# document

id: 5db0992295d6daef16bcacddce49cc0
created_at: 2023-04-19 13:22:10
updated_at: 2023-04-19 13:22:10
content_type: text
index: climate_gpt_v2
vector_id: 13776
content:
"Figure 2.26 | Future CO2 emissions from existing and currently planned fossil fuel infrastructure in the context of Paris carbon budgets in GtCO2 based \r\non historic patterns of infrastructure lifetimes and capacity utilisation. Future CO2 emissions estimates of existing infrastructure for the electricity sector as well as all \r\nother sectors (industry, transport, buildings, other fossil fuel infrastructures) and of proposed infrastructures for coal power as well as gas and oil power. Grey bars on the right \r\ndepict the range (5th-95th percentile) in overall cumulative net CO2 emissions until reaching net zero CO2 in pathways that limit warming to 1.5degC with no or limited overshoot \r\n(1.5degC scenarios), and in pathways that limit warming to 2degC (<67%) (2degC scenarios). Source: based on Edenhofer et al. (2018) and Tong et al. (2019). "


id: bb44b5c8cfd7cd892c1b42ae230d1d73
created_at: 2023-04-19 13:22:10
updated_at: 2023-04-19 13:22:10
content_type: text
index: climate_gpt_v2
vector_id: 30164
content:
"Few quantifications of carbon lock-in from urban infrastructure, in \r\nparticular urban form, have been attempted, in part because they also \r\nrelate to behaviours that are closely tied to routines and norms that \r\nco-evolve with 'hard infrastructures' and technologies, as well as 'soft \r\ninfrastructure' such as social networks and markets (Seto et al. 2016). \r\nThere are some notable exceptions providing early attempts (Guivarch \r\nand Hallegatte 2011; Driscoll 2014; Seto et al.2014; Lucon et al. 2014; \r\nErickson and Tempest 2015; Creutzig et al. 2016). Creutzig et al. (2016) \r\nattempt a synthesis of this literature and estimate the total cumulative \r\nfuture CO2 emissions from existing urban infrastructure at 210 Gt, and \r\nfrom new infrastructures at 495 Gt for the period 2010-2030."


id: f3a4124ebf316745d560f94ebfcd7f24
created_at: 2023-04-19 13:22:10
updated_at: 2023-04-19 13:22:10
content_type: text
index: climate_gpt_v2
vector_id: 40238
content:
"A complementary strand of literature uses IAMs to assess the cumulative \r\ngross amount of unabated CO2 emissions from fossil fuels across \r\ndecarbonisation pathways that are not removed from the system, even \r\nunder strong (short- and long-term) climate policy ambitions. Lower \r\nbound estimates for such a  minimum amount of unabated residual \r\nCO2 emissions across the 21st century that is not removed from the \r\nsystem, even under very ambitious climate policy assumptions, may \r\nbe around 600-700 GtCO2 (Kriegler et al. 2018b). This range increases \r\nto 650-1800 GtCO2 (Table  2.7) as soon as a  broader set of policy \r\nassumptions are considered, including delayed action in scenarios that \r\nlimit warming to 1.5degC and 2degC respectively (Luderer et al. 2018)."


id: b0b69729034d496e7c30e99a95372b8e
created_at: 2023-04-19 13:22:10
updated_at: 2023-04-19 13:22:10
content_type: text
index: climate_gpt_v2
vector_id: 28318
content:
"In accounting studies, estimates of future CO2 emissions from current \r\nfossil fuel infrastructures are dominated by the power sector with \r\nits large fossil fuel capacities. In contrast, scenario studies highlight \r\nresidual emissions from non-electric energy  - particularly in the \r\ntransport and industry sectors. Fossil-fuel infrastructure in the power \r\nsector can be much more easily retired than in those sectors, where \r\nthere are fewer and more costly alternatives. IAMs therefore account \r\nfor continued investments into fossil-based energy technologies \r\nin areas with limited decarbonisation potential, such as some areas of "


id: 735371eda1c1656414362696f65ffb6a
created_at: 2023-04-19 13:22:10
updated_at: 2023-04-19 13:22:10
content_type: text
index: climate_gpt_v2
vector_id: 17540
content:
"region or sector, policies and measures tend to affect GHG emissions \r\nin other parts of the world. Emissions leakage is the key channel \r\nby which such phenomena and complex interactions occur.13\r\nUncertainties in impacts, synergies, and trade-offs between policies \r\nand measures also complicate the evaluation of emissions impacts. \r\nThese make it challenging to identify the impacts of any specific \r\npolicy or measure on emissions of any specific region or sector. \r\nRigorous statistical analyses are necessary for building strong \r\nempirical evidence, but the experience with climate-related policy \r\nexperiments to date is limited."


id: f9b0f2976e66b90d47fa7acfa46eeece
created_at: 2023-04-19 13:22:10
updated_at: 2023-04-19 13:22:10
content_type: text
index: climate_gpt_v2
vector_id: 41292
content:
"It is rather difficult, though not impossible, to discern the genuine \r\nimpacts of climate and non-climate policies on GHG emissions. \r\nMost current and past policies target only a  small part of global \r\nemissions in a  limited geographical area and/or from a  small \r\nnumber of economic sectors. However, in addition to the targeted "


id: e1e91dc7f96ad7e0ae7cb3fa95bf1879
created_at: 2023-04-19 13:22:10
updated_at: 2023-04-19 13:22:10
content_type: text
index: climate_gpt_v2
vector_id: 37028
content:
"rate changes has not been sufficient to stimulate further emissions \r\nreductions (Haites et al. 2018)."


id: 43d39beaea06afbe53a18727b8073dcd
created_at: 2023-04-19 13:22:10
updated_at: 2023-04-19 13:22:10
content_type: text
index: climate_gpt_v2
vector_id: 9165
content:
"Renewable energy policies, such as Renewable Portfolio Standards \r\nand Feed-in-Tariff, have played an essential role in the massive \r\nexpansion of renewable energy capacities, another key driver of GHG \r\nemissions reductions (robust evidence, high agreement). Drivers of \r\ndecreasing CO2 emissions seen in a group of 18 developed economies \r\nthat decarbonised over the period 2005-2015 are the displacement \r\nof fossil fuels by renewable energy and decreases in energy use \r\n(Le Quere et al. 2019). Renewable energy policies both at the EU and \r\nmember states level have played an essential role in abating GHG \r\nemissions (ICF International 2016)."


id: 6a07114924a84e41340568fae54bd8a4
created_at: 2023-04-19 13:22:10
updated_at: 2023-04-19 13:22:10
content_type: text
index: climate_gpt_v2
vector_id: 15923
content:
"Many governments have implemented sector-specific policies, in \r\naddition to nationwide measures, to reduce GHG emissions (high \r\nconfidence). Examples of sectoral climate policies include carbon taxes \r\non transportation fuels, low-carbon fuel standards, and regulation of \r\ncoal power generation."


id: 2f41f9df0db291ba86051dfbc0f978e3
created_at: 2023-04-19 13:22:10
updated_at: 2023-04-19 13:22:10
content_type: text
index: climate_gpt_v2
vector_id: 5579
content:
"The implementation of a carbon tax and value-added tax on gasoline \r\nand diesel in Sweden resulted in significant reductions of CO2\r\nemissions in the transportation sector (Shmelev and Speck 2018; \r\nAndersson 2019). An assessment of a variety of carbon tax schemes \r\nacross various sectors in the EU shows a  negative relationship \r\nbetween CO2 emissions and a CO2 tax (Hajek et al. 2019). In British \r\nColumbia (Canada), the carbon tax resulted in a decrease in demand \r\nfor gasoline and a reduction in total GHG emissions (not exclusive to \r\nthe transportation sector) estimated to be between 5-15% (Murray \r\nand Rivers 2015; Rivers and Schaufele 2015). The Low Carbon Fuel \r\nStandard in California has contributed to reducing carbon emissions \r\nin the transportation sector by approximately 9.85-13.28% during \r\n1997-2014 (Huseynov and Palma 2018)."


id: e6acbe06c52629270e9b34923b2cbdf
created_at: 2023-04-19 13:22:10
updated_at: 2023-04-19 13:22:10
content_type: text
index: climate_gpt_v2
vector_id: 37879
content:
"Carbon prices  - such as carbon taxes and GHG emissions trading \r\nschemes (ETSs)  - are among the most widely used climate policy \r\ninstruments across the globe, together with technology support \r\ninstruments (IRENA 2018). As of May 2020, there were 61 carbon \r\npricing schemes in place or scheduled for implementation, consisting \r\nof 31 ETSs and 30 carbon tax regimes, covering 12 GtCO2-eq or \r\nabout 22% of annual global GHG emissions (World Bank 2020). The \r\nperformance of carbon pricing in practice varies by countries and \r\nsectors, and depends on the policy environment (robust evidence,\r\nhigh agreement)."


id: 170d29ff7f0a664c7dad8ee29d8734e6
created_at: 2023-04-19 13:22:10
updated_at: 2023-04-19 13:22:10
content_type: text
index: climate_gpt_v2
vector_id: 1272
content:
"The European Union Emissions Trading Scheme (EU ETS), the longest\u0002standing regional climate policy instrument to date, has reduced \r\nemissions, though the estimates of the amount vary by study, by \r\ncountry, and by sector; ranging from 3-28% (McGuinness and \r\nEllerman 2008; Ellerman et  al. 2010; Abrell et  al. 2011; Anderson \r\nand Di Maria 2011; Egenhofer et al. 2011; Petrick and Wagner 2014; \r\nArlinghaus 2015; Martin et al. 2016). The EU ETS avoided emitting \r\nabout 1.2 GtCO2 between 2008 and 2016 (3.8%), almost half of \r\nwhat EU governments promised to reduce under their Kyoto Protocol \r\ncommitments (Bayer and Aklin 2020)."


id: 888d2f3f204dc2ec4f98cf7e10ffe2f5
created_at: 2023-04-19 13:22:10
updated_at: 2023-04-19 13:22:10
content_type: text
index: climate_gpt_v2
vector_id: 21189
content:
"China's emission trading pilots have resulted in a decline in carbon \r\nintensity in the pilot provinces by adjusting the industrial structure \r\n(Zhou et  al. 2019). The Regional Greenhouse Gas Initiative (RGGI) \r\nin the USA has induced leakage in emissions through increases in \r\nelectricity generation in surrounding non-RGGI areas, but it has led \r\nto the reduction of emissions by way of changes in the fuel mix from \r\ncoal to gas (Fell and Maniloff 2018). Actual emissions declined in six \r\nof the 10 ETSs for which data is available, although other factors, \r\nsuch as the 2009 recession, have had significant impacts on those \r\nemissions as well (Haites et al. 2018)."


id: 21d3b413a2f46a880fe3b70e74079997
created_at: 2023-04-19 13:22:10
updated_at: 2023-04-19 13:22:10
content_type: text
index: climate_gpt_v2
vector_id: 3169
content:
"An alternative approach to a carbon tax is an indirect emissions tax \r\non fuels such as an excise tax, or on vehicles, based on the expected \r\nCO2 intensity of new passenger vehicles. Vehicle purchase taxes \r\ncan result in a  reduction in GHG emissions through reducing the \r\nCO2 emissions intensity of vehicles, while also discouraging new \r\nvehicle purchases (Aydin and Esen 2018). For example, a vehicle tax \r\npolicy in Norway resulted in a reduction of average CO2 intensity \r\nper  kilometre of 7.5 gCO2  km-1 (Ciccone 2018; Steinsland et  al. \r\n2018). Despite such evidence, studies of carbon pricing find that \r\nadditional policies are often needed to stimulate sufficient emissions \r\nreductions in transportation (medium confidence) (Tvinnereim and \r\nMehling 2018)."


id: d4095593bd60a8d57cbcc2f17fda1c62
created_at: 2023-04-19 13:22:10
updated_at: 2023-04-19 13:22:10
content_type: text
index: climate_gpt_v2
vector_id: 34634
content:
"The evidence of environmental effectiveness of carbon taxes in \r\nWestern European countries is varied depending on country and \r\nstudy (robust evidence, high agreement). A  significant impact is \r\nfound in Finland but insignificant impacts are found in Denmark and \r\nthe Netherlands, and there are mixed results for Sweden (Lin and Li \r\n2011; Brannlund et al. 2014). Only six of the 17 taxes, where data are \r\navailable, have reduced actual emissions subject to the tax. Tax rates \r\ntend to be too low in many cases and the scale and frequency of the "


id: eaf29d12881b70d9978c8cceb446156c
created_at: 2023-04-19 13:22:10
updated_at: 2023-04-19 13:22:10
content_type: text
index: climate_gpt_v2
vector_id: 38698
content:
"Policies other than those intended directly to mitigate GHGs can \r\nalso influence these emissions. Policies to protect the stratospheric \r\nozone layer is a  case in point. Implementing the Montreal \r\nProtocol and its amendments, emissions of controlled ozone\u0002depleting substances (ODSs) (those covered by the protocol) \r\ndeclined to a very low level of about 1.4 GtCO2-eq yr -1 by 2010, \r\navoiding GHG emissions of an estimated 13.3-16.7 GtCO2-eq yr -1\r\n(9.7-12.5 GtCO2-eq yr -1 when accounting for the ozone depletion \r\nand hydrofluorocarbons (HFCs) offsets) (Velders et  al. 2007). Yet \r\nfluorinated gases (F-gases), the substances introduced to substitute \r\nODSs are also potent GHGs. See Section 2.2 for emissions data, and \r\nChapter 13 on current policies to mitigate HFCs and other F-gases. \r\nGHG implications of two other categories of non-climate policies \r\nare briefly assessed in this section."


id: 18376ab8686670c947311758900ccc98
created_at: 2023-04-19 13:22:10
updated_at: 2023-04-19 13:22:10
content_type: text
index: climate_gpt_v2
vector_id: 1461
content:
"Co-impacts of local or regional air pollution abatement policies for \r\nclimate mitigation are widely studied in the literature. Cross-border \r\nexternalities of air pollution have also made these a focus of several \r\ninternational agreements (Mitchell et  al. 2020). Evaluating the \r\neffectiveness of such treaties and policies is difficult because deriving \r\ncausal inferences and accurate attribution requires accounting \r\nfor several confounding factors, and direct and indirect spillovers \r\n(Isaksen 2020). Nevertheless, several studies assess the effectiveness \r\nof such treaties and regulations (De Foy et al. 2016; Li et al. 2017a, \r\n2017b; Morgenstern 2018; Mardones and Cornejo 2020). However, \r\nthere is little ex-post empirical analysis and a  greater focus on \r\nex-ante studies in the literature."


id: 5d3bcb9e7da7f2d431cacd1e8272ac20
created_at: 2023-04-19 13:22:10
updated_at: 2023-04-19 13:22:10
content_type: text
index: climate_gpt_v2
vector_id: 13677
content:
"Bike- and car-sharing programmes can reduce GHG emissions\r\n(medium confidence). Albeit a study of eight cities in the USA with \r\nlarger bike share systems and higher ridership found that their \r\npotential to reduce total emissions is limited to <0.1% of total GHG \r\nemissions from the transportation sectors of these cities (Kou et al. \r\n2020). The emissions reductions effects of car-sharing programmes \r\ndepends on the specifics of programmes: the mode shift from public \r\ntransit to car-sharing services can outweigh the decreases in GHG \r\nemissions associated with a  reduced number of cars on the road \r\n(Jung and Koo 2018), whereas car-sharing programmes with EV \r\nfleets may reduce GHG emissions (Luna et al. 2020)."


id: 58c3e3f7ee9fdc947b9d63da318c54a8
created_at: 2023-04-19 13:22:10
updated_at: 2023-04-19 13:22:10
content_type: text
index: climate_gpt_v2
vector_id: 12858
content:
"agro-industry. For example, small/medium and environmentally \r\nconcerned farmers in Europe are often asking for more policies \r\nand regulations, and see it as necessary from a climate perspective, \r\nand also to maintain competitiveness relative to large agro-industrial \r\ncomplexes. Therefore, the need for governmental support for small \r\nproducers in regulations encompasses all AFOLU sectors."


id: 794c30e247f6edfd05474aba0c25e878
created_at: 2023-04-19 13:22:10
updated_at: 2023-04-19 13:22:10
content_type: text
index: climate_gpt_v2
vector_id: 18534
content:
"Other policies that typically have potential co-benefits for climate \r\nmitigation include those specific to certain sectors and are discussed \r\nin Chapters 5-11. Examples of such policies include those that \r\nencourage active travel modes, which have been found to have \r\nancillary benefits for local air quality, human health, and GHG \r\nemissions (Fujii et al. 2018). Policies to reduce energy use through \r\ngreater efficiency have also been found to have benefits for air quality \r\nand the climate (robust evidence, medium agreement) (Tzeiranaki \r\net al. 2019; Bertoldi and Mosconi 2020). Important air quality and \r\nclimate co-benefits of renewable or nuclear energy policies have also \r\nbeen found (medium evidence, medium agreement) (Lee et al. 2017; \r\nApergis et al. 2018; Sovacool and Monyei 2021)."


id: dabf6a36169470add1b6069bfef70d0f
created_at: 2023-04-19 13:22:10
updated_at: 2023-04-19 13:22:10
content_type: text
index: climate_gpt_v2
vector_id: 35776
content:
"Such efforts have led to the development of the High Carbon \r\nStock (HCS) approach that combines carbon stock values with the \r\nprotection of HCS areas (including peatlands and riparian zones) and \r\nareas important for the livelihoods of local communities (Rosoman \r\net  al. 2017). Long-term positive impacts, however, will need to be \r\nassessed with hindsight and focus on national and global statistics. \r\nSuccessful initiatives targeting zero deforestation at jurisdictional \r\nlevel would also need to improve the enforcement of forest laws and \r\nregulations (EII 2015; Meyer and Miller 2015)."


id: 5962dd22ccdc176b6d9df418136ad66b
created_at: 2023-04-19 13:22:10
updated_at: 2023-04-19 13:22:10
content_type: text
index: climate_gpt_v2
vector_id: 12962
content:
"Policies specific to other sectors, such as encouraging green building \r\ndesign, can also reduce GHG emissions (Eisenstein et  al. 2017). \r\nEvidence from several countries also shows that replacing polluting \r\nsolid biomass cooking with cleaner gas-burning or electric alternatives \r\nhave strong co-benefits for health, air quality, and climate change \r\n(robust evidence, high agreement) (Anenberg et al. 2017; Singh et al. \r\n2017; Tao et al. 2018)."


id: 4be655aafad0c02299a126a2a007fb5a
created_at: 2023-04-19 13:22:10
updated_at: 2023-04-19 13:22:10
content_type: text
index: climate_gpt_v2
vector_id: 10613
content:
"Large-scale agribusiness, banks, and consumer goods companies \r\ndominate supply chain-focused zero-deforestation initiatives, but \r\nonly the producers, including local communities and smallholders, can \r\nchange the production circumstances (TFD 2014). Producers shoulder \r\nmuch of the burden for meeting environmental requirements of \r\npledges. And local communities and small producers are vulnerable \r\nto being cut out when supply chains reorient. The zero-deforestation \r\npledges do not always devise programmes for introducing new \r\nsourcing strategies, and governments may have an important \r\ncontribution to make, particularly in safeguarding the interests of \r\nsmall producers."


id: f5c04ca349126097ced869cf033b565b
created_at: 2023-04-19 13:22:10
updated_at: 2023-04-19 13:22:10
content_type: text
index: climate_gpt_v2
vector_id: 40597
content:
"Policies on agriculture, forestry, and other land use (AFOLU), and \r\nAFOLU sector-related policies have had a  long history in many \r\ndeveloping and developed countries. Co-impacts of these policies \r\non the climate have been only marginally studied, although their \r\nimpacts might be quite important because the AFOLU sector is \r\nresponsible for 22% of total GHG emissions (robust evidence, high \r\nagreement). The results of afforestation policies around the world \r\nand the contribution to CCS are also important."


id: 4cf4744a06d16029176cfb540ef3773a
created_at: 2023-04-19 13:22:10
updated_at: 2023-04-19 13:22:10
content_type: text
index: climate_gpt_v2
vector_id: 10810
content:
"Private and governmental policies can have a major impact on the \r\nclimate. Experience indicates that 'climate proofing' a policy is likely \r\nto require some stimulus, resources, and expertise from agencies \r\nor organisations from outside the country. Stimulus and support \r\nfor adaptation and mitigation can come from the UN system and \r\nfrom international development institutions (FAO 2009). These \r\nfindings are also valid for small/organic farmers vis-a-vis large-scale "


id: 5d2a31f9131cf0414bb250aae51a84ab
created_at: 2023-04-19 13:22:10
updated_at: 2023-04-19 13:22:10
content_type: text
index: climate_gpt_v2
vector_id: 13668
content:
"Other than in Brazil and Indonesia, beyond individual supply chains, \r\nthere is still little evidence on positive results of zero-deforestation \r\ncommitments, as information available for companies to judge \r\ntheir progress is scarce. Moreover, many zero-deforestation pledges \r\nset targets to be achieved by 2020 or 2030, and, consequently, "


id: 2854d0ed3ae3850c6e39e49760bac5f3
created_at: 2023-04-19 13:22:10
updated_at: 2023-04-19 13:22:10
content_type: text
index: climate_gpt_v2
vector_id: 4295
content:
"population control and rapid technological transition to different \r\nextents and in different settings. More research on decoupling \r\nand sustainability transformations would help to answer these \r\nquestions. Key knowledge gaps also remain in the role of trade - in \r\nparticular, how supporting low-carbon technologies in developing \r\nand exporting countries can counteract the upward-driving effect \r\nof trade, and how to achieve decoupling without outsourcing \r\nemissions to others and often to less developed regions."


id: 4e9cc323d589314b97f3c209a547d722
created_at: 2023-04-19 13:22:10
updated_at: 2023-04-19 13:22:10
content_type: text
index: climate_gpt_v2
vector_id: 11102
content:
"many companies have not yet reported publicly on their progress. \r\nSimilarly, only a  few governments have yet shown progress in \r\nreducing deforestation, but the New York Declaration on Forests, \r\nthe Sustainable Development Goals (SDGs) and the Paris Agreement \r\nwere adopted relatively recently. The effectiveness of private-sector \r\nzero-deforestation pledges depends on the extent to which they \r\ncan be supported by governmental action and foster a cooperative \r\nenvironment with the engagement of all stakeholders. Where the \r\npledges are coordinated with regulation, multi-stakeholder dialogues, \r\nand technical and financial support, a true paradigm shift becomes \r\npossible. Many governments are still building the capacity to improve \r\noverall forest governance, but implementing ambitious international \r\ntargets is likely to depend on technical and major financial support \r\nthat has not yet been mobilised."


id: 9bb1f216c04e762befabad0e9850c341
created_at: 2023-04-19 13:22:10
updated_at: 2023-04-19 13:22:10
content_type: text
index: climate_gpt_v2
vector_id: 24649
content:
"Global greenhouse gas (GHG) emissions continued to rise and reached 59 +- 6.6 GtCO2-eq in 2019, although the rate of growth \r\nhas fallen compared to the previous decade. However, emissions were higher than at any point in human history before. Emissions \r\nwere around 12% and 54% higher than in 2010 and 1990, respectively. Average annual GHG emissions for 2009-2019 were higher \r\ncompared to the periods 2000-2009 and 1990-1999, respectively. GHG emissions growth slowed since 2010: while average annual \r\nGHG emissions growth was 2.1% for 2000-2010, it was only 1.3% for 2010-2019. In order to stop the temperature increase, \r\nhowever, net emissions must be zero."


id: b038757e7cff0e4499c8cf8ab8c8c456
created_at: 2023-04-19 13:22:10
updated_at: 2023-04-19 13:22:10
content_type: text
index: climate_gpt_v2
vector_id: 28240
content:
"About 24 countries have reduced territorial CO2 and GHG emissions for more than 10 years. Uncertainties in emission levels and \r\nchanges over time prevent a precise assessment in some country cases. In the short observation period of 2010-2015, 43 out of \r\n166 countries have achieved absolute decoupling of consumption-based CO2 emissions from economic growth, which means that \r\nthese countries experienced GDP growth while their emissions have stabilised or declined. A group of developed countries, such as \r\nsome EU countries and the USA, and some developing countries, such as Cuba, have successfully achieved an absolute decoupling \r\nof consumption-based CO2 emissions and GDP growth. Decoupling has been achieved at various levels of per capita income and \r\nper capita emissions. Overall, the absolute reduction in annual emissions achieved by some countries has been outweighed by \r\ngrowth in emissions elsewhere in the world."


id: 8c42c935c34f8db3dd905da8e3b2bb27
created_at: 2023-04-19 13:22:10
updated_at: 2023-04-19 13:22:10
content_type: text
index: climate_gpt_v2
vector_id: 21850
content:
"Coordinating Lead Authors:\r\nShobhakar Dhakal (Nepal/Thailand), Jan Christoph Minx (Germany), Ferenc L. Toth (Austria/Hungary)"


id: bd56af71cc60509772c24ead08ef992f
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 30558
content:
"Dhakal, S., J.C. Minx, F.L. Toth, Reisinger, W. F. Lamb, N. Dobbeling, T. Wiedmann, K. Hubacek, R. M. Andrew, M. Crippa, \r\nP. M. Forster, J. Olivier, G. P. Peters, J. Pongratz, M.Rigby, M. Saunois, S. J. Smith, E. Solazzo, H. Tian, K. Blok, H. Clark, \r\nA. Cowie, J.S Fuglestvedt, O. Geden, V. Ginzburg, C. Guivarch, J. I. House, R. Mrabet, G. J. Nabuurs, K. Riahi, A. H. Stromman, \r\nR. Schaeffer, D. P. van Vuuren, A. Al Khourdajie, R Slade, 2022: Emissions Trends and Drivers Supplementary Material. \r\nIn IPCC, 2022: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth \r\nAssessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, \r\nR. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S.  Luz, \r\nJ. Malley, (eds.)]. Available from https://www.ipcc.ch/report/ar6/wg3/."


id: feb814d665d593c604dd00db6556bf39
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 42201
content:
"report. The dataset including the sector and region classification, and \r\nGWP100 by gas can be found at https://zenodo.org/record/5566761."


id: 6ae74ded5546d580da27a6390a66aaec
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 16095
content:
"This section provides a brief description of the dataset on historic \r\ngreenhouse gas (GHG) emissions compiled for AR6 WGIII (the \r\ncontribution of Working Group III to the IPCC Sixth Assessment \r\nReport) on climate change mitigation. The dataset is publicly \r\navailable (https://zenodo.org/record/5566761) and has undergone \r\nadditional peer review (Minx et  al. 2021). Sections 2.SM.1 and \r\n2.SM.2 included in this Supplementary Material are taken (in most \r\nparts) directly from Minx et al. (2021). It is included here solely to \r\nprovide full transparency over the data used in this report and enable \r\neasy access to all information."


id: 53f058f403d78a68d25ab547cfdcb40a
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 12038
content:
"The dataset is compiled from four sources: (i) the full EDGARv6.0 \r\nrelease for CO2-FFI as well as non-CO2 GHGs covering the time period \r\n1970-2018 (Crippa et al. 2021); (ii) EDGARv6.0 fast-track data for \r\nCO2-FFI providing preliminary estimates for 2019 and 2020 (Crippa \r\net al. 2021); (iii) CO2-LULUCF as the average of three bookkeeping \r\nmodels, consistent with the approach of the global carbon project \r\n(Friedlingstein et al. 2020); and (iv) 2019 non-CO2 emissions based \r\non Olivier and Peters (2018). The resulting synthetic dataset as \r\npresented here has undergone additional peer review (Minx et  al. \r\n2021)."


id: 98aba5f270f81a9d963ff095535f9575
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 24075
content:
"As shown in Table 2.SM.1, sectoral detail is organised along five major \r\neconomic sectors harmonised with the sector chapters used in this report: \r\nenergy supply (Chapter 6); building (Chapter 9); transport (Chapter 10); \r\nIndustry (Chapter 11); and Agriculture, Forestry and Other Land Use (AFOLU) \r\n(Chapter 7). A further classification for assigning our 228 countries and \r\nterritories to regions is used, combining the standard Annex I/non-Annex I \r\ndistinction with geographical location, as documented in Annex II of this "


id: 34d8d33b2b40e5d8d6af5e7b4372a434
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 6540
content:
"or territory in which they took place (i.e., a territorial accounting \r\nprinciple) (IPCC 2006, 2019). A more complete description of the \r\ndata sources and methodology for EDGARv6 is provided in Crippa \r\net al. (2021)."


id: a91d9a76d21f203556df1f30385dac8a
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 26972
content:
"The activity data are sector dependent and vary from fuel combustion \r\nin energy units of a particular fuel type, to the amount of products \r\nmanufactured, or to the number of animals or the area or yield of \r\ncultivated crops. The technology mixes, (uncontrolled) emission \r\nfactors and end-of-pipe measures are determined at different levels: \r\ncountry-specific, regional, country group (e.g., Annex I/non-Annex I), \r\nor global. Technology-specific emission factors are used to enable \r\nan IPCC Tier-2 approach (see Box 2.SM.1), taking into account the \r\ndifferent management and technology processes or infrastructures \r\n(e.g., different distribution networks) under specific 'technologies', \r\nand modelling explicitly abatements/ emission reductions - for \r\nexample, the CH4 recovery from coal mine gas at country level under \r\nthe 'end-of-pipe measures'. As with national inventories, emissions \r\nare accounted over a period of one calendar year in the country "


id: 1cd6066a90a41148d20f831d7617e7ac
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 2256
content:
"All fluxes of CO2-LULUCF are considered. This includes CO2 fluxes \r\nfrom the clearing of forests and other natural vegetation (by \r\nanthropogenic fire and/or clear-cut), afforestation, harvest activities, \r\nland-use related forest degradation, shifting cultivation (cycles of "


id: c411fed6b0d4ebc29548bb1bab8a08cd
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 31778
content:
"About 3.2 GtCO2 yr-1 (for the period 2005-2014) was found to be \r\nexplicable by these conceptual differences in anthropogenic forest \r\nsink estimation related to the representation of environmental change \r\nimpacts and the areas considered as managed (Grassi et al. 2018). "


id: 39e5343151a7e19141384aa27458e9ff
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 7395
content:
"Estimates of historic GHG emissions - CO2, CH4, N2O and F-gases \r\n(HFCs, PFCs, SF6, NF3) - are uncertain to different degrees. Assessing \r\nand reporting uncertainties is crucial in order to understand whether \r\navailable estimates are sufficiently accurate to answer - for example, \r\nwhether GHG emissions are still rising, or if a country has achieved \r\nan emissions-reduction goal (Marland 2008). These uncertainties can \r\nbe of a scientific nature, such as when a process is not sufficiently \r\nunderstood. They also arise from incomplete or unknown parameter \r\ninformation (activity data, emission factors, and so on), as well as \r\nestimation uncertainties from imperfect modelling techniques. There \r\nare at least three major ways to examine uncertainties in emission \r\nestimates (Marland et  al. 2009): (i) by comparing estimates made "


id: 4dd8d230cbf25ac34d234d8195607c9
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 10964
content:
"by independent methods and observations (e.g., comparing top\u0002down vs bottom-up estimates; modelling against remote sensing \r\ndata) (Li et al. 2020; Saunois et al. 2020; Petrescu et al. 2020a, 2021); \r\n(ii) by comparing estimates from multiple sources and understanding \r\nsources of variation (Macknick, 2011; Andres et  al. 2012; Andrew \r\n2020; Ciais et al. 2021); (iii) by evaluating multiple estimates from \r\na single source (Hoesly and Smith 2018) including approaches such \r\nas uncertainty ranges estimated through statistical sampling across \r\nparameter values applied, for example, at the country or sectoral \r\nlevel (Monni et al. 2007; Andres et al. 2014; Solazzo et al. 2021), or to \r\nspatially distributed emissions (Tian et al. 2019)."


id: e0b220d112f7b5a6b9fc6f3fbd36cca8
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 36789
content:
"increases the resulting uncertainty estimate. Uncertainties calculated \r\nwith this methodology tend to be higher than the range of values \r\nfrom ensemble of dependent inventories (Saunois et al. 2016, 2020). \r\nThe uncertainty of emission estimates derived from ensembles of \r\ngridded results from bio-physical models (Tian et  al. 2018) adds \r\nan additional dimension of spatial variability, and is therefore not \r\ndirectly comparable with aggregate country or regional uncertainty, \r\nestimated with the methods discussed above."


id: 6ff6cf9764ba19df7f0ca5eedc6044e6
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 16955
content:
"This section provides an assessment of uncertainties in GHG emissions \r\ndata at the global level. The uncertainties reported here combine \r\nstatistical analysis, comparisons of global emissions inventories and \r\nexpert judgement of the likelihood of results lying outside a defined \r\nconfidence interval, rooted in an understanding gained from the \r\nrelevant literature. At times, we also use a qualitative assessment \r\nof confidence levels to characterise the annual estimates from each \r\nterm, based on the type, amount, quality, and consistency of the \r\nevidence as defined by the IPCC (IPCC 2014). "


id: a8b198d8022cc2eae15becfc829a1508
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 26884
content:
"Uncertainty estimates can be rather different depending on the \r\nmethod chosen. For example, the range of estimates from multiple \r\nsources is bounded by their interdependency; they can be lower \r\nthan true structural plus parameter uncertainty estimates or than \r\nestimates made by independent methods. In particular, it is important \r\nto account for potential bias in estimates, which can result from \r\nusing common methodological or parameter assumptions across \r\nestimates, or from missing sources, which can result in a systemic \r\nbias in emission estimates (see N2O discussion below). Independent \r\ntop-down observational constraints are, therefore, particularly useful \r\nto bound total emission estimates (Petrescu et al. 2021)."


id: 33259615b29664ad6b5a362c98691cea
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 6253
content:
"Such a comprehensive uncertainty assessment covering all major \r\ngroups of GHGs and considering multiple lines of evidence has \r\nbeen missing in the literature. The absence has provided a serious \r\nchallenge for a transparent, scientific reporting of GHG emissions \r\nin climate change assessments such as those by IPCC's Working \r\nGroup  III or the UN Emissions Gap Report, which have only more \r\nrecently started to deal with the issue (Blanco et  al. 2014; UNEP \r\n2020). Most of the available studies in the peer-reviewed literature \r\nusing multiple lines of evidence for their assessment have focused on \r\nindividual gases as in the Global Carbon Budget (Friedlingstein et al. \r\n2020), the Global Methane Budget (Saunois et al. 2020) or the Global \r\nNitrous Oxide Budget (Tian et al. 2020) or covered multiple gases, but \r\nmainly considered individual lines of evidence (Janssens-Maenhout \r\net al. 2019; Solazzo et al. 2021)."


id: fb1d76a7ee15b8862d74f9eda9479479
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 41553
content:
"We adopt a 90% confidence interval (5th-95th percentile) to report \r\nthe uncertainties in our GHG emissions estimates - that is, there is \r\na 90% likelihood that the true value will be within the provided range \r\nif the errors have a Gaussian distribution, and no bias is assumed. \r\nThis is in line with previous reporting in IPCC AR5 (Blanco et  al. \r\n2014; Ciais et  al. 2014). Note that national emissions inventories \r\nsubmitted to the UNFCCC are requested to report uncertainty using \r\na 95% or 2s confidence interval. The use of this broader uncertainty "


id: 8d4612060784fa0432c1110dbc5a61bd
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 22038
content:
"interval implies, however, a relatively high degree of knowledge \r\nabout the uncertainty structure of the associated data, particularly \r\nregarding the distribution of uncertainty in the tails of the probability \r\ndistributions. Such a high degree of knowledge is not present across \r\nall regions, emission sectors, and species considered here. Note \r\nthat, in some cases below, we convert 1s uncertainty results from \r\nthe literature to a 90% confidence interval by implicitly assuming \r\na normal distribution. While we do this as a necessary assumption \r\nto obtain a consistent estimate across all GHGs, we note that this \r\nitself is an assumption that may not be valid. We have made use of \r\nthe best available information in the literature, but note that much \r\nmore work on uncertainty quantification remains to be done. Using \r\nIPCC uncertainty language, we cannot assign high confidence to the \r\nrobustness of most existing uncertainty estimates."


id: 60a00166f81a1631d90cf94fccb9dbbd
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 14297
content:
"Another important source of divergence between datasets is \r\ndifferences in their respective system boundaries (Macknick 2011; \r\nAndres et al. 2012; Andrew 2020). Hence, differences across CO2-FFI \r\nemissions estimates do not reflect full uncertainty due to source data \r\ndependencies. At the same time, the observed range across estimates \r\nfrom different databases exaggerates uncertainty, to the extent that \r\nthey largely originate in system boundary differences (Macknick \r\n2011; Andrew 2020)."


id: 5c4c79b83ee905efefe8498fa44ab1dd
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 13490
content:
"Across global inventories, mean global annual CO2-FFI emissions \r\ntrack at 34 +- 2  GtCO2 in 2014, reflecting a variability of about \r\n+-5.4% (Figure  2.SM.1). However, this variability is almost halved \r\nwhen system boundaries are harmonised (Andrew 2020). EDGAR \r\nCO2-FFI emissions, as used there, track at the top of the range as \r\nshown in Figure  2.SM.1. This is partly due to the comprehensive \r\nsystem boundaries of EDGAR, but also due to the assumption of \r\n100% oxidation of combusted fuels as per IPCC default assumptions. \r\nOnce system boundaries are harmonised, EDGAR continues to track \r\nat the upper end of the range, but no longer at the top. EDGAR \r\nCO2-FFI estimates are further well-aligned with emission inventories \r\nsubmitted by Annex I countries to the UNFCCC - even though some \r\nvariation can occur for individual countries (Andrew 2020; Minx et al. \r\n2021). Differences in FFI-CO2 emissions across different versions of \r\nthe EDGAR dataset are shown in Figure 2.SM.2."


id: 83ca1b1bcb4fe10c8d9c7644a24844c4
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 20332
content:
"Uncertainties in CO2-FFI emissions arise from the combination \r\nof uncertainty in activity data and uncertainties in emission \r\nfactors including assumptions for combustion completeness and \r\nnon-combustion uses. CO2-FFI emissions estimates are largely derived \r\nfrom energy consumption activity data, where data uncertainties are \r\ncomparatively small due to well-established statistical monitoring \r\nsystems, although there are larger uncertainties in some countries \r\nand time periods (Macknick 2011; Andres et  al. 2012; Ballantyne \r\net  al. 2015; Janssens-Maenhout et  al. 2019; Andrew, 2020). Most "


id: 8574166b6e7af55ac5a3299bf11cae1e
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 20619
content:
"Figure 2.SM.3 | Dependencies of selected global energy and CO2 emissions datasets. Here a 'primary' emissions dataset is one that calculated emissions directly \r\nfrom energy data, rather than collating emissions estimates from other sources. In addition to energy data sources, some emissions datasets include emissions from carbonates, \r\nwhich rely on other data sources. Some national data are first collated by regional organisations. 'UN stats' is the United Nations Statistics Office (not UNFCCC). Source: \r\nAndrew (2020)."


id: d63baa50ee86ce16bf35da7ded795a7f
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 35007
content:
"of the underlying uncertainties are systematic and related to \r\nunderlying biases in the energy statistics and accounting methods \r\nused (Friedlingstein et  al. 2020). Uncertainties are lower for fuels \r\nwith relatively uniform properties such as natural gas, oil or gasoline, \r\nand higher for fuels with more diverse properties, such as coal (IPCC \r\n2006; Blanco et al. 2014). Uncertainties in CO2 emissions estimates \r\nfrom industrial processes - that is, non-combustive oxidation of fossil \r\nfuels and decomposition of carbonates - are higher than for fossil \r\nfuel combustion. At the same time, products such as cement also take \r\nup carbon over their life cycle, which are often not fully considered in \r\ncarbon balances (Xi et al. 2016; Sanjuan et al. 2020; Guo et al. 2021). \r\nHowever, recent versions of the global carbon budget include specific \r\nestimates for the cement carbonation sink and estimate average \r\nannual CO2 uptake at 0.70  GtCO2 for 2010-2019 (Friedlingstein \r\net al. 2020)."


id: d72940e7a4a7fb9aeedf07e043e611f1
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 35163
content:
"statistical reporting systems do not have the same level of maturity \r\nas in many industrialised countries (Gregg et al. 2008; Marland, 2008; \r\nAndres et al. 2012; Guan et al. 2012; Korsbakken et al. 2016; Janssens\u0002Maenhout et al. 2019; Friedlingstein et al. 2019, 2020; Andrew, 2020). \r\nHowever, these customary country groupings do not always predict \r\nthe extent to which a country's energy data has undergone historical \r\nrevisions (Hoesly and Smith 2018). Uncertainties in CO2-FFI emissions \r\nbefore the 1970s are higher than for more recent estimates. Over the \r\nlast two to three decades uncertainties have increased again because \r\nof increased fuel production and consumption in some developing \r\ncountries with less rigorous statistics and more uncertain fuel \r\nproperties (Marland et al. 2009; Ballantyne et al. 2015; Friedlingstein \r\net al. 2020)."


id: 140645be7cd92f1efccd023f046364ca
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 738
content:
"The global carbon project (Le Quere et al. 2018; Friedlingstein et al. \r\n2019, 2020) assesses uncertainties in global anthropogenic CO2-FFI \r\nemissions estimates within one standard deviation (1s) as +-5% \r\n(+-10% at 2s). This is broadly consistent with the +-8.4% uncertainty \r\nestimate for CDIAC (Andres et al. 2014) as well as the +-7 to +-9% \r\nuncertainty estimate for EDGARv4.3.2 and v5 (Janssens-Maenhout \r\net al. 2019; Solazzo et al. 2021) at 2s. It remains at the higher end \r\nof the +-5% to +-10% range provided by Ballantyne et  al. (2015). \r\nConsistent with the above uncertainty assessments, we present "


id: 250aa06947c64ad9b5ecf1ce08d504fd
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 3713
content:
"Uncertainties for energy consumption data (and, therefore, CO2-FFI \r\nemissions) are generally higher for the first year of their publication \r\nwhen less data is available to constrain estimates. In the BP energy \r\nstatistics, 70% of data points are adjusted by an average of 1.3% \r\nof a country's total fossil fuel use in the subsequent year, with \r\nfurther more modest revisions later on (Hoesly and Smith 2018). \r\nUncertainties are also higher for developing countries, where "


id: fb7d47e618196cefe913b0cff956e23
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 41621
content:
"Table 2.SM.3 | System boundaries and other key features of global FFI-CO2 emissions datasets. Comparison of some important general characteristics of nine \r\nemissions datasets, with green indicating a characteristic that might be considered a strength. Columns four to six refer to CO2 emission estimates for industrial processes and \r\nproduct use. Since all datasets are under development, these details are subject to change. Based on Andrew (2020). Source: Minx et al. (2021)."


id: 421f5b942af2b0eb693d53d83cb1c41f
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 8832
content:
"Nonetheless, a CO2-LULUCF estimate from the DGVM multi-model \r\nmean remains consistent with the average estimate from the \r\nbookkeeping models, as shown in Figure  2.SM.1. Variation across \r\nDGVMs is large with a standard deviation at around 1.8 GtCO2 yr-1, \r\nbut is still smaller than the average difference between bookkeeping \r\nmodels at 2.6  GtCO2 yr-1 as well as the current estimate of H&N \r\n(Houghton and Nassikas 2017) and its previous model versions \r\n(Houghton et  al. 2012). DGVMs differ in methodology, input data \r\nand how comprehensively they represent land-use-related processes. \r\nIn particular land management, such as crop harvesting, tillage, or \r\ngrazing (all implicitly included in observation-based carbon densities \r\nof bookkeeping models) can alter CO2 flux estimates substantially, \r\nbut are included to varying extents in DGVMs, thus increasing model "


id: e371df999521207274f8bd6ced7f15c9
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 37314
content:
"Notes: DGVM - dynamic global vegetation model; LUH2 and FAO refer to land-use forcing datasets; arrows indicate tendency of process to increase or decrease emissions \r\ncompared to the other estimates' choice. Source: Minx et al. (2021).\r\nLiterature: a Hansis et al. (2015); b\r\n Houghton and Nassikas (2017); c Gasser et al. (2020); d Hurtt et al. (2020); e Chini et al. (2021); f FAO (2015); g based on rangeland-pasture \r\ndistinction of the HYDE dataset (Goldewijk et al. 2017) and forest cover map of Hurtt et al. (2020); see Friedlingstein et al. (2020) for details; h\r\n Conchedda and Tubiello, (2020); i\r\n Hooijer et al. (2010)."


id: e25aeb2a9d4b4e9043f3f8e5d0063b99
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 37115
content:
"spread (Arneth et al. 2017). For all types of models, land-use forcing \r\nis a major determinant of emissions and removals, and its high \r\nuncertainty impacts CO2-LULUCF estimates (Bastos et al. 2021). The \r\nreconstruction of land-use change of the historical past, which has \r\nto cover decades to centuries of legacy LULUCF fluxes, is based on \r\nsparse data or proxies (Goldewijk et al. 2017; Hurtt et al. 2020), while \r\nsatellite-based products suffer from complications in distinguishing \r\nnatural from anthropogenic drivers (Hansen et  al. 2013; Li et  al. \r\n2018) or accounting for small-scale disturbances and degradation \r\n(Matricardi et  al. 2020). Lastly, regional carbon budgets can be \r\nsubstantially over- or under-estimated when the carbon embodied in \r\ntrade products is not accounted for (Ciais et al. 2021)."


id: cd2de517b60a5ba08ef62369ec2d7199
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 33435
content:
"in various modelling frameworks (Pongratz et al. 2014) as well as \r\ninventory data (Grassi et al. 2018)."


id: af030efeaa2101161c61b0bc1b7755a8
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 28036
content:
"Finally, note that attempts to constrain the estimates of CO2-LULUCF \r\nemissions from bookkeeping models and DGVMs by observed \r\nbiomass densities have been undertaken, but were successful only in \r\nsome non-tropical regions (Li et al. 2017). While providing valuable \r\nindependent and observation-driven information, remote-sensing \r\nderived estimates of carbon stock changes have limited applicability \r\nfor model evaluation for the total CO2-LULUCF flux, since they \r\nusually only quantify vegetation biomass changes and exclude legacy \r\nemissions from the pre-satellite era. Further, with the exception \r\nof the (pan-tropical) estimates by Baccini et  al. (2012) they either \r\ntrack committed instead of actual emissions (Tyukavina et al. 2015), "


id: ebcf546f6a0e5fb33c3dc4c9d282c85
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 38856
content:
"better constrain top-down approaches. Local or regional studies have \r\nproved important as independent estimate of inventories while being \r\nspared of the chemical sink uncertainty (Maasakkers et  al. 2021). \r\nSome top-down systems aim to optimise certain emission sectors \r\nbased on differences in their spatial and temporal distributions \r\n(Bergamaschi et al. 2013), while others only solve for net emissions \r\nat the surface. Then the partitioning of top-down posterior (output) \r\nfluxes between specific source sectors is carried out with various \r\ndegrees of uncertainty, depending of the methods and the degree \r\nof refinement of sectors, but often rely on ratios from the prior \r\nknowledge of fluxes. Comprehensive assessments of methane \r\nsources and sinks have been provided by Saunois et al. (2016, 2020) \r\nand Kirschke et al. (2013)."


id: 93642a4344c2fae85ac6bdd6af9c26b1
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 23136
content:
"combine a static carbon density map with forest cover changes, or \r\ninclude the natural land sink (e.g., Baccini et al. 2017) to infer fluxes \r\ndirectly from the carbon stock time series. None of these approaches \r\ntherefore fully distinguishes natural from anthropogenic disturbances \r\nfor actual emissions as the CO2-LULUCF emissions estimate provided \r\nhere do, based on bookkeeping models and DGVMs, such that \r\na direct evaluation is hampered."


id: 6ebca9a03aa6a686ea87d38d98cca839
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 16747
content:
"estimate the venting component for gas and oil in the venting and \r\nflaring sector (1B2a/b2). Differences across different versions of the \r\nEDGAR dataset are shown in Figure 2.SM.4. US-EPA show the lowest \r\nestimates, probably due to missing estimates from a significant \r\nnumber of countries not reporting to UNFCCC (US-EPA 2020 includes \r\nestimates from only 195 countries) and incomplete sectoral coverage. \r\nEDGARv6 estimates of anthropogenic CH4 emissions, as used here, \r\nare in the upper range of the different inventories across most \r\nanthropogenic sources. However, none of these inventories cover CH4\r\nemissions from forest and grassland burning, which amount to about \r\n10-12 Mt yr-1 globally."


id: b99811d83459021f55f5f51d1b046e38
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 29858
content:
"to UNFCCC, other inventories produce their own estimates using \r\na consistent approach for all countries, and country-specific activity \r\ndata, emission factor and technological abatement when available. \r\nFAOSTAT and EDGAR mostly apply a Tier 1 approach to estimate CH4\r\nemissions while GAINS uses a Tier 2 approach (Box 2.SM.1). CEDS is \r\nbased on pre-existing emission estimates from FAOSTAT and EDGAR, \r\nwhich are then scaled to match country-specific inventories, largely \r\nthose reported to UNFCCC."


id: d4b6de8c0612ebff931d6ee023c48f6b
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 34741
content:
"Global anthropogenic CH4 emission estimates are compared in \r\nFigure 2.SM.4. EDGARv5.0 has revised total global CH4 emissions by \r\nabout 10 MtCH4 yr-1 compared to the previous version due to a higher \r\nestimate for the waste sector (Figure 2.SM.1). Subsequent revisions \r\nof the estimation methodology in EDGARv6 in alignment with the \r\nIPCC guidelines refinement (IPCC 2019) lead to very substantial \r\ndifferences in total CH4 emissions that are up to 50 MtCH4yr-1 lower \r\nbefore the 1990s compared to previous versions, but differences are \r\nsmaller ranging from 1-13 MtCH4 yr-1 since the 2000s (Figure 2.SM.1). \r\nThe cause of these differences is a new procedure to separately "


id: 1f341c7253d5b4c3c313e3c8f85152c4
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 2700
content:
"Saunois et  al. (2020) provide estimates of CH4 sources and sinks \r\nbased on bottom-up and top-down approaches associated with \r\nan uncertainty range based on the minimum and maximum values \r\nof available studies (because for many individual source and sink \r\nestimates the number of studies is often relatively small). Thus, they \r\ndo not consider the uncertainty of the individual estimates. As shown \r\nin Table 2.SM.5, uncertainties in total global CH4 emissions across "


id: a5174d443320f3e5587344db1e1a8243
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 26273
content:
"Table 2.SM.5 | Uncertainties estimated for CH4 sources at the global scale: based on ensembles of bottom-up (BU) and top-down (TD) estimates, national \r\neports and specific uncertainty assessments of EDGAR. Note: this table is not intended to be exhaustive, but provides uncertainty estimates from some of the key \r\niterature based on different methodological approaches. Source: Minx et al. (2021)."


id: e64b3eb14398f083fca6b1a7fdafa618
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 37820
content:
"Notes: a Based on NASEM (2018); b Uncertainty calculated as (min-max)/2)/mean*100 from the estimates of year 2017 of the six inventories plotted in Figure 2.SM.1. This \r\ndoes not consider uncertainty on each individual estimate; c\r\n Uncertainty calculated as (min-max)/2)/mean*100 from individual estimates for the 2008-2017 decade. This does \r\nnot consider uncertainty on each individual estimate, which is probably larger than the range presented here. d\r\n Based on EDGARv432 for year 2010 (Janssens-Maenhout et al. \r\n2019); e\r\n Based on Solazzo et al. (2021)."


id: 17cf941ef5cb2284265eb81575760727
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 1396
content:
"deviation of +-30% (i.e., +-60% for 2s). On the other hand, IPCC AR5 \r\nprovides a comparatively low estimate at +-20% for a 90% confidence \r\ninterval. Overall, we apply a best value judgement of +-30% for global \r\nanthropogenic CH4 emissions for a 90% confidence interval. This is \r\njustified by the larger uncertainties reported in studies on the EDGAR \r\ndataset (Janssens-Maenhout et al. 2019; Solazzo et al. 2021) as well \r\nas for FAO activity statistics by Tubiello et al. (2015)."


id: d149642c80c2b79f52b3418021615156
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 34172
content:
"Uncertainties in EDGAR CH4 emissions using a Tier 1 approach are \r\nestimated at -33% to +46% at 2s, but there is great variability \r\nacross individual sectors ranging from +-30% (agriculture) to more \r\nthan +-100% (fuel combustion), with high uncertainties in oil and \r\ngas sector (+-93%) and coal fugitive emissions (+-65%) (Solazzo et al. \r\n2021). As an example of developed country with well-established \r\nemissions reporting, US methane emissions also report large \r\nuncertainties depending on the sector (NASEM 2018); although the \r\nactivity data uncertainty may be lower than those for less-developed \r\ncountries. For example, global inventories, such as EDGAR, estimate \r\nuncertainties in national anthropogenic emissions of about +- 32% \r\nfor the 24 member countries of OECD, and up to +-57% for other \r\ncountries, which have more uncertain activity data (Janssens\u0002Maenhout et al. 2019). "


id: bad9a2cbce2d0450b728627902f6d5b9
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 30084
content:
"There are a variety of approaches for estimating N2O emissions. \r\nThese include inventories (Tubiello et  al. 2013; Tian et  al. 2018; \r\nJanssens-Maenhout et  al. 2019), statistical extrapolations of flux \r\nmeasurements (Wang et  al. 2020a), and process-based land and \r\nocean modelling (Tian et al. 2019; Yang et al. 2020). There are at least \r\nfive relevant global N2O emissions inventories available: EDGAR \r\n( Crippa et al. 2019, 2021; Janssens-Maenhout et al. 2019), GAINS "


id: 9eec5773f5b2cad01504a81afa26d384
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 25186
content:
"Table 2.SM.6 | Comparison of four global N2O inventories: EDGAR (Crippa et al. 2019; Janssens-Maenhout et al. 2019); GCP (Tian et al. 2020); GAINS \r\n(Winiwarter et al. 2018; Hoglund-Isaksson et al. 2020); FAOSTAT (Tubiello et al. 2013; Tubiello 2018). Source: Minx et al. (2021)."


id: 25caae8ace7ae285b53e760a53acd57e
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 3830
content:
"NF3 (EDGARv6 only). For the higher molecular weight PFCs (C4F10, \r\nC5F12, C6F14, C7F16), top-down estimates were not available in WMO \r\n(2018). Top-down estimates have previously been published for these \r\ncompounds (Ivy et al. 2012), however, this comparison is not included \r\nhere due to their very low emissions. For a small number of species, "


id: cebfbbe1c195ff8f9dbb30f13c6aa955
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 33715
content:
"global top-down estimates are available for some years, based on \r\nan atmospheric model independent to that used in WMO (2018), \r\nalthough most of these inversions use similar measurement datasets: \r\nFortems-Cheiney et  al. (2015) for HFC-134a; Lunt et  al. (2015) for "


id: dd7a1baaade79e7765ea4d0de2641cc8
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 36232
content:
"Figure 2.SM.6 | Comparison of top-down and bottom-up estimates for individual species of fluorinated gases in Olivier and Peters (2020) (EDGARv5FT) \r\nand EDGARv6 for 1980-2016. C4F10, C5F12, C6F14 and C7F16 are excluded. Top-down estimates from WMO 2018 (Engel and Rigby 2018; Montzka and Velders 2018) are \r\nshown as blue lines with blue shading indicating 1s uncertainties. Bottom-up estimates from EDGARv5 and v6 (the emissions data used in Chapter 2) are shown in red dotted \r\nlines and purple dashed lines, respectively. Top-down estimates for some species are shown from Rigby et al. (2010), Fortems-Cheiney et al. (2015) and Lunt et al. (2015). \r\nSource: Minx et al. (2021)."


id: fd03f6020d630567c6b6df829375b2a1
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 41874
content:
"methodologies for these species (Muhle et al. 2010). However, it is \r\nunclear for which other species similar validation has taken place, or \r\nhow these assumptions vary between versions of EDGAR. "


id: 848f6845c847f1dfb1179151786d009a
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 20467
content:
"When species are aggregated into F-gas total emissions, weighted by \r\ntheir current 100-year GWPs based on IPCC AR6 (Forster et al. 2021), \r\nwe note that, in the left panel of Figure 2.SM.7, the Olivier and Peters \r\n(2020) (EDGARv5FT) estimates are around 10% lower than the WMO \r\n2018 values in the 1980s. Subsequently, EDGARv5FT estimates grow \r\nmore rapidly than the top-down values and are almost 30% higher \r\nthan WMO 2018 by the 2010s. EDGARv6 emissions are around 10% \r\nlower than the WMO 2018 values throughout. Given that detailed \r\nuncertainty estimates are not available for all EDGAR F-gas species, \r\nwe base our uncertainty estimate solely on this comparison with the \r\ntop-down values (Figure 2.SM.7, left panel), and therefore suggest \r\na conservative uncertainty in aggregated F-gas emissions of +-30% \r\nfor a 90% confidence interval. For individual species, the magnitude \r\nof this discrepancy can be orders of magnitude larger."


id: fd315aa614ccc6401941f95d9dd8805f
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 41894
content:
"The F-gases in EDGAR exclude species such as chlorofluorocarbons \r\n(CFCs) and hydrochlorofluorocarbons (HCFCs), which are groups of \r\nsubstances regulated under the Montreal Protocol. Historically, total \r\nCO2-eq F-gas emissions have been dominated by the CFCs (Engel and \r\nRigby 2018). In particular, during the 1980s, peak annual emissions due \r\nto CFCs reached 9.1 +- 0.4 GtCO2-eq yr-1 (Figure 2.SM.7), comparable "


id: 321ebdef74c4becb382b75033ba2cb1e
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 6077
content:
"Figure 2.SM.7 | Comparison between top-down estimates and bottom-up EDGAR inventory data on GHG emissions for 1980-2016. Left panel: Total \r\nGWP100-weighted emissions based on IPCC AR6 (Forster et al. 2021) of F-gases in Olivier and Peters (2020) (EDGARv5FT) (red dashed line, excluding C4F10, C5F12, C6F14 and \r\nC7F16) and EDGARv6 (purple dashed line) (Crippa et al. 2021) compared to top-down estimates based on AGAGE and NOAA data from WMO (2018) (blue lines; Engel and \r\nRigby (2018); Montzka and Velders (2018)). Right panel: Top-down aggregated emissions for the three most abundant CFCs (-11, -12 and -113) and HCFCs (-22, -141b, \r\n-142b) not covered in bottom-up emissions inventories are shown in green and orange. For top-down estimates the shaded areas between two respective lines represent 1s\r\nuncertainties. Source: Minx et al. (2021)."


id: 79608faeaccbaa4b1c828a0341587138
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 18551
content:
"to that of CH4, and substantially larger than the 2019 emissions of the \r\ngases included in EDGARv5FT and v6 (1.4 GtCO2-eq). Subsequently, \r\nfollowing the controls of the Montreal Protocol, emissions of CFCs \r\ndeclined substantially, while those of HCFCs and HFCs rose, such that \r\nCO2-eq emissions of the HFCs, HCFCs and CFCs were approximately \r\nequal by 2016, with a smaller contribution from PFCs, SF6, NF3 and \r\nsome more minor F-gases. Therefore, the GWP-weighted F-gas \r\nemissions in EDGAR, which are dominated by the HFCs, represent \r\nless than half of the overall CO2-eq F-gas emissions in 2016."


id: 4792ade5eb2ad795b8c3074c3c14cc5c
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 9811
content:
"metric values for SLCFs, if their emissions are falling rapidly (Allen \r\net al. 2018; Cain et al. 2019; Collins et al. 2019; Lynch et al. 2020)."


id: 585c0993e6114f83530b87b6cc449743
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 12799
content:
"The contribution of SLCF emissions to total GHG emissions expressed \r\nin CO2-eq thus depends critically on the choice of GHG metric and its \r\ntime horizon. However, even for a given choice, the metric value for \r\neach gas is also subject to uncertainties. For example, the GWP100 \r\nfor biogenic CH4 has changed from 21 based on the IPCC Second \r\nAssessment Report (SAR) in 1995 to 28 or 34 based on IPCC AR5 \r\n(excluding or including climate-carbon cycle feedbacks), and to 27 \r\nbased on IPCC AR6. These changes and remaining uncertainties \r\narise from parametric uncertainties, differences in methodological \r\nchoices, and changes in metric values over time due to changing \r\nbackground conditions."


id: a72fd3a7ad6a8473d8a7e48148e3576c
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 26627
content:
"Parametric uncertainties arise from uncertainties in climate sensitivity, \r\nradiative efficacy and atmospheric lifetimes of CO2 and non-CO2\r\ngases, etc. The WGI contribution to AR6 assessed the parametric \r\nuncertainty of GWP for CH4 as +-32% and +-40% for time horizons \r\nof 20 and 100 years, +-43% and +-47% for N2O, and +-26-31 and \r\n+-33-38% for various F-gases (Forster et al. 2021). The uncertainty \r\nof GTP100 for CH4 was estimated at +-83%, which is larger than the \r\nuncertainty in a forcing-based metric due to uncertainties in climate \r\nresponses to forcing (e.g., transient climate sensitivity). "


id: fe9abd2d922c79a23f9836c16ad68872
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 42170
content:
"GHG emission metrics are necessary if emissions of non-CO2 gases \r\nand CO2 are to be aggregated into CO2-eq emissions (Section 2.3). \r\nGWP100 is the most common metric and has been adopted for \r\nemissions reporting under the transparency framework for the Paris \r\nAgreement (UNFCCC 2019), but many alternative metrics exist in the \r\nscientific literature. The most appropriate choice of metric depends \r\non the climate policy objective and the specific use of the metric to \r\nsupport that objective (i.e., why do we want to aggregate or compare \r\nemissions of different gases? What specific actions do we wish \r\nto inform?)."


id: 48ab7fcb25ba1f7f087179509a97c826
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 10004
content:
"A third uncertainty arises from changes in metric values over time. \r\nMetric values depend on the radiative efficacy of CO2 and non\u0002CO2 emissions, which in turn depend on the changing atmospheric \r\nbackground concentrations of those gases. Rising temperature can \r\nfurther affect the lifetime of some gases and hence their contribution \r\nto forcing over time for different emission scenarios (Reisinger \r\net  al. 2011). Successive IPCC assessments take changing starting\u0002year background conditions into account, which explains part of \r\nthe changes in GWP100 metric values in different reports. Applying \r\na single metric value to a multi-decadal historical time series of \r\nemissions is therefore only an approximation of the correct metric \r\nvalue for any given emissions year - as, for example, the correct "


id: f457832d5413dc07d2c6b61b0b3b2a38
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 40365
content:
"Different metric choices and time horizons can result in very different \r\nweightings of the emissions of short-lived climate forcers (SLCFs), \r\nsuch as CH4. For example, 1t CH4 represents as much as 81 tCO2-eq \r\nif a global warming potential (GWP) is used with a time horizon of \r\n20 years, or as little as 5.4t CO2-eq if the global temperature change \r\npotential (GTP) is used with a time horizon of 100 years (Forster et al. \r\n2021a). More recent metric developments that compare emissions \r\nin new ways - e.g., the additional warming from sustained changes \r\nin SLCF emissions compared to pulse emissions of CO2 - increase \r\nthe range of metric values further and can even result in negative "


id: e997e15c6e95feee0dfd86a7e0efec34
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 38449
content:
"GWP100 value for CH4 emitted in the year 1970 will be different to \r\nthe GWP100 value for an emission in the year 2018. However, the \r\nliterature does not offer a complete set of GWP100 metric values \r\nfor past concentrations and climate conditions covered in our time \r\nseries."


id: 430407513393fe26e71031c8282f0a1a
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 8992
content:
"(GTP), global damage potential, and GWP*), use carbon dioxide (CO2) \r\nas the reference gas. Emissions of non-CO2 gases, when expressed \r\nusing such metrics, are often referred to as 'carbon dioxide equivalent' \r\nemissions. A metric that establishes equivalence regarding one key \r\nmeasure of the climate system response to emissions does not imply \r\nequivalence regarding other key measures. The choice of a metric, \r\nincluding its time horizon, should reflect the policy objectives for \r\nwhich the metric is applied."


id: 45cb8165bb886e25c331e543295c17a7
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 9497
content:
"Overall, we estimate the uncertainty in GWP100 metric values, if \r\napplied to an extended historical emission time series, as +-50% \r\nfor CH4 and other SLCFs, and +-40% for non-CO2 gases with longer \r\natmospheric lifetimes (specifically, those with lifetimes longer than 20 \r\nyears). If uncertainties in GHG metrics are considered and assumed to \r\nbe independent for each gas (which may lead to an underestimate), \r\nthe overall uncertainty of total GHG emissions in 2019 increases from \r\n+-11% to +-13%. However, these GWP-related uncertainties are not \r\nincluded in the global, regional or sectoral emissions estimates in the \r\nremainder of the assessment. "


id: 6266c9232c2de98982ca5d078695ea2b
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 14593
content:
"Emission metrics also exist for aerosols, but these are not commonly \r\nused in climate policy. This assessment focuses on GHG emission \r\nmetrics only."


id: 3eb46665a11d0084bc98699e6dbd5b04
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 8221
content:
"Parties to the Paris Agreement decided in the Paris Agreement \r\nRulebook to report aggregated emissions based on the global \r\nwarming potential with a time horizon of 100 years (GWP100) \r\nfrom AR5, or to use GWP100 values from a subsequent IPCC report \r\nas agreed upon by the CMA (UNFCCC 2019, 18/CMA.1), and to \r\naccount for their second and subsequent Nationally Determined \r\nContributions in accordance with this approach (UNFCCC 2019, \r\n4/CMA.1). However, parties can report supplemental information \r\nabout aggregate emissions and removals using other GHG emission \r\nmetrics (e.g., global temperature change potential) expressed in \r\nCO2-eq and assessed by the IPCC."


id: fed51dd09fe7697d1c1e9af5c8cccd89
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 42230
content:
"The WGIII assessment uses GWP100 metric values from the WGI \r\ncontribution to AR6 (Forster et al. 2021) as a default metric when \r\npresenting aggregated emissions and removals of different GHGs \r\n(Cross-Chapter Box 2, Supplementary Material Section 2.SM.3, and \r\nAnnex II.8)."


id: b43efadf660f54d73defb2a8703010bc
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 28945
content:
"Apart from international reporting and accounting, countries or \r\nsectors might consider other GHG emission metrics to help achieve \r\nspecific domestic policy objectives. A clear assessment of metrics \r\ncan help decision-makers determine the consistency between policy \r\ngoals and metrics and avoid potentially inadvertent consequences of \r\nalternative metric choices."


id: 310ee93448cd159000aee9f64d724d06
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 5880
content:
"GHG emission metrics are used to compare climate effects of \r\ndifferent GHGs and to aggregate emissions and removals of different \r\nGHGs, such as for national inventory reporting and development \r\nof mitigation policies. GHG emission metrics provide simplified \r\ninformation about the effects that emissions of different gases GHGs \r\nhave on global temperature or other aspects of climate, usually \r\nexpressed relative to the effect of emitting CO2. "


id: b490f7b60c733f8923b443774db1d9f3
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
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"This Supplementary Material provides additional explanations, \r\nreferences and figures to the assessment of GHG emission metrics \r\nfrom a mitigation perspective in Cross-Chapter Box  2 on GHG \r\nemission metrics in Chapter 2. Both the Cross-Chapter Box and this \r\nSupplementary Material build on the physical science assessment of \r\nGHG emission metrics by WGI (Forster et al. 2021, Section 7.6)."


id: f2b6361e5909cf0819c35e85a4e09d41
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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content:
"The global warming potential (GWP) and the global temperature \r\nchange potential (GTP) were the main metrics assessed in AR5 \r\n(Myhre et al. 2013; IPCC 2014; Kolstad et al. 2014). GWP with a time \r\nhorizon of 100 years (GWP100) is the predominant metric used in \r\nliterature assessed by WGIII."


id: 58414089534b2abebbe3ae9fe1ef5243
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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content:
"These metrics compare the effect on climate of emitting a unit mass \r\nof a non-CO2 gas over a chosen time horizon with the effect of \r\nemitting the same unit mass of CO2. GWP compares CO2 and non-CO2\r\nemissions based on the radiative forcing they would cause integrated \r\nover the entire time horizon, whereas GTP compares emissions based \r\non the global mean surface temperature change they would cause \r\nonly at the end point of the chosen time horizon."


id: a12ee431dea7d26c7da96caa9f3b76ec
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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content:
"Table 2.SM.7 | Illustrative metric values for CH4 under a range of metrics and time horizons. GWP and GTP compare pulse emissions of non-CO2 gases with \r\na pulse emission of CO2. Combined global temperature change potential (CGTP) compares a sustained step-change in non-CO2 emissions with a pulse emission of CO2. Values \r\nare based on Forster et al. (2021)."


id: bfed2bf1abf0ab9d0a070bb169ca764a
created_at: 2023-04-19 13:22:11
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"The WGI contribution to AR6 includes updated values for these \r\nmetrics based on updated scientific understanding of the response \r\nof the climate system to emissions of different gases, including \r\nchanging background concentrations (Forster et  al. 2021). It also \r\nassess new metrics published since AR5. Metric values in AR6 include \r\nclimate-carbon cycle feedbacks by default; this provides an important \r\nupdate and clarification from AR5 which reported metric values both \r\nwith and without such feedbacks (Table 2.SM.7)."


id: 86a02a19e67981b51247169400ae2ab0
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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content:
"CO2 is nearly constant over centuries. Universal use of a single metric \r\nand time horizon can thus result in mismatches between policy goals \r\nand actual climate outcomes. Moreover, 'CO2 equivalence' of pulse \r\nemissions based on GWP or GTP does not imply equivalent climate \r\noutcomes from cumulative emissions, nor at all times even from \r\na single emissions pulse."


id: f57625270094f535b848ca6e24aa92a2
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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"By far the most commonly used, static time horizon for GWP, \r\nincluding in reporting under the UNFCCC and the Paris Agreement, is \r\n100 years, but other time horizons (e.g., GWP20, GWP500) have also \r\nbeen applied (recent examples include Tanaka et al. 2019, 2021; and \r\nSkytt et al. 2020)."


id: d58f8479c7d0172d27683ca363f691f6
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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content:
"For GTP, both static and dynamic time horizons are used in the literature. \r\nA static GTP evaluates warming due to an emissions pulse at the end \r\npoint of the stated time horizon (Shine et al. 2005). For example, the \r\nstatic GTP100 would evaluate emissions occurring in 2020 based on \r\nthe warming they would cause in the year 2120, whereas emissions \r\noccurring in 2030 would be evaluated based on the warming they \r\nwould cause in the year 2130. By contrast, the dynamic GTP (Shine \r\net  al. 2007) evaluates each emission based on its contribution to \r\nwarming in a specified future target year. Depending on application, \r\nthis can be the year in which global average temperature is expected \r\nto peak within a mitigation scenario, or any other time-bound \r\ntemperature-related climate target. Policy-relevant time horizons and \r\nresulting metric values for the dynamic GTP therefore depend on the \r\nchosen temperature goal and implied target year."


id: 63964607523e4831a659fa4c720064dd
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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content:
"A more detailed discussion of recently developed step-change \r\nmetrics GWP* (Allen et al. 2018; Cain et al. 2019; Smith et al. 2021) \r\nand combined global temperature change potential (CGTP) (Collins \r\net  al. 2019) and their ability to reproduce temperature changes \r\nresulting from sustained changes in SLCF emissions is provided in \r\nForster et al. (2021). These metrics indicate greater climate benefits \r\nfrom rapid and sustained CH4 reductions compared to CO2 over the \r\nnext few decades than if such reductions are weighted by GWP100, \r\nwhile conversely, sustained methane increases have greater adverse \r\nclimate impacts (Collins et al. 2019; Lynch et al. 2020; Brazzola et al. \r\n2021). However, as indicated in Figure  2.SM.8, the warming from \r\nCH4 (or conversely, the benefits of CH4 reduction) do not continue to \r\naccumulate at the initial rate."


id: f3b0281bac8784bf955508eb321f625
created_at: 2023-04-19 13:22:11
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content:
"The time horizon of a dynamic GTP shrinks for successive emissions \r\nas the target year is approached, which increases the weight given to \r\nemissions of short-lived climate forcers (SLCFs) such as CH4 over time. \r\nFor example, for a climate policy goal of limiting warming to 1.5degC \r\nwith no or limited overshoot (scenario Category C1 in Chapter 3), \r\nglobal average surface temperature would peak by around 2055. To \r\ncompare the importance of abating non-CO2 and CO2 emissions in \r\nany given year relative to that policy goal, emissions occurring in the \r\nyear 2020 would be evaluated using GTP35, whereas emissions in \r\n2030 would be evaluated using GTP25, and so on (see Table 2.SM.7 \r\nfor illustrative values)."


id: 4768ea22f3a54fb235a68f9ff90fbbc7
created_at: 2023-04-19 13:22:11
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index: climate_gpt_v2
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"The GWP with a static time horizon approximates the global damage \r\npotential - that is, the notion that the emission of a non-CO2 forcer \r\nat any point in time should be weighted by the marginal economic \r\ndamages from this emission, relative to the marginal damages from \r\nemitting a unit mass of CO2 (Reilly and Richards 1993; Kandlikar \r\n1996; Kolstad et al. 2014). "


id: 5c778e00bbe75c66e04c4fd265a4cc4b
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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index: climate_gpt_v2
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content:
"A key limitation of pulse emission metrics such as GWP and GTP, \r\nnoted in AR5 and emphasised in more recent literature (Allen et al. \r\n2018; Cain et al. 2019; Collins et al. 2019; Allen et al. 2021; see Forster \r\net  al. 2021 for the WGI assessment), is that metric values depend \r\nstrongly on the selected time horizon, given that warming from a CH4\r\nemission pulse declines over time, whereas warming from a pulse of "


id: 8ed7599a4c616c6c234b4627c111d33b
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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content:
"These studies indicate that, even though the GWP100 was not \r\ndesigned to meet any economic objectives and was not designed \r\nas a damage potential, the discount rate implied in GWP100 for CH4\r\nis broadly similar to social discount rates of 3-5% that are used in \r\nintegrated assessment models (Chapter  3) and investments with \r\nmulti-decadal lifetimes (Giglio et al. 2015; HM Treasury 2018)."


id: 51171bef89010ceabb5f0c8acfec732f
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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index: climate_gpt_v2
vector_id: 11546
content:
"Shindell et al. (2017) evaluated the social cost of methane emissions \r\ndirectly based on time-varying changes in climate and inferred \r\neconomic damages, and found a wide range of possible values \r\nThis reflects the range of judgements in determining social costs of \r\npollutants including non-climate effects. However, their results are \r\nbroadly consistent with a GWP100-based weighting of CH4 relative \r\nto CO2 when similar discount rates and consistent assumptions \r\nabout climate-related damages and the temperature dependence of \r\ndamage functions are chosen for both gases. "


id: 1fa51da7dd298e0434ef280c36ab243b
created_at: 2023-04-19 13:22:11
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content:
"The GTP with any static time horizon (e.g., GTP50 or GTP100) is \r\nnot clearly matched to either a cost-benefit or a cost-effectiveness \r\nframework, as the year for which temperature outcomes are evaluated \r\nwould shift forward each year and hence would not match the year "


id: 75f69ad143525ee4b4e93936378f0dbc
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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content:
"when the global temperature limit is reached or the overall damages \r\ncaused by each emission (Tol et al. 2012; Edwards and Trancik 2014; \r\nStrefler et al. 2014; Mallapragada and Mignone 2017). However, use \r\nof GTP with a static time horizon may be relevant where it is applied \r\nto emissions only in a given year or finite period, and if the time \r\nhorizon matches a relevant climate policy goal (Fuglestvedt et  al. \r\n2010; Grewe and Dahlmann 2015; Balcombe et al. 2018)."


id: 7a9fac9a12224f06ac9105ae422c7651
created_at: 2023-04-19 13:22:11
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content:
"Studies available for AR5 suggested that using a dynamic GTP or \r\neconomic optimisation approaches, which defer high-cost CH4\r\nabatement until closer to the target year, could reduce global \r\nabatement costs compared to GWP100 by a few percent (Manne and \r\nRichels 2001; Shine et al. 2007; Johansson 2011; Reisinger et al. 2012). \r\nMore recent studies confirm this theoretical cost saving in principle. \r\nHowever, these studies also demonstrate that the extent to which this \r\ncost saving would be realised depends on a range of assumptions, \r\nincluding the stringency of the target, degree of policy foresight, the \r\nspeed with which CH4 emissions can be reduced as metric values \r\nincrease, allowance for any temporary temperature overshoot for \r\nend-of-century targets, the shape of marginal abatement cost curves, \r\nand the treatment of uncertainty (Ekholm et al. 2013; Strefler et al. \r\n2014; Huntingford et al. 2015; Van Den Berg et al. 2015; Harmsen \r\net al. 2016; Tanaka et al. 2020)."


id: a6fa1df9320f548d08cbfb9c861af5a5
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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content:
"The AR5 noted that the choice of metric and time horizon could have \r\nsignificant implications for regions or sectors with high fractions \r\nof SLCF emissions (Brennan and Zaitchik 2013; Myhre et al. 2013; \r\nIPCC 2014; Strefler et al. 2014). The choice of GHG emission metric \r\nis therefore linked not only to cost-effectiveness but also to equity. \r\nSectoral and national perspectives on mitigation pathways, including \r\nGHG emission metrics to inform such pathways, may therefore differ \r\nfrom a global least-cost perspective (Klinsky and Winkler 2018), but "


id: d01f6092894b449d35a389f9b2fcd3b
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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content:
"the literature has not provided a consistent framework for assessing \r\nGHG emission metrics based on a wider set of equity principles."


id: bc40870322d3c5e62580c61cd6a3f16f
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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content:
"The shifting of costs between emitters due to different metrics has \r\nbeen demonstrated for the case of agriculture in New Zealand, which \r\nhas a high fraction of enteric methane emissions. Even though global \r\nmitigation costs to limit warming to below 2degC would be lower \r\nunder GWP100 than GTP100, costs to farmers would be greater \r\nunder GWP100 than GTP100 if climate policy were to price all \r\nGHG emissions and place the cost burden on emitters (Dorner and \r\nKerr 2017)."


id: 9b5bc26b61029a659a3af603a22f4573
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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index: climate_gpt_v2
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content:
"Some studies use simple climate models or pulse-response functions \r\nto understand the climate impacts of emissions of different gases \r\ndirectly rather than relying on emission metrics (Berntsen and \r\nFuglestvedt 2008; Reisinger and Clark 2017; Lynch and Pierrehumbert \r\n2019; Mayfield et  al. 2019; Cooper et  al. 2020; Lee et  al. 2021; \r\nReisinger et  al. 2021). Treating GHGs with different lifetimes \r\nseparately supports the targeted treatment of different pollutants \r\nand avoids embedding value judgements about the climate outcome \r\nof concern, time horizons and reference levels into GHG emission \r\nmetrics. This does not avoid the need for such value judgements to be \r\nmade, but can allow them to be made more explicitly."


id: 1f1014531ee50c3ca130885ae053ffc0
created_at: 2023-04-19 13:22:11
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index: climate_gpt_v2
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content:
"Cross-Chapter Box  2 in Chapter  2 notes that GWP* can calculate \r\nnegative CO2-eq emissions, while GWP or GTP calculate positive \r\nCO2-eq emissions for the same CH4 emissions path."


id: b706baaab65f127617ac645a5e36a69
created_at: 2023-04-19 13:22:11
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content:
"Rapidly declining CH4 emissions can have a negative CO2-warming\u0002equivalent value based on GWP* because SLCF emissions that \r\ndecline at a sufficient rate result in declining temperature, relative \r\nto the warming at a previous point in time caused by past SLCF \r\nemissions from that same source. The rate at which SLCF emissions \r\nhave to decline to result in a roughly constant contribution to \r\nwarming depends on the emissions history, changing background \r\nconcentrations, and lifetime of the gas; for global CH4 emissions, this \r\nhas been estimated at about 0.3% per year (Forster et al. 2021)."


id: ec00285f40eeb1bf4f785f33e9f25323
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content:
"over time. The amount of climate change (integrated radiative \r\nforcing, or temperature change at a given point in time) that occurs \r\nfrom these emissions, relative to the absence of these emissions \r\n(everything else being equal), has also been referred to as 'marginal\r\nwarming' (Reisinger et al. 2021), in alignment with the concepts of \r\nmarginal damages and marginal costs that underpin the economics \r\nliterature on multi-pollutant problems (Michaelis 1992, 1999; Reilly \r\nand Richards 1993; Kandlikar 1996; Manne and Richels 2001; Tol \r\net al. 2012)."


id: e55de23fc59902627c15e37db8387e92
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content:
"1 The GWP* formula was applied to the following gases: CH4, HFC-134a, HFC-32, HFC-43-10-mee, HFC-152a, HFC-365-mfc. The parameters used in the calculation are \r\nbased on the atmospheric lifetime of CH4 and are not necessarily matched to other short-lived gases. Results should therefore be seen as indicative only; the existing \r\nliterature provides parameters only for CH4. Using further updated parameters from Smith et al. (2021) would not change the overall results substantially."


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"The difference in timing of net zero GHG emissions under GTP100 \r\ncompared to GWP100 depends on the magnitude of SLCF (mostly CH4) \r\nemissions at that point, as well as the slope of the emissions pathway \r\nwhen approaching net zero. IMP-SP has the largest reductions in CH4\r\nemissions and hence the difference between GTP100 and GWP100 \r\nis relatively smaller than for other pathways. Conversely, IMP-Ren2 \r\nrelatively high residual CH4 emissions. Therefore, expressing \r\nCO2-equivalent emissions using GTP100 has a bigger impact on total \r\nCO2-eq emissions compared to GWP100."


id: e4694c99f9b1f7e05b1994628201e2bb
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"Cross-Chapter Box 2 in Chapter 2 notes that different metric choices \r\ncan alter the reported quantity of CO2-eq emissions and the time \r\nat which net zero GHG emissions are calculated to be reached, or \r\nwhether net zero GHG emissions are reached at all. This is also an \r\nimportant conclusion from the assessment by WGI (Forster et  al. \r\n2021) building on Fuglestvedt et al. (2018) and Tanaka and O'Neill \r\n(2018), and highlighted by Schleussner et  al. (2019) in relation to \r\nArticle 4.1 of the Paris Agreement."


id: 38abcf177d337b244119bd1135892258
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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index: climate_gpt_v2
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"Using GWP20 gives consistently higher weighting to SLCF emissions \r\ncompared to GWP100. This shifts the year of net zero emissions back \r\nby more than 20 years, as more net negative CO2 emissions are \r\nneeded to balance residual SLCF emissions; again the extent to \r\nwhich timing shifts depends on the amount of CH4 emissions in the \r\ndifferent pathways. Under GWP20, only IMP-REN2 reaches net zero \r\nin 2100 as it has the largest net-negative CO2 emissions in 2100 \r\nof those four pathways; the three other pathways would remain at \r\ngreater than net zero GHG emissions in 2100."


id: 45a393df853eba861b0ee745217185e7
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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"The degree to which reported CO2-eq emissions would differ under \r\nalternative metrics, for the same actual emissions of different gases, \r\nis illustrated in Figure 2.SM.10. It shows calculated CO2-eq emissions \r\nfor four different IMPs from Chapter  3 (IMP-REN15, IMP-SP, \r\nIMP-REN2, and IMP-GS; see Chapter 3 for details on these pathways) \r\nfor an illustrative range of metrics. "


id: 84c7dfcc64454df326e3ed9d8fd7a815
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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index: climate_gpt_v2
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content:
"Using GWP* as a metric results in a significant change, not only in \r\nthe timing of net zero emissions, but also the overall shape of the \r\nCO2-eq emissions pathway. In the two pathways consistent with \r\nlimiting warming to 1.5degC with no or limited overshoot (IMP-Ren15 \r\nand IMP-SP), CO2-equivalent emissions using GWP* drop well below \r\nnet zero before 2040 but then rebound again. IMP-Ren15 returns to \r\nnet-positive GHG emissions before returning to net zero by 2100, \r\nwhile IMP-SP has emissions close to net zero for most of the second \r\nhalf of the 21st century. "


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content:
"CO2-equivalent emissions using GWP* for IMP-GS follow a similar \r\nshape but have higher overall levels; net GHG emissions would briefly \r\nreach net zero in 2040 before returning to positive levels and dropping \r\nto net zero by 2080. For IMP-Ren2, CO2-equivalent emissions based \r\non GWP* look more similar to the emissions pathway based on other \r\nmetrics but reach net zero GHG emissions about 20 years earlier than \r\nif using GWP100. "


id: fdb47349327eb773decbda579753f841
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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content:
"Overall, differences in the timing of net zero GHG (CO2-eq) emissions \r\nare smaller for different versions of GWP100 than for fundamentally \r\ndifferent choices of metric and/or time horizon (GWP20 or GTP100), \r\nand differ materially for GWP*. "


id: 40e358412ebdd2799c1e465e0fe514de
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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index: climate_gpt_v2
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"Using GWP100 values from different IPCC assessment reports has \r\na  relatively minor effect on CO2-eq emissions. It shifts the timing \r\nof net zero emissions by up to 10 years for those pathways that \r\nreach net zero before 2100. For pathways that reach net zero GHG \r\nemissions only very late in the 21st century, this could result in net \r\nzero not being reached at all before 2100 under some versions of \r\nGWP100. For example, IMP-GS reaches net zero GHG emissions in \r\n2095 for GWP100 (SAR) but remains (just) above zero until after \r\n2100 for GWP100 (AR5-ccfb) and for GWP100 (AR6)."


id: c55d90e0cd99c3f5ad484bbe252745d8
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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index: climate_gpt_v2
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content:
"The reason for those different shapes of CO2-equivalent emission \r\ntrajectories under GWP* is that this metric translates rapid reductions \r\nof CH4 emissions into negative CO2-equivalent emissions. IMP-Ren2 \r\npathway has less rapid reductions of CH4 emissions in the near term \r\nthan the three other pathways. The rapid reduction of methane in \r\nthese three pathways results in a significantly faster and greater \r\nreduction of total CO2-equivalent emissions under GWP*. As a result, \r\nnet zero GHG emissions would be reached well before 2050, although \r\n(depending on further reductions) only temporarily in some pathways \r\nas the reduction of CH4 emissions does not continue at the same rate."


id: c6af263f0c31b1d9de7d2ca8f5ea9c53
created_at: 2023-04-19 13:22:11
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index: climate_gpt_v2
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content:
"Using GTP100 gives consistently lower weighting to SLCF emissions \r\ncompared to GWP100. This brings the year of net zero GHG \r\nemissions  forward by 12-18 years compared to GWP100 (AR6), \r\nsince the remaining gross SLCF emissions would be aggregated into \r\nlower CO2-eq emissions and hence would be compensated by a lower \r\namount of net negative CO2 emissions, which is reached earlier. "


id: 8a4450039553be554188cfe8118656ea
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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index: climate_gpt_v2
vector_id: 21504
content:
"Note that the different reported CO2-equivalent emissions do not \r\naffect the climate outcome, as the actual emissions of individual \r\ngases in these pathways are unchanged. What Figure 2.SM.10 shows "


id: 59b9f8dd78d4ed1f67d12ed0f1fc6ea2
created_at: 2023-04-19 13:22:11
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index: climate_gpt_v2
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content:
"net zero GHG emissions. Emission metrics that give less weight to \r\nongoing SLCF emissions imply a lesser rate of CO2 removal and hence \r\ngreater overall warming and/or lesser reduction in warming over time \r\nafter net zero GHG emissions have been reached. Conversely, a given \r\namount of CDR would permit different rates of SLCF emissions to \r\nachieve net zero GHG emissions under different metrics. This would \r\nresult in different amounts of warming contributed by SLCF emissions \r\nin addition to the warming from CO2."


id: 3570a5d7bfceae298b7ce3449b279b4a
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 6648
content:
"is only how the global aggregated emissions and removals would be \r\nreported for each pathway under different metrics."


id: 8c96da5013df5b8244165500892a7b80
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 21922
content:
"The significant differences in the timing of net zero GHG emissions \r\nimply, however, that alternative emissions pathways that reach the \r\nsame net zero GHG emissions target, but do so based on different \r\nGHG metrics, would necessarily result in different climate outcomes \r\nand would imply different levels of ambition to reach such an \r\nemissions target."


id: 2b55de2c5f81742fda1f4541133764b
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 4875
content:
"because emissions reductions in the former group are counteracted \r\nby higher increases in the latter group of countries (Yao et al. 2019). "


id: 2f1bda82d2fe713c92d1ac3f793baa81
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 5558
content:
"change if net zero GHG emissions are sustained after the peak \r\n(Fuglestvedt et al. 2018; Tanaka and O'Neill 2018; Schleussner et al. \r\n2019). This is before taking into account how the use of different \r\nGHG emission metrics might shape abatement choices leading up to \r\nan emission target."


id: 4462296618fc5356bea2422d4fd275cc
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 9268
content:
"This section assesses how trade openness and liberalisation may \r\nhave changed the global level of GHG emissions, and complements \r\nSections 2.3 and 2.4 in Chapter  2. It does not describe whether \r\ntrade has shifted emissions between countries (transfer of embodied \r\nemissions) or has changed the level of emissions in individual \r\ncountries (this is described in Chapter 2, Section 2.3). The effect of \r\ninternational emissions trading schemes, mechanisms, and policies \r\nare described in Chapter 2, Sections 2.8 and 14.5, respectively. "


id: 838fd882be1c2851ce96e837387cf4e9
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 20286
content:
"The question of whether international trade drives increases or \r\ndecreases in global GHG emissions is difficult to answer since it not \r\nonly depends on the emissions intensity of traded products, but also \r\non the synergistic influence of trade on economic growth, income, \r\nconsumption patterns, and the diffusion of low-carbon technologies \r\nor practices. All of these in turn are drivers of GHG emissions and the \r\ncounterfactual question to answer is: What would happen without \r\ntrade? (Jakob and Marschinski 2013). Trade also affects emissions \r\nthrough enhancing innovation and exchanging technologies between \r\ntrading partners. These complex interactions are currently not fully \r\nunderstood (limited evidence, low agreement) (Cherniwchan et  al. \r\n2017). Consumption-based accounting alone (Chapter 2, Section 2.3) \r\nis therefore not suited to assess whether or not trade is driving global \r\nGHG emissions (Jakob and Marschinski 2013; Kander et  al. 2015; \r\nJiborn et al. 2018)."


id: 5ecbae15afd0aba6e750316bdb9283ff
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 13968
content:
"In summary, there is low agreement and limited evidence on how \r\ninternational trade influences global GHG emissions. Since the \r\npricing of energy resources and GHG emissions is inconsistent \r\nacross countries, the overall outcome of trade on global emissions \r\nis coincidental rather than by design. If shifts in production are \r\naccompanied by large-scale transfers of and investment in low\u0002carbon technologies in carbon-intensive countries, the effects of \r\ntrade on emissions can be mitigated (Jiang and Green 2017; Gozgor \r\net  al. 2020). While such investments and knowledge transfers are \r\nmore likely to come from net importing nations leading in low-carbon \r\ntechnology, net exporters can help by targeting carbon-intensive \r\nexport industries with additional mitigation measures (Ren et  al. \r\n2014; Liu et al. 2015b; Ji et al. 2017). Section 13.7 of this report deals \r\nwith international interactions of national mitigation policies."


id: d3c4ebba3de9db452d87da8a35643bbc
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 34603
content:
"Only very few studies over the AR6 target time frame of 2010-2019 \r\ninvestigated the impacts of trade. Studies investigating global CO2\r\nemissions changes between 1995 and 2007/2008 found that the \r\ncontribution of trade was moderately positive, whereas increases in \r\noverall and per capita consumption levels contributed much more \r\nstrongly to the increase and improved technology had a significant \r\ndecreasing effect (Arto and Dietzenbacher 2014; Hoekstra et  al. \r\n2016). A recent study modelled that international trade in 2015 \r\nincreased global GDP by 10% and global total GHG emissions by 2% \r\ncompared to a scenario where there was no trade (Wu et al. 2021). "


id: 2228748ecf8da576618f50cca2a45241
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 3231
content:
"Lin et al. (2019) investigated different scenarios on trade restrictions \r\nand found that a scenario with significant trade barriers based on \r\nadditional 25% of tariffs would reduce global CO2 emissions by 6.3% \r\nand GDP by 9.0%. On the other hand, the free trade scenario would \r\nincrease global export volume by 5.4% and global CO2 emissions \r\nby 1.2% for the base year of 2014 because of enhanced global \r\nproduction, especially in developing regions with high emissions \r\nintensities (Lin et al. 2019). It seems, however, that increased global \r\nGHG emissions only occur when the free trade agreements are \r\nbetween developed and developing countries (Nemati et al. 2019) "


id: 5fb6485f564e42caa38001ba5561c53e
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 14142
content:
"Figure 2.SM.12 | Global GHG emissions trends 1990-2019: CH4 (blue); N2O (orange). Aggregate GHG emissions trends by groups of gases reported in original \r\nmass units. Coloured shadings show the associated uncertainties at a 90% confidence interval without considering uncertainties in GDP and population data (see below). First \r\ncolumn shows emissions trends in absolute levels (MtCO2-eq). Second column shows per capita emissions trends (kg per capita) using UN population data for normalisation \r\n(World Bank 2021). Third column shows emissions trends per unit of GDP (g per USD) using GDP data in constant USD2010 from the World Bank for normalisation (World \r\nBank 2021). Data: Minx et al. (2021)."


id: 299723e9f677fdd545c5899615c4286d
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 4544
content:
"Figure 2.SM.14 | Total annual anthropogenic GHG emissions by major economic sector and their underlying trends by region. Panel (a): Trends in total \r\nannual anthropogenic GHG emissions (in GtCO2-eq yr-1) by major economic sector. Panel (b): Trends in total annual anthropogenic GHG emissions (in GtCO2-eq yr-1) by \r\nmajor economic sector and region. Panels (c) and (d): Largest sub-sectoral changes in GHG emissions for the reporting period 1990-2019 in relative (% annual change) and \r\nabsolute terms (GtCO2-eq). Emissions are converted into CO2-equivalents based on global warming potential with a 100-year time horizon (GWP100) from IPCC's AR6. Based \r\non Lamb et al. (2021); Data: Crippa et al. (2021); Minx et al. (2021). "


id: 14399e3dc8efb9180328d3173d2fb864
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 778
content:
"UNFCCC, 2021: National Inventory Submissions 2021. https://unfccc.int/ghg\u0002inventories-annex-i-parties/2021 (Accessed September 30, 2021)."


id: 1064d7931c8d0ed90cc2b25246e386d8
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 73
content:
"Jacobo Arango (Colombia), Katherine Calvin (the United States of America), Celine Guivarch \r\n(France), Tomoko Hasegawa, (Japan), Kejun Jiang (China), Elmar Kriegler (Germany), \r\nRobert  Matthews (United Kingdom), Glen  P. Peters (Norway/Australia), Anand Rao (India), \r\nSimon Robertson (Australia), Adam Mohammed Sebbit (Uganda), Julia Steinberger (Switzerland/\r\nUnited Kingdom), Massimo Tavoni (Italy), Detlef P. van Vuuren (the Netherlands)"


id: 1690fe9b065ece31bc2f88c54150072f
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 1183
content:
"Riahi,  K.,  R. Schaeffer,  J. Arango,  K. Calvin,  C. Guivarch,  T. Hasegawa,  K. Jiang,  E. Kriegler,  R. Matthews, \r\nG.P. Peters, A. Rao, S. Robertson, A.M. Sebbit, J. Steinberger, M. Tavoni, D.P. van Vuuren, 2022: Mitigation pathways \r\ncompatible with long-term goals. In IPCC, 2022: Climate Change 2022: Mitigation of Climate Change. Contribution \r\nof Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change\r\n[P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, \r\nM. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)]. Cambridge University Press, Cambridge, UK and New York, \r\nNY, USA. doi: 10.1017/9781009157926.005"


id: d31071b0d55cd55a61f59d0df28806c8
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 34475
content:
"Cost-effective mitigation pathways assuming immediate \r\naction1 to limit warming to 2degC (>67%) are associated with \r\nnet global GHG emissions of 30-49 GtCO2-eq yr-1 by 2030 \r\nand 14-26 GtCO2-eq yr-1 by 2050 (medium confidence). This \r\ncorresponds to reductions, relative to 2019 levels, of 13-45% by \r\n2030 and 52-76% by 2050. Pathways that limit global warming to \r\nbelow 1.5degC with no or limited overshoot require a further acceleration \r\nin the pace of the transformation, with net GHG emissions typically \r\naround 21-36 GtCO2-eq yr-1 by 2030 and 1-15 GtCO2-eq yr-1 by \r\n2050; thus, reductions of 34-60% by 2030 and 73-98% by 2050 \r\nrelative to 2019 levels. {3.3}"


id: a6ad8c22b95565983192da55495502d9
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 26539
content:
"Pathways accelerating actions compared to NDCs announced \r\nprior to COP26 that reduce annual GHG emissions to \r\n48  (38-52) GtCO2-eq by 2030, or  2-9 GtCO2-eq below \r\nprojected emissions from fully implementing NDCs announced \r\nprior to COP26, reduce the mitigation challenge for limiting \r\nwarming to 2degC (>67%) after 2030 (medium confidence).\r\nThe accelerated action pathways are characterised by a  global, \r\nbut regionally differentiated, roll out of regulatory and  pricing \r\npolicies. Compared to NDCs, they see less fossil fuels  and more \r\nlow-carbon fuels until 2030, and narrow, but do not close the gap \r\nto pathways assuming immediate global action using all available \r\nleast-cost abatement options. All delayed or accelerated action \r\npathways that limit warming to 2degC (>67%) converge to a global \r\nmitigation regime at some point after 2030 by putting a significant \r\nvalue on reducing carbon and other GHG emissions in all sectors \r\nand regions. {3.5}"


id: b5ec819011edf34784f2ed8ef3d50994
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 29229
content:
"Many pathways in the literature show how to limit global \r\nwarming compared to pre-industrial times to 2degC (>67%) with \r\nno overshoot or to limit warming to 1.5degC (>50%) with no or \r\nlimited overshoot. The likelihood of limiting warming to 1.5degC \r\nwith no or limited overshoot has dropped in AR6 compared \r\nto the Special Report on Global Warming of  1.5degC (SR1.5) \r\nbecause global GHG emissions have risen since the time SR1.5 \r\nwas published, leading to higher near-term emissions (2030) \r\nand higher cumulative CO2 emissions until the time of net zero \r\n(medium confidence). Only a small number of published pathways \r\nlimit global warming to 1.5degC without overshoot over the course of \r\nthe 21st century. {3.3, Annex III.II.3}"


id: ad52d73707a8b198790f4275553219f0
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 27738
content:
"(>67%), projected CO2 emissions are reduced between 2019 and \r\n2050 by around 49% for energy demand, 97% for energy supply, and \r\n136% for AFOLU (medium confidence). {3.4}"


id: 59b2ba579b1879a2033f86737d2d1bc4
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 13018
content:
"Mitigation pathways limiting warming to 1.5degC (>50%) with \r\nno or limited overshoot reach 50% reductions of CO2 in the \r\n2030s, relative to 2019, then reduce emissions further to \r\nreach net zero CO2 emissions in the 2050s. Pathways limiting \r\nwarming to 2degC (>67%) reach 50% reductions in the 2040s \r\nand net zero CO2 by 2070s (medium confidence). {3.3, Cross\u0002Chapter Box 3 in this chapter}"


id: 34c5becc0d9325c228680460f855078c
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 6524
content:
"Delaying or sacrificing emissions reductions in one sector \r\nor region involves compensating reductions in other sectors or \r\nregions if warming is to be limited (high confidence). Mitigation \r\npathways show differences in the timing of decarbonisation and \r\nwhen net zero CO2 emissions are achieved across sectors and \r\nregions. At the time of global net zero CO2 emissions, emissions in \r\nsome sectors and regions are positive while others are negative; \r\nthe ordering depends on the mitigation options available, the cost \r\nof those options, and the policies implemented. In cost-effective \r\nmitigation pathways, the energy-supply sector typically reaches net \r\nzero CO2 before the economy as a whole, while the demand sectors \r\nreach net zero CO2 later, if ever (high confidence). {3.4}"


id: 688e448255a75ee174272b5629f6455e
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 15665
content:
"Peak warming in mitigation pathways is determined by the \r\ncumulative net CO2 emissions until the time of net zero CO2 and \r\nthe warming contribution of other GHGs and climate forcers \r\nat that time (high confidence). Cumulative net CO2 emissions \r\nfrom 2020 to the time of net zero CO2 are 510 (330-710) GtCO2\r\nin pathways that limit warming to 1.5degC (>50%) with no or limited \r\novershoot and 890 (640-1160) GtCO2 in pathways limiting warming \r\nto 2degC (>67%). These estimates are consistent with the assessment \r\nof remaining carbon budgets by WGI after adjusting for differences \r\nin peak warming levels. {3.3, Box 3.4}"


id: c997af5e2fc467d2b7002cfcef7a3d56
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 32792
content:
"Pathways limiting warming to 2degC (>67%) or lower involve \r\nsubstantial reductions in fossil fuel consumption and a near \r\nelimination of the use of coal without carbon capture and \r\nstorage (CCS) (high confidence). These pathways show an increase \r\nin low-carbon energy, with 88% (69-97%) of primary energy coming \r\nfrom these sources by 2100. {3.4}"


id: f668436bbdf817c9dd896b11ba4ec47c
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 40718
content:
"Stringent emissions reductions at the level required for \r\n2degC (>67%) or lower are achieved through increased direct \r\nelectrification of buildings, transport, and industry, resulting \r\nin increased electricity generation in all pathways (high \r\nconfidence). Nearly all electricity in pathways limiting warming to \r\n2degC (>67%) or lower is from low- or no-carbon technologies, with \r\ndifferent shares of nuclear, biomass, non-biomass renewables, and \r\nfossil CCS across pathways. {3.4}"


id: 2848eea2575784786fc8497714623afc
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 4285
content:
"The measures required to limit warming to 2degC (>67%) or \r\nlower can result in large-scale transformation of the land \r\nsurface (high confidence). Pathways limiting warming to 2degC \r\n(>67%) or lower are projected to reach net zero CO2 emissions in \r\nthe AFOLU sector between the 2020s and 2070, with an increase of \r\nforest cover of about 322 million ha (-67 to 890 million ha) in 2050 \r\nin pathways limiting warming to 1.5degC (>50%) with no or limited \r\novershoot. Cropland area to supply biomass for bioenergy (including \r\nbioenergy with carbon capture and storage  - BECCS) is around \r\n199  (56-482) million ha in 2050 in pathways limiting warming \r\nto 1.5degC (>50%) with no or limited overshoot. The use of bioenergy \r\ncan lead to either increased or reduced emissions, depending on the \r\nscale of deployment, conversion technology, fuel displaced, and how/\r\nwhere the biomass is produced (high confidence). {3.4}"


id: 3ac4dee2db4493982335480985ff8535
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 7539
content:
"Net zero GHG emissions imply net negative CO2 emissions at \r\na level compensating residual non-CO2 emissions. Only 30% of \r\nthe pathways limiting warming to 2degC (>67%) or lower reach \r\nnet zero GHG emissions in the 21st century (high confidence).\r\nIn those pathways reaching net zero GHGs, it is achieved around \r\n10 to 40 years later than for net zero CO2 (medium confidence). \r\nThe reported quantity of residual non-CO2 emissions depends on \r\naccounting: the choice of GHG metric. Reaching and sustaining global \r\nnet zero GHG emissions, measured in terms of GWP-100, results in \r\na gradual decline of temperature (high confidence). {Cross-Chapter \r\nBox 2 in Chapter 2, 3.3, Cross-Chapter Box 3 in this chapter}"


id: 3e370eb9fd88b35119ac286fa0b4e93f
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 8126
content:
"Anthropogenic land CO2 emissions and removals in Integrated \r\nAssessment Model (IAM) pathways cannot be directly \r\ncompared with those reported in national GHG inventories\r\n(high confidence). Methodologies enabling a  more like-for-like \r\ncomparison between models' and countries' approaches would \r\nsupport more accurate assessment of the collective progress achieved \r\nunder the Paris Agreement. {3.4, 7.2.2.5}"


id: 6fe3e3be968c422d9701db0ed13268d2
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 16942
content:
"Pathways limiting warming to 2degC (>67%) or lower exhibit \r\nsubstantial reductions in emissions from all sectors (high \r\nconfidence). Projected CO2 emissions reductions between 2019 \r\nand 2050 in 1.5degC (>50%) pathways with no or limited overshoot \r\nare around 77% (31-96%) for energy demand, 115% (90-167%) \r\nfor energy supply, and 148% (94-387%) for agriculture, forestry \r\nand other land use (AFOLU). In pathways limiting warming to 2degC "


id: 21e101af8918711c576c6a9deb585eeb
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 3182
content:
"Pathways that limit warming to 2degC (>67%) or lower involve \r\nsome amount of CDR to compensate for residual GHGemissions \r\nremaining after substantial direct emissions reductions in all \r\nsectors and regions (high confidence). CDR deployment in \r\npathways serves multiple purposes: accelerating the pace of emissions \r\nreductions, offsetting residual emissions, and creating the option for \r\nnet negative CO2 emissions in case temperature reductions need to \r\nbe achieved in the long term (high confidence). CDR options in the \r\npathways are mostly limited to BECCS, afforestation and direct air \r\ncarbon capture and storage (DACCS). CDR through some measures in \r\nAFOLU can be maintained for decades but not in the very long term \r\nbecause these sinks will ultimately saturate (high confidence). {3.4} "


id: c61c95f1e6c50da1191880fc7f10c210
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 32164
content:
"Mitigation pathways show reductions in energy demand \r\nrelative to reference scenarios, through a  diverse set of \r\ndemand-side interventions (high confidence). Bottom-up \r\nand non-IAM studies show significant potential for demand-side \r\nmitigation. A stronger emphasis on demand-side mitigation implies \r\nless dependence on CDR and, consequently, reduced pressure on \r\nland and biodiversity. {3.4, 3.7}"


id: bbfe8210e21538cb3ab6077599793ada
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 30310
content:
"Limiting warming requires shifting energy investments away \r\nfrom fossil fuels and towards low-carbon technologies (high \r\nconfidence). The bulk of investments are needed in medium- and \r\nlow-income regions. Investment needs in the electricity sector are on \r\naverage 2.3 trillion USD2015 yr-1 over 2023 to 2052 for pathways \r\nthat limit warming to 1.5degC (>50%) with no or limited overshoot, \r\nand 1.7 trillion USD2015 yr-1 for pathways that limit warming to 2degC \r\n(>67%). {3.6.1}"


id: 5ecddeb295e8b9b4216d85df1758126
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 13969
content:
"Differences between aggregate employment in mitigation \r\npathways compared to reference scenarios are relatively \r\nsmall, although there may be substantial reallocations across \r\nsectors, with job creation in some sectors and job losses in \r\nothers (medium confidence). The net employment effect (and its \r\nsign) depends on scenario assumptions, modelling framework, and \r\nmodelled policy design. Mitigation has implications for employment \r\nthrough multiple channels, each of which impacts geographies, \r\nsectors and skill categories differently (medium confidence). {3.6.4}"


id: daefe3cb17ae0e3a8e3efdb131bf118c
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 35804
content:
"Pathways limiting warming to 2degC (>67%) require more rapid \r\nnear-term transformations and are associated with higher \r\nupfront transition costs, but meanwhile bring long-term gains \r\nfor the economy as well as earlier benefits in avoided climate \r\nchange impacts (high confidence). This conclusion is independent \r\nof the discount rate applied, though the modelled cost-optimal \r\nbalance of mitigation action over time does depend on the discount \r\nrate. Lower discount rates favour earlier mitigation, reducing reliance \r\non CDR and temperature overshoot. {3.6.1, 3.8}"


id: c3b06b34faf126cb68a3cbd86aaeee73
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 31704
content:
"Mitigation pathways that limit warming to 2degC (>67%) entail \r\nlosses in global GDP with respect to reference scenarios \r\nof between  1.3% and  2.7% in 2050; and in pathways that \r\nlimit warming to 1.5degC (>50%) with no or limited overshoot, \r\nlosses are between  2.6% and  4.2%. Yet, these estimates do \r\nnot account for the economic benefits of avoided climate \r\nchange impacts (medium confidence). In mitigation pathways \r\nthat limit warming to 2degC (>67%), marginal abatement costs \r\nof carbon are about 90  (60-120) USD2015 tCO2 in 2030 and \r\nabout 210 (140-340) USD2015 tCO2 in 2050; in pathways that \r\nlimit warming to  1.5degC (>50%) with no or limited overshoot, \r\nthey are about 220 (170-290) USD2015 tCO2 in 2030 and about \r\n630 (430-990) USD2015 tCO2 in 2050.3\r\n {3.6.1}"


id: edfdb8dd58b1222cd31ae6b1b16e5fd3
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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content:
"The timing of mitigation actions and their effectiveness \r\nwill have significant consequences for broader sustainable \r\ndevelopment outcomes in the longer term (high confidence). \r\nAmbitious mitigation can be considered a precondition for achieving \r\nthe Sustainable Development Goals (SDGs), especially for vulnerable \r\npopulations and ecosystems with little capacity to adapt to climate \r\nimpacts. Dimensions with anticipated co-benefits include health, \r\nespecially regarding air pollution, clean energy access and water \r\navailability. Dimensions with potential trade-offs include food, \r\nemployment, water stress, and biodiversity, which come under \r\npressure from large-scale CDR deployment, energy affordability/\r\naccess, and mineral-resource extraction (high confidence). {3.7}"


id: 39c982d5519438363a0671675424e01
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
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content:
"Many of the potential trade-offs of mitigation measures \r\nfor other sustainable development outcomes depend on \r\npolicy design and can thus be compensated or avoided with \r\nadditional policies and investments or through policies that \r\nintegrate mitigation with other SDGs (high confidence). \r\nTargeted SDG policies and investments, for example in the areas \r\nof healthy nutrition, sustainable consumption and production, and \r\ninternational collaboration, can support climate change mitigation \r\npolicies and resolve or alleviate trade-offs. Trade-offs can be addressed \r\nby complementary policies and investments, as well as through the \r\ndesign of cross-sectoral policies integrating mitigation with \r\nthe Sustainable Development Goals of health, nutrition, sustainable \r\nconsumption and production, equity and biodiversity. {3.7}"


id: daa828d711ac27b7bb3d2e3ebfdc80a7
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 35763
content:
"Decent living standards, which encompass many SDG \r\ndimensions, are achievable at lower energy use than previously \r\nthought(high confidence). Mitigation strategies that focus on \r\nlower demands for energy and land-based resources exhibit reduced \r\ntrade-offs and negative consequences for sustainable development \r\nrelative to pathways involving either high emissions and climate \r\nimpacts or those with high consumption and emissions that are \r\nultimately compensated by large quantities of BECCS. {3.7}"


id: 16da19287cb843a68a8645de84c38c0
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 1236
content:
"Different mitigation pathways are associated with different \r\nfeasibility challenges, though appropriate enabling conditions \r\ncan reduce these challenges (high confidence). Feasibility \r\nchallenges are transient and concentrated in the next two to \r\nthree decades (high confidence).  They are multidimensional, \r\ncontext-dependent and malleable to policy, technological and \r\nsocietal trends. {3.8}"


id: 5114431ede6aaef899ace8b815c6cf81
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 11543
content:
"Mitigation pathways are associated with significant \r\ninstitutional and economic feasibility challenges rather than \r\ntechnological and geophysical feasibility challenges (medium \r\nconfidence). The rapid pace of technological development and \r\ndeployment in mitigation pathways is not incompatible with \r\nhistorical records. Institutional capacity is rather a key limiting factor \r\nfor a successful transition. Emerging economies appear to have the \r\nhighest feasibility challenges in the short to medium term. {3.8}"


id: 19c0bcc51b0a53a8ee1154dd84c89fa3
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 1729
content:
"Pathways relying on a broad portfolio of mitigation strategies \r\nare more robust and resilient (high confidence). Portfolios of \r\ntechnological solutions reduce the feasibility risks associated with \r\nthe low-carbon transition. {3.8}"


id: 93eb0b9006c3c97ad61f537c33e9a7d2
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
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content:
"that permits the evaluation of mitigation pathways across a range \r\nof feasibility concerns. Finally, methods of the assessment and \r\nknowledge gaps are discussed in Section 3.9, followed by Frequently \r\nAsked Questions (FAQs)."


id: f2223c5dec769d8732d31c93f28b71f3
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 39994
content:
"Chapter 3 takes a long-term perspective on climate change mitigation \r\npathways. Its focus is on the implications of long-term targets for the \r\nrequired short- and medium-term system changes and associated \r\ngreenhouse gas (GHG) emissions. This focus dictates a more global \r\nview and on issues related to path-dependency and up-scaling \r\nof mitigation options necessary to achieve different emissions \r\ntrajectories, including particularly deep mitigation pathways that \r\nrequire rapid and fundamental changes."


id: 8d97dfae323ca243dfe0f23f766386ab
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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content:
"Chapter 3 is linked to many other chapters in the report. The most \r\nimportant connections exist with Chapter  4 on mitigation and \r\ndevelopment pathways in the near to mid-term; with the sectoral \r\nchapters (Chapters  6-11); with the chapters dealing with cross\u0002cutting issues (Chapters 12 and 17, e.g., feasibility); and finally also \r\nwith AR6 WGI and WGII."


id: 64827bc7c3838e988ca1735b23263ae3
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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index: climate_gpt_v2
vector_id: 14956
content:
"Within the overall framing of the AR6 report, Chapter 3 and Chapter 4 \r\nprovide important complementary views of the required systems \r\ntransitions across different temporal and spatial scales. While \r\nChapter 3 focuses on the questions concerning the implications of the \r\nlong-term objectives for the medium-to-near-term transformations, \r\nChapter 4 comes from the other direction, and focuses on current \r\nnear-term trends and policies (such as the Nationally Determined \r\nContributions  - NDCs) and their consequences with regards to \r\nGHG emissions. The latter chapter naturally focuses much more \r\non the regional and national dimensions, and the heterogeneity of \r\ncurrent and planned policies. Bringing together the information from \r\nthese two chapters enables the assessment of whether current and \r\nplanned actions are consistent with the required systems changes for \r\nthe long-term objectives of the Paris Agreement."


id: 8674ce6c9fe91c7c874c1d477cb0c1d6
created_at: 2023-04-19 13:22:11
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content:
"Important other linkages comprise the collaboration with the \r\n'sectoral' Chapters 6 to 11 to provide an integrated cross-sectoral \r\nperspective. This information (including information also from \r\nthe sectoral chapters) is taken up ultimately also by Chapter 5 on \r\ndemand/services and Chapter 12 for a further assessment of sectoral \r\npotential and costs."


id: 6304063645a93b8f0d562a304a911bb9
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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content:
"Linkages to other chapters exist also on the topic of feasibility, which \r\nare informed by the policy, the sectoral and the demand chapters, \r\nthe technology and finance chapters, as well as Chapter  4 on \r\nnational circumstances."


id: d8b79f00e050ec45c8a9d698b6af599d
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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index: climate_gpt_v2
vector_id: 35423
content:
"Close collaboration with WGI permitted the use of AR6-calibrated \r\nemulators, which assure full consistency across the different \r\nworking groups. Linkages to WGII concern the assessment of \r\nmacroeconomic benefits of avoided impacts that are put into the \r\ncontext of mitigation costs as well as co-benefits and trade-offs for \r\nsustainable development."


id: 39f4d4b350d7765f600d3ef07e9b5eb6
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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index: climate_gpt_v2
vector_id: 7408
content:
"The assessment of mitigation pathways explores a  wide scenario \r\nspace from the literature within which seven Illustrative Pathways \r\n(IPs) are explored. The overall process is indicated in Figure 3.5a."


id: 363bedc3463f5b2174b61107aee8348e
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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vector_id: 6784
content:
"4 Available at: https://doi.org/10.5281/zenodo.5886911. All figures and tables in this chapter source data from the AR6 Scenarios Database, unless otherwise stated."


id: 3c57b7234ee627f5f09114b77c34350e
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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index: climate_gpt_v2
vector_id: 7806
content:
"typically capture interactions and processes that change key driving \r\nforces such as population, GDP, technology, lifestyles, and policy, and \r\nthe consequences on energy use, land use, and emissions. Scenarios \r\nare not predictions or forecasts. An emission pathway is a modelled \r\ntrajectory of anthropogenic emissions (Rogelj et al. 2018a) and, \r\ntherefore, a part of a scenario."


id: 290b240f580828d3ead52d16b7c6026d
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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vector_id: 4430
content:
"For a comprehensive assessment, a large ensemble of scenarios is \r\ncollected and made available through an interactive AR6 Scenarios \r\nDatabase4\r\n. The collected information is shared across the chapters of \r\nAR6 and includes more than 3000 different pathways from a diverse \r\nset of studies. After an initial screening and quality control, scenarios \r\nwere further vetted to assess if they sufficiently represented historical \r\ntrends (Annex III.II.3.1). Subsequently, the climate consequences of \r\neach scenario were assessed using the climate emulator (leading to \r\nfurther classification). The assessment in Chapter 3 is, however, not \r\nlimited to the scenarios from the database, and wherever necessary \r\nother literature sources are also assessed in order to bring together \r\nmultiple lines of evidence."


id: fc802e06cc93e3aa37b6d3852f80c026
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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content:
"There is no unique or preferred method to develop scenarios, and \r\nfuture pathways can be developed from diverse methods, depending \r\non user needs and research questions (Turnheim et al. 2015; \r\nTrutnevyte et al. 2019a; Hirt et al. 2020). The most comprehensive \r\nscenarios in the literature are qualitative narratives that are translated \r\ninto quantitative pathways using models (Clarke et al. 2014; Rogelj \r\net al. 2018a). Schematic or illustrative pathways can also be used \r\nto communicate specific features of more complex scenarios (Allen \r\net al. 2018). Simplified models can be used to explain the mechanisms \r\noperating in more complex models (e.g.,  Emmerling et al. 2019). \r\nUltimately, a diversity of scenario and modelling approaches can lead \r\nto more robust findings (Schinko et al. 2017; Gambhir et al. 2019)."


id: cfca91f21139aa1ac73a1813b7959490
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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index: climate_gpt_v2
vector_id: 33917
content:
"In parallel, based on the overall AR6 assessment, seven illustrative \r\npathways (IP) were defined representing critical mitigation strategies \r\ndiscussed in the assessment. The seven pathways are composed of \r\ntwo sets: (i) one set of five Illustrative Mitigation Pathways (IMPs) and \r\n(ii) one set of two reference pathways illustrative for high emissions. \r\nThe IMPs are on the one hand representative of the scenario spac but \r\nalso help to communicate archetypes of distinctly different systems \r\ntransformations and related policy choices. Subsequently, seven \r\nscenarios were selected from the full database that fitted these \r\nstorylines of each IP best. For these scenarios more strict vetting \r\ncriteria were applied. The selection was done by first applying specific \r\nfilters based on the storyline followed by a final selection (Box 3.1 \r\nand Figure 3.5a)."


id: e4de02c0e8f024347b0ef1349197fb37
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vector_id: 37549
content:
"It is common to define a reference scenario (also called a baseline \r\nscenario). Depending on the research question, a reference scenario \r\ncould be defined in different ways (Grant et al. 2020): (i) a hypothetical \r\nworld with no climate policies or climate impacts (Kriegler \r\net al. 2014b), (ii) assuming current policies or pledged policies are \r\nimplemented (Roelfsema et al. 2020), or (iii) a mitigation scenario \r\nto compare sensitivity with other mitigation scenarios (Kriegler et al. \r\n2014a; Sognnaes et al. 2021)."


id: 4aa931975c5f7dd60da4112beb39ab52
created_at: 2023-04-19 13:22:11
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index: climate_gpt_v2
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content:
"No-climate-policy reference scenarios have often been compared \r\nwith mitigation scenarios (Clarke et al. 2014). A  no-climate-policy \r\nscenario assumes that no future climate policies are implemented, \r\nbeyond what is in the model calibration, effectively implying that \r\nthe carbon price is zero. No-climate-policy reference scenarios \r\nhave a  broad range depending on socio-economic assumptions \r\nand model characteristics, and consequently are important when \r\nassessing mitigation costs (Riahi et al. 2017; Rogelj et al. 2018b). As "


id: 53a7275574985b3f1a601b10c3e28fa7
created_at: 2023-04-19 13:22:11
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index: climate_gpt_v2
vector_id: 11989
content:
"Scenario and emission pathways are used to explore possible \r\nlong-term trajectories, the effectiveness of possible mitigation \r\nstrategies, and to help understand key uncertainties about the \r\nfuture. A scenario is an integrated description of a possible future \r\nof the human-environment system (Clarke et al. 2014), and could \r\nbe a qualitative narrative, quantitative projection, or both. Scenarios "


id: 595dda910b0c7c9cd2bc0ac5db864372
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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content:
"countries move forward with climate policies of varying stringency, \r\nno-climate-policy baselines are becoming increasingly hypothetical \r\n(Hausfather and Peters 2020). Studies clearly show current policies \r\nare having an effect, particularly when combined with the declining \r\ncosts of low-carbon technologies (IEA 2020a; Roelfsema et al. 2020; \r\nSognnaes et al. 2021; UNEP 2020), and, consequently, realised \r\ntrajectories begin to differ from earlier no-climate-policy scenarios \r\n(Burgess et al. 2020). High-end emission scenarios, such as RCP8.5 and \r\nSSP5-8.5, are becoming less likely with climate policy and technology \r\nchange (Box  3.3), but high-end concentration and warming levels \r\nmay still be reached with the inclusion of strong carbon or climate \r\nfeedbacks (Hausfather and Peters 2020; Pedersen et al. 2020)."


id: 8ceebd59c5cd1b04a2a4c55babb56ca8
created_at: 2023-04-19 13:22:11
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index: climate_gpt_v2
vector_id: 21981
content:
"based on optimisation or simulation; have exogenous or endogenous \r\ntechnological change amongst many other characteristics. IAMs take \r\nas input socio-economic and technical variables and parameters \r\nto represent various systems. There is no unique way to integrate \r\nthis knowledge into a model, and due to their complexity, various \r\nsimplifications and omissions are made for tractability. IAMs \r\ntherefore have various advantages and disadvantages which need \r\nto be weighed up when interpreting IAM outcomes. Annex  III.I \r\ncontains an overview of the different types of models and their \r\nkey characteristics."


id: 7dad24f617f38defc7d51b6cba56c154
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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index: climate_gpt_v2
vector_id: 19283
content:
"Most IAMs are necessarily broad as they capture long-term dynamics. \r\nIAMs are strong in showing the key characteristics of emission \r\npathways and are most suited to questions related to short- versus \r\nlong-term trade-offs, key interactions with non-climate objectives, \r\nlong-term energy and land-use characteristics, and implications of \r\ndifferent overarching technological and policy choices (Clarke et al. \r\n2014; Rogelj et al. 2018a). While some IAMs have a  high level of \r\nregional and sectoral detail, for questions that require higher levels \r\nof granularity (e.g., local policy implementation) specific region and \r\nsector models may be better suited. Utility of the IAM pathways \r\nincreases when the quantitative results are contextualized through \r\nqualitative narratives or other additional types of knowledge \r\nto provide deeper insights (Geels et al. 2016a; Weyant 2017; \r\nGambhir et al. 2019)."


id: 758b265dccce2e299916eec74eeebf41
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index: climate_gpt_v2
vector_id: 17910
content:
"Mitigation scenarios developed for a  long-term climate constraint \r\ntypically focus on cost-effective mitigation action towards a  long\u0002term climate goal. Results from IAM as well as sectoral models depend \r\non model structure (Mercure et al. 2019), economic assumptions \r\n(Emmerling et al. 2019), technology assumptions (Pye et al. 2018), \r\nclimate/emissions target formulation (Johansson et al. 2020), and \r\nthe extent to which pre-existing market distortions are considered \r\n(Guivarch et al. 2011). The vast majority of IAM pathways do not \r\nconsider climate impacts (Schultes et al. 2021). Equity hinges upon \r\nethical and normative choices. As most IAM pathways follow the "


id: ff3708a9b3daa5a7135ec5e8431e8b05
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updated_at: 2023-04-19 13:22:11
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index: climate_gpt_v2
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content:
"'IAms can provide very useful information, but this information needs \r\nto be carefully interpreted and integrated with other quantitative and \r\nqualitative inputs in the decision-making process.'"


id: cd7193375aa6b3300ac25657d626a4b
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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index: climate_gpt_v2
vector_id: 33478
content:
"The climate constraint implementation can have a meaningful impact \r\non model results. The literature so far includes many temperature \r\novershoot scenarios with heavy reliance on long-term CDR and net \r\nnegative CO2 emissions to bring back temperatures after the peak \r\n(Rogelj et al. 2019b; Johansson et al. 2020). New approaches have \r\nbeen developed to avoid temperature overshoot. The new generation \r\nof scenarios show that CDR is important beyond its ability to reduce \r\ntemperature, but is essential also for offsetting residual emissions to \r\nreach net zero CO2 emissions (Rogelj et al. 2019b; Johansson et al. \r\n2020; Riahi et al. 2021; Strefler et al. 2021b)."


id: 738cf1a58840e34322875fac8647cf56
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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index: climate_gpt_v2
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content:
"To facilitate this assessment, a  large ensemble of scenarios has \r\nbeen collected and made available through an interactive AR6 \r\nWGIII scenario database. The collection of the scenario outputs \r\nis coordinated by Chapter  3 and expands upon the IPCC SR1.5 \r\nscenario explorer (Huppmann et al. 2018; Rogelj et al. 2018a). \r\nA  complementary database for national pathways has been \r\nestablished by Chapter 4. Annex III.II.3 contains full details on how \r\nthe scenario database was compiled."


id: 16c367088fcb9f9961f0d6a3016c7668
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index: climate_gpt_v2
vector_id: 1218
content:
"intercomparison projects, with around one-fifth originating from \r\nindividual studies (Figure 3.2). Because of the uneven distribution of \r\nscenarios from different models and projects, uncorrected statistics \r\nfrom the database can be misleading."


id: 8477c4c67f9c932d305112b7348c5774
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index: climate_gpt_v2
vector_id: 20450
content:
"The AR6 scenario database contains 3131 scenarios (Figure  3.5a). \r\nAfter an initial screening and quality control, scenarios were further \r\nvetted to assess if they sufficiently represented historical trends \r\n(Annex III.II.3.1). Of the initial 2266 scenarios with global scope, \r\n1686 scenarios passed the vetting process and are assessed in this \r\nchapter. The scenarios that did not pass the vetting are still available in \r\nthe database. The vetted scenarios were from over 50 different model \r\nfamilies, or over 100 when considering all versions of the same family \r\n(Figure 3.1). The scenarios originated from over 15 different model "


id: 20d83f35b0034e73b846960d966dd4f8
created_at: 2023-04-19 13:22:11
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index: climate_gpt_v2
vector_id: 2998
content:
"Each scenario with sufficient data is given a  temperature \r\nclassification using climate model emulators. Three emulators were \r\nused in the assessment: FAIR (Smith et al. 2018), CICERO-SCM \r\n(Skeie et al. 2021), MAGICC (Meinshausen et al. 2020). Only the "


id: 13b7dcd2fdd2cf6af1ba7e9e0af9ae5e
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updated_at: 2023-04-19 13:22:11
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"results of MAGICC are shown in this chapter as it adequately covers the \r\nrange of outcomes. The emulators are calibrated against the behaviour \r\nof complex climate models and observation data, consistent with the \r\noutcomes of AR6 WGI (Cross-Chapter Box 7.1). The climate assessment \r\nis a three-step process of harmonisation, infilling and a probabilistic \r\nclimate model emulator run (Annex  III.II.2.5). Warming projections \r\nuntil the year 2100 were derived for 1574 scenarios, of which 1202 \r\npassed vetting, with the remaining scenarios having insufficient \r\ninformation (Figure 3.3 and Table 3.1). For scenarios that limit warming \r\nto 2degC or lower, the SR1.5 classification was adopted in AR6, with \r\nmore disaggregation provided for higher warming levels (Table 3.1). "


id: f63434bae37b11dd1931d160ead3bb64
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content:
"These choices can be compared with the selection of common global \r\nwarming levels (GWLs) of 1.5degC, 2degC, 3degC and 4degC to classify climate \r\nchange impacts in the WGII assessment."


id: 48746a8f91ecf9e8445a24f47ec18177
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index: climate_gpt_v2
vector_id: 9957
content:
"key characteristics that define an IMP. The quantitative versions of \r\nthe IMPs provide numerical values that are internally consistent and \r\ncomparable across chapters of the report. The quantitative IMPs have \r\nbeen selected from the AR6 scenario database. No assessment of the \r\nlikelihood of each IMP has been made."


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"Successive IPCC Assessment Reports (ARs) have used scenarios to \r\nillustrate key characteristics of possible climate (policy) futures. In AR5 \r\nfour RCPs made the basis of climate modelling in WGI and WGII, with \r\nWGIII assessing over 1000 scenarios spanning those RCPs (Clarke et al. \r\n2014). Of the over 400 scenarios assessed in SR1.5, four scenarios \r\nwere selected to highlight the trade-off between short-term emission \r\nreductions and long-term deployment of BECCS (Rogelj et al. 2018a), \r\nreferred to as 'Illustrative Pathways' (IPs). AR6 WGI and WGII rely on \r\nthe scenarios selected for CMIP6, called ScenarioMIP (O'Neill et al. \r\n2016), to assess warming levels. In addition to the full set of scenarios, \r\nAR6 WGIII also uses selected Illustrative Mitigation Pathways (IMPs)."


id: 31855de4796d2aa22fe45d0134a0f23b
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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content:
"The IMPs are selected to have different mitigation strategies, which \r\ncan be illustrated looking at the energy system and emission pathways \r\n(Figure 3.7 and Figure 3.8). The mitigation strategies show the different \r\noptions in emission reduction (Figure 3.7). Each panel shows the key \r\ncharacteristics leading to total GHG emissions, consisting of residual \r\n(gross) emissions (fossil CO2 emissions, CO2 emissions from industrial \r\nprocesses, and non-CO2 emissions) and removals (net land-use change, \r\nbioenergy with carbon capture and storage  - BECCS, and direct air \r\ncarbon capture and storage - DACCS), in addition to avoided emissions \r\nthrough the use of carbon capture and storage on fossil fuels. The \r\nIMP-Neg and IMP-GS scenarios were shown to illustrate scenarios \r\nwith a significant role of CDR. The energy supply (Figure 3.8) shows \r\nthe phase-out of fossil fuels in the IMP-LD, IMP-Ren and IMP-SP cases, \r\nbut a less substantial decrease in the IMP-Neg case. The IMP-GS case "


id: 30e6b8b4f7b474c6c94d489283cb902f
created_at: 2023-04-19 13:22:11
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content:
"In WGIII, IMPs were selected to denote the implications of different \r\nsocietal choices for the development of future emissions and \r\nassociated transformations of main GHG-emitting sectors (Figure 3.5a \r\nand Box 3.1). The most important function of the IMPs is to illustrate \r\nkey themes that form a  common thread in the report, both with \r\na storyline and a quantitative illustration. The storyline describes the "


id: db16ad58e7fb710dd5cb10a584686d6f
created_at: 2023-04-19 13:22:11
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content:
"needs to make up its slow start by (i)  rapid reductions mid-century \r\nand (ii) massive reliance on net negative emissions by the end of the \r\ncentury. The CurPol and ModAct cases both result in relatively high \r\nemissions, showing a  slight increase and stabilisation compared to \r\ncurrent emissions, respectively."


id: 2daf5af1f32d2f8208eb1332a380f82d
created_at: 2023-04-19 13:22:11
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"Figure 3.9 | Trends in key scenario characteristics and driving forces as included in the SSP scenarios (showing 5-95th percentiles of the reference \r\nscenarios as included in the database in grey shading). Reference (dotted lines) refers to the UN low-, medium- and high-population scenarios (UN 2019), the OECD \r\nlong-term economic growth scenario (OECD 2021), the scenarios from the IEA's World Energy Outlook (IEA 2019), and the scenarios in the FAO assessment (FAO 2018)."


id: 63998a182fec929d04fc45d1ad8a4a10
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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content:
"Figure  3.10 shows the GHG and CO2 emission trajectories for \r\ndifferent temperature categories as defined in Section  3.2 (the \r\ntemperature levels are calculated using simple climate models, \r\nconsistent with the outcomes of the recent WGI assessment, Cross\u0002Chapter Box 7.1). It should be noted that most scenarios currently in \r\nthe literature do not account for the impact of COVID-19 (Box 3.2). "


id: 74d216a48017268f6800c3ea19075fcb
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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"emissions peak in the 21st century. For the lowest categories, \r\nthe emissions peak is mostly before 2030. In fact, for scenarios \r\nin the  category that avoids temperature overshoot for the  1.5degC \r\nscenario (C1 category), GHG emissions are reduced already to almost \r\nzero around the middle of the century. Typically, CO2 emissions reach \r\nnet zero about 10 to 40 years before total GHG emissions reach net \r\nzero. The main reason is that scenarios reduce non-CO2 greenhouse \r\ngas emissions less than CO2 due to a  limited mitigation potential \r\n(Section 3.3.2.2). Figure 3.10 also shows that many scenarios in the \r\nliterature with a temperature outcome below 2degC show net negative \r\nemissions. There are, however, also exceptions in which more \r\nimmediate emission reductions limits the need for CDR. The IMPs \r\nillustrate alternative pathways to reach the C1-C3 temperature levels."


id: fbc8d7c2ebfb333d743dec38561bfae6
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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content:
"The lower categories include increasingly stringent assumed climate \r\npolicies. For all scenario categories, except the highest category, "


id: c8b5cf2c9f96ec6c32060de48c1813d7
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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content:
"Figure 3.11 | Global mean temperature outcome of the ensemble of scenarios included in the climate categories C1-C8 (based on a reduced complexity \r\nmodel - RCM - calibrated to the WGI assessment, both in terms of future and historic warming). The left panel shows the ranges of scenario uncertainty (shaded \r\narea) with the P50 RCM probability (line). The right panel shows the P5 to P95 range of combined RCM climate uncertainty (C1-C8 is explained in Table 3.1) and scenario \r\nuncertainty, and the P50 (line)."


id: f27ef17bb770c13b5d62bb2bb74103b7
created_at: 2023-04-19 13:22:11
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"limited overshoot (typically <0.1degC, C1) compared to those with high \r\novershoot (C2): in emissions, the C1 category is characterised by steep \r\nearly reductions and a relatively small contribution of net negative \r\nemissions (like IMP-LD and IMP-Ren) (Figure  3.10). In addition to \r\nthe temperature caused by the range of scenarios in each category \r\n(main panel), climate uncertainties also contribute to a  range of \r\ntemperature outcomes (including uncertainties regarding the carbon \r\ncycle, climate sensitivity, and the rate of change, see AR6 WGI). The \r\nbars on the right of Figure 3.11 show the uncertainty range for each "


id: b1c6cf82d5be47a321f0e07fd2303191
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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vector_id: 28492
content:
"category (combining scenario and climate uncertainty). While the C1 \r\ncategory more likely than not limits warming to 1.5degC (>50%) by the \r\nend of the century, even with such a scenario, warming above 2degC \r\ncannot be excluded (95th percentile). The uncertainty range for the \r\nhighest emission categories (C7) implies that these scenarios could \r\nlead to a warming above 6degC."


id: 5b9ae75af7c9d3e82da7d52502671d5b
created_at: 2023-04-19 13:22:11
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content:
"Figure 3.12 | (a) The role of CO2 and other greenhouse gases. Emission in CO2-eq in 2100 (using AR6 GWP-100) (other = halogenated gases) and (b) cumulative CO2\r\nemissions in the 2020-2100 period. Panels (c) and (d) show the development of CH4 and N2O emissions over time. Energy emissions include the contribution of BECCS. For both \r\nenergy and AFOLU sectors, the positive and negative values represent the cumulated annual balances. In both panels, the three bars per scenario category represent the lowest 5-15th \r\npercentile, the average value and the highest 5-15th percentile. These illustrate the range of scenarios in each category. The definition of C1-C7 can be found in Table 3.1."


id: 23259cc547dee771ff06571ca963c8dd
created_at: 2023-04-19 13:22:11
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vector_id: 3381
content:
"Box 3.4 | Consistency of Remaining Carbon Budgets in the WGI Assessment and Cumulative \r\nCO2 Emissions in WGIII Mitigation Pathways"


id: ecfaf63ae8bbc3ea2eba3c66f568b1ff
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content:
"The WGI assessment has shown that the increase in global mean temperature has a  near-linear relationship with cumulative \r\nCO2 emissions (Chapter 5, Section 5.5, Box 5.3 of AR6 WGI report). Consistently, WGI has confirmed that net zero CO2 emissions are \r\nrequired to halt CO2-induced warming. This permits the estimation of carbon budgets consistent with specific temperature goals. In \r\nChapter 3, we present the temperature outcomes and cumulative CO2 emissions associated with different warming levels for around \r\n1200 scenarios published in the literature and which were classified according to different warming levels (Section 3.2 and Annex III.\r\nII.3.2). In this box, we discuss the consistency of the assessments presented here and in IPCC AR6 WGI. The box summarises how \r\nthe remaining carbon budgets assessed by AR6 WGI relate to the remaining cumulative CO2 emissions until the time of net zero CO2\r\nemissions in mitigation pathways (Tables 3.2 and SPM.1) assessed by AR6 WGIII."


id: 4c53d824b23d4474439484a842fbcc43
created_at: 2023-04-19 13:22:11
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content:
"In its assessment, AR6 WGI uses a framework in which the various components of the remaining carbon budget are informed by \r\nvarious lines of evidence and assessed climate system characteristics. The AR6 WGIII, instead, uses around 1200 emission scenarios \r\nwith estimated warming levels that cover the scenario range presented in AR6 WGI but also contain many more intermediate \r\nprojections with varying emission profiles and a combination of CO2 emissions and other greenhouse gases. In order to assess their \r\nclimate outcomes, climate model emulators are used. The emulators are reduced complexity climate models that are provided by AR6 \r\nWGI, and which are calibrated to the AR6 WGI assessment of future warming for various purposes (a detailed description of the use of \r\nclimate model emulators in the AR6 WGI and WGIII assessments can be found in Cross-Chapter Box 7.1 in the AR6 WGI report, with \r\nthe connection of WGI and WGIII discussed in Annex III.2.5.1)."


id: c3adf723992811de900714bb859230d2
created_at: 2023-04-19 13:22:11
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"Cumulative CO2 emissions until net zero estimated by AR6 WGIII\r\nThe AR6 WGIII provides estimates of cumulative net CO2 emissions (from 2020 inclusive) until the time of reaching net zero CO2 emissions \r\n(henceforth called 'peak cumulative CO2 emissions') and until the end of the century for eight temperature classes that span a range of \r\nwarming levels. The numbers can be found in Table 3.2 (330-710 GtCO2 for C1; 530-930 for C2; and 640-1160 for C3)."


id: 4087edf545d786d4fc84e74dd047332
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"Comparing the AR6 WGI remaining carbon budgets and remaining cumulative CO2 emissions of the AR6 WGIII scenarios\r\nA comparison between AR6 WGI and WGIII findings requires recognising that, unlike in WGI, cumulative emissions in WGIII are not \r\nprovided for a specific peak-warming threshold or level but are instead provided for a set of scenarios in a category, representing \r\na  specific range of peak-temperature outcomes (for instance the C4 category contains scenarios with a  median peak warming \r\nanywhere between approximately 1.8degC and up to 2degC). When accounting for this difference, the AR6 WGI and WGIII findings are \r\nvery consistent for temperature levels below 2degC. Figure 1 compares the peak temperatures and associated cumulative CO2 emissions \r\n(i.e., peak cumulative CO2 emissions) for the WGIII scenarios to the remaining carbon budgets assessed by WGI. This shows only minor \r\ndifferences between the WGI and WGIII approaches."


id: 3cc1d245323e7757f408da6ecad05038
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"After correcting for the categorisation, some (small) differences between the AR6 WGI and WGIII numbers arise from remaining \r\ndifferences between the outcomes of the climate emulators and their set-up (IPCC AR6 WGI Cross-Chapter Box 7.1) and the differences \r\nin the underlying scenarios. Moreover, the WGI assessment estimated the non-CO2 warming at the time of net zero CO2 emissions \r\nbased on a relationship derived from the SR1.5 scenario database with historical emission estimates as in Meinshausen et al. (2020) \r\n(AR6 WGI Chapter 5). The WGIII assessment uses the same climate emulator with improved historical emissions estimates (Nicholls \r\net al. 2021) (AR6 WGI Cross-Chapter Box  7.1). Annex III.II.2.5.1 further explores the effects of these factors on the relationship \r\nbetween non-CO2 warming at peak cumulative CO2 and peak surface temperature."


id: bb06cfea506467fa0b02625e10900934
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"A detailed comparison of the impact of different assessment steps (i.e., the new emulators, scenarios, and harmonisation methods), \r\nhas been made and is presented in Figure 6 in Annex III.II.3.2 ."


id: cbdcba882410beab69301851b52772ae
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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"The concept of a finite carbon budget means that the world needs to get to net zero CO2, no matter whether global warming is \r\nlimited to 1.5degC or well below 2degC (or any other level). Moreover, exceeding the remaining carbon budget will have consequences by \r\novershooting temperature levels. Still, the relationship between the timing of net zero and temperature targets is a flexible one, as \r\ndiscussed further in Cross-Chapter Box 3 in this chapter. It should be noted that the national-level inventory as used by UNFCCC for \r\nthe land use, land-use change and forestry sector is different from the overall concept of anthropogenic emissions employed by IPCC \r\nAR6 WGI. For emissions estimates based on these inventories, the remaining carbon budgets must be correspondingly reduced by \r\napproximately 15%, depending on the scenarios (Grassi et al., 2021) (Chapter 7). "


id: 212d60ffb21ca6df4eec4731e0926f33
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content:
"One of the uncertainties of the remaining carbon budget is the level of non-CO2 emissions which is a function of policies and technology \r\ndevelopment. This represents a point of leverage for policies rather than an inherent geophysical uncertainty. Stringent non-CO2 emission \r\nreductions hence can provide - to some degree - an option to reach more stringent climate goals with the same carbon budget."


id: 2b786c874595161c7f02357b0511446c
created_at: 2023-04-19 13:22:11
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"The near-linear relationship implies that cumulative CO2 emissions \r\nare critically important for climate outcomes (Collins et al. 2013). \r\nThe maximum temperature increase is a  direct function of the \r\ncumulative emissions until net zero CO2 emissions is reached \r\n(the emission budget) (Figure  3.13, left side). The end-of-century \r\ntemperature correlates well with cumulative emissions across \r\nthe century (right panel). For long-term climate goals, positive \r\nemissions in the first half of the century can be offset by net \r\nremoval of CO2 from the atmosphere (net negative emissions) at \r\nthe cost of a  temporary overshoot of the target (Tokarska et al. \r\n2019). The bottom panels of Figure 3.13 show the contribution of \r\nnet negative CO2 emissions."


id: 2f0114c99bb795a286874ab0b7b56d0d
created_at: 2023-04-19 13:22:11
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"Focusing on cumulative emissions, the right-hand panel of \r\nFigure  3.12b shows that for high-end scenarios (C6-C7), most \r\nemissions originate from fossil fuels, with a  smaller contribution \r\nfrom net deforestation. For C5 and lower, there is also a  negative \r\ncontribution to emissions from both AFOLU emissions and energy "


id: 303d718b3f62a646d00d658b7d105bb6
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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content:
"Figure 3.13 | The near-linear relationship between cumulative CO2 emissions and temperature. The left panel shows cumulative emissions until net zero emission \r\nis reached. The right panel shows cumulative emissions until the end of the century, plotted against peak and end-of-century temperature, respectively. Both are shown as \r\na function of non-CO2 forcing and cumulative net negative CO2 emissions. Position temperature categories (circles) and IPs are also indicated, including two 2degC sensitivity \r\ncases for Neg (Neg-2.0) and Ren (Ren-2.0)."


id: 9af16cfc65407801d52c4f956e1066b7
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updated_at: 2023-04-19 13:22:11
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vector_id: 24504
content:
"igure 3.14 | Net zero year for CO2 and all GHGs (based on AR6 GWP100) as a function of remaining carbon budget and temperature outcomes (note \r\nhat scenarios that stabilise (near) zero are also included in determining the net zero year)."


id: 49161d466d76ae814cadbf1c67ce1e9b
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updated_at: 2023-04-19 13:22:11
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content:
"This Cross-Chapter Box surveys scientific, technical and policy aspects of net zero carbon dioxide (CO2) and net zero greenhouse gas \r\n(GHG) emissions, with a focus on timing, the relationship with warming levels, and sectoral and regional characteristics of net zero \r\nemissions. Assessment of net zero GHG emissions additionally requires consideration of non-CO2 gases and choice of GHG emission \r\nmetrics used to aggregate emissions and removals of different GHGs (Cross-Chapter Box 2 in Chapter 2 and Cross-Chapter Box 7 in \r\nChapter 10). The following considers net zero CO2 and GHG emissions globally, followed by regional and sectoral dimensions."


id: a1390f59717f41598f6cb8b940d163d3
created_at: 2023-04-19 13:22:11
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vector_id: 25587
content:
"Reaching net zero CO2 emissions globally is necessary for limiting global warming to any level. At the point of net zero \r\nCO2, the amount of CO2 human activity is putting into the atmosphere equals the amount of CO2 human activity is removing from \r\nthe atmosphere (see Annex I: Glossary). Reaching and sustaining net zero CO2 emissions globally stabilizes CO2-induced warming. \r\nReaching net zero CO2 emissions and then moving to net negative CO2 emissions globally leads to a peak and decline in CO2-induced \r\nwarming (AR6 WGI Sections 5.5 and 5.6)."


id: cc2f391fa263afaefd550b39b412d50a
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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index: climate_gpt_v2
vector_id: 33241
content:
"Limiting warming to 1.5degC (>50%) or to 2degC (>67%) requires deep, rapid, and sustained reductions of other greenhouse \r\ngases including methane alongside rapid reductions of CO2 emissions to net zero. This ensures that the warming contributions \r\nfrom non-CO2 forcing agents as well as from CO2 emissions are both limited at low levels. The AR6 WGI estimated remaining carbon \r\nbudgets until the time of reaching net zero CO2 emissions for a range of warming limits, taking into account historical CO2 emissions \r\nand projections of the warming from non-CO2 forcing agents (Box 3.4 in Section 3.3, AR6 WGI Section 5.5)."


id: bf8967be083f9ccd45ce418669233666
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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content:
"The earlier global net zero CO2 emissions are reached, the lower the cumulative net amount of CO2 emissions and \r\nhuman-induced global warming, all else being equal (Figure 1a in this Cross-Chapter Box). For a given net zero date, a variation \r\nin the shape of the CO2 emissions profile can lead to a variation in the cumulative net amount of CO2 emissions until the time of net \r\nzero CO2 and as a result to different peak-warming levels. For example, cumulative net CO2 emissions until the time of reaching \r\nnet zero CO2 will be smaller, and peak warming lower, if emissions are reduced steeply and then more slowly compared to reducing \r\nemissions slowly and then more steeply (Figure 1b in this Cross-Chapter Box)."


id: 2caae540bf80408c0772be4ec9c1b67f
created_at: 2023-04-19 13:22:11
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"Cross-Chapter Box 3, Figure 1 | Selected global CO2 emissions trajectories with similar shape and different net zero CO2 date (a), different shape \r\nand similar net zero CO2 date (b), and similar peak warming, but varying shapes and net zero CO2 dates (c). Funnels show pathways limiting warming \r\nto 1.5degC (>50%) with no or limited overshoot (light blue) and limiting warming to 2degC (>67%) (beige). Historic CO2 emissions from Section 2.2 (EDGAR v6)."


id: 9eac6823ec6fe2d06d953c38602b6e31
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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content:
"Pathways following emissions levels projected from the implementation of Nationally Determined Contributions (NDCs) \r\nannounced prior to COP26 until 2030 would result in substantially (>0.1degC) exceeding 1.5degC. They would have to reach \r\nnet zero CO2 around 5-10 years later7\r\n than in pathways with no or limited overshoot in order to reach the net negative emissions \r\nthat would then be required to return warming to 1.5degC (>50%) after a high overshoot by 2100. Those high overshoot pathways \r\nhave higher transient warming and higher reliance on net negative CO2 emissions towards the end of the 21st century. As they need \r\nto reach net zero CO2 emissions in only limited amount of time but from much higher 2030 emissions levels, their post-2030 CO2\r\nemissions reduction rates are substantially higher (by around 30%) than in pathways limiting warming to 1.5degC with no or limited \r\novershoot. (Section 3.5)."


id: 46d71ed084e816a1afdfb573558626c2
created_at: 2023-04-19 13:22:11
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"Pathways following emissions levels projected from the implementation of NDCs announced prior to COP26 until \r\n2030 would have to reach net zero CO2 around 5 years earlier8\r\n than cost-effective pathways that limit warming to 2degC \r\n(>67%). While cost-effective pathways take around 50-55 years to reach net zero CO2 emissions, those pathways would only have \r\n35-40 years left for transitioning to net zero CO2 from 2030 onwards, close to the transition times that 1.5degC pathways are faced with \r\ntoday. Current CO2 emissions and 2030 emission levels projected under the NDCs announced prior to COP26 are in a similar range \r\n(Sections 3.5 and 4.2)."


id: 11a0da92f28cdc97668bf7eb93a287e7
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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content:
"Net zero greenhouse gas (GHG) emissions\r\nThe amount of CO2-equivalent emissions and the point when net zero GHG emissions are reached in multi-GHG emissions \r\npathways depends on the choice of GHG emissions metric. Various GHG emission metrics are available for this purpose.9\r\nGWP-100 is the most commonly used metric for reporting CO2-equivalent emissions and is required for emissions reporting under the \r\nRulebook of the Paris Agreement. (Cross-Chapter Box 2 in Chapter 2, Annex I and Annex II.9)"


id: 8e9ca560c0fc26ed60129d4a00b895e3
created_at: 2023-04-19 13:22:11
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vector_id: 22275
content:
"For most choices of GHG emissions metric, reaching net zero GHG emissions requires net negative CO2 emissions in order \r\nto balance residual CH4, N2O and F-gas emissions. Under foreseen technology developments, some CH4, N2O and F-gas emissions \r\nfrom, for example, agriculture and industry, will remain over the course of this century. Net negative CO2 emissions will therefore be \r\nneeded to balance these remaining non-CO2 GHG emissions to obtain net zero GHG emissions at a point in time after net zero CO2\r\nhas been reached in emissions pathways. Both the amount of net negative CO2 emissions and the time lag to reaching net zero GHG \r\ndepend on the choice of GHG emission metric."


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vector_id: 17776
content:
"Reaching net zero GHG emissions globally in terms of GWP-100 leads to a reduction in global warming from an earlier \r\npeak. This is due to net negative CO2 emissions balancing the GWP-100-equivalent emissions of short-lived GHG emissions, which by \r\nthemselves do not contribute to further warming if sufficiently declining (Fuglestvedt et al. 2018; Rogelj et al. 2021). Hence, 1.5degC-2degC \r\nemissions pathways in the AR6 scenario database that reach global net zero GHG emissions in the second half of the century show \r\nwarming being halted at some peak value followed by a gradual decline towards the end of the century (AR6 WGI Chapter 1, Box 1.4)."


id: daa5be9dbb6370f19fa979a001575903
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 35759
content:
"The adoption and implementation of net zero CO2 or GHG emission targets by countries and regions also depends on \r\nequity and capacity criteria. The Paris Agreement recognises that peaking of emissions will occur later in developing countries \r\n(Art. 4.1). Just transitions to net zero CO2 or GHG could be expected to follow multiple pathways, in different contexts. Regions may \r\ndecide about net zero pathways based on their consideration of potential for rapid transition to low-carbon development pathways, \r\nthe capacity to design and implement those changes, and perceptions of equity within and across countries. Cost-effective pathways \r\nfrom global models have been shown to distribute the mitigation effort unevenly and inequitably in the absence of financial support \r\nmechanisms and capacity building (Budolfson et al. 2021), and hence would require additional measures to become aligned with "


id: 846208b4d8eca9c527bf48b91ae5a49e
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 20439
content:
"equity considerations (Fyson et al. 2020; van Soest et al. 2021b). Formulation of net zero pathways by countries will benefit from clarity \r\non scope, roadmaps and fairness (Rogelj et al. 2021; Smith 2021). Achieving net zero emission targets relies on policies, institutions \r\nand milestones against which to track progress. Milestones can include emissions levels, as well as markers of technological diffusion."


id: cb63c0d61648950a596bc946c1e3dea2
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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index: climate_gpt_v2
vector_id: 33112
content:
"Net zero CO2 and carbon neutrality have different meanings in this assessment, as is the case for net zero GHG and \r\nGHG neutrality. They apply to different boundaries in the emissions and removals being considered. Net zero (GHG or CO2) refers \r\nto emissions and removals under the direct control or territorial responsibility of the reporting entity. In contrast, (GHG or carbon) \r\nneutrality includes anthropogenic emissions and anthropogenic removals within and also those beyond the direct control or territorial \r\nresponsibility of the reporting entity. At the global scale, net zero CO2 and carbon neutrality are equivalent, as is the case for net zero \r\nGHG and GHG neutrality. The term 'climate neutrality' is not used in this assessment because the concept of climate neutrality is \r\ndiffuse, used differently by different communities, and not readily quantified."


id: cf68a248bc8cfdd73cc22e6d99770655
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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index: climate_gpt_v2
vector_id: 33843
content:
"Given the larger contribution of scenarios in the literature that aim \r\nto reduce net negative emissions, emission reductions are somewhat \r\nlarger in the short term compared to similar categories in the IPCC \r\nSR1.5. At the same time, the year of net zero emissions is somewhat \r\nlater (but only if these rapid, short-term emission reductions are \r\nachieved). The scenarios in the literature in C1-C3 show a peak in \r\nglobal emissions before 2025. Not achieving this requires a  more \r\nrapid reduction after 2025 to still meet the Paris goals (Section 3.5)."


id: 10b3eb2440c98615cded954e67afbc89
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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index: climate_gpt_v2
vector_id: 121
content:
"e\r\n Alignment with the categories of the illustrative SSP scenarios considered in AR6 WGI, and the Illustrative (Mitigation) Pathways (IPs/IMPs) of WGIII. The IMPs have \r\ncommon features such as deep and rapid emissions reductions, but also different combinations of sectoral mitigation strategies. See Box SPM.1 for an introduction of the \r\nIPs and IMPs, and Chapter 3 for full descriptions. {3.2, 3.3, Annex III.II.2.4}"


id: 1ada89dd5c7e1bbbec024589710dc1d3
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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index: climate_gpt_v2
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content:
"l\r\n For cases where models do not report all GHGs, missing GHG species are infilled and aggregated into a Kyoto basket of GHG emissions in CO2-eq defined by the 100-year \r\nglobal warming potential. For each pathway, reporting of CO2, CH4, and N2O emissions was the minimum required for the assessment of the climate response and the \r\nassignment to a climate category. Emissions pathways without climate assessment are not included in the ranges presented here. {See Annex III.II.2.5 }\r\nm Cumulative emissions are calculated from the start of 2020 to the time of net zero and 2100, respectively. They are based on harmonised net CO2 emissions, ensuring \r\nconsistency with the WGI assessment of the remaining carbon budget.50 {Box 3.4}"


id: a806cdb62f0f60db63cd88aee8f12eea
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 26773
content:
"Detailed sectoral implications are discussed in Section  3.4 and \r\nChapters 5-11 (see also Table 3.3). The stringency of climate policy \r\nhas clear implications for mitigation action (Figure 3.15). There are \r\na number of important commonalities of pathways limiting warming \r\nto 2degC (>67%) or lower: for instance, they all rely on significant \r\nimprovement of energy efficiency, rapid decarbonisation of supply \r\nand, many of them, CDR (in energy supply or AFOLU), either in terms \r\nof net negative emissions or to compensate residual emissions. \r\nStill, there are also important differences and the (IMPs) show how \r\ndifferent choices can steer the system into alternative directions with \r\ndifferent combinations of response options. For decarbonisation of \r\nenergy supply many options exist, including CCS, nuclear power, and \r\nrenewables (Chapter 6). In the majority of the scenarios reaching low \r\nGHG targets, a considerable amount of CCS is applied (Figure 3.15d). "


id: f45cb404859fc4c0df8e0a5fc26b98b5
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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index: climate_gpt_v2
vector_id: 40371
content:
"options for different decarbonisation pathways with heavy reliance \r\non renewables (IMP-Ren), strong emphasis on energy-demand \r\nreductions (IMP-LD), widespread deployment of CDR methods \r\ncoupled with CCS (BECCS and DACCS) (IMP-Neg), mitigation in \r\nthe context of sustainable development (IMP-SP) (Figure 3.16). For \r\nexample, in some scenarios, a small part of the energy system is still \r\nbased on fossil fuels in 2100 (IMP-Neg), while in others, fossil fuels \r\nare almost or completely phased out (IMP-Ren). Nevertheless, in all \r\nscenarios, fossil fuel use is greatly reduced and unabated coal use \r\nis completely phased out by 2050. Also, nuclear power can be part \r\nof a mitigation strategy (however, the literature only includes some \r\nscenarios with high-nuclear contributions, such as Berger et al. 2017). "


id: 6e1602ef28c459c36d50ba0eed13277c
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 16627
content:
"2014a,b; Lehtveer and Hedenus 2015a,b; Pfenninger and Keirstead \r\n2015; Sepulveda et al. 2018; Zappa et al. 2019; IEA 2021b) (see also \r\nBox  6.6 on 100% renewables in net zero CO2 systems). Panels e \r\nand f, finally, show the contribution of CDR - both in terms of net \r\nnegative emissions and gross CDR. The contribution of total CDR \r\nobviously exceeds the net negative emissions. It should be noted that \r\nwhile a majority of scenarios rely on net negative emissions to reach \r\nstringent mitigation goals - this is not the case for all of them."


id: cf41436161bc79805b9400295b7e8cc5
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 33813
content:
"The spread shown in Figure  3.15 implies different mitigation \r\nstrategies that could all lead to emissions levels consistent with the \r\nParis Agreement (and reach zero emissions). The IMPs illustrate some "


id: 4f4c80fdd9b7cf520cb76ed4e699b06d
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 11234
content:
"b. Sectoral GHG emissions at the time of net-zero CO2 emissions \r\n(compared to modelled 2019 emissions) "


id: 5f0885856e71d5ca49fc11b56ee1eb92
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 14018
content:
"Mitigation pathways also have a regional dimension. In 2010, about \r\n40% of emissions originated from the Developed Countries and Eastern \r\nEurope and West Central Asia regions. According to the projections \r\nshown in Figure 3.17, the share of the latter regions will further increase \r\nto about 70% by 2050. In the scenarios in the literature, emissions are \r\ntypically almost equally reduced across the regions."


id: 276a86dd8f19a2a1f9d9848b7c1d0960
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 4114
content:
"This is explored further in Section 3.5. The different strategies are also \r\nclearly apparent in the way they scenarios reach net zero emissions. \r\nWhile IMP-GS and IMP-Neg rely significantly on BECCS and DACCS, \r\ntheir use is far more restricted in the other IMPs. Consistently, in these \r\nIMPs residual emissions are also significantly lower."


id: d1ccc57ad8268124f1a5eb4681117f01
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 34263
content:
"Steinberg et al. 2020). These  studies show increases in electricity \r\ngeneration in the USA (McFarland et al. 2015; Steinberg et al. 2020) \r\nand increases in CO2 emissions in Australia (Emodi et al. 2019) or the \r\nUSA (McFarland et al. 2015)."


id: c868b5b7b52f6a4a49d33805ecbd2a1c
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 32565
content:
"In summary, a  limited number of studies quantify the impact of \r\nclimate on emissions pathways. The most important impact in \r\nenergy systems might be through the impact on demand, although \r\nclimate change could also impact renewable mitigation potential - \r\ncertainly at the local and regional scale. Climate change might be \r\nmore important for land-use related mitigation measures, including \r\nafforestation, bioenergy and nature-based solutions. The net effect of \r\nchanges in climate and CO2 fertilisation are uncertain but could be \r\nsubstantial (Chapter 7)."


id: 6127ffbe92a47967a0e50a77844fd41d
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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index: climate_gpt_v2
vector_id: 14391
content:
"Most IAM pathways rely heavily on supply-side mitigation \r\nstrategies, including fuel switching, decarbonisation of fuels, and \r\nCDR (Creutzig et al. 2016; Bertram et al. 2018; Rogelj et al. 2018b; \r\nMundaca et al. 2019). For demand-side mitigation, IAMs incorporate \r\nchanges in energy efficiency, but many other demand-side options \r\n(e.g.,  behaviour and lifestyle changes) are often excluded from \r\nmodels (van Sluisveld et al. 2015; Creutzig et al. 2016; van den Berg \r\net al. 2019; Wilson et al. 2019). In addition, this mitigation is typically \r\nprice-driven and limited in magnitude (Yeh et al. 2017; Luderer \r\net al. 2018; Wachsmuth and Duscha 2019; Sharmina et al. 2020). In \r\ncontrast, bottom-up modelling studies show considerable potential \r\nfor demand-side mitigation (Creutzig et al. 2016; Yeh et al. 2017; \r\nMundaca et al. 2019; Wachsmuth and Duscha 2019) (Chapter  5), \r\nwhich can slow emissions growth and/or reduce emissions (Creutzig \r\net al. 2016; Samadi et al. 2017)."


id: bd96d85c02d497e587f2cf281040d65
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 30616
content:
"a Direct energy use and direct emissions only; emissions do not include those \r\nassociated with energy production."


id: 4c2739a32719d4ff0f994eb7fe99061d
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 10658
content:
"b\r\n Other non-specified fuel use, including military. Some models report this category in \r\nthe buildings sector, while others report it in the 'Other' sector."


id: 1d2dee63106c077051b5dcd0d5af789c
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 2308
content:
"Figure 3.18 shows indicators of supply- and demand-side mitigation \r\nin the IMPs, as well as the range across the database. Two of these \r\nIMPs (IMP-SP, IMP-LD) show strong reductions in energy demand, \r\nresulting in less reliance on bioenergy and limited CDR from \r\nenergy supply. In contrast, IMP-Neg has higher energy demand, \r\ndepending more on bioenergy and net negative CO2 emissions from \r\nenergy supply."


id: 80ccfd1d0c9b4c37c6a2bedff192ef1
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 19794
content:
"12 Unless otherwise specified, the values in parentheses in Section 3.4 from this point forward indicate the 5-95th percentile range."


id: b1416d79e4ccafe805412971b512d449
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 28415
content:
"et al. 2021) (Section 6.7). CO2 emissions from transport, industry, and \r\nbuildings are positive, and non-CO2 GHG emissions are also positive \r\nat the time of global net zero CO2 emissions (Figure 3.20)."


id: 2f39f87e658941eb6bba509c92c75d14
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 5575
content:
"Mitigation pathways show differences in the timing of decarbonisation \r\n(Figure 3.20) and the timing of net zero (Figure 3.19) across sectors \r\nand regions (high confidence); the timing in a given sector depends \r\non the cost of abatement in it, the availability of CDR options, the \r\nscenario design, near-term emissions levels, and the amount of non\u0002CO2 abatement (Yeh et al. 2017; Emmerling et al. 2019; Rogelj et al. \r\n2019a,b; Johansson et al. 2020; Azevedo et al. 2021; Ou et al. 2021; \r\nvan Soest et al. 2021b) (Cross-Chapter Box 3 in this chapter). However, \r\ndelaying emissions reductions, or more limited emissions reductions \r\nin one sector or region, involves compensating reductions in other \r\nsectors or regions if warming is to be limited (high confidence) (Price \r\nand Keppo 2017; Grubler et al. 2018; Rochedo et al. 2018; van Soest \r\net al. 2021b)."


id: 883f72b18b4b012476658b5c9cc3e8a
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 21124
content:
"At the time of net zero global CO2 emissions, emissions in some \r\nsectors are positive and some negative. In cost-effective mitigation \r\npathways, the energy supply sector typically reaches net zero CO2\r\nbefore the economy as a whole, while the demand sectors reach net \r\nzero CO2 later, if at all (Pietzcker et al. 2014; Price and Keppo 2017; \r\nLuderer et al. 2018; Rogelj et al. 2018a,b; Mejean et al. 2019; Azevedo "


id: 625ffa1e175662b999cae913ee2cc14d
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 14590
content:
"sions char\r\n Air \r\ne\r\nLo\r\nof P\r\n20\r\n SSP \r\nIPs \r\nment\r\n2030 \r\nD, \r\nD,\r\nen, \r\n1.9 \r\n32 \r\n(17-48\r\nNeg\r\n24 \r\n(11-35\r\n24 \r\n(16-32\r\n2.6\r\n21 \r\n(14-24\r\nS\r\n21 \r\n(12-24\r\n20 \r\n(11-23) \r\n17 \r\n(11-21\r\n4.5 13 \r\nAct (11-17\r\n7.0 32 \r\nol (17-48\r\n8.5 24 \r\n(11-35\r\ne 50th and (5\r\nable 3.1.\r\neg peaks aro\r\nn 'Direct Equ\r\ned to include"


id: c3788c7b4350e1fc0dc9b07fadf6114
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 31669
content:
"Figure 3.20 | Greenhouse gas (GHG) emissions, including CO2 emissions by sector and total non-CO2 GHGs in 2050 (top left), 2100 (top middle), year of \r\nglobal net zero CO2 (top right), cumulative CO2 emissions from 2020-2100 (bottom left), and cumulative CO2 emissions from 2020 until the year of net \r\nzero CO2 for scenarios that limit warming to below 2degC. Scenarios are grouped by their temperature category. 'Industry' includes CO2 emissions associated with industrial \r\nenergy use only; sectors shown in this figure do not necessarily sum to total CO2. In this, and other figures in Section 3.4, unless stated otherwise, only scenarios that pass the \r\nvetting criteria are included (Section 3.2). Boxes indicate the interquartile range, the median is shown with a horizontal black line, while vertical lines show the 5-95% interval."


id: 231e836b77ee45ce6efdbfcce1aab05d
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 3373
content:
"Mitigation in one sector can be dependent upon mitigation in \r\nanother sector, or may involve trade-offs between sectors. Mitigation \r\nin energy demand often includes electrification (Pietzcker et al. 2014; \r\nLuderer et al. 2018; Sharmina et al. 2020; DeAngelo et al. 2021), \r\nhowever such pathways only result in reduced emissions if  the \r\nelectricity sector is decarbonised (Zhang and Fujimori 2020) \r\n(Chapter  12). Relatedly, the mitigation potential of some sectors \r\n(e.g., transportation) depends on the decarbonisation of liquid fuels, \r\nfor example, through biofuels (Pietzcker et al. 2014; Wise et al. 2017; \r\nSharmina et al. 2020) (Chapter 12). In other cases, mitigation in one \r\nsector results in reduced emissions in another sector. For example, \r\nincreased recycling can reduce primary resource extraction; planting \r\ntrees or green roofs in urban areas can reduce the energy demand \r\nassociated with space cooling (Chapter 12)."


id: 2b6554e9230fca51d800cfb9c2dd761
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 4881
content:
"Mitigation in one sector can also result in additional emissions \r\nin  another. One example is electrification of end use which can \r\nresult in increased emissions from energy supply. However, one \r\ncomparitively well-researched example of this linkage is bioenergy. \r\nAn increase in demand for bioenergy within the energy system has \r\nthe potential to influence emissions in the AFOLU sector through the \r\nintensification of land and forest management and/or via land-use \r\nchange (Daioglou et al. 2019; Smith et al. 2019; Smith et al. 2020a; \r\nIPCC 2019a). The effect of bioenergy and BECCS on mitigation \r\ndepends on a variety of factors in modelled pathways. In the energy \r\nsystem, the emissions mitigation depends on the scale of deployment, \r\nthe conversion technology, and the fuel displaced (Calvin et al. 2021). "


id: 1a628ce7c72bfff9a59b0940fab8aab6
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 1842
content:
"with between 75 and 248 EJ yr-1 in 2050 in pathways that limit \r\nwarming to 1.5degC with no or limited overshoot. Finally, IAMs do not \r\ninclude all potential feedstock and management practices, and have \r\nlimited representation of institutions, governance, and local context \r\n(Brown et al. 2019; Butnar et al. 2020; Calvin et al. 2021)."


id: 213d33ffa551bae740b80edb019466c2
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 3066
content:
"Limiting or excluding bioenergy and/or BECCS increases mitigation \r\ncost and may limit the ability of a model to reach a low warming \r\nlevel (Edmonds et al. 2013; Calvin et al. 2014b; Luderer et al. 2018; \r\nMuratori et al. 2020). In AFOLU, bioenergy can increase or decrease \r\nterrestrial carbon stocks and carbon sequestration, depending on the \r\nscale, biomass feedstock, land management practices, and prior land \r\nuse (Calvin et al. 2014c; Wise et al. 2015; IPCC 2019a; Smith et al. \r\n2019, 2020a; Calvin et al. 2021)."


id: 840810b167b7279936522cad983bafc2
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 20382
content:
"Figure 3.22 | Primary energy consumption across scenarios: total primary energy (a), fossil fuels (b), coal without CCS (c), non-biomass renewables (d), \r\nand biomass (e). Scenarios are grouped by their temperature category. Primary energy is reported in direct equivalent, where one unit of nuclear or non-biomass renewable \r\nenergy output is reported as one unit of primary energy. Not all subcategories of primary energy are shown."


id: 835d47be1efc61d285e096d2b38b0059
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 20246
content:
"further reduced to 99% below its 2019 levels in 2100. These pathways \r\nshow an increase in low-carbon energy, with 88% (69-97%) of primary \r\nenergy from low-carbon sources in 2100, with different combinations \r\nof low-carbon fuels (e.g., non-biomass renewables, biomass, nuclear, \r\nand CCS) (Rogelj et al. 2018a,b; van Vuuren et al. 2018) (Sections 3.4.1 \r\nand 6.7). Across all pathways that limit warming to 2degC and below, \r\nnon-biomass renewables account for 52% (24-77%) of primary \r\nenergy in 2100 (Creutzig et al. 2017; Pietzcker et al. 2017; Rogelj et al. \r\n2018b) (Chapter 6 and Figure 3.22). There are some studies analysing \r\nthe potential for 100% renewable energy systems (Hansen et al. 2019); \r\nhowever, there are a range of issues around such systems (Box 6.6)."


id: 73ffd473167f818f3567e7595b0625c5
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 17650
content:
"Stringent emissions reductions at the level required to limit warming \r\nto 2degC (>67%) or 1.5degC are achieved through increased electrification \r\nof end use, resulting in increased electricity generation in all \r\npathways (high confidence) (Rogelj et al. 2018a; Azevedo et al. 2021) \r\n(Figure 3.23). Nearly all electricity in pathways likely to limit warming \r\nto 2degC and below is from low- or no-carbon fuels (Rogelj et al. 2018a; \r\nAzevedo et al. 2021), with different shares of nuclear, biomass, non\u0002biomass renewables, and fossil CCS across pathways. Low-emissions \r\nscenarios also show increases in hydrogen use (Figure 3.23)."


id: bec8a5a3379046c45c37b3377b3b65f7
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 30835
content:
"All pathways that limit warming to 2degC (>67%) or lower show \r\nsubstantial reductions in fossil fuel consumption and a near elimination \r\nof the use of coal without CCS (high confidence) (Bauer et al. 2017; \r\nvan Vuuren et al. 2018; Grubler et al. 2018; Luderer et al. 2018; Rogelj \r\net al. 2018a,b; Azevedo et al. 2021; Mcjeon et al. 2021; Welsby et al. \r\n2021) (Figure 3.22). In these pathways, the use of coal, gas and oil is \r\nreduced by 90%, 25%, and 41%, respectively, between 2019 and 2050 \r\nand 91%, 39%, and 78% between 2019 and 2100; coal without CCS is "


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created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 32736
content:
"13 2019 values are from model results and interpolated from other years when not directly reported."


id: 723af64d38474b2e3fc61009c8ba7a57
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 17339
content:
"(Figure 3.24). There is very little difference in final energy intensity \r\nfor the buildings sector across scenarios. Direct CO2 emissions \r\nfrom the buildings sector vary more widely across temperature \r\nstabilisation levels than energy consumption. In 2100, scenarios \r\nabove 3degC [C7-C8] still show an increase of CO2 emissions from \r\nbuildings around 29% above 2019, while all scenarios likely to limit \r\nwarming to 2degC and below have emission reductions of around \r\n85% (8-100%). Carbon intensity declines in all scenarios, but much \r\nmore sharply as the warming level is reduced."


id: dbd490ce3fcb4fb07a895ed1b1459d9e
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 35961
content:
"In all scenarios, the share of electricity in final energy use increases, \r\na trend that is accelerated by 2050 for the scenarios likely to limit \r\nwarming to 2degC and below (Figure 3.23). By 2100, the low-warming \r\nscenarios show large shares of electricity in final energy consumption \r\nfor buildings. The opposite is observed for gases."


id: 335309285eee891ebc58ae3a204a258d
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 6281
content:
"While several global IAM models have developed their buildings \r\nmodules considerably over the past decade (Daioglou et al. 2012; \r\nKnobloch et al. 2017; Clarke et al. 2018; Edelenbosch et al. 2021; \r\nMastrucci et al. 2021), the extremely limited availability of key \r\nsectoral variables in the AR6 scenarios database (such as floor space \r\nand energy use for individual services) prohibit a detailed analysis \r\nof sectoral dynamics. Individual studies in the literature often \r\nfocus on single aspects of the buildings sector, though collectively \r\nproviding a  more comprehensive overview (Edelenbosch et al. "


id: 34c361b831f9595befebab8fce527bd6
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 6523
content:
"Reference scenarios show growth in transport demand, particularly \r\nin aviation and freight (Yeh et al. 2017; Sharmina et al. 2020; \r\nMuller-Casseres et al. 2021b). Energy consumption continues to be \r\ndominated by fossil fuels in reference scenarios, with some increases \r\nin electrification (Yeh et al. 2017; Edelenbosch et al. 2020; Yeh et al. "


id: c006041ac98f170e00d9b1894f3d89
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 31076
content:
"Across all IAM scenarios assessed, final energy demand for transport \r\ncontinues to grow, including in many stringent mitigation pathways \r\n(Figure  3.25). The carbon intensity of energy declines substantially \r\nby 2100 in likely 2degC (>67%) and below scenarios, leading to \r\nsubstantial declines in transport sector CO2 emissions with increased \r\nelectrification of the transport system (Figure 3.23)."


id: c4cefe2e9e6303dc8b1570c3699e0769
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 31942
content:
"2017). CO2 emissions from transport increase for most models in \r\nreference scenarios (Yeh et al. 2017; Edelenbosch et al. 2020)."


id: 8f98fc4d38eaa67e394913354a2d3c59
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 22473
content:
"The transport sector has more detail than other sectors in many \r\nIAMs (Edelenbosch et al. 2020); however, there is considerable \r\nvariation across models. Some models (e.g.,  GCAM, IMAGE, \r\nMESSAGE-GLOBIOM) represent different transport modes with \r\nendogenous shifts across modes as a  function of income, price, \r\nand modal speed (Edelenbosch et al. 2020).15 However, IAMs, \r\nincluding those with detailed transport, exclude several supply-side \r\n(e.g.,  synthetic fuels) and demand-side (e.g.,  behaviour change, \r\nreduced shipping, telework and automation) mitigation options \r\n(Pietzcker et al. 2014; Creutzig et al. 2016; Mittal et al. 2017; Davis \r\net al. 2018; Kohler et al. 2020; Mittal et al. 2017; Gota et al. 2019; \r\nWilson et al. 2019; Creutzig et al. 2016; Kohler et al. 2020; Sharmina \r\net al. 2020; Pietzcker et al. 2014; Lefevre et al. 2021; Muller-Casseres \r\net al. 2021a,b)."


id: a893e4296d116db4698b474800cbc949
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 26868
content:
"16 2019 values are from model results and interpolated from other years when not directly reported."


id: 237f447575682b734592649f513f0238
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 3444
content:
"Reference scenarios show declines in energy intensity, but increases \r\nin final energy use in the industrial sector (Edelenbosch et al. \r\n2017b). These scenarios show increases in CO2 emissions both for \r\nthe total industrial sector (Edelenbosch et al. 2017b, 2020; Luderer \r\net al. 2018) and individual subsectors such as cement and iron and \r\nsteel (van Ruijven et al. 2016; van Sluisveld et al. 2021) or chemicals \r\n(Daioglou et al. 2014; van Sluisveld et al. 2021)."


id: a623e8dbe11cfa7e0518361ce1e8a402
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 26452
content:
"In mitigation pathways, CO2 emissions reductions are achieved \r\nthrough a  combination of energy savings (via energy-efficiency \r\nimprovements and energy conservation), structural change, fuel \r\nswitching, and decarbonisation of fuels (Edelenbosch et al. 2017b, \r\n2020; Grubler et al. 2018; Luderer et al. 2018). Mitigation pathways \r\nshow reductions in final energy for industry compared to the baseline \r\n(Edelenbosch et al. 2017b; Luderer et al. 2018; Edelenbosch et al. \r\n2020) and reductions in the carbon intensity of the industrial sector \r\nthrough both fuel switching and the use of CCS (van Ruijven et al. \r\n2016; Edelenbosch et al. 2017b, 2020; Luderer et al. 2018; Paltsev \r\net al. 2021; van Sluisveld et al. 2021). The mitigation potential differs \r\ndepending on the industrial subsector and the availability of CCS, \r\nwith larger potential reductions in the steel sector (van Ruijven \r\net al. 2016) and cement industry (Sanjuan et al. 2020) than in the "


id: f94fe2df292df9d4b74c8f195a3932ca
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 41226
content:
"Figure 3.26 | Industrial final energy, including feedstocks (a), CO2 emissions (b), carbon intensity (c), energy intensity (d), share of final energy from \r\nelectricity (e), and share of final energy from gases (f). Energy intensity is final energy per unit of GDP. Carbon intensity is CO2 emissions per EJ of final energy. The first \r\nfour indicators are indexed to 2019,15 where values less than 1 indicate a reduction. Industrial sector CO2 emissions include fuel combustion emissions only."


id: bebfdcb3efe063d8007403f7a4b85791
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 30827
content:
"chemicals sector (Daioglou et al. 2014). Many scenarios, including \r\nstringent mitigation scenarios, show continued growth in final \r\nenergy; however, the carbon intensity of energy declines in all \r\nmitigation scenarios (Figure 3.26)."


id: 43874a0fd3f63ee8cb56cef7c22bd62c
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 9106
content:
"studies with median reductions in CO2 emissions between 2019 and \r\n2050 of 70% in scenarios likely to limit warming to 2degC (>67%) \r\nand below and a maximum reduction of 96% (Figure 3.26). Some \r\ndifferences between IAMs and sectoral models can be attributed to \r\ndifferences in technology availability, with IAMs sometimes including \r\nmore technologies (van Ruijven et al. 2016) and sometimes less \r\n(Sharmina et al. 2020)."


id: ea66871ef36ddfbd722a576e3e749bc4
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 38601
content:
"The representation of the industry sector is very aggregated in most \r\nIAMs, with only a small subset of models disaggregating key sectors \r\nsuch as cement, fertiliser, chemicals, and iron and steel (Daioglou \r\net al. 2014; Edelenbosch et al. 2017b; Pauliuk et al. 2017; Napp \r\net al. 2019; van Sluisveld et al. 2021). IAMs often account for both \r\nenergy combustion and feedstocks (Edelenbosch et al. 2017b), but \r\nIAMs typically ignore material flows and miss linkages between \r\nsectors (Pauliuk et al. 2017; Kermeli et al. 2019). By excluding \r\nthese processes, IAMs misrepresent the mitigation potential of the \r\nindustry sector, for example by overlooking mitigation from material \r\nefficiency and circular economies (Sharmina et al. 2020), which can \r\nhave substantial mitigation potential (Sections 5.3.4 and 11.3)."


id: e53304f3165169446434a24c193a764b
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 37625
content:
"Mitigation pathways show substantial reductions in CO2 emissions, \r\nbut more modest reductions in AFOLU CH4 and N2O emissions \r\n(high confidence) (Popp et al. 2017; Roe et al. 2019; Reisinger et al. \r\n2021) (Figure  3.27). Pathways limiting warming to likely 2degC or \r\nlower are projected to reach net zero CO2 emissions in the AFOLU \r\nsector around 2033 (2024-2060); however, AFOLU CH4 and N2O \r\nemissions remain positive in all pathways (Figure  3.27). While \r\nIAMs include many land-based mitigation options, these models \r\nexclude several options with large mitigation potential, such as \r\nbiochar, agroforestry, restoration/avoided conversion of coastal \r\nwetlands, and restoration/avoided conversion of peatland (IPCC \r\n2019a; Smith et al. 2019) (Chapter  7 and Section  3.4). Sectoral \r\nstudies show higher mitigation potential than IAM pathways, as "


id: 831d610e839cb12152000e8ec15b2ef
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 20192
content:
"Sectoral studies indicate a large mitigation potential in the industrial \r\nsector by 2050, including the potential for net zero CO2 emissions \r\nfor steel, plastics, ammonia, and cement (Section  11.4.1). Detailed \r\nindustry sector pathways show emissions reductions between \r\n39% and 94% by mid-century compared to the present day17\r\n(Section  11.4.2) and a  substantial increase in direct electrification \r\n(IEA 2021a). IAMs show comparable mitigation potential to sectoral "


id: 77b28343241ba9a43a17bdaf1d7cfad0
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 18266
content:
"Figure 3.27 | Reduction in AFOLU GHG emissions from 2019. The AFOLU CO2 estimates in this figure are not necessarily comparable with country GHG inventories \r\nsee Chapter 7)."


id: 8220eefd2540b828bf921752781d24d1
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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index: climate_gpt_v2
vector_id: 20018
content:
"their peak to a given level (Smith et al. 2019; Hasegawa et al. 2021a). \r\nIAM pathways rely on afforestation and BECCS as CDR measures, \r\nso delayed mitigation action results in substantial land-use change \r\nin the second half of the century with implications for sustainable \r\ndevelopment (Hasegawa et al. 2021a) (Section  3.7). Shifting to \r\nearlier mitigation action reduces the amount of land required for \r\nthis, though at the cost of larger land-use transitions earlier in the \r\ncentury (Hasegawa et al. 2021a). Earlier action could also reduce \r\nclimate impacts on agriculture and land-based mitigation options \r\n(Smith et al. 2019)."


id: 2ff0ceeebd7575dc8f9a3c816ee01df7
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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index: climate_gpt_v2
vector_id: 5691
content:
"Delayed mitigation has implications for land-use transitions \r\n(Hasegawa et al. 2021a). Delaying mitigation action can result in \r\na temporary overshoot of temperature and large-scale deployment \r\nof CDR in the second half of the century to reduce temperatures from "


id: a82d9e73e1f735e8853717a2eff06b1a
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 26806
content:
"forest regeneration, can help to remediate and slow any decline in the \r\nforest carbon sink, for example by restoring degraded forest areas, \r\nand so go some way towards addressing the issue of sink saturation \r\n(IPCC 2019) (AR6 WGI Chapter 5; and Chapter 7 in this report). The \r\naccumulated carbon resulting from mitigation options that enhance \r\ncarbon sequestration (e.g., reforestation, soil carbon sequestration) is \r\nalso at risk of future loss due to disturbances (e.g., fire, pests) (Boysen \r\net al. 2017; de Coninck et al. 2018; Fuss et al. 2018; Smith et al. 2019; \r\nIPCC 2019a; Anderegg et al. 2020) (AR6 WGI Chapter 5). Maintaining \r\nthe resultant high vegetation and soil carbon stocks could limit future \r\nland-use options, as maintaining these carbon stocks would require \r\nretaining the land use and land-cover configuration implemented to \r\nachieve the increased stocks."


id: 1b1cb82f4572bbd9381cb20184094c05
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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index: climate_gpt_v2
vector_id: 1965
content:
"(DACCS), enhanced weathering (EW), and ocean-based approaches, \r\nfocusing on the role of these options in long-term mitigation \r\npathways, using both IAMs (Chen and Tavoni 2013; Marcucci \r\net al. 2017; Rickels et al. 2018; Fuhrman et al. 2019, 2020, 2021; \r\nRealmonte et al. 2019; Akimoto et al. 2021; Strefler et al. 2021a) and \r\nnon-IAMs (Fuss et al. 2013; Gonzalez and Ilyina 2016; Bednar et al. \r\n2021; Shayegh et al. 2021). There are other options discussed in the \r\nliterature, such as methane capture (Jackson et al. 2019), however, \r\nthe role of these options in long-term mitigation pathways has not \r\nbeen quantified and is thus excluded here. Chapter 12 includes a more \r\ndetailed description of the individual technologies, including their \r\ncosts, potentials, financing, risks, impacts, maturity and upscaling."


id: 8ad6c027b52768fbb19431d93c379d14
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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index: climate_gpt_v2
vector_id: 21597
content:
"Table 3.5 | Carbon dioxide removal in assessed pathways. Scenarios are grouped by temperature categories, as defined in Section 3.2.4. Quantity indicates the median \r\nand 5-95th percentile range of cumulative sequestration from 2020 to 2100 in GtCO2. Count indicates the number of scenarios with positive values for that option. Source: \r\nAR6 Scenarios Database."


id: 885836e44773ddbeb12fb7903b8e8f86
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 21150
content:
"1\r\n Cumulative CDR from AFOLU cannot be quantified precisely because models use different reporting methodologies that in some cases combine gross emissions and removals, \r\nand use different baselines."


id: 1eafb01a96c32ae0c10c4d31c89f9fd9
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 2601
content:
"available options (Holz et al. 2018; Strefler et al. 2021a). Similar to \r\nDACCS, the deployment of deep-ocean storage depends on cost and \r\nthe strength of carbon-cycle feedbacks (Rickels et al. 2018)."


id: 2a526f7436db172cfc09f34778f75d8c
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 4669
content:
"3.5.1 Relationship Between Long-term Climate Goals \r\nand Near- to Medium-term Emissions Reductions"


id: 537099694f61273c2d86aad5b7f90d7
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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index: climate_gpt_v2
vector_id: 11955
content:
"This section assesses the relationship between long-term climate \r\ngoals and short- to medium-term emissions reduction strategies \r\nbased on the mitigation pathway literature. After an overview of \r\nthis relationship (Section 3.5.1), it provides an assessment of what \r\ncurrently planned near-term action implies for limiting warming \r\nto  1.5degC-2degC (Section  3.5.2), and to what extent pathways with \r\naccelerated action beyond current NDCs can improve the ability to \r\nkeep long-term targets in reach (Section 3.5.3)."


id: fcf1e86fe583969be8fac8ba46b20a83
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 41861
content:
"The close link between cumulative CO2 emissions and warming has \r\nstrong implications for the relationship between near-, medium-, \r\nand long-term climate action to limit global warming. The AR6 \r\nWGI Assessment has estimated a  remaining carbon budget of \r\n500 (400) GtCO2 from the beginning of 2020 onwards for staying \r\nbelow  1.5degC with 50% (67%) likelihood, subject to additional \r\nuncertainties about historic warming and the climate response, \r\nand variations in warming from non-CO2 climate forcers (Canadell and \r\nMonteiro 2019) (AR6 WGI Chapter 5, Section 5.5). For comparison, if \r\ncurrent CO2 emissions of more than 40 GtCO2 are keeping up until \r\n2030, more than 400 GtCO2 will be emitted during 2021-2030, \r\nalready exhausting the remaining carbon budget for 1.5degC by 2030."


id: f02bb4c57aacc989294269b485a43bc0
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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index: climate_gpt_v2
vector_id: 39641
content:
"2017). Early and deep reductions of methane emissions, and other \r\nshort-lived warming agents such as black carbon, provide space for \r\nresidual CO2-induced warming until the point of net zero CO2 emissions \r\nis reached (see purple lines in Figure 3.29). Such emissions reductions \r\nhave also been advocated due to co-benefits for, for example, reducing \r\nair pollution (Rao et al. 2016; Shindell et al. 2017a, 2018; Shindell and \r\nSmith 2019; Rauner et al. 2020a; Vandyck et al. 2020)."


id: 2067f4eb3a4ffeaa821e2f1daf3ac3d7
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 2920
content:
"their reduction drives up non-CO2 warming in the near term, which \r\ncan be attenuated by the simultaneous reduction of methane and \r\nblack carbon (Shindell and Smith 2019; Smith et al. 2020b) (AR6 WGI \r\nSection 6.7). After 2030, the reduction in methane concentrations and \r\nassociated reductions in tropospheric ozone levels tend to dominate \r\nso that a peak and decline in non-CO2 forcing and non-CO2-induced \r\nwarming can occur before net zero CO2 is reached (Figure 3.29) (Rogelj \r\net al. 2018a). The more stringent the reductions in methane and other \r\nshort-lived warming agents such as black carbon, the lower this peak \r\nand the earlier the decline of non-CO2 warming, leading to a reduction \r\nof warming rates and overall warming in the near to medium term \r\n(Harmsen et al. 2020; Smith et al. 2020b). This is important for keeping \r\nwarming below a tight warming limit that is already reached around \r\nmid-century as is the case in  1.5degC pathways (Xu and Ramanathan "


id: e406be3fd2abb958f4124a11b8faf4d7
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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index: climate_gpt_v2
vector_id: 37419
content:
"The relationship between long-term climate goals and near-term \r\naction is further constrained by social, technological, economic and \r\npolitical factors (Cherp et al. 2018; van Sluisveld et al. 2018b; Aghion \r\net al. 2019; Mercure et al. 2019; Trutnevyte et al. 2019b; Jewell and \r\nCherp 2020). These factors influence path dependency and transition \r\nspeed (Pahle et al. 2018; Vogt-Schilb et al. 2018). While detailed "


id: 374b193393e88ff1d528b2a07bd00596
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 6944
content:
"post-2030 mitigation efforts and warming outcomes based on the \r\nliterature and the AR6 scenarios database. "


id: d1a381f1c8620a72166eff4110077490
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 34231
content:
"Without a  strengthening of policies beyond those that are \r\nimplemented by the end of 2020, GHG emissions are projected \r\nto rise beyond 2025, leading to a median global warming of 3.2 \r\n[2.2 to  3.5] degC by 2100. Modelled pathways that are consistent \r\nwith NDCs announced prior to COP26 until 2030 and assume no \r\nincrease in ambition thereafter have lower emissions, leading to \r\na median global warming of 2.8degC [2.1-3.4degC] by 2100."


id: 86f204e6387868f4851a2d63065b16b9
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 20907
content:
"be lower (Gambhir et al. 2017; Napp et al. 2017; Brutschin et al. 2021) \r\n(cf. Section 3.8), increasing the risk of an overshoot of climate goals."


id: 18321290e6debb73eb6d76c9f3801311
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 1455
content:
"Obtaining net negative CO2 emissions requires massive deployment \r\nof carbon dioxide removal (CDR) in the second half of the century, on \r\nthe order of 220 (160-370) GtCO2 for each 0.1degC degree of cooling \r\n(based on the assessment of the likely range of the transient response \r\nto cumulative CO2 emissions in AR6 WGI Section 5.5 in Chapter 5, \r\nnot taking into account potential asymmetries in the temperature \r\nresponse to CO2 emissions and removals). CDR is assessed in detail \r\nin Section  12.3 of this report (see also Cross-Chapter Box  8 in \r\nChapter 12). Here we only point to the finding that CDR ramp-up \r\nrates and absolute deployment levels are tightly limited by techno\u0002economic, social, political, institutional and sustainability constraints \r\n(Smith et al. 2016; Boysen et al. 2017; Fuss et al. 2018, 2020; Nemet "


id: e5d50b4b786a0fd8f47dff882723f888
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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index: climate_gpt_v2
vector_id: 37737
content:
"There already exists a substantial and growing carbon lock-in today, \r\nas measured by committed emissions associated with existing long\u0002lived infrastructure (Section  2.7 and Figure  2.31). If existing fossil \r\nfuel infrastructure would continue to be operated as historically, it \r\nwould entail CO2 emissions exceeding the carbon budget for 1.5degC \r\n(Section  2.7.2 and Figure  2.32). However, owner-operators and \r\nsocieties may choose to retire existing infrastructure earlier than \r\nin the past, and committed emissions are thus contingent on the \r\ncompetitiveness of non-emitting alternative technologies and \r\nclimate policy ambition. Therefore, in mitigation pathways, some \r\ninfrastructure may become stranded assets. Stranded assets have \r\nbeen defined as 'assets that have suffered from unanticipated or "


id: 5ca12832cb1745cab4dde5b361d89f9e
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content:
"Many quantitative estimates of stranded assets along mitigation \r\npathways have focused on fossil fuel power plants in pathways \r\ncharacterised by mitigation ambition until 2030 corresponding to the \r\nNDCs followed by strengthened action afterwards to limit warming \r\nto 2degC (>67%) or lower (Bertram et al. 2015a; Iyer et al. 2015b; \r\nLane  et al. 2016; Farfan and Breyer 2017; van Soest et al. 2017a; \r\nKriegler et al. 2018a; Luderer et al. 2018; Cui et al. 2019; Saygin et al. "


id: 4c1d3735e2b18ce95aefd8dab2077309
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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index: climate_gpt_v2
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content:
"scenarios/pathways in the literature (IEA 2011; Spencer et al. \r\n2015; van Soest et al. 2021a). They rely on regionally differentiated \r\nregulatory and pricing policies to gradually strengthening regional \r\nand sectoral action beyond the mitigation ambition in the NDCs. \r\nThere are limitations to this approach. The tighter the warming \r\nlimit, the more likely it is that disruptive action becomes inevitable \r\nto achieve the speed of transition that would be required (Kriegler \r\net al. 2018a). Cost-effective pathways already have abrupt shifts in \r\ndeployments, investments and prices at the time a stringent warming \r\nlimit is imposed, reflecting the fact that the overall response to \r\nclimate change has so far been misaligned with long-term climate \r\ngoals (Fawcett et al. 2015; Rogelj et al. 2016; Schleussner et al. 2016b; \r\nGeiges et al. 2020). Disruptive action can help to break lock-ins and \r\nenable transformative change (Vogt-Schilb et al. 2018)."


id: 1206172ccd6b4a8850e77fc31af5696b
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 360
content:
"The risk of stranded assets has implications for workers depending \r\non those assets, asset owners, assets portfolio managers, financial \r\ninstitutions and the stability of the financial system. Chapter  6 \r\nassesses the risks and implications of stranded assets for energy \r\nsystems (Section 6.7.3 and Box 6.11) and fossil fuels (Section 6.7.4). \r\nThe implications of stranded assets for inequality and Just Transition \r\nare assessed in Chapter 17 (Section 17.3.2.3). Chapter 15 assesses \r\nthe literature on those implications for the financial system as well \r\nas on coping options (Sections 15.5.2 and 15.6.1)."


id: 59ab2dfd536de9a909751042ef847f5
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 13011
content:
"On the other hand, mitigation, by limiting climate change, reduces \r\nthe risk of destroyed or stranded assets from the physical impacts of \r\nclimate change on natural and human systems, from more frequent, \r\nintense or extended extreme events and from sea level rise (O'Neill \r\net al. 2020a). The literature on mitigation pathways rarely includes an \r\nevaluation of stranded assets from climate change impacts. Unruh \r\n(2019) suggest that these are the real stranded assets of carbon lock\u0002in and could prove much more costly."


id: 719f4b1f744d08fc37e75154176cb7e5
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 17237
content:
"One approach relies on augmenting initially moderate emissions\u0002pricing policies with robust anticipation of ratcheting up climate \r\naction in the future (Spencer et al. 2015). If announcements of \r\nstrong future climate policies are perceived to be credible, they \r\ncan help to prevent carbon lock-in as investors anticipating high \r\nfuture costs of GHG emissions would reduce investment into fossil \r\nfuel infrastructure, such as coal power plants (Bauer et al. 2018b). \r\nHowever, the effectiveness of such announcements strongly \r\nhinges on their credibility. If investors believe that policymakers \r\ncould drop them if anticipatory action did not occur, they may not \r\nundertake such action."


id: b486b5702b410b221ee76cb2dc477850
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 29005
content:
"Another approach relies on international cooperation to strengthen \r\nnear-term climate action. These studies build on international climate \r\npolicy architectures that could incentivise a coalition of like-minded \r\ncountries to raise their mitigation ambition beyond what is stated in \r\ntheir NDC (Graichen et al. 2017). Examples are the idea of climate \r\nclubs characterised by harmonised carbon and technology markets \r\n(Nordhaus 2015; Keohane et al. 2017; Paroussos et al. 2019; Pihl \r\n2020) and the Powering Past Coal Alliance (PPCA) (Jewell et al. 2019). \r\nParoussos et al. (2019) find economic benefits of joining a climate \r\nclub despite the associated higher mitigation effort, in particular due "


id: 2a553f02e0b913fdd4dea6dc01a492a5
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 4673
content:
"A growing literature explores long-term mitigation pathways with \r\naccelerated near-term action going beyond the NDCs (Graichen \r\net al. 2017; Jiang et al. 2017; Kriegler et al. 2018a; Roelfsema et al. \r\n2018; Fekete et al. 2021; van Soest et al. 2021a). Global accelerated \r\naction pathways are designed to transition more gradually from \r\nimplemented policies and planned implementation of NDCs onto \r\na  1.5degC-2degC pathway and at the same time alleviate the abrupt \r\ntransition in 2030 that would be caused by following the NDCs until \r\n2030 and strengthening towards limiting warming to 2degC thereafter \r\n(Section 3.5.2). Therefore, they have sometimes been called bridging "


id: 6e195287679aed4dd34dea61b3b93eed
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 16632
content:
"to access to technology and climate finance. Graichen et al. (2017) \r\nfind an additional reduction of  5-11 GtCO2-eq compared to the \r\nmitigation ambition in the NDCs from the successful implementation \r\nof international climate initiatives. Other studies assess benefits from \r\ninternational transfers of mitigation outcomes (Stua 2017; Edmonds \r\net al. 2021). Edmonds et al. (2021) find economic gains from sharing \r\nNDC emissions-reduction commitments compared to purely domestic \r\nimplementation of NDCs. If reinvested in mitigation efforts, the study \r\nprojects an additional reduction of 9 billion tonnes of CO2 in 2030."


id: 518c0803ac3c330c20469925e60acaa
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 11625
content:
"A complete appraisal of economic effects and welfare effects at \r\ndifferent temperature levels would include the macroeconomic \r\nimpacts of investments in low-carbon solutions and structural \r\nchange away from emitting activities, co-benefits and adverse side \r\neffects of mitigation, (avoided) climate damages, as well as (reduced) \r\nadaptation costs, with high temporal, spatial and social heterogeneity \r\nusing a  harmonised framework. If no such complete appraisal in \r\na harmonised framework exists, key elements are emerging from the \r\nliterature, and assessed in the following subsections: on aggregated \r\neconomy-wide global mitigation costs (Section  3.6.1), on the \r\neconomic benefits of avoiding climate impacts (Section  3.6.2), on \r\neconomic benefits and costs associated with mitigation co-benefits \r\nand co-harms (Section  3.6.3) and on the distribution of economic \r\nimplications between economic sectors and actors (Section 3.6.4)."


id: e3c429075a31b788e7f9f4bc7ddbd665
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 37360
content:
"Accelerated action pathways derived with this approach show \r\nconsiderable scope for narrowing the emissions gap between \r\npathways reflecting the ambition level of the NDCs and cost\u0002effective mitigation pathways in 2030. Kriegler et al. (2018a) find \r\naround 10 GtCO2-eq lower emissions compared to original NDCs \r\nfrom a global roll out of good practice plus net zero policies and \r\na  moderate increase in regionally differentiated carbon pricing. \r\nFekete et al. (2021) show that global replication of sector progress \r\nin five major economies would reduce GHG emissions in 2030 by "


id: 2e6b5b504215c7ea76e7cdebff016c47
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 5442
content:
"Most studies that have developed mitigation pathways have used a  cost-effectiveness analysis (CEA) framework, which aim to \r\ncompare the costs of different mitigation strategies designed to meet a given climate change mitigation goal (e.g., an emission\u0002reduction target or a temperature stabilisation target) but does not represent economic impacts from climate change itself, nor the \r\nassociated economic benefits of avoided impacts. Other studies use modelling frameworks that represent the feedback of damages \r\nfrom climate change on the economy in a cost-benefit analysis (CBA) approach, which balances mitigation costs and benefits. This \r\nsecond type of study is represented in Section 3.6.2."


id: 4eb1265453395f1735882a8c1eea150
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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index: climate_gpt_v2
vector_id: 11114
content:
"The absolute level of economic activity and welfare also strongly depends on the socio-economic pathway assumptions regarding, \r\ninter alia, evolutions in demography, productivity, education levels, inequality, and technical change and innovation. The GDP or \r\nconsumption indicators reported in the database of scenarios, and synthesized below, represent the absolute level of aggregate \r\neconomic activity or consumption but do not reflect welfare and well-being (Roberts et al. 2020), that notably depend on human\u0002needs satisfaction, distribution within society and inequality (Section 3.6.4)."


id: 30e603b4d02fd41a333bd281d3fb141c
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 5857
content:
"Chapter 1 and Annex III.I give further details of the economic concepts and modelling frameworks, including their limitations, used \r\nin this report, respectively."


id: 31bb6d1cb6bf4c2d0c86ea8bf23b7007
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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vector_id: 5995
content:
"Figure 3.32 | Marginal abatement cost of carbon in 2030, 2050 and 2100 for mitigation pathways with immediate global mitigation action (a), and ratio \r\nin 2050 between pathways that correspond to NDCs announced prior to COP26 in 2030 and strengthen action after 2030 and pathways with immediate \r\nglobal mitigation action, for C3 and C4 temperature categories (b)."


id: cf83ae26cb6db203ccd8b5a1576bb2d9
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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index: climate_gpt_v2
vector_id: 33870
content:
"For optimisation modelling frameworks, the time profile of marginal \r\nabatement costs of carbon depends on the discount rate, with lower \r\ndiscount rates implying higher carbon values in the short term but \r\nlower values in the long term (Emmerling et al. 2019) (see also \r\n'Discounting' in Annex I: Glossary, and Annex III.I.2). In that case, the \r\ndiscount rate also influences the shape of the emissions trajectory, \r\nwith low discount rates implying more emissions reduction in the \r\nshort term and, for low-temperature categories, limiting CDR and \r\ntemperature overshoot."


id: 1cb6678230508b559f3a5b3b3a265cd8
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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index: climate_gpt_v2
vector_id: 2233
content:
"Pathways that correspond to NDCs announced prior to COP26 \r\nin 2030 and strengthen action after 2030 imply higher marginal \r\nabatement costs of carbon in the longer run than pathways \r\nwith stronger immediate global mitigation action (Figure  3.32b) \r\n(high confidence)."


id: 8f8213f8d7ce6f56181cbce60bf5ace8
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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index: climate_gpt_v2
vector_id: 22459
content:
"Mitigation costs increase with the stringency of mitigation (Hof \r\net al. 2017; Vrontisi et al. 2018) (Figure 3.34b,c), but are reduced when \r\nenergy demand is moderated through energy efficiency and lifestyle \r\nchanges (Fujimori et al. 2014; Bibas et al. 2015; Liu et al. 2018; Mejean \r\net al. 2019), when sustainable transport policies are implemented \r\n(Zhang et al. 2018c), and when international technology cooperation is \r\nfostered (Schultes et al. 2018; Paroussos et al. 2019). Mitigation costs \r\nalso depend on assumptions on availability and costs of technologies \r\n(Clarke et al. 2014; Bosetti et al. 2015; Dessens et al. 2016; Creutzig et al. "


id: b487c77eeb13ad4a723369897bd80d9
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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index: climate_gpt_v2
vector_id: 29006
content:
"However, the possibility for increased economic activity following \r\nmitigation action, and conversely the risk of large negative economic \r\neffects, are not excluded. Some studies find that mitigation increases \r\nthe speed of economic growth compared to baseline scenarios (Pollitt \r\nand Mercure 2018; Mercure et al. 2019). These studies are based on \r\na  macroeconomic modelling framework that represent baselines "


id: 5fe08aec7fb82c980f360bf3c21ee6e7
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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index: climate_gpt_v2
vector_id: 14177
content:
"2018; Napp et al. 2019; Giannousakis et al. 2021), on the representation \r\nof innovation dynamics in modelling frameworks (Hoekstra et al. \r\n2017; Rengs et al. 2020) (Chapter 16), as well as the representation \r\nof investment dynamics and financing mechanisms (Iyer et al. 2015c; \r\nMercure et al. 2019; Battiston et al. 2021). In particular, endogenous \r\nand induced innovation reduce technology costs over time, create \r\npath dependencies and reduce the macroeconomic cost of reaching \r\na mitigation target (Section 1.7.1.2). Mitigation costs also depend on \r\nsocio-economic assumptions (Hof et al. 2017; van Vuuren et al. 2020)."


id: 4401fbe7042877734d79a082b28fe44a
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 9202
content:
"2019; Riahi et al. 2021). Mitigation pathways with weak early action \r\ncorresponding to NDCs announced prior to COP26 in 2030 and \r\nstrengthening action after 2030 to reach end-of-century temperature \r\ntargets imply limited mitigation costs in 2030, compared to immediate \r\nglobal action pathways, but faster increase in costs post-2030, with \r\nimplications for intergenerational equity (Aldy et al. 2016; Liu et al. \r\n2016; Vrontisi et al. 2018). Emissions trading policies reduce global \r\naggregate mitigation costs, in particular in the context of achieving \r\nNDCs (Fujimori et al. 2015, 2016a; Bohringer et al. 2021; Edmonds \r\net al. 2021), and change the distribution of mitigation costs between \r\nregions and countries (Section 3.6.1.2)."


id: f054ae5001a49ec97099ece463bf829d
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 39666
content:
"Regional policy costs depend on the evaluation framework (Budolfson \r\net al. 2021), policy design, including revenue recycling, and on \r\ninternational coordination, especially among trade partners. By \r\nfostering technological change and finance, climate cooperation can "


id: 22411f6a4f5d8f5330c4bf02b6babb80
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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index: climate_gpt_v2
vector_id: 3249
content:
"(Rogelj et al. 2018b). By contrast, achieving equity without \r\ninternational markets would result in a  large discrepancy in \r\nregional carbon prices, up to a factor of 100 (Bauer et al. 2020). The \r\nefficiency-sovereignty trade-off can be partly resolved by allowing \r\nfor limited differentiation of regional carbon prices: moderate \r\nfinancial transfers substantially reduce inefficiencies by narrowing \r\nthe carbon price spread (Bauer et al. 2020)."


id: 6b26eb22ec0c4b1ccc56d3d0c22fcfb8
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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index: climate_gpt_v2
vector_id: 16146
content:
"generate economic benefits, both in large developing economies such \r\nas China and India (Paroussos et al. 2019) and industrialised regions \r\nsuch as Europe (Vrontisi et al. 2020). International coordination is \r\na major driver of regional policy costs. Delayed participation in global \r\nmitigation efforts raises participation costs, especially in carbon\u0002intensive economies (Figure 3.35a. Trading systems and transfers can \r\ndeliver cost savings and improve equity (Rose et al. 2017a). On the \r\nother hand, measures that reduce imports of energy-intensive goods \r\nsuch as carbon-border tax adjustment may imply costs outside of \r\nthe policy jurisdiction and have international equity repercussions, \r\ndepending on how they are designed (Bohringer et al. 2012, 2017; \r\nCosbey et al. 2019) (Section 13.6.6)."


id: fefe11b63f53f107cffb76ea34235b15
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 42261
content:
"Figures  3.36 and  3.37 show increased investment needs in the \r\nenergy sector in lower temperature categories, and a  major shift \r\naway from fossil fuel generation and extraction towards electricity, \r\nincluding for system enhancements for electricity transmission, \r\ndistribution and storage, and low-carbon technologies. Investment \r\nneeds in the electricity sector are  2.3 trillion USD2015 yr-1\r\nover 2023-2050 on average for C1 pathways, 2 trillion USD \r\nfor C2 pathways,  1.7  trillion  USD for C3,  1.2 trillion USD for C4 \r\nand  0.9-1.1 billion USD for C5/C6/C7 (mean values for pathways \r\nin each temperature category). The regional pattern of power \r\nsector investments broadly mirrors the global picture. However, the \r\nbulk of investment requirements are in medium- and low-income \r\nregions. These results from the AR6 scenarios database corroborate \r\nthe findings from McCollum et al. (2018a), Zhou et al. (2019) and \r\nBertram et al. (2021). "


id: 8105e444ed9c9d1f25fd2539a2cdfdfc
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 19837
content:
"An equitable global emission-trading scheme would require very \r\nlarge international financial transfers, in the order of several \r\nhundred billion USD per year (Tavoni et al. 2015; Bauer et al. 2020; \r\nvan den Berg et al. 2020). The magnitude of transfers depends on the \r\nstringency of the climate goals and on the burden-sharing principle. \r\nSome interpretations of equitable burden sharing compliant with \r\nthe Paris Agreement leads to negative carbon allowances for \r\ndeveloped countries and some developing countries by mid-century \r\n(van  den  Berg et al. 2020), more stringent than cost-optimal \r\npathways. International transfers also depend on the underlying \r\nsocio-economic development (Leimbach and Giannousakis 2019), \r\nas these drive the mitigation costs of meeting the Paris Agreement "


id: 709c18e0908b37ef43ab98b87bce0e5c
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content:
"Figure  3.37 | Average yearly investments from 2023-2052 for the four subcomponents of the energy system representing the larger a\r\nin billion USD2015), by aggregate regions, in pathways by temperature categories. T&D: transmissions and distribution of electricity. Extr.: extraction of fo\r\nBars show the median values (number of pathways at the bottom), and whiskers show the interquartile ranges. For definition of regional classifications used see Annex "


id: 49a71a2391e431488d7d9d8c43942b8e
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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index: climate_gpt_v2
vector_id: 10188
content:
"In the context of the COVID-19 pandemic recovery, Kikstra et al. \r\n(2021a) show that a low-energy-demand recovery scenario reduces \r\nenergy investments required until 2030 for a  1.5degC consistent \r\npathway by  9% (corresponding to reducing total required energy \r\ninvestment by USD1.8 trillion) compared to a scenario with energy \r\ndemand trends restored to pre-pandemic levels."


id: a4074522eb7a465058a58ed8c0954657
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 26046
content:
"pathways. Rozenberg and Fay (2019) estimate the funding needs \r\nto close the service gaps in water and sanitation, transportation, \r\nelectricity, irrigation, and flood protection in thousands of scenarios, \r\nshowing that infrastructure investment paths compatible with full \r\ndecarbonisation in the second half of the century need not cost more \r\nthan more-polluting alternatives. Investment needs are estimated \r\nbetween 2% to 8% of GDP, depending on the quality and quantity of \r\nservices targeted, the timing of investments, construction costs, and \r\ncomplementary policies."


id: 2b60a5bfdce202d93c268a2370aac61a
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 4879
content:
"Few studies extend the scope of the investment needs quantification \r\nbeyond the energy sector. Fisch-Romito and Guivarch (2019) \r\nand O  Broin and Guivarch (2017) assess investment needs for \r\ntransportation infrastructures and find lower investment needs in \r\nlow-carbon pathways, due to a  reduction in transport activity and \r\na  shift towards less road construction, compared to high-carbon "


id: e4e6f19aadfff42292a0aa3816a5004f
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 37559
content:
"Chapter 15 also reports investment requirements in global mitigation \r\npathways in the near term, compares them to recent investment \r\ntrends, and assesses financing issues."


id: 11a95682ba3a82e4588d5f695c2cdbfc
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 298
content:
"Authors: Celine Guivarch (France), Steven Rose (the United States of America), Alaa Al Khourdajie (United Kingdom/Syria), \r\nValentina Bosetti (Italy), Edward Byers (Austria/Ireland), Katherine Calvin (the United States of America), Tamma Carleton (the United \r\nStates of America), Delavane Diaz (the United States of America), Laurent Drouet (France/Italy), Michael Grubb (United Kingdom), \r\nTomoko Hasegawa (Japan), Alexandre C. Koberle (Brazil/United Kingdom), Elmar Kriegler (Germany), David McCollum (the United \r\nStates of America), Aurelie Mejean (France), Brian O'Neill (the United States of America), Franziska Piontek (Germany), Julia Steinberger \r\n(United Kingdom/Switzerland), Massimo Tavoni (Italy)"


id: 272c413ab3ee7fc435e780baad174223
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 4055
content:
"Conceptually, the difference in aggregate economic impacts from climate change between two given temperature levels represents \r\nthe aggregate economic benefits arising from avoided climate change impacts due to mitigation action. A subset of the studies whose \r\nestimates were evaluated by AR6 WGII (5 of 15) are used to derive illustrative estimates of aggregate economic benefits in 2100 arising "


id: 9eef67fdffc2b4fecdead9f3a435639b
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 25190
content:
"Aggregate benefits from avoided impacts expressed in GDP terms, as in Figure 1, do not encompass all avoided climate risks, adaptation \r\npossibilities, and do not represent their influence on well-being and welfare (AR6 WGII Cross-Working Group Box ECONOMIC). \r\nMethodological challenges for economic impact estimates include representing uncertainty and variability, capturing interactions and \r\nspillovers, considering distributional effects, representing micro- and macro-adaptation processes, specifying non-gradual damages \r\nand non-linearities, and improving understanding of potential long-run growth effects. In addition, the economic benefits aggregated "


id: bd5a1d99c3288461a419205284aa5728
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 30561
content:
"at the global scale provide limited insights into regional heterogeneity. Global economic impact studies with regional estimates \r\nfind large differences across regions in absolute and percentage terms, with developing and transitional economies typically more \r\nvulnerable. Furthermore, (avoided) impacts for poorer households and poorer countries can represent a smaller share in aggregate \r\nquantifications expressed in GDP terms or monetary terms, compared to their influence on well-being and welfare (Hallegatte et al. \r\n2020; Markhvida et al. 2020). Finally, as noted by AR6 WGII, other lines of evidence regarding climate risks, beyond monetary estimates, \r\nshould be considered in decision-making, including Key Risks and Reasons for Concern."


id: 504ac4a42c5c04dbfcbf71e29b436fef
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
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vector_id: 11412
content:
"The standard discounted utilitarian framework dominates CBA, thus \r\noften limiting the analysis to the question of discounting. CBA can be \r\nexpanded to accommodate a wider variety of ethical values to assess \r\nmitigation pathways (Fleurbaey et al. 2019). The role of ethical values \r\nwith regard to inequality and the situation of the worse off (Adler \r\net al. 2017), risk (van den Bergh and Botzen 2014; Drouet et al. 2015), \r\nand population size (Scovronick et al. 2017; Mejean et al. 2020) has \r\nbeen explored. In most of these studies, the optimal climate policy is \r\nfound to be more stringent than the one obtained using a standard \r\ndiscounted utilitarian criterion."


id: 7b2f2e5d5e0a96c4a860ec73e0a327c6
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 18867
content:
"Comparing economic costs and benefits of mitigation raises a number \r\nof methodological and fundamental difficulties. Monetising the full \r\nrange of climate change impacts is extremely hard, if not impossible \r\n(AR6 WGII Chapter 16), as is aggregating costs and benefits over time \r\nand across individuals when values are heterogeneous (Chapter 1; \r\nAR5 WGIII Chapter 3). Other approaches should thus be considered \r\nin supplement for decision-making (Chapter 1 and Section 1.7), in \r\nparticular cost-effectiveness approaches that analyse how to achieve \r\na defined mitigation objective at least cost or while also reaching \r\nother societal goals (Koomey 2013; Kaufman et al. 2020; Koberle \r\net al. 2021; Stern and Stiglitz 2021). In cost-effectiveness studies \r\ntoo, incorporating benefits from avoided climate damages influences \r\nthe results and leads to more stringent mitigation in the short term \r\n(Drouet et al. 2021; Schultes et al. 2021)."


id: 19528152165ee26fb5103dcc1fccad3
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 1670
content:
"employment, and of future inequality trends, as overarching trends in \r\ndemographics, technological change (innovation, automation, etc.), \r\neducation and institutions will be prominent drivers. For instance, \r\nRao et al. (2019b) and Benveniste et al. (2021) have shown that \r\nincome inequality projections for the 21st century vary significantly, \r\ndepending on socio-economic assumptions related to demography, \r\neducation levels, social public spending and migrations. However, \r\nthe sections below focus on climate-related factors, both climate\u0002mitigation actions themselves and the climate change impacts \r\navoided along mitigation pathways, effects on structural change, \r\nincluding employment, and distributional effects."


id: 7341615df16d0718b62dea290486e1f2
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 17529
content:
"Mitigation actions have co-benefits and trade-offs with other \r\nsustainable development dimensions (Section  3.7) beyond climate \r\nchange, which imply welfare effects and economic effects, as well \r\nas other implications beyond the economic dimension. The majority \r\nof quantifications of mitigation costs and benefits synthesized \r\nin Sections  3.6.1 and  3.6.2 do not account for these economic \r\nbenefits and costs associated with co-benefits and trade-offs along \r\nmitigation pathways."


id: 8c681c9aada3395d44c24b492c61ee3
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 21882
content:
"Mitigation pathways entail transformation of the energy sector, with \r\nstructural change away from fossil energy and towards low-carbon \r\nenergy (Section 3.3), as well as broader economic structural change, \r\nincluding industrial restructuring and reductions in carbon-intensive \r\nactivities in parallel to extensions in low-carbon activities."


id: 596992bd7f0fb6dd714634b519099c9
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 12967
content:
"Achieving co-benefits is not automatic but results from coordinated \r\npolicies and implementation strategies (Clarke et al. 2014; McCollum \r\net al. 2018a). Similarly, avoiding trade-offs requires targeted policies \r\n(van Vuuren et al. 2015; Bertram et al. 2018). There is limited \r\nevidence of such pathways, but the evidence shows that mitigation \r\npathways designed to reach multiple Sustainable Development \r\nGoals instead of focusing exclusively on emissions reductions, \r\nresult in limited additional costs compared to the increased benefits \r\n(Cameron et al. 2016; McCollum et al. 2018b; Fujimori et al. 2020a; \r\nSognnaes et al. 2021)."


id: a9a44397d98401fbaac85074826687c5
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 27064
content:
"Beyond aggregate effects at the economy-wide level, mitigation \r\npathways have heterogeneous economic implications for different \r\nsectors and different actors. Climate-related factors are only one \r\ndriver of the future structure of the economy, of the future of "


id: 7a4774054cb1f9027012be188bc7a1e8
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 18709
content:
"(AR6 WGII Chapter 16). Through avoiding impacts, mitigation thus \r\nreduces economic inequalities and poverty (high confidence)."


id: a1a81f3fd2afcf29117fc04808abd5cf
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 25657
content:
"from net job destruction (Barker et al. 2016; Pollitt and Mercure 2018; \r\nGarcia-Casals et al. 2019; Malerba and Wiebe 2021). Coal phase-out \r\nraises acute issues of just transition for the coal-dependent countries \r\n(Spencer et al. 2018; Jakob et al. 2020) (Section 4.5 and Box 6.2)."


id: 69be852e52cfc1415478a601e252f4d3
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 15872
content:
"Mitigation action also affects employment through avoided climate \r\nchange impacts. Mitigation reduces the risks to human health and \r\nassociated impacts on labour and helps protect workers from the \r\noccupational health and safety hazards imposed by climate change \r\n(Kjellstrom et al. 2016, 2018, 2019; Levi et al. 2018; Day et al. 2019) \r\n(AR6 WGII Chapter 16)."


id: 81e3f38fbe26dbfe726fed8de55ec9b0
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 19974
content:
"Rapid and effective climate mitigation is a necessary part of sustainable \r\ndevelopment (high confidence) (Cross-Chapter Box 5 in Chapter 4), \r\nbut the latter can only be realised if climate mitigation becomes \r\nintegrated with sustainable development policies (high confidence). \r\nTargeted policy areas must include healthy nutrition, sustainable \r\nconsumption and production, inequality and poverty alleviation, \r\nair quality and international collaboration (high confidence). \r\nLower energy demand enables synergies between mitigation and \r\nsustainability, with lower reliance on CDR (high confidence)."


id: e405894402d436db3bf04a825c0d539e
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 37418
content:
"Mitigation pathways also affect economic inequalities between \r\nand within countries, and poverty, through the reduction of climate \r\nchange impacts that fall more heavily on low-income countries, \r\ncommunities and households, and exacerbate poverty (AR6 WGII \r\nChapters  8 and 16). Higher levels of warming are projected to \r\ngenerate higher inequality between countries as well as within them "


id: bcf9ecb18a563c1b6a89fc0b122e5c5a
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 30491
content:
"mitigation policies (averaged over several models, middle panel), \r\nand policies integrating sustainability considerations (right panel of \r\nFigure  3.39, based on the Illustrative Mitigation Pathway 'Shifting \r\nPathways' (IMP-SP) (Soergel et al. 2021a)). Note that neither middle \r\nnor right panels include climate impacts."


id: 874852815b1487f478cfcd216184cd8a
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 20961
content:
"clean energy) as side effects. These trade-offs can be compensated \r\nthrough targeted support measures and/or additional sustainable \r\ndevelopment policies (Cameron et al. 2016; Bertram et al. 2018; \r\nFujimori et al. 2019; Soergel et al. 2021a)."


id: 67e22f4aebfa0701ff6be96899c34239
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 15550
content:
"The synthesis of the interplay between climate mitigation and \r\nsustainable development is shown in Figure  3.39. Panel a  shows \r\nthe reduction in population affected by climate impacts at  1.5degC \r\ncompared to 3degC according to sustainability domains (Byers et al. \r\n2018). Reducing warming reduces the population impacted by all \r\nimpact categories shown (high confidence). The left panel does not \r\ntake into account any side effects of mitigation efforts or policies to \r\nreduce warming: only reductions in climate impacts. This underscores \r\nthat mitigation is an integral basis for comprehensive sustainable \r\ndevelopment (Watts et al. 2015)."


id: 33e3051ba0e7ca573f7a61ecd1c12694
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 6368
content:
"Areas of co-benefits include human health, ambient air pollution \r\nand other specific kinds of pollution, while areas of trade-off include \r\nfood access, habitat loss and mineral resources (medium confidence). \r\nFor example, action consistent with 1.5degC in the absence of energy\u0002demand reduction measures require large quantities of CDR, which, \r\ndepending on the type used, are likely to negatively impact both food \r\navailability and areas for biodiversity (Fujimori et al. 2018; Ohashi \r\net al. 2019; Roelfsema et al. 2020)."


id: ee3863e65cc4c57b4ffe87a932c425da
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 39283
content:
"Mitigation to 1.5degC reduces climate impacts on sustainability (left). \r\nPolicies integrating sustainability and mitigation (right) have far \r\nfewer trade-offs than narrow mitigation policies (middle)."


id: ff90ded7b312eb90fcec1c7917f39586
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 42363
content:
"Panels b and c of Figure  3.39 show the effects of  1.5degC \r\nmitigation  policies compared to current national policies: narrow "


id: 8cc85f4238a29c2b7b38386c5671d5a8
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 21956
content:
"Lower demand  - for example, for energy and land-intensive \r\nconsumption such as meat  - represents a  synergistic strategy for \r\nachieving ambitious climate mitigation without compromising \r\nSustainable Development Goals (high confidence) (Bertram et al. \r\n2018; Grubler et al. 2018; van Vuuren et al. 2018; Kikstra et al. 2021b; \r\nSoergel et al. 2021a). This is especially true for reliance on BECCS \r\n(Hickel et al. 2021; Keysser and Lenzen 2021). Options that reduce \r\nagricultural demand (e.g., dietary change, reduced food waste) can \r\nhave co-benefits for adaptation through reductions in demand for \r\nland and water (Bertram et al. 2018; Grubler et al. 2018; IPCC 2019a; \r\nSoergel et al. 2021a)."


id: 2ad2f009dd19183c6713340bdc25a8d6
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 4765
content:
"These findings indicate that holistic policymaking integrating \r\nsustainability objectives alongside mitigation will be important in \r\nattaining Sustainable Development Goals (van Vuuren et al. 2015, \r\n2018; Bertram et al. 2018; Fujimori et al. 2018; Hasegawa et al. \r\n2018; Liu et al. 2020a; Honegger et al. 2021; Soergel et al. 2021a). \r\nMitigation policies which target direct sector-level regulation, early \r\nmitigation action, and lifestyle changes have beneficial sustainable \r\ndevelopment outcomes across air pollution, food, energy and water \r\n(Bertram et al. 2018)."


id: 14ba7603535b2e1159e4797da6da50d0
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 874
content:
"While the impacts of climate change on agricultural output are \r\nexpected to increase the population at risk of hunger, there is \r\nevidence suggesting population growth will be the dominant \r\ndriver of hunger and undernourishment in Africa in 2050 (Hall et al. \r\n2017). Meeting SDG 5, relating to gender equality and reproductive \r\nrights, could substantially lower population growth, leading to \r\na  global population lower than the 95% prediction range of the \r\nUN projections (Abel et al. 2016). Meeting SDG 5 (gender equality, \r\nincluding via voluntary family planning (O'Sullivan 2018)) could thus \r\nminimise the risks to SDG 2 (zero hunger) that are posed by meeting \r\nSDG 13 (climate action)."


id: 5d717bc8900753060e9156aaeb0fb577
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 13728
content:
"These policies include ones around stringent air quality (Kinney 2018; \r\nRafaj et al. 2018; Soergel et al. 2021a); efficient and safe demand\u0002side technologies, especially cook stoves (Cameron et al. 2016); \r\nlifestyle changes (Bertram et al. 2018; Grubler et al. 2018; Soergel \r\net al. 2021a); industrial and sectoral policy (Bertram et al. 2018); \r\nagricultural and food policies (including food waste) (van Vuuren \r\net al. 2019; Soergel et al. 2021a); international cooperation (Soergel \r\net al. 2021a); as well as economic policies described in Section 3.6. \r\nRecent research shows that mitigation is compatible with reductions \r\nin inequality and poverty (Box 3.6)."


id: 12b782fb532ed100bcfe728f2acd8035
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 496
content:
"In modelled mitigation pathways, inequality in per-capita emissions between regions are generally reduced over time, and the \r\nreduction is generally more pronounced in lower-temperature pathways (Box 3.6, Figure 1). Already in 2030, if NDCs from the Paris \r\nAgreement, announced prior to COP26, are fully achieved, inequalities in per-capita GHG emissions between countries would be \r\nreduced (Benveniste et al. 2018)."


id: 5d63d9628a6a04210cfa2980c7de7863
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 13719
content:
"Through avoiding impacts of climate change, which fall more heavily on low-income countries, communities and households, and \r\nexacerbate poverty, mitigation reduces inequalities and poverty (Section 3.6.4.2)."


id: 8f1f971bd38b2103b5ec49da6b9cf647
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 22380
content:
"The remainder of this section covers specific domains of sustainable \r\ndevelopment: food (Section  3.7.2), water (Section  3.7.3), energy \r\n(Section  3.7.4), health (Section  3.7.5), biodiversity (Section  3.7.6) \r\nand multi-sector  - cities, infrastructure, industry, production and \r\nconsumption (Section  3.7.7). These represent the areas with the \r\nstrongest research connecting mitigation to sustainable development. \r\nThe links to individual SDGs are given within these sections. Each \r\ndomain covers the benefits of avoided climate impacts and the \r\nimplications (synergies and trade-offs) of mitigation efforts."


id: 292d67c054448078cadd9088e55b303f
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 4457
content:
"Climate change will reduce crop yields, increase food insecurity, and \r\nnegatively influence nutrition and mortality (high confidence) (AR6 \r\nWGII Chapter 5). Climate mitigation will thus reduce these impacts, \r\nand hence reduce food insecurity (high confidence). The yield \r\nreduction of global food production will increase food insecurity and \r\ninfluence nutrition and mortality (Hasegawa et al. 2014; Springmann \r\net al. 2016a). For instance, Springmann et al. (2016a) estimate that \r\nclimate change could lead to 315,000-736,000 additional deaths by \r\n2050, though these could mostly be averted by stringent mitigation \r\nefforts. Reducing warming reduces the impacts of climate change, \r\nincluding extreme climates, on food production and risk of hunger \r\n(Hasegawa et al. 2014, 2021b)."


id: ec3ca9407d9334c918b826c62f6a1ed7
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 38931
content:
"The goal of SDG 2 is to achieve 'zero-hunger' by 2030. According \r\nto the UN (2015), over 25% of the global population currently \r\nexperience food insecurity and nearly 40% of these experience severe \r\nfood insecurity, a  situation worsened by the COVID-19 pandemic \r\n(Paslakis et al. 2021)."


id: 5fdc3dc803947d41aecd33ae46e42243
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 14173
content:
"Recent studies explore the effect of climate change mitigation on \r\nagricultural markets and food security (Havlik et al. 2014; Hasegawa \r\net al. 2018; Doelman et al. 2019; Fujimori et al. 2019). Mitigation \r\npolicies aimed at achieving  1.5degC-2degC, if not managed properly, "


id: 67106b914db9e57403d919ca8804a9a7
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 15399
content:
"could negatively affect food security through changes in land and \r\nfood prices (high confidence), leading to increases in the population \r\nat risk of hunger by 80-280 million people compared to baseline \r\nscenarios. These studies assume uniform carbon prices on AFOLU \r\nsectors (with some sectoral caps) and do not account for climate \r\nimpacts on food production."


id: 85f0c1fe0bbb7a58164c7de12277cad8
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 20709
content:
"of and exposure to hydrological extremes (high confidence) (Arnell \r\nand Lloyd-Hughes 2014; Asadieh and Krakauer 2017; Dottori \r\net al. 2018; Naumann et al. 2018; IPCC 2019a; Do et al. 2020) (AR6 \r\nWGII Chapter 4). Climate models project increases in precipitation \r\nintensity (high confidence), local flooding (medium confidence), and \r\ndrought risk (very high confidence) (Arnell and Lloyd-Hughes 2014; \r\nAsadieh and Krakauer 2017; Dottori et al. 2018; IPCC 2019a) (AR6 \r\nWGII Chapter 4)."


id: 50cc6e5a2be7e9277b3bf0eff82afa3d
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 11485
content:
"The effect of climate change on water availability and hydrological \r\nextremes varies by region (high confidence) due to differences in the \r\nspatial patterns of projected precipitation changes (Hanasaki et al. \r\n2013; Schewe et al. 2014; Schlosser et al. 2014; Asadieh and Krakauer \r\n2017; Dottori et al. 2018; Naumann et al. 2018; Koutroulis et al. 2019) \r\n(AR6 WGII Chapter 4). Global exposure to water stress is projected to \r\nincrease with increased warming, but increases will not occur in all \r\nregions (Hanasaki et al. 2013; Schewe et al. 2014; Arnell and Lloyd\u0002Hughes 2014; Gosling and Arnell 2016; IPCC 2019a)."


id: 73a626d430aaeb7bc58105e14db671cc
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 17598
content:
"Limiting warming could reduce water-related risks (high confidence) \r\n(O'Neill et al. 2017b; Byers et al. 2018; Hurlbert et al. 2019) (AR6 WGII \r\nChapter  4) and the population exposed to increased water stress \r\n(Hanasaki et al. 2013; Arnell and Lloyd-Hughes 2014; Schewe et al. \r\n2014; Gosling and Arnell 2016; IPCC 2019a)."


id: dc942a3b382942fa38818e7df52c8d6e
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 36071
content:
"There are many other synergistic measures for climate mitigation and \r\nfood security. Agricultural technological innovation can improve the \r\nefficiency of land use and food systems, thus reducing the pressure \r\non land from increasing food demand (Foley et al. 2011; Popp et al. \r\n2014; Obersteiner et al. 2016; Humpenoder et al. 2018; Doelman \r\net al. 2019). Furthermore, decreasing consumption of animal \r\nproducts could contribute to SDG 3.4 by reducing the risk of non\u0002communicable diseases (Garnett 2016)."


id: 40dd13150a6a75e9864bf54b1fd1b2d2
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 8598
content:
"The effect of climate change on water depends on the climate model, \r\nthe hydrological model, and the metric (high confidence) stress \r\nHanasaki et al. (2013); Arnell and Lloyd-Hughes (2014); Schewe \r\net al. (2014); Schlosser et al. (2014); Gosling and Arnell (2016); \r\nIPCC (2019a)."


id: 46000c3c16fbe18ba74e140ee2c98d25
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 9537
content:
"However, the effect of socio-economic development could be larger \r\nthan the effect of climate change (high confidence) (Arnell and Lloyd\u0002Hughes 2014; Schlosser et al. 2014; Graham et al. 2020)."


id: 47f519d6779855546f6f678088a06ade
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 9876
content:
"Taken together, climate changes will reduce crop yields, increase food \r\ninsecurity and influence nutrition and mortality (high confidence)\r\n(see 3.7.2.1). However, if measures are not properly designed, mitigating \r\nclimate change will also negatively impact on food consumption and \r\nsecurity. Additional solutions to negative impacts associated with \r\nclimate mitigation on food production and consumption include \r\na transition to a sustainable agriculture and food system that is less \r\nresource intensive, more resilient to a  changing climate, and in line \r\nwith biodiversity and social targets (Kayal et al. 2019)."


id: 7ac469a6ff6a6b591cef5c976287f3a7
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 18798
content:
"Climate change can also affect water quality (both thermal and \r\nchemical) (Liu et al. 2017), leading to increases in stream temperature \r\nand nitrogen loading in rivers (Ballard et al. 2019)."


id: 1669059545e5ae000cd04d887409cd8f
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 1159
content:
"Water is relevant to SDG 6 (clean water and sanitation), SDG 15 (life \r\non land ), and SDG Targets 12.4 and 3.9 (water pollution and health). \r\nThis section discusses water quantity, water quality, and water\u0002related extremes. See Section 3.7.5 for water-related health effects."


id: 2d7777502b357447ebecfb57d2624c2c
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 5288
content:
"Global precipitation, evapotranspiration, runoff and water availability \r\nincrease with warming (Hanasaki et al. 2013; Greve et al. 2018) \r\n(AR6 WGII Chapter 4). Climate change also affects the occurrence "


id: 3c18ee9f65df3e4b2bc353109d9b0ef4
created_at: 2023-04-19 13:22:11
updated_at: 2023-04-19 13:22:11
content_type: text
index: climate_gpt_v2
vector_id: 7764
content:
"et al. 2017) could increase water demand; however, the implications \r\nof CCS depend on the cooling technology and when capture occurs \r\n(Magneschi et al. 2017; Maizi et al. 2017; Giannaris et al. 2020). \r\nDemand-side mitigation (e.g., dietary change, reduced food waste, \r\nreduced energy demand) can reduce water demand (Bajzelj et al. \r\n2014; Aleksandrowicz et al. 2016; Green et al. 2018; Springmann \r\net al. 2018). Introducing specific measures (e.g., environmental flow \r\nrequirements, improved efficiency, priority rules) can reduce water \r\nwithdrawals (Bertram et al. 2018; Bijl et al. 2018; Parkinson et al. 2019)."


id: cc04d51f89cef5d074c181292f17ff9e
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 33218
content:
"Extending energy access to all in line with SDG7 is compatible \r\nwith strong mitigation consistent with the Paris Agreement (high \r\nconfidence). The Low Energy Demand (LED) scenario projects that \r\nthese twin goals can be achieved by relying heavily on energy \r\nefficiency and rapid social transformations (Grubler et al. 2018). \r\nThe IEA's Sustainable Development Scenario (IEA 2020a) achieves \r\ndevelopment outcomes but with higher average energy use, and \r\nbottom-up modelling suggests that decent living standards could be \r\nprovided to all in 2040-2050 with roughly 150 EJ, or 40% of current \r\nfinal energy use (Millward-Hopkins et al. 2020; Kikstra et al. 2021b). \r\nThe trade-offs between climate mitigation and increasing energy \r\nconsumption of the world's poorest are negligible (Rao and Min \r\n2018; Scherer et al. 2018)."


id: 41c725dea2838d3410c0b1b0c1b1bc63
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 8769
content:
"The effect of mitigation on water quality depends on the mitigation \r\noption, its implementation, and the aspect of quality considered \r\n(high confidence) (Ng et al. 2010; Florke et al. 2019; Sinha et al. \r\n2019; Smith et al. 2019; Fuhrman et al. 2020; Karlsson et al. 2020; \r\nMcElwee et al. 2020)."


id: 1d36de488436c778416d39f200360a3f
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 2317
content:
"Energy is relevant to SDG 7 (affordable and clean energy). Access \r\nto sufficient levels of reliable, affordable and renewable energy is \r\nessential for sustainable development. Currently, over 1 billion people \r\nstill lack access to electricity (Ribas et al. 2019)."


id: 648a397ee9c5a2711ed3689544d94e58
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 14963
content:
"In contrast, future energy infrastructure could improve reliability, thus \r\nlowering dependence on high-carbon, high-air pollution back-up diesel \r\ngenerators (Farquharson et al. 2018) that are often used to cope with \r\nunreliable power in developing countries (Maruyama Rentschler et al. \r\n2019). There can be significant reliability issues where mini-grids are \r\nused to electrify rural areas (Numminen and Lund 2019). A  stable, \r\nsustainable energy transition policy that considers national sustainable \r\ndevelopment in the short and long term is critical in driving a transition \r\nto an energy future that addresses the trilemma of energy security, \r\nequity, and sustainability (La Vina et al. 2018)."


id: fdd5310e62474179f4babd90d20d3cbb
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 42018
content:
"The energy sector uses large volumes of water (Fricko \r\net al. 2016), making it highly vulnerable to climate change (Tan and \r\nZhi 2016) (high confidence). Thermoelectric and hydropower sources \r\nare the most vulnerable to water stress (van Vliet et al. 2016). \r\nRestricted water supply to these power sources can affect grid security \r\nand affordable energy access (Koch et al. 2014; Ranzani et al. 2018; \r\nZhang et al. 2018d).The hydropower facilities from high mountain \r\nareas of Central Europe, Iceland, Western USA/Canada, and Latin \r\nAmerica (Hock et al. 2019), as well as Africa and China (Bartos and \r\nChester 2015; Gaupp et al. 2015; Tarroja et al. 2016; Conway et al. \r\n2017; Byers et al. 2018; Eyer and Wichman 2018; Ranzani et al. 2018; \r\nSavelsberg et al. 2018; Zhang et al. 2018d; Zhou et al. 2018; Wang \r\net al. 2019) have experienced changes in seasonality and availability."


id: eac1aec7f39bcf7bb9bcec515e8b7fb
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 38658
content:
"SDG 3 (good health and well-being) aims to ensure healthy lives and \r\npromote well-being for all at all ages. Climate change is increasingly \r\ncausing injuries, illnesses, malnutrition, threats to mental health \r\nand well-being, and deaths (AR6 WGII Chapter 7). Mitigation policies \r\nand technologies to reduce GHG emissions are often beneficial \r\nfor human health on a  shorter time scale than benefits in terms \r\nof slowing climate change (Limaye et al. 2020). The financial value \r\nof health benefits from improved air quality alone is projected to \r\nexceed the costs of meeting the goals of the Paris Agreement \r\n(Markandya et al. 2018)."


id: f66cd6896f2016b32a82da9bd127d840
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 40721
content:
"The avoided health impacts associated with climate change mitigation \r\ncan substantially offset mitigation costs at the societal level (Scasny \r\net al. 2015; Schucht et al. 2015; Chang et al. 2017; Markandya et al. \r\n2018). Models of health co-benefits show that a 1.5degC pathway could \r\nresult in 152 million +- 43 million fewer premature deaths worldwide \r\nbetween 2020 and 2100 in comparison to a  business-as-usual \r\nscenario, particularly due to reductions in exposure to PM2.5 (Shindell \r\net al. 2018; Rauner et al. 2020a; Rafaj et al. 2021). Some of the most \r\nsubstantial health, well-being, and equity benefits associated with \r\nclimate action derive from investing in basic infrastructure: sanitation, \r\nclean drinking water, clean energy, affordable healthy diets, clean \r\npublic transport, and improved air quality from transformative \r\nsolutions across economic sectors including agriculture, energy, \r\ntransport and buildings (Chang et al. 2017)."


id: fc90df449f89933864361cf7b6c7ca52
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 41804
content:
"The human health chapter of the WGII contribution to the AR6 \r\nconcluded that climate change is increasingly affecting a  growing \r\nnumber of health outcomes, with negative net impacts at the global \r\nscale and positive impacts only in a few limited situations. There are \r\nfew estimates of economic costs of increases in climate-sensitive \r\nhealth outcomes. In the USA in 2012, the financial burden in terms \r\nof deaths, hospitalisations, and emergency department visits for \r\nten climate-sensitive events across 11 states were estimated to be \r\n10 (2.7-24.6) billion USD2018 (Limaye et al. 2019)."


id: f3fb902093ccdf2feebfa999b010d290
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 40297
content:
"Transitioning toward equitable, low-carbon societies has multiple \r\nco-benefits for health and well-being (AR6 WGII Chapter 7). Health \r\nbenefits can be gained from improvements in air quality through \r\ntransitioning to renewable energy and active transport (e.g., walking \r\nand cycling); shifting to affordable low-meat, plant-rich diets; and \r\ngreen buildings and nature-based solutions, such as green-and-blue \r\nurban infrastructure, as shown in Figure 3.40 (Iacobucci 2016)."


id: 7e519c10999ca96bb14fea6a9ea824ac
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 19386
content:
"The health co-benefits of the NDCs for 2040 were compared for \r\ntwo scenarios, one consistent with the goal of the Paris Agreement \r\nand the SDGs and the other also placing health as a  central focus \r\nof the policies (i.e., health in all climate policies scenario) (Hamilton \r\net al. 2021), for Brazil, China, Germany, India, Indonesia, Nigeria, \r\nSouth Africa, the UK, and the USA. Modelling of the energy, food \r\nand agriculture, and transport sectors, and associated risk factors "


id: 5e4c7faea0414ba4c0f6ae9072171559
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 13882
content:
"two thirds have advanced their timing of spring events (Parmesan and \r\nHanley 2015). Under 3.2degC warming, 49% of insects, 44% of plants \r\nand 26% of vertebrates are projected to be at risk of extinction. At \r\n2degC, this falls to 18% of insects, 16% of plants and 8% of vertebrates and \r\nat 1.5degC, to 6% of insects, 8% of plants and 4% of vertebrates (Warren \r\net al. 2018). Incidents of migration of invasive species, including pests \r\nand diseases, are also attributable to climate change, with negative \r\nimpacts on food security and vector-borne diseases. Moreover, if \r\nclimate change reduces crop yields, cropland may expand - a primary \r\ndriver of biodiversity loss - in order to meet food demand (Molotoks \r\net al. 2020). Land restoration and halting land degradation under all \r\nmitigation scenarios has the potential for synergy between mitigation \r\nand adaptation."


id: affae34edf83edadb5fca8302ce245ea
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 28202
content:
"related to mortality, suggested the sustainable pathways scenario \r\ncould result in annual reductions of 1.18 million air pollution-related \r\ndeaths, 5.86 million diet-related deaths, and 1.15 million deaths due to \r\nphysical inactivity. Adopting the more ambitious health in all climate \r\npolicies scenario could result in further reductions of 462,000 annual \r\ndeaths attributable to air pollution, 572,000 annual deaths attributable \r\nto diet, and 943,000 annual deaths attributable to physical inactivity. \r\nThese benefits were attributable to the mitigation of direct GHG \r\nemissions and the commensurate actions that reduce exposure to \r\nharmful pollutants, as well as improved diets and safe physical activity."


id: f035f7b37f819048f196a0d99a8bc3e1
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 39649
content:
"Marine ecosystems are being affected by climate change and growing \r\nnon-climate pressures including temperature change, acidification, \r\nland-sourced pollution, sedimentation, resource extraction and \r\nhabitat destruction (high confidence) (Bindoff et al. 2019; IPCC 2019b). \r\nThe impacts of climate drivers and their combinations vary across \r\ntaxa (AR6 WGII Chapter 3). The danger or warming and acidification \r\nto coral reefs, rocky shores and kelp forests is well established \r\n(high confidence) (AR6 WGII Chapter 3). Migration towards optimal \r\nthermal and chemical conditions (Burrows et al. 2019) contributes to \r\nlarge-scale redistribution of fish and invertebrate populations, and \r\nmajor impacts on global marine biomass production and maximum \r\nsustainable yield (Bindoff et al. 2019)."


id: cee0ed2d4810ebc0225423cb7e5a61cc
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 33737
content:
"Mitigation measures have the potential to reduce the progress \r\nof negative impacts on ecosystems, although it is unlikely that all \r\nimpacts can be mitigated (high confidence) (Ohashi et al. 2019). \r\nThe specifics of mitigation achievement are crucial, since large\u0002scale deployment of some climate mitigation and land-based CDR \r\nmeasures could have deleterious impacts on biodiversity (Santangeli \r\net al. 2016; Hof et al. 2018)."


id: ca1c6673c5e282ba44566f9e267b3013
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 32893
content:
"Just in the USA, over the next 50 years, a  2degC pathway could \r\nprevent roughly  4.5 million premature deaths, about  3.5 million \r\nhospitalisations and emergency room visits, and approximately \r\n300 million lost workdays (Shindell 2020). The estimated yearly \r\nbenefits of USD700 billion were more than the estimated cost of the \r\nenergy transition."


id: bfdf69622ba0880a74e7a88e5d314571
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 31047
content:
"Biodiversity covers life below water (SDG 14) and life on land \r\n(SDG  15). Ecosystem services are relevant to the goals of zero \r\nhunger (SDG 2), good health and well-being (SDG 3), clean water \r\nand sanitation (SDG 6) and responsible consumption and production \r\n(SDG 12), as well as being essential to human existence (IPBES 2019)."


id: a3ec546468643dc1fe1fd977493039a8
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 26033
content:
"Climate change is a major driver of species extinction and terrestrial \r\nand freshwater ecosystems destruction (high confidence) (AR6 WGII \r\nChapter 2). Analysis shows that approximately half of all species with \r\nlong-term records have shifted their ranges in elevation and about "


id: a0e2441736cf1679f2254a2886ab6a02
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 25529
content:
"Addressing multiple SDG arenas requires new systemic thinking \r\nin the areas of governance and policy, such as those proposed by \r\nSachs et al. (2019)."


id: 43360fbb18d4e3224104f653fc02f7bf
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 9034
content:
"have evaluated interconnections among key aspects of land and show \r\na pathway to the global sustainable future of land (Popp et al. 2014; \r\nErb et al. 2016; Obersteiner et al. 2016; Humpenoder et al. 2018). Most \r\nstudies agree that many biophysical options exist to achieve global \r\nclimate mitigation and sustainable land use in future. Conserving \r\nlocal biodiversity requires careful policy design in conjunction with \r\nland-use regulations and societal transformation in order to minimise \r\nthe conversion of natural habitats."


id: c79fecb83fb848c794a90a63ffb9abbd
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 32432
content:
"The objective of this section is to discuss concepts of feasibility in \r\nthe context of the low-carbon transition and pathways. We aim to \r\nidentify drivers of low-carbon scenarios feasibility and to highlight \r\nenabling conditions which can ameliorate feasibility concerns."


id: 8ba51a7d87b072fedab07dc38ebfc80b
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 21739
content:
"This subsection focuses upon SDG 9 (industry, innovation and \r\ninfrastructure) and SDG 11 (sustainable cities and communities)."


id: 569741458811589f2e7ae1378a135a07
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 12490
content:
"By 2100, urban population will be almost double and more urban \r\nareas will be built (Jiang and O'Neill 2017), although COVID-19 may \r\nmodify these trends (Kii 2021). Urbanisation will amplify projected air \r\ntemperature changes in cities, including amplifying heatwaves (AR6 \r\nWGI Chapter 10, Box 10.3). Benefits of climate mitigation in urban \r\nareas include reducing heat, air pollution and flooding. Industrial \r\ninfrastructure and production-consumption supply networks also \r\nbenefit from avoided impacts."


id: b5b93a40f6bd1856e717ea3d67cbc353
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 29198
content:
"Many co-benefits to urban mitigation actions (Chapter  8, \r\nSection  8.2.1) improve the liveability of cities and contribute to \r\nachieving SDG 11. In particular, compact urban form, efficient \r\ntechnologies and infrastructure can play a valuable role in mitigation \r\nby reducing energy demand (Creutzig et al. 2016; Guneralp et al. \r\n2017), thus averting carbon lock-in, while reducing land sprawl and \r\nhence increasing carbon storage and biodiversity (D'Amour et al. \r\n2017). Benefits of mitigation include air quality improvements from \r\ndecreased traffic and congestion when private vehicles are displaced \r\nby other modes; health benefits from increases in active travel; and \r\nlowered urban heat island effects from green-blue infrastructures \r\n(Section 8.2.1)."


id: 649eafc28cbd12eda38b24986a6fd2a3
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 14982
content:
"Regarding scenarios, the feasibility 'what' question is the one most \r\ncommonly dealt with in the literature, though most of the studies \r\nhave focused on expanding low-carbon system, and yet political \r\nconstraints might arise mostly from phasing out fossil fuel-based \r\nones (Spencer et al. 2018; Fattouh et al. 2019). The 'when and where' \r\ndimension can also be related to the scenario assessment, but only \r\ninsofar that the models generating them can differentiate time and \r\ngeographical contextual factors. Distinguishing mitigation potential \r\nby regional institutional capacity has a significant influence on the \r\ncosts of stabilising climate (Iyer et al. 2015c). The 'whom' question \r\nis the most difficult to capture by scenarios, given the multitude \r\nof actors involved as well as their complex interactions. The focus of \r\nsocio-technical transition sciences on the co-evolutionary processes \r\ncan shed light on the dynamics of feasibility (Nielsen et al. 2020)."


id: 5bb9c02df0c0aa26d1b91e3f2deb59fa
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 13391
content:
"However, increasing urban density or enlarging urban green spaces \r\ncan increase property prices and reduce affordability (Section 8.2.1). \r\nRaising living conditions for slum dwellers and people living in \r\ninformal settlements will require significant materials and energy; \r\nhowever, regeneration can be conducted in ways that avoid carbon\u0002intense infrastructure lock-in (Chapters 8 and 9). Cities affect other \r\nregions through supply chains (Marinova et al. 2020)."


id: a479603f6b965e86dfed69dbc0008a09
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 26149
content:
"The when-where-whom distinction allows depicting a  feasibility \r\nfrontier beyond which implementation challenges prevent mitigation \r\naction (Jewell and Cherp 2020). Even if the current feasibility frontier \r\nappears restraining in some jurisdictions, it is context-dependent and \r\ndynamic as innovation proceeds and institutional capacity builds up \r\n(Nielsen et al. 2020). The question is whether the feasibility frontier \r\ncan move faster than the pace at which the carbon budget is being "


id: 6caee3d459b29461bfce7d76f53f661b
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 16405
content:
"Sustainable production, consumption and management of natural \r\nresources are consistent with, and necessary for, mitigation \r\n(Chapters 5 and 11). Demand-side measures can lower requirements \r\nfor upstream material and energy use (Chapter  5). In terms of \r\nindustrial production, transformational changes across sectors will \r\nbe necessary for mitigation (Sections 11.3 and 11.4)."


id: 63d02a2b74398738d6188a750509e41f
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 14852
content:
"Although model-feasibility differs distinctly from feasibility in the \r\nreal world, it can indicate the relative challenges of low-carbon \r\nscenarios  - primarily when performed in a  model ensemble of \r\nsufficient size. Riahi et al. (2015) interpreted infeasibility across \r\na large number of models as an indication of increased risk that the \r\ntransformation may not be attainable due to technical or economic \r\nconcerns. All models involved in a model comparison of 1.5degC targets \r\n(Rogelj et al. 2018b) (Table S1) were able to solve under favourable \r\nunderlying socio-economic assumptions (SSP1), but none for the \r\nmore challenging SSP3. This interpretation of feasibility was used \r\nto highlight the importance of socio-economic drivers for attaining \r\nclimate stabilisation. Gambhir et al. (2017) constrained the models \r\nto historically observed rates of change and found that it would \r\nno longer allow to solve for 2degC, highlighting the need for rapid \r\ntechnological change."


id: d1c8a5c451693483d4d55c71691b1f1
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 34260
content:
"exhausted. Jewell et al. (2019) show that the emission savings from \r\nthe pledges of premature retirement of coal plants is 150 times less \r\nthan globally committed emissions from existing coal power plants. \r\nThe pledges come from countries with high institutional capacity \r\nand relatively low shares of coal in electricity. Other factors currently \r\nlimiting the capacity to steer transitions at the necessary speed \r\ninclude the electoral-market orientation of politicians (Willis 2017), \r\nthe status-quo orientation of senior public officials (Geden 2016), \r\npath dependencies created by 'instrument constituencies' (Beland \r\nand Howlett 2016), or the impacts of deliberate inconsistencies \r\nbetween talk, decisions and actions in climate policy (Rickards et al. \r\n2014). All in all, a  number of different delay mechanisms in both \r\nscience and policy have been identified to potentially impede climate \r\ngoal achievement (Karlsson and Gilek 2020) (Chapter 13)."


id: 140af7189a8a8d8aba4dffbadb283aa7
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 743
content:
"In addition to its contextual and dynamic nature, feasibility is a multi\u0002dimensional concept. The IPCC SR1.5 distinguishes six dimensions \r\nof feasibility: geophysical, environmental-ecological, technological, \r\neconomic, socio-cultural and institutional. At the individual option \r\nlevel, different mitigation strategies face various barriers as well as \r\nenablers (see Chapter 6 for the option-level assessment). However, \r\na systemic transformation involves interconnections of a wide range \r\nof indicators. Model-based assessments are meant to capture the \r\nintegrative elements of the transition and of associated feasibility \r\nchallenges. However, the translation of model-generated pathways \r\ninto feasibility concerns (Rogelj et al. 2018b) has developed \r\nonly recently. Furthermore, multiple forms of knowledge can be \r\nmobilised to support strategic decision-making and complement \r\nscenario analysis (Turnheim and Nykvist 2019). We discuss both \r\napproaches next."


id: ac667b37e3b13e5c72afb133285f95d8
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 27568
content:
"Evaluating the feasibility of scenarios involves several steps \r\n(Figure  3.41). First, one needs to identify which dimensions of \r\nfeasibility to focus on. Then, for each dimension, one needs to \r\nselect relevant indicators for which sufficient empirical basis exists \r\nand which are an output of models (or at least of a  sufficient \r\nnumber of them). Then, thresholds marking different levels of \r\nfeasibility concerns are defined based on available literature, \r\nexpert elicitations and empirical analysis based on appropriately \r\nchosen historical precedents. Finally, scenario feasibility scores are \r\nobtained for each indicator, and where needed aggregated up in \r\ntime or dimensions, as a  way to provide an overall appraisal of \r\nfeasibility trade-offs, depending on the timing, disruptiveness and \r\nscale of transformation."


id: 624718039b07d33191c95e70ce79224
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 14567
content:
"Evaluating the feasibility of low-carbon pathways can take different \r\nforms. In the narrowest sense, there is feasibility pertaining the \r\nreporting of model-generated scenarios: here an infeasible scenario \r\nis one which cannot meet the constraints embedded implicitly or \r\nexplicitly in the models which attempted to generate it. Second, \r\nthere is a  feasibility that relates to specific elements or overall \r\nstructure characterising the low-carbon transition compared to some \r\nspecified benchmark."


id: 1c2ac1f03319d29daeba0caae64ae13f
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 2141
content:
"In order to be generated, scenarios must be coherent with \r\nthe constraints and assumptions embedded in the models \r\n(i.e.,  deployment potential of given technologies, physical and \r\ngeological limits) and in the scenario design (i.e.,  carbon budget). \r\nSometimes, models cannot solve specific scenarios. This provides \r\na  first, coarse indication of feasibility concerns. Specific vetting \r\ncriteria can be imposed, such as carbon-price values above which \r\nscenarios should not be reported, as in Clarke et al. (2009). However, \r\nmodel solvability raises issues of aggregation in model ensembles. \r\nSince model solving is not a random process, but a function of the \r\ncharacteristics of the models, analysing only reported outcomes leads \r\nto statistical biases (Tavoni and Tol 2010)."


id: 87f92308601447fc1603534eab2d89f3
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 21077
content:
"Figure  3.42 | Example of multi-dimensional feasibility analysis and indicators used in the IPCC AR6 scenarios. The approach defines relevant indicators \r\ncharacterising the key dimensions of feasibility. Indicators capture the timing, scale and disruptiveness challenges. Low-, medium- and high-feasibility concerns are defined \r\nbased on historical trends and available literature. Details about indicator and threshold values can be found in Annex III.II.2.3."


id: 767b4410da3bf46c6bf0bf08b229be8
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 18064
content:
"Figure 3.43 | Feasibility characteristics of the Paris-consistent scenarios in the AR6 scenarios database : Feasibility corridors for the AR6 scenarios \r\ndatabase, applying the methodology by (Brutschin et al. 2021).  (a) The fraction of scenarios falling within three categories of feasibility concerns (plausible, best case, \r\nunprecedented), for different times (2030, 2050, 2100), different climate categories consistent with the Paris Agreement and five dimensions. (b) Composite feasibility score \r\n(obtained by geometric mean of underlying indicators) over time for scenarios with immediate and delayed global mitigation efforts, for different climate categories (C1, C2, C3. \r\nNote: no C1 scenario has delayed participation). (c) The fraction of scenarios which in any point in time over the century exceed the feasibility concerns, for C1 and C3 climate \r\ncategories. Overlayed are the Illustrative Mitigation Pathways (IMP-LP, IMP-SP, IMP-Ren: C1 category; IMP-Neg, IMP-GS: C3 category)."


id: f191de5858759f8b26d5bcca8fbfbfc1
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 39883
content:
"Recent approaches have addressed multiple dimensions of feasibility, \r\nan important advancement since social and institutional aspects \r\nare as, if not more, important than technology ones (Jewell and \r\nCherp 2020). Feasibility corridors of scenarios based on their scale, \r\nrate of change and disruptiveness have been identified (Kriegler \r\net al. 2018b; Warszawski et al. 2021). The reality check shows that \r\nmany  1.5degC-compatible scenarios violate the feasibility corridors. \r\nThe ones that didn't are associated with a greater coverage of the \r\navailable mitigation levers (Warszawski et al. 2021)."


id: f539a4e89224c895230c1ab801634672
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 40510
content:
"Brutschin et al. (2021) proposed an operational framework covering \r\nall six dimensions of feasibility. They developed a  set of multi\u0002dimensional metrics capturing the timing, disruptiveness and the scale \r\nof the transformative change within each dimension (as in Kriegler \r\net al. 2018b). Thresholds of feasibility risks of different intensity are \r\nobtained through the review of the relevant literature and empirical \r\nanalysis of historical data. Novel indicators include governance \r\nlevels (Andrijevic et al. 2020a). The 17 bottom-up indicators are then \r\naggregated up across time and dimension, as a  way to highlight \r\nfeasibility trade-offs. Aggregation is done via compensatory \r\napproaches such as the geometric mean. This is employed, for \r\ninstance, for the Human Development Index. A conceptual example \r\nof this approach as applied to the IPCC AR6 scenarios database is \r\nshown in Figure 3.42 and further described in the Annex III.II.2.3."


id: ab05eb6a6ededc6340c69778fa573566
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 27312
content:
"Other elements of feasibility relate to social justice, which could be \r\nessential to enhance the political and public acceptability of the \r\nlow-carbon transition. Reviewing the literature, one study finds that \r\nemploying social justice as an orienting principle can increase the \r\npolitical feasibility of low-carbon policies (Patterson et al. 2018). \r\nThree elements are identified as key: (i) protecting vulnerable people \r\nfrom climate change impacts, (ii) protecting people from disruptions \r\nof transformation, (iii) enhancing the process of envisioning and \r\nimplementing an equitable post-carbon society."


id: f1478159ce0534c52cc3b2b95666e5ab
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 39833
content:
"The limitations associated with quantitative low-carbon transition \r\npathways stem from a  predominant reliance on techno-economic \r\nconsiderations with a  simplified or non-existent representation \r\nof the socio-political and institutional agreement. Accompanying \r\nthe required deployment of low-carbon technologies will be the \r\nformation of new socio-technical systems (Bergek et al. 2008). With \r\na  socio-technical system being defined as a  cluster of elements \r\ncomprising of technology, regulation, user practices and markets, \r\ncultural meaning, infrastructure, maintenance networks, and supply \r\nnetworks (Hofman et al. 2004; Geels and Geels 2005); the inter\u0002relationship between technological systems and social systems must "


id: 27bfe0ee94ebc58b2b59ab7d1aa1016c
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 4184
content:
"There is strong agreement that the climate policy institutional \r\nframework as well as technological progress have a profound impact \r\non the attainability of low-carbon pathways. Delaying international \r\ncooperation reduces the available carbon budget and locks into \r\ncarbon-intensive infrastructure exacerbating implementation \r\nchallenges (Keppo and Rao 2007; Bosetti et al. 2009; Boucher et al. \r\n2009; Clarke et al. 2009; Krey and Riahi 2009; van Vliet et al. 2009; "


id: c66e12ae01d7c10f5a8aaa6e7d162a31
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 32219
content:
"innovation market failures, and also address the dynamic nature of \r\npolitical feasibility (Bosetti et al. 2011)."


id: cfc77f2f1ec5324b5f8467a5a2f5c2b
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 33914
content:
"Knopf et al. 2011; Jakob et al. 2012; Luderer et al. 2013; Rogelj \r\net al. 2013a; Aboumahboub et al. 2014; Kriegler et al. 2014a; Popp \r\net al. 2014; Riahi et al. 2015; Gambhir et al. 2017; Bertram et al. \r\n2021). Similarly, technological availability influences the feasibility \r\nof climate stabilisation, though differently for different technologies \r\n(Kriegler et al. 2014a; Iyer et al. 2015a; Riahi et al. 2015)."


id: df68064db15b68ef6264224ec9bf847a
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 36553
content:
"The analysis in this chapter relies on the available literature as well \r\nas an assessment of the scenarios contained in the AR6 scenarios \r\ndatabase. Scenarios were submitted by research and other institutions \r\nfollowing an open call (Annex III.II.3.1). The scenarios included in the \r\nAR6 scenarios database are an unstructured ensemble, as they are \r\nfrom multiple underlying studies and depend on which institutions \r\nchose to submit scenarios to the database. As noted in Section 3.2, \r\nthey do not represent the full scenario literature or the complete set \r\nof possible scenarios. For example, scenarios that include climate \r\nchange impacts or economic degrowth are not fully represented, \r\nas these scenarios, with a  few exceptions, were not submitted \r\nto the database. Additionally, sensitivity studies, which could \r\nhelp elucidate model behaviour and drivers of change, are mostly \r\nabsent from the database - though examples exist in the literature \r\n(Marangoni et al. 2017)."


id: ccfa3f8436b94ad0403927c365f75120
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 33406
content:
"Nonetheless, winning support will require a mix of policies which go \r\nbeyond carbon pricing, and include subsidies, mandates and feebates \r\n(Jenkins 2014; Rozenberg et al. 2018). More recent scenarios take \r\ninto account a more comprehensive range of policies and regional \r\nheterogeneity in the near to medium term (Roelfsema et al. 2020). \r\nRegulatory policies complementing carbon prices could reduce \r\nthe implementation challenges by increasing short-term emission \r\nreduction, though they could eventually reduce economic efficiency \r\n(Bertram et al. 2015b; Kriegler et al. 2018a). Innovation policies \r\nsuch as subsidies to R&D have been shown to be desirable due to "


id: fa3daad168acbd15ee6878c58b20537e
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 41399
content:
"Scenarios are not independent of each other and not representative \r\nof all possible outcomes, nor of the underlying scenario generation \r\nprocess; thus, the statistical power of the database is limited. \r\nDependencies in the data-generation process originate from various \r\nsources. Certain model groups, and types, are over-represented. For \r\nexample, eight model teams contributed 90% of scenarios. Second, \r\nnot all models can generate all scenarios, and these differences are \r\nnot random, thereby creating selection bias (Tavoni and Tol 2010). "


id: 79a5e1834765d2159b7d82229fc6fa89
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 18606
content:
"Third, there are strong model dependencies: the modelling scientific \r\ncommunity shares code and data, and several IAMs are open-source. "


id: 68ada0c78b39640eb973b95966bd185
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 15686
content:
"Halting global warming in the long term requires, at a minimum, that no additional CO2 emissions from human activities are added \r\nto the atmosphere (i.e., CO2 emissions must reach 'net' zero). Given that CO2 emissions constitute the dominant human influence \r\non global climate, global net zero CO2 emissions are a prerequisite for stabilising warming at any level. However, CO2 is not the \r\nonly greenhouse gas that contributes to global warming and reducing emissions of other greenhouse gases (GHGs) alongside \r\nCO2 towards net zero emissions of all GHGs would lower the level at which global temperature would peak. The temperature \r\nimplications of net zero GHG emissions depend on the bundle of gases that is being considered, and the emissions metric used to \r\ncalculate aggregated GHG emissions and removals. If reached and sustained, global net zero GHG emissions using the 100-year \r\nGlobal Warming Potential (GWP-100) will lead to gradually declining global temperature."


id: d70679285658499851b0ce3aef19197
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 35134
content:
"Not all emissions can be avoided. Achieving net zero CO2 emissions globally therefore requires deep emissions cuts across all sectors \r\nand regions, along with active removal of CO2 from the atmosphere to balance remaining emissions that may be too difficult, too \r\ncostly, or impossible to abate at that time. Achieving global net zero GHG emissions would require, in addition, deep reductions of \r\nnon-CO2 emissions and additional CO2 removals to balance remaining non-CO2 emissions."


id: 83db4dd71804ef19fe218ef8dd315576
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 20342
content:
"Total GHG emissions are greater than emissions of CO2 only; reaching net zero CO2 emissions therefore occurs earlier, by up to \r\nseveral decades, than net zero GHG emissions in all modelled pathways. In most modelled pathways that likely limit warming to 2degC \r\n(>67%) above pre-industrial levels and below in the most cost-effective way, the agriculture, forestry and other land-use (AFOLU) \r\nand energy supply sectors reach net zero CO2 emissions several decades earlier than other sectors; however, many pathways show \r\nmuch reduced, but still positive, net GHG emissions in the AFOLU sector in 2100."


id: e117a6a47872c2664fadb95b04bc7d99
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 36867
content:
"Julius Partson Daka (Zambia), Sha Fu (China), James S. Gerber (the United States of America), Sivan \r\nKartha (the United States of America), Volker Krey (Germany/Austria), Hans Lofgren (Sweden/\r\nthe  United States of America), Toshihiko Masui (Japan), Ritu Mathur (India), Joana  Portugal\u0002Pereira (Brazil), Benjamin  K. Sovacool (Denmark/United Kingdom), Maria Virginia Vilarino \r\n(Argentina), Nan Zhou (the United States of America)"


id: 56f3a2667e588300196504328c1a7d02
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 12547
content:
"Lecocq, F., H. Winkler, J.P. Daka, S. Fu, J.S. Gerber, S. Kartha, V. Krey, H. Lofgren, T. Masui, R. Mathur, J. Portugal-Pereira, \r\nB.  K. Sovacool,  M. V. Vilarino,  N. Zhou, 2022: Mitigation and development pathways in the near- to mid-term. In \r\nIPCC, 2022: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth \r\nAssessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, \r\nR. van  Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, \r\n(eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA. doi: 10.1017/9781009157926.006"


id: 78f800772d5eb11d9680c4ccf6e93992
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 18473
content:
"capacity building support. The disruptions triggered by the COVID-19 \r\nepidemic increase uncertainty over range of projections relative \r\nto pre-COVID-19 literature. As indicated by a  growing number of \r\nstudies at the national and global level, how large near- to mid-term \r\nemissions implications of the COVID-19 pandemic are, to a  large \r\ndegree depends on how stimulus or recovery packages are designed. \r\n{4.2, 4.2.2.5, Cross-Chapter Box 4}"


id: f976807251ea52f7b04f6bf9abae9049
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 41252
content:
"This chapter focuses on accelerating mitigation and on shifting \r\ndevelopment pathways to increased sustainability, based on literature \r\nparticularly at national scale. While previous WGIII assessments have \r\ndiscussed mitigation pathways, focus on development pathways is \r\nmore recent. The timeframe is the near term (now up to 2030) to \r\nmid-term (2030 to 2050), complementing Chapter 3 on the long term \r\n(from 2050 onward)."


id: e59a611e208d35891445fb21ed7a0eda
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 37705
content:
"In sum, this chapter suggests that the immediate tasks are \r\nto broaden and deepen mitigation in the near term if the global \r\ncommunity is to deliver emission reductions at the scale required \r\nto keep temperature well below 2degC and pursue efforts at  1.5degC. \r\nDeepening mitigation means more rapid decarbonisation. Shifting \r\ndevelopment pathways to increased sustainability (SDPS) broadens \r\nthe scope of mitigation. Putting the enabling conditions above in \r\nplace supports both. Depending on context, some enabling conditions \r\nsuch as shifting behaviour may take time to establish, underscoring \r\nthe importance of early action. Other enabling conditions, such as \r\nimproved access to financing, can be put in place in a relatively short \r\ntime frame, and can yield results rapidly."


id: 9b434d686cbbf57015d9365bff51b7ca
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 24563
content:
"lower use of fossil fuels than today; (v) converting other uses to low\u0002or zero-carbon fuels (e.g., hydrogen, bioenergy, ammonia) in hard\u0002to-decarbonise sectors; (vi) promote bioenergy, demand reduction, \r\ndietary changes, and policies, incentives, and rules for mitigation in \r\nthe land sector; and (vii) setting and meeting ambitious targets to \r\nreduce methane and other short-lived climate forcers. Charting just \r\ntransitions to net zero may provide a vision, which policy measures \r\ncan help achieve. Though there is increasing experience with \r\npricing carbon directly or indirectly, decision-makers might consider \r\na broader toolbox of enablers and levers that is available in domains \r\nthat have not traditionally been considered climate policy. {4.5, \r\nAnnex II.IV.11}"


id: 1618fbda22fd876742060a2af5f89ab1
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 1102
content:
"Broadening opportunities by focusing on development pathways \r\nand considering how to shift them: Some of the policy measures may \r\nyield rapid results, whereas other, larger transformations may take \r\nlonger. If we are to overcome obstacles, a near-term priority is to put \r\nin place the enabling conditions to shifting development pathways \r\nto increased sustainability. Learning from the examples above, \r\nfocusing on SDPS also provides a broader set of tools to accelerating \r\nmitigation and achieve other sustainable development goals. \r\nConsider climate whenever you make choices about development, \r\nand vice versa. {4.4.1}"


id: 1198012f8a078c872ae7b73d871aeca3
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 287
content:
"Secondly, even if carefully designed, climate policies to accelerate \r\nmitigation may have adverse consequences for other development \r\nobjectives. As a complement to mitigation action, taking action to \r\nshift development pathways towards sustainability broadens the \r\nrange of mitigation options, while increasing the possibility to meet \r\nother development priorities at the same time (medium evidence,\r\nhigh agreement)."


id: a8cbdbc7e547757310f28bf7eb93a68f
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 26904
content:
"The recent IPCC Report on Global Warming of 1.5degC (SR1.5) made \r\nclear that the next three decades are critical if we are to achieve \r\nthe long-term mitigation goal of the Paris Agreement (IPCC 2018a). \r\nThe present chapter assesses the literature on mitigation and \r\ndevelopment pathways over that timeframe, in the near (up to 2030) \r\nand mid-term (up to 2050)."


id: 4e11b805bc101ba9311a8171e5f9b060
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 10999
content:
"Development pathways and shifting them to increased sustainability \r\nare introduced in Chapter  1, and constitute a  thread throughout \r\nthe report (see 'development pathways' in Annex  I: Glossary). \r\nThe AR6 WGII Report highlights the related concept of climate \r\nresilient development pathways (AR6 WGII, Chapter  18). Cross\u0002Chapter Box  5  in this chapter  - on shifting sustainable pathway \r\ntowards sustainability  - elaborates on the concept. The influence \r\nof development pathways on emissions and mitigative capacity is \r\ndiscussed in Chapter  2. Chapter  3  assesses modelling of shifts in \r\ndevelopment pathways, illustrated by the illustrative mitigation \r\npathway called 'shifting pathways'. The importance of behavioural \r\nchange as societies make decisions that intentionally shift their \r\nfuture development pathway is emphasised in Chapter 5. The systems \r\nChapters (6-12) take sectoral perspectives, while pathways that are \r\nsustainable are the specific focus of Chapter 17."


id: 94094f131c4363486b54dd0ac9152fa9
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 23250
content:
"Where are we heading now? Despite the drop in emissions due to \r\nthe COVID-19 crisis, the gap between projected emissions based on \r\nNationally Determined Contributions (NDCs) in 2030 and emissions \r\npathways compatible with the long term temperature goal set in the \r\nParis Agreement remains large (Section  4.2.2). In addition to this \r\npersistent emissions gap, we face an implementation gap, as current \r\npolicies are insufficient to achieve mitigation targets in NDCs, and \r\nsufficient international support is not yet available to developing \r\ncountries who have requested and quantified support needs. \r\nContinuing along a development pathway characterised by the same \r\nunderlying drivers, structural obstacles and insufficient enabling \r\nconditions that led to high emissions will not address the problem \r\n(robust evidence, high agreement)."


id: 63ba858fd488674269044abc708f8c71
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 14832
content:
"The analysis of the gap is conducted together with Chapter 3 (Cross\u0002Chapter Box  4  in this chapter). Chapter  3  is working backward, \r\nassessing mitigation in the long term (beyond 2050 up to 2100) to \r\ndraw the near- and mid-term implications of long-term temperature \r\nand mitigations goals. Chapter  4, on the other hand, works \r\nforward from current and planned mitigation (including NDCs) \r\n(Sections 4.2.1 and 4.2.2) and from current development paths to \r\nassess the implications for near- and mid-term greenhouse gases \r\n(GHG) emissions and development goals. Some countries, regions, \r\ncities, communities and non-state actors are taking leadership in \r\nimplementing more ambitious action (Section  4.2.3). This chapter \r\nalso assesses national low emission development strategies \r\n(Section 4.2.4)."


id: 5dd8676baa99c4e092798b46fd122451
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 13812
content:
"Enabling conditions necessary to accelerate mitigation and shift \r\ndevelopment pathways are discussed in depth in Chapters  5, 13, \r\n14, 15 and 16. In addition, Chapters  13 and 14 detail the policy \r\ninstruments that could help shift development pathways and \r\naccelerate the scale and pace of mitigation, while Chapter 4 describes \r\nthose in broad strategies terms. Chapter  13 adds more texture \r\non  institutional and governance machinery; policy choice, \r\ndesign and implementation; as well as policy formulation processes, \r\nactors and structure across scales."


id: 871e49470bb9df6171029d55f88b7acb
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 20936
content:
"Where do we want to go? Technical alternatives and policy options \r\nexist to bridge the emissions and implementation gaps, and the \r\nliterature illustrates these with a wide range of accelerated techno\u0002economic pathways that deepen decarbonisation closer to the pace \r\nand scale required (Section  4.2.5), and examines their impacts on \r\nother development objectives (Section 4.2.6). In practice, however, \r\nscaling up at the broader, deeper, and faster level required to meet \r\nclimate goals while advancing other development objectives regularly \r\nfaces prohibitive obstacles (Section 4.2.7). Mitigation policies grafted \r\non to existing development pathways are unlikely to achieve rapid \r\nand deep emission reductions."


id: 79687b8f221ae305b6f5bce539953275
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 18557
content:
"Since development pathways and mitigation options depend to \r\nlarge extent on national objectives and circumstances, this chapter \r\nis primarily concerned with literature at national level (or in the case \r\nof the European Union, at regional level), while Chapter 3 is primarily \r\nconcerned with literature at global scale. The national scale selected \r\nin this chapter requires attention as national mitigation pathways \r\ncannot be linked directly to global mitigation goals (Box 4.2). This "


id: 59cdc9fbb6d6f77666f4063e0d6b5133
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 13041
content:
"scholarship on the ethical underpinnings of these assertions and of \r\nvarious quantitative analyses of equitable effort-sharing. Section 4.2.6 \r\ndiscusses certain distributional implications of domestic mitigation \r\nefforts, such as shifts in employment. Sections  4.2.7 and  4.3 note \r\nthe relevance of potential distributional impacts as an obstacle to \r\nclimate action, as well as the inequitable distribution of decision\u0002making authority. Finally, Section  4.5 recognises the structural \r\nrelationship between equity and climate, explores just transitions as \r\nan international focal point tying together social movements, trade \r\nunions, and other stakeholders, and thus an instrumental role in \r\nestablishing consensus."


id: ac77c34a101f1ff4da2367a16600784
created_at: 2023-04-19 13:22:12
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"chapter is also concerned mostly with economy-wide development \r\nand mitigation pathways, as distinct from detailed sectoral work that \r\nis assessed in the systems Chapters 6 to 12. The present chapter also \r\nassesses literature on non-state action."


id: 818110ff4f6fe0d4e364e001bf42157a
created_at: 2023-04-19 13:22:12
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"Chapter 4 draws on five major strands of literature: (i) an emerging \r\nliterature on development pathways  - conceptual, empirical, and \r\nmodel-based, including at the national and sub-national scales; \r\n(ii)  a  rapidly expanding, model-based, literature on mitigation \r\npathways in the near- and mid-term (Lepault and Lecocq 2021); \r\n(iii)  studies of NDCs and mid-century strategies; (iv) a  broader \r\nliterature on transformation and shifts in development pathways, \r\nincluding from non-climate literatures; and (v) a significant literature \r\non equity, including just transitions. This is supported by a database \r\nof country-level mitigation scenarios at country level assembled for \r\nthe preparation of this chapter (Annex III, Table I.10 and I.11)."


id: 1c341c7ae5def679069f65a8a031970e
created_at: 2023-04-19 13:22:12
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"A central instrument of the Paris Agreement is the NDCs, submitted by \r\neach country, and reflecting national efforts to reduce GHG emissions \r\nand build resilience to the impacts of climate change. Every five years, \r\ncollective progress will be compared against long-term goals of the \r\nParis Agreement. Considering the outcome of a  global stocktake, \r\ncountries will prepare subsequent NDCs, showing progression in their \r\nambition and enhancing international cooperation (UNFCCC 2015a)."


id: 5ecfdfa1dc6aa2afc436c1ca465d652e
created_at: 2023-04-19 13:22:12
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"Chapter  4  examines mitigation within the broader context of \r\ndevelopment pathways, and examines how shifting development \r\npathways can have a  major impact on mitigative capacity and \r\nbroadening mitigation options. It is organised as follows."


id: 71860ab63682c5d71267790d74d24405
created_at: 2023-04-19 13:22:12
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"Section 4.2 demonstrates that collective mitigation actions fall short \r\nof pathways that keep in reach the Paris temperature goals in the long \r\nterm. Section 4.3 introduces development pathways (given its relative \r\nnovelty in IPCC assessments), considers the implications of mitigation \r\nfor development and vice versa, and articulates an approach on both\r\naccelerating mitigation and shifting development pathways."


id: 4e8343aea055c9335e4f7cc1f5de1c41
created_at: 2023-04-19 13:22:12
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"Submitted NDCs vary in content, scope and background assumptions. \r\nFirst NDCs contain mitigation targets, and in many cases also \r\nprovisions about adaptation. The mitigation targets range from \r\neconomy-wide absolute emission reduction targets to strategies, \r\nplans and actions for low-emission development. Baseline years vary \r\nfrom 1990 to 2015 and in almost all NDCs the targeted time frame is \r\n2030, with a few specified periods of until 2025, 2035, 2040 or 2050. \r\nAround 43% of the mitigation targets in first NDCs are expressed in \r\nterms of deviation below business-as-usual by a specified target year, \r\neither for the whole economy or for specific sectors, while around \r\n35% include fixed-level targets (either reductions or limitations \r\ncompared to base years), and another 22% refer to intensity targets \r\n(in terms of GHG, CO2 or energy) or policies and measures, with an "


id: 53be6a2033230b8e06a0da95ca09661b
created_at: 2023-04-19 13:22:12
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"Section  4.4 discusses how to shift development pathway and \r\naccelerate the scale and pace of mitigation, what levers are available \r\nto policymakers, and how policies may intersect with adaptation \r\ngoals. It points out that development pathways also drive adaptation \r\nand adaptative capacity, and discusses various risks associated with \r\nshifting development pathways and accelerated mitigation strategies."


id: 7bcaa9041d46f794e28ba7651817b37e
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"Finally, equity and just transitions are recurring themes in the \r\nchapter, specifically in relation to accelerating mitigation and shifting \r\ndevelopment pathways toward sustainability. In Section  4.2.2.7, \r\nequity is discussed in the context of Parties' assertions regarding \r\nthe fairness of their NDCs, alongside reflections from academic "


id: d8e89865c174e1eb0defc5d0f0419de0
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"2 'Unconditional' NDCs refer to abatement efforts pledged without any conditions (this terminology is used by the literature, not by the Paris Agreement). They are based \r\nmainly on domestic abatement actions, although countries can use international cooperation to meet their targets. 'Conditional' NDCs require international cooperation, \r\nfor example bilateral agreements under article 6, financing or monetary and/or technological transfers (14.3.2)."


id: 30dcee6788202cc7966f5fd48f12e88d
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"increasing number of Parties moving to absolute emission reduction \r\ntargets in their new or updated NDCs (UNFCCC 2016a, 2021). Some \r\ndeveloping countries' NDCs include unconditional elements, while \r\nothers include conditional ones, the latter with higher ambition if \r\nfinance, technology and capacity building support from developed \r\ncountries is provided (UNFCCC 2016a).2\r\n In some NDCs, the additional \r\nmitigation is quantified, in others not (Figure 14.2)."


id: cf6d070cf2abccf736ad9cececfb1337
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"There is considerable literature on country-level mitigation \r\npathways, including but not limited to NDCs. Country distribution \r\nof this literature is very unequal (robust evidence, high agreement). \r\nIn particular, there is a growing literature on (I)NDCs, with a wide \r\nscope which includes estimate of emissions levels of NDCs \r\n(Section  4.2.2.2); alignment with sustainable development goals \r\n(Caetano et al. 2020; Campagnolo and Davide 2019; Fuso Nerini \r\net al. 2019; Antwi-Agyei et al. 2018); ambition (Hohne et al. 2018; \r\nVogt-Schilb and Hallegatte 2017; Hermwille et al. 2019); energy \r\ndevelopment (Scott et al. 2018); and the legality of downgrading \r\nNDCs (Rajamani and Brunnee 2017). Other studies note that many \r\nNDCs contain single-year mitigation targets, and suggest that \r\na multi-year trajectory is important for more rigorous monitoring \r\n(Elliott et al. 2017; Dagnet et al. 2017)."


id: 82c468b42ffffcf69135f51771fd0c57
created_at: 2023-04-19 13:22:12
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"The literature reviewed in this section includes globally comprehensive \r\nassessments of NDCs and current policies, both peer-reviewed and \r\nnon-peer-reviewed (but not unpublished model results) as well as \r\nsynthesis reports by the UNFCCC Secretariat, government reports \r\nand national studies."


id: 3148222371995d0761f94207683a73eb
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"The aggregate effects of NDCs provide information on where \r\nemissions might be in 2025/2030, working forward from their recent \r\nlevels. Chapter 3 of this report works backwards from temperature \r\ngoals, defining a  range of long-term global pathways consistent \r\nwith 1.5degC, 2degC and higher temperature levels. By considering the \r\ntwo together, it is possible to assess whether NDCs are collectively \r\nconsistent with 1.5degC, 2degC and other temperature pathways (Cross\u0002Chapter Box 4 in this chapter)."


id: 59f4d52144aa594e304b79cc33d69a35
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"The literature also points out that beyond the 'headline numbers', \r\ninformation in (I)NDCs is difficult to analyse (Pauw et al. 2018). \r\nInformation for 'clarity, transparency and understanding' is to be \r\ncommunicated with NDCs, although initial guidance was not specific \r\n(UNFCCC 2014). While the adoption of the Paris rule-book provided \r\nsome greater specificity (UNFCCC 2018b,c), the information included \r\nin the NDCs remains uneven. Many NDCs omit important mitigation \r\nsectors and do not adequately provide details on costs and financing \r\nof implementation (Pauw et al. 2018). Countries are also invited to \r\nexplain how their NDCs are fair and ambitious, though the way this \r\nhas been done so far has been criticised as insufficiently rigorous \r\n(Winkler et al. 2018)."


id: 66bea246221de7749e17e25c4bfb861c
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"A variety of different methods are used to assess emissions \r\nimplications of NDCs and current policies over the time horizon to \r\n2025 or 2030. Some of these projections were explicitly submitted as \r\npart of an official communication to UNFCCC (e.g., Biennial Report, \r\nBiennial Update Reports or National Communications) while the \r\nmajority is from independent studies."


id: 63bb699c99d88a0309fe3aad3b069cd0
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"emissions, but have poor resolution of remaining countries or regions, \r\nwhich are assessed in aggregate terms. Conversely, studies with high \r\nresolution of a particular country tend to treat interactions with the \r\nglobal scale in a limited way. The recent literature includes attempts \r\nto provide a composite global picture from detailed national studies \r\n(Bataille et al. 2016a; Deep Decarbonization Pathways Project 2015; \r\nRoelfsema et al. 2020)."


id: 520ae8763a68de14cf74546d518c7b2f
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"A second dimension in which the studies are different is their \r\ncomprehensiveness of covering different emitting sectors. Some \r\nstudies focus on the contribution of a  single sector, for example \r\nthe agriculture, forestry and other land use (AFOLU) sector (Fyson \r\nand Jeffery 2019; Grassi et al. 2017) or the energy system (including \r\nboth energy supply and demand sectors), to emission reductions as \r\nspecified in the NDC. Such studies give an indication of the importance \r\nof a given sector to achieving the NDC target of a country and can be \r\nused as a benchmark to compare to comprehensive studies, but adding \r\nsectoral contributions up represents a methodological challenge."


id: c1d7bf87cccdcd9496a25580c88cb885
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"System modelling studies are conducted at global, regional and \r\nnational scales. Global models provide an overview, are necessary \r\nfor assessment of global phenomena (e.g.,  temperature change), \r\ncan integrate climate models and trade effects. National models \r\ntypically include more details on sectors, technology, behaviour and \r\nintersectoral linkages, but often use simplifying assumptions for \r\ninternational trade (e.g., the Armington elasticity approach). Critically, \r\nthey can also better reflect local socio-economic and political \r\nconditions and their evolution (i.e., national development pathways). \r\nA variety of modelling paradigms are found, including optimisation \r\nand simulation models, myopic and with foresight, monolithic and \r\nmodular (Annex III: Scenarios and Modelling Methods)."


id: 58bf9d0087bbd9d8e9d5f0f77015b693
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"Third, GHG coverage is different across studies. Some focus on CO2\r\nonly, while others take into account the full suite of Kyoto gases \r\n(CO2, CH4, N2O, HFCs, PFCs and SF6). For the latter, different metrics \r\nfor aggregating GHGs to a  CO2-equivalent metric are being used, \r\ntypically GWP 100 from different IPCC assessments (Table 4.1)."


id: 2d3437cb3774dcea12626881cd90e0a9
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"Fourth, studies typically cover a set of scenarios, though how these \r\nscenarios are defined varies widely. The literature reporting IAM \r\nresults often includes Nationally Determined Contribution (NDC), \r\nwhich are officially communicated, and Current Policies (CP) as \r\ninterpreted by modellers. Studies based on national modelling, by \r\ncontrast, tend to define scenarios reflecting very different national \r\ncontexts. In both cases, modellers typically include so-called No \r\nPolicy Baseline scenarios (alternatively referred to as Reference \r\nor Business-as-Usual scenarios) which do not necessarily reflect \r\ncurrently implemented policies and thus are not assessed as reference \r\npathways (Section  4.2.6.1). There are also various approaches to \r\nconsidering more ambitious action compared to the CP or NDC \r\nprojections that are covered in addition."


id: ab9a0555420af7ae4d2c55a47736e442
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"Among the hybrid approaches, three broader categories can be \r\ndistinguished, (i) direct use of official emission projection as part of \r\nsubmitted NDC or other communication to UNFCCC, (ii) historical \r\ntrend extrapolation of emissions based on inventory data, possibly \r\ndisaggregated by sector and emission species, and (iii) use of \r\nReference/Business-As-Usual pathways from an independent \r\npublished study (e.g.,  IEA WEO). In all cases, the reductions are \r\nthen estimated on top of the resulting emission trajectory. Note \r\nthat globally comprehensive studies may vary the approach used \r\ndepending on the country."


id: 3526973870ab886b20e8fe1c4e12766a
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"Fifth, studies differ in the way they represent policies (current or \r\nenvisioned in NDCs), depending on their internal structure. For \r\nexample, a subsidy to energy efficiency in buildings may be explicitly \r\nmodelled (e.g.,  in a  sectoral model that represents household \r\ndecisions relative to building insulation), represented by a  proxy \r\n(e.g.,  by an exogenous decrease in the discount rate households \r\nuse to make choices), or captured by its estimated outcome (e.g., by \r\nan exogenous decrease in the household demand for energy, say in an \r\nenergy system model or in a compact CGE). Detailed representations \r\n(such as the former example) do not necessarily yield more accurate \r\nresults than compact ones (the latter example), but the set of \r\nassumptions that are necessary to represent the same policy will be \r\nvery different."


id: 3decb9f36129a1a72c40eaefd38f0cf7
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"Beyond the method applied, studies also differ in a  number of \r\ndimensions, including (i) their spatial resolution and coverage, \r\n(ii) their sectoral resolution and coverage, (iii) the GHGs that are \r\nincluded in the assessment, the GWPs (or other metrics) to aggregate \r\nthem, the emissions inventory (official vs independent inventory \r\ndata) and related accounting approaches used as a  starting point \r\nfor the projections, (iv) the set of scenarios analysed (Reference/\r\nBusiness-As-Usual, Current Policies, NDCs, etc.), and (v) the degree to \r\nwhich individual policies and their impact on emissions are explicitly \r\nrepresented (Table 4.1)."


id: 18f1602c24b47d1b6a65d16ecb65f8ed
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"First, the studies are relevant to different spatial levels, ranging \r\nfrom macro-scale regions with globally comprehensive coverage to \r\nnational level (Section 4.2.2.3) and sub-national and company level in \r\na few cases (Section 4.2.3). It is important to recognise that globally \r\ncomprehensive studies typically resolve a limited number of countries \r\nindividually, in particular those that contribute a high share to global "


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"policies at sub-national level. In addition, in countries with rapidly \r\nevolving policy environments, slightly different cut-off dates for the \r\npolicies considered in an emission projection can make a significant \r\ndifference for the results (Dubash et al. 2018)."


id: cdf792b336a1d450f06f2f8441c695a1
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"to the global level. As different studies report different emission \r\nindicators, the table includes four different indicators: CO2 and GHG \r\nemissions, including or excluding AFOLU emissions. Where possible, \r\nmultiple indicators are included per study."


id: afd2e817616afdf6a52d312f0c007618
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"The challenges described above are dealt with in the assessment \r\nof quantitative results in Section 4.2.2.3 by (i) comparing national \r\nstudies with country-level results from global studies to understand \r\nsystematic biases; (ii) comparing economy-wide emissions (including \r\nAFOLU) as well as energy-related emissions; (iii) using different \r\nemission metrics including CO2 and Kyoto GHG emissions where \r\nthe latter have been harmonised to using AR6 GWP100 metrics; and \r\n(iv) tracking cut-off dates of implemented policies and NDCs used \r\nin different references (Table 4.SM.1). The most notable differences \r\nin quantitative emission estimates related to current policies \r\nand NDCs relate to the COVID-19 pandemic and its implications and \r\nto the updated NDCs mostly submitted since early 2020 which are \r\nseparately dealt with in Sections 4.2.2.4 and 4.2.2.5, respectively."


id: 226b39ea89ff2e33afe2b062770351e8
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"The UNFCCC Secretariat has assessed the aggregate effect of NDCs \r\nmultiple times. The first report considered the intended NDCs in \r\nrelation to 2degC (UNFCCC 2015b), whereas the second considered \r\nNDCs also in relation to 1.5degC (UNFCCC 2016b). New submissions \r\nand updates of NDCs in 2020/21 are assessed in Section  4.2.2.5. \r\nA number of globally comprehensive studies (den Elzen et al. 2016; \r\nLuderer et al. 2016; Rogelj et al. 2016, 2017; Vandyck et al. 2016; \r\nRose et al. 2017; Baumstark et al. 2021) which estimate aggregate \r\nemissions outcomes of NDCs and current policies have previously \r\nbeen assessed in Cross-Chapter-Box 11 of IPCC SR1.5."


id: 6916e118571aac535ffbc68909b4d3dd
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"According to the assessment in this report, studies projecting \r\nemissions of current policies based on pre-COVID assumptions lead \r\nto median global GHG emissions of 60 GtCO2-eq with a full range \r\nof 54-68 by 2030 and original unconditional and conditional NDCs \r\nsubmitted in 2015/16 to 57 (49-63) and 54 (50-60) GtCO2-eq, \r\nrespectively (robust evidence, medium agreement) (Table  4.1). \r\nGlobally comprehensive and national-level studies project emissions \r\nof current policies and NDCs to 2025 and 2030 and, in general, are \r\nin good agreement about projected emissions at the country level."


id: ab816a88e55eec11a7c5865f186464c6
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"In addition to assessing the emissions outcomes of NDCs, some \r\nstudies report development indicators, by which they mean a wide \r\ndiversity of socio-economic indicators (Jiang et al. 2013; Chai and \r\nXu 2014; Delgado et al. 2014; La Rovere et al. 2014a; Zevallos \r\net al. 2014; Benavides et al. 2015; Altieri et al. 2016; Bataille et al. \r\n2016a; Zou et al. 2016; Paladugula et al. 2018; Parikh et al. 2018; \r\nYang et al. 2021), share of low-carbon energy (Bertram et al. 2015; \r\nRiahi et al. 2015), renewable energy deployment (Roelfsema et al. \r\n2018), production of fossil fuels (SEI et al. 2020) or investments into \r\nlow-carbon mitigation measures (McCollum et al. 2018) to track \r\nprogress towards long-term temperature goals."


id: bb2148d91744469c5f0e8a3690553c09
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"These estimates are close to the ones provided by the IPCC \r\nSR1.5, Cross-Chapter-Box  11, and the UNEP emissions gap \r\nreport (UNEP 2020a).3"


id: a87a121b4fb78486a5bf54ad08bdb479
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
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"A large body of literature on national and regional emissions \r\nprojections, including official communications of as part of the \r\nNDC submissions and independent studies exist. A  subset of this \r\nliterature provides quantitative estimates for the 2030 timeframe. \r\nAs highlighted in Section 4.2.1, the number of independent studies \r\nvaries considerably across countries with an emphasis on the largest \r\nemitting countries. This is reflected in Table 4.1 (see also Table 4.SM.1). \r\nDespite smaller differences between globally comprehensive \r\nand national studies for a  few countries, there is generally good \r\nagreement between the different types of studies, providing evidence \r\nthat these quantitative estimates are fairly robust."


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"The emissions projections presented in this section relate to the first \r\nNDCs, as communicated in 2015 and 2016, and on which an extensive \r\nliterature exists. New and updated NDCs, mostly submitted since the \r\nbeginning of 2020, are dealt with in Section  4.2.2.5. Similarly, \r\nthe implications of COVID-19 and the related government responses \r\non emissions projections is specifically dealt with in Section 4.2.2.4."


id: ff3a75d451ce7c39ad9acfc71385ec4f
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"Table 4.1 presents the evidence base for the assessment of projected \r\nemissions of original NDCs and current policies until 2030. It covers \r\n31 countries and regions responsible for about 82% of global GHG \r\nemission (excluding FOLU CO2 emissions) and draws quantitative \r\nestimates from more than 40 studies (Table  4.SM.1 in the \r\nSupplementary Material to this chapter). The table allows comparing \r\nemission projections from national and globally comprehensive \r\nstudies as well as official communications by countries to the \r\nUNFCCC at the national/regional level. The global aggregates \r\npresented in Table  4.1 derive from globally comprehensive studies \r\nonly and are not the result of aggregating country projections up "


id: ce4f3095435ae51e2870526ef6dcd009
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"Sectoral studies are essential to understand the contributions of \r\nconcrete measures of NDCs and current policies. For example, \r\napproximately 98% of NDCs include the energy sector in their \r\nmitigation contributions, of which nearly 50% include a  specific "


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"target for the share of renewables, and about 5% aim at increasing \r\nnuclear energy production (Stephan et al. 2016). Transport is covered \r\nexplicitly in 75% of NDCs, although specific targets for the sector \r\nexist in only 21% of NDCs (PPMC and SLoCaT 2016). Measures or \r\ntargets for buildings are referred to explicitly in 27% of NDCs (GIZ \r\n2017). Additionally, 36% of NDCs include targets or actions that are \r\nspecific to the agriculture sector (FAO 2016). LULUCF (mitigation) is \r\nincluded in 80% of all submitted NDCs, while 59% include adaptation \r\nand 29% refer to REDD+."


id: a4566d4e83b2a69589049204385d54b7
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 26116
content:
"(Table  4.SM.2). Nascimento et al. (2021) analyse the impacts of \r\nCOVID-19 on current policy emission projections for 26 countries and \r\nregions and find a large range of emission reduction - between -1% \r\nand -21% - across these."


id: 154ffa46f9b7badb50bea2587e20ce52
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 964
content:
"As indicated by a  growing number of studies at the national and \r\nglobal level, how large near- to mid-term emissions implications \r\nof the COVID-19 pandemic are to a large degree depends on how \r\nstimulus or  recovery packages are designed (Forster et al. 2020; \r\nGillingham et al. 2020; IEA 2020; Le Quere et al. 2020; Malliet \r\net al. 2020; Wang  et al. 2020; Obergassel et al. 2021; Pollitt et al. \r\n2021; UNEP 2020a)."


id: 8c6c91544bc0777f2ce0ff6eda94cf6c
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 21889
content:
"Greater sectoral expertise and involvement will be critical to \r\naccomplishing development and climate goals due to enhanced \r\navailability of information and expertise on specific sectoral options, \r\ngreater ease of aligning the NDCs with sectoral strategies, and greater \r\nawareness among sector-level decision-makers and stakeholders \r\n(Fekete et al. 2015; NDC Partnership 2017). Sector-specific studies \r\nare assessed in the sectoral Chapters (6 to 11) of this report."


id: 4a6285ee4eb7a965ee87f05cfc092789
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 10326
content:
"Four studies (Climate Action Tracker 2021; den Elzen et al. 2021; JRC \r\n2021; Riahi et al. 2021) provide an update of the current policies \r\nassessment presented in Section 4.2.2.3 by taking into account the \r\neffects of COVID-19 as well as potential updates of policies. The \r\nresulting GHG emissions in 2030 are estimated to be 57 GtCO2-eq \r\nwith a full range of 52 to 60 GtCO2-eq (Table 4.2). This is a reduction \r\nof about 3 GtCO2-eq or 5% compared to the pre-COVID estimates \r\nfrom Section 4.2.2.3."


id: f76188c0dd5244250bf47041c4931d66
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 40899
content:
"The impacts of COVID-19 and national governments' economic \r\nrecovery measures on current (Section 2.2.2) and projected emissions \r\nof individual countries and globally under current policies scenarios \r\nuntil 2030 may be significant, although estimates are highly uncertain \r\nand vary across the few available studies. The analyses published to \r\ndate (October 2021) are based on limited information about how \r\nCOVID-19 has affected the economy and hence GHG emissions \r\nacross countries so far in 2020, and also based on assumptions about \r\nCOVID-19's longer term impact. Moreover, the comparison of pre\u0002and post-COVID-19 projections captures the impact of COVID-19 as \r\nwell as other factors such as the consideration of recently adopted \r\npolicies not related to COVID-19, and methodological changes."


id: 90b76a6756574e348a100af66aceb651
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 22664
content:
"Across different studies (Kikstra et al. 2021; IEA 2020; Dafnomilis \r\net al. 2021; Pollitt et al. 2021; UNEP 2020a; Climate Action Tracker \r\n2020; Keramidas et al. 2021; Dafnomilis et al. 2020), the impact of the \r\ngeneral slowdown of the economy due to the COVID-19 pandemic \r\nand its associated policy responses would lead to a  reduced \r\nestimate of global GHG emissions in 2030 of about 1 to 5 GtCO2-eq, \r\nequivalent to  1.5-8.5%, compared to the pre-COVID-19 estimates "


id: 4b18ea3c089e21f9cff57d2c8dc15e2d
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 10456
content:
"Notes: a GHG emissions expressed in CO2-eq emission using AR6 100-year GWPs. GHG emissions from studies that provide aggregate GHG emissions using other GWPs are \r\nrescaled using 2019 GHG emissions from Chapter 2 (Minx et al. 2021; Crippa et al. 2021). b\r\n If a range is available from a study, a median is provided in addition to the range. c\r\n Range includes estimates from four models: GEM-E3, MESSAGEix-GLOBIOM, POLES, REMIND-MAgPIE, based on sensitivity analysis. d To avoid a bias due to multiple estimates \r\nprovided by the same model, only one estimate per model, typically the most recent update, is included in the median estimate for the total."


id: 9466a99a78cf89c37068b00f3f0d59c9
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
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index: climate_gpt_v2
vector_id: 23318
content:
"An additional challenge lies in the fact that these studies do not all \r\napply the same cut-off date for NDC updates, potentially leading \r\nto larger systematic deviations in the resulting emission estimates. \r\nAnother complication is the fact that publicly announced mitigation \r\npledges on global 2030 emissions that have not been officially \r\nsubmitted to the UNFCCC NDC registry yet, have been included in \r\nseveral of the studies to anticipate their impact on emission levels \r\n(see notes to Table 4.3). In addition to the updates of NDC targets, \r\nmost of the new studies also include impacts of COVID-19 on future \r\nemission levels (as discussed in Section 4.2.2.4) which may have led \r\nto considerable downward revisions of emission trends unrelated to \r\nNDCs. Table 4.3 presents the emission estimates of the four studies \r\nthat form the basis of the quantitative assessment presented here \r\nand three other studies to compare with."


id: 4d804969b917d4434d378c971a02a488
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
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content:
"22 GtCO2-eq (19-26). New and updated conditional NDCs reduce the \r\nmedian gap with emissions pathways that limit warming to 1.5degC \r\n(>50%) with no or limited overshoot in 2030 by about 20%, from \r\na  median gap of 24  GtCO2-eq (20-29) to 19  GtCO2-eq (16-23). \r\nBox 4.1 discusses the adaptation gap."


id: 449f3ae5ce47aea7c014a923b0aea3ec
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
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content:
"Globally, the implementation gap between projected emissions of \r\ncurrent policies and the unconditional and conditional new and \r\nupdated NDCs is estimated to be around 4 and 7 GtCO2-eq in 2030, \r\nrespectively (medium evidence, medium agreement) (Tables  4.2 \r\nand  4.3), with many countries requiring additional policies and \r\nassociated climate action to meet their mitigation targets as \r\nspecified under the NDCs (limited evidence) (Section  4.2.2.6). It \r\nshould be noted that the implementation gap varies considerably \r\nacross countries, with some having policies in place estimated to be \r\nsufficient to achieve the emission targets their NDCs, some where \r\nadditional policies may be required to be sufficient, as well as \r\ndifferences between the policies in place and action on the ground."


id: 64ae6838b5261c9b91c3d92e933cb2e4
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 14996
content:
"Under the Enhanced Transparency Framework, countries will transition \r\nfrom reporting biennial reports (BRs) and biennial update reports \r\n(BURs) to reporting biennial transparency reports (BTRs) starting, at \r\nthe latest, by December 2024. Each Party will be required to report \r\ninformation necessary to track progress made in implementing and \r\nachieving its NDC under the Paris Agreement (UNFCCC 2018b). Thus, \r\nno official data exists yet on tracking progress of individual NDCs."


id: 49d2969dbfe8001187644f4ad4e04b9c
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 10226
content:
"With an implementation gap in 2030 of 4  to 7  GtCO2-eq \r\n(Section 4.2.2.5), many countries will need to implement additional \r\npolicies to meet their self-determined mitigation targets as specified \r\nunder the NDCs. Studies that assess the level of projected emissions \r\nunder current policies indicate that new policies (that have been \r\nimplemented since the first assessment of the NDCs in 2015 and are \r\nthus covered in more recent projections) have reduced projections, \r\nby about two GtCO2-eq since the adoption of the Paris Agreement \r\nin 2015 to 2019 (Climate Action Tracker 2019; UNEP 2020a; \r\nden Elzen et al. 2019)."


id: d4785c0f951dbbc9ed654e758c55c6eb
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
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content:
"There are many factors that influence the global aggregated effects of \r\nNDCs. There is limited literature on systematically analysing the impact \r\nof uncertainties on the NDC projections with some exception (Rogelj \r\net al. 2017; Benveniste et al. 2018). The UNEP Gap Report (UNEP \r\n2017a) discusses uncertainties of NDC estimates in some detail. The \r\nmain factors include variations in overall socio-economic development; \r\nuncertainties in GHG inventories; conditionality; targets with ranges or \r\nfor single years; accounting of biomass; and different GHG aggregation \r\nmetrics (e.g., GWP values from different IPCC assessments). In addition, \r\nwhen mitigation effort in NDCs is described as measures that do only \r\nindirectly translate into emission reductions, assumptions necessary \r\nfor the translation come into play (Doelle 2019). For a more elaborate \r\ndiscussion of uncertainties in NDCs (Section 14.3.2)."


id: b86dd8cc34e42971fa56037441f99eba
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
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index: climate_gpt_v2
vector_id: 29657
content:
"Most countries provided information on how they consider their \r\nNDCs to be fair and ambitious in the NDCs submitted to UNFCCC and \r\nmany of these NDCs refer to specific national circumstances such as \r\nsocial, economic and geographical factors when outlining why they \r\nare fair and ambitious. Further, several Parties provided information \r\non specific criteria for evaluating fairness and ambition, including \r\ncriteria relating to: responsibility and capability; share of emissions; \r\ndevelopment and/or technological capacity; mitigation potential; cost \r\nof mitigation actions; the degree of progression or stretching beyond \r\nthe current level of effort; and the link to objectives and global goals \r\n(UNFCCC 2016a)."


id: 32bf4e4c3d088f0d43e5023dffd28f6a
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
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index: climate_gpt_v2
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content:
"Some studies assume successful implementation of all of the NDCs' \r\nproposed measures, sometimes including varying assumptions to \r\naccount for some of the NDC features which are subject to assumed \r\nconditions related to finance and technology transfer. Countries 'shall \r\npursue domestic mitigation measures' under Article 4.2 of the Paris \r\nAgreement (UNFCCC 2015a), but they are not legally bound to the \r\nresult of reducing emissions (Winkler 2017a). Some authors consider \r\nthis to be a  lack of a  strong guarantee that mitigation targets in \r\nNDCs will be implemented (Nemet et al. 2017). Others point to \r\ngrowing extent of national legislation to provide a  legal basis for \r\naction (Iacobuta et al. 2018) (Section 13.2). These factors together \r\nwith incomplete information in NDCs mean there is uncertainty \r\nabout the estimates of anticipated 2030 emission levels."


id: edc2b2f852c04d3917b6cb5bcc750b00
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
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index: climate_gpt_v2
vector_id: 39187
content:
"According to its Article 2.2, the Paris Agreement will be implemented \r\nto reflect equity and the principle of common but differentiated \r\nresponsibilities and respective capabilities, in the light of different \r\nnational circumstances, the latter clause being new, added to \r\nthe UNFCCC principle (Voigt and Ferreira 2016; Rajamani 2017). \r\nPossible different interpretations of equity principles lead to different \r\nassessment frameworks (Lahn and Sundqvist 2017; Lahn 2018)."


id: 1302ec10b0693565a9e0b2713946f235
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 551
content:
"Various assessment frameworks have been proposed to analyse \r\nfair share ranges for NDCs. The literature on equity frameworks \r\nincluding quantification of national emissions allocation is assessed \r\nin section 4.5 (Sections 13.4.2, 14.3.2 and 14.5.3). Recent literature "


id: b315f10e214a6a407fedfec0c2e4b245
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 28750
content:
"The aggregation of targets results in large uncertainty (Rogelj et al. \r\n2017; Benveniste et al. 2018). In particular, clarity on the contributions \r\nfrom the land use sector to NDCs is needed 'to prevent high \r\nLULUCF uncertainties from undermining the strength and clarity of \r\nmitigation in other sectors' (Fyson and Jeffery 2019). Methodological \r\ndifferences in the accounting of the LULUCF anthropogenic CO2\r\nsink between scientific studies and national GHG inventories (as \r\nsubmitted to UNFCCC) further complicate the comparison and "


id: 91e59de5411d2b3306cce6655f343761
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
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index: climate_gpt_v2
vector_id: 22886
content:
"aggregation of emissions of NDC implementation (Grassi et al. 2018, \r\n2021) (Section  7.2.3 and Cross-Chapter Box  6  in Chapter  7). This \r\nuncertainty could be reduced with clearer guidelines for compiling \r\nfuture NDCs, in particular when it comes to mitigation efforts not \r\nexpressed as absolute economy-wide targets (Doelle 2019), and \r\nexplicit specification of technical details, including energy accounting \r\nmethods, harmonised emission inventories (Rogelj et al. 2017) and \r\nfinally, increased transparency and comparability (Pauw et al. 2018)."


id: 872569cff21d74dd000db95d5045d0dd
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 20940
content:
"The Paris Agreement (PA) sets a long-term goal of holding the increase of global average temperature to 'well below 2degC above pre\u0002industrial levels' and pursuing efforts to limit the temperature increase to 1.5degC above pre-industrial levels. This is underpinned by \r\nthe 'aim to reach global peaking of greenhouse gas emissions as soon as possible' and 'achieve a balance between anthropogenic \r\nemissions by sources and removals by sinks of GHG in the second half of this century' (UNFCCC 2015a). The PA adopts a bottom-up \r\napproach in which countries determine their contribution to reach the PA's long-term goal. These national targets, plans and measures \r\nare called 'nationally determined contributions' or NDCs."


id: c91c02114a438d8a7221ebcd13b3d89d
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 32703
content:
"Cross-Chapter Box 4, Figure 1 | Global GHG emissions of modelled pathways (funnels in Panel a, and associated bars in Panels b, c, d) and \r\nprojected emission outcomes from near-term policy assessments for 2030 (Panel b)."


id: c2f44c326a931e59f1eee9ee6d2cf5a0
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 31573
content:
"Cross-Chapter Box 4, Figure 1 (continued): Global GHG emissions of modelled pathways (funnels in Panel a, and associated bars in Panels b, c, d) \r\nand projected emission outcomes from near-term policy assessments for 2030 (Panel b)."


id: 91616ec4dbcbb9f0d220fb05a2f702a1
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 22785
content:
"Past GHG emissions for 2010-2015 used to project global warming outcomes of the modelled pathways are shown by a black line4\r\n and past global \r\nGHG emissions in 2015 and 2019 as assessed in Chapter 2 are shown by whiskers."


id: 26cc7f1e14fe0ad8e81902b57bee39f1
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
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index: climate_gpt_v2
vector_id: 3981
content:
"Panels b, c and d show snapshots of the GHG emission ranges of the modelled pathways in 2030, 2050, and 2100, respectively. Panel b also \r\nshows projected emissions outcomes from near-term policy assessments in 2030 from Chapter 4.2 (Tables 4.2 and 4.3; median and full range). GHG \r\nemissions are in CO2-equivalent using GWP100 from AR6 WGI. {3.5, 4.2, Table 4.2, Table 4.3, Cross-Chapter Box 4 in Chapter 4}"


id: 96246c009a6ae2fc6f6724b9c10ac16
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 23610
content:
"The NDCs are a central instrument of the PA to achieve its long-term goal. It thus combines a global goal with a country-driven \r\n(bottom-up) instrument to a hybrid climate policy architecture to strengthen the global response to climate change. All signatory \r\ncountries committed to communicating nationally determined contributions including mitigation targets, every five years. While the \r\nNDCs mostly state targets, countries are also obliged to pursue domestic mitigation measures to achieve the objectives. The literature \r\nexamines the emissions outcome of the range of policies implemented to reach these targets."


id: cdd8987f62d4408dc64b85b8358c5091
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 33543
content:
"Beyond the quantification of different GHG emissions gaps, there is an emerging literature that identifies gaps between current \r\npolicies, NDCs and long-term temperature in terms of other indicators, including for example the deployment of low-carbon energy \r\nsources, energy efficiency improvements, fossil fuel production levels or investments into mitigation measures (Roelfsema et al. 2020; \r\nMcCollum et al. 2018; SEI et al. 2020)."


id: 2073ce886e7aa40dc3527ed88dd5e72c
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 2928
content:
"A 2030 gap in the contribution of low-carbon energy sources to the energy mix in 2030 between current policies and cost-effective \r\nlong-term temperature pathways is calculated to be around 7percentage-points (2degC) and 13percentage-points (1.5degC) by Roelfsema \r\net al. (Roelfsema et al. 2020). The same authors estimate an energy intensity improvement gap 10% and 18% for 2030 between current \r\npolicies pathways and 2degC and 1.5degC pathways, respectively. SEI et al. (2020) estimates the 'fossil fuel production gap', by which they \r\nmean 'the level of countries' planned fossil fuel production expressed in their carbon content to be 120% and 50% higher compared \r\nto the fossil fuel production consistent with 1.5degC and 2degC pathways, respectively, as assessed in IPCC SR1.5 (Rogelj et al. 2018a). "


id: ef3425788d8eab5b8312c33fa726add0
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
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index: climate_gpt_v2
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content:
"The methodology used for this estimation is very similar to how emissions gaps are derived (SEI et al. 2019). The gap of global \r\nannual average investments in low-carbon energy and energy efficiency in 2030 between following current policy on the one \r\nhand and achieving the NDCs, the 2degC and 1.5degC targets on the other hand, is estimated to be approximately USD 130, 320, or \r\n480 billion per year (McCollum et al. 2018)."


id: deef0e6a4ff6b044805c66352e5f0cc5
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
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index: climate_gpt_v2
vector_id: 36481
content:
"It is important to note that such comparisons are less straight forward as the link between long-term temperature goals and these \r\nindicators is less pronounced compared to the emission levels themselves; they are therefore associated with greater uncertainty \r\ncompared to the emissions gap."


id: df36cd77ec62986a106fd104a115739c
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
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index: climate_gpt_v2
vector_id: 36526
content:
"NDCs have been an important driver of national adaptation planning, with cascading effects on sectors and sub-national action, \r\nespecially in developing countries. Yet, only 40 developing countries have quantifiable adaptation targets in their current NDCs; \r\n49 countries include quantifiable targets in their national legislation (UNEP 2018a)."


id: 59d853e4302d2c36ccf1ed9cfc5036dc
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
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index: climate_gpt_v2
vector_id: 13050
content:
"members to tackle emissions reductions in specific sectors or aim for \r\ntransformational change."


id: c0248966c021a3c9ecc11f720d8333d4
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 31100
content:
"Quantification of the (potential) impact of these actions is still \r\nlimited. Almost all studies estimate the potential impact of the \r\nimplementation of actions by NSAs and ICIs, but do not factor in \r\nthat they may not reach their targets. The main reason for this is that \r\nthere is very limited data currently available from individual actors \r\n(e.g., annual GHG inventory reports) and initiatives to assess their \r\nprogress towards their targets. A few studies have attempted to assess \r\nprogress of initiatives by looking into the initiatives' production of \r\nrelevant outputs (Chan et al. 2018). Quantification does not yet cover \r\nall commitments and only a selected number of ICIs are analysed in \r\nthe existing literature. Most of these studies exclude commitments \r\nthat are not (self-)identified as related to climate change mitigation, \r\nthose that are not connected to international networks, or those that \r\nare communicating in languages other than English."


id: d250ac0dba5b596faafd7c8833fa0887
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
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index: climate_gpt_v2
vector_id: 34355
content:
"Non state action could make significant contributions to achieving \r\nthe Paris climate goals (limited evidence, high agreement). However, \r\nefforts to measure the extent to which non-state and sub-national \r\nactors go beyond national policy are still nascent (Hsu et al. 2019; \r\nKuramochi et al. 2020) and we do not fully understand the extent "


id: 8c9a7f6785cde56749193f8658d226b9
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 21925
content:
"to which ambitious action by non-state actors is additional to what \r\nnational governments intend to do. Sub-national and non-state \r\nclimate action may also have benefits in reinforcing, implementing, \r\nor piloting national policy, in place of or in addition to achieving \r\nadditional emissions reductions (Broekhoff et al. 2015; Heidrich et al. \r\n2016; Hsu et al. 2017)."


id: 4f8fcd6b80b09c37aa5438dbd2b39363
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 11284
content:
"Quantification of commitments by individual NSAs are limited \r\nto date. Attempts to quantify aggregate effects in 2030 of \r\ncommitments by individual non-state and sub-national actors are \r\nreported by (Hsu et al. 2019; Kuramochi et al. 2020). Kuramochi \r\net al. (2020) estimate potential mitigation by more than  1,600 \r\ncompanies, around 6,000 cities and many regions (cities assessed \r\nhave a  collective population of 579 million, and regions 514 \r\nmillion). Individual commitments by these sub-national regions, \r\ncities and companies could reduce GHG emissions in 2030 by 1.2 \r\nto 2.0 GtCO2-eq yr-1 compared to current national policies scenario \r\nprojections, reducing projected emissions by 3.8-5.5% in 2030, if \r\ncommitments are fully implemented and do not lead to weaker \r\nmitigation actions by others (Figure 4.1 left). In several countries, \r\nNSA commitments could potentially help meet or exceed national \r\nmitigation targets."


id: ccb9abc6bc51b0fa7f8db52b20506e7d
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
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index: climate_gpt_v2
vector_id: 33345
content:
"Non-state action may be broader than assessed in the literature \r\nso far, though subject to uncertainty. The examples in Table  4.4 \r\nand Figure 4.1 do not include initiatives that target the emissions \r\nfrom religious organisations, colleges and universities, civic and \r\ncultural groups, and, to some extent, households, and in this sense \r\nmay underestimate sub-national potential for mitigating emissions, \r\nrather than overestimate it. That said, the estimates are contingent \r\non assumptions that sub-national and non-state actors achieve \r\ncommitments - both with respect to mitigation and in some cases "


id: 21c9e2b1915fa8910cce292dc6bd632e
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 3158
content:
"Quantification of potential emission reductions from international \r\ncooperative initiatives have been assessed in several studies, and \r\nrecently synthesised (Hsu et al. 2020; Lui et al. 2021), with some "


id: 3444e70fdf945643cbfcf231e6ed6f8f
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
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content:
"that insufficient progress has to-date been made towards this goal \r\n(NYDF Assessment Partners 2020). On the other hand, uncertainties \r\nin global forest carbon emissions (and therefore potential reductions) \r\nare high and despite a multitude of initiatives in the sector, actually \r\nmeasured deforestation rates have not declined since the initiative \r\nwas announced in 2014 (Sections 7.2 and 7.3.1). Moreover, not all \r\ninitiatives are transparent about how they plan to reach their goals \r\nand may also rely on offsets."


id: 533b7575e5a0eba17ec535acd743115d
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
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vector_id: 11912
content:
"membership - and that these actions are not accounted for in nor \r\nlead to weakening of national actions."


id: db9026933c6013b52babdf2ab3fa08a0
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
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index: climate_gpt_v2
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content:
"Care is to be taken not to depict these efforts as additional to action \r\nwithin national NDCs, unless this is clearly established (Broekhoff \r\net al. 2015). There are potential overlaps between individual NSAs \r\nand ICIs, and across ICIs. Kuramochi et al. (2020) propose partial \r\nand conservative partial effect methods to avoid double counting \r\nwhen comparing ambition, a matter that merits further attention. As \r\nthe diversity of actions increased, the potential to count the same \r\nreductions multiple times increases."


id: 27be4dd6ffdc08b573a31aeb826cda2a
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 4179
content:
"Initiatives focused on non-CO2 emissions, and particularly on \r\nmethane, can achieve sizable reductions, in the order of multiple \r\nGtCO2-eq yr-1 (Table  4.4). The Global Cement and Concrete \r\nAssociation (formerly the Cement Sustainability Initiative), has \r\ncontributed to the development of consistent energy and emissions \r\nreporting from member companies. The CSI also suggested \r\npossible approaches to balance GHG mitigation and the issues of \r\ncompetitiveness and leakage (Cook and Ponssard 2011). The member \r\ncompanies of the GCCA (CSI) have become better prepared for future \r\nlegislation on managing GHG emissions and developed management \r\ncompetence to respond to climate change compared to non-member \r\ncompanies in the cement sector (Busch et al. 2008; Global Cement \r\nand Concrete Association 2020). Accordingly, the cement industry \r\nhas developed some roadmaps to reach net zero GHG around 2050 \r\n(Sanjuan et al. 2020)."


id: feb515cf3938e5943f1c4e2aa3f8019a
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
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content:
"It is also important to note that individual NSAs and ICIs that commit \r\nto GHG mitigation activities are often scarce in many crucial and \r\n'hard-to-abate' sectors, such as iron and steel, cement and freight \r\ntransport (Chapters 10 and 11). Sub-national and non-state action \r\nefforts could help these sectors meet an urgent need to accelerate \r\nthe commercialisation and uptake of technical options to achieve low \r\nzero emissions (Bataille 2020)."


id: fd79eb4ec972059ff007c0bccf2f763d
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
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content:
"An increasing amount of literature describes mitigation pathways \r\nfor the mid-term (up to 2050). We assess literature reflecting on the \r\nUNFCCC process (Section 4.2.4.1), other official plans and strategies \r\n(Section  4.2.4.2) and academic literature on mid-century low\u0002emission pathways at the national level (Section 4.2.4.3). After the \r\nParis Agreement and the IPCC SR1.5 Report, the number of academic \r\npapers analysing domestic emission pathways compatible with \r\nthe 1.5degC limit has been increasing. Governments have developed \r\nan increasing number of mitigation strategies up to 2050. Several \r\namong these strategies aim at net zero CO2 or net zero GHG, but it is \r\nnot yet possible to draw global implications due to the limited size of \r\nsample (limited evidence, limited agreement)."


id: 5e96909177f01a43e1c31de8ecf5c45c
created_at: 2023-04-19 13:22:12
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content:
"Box 4.2 | Direct Links Between an Individual Actor's Mitigation Efforts in the Near Term and \r\nGlobal Temperature Goals in the Long Term Cannot be Inferred: Making direct links requires \r\nclear distinctions of spatial and temporal scales (Robertson 2021; Rogelj et al. 2021) and \r\nexplicit treatment of ethical judgements made (Klinsky et al. 2017a; Holz et al. 2018; Klinsky \r\nand Winkler 2018; Rajamani et al. 2021)"


id: a17a680b77900e45c30f68ef6a6e99ea
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
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index: climate_gpt_v2
vector_id: 25625
content:
"As of August 25, 2021, 31 countries and the European Union had \r\nsubmitted low-emissions development strategies (LEDS) (Table 4.5)."


id: 377b3fc20acbcdc8b6131e8b7ef5b4bd
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
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index: climate_gpt_v2
vector_id: 6972
content:
"The Paris Agreement requests that Parties should strive to formulate \r\nand communicate long-term low GHG development strategies by \r\n2020. (Note that by 'long-term', the UNFCCC means 2050, which is \r\nthe end point of the 'mid-term' horizon range in the present report.) "


id: bfd2b192c9e0c626af73f656b211d4cd
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
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vector_id: 31028
content:
"Table 4.5 | Countries having submitted long-term low-GHG emission development strategy (as of 25 August 2021)."


id: cdd13139044ae085f161388e3fc6a737
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 33541
content:
"6 Specifying gases aids clarity, see Cross-Chapter Boxes 2 and 3 in chapters 2 and 3, respectively. Some countries refer to net zero GHG emissions as 'climate neutrality' or \r\n'carbon neutrality'; the more precise terms are used where supported by the information assessed in this report."


id: e9fc67d6bc8066250a0cd548c4883c8f
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 38518
content:
"Table 4.6 | Countries with a national net zero CO2 or GHG target by 2050 \r\n(as of 25 August 2021)."


id: 90037208297e8feb2c05d2e82f8a87e3
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 22540
content:
"Note: In addition to the above list, the numbers of 'In Policy Document' and 'Target \r\nUnder discussion' as Target status are 37 countries and 79 countries, respectively."


id: b9bd28d797b581ad40163b00ca7b4e77
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 29889
content:
"Since the 2000s, an increasing number of studies have quantified \r\nthe emission pathways to mid-century by using national scale \r\nmodels. In the early stages, the national emission pathways were \r\nmainly assessed in the developed countries such as Germany, UK, \r\nFrance, the Netherlands, Japan, Canada, and USA. For example, the \r\nEnquete Commission in Germany identified robust and sustainable \r\n80% emission reduction pathways (Deutscher Bundestag 2002). In \r\nJapan, 2050 Japan Low-Carbon Society scenario team (2008) assessed \r\nthe 70% reduction scenarios in Japan, and summarised the necessary \r\nmeasures to 'Dozen Actions towards Low-Carbon Societies'."


id: b477f31aac07d4c566d5194e03318b59
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
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index: climate_gpt_v2
vector_id: 28995
content:
"Among developing countries, China, India, South Africa assessed \r\ntheir national emission pathways. For example, detailed analysis was \r\nundertaken to analyse pathways to China's goal for carbon neutrality \r\n(EFC 2020). In South Africa, a  Scenario Building Team (2007) \r\nquantified the Long Term Mitigation Scenarios for South Africa."


id: c9fd2f9a18aba6b76f03572e50fdf417
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
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index: climate_gpt_v2
vector_id: 32867
content:
"The literature underlines the differences induced by the shift from \r\n'2degC scenarios' (typically assumed to imply mitigation in 2050 \r\naround 80% relative to 1990) to '1.5degC scenarios' (typically assumed \r\nto imply net zero CO2 or GHG emissions in 2050) (Box 4.2). For Japan, \r\nOshiro et al. (2018) shows the difference between the implications of \r\na 2degC scenario (80% reduction of CO2 in 2050) and a 1.5degC scenario \r\n(net zero CO2 emission in 2050), suggesting that for a net zero CO2\r\nemission scenario, BECCS is a key technology. Their sectoral analysis \r\naims in 2050 at negative CO2 emissions in the energy sector, and \r\nnear-zero emissions in the buildings and transport sectors, requiring \r\nenergy efficiency improvement and electrification. To do so, drastic \r\nmitigation is introduced immediately, and, as a result, the mitigation "


id: 85c2924c408f35087d42a016691fc19a
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
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vector_id: 20676
content:
"Prior to COP21, most of the literature on mid-century mitigation \r\npathways at the national level was dedicated to pathways compatible \r\nwith a  2degC limit (see Box  4.2 for a  discussion on the relationship \r\nbetween national mitigation pathways and global, long-term targets). \r\nAfter COP21 and the IPCC SR1.5, literature increasingly explored just \r\ntransition to net zero emissions around 2050. This literature reflects \r\non low-emissions development strategies (cognate with SDPS, \r\nSection 4.3.1) and policies to get to net zero CO2 or GHG emissions \r\n(Garg and Waisman 2021) (Cross-Chapter Box 5 in this chapter)."


id: b5b5fd757a58c342d81fd6f017a78c7e
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
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vector_id: 29194
content:
"Figure 4.2 | Energy related CO2 emission pathways to mid-century from existing studies. Source of the historical data: Greenhouse Gas Inventory Data of UNFCCC \r\nhttps://di.unfccc.int/detailed_data_by_party)"


id: b211dd9a04a5428fde5c0e53917ec4
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 28552
content:
"In addition to those analyses, Vishwanathan et al. (2018b), Chunark \r\nand Limmeechokchai (2018) and Pradhan et al. (2018b) build national \r\nscenarios in India, Thailand and Nepal, respectively, compatible \r\nwith a global 1.5degC. Unlike the studies mentioned in the previous \r\nparagraph, they translate the 1.5degC goal by introducing in their model \r\na carbon price trajectory estimated by global models as sufficient to \r\nachieve the 1.5degC target. Because of the high economic growth and "


id: 6befd6993f402da67cb1a6c5d2010509
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
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index: climate_gpt_v2
vector_id: 16290
content:
"target of Japan's current NDC is considered not sufficient to achieve \r\na 1.5degC scenario. Jiang et al. (2018) also show the possibility of net \r\nnegative emissions in the power sector in China by 2050, indicating \r\nthat biomass energy with carbon capture and storage (CCS) must be \r\nadopted on a large scale by 2040. Samadi et al. (2018) indicate the \r\nwidespread use of electricity-derived synthetic fuels in end-use sectors \r\nas well as behavioural change for the 1.5degC scenario in Germany."


id: 5a04c8835ea650bff15424cc15972022
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
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index: climate_gpt_v2
vector_id: 13084
content:
"Specific policies, measures and technologies are needed to reach \r\nsuch targets. These include, broadly, decarbonising electricity supply, \r\nincluding through low-carbon energy, radically more efficient use of \r\nenergy than today; electrification of end-uses (including transport/\r\nelectric vehicles); dramatically lower use of fossil fuels than today; \r\nconverting other uses to low- or zero-carbon fuels (e.g., hydrogen, \r\nbioenergy, ammonia) in hard-to-decarbonise sectors; and setting \r\nambitious targets to reduce methane and other short-lived climate \r\nforcers (SLCFs)."


id: 26463d2ab04ffbf7862e2384154bd79e
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
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index: climate_gpt_v2
vector_id: 3904
content:
"increase of GHG emissions in the reference case, CO2 emissions in \r\n2050 do not reach zero. Finally, the literature also underlines that \r\nto achieve a 1.5degC target, mitigation measures relative to non-CO2\r\nemissions become important, especially in developing countries \r\nwhere the share of non-CO2 emissions is relatively high. (La Rovere \r\net al. 2018) treat mitigation actions in AFOLU sector."


id: e3295075e580e50fb785cca133c8ec9e
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 37265
content:
"Chapter 3 reported on multi-model analyses, comparison of results \r\nusing different models, of global emissions in the long term. At the \r\nnational scale, multi-model analyses are still limited, though such \r\nanalyses are growing as shown in Table 4.7. By comparing the results \r\namong different models and different scenarios in a  country, the \r\nuncertainties on the emission pathways including the mitigation \r\nmeasures to achieve a given emission target can be assessed."


id: 499fafdd4466899790468680bb7bf7b8
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 10181
content:
"Another type of multi-model analysis is international, in other \r\nwords, different countries join the same project and use their own \r\nnational models to assess a  pre-agreed joint mitigation scenario. \r\nBy comparing the results of various national models, such projects \r\nhelp highlight specific features of each country. More robust \r\nmitigation measures can be proposed if different types of models \r\nparticipate. These activities can also contribute to capacity building \r\nin developing countries."


id: 2c4d90692ac1ad224f1e75e5d70f3e40
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
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index: climate_gpt_v2
vector_id: 5060
content:
"The literature reports an increasing number of accelerated mitigation \r\npathways that are beyond NDCs in different regions and countries. \r\nThere is increasing understanding of the technical content of such \r\npathways, though the literature remains limited on some dimensions, \r\nsuch as demand-side options, systems analysis, or mitigation of \r\nAFOLU non-CO2 GHGs. The present section describes insights from \r\nthis literature."


id: cd15a8c3b62bd2ed8ca9f87570dd4b00
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
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index: climate_gpt_v2
vector_id: 33419
content:
"Overall, the literature shows that pathways considered consistent \r\nwith below 2degC (>67%) or 1.5degC (Box 4.2) - including inter alia \r\n80% reduction of GHG emissions in 2050 relative to 1990 or \r\n100% renewable electricity scenarios  - are technically feasible \r\n(Lund and Mathiesen 2009; Mathiesen et al. 2011; Esteban and \r\nPortugal-Pereira 2014; Young and Brans 2017; Esteban et al. 2018; \r\nChild et al. 2019; Hansen et al. 2019). They entail increased end\u0002use energy efficiency, significant increases in low-carbon energy, \r\nelectrification, other new and transformative technologies in \r\ndemand sectors, adoption of carbon capture and sequestration \r\n(CCS) to reduce gross emissions, and contribution to net negative \r\nemissions through carbon dioxide removal (CDR) and carbon sinks. \r\nFor these pathways to be realised, the literature assumes higher \r\ncarbon prices, combined in policy packages with a range of other \r\npolicy measures."


id: 3f2aa655d224c03f10506a3d23bef53f
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 8300
content:
"Though they differ across countries, accelerated mitigation pathways \r\nshare common characteristics as follows. First, energy  efficiency, \r\nconservation, and reducing energy use in all energy demand \r\nsectors (buildings, transport, and industry) are included in nearly \r\nall literature that addresses future demand growth (Ashina et al. \r\n2012; Saveyn et al. 2012; Schmid and Knopf 2012; Chiodi et al. 2013; \r\nDeetman et al. 2013; Jiang et al. 2013; Thepkhun et al. 2013; Schiffer \r\n2015; Altieri et al. 2016; Jiang et al. 2016; McNeil et al. 2016; \r\nNogueira de Oliveira et al. 2016; Chilvers et al. 2017; Elizondo et al. \r\n2017; Fragkos et al. 2017; Jacobson et al. 2017, 2019; Kuramochi \r\net al. 2017; Oshiro et al. 2017a; Ouedraogo 2017; Shahiduzzaman \r\nand Layton 2017; Vaillancourt et al. 2017; Hanaoka and Masui 2018; \r\nHodson et al. 2018; Lee et al. 2018; Lefevre et al. Oshiro et al. 2018; \r\n2018; Capros et al. 2019; Dioha et al. 2019; Duscha et al. 2019; "


id: 379185d82ef7fc280a6df3232726ce4e
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
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index: climate_gpt_v2
vector_id: 6988
content:
"The most recent literature also reflects on accelerated mitigation \r\npathways aiming at reaching net zero CO2 emissions or net zero \r\nGHG emissions by 2050 (Section 4.2.4 and Table 4.6; see Glossary \r\nentries on 'net zero CO2 emissions' and 'net zero GHG emissions'). "


id: f6a337d238f44768de44ad34b4bfc8f0
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 40763
content:
"Khanna et al. 2019; Kato and Kurosawa 2019; Nieves et al. 2019; \r\nSugiyama et al. 2019; Zhou et al. 2019; Dioha and Kumar 2020).\r\n4.2.5.2 Accelerated Decarbonisation of Electricity Through \r\nRenewable Energy"


id: fa461bc9a0777e4e0c5f67fed818349f
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
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index: climate_gpt_v2
vector_id: 41403
content:
"Similarly, electrification of industrial processes (up to 50% for EU and \r\nChina) and transport (e.g., 30-60% for trucks in Canada), buildings, \r\nand district heating and cooling are commonplace (Ashina et al. 2012; \r\nMassetti 2012; Saveyn et al. 2012; Chiodi et al. 2013; Deetman et al. \r\n2013; Fragkos et al. 2017; Oshiro et al. 2017b; Vaillancourt et al. 2017; \r\nXunzhang et al. 2017; Jiang et al. 2018; Mittal et al. 2018; Oshiro et al. \r\n2018; Capros et al. 2019; Zhou et al. 2019; Hammond et al. 2020)."


id: e395b7ee7f3a95758f82ba104199c0a5
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 37334
content:
"Power generation could decarbonise much faster with scaled up \r\ndeployment of renewable energy and storage. Both technologies are \r\nmature, available, and fast decreasing in costs, more than for many \r\nother mitigation options. Models continuously underestimate the \r\nspeed at which renewables and storage expand. Higher penetration of \r\nrenewable energy in the power sector is a common theme in scenarios. \r\nSome studies provide cost optimal electricity mix under emission \r\nconstraints, while others explicitly explore a  100%  renewables or \r\n100% emission free electricity sector (Box 4.3)."


id: 95efba5f8b2228e22d4119464f452589
created_at: 2023-04-19 13:22:12
updated_at: 2023-04-19 13:22:12
content_type: text
index: climate_gpt_v2
vector_id: 23576
content:
"Third, lower emissions sources of energy, such as nuclear, renewables, \r\nand some biofuels, are seen as necessary in all pathways. However, \r\nthe extent of deployment depends on resource availability. Some \r\ncountries have set targets of up to 100% renewable electricity, while \r\nothers such as Brazil rely on increasing biomass up to 40-45% of \r\ntotal or industry energy consumption by 2050."


