# document

id: 6e06b3747f9b6f43714a98d396a6d11f
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
index: climate_gpt_v2
vector_id: 16621
content:
"Pathways following current NDCs until 2030 reach annual \r emissions of 47-57 GtCO2-eq yr-1 by 2030, thereby making it \r impossible to limit warming to 1.5degC (>50%) with no or limited \r overshoot and strongly increasing the challenge of limiting \r warming to 2degC (>67%) (high confidence). A high overshoot \r of 1.5degC increases the risks from climate impacts and increases \r dependence on large-scale carbon dioxide removal (CDR) from the \r atmosphere. A future consistent with current NDCs implies higher fossil \r fuel deployment and lower reliance on low-carbon alternatives until \r 2030, compared to mitigation pathways describing immediate action \r that limits warming to 1.5degC (>50%) with no or limited overshoot, or \r limits warming to 2degC (>67%) and below. After following the NDCs to \r 2030, to limit warming to 2degC (>67%) the pace of global GHG emission \r reductions would need to abruptly increase from 2030 onward to an \r [...]"


id: dccad4131da103ac5c2868766ca28d6c
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
index: climate_gpt_v2
vector_id: 36109
content:
"[...] average of 1.3-2.1 GtCO2-eq per year between 2030 and 2050. This \r is similar to the global CO2 emission reductions in 2020 that occurred \r due to the COVID-19 pandemic lockdowns, and around 70% faster \r than in pathways where immediate action is taken to limit warming \r to 2degC (>67%). Accelerating emission reductions after following an \r NDC pathway to 2030 would also be particularly challenging because \r of the continued buildup of fossil fuel infrastructure that would take \r place between now and 2030. (TS4.1, Table TS.3) {3.5, 4.2}"


id: 87fdc329844d87aadf2f4776da0c209a
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
index: climate_gpt_v2
vector_id: 21079
content:
"Figure TS.10 (continued): Mitigation pathways that limit warming to 1.5degC, or 2degC, involve deep, rapid and sustained emissions reductions. Net zero CO2\r and net zero GHG emissions are possible through different mitigation portfolios. Panels (a) and (b) show the development of global GHG and CO2 emissions \r in modelled global pathways (upper sub-panels) and the associated timing of when GHG and CO2 emissions reach net zero (lower sub-panels). Panels (c) and (d) show the \r development of global CH4 and N2O emissions, respectively. Coloured ranges denote the 5th to 95th percentile across pathways. The red ranges depict emissions pathways \r assuming policies that were implemented by the end of 2020 and pathways assuming implementation of NDCs (announced prior to COP26). Ranges of modelled pathways that \r limit warming to 1.5degC (>50%) with no or limited overshoot are shown in light blue (category C1) and pathways that limit warming to 2degC (>67%) are shown in light purple \r [...]"


id: 5bba33a7b2d0dd9ac695c81b5831d33d
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
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vector_id: 13393
content:
"[...] (category C3). The grey range comprises all assessed pathways (C1-C8) from the 5th percentile of the lowest warming category (C1) to the 95th percentile of the highest \r warming category (C8). The modelled pathway ranges are compared to the emissions from two pathways illustrative of high emissions (CurPol and ModAct) and five IMPs: \r IMP-LD, IMP-Ren, IMP-SP, IMP-Neg and IMP-GS. Emissions are harmonised to the same 2015 base year. The vertical error bars in 2015 show the 5-95th percentile uncertainty \r range of the non-harmonised emissions across the pathways, and the uncertainty range, and median value, in emission estimates for 2015 and 2019. The vertical error bars \r in 2030 (panel a) depict the assessed range of the NDCs, as announced prior to COP26.17 Panel (e) shows the sectoral contributions of CO2 and non-CO2 emissions sources \r [...]"


id: 235f9dddc0cb7fb36397aad6677f2576
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
index: climate_gpt_v2
vector_id: 3421
content:
"[...] and sinks at the time when net zero CO2 emissions are reached in the IMPs. Positive and negative emissions for different IMPs are compared to the GHG emissions from the \r year 2019. Energy supply (neg.) includes BECCS and DACCS. DACCS features in only two of the five IMPs (IMP-REN and IMP-GS) and contributes <1% and 64%, respectively, \r to the net negative emissions in Energy Supply (neg.). Panel (f) shows the contribution of different sectors and sources to the emissions reductions from a 2019 baseline for \r reaching net zero GHG emissions. Bars denote the median emissions reductions for all pathways that reach net zero GHG emissions. The whiskers indicate the p5-p95 range. \r The contributions of the service sectors (transport, buildings, industry) are split into direct (demand-side) as well as indirect (supply-side) CO2 emissions reductions. Direct \r [...]"


id: 254d3f4e262cbe6460282cadc373f665
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
index: climate_gpt_v2
vector_id: 3750
content:
"[...] emissions represent demand-side emissions due to the fuel use in the respective demand sector. Indirect emissions represent upstream emissions due to industrial processes \r and energy conversion, transmission and distribution. In addition, the contributions from the LULUCF sector and reductions from non-CO2 emissions sources (green and grey \r bars) are displayed. {3.3, 3.4}"


id: a075289749b738bb054e49baab6794d5
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
index: climate_gpt_v2
vector_id: 25444
content:
"Mitigation pathways that limit warming to 2degC (>67%) entail \r losses in global GDP with respect to reference scenarios \r of between 1.3% and 2.7% in 2050. In pathways limiting \r warming to 1.5degC (>50%) with no or limited overshoot, \r losses are between 2.6% and 4.2%. These estimates do not \r account for the economic benefits of avoided climate change \r impacts (medium confidence). In mitigation pathways limiting \r warming to 2degC (>67%), marginal abatement costs of carbon are \r about 90 (60-120) USD2015 tCO2 in 2030 and about 210 (140-340) \r USD2015/tCO2 in 2050. This compares with about 220 (170-290) \r USD2015  tCO2 in 2030 and about 630 (430-990) USD2015 tCO2\r in 205018 in pathways that limit warming to 1.5degC (>50%) with \r no or limited overshoot. Reference scenarios, in the AR6 scenarios \r database, describe possible emission trajectories in the absence of \r new stringent climate policies. Reference scenarios have a  broad \r [...]"


id: 601628d067248443e96c736670c4d55b
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
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vector_id: 14212
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"The global benefits of pathways limiting warming to 2degC \r (>67%) outweigh global mitigation costs over the 21st century, \r if aggregated economic impacts of climate change are at the \r moderate to high end of the assessed range, and a  weight \r consistent with economic theory is given to economic impacts \r over the long term. This holds true even without accounting \r for benefits in other sustainable development dimensions or \r non-market damages from climate change (medium confidence).\r The aggregate global economic repercussions of mitigation pathways \r include: the macroeconomic impacts of investments in low-carbon \r solutions and structural changes away from emitting activities; \r co-benefits and adverse side effects of mitigation; avoided climate \r change impacts; and reduced adaptation costs. Existing quantifications \r of the global aggregate economic impacts show a strong dependence \r on socio-economic development conditions, as these shape exposure \r [...]"


id: 7998ac2c55733ad4bf82e2884f3759c7
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
index: climate_gpt_v2
vector_id: 18598
content:
"[...] and vulnerability and adaptation opportunities and responses. \r Avoided impacts for poorer households and poorer countries represent \r a  smaller share in aggregate economic quantifications expressed in \r GDP or monetary terms, whereas their well-being and welfare effects \r are comparatively larger. When aggregate economic benefits from \r avoided climate change impacts are accounted for, mitigation is \r a welfare-enhancing strategy (high confidence). {3.6.2}"


id: a2e3c047be865874f1b939ae4d7ef082
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
index: climate_gpt_v2
vector_id: 25873
content:
"CBA analysis and CBA integrated assessment models (IAMs) remain limited in their ability to represent all damages from climate \r change, including non-monetary damages, and capture the uncertain and heterogeneous nature of damages and the risk of catastrophic \r damages, such that other lines of evidence should be considered in decision-making. However, emerging evidence suggests that, even \r without accounting for co-benefits of mitigation on other sustainable development dimensions, the global benefits of pathways \r limiting warming to 2degC (>67%) outweigh global mitigation costs over the 21st century (medium confidence). Depending on the \r study, the reason for this result lies in assumptions of economic damages from climate change in the higher end of available estimates, \r in the consideration of risks of tipping points or damages to natural capital and non-market goods, or in the combination of updated \r [...]"


id: c5dd8e2f298bbb39ea75dffb18039dc2
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
index: climate_gpt_v2
vector_id: 32118
content:
"[...] representations of carbon cycle and climate modules, updated damage estimates and updated representations of economic and \r mitigation dynamics. In the studies that perform a sensitivity analysis, this result is found to be robust to a wide range of assumptions \r on social preferences (in particular on inequality aversion and pure rate of time preference), and holds except if assumptions of \r economic damages from climate change are in the lower end of available estimates and the pure rate of time preference is in the \r higher range of values usually considered (typically above 1.5%). However, although such pathways bring overall net benefits over time \r (in terms of aggregate discounted present value), they involve distributional consequences between and within generations. {3.6.2}"


id: d3f9990023448c3b18d782dfafc0d00b
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
index: climate_gpt_v2
vector_id: 34625
content:
"Low-carbon energy systems are expected to rely heavily on end-use electrification, where electricity produced with low GHG emissions \r is used for building and industrial heating, transport and other applications that rely heavily on fossil fuels at present. But not all \r end-uses are expected to be commercially electrifiable in the short to medium term {11.3.5}, and many will require low GHG liquid and \r gaseous fuels, that is, hydrogen, ammonia, and biogenic and synthetic low GHG hydrocarbons made from low GHG hydrogen, oxygen \r and carbon sources (the latter from CCU,20 biomass, or direct air capture {11.3.6}). The future role of hydrogen and hydrogen derivatives \r will depend on how quickly and how far production technology improves, that is, from electrolysis ('green'), biogasification, and fossil \r fuel reforming with CCS ('blue') sources. As a general rule, and across all sectors, it is more efficient to use electricity directly and avoid \r [...]"


id: a1721d00ca482c3aaf877d5e01f68388
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
index: climate_gpt_v2
vector_id: 25620
content:
"[...] the progressively larger conversion losses from producing hydrogen, ammonia, or constructed low GHG hydrocarbons. What hydrogen \r does do, however, is add time and space option value to electricity produced using variable clean sources, for use as hydrogen, as \r stored future electricity via a fuel cell or turbine, or as an industrial feedstock. Furthermore, electrification and hydrogen involve \r a symbiotic range of general-purpose technologies, such as electric motors, power electronics, heat pumps, batteries, electrolysis, fuel \r cells, and so on, that have different applications across sectors but cumulative economies of innovation and production scale benefits. \r Finally, neither electrification nor hydrogen produce local air pollutants at point of end-use."


id: 6b053801f964d346ca4d78f44c51435d
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
index: climate_gpt_v2
vector_id: 16125
content:
"An increasing reliance on electricity from variable renewable sources, notably wind and solar power, disrupts old concepts and makes \r many existing guidelines obsolete for power system planning, for example, that specific generation types are needed for baseload, \r intermediate load, and peak load to follow and meet demand. In future power systems with high shares of variable electricity from \r renewable sources, system planning and markets will focus more on demand flexibility, grid infrastructure and interconnections, \r storage on various timelines (on the minute, hourly, overnight and seasonal scale), and increased coupling between the energy sector \r and the building, transport and industrial sectors. This shifts the focus to energy systems that can handle variable supply rather than \r always follow demand. Hydrogen may prove valuable to improve the resilience of electricity systems with high penetration of variable \r [...]"


id: 85b46b1f337f8d45c4ee2c0d83100ba6
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
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vector_id: 20667
content:
"[...] renewable electricity. Flexible hydrogen electrolysis, hydrogen power plants and long-duration hydrogen storage may all improve \r resilience. Electricity-to-hydrogen-to-electricity round-trip efficiencies are projected to reach up to 50% by 2030. {6.4.3}"


id: cdf2d9eae6d3d3848c1b07879fe1b048
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
index: climate_gpt_v2
vector_id: 33568
content:
"Electrification is expected to be the dominant strategy in buildings as electricity is increasingly used for heating and for cooking. \r Electricity will help to integrate renewable energy into buildings and will also lead to more flexible demand for heating, cooling, and \r electricity. District heating and cooling offers potential for demand flexibility through energy storage and supply flexibility through \r cogeneration. Heat pumps are increasingly used in buildings and industry for heating and cooling {9.3.3, Box  9.3}. The ease of \r switching to electricity means that hydrogen is not expected to be a dominant pathway for buildings {Box 9.6}. Using electricity \r directly for heating, cooling and other building energy demand is more efficient than using hydrogen as a fuel, for example, in boilers \r or fuel cells. In addition, electricity distribution is already well developed in many regions compared to essentially non-existent \r [...]"


id: dfdc5cd76c2368ab627f06c598f93dd8
created_at: 2023-04-19 13:23:06
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vector_id: 36631
content:
"[...] hydrogen infrastructure, except for a few chemicals industry pipelines. At the same time, hydrogen could potentially be used for on-site \r storage should technology advance sufficiently."


id: 3ebbaf0ff5468d5973d4e1282b96f65c
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
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content:
"Figure TS.12 | Changes in six metrics associated with urban and national-scale combined CO2 and CH4 emissions represented in the AR6 WGIII six-region \r aggregation, with (a) 2000 and (b) 2015. The trends in Luqman et al. (2021) were combined with the work of Moran et al. (2018) to estimate the regional urban CO2-eq \r share of global urban emissions, the urban share of national CO2-eq emissions, and the urban per capita CO2-eq emissions by region. This estimate is derived from consumption\u0002based accounting that includes both direct emissions from within urban areas and indirect emissions from outside urban areas related to the production of electricity, goods, \r and services consumed in cities. It incorporates all CO2 and CH4 emissions except aviation, shipping and biogenic sources (i.e., land-use change, forestry, and agriculture). The \r [...]"


id: 43b26b32fe8b88ccda571e1946993173
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
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"[...] dashed grey line represents the global average urban per capita CO2-eq emissions. The regional urban population share, regional CO2-eq share in total emissions, and national \r per capita CO2-eq emissions by region are given for comparison. Source: adapted from Gurney et al. (2022)."


id: 6dd8bd670641a5a8bcda26c3bae75c5f
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
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vector_id: 16589
content:
"Urban green and blue infrastructure can mitigate climate \r change through carbon sinks, avoided emissions, and \r reduced energy use while offering multiple co-benefits (high \r confidence). Urban green and blue infrastructure, including urban \r forests and street trees, permeable surfaces, and green roofs24 offer \r potentials to mitigate climate change directly through storing carbon, \r and indirectly by inducing a cooling effect that both reduces energy \r demand and reduces energy use for water treatment. Globally, urban \r trees store approximately 7.4 billion tonnes of carbon, and sequester \r approximately 217 million tonnes of carbon annually, although \r carbon storage is highly dependent on biome. Among the multiple \r co-benefits of green and blue infrastructure are reducing the urban \r heat island (UHI) effect and heat stress, reducing stormwater runoff, \r improving air quality, and improving the mental and physical health \r [...]"


id: aa04dccbfaa8fdb5c961772c88e209c8
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
index: climate_gpt_v2
vector_id: 27138
content:
"[...] of urban dwellers. Many of these options also provide benefits to \r climate adaptation. (high agreement, robust evidence) {8.2, 8.4.4}"


id: 2762f160e658e4eeb579bf893668d675
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
index: climate_gpt_v2
vector_id: 4107
content:
"The potential and sequencing of mitigation strategies to reduce \r GHG emissions will vary depending on a city's land use, spatial \r form, development level, and state of urbanisation (i.e., whether \r it is an established city with existing infrastructure, a rapidly \r growing city with new infrastructure, or an emerging city with \r infrastructure buildup) (high confidence). New and emerging cities \r will have significant infrastructure development needs to achieve high \r quality of life, which can be met through energy-efficient infrastructures \r and services, and people-centred urban design (high confidence). \r The long lifespan of urban infrastructures locks in behaviour and \r committed emissions. Urban  infrastructures  and urban form can \r enable sociocultural and lifestyle changes that can significantly reduce \r carbon footprints. Rapidly growing cities can  avoid higher future \r emissions through urban planning to co-locate jobs and housing to \r [...]"


id: f6d69d8a24731808d59e2ed50f682536
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
index: climate_gpt_v2
vector_id: 40797
content:
"[...] achieve compact urban form, and by leapfrogging to low-carbon \r technologies. Established cities will achieve the largest GHG emissions \r savings by replacing, repurposing, or retrofitting the building stock, \r targeted infilling and densifying, as well as through modal shift and \r the electrification of the urban energy system. New and emerging cities "


id: bec38b4c84ddf321ab1b7bd5939360b1
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
index: climate_gpt_v2
vector_id: 30829
content:
"Changes in urban form, behaviour programs, the circular \r economy, the shared economy, and digitalisation trends \r can support systemic changes that lead to reductions in \r demand for transport services or expand the use of more \r efficient transport modes (high confidence). Cities can reduce \r their transport-related fuel consumption by around 25% through \r combinations of more compact land use and the provision of less \r car-dependent transport infrastructure. Appropriate infrastructure, \r including protected pedestrian and bike pathways, can also \r support much greater localised active travel.25 Transport demand \r management incentives are expected to be necessary to support \r these systemic changes. There is mixed evidence of the effect \r of circular economy initiatives, shared economy initiatives, and \r digitalisation on demand for transport services (Box TS.14). For \r example, while dematerialisation can reduce the amount of material \r [...]"


id: 86bba34c4f416b589480623d2c75e8cf
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
index: climate_gpt_v2
vector_id: 20867
content:
"[...] that needs to be transported to manufacturing facilities, an increase \r in online shopping with priority delivery can increase demand for \r freight transport. Similarly, while teleworking could reduce travel \r demand, increased ride-sharing could increase vehicle kilometres \r travelled (VKT). {Chapters 1 and 5, 10.2, 10.8}"


id: f1a1202da17a22421604a0809d134f92
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
index: climate_gpt_v2
vector_id: 39893
content:
"Battery electric vehicles (BEVs) have lower lifecycle greenhouse \r gas (GHG) emissions than internal combustion engine vehicles \r (ICEVs) when BEVs are charged with low-carbon electricity \r (high confidence). Electromobility is being rapidly implemented \r in micro-mobility (e-autorickshaws, e-scooters, e-bikes), in transit \r systems, especially buses, and to a  lesser degree, in personal \r vehicles. BEVs could also have the added benefit of supporting \r grid operations. The commercial availability of mature lithium-ion \r batteries (LIBs) has underpinned this growth in electromobility. As \r global battery production increases, unit costs are declining. Further \r efforts to reduce the GHG footprint of battery production, however, \r are essential for maximising the mitigation potential of BEVs. The \r continued growth of electromobility for land transport would entail \r investments in electric charging and related grid infrastructure. \r [...]"


id: c281e9aae3a2ad75c54135385a03ed02
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
index: climate_gpt_v2
vector_id: 31508
content:
"[...] Electromobility powered by low-carbon electricity has the potential \r to rapidly reduce transport GHG and can be applied with multiple \r co-benefits, especially in developing countries. {10.3, 10.4, 10.8}"


id: bc476fd267621d7998a4ba1a9fc0e9d2
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
index: climate_gpt_v2
vector_id: 30370
content:
"Decarbonisation options for shipping and aviation still require \r R&D, though advanced biofuels, ammonia, and synthetic \r fuels are emerging as viable options (medium confidence). \r Increased efficiency has been insufficient to limit the emissions from \r shipping and aviation, and natural gas-based fuels are expected to \r be inadequate to meet stringent decarbonisation goals for these \r segments (high confidence). High-energy density, low-carbon fuels \r are required, but they have not yet reached commercial scale. \r Advanced biofuels could provide low-carbon jet fuel (medium \r confidence). The production of synthetic fuels using low-carbon \r hydrogen with CO2 captured through DACCS/BECCS could provide \r jet and marine fuels but these options still require demonstration \r at scale (low confidence). Ammonia produced with low-carbon \r hydrogen could also serve as a  marine fuel (medium confidence). \r Deployment of these fuels requires reductions in production costs. \r [...]"


id: c191a37a5370cb9ba0d4172332043b35
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
index: climate_gpt_v2
vector_id: 31359
content:
"Scenarios from bottom-up and top-down models indicate \r that, without intervention, CO2 emissions from transport \r could grow in the range of 16% and 50% by 2050 (medium \r confidence). The scenarios literature projects continued growth in \r demand for freight and passenger services, particularly in developing \r countries in Africa and Asia (high confidence). This growth is \r projected to take place across all transport modes. Increases in \r demand notwithstanding, scenarios that limit warming to 1.5degC \r degree with no or limited overshoot suggest that a 59% reduction \r (42-68% interquartile range) in transport-related CO2 emissions by \r 2050, compared to modelled 2020 levels is required. While many \r global scenarios place greater reliance on emissions reduction \r in sectors other than transport, a  quarter of the 1.5degC scenarios \r describe transport-related CO2 emissions reductions in excess of 68% \r (relative to modelled 2020 levels) (medium confidence). Illustrative \r [...]"


id: 87ede63db7d7d53048d3985e7ecb17e9
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
index: climate_gpt_v2
vector_id: 21071
content:
"[...] Mitigation Pathways IMP-Ren and IMP-LD (TS 4.2) describe emission \r reductions of 80% and 90% in the transport sector, respectively, \r by 2050. Transport-related emission reductions, however, may not \r happen uniformly across regions. For example, transport emissions \r from the Developed Countries, and Eastern Europe and West Central \r Asia countries decrease from 2020 levels by 2050 across all scenarios \r limiting global warming to 1.5degC by 2100, but could increase in \r Africa, Asia and Pacific (APC), Latin America and Caribbean, and the \r Middle East in some of these scenarios. {10.7}"


id: ea4818614eeb3b10edf3ba02c5415b0d
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
index: climate_gpt_v2
vector_id: 38575
content:
"Legislated climate strategies are emerging at all levels of \r government, and together with pledges for personal choices, \r could spur the deployment of demand- and supply-side \r transport mitigation strategies (medium confidence). At the \r local level, legislation can support local transport plans that include \r commitments or pledges from local institutions to encourage \r behaviour change by adopting an organisational culture that \r motivates sustainable behaviour with inputs from the creative \r arts. Such institution-led mechanisms could include bike-to-work \r campaigns, free transport passes, parking charges, or eliminating \r car benefits. Community-based solutions such as solar sharing, \r community charging, and mobility as a  service can generate new \r opportunities to facilitate low-carbon transport futures. At the \r regional and national levels, legislation can include vehicle and fuel \r efficiency standards, R&D support, and large-scale investments in \r [...]"


id: 7b3edb757cbddcdada6170ac5e51d45c
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
index: climate_gpt_v2
vector_id: 18884
content:
"In most regions, historical improvements in efficiency have \r been approximately matched by growth in floor area per capita \r (high confidence). At the global level, building-specific drivers of \r GHG emissions include: (i) population growth, especially in developing \r countries; (ii) increasing floor area per capita, driven by the increasing \r size of dwellings while the size of households kept decreasing, especially \r in developed countries; (iii) the inefficiency of newly constructed \r buildings, especially in developing countries, and the low renovation \r rates and low ambition level in developed countries when existing \r buildings are renovated; (iv) the increase in use, number and size of \r appliances and equipment, especially information and communication \r technologies (ICT) and cooling, driven by income; and, (v) the continued \r reliance on carbon-intensive electricity and heat. These factors taken \r together are projected to continue driving increased GHG emissions in \r [...]"


id: ffd9ffb4638f65eace605515c8ae41c1
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
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vector_id: 42409
content:
"The potential associated with sufficiency measures, as well as \r the replacement of appliances, equipment and lights by efficient \r ones, is below zero cost (high confidence). The construction of \r high-performance buildings is expected to become a  business-as\u0002usual technology by 2050 with costs below USD20 tCO2\r -1 in developed \r countries and below USD100 tCO2\r -1 in developing countries (medium \r confidence). For existing buildings, there have been many examples \r of deep retrofits where additional costs per CO2 abated are not \r significantly higher than those of shallow retrofits. However, \r for the whole building stock they tend to be in cost intervals of \r USD-200 tCO2\r -1 and >USD200 tCO2-1 (medium confidence). Literature \r emphasises the critical role of the 2020-2030 decade in accelerating \r the learning of know-how and skills to reduce the costs and remove \r feasibility constraints for achieving high-efficiency buildings at scale \r [...]"


id: 54d89d5697479dcc9819fc41d27ded4b
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
index: climate_gpt_v2
vector_id: 12197
content:
"[...] and set the sector on the pathway to realise its full potential (high \r confidence). {9.3, 9.6, 9.9}."


id: 354c795e0180cb9eaf97af4fb4159af1
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
index: climate_gpt_v2
vector_id: 6625
content:
"Figure TS.15 (continued): Decompositions of changes in historical residential energy emissions 1990-2019, changes in emissions projected by baseline \r scenarios for 2020-2050, and differences between scenarios in 2050 using scenarios from three models: IEA, IMAGE, and RECC. RECC-LED data for \r (a) global, and (b) for nine world regions, include only space heating and cooling and water heating in residential buildings. Emissions are decomposed using the equation, \r which shows changes in driver variables of population, sufficiency (floor area per capita), efficiency (final energy per floor area), and renewables (GHG emissions per final \r energy). 'Renewables' is a summary term describing changes in GHG intensity of energy supply. Emission projections to 2050, and differences between scenarios in 2050, \r demonstrate mitigation potentials from the dimensions of the SER framework realised in each model scenario. In most regions, historical improvements in efficiency have been \r [...]"


id: 8f2ef2ebc26d10da511f97f25546759d
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
index: climate_gpt_v2
vector_id: 22391
content:
"[...] approximately matched by growth in floor area per capita. Implementing sufficiency measures that limit growth in floor area per capita, particularly in developed regions, \r reduces the dependence of climate mitigation on technological solutions. {Figure 9.5, Box 9.2}"


id: 35818f596eb180b01ca7085192705b22
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
index: climate_gpt_v2
vector_id: 6663
content:
"Adapting buildings to future climate while ensuring well-being \r for all requires action. Expected heatwaves will inevitably \r increase cooling needs to limit the health impacts of climate \r change (medium confidence). Global warming will impact cooling \r and heating needs but also the performance, durability and safety \r of buildings, especially historical and coastal ones, through changes \r in temperature, humidity, atmospheric concentrations of CO2 and \r chloride, and sea level rise. Adaptation measures to cope with climate \r change may increase the demand for energy and materials leading to \r an increase in GHG emissions if not mitigated. Sufficiency measures \r which anticipate climate change, and include natural ventilation, \r white walls, and nature-based solutions (e.g.,  green roofs) will \r decrease the demand for cooling. Shared cooled spaces with highly \r efficient cooling solutions are among the mitigation strategies which \r [...]"


id: 979a9907ad6bd3a7bd15d434c7ecb315
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
index: climate_gpt_v2
vector_id: 23888
content:
"Net zero CO2 industrial-sector emissions are possible but \r challenging (high confidence). Energy efficiency will continue to \r be important. Reduced materials demand, material efficiency, and \r circular economy solutions can reduce the need for primary production. \r Primary production options include switching to new processes that \r use low-to-zero GHG energy carriers and feedstocks (e.g., electricity, \r hydrogen, biofuels, and carbon dioxide capture and utilisation \r (CCU) to provide carbon feedstocks). Carbon capture and  storage \r (CCS) will be required to mitigate remaining CO2 emissions {11.3}. \r These options require substantial scaling up of electricity, hydrogen, \r recycling, CO2, and other infrastructure, as well as phase-out or \r conversion of existing industrial plants. While improvements in the \r GHG intensities of major basic materials have nearly stagnated over \r the last 30 years, analysis of historical technology shifts and newly \r [...]"


id: 145c53c09cf9a9bdaecd736fde1b814a
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
index: climate_gpt_v2
vector_id: 800
content:
"[...] available technologies indicate these intensities can be significantly \r reduced by mid-century. {11.2, 11.3, 11.4}"


id: 585ee38dbdb27fe1f257e8491990a07d
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
index: climate_gpt_v2
vector_id: 12801
content:
"Figure TS.17 (continued): Potentials and costs for zero-carbon mitigation options for industry and basic materials. CIEl - carbon intensity of electricity for \r indirect emissions; EE - energy efficiency; ME - material efficiency; Circularity - material flows (clinker substituted by coal fly ash, blast furnace slag or other by-products and \r waste, steel scrap, plastic recycling, etc.); FeedCI - feedstock carbon intensity (hydrogen, biomass, novel cement, natural clinker substitutes); FSW+El - fuel switch and processes \r electrification with low-carbon electricity. Ranges for mitigation options are shown based on bottom-up studies for grouped technologies packages, not for single technologies. \r In circles, contribution to mitigation from technologies based on their readiness are shown for 2050 (2040) and 2070. Direct emissions include fuel combustion and process \r [...]"


id: 479e1066f5c4a37060bdb0a97155eb05
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
index: climate_gpt_v2
vector_id: 9822
content:
"[...] emissions. Indirect emissions include emissions attributed to consumed electricity and purchased heat. For basic chemicals, only methanol, ammonia and high-value chemicals \r are considered. Total for industry does not include emissions from waste. Negative mitigation costs for some options such as Circularity are not reflected. {Figure 11.13}"


id: a49b537406d8920b5febd4947fc99e58
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
index: climate_gpt_v2
vector_id: 26174
content:
"The AFOLU sector, on average, accounted for 13-21% of \r global total anthropogenic GHG emissions in the period \r 2010-2019. At the same time managed and natural terrestrial \r ecosystems were a carbon sink, absorbing around one third \r of anthropogenic CO2 emissions (medium confidence). \r Estimated anthropogenic net CO2 emissions from AFOLU (based on \r bookkeeping models) result in a net source of +5.9 +- 4.1 GtCO2 yr-1\r between 2010 and 2019 with an unclear trend. Based on FAOSTAT \r or national GHG inventories, the net CO2 emissions from AFOLU were \r 0.0 to +0.8 GtCO2 yr -1 over the same period. There is a discrepancy \r in the reported CO2 AFOLU emissions magnitude because alternative \r methodological approaches that incorporate different assumptions \r are used {7.2.2}. If the responses of all managed and natural land \r to both anthropogenic environmental change and natural climate \r variability, estimated to be a gross sink of -12.5 +- 3.2 GtCO2 yr-1 for \r [...]"


id: f6f8817ee57d2050857db0868d5e5bba
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
index: climate_gpt_v2
vector_id: 40820
content:
"[...] the period 2010-2019, are added to land-use emissions, then land \r overall constituted a net sink of -6.6 +- 5.2 GtCO2 yr-1 in terms of CO2\r emissions (medium confidence). (Table TS.4) {7.2, Table 7.1} "


id: bd696e9589e80cc8ca78f8dd132effcb
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
index: climate_gpt_v2
vector_id: 30576
content:
"Land-use change drives net AFOLU CO2 emission fluxes. The \r rate of deforestation, which accounts for 45% of total AFOLU \r emissions, has generally declined, while global tree cover \r and global forest-growing stock levels are likely increasing \r (medium confidence). There are substantial regional differences, \r with losses of carbon generally observed in tropical regions and \r gains in temperate and boreal regions. Agricultural CH4 and N2O \r emissions are estimated to average 157 +- 47.1 MtCH4 yr -1 and \r 6.6 +- 4.0 MtN2O yr -1 or 4.2 +- 1.3 and 1.8 +- 1.1 GtCO2-eq yr-1 (using \r IPCC AR6 GWP100 values for CH4 and N2O) respectively between \r 2010 and 2019 {7.2.1, 7.2.3}. AFOLU CH4 emissions continue to \r increase, the main source of which is enteric fermentation from \r ruminant animals. Similarly, AFOLU N2O emissions are increasing, \r dominated by agriculture, notably from manure application, nitrogen \r deposition, and nitrogen fertiliser use (high confidence). In addition \r [...]"


id: 301022acc631675839c70fe5ebab353b
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
index: climate_gpt_v2
vector_id: 5720
content:
"[...] to being a net carbon sink and source of GHG emissions, land plays an \r important role in climate through albedo effects, evapotranspiration, \r and aerosol loading through emissions of volatile organic compounds \r (VOCs). The combined role of CH4, N2O and aerosols in total climate \r forcing, however, is unclear and varies strongly with bioclimatic \r region and management practice. {2.4.2.5, 7.2, 7.3}"


id: e44f4c8db370266b08fbbc98c56a46cf
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
index: climate_gpt_v2
vector_id: 37466
content:
"The agriculture, forestry and other land use (AFOLU)27 sector \r encompasses managed ecosystems and offers significant \r mitigation opportunities while providing food, wood \r and other renewable resources as well as biodiversity \r conservation, provided the sector adapts to climate change.\r Land-based mitigation measures can reduce GHG emissions within \r the AFOLU sector, deliver CDR and provide biomass thereby enabling \r emission reductions in other sectors.28 The rapid deployment of \r AFOLU measures features in all pathways that limit global warming \r to 1.5degC. Where carefully and appropriately implemented, AFOLU \r mitigation measures are positioned to deliver substantial co-benefits \r and help address many of the wider challenges associated with \r land management. If AFOLU measures are deployed badly, when \r taken together with the increasing need to produce sufficient \r food, feed, fuel and wood, they may exacerbate trade-offs with the \r [...]"


id: 140a7052c68c38595c724efc1a70c9bf
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
index: climate_gpt_v2
vector_id: 742
content:
"savannas and grasslands have the potential to reduce emissions \r and/or sequester 7.3 mean (3.9-13.1) GtCO2-eq yr-1. Agriculture \r provides the second largest share of the mitigation potential, with \r 4.1 (1.7-6.7) GtCO2-eq yr -1 (up to USD100 tCO2-eq-1) from cropland \r and grassland soil carbon management, agroforestry, use of biochar, \r improved rice cultivation, and livestock and nutrient management. \r Demand-side measures including shifting to sustainable healthy \r diets, reducing food waste, building with wood, biochemicals, and \r bio-textiles, have a mitigation potential of 2.2 (1.1-3.6) GtCO2-eq yr -1. \r Most mitigation options are available and ready to deploy. Emissions \r reductions can be achieved relatively quickly, whereas CDR needs \r upfront investment. Sustainable intensification in agriculture, shifting \r diets, and reducing food waste could enhance efficiencies and reduce \r agricultural land needs, and are therefore critical for enabling \r [...]"


id: 5ad5f8112150291b01f7efe72bf1d910
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
index: climate_gpt_v2
vector_id: 13230
content:
"[...] supply-side measures such as reforestation, restoration, as well as \r decreasing CH4 and N2O emissions from agricultural production. In \r addition, emerging technologies (e.g.,  vaccines or CH4 inhibitors) \r have the potential to substantially increase the CH4 mitigation \r potential beyond current estimates. AFOLU mitigation is not only \r relevant in countries with large land areas. Many smaller countries \r and regions, particularly with wetlands, have disproportionately high \r levels of AFOLU mitigation potential density. {7.4, 7.5}"


id: 93b52481d276f21eb467e783aa2a80e8
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
index: climate_gpt_v2
vector_id: 23192
content:
"The AFOLU sector offers significant near-term mitigation \r potential at relatively low cost and can provide 20-30% of \r the 2050 emissions reduction described in scenarios that limit \r warming to 2degC  (>67%) or lower (high evidence, medium \r agreement). The AFOLU sector can provide 20-30% (interquartile \r range) of the global mitigation needed for a 1.5degC or 2degC pathway \r towards 2050, though there are highly variable mitigation strategies \r for how AFOLU potential can be deployed for achieving climate \r targets {Illustrative Mitigation Pathways in 7.5}. The estimated \r economic (<USD100 tCO2-eq-1) AFOLU sector mitigation potential \r is 8 to 14 GtCO2-eq yr-1 between 2020-2050, with the bottom \r end of this range representing the mean from IAMs and the upper \r end  representing the mean estimate from global sectoral studies. \r The economic potential is about half of the technical potential from \r AFOLU, and about 30-50% could be achieved under USD20 tCO2-eq-1\r [...]"


id: 304bad6ef4768d54eb28c814b4492d7c
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
index: climate_gpt_v2
vector_id: 5748
content:
"[...] {7.4}. The implementation of robust measurement, reporting and \r verification processes is paramount to improving the transparency of \r changes in land carbon stocks and this can help prevent misleading \r assumptions or claims on mitigation. {7.1, 7.4, 7.5} "


id: 6a9926bdf9a355eff9b2e4d28aa301db
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
index: climate_gpt_v2
vector_id: 16038
content:
"while limiting trade-offs (medium confidence). The results of \r implementing AFOLU measures are often variable and highly context\u0002specific. Depending on local conditions (e.g., ecosystem, climate, food \r system, land ownership) and management strategies (e.g.,  scale, \r method), mitigation measures can positively or negatively affect \r biodiversity, ecosystem functioning, air quality, water availability \r and quality, soil productivity, rights infringements, food security, and \r human well-being. The agriculture and forestry sectors can devise \r management approaches that enable biomass production and use \r for energy in conjunction with the production of food and timber, \r thereby reducing the conversion pressure on natural ecosystems \r (medium confidence). Mitigation measures addressing GHGs may also \r affect other climate forcers such as albedo and evapotranspiration. \r Integrated responses that contribute to mitigation, adaptation, and \r [...]"


id: d9298354a6526dd1d44d9463906f8630
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
index: climate_gpt_v2
vector_id: 35503
content:
"[...] other land challenges will have greater likelihood of being successful \r (high confidence); measures which provide additional benefits to \r biodiversity and human well-being are sometimes described as \r 'Nature-based Solutions'. {7.1, 7.4, 7.6, 12.4, 12.5}"


id: 98fbf025e06ecf5b73840ada2f9c5d8a
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
index: climate_gpt_v2
vector_id: 24131
content:
"AFOLU mitigation measures have been well understood \r for decades but deployment remains slow, and emissions \r trends indicate unsatisfactory progress despite beneficial \r contributions to global emissions reduction from forest\u0002related options (high confidence). Globally, the AFOLU sector has \r so far contributed modestly to net mitigation, as past policies have \r delivered about 0.65 GtCO2 yr-1 of mitigation during 2010-2019 or \r 1.4% of global gross emissions. The majority (>80%) of emission \r reduction resulted from forestry measures. Although the mitigation \r potential of AFOLU measures is large from a biophysical and ecological \r perspective, its feasibility is hampered by lack of institutional support, \r uncertainty over long-term additionality and trade-offs, weak \r governance, fragmented land ownership, and uncertain permanence \r effects. Despite these impediments to change, AFOLU mitigation \r options are demonstrably effective and with appropriate support can \r [...]"


id: f9c92fb28dd0af17a4be9fc1df489a34
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
index: climate_gpt_v2
vector_id: 41304
content:
"Bioenergy is the most land-intensive energy option, but total \r land occupation of other renewable energy options can also \r become significant in high deployment scenarios. While not \r as closely connected to the AFOLU sector as bioenergy, other \r renewable energy options can influence AFOLU activities \r in both synergistic and detrimental ways (high confidence). \r The character of land occupation, and associated impacts, vary \r considerably among mitigation options and also for the same \r option depending on geographic location, scale, system design and \r deployment strategy. Land occupation can be large uniform areas, \r for example, reservoir hydropower dams and tree plantations, and \r more distributed occupation that is integrated with other land uses, \r for example, wind turbines and agroforestry in agriculture landscapes. \r Deployment can be partly decoupled from additional land use, for \r example, use of organic waste and residues and integration of solar \r [...]"


id: 5f73a9680bfe23313377a9f09e84a500
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
index: climate_gpt_v2
vector_id: 14103
content:
"[...] PV into buildings and other infrastructure (high confidence). Wind and \r solar power can coexist with agriculture in beneficial ways (medium \r confidence). Indirect land occupation includes new agriculture areas \r following displacement of food production with bioenergy plantations \r and expansion of mining activities providing minerals required for \r manufacture of EV batteries, PV, and wind power. {7.4, 12.5}"


id: f25a391107092079ecb97af0fbb2e7d5
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
index: climate_gpt_v2
vector_id: 40034
content:
"Concerted, rapid and sustained effort by all stakeholders, from \r policymakers and investors to land owners and managers is \r a  pre-requisite for achieving high levels of mitigation in the \r AFOLU sector (high confidence). To date USD0.7 billion yr-1 is \r estimated to have been spent on AFOLU mitigation. This is well short \r of the more than USD400 billion yr-1 that is estimated to be necessary \r to deliver the up to 30% of global mitigation effort envisaged in deep \r mitigation scenarios (medium confidence). This estimate of the global \r funding requirement is smaller than current subsidies provided to \r agriculture and forestry. A gradual redirection of existing agriculture \r and forestry subsidies would greatly advance mitigation. Effective policy \r interventions and national (investment) plans as part of NDCs, specific \r to local circumstances and needs, are urgently needed to accelerate \r the deployment of AFOLU mitigation options. These interventions are \r [...]"


id: 4b5ee0a7e497db6151d51696b6c7f3
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
index: climate_gpt_v2
vector_id: 10508
content:
"[...] effective when they include funding schemes and long-term consistent \r support for implementation with governments taking the initiative \r together with private funders and non-state actors. {7.6}"


id: b09cbea12f28a1fb9cccaf717b4104
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
index: climate_gpt_v2
vector_id: 28305
content:
"Addressing the many knowledge gaps in the development and \r testing of AFOLU mitigation options can rapidly advance the \r likelihood of achieving sustained mitigation (high confidence). \r Research priorities include improved quantification of anthropogenic \r and natural GHG fluxes and emissions modelling, better understanding \r of the impacts of climate change on the mitigation potential, \r permanence and additionality of estimated mitigation  actions, \r and improved (real-time and cheap) measurement, reporting and \r verification. There is a need to include a greater suite of mitigation \r measures in IAMs, informed by more realistic assessments that take \r into account local circumstances and socio-economic factors and \r cross-sector synergies and trade-offs. Finally, there is a critical need \r for more targeted research to develop appropriate country-level, \r locally specific, policy and land-management response options. These \r [...]"


id: 8858319df190d2341b547060f4bc4687
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
index: climate_gpt_v2
vector_id: 21149
content:
"[...] options could support more specific NDCs with AFOLU measures \r that enable mitigation while also contributing to biodiversity \r conservation, ecosystem functioning, livelihoods for millions of \r farmers and foresters, and many other SDGs. {7.7, Figure 17.1} "


id: e1091c22c4d65839b6d03e1aeca7b462
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
index: climate_gpt_v2
vector_id: 36853
content:
"Realising the mitigation potential of the AFOLU sector \r depends strongly on policies that directly address emissions \r and drive the deployment of land-based mitigation options, \r consistent with carbon prices in deep mitigation scenarios \r (high confidence). Examples of successful policies and measures \r include establishing and respecting tenure rights and community \r forestry, improved agricultural management and sustainable \r intensification, biodiversity conservation, payments for ecosystem \r services, improved forest management and wood-chain usage, \r bioenergy, voluntary supply chain management efforts, consumer \r behaviour campaigns, private funding and joint regulatory efforts \r to avoid, for example, leakage. The efficacy of different policies, \r however, will depend on numerous region-specific factors. In addition \r to funding, these factors include governance, institutions, long-term \r consistent execution of measures, and the specific policy setting. \r [...]"


id: 5abb1e517de12388e6887f0dce50abbf
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
index: climate_gpt_v2
vector_id: 13216
content:
"[...] While the governance of land-based mitigation can draw on lessons \r from previous experience with regulating biofuels and forest carbon, \r integrating these insights requires governance that goes beyond \r project-level approaches emphasising integrated land-use planning \r and management within the frame of the Sustainable Development \r Goals. {7.4, Box 7.2, 7.6}"


id: e27b35f652664823954040215ede0728
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
index: climate_gpt_v2
vector_id: 37143
content:
"There is a great variety of CDR methods and respective implementation options {Cross-Chapter Box 8, Figure 1 in Chapter 12}. Some \r of these methods (like afforestation and soil carbon sequestration) have been practiced for decades to millennia, although not \r necessarily with the intention to remove carbon from the atmosphere. Conversely, for methods such as DACCS and BECCS, experience \r is growing but still limited in scale. A categorisation of CDR methods can be based on several criteria, depending on the highlighted \r characteristics. In this report, the categorisation is focused on the role of CDR methods in the carbon cycle, that is on the removal \r process (land-based biological; ocean-based biological; geochemical; chemical) and on the time scale of storage (decades to centuries;\r centuries to millennia; 10,000 years or longer), the latter being closely linked to different carbon storage media. Within one category \r [...]"


id: 93e7da55f0b92bc208db31155d459ef3
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
index: climate_gpt_v2
vector_id: 23226
content:
"[...] (e.g., ocean-based biological CDR) options often differ with respect to other dynamic or context-specific dimensions such as mitigation \r potential, cost, potential for co-benefits and adverse side effects, and technology readiness level. (Table TS.7, TS.5.6, TS. 5.7) {12.3}"


id: 8f6f95c97f08090054537c91b3f02e5d
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
index: climate_gpt_v2
vector_id: 22448
content:
"It is useful to distinguish between CO2 removal from the atmosphere as the outcome of deliberate activities implementing CDR \r options, and the net emissions outcome achieved with the help of CDR deployment (i.e., gross emissions minus gross removals). \r As part of ambitious mitigation strategies at global or national levels, gross CDR can fulfil three different roles in complementing \r emissions abatement: (i) lowering net CO2 or GHG emissions in the near term; (ii) counterbalancing 'hard-to-abate' residual emissions \r such as CO2 from industrial activities and long-distance transport, or CH4 and nitrous oxide from agriculture, in order to help reach net \r zero CO2 or GHG emissions in the mid-term; (iii) achieving net negative CO2 or GHG emissions in the long term if deployed at levels \r exceeding annual residual emissions {2.7, 3.3, 3.4, 3.5}. These roles of CDR are not mutually exclusive: for example, achieving net zero \r [...]"


id: 88495ba0aeb0d4842aa57c24b88f8faf
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
index: climate_gpt_v2
vector_id: 21138
content:
"[...] CO2 or GHG emissions globally might involve individual developed countries attaining net negative CO2 emissions at the time of global \r net zero, thereby allowing developing countries a smoother transition. {Cross-Chapter Box 8, Figure 2 in Chapter 12}"


id: b80921111e3c2366393031dc775d7918
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
index: climate_gpt_v2
vector_id: 29593
content:
"A social science perspective is important in two ways. By adding new actors and perspectives, it (i) provides more options for climate mitigation; \r and (ii) helps to identify and address important social and cultural barriers and opportunities to socio-economic, technological, and institutional \r change. Demand-side mitigation involves five sets of social actors: individuals (e.g., consumption choices, habits), groups and collectives \r (e.g., social movements, values), corporate actors (e.g., investments, advertising), institutions (e.g., political agency, regulations), and infrastructure \r actors (e.g., very long-term investments and financing). Actors either contribute to the status-quo of global high-carbon consumption, and a GDP \r growth-oriented economy, or help generate the desired change to a low-carbon energy-services, well-being, and equity-oriented economy. \r [...]"


id: 3f1c537136f194eda89c62518fde1990
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updated_at: 2023-04-19 13:23:06
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"[...] Each set of actors has novel implications for the design and implementation of both demand- and supply-side mitigation policies. They show \r important synergies, making energy demand mitigation a dynamic problem where the packaging and/or sequencing of different policies play \r a role in their effectiveness {5.5, 5.6}. Incremental interventions change social practices, simultaneously affecting emissions and well-being. \r The transformative change requires coordinated action across all five sets of actors (Table 5.4), using social science insights about intersection \r of behaviour, culture, institutional and infrastructural changes for policy design and implementation. Avoid, Shift, and Improve choices by \r individuals, households and communities support mitigation {5.3.1.1, Table 5.1}. They are instigated by role models, changing social norms \r [...]"


id: 487712bfd8d6fb7efd49a1f511d329e2
created_at: 2023-04-19 13:23:06
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"[...] driven by policies and social movements. They also require appropriate infrastructures designed by urban planners and building and transport \r professionals, corresponding investments, and a political culture supportive of demand-side mitigation action."


id: eb8d4a71a7b13d4ab701f3823942684
created_at: 2023-04-19 13:23:06
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index: climate_gpt_v2
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content:
"Figure TS.21 | Demand-side mitigation can be achieved through changes in socio-cultural factors, infrastructure design and use, and technology adoption. \r Mitigation response options related to demand for services have been categorised into three domains: 'socio-cultural factors', related to social norms, culture, and individual \r choices and behaviour; 'infrastructure use', related to the provision and use of supporting infrastructure that enables individual choices and behaviour; and 'technology adoption', \r which refers to the uptake of technologies by end users. Potentials in 2050 are estimated using the International Energy Agency's 2020 World Energy Outlook STEPS (Stated \r Policy Scenarios) as a baseline. This scenario is based on a sector-by-sector assessment of specific policies in place, as well as those that have been announced by countries by \r [...]"


id: 11716a2affdb813935b637b9c06bf661
created_at: 2023-04-19 13:23:06
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"[...] mid-2020. This scenario was selected due to the detailed representation of options across sectors and sub-sectors. The heights of the coloured columns represent the potentials \r on which there is a high level of agreement in the literature, based on a range of case studies. The range shown by the dots connected by dotted lines represents the highest \r and lowest potentials reported in the literature which have low to medium levels of agreement. The demand-side potential of socio-cultural factors in the food system has \r two parts. The economic potential of direct emissions (mostly non-CO2) demand reduction through socio-cultural factors alone is 1.9 GtCO2-eq without considering land-use \r change by diversion of agricultural land from food production to carbon sequestration. If further changes in land use enabled by this change in demand are considered, the \r [...]"


id: 6acb4557df86947c5bb28e32d830c5eb
created_at: 2023-04-19 13:23:06
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index: climate_gpt_v2
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content:
"[...] indicative potential could reach 7 GtCO2-eq. The electricity panel presents separately the mitigation potential from changes in electricity demand and changes associated with \r enhanced electrification in end-use sectors. Electrification increases electricity demand, while it is avoided though demand-side mitigation strategies. Load management refers \r to demand-side flexibility that can be achieved through incentive design such as time-of-use pricing/monitoring by artificial intelligence, diversification of storage facilities, and \r so on. NZE (IEA Net-Zero Emissions by 2050 scenario) is used to compute the impact of end-use sector electrification, while the impact of demand-side response options is \r [...]"


id: 5576e9b97455105097994f9557e12096
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
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index: climate_gpt_v2
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"[...] based on bottom-up assessments. Dark grey columns show the emissions that cannot be avoided through demand-side mitigation options. The table indicates which demand\u0002side mitigation options are included. Options are categorised according to: socio-cultural factors, infrastructure use, and technology adoption. Figure SPM.7 covers potential of \r demand-side options for the year 2050. Figure SPM.8 covers both supply- and demand-side options and their potentials for the year 2030. {5.3, Figure 5.7, 5.SM.II}"


id: d1ec065439c6031f531e8c74d8233b14
created_at: 2023-04-19 13:23:06
updated_at: 2023-04-19 13:23:06
content_type: text
index: climate_gpt_v2
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content:
"Figure TS.23 | Overview of emission mitigation options and their cost and potential for the year 2030. The mitigation potential of each option is the quantity \r of net greenhouse gas emission reductions that can be achieved by a given mitigation option relative to specified emission baselines that reflects what would be considered \r current policies in the period 2015-2019. Mitigation options may overlap or interact and cannot simply be summed together. The potential for each option is broken down into \r cost categories (see legend). Only monetary costs and revenues are considered. If costs are less than zero, lifetime monetary revenues are higher than lifetime monetary costs. \r For wind energy, for example, negative cost indicates that the cost is lower than that of fossil-based electricity production. The error bars refer to the total potential for each \r [...]"


id: 30aaf3ebdff2050c5d5f741e6a3aeca1
created_at: 2023-04-19 13:23:06
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content:
"[...] option. The breakdown into cost categories is subject to uncertainty. Where a smooth colour transition is shown, the breakdown of the potential into cost categories is not \r well researched, and the colours indicate only into which cost category the potential can predominantly be found in the literature. {Figure SPM.8, 6.4, Table 7.3, Supplementary \r Material Table 9.SM.2, Supplementary Material Table 9.SM.3, 10.6, 11.4, Figure 11.13, 12.2, Supplementary Material 12.SM.1.2.3}"


id: df54807c3e9f8a3e899e393822703960
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
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content:
"Figure TS.24 | Prevalence of legislation and emissions targets across regions. Panel (a): shares of global GHG emissions under national climate change legislations - \r in 2010, 2015 and 2020. Climate legislation is defined as an act passed by a parliament that includes the reduction of GHGs in its title or objectives. Panel (b): shares of global \r GHG emissions under national climate emission targets - in 2010, 2015 and 2020. Emissions reductions targets were taken into account as a legislative target when they were \r defined in a law or as part of a country's submission under the Kyoto Protocol, or as an executive target when they were included in a national policy or official submissions \r under the UNFCCC. Targets were included if they were economy-wide or included at least the energy sector. The proportion of national emissions covered are scaled to reflect \r [...]"


id: b4e826dd67aba9427f70b3781c656e4b
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
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content:
"[...] coverage and whether targets are in GHG or CO2 terms. Emissions data used are for 2019. 2020 data was excluded as emissions shares across regions deviated from past \r patterns due to COVID-19. AR6 regions: DEV = Developed countries; APC = Asia and Pacific; EEA = Eastern Europe and West Central Asia; AFR = Africa; LAM = Latin America \r and the Caribbean; ME = Middle East. {Figure 13.1 and 13.2}"


id: 214ab29ad98e9b352878a97d20ad2f87
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
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content:
"Climate change mitigation is accelerated when attention is \r given to integrated policy and economy-wide approaches, \r and when enabling conditions (governance, institutions, \r behaviour and lifestyle, innovation, policy, and finance), are \r present (robust evidence, medium agreement). Accelerating \r climate mitigation includes simultaneously weakening high-carbon \r systems and encouraging low-carbon systems; ensuring interaction \r between adjacent systems (e.g., energy and agriculture); overcoming \r resistance to policies (e.g.,  from incumbents in high-carbon\u0002emitting industries), including by providing transitional support to \r the vulnerable and negatively affected by distributional impacts; \r inducing changes in consumer practices and routines; providing \r transition support; and addressing coordination challenges in policy \r and governance. Table TS.9 elucidates the complexity of policymaking \r in driving sectoral transitions by summarising case studies of \r [...]"


id: b42a38dc6386f5eebd4ca69a07ec488e
created_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 28933
content:
"[...] sectoral transitions from Chapters 5 to 12. These real-world sectoral \r transitions reinforce critical lessons on policy integration. (Table TS.9) \r {13.7, 13.9}"


id: 457a73796535d04cd7877dc99f54618d
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 9440
content:
"its stated goals (medium confidence).Arguments in support of the \r Paris Agreement are that the processes it initiates and supports will \r in multiple ways lead, and indeed have already led, to rising levels of \r ambition over time. The recent proliferation of national mid-century \r net zero GHG targets can be attributed in part to the Paris Agreement. \r Moreover, its processes and commitments will enhance countries' \r abilities to achieve their stated level of ambition, particularly among \r developing countries. Arguments against the Paris Agreement are that \r it lacks a mechanism to review the adequacy of individual Parties' \r Nationally Determined Contributions (NDCs), that collectively current \r NDCs are inconsistent in their level of ambition with achieving the \r Paris Agreement's long-term temperature goal, that its processes will \r not lead to sufficiently rising levels of ambition in the NDCs, and that \r NDCs will not be achieved because the targets, policies and measures \r [...]"


id: cb760822eb67ee67ad41921a5c2f7c35
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 33122
content:
"[...] they contain are not legally binding at the international level. To \r some extent, arguments on both sides are aligned with different \r analytic frameworks, including assumptions about the main barriers \r to mitigation that international cooperation can help overcome. The \r extent to which countries increase the ambition of their NDCs and \r ensure they are effectively implemented will depend in part on the \r successful implementation of the support mechanisms in the Paris \r Agreement, and in turn will determine whether the goals of the \r Paris Agreement are met (high confidence). {14.2, 14.3, 14.4} "


id: 34d07689fa3585f9baeae5c43cfc3c06
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 6534
content:
"International cooperation outside the UNFCCC processes \r and agreements provides critical support for mitigation in \r particular regions, sectors and industries, for particular types \r of emissions, and at the sub- and trans-national levels (high \r confidence).Agreements addressing ozone depletion, transboundary \r air pollution, and release of mercury are all leading to reductions \r in the emissions of specific greenhouse gases. Cooperation is \r occurring at multiple governance levels including cities. Transnational \r partnerships and alliances involving non-state and sub-national \r actors are also playing a  growing role in stimulating low-carbon \r technology diffusion and emissions reductions (medium confidence). \r Such transnational efforts include those focused on climate litigation; \r the impacts of these are unclear but promising. Climate change is \r being addressed in a growing number of international agreements \r [...]"


id: 72bf2f04fd8f16b05930f42f7611eb06
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index: climate_gpt_v2
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content:
"[...] operating at sectoral levels, as well as within the practices of many \r multilateral organisations and institutions. Sub-global and regional \r cooperation, often described as climate clubs, can play an important \r role in accelerating mitigation, including the potential for reducing \r mitigation costs through linking national carbon markets, although \r actual examples of these remain limited. {14.2, 14.4, 14.5, 14.6} "


id: 1f919c15f32de9020978060d019f7cd8
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 2764
content:
"International cooperation under the UN climate regime took \r an important new direction with the entry into force of the \r 2015 Paris Agreement, which strengthened the objective of \r the UN climate regime, including its long-term temperature \r goal, while adopting a  different architecture to that of the \r Kyoto Protocol (high confidence). The core national commitments \r under the Kyoto Protocol were legally binding quantified emission \r targets for developed countries tied to well-defined mechanisms for \r monitoring and enforcement. By contrast, the commitments under \r the Paris Agreement are primarily procedural, extend to all parties, \r and are designed to trigger domestic policies and measures, enhance \r transparency, and stimulate climate investments, particularly in \r developing countries, and to lead iteratively to rising levels of \r ambition across all countries. Issues of equity remain of central \r importance in the UN climate regime, notwithstanding shifts in the \r [...]"


id: dcc5a60aab33a832d9bacda28b2ba1e6
created_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
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content:
"[...] operationalisation of 'common but differentiated responsibilities and \r respective capabilities' from Kyoto to Paris. {14.3} "


id: 1fed25b34b540ef9b0434ffb6330b0cb
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 2828
content:
"Finance to reduce net GHG emissions and enhance resilience to \r climate impacts is a critical enabling factor for the low-carbon \r transition. Fundamental inequities in access to finance as well as \r finance terms and conditions, and countries' exposure to physical \r impacts of climate change overall, result in a worsening outlook \r for a global Just Transition (high confidence). Decarbonising the \r economy requires global action to address fundamental economic \r inequities and overcome the climate investment trap that exists for \r many developing countries. For these countries the costs and risks of \r financing often represent a significant challenge for stakeholders at \r all levels. This challenge is exacerbated by these countries' general \r economic vulnerability and indebtedness. The rising public fiscal costs \r of mitigation, and of adapting to climate shocks, is affecting many \r countries and worsening public indebtedness and country credit \r [...]"


id: 1ec7685248afd7667d3f8d52c7b95c60
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 2616
content:
"[...] ratings at a time when there were already significant stresses on public \r finances. The COVID-19 pandemic has made these stresses worse and \r tightened public finances still further. Other major challenges for \r commercial climate finance include: the mismatch between capital \r and investment needs, home bias31 considerations, differences in risk \r perceptions for regions, as well as limited institutional capacity to \r ensure safeguards are effective (high confidence). {15.2, 15.6.3}"


id: bcc0cef305f42a566bf99e3589f9c17
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 30454
content:
"Investors, central banks, and financial regulators are driving \r increased awareness of climate risk. This increased awareness \r can support climate policy development and implementation \r (high confidence) {15.2, 15.6}. Climate-related financial risks arise \r from physical impacts of climate change (already relevant in the short \r term), and from a  disorderly transition to a  low-carbon economy. \r Awareness of these risks is increasing, leading also to concerns about \r financial stability. Financial regulators and institutions have responded \r with multiple regulatory and voluntary initiatives to assess and address \r these risks. Yet despite these initiatives, climate-related financial risks \r remain greatly underestimated by financial institutions and markets, \r limiting the capital reallocation needed for the low-carbon transition. \r Moreover, risks relating to national and international inequity - which \r [...]"


id: ffd814ceeb7761305a7a73832e4ca9e8
created_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
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content:
"[...] act as a barrier to the transformation - are not yet reflected in decisions \r by the financial community. Stronger steering by regulators and \r policymakers has the potential to close this gap. Despite the increasing \r attention of investors to climate change, there is limited evidence that \r this attention has directly impacted emission reductions. This leaves \r high uncertainty, both near term (2021-30) and longer term (2021-\r 50), on the feasibility of an alignment of financial flows with the Paris \r Agreement goals (high confidence). {15.2, 15.6}"


id: d9864ffb382db62b9e8a1e7edc936b53
created_at: 2023-04-19 13:23:07
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content:
"Figure TS.25 | Breakdown of recent average (downstream) mitigation investments and model-based investment requirements for 2020-2030 (USD billion) \r in scenarios that likely limit warming to 2degC or lower. Mitigation investment flows and model-based investment requirements by sector / segment (energy efficiency \r in buildings and industry, transport including efficiency, electricity generation, transmission and distribution including electrification, and agriculture, forestry and other land \r use), by type of economy, and by region (see Annex II Part I Section 1: By region is based on intermediate level (R10) classification scheme. By type of economy is based on \r intermediate level (R10) classification scheme, which considers 'North America', 'Europe', and 'Australia, Japan and New Zealand' as developed countries, and the other seven \r [...]"


id: 4dfef773ca8625b52e706fbd96d90394
created_at: 2023-04-19 13:23:07
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"[...] regions as developing countries). Breakdown by sector / segment may differ slightly from sectoral analysis in other contexts due to the availability of investment needs data. \r The granularity of the models assessed in Chapter 3, and other studies, do not allow for a robust assessment of the specific investment needs of LDCs or SIDSs. Investment \r requirements in developing countries might be underestimated due to missing data points as well as underestimated technology costs. In modelled pathways, regional \r investments are projected to occur when and where they are cost cost-effective to limit global warming. The model quantifications help to identify high-priority areas for cost\u0002effective investments, but do not provide any indication on who would finance the regional investments. Investment requirements and flows covering downstream / mitigation \r [...]"


id: dc3c4128d6719996d91e10dec3d0e897
created_at: 2023-04-19 13:23:07
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content:
"[...] technology deployment only. Data includes investments with a direct mitigation effect, and in the case of electricity, additional transmission and distribution investments. See \r section 15.4.2 Quantitative assessment of financing needs for detailed data on investment requirements. Data on mitigation investment flows are based on a single series of \r reports (Climate Policy Initiative, CPI) which assembles data from multiple sources. Investment flows for energy efficiency are adjusted based on data from the International \r Energy Agency (IEA). Data on mitigation investments do not include technical assistance (i.e., policy and national budget support or capacity building), other non-technology \r deployment financing. Adaptation only flows are also excluded. Data on mitigation investment requirements for electricity are based on emission pathways C1, C2 and C3 \r [...]"


id: e2704e70a8e28801c8916eb4f4eff6f8
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 37136
content:
"[...] (Table SPM.1). For electricity investment requirements, the upper end refers to the mean of C1 pathways and the lower end to the mean of C3 pathways. Data points for energy \r efficiency, transport and AFOLU cannot always be linked to C1-C3 scenarios. Data do not include needs for adaptation or general infrastructure investment or investment \r related to meeting the SDGs other than mitigation, which may be at least partially required to facilitate mitigation. The multiplication factors show the ratio of average annual \r model-based mitigation investment requirements (2020-2030) and most recent annual mitigation investments (averaged for 2017-2020). The lower and upper multiplication \r factors refer to the lower and upper ends of the range of investment needs."


id: ac49034018b4dd8ba6f4619118594e87
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 27555
content:
"Global climate finance is heavily focused on mitigation (more \r than 90% on average between 2017-2020) (high confidence) \r {15.4, 15.5}. This is despite the significant economic effects of climate \r change's expected physical impacts, and the increasing awareness \r of these effects on financial stability. To meet the needs for rapid \r deployment of mitigation options, global mitigation investments \r are expected to need to increase by the factor of three to six (high \r confidence). The gaps represent a  major challenge for developing \r countries, especially Least-Developed Countries (LDCs), where flows \r have to increase by the factor of four to seven for specific sectors such \r as AFOLU, and for specific groups with limited access to, and high \r costs of, climate finance (high confidence) (Figure TS.25) {15.4, 15.5}. \r The actual size of sectoral and regional climate financing gaps is only \r one component driving the magnitude of the challenge. Financial \r [...]"


id: 7f0cda93f9849815000f6bf0b7e5270e
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index: climate_gpt_v2
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content:
"[...] and economic viability, access to capital markets, appropriate \r regulatory frameworks, and institutional capacity to attract and \r facilitate investments and ensure safeguards are decisive to scaling\u0002up funding. Soft costs for regulatory environment and institutional \r capacity, upstream funding needs as well as R&D and venture capital \r for development of new technologies and business models are often \r overlooked despite their critical role to facilitate the deployment of \r scaled-up climate finance (high confidence). {15.4.1, 15.5.2}"


id: 2a23eddef1ba97592db0fc281b0b8917
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
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content:
"New business models (e.g.,  pay-as-you-go) can facilitate \r the aggregation of small-scale financing needs and provide \r scalable investment opportunities with more attractive \r risk-return profiles (high confidence). Support and guidance \r for enhancing transparency can promote capital markets' climate \r financing by providing quality information to price climate risks \r and opportunities. Examples include SDG and environmental, social \r and governance (ESG) disclosure, scenario analysis and climate risk \r assessments, including the Task Force on Climate-related Financial \r Disclosures (TCFD). The outcome of these market-correcting \r approaches on capital flows cannot be taken for granted, however, \r without appropriate fiscal, monetary and financial policies. Mitigation \r policies will be required to enhance the risk-weighted return of low\u0002emission and climate-resilient options, accelerate the emergence and \r support for financial products based on real projects, such as green \r [...]"


id: 5f160a8b430d4af3a3b6d9152f01021b
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content:
"[...] bonds, and phase-out fossil fuel subsidies. Greater public-private \r cooperation can also encourage the private sector to increase and \r broaden investments, within a context of safeguards and standards, \r and this can be integrated into national climate change policies and \r plans (high confidence). {15.1, 15.2.4, 15.3.1, 15.3.2, 15.3.3, 15.5.2, \r 15.6.1, 15.6.2, 15.6.6, 15.6.7, 15.6.8} "


id: c66df679504925efaca66312d2565103
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updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 32218
content:
"Ambitious global climate policy coordination and stepped-up \r public climate financing over the next decade (2021-2030) \r can help redirect capital markets and overcome challenges \r relating to the need for parallel investments in mitigation. \r It can also help address macroeconomic uncertainty and \r alleviate developing countries' debt burden post-COVID-19 \r (high confidence). Providing strong climate policy signals helps \r guide investment decisions. Credible signalling by governments and \r the international community can reduce uncertainty for financial \r decision-makers and help reduce transition risk. In addition to indirect \r and direct subsidies, the public sector's role in addressing market \r failures, barriers, provision of information, and risk-sharing can \r encourage the efficient mobilisation of private sector finance (high \r confidence) {15.2, 15.6.1, 15.6.2}. The mutual benefits of coordinated \r support for climate mitigation and adaptation in the next decade \r [...]"


id: cab8dbdbc8e943182d615cb651c1f579
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 33006
content:
"[...] for both developed and developing regions could potentially be very \r high in the post-COVID era. Climate-compatible stimulus packages \r could significantly reduce the macro-financial uncertainty generated \r by the pandemic and increase the sustainability of the world \r economic recovery {15.2, 15.3.2.3, 15.5.2, 15.6.1, 15.6.7}. Political \r leadership and intervention remain central to addressing uncertainty, \r which is a fundamental barrier for the redirection of financial flows. \r Existing policy misalignments - for example, in fossil fuel subsidies - \r undermine the credibility of public commitments, reduce perceived \r transition risks and limit financial sector action (high confidence). \r {15.2, 15.3.3, 15.6.1, 15.6.2, 15.6.3}"


id: 13cd3f6c32f67619c9e5e7e44baa2a34
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 696
content:
"action to mitigate climate change by reinforcing other interventions. \r In conjunction with other enabling conditions, innovation can support \r system transitions to limit warming and help shift development \r pathways. The currently widespread implementation of solar PV \r and LED lighting, for instance, could not have happened without \r technological innovation. Technological innovation can also bring \r about new and improved ways of delivering services that are essential \r to human well-being (high confidence) {16.1, 16.3, 16.4, 16.6}. At the \r same time as delivering benefits, innovation can result in trade-offs that \r undermine both progress on mitigation and progress towards other \r Sustainable Development Goals (SDGs). Trade-offs include negative \r externalities' - for instance, greater environmental pollution and social \r inequalities - rebound effects leading to lower net emission reductions \r or even increases in emissions, and increased dependency on foreign \r [...]"


id: 1ce526de68c03ecd75ff4887b5d4f0c3
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content:
"[...] knowledge and providers (high confidence). Effective governance and \r policy have the potential to avoid and minimise such misalignments \r (medium evidence, high agreement). {16.2, 16.3, 16.4, 16.5.1, 16.6} "


id: 5029ea3658a5b2b77488e6430f6b2bcf
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"Experience and analyses show that technological change is \r inhibited if technological innovation system functions are \r not adequately fulfilled; this inhibition occurs more often \r in developing countries (high confidence). Examples of such \r functions are knowledge development, resource mobilisation, and \r activities that shape the needs, requirements and expectations \r of actors within the innovation system (guidance of the search). \r Capabilities play a key role in these functions, the buildup of which \r can be enhanced by domestic measures, but also by international \r cooperation. For instance, innovation cooperation on wind energy \r has contributed to the accelerated global spread of this technology. \r As another example, the policy guidance by the Indian government, \r which also promoted development of data, testing capabilities and \r knowledge within the private sector, has been a key determinant of \r the success of an energy-efficiency programme for air conditioners \r [...]"


id: d23f0b836917e760c05d1ac790a944d
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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content:
"[...] and refrigerators in India. {16.3, 16.5, 16.6, Cross-Chapter Box 12 in \r Chapter 16, Box 16.3}"


id: 3e28442636300998b3ea53e8edcac187
created_at: 2023-04-19 13:23:07
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content:
"A systemic perspective on technological change can provide \r insights to policymakers supporting their selection of effective \r innovation policy instruments (high confidence) {16.4, 16.5}.\r A combination of scaled-up innovation investments with demand\u0002pull interventions can achieve faster technology unit cost reductions \r and more rapid scale-up than either approach in isolation. These \r innovation policy instruments would nonetheless have to be tailored \r to local development priorities, to the specific context of different \r countries, and to the technology being supported. The timing of \r interventions and any trade-offs with sustainable development also \r need to be addressed. Public R&D funding and support, as well as \r innovation procurement, have shown to be valuable for fostering \r innovation in small-to-medium clean-tech firms (Figure  TS.27) \r {16.4.4.3}. Innovation outcomes of policy instruments not necessarily \r [...]"


id: 3f0b90709874e1b9ac90881e3bf8f516
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 8277
content:
"Strategies for climate change mitigation can be most effective \r in accelerating transformative change when actions taken to \r strengthen one set of enabling conditions also reinforce and \r strengthen the effectiveness of other enabling conditions \r (medium confidence). Applying transition or system dynamics \r to decisions can help policymakers take advantage of such \r high-leverage intervention points, address the specific characteristics \r of technological stages, and respond to societal dynamics. Inspiration \r can be drawn from the global unit-cost reductions of solar PV, \r which were accelerated by a  combination of factors interacting \r in a  mutually reinforcing way across a  limited group of countries \r (high confidence) {Box 16.2, Cross-Chapter Box 12 in Chapter 16}. \r Transitions can be accelerated by policies appropriately targeted, \r which may be grouped in different 'pillars of policy'. The relative \r importance of different 'pillars' differs according to the stage of the \r [...]"


id: d0ec19ff0c07bca852aa7d27eb8fe7c5
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 34104
content:
"Although some initiatives have mobilised investments in \r developing countries, gaps in innovation cooperation remain, \r including in the Paris Agreement instruments. These gaps \r could be filled by enhancing financial support for international \r technology cooperation, by strengthening cooperative \r approaches, and by helping build suitable capacity in developing \r countries across all technological innovation system functions \r (high confidence). The implementation of current arrangements of \r international cooperation for technology development and transfer, \r as well as capacity-building, are insufficient to meet climate objectives \r and contribute to sustainable development. For example, despite \r building a  large market for mitigation technologies in developing \r countries, the lack of a systemic perspective in the implementation \r of the Clean Development Mechanism (CDM), operational since \r the mid-2000s, has only led to some technology transfer, especially \r [...]"


id: 255eeb5c0cfa6a62a72797c6639d3087
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 3762
content:
"[...] to larger developing countries, but limited capacity building and \r minimal technology development (medium confidence). In the \r current climate regime, a  more systemic approach to innovation \r cooperation could be introduced by linking technology institutions, \r such as the Technology Mechanism, and financial actors, such as the \r Financial Mechanism. {16.5.3} "


id: 9778d6393ec87f1dad2273f46a141dfe
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 23860
content:
"Better and more comprehensive data on innovation indicators \r can provide timely insights for policymakers and policy design \r locally, nationally and internationally, especially for developing \r countries, where such insights are often missing. Data needed \r include those that can show the strength of technological, sectoral \r and national innovation systems. It is also necessary to validate \r current results and generate insights from theoretical frameworks \r and empirical studies for developing countries' contexts. Innovation \r studies on adaptation and mitigation other than energy and ex-post\r assessments of the effectiveness of various innovation-related \r policies and interventions, including R&D, would also provide benefits. \r Furthermore, methodological developments to improve the ability of \r IAMs to capture energy innovation system dynamics and the relevant \r institutions and policies (including design and implementation), \r [...]"


id: 4bcd5ff8c242321ba7d23c4997e11028
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 10597
content:
"The digital transformation is a megatrend that is fundamentally changing all economies and societies, albeit in very different ways \r depending on the level of development of a given country and on the nature of its economic system. Digital technologies have \r significant potential to contribute to decarbonisation due to their ability to increase energy and material efficiency, make transport \r and building systems less wasteful, and improve the access to services for consumers and citizens. Yet, if left unmanaged, the digital \r transformation will probably increase energy demand, exacerbate inequities and the concentration of power, leaving developing \r economies with less access to digital technologies behind, raise ethical issues, reduce labour demand and compromise citizens' \r welfare. Appropriate governance of the digital transformation can ensure that digitalisation works as an enabler, rather than as \r [...]"


id: 154ebe58f61ccd814027c8f8cb4d0562
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 963
content:
"[...] a barrier and further strain in decarbonisation pathways. Governance can ensure that digitalisation not only reduces GHG emissions \r intensity but also contributes to reducing absolute GHG emission, constraining run-away consumption. {Cross-Chapter Box 11 in \r Chapter 16, 16.2} "


id: 97b81687b1a6ca20801c7010939e93
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 23911
content:
"The SDG framework33 can serve as a  template to evaluate \r the long-term implications of mitigation on sustainable \r development and vice versa (high confidence). Understanding \r the co-benefits and trade-offs associated with mitigation \r is key to understanding how societies prioritise among the \r various sectoral policy options (medium confidence). Areas \r with anticipated trade-offs include food and biodiversity, energy \r affordability/access, and mineral-resource extraction. Areas with \r anticipated co-benefits include health, especially regarding air \r pollution, clean energy access and water availability. The possible \r implementation of the different sectoral mitigation options therefore \r depends on how societies prioritise mitigation versus other products \r and services: not least, how societies prioritise food, material well\u0002being, nature conservation and biodiversity protection, as well as \r considerations such as their future dependence on CDR. Figure TS.29 \r [...]"


id: a4bdefffd4d3e0290d4aa8bd892c3834
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updated_at: 2023-04-19 13:23:07
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vector_id: 26204
content:
"[...] summarises the assessment of where key synergies and trade-offs \r exist between mitigation options and the SDGs. (Figures TS.29 and \r TS.31, Table TS.7) {12.3, 12.4, 12.5, 12.6.1, Figures 3.39 and 17.1} "


id: 9db5ad24a85bfddd9ab3e3719e89a835
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 24976
content:
"Adopting coordinated cross-sectoral approaches to climate \r mitigation can target synergies and minimise trade-offs, \r both between sectors and between sustainable development \r objectives (high confidence). This requires integrated planning \r using multiple-objective-multiple-impact policy frameworks. \r Strong inter-dependencies and cross-sectoral linkages create both \r opportunities for synergies and need to address trade-offs related \r to mitigation options and technologies. This can only be done if \r coordinated sectoral approaches  to climate change mitigation \r policies are adopted that mainstream these interactions and ensure \r local people are involved in the development of new products, as \r well as production and consumption practices. For instance, there can \r be many synergies in urban areas between mitigation policies and \r the SDGs but capturing these depends on the overall planning of \r urban structures and on local integrated policies such as combining \r [...]"


id: b190c0ef955dbd697c216bad3d46aab3
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updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 28464
content:
"[...] affordable housing and spatial planning with walkable urban \r areas, green electrification and clean renewable energy (medium \r confidence). Integrated planning and cross-sectoral alignment of \r climate change policies are also particularly evident in developing \r countries' NDCs under the Paris Agreement, where key priority \r sectors such as agriculture and energy are closely aligned with the \r proposed mitigation and adaptation actions and the SDGs. {12.6.2, \r Supplementary Material Table 17.SM.1, 17.3.3}"


id: 9aea5a1c68990e4f3cc4dd419605222d
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 24496
content:
"Figure TS.29 (continued): Mitigation options have synergies with many Sustainable Development Goals (SDGs), but there are trade-offs associated with \r some options especially when implemented at scale. The synergies and trade-offs vary widely and depend on the context. Figure presents a summary of the chapter\u0002level qualitative assessment of the synergies and trade-offs for selected mitigation options. Overlaps may exist in the mitigation options assessed and presented by sector and \r system, and interlinkages with the SDGs might differ depending on the application of that option by sector. Interactions of mitigation options with the SDGs are context-specific \r and dependent on the scale of implementation. For some mitigation options, these scaling and context-specific issues imply that there are both synergies and trade-offs in \r [...]"


id: febffad91159fefe33eca944be1f0624
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 42208
content:
"[...] relation to specific SDGs. The SDGs are displayed as coloured squares. They indicate whether a synergy, trade-off, or both synergies and trade-offs exist between the SDG and \r the mitigation option. Confidence levels are indicated through the solidity of the squares. A solid square indicates high confidence, a partially filled square indicates medium \r confidence, and an outlined square indicates low confidence. The final column in the figure provides a line of sight to the chapters that provide details on context-specificity and \r scale of implementation. {6.3, 6.4, 6.7, 7.3, 7.4, 7.5, 7.6, 8.2, 8.4, 8.6, 9.4, 9.5, 9.8, Table 9.5, 10.3, 10.4, 10.5, 10.6, 10.8, 11.5, Table 10.3, 17.3, Figure 17.1, Supplementary \r Material Table 17.SM.1, Annex II.IV.12}"


id: 23eac2d5b60ea3f0b85c945fd5ffe123
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 3506
content:
"Figure TS.31 | Geophysical, environmental-ecological, technological, economic, socio-cultural and institutional factors can enable or act as barriers to \r the deployment of response options. Chapter-level assessment for selected mitigation options. Overlaps may exist in the mitigation options assessed and presented \r by sector and system, and feasibility might differ depending on the demarcation of that option in each sector. Chapters 6, 8, 9, 10, and 12 assess mitigation response \r options across six  feasibility dimensions: geophysical, environmental-ecological, technological, economic, socio-cultural and institutional. AFOLU (Chapter  7) and industry \r (Chapter 11) are not included because of the heterogeneity of options in these sectors. For each dimension, a set of feasibility indicators was identified. Examples of indicators \r [...]"


id: 6055e48df2fe3a7f9bac5278ab5eb714
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 14254
content:
"[...] include impacts on land use, air pollution, economic costs, technology scalability, public acceptance and political acceptance (see Box TS.15, and Annex II.IV.11 for a detailed \r explanation). An indicator could refer to a barrier or an enabler to implementation, or could refer to both a barrier or an enabler, depending on the context, speed, and scale \r of implementation. Dark blue bars indicate the extent of enablers to deployment within each dimension. This is shown relative to the maximum number of possible enablers, \r as indicated by the light blue shading. Dark orange bars indicate the extent of barriers to deployment within each dimension. This is shown relative to the maximum number \r of possible barriers, as indicated by light orange shading. A light grey dot indicates that there is limited or no evidence to assess the option. A dark grey dot indicates that one \r [...]"


id: 92242d712878e5922af60a0be6500dbb
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 22924
content:
"[...] of the feasibility indicators within that dimension is not relevant for the deployment of the option. The relevant sections in the underlying chapters include references to the \r literature on which the assessment is based and indicate whether the feasibility of an option varies depending on context (e.g., region), scale (e.g., small, medium or full scale), \r speed (e.g., implementation in 2030 versus 2050) and warming level (e.g., 1.5degC versus 2degC). {6.4, 8.5, 9.10, 10.8, 12.3, Annex II.IV.11}"


id: 69c9968672a047a5c538d6a5d1e776d3
created_at: 2023-04-19 13:23:07
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content:
"Figure TS.32 | The feasibility of mitigation scenarios. Figure TS.32 shows the proportion of scenarios in the AR6 scenarios database - falling within the warming \r level classifications C1 and C3 (C1: below 1.5degC (>50%), no or limited overshoot; C3: below 2degC (>67%)) - that exceed threshold values in 2030, 2050 and 2100 for five \r dimensions of feasibility (Boxes TS.5 and TS.15). The feasibility dimensions shown are: geophysical, technological, economic, socio-cultural and institutional. The thresholds \r shown are: (i) plausible - range of values based on past historical trends or other peer reviewed assessments; (ii) best-case scenario - range of values assuming major political \r support or technological breakthrough; (iii) unprecedented - values going beyond those observed or reported in peer-reviewed assessments. Overlayed are the Illustrative \r [...]"


id: 6167578d83b7d9f18a9e7eb5278f4266
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index: climate_gpt_v2
vector_id: 14436
content:
"[...] Mitigation Pathways consistent with SSP2 (LD, SP, Ren: C1 category; Neg, GS: C3 category). The positioning of the illustrative pathways is simply indicative of the general trade\u0002offs over time and across the feasibility dimensions, it is not determined mathematically. (Box TS.5) {3.8}"


id: a06d52b42a4a808387b4815c3347a8a0
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 25438
content:
"A wide range of factors have been found to enable sustainability \r transitions, ranging from technological innovations to shifts in \r markets, and from policies and governance arrangements to \r shifts in belief systems and market forces (high confidence).\r Many of these factors have come together in a co-evolutionary process \r that has unfolded globally, internationally and locally over several \r decades (low evidence, high agreement). Those same conditions that \r may serve to impede the transition (i.e., organisational structure, \r behaviour, technological lock-in) can also 'flip' to enable both the \r transition and the framing of sustainable development policies to \r create a  stronger basis for policy support (high confidence). It is \r important to note that strong shocks to these systems, including \r accelerating climate change impacts, economic crises and political \r changes, may provide crucial openings for accelerated transitions \r [...]"


id: 176f4968e808d495d651f7f6fcfd3931
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updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 1342
content:
"[...] to sustainable systems. For example, rebuilding more sustainably \r after an extreme event, or renewed public debate about the drivers \r of social and economic vulnerability to multiple stressors (medium \r confidence). {17.4} "


id: 3172f74f5a76b3fdcc4e7f65d1d4e6c8
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 5940
content:
"The transition to a  low-carbon economy depends on \r a wide range of closely intertwined drivers and constraints, \r including policies and technologies where notable advances \r over the past decade have opened up new and large-scale \r opportunities for deep decarbonisation, and for alternative \r development pathways which could deliver multiple social and \r developmental goals (robust evidence, medium agreement). \r Drivers for and constraints against low-carbon societal transition \r comprise economic and technological factors (the means by which \r services such as food, heating and shelter are provided and for whom, \r the emissions intensity of traded products, finance, and investment), \r socio-political issues (political economy, equity and fairness, \r social innovation and behaviour change), and institutional factors\r (legal framework and institutions, and the quality of international \r cooperation). In addition to being deeply intertwined all the factors \r [...]"


id: 88bbc19fb99ff0089ec341b87185763d
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 21217
content:
"[...] matter to varying degrees, depending on the prevailing social, \r economic, cultural and political context. They often exert both push \r and pull forces at the same time, within and across different scales. \r The development and deployment of innovative technologies and \r systems at scale are important for achieving deep decarbonisation. \r In recent years, the cost of several low-carbon technologies has \r declined sharply, alongside rapid deployment. Over 20 countries \r have also sustained emission reductions, and many more have \r accelerated energy efficiency and/or land-use improvements. Overall, \r however, the global contribution is so far modest, at a few billion \r tonnes (tCO2-eq) of avoided emissions annually. {1.3, 1.4, Chapters 2, \r 4, 13 and 14}"


id: 719783138d173ba7877bd4f0e8250456
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 17230
content:
"Global greenhouse gas (GHG) emissions continued to rise to \r 2019: the aggregate reductions implied by current Nationally \r Determined Contributions (NDCs) to 2030 would still make \r it impossible to limit warming to 1.5degC with no or limited \r overshoot, and would only be compatible with likely limiting \r warming below 2degC if followed by much steeper decline, hence \r limiting warming to either level implies accelerated mitigation \r actions at all scales (robust evidence, high agreement). Since \r the IPCC's Fifth Assessment Report (AR5), important changes that \r have emerged include the specific objectives established in the Paris \r Agreement of 2015 (for temperature, adaptation and finance), rising \r climate impacts, and higher levels of societal awareness and support for \r climate action. The growth of global GHG emissions has slowed over the \r past decade, and delivering the updated NDCs to 2030 would turn this \r [...]"


id: 1bdd04ce53eb1861cd1e1e1ac330647f
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 2089
content:
"[...] into decline, but the implied global emissions by 2030 exceed pathways \r consistent with 1.5degC by a large margin, and are near the upper end of \r the range of modelled pathways which keep temperatures likely limit \r warming to 2degC (with >65% probability). Continuing investments in \r carbon-intensive activities at scale will heighten the multitude of risks \r associated with climate change and impede societal and industrial \r transformation towards low-carbon development. Meeting the long\u0002term temperature objective in the Paris Agreement therefore implies \r a rapid turn to an accelerating decline of GHG emissions towards 'net \r zero', which is implausible without urgent and ambitious action at all \r scales. The unprecedented COVID-19 pandemic has had far-reaching \r impacts on the global economic and social system, and recovery will \r present both challenges and opportunities for climate mitigation. {1.2, \r 1.3, 1.5, 1.6, Chapters 3 and 4}"


id: 69bca40ff34d5c286279e1bb2b31c847
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 15869
content:
"Accelerating mitigation to prevent dangerous anthropogenic \r interference within the climate system will require the \r integration of broadened assessment frameworks and tools \r that combine multiple perspectives, applied in a  context of \r multi-level governance (robust evidence, medium agreement). \r Analysing a  challenge on the scale of fully decarbonising our \r economies entails integration of multiple analytic frameworks. \r Approaches to risk assessment and resilience, established across \r IPCC Working Groups, are complemented by frameworks for probing \r the challenges in implementing mitigation. Aggregate frameworks \r include cost-effectiveness analysis towards given objectives, and \r cost-benefit analysis, both of which have been developing to take \r fuller account of advances in understanding risks and innovation, \r the dynamics of emitting systems and of climate impacts, and \r welfare economic theory including growing consensus on long-term \r [...]"


id: e6a22427d02ba5641ef66ac615c951f5
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 37871
content:
"[...] discounting. Ethical frameworks consider the fairness of processes \r and outcomes which can help ameliorate distributional impacts \r across income groups, countries and generations. Transition and \r transformation frameworks explain and evaluate the dynamics of \r transitions to low-carbon systems arising from interactions amongst \r levels, with inevitable resistance from established socio-technical \r structures. Psychological, behavioural and political frameworks \r outline the constraints (and opportunities) arising from human \r psychology and the power of incumbent interests. A comprehensive \r understanding of climate mitigation must combine these multiple \r frameworks. Together with established risk frameworks, collectively \r these help to explain potential synergies and trade-offs in mitigation, \r imply a  need for a  wide portfolio of policies attuned to different "


id: 533f10264b952ecfbd950e3eabfd2f3a
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 11916
content:
"While there are some trade-offs, effective and equitable \r climate policies are largely compatible with the broader goal \r of sustainable development and efforts to eradicate poverty \r as enshrined in the 17 Sustainable Development Goals (SDGs) \r (robust evidence, high agreement). Climate mitigation is one \r of many goals that societies pursue in the context of sustainable \r development, as evidenced by the wide range of the SDGs. Climate \r mitigation has synergies and/or trade-offs with many other SDGs. \r There has been a strong relationship between development and GHG \r emissions, as historically both per capita and absolute emissions \r have risen with industrialisation. However, recent evidence shows \r countries can grow their economies while reducing emissions. \r Countries have different priorities in achieving the SDGs and reducing \r emissions as informed by their respective national conditions and \r capabilities. Given the differences in GHG emissions contributions, \r [...]"


id: 9b6644b9dbe2b754d7ff9f91d178d104
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 24593
content:
"[...] degree of vulnerabilities and impacts, as well as capacities within and \r between nations, equity and justice are important considerations for \r effective climate policy and for securing national and international \r support for deep decarbonisation. Achieving sustainable global \r development and eradicating poverty as enshrined in the 17 SDGS \r would involve effective and equitable climate policies at all levels \r from local to global scale. Failure to address questions of equity \r and justice over time can undermine social cohesion and stability. \r International cooperation can enhance efforts to achieve ambitious \r global climate mitigation in the context of sustainable development. \r {1.4, 1.6, Chapters 2, 3, 4, 5, 13 and 17}"


id: 6ace142fa00643cb4b104ad191c04177
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 16070
content:
"The speed, direction and depth of any transition will be \r determined by choices in the, environmental, technological, \r economic, socio-cultural and institutional realms (robust \r evidence, high agreement). Transitions in specific systems can be \r gradual or rapid and disruptive. The pace of a transition can be impeded \r by 'lock-in' generated by existing physical capital, institutions, and \r social norms. The interaction between power, politics and economy \r is central in explaining why broad commitments do not always \r translate to urgent action. At the same time, attention to and support \r for climate policies and low-carbon societal transition has generally \r increased, as the impacts have become more salient. Both public and \r private financing and financial structures strongly affect the scale \r and balance of high- and low-carbon investments. COVID-19 has \r strained public finances, and integrating climate finance into ongoing \r [...]"


id: 1f93af27af5731c86b7e1aef414243f9
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 2765
content:
"[...] recovery strategies, nationally and internationally, can accelerate the \r diffusion of low-carbon technologies and also help poorer countries \r to minimise future stranded assets. Societal and behavioural norms, \r regulations and institutions are essential conditions to accelerate \r low-carbon transitions in multiple sectors, whilst addressing \r distributional concerns endemic to any major transition. {1.3.3, 1.4, \r 1.8, Chapters 2, 4 and 15, and Cross-Chapter Box 1 in this chapter}"


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"Achieving the global transition to a  low-carbon, climate\u0002resilient and sustainable world requires purposeful and \r increasingly coordinated planning and decisions at many \r scales of governance including local, sub-national, national \r and global levels (robust evidence, high agreement).\r Accelerating mitigation globally would imply strengthening policies \r adopted to date, expanding the effort across options, sectors, and \r countries, and broadening responses to include more diverse actors \r and societal processes at multiple - including international - levels. \r Effective governance of climate change entails strong action across \r multiple jurisdictions and decision-making levels, including regular \r evaluation and learning. Choices that cause climate change as well \r as the processes for making and implementing relevant decisions \r involve a range of non-nation state actors such as cities, businesses, \r and civil society organisations. At global, national and sub-national \r [...]"


id: 81edc790add5f7a1b39599cd94fde17e
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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"[...] levels, climate change actions are interwoven with and embedded \r in the context of much broader social, economic and political goals. \r Therefore, the governance required to address climate change has to \r navigate power, political, economic, and social dynamics at all levels \r of decision-making. Effective climate-governing institutions,  and \r openness to experimentation on a  variety of institutional \r arrangements, policies and programmes can play a  vital role in \r engaging stakeholders and building momentum for effective climate \r action. {1.4, 1.9, Chapters 8, 15 and 17}"


id: 9acd1308be1c4553f9b474b3e852de73
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 24474
content:
"The Special Report on Global Warming of 1.5degC (SR1.5) underlined \r that humanity is now living with the 'unifying lens of the \r Anthropocene' (IPCC 2018a, pp. 52-53), that requires a sharpened \r focus on the impact of human activity on the climate system and the \r planet more broadly given 'planetary boundaries' (Steffen et al. 2015) \r including interdependencies of climate change and biodiversity \r (Dasgupta 2021). Recent literature assessed by Working Groups I and \r II of this AR6 underlines the urgency of climate action as cumulative \r CO2 emissions, along with other greenhouses gases (GHGs), drives \r the temperature change. Across AR6, global temperature changes \r are defined relative to the period 1850-1900, as in SR1.5 and \r collaboration with WGI enabled the use of AR6-calibrated emulators \r to assure consistency across the three Working Groups. The remaining \r 'carbon budgets' (see Annex I: Glossary) associated with 1.5degC and \r [...]"


id: 1704fa32ec3f68876c0f96d041ff285d
created_at: 2023-04-19 13:23:07
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content:
"[...] 2degC temperature targets equate to about one (for 1.5degC) to three \r (for 2degC) decades of current emissions, as from 2020, but with \r significant variation depending on multiple factors including other \r gases (Figure 2.7, and Cross-Working Group Box 1 in Chapter 3). For \r an outline of the WGIII approach to mitigation scenarios, emission \r pathways implied by the Paris goals, and the timing of peak and 'net \r zero' (see Glossary and FAQ 1.3), see Section 1.5 and Chapter 3."


id: 226433d12aae4209369656ce490f5866
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updated_at: 2023-04-19 13:23:07
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content:
"Successive IPCC reports highlight a  strong connection between \r climate mitigation and sustainable development. Climate \r mitigation and adaptation goals have synergies and trade-offs \r with efforts to achieve sustainable development, including poverty \r eradication. A comprehensive assessment of climate policy therefore \r involves going beyond a  narrow focus on specific mitigation and \r adaptation options to incorporate climate issues into the design of \r comprehensive strategies for equitable sustainable development. At \r the same time, some climate mitigation policies can run counter to \r sustainable development and eradicating poverty, which highlights \r the need to consider trade-offs alongside benefits. Examples include \r synergies between climate policy and improved air quality, reducing \r premature deaths and morbidity (IPCC 2014b, Figure SPM.6) (AR6 \r WGI Sections 6.6.3 and 6.7.3), but there would be trade-offs if policy \r [...]"


id: 6b3dfc4560862a7ca20e246b3c5bbf19
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"[...] raises net energy bills, with distributional implications. The Special \r Report on Climate Change and Land (SRCCL) also emphasises \r important synergies and trade-offs, bringing new light on the link \r between healthy and sustainable food consumption and emissions \r caused by the agricultural sector. Land-related responses that \r contribute to climate change adaptation and mitigation can also \r combat desertification and land degradation, and enhance food \r security (IPCC 2019a)."


id: 681bc6da502d7af174e89dc0e70355a2
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"The AR5 projected that in baseline scenarios (i.e., based on prevailing \r trends without explicit additional mitigation efforts), agriculture, \r forestry and other land use (AFOLU) would be the only sector where \r emissions could fall by 2100, with some CO2 removal (IPCC 2014b, \r p. 17). Direct CO2 emissions from energy were projected to double \r or even triple by 2050 (IPCC 2014b, p. 20) due to global population \r and economic growth, resulting in global mean surface temperature \r increases in 2100 from 3.7degC to 4.8degC compared to pre-industrial \r levels. The AR5 noted that mitigation effort and the costs associated \r with ambitious mitigation differ significantly across countries, and in \r 'globally cost-effective' scenarios, the biggest reductions (relative to \r projections) occur in the countries with the highest future emissions \r in the baseline scenarios (IPCC 2014b, p. 17). Since most physical \r capital (e.g.,  power plants, buildings, transport infrastructure) \r [...]"


id: 879b92ac28e657c19a73170ae34b0d51
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index: climate_gpt_v2
vector_id: 21015
content:
"[...] involved in GHG emissions is long-lived, the timing of the shift in \r investments and strategies will be crucial (IPCC 2014b, p. 18)."


id: f6729b16ec4afbc90528111c66605e28
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
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content:
"A key message from recent Special Reports is the urgency to mitigate \r GHG emissions in order to avoid rapid and potentially irreversible \r changes in natural and human systems (IPCC 2018b, 2019b, 2019c). \r Successive IPCC reports have drawn upon increasing sophistication \r of modelling tools to project emissions in the absence of ambitious \r decarbonisation action, as well as the emission pathways that meet \r long-term temperature targets. The SR1.5 examined pathways limiting \r warming to 1.5degC, compared to the historical baseline of 1850-1900, \r finding that 'in pathways with no or limited overshoot of 1.5degC, \r global net anthropogenic CO2 emissions decline by about 45% from \r 2010 levels by 2030, reaching net zero around 2050' (2045-2055 \r interquartile range); with 'overshoot' referring to higher temperatures, \r then brought down by 2100 through 'net negative' emissions. It \r found this would require rapid and far-reaching transitions in energy, \r [...]"


id: 9bf184f78a17c038087c0de87ea6f470
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updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 24686
content:
"[...] land, urban and infrastructure (including transport and buildings), \r and industrial systems (high confidence) (IPCC 2018b)."


id: 45d4609c17966ef4c0882f4a97d7f38a
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 9507
content:
"The assessment of the Physical Science Basis (IPCC AR6 WGI) \r documents sustained and widespread changes in the atmosphere, \r cryosphere, biosphere and ocean, providing unequivocal evidence of \r a  world that has warmed, associated with rising atmospheric CO2\r concentrations reaching levels not experienced in at least the last \r 2  million years. Aside from temperature, other clearly discernible, \r human-induced changes beyond natural variations include \r declines in Arctic Sea ice and glaciers, thawing of permafrost, and \r a  strengthening of the global water cycle (AR6 WGI SPM A.2, B.3 \r and B.4). Oceanic changes include rising sea level, acidification, \r deoxygenation, and changing salinity (WGI SPM B.3). Over land, in \r recent decades, both frequency and severity have increased for hot \r extremes but decreased for cold extremes; intensification of heavy \r precipitation is observed in parallel with a  decrease in available \r [...]"


id: 8d137a0fdc3f72304b12bebf1cb34360
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updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 22010
content:
"[...] water in dry seasons, along with an increased occurrence of weather \r conditions that promote wildfires."


id: f5e0f0bb9c6866f986193f5151920d14
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updated_at: 2023-04-19 13:23:07
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vector_id: 40616
content:
"SDGs. In September 2015, the UN endorsed a universal agenda - \r 'Transforming our World: the 2030 Agenda for Sustainable \r Development'. The agenda adopted 17 non-legally-binding SDGs \r and 169 targets to support people, peace, prosperity, partnerships \r and the planet. While climate change is explicitly listed as SDG 13, \r the pursuit of the implementation of the UNFCCC is relevant for \r a number of other goals including SDG 7 (clean energy for all), SDG \r 9  (sustainable industry), and SDG 11 (sustainable cities), SDG 12 \r (responsible consumption and production) as well as those relating \r to life below water (SDG 14) and on land (SDG 15) (Biermann et al. \r 2017). Mitigation actions could have multiple synergies and trade\u0002offs across the SDGs (Pradhan et al. 2017) (Chapter  17) and their \r net effects depend on the pace and magnitude of changes, the \r specific mitigation choices and the management of the transition. \r [...]"


id: 4052545db6932d01f0f15329b9c367df
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content:
"[...] This suggests that mitigation must be pursued in the broader context \r of sustainable development as explained in Section 1.6."


id: 21da6a2be56119276cabb16cae414a75
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updated_at: 2023-04-19 13:23:07
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vector_id: 3179
content:
"Global GHG emissions have continued to rise since AR5, though \r the average rate of emissions growth slowed, from 2.4% (from \r 2000-2010) to 1.3% for 2010-2019 (Figure  1.1). After a  period \r of exceptionally rapid growth from 2000 as charted in AR5, global \r fossil fuel- and industry-related (FFI) CO2 emissions almost plateaued \r between 2014 and 2016 (while the global economy continued to \r expand (World Bank 2020), but increased again over 2017-19, the \r average annual growth rate for all GHGs since 2014 being around \r 0.8% yr-1 (IPCC/EDGAR emissions database; see also Chapter  11, \r Figure 11.2)). Important driving factors include population and GDP \r growth, as illustrated in panels (b) and (c) of Figure 1.1 respectively. \r The pause in emissions growth reflected the interplay of strong energy \r efficiency improvements and low-carbon technology deployment, but \r these did not expand fast enough to offset the continued pressures \r [...]"


id: fa845ad2a3129cb62e56616b74ce46bb
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content:
"[...] for overall growth at global level (UNEP 2018a; IEA 2019a). However, \r since 2013/14, the decline in global emissions intensity (GHG/\r GDP) has accelerated somewhat, and global emissions growth has \r averaged slightly slower than population growth (Figure 1.1d), which \r if sustained would imply a peak of global CO2 (GHG) emissions per \r capita, at about 5 tCO2 per person (7 tCO2-eq per person) respectively."


id: e2c5a3d0a143a656eceffab4c8181218
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updated_at: 2023-04-19 13:23:07
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vector_id: 37195
content:
"Alongside this, private and blended climate finance is increasing \r but is still short of projected requirements consistent with Paris \r Agreement goals (Section 15.3.2.1). The financing gap is particularly \r acute for adaptation projects, especially in vulnerable developing \r countries. From a  macro-regulatory perspective, there is growing \r recognition that substantial financial value may be at risk from \r changing regulation and technology in a low-carbon transition, with \r potential implications for global financial stability (Section 15.6.3). \r To date, the most significant governance development is the \r Financial Stability Board's Task Force on Climate-related Financial \r Disclosures (TCFD) and its recommendations that investors and \r companies consider climate change risks in their strategies and \r capital allocation, so investors can make informed decisions (TCFD \r 2018), welcomed by over 500 financial institutions and companies \r [...]"


id: 127c8ce7b3569931ea5f691bb018ebb5
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updated_at: 2023-04-19 13:23:07
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content:
"Figure 1.2 | Distribution of regional greenhouse gas (GHG) emissions for 10 broad global regions according to territorial accounting (panels (a) and (b), \r GHG emissions) and consumption-based accounting (panels (c) and (d), CO2-FFI emissions only). GHG emissions are categorised into: fossil fuel and industry \r (CO2-FFI); land use, land-use change and forestry (CO2-LULUCF); and other greenhouse gases (methane, nitrous oxide and F-gas - converted to 100-year global warming \r potentials). Per-capita GHGs for territorial (panel a) and CO2-FFI emissions vs population for consumption-based accounting (panel c). Panels (b) and (d): GHG emissions \r per unit GDPppp vs GDPppp, weighted with purchasing power parity for territorial accounting (panel b), CO2-FFI emissions per unit GDPppp for consumption-based accounting \r (panel d). The area of the rectangles refers to the total emissions for each regional category, with the height capturing per-capita emissions (panels a and c) or emissions per \r [...]"


id: 340d4bfd55243018a50ff67fd08cb0f
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content:
"[...] unit GDPppp (panels (b) and (d)), and the width proportional to the population of the regions and GDPppp. Emissions from international aviation and shipping (2.4% of the total \r GHG emissions) are not included."


id: face10b570effc7526965d419f4bcd87
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updated_at: 2023-04-19 13:23:07
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content:
"The COVID-19 aftermath consequently also changes the economic \r context for mitigation (Sections  15.2 and 15.4). Many traditional \r forms of economic analysis (expressed as general equilibrium) \r assume that available economic resources are fully employed, \r with limited scope for beneficial economic 'multiplier effects' of \r government-led investment. After COVID-19 however, no country is \r in this state. Very low interest rates amplify opportunities for large\u0002scale investments which could bring 'economic multiplier' benefits, \r especially if they help to build the industries and infrastructures for \r further clean growth (Hepburn et al. 2020). However, the capability \r to mobilise low-interest finance varies markedly across countries and \r large public debts - including bringing some developing countries \r close to default - undermine both the political appetite and feasibility \r of large-scale clean investments. In practice the current orientation of \r [...]"


id: 3f84dea372614f9d9dbdaec77899e9a8
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updated_at: 2023-04-19 13:23:07
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vector_id: 8363
content:
"[...] COVID-19 recovery packages is very varied, pointing to a very mixed \r picture about whether or not countries are exploiting this opportunity \r (Cross-Chapter Box 1 in this chapter)."


id: 715c28c59ab9e84e2b377b36a4a4d641
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updated_at: 2023-04-19 13:23:07
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vector_id: 17201
content:
"Lessons. Important lessons can be drawn from the pandemic to climate change including the value of forward-looking risk management, \r the role of scientific assessment, preparatory action and international process and institutions (Chapter 5 and Section 1.3). There had \r been long-standing warnings of pandemic risks and precursors - with both pandemic and climate risks being identified by social \r scientists as 'uncomfortable knowledge' or 'unknown knowns', which tend to be marginalised in practical policy (Rayner 2012; \r Sarewitz 2020). This echoes long-standing climate literature on potential 'high impact' events, including those perceived as low \r probability (Dietz 2011; Weitzman 2011). The costs of preparatory action, mainly in those countries that had suffered from earlier \r pandemics were negligible in comparison, suggesting the importance not just of knowledge but its effective communication and \r [...]"


id: 10ce726411000e3d68f101db8d261f53
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updated_at: 2023-04-19 13:23:07
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vector_id: 143
content:
"[...] embodiment in society (Chapter 5). Klenert et al. (2020) offer five early lessons for climate policy, concerning: the cost of delay; the bias \r in human judgement; the inequality of impacts; the need for multiple forms of international cooperation; and finally, 'transparency in \r value judgements at the science-policy interface'."


id: 5144ccadca1a16d127bac8f1ea219962
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updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 11577
content:
"Emissions and behavioural changes. Overall, global CO2 FFI emissions declined by about 5.8% (5.1-6.3%) from 2019 to 2020, or \r about 2.2 (1.8-2.4) GtCO2 in total (Section 2.2.2). Analysis from previous economic crises suggest significant rebound in emissions \r without policy-induced structural shifts (Jaeger et al. 2020) (Section 2.2.2.1 and Figure 2.5). Initial projections suggest the COVID \r aftermath may reduce emissions by 4-5% over 2025-2030 (Shan and Et.al 2020; Reilly et al. 2021), below a 'no-pandemic' baseline. \r The long-term impacts on behaviour, technology and associated emissions remain to be seen, but may be particularly significant in \r transport - lockdowns reduced mobility-related emissions, alongside two major growth areas: electronic communications replacing \r many work and personal travel requirements (Chapter 10 and Section 4.4.3.4); and revitalised local active transport and e-micromobility \r [...]"


id: 5dea365818bec665c16d2a2dc56772ec
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updated_at: 2023-04-19 13:23:07
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content:
"[...] (Earley and Newman 2021). Temporary 'clear skies' may also have raised awareness of the potential environment and health \r co-benefits of reduced fossil fuel use particularly in urban areas (Section 8.7), with evidence also indicating that air pollution itself \r amplified vulnerability to COVID-19 (Gudka et al. 2020; Wu et al. 2020). The significant impacts on passenger aviation are projected \r to extend not just through behavioural changes, but also fleet changes from retiring older planes, and reduced new orders indicating \r expectations of reduced demand and associated GHG emissions until 2030 (Sections 5.1.2 and 10.5) (AR6 WGI Box 6.1 in Chapter 6). \r However, air cargo has recovered more rapidly (IATA 2020), possibly enhanced by online ordering."


id: b61eb8c16f027715bb8d56736e7729c0
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updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
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content:
"Fiscal, growth and inequality impacts. Aspects of the global and regional economic crises from COVID-19 may prevail much \r longer than the crisis itself, potentially compromising mitigation. Most countries have undertaken unprecedented levels of short-term \r public expenditures. The International Monetary Fund (IMF) projects sovereign debt to GDP to have increased by 20% in advanced \r economies and 10% in emerging economies by the end of 2021 (IMF 2020). This is likely to slow economic growth, and may squeeze \r financial resources for mitigation and relevant investments for many years to come (Sections 15.2.3 and 15.6.3). COVID-19 further \r lowered interest rates which should facilitate low-carbon investment, but pandemic responses have increased sovereign debt across \r countries in all income bands (IMF 2021), and, particularly in some developing economies and regions, it has caused debt distress \r [...]"


id: 6170d4ba4a23d102d93927ff10c15180
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updated_at: 2023-04-19 13:23:07
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content:
"[...] (Bulow et al. 2021), widening the gap in developing countries' access to capital (Hourcade et al. 2021b) (Section 15.6.3). After decades \r of global progress in reducing poverty, COVID-19 has pushed hundreds of millions of people below poverty thresholds and raises the \r spectre of intersecting health and climate crises that are devastating for the most vulnerable (Section 5.1.2 and Box 5.1). Like those \r of climate change, pandemic impacts fall heavily on disadvantaged groups, exacerbate the uneven distribution of future benefits, \r amplify existing inequities, and introduce new ones. Increased poverty also hinders efforts towards sustainable low-carbon transitions \r (Section 1.6)."


id: cfa9d2c38c9b305f95b0e1e18dcadc9b
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vector_id: 33894
content:
"Recovery packages are motivated by assessments of the macroeconomic effectiveness ('multipliers') of public spending in ways that \r can crowd-in and revive private investment (Hepburn et al. 2020). There are clear reasons why a low-carbon response can create more \r enduring jobs, better aligned to future growth sectors: by also crowding-in and reviving private investment (e.g., from capital markets \r and institutional investors, including the growing profile of environmental, social and governance (ESG) and green bond markets \r (Section 15.6)), this can boost the effectiveness of public spending (IMF 2020). Stern and Valero (2021) argue that investment in low\u0002carbon innovation and its diffusion, complemented by investments in sustainable infrastructure, are key to shaping environmentally \r sustainable and inclusive growth in the aftermath of the COVID-19 pandemic crisis. This would be the case both for high-income \r [...]"


id: f77bc3fc8bd59c7d4deabbef7da3a08f
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content:
"[...] economies on the global innovation frontier, and to promote sustainable development in poorer economies."


id: 2e9ab679396ea5b9b21efa0565c0eb8c
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 5477
content:
"Crises and opportunities: the wider context for mitigation and transformation. The impacts of COVID-19 have been \r devastating in many ways, in many countries, and may distract political and financial capacity away from efforts to mitigate climate \r change. Yet, studies of previous post-shock periods suggest that waves of innovation that are ready to emerge can be accelerated \r by crises, which may prompt new behaviours, weaken incumbent ('meso-level') systems, and prompt rapid reforms (Roberts and \r Geels 2019a) (Section 1.6.5). Lessons from the collective effort to 'flatten the curve' during the pandemic, illustrating aspects of \r science-society interactions for public health in many countries, may carry over to climate mitigation, and open new opportunities \r (Section 5.1.2). COVID-19 appears to have accelerated the emergence of renewable power, electromobility and digitalisation (Newman \r [...]"


id: d0f72a7a5a692a61c0e3fc8ce674b976
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updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 34109
content:
"[...] 2020) (Sections 5.1.2, 6.3 and 10.2). Institutional change is often very slow but major economic dislocation can create significant \r opportunities for new ways of financing and enabling 'leapfrogging' investment to happen (Section 10.8). Given the unambiguous \r risks of climate change, and consequent stranded asset risks from new fossil fuel investments (Box 6.11), the most robust recoveries \r are likely to be those which emerge on lower carbon and resilient pathways (Obergassel et al. 2021). Noting the critical global post\u0002COVID-19 challenge as the double impact of heightened credit risk in developing countries, along with indebtedness in developed \r countries, Hourcade et al. (2021a) estimate that a 'multilateral' sovereign guarantee structure to underwrite low-carbon investments \r could leverage projects up to 15 times its value, contributing to shifting development pathways consistent with the SDGs and Paris goals."


id: 3e0359e30c4fdaee60a0aed7abe5fb3a
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updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 8079
content:
"Civil society pressures for stronger action. Civic engagement \r increased leading up to the Paris Agreement (Backstrand and \r Lovbrand 2019) and after. Youth movements in several countries \r show young people's awareness about climate change, evidenced \r by the school strikes for the climate (Hagedorn et al. 2019; Buettner \r 2020; Thackeray et al. 2020; Walker 2020). Senior figures across many \r religions (Francis 2015; IFEES 2015) stressed the duty of humanity \r to protect future generations and the natural world, and warned \r about the inequities of climate change. Growing awareness of local \r environmental problems such as air pollution in Asia and Africa \r (Karlsson et al. 2020), and the threat to indigenous people's rights \r and existence has also fuelled climate activism (Etchart 2017). Grass\u0002roots movements (Cheon and Urpelainen 2018; Fisher et al. 2019), \r build political pressure for accelerating climate change mitigation, \r [...]"


id: dadacd94b547888e222c4670d8f573b
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updated_at: 2023-04-19 13:23:07
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vector_id: 35797
content:
"The AR5 introduced six 'enabling conditions' for shifting development \r pathways which are presented in Chapter 4 of this report and some \r of which overlap with the drivers reviewed here. However, the \r terminology of drivers and constraints have been chosen here to \r reflect the fact that each of these factors can serve as an enabling \r condition or a constraint to ambitious climate action depending on \r the context and how they are deployed. Often one sees the factors \r exerting both push and pull forces at the same time in the same \r and across different scales. For example, finance and investments can \r serve as a barrier or an enabler to climate action (Battiston et al. 2021). \r Similarly, political economy factors can align in favour of ambitious \r climate action or act in ways that inhibit strong cooperation and \r low-carbon transition. The other key insight from the assessment of \r the  system drivers and constraints undertaken below is that none \r [...]"


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"[...] of the factors or conditions by themselves is more or less important \r than the others. In addition to being deeply intertwined all the factors "


id: 210c94dda1aef0a017beec57db34df0c
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
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content:
"Continued growth in population and income are expected to \r continue driving up demand for goods and services (Chapters  2, \r 3 and 5), with an important role for urbanisation which is proceeding \r at an unprecedented speed and scale. In the last decade, the urban \r population grew by 70 million people each year, or about 1.3 million \r people per week, with urban area expanding by about 102 km2\r  per \r day (Chapter  8). Urban areas account for most (45-87%) of the \r global carbon footprint (8.1) and the strong and positive correlation \r between urbanisation and incomes means higher consumption \r from urban lifestyles will continue driving direct and indirect GHG \r emissions. Cities provide a conduit to many of the services such as \r transportation, housing, water, food, medical care and recreation, \r and other services and urban carbon emissions are driven not only \r by population and income but also by the form and structure of \r [...]"


id: b34ea665ca1b838d3cb1ff17ae0ec45a
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 28790
content:
"[...] urban areas (Sections  8.1 and 8.3-8.6). This creates opportunities \r for decarbonisation through urban planning and purposeful \r 'experimentation' (Newman et al. 2017) (Chapter 8)."


id: 494e193abc1e9fbfb57033eb6a912100
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 10119
content:
"Official inventories report territorial emissions, which do not \r consider the impacts embodied in imports of goods. Global supply \r chains undoubtedly lead to a  growth in trade volumes (Federico \r and Tena-Junguito 2017), alternative methods have been suggested \r to account for emissions associated with international trade, such \r as shared responsibility (Lenzen et al. 2007), technology-adjusted \r consumption-based accounting (Kander et al. 2015), value\u0002added-based responsibility (Pinero et al. 2019) and exergy-based \r responsibility based on thermodynamics (Khajehpour et al. 2019). \r Consumption-based emissions (i.e., attribution of emissions related \r to domestic consumption and imports to final destination) are not \r officially reported in global emissions datasets but data has improved \r (Tukker and Dietzenbacher 2013; Afionis et al. 2017). This analysis \r has been used extensively for consumption-based accounting of \r [...]"


id: f9dc2f213763372805843641679b2d9c
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 41321
content:
"[...] emissions, and other environmental impacts (Wiedmann and Lenzen \r 2018; Malik et al. 2019) (Section 2.3)."


id: 5721e086d71320d415d7222846abc923
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 12582
content:
"A broad class of more speculative technologies propose to counteract \r effects of climate change by removing CO2 from the atmosphere \r (CDR), or by directly modifying the Earth's energy balance at a large \r scale (solar radiation modification or SRM). CDR technologies \r include ocean iron fertilisation, enhanced weathering and ocean \r alkalinisation (Council 2015a), along with direct air carbon capture \r and storage (DACCS). They could potentially draw down atmospheric \r CO2 much faster than the Earth's natural carbon cycle, and reduce \r reliance on biomass-based removal (Koberle 2019; Realmonte et al. \r 2019), but some present novel risks to the environment and DACCS \r is currently more expensive than most other forms of mitigation (Fuss \r et al. 2018) (Cross-Chapter Box  8  in Chapter  12). Solar radiation \r modification (SRM) could potentially cool the planet rapidly at low \r estimated direct costs by reflecting incoming sunlight (Council 2015b), \r [...]"


id: 6c3acb70665f013a24bfe3759668af7
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
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content:
"[...] but entails uncertain side effects and thorny international equity and \r governance challenges (Netra et al. 2018; Florin et al. 2020; National \r Academies of Sciences 2021) (Chapter 14). Understanding the climate \r response to SRM remains subject to large uncertainties (AR6 WGI). \r Some literature uses the term 'geoengineering' for both CDR or SRM \r when applied at a planetary scale (Shepherd 2009; GESAMP 2019). \r In this report, CDR and SRM are discussed separately, reflecting their \r very different geophysical characteristics."


id: b268e412df4f61f5f6f34d5e3025977c
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 28626
content:
"Climate finance is a multi-actor, multi-objective domain that includes \r central banks, commercial banks, asset managers, underwriters, \r development banks, and corporate planners. Climate change \r presents both risks and opportunities for the financial sector. The \r risks include physical risks related to the impacts of climate change \r itself; transition risks related to the exposure to policy, technology \r and behavioural changes in line with a low-carbon transition; and \r liability risks from litigation for climate-related damages (Box 15.2). \r These could potentially lead to stranded assets (the loss of economic \r value of existing assets before the end of their useful lifetimes (Bos \r and Gupta 2019) (Sections 6.7 and 15.6.3). Such risks continue to \r be underestimated by financial institutions (Section  15.6.1). The \r continuing expansion of fossil fuel infrastructure and insufficient \r transparency on how these are valued raises concerns that systemic \r [...]"


id: 2523bf3bfcb48cad6ce9a2fd64a3b602
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
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content:
"[...] risk may be accumulating in the financial sector in relation to \r a  potential low-carbon transition that may already be under way \r (Battiston et al. 2017) (Section  15.6.3). The Financial Stability \r Board's Taskforce on Climate-related Financial Disclosures' (TCFD) \r recommendations on  transparency aim to ensure that investors \r and companies consider climate change risks in their strategies and \r capital allocation (TCFD 2018). This is helping 'investors to reassess \r core assumptions' and may lead to 'significant' capital reallocation \r (Fink 2020). However, metrics and indicators of assets risk exposure \r are inadequate (Monasterolo 2017; Campiglio et al. 2018) and \r transparency alone is insufficient to drive the required asset \r reallocation in the absence of clear regulatory frameworks (Ameli \r et al. 2020; Chenet et al. 2021). A  coalition of central banks have \r formed the Network for Greening the Financial Sector, to support \r [...]"


id: df631652cb4aec1b732d4c827704572b
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 36547
content:
"[...] and advance the transformation of the financial system (Allen et al. \r 2020; NGFS 2020), with some of them conducting climate-related \r institutional stress tests."


id: f1fd1b7c4f928023c802e595bbf66593
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 39967
content:
"There is often a  disconnect between stated national climate \r ambition and finance flows, and overseas direct investment (ODI) \r from donor countries may be at odds with national climate pledges \r such as NDCs. One report found funds supported by foreign state\u0002owned enterprises into 56 recipient countries in Asia and Africa in \r 2014-2017 went mostly to fossil fuel-based projects not strongly \r aligned with low-carbon priorities of recipient countries' NDCs (Zhou \r et al. 2018). Similarly, Steffen and Schmidt (2019) found that even \r within multilateral development banks, 'public- and private-sector \r branches differ considerably', with public-sector lending used mainly \r in non-renewable and hydropower projects. Political leadership is \r therefore essential to steer financial flows to support low-carbon \r transition (Section  15.6). Voituriez et al. (2019) identify significant \r mitigation potential if financing countries simply applied their own \r [...]"


id: 942e5b75763ad0b1218ade7970ec2cb
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 23278
content:
"The politics of interest (most especially economic interest) of key \r actors at sub-national, national and global levels can be important \r determinants of climate (in)action (O'Hara 2009; Lo 2010; Tanner \r and Allouche 2011; Sovacool et al. 2015; Lohmann 2017; Clapp et al. \r 2018; Newell and Taylor 2018; Lohmann 2019). Political economy \r approaches can be crudely divided into 'economic approaches to \r politics', and those used by other social scientists (Paterson and \r P-Laberge 2018). The former shows how electoral concerns lead \r to weak treaties (Battaglini and Harstad 2016) and when policy \r negotiations cause status-quo biases and the use of inefficient \r policy instruments (Austen-Smith et al. 2019) or delays and excessive \r harmonisation (Harstad 2007). The latter emphasises the central \r role of structures of power and production, and a  commitment to \r economic growth and capital accumulation in relation to climate \r [...]"


id: efa208714f9c5fa8b4e6d47e033b213e
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 39539
content:
"[...] action, given the historically central role of fossil fuels to economic \r development and the deep embedding of fossil energy in daily life \r (Newell and Paterson 2010; Huber 2012; Di Muzio 2015; Malm 2015)."


id: ee2b50ec8a07e5ec73a91b6242ccefeb
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 39273
content:
"Governments cannot single-handedly fund the transition \r (Section  15.6.7), least of all in low-income developing countries \r with large sovereign debt and poor access to global financial \r markets. Long-term sources of private capital are required to close \r the financing gap across sectors and geographies (Section 15.6.7). \r Future investment needs are greatest in emerging and developing \r economies (Section 15.5.2) which already face higher costs of capital, \r hindering capacity to finance a  transition (Buhr et al. 2018; Ameli \r et al. 2020). Requisite North-South financial flows are impeded by \r both geographic and technological risk premiums (Iyer et al. 2015), \r and the COVID-19 pandemic has further compromised the ability \r of developing and emerging economies to finance development \r activities or attract additional climate finance from developed \r countries (Section 15.6.3, and Cross-Chapter Box 1 in this chapter). \r [...]"


id: a93f27420e63830021d376ad60e92043
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 26997
content:
"[...] Climate-related investments in developing countries also suffer from \r structural barriers such as sovereign risk and exchange rate volatility \r (Farooquee and Shrimali 2016; Guzman et al. 2018) which affect not "


id: 51a7090ec0bc5c4e6909f764c9183f5b
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 11647
content:
"One factor limiting the ambition of climate policy has been the ability \r of incumbent industries to shape government action on climate \r change (Newell and Paterson 1998; Jones and Levy 2009; Geels 2014; \r Breetz et al. 2018). Incumbent industries are often more concentrated \r than those benefiting from climate policy and lobby more effectively \r to prevent losses than those who would gain (Meng and Rode 2019). \r Drawing upon wider networks (Brulle 2014), campaigns by oil and \r coal companies against climate action in the United States of America \r and Australia are perhaps the most well known and largely successful \r of these (Pearse 2017; Brulle et al. 2020; Mildenberger 2020; Stokes \r 2020), although similar dynamics have been demonstrated in Brazil \r and South Africa (Hochstetler 2020), Canada (Harrison 2018), \r and Norway and Germany (Fitzgerald et al. 2019), for example. \r In other contexts, resistance by incumbent companies is more \r [...]"


id: e538ca21ac3faee8d103b74cad672ec1
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 37629
content:
"[...] subtle but nevertheless has weakened policy design on emissions \r trading systems (Rosembloom and Markard 2020), and limited the \r development of alternative-fuelled automobiles (Levy and Egan \r 2003; Wells and Nieuwenhuis 2012)."


id: 17ad1ffed6eedae17a1c1821047f19e7
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 1374
content:
"Climate change and climate policies affect countries and people \r differently. Low-income countries tend to be more dependent \r on primary industries (agriculture and fisheries, etc.) than richer \r countries, and their infrastructure may be less robust to tackle more \r severe weather conditions. Within a country, the burdens may not be \r equally distributed either, due to policy measures implemented and \r from differences in vulnerability and adaptive capacity following from \r e.g. income and wealth distribution, race and gender. For instance, \r unequal social structures can result in women being more vulnerable \r to the effects of climate change compared to men, especially in poor \r countries (Arora-Jonsson 2011; Jost et al. 2016; Rao et al. 2019). \r Costs of mitigation also differ across countries. Studies show there \r are large disparities of economic impacts of NDCs across regions, \r and also between relatively similar countries when it comes to the \r [...]"


id: 9ca8e4dbeb30bdf380986c7b88f97213
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 24802
content:
"[...] level of development, due to large differences in marginal abatement \r costs for the emission-reduction goal of NDCs (Fujimori et al. 2016; \r Hof et al. 2017; Akimoto et al. 2018; Evans &  Gabbatiss 2019). \r Equalising the burdens from climate policies may give more support \r for mitigation policies (Maestre-Andres et al. 2019)."


id: e974edee1d1631a55b827bd3b114da75
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 38427
content:
"Equity and fairness can serve as both drivers and barriers to climate \r mitigation at different scales of governance. Literature regularly \r highlights equity and justice issues as critical components in local \r politics and international diplomacy regarding all SDGs, such as goals \r for no poverty, zero hunger, gender equality, affordable clean energy, \r reducing inequality, but also for climate action (SDG 13) (Marmot and \r Bell 2018; Spijkers 2018). Equity issues help explain why it has proved \r hard to reach more substantive global agreements, as it is hard to \r agree on a level of greenhouse gas (GHG) mitigation (or emissions) \r and how to distribute mitigation efforts among countries (Kverndokk \r 2018) for several reasons. First, an optimal trade-off between \r mitigation costs and damage costs of climate change depends on \r ethical considerations, and simulations from integrated assessment \r models using different ethical parameters producing different optimal \r [...]"


id: 740a1fccee847776a39061a8c7b75b5b
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 17655
content:
"Tackling climate change is often mentioned as an important reason \r for strong international cooperation in the 21st century (Falkner \r 2016; Keohane and Victor 2016; Bodansky et al. 2017; Cramton \r et al. 2017b). Mitigation costs are borne by countries taking action, \r while the benefits of reduced climate change are not limited to \r them, being in economic terms 'global and non-excludable'. Hence \r anthropogenic climate change is typically seen as a global commons \r problem (Falkner 2016; Wapner and Elver 2017). Moreover, the belief \r that mitigation will raise energy costs and may adversely affect \r competitiveness creates incentives for free riding, where states avoid \r taking their fair share of action (Barrett 2005; Keohane and Victor \r 2016). International cooperation has the potential to address these \r challenges through collective action (Tulkens 2019) and international \r institutions offer the opportunity for actors to engage in meaningful \r [...]"


id: 4706a886695a491a9acb68fee8e2c359
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updated_at: 2023-04-19 13:23:07
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vector_id: 9698
content:
"[...] communication and exchange of ideas about potential solutions \r (Cole 2015). International cooperation is also vital for the creation \r and diffusion of norms and the framework for stabilising expectations \r among actors (Pettenger 2016)."


id: 23b031a6c6a50febee8bf0911d8bf8b
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 3475
content:
"Some key roles of the UNFCCC have been detailed by its former \r heads (Kinley et al. 2021). In addition to specific agreements (most \r recently the PA) it has enhanced transparency through reporting \r and data, and generated or reinforced several important norms for \r global climate action including the principles of equity, common \r but differentiated responsibility and respective capabilities, and the \r precautionary principles for maintaining global cooperation among \r states with unevenly distributed emissions sources, climate impacts, \r and varying mitigation costs across countries (Keohane and Victor, \r 2016). In addition to formal negotiations, the annual Conference of \r the Parties (COPs) have increased awareness, and motivated more \r ambitious actions, sometimes through the formation of 'coalitions \r of the willing', for example. It provides a  structure for measuring \r and monitoring action towards a global goal (Milkoreit and Haapala \r [...]"


id: cb9c7785aa360afd835525e9f63039c1
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 33149
content:
"[...] 2019). International cooperation (including the UNFCCC) can also \r promote technology development and transfer and capacity building; \r mobilise finance for mitigation and adaptation; and help address \r concerns on climate justice (Okereke and Coventry 2016; Chan et al. \r 2018) (Chapters 14-16)."


id: b446cf099d679eb398d2ed6bc9c14a8
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 28950
content:
"International collaboration works best if an agreement can be made \r self-reinforcing with incentives for mutual gains and joint action \r (Barrett 2016; Keohane and Victor 2016), but the structure of the \r climate challenge makes this hard to achieve. The evidence from the \r Montreal Protocol on ozone-depleting substances and from the Kyoto \r Protocol on GHGs, is that legally binding targets have been effective \r in that participating Parties complied with them (Shishlov et al. 2016; \r Albrecht and Parker 2019), and (for Kyoto) these account for most of \r the countries that have sustained emission reductions for at least the \r past 10 to 15 years (Sections 1.3.2 and 2.2). However, such binding \r commitments may deter participation if there are no clear incentives \r to sustain participation and especially if other growing emitters are \r omitted by design, as with the Kyoto Protocol. Consequently the USA \r refused to ratify (and Canada withdrew), particularly on the grounds \r [...]"


id: 1b835a738f80b31da7fad8f5fbe961c0
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 2029
content:
"[...] that developing countries had no targets; with participation in Kyoto's \r second period commitments declining further, the net result was \r limited global progress in emissions under Kyoto (Bodansky 2016; \r Okereke and Coventry 2016; Scavenius and Rayner 2018) despite full \r legal compliance in both commitment periods (Chapter 14)."


id: 324e0f0837183038ceffa0f0147feabe
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 6106
content:
"Drawing from the scenarios database, five Illustrative Mitigation \r Pathways (IMPs) were defined for this report (Figure  3.5 and \r Table  1.1). These are introduced here, with a  more complete \r description and discussion provided in Section  3.2.5. These IMPs \r were chosen to illustrate key themes with respect to mitigation \r strategies across the entire WGIII assessment. The IMPs embody \r both a  storyline, which describes in narrative form the key socio\u0002economic characteristics of that scenario, and a  quantitative \r illustration providing numerical values that are internally consistent \r and comparable across chapters of this report. Quantitative IMPs \r can be associated directly with specific human activities and provide \r a quantitative point of reference that links activities in different parts \r of socio-economic systems. Some parts of the report draw on these \r quantitative scenarios, whilst others use only the narratives. No \r [...]"


id: 6a303d4edf525679bae21991585926e6
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updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 15960
content:
"[...] assessment of the likelihood of each IMP has been made (as they \r reflect both human choice and deep uncertainty)."


id: 30b24f64aac3598a31fafebdda2bc6f7
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 5820
content:
"IMPs are not intended to be comprehensive. They are not intended \r to illustrate all possible themes in this report. They do not, for \r example, attempt to illustrate the range of alternative socio\u0002economic pathways against which efforts to implement Paris goals \r may be set, or to reflect variations in potential regional development \r pathways. They do not explore issues around income distribution or \r environmental justice, but assume implicitly that where and how \r action occurs can be separated from who pays, in ways to adequately \r address such issues. They are essentially pathways of technological \r evolution and demand shifts reflecting broad global trends in social \r choice. The IMPs do not directly assess issues of realisation linked to \r the 'drivers and constraints' summarised in our previous section, and \r the quantifications use, for the most part, models that are grounded \r mainly in the Aggregate Economics Frameworks (Section 7.1). As such \r [...]"


id: 6a2eddd298dd41e0283828b3b0de3c4c
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updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 15957
content:
"[...] they reflect primarily the geophysical, economic and technological \r Dimensions of Assessment, but can be assessed in relation to the full \r set of Feasibility criteria (Section 1.8.1)."


id: 5702f42fb811497d7f3d5640eac2f3fa
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 12560
content:
"The impact of climate change in limiting well-being is most acutely \r felt by the world's poorest people, communities, and nations, who \r have the smallest carbon footprint, constrained capacity to respond \r and limited voice in important decision-making circles (Okereke and \r Ehresman 2015; Tosam and Mbih 2015; Mugambiwa and Tirivangasi \r 2017). The wide variation in the contribution to, and impact of climate \r change within and across countries makes equity, inequality, justice, \r and poverty eradication, inescapable aspects of the relationship \r between sustainable development and climate change (Okereke \r and Coventry 2016; Klinsky et al. 2017; Reckien et al. 2017; Bos \r and Gupta 2019; Kayal et al. 2019; Diffenbaugh and Burke 2019; \r Baarsch et al. 2020). This underpins the conclusion, as commonly \r expressed, that climate action needs to be pursued in the context of \r sustainable development, equity and poverty eradication (Smit et al. \r [...]"


id: 8618b16b82ac44d489bbcc0800b323dc
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updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 20741
content:
"Climate change mitigation is one of many goals that societies pursue \r in the context of sustainable development, as evidenced by the wide \r range of the Sustainable Development Goals (SDGs). Climate change \r and sustainable development, as well as development more broadly, \r are interwoven along multiple and complex lines of relationship \r (Okereke et al. 2009; Fankhauser and McDermott 2016; Okereke \r and Massaquoi 2017; Gomez-Echeverri 2018a), as highlighted in \r several previous IPCC reports (IPCC 2007, 2011a, 2014a, 2018b, \r 2019a). With its significant negative impact on natural systems, \r food security and infrastructure, loss of lives and territories, species \r extinction, conflict health, among several other risks, climate change \r poses a serious threat to development and wellbeing in both rich and \r poor countries (IPCC 2007, 2011a, 2014a, 2018b, 2019b). Without \r serious efforts at mitigation and adaptation, climate change could \r [...]"


id: fc4047dacabc90558bb2e0ddbe576383
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 41746
content:
"[...] push millions further into poverty and limit the opportunities for \r economic development (Chapters 4 and 17). It follows that ambitious \r climate mitigation is necessary to secure a safe climate within which \r development and well-being can be pursued and sustained."


id: 7806916fcb762ad24ae6060474ab6dda
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 18327
content:
"Other concepts such as 'Doughnut Economics' (Raworth 2018), \r ecological modernisation, and mainstreaming are also used to \r convey ideals of development pathways that take sustainability, \r climate mitigation, and environmental limits seriously (Dale et al. \r 2015a). Mainstreaming focuses on incorporating climate change \r into national development activities, such as the building of \r infrastructure (Wamsler and Pauleit 2016; Runhaar et al. 2018). The \r 'green economy' and green growth - growth without undermining \r ecological systems, partly by gaining economic value from cleaner \r technologies and systems and is inclusive and equitable in its \r outcomes - has gained popularity in both developed and developing \r countries as an approach for harnessing economic growth to address \r environmental issues (Bina 2013; Georgeson et al. 2017; Capasso \r et al. 2019; Song et al. 2020; Hao et al. 2021). However, critics argue \r that green economy ultimately emphasises economic growth to the \r [...]"


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"Literature on degrowth, post growth, and post development \r questions the sustainability and imperative of more growth \r especially in already industrialised countries and argues that \r prosperity and the 'Good Life' are not immutably tied to economic \r growth (Asara et al. 2015; Escobar 2015; Latouche 2018; Kallis \r 2019) (Section  5.2.1). The concept of Just Transition also stresses \r the need to integrate justice concerns so as to not impose hardship \r on already marginalised populations within and between countries \r (Evans and Phelan 2016; Goddard and Farrelly 2018; Heffron and \r McCauley 2018; Smith, Jackie and Patterson 2018; McCauley \r and Heffron 2018) (Section 1.7.2). The key insight is that pursuing \r climate goals in the context of sustainable development requires \r holistic thinking including on how to measure well-being, serious \r consideration of the notion of ecological limits, at least some level \r of decoupling and certainly choices and decision-making approaches \r [...]"


id: 31c957c92e56b1598c5a54771e5d45e0
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"[...] that exploit and maximise the synergy and minimise the trade-off \r between climate mitigation and other sustainable development \r goals. It also requires consideration of equity and justice within \r and between countries. However, ideas of a synergistic relationship \r between development and climate mitigation can sometimes offer \r limited practical guidelines for reconciling the tensions that are often \r present in practical policymaking (Ferguson et al. 2014; Dale et al. \r 2015b; Kasztelan 2017; Kotze 2018)."


id: c9349961813c6fbcdb040134a5d5d591
created_at: 2023-04-19 13:23:07
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"Figure 1.6 | Sustainable development pathways towards fulfilling the Sustainable Development Goals (SDGs). The graph shows global average per-capita \r GHG emissions (vertical axis) and relative 'Historic Index of Human Development' (HIHD) levels (horizontal) have increased globally since the industrial revolution (grey line). \r The bubbles on the graph show regional per-capita GHG emissions and human development levels in the year 2015, illustrating large disparities. Pathways towards fulfilling the \r Paris Agreement (and SDG 13) involve global average per-capita GHG emissions below about 5 tCO2-eq by 2030. Likewise, to fulfil SDGs 3, 4 and 8, HIHD levels (see footnote 7) \r need to be at least 0.5 or greater. This suggests a 'sustainable development zone' for year 2030 (in pale brown); the in-figure text also suggests a 'sustainable development \r [...]"


id: d90d4d4fdc05dcdc60f4c02c5775e1a5
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"[...] corridor', where countries limit per-capita GHG emissions while improving levels of human development over time. The emphasis of pathways into the sustainable development \r zone differ (dashed brown arrows), but in each case transformations are needed in how human development is attained while limiting GHG emissions."


id: 1929ea027283d131e0ad5565257fc9d
created_at: 2023-04-19 13:23:07
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"In conclusion, achieving climate stabilisation in the context of \r sustainable development and efforts to eradicate poverty requires \r collective action and exploiting synergies between climate action \r and sustainable development, while minimising the impact of trade\u0002offs (Najam 2005; Okereke and Massaquoi 2017; Makomere and Liti \r Mbeva 2018; Dooley et al. 2021). It also requires a focus on equity \r considerations to avoid climate-induced harm, as well as unfairness \r that can result from urgent actions to cut emissions (Pan et al. \r 2014; Robiou du Pont et al. 2017; Kartha et al. 2018a). This is ever \r more important as the diminishing carbon budget has intensified \r debates on which countries should have the greatest claim to the \r 'remaining space' for emissions (Raupach et al. 2014) or production \r (McGlade and Ekins 2015), amplified by persistent concerns over \r the insufficiency of support for means of implementation, to support \r [...]"


id: df2282da8debfaf50f22e005667a177b
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
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"Such global aggregate economic studies coalesce around two main \r questions. One, as pioneered by Nordhaus (1992, 2008) attempts to \r monetise overall climate damages and mitigation costs so as to strike \r a 'cost-benefit optimum' pathway. More detailed and empirically\u0002grounded 'cost-effectiveness analysis' explores pathways that would \r minimise mitigation costs (Ekholm 2014; IPCC 2014a Section  2.5; \r Weyant 2017) for given targets (e.g.,  as agreed in international \r negotiations, see Section 3.2 in Chapter 3). Both approaches recognise \r that resources are limited and climate change competes with other \r priorities in government policymaking, and are generally examined \r with some form of Integrated Assessment Model (IAM) (Section 1.5 \r and Appendix III). Depending on the regional disaggregation of the \r modelling tools used and on the scope of the analyses, these studies \r may or may not address distributional aspects within and across \r [...]"


id: 275b0ab86569ab0b2689f2e99ef627
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"To complement the extensive literature on risks and decision\u0002making under uncertainty reviewed in AR6 WGII (notably, \r Chapter  19), this section summarises insights and developments \r in key analytic frameworks and tools particularly relevant to \r understanding specific mitigation strategies, policies and other \r actions, including explaining the observed if limited progress \r to date. Organised partly as reflected in the quotes above, these \r include aggregated (principally, economic) frameworks to evaluate \r system-level choices; ethical perspectives on values and equity \r including stages of development and distributional concerns; and \r transition frameworks which focus on the processes and actors \r involved in major technological and social transitions. These need \r to be complemented by a  fourth set of approaches which shine \r more light on psychological/behavioural and political factors. All \r these frameworks are relevant, and together they point to the \r [...]"


id: 5544a610d07267c32aa39c31627b9126
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"[...] multiple perspectives and actions required if the positive drivers \r of emission reduction summarised in Section 4 are to outweigh the \r barriers and overcome the constraints."


id: 61e67fe9c6353b635f56658ef58ddc58
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updated_at: 2023-04-19 13:23:07
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"The latter especially are technically grounded in complex system \r theories (e.g.,  Arthur 1989, 1999; Beinhocker 2007; Hidalgo and \r Hausmann 2009). These take inherently dynamic views of economies \r as continually evolving systems with continuously unfolding and \r path-dependent properties, and emphasise uncertainty in contrast to \r any predictable or default optimality. Such approaches have been \r variously applied in policy evaluation (Walton 2014; Moore et al. \r 2018), and specifically for global decarbonisation (e.g., Barker and \r Crawford-Brown 2014) using global simulation models. Because \r these have no natural reference 'least lost' trajectory, they illustrate \r varied and divergent pathways and tend to emphasise the diversity \r of possibilities and relevant policies, particularly linked to innovation \r and potentially 'sensitive intervention points' (Farmer et al. 2019) \r (Section  1.7.3). They also illustrate that different representations \r [...]"


id: 4275efb6ab84380506ccde13595b3643
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content:
"[...] of innovation and financial markets together can explain why \r estimated impacts of mitigation on GDP can differ very widely \r (potentially even in sign), between different model types (Chapter 15, \r Section 15.6.3 and Box 15.7)."


id: 191acb3ecc1a89fa145c3a37bd48b1b6
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"This report uses the term transition as the process, and transformation \r as the overall change or outcome, of large-scale shifts in technological, \r economic and social systems, called socio-technical systems in \r the innovation literature. Typically, new technologies, ideas and \r associated systems initially grow slowly in absolute terms, but may \r then 'take-off' in a phase of exponential growth as they emerge from \r a  position of niche into mainstream diffusion, as indicated by the \r 'S-curve' growth in Figure  1.7 (lower panel). These dynamics arise \r from interactions between innovation (in technologies, companies \r and other organisations), markets, infrastructure and institutions, \r at multiple levels (Geels et al. 2017; Kramer 2018). Consequently, \r interdisciplinary perspectives are needed (Turnheim et al. 2015; \r Geels et al. 2016; Hof et al. 2020). Beyond aggregated economic \r perspectives on dynamics (Section  1.7.1.2), these emphasise the \r [...]"


id: 703500892fcbee9e0849711723b8c087
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"The widely-used Multi-Level Perspective or MLP (Geels 2002) identifies \r the meso level as the established 'socio-technical (ST) regime', \r a set of interrelated sub-systems which define rules and regulatory \r structures around existing technologies and practices. The micro level \r is an ecosystem of varied niche alternatives, and overlaying the ST \r regime is a macro 'landscape' level. Transitions often start with niche \r alternatives (Grin et al. 2010; Kohler et al. 2019), which may break \r through to wider diffusion (second stage in Figure 1.8), especially if \r external landscape developments 'create pressures on the regime that \r lead to cracks, tensions and windows of opportunity' (Rotmans et al. \r 2001; Geels 2010); an example is climate change putting sustained \r pressure on current regimes of energy production and consumption \r (Kuzemko et al. 2016). There are continual interactions between \r landscape, regime and niches, with varied implications for Transition \r [...]"


id: f90ff6fa2680181977fa4162775a558e
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"Figure 1.7 | Transition dynamics: levels, policies and processes. Note: the lower panel illustrates growth of innovative technologies or practices, which if successful \r emerge from niches into an S-shape dynamic of exponential growth. The diffusion stage often involves new infrastructure and reconfiguration of existing market and regulatory \r structures (known in the literature as the 'socio-technical regime'). During the phase of more widespread diffusion, growth levels off to linear, then slows as the industry and \r market matures. The processes displace incumbent technologies/practices which decline, initially slowly but then at an accelerating pace. Many related literatures identify three \r main levels with different characteristics, most generally termed micro, meso and macro. Transitions can be accelerated by policies appropriately targeted, which may be similarly \r [...]"


id: 8b2ac20ca8dda4832ffaf51ef32b8146
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content:
"[...] grouped and sequenced (upper panel) in terms of three corresponding pillars of policy (Section 1.7.3): generally all are relevant, but their relative importance differs according \r to the stage of the transition."


id: 7b3acf1a2d70be5b60e521bba26b2cb9
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content:
"Transition theories tend to come from very different disciplines and \r approaches compared to either economics or other social sciences, \r with less quantification, notwithstanding evolutionary and complex \r system models (Section  1.7.1.3). However, a  few distinct types of \r quantitative models of 'socio-technical energy transition' (Li et al. \r 2015) have emerged. For policy evaluation, transitions can be viewed \r as processes in which dynamic efficiency (Section 1.7.2) dominates \r over static allocative efficiency, with potential 'positive intervention \r points' (Farmer et al. 2019). Given inherent uncertainties, there are \r obvious risks (e.g., Alic and Sarewitz 2016). All this may make an \r evaluation framework of risks and opportunities more appropriate \r than traditional cost-benefit (Mercure et al. 2021), and (drawing on \r lessons from renewables and electric vehicles) create foundations \r for sector-based international 'positive sum cooperation' in climate \r [...]"


id: 6dd0f42f1967a92e2b908db5f75d2a73
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"Sustainability may require purposeful actions at the different levels \r to foster the growth of sustainable technologies and practices, \r including support for niche alternatives (Grin et al. 2010). The middle \r level (established 'socio-technical regime') tends to resist major \r change, reforms generally involve pressures from the other two \r levels. Thus, transitions can be accelerated by policies appropriately \r targeting relevant actors at the different levels (Kohler et al. 2019), \r the foundations for 'three pillars of policy' (Grubb et al. 2014), which \r logically evolve in the course of transition (Figure 2.6a). Incumbent \r industries have to adapt if they are to thrive within the growth of \r new systems. Policy may need to balance existing socio-technical \r systems with strategic investment and institutional development \r of the emerging niches (e.g., the maintenance of energy provision \r and energy security with the development of renewables), and help \r [...]"


id: 6ddcfaa66fc28713e4f5cb0d71702a47
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"The AR5 emphasised that decision processes often include both \r deliberate ('calculate the costs and benefits') and intuitive thinking, \r the latter utilising emotion- and rule-based responses that are \r conditioned by personal past experience, social context, and cultural \r factors (e.g.,  Kahneman 2003), and that laypersons tend to judge \r risks differently than experts  - for example, 'intuitive' reactions \r are often characterised by biases to the status quo and aversion to \r perceived risks and ambiguity (Kahneman and Tversky 1979). Many \r of these features of human reasoning create 'psychological distance' \r from climate change (Spence et al. 2012; Marshall 2014). These can \r impede adequate personal responses, in addition to the collective \r nature of the problem, where such problems can take the form of \r 'uncomfortable knowledge', neglected and so becoming 'unknown \r knowns' (Sarewitz 2020). These decision processes, and the \r [...]"


id: c9ca1abc972e32555b3569f72c891add
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"[...] perceptions that shape them, have been studied through different \r lenses from psychology (Weber 2016) to sociology (Guilbeault \r et al. 2018), and media studies (Boykoff 2011). Karlsson and Gilek \r (2020) identify science denialism and 'decision thresholds' as key \r mechanisms of delay."


id: 5e587d0c6435278e80881c6872a71e7f
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content:
"There is usually a  social dimension to such transitions. Key \r elements include capacity to transform (Folke et al. 2010), planning, \r and interdisciplinarity (Woiwode 2013). The Second World War \r demonstrated the extent to which crises can motivate (sometimes \r positive) change across complex social and technical systems, \r including industry, and agriculture which then doubled its productivity \r over 15 years (Roberts and Geels 2019b). In practice, climate change \r may involve a combination of (reactive) transformational adaptation, \r and (proactive) societal transformation (Feola 2015), the latter \r seen as reorientation (including values and norms) in a sustainable \r direction (Section 5.4), including, for example, 'democratisation' in \r energy systems (Sorman et al. 2020). Business change management \r principles could be relevant to support positive social change (Stephan \r et al. 2016). Overall, effective transitions rest on appropriate enabling \r [...]"


id: 948735f3e95acc93c83e5cb465e9ccbf
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"[...] conditions, which can also link socio-technical transitions to broader \r development pathways (Cross-Chapter Box 12 in Chapter 16)."


id: 272d9193aa6ef49576f9cb7e239d03f3
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content:
"Balancing the forces outlined in Section  4.6 in Chapter  4  typically \r involves building coalitions of actors who benefit economically from \r climate policy (Levin et al. 2012). Policy stability is critical to enabling \r long-term investments in decarbonisation (Rietig and Laing 2017; \r Rosenbloom et al. 2018). Policy design can encourage coalitions \r to form that sustain momentum by supporting further policy \r development to accelerate decarbonisation (Roberts et al. 2018), for \r example, by generating concentrated benefits to coalition members \r (Bernstein and Hoffmann 2018; Meckling 2019; Millar et al. 2020), as \r with renewable feed-in tariffs (FiTs) in Germany (Michaelowa et al. \r 2018). Coalitions may also be sustained by overarching framings, \r especially to involve actors (e.g.,  NGOs) for whom the benefits of \r climate policy are not narrowly economic. However, policy design \r can also provoke coalitions to oppose climate policy, as in the FiT \r [...]"


id: d5f5b7de390b5a45a2c7e3b7ab999f78
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content:
"[...] programme in Ontario (Stokes 2013; Raymond 2020) or the yellow \r vest protests against carbon taxation in France (Berry and Laurent \r 2019). The Just Transitions frame can thus also be understood in \r terms of coalition-building, as well as ethics, as the pursuit of low\u0002carbon transitions which spread the economic benefits broadly, \r through 'green jobs', and the redistributive policies embedded in \r them both nationally and globally (Healy and Barry 2017; Winkler \r 2020). Appropriate policy design will be different at different stages \r of the transition process (Meckling et al. 2017; Breetz et al. 2018)."


id: b398e5115045b159222a5d501a610076
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content:
"Carbon pricing. Appraisal of policy instruments also requires such \r multi-dimensional assessment. Stern's (2007) reference to climate \r change as 'the greatest market failure in history' highlights that \r damages inflicted by climate change are not properly costed in \r most economic decision-making. Economic perspectives emphasise \r the value of removing fossil fuel subsidies, and pricing emissions to \r 'internalise' in economic decision-making the 'external' damages \r imposed by GHG emissions, and/or to meet agreed goals. Aggregate \r economic frameworks generally indicate carbon pricing (on principles \r which extend to other gases) as the most cost-effective way to \r reduce emissions, notwithstanding various market failures which \r complicate this logic.9\r  The High-Level Commission on carbon pricing \r (Stern and Stiglitz 2017) estimated an appropriate range as USD40-\r 80 tCO2 in 2020, rising steadily thereafter. In practice the extent and \r [...]"


id: 7cbcda164d4b5ea5cb216d239346340d
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updated_at: 2023-04-19 13:23:07
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content:
"A socio-cultural and equity perspective emphasises that the faith in \r and role of markets varies widely between countries - many energy \r systems do not in fact operate on a basis of competitive markets - \r and that because market-based carbon pricing involves large revenue \r transfers, it must also contend with major distributional effects and \r political viability (Prinn et al. 2017; Klenert et al. 2018), both domestic \r (Chapter 13) and international (Chapter 14). A major review (Maestre\u0002Andres et al. 2019) finds persistent distributional concerns (rich \r incumbents have also been vocal in using arguments about impacts \r on the poor (Rennkamp 2019)), but suggests these may be addressed \r by combining redistribution of revenues with support for low-carbon \r innovation. Measures could include redistributing the tax revenue to \r favour of low-income groups or differentiated carbon taxes (Metcalf \r 2009; Klenert and Mattauch 2016; Stiglitz 2019), including 'dual \r [...]"


id: 894b5d066c69ed9f85e7bfa40e37b58d
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content:
"[...] track' approaches (van den Bergh et al. 2020). To an extent though, \r all these depend on levels of trust, and institutional capacity."


id: 856a24214c86076d744ace4aba947a37
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content:
"Previous sections have highlighted the multiple factors that drive \r and constrain climate action, the complex interconnection between \r climate mitigation and other societal objectives, and the diversity \r of analytical frames for interpreting these connections. Despite \r the complexities, there are signs of progress including increased \r societal awareness, change in social attitudes, policy commitments \r by a  broad range of actors and sustained emission reductions in \r some jurisdictions. Nevertheless, emission trends at the global level \r remains incompatible with the goals agreed in the Paris Agreement. \r Fundamentally, the challenge of how best to urgently scale up and \r speed up the climate-mitigation effort at all scales - from local to \r global  - to the pace needed to address the climate challenge is \r that of governance understood as 'modes and mechanisms to steer \r society' (Jordan et al. 2015). The concept of governance encompasses \r [...]"


id: 37a7d37f09f87dcc397db2f1edf7223f
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content:
"[...] the ability to plan and create the organisations needed to achieve \r a desired goal (Guney 2017) and the process of interaction among \r actors involved in a common problem for making and implementing \r decisions (Kooiman 2003; Hufty 2012)."


id: 934ceac4a6fe31dfe85c820d4f139c79
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content:
"Crucially, climate governance takes place in the context of embedded \r power relations, operating in global, national and local contexts. \r Effective rules and institutions to govern climate change are more \r likely to emerge where and when power structures and interests \r favour action. However widespread and enduring cooperation can \r only be expected when the benefits outweigh the cost of cooperation \r and when the interests of key actors are sufficiently aligned (Barrett \r 1994; Finus and Rubbelke 2008; Victor 2011; Mainali et al. 2018; \r Tulkens 2019). Investigating the distribution and role of hard and \r soft power resources, capacities and power relations within and \r across different jurisdictional levels is therefore important to uncover \r hindrances to effective climate governance (Marquardt 2017). \r Institutions at international and national levels are also critical as \r they have the ability to mediate the power and interest of actors, and \r [...]"


id: b7544673be374981e36382e0ed30dc79
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updated_at: 2023-04-19 13:23:07
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"[...] sustain cooperation based on equity and fair rules and outcomes. \r Governance, in fact, helps to align and moderate the interests of \r actors as well as to shift perceptions, including the negative, burden\u0002sharing narratives that often accompany discussion about climate \r action, especially in international negotiations. It is also useful for \r engaging the wider public and international networks in imagining \r low-carbon societies (e.g.,  Levy and Spicer 2013; Milkoreit 2017; \r Nikoleris et al. 2017; Wapner and Elver 2017; Bengtsson Sonesson \r et al. 2019; Fatemi et al. 2020). Experimentation also represents an \r important source of governance innovation and capability formation, \r linked to global knowledge and technology flows, which could \r reshape emergent socio-technical regimes and so contribute to \r alternative development pathways (Berkhout et al. 2010; Roberts \r et al. 2018; Turnheim et al. 2018; Lo and Castan Broto 2019)."


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vector_id: 37362
content:
"Global conditions have changed substantially since the IPCC's Fifth \r Assessment Report in 2014. The Paris Agreement and the SDGs \r provided a new international context, but global intergovernmental \r cooperation has been under intense stress. Growing direct impacts \r of climate change are unambiguous and movements of protest and \r activism  - in countries and transnational organisations at many \r levels - have grown. Global emissions growth had slowed but not \r stopped up to 2018/19, albeit with more diverse national trends. \r Growing numbers of countries have adopted 'net zero' CO2 and/\r or GHG emission goals and decarbonisation or low-carbon growth \r strategies, but the current NDCs to 2030 collectively would barely \r reduce global emissions below present levels (Section  1.3.3). An \r unfolding technology revolution is making significant contributions \r in some countries, but as yet its global impact is limited. Global \r [...]"


id: e8a05d1d423de1ff3a40e242e78ab1
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content:
"[...] climate change can only be tackled within, and if integrated with, the \r wider context of sustainable development, and related social goals \r including equity concerns. Countries and their populations have \r many conflicting priorities. Developing countries in particular have \r multiple urgent needs associated with earlier stages of sustainable \r development as reflected in the non-climate SDGs. Developed \r countries are amongst the most unsustainable in terms of overall \r consumption, but also face social constraints particularly arising from \r distributional impacts of climate policies."


id: 119f82cf89ad0ee8d7f5b61b4afbd86a
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content:
"The assessment of the key drivers for, and barriers against mitigation \r undertaken in this chapter underscore the complexity and multi\u0002dimensional nature of climate mitigation. Historically, much of the \r academic analysis of mitigating climate change, particularly global \r approaches, has focused on modelling costs and pathways, and \r discussion about 'optimal' policy instruments. Developments since \r AR5 have continued to highlight the role of a wide range of factors \r intersecting the political, economic, social and institutional domains. \r Yet despite such complexities, there are signs of progress emerging \r from years of policy effort in terms of technology, social attitudes, \r and emission reductions in some countries, with tentative signs of \r impact on the trajectory of global emissions. The challenge remains \r how best to urgently scale up and speed up the climate mitigation \r effort at all scales - from local to global - to achieve the level of \r [...]"


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"[...] mitigation needed to address the problem as indicated by climate \r science. A related challenge is how to ensure that mitigation effort \r and any associated benefits of action are distributed fairly within and \r between countries and aligned to the overarching objective of global \r sustainable development. Lastly, globally effective and efficient \r mitigation will require international cooperation especially in the \r realms of finance and technology."


id: 93f7879377d844778bd21f00c65fcb27
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 23234
content:
"Four chapters then review thematic issues in implementation and \r governance of mitigation. Chapter  13 explores national and sub\u0002national policies and institutions, bringing together lessons of policies \r examined in the sectoral chapters, as well as insights from service \r and demand-side perspectives (Chapter 5), along with governance \r approaches and capacity-building, and the role and relationships of \r sub-national actors. Chapter 14 then considers the roles and status \r of international cooperation, including the UNFCCC agreements \r and international institutions, sectoral agreements and multiple \r forms of international partnerships, and the ethics and governance \r challenges of solar radiation modification. Chapter  15 explores \r investment and finance, including current trends, the investment \r needs for deep decarbonisation, and the complementary roles of \r public and private finance. This includes climate-related investment \r [...]"


id: a489d154a9197c6184542f156a96dd35
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 26160
content:
"[...] opportunities and risks (e.g.,  'stranded assets'), linkages between \r finance and investments in adaptation and mitigation; and the \r impact of COVID-19. A new chapter on innovation (Chapter 16) looks \r at technology development, accelerated deployment and global \r diffusion as systemic issues that hold potential for transformative \r changes, and the challenges of managing such changes at multiple \r levels including the role of international cooperation."


id: 21abbe733c41712cd4a8866208588a13
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 3136
content:
"Chapters 6 to 12 examine sectoral contributions and possibilities for \r mitigation. Chapter 6 summarises characteristics and trends in the \r energy sector, specifically supply, including the remarkable changes \r in the cost of some key technologies since AR5. Chapter 7 examines \r the roles of AFOLU, drawing upon and updating the recent Special \r Report, including the potential tensions between the multiple \r uses of land. Chapter  8  presents a  holistic view of the trends \r and pressures of  urban systems, as both a  challenge and an \r opportunity for mitigation. Chapters  9 and 10 then examine two \r sectors which entwine with, but go well beyond, urban systems: \r buildings (Chapter  9) including construction materials and zero\u0002carbon buildings; and transport (Chapter  10), including shipping \r and aviation and a  wider look at mobility as a  general service. \r Chapter  11 explores the contribution of industry, including supply \r [...]"


id: 45f55ce7ba544bd3347d2b8534f7fc1a
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 9532
content:
"[...] chain developments, resource efficiency/circular economy, and the \r cross-system implications of decarbonisation for industrial systems. \r Finally, Chapter 12 takes a cross-sectoral perspective and explores \r cross-cutting issues like the interactions of biomass energy, food and \r land, and carbon dioxide removal."


id: cffd6f0eb50bc305158d550d938656c0
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 33945
content:
"Different combinations of gases are emitted from different activities. The largest source of CO2 is combustion of fossil fuels in energy \r conversion systems like boilers in electric power plants, engines in aircraft and automobiles, and in cooking and heating within \r homes and businesses (approximately 64% of emissions, Figure SPM.2). Fossil fuels are also a major source of methane (CH4), \r the second biggest contributor to global warming. While most GHGs come from fossil fuel combustion, about one quarter comes \r from land-related activities like agriculture (mainly CH4 and N2O) and deforestation (mainly CO2), with additional emissions from \r industrial processes (mainly CO2, N2O and F-gases), and municipal waste and wastewater (mainly CH4) (Chapter 2). In addition to \r these emissions, black carbon - an aerosol that is, for example, emitted during incomplete combustion of fossil fuels - contributes \r [...]"


id: 1d7aa7a21e1eda00da59be3f31d98a12
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 2366
content:
"[...] to warming of the Earth's atmosphere, whilst some other short-lived pollutants temporarily cool the surface (IPCC AR6 WGI \r Section 6.5.4.3)."


id: 343fbd6a6c5b9639ce196cc70ebef6bc
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 6429
content:
"Alaa Al Khourdajie (United Kingdom/Syria), Robbie M. Andrew (Norway), Giovanni Baiocchi (the \r United States of America/Italy), Igor A. Bashmakov (the Russian Federation), Alexandre Bizeul \r (France), Kornelis Blok (the Netherlands), Lazarus Chapungu (Zimbabwe), Harry Clark (New \r Zealand), William Collins (United Kingdom), Annette Cowie (Australia), Monica  Crippa (Italy), \r Hancheng Dai (China), Steven J. Davis (the United States of America), Stephane de la Rue du Can \r (the United States of America), Niklas Dobbeling (Germany), Kuishuang Feng (the United States \r of America), Vivien Fisch-Romito (France), Piers M. Forster (United Kingdom), Jan S. Fuglestvedt \r (Norway), Victor  Garcia Tapia (Spain), Oliver Geden (Germany), Yong Geng (China), Veronika \r Ginzburg (the Russian Federation), Giacomo  Grassi (Italy/European Union), Baihe Gu (China), \r Celine  Guivarch (France), Diego  Guizzardi (Italy), Joanna I. House (United Kingdom), Suzana \r [...]"


id: 262d99495103adf6f812e0fde0ac2928
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 3885
content:
"[...] Kahn Ribeiro (Brazil), Smail  Khennas (Algeria/United Kingdom), William F. Lamb (Germany/\r United Kingdom), Gunnar  Luderer (Germany), Giulio  Mattioli (Germany/Italy), Francesco \r Mattion (Italy), Rachid Mrabet (Morocco), Gert-Jan Nabuurs (the Netherlands), Jos Olivier (the \r Netherlands), Anne Owen (United Kingdom), Glen P. Peters (Norway/Australia), Julia Pongratz \r (Germany), Roberta Quadrelli (Italy), Andy  Reisinger (New Zealand), Keywan Riahi (Austria), \r Matt Rigby (United Kingdom), Joeri Rogelj (Belgium/United Kingdom), Yamina Saheb (France/\r Algeria), Marielle Saunois (France), Roberto Schaeffer (Brazil), Karen C. Seto (the United States of \r America), Yuli Shan (China), Raphael Slade (United Kingdom), Steven J. Smith (the United States \r of America), Efisio Solazzo (Italy), Jan Steckel (Germany), Anders Hammer Stromman (Norway), \r Laixiang Sun (the United States of America), Pouya Taghavi-Moharamli (Canada), Hanqin Tian \r [...]"


id: 634bf2cb52561395b307aeec70ac2d42
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 14766
content:
"[...] (the United States of America), Detlef  P.  van Vuuren (the Netherlands), Dominik Wiedenhofer \r (Austria)"


id: 4c9a8bd1f98da24d78259ec3ff595663
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 10742
content:
"Emissions growth has varied, but persisted across all groups of \r GHGs (high confidence). The average annual emission levels of the \r last decade (2010-2019) were higher than in any previous decade for \r each group of GHGs (high confidence). In 2019, CO2 emissions were \r 45 +- 5.5 GtCO2,\r 2\r  CH4 11 +- 3.2 GtCO2-eq, N2O 2.7 +- 1.6 GtCO2-eq and \r fluorinated gases (F-gases: HFCs, PFCs, SF6, NF3) 1.4 +- 0.41 GtCO2-eq. \r Compared to 1990, the magnitude and speed of these increases \r differed across gases: CO2 from fossil fuel and industry (FFI) grew by \r 15 GtCO2-eq yr-1 (67%), CH4 by 2.4 GtCO2-eq yr -1 (29%), F-gases \r by 0.97 GtCO2-eq yr -1 (254%), and N2O by 0.65  GtCO2-eq  yr-1\r (33%). CO2 emissions from net land use, land-use change and \r forestry (LULUCF) have shown little long-term change, with large \r uncertainties preventing the detection of statistically significant \r trends. F-gases excluded from GHG emissions inventories such as \r [...]"


id: f7a316612a565940dd2fd21f0ec65f93
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 40940
content:
"[...] chlorofluorocarbons and hydrochlorofluorocarbons are about the \r same size as those included (high confidence). {2.2.1, 2.2.2, Table 2.1, \r Figures 2.2, 2.3 and 2.5}"


id: c80c7127822dc758bbf928dff907edd2
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 32503
content:
"Emissions from developing countries have continued to grow, \r starting from a  low base of per  capita emissions and with \r a lower contribution to cumulative emissions than Developed \r Countries (robust evidence, high agreement). Average 2019 \r per  capita CO2-FFI emissions in three developing regions  - Africa \r (1.2  tCO2 per  capita), Asia and Pacific (4.4 tCO2 per  capita), and \r Latin America and Caribbean (2.7 tCO2 per capita) - remained less \r than half that of Developed Countries (9.5 tCO2 per capita) in 2019. \r CO2-FFI emissions in the three developing regions together grew by \r 26% between 2010 and 2019, compared to 260% between 1990 \r and 2010, while in Developed Countries emissions contracted by \r 9.9% between 2010 and 2019, and by 9.6% between 1990 and \r 2010. Historically, the three developing regions together contributed \r 28% to cumulative CO2-FFI emissions between 1850 and 2019, \r whereas Developed Countries contributed 57% and Least-Developed \r [...]"


id: bd0e37617a749b70b9e441fbc1c70986
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updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 30508
content:
"Globally, GHG emissions continued to rise across all sectors \r and subsectors; most rapidly in transport and industry (high \r confidence). In 2019, 34% (20 GtCO2-eq) of global GHG emissions \r came from the energy sector, 24% (14 GtCO2-eq) from industry, 22% \r (13 GtCO2-eq) from agriculture, forestry and other land use (AFOLU), \r 15% (8.7 GtCO2-eq) from transport and 5.6% (3.3 GtCO2-eq) from \r buildings. Once indirect emissions from energy use are considered, \r the relative shares of industry and buildings emissions rise to 34% \r and 16%, respectively. Average annual GHG emissions growth during \r 2010 to 2019 slowed compared to the previous decade in energy \r supply (from 2.3% to 1.0%) and industry (from 3.4% to 1.4%, direct \r emissions only), but remained roughly constant at about 2% per year \r in the transport sector (high confidence). Emission growth in AFOLU \r is more uncertain due to the high share of CO2-LULUCF emissions \r [...]"


id: 1f75f516d657e389694ff66b614ace8b
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 2744
content:
"Estimates of future CO2 emissions from existing fossil fuel \r infrastructures already exceed remaining cumulative net CO2\r emissions in pathways limiting warming to 1.5degC with no or \r limited overshoot (high confidence). Assuming variations in \r historical patterns of use and decommissioning, estimated future \r CO2 emissions from existing fossil fuel infrastructure alone are \r 660 (460-890) GtCO2 and from existing and currently planned \r infrastructure 850 (600-1100) GtCO2. This compares to overall \r cumulative net CO2 emissions until reaching net zero CO2 of 510 \r (330-710) Gt in pathways that limit warming to 1.5degC with no or \r limited overshoot, and 890 (640-1160) Gt in pathways that limit \r warming to 2degC (<67%) (high confidence). While most future CO2\r emissions from existing and currently planned fossil fuel infrastructure \r are situated in the power sector, most remaining fossil fuel CO2\r emissions in pathways that limit warming to 2degC (<67%) and below \r [...]"


id: 6a2f518f167dc9d076e783fb71cf280e
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updated_at: 2023-04-19 13:23:07
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vector_id: 15959
content:
"[...] are from non-electric energy - most importantly from the industry \r and transportation sectors (high confidence). Decommissioning and \r reduced utilisation of existing fossil fuel installations in the power \r sector as well as cancellation of new installations are required to \r align future CO2 emissions from the power sector with projections \r in these pathways (high confidence). {2.7.2, 2.7.3, Figure  2.26, \r Table 2.6, Table 2.7}"


id: 6dea95e9e5b55aa3f43f6ea08e254ea1
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 16599
content:
"A growing number of global GHG emissions inventories have \r become available since AR5 (Minx et al. 2021). However, only a few \r are comprehensive in their coverage of sectors, countries and gases - \r namely EDGAR (Emissions Database for Global Atmospheric Research) \r (Crippa et al. 2021), PRIMAP (Potsdam Real-time Integrated Model \r for probabilistic Assessment of emissions Paths) (Gutschow et  al. \r 2021a), CAIT (Climate Analysis Indicators Tool) (WRI 2019) and CEDS \r (A Community Emissions Data System for Historical Emissions) (Hoesly \r et al. 2018). None of these inventories presently cover CO2-LULUCF, \r while CEDS excludes F-gases. For individual gases and sectors, \r additional GHG inventories are available, as shown in Figure 2.2, but \r each has varying system boundaries leading to important differences \r between their respective estimates (Section 2.2.1). Some inventories \r are compiled bottom-up, while others are produced synthetically \r [...]"


id: f8878f57b0bc8abc1940d2e8b006db0
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updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 41084
content:
"[...] and are dependent on other inventories. A more comprehensive list \r and discussion of different datasets is provided in the Chapter  2 \r Supplementary Material (2.SM.1) and in Minx et al. (2021)."


id: 719d3a5f0caf9c5c6ccad706328b0da2
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 17235
content:
"Accordingly, the grey-outlined boxes at the top of Figure 2.1 show \r that the first part of the chapter presents GHG emissions from two \r main perspectives: their geographical locations; and the places where \r goods are consumed and services are utilised. A complicated chain of \r drivers underlie these emissions. They are linked across time, space, \r and various segments of the economy and society in complex non\u0002linear relationships. Sections shown in the second row of grey-outlined \r boxes assess the latest literature and improve the understanding of \r the relative importance of these drivers in mitigating GHG emissions. \r A huge mass of physical capital embodying immense financial assets \r and potentially operating over a  long lifetime produces vast GHG \r emissions. This long-lived infrastructure can be a significant hindrance \r to fast and deep reductions of emissions; it is therefore also shown as \r an important driver. A large range of economic, social, environmental, \r [...]"


id: aa10efbdac91a31ab4da3b0f70b82820
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 27147
content:
"[...] and other policies has been shaping these drivers of GHG emissions \r in the past and are anticipated to influence them in the future, as \r indicated by the grey-outlined policies box and its manifold linkages. \r As noted, blue lines show linkages of sections to other chapters that \r discuss these drivers and their operating mechanisms in detail."


id: c3fcb3006e7c854af07f72fbd6f6728a
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 31763
content:
"Across this report, version 6 of EDGAR (Crippa et al. 2021) provided \r by the Joint Research Centre of the European Commission, is used \r for a  consistent assessment of GHG emissions trends and drivers. \r It covers anthropogenic releases of CO2-FFI, CH4, N2O, and F-gas \r (HFCs, PFCs, SF6, NF3) emissions by 228 countries and territories and \r across five sectors and 27 subsectors. EDGAR is chosen because it \r provides the most comprehensive global dataset in its coverage \r of sources, sectors and gases. For transparency, and as part of the \r uncertainty assessment, EDGAR is compared to other global datasets \r in Section 2.2.1 as well as in the Chapter 2 Supplementary Material \r (2.SM.1). For individual country estimates of GHG emissions, it may \r be more appropriate to use inventory data submitted to the United \r Nations Framework Convention on Climate Change (UNFCCC) under \r the common reporting format (CRF) (UNFCCC 2021). However, these \r [...]"


id: 2c15be0ea6c9010d2f5531f3a83dd218
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 5017
content:
"[...] inventories are only up to date for Annex I countries and cannot be \r used to estimate global or regional totals. As part of the regional \r analysis, a comparison of EDGAR and CRF estimates at the country\u0002level is provided, where the latter is available (Figure 2.9)."


id: 821895e9f61b31ccf754b5a5b08f190d
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 20009
content:
"Uncertainty estimates can be rather different depending on the \r method chosen. For example, the range of estimates from multiple \r sources is bounded by their interdependency; they can be lower than \r true structural plus parameter uncertainty, or than estimates made \r by independent methods. In particular, it is important to account for \r potential bias in estimates, which can result from using common \r methodological or parameter assumptions, or from missing sources \r (systemic bias). It is further crucial to account for differences in \r system boundaries - that is, which emissions sources are included \r in a  dataset and which are not, otherwise direct comparisons \r can exaggerate uncertainties (Macknick 2011; Andrew 2020). \r Independent top-down observational constraints are, therefore, \r particularly useful to bound total emission estimates, but are not \r yet capable of verifying emission levels or trends (Petrescu et  al. \r [...]"


id: d037908d21aa8f7bed528d98fdc41f88
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 33997
content:
"[...] 2021a, 2021b). Similarly, uncertainties estimates are influenced by \r specific modelling choices. For example, uncertainty estimates from \r studies on the propagation of uncertainties associated with key input \r parameters (activity data, emissions factors) following the IPCC \r Guidelines (IPCC 2006) are strongly determined by assumptions on \r how these parameters are correlated between sectors, countries, \r and regions (Janssens-Maenhout et  al. 2019; Solazzo et  al. 2021). \r Assuming (full) covariance between source categories, and therefore \r dependence between them, increases uncertainty estimates. \r Estimates allowing for some covariance as in Sollazo et al. (2021) \r also tend to yield higher estimates than the range of values from \r ensemble of dependent inventories (Saunois et al. 2016, 2020)."


id: 7d404cddd257151924051986c863346e
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 19212
content:
"In this chapter and the report as a  whole, different GHGs are \r frequently converted into common units of CO2 equivalent (CO2-eq) \r emissions using 100-year global warming potentials (GWP100) from \r AR6 WGI (Forster et al. 2021a). This reflects the dominant use in the \r scientific literature and is consistent with decisions made by Parties \r to the Paris Agreement for reporting and accounting of emissions \r and removals (UNFCCC 2019). Other GHG emissions metrics exist, \r all of which, like GWP100, are designed for specific purposes and \r have limitations and uncertainties. The appropriate choice of GHG \r emissions metrics depends on policy objective and context (Myhre \r et  al. 2013; Kolstad et  al. 2015). A  discussion of GHG metrics is \r provided in a Cross-Chapter Box later in the chapter (Cross-Chapter \r Box  2) and at length in the Chapter  2 Supplementary Material. \r Throughout the chapter GHG emissions are reported (in GtCO2-eq) at \r [...]"


id: 6ffe7684538228a9b56a9b27623b909
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index: climate_gpt_v2
vector_id: 16959
content:
"[...] two significant digits to reflect prevailing uncertainties in emissions \r estimates. Estimates are subject to uncertainty, which we report for \r a 90% confidence interval."


id: 70de414b18b6a45fafc753719417c822
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 17121
content:
"Estimates of historical GHG emissions  - CO2, CH4, N2O and \r F-gases - are uncertain to different degrees. Assessing and reporting \r uncertainties is crucial in order to understand whether available \r estimates are sufficiently robust to answer policy questions  - for \r example, if GHG emissions are still rising, or if a country has achieved \r an emission reduction goal (Marland 2008). These uncertainties can \r be of scientific nature, such as when a  process is not sufficiently \r understood. They also arise from incomplete or unknown parameter \r information (e.g.,  activity data, or emission factors), as well as \r estimation uncertainties from imperfect modelling techniques. There \r are at least three major ways to examine uncertainties in emission \r estimates (Marland et al. 2009): (i) by comparing estimates made by \r independent methods and observations (e.g., comparing atmospheric \r measurements with bottom-up emissions inventory estimates) \r [...]"


id: e27f517c95a7bf1623dc45c8b335d5da
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 37147
content:
"[...] (Saunois et al. 2020; Petrescu et al. 2020a and 2020b; Tian et al. 2020); \r (ii) by comparing estimates from multiple sources and understanding \r sources of variation (Macknick 2011; Andres et  al. 2012; Andrew \r 2020; Ciais et al. 2021); and (iii) by evaluating estimates from a single \r source (Hoesly and Smith 2018), for instance via statistical sampling "


id: e358725f5572870e4b8c564cf25fabc4
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 37296
content:
"Figure 2.2 (continued): Estimates of global anthropogenic greenhouse gas emissions from different data sources 1970-2019. Panel (a): CO2 FFI emissions \r from: EDGAR - Emissions Database for Global Atmospheric Research (this dataset) (Crippa et al. 2021); GCP - Global Carbon Project (Friedlingstein et al. 2020; Andrew and \r Peters 2021); CEDS - Community Emissions Data System (Hoesly et al. 2018; O'Rourke et al. 2021); CDIAC Global, Regional, and National Fossil-Fuel CO2 Emissions (Gilfillan \r et al. 2020); PRIMAP-hist - Potsdam Real-time Integrated Model for probabilistic Assessment of emissions Paths (Gutschow et al. 2016, 2021b); EIA - Energy Information \r Administration International Energy Statistics (EIA 2021); BP - BP Statistical Review of World Energy (BP 2021); IEA - International Energy Agency (IEA 2021a, 2021b); \r [...]"


id: 384f71a3a433c474d4bc7b63bde123b7
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 7113
content:
"[...] IPPU refers to emissions from industrial processes and product use. Panel (b): Net anthropogenic CO2-LULUCF emissions from: BLUE - Bookkeeping of land-use emissions \r (Hansis et al. 2015; Friedlingstein et al. 2020); DGVM-mean - multi-model mean of CO2-LULUCF emissions from dynamic global vegetation models (Friedlingstein et al. 2020); \r OSCAR - an earth system compact model (Friedlingstein et al. 2020; Gasser et al. 2020); HN - Houghton and Nassikas Bookkeeping Model (Houghton and Nassikas 2017; \r Friedlingstein et al. 2020); for comparison, the net CO2 flux from FAOSTAT (FAO Tier 1) is plotted, which comprises net emissions and removals on forest land and from net \r forest conversion (FAOSTAT 2021; Tubiello et al. 2021), emissions from drained organic soils under cropland/grassland (Conchedda and Tubiello 2020), and fires in organic soils \r [...]"


id: ad4dafdb26e07159c1f041fb59a20ec7
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 27733
content:
"[...] (Prosperi et al. 2020), as well as a net CO2 flux estimate from National Greenhouse Gas Inventories (NGHGI) based on country reports to the UNFCCC, which include land-use \r change, and fluxes in managed lands (Grassi et al. 2021). Panel (c): Anthropogenic CH4 emissions from: EDGAR (above); CEDS (above); PRIMAP-hist (above); GAINS - The \r Greenhouse gas - Air pollution Interactions and Synergies Model (Hoglund-Isaksson et al. 2020); EPA-2019: Greenhouse gas emission inventory (US-EPA, 2019); FAO -FAOSTAT \r inventory emissions (Tubiello et al. 2013; Tubiello 2018; FAOSTAT 2021); Panel (d): Anthropogenic N2O emissions from: GCP - global nitrous oxide budget (Tian et al. 2020); \r CEDS (above); EDGAR (above); PRIMAP-hist (above); GAINS (Winiwarter et al. 2018); EPA-2019 (above); FAO (above). Differences in emissions across different versions of the \r EDGAR dataset are shown in the Supplementary Material (Figure 2.SM.2). Source: Minx et al. (2021)."


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vector_id: 21238
content:
"The most appropriate metric to aggregate GHG emissions depends \r on the objective (Cross-Chapter Box 2). One such objective can be to \r understand the contribution of emissions in any given year to warming, \r while another can be to understand the contribution of cumulative \r emissions over an extended time period to warming. In Figure 2.4 the \r modelled warming from emissions of each gas or group of gases is \r also shown - calculated using the reduced-complexity climate model \r Finite Amplitude Impulse Response (FaIR) model v1.6, which has been \r calibrated to match several aspects of the overall WGI assessment \r (Forster et al. 2021a; specifically Cross-Chapter Box 7 in Chapter 10 \r therein). Additionally, its temperature response to emissions with \r shorter atmospheric lifetimes such as aerosols, methane or ozone \r has been adjusted to broadly match those presented in Szopa et al. \r (2021a). There are some differences in actual warming compared to \r [...]"


id: e0d6b6eba57ff1b0462fef534e460424
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 36820
content:
"[...] the GWP100 weighted emissions of each gas (Figure 2.4), in particular \r a greater contribution from CH4 emissions to historical warming. This "


id: a886043f110473cadcb85020fb97696b
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 26860
content:
"The choice of metric, including time horizon, should reflect the policy objectives for which the metric is applied (Plattner et al. 2009). \r Recent studies confirm earlier findings that the GWP is consistent with a cost-benefit framework (Kolstad et al. 2014), which implies \r weighting each emission based on the economic damages that this emission will cause over time, or conversely, the avoided damages \r from avoiding that emission. The GWP time horizon can be linked to the discount rate used to evaluate economic damages from each \r emission. For methane, GWP100 implies a social discount rate of about 3-5% depending on the assumed damage function, whereas \r GWP20 implies a much higher discount rate, greater than 10% (medium confidence) (Mallapragada and Mignone 2019; Sarofim and \r Giordano 2018). The dynamic GTP is aligned with a cost-effectiveness framework, as it weights each emission based on its contribution \r [...]"


id: 30a56baf0551f22a471cd0aea4eefc0a
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 5812
content:
"[...] to global warming in a specified future year (e.g., the expected year of peak warming for a given temperature goal). This implies \r a shrinking time horizon and increasing relative importance of SLCF emissions as the target year is approached (Johansson 2011; \r Aaheim and Mideksa 2017). The GTP with a static time horizon (e.g., GTP100) is not well-matched to either a cost-benefit or a cost\u0002effectiveness framework, as the year for which the temperature outcome is evaluated would not match the year of peak warming, nor \r the overall damages caused by each emission (Edwards and Trancik 2014; Strefler et al. 2014; Mallapragada and Mignone 2017)."


id: bd742877511199b6c7842ddecff93c94
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
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content:
"A number of studies since AR5 have evaluated the impact of various GHG emission metrics and time horizons on the global economic \r costs of limiting global average temperature change to a pre-determined level (e.g. Strefler et al. 2014; Harmsen et al. 2016; Tanaka \r et al. 2021) (see 2.SM.3 for additional detail). These studies indicate that, for mitigation pathways that limit warming to 2degC (<67%) \r above pre-industrial levels or lower, using GWP100 to inform cost-effective abatement choices between gases would achieve such \r long-term temperature goals at close to least global cost within a few percent (high confidence). Using the dynamic GTP instead of \r GWP100 could reduce global mitigation costs by a few percent in theory (high confidence), but the ability to realise those cost savings \r depends on the temperature limit, policy foresight and flexibility in abatement choices as the weighting of SLCF emissions increases \r [...]"


id: db8f01a2bdbee7979d06d5d5d3d1382e
created_at: 2023-04-19 13:23:07
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content:
"[...] over time (medium confidence) (van den Berg et al. 2015; Huntingford et al. 2015). Similar benefits as for the dynamic GTP might be \r obtained by regularly reviewing and potentially updating the time horizon used for GWP in light of actual emissions trends compared \r to climate goals (Tanaka et al. 2020)."


id: 7f43924a0a0303ff78bb511d3888cb5
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
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content:
"Recently developed step/pulse metrics such as the combined global temperature change potential (CGTP) (Collins et al. 2019) and \r GWP* (Allen et al. 2018; Cain et al. 2019) recognise that a sustained increase/decrease in the rate of SLCF emissions has a similar \r effect on global surface temperature over multiple decades as a one-off pulse emission/removal of CO2. These metrics use this \r relationship to calculate the CO2 emissions or removals that would result in roughly the same temperature change as a sustained \r change in the rate of SLCF emissions (CGTP) over a given time period, or as a varying time series of CH4 emissions (GWP*). From \r a mitigation perspective, these metrics indicate greater climate benefits from rapid and sustained methane reductions over the next \r few decades than if such reductions are weighted by GWP100, while conversely, sustained methane increases have greater adverse \r [...]"


id: fee8e52737eef2d76e4faa71de62b5e
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 42241
content:
"[...] climate impacts (Collins et al. 2019; Lynch et al. 2020). The ability of these metrics to relate changes in emission rates of short-lived \r gases to cumulative CO2 emissions makes them well-suited, in principle, to estimating the effect on the remaining carbon budget from \r more, or less, ambitious SLCF mitigation over multiple decades compared to a given reference scenario (high confidence) (Collins et al. \r 2019; Forster et al. 2021)."


id: 61ea34644345c471649b7cda4113c9df
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
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content:
"The potential application of GWP* in wider climate policy (e.g., to inform equitable and ambitious emission targets or to support \r sector-specific mitigation policies) is contested, although relevant literature is still limited (Rogelj and Schleussner 2019, 2021; \r Schleussner et al. 2019; Allen et al. 2021; Cain et al. 2021). Whereas GWP and GTP describe the marginal effect of each emission \r relative to the absence of that emission, GWP* describes the equivalent CO2 emissions that would give the same temperature \r change as an emissions trajectory of the gas considered, starting at a (user-determined) reference point. The warming based on \r those cumulative CO2-equivalent emission at any point in time is relative to the warming caused by emissions of that gas before the \r reference point. Because of their different focus, GWP* and GWP100 can equate radically different CO2 emissions to the same CH4\r [...]"


id: 452633804afaee021cfe1e5432f2167c
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 9390
content:
"[...] emissions: rapidly declining CH4 emissions have a negative CO2-warming-equivalent value based on GWP* (rapidly declining SLCF \r emissions result in declining temperature, relative to the warming caused by past SLCF emissions at a previous point in time) but a \r positive CO2-equivalent value based on GWP or GTP (each SLCF emission from any source results in increased future radiative forcing \r and global average temperature than without this emission, regardless of whether the rate of SLCF emissions is rising or declining). \r The different focus in these metrics can have important distributional consequences, depending on how they are used to inform \r emission targets (Lynch et al. 2021; Reisinger et al. 2021), but this has only begun to be explored in the scientific literature."


id: d81305fa64fe72ddfd0eb843992fcae6
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 35315
content:
"A key insight from WGI is that, for a  given emissions scenario, different metric choices can alter the time at which net zero \r GHG emissions are calculated to be reached, or whether net zero GHG emissions are reached at all (2.SM.3). From a mitigation \r perspective, this implies that changing GHG emission metrics but retaining the same numerical CO2-equivalent emissions targets \r would result in different climate outcomes. For example, achieving a balance of global anthropogenic GHG emissions and removals, as \r stated in Article 4.1 of the Paris Agreement could, depending on the GHG emission metric used, result in different peak temperatures \r and in either stable, or slowly or rapidly declining temperature after the peak (Allen et al. 2018; Fuglestvedt et al. 2018; Tanaka and \r O'Neill 2018; Schleussner et al. 2019). A fundamental change in GHG emission metrics used to monitor achievement of existing \r [...]"


id: ff42061fc9763a9bc53936d42ef297fe
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 42312
content:
"[...] emission targets could therefore inadvertently change their intended climate outcomes or ambition, unless existing emission targets \r are re-evaluated at the same time (very high confidence)."


id: 4c7a42a02ec4cda80ee2dc8124ba5ec8
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 10712
content:
"The only exception to these patterns of GHG emissions growth is net \r anthropogenic CO2-LULUCF emissions, where there is no statistically \r significant trend due to high uncertainties in estimates (Figures 2.2 and \r 2.5; Chapter 2 Supplementary Material). While the average estimate \r from the bookkeeping models report a  slightly increasing trend in \r emissions, NGHGI and FAOSTAT estimates show a slightly decreasing \r trend, which diverges in recent years (Figure 2.2). Similarly, trends in \r CO2-LULUCF estimates from individual bookkeeping models differ: \r while two models (BLUE and OSCAR) show a sustained increase in \r emissions levels since the mid-1990s, emissions from the third model \r (Houghton and Nassikas (HN)) declined (Figure 2.2 in this chapter; \r Friedlingstein et al. 2020). Differences in accounting approaches and \r their impacts CO2 emissions estimates from land use is covered in \r Chapter  7 and in the Chapter  2 Supplementary Material (2.SM.2). \r [...]"


id: 4a144386b8971387c3b2cf45bef3cc56
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 10276
content:
"[...] Note that anthropogenic net emissions from bioenergy are covered \r by the CO2-LULUCF estimates presented here."


id: dd65e103d1d359cfb12cadd661950ab1
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 36220
content:
"Starting in the spring of 2020 a major break in global emissions \r trends was observed due to lockdown policies implemented in \r response to the COVID-19 pandemic (high confidence) (Forster \r et al. 2020; Le Quere et al. 2020, 2021; Z. Liu et al. 2020b; Bertram \r et  al. 2021). Overall, global CO2-FFI emissions are estimated \r to have declined by 5.8% (5.1%-6.3%) in 2020, or about 2.2 \r (1.9-2.4)  GtCO2 in total (Friedlingstein et  al. 2020; Z. Liu et  al. \r 2020b; BP 2021; Crippa et al. 2021; IEA 2021a). This exceeds any \r previous global emissions decline since 1970, both in relative \r and absolute terms (Figure 2.6). Daily emissions, estimated based \r on activity and power-generation data, declined substantially \r compared to 2019 during periods of economic lockdown, \r particularly in April 2020 - as shown in Figure 2.6 - but rebounded \r by the end of 2020 (medium confidence) (Le Quere et  al. 2020, \r 2021; Z. Liu et  al. 2020b). Impacts were differentiated by sector, \r [...]"


id: c90c2a89f9a35eab2f2020ed980953f8
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 32691
content:
"[...] with road transport and aviation particularly affected. Inventories \r estimate the total power sector CO2 reduction from 2019 to 2020 \r at 3% (IEA 2021a) and 4.5% (Crippa et al. 2021). Approaches that \r predict near real-time estimates of the power sector reduction are \r more uncertain and estimates range more widely, between 1.8% "


id: 2cc65f85f4ed4b8ba5ebe3ceb6bfa9cb
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 5143
content:
"Even when taking uncertainties into account, historical emissions \r between 1850 and 2019 constitute a  large share of total carbon \r budgets from 2020 onwards for limiting warming to 1.5degC with \r a 50% probability as well as for limiting warming to 2degC with a 67% \r probability. Based on central estimates only, historical cumulative \r net CO2 emissions between 1850-2019 amount to about four fifths \r of the total carbon budget for a 50% probability of limiting global \r warming to 1.5degC (central estimate about 2900 GtCO2), and to \r about two thirds of the total carbon budget for a 67% probability \r to limit global warming to 2degC (central estimate about 3550 GtCO2). \r The carbon budget is the maximum amount of cumulative net \r global anthropogenic CO2 emissions that would result in limiting \r global warming to a given level with a given likelihood, taking into \r account the effect of other anthropogenic climate forcers. This is \r [...]"


id: b852c9f04406cb41d72f8594b8b4d6bc
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 29642
content:
"[...] referred to as the total carbon budget when expressed starting from \r the pre-industrial period, and as the remaining carbon budget when \r expressed from a  recent specified date. The total carbon budgets \r reported here are the sum of historical emissions from 1850 to 2019 \r and the remaining carbon budgets from 2020 onwards, which extend "


id: 3f963ed183cb52b946ca45139c6e9622
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 8378
content:
"Least-Developed Countries and Small Island Developing States \r contributed only a negligible proportion of historic GHG emissions \r growth and have the lowest per capita emissions. As of 2019 Least \r Developed Countries contribute 3.3% of global GHG emissions, \r excluding LULUCF CO2, despite making up 13% of the global \r population. Small Island Developing States contributed 0.6% of \r global GHG emissions in 2019, excluding LULUCF CO2, with 0.9% \r of the global population. Since the start of the industrial revolution in \r 1850 up until 2019, Least Developed Countries contributed 0.4% of \r total cumulative CO2 emissions, while Small Island Developing States \r contributed 0.5% (Figure  2.10). Conversely, Developed Countries \r have the highest share of historic cumulative emissions (Rocha \r et  al. 2015; Gutschow et  al. 2016; Matthews 2016), contributing \r approximately 57% (Figure 2.10), followed by Asia and Pacific (21%), \r [...]"


id: 19d5ba3916e16d5f5f116f5723d71711
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"[...] Eastern Europe and West Central Asia (9%), Latin America and the \r Caribbean (4%), the Middle East (3%), and Africa (3%). Developed \r Countries still have the highest share of historic cumulative emissions \r (45%) when CO2-LULUCF emissions are included, which typically \r account for a higher proportion of emissions in developing regions \r (Figure 2.10)."


id: 8f298ddf515443549fef5b0b93dbf3bd
created_at: 2023-04-19 13:23:07
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content:
"Figure  2.9 (continued): Change in regional GHGs from multiple perspectives and their underlying drivers. Panel (a): Regional GHG emissions trends \r (in GtCO2-eq yr -1) for the time period 1990-2019. GHG emissions from international aviation and shipping are not assigned to individual countries and shown separately.\r Panels (b) and (c): Changes in GHG emissions for the 20 largest emitters (as of 2019) for the post-AR5 reporting period 2010-2019 in relative (% annual change) and \r absolute terms (GtCO2-eq). Panels (d) and (e): GHG emissions per capita and per GDP in 2019 for the 20 largest emitters (as of 2019). GDP estimated using constant \r international purchasing power parity (USD2017). Emissions are converted into CO2-equivalents based on global warming potentials with a 100-year time horizon (GWP100) \r from the IPCC Sixth Assessment Report (Forster et al. 2021a). The black dots represent the emissions data from UNFCCC-CRFs (2021) that were accessed through Gutschow \r [...]"


id: f453df3a563ed99d1fa179682595af76
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 40360
content:
"[...] et al. (2021a). Net LULUCF CO2 emissions are included in panel (a), based on the average of three bookkeeping models (Section 2.2), but are excluded in panels (b-e) due to \r a lack of country resolution."


id: 9c6967f3e442f917567315b050bcc8f6
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 24769
content:
"Figure 2.10 | Different perspectives on historic emissions and equity. Panel (a) shows the regional proportion (%) of total global population or emissions in 2018 or \r 2019, for five categories: population (persons); consumption-based CO2-FFI emissions (GtCO2); production-based CO2-FFI emissions (GtCO2); production-based GHG emissions \r excluding CO2-LULUCF (GtCO2-eq); and production-based GHG emissions including CO2-LULUCF (GtCO2-eq). Panel (b) shows the regional proportion (%) of total cumulative \r production-based CO2 emissions from 1850 to 2019, including and excluding CO2-LULUCF (GtCO2). In the lower panels, the proportion of each population or emissions \r category attributable to Least-Developed Countries and Small Island Developing States (SIDS) are shown, where available (CO2-LULUCF data is not available for these regions). \r [...]"


id: de4fd8ca63969e1d478e71aab48d5c2a
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 36373
content:
"[...] GHG emissions are converted into CO2-equivalents based on global warming potentials with a 100-year time horizon (GWP100) from the IPCC Sixth Assessment Report\r (Forster et al. 2021a). Source: data from Friedglinstein et al. (2020)."


id: 31e64c490338ac212ad94ade7da36770
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 6032
content:
"A growing number of countries have reduced CO2 and GHG emissions \r for longer than 10 years (high confidence) (Le Quere et  al. 2019; \r Burck et al. 2021; Lamb et al. 2021a; Wu et al. 2021). Data up to 2018 \r indicates that about 24 countries have reduced territorial CO2 and GHG \r emissions (excluding LULUCF CO2), as well as consumption-based CO2\r emissions, for at least 10 years (Lamb et al. 2021a). Uncertainties in \r emissions levels and changes over time prevents a precise assessment \r of reductions in some cases. Of these 24  countries, 12 peaked \r emissions in the 2000s; six have sustained longer reductions since \r the 1970s; and six are former members of the Eastern Bloc, where \r emissions dropped rapidly in the 1990s and continued declining \r at a  slower pace thereafter. Country emissions reductions have \r been driven by both climate and non-climate policies and factors, \r including structural changes. To date, most territorial emissions \r [...]"


id: f48537b8f80acee2145cbd5991788297
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 40393
content:
"[...] reductions were realised in the electricity and heat sector, followed \r by industry and buildings, while in many cases transport emissions \r have increased since countries reached their overall emissions peak \r (Climate Transparency 2021; Lamb et al. 2021a). One estimate of the \r total reduction in annual GHG emissions - from peak years to 2018 - \r sums to 3.2 GtCO2-eq across all decarbonising countries (Lamb et al. \r 2021a). These reductions have therefore been far outweighed by \r recent emissions growth. However, climate policy related reductions \r may be even larger when compared against a counterfactual case of \r emissions growth across different sectors (Eskander and Fankhauser \r 2020) (Cross-Chapter Box 1 in Chapter 1; Section 2.8)."


id: c0e53d48dfbae21024bebdad724febe1
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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vector_id: 31239
content:
"Figure 2.12 | Total anthropogenic direct and indirect GHG emissions for the year 2019 (in GtCO2-eq) by sector and subsector. Direct emissions estimates \r assign emissions to the sector in which they arise (scope 1 reporting). Indirect emissions - as used here - refer to the reallocation of emissions from electricity and heat to the \r sector of final use (scope 2 reporting). Note that cement refers to process emissions only, as a lack of data prevents the full reallocation of indirect emissions to this sector. \r More comprehensive conceptualisations of indirect emissions including all products and services (scope 3 reporting) are discussed in Section 2.3 of this chapter. Emissions are \r converted into CO2-equivalents based on global warming potentials with a 100-year time horizon (GWP100) from the IPCC Sixth Assessment Report. Percentages may not add \r [...]"


id: b35057ea59550a4d45a1266ad54d8975
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updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 28791
content:
"[...] up to 100 across categories due to rounding at the second significant digit. Source: based on Lamb et al. (2021b); data: Minx et al. (2021)."


id: e0bfb8dddfb6d2e017a382dc7d3ec491
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 36800
content:
"There are other proposed emission accounting approaches used \r in different circumstances. Historical cumulative emissions (HCEs) \r are used when analysing countries' historic contribution to \r emissions and responsibility for emission reduction. HCEs account \r for a  country's cumulative past emissions, which may be different \r from the country's  current annual emissions (Botzen et  al. 2008; \r Ritchie 2019), but are sensitive to the choice of cut-off period. For \r example, the USA and EU-27 countries plus the UK contributed \r respectively 13.3% and 8.7% to global PBEs in 2019 (Crippa et al. \r 2020), however, they emitted around 25% and 22% of global \r historical PBEs since 1751 (Ritchie 2019). Extraction-based emissions \r (EBEs) accounting allocates all emissions from burning fossil fuels \r throughout the supply chains to the country where the fuels were \r extracted (Steininger and Schinko 2015). EBEs can be calculated \r [...]"


id: 4b7c05480e40408f7be54a265bba3ac4
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 10533
content:
"[...] by multiplying primary energy extraction of fossil fuels with their \r respective carbon content (Erickson and Lazarus 2013). Another \r approach for accounting emissions is income-based emission (IBE), \r which traces emissions throughout all supply chains and allocates \r emissions to primary inputs (e.g., capital and labour). In other words, \r IBEs investigate a  country's direct and indirect downstream GHG \r emissions enabled by its primary inputs (Liang et al. 2017a). All these \r approaches provide complementary information and different angles \r to assigning responsibility for emissions reductions."


id: a1dc60b2288ee8b94a2a14a1395eeb57
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 25700
content:
"Production-based emissions (PBEs) and territorial emissions \r resulting from the production and consumption of goods and \r services within a  region (for both domestic use and export) are \r often used by authorities to report carbon emissions (Peters 2008) \r (Section  2.2). PBEs also include emissions from international \r activities (e.g.,  international aviation/shipping and non-resident \r activities), which are excluded from territorial emissions (Karstensen \r et al. 2018; Shan et al. 2018). In contrast, CBEs refer to emissions \r along the entire supply chains induced by consumption, irrespective \r of the place of production (Liu et al. 2015b). This reflects a shared \r understanding that a wider system boundary going beyond territorial \r emissions is important to avoid outsourcing of pollution and to \r achieve global decarbonisation. CBEs allow for the identification of \r new policy levers through information on a country's trade balance \r [...]"


id: 45c15f935b55ca4152ca4289ef3f4432
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 9486
content:
"[...] of embodied emissions, households' carbon implications of their \r lifestyle choices, companies' upstream emissions as input for supply "


id: 6f8a4839c04cecbc3c2d14c7d9283b7c
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 16887
content:
"The dominant method for calculating nations' CBEs is global multi\u0002region input-output (GMRIO) analysis (Wiedmann and Lenzen \r 2018). Other frequently used approaches include analysing bilateral \r trade flows of products and their lifecycle emission factors (Sato \r 2014). Generally, the uncertainties associated with CBEs depends \r on the choice of the dataset/model used for calculation, which \r differs according to: (i) the national economic and trade data \r used; (ii) the emissions data used; (iii) the sector or product-level \r aggregation; (iv) the regional aggregation; (v) the conceptual scope \r (e.g., residential vs territorial accounting principle); and (vi) the model \r construction techniques, which include table-balancing algorithms \r and ways of dealing with missing or conflicting data (Moran and \r Wood 2014; Owen, 2017; Wieland et  al. 2018; Wood et  al. 2018, \r 2019). When excluding systematic error sources, research has shown \r [...]"


id: 6d0d3634d3104514a8310aba1e91b98d
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 16464
content:
"[...] that the stochastic relative standard deviation (RSD) of total national \r CBEs is not significantly different to that from PBEs accounts and in \r the region of 5-15% (Wood et al. 2018, 2019)."


id: 2eee5b37d4a21bd79c1760bde411d4d2
created_at: 2023-04-19 13:23:07
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content:
"The two panels at left in Figure  2.14 show total and per  capita \r CBEs for six regions. The three panels on the right show additional \r information for the 18 top-emitting countries with the highest \r CBEs in 2018. In  Developed Countries, consumption-based CO2\r emissions peaked at 15 GtCO2 in 2007 with a  subsequent 16% \r decline until 2016 (to 12.7  GtCO2) and a  slight rebound of 1.6% \r until 2018 (to 12.9  GtCO2). Asia and Pacific has been a  major \r contributor to consumption-based CO2 emissions growth since 2000 \r and exceeded Developed Countries as the global largest emissions \r source in 2015. From 1990 to 2018, the average growth rate of Asia \r and Pacific was  4.8% per  year, while in other regions emissions \r declined by -1.1%-4.3% per  year on average. In 2018, 35% of \r global consumption-based CO2 emissions were from Developed \r Countries and 39% from Asia and Pacific, 5% from Latin American \r and Caribbean, 5% from Eastern Europe and West Central Asia, 5% \r [...]"


id: 4cce01ac74bfafd82d97d87e90befc2a
created_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
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content:
"[...] from Middle East, and 3% from Africa (Hubacek et al. 2021). Global \r CBEs kept growing over the period with a short-lived decline in 2008 \r due to the global financial crisis. In 2020, lockdowns associated with \r COVID-19 significantly reduced global emissions (Section  2.2.2), \r including CBEs (Shan et al. 2021a)."


id: a422d3906b5533b867af0bfbd575ef3f
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 26072
content:
"Located downstream in global supply chains, developed countries \r (mostly in Western Europe and North America) tend to be net \r emission importers, that is, EEI are larger than EEE. For example, over \r 40% of national CO2 footprints in France, Germany, Italy, and Spain \r are from imports (Fan et al. 2017). Developing countries tend to be \r net emission exporters with higher PBEs than their CBEs (Peters et al. \r 2011a), especially for Asia and Pacific (as shown in Figure 2.15). That \r is to say, there is a net emission transfer and outsourcing of carbon\u0002intensive production from developed to developing economies via \r global trade (Jiang et al. 2018), mainly caused by cheap labour costs \r (Tate and Bals 2017) and cheap raw materials (Mukherjee 2018). \r Increasing openness to trade (Fernandez-Amador et  al. 2016) and \r less stringent environmental legislation (acting as so-called pollution \r havens) are also possible reasons (Hoekstra et al. 2016; Malik and \r [...]"


id: 743fcc07fbdea9ee6689027ca14dfdb8
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
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content:
"Economic growth (measured as GDP) and its main components  - \r GDP per  capita and population growth  - remained the strongest \r drivers of GHG emissions in the last decade, following a long-term \r trend (robust evidence, high agreement) (Liddle 2015; Malik et al. \r 2016; Sanchez and Stern 2016; Chang et al. 2019; Dong et al. 2019; \r Liobikiene and Butkus 2019; Liu et al. 2019a; Mardani et al. 2019; Pan \r et al. 2019; Dong et al. 2020; Parker and Bhatti 2020; Xia et al. 2021). \r Globally, GDP per  capita remained by far the strongest upward \r driver, increasing almost in tandem with energy consumption and \r CO2 emissions up until 2015, after which some modest decoupling \r occurred (Deutch 2017; Wood et al. 2018) (Section 2.3.3). The main \r counteracting, yet insufficient, factor that led to emissions reductions \r was decreased energy use per  unit of GDP in almost all regions \r (-2.0% yr -1 between 2010 and 2019 globally) (see also Lamb et al. \r [...]"


id: f0dafcf03804fa406f1f88031128d2f2
created_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
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content:
"[...] 2021b) (Figure  2.16) (robust evidence, high agreement). These \r reductions in energy intensity are a result of technological innovation, \r structural changes, regulation, fiscal support, and direct investment, \r as well as increased economic efficiency in underlying sectors (Yao \r et al. 2015; Sanchez and Stern 2016; Chang et al. 2019; Dong et al. \r 2019a; Mohmmed et al. 2019; Stern 2019; Azhgaliyeva et al. 2020; \r Goldemberg 2020; Gao et al. 2021; Liddle and Huntington 2021; Liu \r et al. 2019b; Xia et al. 2021)."


id: 3f35a12f4f14d5baf61f7a47ed945ca
created_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
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content:
"Developing countries remained major accelerators of global \r CO2 emissions growth since 2010, mostly driven by increased \r consumption and production, in particular in East Asia (robust \r evidence, high agreement) (Jiborn et al. 2020). While energy intensity \r declined to a  similar extent in countries of the Organisation for \r Economic Co-operation and Development (OECD) and non-OECD \r countries over the last 30 years, economic growth has been much \r stronger in non-OECD countries (Gonzalez-Torres et al. 2021). This led \r to an average annual growth rate of 2.8% of CO2 emissions in these \r countries, whereas they decreased by 0.3% yr-1 in OECD countries \r (UNEP 2019). The majority of developed economies reduced both \r production-based and consumption-based CO2 emissions modestly \r (Jiborn et al. 2020; Xia et al. 2021). This was due to slower economic \r growth, increased energy efficiency (less energy per unit of GDP), fuel \r [...]"


id: 93681a59d948f545fd95012553242135
created_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 23139
content:
"[...] switching from coal to gas (mostly in North America) (Wang et al. \r 2020b), and the use of less and cleaner energy from renewables in \r Europe (Peters et al. 2017; Karstensen et al. 2018; Chang et al. 2019; \r Wood et al. 2019c)."


id: 56c65855d33470721987fa95efc26023
created_at: 2023-04-19 13:23:07
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content:
"Economic growth as the main driver of GHG emissions is particularly \r strong in China and India (robust evidence, high agreement) (Liu et al. \r 2019b; Ortega-Ruiz et al. 2020; Z. Wang et al. 2020b; Yang et al. 2020; \r Zheng et  al. 2020; Xia et  al. 2021), although both countries show \r signs of relative decoupling because of structural changes (Marin \r and Mazzanti 2019). A change in China's production structure (with \r relatively less heavy industry and lower-carbon manufacturing) and \r consumption patterns (i.e., the type of goods and services consumed) \r has become the main moderating factor of emissions after 2010, \r while economic growth, consumption levels, and investment remain \r the dominating factors driving up emissions (Wang and Jiang 2019; \r Jiborn et  al. 2020; Zheng et  al. 2020). In India, an expansion of \r production and trade as well as a higher energy intensity between \r 2010 and 2014 caused increased emissions (Kanitkar et  al. 2015; \r [...]"


id: 91438feff155a45c145bc329dd77952c
created_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
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content:
"The carbon intensity of power generation varies widely between \r (and also within) regions (Chapter  6). In North America, a  switch \r from coal to gas for power generation (Peters et al. 2017, 2020; Feng \r 2019; Mohlin et al. 2019) as well as an overall decline in the share \r of fossil fuels in electricity production (from 66% in 2010 to 59% in \r 2018) (Mohlin et al. 2019) has decreased carbon intensity and CO2\r emissions. Since 2007, Europe's carbon intensity improvements have \r been driven by the steady expansion of renewables in the share of \r electricity generation (medium evidence, high agreement) (Peters \r et al. 2017, 2020; Le Quere et al. 2019; Rodrigues et al. 2020). Some \r studies attribute these effects to climate policies, such as the carbon \r floor price in the UK, the EU emissions trading scheme, and generous \r renewable energy subsidies across the continent (Dyrstad et  al. \r 2019; H. Wang et  al. 2020). South-East Asian developed countries \r [...]"


id: 58f19c3ace2f01ef001d78c3b78f1706
created_at: 2023-04-19 13:23:07
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content:
"[...] and Australia, Japan and New Zealand stand out in contrast to other \r developed regions, with an increase of regional carbon intensity of \r 1.8 and 1.9% yr -1, respectively (Figure 2.17). Generally, the use of \r natural gas for electricity production is growing strongly in most \r countries and gas has contributed to the largest increase in global \r fossil CO2 emissions in recent years (Jackson et  al. 2019; Peters \r et al. 2020). Furthermore, gas brings the risk of increased methane \r (CH4) emissions from fugitive sources, as well as large cumulative \r emissions over the lifetime of new gas power plants that may erase \r early carbon intensity reductions (Shearer et al. 2020)."


id: 5b7699310f48ed28d2e334ef9dd0933b
created_at: 2023-04-19 13:23:07
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content_type: text
index: climate_gpt_v2
vector_id: 13332
content:
"More so than any other sector, transport energy use has tracked GDP \r per capita growth (Figure 2.20), (Lamb et al. 2021). With the exception \r of road gasoline demand in OECD countries, the demand for all road \r fuels generally increases at least as fast as the rate at which GDP \r per  capita increases (Liddle and Huntington 2020). Developments \r since 1990 continue a historical trend of increasing travel distances \r and a shift from low- to high-speed transport modes that goes along \r with GDP growth (Schafer et  al. 2009; Gota et  al. 2019). Modest \r improvements in energy efficiency have been realised between 2010 \r and 2019, averaging -1.5% yr -1 in energy intensity globally, while \r carbon intensities of the transport sector have remained stable in all \r world regions (Figure  2.20). Overall, global increases in passenger \r and freight travel activity levels have outpaced energy efficiency and \r fuel economy improvements, continuing a  long-term trend for the \r [...]"


id: e23e139d0065cfaa44aab0d0a08cf0f1
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index: climate_gpt_v2
vector_id: 37086
content:
"[...] transport sector (medium evidence, high agreement) (Gucwa and \r Schafer 2013; Grubler 2015; McKinnon 2016)."


id: 3218c6a95e9215e979158bb2a3679d6f
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 6074
content:
"Steady improvements in building energy intensities across regions can \r be attributed to baseline improvements in building fabrics, appliance \r efficiencies, energy prices, and fuel shifts. Many countries have \r adopted a mix of relevant policies, such as energy labelling, building \r energy codes, and mandatory energy performance requirements \r (Nie and Kemp 2014; Nejat et  al. 2015; Economidou et  al. 2020). \r Efforts towards building refurbishments and retrofits have also \r been pursued in several nations, especially for historical buildings in \r Europe, but evidence suggests that the recent retrofit rates have not \r made a significant dent on emissions (Corrado and Ballarini 2016). \r The Chinese central government launched various policies, including \r command and control, economic incentives, and technology measures, \r but a big gap remains between the total rate of building green retrofit \r in the nation and the future retrofit potential (G. Liu et  al. 2020a, \r [...]"


id: 179b9e97264733caa53056a39ce4238a
created_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
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content:
"[...] 2020b). Still, one major global factor driving down energy intensities \r has been the global transition from inefficient coal and biomass use in \r buildings for heating and cooking, towards natural gas and electricity, \r in part led by concerted policy action in Asian countries (Urge-Vorsatz \r et al. 2015; Kerimray et al. 2017; Thoday et al. 2018). As developing \r countries construct new buildings, there is sizable potential to reduce \r and use less carbon-intensive building materials and adopt building \r designs and standards that lower lifecycle buildings energy use and \r allow for passive comfort. Chapter 9 describes the mitigation options \r of the buildings sector."


id: 7ceda8138a5c6862ee488f9b0b3bf02
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 19170
content:
"Literature shows that more equitable income distributions can \r improve environmental quality, but the nature of this relationship \r can vary by level of development (low evidence, medium agreement) \r (Knight et al. 2017; Chen et al. 2020; Hailemariam et al. 2020; Huang \r and Duan 2020; Liobikiene and Rimkuviene 2020; Rojas-Vallejos \r and Lastuka 2020; Uddin et  al. 2020). Differences in the energy \r and carbon intensities of consumption and the composition of \r consumption baskets across populations and nations matter for \r emissions. (Jorgenson et al. 2016; Grunewald et al. 2017). There is \r evidence to suggest that more equal societies place a higher value \r on environmental public goods (Baumgartner et  al. 2017; Drupp \r et  al. 2018). Additional research shows that reducing top income \r inequality in OECD countries can reduce carbon emissions and \r improve environmental quality (Hailemariam et  al. 2020) and that \r the effect of wealth inequality, measured as the wealth share of \r [...]"


id: 9b283e5e12e76101ff41edc004269aaa
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vector_id: 24547
content:
"[...] the top decile, on per capita emissions in high-income countries, is \r positive (Knight et al. 2017). Evidence from 40 sub-Saharan African \r countries suggests that a  rise in income inequality contributed to \r increasing CO2 emissions between 2010 and 2016, controlling for \r other drivers such as economic growth, population size, and inflation \r (Baloch et al. 2020)."


id: 45fcbf42bdb14c59801bf947eca5e471
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content_type: text
index: climate_gpt_v2
vector_id: 9535
content:
"Global diets are a  key driver of production per  capita, and thus \r land pressure and AFOLU emissions (Chapter  7). As per  capita \r incomes rise and populations urbanise, traditional, low-calorie diets \r that emphasise starchy foods, legumes, and vegetables transition \r towards energy-intensive products such as refined sugars, fats, oils, \r and meat (Pradhan et al. 2013; Tilman and Clark 2014). At a certain \r point in national development, affluence and associated diets thus \r override population growth as the main driver of AFOLU emissions \r (Kastner et  al. 2012). Very high calorie diets have high total GHG \r emissions per  capita (Heller and Keoleian 2015) and are common \r in the developed world (Pradhan et  al. 2013). Over the last few \r decades, a  'westernisation' of diets has also been occurring in \r developing countries (Pradhan et al. 2013). Low- and middle-income \r countries such as India, Brazil, Egypt, Mexico, and South Africa \r [...]"


id: 3850ee998951050f7430a718d8f2f182
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"[...] have experienced a  rapid dietary shift towards western-style diets \r (De Carvalho et al. 2013; Pradhan et al. 2013; Popkin 2015). Another \r driver of higher food requirements per capita is food waste, which \r has increased more or less continuously since the 1960s in all regions \r but Europe (Porter and Reay 2016)."


id: c45b44347fb90b6a69e09cf9c60a6131
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index: climate_gpt_v2
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content:
"The key development objective of eradicating extreme poverty \r (Chakravarty and Tavoni 2013; Hubacek et al. 2017a; Malerba 2020) \r and providing universal access to modern energy services (Pachauri \r et al. 2013, 2018; Pachauri 2014; Singh et al. 2017) only marginally \r affects GHG emissions (medium evidence, high agreement). Shifts \r from biomass to more efficient energy sources and collective \r provisioning systems for safe water, health, and education are \r associated with reduced energy demand (Baltruszewicz et al. 2021). \r Efforts to alleviate multi-dimensional poverty by providing minimum \r decent living standards universally, however, may require more energy \r and resources. Recent estimates of the additional energy needed are \r still within bounds of projections of energy demand under climate \r stabilisation scenarios (Hubacek et  al. 2017a, 2017b; Rao et  al. \r 2019; Pascale et al. 2020; Kikstra et al. 2021). Bottom-up estimates \r [...]"


id: 7165c2ed9af8a3916d73b7818b3d1466
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content:
"[...] suggest that achieving decent living standards requires 13-40  GJ \r per capita annually, much less than the current world average energy \r consumption of 80 GJ per  capita in 2020 (Millward-Hopkins et  al. \r 2020) (medium evidence, high agreement). Aggregate top-down \r estimates suggest that achieving a  high Human Development \r Index (HDI) score above 0.8 requires energy consumption between "


id: f9b06f5b664136a6366b97acfeb98a4a
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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content:
"Technological change for climate change mitigation involves \r improvement in and adoption of technologies, primarily those \r associated with energy production and use. Technological change \r has had a  mitigating effect on emissions over the long term and \r is central to efforts to achieving climate goals (high confidence). \r Progress since AR5 shows that multiple low-carbon technologies are \r improving and falling in cost (high confidence); technology adoption \r is reaching substantial shares, and small-scale technologies are \r particularly promising on both (medium confidence). Faster adoption \r and continued technological progress can play a  crucial role in \r accelerating the energy transition. However, the historical pace of \r technological change is still insufficient to catalyse a complete and \r timely transition to a low-carbon energy system: technological change \r needs to accelerate (high confidence). This section assesses the role of \r [...]"


id: 410e2f6219be3bfa632e2a10d584464f
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 8629
content:
"[...] technological change in driving emissions reductions and the factors \r that drive technological change, with an emphasis on the speed of \r transitions. Incentives and support for technological change affect \r technology outcomes (Sivaram et al. 2018; Wilson et al. 2020a). Work \r since AR5 has focused on evaluating the effectiveness of policies: \r those that accelerate technological change by enhancing knowledge \r (technology push) and those that increase market opportunities \r for successful technologies (demand pull) (Nemet 2013); as well \r as the importance of tailoring support to country contexts (Barido \r et al. 2020; Rosenbloom et al. 2020), including the limits of carbon\u0002pricing policies to date (Lilliestam et  al. 2020). Section  2.8 and \r Chapter 13 describe how these polices affect emissions; Chapter 14 \r and Cross-Chapter Box 12 in Chapter 16 discuss transition dynamics; \r and Chapter 16 provides a more detailed assessment of the evolution \r [...]"


id: b40aee94a4c6be487825d5e7712d2b7c
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content:
"Recent studies have identified examples supporting fast energy \r transitions (Sovacool 2016; Bond et  al. 2019; Reed et  al. 2019). \r One describes five rapid national-scale transitions in end-use \r technologies, including lighting in Sweden, cook-stoves in China, \r liquefied petroleum gas stoves in Indonesia, ethanol vehicles in \r Brazil, and air conditioning in the USA (Sovacool 2016). Adoption of \r electric vehicles in Norway and in cities in China have also been rapid \r (Rietmann and Lieven 2019; Li et al. 2020; Fridstrom 2021). Examples \r in energy supply, include electrification in Kuwait, natural gas in the \r Netherlands, nuclear electricity in France and Sweden, combined \r heat and power in Denmark, renewable energy in Uruguay, and coal \r retirements in Ontario, Canada (Qvist and Brook 2015). Reasons \r that these exemplars could be applied more broadly in the future \r include: growing urgency on climate change, shifting motivation \r [...]"


id: 9796b6298b4ec04073b4a02541206bd4
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 23885
content:
"[...] from price response to proactive resource scarcity, and an increase in \r the likelihood of technological breakthroughs (medium confidence) \r (Sovacool 2016; Bazilian et  al. 2020). The emergence of smaller \r unit scale, granular technologies (described below) also creates the \r potential for faster system change (Trancik 2006; Grubler et al. 2018; \r Wilson et al. 2020a). Energy service prices and government actions \r that affect demand are critical to the speed and extent of energy \r transitions (Kramer and Haigh 2009). Reasons scholars consider for \r expecting a fast transition include: intentional policy and alignment \r with goals; globalisation which diversifies sources and integrates \r supply chains; collective action via the Paris Agreement; as well \r as bottom-up grassroots movements and private sector initiatives \r (Kern and Rogge 2016). Political support for change can also speed \r transitions (Burke and Stephens 2017; Stokes and Breetz 2018), as \r [...]"


id: 65a0a3b1b9b1d2f9aebc707ded3e367c
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index: climate_gpt_v2
vector_id: 15143
content:
"[...] can the credibility of transition-related targets (Li and Pye 2018; \r Rogge and Dutschke 2018)."


id: 9495716ce738fa90281db8475fc535f4
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 23347
content:
"Multiple challenges exist in accelerating the past rate of \r technological change. First, an array of physical assets in the energy \r system are long-lived and thus involve substantial committed carbon \r (Section 2.7) (Knapp 1999; Cui et al. 2019). A process of 'exnovation', \r accelerating the phase-out of incumbent technology through \r intentional policy (such as by pricing carbon), provides a means to \r address long lifetimes (Davidson 2019; Rosenbloom and Rinscheid \r 2020). Second, countries may not have the capacity to absorb the \r flows of ideas and research results from international knowledge \r spillovers due to weak infrastructure, limited research capacity, lack \r of credit facilities (Chapter 15, Section 15.5), and other barriers to \r technology transfer (Adenle et  al. 2015). In a  developing country \r context, processes of innovation and diffusion need to include \r competence-building systems (Lema et  al. 2015; Perrot and Sanni \r [...]"


id: 55ea8cb3b9616ae16b4ef166764b4cd6
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vector_id: 12388
content:
"[...] 2018; Stender et al. 2020). Third, public policy is central to stimulating \r technological change to reduce emissions; policy depends on creating \r credible expectations of future market opportunities (Alkemade and \r Suurs 2012), but the historical evidence shows that, despite recent \r progress, policies related to energy and climate over the long term \r have been inconsistent (Taylor 2012; Nemet et al. 2013; Koch et al. \r 2016). Bolstering the credibility and durability of policies related \r to low-carbon technology are crucial to accelerating technological \r change and inducing the private sector investment required (Helm \r et al. 2003; Habermacher et al. 2020)."


id: 5eed4c48df2c69251c0ce2e8dc455a83
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 13997
content:
"The important role of leader countries is often missed when looking \r only at global aggregates (Meckling and Hughes 2018); leaders \r accumulate important knowledge, provide scaled market, and set \r positive examples for followers (medium confidence) (Schwerhoff \r 2016; Buchholz et  al. 2019). In recent years, the conception of \r where leadership, climate-relevant innovation, and technology \r transfer originate has shifted to considering more meaningfully \r direct South-South and South-North forms of technology transfer, \r flows of capital, drivers for market access, origins of innovation, \r and other forms of cooperation (Urban 2018; Kohler et al. 2019). \r Recent evidence shows that South-South trade is enabling clean \r technology transfer (Gosens 2020). Leaders can initiate a process \r of 'catalytic cooperation' in which they overcome collective action \r problems and stimulate rapid change (Hale 2018). Similarly, \r 'sensitive intervention points'  - targeted support of social \r [...]"


id: 3675f541627fca454f9001fce782913f
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index: climate_gpt_v2
vector_id: 6819
content:
"[...] movements, technologies, or policies themselves - can lead to rapid \r and self-sustaining change (Farmer et  al. 2019), such as support \r for photovoltaics in Germany in the 2000s and student climate \r activism in Europe in  2019. The focus on leadership, catalysts, \r and intervention points reflects a  systemic view of transitions \r that emphasises interactions and interdependence (Geels 2018; \r Meckling and Hughes 2018). Technological change has been at the \r core of transitions, but is best understood as part of a system in \r which social aspects are crucial (medium confidence) (Cherp et al. \r 2018; Kohler et al. 2019; Overland and Sovacool 2020)."


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"A wide array of technologies shows long-term improvements in \r performance, efficiency, and cost. Among the most notable are \r solar PV, wind power, and batteries (high confidence) (Chapters 6 \r and 16). The dynamics for PVs are the most impressive, having fallen \r in cost by a factor of 10,000 from the first commercial application \r on a  satellite in 1958 (Maycock and Wakefield 1975) to power \r purchase agreements signed in 2019 (IRENA 2020). Wind has been \r on a  nearly as steep trajectory (Wiser and Bolinger 2019) as are \r lithium-ion battery packs for electric vehicles (Nykvist and Nilsson \r 2015; Service 2019). The future potential for PV and batteries seems \r especially promising given that neither industry has yet begun to \r adopt alternative materials with attractive properties as the cost \r reductions and performance improvements associated with the \r current generation of each technology continue (medium confidence) \r [...]"


id: 3c443c7b04499018cbba868ece3b67e9
created_at: 2023-04-19 13:23:07
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content:
"[...] (Kwade et al. 2018). A key challenge is improving access to finance, \r especially in developing country contexts, where the costs of financing \r are of crucial importance (Creutzig et al. 2017; Schmidt 2019)."


id: c43234e929ffb876a03e26cdef960a86
created_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 31804
content:
"Figure  2.22 shows changes in the costs of four dynamic energy \r technologies. One can see rapid changes since AR5, cost data for \r which ended in 2010. Solar PV is by far the most dynamic technology, \r and its cost since AR5 has continued on its steep decline at about the \r same rate of change as before AR5, but now costs are well within \r the range of fossil fuels (high confidence) (Chapter  6). Very few \r concentrating solar power (CSP) plants had been built between the \r 1980s and 2012. Since AR5, 4GW have been built and costs have \r fallen by half. Onshore wind has continued its pace of cost reductions \r such that it is well within the range of fossil fuels. Offshore wind has \r changed the most since AR5. Whereas costs were increasing before \r AR5, they have decreased by 50% since. None of these technologies \r shows indications of reaching a limit in their cost reductions. Crucial \r to their impact will be extending these gains in the electricity and \r [...]"


id: ec652014db8989a4a86d71934e31c5fe
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 38948
content:
"The array of evidence of technology learning that has accumulated \r both before and since AR5 (Thomassen et al. 2020) has prompted \r investigations about the factors that enable rapid technology \r learning. From the wide variety of factors considered, unit size has \r generated the strongest and most robust results. Smaller unit sizes, \r sometimes referred to as 'granularity', tend to be associated with \r faster learning rates (medium confidence) (Sweerts et  al. 2020; \r Wilson et al. 2020). Examples include solar PV, batteries, heat pumps, \r and to some extent wind power. The explanatory mechanisms \r for these observations are manifold and well established: more \r iterations are available with which to make improvements (Trancik \r 2006); mass production can be more powerful than economies of \r scale (Dahlgren et al. 2013); project management is simpler and less \r risky (Wilson et al. 2020); the ease of early retirement can enable \r [...]"


id: c891d3c261ea5145a36e091510079bf3
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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vector_id: 32602
content:
"[...] risk-taking for innovative designs (Sweerts et  al. 2020); and they \r tend to be less complicated (Malhotra and Schmidt 2020; Wilson "


id: 2d1dbc7998f45d132ef9ce5020cf0624
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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content:
"Figure  2.22 | Unit cost reductions and use in some rapidly changing mitigation technologies. The top panel shows global costs per  unit of energy \r (USD per MWh) for some rapidly changing mitigation technologies. Solid blue lines indicate average unit cost in each year. Light blue shaded areas show the range \r between the 5th and 95th percentiles in each year. Grey shading indicates the range of unit costs for new fossil fuel (coal and gas) power in 2020 (corresponding to \r USD55-148 per MWh). In 2020, the levelised costs of energy (LCOE) of the four renewable energy technologies could compete with fossil fuels in many places. For batteries, \r costs shown are for 1 kWh of battery storage capacity; for the others, costs are LCOE, which includes installation, capital, operations, and maintenance costs per MWh of \r electricity produced. The literature uses LCOE because it allows consistent comparisons of cost trends across a diverse set of energy technologies to be made. However, it \r [...]"


id: d8a04fad273cebacf08a5a2647deee39
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 35405
content:
"[...] does not include the costs of grid integration or climate impacts. Further, LCOE does not take into account other environmental and social externalities that may modify the \r overall (monetary and non-monetary) costs of technologies and alter their deployment. The bottom panel shows cumulative global adoption for each technology, in GW \r of installed capacity for renewable energy and in millions of vehicles for battery-electric vehicles. A vertical dashed line is placed in 2010 to indicate the change since AR5. \r Shares of electricity produced and share of passenger vehicle fleet are indicated in text for 2020 based on provisional data, i.e., percentage of total electricity production \r (for PV, onshore wind, offshore wind, CSP) and of total stock of passenger vehicles (for EVs). The electricity production share reflects different capacity factors; for example, \r [...]"


id: a6acfbcaf28d460b205f3aa470a5f312
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 26538
content:
"[...] for the same amount of installed capacity, wind produces about twice as much electricity as solar PV. {2.5, 6.4} Renewable energy and battery technologies were selected as \r illustrative examples because they have recently shown rapid changes in costs and adoption, and because consistent data are available. Other mitigation options assessed \r in the report are not included as they do not meet these criteria."


id: 7fcd51570c8a793867e39c23b3d89596
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 19623
content:
"et al. 2020). Small technologies often involve iterative production \r processes with many opportunities for learning by doing, and have \r much of the most advanced technology in the production equipment \r than in the product itself. In contrast, large unit scale technologies - \r such as full-scale nuclear power, carbon capture and storage (CCS), \r low-carbon steel making, and negative emissions technologies such \r as bioenergy with carbon capture and storage (BECCS) - are often \r primarily built on site and include thousands to millions of parts, \r such that complexity and system integration issues are paramount \r (Nemet 2019). Despite the accumulating evidence of the benefits \r of granularity, these studies are careful to acknowledge the role \r of other factors in explaining learning. In a  study of 41  energy \r technologies (Figure 2.23), unit size explained 22% of the variation \r in learning rates (Sweerts et al. 2020) and a study of 31 low-carbon \r [...]"


id: 304ebbd6fbe151fc9546caa810c4fa0d
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 5750
content:
"[...] technologies showed that unit size explained 33% (Wilson et  al. \r 2020). Attributing that amount of variation to a  single factor is \r rare in studies of technological change. The large residual has \r motivated studies, which find that small-scale technologies provide "


id: 5abe301eb1a73c9b09930f5be80ddcb4
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 13217
content:
"Results of integrated assessment models (IAMs) show that scale-up \r needs are massive for 2degC scenarios. Using logistic growth rates of \r energy shares as in previous work (Wilson 2012; Cherp et al. 2021), \r most of these technologies include annual adoption growth rates of \r 20% in the 2020s and 2030s, and are in line with recent adoption \r of wind and solar. However, it is important to realise that IAMs \r include faster adoption rates for some mitigation technologies than \r for others (Peters et al. 2017). Growth rates in IAMs for large-scale \r CCS - biomass, coal, and gas - are between 15-30% (25th and 75th \r percentiles) (Figure 2.24). So few plants have been built that there \r is little historical data to analyse expected growth; with only two \r full-scale CCS power plants built and a 7% growth rate, if including \r industrial CCS. In contrast, IAMs indicate that they expect much \r lower rates of growth in future years for the technologies that have \r [...]"


id: 5ad63826bc8bb85867451ce95cef6d0b
created_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 13231
content:
"[...] been growing fastest in recent years (wind and solar), without strong \r evidence for why this should occur."


id: cc276aa958aceee6d0234e3f202b77b3
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 33237
content:
"In western countries, the largest contribution to the household \r carbon footprint is from transportation, housing, and consumption \r of food (Druckman and Jackson 2015). The joint contribution \r of these three items varies in different countries, depending on \r consumption patterns, and account for 58.5%, on average, in EU25 \r countries (Tukker and Jansen 2006). However, different countries, \r and regions within countries, may have different emission patterns \r due to differences in income, lifestyle, geography, infrastructure, \r political and economic situation. For example, the main contributors \r to the average US household is private transport (19.6%), followed \r by electricity (14.8%) and meat (5.2%) (Jones and Kammen 2011), \r while UK households have 24.6% emissions on energy and housing, \r 13.7% emissions on food, and 12.2% emissions on consumables \r (Gough et al. 2011). A study of 49 Japanese cities found that energy \r [...]"


id: 12b158d6e5f97fc3cfd3e685f5b23565
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 492
content:
"[...] (31%), food (27%), and accommodation (15%) were the largest \r sources of household emissions (Long et al. 2017). An investigation \r of Japan's household emissions found that energy, food, and utility \r are the three main emissions sources, but their shares are dependent \r on age (Shigetomi et al. 2014). See Section 12.4 (Chapter 12) and \r Box 5.4 (Chapter 5) for a more in-depth discussion on food systems \r and dietary shifts towards lower emission food."


id: 30c11c6b04771acf29b2d6e6c2298133
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 5833
content:
"Households' carbon emissions are closely linked to activities and \r consumption patterns of individuals and as a group in households. \r Individual and group behaviour, in turn, is shaped by economic, \r technological, and psychological factors, social contexts (such as \r family ties, friends and peer pressure) and cultural contexts (social \r identity, status, and norms) as well as the natural environment \r (number of hot and cold days) and physical infrastructure, or \r geography (Jorgenson et  al. 2019). For example, a  city with an \r excellent bicycle infrastructure will make it safer and easier for \r citizens to become highly mobile by using their bikes; a city that \r has less density and is dominated by automobile infrastructure \r induces more people to travel by car (Chapters 8 and 10). \r As  a  consequence, many climate relevant consumption acts are \r not consciously decided on or deliberately made part of a lifestyle, \r [...]"


id: d7aa20f277a7164922e3aebd7e8cd5bb
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updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 35255
content:
"[...] but are strongly influenced by the factors listed above. Chapter 5 \r provides a  more in-depth discussion on behavioural drivers and \r examples of behavioural interventions and policies that can be \r used to reduce emissions."


id: 50fbe3a816dc4b73daee761582493445
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 11529
content:
"Age. The effect of population ageing on emissions is contested in \r the literature. Ageing when accompanied by shrinking household \r size and more energy-intensive consumption and activity patterns \r results in increased emissions. However, an ageing labour force can \r also dampen economic growth and result in less energy-intensive \r activity such as driving, which decreases emissions (Liddle and \r Lung 2010; Liddle 2011). Ageing of the population characterises the \r demographic transition in both developed and developing countries. \r The implications of ageing for emissions depend on labour force \r participation of the elderly and differences in the consumption and \r investment patterns of different age groups (O'Neill et  al. 2012). \r Analysis using panel macro data from OECD countries suggests that \r shifts in age and cohort composition have contributed to rising GHG \r emissions since the 1960s (Menz and Welsch 2012; Nassen 2014). \r [...]"


id: d025c9ba9d49bbce02c6ec7574434e25
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 33978
content:
"[...] Household-level data over time for the USA provides evidence \r that residential energy consumption increases over the lifetime \r of household members, largely due to accompanying changes in \r household size (Estiri and Zagheni 2019). Similar insights emerge \r from Japan, where analysis shows that those in their 70s or older, \r a group that is growing in size in Japan, have higher emissions than \r other age groups (Shigetomi et al. 2014, 2018, 2019). Recent analysis \r from China suggests that the shift to smaller and ageing households \r is resulting in higher carbon emissions because of the accompanying \r time-use and consumption shifts (Yu et al. 2018; Li and Zhou 2019). \r An increase in the dependency ratio  - that is, the proportion of \r children aged under 15 and people over 65 relative to the working\u0002age population  - in other analyses, has been shown to  lead to \r reduced CO2 emissions in China (Wei et al. 2018; Li and Zhou 2019). \r [...]"


id: dd097d94712a982ca41a551ed56769ba
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 36149
content:
"[...] Implications of the nature of this relationship are important to policy \r discussions of working hours and retirement age that are likely to \r have an influence on emissions. For example, children and youth \r tend to emit more education-related emissions than adults (Han \r et al. 2015). Older people tend to have higher emissions related to \r heating and cooling being more sensitive to temperature (Meier and \r Rehdanz 2010)."


id: 207ad49bab9fbb85eccd9d09a7733c1d
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updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 2937
content:
"Broader contextual factors and cultural trends towards consumerism, \r individualisation and defining self-worth through conspicuous \r consumption can drive emissions up (Chancel and Piketty, 2015). \r However, cohort and generational shifts can drive emissions down. \r For instance, evidence, from millennials in the OECD shows that fewer \r younger people have driving licenses compared to older generations \r (Kuhnimhof et al. 2012). Similar, findings are evident from analysis for \r the US, where changing attitudes, decreased employment and rising \r virtual mobility explain decreased travel by Millennials (McDonald, \r 2015). Analysis for France shows that baby boomers are higher \r emitters than other generations (Chancel, 2014). A change in social \r norms is taking place with the spread of the sharing economy by \r which consumers share or borrow goods from other consumers. \r Sharing opportunities are more advanced within the mobility sector \r [...]"


id: ac74d3143d2ddebe5e8f49e5b31e7bd1
created_at: 2023-04-19 13:23:07
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content:
"[...] (Greenblatt and Shaheen, 2015). Successful car and bike sharing \r have rapidly expanded in countries such as China, Indonesia, Mexico, \r Brazil and Turkey. Technology and data advances are currently barriers \r to spreading of sharing in low- and lower middle-income cities but \r the potential offered by these technologies to allow poor countries \r to leapfrog to more integrated, efficient, multimodal transport \r systems is important (Yanocha et  al. 2020). Despite this potential \r it is unclear how much shared mobility contributes to transport "


id: 21918356f8facb2e09827afc86f8b3db
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 3115
content:
"Inequality. Global inequality within and between countries has \r shifted over the last decade's expanding consumption and consumer \r culture (Castilhos and Fonseca 2016; Alvaredo et al. 2018; Short and \r Martinez 2020). The rise of income of middle-class in countries, mostly \r in Asia - for example, China, India, Indonesia and Vietnam - and the \r stagnating incomes of the middle classes in developed economies \r reduced between countries' income differences; meanwhile, the \r population under extreme poverty (a threshold of USD1.9 per person \r per day) is now concentrated in Sub-Saharan Africa and South Asia \r (Milanovic 2016). A  major gap between top and bottom incomes \r occurred in parallel within countries. Since 1980, the top 1% richest \r individuals in the world captured twice as much growth as the \r bottom 50% individuals (Friedman and Savage 2017; Alvaredo et al. \r 2018). The influence of these dual inequality trends on lifestyles, \r [...]"


id: 7744345e86137aaa4e5f41cf1a833631
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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content:
"[...] new consumption patterns and carbon emissions at regional, local \r and global scale are large and have led to the fastest growth of \r global carbon emissions, in particular, for fast emerging economies \r (Sections 2.2. and 2.3). Emissions remain highly concentrated, with \r the top 10% per capita emitters contributing to between 35-45% \r of global emissions, while the bottom 50% emitters contribute to \r 13-15% of global emissions (Hubacek et  al. 2017a). Furthermore, \r the top 1% of income earners by some estimates could have an \r average carbon footprint 175 times that of an average person in the \r bottom 10% (Otto et al. 2020). The top 10% high emitters live in all \r continents, and one-third of them live in emerging countries (Chancel \r and Piketty 2015; Hubacek et al. 2017a; Semieniuk and Yakovenko \r 2020). Mitigation pathways need to consider how to minimise the \r impacts of inequality on climate change and the different mechanisms \r [...]"


id: 8bbdfdbd12d585a035a0ae621e508679
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 21755
content:
"[...] and effects coming into play between the inequality of income and \r emissions (Baek and Gweisah 2013; Berthe and Elie 2015; Hao et al. \r 2016; Grunewald et al. 2017) (Section 2.4.3)."


id: 9b377d2bcd76a93ef8888bb2b4df3e7b
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 24556
content:
"Status competition. As part of a  larger consumer society and \r consumer culture, based on consumer-oriented lifestyles, products \r frequently provide a  source for identity and fulfilment (Stearns \r 2001; Baudrillard 2017; Jorgenson et  al. 2019). People pursue \r cultural constructs such as status, comfort, convenience, hygiene, \r nutrition, and necessity. Consumption is, by and large, not an end \r in itself but a  means to achieve some other end, and those ends \r are diverse and not necessarily connected to one another (Wilk \r 2010). This shows that consumption patterns cannot be sufficiently \r understood without also considering the context - for example, the \r cultural and social contexts leading to status competition and status\u0002related consumption (Veblen 2009; Ehrhardt-Martinez, K. et al. 2015; \r Wilk 2017). Status seeking can work to reduce emissions when \r 'green products' such as an electric car or photovoltaics on the roof \r [...]"


id: 20f021f57f58c51f97e346fb66cd8f80
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 3015
content:
"[...] become a sign for high-status (Griskevicius et al. 2010). It also can \r work to increase emissions through visible and high-carbon intensive \r consumption items, such a larger homes, fuel-inefficient sport utility \r vehicles (SUVs), and long-distance vacations (Schor 1998), driven \r by a  notion of having 'to keep up with the Joneses' (Hamilton \r 2011). This can lead to formation of new habits and needs, where \r products and services become normalised and are quickly perceived \r as needed, reinforced through social networks and advertisement, \r making it psychologically easy to convert a luxury item to a perceived \r necessity (Assadour 2012). For example, the share of adults who "


id: 857c67ecb6f000f61a43d0af150c30f2
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 20625
content:
"Inequality trends catalyse impact at a  demand level, mobilising \r rapid lifestyles changes, symbolic consumption and ideals of \r material improvements and upward mobility (Castilhos et al. 2017) \r and emulation of high-carbon emissions intensive lifestyle of the \r wealthy (Gough 2017). Decoupling energy use and emissions from \r income growth and the decarbonisation of energy services have \r not counteracted these trends (Section 2.4.1). Alternative options to \r deal with carbon inequality, such as sharing global carbon emissions \r among high emitters (Chakravarty et  al. 2009; Chakravarty and \r Tavoni 2013) or addressing the discourse of income distribution \r and the carbon intensity of high emitters lifestyles (Hubacek et al., \r 2017b; Gossling 2019; Otto et al. 2019) are met with caution that \r such alternatives may necessitate hard-to-implement institutional \r changes (Semieniuk and Yakovenko 2020). Growing inequality within \r [...]"


id: eca2f17b22765f0cad70b53d25bdcfa
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 38991
content:
"looking using scenarios (Fisch-Romito et  al. 2020). Quantifications \r also differ in the indicators used to describe carbon lock-in. Literature \r has assessed how delays in climate policy affect the evolution of \r fossil-fuel infrastructure stock in the short term (Bertram et al. 2015; \r Kefford et  al. 2018; McGlade et  al. 2018), overall mitigation costs \r (Riahi et al. 2015; Luderer et al. 2016), or the transition risks from \r premature retirements or underutilisation of existing assets (Iyer \r et al. 2015; Johnson et al. 2015; Lane et al. 2016; Luderer et al. 2016; \r Farfan and Breyer 2017; van Soest et al. 2017; Kefford et al. 2018; Cui \r et al. 2019; Fofrich et al. 2020; Malik et al. 2020; H. Wang et al. 2020; \r Pradhan et al. 2021). Only a few authors have relied on indicators \r related to institutional factors such as technology scale or employment \r (Erickson et al. 2015; Spencer et al. 2018). Complementary literature \r [...]"


id: 91c15b124f126f1ea0db64150cf2f272
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 22868
content:
"[...] has explored how the sheer size of the world's fossil fuel reserves (and \r resources) and owners' financial interests could contribute to supply\u0002side dynamics that sustain the use of fossil fuels (Jewell et al. 2013; \r Jakob and Hilaire 2015; McGlade and Ekins 2015; Bauer et al. 2016; \r Heede and Oreskes 2016; Welsby et al. 2021)."


id: a3552ee5420152c8367da85af501d67f
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 25944
content:
"more difficult and, it may also exacerbate redistribution and social \r cohesion dilemmas (Gough 2017; Rompke et  al. 2019). Climate \r mitigation action has different motivational departures in unequal \r context. An emerging global 'middle class' strengthens consumption \r at the margin as evidence by first-time purchases of white goods \r with likely impacts on energy demand (Wolfram et  al. 2012), and \r with a warming climate, the increased use of air conditioning (Davis \r and Gertler 2015). Inequality may affect the willingness of rich and \r poor to pay for environmental goods or accept policies to protect \r the environment (Baumgartner et  al. 2017). Unequal departure \r for action is strongly manifested in cities of all sizes in developing \r countries with low-income urban residents hardest hit in lock-in \r situations such as lack of access to transportation and jobs (Altshuler \r 2013; Mattioli 2017), lack of green spaces (Joassart-Marcelli et al. \r [...]"


id: 14a829ef32f84931588992b75e17065c
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 859
content:
"[...] 2011), poor access to waste collection (King and Gutberlet 2013) \r and to energy and clean water provision. The exacerbation of \r these conditions constrains the feasibility for achieving emissions \r reductions through lifestyle or behavioural changes alone (Baiocchi \r et al. 2010; Oxfam 2015). High inequality limits mitigation efforts and \r conversely, advancing mitigation should not contribute to deepen \r existing inequalities (Rao and Min 2018; Saheb et  al. 2019). It is \r critically important to account for varying demands and affordability \r across heterogeneous household groups in access to quality energy, \r education, health, decent jobs and services, while recomposing \r consumption and balancing societal trade-offs via policies to boost \r the inclusion of low-income and energy-poor population groups \r (Pachauri et al. 2013). Further, there is a need to reduce inequalities \r and improve the capabilities people have to live the lives they value \r [...]"


id: bb5a128f1a99039e8dc322e9645039dd
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 30177
content:
"Carbon lock-in can be understood as inertia in a system that limits \r the rate of transformation by a path-dependent process (Seto et al. \r 2016). For example, long lifetimes of infrastructures such as power \r plants, roads, buildings or industrial plants may influence the rate \r of transformation substantially and lock societies into carbon\u0002intensive lifestyles and practices for many decades (Unruh 2000, \r 2002; Unruh and Carrillo-Hermosilla 2006; Grubler 2012; Seto et al. \r 2016; Sovacool 2016). Infrastructure stock evolution depends on \r technological and economic factors, but also on institutional and \r behavioural ones that are often mutually reinforcing. That is, physical \r infrastructure such as the built environment of urban areas can shape \r people's behaviour and practices, which in turn change the demand \r for such infrastructure and lock-in energy demand patterns (Banister \r et al. 1997; Makido et al. 2012; Creutzig et al. 2016; Seto et al. 2016; \r [...]"


id: a0c4ee41c25f3779e9c9ba4ac3528f11
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 25493
content:
"Estimates of total cumulative future CO2 commitments from proposed \r infrastructure focus only on the power sector due to data availability \r (Table 2.6 and Figure 2.26). Infrastructure proposals can be at various \r stages of development involving very different probabilities of \r implementation. About one-third of the currently proposed projects \r are more probable as they are already under construction (Cui \r et al. 2019). Pfeiffer et al. (2018) and Tong et al. (2019) assess the \r cumulated CO2 emissions from proposed infrastructure in the entire \r power sector at 270 GtCO2 and 190 GtCO2 respectively. Estimates of \r CO2 emissions implications for new coal power infrastructure plans \r are more frequent (Edenhofer et  al. 2018; Pfeiffer et  al. 2018; Cui \r et al. 2019; Tong et al. 2019) ranging between 100 and 210 GtCO2. \r Differences across estimates of future CO2 emissions from proposed \r power infrastructure mostly reflect substantial cancellations of coal \r [...]"


id: 7e1f1a571d23e613d0b2df9f31e96436
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 19361
content:
"Hence, the Paris climate goals could move out of reach unless there are \r dedicated efforts for early decommissioning, and reduced utilisation of \r existing fossil fuel infrastructures, cancellation of plans for new fossil \r fuel infrastructures, or compensation efforts by removing some of the \r CO2 emissions from the atmosphere (Cui et al. 2019; Smith et al. 2019; \r Tong et al. 2019; Pradhan et al. 2021). For example, Fofrich et al. (2020) \r suggest in a multi-model study that coal and gas power infrastructure \r would need to be retired 30 (19-34) and 24 (21-26) years earlier \r than the historical averages of 39 and 36 years when following 1.5degC \r pathways and 23 (11-33) and 19 (11-16) years earlier when following \r 2degC pathways. Cui et al. (2019) arrive at more conservative estimates \r for coal power plants, but only consider the existing and currently \r proposed capacity. Premature retirement of power plants pledged \r [...]"


id: bf0fd1a026cd6f858ce04997768196a8
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 30888
content:
"[...] by members of the Powering Past Coal Alliance would cut emissions \r by 1.6 GtCO2, which is 150 times less than future CO2 emissions from \r existing coal power plants (Jewell et al. 2019)."


id: 793b7198b151ae485d0766d7b5cc1814
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 18521
content:
"Notably, the lower end of residual fossil fuel emissions in IAM scenarios \r (Luderer et al. 2018) is remarkably similar to global estimates from \r the accounting studies of the previous section, as shown in Table 2.6. \r Yet, there are important conceptual and interpretative differences \r that are also reflected in the very different distribution of reported \r future CO2 emissions attached to current and future fossil fuel \r infrastructures (Table  2.7). Accounting studies start from granular, \r plant-based data for existing fossil fuel infrastructure and make \r statements about their future CO2 emissions, assuming variations \r of historic patterns of use and decommissioning. Expansions to the \r future are limited to proposals for new infrastructures that we know \r of today. Scenario studies quantifying residual fossil fuel emissions \r start from aggregate infrastructure descriptions, but dynamically \r update those through new investment decisions in each time step \r [...]"


id: a206a2070e4498b4e6b0cb79c0cac70d
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 25742
content:
"[...] across the 21st  century based on the development of energy and \r energy service demands, as well as technology availability, guided by \r defined climate policy goals (or their absence)."


id: fa8dbeb6231c153a72b97ce5b6f703e8
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 41453
content:
"Table 2.7 | Residual (gross) fossil fuel emissions (GtCO2) in climate change mitigation scenarios strengthening mitigation action after 2020 ('early \r strengthening'), compared to scenarios that keep Nationally Determined Contribution (NDC) ambition level until 2030 and only strengthen thereafter.\r Cumulative gross CO2 emissions from fossil fuel and industry until reaching net zero CO2 emissions are given in terms of the mean as well as minimum and maximum (in \r parentheses) across seven participating models: AIM/CGE, GCAM, IMAGE, MESSAGES, POLES, REMIND, WITCH. Scenario design prescribes a harmonised, global carbon price \r in line with long-term carbon budget. Delay scenarios follow the same price trajectory, but 10 years later. Carbon dioxide removal requirements represent ex-post calculations \r that subtract gross fossil fuel emissions from the carbon budget associated with the respective long-term warming limit. We take the carbon budget for limiting warming to \r [...]"


id: 9c9e981c2dd740aec6bfc98c8fd2136
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 24798
content:
"[...] 1.5degC with a 50% probability and to 2degC with a 67% probability (Canadell et al. 2021). Hence, carbon dioxide removal (CDR) requirements reflect a minimum amount of CDR \r for a given mitigation trajectory. Results are reported at two significant digits. Sources: Luderer et al. (2018); Tong et al. (2019)."


id: a9ac12c71760b90d28cec07ab8b1158e
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 27069
content:
"transportation (in particular aviation, shipping and road-based freight) \r or some industrial processes (such as cement production or feedstocks \r for chemicals). This explains the key discrepancies observable in \r Table 2.7. Therefore, our overall assessment of these available lines \r of evidence strongly emphasises the importance of decommissioning, \r reduced utilisation of existing power sector infrastructure, as well as \r continued cancellation of new power sector infrastructures in order \r to limit warming to well below 2degC (high confidence) (Kriegler et al. \r 2018b; Luderer et al. 2018; Chen et al. 2019; Cui et al. 2019; Fofrich \r et al. 2020). This is important as the power sector is comparatively easy \r to decarbonise (IPCC 2014a; Krey et al. 2014; Davis et al. 2018; Mejean \r et al. 2019) and it is crucial to make space for residual emissions from \r non-electric energy end uses that are more difficult to mitigate (high \r [...]"


id: 49a6538d1b78e7f02acfa74ab944b4f8
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 10187
content:
"[...] confidence). Any further delay in climate policy substantially increases \r carbon lock-in and mitigation challenges as well as a dependence on \r carbon dioxide removal technologies for meeting the Paris climate \r goals (Kriegler et al. 2018b; Luderer et al. 2018)."


id: 190ce10d5d53f92d8221efb808f15d22
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 1622
content:
"Comprehensive multinational evaluations with wider regional and \r sectoral coverage enable the assessment of emissions impacts \r without distortions from emissions leakage. Among the wide range \r of climate policy instruments, pricing carbon - such as a carbon tax or \r an emissions trading system - has been one of the most widely used \r and effective options to reduce GHG emissions (robust evidence,\r high agreement). In a comparison of 142 countries with and without \r carbon pricing, countries with a  carbon price show annual CO2\r emission growth rates of 2 percentage points lower than countries \r without such policies (Best et  al. 2020). A  more comprehensive \r evaluation of carbon prices shows that countries with a lower carbon \r pricing gap (a higher carbon price) tend to be more carbon-efficient, \r that is, they have a lower carbon intensity of GDP (OECD 2018).14\r An empirical analysis of the effects of environmental regulation and \r [...]"


id: 8f326a7fef5ceff00a5f332f5c035d13
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 22394
content:
"[...] innovation on the carbon emissions of OECD countries during the \r period 1999-2014 indicates that a 1% increase in environmentally \r friendly patents reduced carbon emissions by 0.017%, and a  1% \r increase in environmental tax revenue per  capita reduced carbon \r emissions by 0.03% (Hashmi and Alam 2019)."


id: ec90e58afe629a01a9df3139932c5f56
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 38979
content:
"The key to achieving climate change mitigation targets includes \r crafting environmentally effective, economically efficient and socially \r equitable policies. For the purposes of this section, policies are defined \r broadly as actions to guide decisions to reach explicit goals and, \r accordingly, climate (mitigation) policies are the ones whose primary \r objective is to reduce GHG emissions. They include a range of domains \r from economic and institutional to research and development (R&D) \r and social policies, and are implemented by various instruments \r (e.g.,  market-based and regulatory in the economic domain) and \r measures (e.g.,  legal provisions and governance arrangements in \r the institutional domain) (Chapter  13, and see 'mitigation policies' \r in Glossary). Yet GHG emissions are also affected by policies enacted in \r various social, economic and environmental areas to pursue primarily \r non-climatic objectives. This section presents succinct assessments of \r [...]"


id: baddae1ab6ae6c38828d049df57b7c87
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 30092
content:
"[...] the outcomes and effectiveness of a few selected policy instruments \r applied in the last two decades that target climate protection \r (Sections  2.8.2 and 2.8.3) and GHG emissions impacts of selected \r other policies primarily aimed at improvements in environmental \r quality and natural resource management (Section 2.8.4).12"


id: 84b100977c871e70cc3d2854dcfb5367
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 20486
content:
"Domestic and international climate legislation have also contributed \r to the reduction of GHG emissions. An empirical analysis of legislative \r activity in 133 countries over the period 1999-2016 based on panel \r data indicates that each new law reduced annual CO2 emissions \r per unit of GDP by 0.78% nationally in the first three years, and by \r 1.79% beyond three years. Additionally, climate laws as of 2016 \r were associated with an annual reduction in global CO2 emissions \r of 5.9 GtCO2 and 38 GtCO2 cumulatively since 1999 (Eskander and \r Fankhauser 2020). It is notable that 36 countries that accepted legally \r binding targets under the Kyoto Protocol all complied (Shishlov et al. \r 2016). It is impossible to disentangle precisely the contribution of \r individual mitigation policies, but it is clear that the participating \r countries, especially those in the OECD, did make substantial policy \r efforts with material impact (Grubb 2016). An ex-post evaluation \r [...]"


id: 113b39d981b9c96f82905fcee3631fef
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 226
content:
"The power sector typically accounts for a large portion of countries' \r CO2 emissions. Market-based regulation and government subsidies in \r China contributed to improving operational efficiency and reducing \r emissions (Zhao et al. 2015). In addition, the implementation of ultra\u0002low emission standards has also resulted in a significant reduction in \r emissions from China's power plants (Tang et al. 2019). Mandatory \r climate and energy policies, including the California Global Warming \r Solutions Act, reduced CO2 emissions by 2.7-25% of the average \r state-level annual emissions from the power sector over the period \r 1990-2014 in the USA. Mandatory GHG registry/reporting, electric \r decoupling and a public benefit fund have been effective in further \r decreasing power sector emissions in the USA (Martin and Saikawa \r 2017). In the UK electricity sector, a carbon price floor, combined with \r electricity market reform (competitive auctions for both firm capacity \r [...]"


id: 3f1497674e989ebcf2e998221916c157
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 8286
content:
"[...] and renewable energy), displaced coal, whose share fell from 46% in \r 1995 to 7% in 2017, halving CO2 emissions, while renewables grew \r from under 4% in 2008 to 22% by 2017 (Grubb and Newbery 2018). \r See Chapter 13 for more information."


id: 349efa1211d92de9d9a4558a2dfda30e
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 6495
content:
"Electric vehicles (EVs) powered by clean electricity can reduce GHG \r emissions, and such policies are important for spurring adoption of \r such vehicles (Kumar and Alok 2020; Thiel et al. 2020). The extent to \r which EV deployment can decrease emissions by replacing internal \r combustion engine-based vehicles depends on the generation \r mix of the electric grid (Abdul-Manan 2015; Nichols et  al. 2015; \r Canals Casals et  al. 2016; Hofmann et  al. 2016; Choi et  al. 2018; \r Teixeira and Sodre 2018) although, even with current grids, EVs \r reduce emissions in almost all cases (Knobloch et al. 2020). Policy \r incentives for EV adoption can be an effective mechanism to \r increase EV sales (Langbroek et al. 2016) and may include discounts, \r purchase subsidies, regulations, and government leadership\r (medium confidence) (Bakker and Jacob Trip 2013; Silvia and Krause \r 2016; Teixeira and Sodre 2018; Qiu et al. 2019; Santos and Davies \r [...]"


id: 7a21153f2da124ccaeba325e6e1dc39b
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 18688
content:
"[...] 2020). The presence of charging infrastructure and publicly available \r charging increases the adoption rate of EVs (Vergis and Chen \r 2015; Javid et al. 2019). A comparison of EV adoption rates across \r 30 countries shows a positive correlation between charging stations \r and EV market share (Sierzchula et al. 2014). A rollout of 80,000 DC \r fast chargers across the USA is estimated to have resulted in a 4% \r reduction in emissions compared to a baseline of no additional fast \r chargers (Levinson and West 2018). More recently, bans on internal \r combustion engine vehicles have provided a  much more direct \r approach to stimulating the adoption of EVs and its supporting \r infrastructure; however, the efficacy of such measures depends on \r enforcement (Plotz et al. 2019)."


id: 5b20b6f8d5c28ef92b24bfb26756cefa
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 13282
content:
"Public transit can reduce vehicle travel and lower GHG emissions by \r reducing the number of trips taken by private vehicles and the length \r of those trips (medium confidence). Changes to the operation of \r public transportation systems (such as density of bus stops, distance \r from stops to households, duration and frequency of trip times, and \r lowering ridership costs) can result in a  mode shift from private \r car trips to public transit trips (Cats et al. 2017; Choi 2018; Carroll \r et  al. 2019). These changes in the public transit system operation \r and network optimisation have been shown to have reduced GHG \r emissions in cases such as San Francisco, where the cost optimisation \r of the transit network was estimated to decrease emissions by \r a  factor of three (Cheng et  al. 2018) and Barcelona, where the \r optimisation of the urban bus system was estimated to reduce GHG \r emissions by 50% (Griswold et al. 2017). For every 1% increase in \r [...]"


id: 34aa8a4477ef6dbbb055fc0a41d10ad5
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 6503
content:
"[...] investment in transit services and transit-oriented design, there is \r an estimated 0.16% reduction in private vehicle kilometres travelled \r per capita (McIntosh et al. 2014)."


id: f6455c48b9ca86c509a387b6948e62c6
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 40692
content:
"At a  local scale, air pollutants are often co-emitted with GHGs in \r combustion processes. Many air quality policies and regulations focus \r on local pollution from specific sources that can potentially either \r substitute or complement global GHG emissions in production and \r generation processes. Also, policies that reduce certain air pollutants, \r such as sulphur dioxide (SO2), have a  positive radiative forcing \r effect (Navarro et al. 2016). The evidence on individual air pollution \r control regulation and policies for GHG emissions is therefore mixed \r (medium evidence, medium agreement). Evidence from the USA \r suggests that increased stringency of local pollution regulation had \r no statistically detectable co-benefits or costs on GHG emissions \r (Brunel and Johnson 2019). Evidence from China suggests that the \r effectiveness of policies addressing local point sources differed from \r those of non-point sources and the co-benefits for climate are mixed, \r [...]"


id: 8a8aba21175e6249c8eba93056230bf2
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 21549
content:
"[...] though policies addressing large industrial point sources have been \r easier to implement and have had significant impact (Huang and \r Wang 2016; Xu et al. 2016; van der A et al. 2017; Dang and Liao 2019; \r Fang et al. 2019; Yu et al. 2019). Legislation to reduce emissions of \r air pollutants in Europe have significantly improved air quality and \r health but have had an unintended warming effect on the climate \r (Turnock et al. 2016)."


id: a22882e6491debd3128bf19f73604dc5
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 25764
content:
"Often, the realisation of potential co-benefits depends on the \r type of pollutant addressed by the specific policy, and whether \r complementarities between local pollution and global GHG emissions \r are considered in policy design (medium evidence, high agreement) \r (Rafaj et al. 2014; Li et al. 2017a). Effective environmental regulations \r that also deliver co-benefits for climate mitigation require integrated \r policies (Schmale et  al. 2014; Haines et  al. 2017). Uncoordinated \r policies can have unintended consequences and even increase \r emissions (Holland et al. 2015). Many studies suggest that policies that \r target both local and global environmental benefits simultaneously \r may be more effective (medium evidence, medium agreement) \r (Klemun et  al. 2020). Furthermore, air pollution policies aimed at \r inducing structural changes - for example, closure of polluting coal \r power plants or reducing motorised miles travelled - are more likely \r [...]"


id: 2e1b3c427426251f75721be9b322b0ad
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 5387
content:
"[...] to have potential positive spillover effects for climate mitigation, as \r compared to policies incentivising end-of-pipe controls (Wang 2021)."


id: 23e6ca0751adffae852022e202d3e432
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 3502
content:
"Forest is generally defined as land spanning more than 0.5 hectares \r with trees higher than 5 metres and a canopy cover of more than \r 10%, or trees able to reach these thresholds in situ (FAO 1998). \r Zero-deforestation (i.e.,  both gross and net zero deforestation) \r initiatives generate results at multiple levels (Meijer 2014). Efforts \r to achieve zero-deforestation (and consequently emissions) are \r announced by non-governmental organisations (NGOs), companies, \r governments, and other stakeholder groups. NGOs engage through \r their campaigning, but also propose tools and approaches for \r companies (Leijten et al. 2020). The extent to which companies can \r actually monitor actions conducive to zero-deforestation pledges \r depends on their position in the supply chain. Beyond the business \r practices of participating companies, achieving long-term positive \r societal impacts requires upscaling from supply chains towards \r [...]"


id: 52a4855b4610d801c1f10ae3c6c92ef0
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 11821
content:
"[...] landscapes, with engagement of all stakeholders, and in particular \r small producers. The various success indicators for zero deforestation \r mirror the multiple levels at which such initiatives develop: progress \r towards certification, improved traceability, and legality are apparent \r output measures, whereas direct-area monitoring and site selection \r approaches target the business practices themselves."


id: 8a8dbd16a0c7cf8d0862fa2cf6652839
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 21551
content:
"If global CO2 emissions continue at current rates, the remaining carbon budget for keeping warming to 1.5degC will likely be \r exhausted before 2030. Between 1850 and 2019, total cumulative CO2 emissions from the fossil fuel industry (FFI) and agriculture, \r forestry, and other land use (AFOLU) were 2400 (+-240 GtCO2). Of these, about 410 +- 30 GtCO2 were added since 2010. This is \r about the same size as the remaining carbon budget for keeping global warming to 1.5degC and between one-third and one-half \r of the 1150 +- 220 (1350, 1700) GtCO2 for limiting global warming below 2degC with a 67% (50%, 33%) probability, respectively \r (Canadell et al. 2021). At current (2019) rates of emissions, it would only take 8 (2-15) and 25 (18-35) years to emit the equivalent \r amount of CO2 for a 67th percentile 1.5degC and 2degC remaining carbon budget, respectively. This highlights the dependence of 1.5degC \r [...]"


id: 6e918e03258838c644724e4a59110b0c
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 16717
content:
"[...] pathways on the availability of substantial CO2 removal capacities, as discussed in Chapters 3, 4, and 12, but also Section 2.7 of \r this chapter."


id: b73797ed1ca998f60ef6b70f355e7d15
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 29446
content:
"Andy Reisinger (New Zealand), William F. Lamb (Germany/United Kingdom), Niklas Dobbeling \r (Germany), Thomas Wiedmann (Australia/Germany), Klaus Hubacek (the Netherlands/the United \r States of America), Robbie M Andrew (Norway), Monica Crippa (Italy), Piers M. Forster (United \r Kingdom), Jos Olivier (the Netherlands), Glen P.Peters (Norway), Julia Pongratz (Germany), Matt \r Rigby (United Kingdom), Marielle Saunois (France), Steven J. Smith (the United States of America), \r Efisio Solazzo (Italy), Hanqin Tian (the United States of America), Kornelis Blok (the Netherlands), \r Harry Clark (New Zealand), Annette Cowie (Australia), Jan Sigurd Fuglestvedt (Norway), Oliver \r Geden (Germany), Veronika Ginzburg (the Russian Federation), Celine Guivarch (France), Joanna \r I. House (United Kingdom), Rachid Mrabet (Morocco), Gert-Jan Nabuurs (the Netherlands), \r Keywan Riahi (Austria), Anders Hammer Stromman (Norway), Roberto Schaeffer (Brazil), Detlef P. \r [...]"


id: 6f7a64bf5f08674db9e07a5f09818ddc
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 16878
content:
"[...] van Vuuren (the Netherlands), Alaa Al Khourdajie (United Kingdom/Syria), Raphael Slade (United \r Kingdom)"


id: 4c9471512711d8876adeee3a0262ffd0
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 10739
content:
"While there is a growing number of global emissions inventories, \r only a few of them provide a wide coverage of gases, sectors, \r activities, and countries or regions that are sufficiently up to date \r to comprehensively track progress and thereby aid discussions in \r science and policy. Table  2.SM.2 provides an overview of global \r emission inventories. Many inventories focus on individual gases \r and subsets of activities. Few provide sectoral detail and, particularly \r for non-CO2 GHG emissions, there is often a considerable time-lag \r in reporting. GHG emissions reporting under the United Nations \r Framework Convention on Climate Change (UNFCCC) provides \r reliable, comprehensive and up-to-date statistics for Annex I \r countries across all major GHGs. Non-Annex I countries - except \r least-developed countries and small island states for which this is \r not mandatory - provide GHG emissions inventory information \r [...]"


id: 707b0b9d53549aad8433002c022c1391
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 17044
content:
"[...] through biennial update reports (BURs), but with much less stringent \r reporting requirements in terms of sector, gas and time coverage \r (Gutschow et al. 2016; Deng et al. 2021). As a result, many still lack \r a well-developed statistical infrastructure to provide detailed and \r timely reports (Janssens-Maenhout et al. 2019)."


id: bb0f3f8e45909e0af4fb7bf938dc89ad
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updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 30131
content:
"The historic emissions dataset used in Chapter  2 provides \r a  comprehensive, synthetic set of estimates for global GHG \r emissions disaggregated by 27 economic sectors and 228 countries \r and territories. Its focus is on anthropogenic GHG emissions: \r natural sources and sinks are not included. Five groups of gases \r are distinguished: (i) CO2  emissions from fossil fuel combustion \r and industry (CO2-FFI); (ii) CO2 emissions from land use, land-use \r change and forestry (CO2-LULUCF); (iii) methane emissions (CH4); \r (iv) nitrous oxide emissions (N2O); (v) fluorinated gases (F-gases) \r comprising hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), \r sulphur hexafluoride (SF6) as well as nitrogen trifluoride (NF3). \r Other F-gases that are internationally regulated as ozone-depleting \r substances under the Montreal Protocol such as chlorofluorocarbons \r (CFCs) and hydrochlorofluorocarbons (HCFCs) are not included. GHG \r [...]"


id: ef694137ab58385a71b609f0abbfce63
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content:
"[...] emissions data are analysed both in native units (except F-gases) as \r well as in CO2-equivalents (CO2-eq) as commonly done in wide parts \r of the climate change mitigation community using global warming \r potential with a 100-year time horizon (GWP100) from AR6 (Forster \r et al. 2021). The impact of using alternative metric choices in tracking \r aggregated GHG emissions is discussed in Section  2.SM.3 of this \r Supplementary Material. "


id: 144cb8d334ca24aba11b7b199dcae3e3
created_at: 2023-04-19 13:23:07
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content:
"EDGAR emission estimates included in Chapter 2 emissions dataset \r are derived from the full version 6 release (Crippa et al. 2021). This \r includes CO2 and non-CO2 GHG emission estimates from 1970 to \r 2018 computed from stable international statistics, and fast-track \r estimates of fossil CO2 emissions up to the year 2020. The following \r general EDGAR methodological description is largely taken from \r Janssens-Maenhout et  al. (2019). EDGAR bottom-up emission \r inventory estimates are calculated from international activity data \r and emission factors following the 2006 IPCC Guidelines for National \r Greenhouse Gas Inventories (IPCC 2006) - updated according to the \r latest scientific knowledge. Emissions (EMs) from a given sector i in a \r country C accumulated during a year t for a chemical compound x are \r calculated with the country-specific activity data (AD), quantifying \r the activity in sector i, with the mix of j technologies (TECH) and \r [...]"


id: 59c8fb4ee76ff46ca50b3a0db7c42724
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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content:
"[...] with the mix of k (end-of-pipe) abatement measures (EOP) installed \r with the share k for each technology j, the emission rate with an \r uncontrolled emission factor (EF) for each sector i and technology j\r and relative reduction (RED) by abatement measure k, as summarised \r in the following formula:"


id: fc6a2ad1934c616e00908f078c26dd8c
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 41777
content:
"To compute emissions up to the most recent years, a fast-track \r methodology is applied, as described in Oreggioni et  al. (2021). \r The underlying principle is to extrapolate trends based on observed \r activity patterns in representative sectors. For CO2-FFI emissions, \r the fast track estimates were based on the latest BP coal, oil and \r natural gas consumption data (BP 2021). Emission updates for \r cement, lime, ammonia and ferroalloys production beyond 2018 are \r based on stable statistics. In particular these include US Geological \r Survey statistics, urea production and consumption statistics from \r the International Fertilizer Association, gas flaring statistics from the \r Global Gas Flaring Reduction Partnership, steel production statistics \r from the World Steel Association, and cement clinker production \r statistics from UNFCCC data. Fast-track extensions for non-CO2 GHG \r emissions are based on Olivier and Peters (2018). For CH4 and N2O \r [...]"


id: 612148ece64f93e77abcb9adfdf439c7
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 14383
content:
"[...] these are based on agricultural statistics from the UN's Food and \r Agricultural Organization (FAO) (CH4 and N2O), fuel production and \r transmission statistics from the International Energy Agency (IEA) \r and BP (CH4) as well as data from national GHG inventory reports \r on coal production (CH4 recovery) and the production of chemicals \r (N2O abatement) submitted by Annex-I countries to the UNFCCC \r following a common reporting format (CRF) (e.g., UNFCCC 2021). \r For F-gases the fast-track extension was based on the most recent \r national emission inventories, submitted under the UNFCCC (up to \r 2018). Given the absence of international statistics, for all remaining \r countries and years, a simple extrapolation was used, with fast-track \r data by Olivier and Peters (2020). Here the procedure was to calculate \r the country- and sector-specific emissions growth between 2018 and \r 2019 in Olivier and Peters (2020), then multiply each growth rate \r [...]"


id: f7e3d67a16b77b43b672aba94427602d
created_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 40988
content:
"The 2006 Guidelines for National Greenhouse Gas Inventories and their 2019 refinements by the Intergovernmental Panel on Climate \r Change (IPCC) provide methodological guidance for compiling greenhouse gas (GHG) emissions inventories at different levels of \r sophistication (IPCC 2006, 2019). The levels of methodological complexity for estimating GHG emissions and removals are organised \r according to different tiers. Tier 1 is the most basic method. It applies a simple default methodology as well as default emission factors \r and other parameters defined in the IPCC Guidelines. Tier 2 methods replace those default values by country-specific data and can \r use more detailed calculations and activity data. Tier 3 refers to methods that may apply country-specific equations for calculating \r emissions along with more details regarding activity data, technologies and practices, providing the most granular approach to \r [...]"


id: bfa6c5d230b04abeb68cecf6ebe05339
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vector_id: 30990
content:
"[...] estimation. Tier 2 and Tier 3 are also referred to as higher tier methods and are generally considered to be more accurate than a Tier 1\r method, especially when it comes to reporting changes in emissions over time (IPCC 2006)."


id: 63d61756e1bbb371185a7f0646b5c5a7
created_at: 2023-04-19 13:23:07
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vector_id: 14855
content:
"forest clearing for agriculture, then abandonment), and regrowth \r of forests and other natural vegetation following wood harvest or \r abandonment of agriculture, and emissions from peat burning and \r drainage. Some of these activities lead to emissions of CO2 to the \r atmosphere, while others lead to CO2 sinks. CO2-LULUCF therefore \r is the net sum of emissions and removals from all human-induced \r land-use changes and land management. Note that CO2-LULUCF is \r referred to as (net) land-use change emissions, ELUC, in the context \r of the global carbon budget (Friedlingstein et al. 2020). Agriculture \r per se, apart from conversions between different agricultural types, \r does not lead to substantial CO2 emissions as compared to land-use \r changes such as clearing or regrowth of natural vegetation. Therefore, \r CO2 fluxes in the AFOLU sector refer almost exclusively to forestry \r and other land use (changes), while the agricultural part of the sector \r [...]"


id: 80d7165bc0e17f22951ca93f5ae2d288
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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vector_id: 19802
content:
"These two conceptually different approaches have different aims: \r the global models' approach separates natural from anthropogenic \r drivers - that is, the effects of changes in environmental conditions \r from effects of land-use change and land management. By contrast, \r the NGHGI approach separates fluxes based on areas, with all those \r occurring on managed land being declared anthropogenic. Given that \r observational data of carbon stocks or fluxes cannot distinguish the \r co-occurring effects of environmental changes and land-use activities, \r an area-based approach that does not require this distinction can \r more consistently be implemented across countries. These conceptual \r differences between global models and NGHGI approaches have \r been acknowledged (Petrescu et al. 2020a; Canadell et al. 2021) and \r approaches have been developed to map the two definitions to each \r other (Grassi et al. 2018, 2021). For non-CO2 GHGs, drivers and areas \r [...]"


id: 1d43f65bfabab3bbcb63aabf9d87c2a4
created_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 2328
content:
"[...] coincide, such that FAOSTAT data for CH4 and N2O is complementary \r to bookkeeping CO2-LULUCF emissions."


id: f7dfdfc19385ed05aa2914e0594470a2
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
content_type: text
index: climate_gpt_v2
vector_id: 40986
content:
"Since in reality anthropogenic CO2-LULUCF emissions co-occur with \r natural CO2 fluxes in the terrestrial biosphere, models have to be used \r to distinguish anthropogenic and natural fluxes (Friedlingstein et al. \r 2020). CO2-LULUCF as reported here is calculated via a bookkeeping \r approach, as originally proposed by Houghton et  al. (2003), \r tracking carbon stored in vegetation and soils before and after \r land-use change. Response curves are derived from the literature \r and observations to describe the temporal evolution of the  decay \r and regrowth of vegetation and soil carbon pools for different \r ecosystems and land-use transitions, including product pools of \r different lifetimes. These dynamics distinguish bookkeeping models \r from the common approach of estimating 'committed emissions' \r (assigning all present and future emissions to the time of the land\u0002use change event), which is frequently derived from remotely-sensed \r [...]"


id: 23821191c353a2e0038fb67506083239
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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content:
"[...] land-use area or biomass observations (Ramankutty et  al. 2007). \r Most bookkeeping models also represent the long-term degradation \r of primary forest as the reduction of standing vegetation and soil \r carbon stocks in secondary forests, and include forest management \r practices such as wood harvesting. Since the effects of environmental \r changes are excluded by the bookkeeping approach, bookkeeping \r CO2-LULUCF emissions estimates isolate the effects of anthropogenic \r (land-use-related) drivers."


id: 58f0ef3b71e7378e696783c676926eb5
created_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 12888
content:
"Following the approach taken by the global carbon budget \r (Friedlingstein et  al. 2020), the approach taken here is to use the \r average of estimates from three bookkeeping models: Bookkeeping \r of land-use emissions (BLUE) (Hansis et al. 2015), H&N (Houghton \r and Nassikas, 2017), and an earth system compact model (OSCAR) \r (Gasser et al. 2020). Key differences across these estimates, including \r land-use forcing, are summarised in Table 2.SM.4. Since bookkeeping \r models do not include emissions from organic soils, emissions from \r peat fires and peat drainage are added from external datasets: Peat \r burning is based on the Global Fire Emissions Database (GFEDv4) \r (van der Werf et al. 2017) and introduces large interannual variability \r to the CO2-LULUCF emissions due to synergies of land-use and \r climate variability, particularly in Southeast Asia, strongly noticeable \r during El Nino events such as in 1997. Peat drainage is based on \r [...]"


id: a8592a0f5e822e9f3bab040d7dadb478
created_at: 2023-04-19 13:23:07
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content:
"[...] estimates by Hooijer et  al. (2010) for Indonesia and Malaysia in \r H&N, and added to BLUE and OSCAR from the global FAO data on \r organic soils emissions from croplands and grasslands (Conchedda \r and Tubiello 2020). "


id: d01d220610a8befa42f966089551598b
created_at: 2023-04-19 13:23:07
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vector_id: 33971
content:
"The definition of CO2-LULUCF emissions by global carbon cycle \r models, as used here and in Canadell et al. (2021), differs from IPCC \r definitions (IPCC 2006) applied in national greenhouse gas inventories \r (NGHGI) for reporting under the climate convention (Grassi et  al. \r 2018) and, similarly, from FAO estimates of carbon fluxes on forest \r land (Tubiello et al. 2021). This means that NGHGI data include natural \r terrestrial fluxes caused by changes in environmental conditions, \r such as the effects of rising atmospheric CO2 (CO2-fertilisation), \r climate change, and nitrogen deposition - sometimes called 'indirect \r effects' as opposed to the direct anthropogenic effects of land-use \r change and management (Houghton et  al. 2012) (Section  2.2.2.1 \r and Chapter 7) - through adoption of the IPCC so-called land-use \r proxy approach when they occur on areas that countries declare \r as managed. Since environmental changes turned the terrestrial \r [...]"


id: a60265ff1421ecef8b1ca37636e6ab6a
created_at: 2023-04-19 13:23:07
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vector_id: 26430
content:
"[...] biosphere into a massive sink, removing about one-third of annual \r anthropogenic emissions in the last decade (Friedlingstein et  al. \r 2020), it is unsurprising that global emission estimates are smaller \r based on NGHGI than for global models' definitions (Figure 2.SM.1). "


id: c3f9e7ad761c1361db28119a933c0537
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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vector_id: 31762
content:
"Figure  2.SM.1 (continued): Estimates of global anthropogenic greenhouse gas emissions from different data sources 1970-2019. Top-left panel:\r CO2-FFI emissions from: EDGAR - Emissions Database for Global Atmospheric Research (this dataset) (Crippa et  al. 2021); GCP - Global Carbon Project (Friedlingstein \r et al. 2020; Andrew and Peters 2021); CEDS - Community Emissions Data System (Hoesly et al. 2018; O'Rourke et al. 2021); CDIAC Global, Regional, and National Fossil\u0002Fuel CO2 Emissions (Gilfillan et  al. 2020); PRIMAP-hist - Potsdam Real-time Integrated Model for probabilistic Assessment of emissions Paths (Gutschow et  al. 2016, \r 2021b); EIA - Energy Information Administration International Energy Statistics (EIA 2019); BP - BP Statistical Review of World Energy (BP 2021); IEA - International \r Energy Agency (IEA 2021b; (a); IPPU refers to emissions from industrial processes and product use. Top-right panel: Net anthropogenic CO2-LULUCF emissions from: \r [...]"


id: 87b0bdfb55cfce81791777a728c40491
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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vector_id: 21032
content:
"[...] BLUE - Bookkeeping of land-use emissions (Hansis et  al. 2015; Friedlingstein et  al. 2020); DGVM-mean - Multi-model mean of CO2-LULUCF emissions from dynamic \r global vegetation models (Friedlingstein et  al. 2020); OSCAR - an earth system compact model (Friedlingstein et  al. 2020; Gasser et  al. 2020); H&N - Houghton and \r Nassikas Bookkeeping Model (Houghton and Nassikas 2017; Friedlingstein et  al. 2020); for comparison, the net CO2 flux from FAOSTAT (FAO Tier 1) is plotted, which \r comprises net emissions and removals on forest land and from net forest conversion (FAOSTAT 2021; Tubiello et  al. 2021), emissions from drained organic soils under \r cropland/grassland (Conchedda and Tubiello 2020), and fires in organic soils (Conchedda and Tubiello 2020; Prosperi et al. 2020), as well as a net CO2 flux estimate from \r [...]"


id: e2182a7a18876c901b2bfc6c0c6c9b2d
created_at: 2023-04-19 13:23:07
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content:
"[...] National Greenhouse Gas Inventories (NGHGI) based on country reports to the UNFCCC, which include land-use change, and fluxes in managed lands (Grassi et al. 2021). \r Bottom-left panel: Anthropogenic CH4 emissions from: EDGAR (above); CEDS (above); PRIMAP-hist (above); GAINS - The Greenhouse gas - Air pollution Interactions and \r Synergies Model (Hoglund-Isaksson et al. 2020); EPA-2019: Greenhouse gas emission inventory (US-EPA 2019); FAO -FAOSTAT inventory emissions (Tubiello et al. 2013; \r Tubiello 2018; FAOSTAT 2021); Bottom-right panel: Anthropogenic N2O emissions from: GCP - global nitrous oxide budget (Tian et al. 2020); CEDS (above); EDGAR (above); \r PRIMAP-hist (above); GAINS (Winiwarter et al. 2018); EPA-2019 (above); FAO (above). Differences in emissions across different versions of the EDGAR dataset are shown in the \r Supplementary Material (Figure 2.SM.2). Source: Minx et al. (2021)."


id: e1283f1716836e9a844dc78c19152900
created_at: 2023-04-19 13:23:07
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vector_id: 36881
content:
"Solazzo et al. (2021) evaluated the uncertainty of EDGAR's source \r categories and totals for the main GHGs (CO2-FFI, CH4, N2O). This \r study is based on the propagation of the uncertainty associated with \r input parameters (activity data and emission factors) as estimated \r by expert judgement (Tier-1) and compiled by the IPCC (2019, 2006). \r A key methodological challenge is determining how well uncertain \r parameters are correlated between sectors, countries, and regions. \r The more highly correlated parameters (e.g., emission factors) are \r across scales, the higher the resulting overall uncertainty estimate. \r Solazzo et  al. (2021) assume full covariance between the same \r source categories where similar assumptions are being used, and \r independence otherwise. For example, they assume full covariance \r where the same emission factor is used between countries or sectors, \r while assuming independence where country-specific emission \r [...]"


id: 36fbc964cd2c5064d56651df6ecea8d5
created_at: 2023-04-19 13:23:07
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vector_id: 6900
content:
"[...] factors are used. This strikes a balance between extreme assumptions \r (full independence or full covariance in all cases) that are likely \r unrealistic, but still leans towards higher uncertainty estimates. \r When aggregating emission sources, assuming full covariance "


id: 6cd5c8ead73aa2d0fe1960fe7f05c23c
created_at: 2023-04-19 13:23:07
updated_at: 2023-04-19 13:23:07
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index: climate_gpt_v2
vector_id: 16427
content:
"Several studies have compared estimates of annual CO2-FFI emissions \r from different global inventories (Macknick 2011; Andres et al. 2012; \r Gutschow et al. 2016; Janssens-Maenhout et al. 2019; Andrew, 2020; \r Petrescu et al. 2020b). However, estimates are not fully independent \r as they all ultimately rely on many of the same data sources. For \r example, all global inventories use one of four global energy datasets \r to estimate CO2 emissions from energy use, and these energy \r datasets themselves all rely on the same national energy statistics, \r with few exceptions (Andrew 2020). Some divergence between these \r estimates (Figure 2.SM.2) are related to differences in the estimation \r methodology, conversion factors, emission coefficients, assumptions \r about combustion efficiency, and calculation errors (Marland et al. \r 2009; Andrew 2020). Key differences for nine global datasets are \r highlighted in Table  2.SM.3 (see also Table  2.SM.2 for further \r [...]"


id: 59930fd995c62a667b7a9fd030d07f89
created_at: 2023-04-19 13:23:08
updated_at: 2023-04-19 13:23:08
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vector_id: 12997
content:
"Uncertainties in CO2-LULUCF emissions can be more comprehensively \r assessed through comparisons across a suite of dynamic global \r vegetation models (DGVM) (Friedlingstein et al. 2020). DGVM models \r are not included in the CO2-LULUCF mean estimate provided here, \r because the typical DGVM setup includes the loss of additional sink \r capacity. The loss of additional sink capacity arises because DGVMs \r isolate LULUCF emissions from natural fluxes caused by historical \r environmental changes by subtracting a counterfactual simulation \r without land-use change from one with land-use change (Pongratz \r et al. 2014). In particular, forests have increased their carbon density \r over time due to CO2 and other environmental effects beneficial \r for plant growth. The 'additional sink capacity' forests would have \r created at the unaltered pre-industrial extent is 'lost' through \r land-use change and included in the DGVM estimates of CO2-LULUCF, \r [...]"


id: 292d36c0450215a5b93a7d6604f387d6
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index: climate_gpt_v2
vector_id: 4456
content:
"[...] but excluded in bookkeeping estimates that disregard changes in \r carbon densities in response to environmental changes. The loss of \r additional sink capacity makes up about 40% of the DGVM estimate \r in recent years (Obermeier et al. 2021). "


id: 8fe55bc48b2f6d0299cd7c12394f8aaf
created_at: 2023-04-19 13:23:08
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index: climate_gpt_v2
vector_id: 22526
content:
"CO2-LULUCF emissions are drawn from three global bookkeeping \r models. For 1990-2019, average net CO2-LULUCF emissions are \r estimated at 6.1, 4.3, and 5.6  GtCO2 yr-1 for Bookkeeping of \r land-use emissions (BLUE), Houghton and Nassikas Bookkeeping \r Model (H&N), and an earth system compact model (OSCAR) \r (Friedlingstein et  al. 2020). Gross emissions 1990-2019 for BLUE, \r H&N, OSCAR are 17, 9.6 and 19 GtCO2 yr-1, while gross removals \r are 11, 5.3, 13  GtCO2 yr-1, respectively. For 1990-2019 maximum \r average differences are 9.1 and 7.8 GtCO2 yr-1 for gross emissions \r and removals, respectively (Friedlingstein et  al. 2020). Note that \r 2016-2019 is extrapolated in H&N and 2019 in OSCAR based on \r the anomalies of the net flux for the gross fluxes. Differences in \r the models underlying this observed variability are reported in \r Table 2.SM.4. In the longer term, a consistent general upward trend \r since 1850 across models is reversed during the second part of \r [...]"


id: 91c54f263a4844d35f1d431d697b4f67
created_at: 2023-04-19 13:23:08
updated_at: 2023-04-19 13:23:08
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index: climate_gpt_v2
vector_id: 22871
content:
"[...] the 20th century. Since the 1980s, however, differing trends across \r models are related to, among other things, different land-use forcings \r (Gasser et al. 2020). Further differences between BLUE and H&N can \r be traced in particular to: (i) differences in carbon densities between \r natural and managed vegetation, or between primary and secondary \r vegetation; (ii) a higher allocation of cleared and harvested material \r to fast turnover pools in BLUE compared to H&N; and (iii) to the \r inclusion sub-grid scale transitions (Bastos et al. 2021). "


id: 44426f0fc6957dfca2b2126a457e48f2
created_at: 2023-04-19 13:23:08
updated_at: 2023-04-19 13:23:08
content_type: text
index: climate_gpt_v2
vector_id: 9245
content:
"Uncertainties can be much higher at a national level than at global \r level, since regional biases tend to cancel out. Land-use forcing has \r been identified as major driver of differences at regional and global \r level (Gasser et  al. 2020; Hartung et  al. 2021; Rosan et  al. 2021), \r as have assumptions on carbon densities and the allocation of \r cleared or harvested material to slash or product pools of various \r lifetimes, for which accurate global data over long time periods is \r missing (Bastos et al. 2021). Although the bookkeeping models are \r conceptually similar, the bookkeeping estimates include country\u0002specific information to different extents: for example, fire suppression \r (for the USA) is included in H&N (Houghton and Nassikas 2017), but \r not the other estimates. H&N includes peat drainage emissions only \r for Southeast Asia, while the FAO emissions estimates for organic soil \r drainage added to BLUE and OSCAR cover all countries (Friedlingstein \r [...]"


id: e93f3bbe8218caea0fe5e524071f049c
created_at: 2023-04-19 13:23:08
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index: climate_gpt_v2
vector_id: 38394
content:
"[...] et al. 2020). The effect of smoothing the FAO cropland and pasture \r information, which can be very variable in some countries, with \r a five-year running mean in H&N, while the annual data is used for \r the recent decades in History database of the Global Environment \r (HYDE) underlying BLUE and OSCAR, must also be expected to \r contribute to the spread in estimates on a country level. Overall, \r great care has to be taken when comparing estimates of individual \r countries across models so as not to over-interpret differences."


id: 247dae4a152088ef7c3e1e87b91610d7
created_at: 2023-04-19 13:23:08
updated_at: 2023-04-19 13:23:08
content_type: text
index: climate_gpt_v2
vector_id: 3611
content:
"Friedlingstein et  al. (2020) is taken as the reference point for \r our uncertainty assessment. The Global Carbon Budget provides \r a best-value judgement for the +-1s absolute uncertainty range of \r CO2-LULUCF emissions at +-2.6  GtCO2 yr-1, constant over the last \r decades. This constant, absolute uncertainty estimate corresponds \r roughly to a relative uncertainty of about +-50% over 1970-2019, \r which is much higher than for most fossil-fuel related emissions, but \r reflects the large model spread and large differences between the \r current estimate of H&N and its previous model versions (Houghton \r et al. 2012). This corresponds to a relative uncertainty of about +-80% \r for a 90% confidence interval (5th-95th percentile). However, here \r we opt for a slightly lower relative uncertainty estimate of about \r +-70% for a 90% confidence interval, given that the mean of the \r CO2-LULUCF estimates has been increasing over the last few decades. \r [...]"


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vector_id: 6447
content:
"[...] This provides absolute uncertainty estimates that are consistent in \r magnitude with the constant value in Friedlingstein et  al. (2020) \r over time - slightly lower for earlier years and slightly higher for the \r most recent years. Compared to AR5 this is larger than the +-50% \r uncertainty estimate applied in the assessment, but still in line with \r the upper end of the broader relative uncertainty range considered \r of +-50% to +-75% (Blanco et al. 2014). Finally note that much larger \r uncertainties in CO2-LULUCF emissions have been identified across \r the literature, but were traced back to different definitions used "


id: 28dc5b827d10e7eb0f006056c2c05e83
created_at: 2023-04-19 13:23:08
updated_at: 2023-04-19 13:23:08
content_type: text
index: climate_gpt_v2
vector_id: 4406
content:
"About 60% of total global methane emissions come from \r anthropogenic sources - that is, they are caused by direct human \r activities since pre-industrial times/pre-agricultural times (Saunois \r et al. 2020). Some studies suggest larger anthropogenic fossil emissions \r than currently estimated (e.g., Hmiel et  al. 2020). Anthropogenic \r methane emissions cover a range of different sectors: livestock \r (enteric fermentation and manure management, rice cultivation, \r fossil fuel production, distribution and use, waste handling (solid and \r water waste) as well as biomass and biofuel burning. About 90% of \r biomass burning events are thought to be triggered by human action \r (Andreae 1991); as biomass burning contributes less than 5% to \r anthropogenic methane emissions, the misallocations of natural fires \r is likely lower than the overall uncertainty. Methane emissions can be \r derived either using bottom-up estimates that rely on anthropogenic \r [...]"


id: 4d2ce654d4ac1baf2f4e1c82c348677e
created_at: 2023-04-19 13:23:08
updated_at: 2023-04-19 13:23:08
content_type: text
index: climate_gpt_v2
vector_id: 10857
content:
"[...] inventories such as EDGAR (Janssens-Maenhout et  al. 2019), land \r surface models that infer part of natural emissions (Wania et  al. \r 2013) or flux observation-based estimates for some specific sources \r such as geological sources (Etiope et  al. 2019). Alternatively, top\u0002down approaches can be used, such as atmospheric transport models \r that assimilate methane atmospheric observations to estimate past \r methane emissions (Houweling et  al. 2017). These techniques are \r applied to infer emissions for a specific facility, sector, region or other \r aggregation, based on in-situ or satellite-based observations. Satellite \r observations have greatly improved the coverage of available data to "


id: 6dbc6cf82da0f51fce81389b71a1bf63
created_at: 2023-04-19 13:23:08
updated_at: 2023-04-19 13:23:08
content_type: text
index: climate_gpt_v2
vector_id: 16568
content:
"EDGAR (Crippa et  al. 2019, 2021; Janssens-Maenhout et  al. 2019) \r is one of multiple global methane bottom-up inventories available. \r Other inventories - namely GAINS (Hoglund-Isaksson 2012; Hoglund\u0002Isaksson et al. 2020), US-EPA (EPA 2011, 2021), CEDS (Hoesly et al. \r 2018; McDuffie et  al. 2020; O'Rourke et  al. 2020), PRIMAP-hist \r (Gutschow et  al. 2016, 2021b) as well as FAOSTAT-CH4 (Tubiello \r 2013, 2018, 2019; Federici et al. 2015) - can differ in terms of their \r country and sector coverage as well as detail. EDGAR, CEDS, US-EPA \r and GAINS cover all major source sectors (fossil fuels, agriculture and \r waste, biofuel) - except large-scale biomass burning - but this can \r be added from different databases such as FINN (Wiedinmyer et al. \r 2011), GFAS (Kaiser et al. 2012), GFED (Giglio et al. 2013) or QFED \r (Darmenov and da Silva 2013). Much like CO2-FFI, these inventories \r of anthropogenic emissions are not completely independent as they \r [...]"


id: 934c52a292b72005c5e7bd6138f4b5ba
created_at: 2023-04-19 13:23:08
updated_at: 2023-04-19 13:23:08
content_type: text
index: climate_gpt_v2
vector_id: 23116
content:
"[...] either follow the same IPCC methodology to derive emissions, rely \r on similar data sources (e.g., FAOSTAT activity data for agriculture, \r reported fossil fuel production), or draw on reported country \r inventory data (Petrescu et al. 2020a). However, they may differ in the \r assumptions and data used for the calculation, and in the choice of \r IPCC Tier levels for the methodology (Box 2.SM.1). For example, while \r the US-EPA inventory uses the reported emissions by the countries "


id: 36f8028a4911ed70079bee942886bc3f
created_at: 2023-04-19 13:23:08
updated_at: 2023-04-19 13:23:08
content_type: text
index: climate_gpt_v2
vector_id: 6896
content:
"all anthropogenic and natural sources are comparatively small at \r +-6% - a range larger than errors in transport models only (Locatelli \r et al. 2015). However, this uncertainty on total emissions is probably \r underestimated as the uncertainty in the chemical sink was not \r fully considered in the top-down estimates in Saunois et al. (2020). \r About 90% of the chemical sink of methane is due to the oxidation \r by the hydroxyl radical (OH). Uncertainty on the global burden of \r OH is about +-5%, much lower than uncertainties derived from \r detailed analysis using EDGAR data by Janssens-Maenhout (2019) \r and Solazzo et  al. (2021), reaching around +-45% at 2s. Saunois \r et al. (2020) reported uncertainty of 10-15%, which translates to an \r uncertainty of about +-10% to +-30% depending on the category, with \r larger uncertainty in the fossil fuel sectors than in the agriculture and \r waste sector (Saunois et al. 2020). However, these uncertainties are \r [...]"


id: 534dd0f3cae36d2bb0d932f5e51f81b3
created_at: 2023-04-19 13:23:08
updated_at: 2023-04-19 13:23:08
content_type: text
index: climate_gpt_v2
vector_id: 11928
content:
"[...] also underestimated as they do not consider the uncertainty in each \r individual estimate, which includes potential uncertainties in activity \r data, emission factors, and equations used to estimate emissions."


id: d94292dc8aea0bcfa47f0caec164dab1
created_at: 2023-04-19 13:23:08
updated_at: 2023-04-19 13:23:08
content_type: text
index: climate_gpt_v2
vector_id: 35520
content:
"Anthropogenic nitrous oxide (N2O) emissions occur in a number \r of sectors, namely agriculture, fossil fuel and industry, biomass \r burning, and waste. The emissions from the agriculture sector have \r four components: (i) direct and indirect emissions from soil and \r water bodies (inland, coastal, and oceanic waters); (ii) manure left \r on pasture; (iii) manure management; and (iv) aquaculture. Besides \r these main sectors, a final 'other' category represents the sum of \r the effects of climate, elevated atmospheric CO2, and land cover \r change. This is a new sector that was developed as part of the global \r N2O budget (Tian et al. 2020) - a recent assessment to quantify all \r sources and sinks of N2O emissions updating previous work (Mosier \r et al. 1998; Kroeze et al. 1999; Mosier and Kroeze, 2000; Syakila and \r Kroeze, 2011). Estimates from the global N2O budget are referred \r to as GCP-N2O since the assessment was facilitated by the Global \r [...]"


id: b92d5487528496983d4d4a1c8058d182
created_at: 2023-04-19 13:23:08
updated_at: 2023-04-19 13:23:08
content_type: text
index: climate_gpt_v2
vector_id: 29781
content:
"[...] Carbon Project (GCP). Overall, anthropogenic sources contributed \r just over 40% to total global N2O emissions (Tian et al. 2020)."


id: 7424a8caf3aa11f8bcafa61f6743f7d4
created_at: 2023-04-19 13:23:08
updated_at: 2023-04-19 13:23:08
content_type: text
index: climate_gpt_v2
vector_id: 17669
content:
"(Winiwarter et al. 2018), FAOSTAT-N2O (Tubiello et al. 2013; Tubiello \r 2018), CEDS (Hoesly et al. 2018; McDuffie et al. 2020; O'Rourke et al. \r 2020), PRIMAP-hist (Gutschow et al. 2016, 2021b), and GFED (van \r der Werf et  al. 2017). While EDGAR and GAINS cover all sectors \r except biomass burning, FAOSTAT-N2O is focused on agriculture and \r biomass burning and GFED on biomass burning only. As shown in \r Figure  2.SM.1, EDGAR, GAINS, CEDS and FAOSTAT emissions are \r consistent in magnitude and trend. Recent revisions in estimating \r indirect N2O emissions in EDGARv6 led to an average increase of \r 1.5% yr-1 in total N2O emissions estimates between 1999 and 2018 \r compared to the two previous versions (differences before 1999 were \r negligible at less than 1% yr-1). Differences across versions of the \r EDGAR dataset are shown in Figure 2.SM.5. The main discrepancies \r across different global inventories are in agriculture, where emissions \r [...]"


id: c154dfc56bb5435ec88590393cc4d1af
created_at: 2023-04-19 13:23:08
updated_at: 2023-04-19 13:23:08
content_type: text
index: climate_gpt_v2
vector_id: 31316
content:
"[...] estimates from the global N2O budget and FAOSTAT are, on average, \r 1.5  MtN2O yr-1 higher than those from GAINS and EDGAR during \r 1990-2016. This is due to higher estimates of direct emissions from \r fertilised soils and manure left on pasture. GCP-N2O provides the \r largest estimate (Figure  2.SM.1), because it synthesised from the \r other three inventories and further informed by additional bottom\u0002up modelling estimates - and is more comprehensive in scope due to \r the new sector discussed above. EDGAR estimates of anthropogenic \r N2O emissions as used in this dataset should therefore be considered \r as lower bound estimates (see also Table 2.SM.6)."


id: b8c9b823d363878f92a1596224b592c5
created_at: 2023-04-19 13:23:08
updated_at: 2023-04-19 13:23:08
content_type: text
index: climate_gpt_v2
vector_id: 29716
content:
"For EDGAR, uncertainties in N2O emissions are estimated based on \r default values (IPCC 2006) at +-42% for 24 OECD90 countries and at \r +-93% for other countries for a 95% confidence interval (Janssens\u0002Maenhout et  al. 2019). However, Solazzo et  al. (2021) arrive at \r substantially larger values allowing for correlation of uncertainties \r between sectors, countries and regions. At a sector level, uncertainties \r are larger for agriculture (263%) than for energy (113%), waste \r (181%), industrial processes and product use (14%) and other \r (112%). In the recent Emissions Gap Report (UNEP 2020) relative \r uncertainties for global anthropogenic N2O emissions are estimated \r at +-50% for a 68% (1s) confidence interval. This is larger than the \r +-60% uncertainties reported in IPCC AR5 for a 90% confidence \r interval (Blanco et al. 2014), but is comparable with the ranges for \r anthropogenic emissions in the global N2O budget (Tian et al. 2020). \r [...]"


id: 4485d4498eee642bdb896eb36099fce8
created_at: 2023-04-19 13:23:08
updated_at: 2023-04-19 13:23:08
content_type: text
index: climate_gpt_v2
vector_id: 9286
content:
"[...] Overall, we assess the relative uncertainty for global anthropogenic \r N2O emissions at +-60% for a 90% confidence interval."


id: 5837c1fe25071f950e59cd98c9f17ac4
created_at: 2023-04-19 13:23:08
updated_at: 2023-04-19 13:23:08
content_type: text
index: climate_gpt_v2
vector_id: 12774
content:
"Fluorinated gases comprise over a dozen different species that \r are primarily used as refrigerants, solvents and aerosols. Here we \r compare global emissions of F-gases estimated in EDGAR to top\u0002down estimates from the 2018 World Meteorological Organization's \r (WMO) Scientific Assessment of Ozone Depletion (Engel and Rigby \r 2018; Montzka and Velders 2018). The top-down estimates were \r based on measurements by the Advanced Global Atmospheric \r Gases Experiment (AGAGE) (Prinn et al. 2018) and National Oceanic \r and Atmospheric Administration (NOAA) (Montzka et  al. 2015), \r assimilated into a global box model - using the method described in \r Rigby et al. (2014) and Engel and Rigby, et al. (2018). Uncertainties \r in the top-down estimates are due to measurement and transport \r model uncertainty. As F-gas emissions are almost entirely \r anthropogenic in nature, top-down estimates of anthropogenic \r fluxes are much better known than CO2, CH4, N2O, where large \r [...]"


id: 18ed7877b6a45d4f7a0f1366049c4065
created_at: 2023-04-19 13:23:08
updated_at: 2023-04-19 13:23:08
content_type: text
index: climate_gpt_v2
vector_id: 1600
content:
"[...] natural fluxes contribute to the observed trends. For substances \r with relatively short lifetimes (~50 years or less), uncertainties are \r typically dominated by uncertainties in the atmospheric lifetimes. \r Comparisons between the EDGAR and WMO 2018 estimates were \r available for HFCs 125, 134a, 143a, 152a, 227ea, 23, 236fa, 245fa, \r 32, 365mfc and 43-10-mee, PFCs CF4, C2F6, C3F8 and c-C4F8, SF6 and "


id: e19a82ae48e1cb0edc4799d79437d854
created_at: 2023-04-19 13:23:08
updated_at: 2023-04-19 13:23:08
content_type: text
index: climate_gpt_v2
vector_id: 36963
content:
"Anthropogenic N2O emissions estimates are subject to considerable \r uncertainty - larger than those from FFI-CO2 or CH4 emissions. N2O \r inventories suffer from high uncertainty on input data, including \r fertiliser use, livestock manure availability, storage and applications \r (Galloway et al. 2010; Steinfeld et al. 2010) as well as nutrient, crops \r and soils management (Ciais et  al. 2014; Shcherbak et  al. 2014). \r Emission factors are also uncertain (Crutzen et  al. 2008; Hu et  al. \r 2012; IPCC 2019; Yuan et al. 2019) and several sources are not yet \r well understood (e.g., peatland degradation, permafrost) (Elberling \r et al. 2010; Wagner-Riddle et al. 2017; Winiwarter et al. 2018). Model\u0002based estimates face uncertainties associated with the specific model \r configuration as well as parametrisation (Buitenhuis et  al. 2018; \r Tian et al. 2018, 2019). Total uncertainty is also large, because N2O \r emissions are dominated by emissions from soils, where the level of \r [...]"


id: f8620f3f4fbd595c6dcee686df9f5a32
created_at: 2023-04-19 13:23:08
updated_at: 2023-04-19 13:23:08
content_type: text
index: climate_gpt_v2
vector_id: 41067
content:
"The comparison of global top-down and bottom-up emissions for \r EDGARv6 and Olivier and Peters (2020) (EDGARv5FT) F-gas species \r (excluding heavy PFCs) is shown in Figure  2.SM.6 for the years \r 1980-2016 (or a subset thereof, depending on the availability of \r the top-down estimates). Where available, the various top-down \r estimates agree with each other within uncertainties. The magnitude \r of the difference between the WMO (2018) and EDGAR estimates \r varies markedly between species, years and versions of EDGAR; for \r several HFCs, the top-down and bottom-up estimates often agree \r within uncertainties for EDGARv6 (but much less often in v5), whereas \r for c-C4F8, the top-down estimate is more than 100 times the EDGAR \r estimates. Some similarities and differences have been previously \r noted for earlier versions of EDGAR (Muhle et al. 2010, 2019; Rigby \r et al. 2010; Lunt et al. 2015). For SF6, the relatively close agreement \r [...]"


id: 100089c8f711592379f3acb91877dcef
created_at: 2023-04-19 13:23:08
updated_at: 2023-04-19 13:23:08
content_type: text
index: climate_gpt_v2
vector_id: 1
content:
"[...] between EDGARv4.0 and a top-down estimate has been discussed \r in Rigby, et al. (2010). They estimated uncertainties in EDGARv4.0 of \r +-10% to +-15%, depending on the year, and top-down values were \r consistent within these uncertainties. However, the agreement is now \r poorer during the 1980s in EDGARv6. For some PFCs (e.g., CF4, C2F6), \r it was previously noted that some assumptions within EDGARv4.0 \r had been validated against atmospheric observations, hence \r EDGAR might be considered a hybrid of top-down and bottom-up "


id: d6ee23b3b68f3487b99060adb0c0ef27
created_at: 2023-04-19 13:23:08
updated_at: 2023-04-19 13:23:08
content_type: text
index: climate_gpt_v2
vector_id: 35121
content:
"Based on the assessment of relevant uncertainties above, constant, \r relative uncertainty estimates for GHGs are applied at a 90% \r confidence interval that range from relatively low for CO2 FFI \r (+-8%), to intermediate values for CH4 and F-gases (+-30%), to \r higher values for N2O (+-60%) and CO2 from LULUCF (+-70%). To \r aggregate these and estimate uncertainties for total GHGs in terms \r of CO2-eq emissions, the square root of the squared sums of absolute \r uncertainties for individual (groups of) gases are taken, using 100-\r year global warming potentials (GWP100) with values from IPCC AR6 \r (Forster et al. 2021, Section 7.6 and Supplementary Material 7.SM.6) \r to weight emissions of non-CO2 gases but excluding uncertainties in \r the metric itself. An estimate of this 'metric uncertainty' is provided \r in the next section. Overall, this uncertainty assessment is broadly \r in line with IPCC AR5 (Blanco et al. 2014), but provides important \r [...]"


id: 2d15618af15bbd1c9a302b1fcb0075c0
created_at: 2023-04-19 13:23:08
updated_at: 2023-04-19 13:23:08
content_type: text
index: climate_gpt_v2
vector_id: 5211
content:
"[...] adjustments in the evaluation of uncertainties of individual gases \r (CH4, F-gases, CO2-LULUCF) as well as the approach in reporting total \r uncertainties across GHGs. "


id: 3384858148bd7487273616b1eb6515b6
created_at: 2023-04-19 13:23:08
updated_at: 2023-04-19 13:23:08
content_type: text
index: climate_gpt_v2
vector_id: 6314
content:
"Methodological choices introduce a different type of uncertainty, \r namely which indirect effects are included in the calculation of metric \r values and the strength of those feedbacks. For CH4, indirect forcing \r caused by photochemical decay products (mainly tropospheric ozone \r and stratospheric water vapour) contributes almost 40% of the total \r forcing from CH4 emissions. More than half of the changes in GWP100 \r values for CH4 in successive IPCC assessments from 1995 to 2013 are \r due to re-evaluations of these indirect forcings. In addition, warming \r due to the emission of non-CO2 gases extends the lifetime of CO2\r already in the atmosphere through climate-carbon cycle feedbacks \r (Friedlingstein et  al. 2013). Including these feedbacks results in \r higher metric values for all non-CO2 gases, but the magnitude of \r this effect is uncertain - for example, AR5 found the GWP100 value \r for CH4 without climate-carbon cycle feedbacks to be 28, whereas \r [...]"


id: 5ff40542ef0b9c48e1631496ae96e55
created_at: 2023-04-19 13:23:08
updated_at: 2023-04-19 13:23:08
content_type: text
index: climate_gpt_v2
vector_id: 14190
content:
"[...] including this feedback would raise the value to between 31 and 34 \r (Myhre et al. 2013; Gasser et al. 2016; Sterner and Johansson 2017). \r The AR6 includes climate-carbon cycle feedbacks (Forster et al. 2021). \r These parametric uncertainties associated with different feedbacks \r are incorporated into the above uncertainty estimates by WGI."


id: c5a8a6d6d40a01eaad31fe177c976fdf
created_at: 2023-04-19 13:23:08
updated_at: 2023-04-19 13:23:08
content_type: text
index: climate_gpt_v2
vector_id: 32083
content:
"A simplified relationship used to quantify the effect of emitting a unit \r mass of a given greenhouse gas (GHG) on a specified key measure \r of climate change. A relative GHG emission metric expresses the \r effect from one gas relative to the effect of emitting a unit mass of \r a reference GHG on the same measure of climate change. There are \r multiple emission metrics, and the most appropriate metric depends \r on the application. GHG emission metrics may differ with respect to: \r (i) the key measure of climate change they consider; (ii) whether they \r consider climate outcomes for a specified point in time or integrated \r over a specified time horizon; (iii) the time horizon over which the \r metric is applied; (iv) whether they apply to a single emission pulse, \r emissions sustained over a period of time, or a combination of both; \r and (v) whether they consider the climate effect from an emission \r compared to the absence of that emission or compared to a reference \r [...]"


id: a84c12594b547f97763251eddcb3a145
created_at: 2023-04-19 13:23:08
updated_at: 2023-04-19 13:23:08
content_type: text
index: climate_gpt_v2
vector_id: 26829
content:
"This is illustrated in Figure 2.SM.8, which shows that the warming \r from CH4 emissions sustained at a constant rate is greater than \r the warming from an 'equivalent' (based on GWP100) amount \r of sustained CO2 emissions for the first 100 years, but the rate of \r warming from sustained CH4 emissions declines over time and the \r total warming becomes less than that from sustained CO2 emissions \r beyond the first century. The different cumulative behaviour of CO2\r and SLCF emissions is particularly relevant in mitigation scenarios: \r each tonne of additional CO2 emissions causes further warming \r until emissions reach net zero (Canadell et  al. 2021). By contrast, \r declining SLCF emissions can result in a declining SLCF contribution \r to global temperature since the warming from past emissions does \r not persist and declines over time. This behaviour is well known and \r can be readily replicated with simple climate models (Figure 2.SM.8) \r [...]"


id: 58d78e8009e0aa68ce9368fb1c54fd07
created_at: 2023-04-19 13:23:08
updated_at: 2023-04-19 13:23:08
content_type: text
index: climate_gpt_v2
vector_id: 12874
content:
"[...] but cumulative SLCF emissions based on GWP100 do not capture this \r decline (Lynch et al. 2020)."


id: 4cf6379feee9d8ac744b3879e9edb918
created_at: 2023-04-19 13:23:08
updated_at: 2023-04-19 13:23:08
content_type: text
index: climate_gpt_v2
vector_id: 10813
content:
"The GWP time horizon can be linked to the social discount rate used \r in the global damage potential to calculate the net present value \r of economic damages over time from each emission. Recent studies \r (Sarofim and Giordano 2018; Mallapragada and Mignone 2019) \r confirm earlier work (Fuglestvedt et al. 2003; Boucher 2012) that, for \r methane, GWP100 is consistent with a discount rate of about 3%, \r with the specific value depending on the gas and other assumptions \r such as non-linearity of damages with warming. Detailed sensitivity \r analysis by Sarofim and Giordano (2018) gives an interquartile \r range of 2.7-4.1% for the implied discount rate for GWP100 in the \r case of CH4, depending on a range of assumptions about climate \r scenarios, shape of damage functions, climate feedbacks and global \r economic growth. GWP20 would imply much higher discount rates \r of 11.1-14.6%, given the stronger weighting of near-term effects on \r [...]"


id: 7e3a8fe352bd62fc133ec41cbf9846c2
created_at: 2023-04-19 13:23:08
updated_at: 2023-04-19 13:23:08
content_type: text
index: climate_gpt_v2
vector_id: 19379
content:
"[...] climate. Use of a single discount rate based on pure time preference \r and future growth in wealth and its effects (known as the simple \r Ramsey rule) can be problematic (Drupp et al. 2018) but no studies \r so far have evaluated metrics with varying discount rates over \r time. In addition, the relationship between GWP time horizon and \r discount rate is not universal as it depends on the lifetime of the SLCF \r (Fuglestvedt et al. 2003)."


id: aac76aab0d6cafd2da550d718932995f
created_at: 2023-04-19 13:23:08
updated_at: 2023-04-19 13:23:08
content_type: text
index: climate_gpt_v2
vector_id: 27271
content:
"In principle, GHG emission metrics focused on cost-effectiveness \r are better matched to the Paris Agreement's temperature goal \r than cost-benefit metrics, and are also supported by the UNFCCC \r principle that mitigation policies and measures should be cost\u0002effective (Johansson, 2011; Tol et  al. 2012; Tanaka et  al. 2020). In \r cost-effectiveness metrics, values for SLCF emissions necessarily \r change over time since the closer SLCF emissions occur to the target \r year, the greater their contribution to climate change in that year \r (Aaheim and Mideksa 2017). The dynamic GTP (Shine et al. 2007) \r reflects such a cost-effectiveness approach by providing information \r on the marginal contribution of SLCF emissions in any given year \r to the expected peak warming at a future date (Tol et  al. 2012; \r Mallapragada and Mignone 2017; Tanaka et al. 2020). However, the \r dynamic GTP does not fully match the optimal weighting of gases in \r [...]"


id: 3a1244f4ba422b897c3cd96396697f3a
created_at: 2023-04-19 13:23:08
updated_at: 2023-04-19 13:23:08
content_type: text
index: climate_gpt_v2
vector_id: 7429
content:
"[...] least-cost mitigation pathways (also referred to as the global cost \r potential; e.g., Michaelis 1992; Manne and Richels 2001) because \r overall mitigation costs and hence the economically optimal amount \r and timing of SLCF abatement also depends on the discount rate \r as well as treatment of uncertainties, not only their contribution to \r warming in the target year (Johansson 2011; Ekholm 2014; Strefler \r et al. 2014; Tanaka et al. 2020)."


id: a608fd5ca6daf583ed00918fd4808de3
created_at: 2023-04-19 13:23:08
updated_at: 2023-04-19 13:23:08
content_type: text
index: climate_gpt_v2
vector_id: 26434
content:
"of likely below 2degC and lower. For such temperature limits, peak \r temperature would be reached between about 2050 and 2080 \r (Chapter 3). This means that emissions occurring in the year 2030 \r would be weighted by GTP20 to GTP50, but emissions in the year \r 2040 by GTP10 to GTP40, and so on. Across such time horizons, the \r numerical values of the dynamic GTP for CH4 (as the main short\u0002lived GHG) over the next few decades are broadly comparable on \r average to GWP100 (Table  2.SM.7). Since a large fraction of the \r total abatement potential for CH4 is assumed to be available at \r relatively low costs (Harmsen et al. 2019) or co-abated with fossil \r CO2 (Rogelj et al. 2014), abatement choices based on GWP100 differ \r little in such pathways from those based on the dynamic GTP or \r economic optimisation. For modelled mitigation pathways that likely\r limit warming to 2degC or below and with limited overshoot, GWP100 \r [...]"


id: f4585897f7588cd3ca0433f003e981fa
created_at: 2023-04-19 13:23:08
updated_at: 2023-04-19 13:23:08
content_type: text
index: climate_gpt_v2
vector_id: 40366
content:
"[...] therefore results in overall abatement levels and costs at the global \r scale that are not very different from those based on dynamic GTP or \r economic optimisation, even though GWP100 reflects a cost-benefit \r rather than cost-effectiveness framework. However, differences can \r be more pronounced for individual sectors."


id: 334c5708063147d17b8636e8d7c28563
created_at: 2023-04-19 13:23:08
updated_at: 2023-04-19 13:23:08
content_type: text
index: climate_gpt_v2
vector_id: 6276
content:
"A number of studies since AR5 have evaluated the impact of \r different pulse GHG emission metrics and time horizons on the \r global economic costs of limiting global average temperature \r change to a pre-determined level, including to likely below 2degC and \r to 1.5degC (Ekholm et al. 2013; Deuber et al. 2014; Strefler et al. 2014; \r Huntingford et  al. 2015; Van Den Berg et  al. 2015; Harmsen et  al. \r 2016; Tanaka et al. 2020). These studies show consistently, with very \r few exceptions, that global costs to achieve the same temperature \r target below 2degC in 2100, or the same peak temperature before \r 2100, are higher if CH4 emissions are weighted consistently less \r than indicated by GWP100 (e.g., if using GTP100 or GWP500). The \r increase in global mitigation costs ranges from a few percent to more \r than 30% in most studies, depending not only on the specific metric \r values used but also on the temperature limit, degree of overshoot, \r [...]"


id: bf04029034aa17c79052dc62f49107f
created_at: 2023-04-19 13:23:08
updated_at: 2023-04-19 13:23:08
content_type: text
index: climate_gpt_v2
vector_id: 30874
content:
"[...] and abatement costs and potentials of different gases assumed in \r those studies. These studies also indicate, albeit less consistently and \r less significantly than for GTP100, that global mitigation costs would \r also increase if CH4 emissions are valued consistently more highly \r than in GWP100 (e.g., using GWP20). Collectively, these studies \r indicate that, even though GWP100 does not represent the most \r cost-effective metric and time horizon choice possible (Tanaka et al. \r 2020), it is more cost-effective than any of the other static metrics \r and time horizons that have been tested in economic models and are \r used most commonly in the scientific literature."


id: 8140631c7914c8c8af0b7e72bb1c4a01
created_at: 2023-04-19 13:23:08
updated_at: 2023-04-19 13:23:08
content_type: text
index: climate_gpt_v2
vector_id: 19871
content:
"A common feature of virtually all GHG emission metrics studies to \r date is that they use a single emission metric (either static GWP or \r GTP, or dynamic GTP with predictably changing values) to inform \r abatement choices over the entire 21st century and beyond. This is \r not well matched to the new scenario logic proposed by Rogelj et al. \r (2019) for the Paris Agreement, which suggests that separate policy \r choices exist regarding the timing and magnitude of the temperature \r peak and the post-peak rate of temperature decline. This new scenario \r logic has not yet been used to evaluate GHG metrics, but Tanaka \r et  al. (2021) show that global cost reductions could be obtained \r by using GWP100 as a starting metric and updating the GWP time \r horizon in discrete steps, depending on when and by how much the \r temperature goal might be exceeded based on actual emissions. This \r approach could reduce mitigation costs by a few percent, relative \r [...]"


