# document

id: 8a71cc31e23ab9d022e123b70f4e3f2b
created_at: 2023-03-16 14:02:41
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" They also illustrate that different representations \r\nof innovation and financial markets together can explain why \r\nestimated impacts of mitigation on GDP can differ very widely \r\n(potentially even in sign), between different model types (Chapter 15, \r\nSection 15.6.3 and Box 15.7)."


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"This report uses the term transition as the process, and transformation \r\nas the overall change or outcome, of large-scale shifts in technological, \r\neconomic and social systems, called socio-technical systems in \r\nthe innovation literature. Typically, new technologies, ideas and \r\nassociated systems initially grow slowly in absolute terms, but may \r\nthen 'take-off' in a phase of exponential growth as they emerge from \r\na  position of niche into mainstream diffusion, as indicated by the \r\n'S-curve' growth in Figure  1.7 (lower panel). These dynamics arise \r\nfrom interactions between innovation (in technologies, companies \r\nand other organisations), markets, infrastructure and institutions, \r\nat multiple levels (Geels et al. 2017; Kramer 2018). Consequently, \r\ninterdisciplinary perspectives are needed (Turnheim et al. 2015; \r\nGeels et al. 2016; Hof et al. 2020). Beyond aggregated economic \r\nperspectives on dynamics (Section  1.7.1"


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"The widely-used Multi-Level Perspective or MLP (Geels 2002) identifies \r\nthe meso level as the established 'socio-technical (ST) regime', \r\na set of interrelated sub-systems which define rules and regulatory \r\nstructures around existing technologies and practices. The micro level \r\nis an ecosystem of varied niche alternatives, and overlaying the ST \r\nregime is a macro 'landscape' level. Transitions often start with niche \r\nalternatives (Grin et al. 2010; Kohler et al. 2019), which may break \r\nthrough to wider diffusion (second stage in Figure 1.8), especially if \r\nexternal landscape developments 'create pressures on the regime that \r\nlead to cracks, tensions and windows of opportunity' (Rotmans et al. \r\n2001; Geels 2010); an example is climate change putting sustained \r\npressure on current regimes of energy production and consumption \r\n(Kuzemko et al. 2016)"


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" There are continual interactions between \r\nlandscape, regime and niches, with varied implications for Transition \r\nManagement (Rotmans et al. 2001; Loorbach 2010)."


id: f8484196570c9aa7a0ba867ed0bdfd92
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"Figure 1.7 | Transition dynamics: levels, policies and processes. Note: the lower panel illustrates growth of innovative technologies or practices, which if successful \r\nemerge from niches into an S-shape dynamic of exponential growth. The diffusion stage often involves new infrastructure and reconfiguration of existing market and regulatory \r\nstructures (known in the literature as the 'socio-technical regime'). During the phase of more widespread diffusion, growth levels off to linear, then slows as the industry and \r\nmarket matures. The processes displace incumbent technologies/practices which decline, initially slowly but then at an accelerating pace. Many related literatures identify three \r\nmain levels with different characteristics, most generally termed micro, meso and macro. Transitions can be accelerated by policies appropriately targeted, which may be similarly \r\ngrouped and sequenced (upper panel) in terms of three corresponding pillars of policy (Section 1.7"


id: 2c4b488edf15a050ab6477b753754178
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"3): generally all are relevant, but their relative importance differs according \r\nto the stage of the transition."


id: 8426bebd6da1bb79579c9372691ec08b
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"Transition theories tend to come from very different disciplines and \r\napproaches compared to either economics or other social sciences, \r\nwith less quantification, notwithstanding evolutionary and complex \r\nsystem models (Section  1.7.1.3). However, a  few distinct types of \r\nquantitative models of 'socio-technical energy transition' (Li et al. \r\n2015) have emerged. For policy evaluation, transitions can be viewed \r\nas processes in which dynamic efficiency (Section 1.7.2) dominates \r\nover static allocative efficiency, with potential 'positive intervention \r\npoints' (Farmer et al. 2019). Given inherent uncertainties, there are \r\nobvious risks (e.g., Alic and Sarewitz 2016). All this may make an \r\nevaluation framework of risks and opportunities more appropriate \r\nthan traditional cost-benefit (Mercure et al"


id: 65b5bd790f18a0c8a30453c6d312e641
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" 2021), and (drawing on \r\nlessons from renewables and electric vehicles) create foundations \r\nfor sector-based international 'positive sum cooperation' in climate \r\nmitigation (Sharpe and Lenton 2021)."


id: 8d9b23335c07a67efcf84734a035dca9
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"Sustainability may require purposeful actions at the different levels \r\nto foster the growth of sustainable technologies and practices, \r\nincluding support for niche alternatives (Grin et al. 2010). The middle \r\nlevel (established 'socio-technical regime') tends to resist major \r\nchange, reforms generally involve pressures from the other two \r\nlevels. Thus, transitions can be accelerated by policies appropriately \r\ntargeting relevant actors at the different levels (Kohler et al. 2019), \r\nthe foundations for 'three pillars of policy' (Grubb et al. 2014), which \r\nlogically evolve in the course of transition (Figure 2.6a). Incumbent \r\nindustries have to adapt if they are to thrive within the growth of \r\nnew systems. Policy may need to balance existing socio-technical \r\nsystems with strategic investment and institutional development \r\nof the emerging niches (e.g"


id: 6406ce5ab7d423691131076155f41024
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", the maintenance of energy provision \r\nand energy security with the development of renewables), and help \r\nmanage declining industries (Koasidis et al. 2020)."


id: 231c78a7d3b811da9a3974f337ed17fc
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"The AR5 emphasised that decision processes often include both \r\ndeliberate ('calculate the costs and benefits') and intuitive thinking, \r\nthe latter utilising emotion- and rule-based responses that are \r\nconditioned by personal past experience, social context, and cultural \r\nfactors (e.g.,  Kahneman 2003), and that laypersons tend to judge \r\nrisks differently than experts  - for example, 'intuitive' reactions \r\nare often characterised by biases to the status quo and aversion to \r\nperceived risks and ambiguity (Kahneman and Tversky 1979). Many \r\nof these features of human reasoning create 'psychological distance' \r\nfrom climate change (Spence et al. 2012; Marshall 2014). These can \r\nimpede adequate personal responses, in addition to the collective \r\nnature of the problem, where such problems can take the form of \r\n'uncomfortable knowledge', neglected and so becoming 'unknown \r\nknowns' (Sarewitz 2020)"


id: 545b6b0281e12c744e7142ccc3badcd7
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" These decision processes, and the \r\nperceptions that shape them, have been studied through different \r\nlenses from psychology (Weber 2016) to sociology (Guilbeault \r\net al. 2018), and media studies (Boykoff 2011). Karlsson and Gilek \r\n(2020) identify science denialism and 'decision thresholds' as key \r\nmechanisms of delay."


id: 797caa69302c7a18a659e62477ca0a3
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"There is usually a  social dimension to such transitions. Key \r\nelements include capacity to transform (Folke et al. 2010), planning, \r\nand interdisciplinarity (Woiwode 2013). The Second World War \r\ndemonstrated the extent to which crises can motivate (sometimes \r\npositive) change across complex social and technical systems, \r\nincluding industry, and agriculture which then doubled its productivity \r\nover 15 years (Roberts and Geels 2019b). In practice, climate change \r\nmay involve a combination of (reactive) transformational adaptation, \r\nand (proactive) societal transformation (Feola 2015), the latter \r\nseen as reorientation (including values and norms) in a sustainable \r\ndirection (Section 5.4), including, for example, 'democratisation' in \r\nenergy systems (Sorman et al. 2020). Business change management \r\nprinciples could be relevant to support positive social change (Stephan \r\net al. 2016)"


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" Overall, effective transitions rest on appropriate enabling \r\nconditions, which can also link socio-technical transitions to broader \r\ndevelopment pathways (Cross-Chapter Box 12 in Chapter 16)."


id: 344f989cf237b6c05acf3a98f0421da8
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"Balancing the forces outlined in Section  4.6 in Chapter  4  typically \r\ninvolves building coalitions of actors who benefit economically from \r\nclimate policy (Levin et al. 2012). Policy stability is critical to enabling \r\nlong-term investments in decarbonisation (Rietig and Laing 2017; \r\nRosenbloom et al. 2018). Policy design can encourage coalitions \r\nto form that sustain momentum by supporting further policy \r\ndevelopment to accelerate decarbonisation (Roberts et al. 2018), for \r\nexample, by generating concentrated benefits to coalition members \r\n(Bernstein and Hoffmann 2018; Meckling 2019; Millar et al. 2020), as \r\nwith renewable feed-in tariffs (FiTs) in Germany (Michaelowa et al. \r\n2018). Coalitions may also be sustained by overarching framings, \r\nespecially to involve actors (e.g.,  NGOs) for whom the benefits of \r\nclimate policy are not narrowly economic"


id: 1a2f484526a9a4f1180509d13e6e986d
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" However, policy design \r\ncan also provoke coalitions to oppose climate policy, as in the FiT \r\nprogramme in Ontario (Stokes 2013; Raymond 2020) or the yellow \r\nvest protests against carbon taxation in France (Berry and Laurent \r\n2019). The Just Transitions frame can thus also be understood in \r\nterms of coalition-building, as well as ethics, as the pursuit of low\u0002carbon transitions which spread the economic benefits broadly, \r\nthrough 'green jobs', and the redistributive policies embedded in \r\nthem both nationally and globally (Healy and Barry 2017; Winkler \r\n2020). Appropriate policy design will be different at different stages \r\nof the transition process (Meckling et al. 2017; Breetz et al. 2018)."


id: 751f23a8d455f4c872613ce4b89ca9be
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"Carbon pricing. Appraisal of policy instruments also requires such \r\nmulti-dimensional assessment. Stern's (2007) reference to climate \r\nchange as 'the greatest market failure in history' highlights that \r\ndamages inflicted by climate change are not properly costed in \r\nmost economic decision-making. Economic perspectives emphasise \r\nthe value of removing fossil fuel subsidies, and pricing emissions to \r\n'internalise' in economic decision-making the 'external' damages \r\nimposed by GHG emissions, and/or to meet agreed goals. Aggregate \r\neconomic frameworks generally indicate carbon pricing (on principles \r\nwhich extend to other gases) as the most cost-effective way to \r\nreduce emissions, notwithstanding various market failures which \r\ncomplicate this logic.9\r\n The High-Level Commission on carbon pricing \r\n(Stern and Stiglitz 2017) estimated an appropriate range as USD40-\r\n80 tCO2 in 2020, rising steadily thereafter"


id: 4d045c7ec31ea1ec4ae0414fecbacc54
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"A socio-cultural and equity perspective emphasises that the faith in \r\nand role of markets varies widely between countries - many energy \r\nsystems do not in fact operate on a basis of competitive markets - \r\nand that because market-based carbon pricing involves large revenue \r\ntransfers, it must also contend with major distributional effects and \r\npolitical viability (Prinn et al. 2017; Klenert et al. 2018), both domestic \r\n(Chapter 13) and international (Chapter 14). A major review (Maestre\u0002Andres et al. 2019) finds persistent distributional concerns (rich \r\nincumbents have also been vocal in using arguments about impacts \r\non the poor (Rennkamp 2019)), but suggests these may be addressed \r\nby combining redistribution of revenues with support for low-carbon \r\ninnovation"


id: 78f06e5dfe60e004aa600367e6bc8cd5
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" Measures could include redistributing the tax revenue to \r\nfavour of low-income groups or differentiated carbon taxes (Metcalf \r\n2009; Klenert and Mattauch 2016; Stiglitz 2019), including 'dual \r\ntrack' approaches (van den Bergh et al. 2020). To an extent though, \r\nall these depend on levels of trust, and institutional capacity."


id: 75babcff3c3515f09209171edf41f775
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"Previous sections have highlighted the multiple factors that drive \r\nand constrain climate action, the complex interconnection between \r\nclimate mitigation and other societal objectives, and the diversity \r\nof analytical frames for interpreting these connections. Despite \r\nthe complexities, there are signs of progress including increased \r\nsocietal awareness, change in social attitudes, policy commitments \r\nby a  broad range of actors and sustained emission reductions in \r\nsome jurisdictions. Nevertheless, emission trends at the global level \r\nremains incompatible with the goals agreed in the Paris Agreement. \r\nFundamentally, the challenge of how best to urgently scale up and \r\nspeed up the climate-mitigation effort at all scales - from local to \r\nglobal  - to the pace needed to address the climate challenge is \r\nthat of governance understood as 'modes and mechanisms to steer \r\nsociety' (Jordan et al. 2015)"


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" The concept of governance encompasses \r\nthe ability to plan and create the organisations needed to achieve \r\na desired goal (Guney 2017) and the process of interaction among \r\nactors involved in a common problem for making and implementing \r\ndecisions (Kooiman 2003; Hufty 2012)."


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"Crucially, climate governance takes place in the context of embedded \r\npower relations, operating in global, national and local contexts. \r\nEffective rules and institutions to govern climate change are more \r\nlikely to emerge where and when power structures and interests \r\nfavour action. However widespread and enduring cooperation can \r\nonly be expected when the benefits outweigh the cost of cooperation \r\nand when the interests of key actors are sufficiently aligned (Barrett \r\n1994; Finus and Rubbelke 2008; Victor 2011; Mainali et al. 2018; \r\nTulkens 2019). Investigating the distribution and role of hard and \r\nsoft power resources, capacities and power relations within and \r\nacross different jurisdictional levels is therefore important to uncover \r\nhindrances to effective climate governance (Marquardt 2017)"


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" \r\nInstitutions at international and national levels are also critical as \r\nthey have the ability to mediate the power and interest of actors, and \r\nsustain cooperation based on equity and fair rules and outcomes. \r\nGovernance, in fact, helps to align and moderate the interests of \r\nactors as well as to shift perceptions, including the negative, burden\u0002sharing narratives that often accompany discussion about climate \r\naction, especially in international negotiations. It is also useful for \r\nengaging the wider public and international networks in imagining \r\nlow-carbon societies (e.g.,  Levy and Spicer 2013; Milkoreit 2017; \r\nNikoleris et al. 2017; Wapner and Elver 2017; Bengtsson Sonesson \r\net al. 2019; Fatemi et al. 2020)"


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" Experimentation also represents an \r\nimportant source of governance innovation and capability formation, \r\nlinked to global knowledge and technology flows, which could \r\nreshape emergent socio-technical regimes and so contribute to \r\nalternative development pathways (Berkhout et al. 2010; Roberts \r\net al. 2018; Turnheim et al. 2018; Lo and Castan Broto 2019)."


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"Global conditions have changed substantially since the IPCC's Fifth \r\nAssessment Report in 2014. The Paris Agreement and the SDGs \r\nprovided a new international context, but global intergovernmental \r\ncooperation has been under intense stress. Growing direct impacts \r\nof climate change are unambiguous and movements of protest and \r\nactivism  - in countries and transnational organisations at many \r\nlevels - have grown. Global emissions growth had slowed but not \r\nstopped up to 2018/19, albeit with more diverse national trends. \r\nGrowing numbers of countries have adopted 'net zero' CO2 and/\r\nor GHG emission goals and decarbonisation or low-carbon growth \r\nstrategies, but the current NDCs to 2030 collectively would barely \r\nreduce global emissions below present levels (Section  1.3.3). An \r\nunfolding technology revolution is making significant contributions \r\nin some countries, but as yet its global impact is limited"


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" Global \r\nclimate change can only be tackled within, and if integrated with, the \r\nwider context of sustainable development, and related social goals \r\nincluding equity concerns. Countries and their populations have \r\nmany conflicting priorities. Developing countries in particular have \r\nmultiple urgent needs associated with earlier stages of sustainable \r\ndevelopment as reflected in the non-climate SDGs. Developed \r\ncountries are amongst the most unsustainable in terms of overall \r\nconsumption, but also face social constraints particularly arising from \r\ndistributional impacts of climate policies."


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"The assessment of the key drivers for, and barriers against mitigation \r\nundertaken in this chapter underscore the complexity and multi\u0002dimensional nature of climate mitigation. Historically, much of the \r\nacademic analysis of mitigating climate change, particularly global \r\napproaches, has focused on modelling costs and pathways, and \r\ndiscussion about 'optimal' policy instruments. Developments since \r\nAR5 have continued to highlight the role of a wide range of factors \r\nintersecting the political, economic, social and institutional domains. \r\nYet despite such complexities, there are signs of progress emerging \r\nfrom years of policy effort in terms of technology, social attitudes, \r\nand emission reductions in some countries, with tentative signs of \r\nimpact on the trajectory of global emissions"


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" The challenge remains \r\nhow best to urgently scale up and speed up the climate mitigation \r\neffort at all scales - from local to global - to achieve the level of \r\nmitigation needed to address the problem as indicated by climate \r\nscience. A related challenge is how to ensure that mitigation effort \r\nand any associated benefits of action are distributed fairly within and \r\nbetween countries and aligned to the overarching objective of global \r\nsustainable development. Lastly, globally effective and efficient \r\nmitigation will require international cooperation especially in the \r\nrealms of finance and technology."


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"Four chapters then review thematic issues in implementation and \r\ngovernance of mitigation. Chapter  13 explores national and sub\u0002national policies and institutions, bringing together lessons of policies \r\nexamined in the sectoral chapters, as well as insights from service \r\nand demand-side perspectives (Chapter 5), along with governance \r\napproaches and capacity-building, and the role and relationships of \r\nsub-national actors. Chapter 14 then considers the roles and status \r\nof international cooperation, including the UNFCCC agreements \r\nand international institutions, sectoral agreements and multiple \r\nforms of international partnerships, and the ethics and governance \r\nchallenges of solar radiation modification. Chapter  15 explores \r\ninvestment and finance, including current trends, the investment \r\nneeds for deep decarbonisation, and the complementary roles of \r\npublic and private finance. This includes climate-related investment \r\nopportunities and risks (e.g"


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",  'stranded assets'), linkages between \r\nfinance and investments in adaptation and mitigation; and the \r\nimpact of COVID-19. A new chapter on innovation (Chapter 16) looks \r\nat technology development, accelerated deployment and global \r\ndiffusion as systemic issues that hold potential for transformative \r\nchanges, and the challenges of managing such changes at multiple \r\nlevels including the role of international cooperation."


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"Chapters 6 to 12 examine sectoral contributions and possibilities for \r\nmitigation. Chapter 6 summarises characteristics and trends in the \r\nenergy sector, specifically supply, including the remarkable changes \r\nin the cost of some key technologies since AR5. Chapter 7 examines \r\nthe roles of AFOLU, drawing upon and updating the recent Special \r\nReport, including the potential tensions between the multiple \r\nuses of land. Chapter  8  presents a  holistic view of the trends \r\nand pressures of  urban systems, as both a  challenge and an \r\nopportunity for mitigation. Chapters  9 and 10 then examine two \r\nsectors which entwine with, but go well beyond, urban systems: \r\nbuildings (Chapter  9) including construction materials and zero\u0002carbon buildings; and transport (Chapter  10), including shipping \r\nand aviation and a  wider look at mobility as a  general service"


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" \r\nChapter  11 explores the contribution of industry, including supply \r\nchain developments, resource efficiency/circular economy, and the \r\ncross-system implications of decarbonisation for industrial systems. \r\nFinally, Chapter 12 takes a cross-sectoral perspective and explores \r\ncross-cutting issues like the interactions of biomass energy, food and \r\nland, and carbon dioxide removal."


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"Different combinations of gases are emitted from different activities. The largest source of CO2 is combustion of fossil fuels in energy \r\nconversion systems like boilers in electric power plants, engines in aircraft and automobiles, and in cooking and heating within \r\nhomes and businesses (approximately 64% of emissions, Figure SPM.2). Fossil fuels are also a major source of methane (CH4), \r\nthe second biggest contributor to global warming. While most GHGs come from fossil fuel combustion, about one quarter comes \r\nfrom land-related activities like agriculture (mainly CH4 and N2O) and deforestation (mainly CO2), with additional emissions from \r\nindustrial processes (mainly CO2, N2O and F-gases), and municipal waste and wastewater (mainly CH4) (Chapter 2)"


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content:
" In addition to \r\nthese emissions, black carbon - an aerosol that is, for example, emitted during incomplete combustion of fossil fuels - contributes \r\nto warming of the Earth's atmosphere, whilst some other short-lived pollutants temporarily cool the surface (IPCC AR6 WGI \r\nSection 6.5.4.3)."


id: d5e2ebdda22695a29a73f579acc7473f
created_at: 2023-03-16 14:02:41
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content:
"Alaa Al Khourdajie (United Kingdom/Syria), Robbie M. Andrew (Norway), Giovanni Baiocchi (the \r\nUnited States of America/Italy), Igor A. Bashmakov (the Russian Federation), Alexandre Bizeul \r\n(France), Kornelis Blok (the Netherlands), Lazarus Chapungu (Zimbabwe), Harry Clark (New \r\nZealand), William Collins (United Kingdom), Annette Cowie (Australia), Monica  Crippa (Italy), \r\nHancheng Dai (China), Steven J. Davis (the United States of America), Stephane de la Rue du Can \r\n(the United States of America), Niklas Dobbeling (Germany), Kuishuang Feng (the United States \r\nof America), Vivien Fisch-Romito (France), Piers M. Forster (United Kingdom), Jan S. Fuglestvedt \r\n(Norway), Victor  Garcia Tapia (Spain), Oliver Geden (Germany), Yong Geng (China), Veronika \r\nGinzburg (the Russian Federation), Giacomo  Grassi (Italy/European Union), Baihe Gu (China), \r\nCeline  Guivarch (France), Diego  Guizzardi (Italy), Joanna I"


id: 59ecc58103cfa36a26bccaab4616c7b8
created_at: 2023-03-16 14:02:41
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vector_id: 11656
content:
" House (United Kingdom), Suzana \r\nKahn Ribeiro (Brazil), Smail  Khennas (Algeria/United Kingdom), William F. Lamb (Germany/\r\nUnited Kingdom), Gunnar  Luderer (Germany), Giulio  Mattioli (Germany/Italy), Francesco \r\nMattion (Italy), Rachid Mrabet (Morocco), Gert-Jan Nabuurs (the Netherlands), Jos Olivier (the \r\nNetherlands), Anne Owen (United Kingdom), Glen P. Peters (Norway/Australia), Julia Pongratz \r\n(Germany), Roberta Quadrelli (Italy), Andy  Reisinger (New Zealand), Keywan Riahi (Austria), \r\nMatt Rigby (United Kingdom), Joeri Rogelj (Belgium/United Kingdom), Yamina Saheb (France/\r\nAlgeria), Marielle Saunois (France), Roberto Schaeffer (Brazil), Karen C. Seto (the United States of \r\nAmerica), Yuli Shan (China), Raphael Slade (United Kingdom), Steven J"


id: b8b00b6638a0e31675027b6761e9e840
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vector_id: 26581
content:
" Smith (the United States \r\nof America), Efisio Solazzo (Italy), Jan Steckel (Germany), Anders Hammer Stromman (Norway), \r\nLaixiang Sun (the United States of America), Pouya Taghavi-Moharamli (Canada), Hanqin Tian \r\n(the United States of America), Detlef  P.  van Vuuren (the Netherlands), Dominik Wiedenhofer \r\n(Austria)"


id: f237608d72b1086b95f654d436f0da93
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content:
"Emissions growth has varied, but persisted across all groups of \r\nGHGs (high confidence). The average annual emission levels of the \r\nlast decade (2010-2019) were higher than in any previous decade for \r\neach group of GHGs (high confidence). In 2019, CO2 emissions were \r\n45 +- 5.5 GtCO2,\r\n2\r\n CH4 11 +- 3.2 GtCO2-eq, N2O 2.7 +- 1.6 GtCO2-eq and \r\nfluorinated gases (F-gases: HFCs, PFCs, SF6, NF3) 1.4 +- 0.41 GtCO2-eq. \r\nCompared to 1990, the magnitude and speed of these increases \r\ndiffered across gases: CO2 from fossil fuel and industry (FFI) grew by \r\n15 GtCO2-eq yr-1 (67%), CH4 by 2.4 GtCO2-eq yr -1 (29%), F-gases \r\nby 0.97 GtCO2-eq yr -1 (254%), and N2O by 0.65  GtCO2-eq  yr-1\r\n(33%). CO2 emissions from net land use, land-use change and \r\nforestry (LULUCF) have shown little long-term change, with large \r\nuncertainties preventing the detection of statistically significant \r\ntrends"


id: 9f35f4fffd8318e92059f0b7e50c69c9
created_at: 2023-03-16 14:02:41
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content:
" F-gases excluded from GHG emissions inventories such as \r\nchlorofluorocarbons and hydrochlorofluorocarbons are about the \r\nsame size as those included (high confidence). {2.2.1, 2.2.2, Table 2.1, \r\nFigures 2.2, 2.3 and 2.5}"


id: ff937819372c05a5b35ff0a6a9918181
created_at: 2023-03-16 14:02:41
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content:
"Emissions from developing countries have continued to grow, \r\nstarting from a  low base of per  capita emissions and with \r\na lower contribution to cumulative emissions than Developed \r\nCountries (robust evidence, high agreement). Average 2019 \r\nper  capita CO2-FFI emissions in three developing regions  - Africa \r\n(1.2  tCO2 per  capita), Asia and Pacific (4.4 tCO2 per  capita), and \r\nLatin America and Caribbean (2.7 tCO2 per capita) - remained less \r\nthan half that of Developed Countries (9.5 tCO2 per capita) in 2019. \r\nCO2-FFI emissions in the three developing regions together grew by \r\n26% between 2010 and 2019, compared to 260% between 1990 \r\nand 2010, while in Developed Countries emissions contracted by \r\n9.9% between 2010 and 2019, and by 9.6% between 1990 and \r\n2010"


id: ba0c043e409dfd5a49b5db28b689dfb5
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content:
" Historically, the three developing regions together contributed \r\n28% to cumulative CO2-FFI emissions between 1850 and 2019, \r\nwhereas Developed Countries contributed 57% and Least-Developed \r\nCountries contributed 0.4%. {2.2.3, Figures 2.9 and 2.10}"


id: 6dbea03ecd294829ed115be3ededb7c6
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content:
"Globally, GHG emissions continued to rise across all sectors \r\nand subsectors; most rapidly in transport and industry (high \r\nconfidence). In 2019, 34% (20 GtCO2-eq) of global GHG emissions \r\ncame from the energy sector, 24% (14 GtCO2-eq) from industry, 22% \r\n(13 GtCO2-eq) from agriculture, forestry and other land use (AFOLU), \r\n15% (8.7 GtCO2-eq) from transport and 5.6% (3.3 GtCO2-eq) from \r\nbuildings. Once indirect emissions from energy use are considered, \r\nthe relative shares of industry and buildings emissions rise to 34% \r\nand 16%, respectively. Average annual GHG emissions growth during \r\n2010 to 2019 slowed compared to the previous decade in energy \r\nsupply (from 2.3% to 1.0%) and industry (from 3.4% to 1.4%, direct \r\nemissions only), but remained roughly constant at about 2% per year \r\nin the transport sector (high confidence). Emission growth in AFOLU \r\nis more uncertain due to the high share of CO2-LULUCF emissions \r\n(medium confidence). {2.4.2, Figure 2.13, Figures 2"


id: 4a1878950c29588927edb059020dc4c2
created_at: 2023-03-16 14:02:41
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vector_id: 9061
content:
"Estimates of future CO2 emissions from existing fossil fuel \r\ninfrastructures already exceed remaining cumulative net CO2\r\nemissions in pathways limiting warming to 1.5degC with no or \r\nlimited overshoot (high confidence). Assuming variations in \r\nhistorical patterns of use and decommissioning, estimated future \r\nCO2 emissions from existing fossil fuel infrastructure alone are \r\n660 (460-890) GtCO2 and from existing and currently planned \r\ninfrastructure 850 (600-1100) GtCO2. This compares to overall \r\ncumulative net CO2 emissions until reaching net zero CO2 of 510 \r\n(330-710) Gt in pathways that limit warming to 1.5degC with no or \r\nlimited overshoot, and 890 (640-1160) Gt in pathways that limit \r\nwarming to 2degC (<67%) (high confidence)"


id: e7594a89bccc243c66c34f51a0404042
created_at: 2023-03-16 14:02:41
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content:
" While most future CO2\r\nemissions from existing and currently planned fossil fuel infrastructure \r\nare situated in the power sector, most remaining fossil fuel CO2\r\nemissions in pathways that limit warming to 2degC (<67%) and below \r\nare from non-electric energy - most importantly from the industry \r\nand transportation sectors (high confidence). Decommissioning and \r\nreduced utilisation of existing fossil fuel installations in the power \r\nsector as well as cancellation of new installations are required to \r\nalign future CO2 emissions from the power sector with projections \r\nin these pathways (high confidence). {2.7.2, 2.7.3, Figure  2.26, \r\nTable 2.6, Table 2.7}"


id: cc2a0f972f61bc076ccc2a9c7ff91314
created_at: 2023-03-16 14:02:41
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content:
"A growing number of global GHG emissions inventories have \r\nbecome available since AR5 (Minx et al. 2021). However, only a few \r\nare comprehensive in their coverage of sectors, countries and gases - \r\nnamely EDGAR (Emissions Database for Global Atmospheric Research) \r\n(Crippa et al. 2021), PRIMAP (Potsdam Real-time Integrated Model \r\nfor probabilistic Assessment of emissions Paths) (Gutschow et  al. \r\n2021a), CAIT (Climate Analysis Indicators Tool) (WRI 2019) and CEDS \r\n(A Community Emissions Data System for Historical Emissions) (Hoesly \r\net al. 2018). None of these inventories presently cover CO2-LULUCF, \r\nwhile CEDS excludes F-gases. For individual gases and sectors, \r\nadditional GHG inventories are available, as shown in Figure 2.2, but \r\neach has varying system boundaries leading to important differences \r\nbetween their respective estimates (Section 2.2.1). Some inventories \r\nare compiled bottom-up, while others are produced synthetically \r\nand are dependent on other inventories"


id: ed461b5634ab302b534d17249d15f507
created_at: 2023-03-16 14:02:41
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content:
" A more comprehensive list \r\nand discussion of different datasets is provided in the Chapter  2 \r\nSupplementary Material (2.SM.1) and in Minx et al. (2021)."


id: a96b47156fe94fa88c98345699db784e
created_at: 2023-03-16 14:02:41
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content:
"Accordingly, the grey-outlined boxes at the top of Figure 2.1 show \r\nthat the first part of the chapter presents GHG emissions from two \r\nmain perspectives: their geographical locations; and the places where \r\ngoods are consumed and services are utilised. A complicated chain of \r\ndrivers underlie these emissions. They are linked across time, space, \r\nand various segments of the economy and society in complex non\u0002linear relationships. Sections shown in the second row of grey-outlined \r\nboxes assess the latest literature and improve the understanding of \r\nthe relative importance of these drivers in mitigating GHG emissions. \r\nA huge mass of physical capital embodying immense financial assets \r\nand potentially operating over a  long lifetime produces vast GHG \r\nemissions. This long-lived infrastructure can be a significant hindrance \r\nto fast and deep reductions of emissions; it is therefore also shown as \r\nan important driver"


id: 8a9acba57fd79965ac1bf4e285dc865b
created_at: 2023-03-16 14:02:41
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content:
" A large range of economic, social, environmental, \r\nand other policies has been shaping these drivers of GHG emissions \r\nin the past and are anticipated to influence them in the future, as \r\nindicated by the grey-outlined policies box and its manifold linkages. \r\nAs noted, blue lines show linkages of sections to other chapters that \r\ndiscuss these drivers and their operating mechanisms in detail."


id: 3673223a09e1b0548818bb25397e2b44
created_at: 2023-03-16 14:02:41
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vector_id: 5959
content:
"Across this report, version 6 of EDGAR (Crippa et al. 2021) provided \r\nby the Joint Research Centre of the European Commission, is used \r\nfor a  consistent assessment of GHG emissions trends and drivers. \r\nIt covers anthropogenic releases of CO2-FFI, CH4, N2O, and F-gas \r\n(HFCs, PFCs, SF6, NF3) emissions by 228 countries and territories and \r\nacross five sectors and 27 subsectors. EDGAR is chosen because it \r\nprovides the most comprehensive global dataset in its coverage \r\nof sources, sectors and gases. For transparency, and as part of the \r\nuncertainty assessment, EDGAR is compared to other global datasets \r\nin Section 2.2.1 as well as in the Chapter 2 Supplementary Material \r\n(2.SM.1). For individual country estimates of GHG emissions, it may \r\nbe more appropriate to use inventory data submitted to the United \r\nNations Framework Convention on Climate Change (UNFCCC) under \r\nthe common reporting format (CRF) (UNFCCC 2021)"


id: 62ac4019fa9b9723096799747de332ca
created_at: 2023-03-16 14:02:41
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index: climate_gpt_only_giec
vector_id: 13093
content:
" However, these \r\ninventories are only up to date for Annex I countries and cannot be \r\nused to estimate global or regional totals. As part of the regional \r\nanalysis, a comparison of EDGAR and CRF estimates at the country\u0002level is provided, where the latter is available (Figure 2.9)."


id: 3d9e28d976833b590bc3e0cfa45b3cbe
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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 than \r\ntrue structural plus parameter uncertainty, or than estimates made \r\nby independent methods. In particular, it is important to account for \r\npotential bias in estimates, which can result from using common \r\nmethodological or parameter assumptions, or from missing sources \r\n(systemic bias). It is further crucial to account for differences in \r\nsystem boundaries - that is, which emissions sources are included \r\nin a  dataset and which are not, otherwise direct comparisons \r\ncan exaggerate uncertainties (Macknick 2011; Andrew 2020). \r\nIndependent top-down observational constraints are, therefore, \r\nparticularly useful to bound total emission estimates, but are not \r\nyet capable of verifying emission levels or trends (Petrescu et  al. \r\n2021a, 2021b)"


id: e4060632dd68e53e28d6ecec8a792c01
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content:
" Similarly, uncertainties estimates are influenced by \r\nspecific modelling choices. For example, uncertainty estimates from \r\nstudies on the propagation of uncertainties associated with key input \r\nparameters (activity data, emissions factors) following the IPCC \r\nGuidelines (IPCC 2006) are strongly determined by assumptions on \r\nhow these parameters are correlated between sectors, countries, \r\nand regions (Janssens-Maenhout et  al. 2019; Solazzo et  al. 2021). \r\nAssuming (full) covariance between source categories, and therefore \r\ndependence between them, increases uncertainty estimates. \r\nEstimates allowing for some covariance as in Sollazo et al. (2021) \r\nalso tend to yield higher estimates than the range of values from \r\nensemble of dependent inventories (Saunois et al. 2016, 2020)."


id: de774961412ae280ecc870949859dc05
created_at: 2023-03-16 14:02:41
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content:
"In this chapter and the report as a  whole, different GHGs are \r\nfrequently converted into common units of CO2 equivalent (CO2-eq) \r\nemissions using 100-year global warming potentials (GWP100) from \r\nAR6 WGI (Forster et al. 2021a). This reflects the dominant use in the \r\nscientific literature and is consistent with decisions made by Parties \r\nto the Paris Agreement for reporting and accounting of emissions \r\nand removals (UNFCCC 2019). Other GHG emissions metrics exist, \r\nall of which, like GWP100, are designed for specific purposes and \r\nhave limitations and uncertainties. The appropriate choice of GHG \r\nemissions metrics depends on policy objective and context (Myhre \r\net  al. 2013; Kolstad et  al. 2015). A  discussion of GHG metrics is \r\nprovided in a Cross-Chapter Box later in the chapter (Cross-Chapter \r\nBox  2) and at length in the Chapter  2 Supplementary Material"


id: 856c6eb5b20c9159592f12ddf7aa2a5e
created_at: 2023-03-16 14:02:41
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vector_id: 18520
content:
" \r\nThroughout the chapter GHG emissions are reported (in GtCO2-eq) at \r\ntwo significant digits to reflect prevailing uncertainties in emissions \r\nestimates. Estimates are subject to uncertainty, which we report for \r\na 90% confidence interval."


id: 35065f886b7333cf187200898d7f3c2b
created_at: 2023-03-16 14:02:41
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content:
"Estimates of historical GHG emissions  - CO2, CH4, N2O and \r\nF-gases - are uncertain to different degrees. Assessing and reporting \r\nuncertainties is crucial in order to understand whether available \r\nestimates are sufficiently robust to answer policy questions  - for \r\nexample, if GHG emissions are still rising, or if a country has achieved \r\nan emission reduction goal (Marland 2008). These uncertainties can \r\nbe of scientific nature, such as when a  process is not sufficiently \r\nunderstood. They also arise from incomplete or unknown parameter \r\ninformation (e.g.,  activity data, or emission factors), 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 by \r\nindependent methods and observations (e.g., comparing atmospheric \r\nmeasurements with bottom-up emissions inventory estimates) \r\n(Saunois et al. 2020; Petrescu et al"


id: 5a80d255e4526de84c857875dba91873
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content:
" 2020a and 2020b; Tian et al. 2020); \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); and (iii) by evaluating estimates from a single \r\nsource (Hoesly and Smith 2018), for instance via statistical sampling "


id: 78b39fd5804204bdd809ca2d9b6975e2
created_at: 2023-03-16 14:02:41
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content:
"Figure 2.2 (continued): Estimates of global anthropogenic greenhouse gas emissions from different data sources 1970-2019. Panel (a): CO2 FFI emissions \r\nfrom: EDGAR - Emissions Database for Global Atmospheric Research (this dataset) (Crippa et al. 2021); GCP - Global Carbon Project (Friedlingstein et al. 2020; Andrew and \r\nPeters 2021); CEDS - Community Emissions Data System (Hoesly et al. 2018; O'Rourke et al. 2021); CDIAC Global, Regional, and National Fossil-Fuel CO2 Emissions (Gilfillan \r\net al. 2020); PRIMAP-hist - Potsdam Real-time Integrated Model for probabilistic Assessment of emissions Paths (Gutschow et al. 2016, 2021b); EIA - Energy Information \r\nAdministration International Energy Statistics (EIA 2021); BP - BP Statistical Review of World Energy (BP 2021); IEA - International Energy Agency (IEA 2021a, 2021b); \r\nIPPU refers to emissions from industrial processes and product use"


id: 153afc27c52daa963bde146d572eb95c
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
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index: climate_gpt_only_giec
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content:
" Panel (b): Net anthropogenic CO2-LULUCF emissions from: BLUE - Bookkeeping of land-use emissions \r\n(Hansis et al. 2015; Friedlingstein et al. 2020); DGVM-mean - multi-model mean of CO2-LULUCF emissions from dynamic global vegetation models (Friedlingstein et al. 2020); \r\nOSCAR - an earth system compact model (Friedlingstein et al. 2020; Gasser et al. 2020); HN - Houghton and Nassikas Bookkeeping Model (Houghton and Nassikas 2017; \r\nFriedlingstein et al. 2020); for comparison, the net CO2 flux from FAOSTAT (FAO Tier 1) is plotted, which comprises net emissions and removals on forest land and from net \r\nforest conversion (FAOSTAT 2021; Tubiello et al. 2021), emissions from drained organic soils under cropland/grassland (Conchedda and Tubiello 2020), and fires in organic soils \r\n(Prosperi et al"


id: fadaf0b18afe3cf40e29b292882dbe7a
created_at: 2023-03-16 14:02:41
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index: climate_gpt_only_giec
vector_id: 36988
content:
" 2020), as well as a net CO2 flux estimate from National Greenhouse Gas Inventories (NGHGI) based on country reports to the UNFCCC, which include land-use \r\nchange, and fluxes in managed lands (Grassi et al. 2021). Panel (c): Anthropogenic CH4 emissions from: EDGAR (above); CEDS (above); PRIMAP-hist (above); GAINS - The \r\nGreenhouse gas - Air pollution Interactions and Synergies Model (Hoglund-Isaksson et al. 2020); EPA-2019: Greenhouse gas emission inventory (US-EPA, 2019); FAO -FAOSTAT \r\ninventory emissions (Tubiello et al. 2013; Tubiello 2018; FAOSTAT 2021); Panel (d): Anthropogenic N2O emissions from: GCP - global nitrous oxide budget (Tian et al. 2020); \r\nCEDS (above); EDGAR (above); PRIMAP-hist (above); GAINS (Winiwarter et al. 2018); EPA-2019 (above); FAO (above). Differences in emissions across different versions of the \r\nEDGAR dataset are shown in the Supplementary Material (Figure 2.SM.2). Source: Minx et al. (2021)."


id: 60be88d362dc9979fbddc732c2efc11e
created_at: 2023-03-16 14:02:41
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content:
"The most appropriate metric to aggregate GHG emissions depends \r\non the objective (Cross-Chapter Box 2). One such objective can be to \r\nunderstand the contribution of emissions in any given year to warming, \r\nwhile another can be to understand the contribution of cumulative \r\nemissions over an extended time period to warming. In Figure 2.4 the \r\nmodelled warming from emissions of each gas or group of gases is \r\nalso shown - calculated using the reduced-complexity climate model \r\nFinite Amplitude Impulse Response (FaIR) model v1.6, which has been \r\ncalibrated to match several aspects of the overall WGI assessment \r\n(Forster et al. 2021a; specifically Cross-Chapter Box 7 in Chapter 10 \r\ntherein). Additionally, its temperature response to emissions with \r\nshorter atmospheric lifetimes such as aerosols, methane or ozone \r\nhas been adjusted to broadly match those presented in Szopa et al. \r\n(2021a)"


id: 7fc1c77bfa70fefc5e50b45434343a41
created_at: 2023-03-16 14:02:41
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vector_id: 17644
content:
" There are some differences in actual warming compared to \r\nthe GWP100 weighted emissions of each gas (Figure 2.4), in particular \r\na greater contribution from CH4 emissions to historical warming. This "


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"The choice of metric, including time horizon, should reflect the policy objectives for which the metric is applied (Plattner et al. 2009). \r\nRecent studies confirm earlier findings that the GWP is consistent with a cost-benefit framework (Kolstad et al. 2014), which implies \r\nweighting each emission based on the economic damages that this emission will cause over time, or conversely, the avoided damages \r\nfrom avoiding that emission. The GWP time horizon can be linked to the discount rate used to evaluate economic damages from each \r\nemission. For methane, GWP100 implies a social discount rate of about 3-5% depending on the assumed damage function, whereas \r\nGWP20 implies a much higher discount rate, greater than 10% (medium confidence) (Mallapragada and Mignone 2019; Sarofim and \r\nGiordano 2018). The dynamic GTP is aligned with a cost-effectiveness framework, as it weights each emission based on its contribution \r\nto global warming in a specified future year (e.g"


id: d8de0c82306e1f3a53347ddc6a43cfd1
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", the expected year of peak warming for a given temperature goal). This implies \r\na shrinking time horizon and increasing relative importance of SLCF emissions as the target year is approached (Johansson 2011; \r\nAaheim and Mideksa 2017). The GTP with a static time horizon (e.g., GTP100) is not well-matched to either a cost-benefit or a cost\u0002effectiveness framework, as the year for which the temperature outcome is evaluated would not match the year of peak warming, nor \r\nthe overall damages caused by each emission (Edwards and Trancik 2014; Strefler et al. 2014; Mallapragada and Mignone 2017)."


id: 5164fee5893319d2651cecc30c2a8b63
created_at: 2023-03-16 14:02:41
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"A number of studies since AR5 have evaluated the impact of various GHG emission metrics and time horizons on the global economic \r\ncosts of limiting global average temperature change to a pre-determined level (e.g. Strefler et al. 2014; Harmsen et al. 2016; Tanaka \r\net al. 2021) (see 2.SM.3 for additional detail). These studies indicate that, for mitigation pathways that limit warming to 2degC (<67%) \r\nabove pre-industrial levels or lower, using GWP100 to inform cost-effective abatement choices between gases would achieve such \r\nlong-term temperature goals at close to least global cost within a few percent (high confidence). Using the dynamic GTP instead of \r\nGWP100 could reduce global mitigation costs by a few percent in theory (high confidence), but the ability to realise those cost savings \r\ndepends on the temperature limit, policy foresight and flexibility in abatement choices as the weighting of SLCF emissions increases \r\nover time (medium confidence) (van den Berg et al"


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" 2015; Huntingford et al. 2015). Similar benefits as for the dynamic GTP might be \r\nobtained by regularly reviewing and potentially updating the time horizon used for GWP in light of actual emissions trends compared \r\nto climate goals (Tanaka et al. 2020)."


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"Recently developed step/pulse metrics such as the combined global temperature change potential (CGTP) (Collins et al. 2019) and \r\nGWP* (Allen et al. 2018; Cain et al. 2019) recognise that a sustained increase/decrease in the rate of SLCF emissions has a similar \r\neffect on global surface temperature over multiple decades as a one-off pulse emission/removal of CO2. These metrics use this \r\nrelationship to calculate the CO2 emissions or removals that would result in roughly the same temperature change as a sustained \r\nchange in the rate of SLCF emissions (CGTP) over a given time period, or as a varying time series of CH4 emissions (GWP*). From \r\na mitigation perspective, these metrics indicate greater climate benefits from rapid and sustained methane reductions over the next \r\nfew decades than if such reductions are weighted by GWP100, while conversely, sustained methane increases have greater adverse \r\nclimate impacts (Collins et al. 2019; Lynch et al. 2020)"


id: 7b70bb3e82d7615e93ad328f0cc5d2ee
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" The ability of these metrics to relate changes in emission rates of short-lived \r\ngases to cumulative CO2 emissions makes them well-suited, in principle, to estimating the effect on the remaining carbon budget from \r\nmore, or less, ambitious SLCF mitigation over multiple decades compared to a given reference scenario (high confidence) (Collins et al. \r\n2019; Forster et al. 2021)."


id: eae38d7131c62b3cec6bfad6f348cc35
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"The potential application of GWP* in wider climate policy (e.g., to inform equitable and ambitious emission targets or to support \r\nsector-specific mitigation policies) is contested, although relevant literature is still limited (Rogelj and Schleussner 2019, 2021; \r\nSchleussner et al. 2019; Allen et al. 2021; Cain et al. 2021). Whereas GWP and GTP describe the marginal effect of each emission \r\nrelative to the absence of that emission, GWP* describes the equivalent CO2 emissions that would give the same temperature \r\nchange as an emissions trajectory of the gas considered, starting at a (user-determined) reference point. The warming based on \r\nthose cumulative CO2-equivalent emission at any point in time is relative to the warming caused by emissions of that gas before the \r\nreference point"


id: c1054b9dfa6752b1a36bffae1c5d0662
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" Because of their different focus, GWP* and GWP100 can equate radically different CO2 emissions to the same CH4\r\nemissions: rapidly declining CH4 emissions have a negative CO2-warming-equivalent value based on GWP* (rapidly declining SLCF \r\nemissions result in declining temperature, relative to the warming caused by past SLCF emissions at a previous point in time) but a \r\npositive CO2-equivalent value based on GWP or GTP (each SLCF emission from any source results in increased future radiative forcing \r\nand global average temperature than without this emission, regardless of whether the rate of SLCF emissions is rising or declining). \r\nThe different focus in these metrics can have important distributional consequences, depending on how they are used to inform \r\nemission targets (Lynch et al. 2021; Reisinger et al. 2021), but this has only begun to be explored in the scientific literature."


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"A key insight from WGI is that, for a  given emissions scenario, different metric choices can alter the time at which net zero \r\nGHG emissions are calculated to be reached, or whether net zero GHG emissions are reached at all (2.SM.3). From a mitigation \r\nperspective, this implies that changing GHG emission metrics but retaining the same numerical CO2-equivalent emissions targets \r\nwould result in different climate outcomes. For example, achieving a balance of global anthropogenic GHG emissions and removals, as \r\nstated in Article 4.1 of the Paris Agreement could, depending on the GHG emission metric used, result in different peak temperatures \r\nand in either stable, or slowly or rapidly declining temperature after the peak (Allen et al. 2018; Fuglestvedt et al. 2018; Tanaka and \r\nO'Neill 2018; Schleussner et al. 2019)"


id: 46a0c1fccc7798f2b57757d23f0bf548
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" A fundamental change in GHG emission metrics used to monitor achievement of existing \r\nemission targets could therefore inadvertently change their intended climate outcomes or ambition, unless existing emission targets \r\nare re-evaluated at the same time (very high confidence)."


id: 22fbd45da44ca8267b23f72180883adf
created_at: 2023-03-16 14:02:41
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"The only exception to these patterns of GHG emissions growth is net \r\nanthropogenic CO2-LULUCF emissions, where there is no statistically \r\nsignificant trend due to high uncertainties in estimates (Figures 2.2 and \r\n2.5; Chapter 2 Supplementary Material). While the average estimate \r\nfrom the bookkeeping models report a  slightly increasing trend in \r\nemissions, NGHGI and FAOSTAT estimates show a slightly decreasing \r\ntrend, which diverges in recent years (Figure 2.2). Similarly, trends in \r\nCO2-LULUCF estimates from individual bookkeeping models differ: \r\nwhile two models (BLUE and OSCAR) show a sustained increase in \r\nemissions levels since the mid-1990s, emissions from the third model \r\n(Houghton and Nassikas (HN)) declined (Figure 2.2 in this chapter; \r\nFriedlingstein et al. 2020). Differences in accounting approaches and \r\ntheir impacts CO2 emissions estimates from land use is covered in \r\nChapter  7 and in the Chapter  2 Supplementary Material (2.SM.2)"


id: 24c604e609d7ec9e0477f02f1788cfc3
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" \r\nNote that anthropogenic net emissions from bioenergy are covered \r\nby the CO2-LULUCF estimates presented here."


id: dca20017963bf1693610cbfea59d1500
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"Starting in the spring of 2020 a major break in global emissions \r\ntrends was observed due to lockdown policies implemented in \r\nresponse to the COVID-19 pandemic (high confidence) (Forster \r\net al. 2020; Le Quere et al. 2020, 2021; Z. Liu et al. 2020b; Bertram \r\net  al. 2021). Overall, global CO2-FFI emissions are estimated \r\nto have declined by 5.8% (5.1%-6.3%) in 2020, or about 2.2 \r\n(1.9-2.4)  GtCO2 in total (Friedlingstein et  al. 2020; Z. Liu et  al. \r\n2020b; BP 2021; Crippa et al. 2021; IEA 2021a). This exceeds any \r\nprevious global emissions decline since 1970, both in relative \r\nand absolute terms (Figure 2.6). Daily emissions, estimated based \r\non activity and power-generation data, declined substantially \r\ncompared to 2019 during periods of economic lockdown, \r\nparticularly in April 2020 - as shown in Figure 2.6 - but rebounded \r\nby the end of 2020 (medium confidence) (Le Quere et  al. 2020, \r\n2021; Z. Liu et  al. 2020b)"


id: 6e52f95f81b0e4a153494caa7faba863
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" Impacts were differentiated by sector, \r\nwith road transport and aviation particularly affected. Inventories \r\nestimate the total power sector CO2 reduction from 2019 to 2020 \r\nat 3% (IEA 2021a) and 4.5% (Crippa et al. 2021). Approaches that \r\npredict near real-time estimates of the power sector reduction are \r\nmore uncertain and estimates range more widely, between 1.8% "


id: 3f2fa997db436b81e67ce53bcb84f64e
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"Even when taking uncertainties into account, historical emissions \r\nbetween 1850 and 2019 constitute a  large share of total carbon \r\nbudgets from 2020 onwards for limiting warming to 1.5degC with \r\na 50% probability as well as for limiting warming to 2degC with a 67% \r\nprobability. Based on central estimates only, historical cumulative \r\nnet CO2 emissions between 1850-2019 amount to about four fifths \r\nof the total carbon budget for a 50% probability of limiting global \r\nwarming to 1.5degC (central estimate about 2900 GtCO2), and to \r\nabout two thirds of the total carbon budget for a 67% probability \r\nto limit global warming to 2degC (central estimate about 3550 GtCO2). \r\nThe carbon budget is the maximum amount of cumulative net \r\nglobal anthropogenic CO2 emissions that would result in limiting \r\nglobal warming to a given level with a given likelihood, taking into \r\naccount the effect of other anthropogenic climate forcers"


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" This is \r\nreferred to as the total carbon budget when expressed starting from \r\nthe pre-industrial period, and as the remaining carbon budget when \r\nexpressed from a  recent specified date. The total carbon budgets \r\nreported here are the sum of historical emissions from 1850 to 2019 \r\nand the remaining carbon budgets from 2020 onwards, which extend "


id: d0ce87d4c1604517f5b5bfa7f37931d4
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"Least-Developed Countries and Small Island Developing States \r\ncontributed only a negligible proportion of historic GHG emissions \r\ngrowth and have the lowest per capita emissions. As of 2019 Least \r\nDeveloped Countries contribute 3.3% of global GHG emissions, \r\nexcluding LULUCF CO2, despite making up 13% of the global \r\npopulation. Small Island Developing States contributed 0.6% of \r\nglobal GHG emissions in 2019, excluding LULUCF CO2, with 0.9% \r\nof the global population. Since the start of the industrial revolution in \r\n1850 up until 2019, Least Developed Countries contributed 0.4% of \r\ntotal cumulative CO2 emissions, while Small Island Developing States \r\ncontributed 0.5% (Figure  2.10). Conversely, Developed Countries \r\nhave the highest share of historic cumulative emissions (Rocha \r\net  al. 2015; Gutschow et  al. 2016; Matthews 2016), contributing \r\napproximately 57% (Figure 2"


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"10), followed by Asia and Pacific (21%), \r\nEastern Europe and West Central Asia (9%), Latin America and the \r\nCaribbean (4%), the Middle East (3%), and Africa (3%). Developed \r\nCountries still have the highest share of historic cumulative emissions \r\n(45%) when CO2-LULUCF emissions are included, which typically \r\naccount for a higher proportion of emissions in developing regions \r\n(Figure 2.10)."


id: dbf1164f5a8bf43f8f048506e3f85fee
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"Figure  2.9 (continued): Change in regional GHGs from multiple perspectives and their underlying drivers. Panel (a): Regional GHG emissions trends \r\n(in GtCO2-eq yr -1) for the time period 1990-2019. GHG emissions from international aviation and shipping are not assigned to individual countries and shown separately.\r\nPanels (b) and (c): Changes in GHG emissions for the 20 largest emitters (as of 2019) for the post-AR5 reporting period 2010-2019 in relative (% annual change) and \r\nabsolute terms (GtCO2-eq). Panels (d) and (e): GHG emissions per capita and per GDP in 2019 for the 20 largest emitters (as of 2019). GDP estimated using constant \r\ninternational purchasing power parity (USD2017). Emissions are converted into CO2-equivalents based on global warming potentials with a 100-year time horizon (GWP100) \r\nfrom the IPCC Sixth Assessment Report (Forster et al. 2021a). The black dots represent the emissions data from UNFCCC-CRFs (2021) that were accessed through Gutschow \r\net al"


id: 5326c99eff1a7a744bed6beae98d89b9
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" (2021a). Net LULUCF CO2 emissions are included in panel (a), based on the average of three bookkeeping models (Section 2.2), but are excluded in panels (b-e) due to \r\na lack of country resolution."


id: 2cd1f4ab1860bea0836cb7098d470478
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"Figure 2.10 | Different perspectives on historic emissions and equity. Panel (a) shows the regional proportion (%) of total global population or emissions in 2018 or \r\n2019, for five categories: population (persons); consumption-based CO2-FFI emissions (GtCO2); production-based CO2-FFI emissions (GtCO2); production-based GHG emissions \r\nexcluding CO2-LULUCF (GtCO2-eq); and production-based GHG emissions including CO2-LULUCF (GtCO2-eq). Panel (b) shows the regional proportion (%) of total cumulative \r\nproduction-based CO2 emissions from 1850 to 2019, including and excluding CO2-LULUCF (GtCO2). In the lower panels, the proportion of each population or emissions \r\ncategory attributable to Least-Developed Countries and Small Island Developing States (SIDS) are shown, where available (CO2-LULUCF data is not available for these regions)"


id: b2e3c3ffd2ae325a5e2254dcaa58d251
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" \r\nGHG emissions are converted into CO2-equivalents based on global warming potentials with a 100-year time horizon (GWP100) from the IPCC Sixth Assessment Report\r\n(Forster et al. 2021a). Source: data from Friedglinstein et al. (2020)."


id: 3444cb71de8bfcc2e7f0cc1cb10a5e3d
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content:
"A growing number of countries have reduced CO2 and GHG emissions \r\nfor longer than 10 years (high confidence) (Le Quere et  al. 2019; \r\nBurck et al. 2021; Lamb et al. 2021a; Wu et al. 2021). Data up to 2018 \r\nindicates that about 24 countries have reduced territorial CO2 and GHG \r\nemissions (excluding LULUCF CO2), as well as consumption-based CO2\r\nemissions, for at least 10 years (Lamb et al. 2021a). Uncertainties in \r\nemissions levels and changes over time prevents a precise assessment \r\nof reductions in some cases. Of these 24  countries, 12 peaked \r\nemissions in the 2000s; six have sustained longer reductions since \r\nthe 1970s; and six are former members of the Eastern Bloc, where \r\nemissions dropped rapidly in the 1990s and continued declining \r\nat a  slower pace thereafter. Country emissions reductions have \r\nbeen driven by both climate and non-climate policies and factors, \r\nincluding structural changes"


id: 83af663de4654ec1829ffdf8b890c8fa
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" To date, most territorial emissions \r\nreductions were realised in the electricity and heat sector, followed \r\nby industry and buildings, while in many cases transport emissions \r\nhave increased since countries reached their overall emissions peak \r\n(Climate Transparency 2021; Lamb et al. 2021a). One estimate of the \r\ntotal reduction in annual GHG emissions - from peak years to 2018 - \r\nsums to 3.2 GtCO2-eq across all decarbonising countries (Lamb et al. \r\n2021a). These reductions have therefore been far outweighed by \r\nrecent emissions growth. However, climate policy related reductions \r\nmay be even larger when compared against a counterfactual case of \r\nemissions growth across different sectors (Eskander and Fankhauser \r\n2020) (Cross-Chapter Box 1 in Chapter 1; Section 2.8)."


id: f773c10912e937df2dd1f0c4bb9e67ed
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"Figure 2.12 | Total anthropogenic direct and indirect GHG emissions for the year 2019 (in GtCO2-eq) by sector and subsector. Direct emissions estimates \r\nassign emissions to the sector in which they arise (scope 1 reporting). Indirect emissions - as used here - refer to the reallocation of emissions from electricity and heat to the \r\nsector of final use (scope 2 reporting). Note that cement refers to process emissions only, as a lack of data prevents the full reallocation of indirect emissions to this sector. \r\nMore comprehensive conceptualisations of indirect emissions including all products and services (scope 3 reporting) are discussed in Section 2.3 of this chapter. Emissions are \r\nconverted into CO2-equivalents based on global warming potentials with a 100-year time horizon (GWP100) from the IPCC Sixth Assessment Report. Percentages may not add \r\nup to 100 across categories due to rounding at the second significant digit. Source: based on Lamb et al. (2021b); data: Minx et al"


id: b5df152db45db442225e3ba4cb8150ea
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content:
"There are other proposed emission accounting approaches used \r\nin different circumstances. Historical cumulative emissions (HCEs) \r\nare used when analysing countries' historic contribution to \r\nemissions and responsibility for emission reduction. HCEs account \r\nfor a  country's cumulative past emissions, which may be different \r\nfrom the country's  current annual emissions (Botzen et  al. 2008; \r\nRitchie 2019), but are sensitive to the choice of cut-off period. For \r\nexample, the USA and EU-27 countries plus the UK contributed \r\nrespectively 13.3% and 8.7% to global PBEs in 2019 (Crippa et al. \r\n2020), however, they emitted around 25% and 22% of global \r\nhistorical PBEs since 1751 (Ritchie 2019). Extraction-based emissions \r\n(EBEs) accounting allocates all emissions from burning fossil fuels \r\nthroughout the supply chains to the country where the fuels were \r\nextracted (Steininger and Schinko 2015)"


id: 48d4739cf261694f7d3838018b6c6079
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content:
" EBEs can be calculated \r\nby multiplying primary energy extraction of fossil fuels with their \r\nrespective carbon content (Erickson and Lazarus 2013). Another \r\napproach for accounting emissions is income-based emission (IBE), \r\nwhich traces emissions throughout all supply chains and allocates \r\nemissions to primary inputs (e.g., capital and labour). In other words, \r\nIBEs investigate a  country's direct and indirect downstream GHG \r\nemissions enabled by its primary inputs (Liang et al. 2017a). All these \r\napproaches provide complementary information and different angles \r\nto assigning responsibility for emissions reductions."


id: dd9050c549c4b48041dd1fe6425facb0
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content:
"Production-based emissions (PBEs) and territorial emissions \r\nresulting from the production and consumption of goods and \r\nservices within a  region (for both domestic use and export) are \r\noften used by authorities to report carbon emissions (Peters 2008) \r\n(Section  2.2). PBEs also include emissions from international \r\nactivities (e.g.,  international aviation/shipping and non-resident \r\nactivities), which are excluded from territorial emissions (Karstensen \r\net al. 2018; Shan et al. 2018). In contrast, CBEs refer to emissions \r\nalong the entire supply chains induced by consumption, irrespective \r\nof the place of production (Liu et al. 2015b). This reflects a shared \r\nunderstanding that a wider system boundary going beyond territorial \r\nemissions is important to avoid outsourcing of pollution and to \r\nachieve global decarbonisation"


id: 779bfad49a1dca69410649bbafc4b41b
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content:
"The dominant method for calculating nations' CBEs is global multi\u0002region input-output (GMRIO) analysis (Wiedmann and Lenzen \r\n2018). Other frequently used approaches include analysing bilateral \r\ntrade flows of products and their lifecycle emission factors (Sato \r\n2014). Generally, the uncertainties associated with CBEs depends \r\non the choice of the dataset/model used for calculation, which \r\ndiffers according to: (i) the national economic and trade data \r\nused; (ii) the emissions data used; (iii) the sector or product-level \r\naggregation; (iv) the regional aggregation; (v) the conceptual scope \r\n(e.g., residential vs territorial accounting principle); and (vi) the model \r\nconstruction techniques, which include table-balancing algorithms \r\nand ways of dealing with missing or conflicting data (Moran and \r\nWood 2014; Owen, 2017; Wieland et  al. 2018; Wood et  al. 2018, \r\n2019)"


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content:
" When excluding systematic error sources, research has shown \r\nthat the stochastic relative standard deviation (RSD) of total national \r\nCBEs is not significantly different to that from PBEs accounts and in \r\nthe region of 5-15% (Wood et al. 2018, 2019)."


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"The two panels at left in Figure  2.14 show total and per  capita \r\nCBEs for six regions. The three panels on the right show additional \r\ninformation for the 18 top-emitting countries with the highest \r\nCBEs in 2018. In  Developed Countries, consumption-based CO2\r\nemissions peaked at 15 GtCO2 in 2007 with a  subsequent 16% \r\ndecline until 2016 (to 12.7  GtCO2) and a  slight rebound of 1.6% \r\nuntil 2018 (to 12.9  GtCO2). Asia and Pacific has been a  major \r\ncontributor to consumption-based CO2 emissions growth since 2000 \r\nand exceeded Developed Countries as the global largest emissions \r\nsource in 2015. From 1990 to 2018, the average growth rate of Asia \r\nand Pacific was  4.8% per  year, while in other regions emissions \r\ndeclined by -1.1%-4.3% per  year on average"


id: b1e32354c9666968a2c5c822cc3d7610
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" In 2018, 35% of \r\nglobal consumption-based CO2 emissions were from Developed \r\nCountries and 39% from Asia and Pacific, 5% from Latin American \r\nand Caribbean, 5% from Eastern Europe and West Central Asia, 5% \r\nfrom Middle East, and 3% from Africa (Hubacek et al. 2021). Global \r\nCBEs kept growing over the period with a short-lived decline in 2008 \r\ndue to the global financial crisis. In 2020, lockdowns associated with \r\nCOVID-19 significantly reduced global emissions (Section  2.2.2), \r\nincluding CBEs (Shan et al. 2021a)."


id: d7ebb32ab623e34bd78f268292bdfe0
created_at: 2023-03-16 14:02:41
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content:
"Located downstream in global supply chains, developed countries \r\n(mostly in Western Europe and North America) tend to be net \r\nemission importers, that is, EEI are larger than EEE. For example, over \r\n40% of national CO2 footprints in France, Germany, Italy, and Spain \r\nare from imports (Fan et al. 2017). Developing countries tend to be \r\nnet emission exporters with higher PBEs than their CBEs (Peters et al. \r\n2011a), especially for Asia and Pacific (as shown in Figure 2.15). That \r\nis to say, there is a net emission transfer and outsourcing of carbon\u0002intensive production from developed to developing economies via \r\nglobal trade (Jiang et al. 2018), mainly caused by cheap labour costs \r\n(Tate and Bals 2017) and cheap raw materials (Mukherjee 2018). \r\nIncreasing openness to trade (Fernandez-Amador et  al. 2016) and \r\nless stringent environmental legislation (acting as so-called pollution \r\nhavens) are also possible reasons (Hoekstra et al"


id: 60fab6c2e2a3790b7dfa20ddade3958a
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content:
"Economic growth (measured as GDP) and its main components  - \r\nGDP per  capita and population growth  - remained the strongest \r\ndrivers of GHG emissions in the last decade, following a long-term \r\ntrend (robust evidence, high agreement) (Liddle 2015; Malik et al. \r\n2016; Sanchez and Stern 2016; Chang et al. 2019; Dong et al. 2019; \r\nLiobikiene and Butkus 2019; Liu et al. 2019a; Mardani et al. 2019; Pan \r\net al. 2019; Dong et al. 2020; Parker and Bhatti 2020; Xia et al. 2021). \r\nGlobally, GDP per  capita remained by far the strongest upward \r\ndriver, increasing almost in tandem with energy consumption and \r\nCO2 emissions up until 2015, after which some modest decoupling \r\noccurred (Deutch 2017; Wood et al. 2018) (Section 2.3.3). The main \r\ncounteracting, yet insufficient, factor that led to emissions reductions \r\nwas decreased energy use per  unit of GDP in almost all regions \r\n(-2.0% yr -1 between 2010 and 2019 globally) (see also Lamb et al. \r\n2021b) (Figure  2"


id: 7a4a4390072eebe23d6419a17298fa1a
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"16) (robust evidence, high agreement). These \r\nreductions in energy intensity are a result of technological innovation, \r\nstructural changes, regulation, fiscal support, and direct investment, \r\nas well as increased economic efficiency in underlying sectors (Yao \r\net al. 2015; Sanchez and Stern 2016; Chang et al. 2019; Dong et al. \r\n2019a; Mohmmed et al. 2019; Stern 2019; Azhgaliyeva et al. 2020; \r\nGoldemberg 2020; Gao et al. 2021; Liddle and Huntington 2021; Liu \r\net al. 2019b; Xia et al. 2021)."


id: dfa36fa8122d4c053e84040b1f10055c
created_at: 2023-03-16 14:02:41
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content:
"Developing countries remained major accelerators of global \r\nCO2 emissions growth since 2010, mostly driven by increased \r\nconsumption and production, in particular in East Asia (robust \r\nevidence, high agreement) (Jiborn et al. 2020). While energy intensity \r\ndeclined to a  similar extent in countries of the Organisation for \r\nEconomic Co-operation and Development (OECD) and non-OECD \r\ncountries over the last 30 years, economic growth has been much \r\nstronger in non-OECD countries (Gonzalez-Torres et al. 2021). This led \r\nto an average annual growth rate of 2.8% of CO2 emissions in these \r\ncountries, whereas they decreased by 0.3% yr-1 in OECD countries \r\n(UNEP 2019). The majority of developed economies reduced both \r\nproduction-based and consumption-based CO2 emissions modestly \r\n(Jiborn et al. 2020; Xia et al. 2021)"


id: 18cdc045d8b2bbbe8d2887db4c69d443
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content:
" This was due to slower economic \r\ngrowth, increased energy efficiency (less energy per unit of GDP), fuel \r\nswitching from coal to gas (mostly in North America) (Wang et al. \r\n2020b), and the use of less and cleaner energy from renewables in \r\nEurope (Peters et al. 2017; Karstensen et al. 2018; Chang et al. 2019; \r\nWood et al. 2019c)."


id: d651a3e2134074e9be9a33ead4d7f90e
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"Economic growth as the main driver of GHG emissions is particularly \r\nstrong in China and India (robust evidence, high agreement) (Liu et al. \r\n2019b; Ortega-Ruiz et al. 2020; Z. Wang et al. 2020b; Yang et al. 2020; \r\nZheng et  al. 2020; Xia et  al. 2021), although both countries show \r\nsigns of relative decoupling because of structural changes (Marin \r\nand Mazzanti 2019). A change in China's production structure (with \r\nrelatively less heavy industry and lower-carbon manufacturing) and \r\nconsumption patterns (i.e., the type of goods and services consumed) \r\nhas become the main moderating factor of emissions after 2010, \r\nwhile economic growth, consumption levels, and investment remain \r\nthe dominating factors driving up emissions (Wang and Jiang 2019; \r\nJiborn et  al. 2020; Zheng et  al. 2020). In India, an expansion of \r\nproduction and trade as well as a higher energy intensity between \r\n2010 and 2014 caused increased emissions (Kanitkar et  al. 2015; \r\nWang and Zhou 2020; Z"


id: 11847caa8b069cc5214d8e932b1106b0
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content:
"The carbon intensity of power generation varies widely between \r\n(and also within) regions (Chapter  6). In North America, a  switch \r\nfrom coal to gas for power generation (Peters et al. 2017, 2020; Feng \r\n2019; Mohlin et al. 2019) as well as an overall decline in the share \r\nof fossil fuels in electricity production (from 66% in 2010 to 59% in \r\n2018) (Mohlin et al. 2019) has decreased carbon intensity and CO2\r\nemissions. Since 2007, Europe's carbon intensity improvements have \r\nbeen driven by the steady expansion of renewables in the share of \r\nelectricity generation (medium evidence, high agreement) (Peters \r\net al. 2017, 2020; Le Quere et al. 2019; Rodrigues et al. 2020). Some \r\nstudies attribute these effects to climate policies, such as the carbon \r\nfloor price in the UK, the EU emissions trading scheme, and generous \r\nrenewable energy subsidies across the continent (Dyrstad et  al. \r\n2019; H. Wang et  al. 2020)"


id: 2782664742b6f9e29b94c2a3d37691c8
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" South-East Asian developed countries \r\nand Australia, Japan and New Zealand stand out in contrast to other \r\ndeveloped regions, with an increase of regional carbon intensity of \r\n1.8 and 1.9% yr -1, respectively (Figure 2.17). Generally, the use of \r\nnatural gas for electricity production is growing strongly in most \r\ncountries and gas has contributed to the largest increase in global \r\nfossil CO2 emissions in recent years (Jackson et  al. 2019; Peters \r\net al. 2020). Furthermore, gas brings the risk of increased methane \r\n(CH4) emissions from fugitive sources, as well as large cumulative \r\nemissions over the lifetime of new gas power plants that may erase \r\nearly carbon intensity reductions (Shearer et al. 2020)."


id: 669caf2ee24da7d20ecb5c798ec13d7
created_at: 2023-03-16 14:02:41
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vector_id: 13736
content:
"More so than any other sector, transport energy use has tracked GDP \r\nper capita growth (Figure 2.20), (Lamb et al. 2021). With the exception \r\nof road gasoline demand in OECD countries, the demand for all road \r\nfuels generally increases at least as fast as the rate at which GDP \r\nper  capita increases (Liddle and Huntington 2020). Developments \r\nsince 1990 continue a historical trend of increasing travel distances \r\nand a shift from low- to high-speed transport modes that goes along \r\nwith GDP growth (Schafer et  al. 2009; Gota et  al. 2019). Modest \r\nimprovements in energy efficiency have been realised between 2010 \r\nand 2019, averaging -1.5% yr -1 in energy intensity globally, while \r\ncarbon intensities of the transport sector have remained stable in all \r\nworld regions (Figure  2.20)"


id: 36627c619b56b06f2d912987805d59f8
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vector_id: 5950
content:
" Overall, global increases in passenger \r\nand freight travel activity levels have outpaced energy efficiency and \r\nfuel economy improvements, continuing a  long-term trend for the \r\ntransport sector (medium evidence, high agreement) (Gucwa and \r\nSchafer 2013; Grubler 2015; McKinnon 2016)."


id: d4c7553fcc47fced73109caf8e4c7ae8
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"Steady improvements in building energy intensities across regions can \r\nbe attributed to baseline improvements in building fabrics, appliance \r\nefficiencies, energy prices, and fuel shifts. Many countries have \r\nadopted a mix of relevant policies, such as energy labelling, building \r\nenergy codes, and mandatory energy performance requirements \r\n(Nie and Kemp 2014; Nejat et  al. 2015; Economidou et  al. 2020). \r\nEfforts towards building refurbishments and retrofits have also \r\nbeen pursued in several nations, especially for historical buildings in \r\nEurope, but evidence suggests that the recent retrofit rates have not \r\nmade a significant dent on emissions (Corrado and Ballarini 2016). \r\nThe Chinese central government launched various policies, including \r\ncommand and control, economic incentives, and technology measures, \r\nbut a big gap remains between the total rate of building green retrofit \r\nin the nation and the future retrofit potential (G. Liu et  al. 2020a, \r\n2020b)"


id: 2bc73fa5c96b20477df863c7c1023529
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" Still, one major global factor driving down energy intensities \r\nhas been the global transition from inefficient coal and biomass use in \r\nbuildings for heating and cooking, towards natural gas and electricity, \r\nin part led by concerted policy action in Asian countries (Urge-Vorsatz \r\net al. 2015; Kerimray et al. 2017; Thoday et al. 2018). As developing \r\ncountries construct new buildings, there is sizable potential to reduce \r\nand use less carbon-intensive building materials and adopt building \r\ndesigns and standards that lower lifecycle buildings energy use and \r\nallow for passive comfort. Chapter 9 describes the mitigation options \r\nof the buildings sector."


id: 5dc56b69f3c3212c8d4ed60c90ed78ae
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content:
"Literature shows that more equitable income distributions can \r\nimprove environmental quality, but the nature of this relationship \r\ncan vary by level of development (low evidence, medium agreement) \r\n(Knight et al. 2017; Chen et al. 2020; Hailemariam et al. 2020; Huang \r\nand Duan 2020; Liobikiene and Rimkuviene 2020; Rojas-Vallejos \r\nand Lastuka 2020; Uddin et  al. 2020). Differences in the energy \r\nand carbon intensities of consumption and the composition of \r\nconsumption baskets across populations and nations matter for \r\nemissions. (Jorgenson et al. 2016; Grunewald et al. 2017). There is \r\nevidence to suggest that more equal societies place a higher value \r\non environmental public goods (Baumgartner et  al. 2017; Drupp \r\net  al. 2018). Additional research shows that reducing top income \r\ninequality in OECD countries can reduce carbon emissions and \r\nimprove environmental quality (Hailemariam et  al"


id: c3a9615fe39040216e4118ba6aeb25da
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content:
" 2020) and that \r\nthe effect of wealth inequality, measured as the wealth share of \r\nthe top decile, on per capita emissions in high-income countries, is \r\npositive (Knight et al. 2017). Evidence from 40 sub-Saharan African \r\ncountries suggests that a  rise in income inequality contributed to \r\nincreasing CO2 emissions between 2010 and 2016, controlling for \r\nother drivers such as economic growth, population size, and inflation \r\n(Baloch et al. 2020)."


id: 9880cfb47a248f3abc92b535e0a0faba
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content:
"Global diets are a  key driver of production per  capita, and thus \r\nland pressure and AFOLU emissions (Chapter  7). As per  capita \r\nincomes rise and populations urbanise, traditional, low-calorie diets \r\nthat emphasise starchy foods, legumes, and vegetables transition \r\ntowards energy-intensive products such as refined sugars, fats, oils, \r\nand meat (Pradhan et al. 2013; Tilman and Clark 2014). At a certain \r\npoint in national development, affluence and associated diets thus \r\noverride population growth as the main driver of AFOLU emissions \r\n(Kastner et  al. 2012). Very high calorie diets have high total GHG \r\nemissions per  capita (Heller and Keoleian 2015) and are common \r\nin the developed world (Pradhan et  al. 2013). Over the last few \r\ndecades, a  'westernisation' of diets has also been occurring in \r\ndeveloping countries (Pradhan et al. 2013)"


id: 6fbebf81d8880cf323e159f991fddeec
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" Low- and middle-income \r\ncountries such as India, Brazil, Egypt, Mexico, and South Africa \r\nhave experienced a  rapid dietary shift towards western-style diets \r\n(De Carvalho et al. 2013; Pradhan et al. 2013; Popkin 2015). Another \r\ndriver of higher food requirements per capita is food waste, which \r\nhas increased more or less continuously since the 1960s in all regions \r\nbut Europe (Porter and Reay 2016)."


id: fb6071a6e2c0835e98837c51e56f28f1
created_at: 2023-03-16 14:02:41
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content:
"The key development objective of eradicating extreme poverty \r\n(Chakravarty and Tavoni 2013; Hubacek et al. 2017a; Malerba 2020) \r\nand providing universal access to modern energy services (Pachauri \r\net al. 2013, 2018; Pachauri 2014; Singh et al. 2017) only marginally \r\naffects GHG emissions (medium evidence, high agreement). Shifts \r\nfrom biomass to more efficient energy sources and collective \r\nprovisioning systems for safe water, health, and education are \r\nassociated with reduced energy demand (Baltruszewicz et al. 2021). \r\nEfforts to alleviate multi-dimensional poverty by providing minimum \r\ndecent living standards universally, however, may require more energy \r\nand resources. Recent estimates of the additional energy needed are \r\nstill within bounds of projections of energy demand under climate \r\nstabilisation scenarios (Hubacek et  al. 2017a, 2017b; Rao et  al. \r\n2019; Pascale et al. 2020; Kikstra et al. 2021)"


id: 875942a2462fa10c913e68c540106e7e
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" Bottom-up estimates \r\nsuggest that achieving decent living standards requires 13-40  GJ \r\nper capita annually, much less than the current world average energy \r\nconsumption of 80 GJ per  capita in 2020 (Millward-Hopkins et  al. \r\n2020) (medium evidence, high agreement). Aggregate top-down \r\nestimates suggest that achieving a  high Human Development \r\nIndex (HDI) score above 0.8 requires energy consumption between "


id: 1784b208a8976542c6caa81f98168a9c
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
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index: climate_gpt_only_giec
vector_id: 1199
content:
"Technological change for climate change mitigation involves \r\nimprovement in and adoption of technologies, primarily those \r\nassociated with energy production and use. Technological change \r\nhas had a  mitigating effect on emissions over the long term and \r\nis central to efforts to achieving climate goals (high confidence). \r\nProgress since AR5 shows that multiple low-carbon technologies are \r\nimproving and falling in cost (high confidence); technology adoption \r\nis reaching substantial shares, and small-scale technologies are \r\nparticularly promising on both (medium confidence). Faster adoption \r\nand continued technological progress can play a  crucial role in \r\naccelerating the energy transition. However, the historical pace of \r\ntechnological change is still insufficient to catalyse a complete and \r\ntimely transition to a low-carbon energy system: technological change \r\nneeds to accelerate (high confidence)"


id: d8d268b0c0a497716e525e636be0e48b
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" This section assesses the role of \r\ntechnological change in driving emissions reductions and the factors \r\nthat drive technological change, with an emphasis on the speed of \r\ntransitions. Incentives and support for technological change affect \r\ntechnology outcomes (Sivaram et al. 2018; Wilson et al. 2020a). Work \r\nsince AR5 has focused on evaluating the effectiveness of policies: \r\nthose that accelerate technological change by enhancing knowledge \r\n(technology push) and those that increase market opportunities \r\nfor successful technologies (demand pull) (Nemet 2013); as well \r\nas the importance of tailoring support to country contexts (Barido \r\net al. 2020; Rosenbloom et al. 2020), including the limits of carbon\u0002pricing policies to date (Lilliestam et  al. 2020). Section  2"


id: 27fb1424a7925ff28207039164d31204
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"8 and \r\nChapter 13 describe how these polices affect emissions; Chapter 14 \r\nand Cross-Chapter Box 12 in Chapter 16 discuss transition dynamics; \r\nand Chapter 16 provides a more detailed assessment of the evolution \r\nand mitigation impacts of technology development, innovation, \r\nand transfer."


id: 89ac347ebfde90d39204904e2c8f3191
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"Recent studies have identified examples supporting fast energy \r\ntransitions (Sovacool 2016; Bond et  al. 2019; Reed et  al. 2019). \r\nOne describes five rapid national-scale transitions in end-use \r\ntechnologies, including lighting in Sweden, cook-stoves in China, \r\nliquefied petroleum gas stoves in Indonesia, ethanol vehicles in \r\nBrazil, and air conditioning in the USA (Sovacool 2016). Adoption of \r\nelectric vehicles in Norway and in cities in China have also been rapid \r\n(Rietmann and Lieven 2019; Li et al. 2020; Fridstrom 2021). Examples \r\nin energy supply, include electrification in Kuwait, natural gas in the \r\nNetherlands, nuclear electricity in France and Sweden, combined \r\nheat and power in Denmark, renewable energy in Uruguay, and coal \r\nretirements in Ontario, Canada (Qvist and Brook 2015)"


id: e63ff801b4da9dc482af150d740b15f2
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" Reasons \r\nthat these exemplars could be applied more broadly in the future \r\ninclude: growing urgency on climate change, shifting motivation \r\nfrom price response to proactive resource scarcity, and an increase in \r\nthe likelihood of technological breakthroughs (medium confidence) \r\n(Sovacool 2016; Bazilian et  al. 2020). The emergence of smaller \r\nunit scale, granular technologies (described below) also creates the \r\npotential for faster system change (Trancik 2006; Grubler et al. 2018; \r\nWilson et al. 2020a). Energy service prices and government actions \r\nthat affect demand are critical to the speed and extent of energy \r\ntransitions (Kramer and Haigh 2009). Reasons scholars consider for \r\nexpecting a fast transition include: intentional policy and alignment \r\nwith goals; globalisation which diversifies sources and integrates \r\nsupply chains; collective action via the Paris Agreement; as well \r\nas bottom-up grassroots movements and private sector initiatives \r\n(Kern and Rogge 2016)"


id: 9e917fae757fe7ee486b613d339f85f5
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content:
" Political support for change can also speed \r\ntransitions (Burke and Stephens 2017; Stokes and Breetz 2018), as \r\ncan the credibility of transition-related targets (Li and Pye 2018; \r\nRogge and Dutschke 2018)."


id: 32c39fe046743072f18a2baa562e176d
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content:
"Multiple challenges exist in accelerating the past rate of \r\ntechnological change. First, an array of physical assets in the energy \r\nsystem are long-lived and thus involve substantial committed carbon \r\n(Section 2.7) (Knapp 1999; Cui et al. 2019). A process of 'exnovation', \r\naccelerating the phase-out of incumbent technology through \r\nintentional policy (such as by pricing carbon), provides a means to \r\naddress long lifetimes (Davidson 2019; Rosenbloom and Rinscheid \r\n2020). Second, countries may not have the capacity to absorb the \r\nflows of ideas and research results from international knowledge \r\nspillovers due to weak infrastructure, limited research capacity, lack \r\nof credit facilities (Chapter 15, Section 15.5), and other barriers to \r\ntechnology transfer (Adenle et  al. 2015). In a  developing country \r\ncontext, processes of innovation and diffusion need to include \r\ncompetence-building systems (Lema et  al. 2015; Perrot and Sanni \r\n2018; Stender et al. 2020)"


id: b8bc9cb45b1832bca9242b6a7b2bcf15
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" Third, public policy is central to stimulating \r\ntechnological change to reduce emissions; policy depends on creating \r\ncredible expectations of future market opportunities (Alkemade and \r\nSuurs 2012), but the historical evidence shows that, despite recent \r\nprogress, policies related to energy and climate over the long term \r\nhave been inconsistent (Taylor 2012; Nemet et al. 2013; Koch et al. \r\n2016). Bolstering the credibility and durability of policies related \r\nto low-carbon technology are crucial to accelerating technological \r\nchange and inducing the private sector investment required (Helm \r\net al. 2003; Habermacher et al. 2020)."


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"The important role of leader countries is often missed when looking \r\nonly at global aggregates (Meckling and Hughes 2018); leaders \r\naccumulate important knowledge, provide scaled market, and set \r\npositive examples for followers (medium confidence) (Schwerhoff \r\n2016; Buchholz et  al. 2019). In recent years, the conception of \r\nwhere leadership, climate-relevant innovation, and technology \r\ntransfer originate has shifted to considering more meaningfully \r\ndirect South-South and South-North forms of technology transfer, \r\nflows of capital, drivers for market access, origins of innovation, \r\nand other forms of cooperation (Urban 2018; Kohler et al. 2019). \r\nRecent evidence shows that South-South trade is enabling clean \r\ntechnology transfer (Gosens 2020). Leaders can initiate a process \r\nof 'catalytic cooperation' in which they overcome collective action \r\nproblems and stimulate rapid change (Hale 2018)"


id: a3b4b53fe348aceb0ea60aa29fa67062
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
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content:
" Similarly, \r\n'sensitive intervention points'  - targeted support of social \r\nmovements, technologies, or policies themselves - can lead to rapid \r\nand self-sustaining change (Farmer et  al. 2019), such as support \r\nfor photovoltaics in Germany in the 2000s and student climate \r\nactivism in Europe in  2019. The focus on leadership, catalysts, \r\nand intervention points reflects a  systemic view of transitions \r\nthat emphasises interactions and interdependence (Geels 2018; \r\nMeckling and Hughes 2018). Technological change has been at the \r\ncore of transitions, but is best understood as part of a system in \r\nwhich social aspects are crucial (medium confidence) (Cherp et al. \r\n2018; Kohler et al. 2019; Overland and Sovacool 2020)."


id: d84028bb0d10a42502b5ac0c438c8612
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
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content:
"A wide array of technologies shows long-term improvements in \r\nperformance, efficiency, and cost. Among the most notable are \r\nsolar PV, wind power, and batteries (high confidence) (Chapters 6 \r\nand 16). The dynamics for PVs are the most impressive, having fallen \r\nin cost by a factor of 10,000 from the first commercial application \r\non a  satellite in 1958 (Maycock and Wakefield 1975) to power \r\npurchase agreements signed in 2019 (IRENA 2020). Wind has been \r\non a  nearly as steep trajectory (Wiser and Bolinger 2019) as are \r\nlithium-ion battery packs for electric vehicles (Nykvist and Nilsson \r\n2015; Service 2019). The future potential for PV and batteries seems \r\nespecially promising given that neither industry has yet begun to \r\nadopt alternative materials with attractive properties as the cost \r\nreductions and performance improvements associated with the \r\ncurrent generation of each technology continue (medium confidence) \r\n(Kwade et al. 2018)"


id: bf531e65052e836ae404578aad9c68
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
content_type: text
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vector_id: 27653
content:
" A key challenge is improving access to finance, \r\nespecially in developing country contexts, where the costs of financing \r\nare of crucial importance (Creutzig et al. 2017; Schmidt 2019)."


id: 5b07b4c26d6c9b022fba8bc97cfb6f26
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
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vector_id: 11834
content:
"Figure  2.22 shows changes in the costs of four dynamic energy \r\ntechnologies. One can see rapid changes since AR5, cost data for \r\nwhich ended in 2010. Solar PV is by far the most dynamic technology, \r\nand its cost since AR5 has continued on its steep decline at about the \r\nsame rate of change as before AR5, but now costs are well within \r\nthe range of fossil fuels (high confidence) (Chapter  6). Very few \r\nconcentrating solar power (CSP) plants had been built between the \r\n1980s and 2012. Since AR5, 4GW have been built and costs have \r\nfallen by half. Onshore wind has continued its pace of cost reductions \r\nsuch that it is well within the range of fossil fuels. Offshore wind has \r\nchanged the most since AR5. Whereas costs were increasing before \r\nAR5, they have decreased by 50% since. None of these technologies \r\nshows indications of reaching a limit in their cost reductions"


id: 53a7ac7b2cf6a696d339739c13abc1f5
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
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index: climate_gpt_only_giec
vector_id: 10631
content:
" Crucial \r\nto their impact will be extending these gains in the electricity and \r\ntransportation sectors to the industrial sector (Davis et al. 2018)."


id: 37c8fb9e84dcfaa005f0180c3f899df8
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
content_type: text
index: climate_gpt_only_giec
vector_id: 6150
content:
"The array of evidence of technology learning that has accumulated \r\nboth before and since AR5 (Thomassen et al. 2020) has prompted \r\ninvestigations about the factors that enable rapid technology \r\nlearning. From the wide variety of factors considered, unit size has \r\ngenerated the strongest and most robust results. Smaller unit sizes, \r\nsometimes referred to as 'granularity', tend to be associated with \r\nfaster learning rates (medium confidence) (Sweerts et  al. 2020; \r\nWilson et al. 2020). Examples include solar PV, batteries, heat pumps, \r\nand to some extent wind power. The explanatory mechanisms \r\nfor these observations are manifold and well established: more \r\niterations are available with which to make improvements (Trancik \r\n2006); mass production can be more powerful than economies of \r\nscale (Dahlgren et al. 2013); project management is simpler and less \r\nrisky (Wilson et al. 2020); the ease of early retirement can enable \r\nrisk-taking for innovative designs (Sweerts et  al"


id: dfca5a9139ea7f740c0fa333f9ed3f8f
created_at: 2023-03-16 14:02:41
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content:
"Figure  2.22 | Unit cost reductions and use in some rapidly changing mitigation technologies. The top panel shows global costs per  unit of energy \r\n(USD per MWh) for some rapidly changing mitigation technologies. Solid blue lines indicate average unit cost in each year. Light blue shaded areas show the range \r\nbetween the 5th and 95th percentiles in each year. Grey shading indicates the range of unit costs for new fossil fuel (coal and gas) power in 2020 (corresponding to \r\nUSD55-148 per MWh). In 2020, the levelised costs of energy (LCOE) of the four renewable energy technologies could compete with fossil fuels in many places. For batteries, \r\ncosts shown are for 1 kWh of battery storage capacity; for the others, costs are LCOE, which includes installation, capital, operations, and maintenance costs per MWh of \r\nelectricity produced. The literature uses LCOE because it allows consistent comparisons of cost trends across a diverse set of energy technologies to be made"


id: 899138f7a89b9d94dea856cd449a7333
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
content_type: text
index: climate_gpt_only_giec
vector_id: 19178
content:
" However, it \r\ndoes not include the costs of grid integration or climate impacts. Further, LCOE does not take into account other environmental and social externalities that may modify the \r\noverall (monetary and non-monetary) costs of technologies and alter their deployment. The bottom panel shows cumulative global adoption for each technology, in GW \r\nof installed capacity for renewable energy and in millions of vehicles for battery-electric vehicles. A vertical dashed line is placed in 2010 to indicate the change since AR5. \r\nShares of electricity produced and share of passenger vehicle fleet are indicated in text for 2020 based on provisional data, i.e., percentage of total electricity production \r\n(for PV, onshore wind, offshore wind, CSP) and of total stock of passenger vehicles (for EVs). The electricity production share reflects different capacity factors; for example, \r\nfor the same amount of installed capacity, wind produces about twice as much electricity as solar PV. {2.5, 6"


id: c3300289a22399dfc9a338df663548e2
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
content_type: text
index: climate_gpt_only_giec
vector_id: 28280
content:
"4} Renewable energy and battery technologies were selected as \r\nillustrative examples because they have recently shown rapid changes in costs and adoption, and because consistent data are available. Other mitigation options assessed \r\nin the report are not included as they do not meet these criteria."


id: 5b9c73a0fb5b6a36403c7bae176f9752
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
content_type: text
index: climate_gpt_only_giec
vector_id: 11916
content:
"et al. 2020). Small technologies often involve iterative production \r\nprocesses with many opportunities for learning by doing, and have \r\nmuch of the most advanced technology in the production equipment \r\nthan in the product itself. In contrast, large unit scale technologies - \r\nsuch as full-scale nuclear power, carbon capture and storage (CCS), \r\nlow-carbon steel making, and negative emissions technologies such \r\nas bioenergy with carbon capture and storage (BECCS) - are often \r\nprimarily built on site and include thousands to millions of parts, \r\nsuch that complexity and system integration issues are paramount \r\n(Nemet 2019). Despite the accumulating evidence of the benefits \r\nof granularity, these studies are careful to acknowledge the role \r\nof other factors in explaining learning. In a  study of 41  energy \r\ntechnologies (Figure 2.23), unit size explained 22% of the variation \r\nin learning rates (Sweerts et al"


id: fdbc6c52d439a527c6fcd9d9918956cc
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
content_type: text
index: climate_gpt_only_giec
vector_id: 37440
content:
" 2020) and a study of 31 low-carbon \r\ntechnologies showed that unit size explained 33% (Wilson et  al. \r\n2020). Attributing that amount of variation to a  single factor is \r\nrare in studies of technological change. The large residual has \r\nmotivated studies, which find that small-scale technologies provide "


id: f7ca499588644001c4b38967af9f38c0
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
content_type: text
index: climate_gpt_only_giec
vector_id: 36505
content:
"Results of integrated assessment models (IAMs) show that scale-up \r\nneeds are massive for 2degC scenarios. Using logistic growth rates of \r\nenergy shares as in previous work (Wilson 2012; Cherp et al. 2021), \r\nmost of these technologies include annual adoption growth rates of \r\n20% in the 2020s and 2030s, and are in line with recent adoption \r\nof wind and solar. However, it is important to realise that IAMs \r\ninclude faster adoption rates for some mitigation technologies than \r\nfor others (Peters et al. 2017). Growth rates in IAMs for large-scale \r\nCCS - biomass, coal, and gas - are between 15-30% (25th and 75th \r\npercentiles) (Figure 2.24). So few plants have been built that there \r\nis little historical data to analyse expected growth; with only two \r\nfull-scale CCS power plants built and a 7% growth rate, if including \r\nindustrial CCS"


id: 1563a0d1faa9cacb3cb225235a7ec272
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
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vector_id: 881
content:
" In contrast, IAMs indicate that they expect much \r\nlower rates of growth in future years for the technologies that have \r\nbeen growing fastest in recent years (wind and solar), without strong \r\nevidence for why this should occur."


id: c22d86fee6ac64589ea26d8724b0a7a1
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
content_type: text
index: climate_gpt_only_giec
vector_id: 28132
content:
"In western countries, the largest contribution to the household \r\ncarbon footprint is from transportation, housing, and consumption \r\nof food (Druckman and Jackson 2015). The joint contribution \r\nof these three items varies in different countries, depending on \r\nconsumption patterns, and account for 58.5%, on average, in EU25 \r\ncountries (Tukker and Jansen 2006). However, different countries, \r\nand regions within countries, may have different emission patterns \r\ndue to differences in income, lifestyle, geography, infrastructure, \r\npolitical and economic situation. For example, the main contributors \r\nto the average US household is private transport (19.6%), followed \r\nby electricity (14.8%) and meat (5.2%) (Jones and Kammen 2011), \r\nwhile UK households have 24.6% emissions on energy and housing, \r\n13.7% emissions on food, and 12.2% emissions on consumables \r\n(Gough et al. 2011)"


id: cd3881b651b0a1f4d3c803e7dcb6e588
created_at: 2023-03-16 14:02:41
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vector_id: 29836
content:
" A study of 49 Japanese cities found that energy \r\n(31%), food (27%), and accommodation (15%) were the largest \r\nsources of household emissions (Long et al. 2017). An investigation \r\nof Japan's household emissions found that energy, food, and utility \r\nare the three main emissions sources, but their shares are dependent \r\non age (Shigetomi et al. 2014). See Section 12.4 (Chapter 12) and \r\nBox 5.4 (Chapter 5) for a more in-depth discussion on food systems \r\nand dietary shifts towards lower emission food."


id: b02f0a0554d99361178ce6b217d0dfab
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
content_type: text
index: climate_gpt_only_giec
vector_id: 25243
content:
"Households' carbon emissions are closely linked to activities and \r\nconsumption patterns of individuals and as a group in households. \r\nIndividual and group behaviour, in turn, is shaped by economic, \r\ntechnological, and psychological factors, social contexts (such as \r\nfamily ties, friends and peer pressure) and cultural contexts (social \r\nidentity, status, and norms) as well as the natural environment \r\n(number of hot and cold days) and physical infrastructure, or \r\ngeography (Jorgenson et  al. 2019). For example, a  city with an \r\nexcellent bicycle infrastructure will make it safer and easier for \r\ncitizens to become highly mobile by using their bikes; a city that \r\nhas less density and is dominated by automobile infrastructure \r\ninduces more people to travel by car (Chapters 8 and 10). \r\nAs  a  consequence, many climate relevant consumption acts are \r\nnot consciously decided on or deliberately made part of a lifestyle, \r\nbut are strongly influenced by the factors listed above"


id: 531adfb0d6492feb201eeb84f965bd48
created_at: 2023-03-16 14:02:41
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index: climate_gpt_only_giec
vector_id: 10543
content:
" Chapter 5 \r\nprovides a  more in-depth discussion on behavioural drivers and \r\nexamples of behavioural interventions and policies that can be \r\nused to reduce emissions."


id: 10c8c75dd01bc57fd8f1acfa734156b1
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
content_type: text
index: climate_gpt_only_giec
vector_id: 115
content:
"Age. The effect of population ageing on emissions is contested in \r\nthe literature. Ageing when accompanied by shrinking household \r\nsize and more energy-intensive consumption and activity patterns \r\nresults in increased emissions. However, an ageing labour force can \r\nalso dampen economic growth and result in less energy-intensive \r\nactivity such as driving, which decreases emissions (Liddle and \r\nLung 2010; Liddle 2011). Ageing of the population characterises the \r\ndemographic transition in both developed and developing countries. \r\nThe implications of ageing for emissions depend on labour force \r\nparticipation of the elderly and differences in the consumption and \r\ninvestment patterns of different age groups (O'Neill et  al. 2012). \r\nAnalysis using panel macro data from OECD countries suggests that \r\nshifts in age and cohort composition have contributed to rising GHG \r\nemissions since the 1960s (Menz and Welsch 2012; Nassen 2014)"


id: dc63adfe208ab837861648e549456791
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
content_type: text
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vector_id: 32177
content:
" \r\nHousehold-level data over time for the USA provides evidence \r\nthat residential energy consumption increases over the lifetime \r\nof household members, largely due to accompanying changes in \r\nhousehold size (Estiri and Zagheni 2019). Similar insights emerge \r\nfrom Japan, where analysis shows that those in their 70s or older, \r\na group that is growing in size in Japan, have higher emissions than \r\nother age groups (Shigetomi et al. 2014, 2018, 2019). Recent analysis \r\nfrom China suggests that the shift to smaller and ageing households \r\nis resulting in higher carbon emissions because of the accompanying \r\ntime-use and consumption shifts (Yu et al. 2018; Li and Zhou 2019). \r\nAn increase in the dependency ratio  - that is, the proportion of \r\nchildren aged under 15 and people over 65 relative to the working\u0002age population  - in other analyses, has been shown to  lead to \r\nreduced CO2 emissions in China (Wei et al. 2018; Li and Zhou 2019)"


id: e26fe85ce3e571d89e85d038b19bbf0d
created_at: 2023-03-16 14:02:41
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content:
" \r\nImplications of the nature of this relationship are important to policy \r\ndiscussions of working hours and retirement age that are likely to \r\nhave an influence on emissions. For example, children and youth \r\ntend to emit more education-related emissions than adults (Han \r\net al. 2015). Older people tend to have higher emissions related to \r\nheating and cooling being more sensitive to temperature (Meier and \r\nRehdanz 2010)."


id: da5fbc4979ba7e7cced1acdeb55166c3
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
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index: climate_gpt_only_giec
vector_id: 31880
content:
"Broader contextual factors and cultural trends towards consumerism, \r\nindividualisation and defining self-worth through conspicuous \r\nconsumption can drive emissions up (Chancel and Piketty, 2015). \r\nHowever, cohort and generational shifts can drive emissions down. \r\nFor instance, evidence, from millennials in the OECD shows that fewer \r\nyounger people have driving licenses compared to older generations \r\n(Kuhnimhof et al. 2012). Similar, findings are evident from analysis for \r\nthe US, where changing attitudes, decreased employment and rising \r\nvirtual mobility explain decreased travel by Millennials (McDonald, \r\n2015). Analysis for France shows that baby boomers are higher \r\nemitters than other generations (Chancel, 2014). A change in social \r\nnorms is taking place with the spread of the sharing economy by \r\nwhich consumers share or borrow goods from other consumers. \r\nSharing opportunities are more advanced within the mobility sector \r\n(Greenblatt and Shaheen, 2015)"


id: 2fbe96e4736176a51f139f448462c0c7
created_at: 2023-03-16 14:02:41
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vector_id: 4931
content:
" Successful car and bike sharing \r\nhave rapidly expanded in countries such as China, Indonesia, Mexico, \r\nBrazil and Turkey. Technology and data advances are currently barriers \r\nto spreading of sharing in low- and lower middle-income cities but \r\nthe potential offered by these technologies to allow poor countries \r\nto leapfrog to more integrated, efficient, multimodal transport \r\nsystems is important (Yanocha et  al. 2020). Despite this potential \r\nit is unclear how much shared mobility contributes to transport "


id: 773a7728f6e3579f99d036e77433c2b0
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
content_type: text
index: climate_gpt_only_giec
vector_id: 16322
content:
"Inequality. Global inequality within and between countries has \r\nshifted over the last decade's expanding consumption and consumer \r\nculture (Castilhos and Fonseca 2016; Alvaredo et al. 2018; Short and \r\nMartinez 2020). The rise of income of middle-class in countries, mostly \r\nin Asia - for example, China, India, Indonesia and Vietnam - and the \r\nstagnating incomes of the middle classes in developed economies \r\nreduced between countries' income differences; meanwhile, the \r\npopulation under extreme poverty (a threshold of USD1.9 per person \r\nper day) is now concentrated in Sub-Saharan Africa and South Asia \r\n(Milanovic 2016). A  major gap between top and bottom incomes \r\noccurred in parallel within countries. Since 1980, the top 1% richest \r\nindividuals in the world captured twice as much growth as the \r\nbottom 50% individuals (Friedman and Savage 2017; Alvaredo et al. \r\n2018)"


id: 527a9a5f347d419e3931e7dec80f6598
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
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vector_id: 10437
content:
" The influence of these dual inequality trends on lifestyles, \r\nnew consumption patterns and carbon emissions at regional, local \r\nand global scale are large and have led to the fastest growth of \r\nglobal carbon emissions, in particular, for fast emerging economies \r\n(Sections 2.2. and 2.3). Emissions remain highly concentrated, with \r\nthe top 10% per capita emitters contributing to between 35-45% \r\nof global emissions, while the bottom 50% emitters contribute to \r\n13-15% of global emissions (Hubacek et  al. 2017a). Furthermore, \r\nthe top 1% of income earners by some estimates could have an \r\naverage carbon footprint 175 times that of an average person in the \r\nbottom 10% (Otto et al. 2020). The top 10% high emitters live in all \r\ncontinents, and one-third of them live in emerging countries (Chancel \r\nand Piketty 2015; Hubacek et al. 2017a; Semieniuk and Yakovenko \r\n2020)"


id: 9e415890a065be2ed17e18ef5fc1de52
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
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content:
" Mitigation pathways need to consider how to minimise the \r\nimpacts of inequality on climate change and the different mechanisms \r\nand effects coming into play between the inequality of income and \r\nemissions (Baek and Gweisah 2013; Berthe and Elie 2015; Hao et al. \r\n2016; Grunewald et al. 2017) (Section 2.4.3)."


id: 6ffc17c63cab8a95fa6b8f4f9de19e96
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
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index: climate_gpt_only_giec
vector_id: 15234
content:
"Status competition. As part of a  larger consumer society and \r\nconsumer culture, based on consumer-oriented lifestyles, products \r\nfrequently provide a  source for identity and fulfilment (Stearns \r\n2001; Baudrillard 2017; Jorgenson et  al. 2019). People pursue \r\ncultural constructs such as status, comfort, convenience, hygiene, \r\nnutrition, and necessity. Consumption is, by and large, not an end \r\nin itself but a  means to achieve some other end, and those ends \r\nare diverse and not necessarily connected to one another (Wilk \r\n2010). This shows that consumption patterns cannot be sufficiently \r\nunderstood without also considering the context - for example, the \r\ncultural and social contexts leading to status competition and status\u0002related consumption (Veblen 2009; Ehrhardt-Martinez, K. et al. 2015; \r\nWilk 2017). Status seeking can work to reduce emissions when \r\n'green products' such as an electric car or photovoltaics on the roof \r\nbecome a sign for high-status (Griskevicius et al"


id: b1938974a3c4e4b12f21e4637db4bb4
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index: climate_gpt_only_giec
vector_id: 25436
content:
" 2010). It also can \r\nwork to increase emissions through visible and high-carbon intensive \r\nconsumption items, such a larger homes, fuel-inefficient sport utility \r\nvehicles (SUVs), and long-distance vacations (Schor 1998), driven \r\nby a  notion of having 'to keep up with the Joneses' (Hamilton \r\n2011). This can lead to formation of new habits and needs, where \r\nproducts and services become normalised and are quickly perceived \r\nas needed, reinforced through social networks and advertisement, \r\nmaking it psychologically easy to convert a luxury item to a perceived \r\nnecessity (Assadour 2012). For example, the share of adults who "


id: cc3d3c37ef053e0936d45cb02a7eba2b
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
content_type: text
index: climate_gpt_only_giec
vector_id: 29677
content:
"Inequality trends catalyse impact at a  demand level, mobilising \r\nrapid lifestyles changes, symbolic consumption and ideals of \r\nmaterial improvements and upward mobility (Castilhos et al. 2017) \r\nand emulation of high-carbon emissions intensive lifestyle of the \r\nwealthy (Gough 2017). Decoupling energy use and emissions from \r\nincome growth and the decarbonisation of energy services have \r\nnot counteracted these trends (Section 2.4.1). Alternative options to \r\ndeal with carbon inequality, such as sharing global carbon emissions \r\namong high emitters (Chakravarty et  al. 2009; Chakravarty and \r\nTavoni 2013) or addressing the discourse of income distribution \r\nand the carbon intensity of high emitters lifestyles (Hubacek et al., \r\n2017b; Gossling 2019; Otto et al. 2019) are met with caution that \r\nsuch alternatives may necessitate hard-to-implement institutional \r\nchanges (Semieniuk and Yakovenko 2020)"


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vector_id: 20713
content:
"looking using scenarios (Fisch-Romito et  al. 2020). Quantifications \r\nalso differ in the indicators used to describe carbon lock-in. Literature \r\nhas assessed how delays in climate policy affect the evolution of \r\nfossil-fuel infrastructure stock in the short term (Bertram et al. 2015; \r\nKefford et  al. 2018; McGlade et  al. 2018), overall mitigation costs \r\n(Riahi et al. 2015; Luderer et al. 2016), or the transition risks from \r\npremature retirements or underutilisation of existing assets (Iyer \r\net al. 2015; Johnson et al. 2015; Lane et al. 2016; Luderer et al. 2016; \r\nFarfan and Breyer 2017; van Soest et al. 2017; Kefford et al. 2018; Cui \r\net al. 2019; Fofrich et al. 2020; Malik et al. 2020; H. Wang et al. 2020; \r\nPradhan et al. 2021). Only a few authors have relied on indicators \r\nrelated to institutional factors such as technology scale or employment \r\n(Erickson et al. 2015; Spencer et al. 2018)"


id: 68742ee69fdaaf19ce092e49b7449809
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
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vector_id: 14020
content:
" Complementary literature \r\nhas explored how the sheer size of the world's fossil fuel reserves (and \r\nresources) and owners' financial interests could contribute to supply\u0002side dynamics that sustain the use of fossil fuels (Jewell et al. 2013; \r\nJakob and Hilaire 2015; McGlade and Ekins 2015; Bauer et al. 2016; \r\nHeede and Oreskes 2016; Welsby et al. 2021)."


id: ee5d19520f94b7aaec4160e51e22f763
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
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index: climate_gpt_only_giec
vector_id: 34970
content:
"more difficult and, it may also exacerbate redistribution and social \r\ncohesion dilemmas (Gough 2017; Rompke et  al. 2019). Climate \r\nmitigation action has different motivational departures in unequal \r\ncontext. An emerging global 'middle class' strengthens consumption \r\nat the margin as evidence by first-time purchases of white goods \r\nwith likely impacts on energy demand (Wolfram et  al. 2012), and \r\nwith a warming climate, the increased use of air conditioning (Davis \r\nand Gertler 2015). Inequality may affect the willingness of rich and \r\npoor to pay for environmental goods or accept policies to protect \r\nthe environment (Baumgartner et  al. 2017). Unequal departure \r\nfor action is strongly manifested in cities of all sizes in developing \r\ncountries with low-income urban residents hardest hit in lock-in \r\nsituations such as lack of access to transportation and jobs (Altshuler \r\n2013; Mattioli 2017), lack of green spaces (Joassart-Marcelli et al"


id: b302684e530dfb270e5633c5a05b5f68
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
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index: climate_gpt_only_giec
vector_id: 25679
content:
" \r\n2011), poor access to waste collection (King and Gutberlet 2013) \r\nand to energy and clean water provision. The exacerbation of \r\nthese conditions constrains the feasibility for achieving emissions \r\nreductions through lifestyle or behavioural changes alone (Baiocchi \r\net al. 2010; Oxfam 2015). High inequality limits mitigation efforts and \r\nconversely, advancing mitigation should not contribute to deepen \r\nexisting inequalities (Rao and Min 2018; Saheb et  al. 2019). It is \r\ncritically important to account for varying demands and affordability \r\nacross heterogeneous household groups in access to quality energy, \r\neducation, health, decent jobs and services, while recomposing \r\nconsumption and balancing societal trade-offs via policies to boost \r\nthe inclusion of low-income and energy-poor population groups \r\n(Pachauri et al. 2013). Further, there is a need to reduce inequalities \r\nand improve the capabilities people have to live the lives they value \r\n(Sen 1999; Gough et al"


id: 31e6248c0fca0cf48a9536ac920235d0
created_at: 2023-03-16 14:02:41
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vector_id: 5265
content:
"Carbon lock-in can be understood as inertia in a system that limits \r\nthe rate of transformation by a path-dependent process (Seto et al. \r\n2016). For example, long lifetimes of infrastructures such as power \r\nplants, roads, buildings or industrial plants may influence the rate \r\nof transformation substantially and lock societies into carbon\u0002intensive lifestyles and practices for many decades (Unruh 2000, \r\n2002; Unruh and Carrillo-Hermosilla 2006; Grubler 2012; Seto et al. \r\n2016; Sovacool 2016). Infrastructure stock evolution depends on \r\ntechnological and economic factors, but also on institutional and \r\nbehavioural ones that are often mutually reinforcing. That is, physical \r\ninfrastructure such as the built environment of urban areas can shape \r\npeople's behaviour and practices, which in turn change the demand \r\nfor such infrastructure and lock-in energy demand patterns (Banister \r\net al. 1997; Makido et al. 2012; Creutzig et al. 2016; Seto et al. 2016; \r\nShove and Trentmann 2018)"


id: 5f39cec9982882bd5be98c7798d27566
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
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index: climate_gpt_only_giec
vector_id: 12546
content:
"Estimates of total cumulative future CO2 commitments from proposed \r\ninfrastructure focus only on the power sector due to data availability \r\n(Table 2.6 and Figure 2.26). Infrastructure proposals can be at various \r\nstages of development involving very different probabilities of \r\nimplementation. About one-third of the currently proposed projects \r\nare more probable as they are already under construction (Cui \r\net al. 2019). Pfeiffer et al. (2018) and Tong et al. (2019) assess the \r\ncumulated CO2 emissions from proposed infrastructure in the entire \r\npower sector at 270 GtCO2 and 190 GtCO2 respectively. Estimates of \r\nCO2 emissions implications for new coal power infrastructure plans \r\nare more frequent (Edenhofer et  al. 2018; Pfeiffer et  al. 2018; Cui \r\net al. 2019; Tong et al. 2019) ranging between 100 and 210 GtCO2"


id: 49e1c790d2defde5cdd9924d12216621
created_at: 2023-03-16 14:02:41
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vector_id: 9026
content:
" \r\nDifferences across estimates of future CO2 emissions from proposed \r\npower infrastructure mostly reflect substantial cancellations of coal \r\ninfrastructure proposals in 2017 and 2018 (Tong et al. 2019)."


id: e9e7bcb56ddd7b71e9cc8b80ae7abc73
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
content_type: text
index: climate_gpt_only_giec
vector_id: 34301
content:
"Hence, the Paris climate goals could move out of reach unless there are \r\ndedicated efforts for early decommissioning, and reduced utilisation of \r\nexisting fossil fuel infrastructures, cancellation of plans for new fossil \r\nfuel infrastructures, or compensation efforts by removing some of the \r\nCO2 emissions from the atmosphere (Cui et al. 2019; Smith et al. 2019; \r\nTong et al. 2019; Pradhan et al. 2021). For example, Fofrich et al. (2020) \r\nsuggest in a multi-model study that coal and gas power infrastructure \r\nwould need to be retired 30 (19-34) and 24 (21-26) years earlier \r\nthan the historical averages of 39 and 36 years when following 1.5degC \r\npathways and 23 (11-33) and 19 (11-16) years earlier when following \r\n2degC pathways. Cui et al. (2019) arrive at more conservative estimates \r\nfor coal power plants, but only consider the existing and currently \r\nproposed capacity"


id: cf9d2fd7d574826746ff119fe7ddfd98
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
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vector_id: 30194
content:
" Premature retirement of power plants pledged \r\nby members of the Powering Past Coal Alliance would cut emissions \r\nby 1.6 GtCO2, which is 150 times less than future CO2 emissions from \r\nexisting coal power plants (Jewell et al. 2019)."


id: f9c4e7a4b23d27430546bf2622ffddd3
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
content_type: text
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vector_id: 36807
content:
"Notably, the lower end of residual fossil fuel emissions in IAM scenarios \r\n(Luderer et al. 2018) is remarkably similar to global estimates from \r\nthe accounting studies of the previous section, as shown in Table 2.6. \r\nYet, there are important conceptual and interpretative differences \r\nthat are also reflected in the very different distribution of reported \r\nfuture CO2 emissions attached to current and future fossil fuel \r\ninfrastructures (Table  2.7). Accounting studies start from granular, \r\nplant-based data for existing fossil fuel infrastructure and make \r\nstatements about their future CO2 emissions, assuming variations \r\nof historic patterns of use and decommissioning. Expansions to the \r\nfuture are limited to proposals for new infrastructures that we know \r\nof today"


id: 5b2461132e6b71821a9e8c6806c58f8d
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
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vector_id: 11847
content:
" Scenario studies quantifying residual fossil fuel emissions \r\nstart from aggregate infrastructure descriptions, but dynamically \r\nupdate those through new investment decisions in each time step \r\nacross the 21st  century based on the development of energy and \r\nenergy service demands, as well as technology availability, guided by \r\ndefined climate policy goals (or their absence)."


id: 52782871852233fb926082cd58f41a8c
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
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vector_id: 10434
content:
"Table 2.7 | Residual (gross) fossil fuel emissions (GtCO2) in climate change mitigation scenarios strengthening mitigation action after 2020 ('early \r\nstrengthening'), compared to scenarios that keep Nationally Determined Contribution (NDC) ambition level until 2030 and only strengthen thereafter.\r\nCumulative gross CO2 emissions from fossil fuel and industry until reaching net zero CO2 emissions are given in terms of the mean as well as minimum and maximum (in \r\nparentheses) across seven participating models: AIM/CGE, GCAM, IMAGE, MESSAGES, POLES, REMIND, WITCH. Scenario design prescribes a harmonised, global carbon price \r\nin line with long-term carbon budget. Delay scenarios follow the same price trajectory, but 10 years later. Carbon dioxide removal requirements represent ex-post calculations \r\nthat subtract gross fossil fuel emissions from the carbon budget associated with the respective long-term warming limit. We take the carbon budget for limiting warming to \r\n1"


id: 6c0ed7f11e42e2864605a8c0b4a92034
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
content_type: text
index: climate_gpt_only_giec
vector_id: 14631
content:
"5degC with a 50% probability and to 2degC with a 67% probability (Canadell et al. 2021). Hence, carbon dioxide removal (CDR) requirements reflect a minimum amount of CDR \r\nfor a given mitigation trajectory. Results are reported at two significant digits. Sources: Luderer et al. (2018); Tong et al. (2019)."


id: c2165ad3eb308a7bd0a9da96788646ef
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
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vector_id: 28116
content:
"transportation (in particular aviation, shipping and road-based freight) \r\nor some industrial processes (such as cement production or feedstocks \r\nfor chemicals). This explains the key discrepancies observable in \r\nTable 2.7. Therefore, our overall assessment of these available lines \r\nof evidence strongly emphasises the importance of decommissioning, \r\nreduced utilisation of existing power sector infrastructure, as well as \r\ncontinued cancellation of new power sector infrastructures in order \r\nto limit warming to well below 2degC (high confidence) (Kriegler et al. \r\n2018b; Luderer et al. 2018; Chen et al. 2019; Cui et al. 2019; Fofrich \r\net al. 2020). This is important as the power sector is comparatively easy \r\nto decarbonise (IPCC 2014a; Krey et al. 2014; Davis et al. 2018; Mejean \r\net al. 2019) and it is crucial to make space for residual emissions from \r\nnon-electric energy end uses that are more difficult to mitigate (high \r\nconfidence)"


id: b6e988b0cd0bf49ca1724fcd0930ba48
created_at: 2023-03-16 14:02:41
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vector_id: 26305
content:
" Any further delay in climate policy substantially increases \r\ncarbon lock-in and mitigation challenges as well as a dependence on \r\ncarbon dioxide removal technologies for meeting the Paris climate \r\ngoals (Kriegler et al. 2018b; Luderer et al. 2018)."


id: 88e1a6448199f6a841f27ae17509b8d0
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
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index: climate_gpt_only_giec
vector_id: 19071
content:
"Comprehensive multinational evaluations with wider regional and \r\nsectoral coverage enable the assessment of emissions impacts \r\nwithout distortions from emissions leakage. Among the wide range \r\nof climate policy instruments, pricing carbon - such as a carbon tax or \r\nan emissions trading system - has been one of the most widely used \r\nand effective options to reduce GHG emissions (robust evidence,\r\nhigh agreement). In a comparison of 142 countries with and without \r\ncarbon pricing, countries with a  carbon price show annual CO2\r\nemission growth rates of 2 percentage points lower than countries \r\nwithout such policies (Best et  al. 2020). A  more comprehensive \r\nevaluation of carbon prices shows that countries with a lower carbon \r\npricing gap (a higher carbon price) tend to be more carbon-efficient, \r\nthat is, they have a lower carbon intensity of GDP (OECD 2018)"


id: c2f2f91c785e453349c585732f7e43b5
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
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index: climate_gpt_only_giec
vector_id: 28250
content:
"14\r\nAn empirical analysis of the effects of environmental regulation and \r\ninnovation on the carbon emissions of OECD countries during the \r\nperiod 1999-2014 indicates that a 1% increase in environmentally \r\nfriendly patents reduced carbon emissions by 0.017%, and a  1% \r\nincrease in environmental tax revenue per  capita reduced carbon \r\nemissions by 0.03% (Hashmi and Alam 2019)."


id: 93c57dcddccf68c5f8363e098fd469ba
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
content_type: text
index: climate_gpt_only_giec
vector_id: 20761
content:
"The key to achieving climate change mitigation targets includes \r\ncrafting environmentally effective, economically efficient and socially \r\nequitable policies. For the purposes of this section, policies are defined \r\nbroadly as actions to guide decisions to reach explicit goals and, \r\naccordingly, climate (mitigation) policies are the ones whose primary \r\nobjective is to reduce GHG emissions. They include a range of domains \r\nfrom economic and institutional to research and development (R&D) \r\nand social policies, and are implemented by various instruments \r\n(e.g.,  market-based and regulatory in the economic domain) and \r\nmeasures (e.g.,  legal provisions and governance arrangements in \r\nthe institutional domain) (Chapter  13, and see 'mitigation policies' \r\nin Glossary). Yet GHG emissions are also affected by policies enacted in \r\nvarious social, economic and environmental areas to pursue primarily \r\nnon-climatic objectives"


id: 9e3d82e4bb0ca2e7aed36735100b9fe7
created_at: 2023-03-16 14:02:41
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index: climate_gpt_only_giec
vector_id: 22418
content:
" This section presents succinct assessments of \r\nthe outcomes and effectiveness of a few selected policy instruments \r\napplied in the last two decades that target climate protection \r\n(Sections  2.8.2 and 2.8.3) and GHG emissions impacts of selected \r\nother policies primarily aimed at improvements in environmental \r\nquality and natural resource management (Section 2.8.4).12"


id: e5fea3cebeb08c37b690b68d0c8a1c60
created_at: 2023-03-16 14:02:41
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vector_id: 33675
content:
"Domestic and international climate legislation have also contributed \r\nto the reduction of GHG emissions. An empirical analysis of legislative \r\nactivity in 133 countries over the period 1999-2016 based on panel \r\ndata indicates that each new law reduced annual CO2 emissions \r\nper unit of GDP by 0.78% nationally in the first three years, and by \r\n1.79% beyond three years. Additionally, climate laws as of 2016 \r\nwere associated with an annual reduction in global CO2 emissions \r\nof 5.9 GtCO2 and 38 GtCO2 cumulatively since 1999 (Eskander and \r\nFankhauser 2020). It is notable that 36 countries that accepted legally \r\nbinding targets under the Kyoto Protocol all complied (Shishlov et al. \r\n2016). It is impossible to disentangle precisely the contribution of \r\nindividual mitigation policies, but it is clear that the participating \r\ncountries, especially those in the OECD, did make substantial policy \r\nefforts with material impact (Grubb 2016)"


id: ca3ca4730ab89c3cbe45852c8438e1df
created_at: 2023-03-16 14:02:41
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vector_id: 29375
content:
" An ex-post evaluation \r\nshows a significant impact of the Protocol on emissions reductions \r\n(Maamoun 2019)."


id: 34d1ab67ede77c0a39e86434e655c323
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
content_type: text
index: climate_gpt_only_giec
vector_id: 5716
content:
"The power sector typically accounts for a large portion of countries' \r\nCO2 emissions. Market-based regulation and government subsidies in \r\nChina contributed to improving operational efficiency and reducing \r\nemissions (Zhao et al. 2015). In addition, the implementation of ultra\u0002low emission standards has also resulted in a significant reduction in \r\nemissions from China's power plants (Tang et al. 2019). Mandatory \r\nclimate and energy policies, including the California Global Warming \r\nSolutions Act, reduced CO2 emissions by 2.7-25% of the average \r\nstate-level annual emissions from the power sector over the period \r\n1990-2014 in the USA. Mandatory GHG registry/reporting, electric \r\ndecoupling and a public benefit fund have been effective in further \r\ndecreasing power sector emissions in the USA (Martin and Saikawa \r\n2017)"


id: 508f8927ef730d6c2a80ffe3d474dd1e
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
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index: climate_gpt_only_giec
vector_id: 10120
content:
" In the UK electricity sector, a carbon price floor, combined with \r\nelectricity market reform (competitive auctions for both firm capacity \r\nand renewable energy), displaced coal, whose share fell from 46% in \r\n1995 to 7% in 2017, halving CO2 emissions, while renewables grew \r\nfrom under 4% in 2008 to 22% by 2017 (Grubb and Newbery 2018). \r\nSee Chapter 13 for more information."


id: a9976207476ac8b7cb97e04032a1b1c0
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
content_type: text
index: climate_gpt_only_giec
vector_id: 24215
content:
"Electric vehicles (EVs) powered by clean electricity can reduce GHG \r\nemissions, and such policies are important for spurring adoption of \r\nsuch vehicles (Kumar and Alok 2020; Thiel et al. 2020). The extent to \r\nwhich EV deployment can decrease emissions by replacing internal \r\ncombustion engine-based vehicles depends on the generation \r\nmix of the electric grid (Abdul-Manan 2015; Nichols et  al. 2015; \r\nCanals Casals et  al. 2016; Hofmann et  al. 2016; Choi et  al. 2018; \r\nTeixeira and Sodre 2018) although, even with current grids, EVs \r\nreduce emissions in almost all cases (Knobloch et al. 2020). Policy \r\nincentives for EV adoption can be an effective mechanism to \r\nincrease EV sales (Langbroek et al. 2016) and may include discounts, \r\npurchase subsidies, regulations, and government leadership\r\n(medium confidence) (Bakker and Jacob Trip 2013; Silvia and Krause \r\n2016; Teixeira and Sodre 2018; Qiu et al. 2019; Santos and Davies \r\n2020)"


id: 2c92dcb98207db005d7da64f8f067b72
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
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vector_id: 4441
content:
" The presence of charging infrastructure and publicly available \r\ncharging increases the adoption rate of EVs (Vergis and Chen \r\n2015; Javid et al. 2019). A comparison of EV adoption rates across \r\n30 countries shows a positive correlation between charging stations \r\nand EV market share (Sierzchula et al. 2014). A rollout of 80,000 DC \r\nfast chargers across the USA is estimated to have resulted in a 4% \r\nreduction in emissions compared to a baseline of no additional fast \r\nchargers (Levinson and West 2018). More recently, bans on internal \r\ncombustion engine vehicles have provided a  much more direct \r\napproach to stimulating the adoption of EVs and its supporting \r\ninfrastructure; however, the efficacy of such measures depends on \r\nenforcement (Plotz et al. 2019)."


id: 9a5411098a1e8bea135751941ca1b7eb
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
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index: climate_gpt_only_giec
vector_id: 21811
content:
"Public transit can reduce vehicle travel and lower GHG emissions by \r\nreducing the number of trips taken by private vehicles and the length \r\nof those trips (medium confidence). Changes to the operation of \r\npublic transportation systems (such as density of bus stops, distance \r\nfrom stops to households, duration and frequency of trip times, and \r\nlowering ridership costs) can result in a  mode shift from private \r\ncar trips to public transit trips (Cats et al. 2017; Choi 2018; Carroll \r\net  al. 2019). These changes in the public transit system operation \r\nand network optimisation have been shown to have reduced GHG \r\nemissions in cases such as San Francisco, where the cost optimisation \r\nof the transit network was estimated to decrease emissions by \r\na  factor of three (Cheng et  al. 2018) and Barcelona, where the \r\noptimisation of the urban bus system was estimated to reduce GHG \r\nemissions by 50% (Griswold et al. 2017)"


id: 4f8761b4df74b8f3669d1bdd60b619da
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
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index: climate_gpt_only_giec
vector_id: 9945
content:
" For every 1% increase in \r\ninvestment in transit services and transit-oriented design, there is \r\nan estimated 0.16% reduction in private vehicle kilometres travelled \r\nper capita (McIntosh et al. 2014)."


id: ebe22f15e5ac4c268c154d3a5cd0fec8
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
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vector_id: 34612
content:
"At a  local scale, air pollutants are often co-emitted with GHGs in \r\ncombustion processes. Many air quality policies and regulations focus \r\non local pollution from specific sources that can potentially either \r\nsubstitute or complement global GHG emissions in production and \r\ngeneration processes. Also, policies that reduce certain air pollutants, \r\nsuch as sulphur dioxide (SO2), have a  positive radiative forcing \r\neffect (Navarro et al. 2016). The evidence on individual air pollution \r\ncontrol regulation and policies for GHG emissions is therefore mixed \r\n(medium evidence, medium agreement). Evidence from the USA \r\nsuggests that increased stringency of local pollution regulation had \r\nno statistically detectable co-benefits or costs on GHG emissions \r\n(Brunel and Johnson 2019)"


id: 2626d863004518a1525d2cfd9d7b3c92
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
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index: climate_gpt_only_giec
vector_id: 3420
content:
" Evidence from China suggests that the \r\neffectiveness of policies addressing local point sources differed from \r\nthose of non-point sources and the co-benefits for climate are mixed, \r\nthough policies addressing large industrial point sources have been \r\neasier to implement and have had significant impact (Huang and \r\nWang 2016; Xu et al. 2016; van der A et al. 2017; Dang and Liao 2019; \r\nFang et al. 2019; Yu et al. 2019). Legislation to reduce emissions of \r\nair pollutants in Europe have significantly improved air quality and \r\nhealth but have had an unintended warming effect on the climate \r\n(Turnock et al. 2016)."


id: 885a3c21a2392bafff93fdcb7d6132c5
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
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index: climate_gpt_only_giec
vector_id: 18983
content:
"Often, the realisation of potential co-benefits depends on the \r\ntype of pollutant addressed by the specific policy, and whether \r\ncomplementarities between local pollution and global GHG emissions \r\nare considered in policy design (medium evidence, high agreement) \r\n(Rafaj et al. 2014; Li et al. 2017a). Effective environmental regulations \r\nthat also deliver co-benefits for climate mitigation require integrated \r\npolicies (Schmale et  al. 2014; Haines et  al. 2017). Uncoordinated \r\npolicies can have unintended consequences and even increase \r\nemissions (Holland et al. 2015). Many studies suggest that policies that \r\ntarget both local and global environmental benefits simultaneously \r\nmay be more effective (medium evidence, medium agreement) \r\n(Klemun et  al. 2020)"


id: ccc10e107c512c19494a2b9cebfd19ea
created_at: 2023-03-16 14:02:41
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index: climate_gpt_only_giec
vector_id: 29755
content:
" Furthermore, air pollution policies aimed at \r\ninducing structural changes - for example, closure of polluting coal \r\npower plants or reducing motorised miles travelled - are more likely \r\nto have potential positive spillover effects for climate mitigation, as \r\ncompared to policies incentivising end-of-pipe controls (Wang 2021)."


id: 98ce2229bff8ca65a4e9f22544cdd52d
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vector_id: 21556
content:
"Forest is generally defined as land spanning more than 0.5 hectares \r\nwith trees higher than 5 metres and a canopy cover of more than \r\n10%, or trees able to reach these thresholds in situ (FAO 1998). \r\nZero-deforestation (i.e.,  both gross and net zero deforestation) \r\ninitiatives generate results at multiple levels (Meijer 2014). Efforts \r\nto achieve zero-deforestation (and consequently emissions) are \r\nannounced by non-governmental organisations (NGOs), companies, \r\ngovernments, and other stakeholder groups. NGOs engage through \r\ntheir campaigning, but also propose tools and approaches for \r\ncompanies (Leijten et al. 2020). The extent to which companies can \r\nactually monitor actions conducive to zero-deforestation pledges \r\ndepends on their position in the supply chain"


id: ce80fa0993821cba5590c76bff7ec72a
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
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index: climate_gpt_only_giec
vector_id: 30027
content:
" Beyond the business \r\npractices of participating companies, achieving long-term positive \r\nsocietal impacts requires upscaling from supply chains towards \r\nlandscapes, with engagement of all stakeholders, and in particular \r\nsmall producers. The various success indicators for zero deforestation \r\nmirror the multiple levels at which such initiatives develop: progress \r\ntowards certification, improved traceability, and legality are apparent \r\noutput measures, whereas direct-area monitoring and site selection \r\napproaches target the business practices themselves."


id: b12d9aef19f6627a59456b2ab2dcf1ab
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
content_type: text
index: climate_gpt_only_giec
vector_id: 25375
content:
"If global CO2 emissions continue at current rates, the remaining carbon budget for keeping warming to 1.5degC will likely be \r\nexhausted before 2030. Between 1850 and 2019, total cumulative CO2 emissions from the fossil fuel industry (FFI) and agriculture, \r\nforestry, and other land use (AFOLU) were 2400 (+-240 GtCO2). Of these, about 410 +- 30 GtCO2 were added since 2010. This is \r\nabout the same size as the remaining carbon budget for keeping global warming to 1.5degC and between one-third and one-half \r\nof the 1150 +- 220 (1350, 1700) GtCO2 for limiting global warming below 2degC with a 67% (50%, 33%) probability, respectively \r\n(Canadell et al. 2021). At current (2019) rates of emissions, it would only take 8 (2-15) and 25 (18-35) years to emit the equivalent \r\namount of CO2 for a 67th percentile 1.5degC and 2degC remaining carbon budget, respectively. This highlights the dependence of 1"


id: e1acf556c7a5a04aa0dbb7aa94796aa2
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
content_type: text
index: climate_gpt_only_giec
vector_id: 32999
content:
"5degC \r\npathways on the availability of substantial CO2 removal capacities, as discussed in Chapters 3, 4, and 12, but also Section 2.7 of \r\nthis chapter."


id: 702a4f1750e56ecd6abef91657365434
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
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index: climate_gpt_only_giec
vector_id: 15263
content:
"Alaa Al Khourdajie (United Kingdom/Syria), Christoph Bertram (Germany), Valentina Bosetti (Italy), \r\nElina Brutschin (Austria), Edward Byers (Austria/Ireland), Tamma Carleton (the United States of \r\nAmerica), Leon Clarke (the United States of America), Annette Cowie (Australia), Delavane Diaz \r\n(the United States of America), Laurent Drouet (Italy/France), Navroz  K.  Dubash  (India), \r\nJames Edmonds (the United States of America) Jan S"


id: 396253566e51a84ead93710d3f99b033
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
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vector_id: 6406
content:
"Chapter 3 assesses the emissions pathways literature in order \r\nto identify their key characteristics (both in commonalities and \r\ndifferences) and to understand how societal choices may steer \r\nthe system into a particular direction (high confidence). More \r\nthan 2000 quantitative emissions pathways were submitted to the \r\nIPCC's Sixth Assessment Report AR6 scenarios database, out of which \r\n1202 scenarios included sufficient information for assessing the \r\nassociated warming consistent with WGI"


id: c6a27638f393df96251fa517a24e20db
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
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index: climate_gpt_only_giec
vector_id: 28818
content:
" Five Illustrative Mitigation \r\nPathways (IMPs) were selected, each emphasising a different scenario \r\nelement as its defining feature: heavy reliance on renewables (IMP\u0002Ren), strong emphasis on energy demand reductions (IMP-LD), \r\nextensive use of carbon dioxide removal (CDR) in the energy and \r\nthe industry sectors to achieve net negative emissions (IMP-Neg), \r\nmitigation in the context of broader sustainable development (IMP\u0002SP), and the implications of a less rapid and gradual strengthening of \r\nnear-term mitigation actions (IMP-GS). {3.2, 3.3}"


id: 7deb1e331abc3d6b7dcb0bdc94bdcefa
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
content_type: text
index: climate_gpt_only_giec
vector_id: 17384
content:
"Pathways following Nationally Determined Contributions \r\n(NDCs) announced prior to COP262 until 2030 reach annual \r\nemissions of 47-57 GtCO2-eq by 2030, thereby making it \r\nimpossible to limit warming to  1.5degC with no or limited \r\novershoot and strongly increasing the challenge to limit \r\nwarming to 2degC (>67%) (high confidence). A  high overshoot \r\nof  1.5degC increases the risks from climate impacts and increases \r\nthe dependence on large-scale carbon dioxide removal from the \r\natmosphere. A  future consistent with NDCs announced prior to \r\nCOP26 implies higher fossil fuel deployment and lower reliance on \r\nlow-carbon alternatives until 2030, compared to mitigation pathways \r\nwith immediate action to limit warming to 2degC (>67%) or lower. To \r\nlimit warming to 2degC (>67%) after following the NDCs to 2030, the \r\npace of global GHG emission reductions would need to accelerate \r\nrapidly from 2030 onward: to an average of 1.4-2"


id: c21bb32eca7b170e5923f72f455942f5
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
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index: climate_gpt_only_giec
vector_id: 28119
content:
"0 GtCO2-eq yr-1\r\nbetween 2030 and 2050, which is around two-thirds of the global \r\nCO2 emission reductions in 2020 due to the COVID-19 pandemic, \r\nand around 70% faster than in immediate action pathways that \r\nlimit warming to 2degC (>67%). Accelerating emission reductions after \r\nfollowing an NDC pathway to 2030 would be particularly challenging \r\nbecause of the continued buildup of fossil fuel infrastructure that \r\nwould be expected to take place between now and 2030. {3.5, 4.2}"


id: 4ffac306d54b5a5029ba231f59c40d2a
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
content_type: text
index: climate_gpt_only_giec
vector_id: 10026
content:
"Pathways consistent with the implementation and \r\nextrapolation of countries' implemented policies until the end \r\nof 2020 see greenhouse gas (GHG) emissions reaching 54-\r\n61 GtCO2-eq yr-1 by 2030 and to 47-67 GtCO2-eq yr-1 by 2050, \r\nleading to a median global warming of 2.2degC to 3.5degC by 2100\r\n(medium confidence). These pathways consider policies at the time \r\nthat they were developed. The Shared Socio-economic Pathways \r\n(SSPs) permit a more systematic assessment of future GHG emissions \r\nand their uncertainties than was possible in AR5. The main emissions \r\ndrivers include growth in population, reaching  8.5-9.7 billion by \r\n2050, and an increase in global GDP of 2.7-4.1% per year between \r\n2015 and 2050. Final energy demand in the absence of any new \r\nclimate policies is projected to grow to around 480-750 EJ yr-1 in \r\n2050 (compared to around 390 EJ in 2015) (medium confidence)"


id: 4a38acb4a139b3469c11271a03af8f3e
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
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index: climate_gpt_only_giec
vector_id: 9085
content:
" The \r\nhighest emissions scenarios in the literature result in global warming \r\nof >5degC by 2100, based on assumptions of rapid economic growth \r\nand pervasive climate policy failures (high confidence). {3.3} "


id: 8700850b6782a49a6fadc4445fbea153
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
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vector_id: 18770
content:
"Rapid reductions in non-CO2 GHGs, particularly methane, \r\nwould lower the level of peak warming (high confidence).\r\nResidual non-CO2 emissions at the time of reaching net zero \r\nCO2 range between 5 and 11 GtCO2-eq yr-1 in pathways limiting \r\nwarming to 2degC  (>67%) or lower. Methane (CH4) is reduced by \r\naround 19% (4-46%) in 2030 and 45% (29-64%) in 2050, relative \r\nto 2019. Methane emission reductions in pathways limiting warming \r\nto  1.5degC (>50%) with no or limited overshoot are substantially \r\nhigher by 2030, 34% (21-57%), but only moderately so by 2050, \r\n51% (35-70%). Methane emissions reductions are thus attainable \r\nat relatively lower GHG prices but are at the same time limited in \r\nscope in most  1.5degC-2degC pathways. Deeper methane emissions \r\nreductions by 2050 could further constrain the peak warming. N2O \r\nemissions are reduced too, but similar to CH4, emission reductions \r\nsaturate for more stringent climate goals"


id: 4aadb0aee532a39f8fc8561470b5fa99
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
content_type: text
index: climate_gpt_only_giec
vector_id: 9157
content:
" In the mitigation pathways, \r\nthe emissions of cooling aerosols are reduced due to reduced use of \r\nfossil fuels. The overall impact on non-CO2-related warming combines \r\nthese factors. {3.3}"


id: ed4138c92296cc0f723f9276a9d45a1b
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
content_type: text
index: climate_gpt_only_giec
vector_id: 34805
content:
"The global benefits of pathways limiting warming to 2degC \r\n(>67%) outweigh global mitigation costs over the 21st century, \r\nif aggregated economic impacts of climate change are at the \r\nmoderate to high end of the assessed range, and a  weight \r\nconsistent with economic theory is given to economic impacts \r\nover the long term. This holds true even without accounting for \r\nbenefits in other sustainable development dimensions or non\u0002market damages from climate change (medium confidence).\r\nThe aggregate global economic repercussions of mitigation \r\npathways include the macroeconomic impacts of investments in \r\nlow-carbon solutions and structural changes away from emitting \r\nactivities, co-benefits and adverse side effects of mitigation, \r\n(avoided) climate change impacts, and (reduced) adaptation costs"


id: 74a70a1a43236042433b2bda102a6a08
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
content_type: text
index: climate_gpt_only_giec
vector_id: 15950
content:
" \r\nExisting quantifications of global aggregate economic impacts show \r\na strong dependence on socio-economic development conditions, as \r\nthese shape exposure and vulnerability and adaptation opportunities \r\nand responses. (Avoided) impacts for poorer households and \r\npoorer countries represent a  smaller share in aggregate economic \r\nquantifications expressed in GDP or monetary terms, whereas their \r\nwell-being and welfare effects are comparatively larger. When \r\naggregate economic benefits from avoided climate change impacts \r\nare accounted for, mitigation is a welfare-enhancing strategy (high \r\nconfidence). {3.6.2}"


id: fe02eb876a626033174a5303041f3659
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
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index: climate_gpt_only_giec
vector_id: 37488
content:
"The economic repercussions of mitigation vary widely across \r\nregions and households, depending on policy design and level \r\nof international cooperation (high confidence). Delayed global \r\ncooperation increases policy costs across regions, especially in those \r\nthat are relatively carbon intensive at present (high confidence). \r\nPathways with uniform carbon values show higher mitigation costs \r\nin more carbon-intensive regions, in fossil fuel exporting regions \r\nand in poorer regions (high confidence). Aggregate quantifications \r\nexpressed in GDP or monetary terms undervalue the economic effects \r\non households in poorer countries; the actual effects on welfare and \r\nwell-being are comparatively larger (high confidence). Mitigation \r\nat the speed and scale required to limit warming to 2degC (>67%) or \r\nlower implies deep economic and structural changes, thereby raising \r\nmultiple types of distributional concerns across regions, income \r\nclasses and sectors (high confidence). {3.6.1, 3"


id: 6202d20f5bff5dfe5810989055a3451e
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content:
"Stabilising global average-temperature change requires reducing \r\nCO2 emissions to net zero. Thus, a central cross-cutting topic within \r\nthe chapter is the timing of reaching net zero CO2 emissions and \r\nhow a  'balance between anthropogenic emissions by sources \r\nand removals by sinks' could be achieved across time and space. This \r\nincludes particularly the increasing body of literature since the IPCC \r\nSpecial Report on Global Warming of 1.5degC (SR1.5) which focuses on \r\nnet zero CO2 emissions pathways that avoid temperature overshoot \r\nand hence do not rely on net negative CO2 emissions. The chapter \r\nconducts a systematic assessment of the associated economic costs \r\nas well as the benefits of mitigation for other societal objectives, \r\nsuch as the Sustainable Development Goals (SDGs). In addition, the \r\nchapter builds on SR1"


id: 46222301d3fade6509e6abd214caae1a
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vector_id: 8414
content:
"5 and introduces a new conceptual framing for \r\nthe assessment of possible social, economic, technical, political, and \r\ngeophysical 'feasibility' concerns of alternative pathways, including \r\nthe enabling conditions that would need to fall into place so that \r\nstringent climate goals become attainable."


id: c0f0529017bdd8335a78c05de68d1a96
created_at: 2023-03-16 14:02:41
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content:
"The structure of the chapter is as follows: Section  3.2 introduces \r\ndifferent types of mitigation pathways as well as the available \r\nmodelling. Section 3.3 explores different emissions trajectories given \r\nsocio-economic uncertainties and consistent with different long-term \r\nclimate outcomes. A central element in this section is the systematic \r\ncategorisation of the scenario space according to key characteristics of \r\nthe mitigation pathways (including e.g., global average-temperature \r\nchange, socio-economic development, technology assumptions, etc.). \r\nIn addition, the section introduces selected Illustrative Mitigation \r\nPathways (IMPs) that are used across the whole report. Section 3.4 \r\nconducts a sectoral analysis of the mitigation pathways, assessing \r\nthe pace and direction of systems changes across sectors"


id: d8506b904773d328c612456fe4111714
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
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index: climate_gpt_only_giec
vector_id: 31560
content:
" Among \r\nothers, this section aims at the integration of the sectoral information \r\nacross AR6 WGIII chapters through a comparative assessment of the \r\nsectoral dynamics in economy-wide systems models compared to \r\nthe insights from bottom-up sectoral models (from Chapters 6 to 11). \r\nSection 3.5 focuses on the required timing of mitigation actions, and \r\nthe implication of near-term choices for the attainability of a range \r\nof long-term climate goals. After having explored the underlying \r\nsystems transitions and the required timing of the mitigation actions, \r\nSection 3.6 assesses the economic implications, mitigation costs and \r\nbenefits; and Section  3.7 assesses related co-benefits, synergies, \r\nand possible trade-offs for sustainable development and other \r\nsocietal (non-climate) objectives. Section 3.8 assumes a central role \r\nin the chapter and introduces a multidimensional feasibility metric "


id: 79ab185b33bd460079e6418c3e4672fe
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content:
"The literature shows a wide range of possible emissions trajectories, depicting developments in the absence of new climate policies or \r\nshowing pathways consistent with the Paris Agreement. From the literature, a set of five Illustrative Mitigation Pathways (IMPs) was \r\nselected to denote implications of choices on socio-economic development and climate policies, and the associated transformations of \r\nthe main GHG-emitting sectors (Figure 3.5b). The IMPs include a set of transformative pathways that illustrate how choices may lead \r\nto distinctly different transformations that may keep temperature increase to below 2degC (>67%) or 1.5degC. These pathways illustrate \r\nthe implications of a focus on renewable energy such as solar and wind; reduced energy demand; extensive use of CDR in the energy \r\nand the industry sectors to achieve net negative emissions and reliance on other supply-side measures; strategies that avoid net \r\nnegative carbon emissions, and gradual strengthening"


id: 923e4839726303e3ec9fc10b5b58b5a2
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index: climate_gpt_only_giec
vector_id: 20531
content:
" In addition, one IMP explores how climate policies consistent with keeping \r\nlimit warming to 1.5C (>50%) can be combined with a broader shift towards sustainable development. These IMPs are used in various \r\nchapters, exploring for instance their implications for different sectors, regions, and innovation characteristics (Figure 3.5b)."


id: b78953b4521373d1883201f32291de11
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
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index: climate_gpt_only_giec
vector_id: 26401
content:
"Mitigation scenarios explore different strategies to meet climate goals \r\nand are typically derived from reference scenarios by adding climate \r\nor other policies. Mitigation pathways are often developed to meet \r\na predefined level of climate change, often referred to as a backcast. \r\nThere are relatively few IAMs that include an endogenous climate \r\nmodel or emulator due to the added computational complexity, \r\nthough exceptions do exist. In practice, models implement climate \r\nconstraints by either iterating carbon-price assumptions (Strefler \r\net al. 2021b) or by adopting an associated carbon budget (Riahi \r\net al. 2021). In both cases, other GHGs are typically controlled by \r\nCO2-equivalent pricing. A large part of the AR5 literature has focused \r\non forcing pathways towards a target at the end of the century (van \r\nVuuren et al. 2007, 2011; Clarke et al. 2009; Blanford et al. 2014; Riahi \r\net al"


id: 21214d91f914b9fb40ff0f8a107e6012
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vector_id: 2647
content:
" 2017), featuring a  temporary overshoot of the warming and \r\nforcing levels (Geden and Loschel 2017). In comparison, many recent \r\nstudies explore mitigation strategies that limit overshoot (Johansson \r\net al. 2020; Riahi et al. 2021). An increasing number of IAM studies \r\nalso explore climate pathways that limit adverse side effects with \r\nrespect to other societal objectives, such as food security (van Vuuren \r\net al. 2019; Riahi et al. 2021) or larger sets of sustainability objectives \r\n(Soergel et al. 2021a)."


id: 58033c07c12c1985e25402f1bdbe6126
created_at: 2023-03-16 14:02:41
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index: climate_gpt_only_giec
vector_id: 11351
content:
"IAMs have a  long history in addressing environmental \r\nproblems, particularly in the IPCC assessment process (van Beek \r\net al. 2020). Many policy discussions have been guided by IAM-based \r\nquantifications, such as the required emission reduction rates, net \r\nzero years, or technology deployment rates required to meet certain \r\nclimate outcomes. This has led to the discussion about whether IAM \r\nscenarios have become performative, meaning that they act upon, \r\ntransform or bring into being the scenarios they describe (Beck and \r\nMahony 2017, 2018). Transparency of underlying data and methods \r\nis critical for scenario users to understand what drives different \r\nscenario results (Robertson 2020). A number of community activities \r\nhave thus focused on the provision of transparent and publicly \r\naccessible databases of both input and output data (Riahi et al. 2012; \r\nHuppmann et al. 2018; Krey et al. 2019; Daioglou et al"


id: b41a47de0396622e6be7872586aaa8c3
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index: climate_gpt_only_giec
vector_id: 25854
content:
" 2020), as well \r\nas the provision of open-source code, and increased documentation \r\n(Annex III.I.9). Transparency is needed to reveal conditionality of results \r\non specific choices in terms of assumptions (e.g., discount rates) and \r\nmodel architecture. More detailed explanations of underlying model \r\ndynamics would be critical to increase the understanding of what \r\ndrives results (Bistline et al. 2020; Butnar et al. 2020; Robertson 2020)."


id: 90aa5eedf5030f1e8d1884c33bd72a51
created_at: 2023-03-16 14:02:41
updated_at: 2023-03-16 14:02:41
content_type: text
index: climate_gpt_only_giec
vector_id: 20297
content:
"Integrated Assessment Models (IAMs) are critical for understanding \r\nthe implications of long-term climate objectives for the required \r\nnear-term transition. For doing so, an integrated systems perspective \r\nincluding the representation of all sectors and GHGs is necessary. \r\nIAMs are used to explore the response of complex systems in a formal \r\nand consistent framework. They cover a broad range of modelling \r\nframeworks (Keppo et al. 2021). Given the complexity of the systems \r\nunder investigation, IAMs necessarily make simplifying assumptions \r\nand therefore results need to be interpreted in the context of these \r\nassumptions"


id: 9552130e063425e73483d6365f81daa4
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 20989
content:
" IAMs can range from economic models that consider \r\nonly carbon dioxide emissions through to detailed process-based \r\nrepresentations of the global energy system, covering separate \r\nregions and sectors (such as energy, transport, and land use), all GHG \r\nemissions and air pollutants, interactions with land and water, and \r\na reduced representation of the climate system. IAMs are generally \r\ndriven by economics and can have a variety of characteristics such as \r\npartial-, general- or non-equilibrium; myopic or perfect foresight; be "


id: 131490d3b92f6b62386a8b113d0223ed
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 516
content:
"cost-effectiveness approach, they do not make any additional equity \r\nassumptions. Notable exceptions include Tavoni et al. (2015), Pan et al. \r\n(2017), van den Berg et al. (2020), and Bauer et al. (2020). Regional \r\nIAM results therefore need to be assessed with care, considering that \r\nemissions reductions are happening where it is most cost-effective, \r\nwhich needs to be separated from who is ultimately paying for \r\nthe mitigation costs. Cost-effective pathways can provide a  useful \r\nbenchmark, but may not reflect real-world developments (Calvin \r\net al. 2014a; Trutnevyte 2016). Different modelling frameworks may \r\nlead to different outcomes (Mercure et al. 2019). Recent studies have \r\nshown that other desirable outcomes can evolve with only minor \r\ndeviations from cost-effective pathways (Bauer et al. 2020; Neumann \r\nand Brown 2021). IAM and sectoral models represent social, political, \r\nand institutional factors only in a rudimentary way"


id: 817400c4f0ddc2ea94ea88025466dba0
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 17896
content:
" This assessment is \r\nthus relying on new methods for the ex post assessment of feasibility \r\nconcerns (Jewell and Cherp 2020; Brutschin et al. 2021). A literature is \r\nemerging that recognises and reflects on the diversity and strengths/\r\nweaknesses of model-based scenario analysis (Keppo et al. 2021)."


id: bdec5b697764e945f15bcbb78c440917
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 27440
content:
"IPCC reports have often used voluntary submissions to a  scenario \r\ndatabase in its assessments. The database is an ensemble of opportunity, \r\nas there is not a well-designed statistical sampling of the hypothetical \r\nmodel or scenario space: the literature is unlikely to cover all possible \r\nmodels and scenarios, and not all scenarios in the literature are \r\nsubmitted to the database. Model intercomparisons are often the core \r\nof scenario databases assessed by the IPCC (Cointe et al. 2019; Nikas \r\net al. 2021). Single-model studies may allow more detailed sensitivity \r\nanalyses or address specific research questions. The scenarios that are \r\norganised within the scientific community are more likely to enter the \r\nassessment process via the scenario database (Cointe et al. 2019), \r\nwhile scenarios from different communities, in the emerging literature, \r\nor not structurally consistent with the database may be overlooked"


id: 63ea97fde2ed940c1a7d05f95a3428f8
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index: climate_gpt_only_giec
vector_id: 13286
content:
" \r\nScenarios in the grey literature may not be assessed even though they \r\nmay have greater weight in a policy context."


id: 39c4d0aac93b7bc9d46b1d0af7673d9
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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vector_id: 6462
content:
"One notable development since AR5 is the Shared Socio-economic \r\nPathways (SSPs), conceptually outlined in Moss et al. (2010) and \r\nsubsequently developed to support integrated climate research across \r\nthe IPCC Working Groups (O'Neill et al. 2014). Initially, a set of SSP \r\nnarratives were developed, describing worlds with different challenges \r\nto mitigation and adaptation (O'Neill et al. 2017a): SSP1 (sustainability), \r\nSSP2 (middle of the road), SSP3 (regional rivalry), SSP4 (inequality) \r\nand SSP5 (rapid growth). The SSPs have now been quantified in terms \r\nof energy, land-use, and emission pathways (Riahi et al. 2017), for \r\nboth no-climate-policy reference scenarios and mitigation scenarios \r\nthat follow similar radiative-forcing pathways as the Representative \r\nConcentration Pathways (RCPs) assessed in AR5 WGI. Since then the \r\nSSPs have been successfully applied in thousands of studies (O'Neill \r\net al"


id: c6829a326e5805c7a0ac9ca4392cb4b8
created_at: 2023-03-16 14:02:42
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content:
" 2020b) including some critiques on the use and application of \r\nthe SSP framework (Pielke and Ritchie 2021; Rosen 2021). A selection \r\nof the quantified SSPs are used prominently in AR6 WGI as they were \r\nthe basis for most climate modelling since AR5 (O'Neill et al. 2016). \r\nSince 2014, when the first set of SSP data was made available, there \r\nhas been a divergence between scenario and historic trends (Burgess \r\net al. 2020). As a result, the SSPs require updating (O'Neill et al. 2020b). \r\nMost of the scenarios in the AR6 database are SSP-based and consider \r\nvarious updates compared to the first release (Riahi et al. 2017)."


id: cb3a8a33a0ea3de48535addd7675381e
created_at: 2023-03-16 14:02:42
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content:
"Many factors influence the deployment of technologies in the \r\nIAMs. Since AR5, there has been fervent debate on the large-scale \r\ndeployment of bioenergy with carbon capture and storage (BECCS) in \r\nscenarios (Fuss et al. 2014; Geden 2015; Anderson and Peters 2016; \r\nSmith et al. 2016; van Vuuren et al. 2017; Galik 2020; Koberle 2019). \r\nHence, many recent studies explore mitigation pathways with limited \r\nBECCS deployment (Grubler et al. 2018; van Vuuren et al. 2019; \r\nRiahi et al. 2021; Soergel et al. 2021a). While some have argued that \r\ntechnology diffusion in IAMs occurs too rapidly (Gambhir et al. 2019), \r\nothers argued that most models prefer large-scale solutions resulting \r\nin a  relatively slow phase-out of fossil fuels (Carton 2019). While \r\nIAMs are particularly strong on supply-side representation, demand\u0002side measures still lag in detail of representation despite progress \r\nsince AR5 (Grubler et al. 2018; Lovins et al. 2019; van den Berg et al. \r\n2019;  O'Neill et al"


id: d58110c97f9cb4c7a7534e52836eed8f
created_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 31113
content:
" 2020b; Hickel et al. 2021; Keysser and Lenzen \r\n2021). The discount rate has a  significant impact on the balance \r\nbetween near-term and long-term mitigation. Lower discount rates \r\n<4% (than used in IAMs) may lead to more near-term emissions \r\nreductions - depending on the stringency of the target (Emmerling \r\net al. 2019; Riahi et al. 2021). Models often use simplified policy \r\nassumptions (O'Neill et al. 2020b) which can affect the deployment \r\nof technologies (Sognnaes et al. 2021). Uncertainty in technologies \r\ncan lead to more or less short-term mitigation (Grant et al. 2021; \r\nBednar et al. 2021). There is also a recognition to put more emphasis \r\non what drives the results of different IAMs (Gambhir et al. 2019) \r\nand suggestions to focus more on what is driving differences in result \r\nacross IAMs (Nikas et al. 2021). As noted by Weyant (2017, p. 131), "


id: 382c84b6ae5337a52fdbd67888da833b
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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vector_id: 6209
content:
"action (634) and (P2a) without any transfer of emission permits - 435, \r\n(P2b) with transfers - 70; or (P2c) with additional policy assumptions - \r\n55; (P3) globally coordinated climate policies with delayed (i.e., from \r\n2030 onwards or after 2030) action (451), preceded by (P3a) no \r\nmitigation commitment or current national policies - 7, (P3b) NDCs - \r\n426, (P3c) NDCs and additional policies - 18; (P4) cost-benefit analysis \r\n(CBA)  -  2. The policy categories were identified using text pattern \r\nmatching on the scenario metadata and calibrated on the best-known \r\nscenarios from model intercomparisons, with further validation against \r\nthe related literature, reported emission and carbon price trajectories, \r\nand exchanges with modellers. If the information available is enough \r\nto qualify a policy category number but not sufficient for a subcategory, \r\nthen only the number is retained (e.g., P2 instead of P2a/b/c)"


id: f22a95a2e8048360af723edd51388932
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 35632
content:
" A suffix \r\nadded after P0 further qualifies a diagnostic scenario as one of the other \r\npolicy categories. To demonstrate the diversity of the scenarios, the vetted \r\nscenarios were classified into different categories along the dimensions \r\nof population, GDP, energy, and cumulative emissions (Figure 3.4). The \r\nnumber of scenarios in each category provides some insight into the \r\ncurrent literature, but this does not indicate a higher probability of that \r\ncategory occurring in reality. For population, the majority of scenarios \r\nare consistent with the SSP2 'middle of the road' category, with very \r\nfew scenarios exploring the outer extremes. GDP has a slightly larger \r\nvariation, but overall most scenarios are around the SSP2 socio\u0002economic assumptions. The level of CCS and CDR is expected to change \r\ndepending on the extent of mitigation, but there remains extensive use \r\nof both CDR and CCS in scenarios"


id: 463204e2865cf8afb1e709e4b2f6c27f
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 8428
content:
" CDR is dominated by bioenergy with \r\nCCS (BECCS) and sequestration on land, with relatively few scenarios \r\nusing direct air capture with carbon storage (DACCS) and even less \r\nwith enhanced weathering (EW) and other technologies (not shown). \r\nIn terms of energy consumption, final energy has a much smaller range \r\nthan primary energy as conversion losses are not included in final \r\nenergy. Both mitigation and reference scenarios are shown, so there is \r\na broad spread in different energy carriers represented in the database. \r\nBioenergy has a number of scenarios at around 100 EJ, representing \r\na constraint used in many model intercomparisons."


id: 4f0d20347f0d7fbdef257d64475f906d
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 9880
content:
"The selected scenarios (IPs) are divided into two sets (Figures  3.5 \r\nand 3.6): two reference pathways illustrative of high emissions and five \r\nIllustrative Mitigation Pathways (IMPs). The narratives are explained \r\nin full in Annex III.II.2.4. The two reference pathways explore the \r\nconsequences of current policies and pledges: Current Policies (CurPol) \r\nand Moderate Action (ModAct). The CurPol pathway explores the \r\nconsequences of continuing along the path of implemented climate \r\npolicies in 2020 and only a  gradual strengthening after that. The \r\nscenario illustrates the outcomes of many scenarios in the literature \r\nthat project the trend from implemented policies until the end of \r\n2020. The ModAct pathway explores the impact of implementing \r\nthe Nationally Determined Contributions (NDCs) as formulated in \r\n2020 and some further strengthening after that"


id: daaf9f36e7c5e72c87b398513e90c615
created_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 31934
content:
" In line with current \r\nliterature, these two reference pathways lead to an increase in global \r\nmean temperature of more than 2degC (Section 3.3)."


id: 874c7a6e872d90866b2ddbad832aa286
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 18827
content:
"The Illustrative Mitigation Pathways (IMPs) properly explore different \r\npathways consistent with meeting the long-term temperature goals \r\nof the Paris Agreement. They represent five different pathways that \r\nemerge from the overall assessment. The IMPs differ in terms of their \r\nfocus, for example, placing greater emphasis on renewables (IMP-Ren), \r\ndeployment of carbon dioxide removal that results in net negative \r\nglobal GHG emissions (IMP-Neg), and efficient resource use and \r\nshifts in consumption patterns, leading to low demand for resources, \r\nwhile ensuring a high level of services (IMP-LD)"


id: c5910f5aa3949291cee36eec6e35e383
created_at: 2023-03-16 14:02:42
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vector_id: 28665
content:
" Other IMPs illustrate \r\nthe implications of a less rapid introduction of mitigation measures \r\nfollowed by a subsequent gradual strengthening (IMP-GS), and how \r\nshifting global pathways towards sustainable development, including \r\nby reducing inequality, can lead to mitigation (IMP-SP) In the IMP \r\nframework, IMP-GS is consistent with limiting warming to 2degC (>67%) \r\n(C3), IMP-Neg shows a strategy that also limits warming to 2degC (>67%) \r\nbut returns to nearly 1.5degC (>50%) by the end of the century (hence \r\nindicated as C2*). The other variants that can limit warming to 1.5degC \r\n(>50%) (C1) were selected. In addition to these IMPs, sensitivity cases \r\nthat explore alternative warming levels (C3) for IMP-Neg and IMP-Ren\r\nare assessed (IMP-Neg-2.0 and IMP-Ren-2.0)."


id: 51d4ec44edc131df40fcec4ac7b2ed3a
created_at: 2023-03-16 14:02:42
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vector_id: 10335
content:
"Economic growth is even more uncertain than the population projections \r\n(Figure  3.9c). The average growth rate of GDP was about  2.8% per \r\nyear (constant USD) in the 1990-2019 period (The World Bank 2021). \r\nIn 2020, the COVID-19 crisis resulted in a considerable drop in GDP \r\n(estimated around  4-5%) (IMF 2021). After a  recovery period, most \r\neconomic projections assume growth rates to converge back to previous \r\nprojections, although at a lower level (IMF 2021; OECD 2021) (see also \r\nBox  3.2). In the long term, assumptions on future growth relate to \r\npolitical stability, the role of the progress of the technology frontier and \r\nthe degree to which countries can catch up (Johansson et al. 2013). The \r\nSSP scenarios cover an extensive range, with low per-capita growth in \r\nSSP3 and SSP4 (mostly in developing countries) and rapid growth in \r\nSSP1 and SSP5"


id: 8a81fc85dae627b97678bb62d96b5823
created_at: 2023-03-16 14:02:42
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vector_id: 19329
content:
" At the same, however, also scenarios outside the range \r\nhave some plausibility - including the option of economic decline (Kallis \r\net al. 2012) or much faster economic development (Christensen et al. \r\n2018). The OECD long-term projection is at the global level reasonably \r\nconsistent with SSP2. Equally important economic parameters include \r\nincome distribution (inequity) and the type of growth (structural \r\nchange,  i.e.,  services vs manufacturing industries). Some projections \r\n(like SSP1) show a considerable convergence of income levels within \r\nand across countries, while in other projections, this does not occur \r\n(e.g., SSP3). Most scenarios reflect the suggested inverse relationship \r\nbetween the assumed growth rate for income and population growth \r\n(Figure  3.9e). SSP1 and SSP5 represent examples of scenarios with \r\nrelatively low population increase and relatively high-income increase \r\nover the century"


id: dc4311c68bd78c8d81dfd9291232f224
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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vector_id: 32162
content:
" SSP3 represents an example of the opposite - while \r\nSSP2 and SSP4 are placed more in the middle. Nearly all scenarios \r\nassessed here do not account for climate impacts on growth (mostly \r\nfor methodological reasons). As discussed in Section 3.5 these impacts \r\ncan be considerable. An emerging area of literature emphasises the \r\npossibility of stabilisation (or even decline) of income levels in developed \r\ncountries, arguing that such a trend would be preferred or even needed \r\nfor environmental reasons (Anderson and Larkin 2013; Hickel and Kallis \r\n2020; Kallis et al. 2020; Hickel et al. 2021; Keysser and Lenzen 2021) (see \r\nalso Chapter 5). Such scenarios are not common among IAM outcomes, \r\nthat are more commonly based on the idea that decarbonisation can \r\nbe combined with economic growth by a combination of technology, \r\nlifestyle and structural economic changes. Still, such scenarios could \r\nresult in a dramatic reduction of energy and resource consumption."


id: 89d810c479ee51aeb6dd726e557b269e
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 19214
content:
"Greenhouse gas (GHG) emissions mainly originate from the use and \r\ntransformation of energy, agriculture, land use (change) and industrial \r\nactivities. The future development of these sources is  influenced \r\nby trends in socio-economic development, including  population, \r\neconomic activity, technology, politics, lifestyles, and climate policy. \r\nTrends for these factors are not independent, and scenarios provide \r\na consistent outlook for these factors together (Section 3.2). Marangoni \r\net al. (2017) show that in projections, assumptions influencing \r\nenergy intensity (e.g., structural change, lifestyle and efficiency) and \r\neconomic growth are the most important determinants of future \r\nCO2 emissions from energy combustion. Other critical factors include \r\ntechnology assumptions, preferences, resource assumptions and \r\npolicy (van Vuuren et al. 2008)"


id: 41506571b26582b1d9b46194422e5394
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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vector_id: 7673
content:
" As many of the factors are represented \r\ndifferently in specific models, the model itself is also an important \r\nfactor  - providing a  reason for the importance of model diversity \r\n(Sognnaes et al. 2021). For land use, Stehfest et al. (2019) show that \r\nassumptions on population growth are more dominant given that \r\nvariations in per capita consumption of food are smaller than for \r\nenergy. Here, we only provide a brief overview of some key drivers. We \r\nfocus first on so-called reference scenarios (without stringent climate \r\npolicy) and look at mitigation scenarios in detail later. We use the SSPs \r\nto discuss trends in more detail. The SSPs were published in 2017, and \r\nby now, some elements will have to be updated (O'Neill et al. 2020b). \r\nStill, the ranges represent the full literature relatively well."


id: 1b1b9c7c96ab63be9222180ef51e537e
created_at: 2023-03-16 14:02:42
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content:
"Historically, population and GDP have been growing over time. \r\nScenario studies agree that further global population growth \r\nis likely up to 2050, leading to a  range of possible outcomes of \r\naround 8.5-11 billion people (Figure 3.9a). After 2050, projections \r\nshow a much wider range. If fertility drops below replacement levels, \r\na decline in the global population is possible (as illustrated by SSP1 \r\nand SSP5). This typically includes scenarios with rapid development \r\nand investment in education. However, median projections mostly \r\nshow a  stabilisation of the world population (e.g.,  SSP2), while \r\nhigh-end projections show a continued growth (e.g., SSP3). The UN \r\nPopulation Prospects include considerably higher values for both \r\nthe medium projection and the high end of the range than the SSP \r\nscenarios (KC and Lutz 2017; UN 2019). The most recent median \r\nUN projection reaches almost 11 billion people in 2100"


id: 86640dd6b15e401a5afb52903916925c
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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vector_id: 18685
content:
" The key \r\ndifferences are in Africa and China: here, the population projections \r\nare strongly influenced by the rate of fertility change (faster drop in \r\nSSPs). Underlying these differences, the UN approach is more based \r\non current demographic trends while the SSPs assume a  broader \r\nrange of factors (including education) driving future fertility."


id: 326e09b29a4a7baffcfc4ab3c6242d7
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 5346
content:
"Scenarios show a range of possible energy projections. In the absence \r\nof climate policy, most scenarios project the final energy demand \r\nto continue to grow to around 650-800 EJ yr-1 in 2100 (based on \r\nthe AR6 Scenarios Database, Figure  3.9b). Some projections show \r\na very high energy demand up to 1000 EJ yr-1 (comparable to SSP5). \r\nThe scenario of the IEA lies within the SSP range but near the SSP1 \r\nprojection. However, it should be noted that the IEA scenario includes \r\ncurrent policies (most reference scenarios do not) and many scenarios \r\npublished before 2021 did not account for the COVID-19 crisis. Several \r\nresearchers discuss the possibility of decoupling material and energy \r\ndemand from economic growth in the literature, mainly in developed \r\ncountries (Kemp-Benedict 2018) (decoupling here refers to either \r\na much slower increase in demand or even a decrease)"


id: 5df8b477a77b231ed9b9ec6a6218cd6
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
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content:
" In the scenario \r\nliterature, this is reflected by scenarios with very low demand for final \r\nenergy based on increased energy efficiency and less energy-intensive \r\nlifestyles (e.g., SSP1 and the LED scenario) (Grubler et al. 2018; van \r\nVuuren et al. 2018). While these studies show the feasibility of such "


id: 63433cf694e500c3ccb34735a17cc8ed
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 13182
content:
"pathways, their energy efficiency improvement rates are considerably \r\nabove the historic range of around 2% (Gutschow et al. 2018; Jeffery et \r\nal. 2018; Vrontisi et al. 2018; Haberl et al. 2020; Roelfsema et al. 2020; \r\nGiarola et al. 2021; Hohne et al. 2021; IEA 2021a; Hohne et al. 2021; \r\nSognnaes et al. 2021). These scenarios also show clear differences in \r\nfood consumption and the amount of land used for agriculture. Food \r\ndemand in terms of per-capita caloric intake is projected to increase in \r\nmost scenarios (Figure 3.9d). However, it should be noted that there \r\nare large differences in dietary composition across the scenarios (from \r\nmore meat-intensive in scenarios such as SSP5 to a decrease in meat \r\nconsumptions in other scenarios such as SSP1). Land-use projections \r\nalso depend on assumed changes in yield and the population scenarios \r\n(Figure 3.9f)"


id: dd90243b82066b96ac9a418d65946702
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 32358
content:
" Typically, changes in land use are less drastic than some \r\nother parameters (in fact, the  5-95th percentile database range \r\nis almost stable). Agriculture land is projected to increase in SSP3, "


id: e9f8672b6b961b022458f9dd9c8a24cd
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 34308
content:
"The reduction in CO2 emissions of the COVID-19 pandemic in 2020 was estimated to be about 6% (Section 4.2.2.4 and Table 4.SM.2) \r\nlower than 2019 levels (Forster et al. 2020; Friedlingstein et al. 2020; Liu et al. 2020c; BP 2021; Crippa et al. 2021; IEA 2021; Le Quere \r\net al. 2021). Near-real-time monitoring estimates show a rebound in emissions levels, meaning 2021 emissions levels are expected \r\nto be higher than 2020 (Le Quere et al. 2021). The longer-term effects are uncertain but so far do not indicate a clear structural \r\nchange for climate policy related to the pandemic. The increase in renewable shares in 2020 could stimulate a further transition, \r\nbut slow economic growth can also slow down (renewable) energy investments. Also, lifestyle changes during the crisis can still \r\ndevelop in different directions (working from home, but maybe also living further away from work)"


id: e4508da6a600b04b72179142d4bcb346
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 33397
content:
" Without a major intervention, \r\nmost long-term scenarios project that emissions will start to follow a similar pathway as earlier projections (although at a reduced \r\nlevel) (IEA 2020b; Kikstra et al. 2021a; Rochedo et al. 2021). If emissions reductions are limited to only a short time, the adjustment \r\nof pathways will lead to negligible outcomes in the order of 0.01K (Forster et al. 2020; Jones et al. 2021). At the same time, however, \r\nthe large amount of investments pledged in the recovery packages could provide a  unique opportunity to determine the long\u0002term development of infrastructure, energy systems and land use (Andrijevic et al. 2020b; Hepburn et al. 2020; Pianta et al. 2021). \r\nNear-term alternative recovery pathways have been shown to have the potential to influence carbon-price pathways, and energy \r\ninvestments and electrification requirements under stringent mitigation targets (Bertram et al. 2021; Kikstra et al. 2021a; Pollitt et al. \r\n2021; Rochedo et al"


id: db3d932cbeec79729e330a8349473f17
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 32002
content:
" 2021; Shan et al. 202). Most studies suggest a noticeable reduction in 2030 emissions. However, much further \r\nreductions would be needed to reach the emission levels consistent with mitigation scenarios that limit warming to 2degC (>67%) \r\nor lower (see Chapter 4). At the moment, the share of investments in greenhouse gas reduction is relatively small in most recovery \r\npackages, and no structural shifts for climate policies are observed linked to the pandemic. Finally, most of the scenarios analysed \r\nin this Chapter do not include the 2020 emissions reduction related to the COVID-19 pandemic. The effect of the pandemic on the \r\npathways will likely be very small. The assessment of climate mitigation pathways in this chapter should be interpreted as being \r\nalmost exclusively based on the assumption of a fast recovery with limited persistent effects on emissions or structural changes."


id: 824791c751324da7b2425160b44f2a66
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 18023
content:
"The higher categories (C6 and C7) mostly included scenarios with \r\nno or modest climate policy. Because of the progression of climate \r\npolicy, it is becoming more common that reference scenarios \r\nincorporate implemented climate policies. Modelling studies typically \r\nimplement current or pledged policies up until 2030 (Vrontisi et al. \r\n2018; Roelfsema et al. 2020; Sognnaes et al. 2021) with some studies \r\nfocusing also on the policy development in the long term (Hohne \r\net al. 2021; IEA  2021a; Jeffery et al. 2018; Gutschow et al. 2018). \r\nBased on the assessment in Chapter 4, reference pathways consistent \r\nwith the implementation and trend from implemented policies until \r\nthe  end of 2020 are associated with increased GHG emissions \r\nfrom 59 (53-65) GtCO2-eq yr-1 in 2019 to 54-60 GtCO2-eq yr-1 by \r\n2030 and to 47-67 GtCO2-eq yr-1 by 2050 (Figure  3.6). Pathways \r\nwith these near-term emissions characteristics lead to a  median \r\nglobal warming of 2.2degC to 3"


id: 1eb88d2acfe75a37b473df4fb163262c
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 2279
content:
"5degC by 2100 (see also further in this \r\nsection). These pathways consider policies at the time that they \r\nwere developed. A recent model comparison that harmonised socio\u0002economic, technological, and policy assumptions (Giarola et al. 2021) \r\nfound a  2.2degC-2.9degC median temperature rise in 2100 for current \r\nand stated policies, with the results sensitive to the model used and \r\nthe method of implementing policies (Sognnaes et al. 2021). Scenario \r\ninference and construction methods using similar policy assumptions \r\nlead to a median range of 2.9degC-3.2degC in 2100 for current policies \r\nand 2.4degC-2.9degC in 2100 for 2030 pledges (Hohne et al. 2021). The \r\nmedian spread of 1degC across these studies (2.2degC-3.2degC) indicates \r\nthe deep uncertainties involved with modelling temperature \r\noutcomes of 2030 policies through to 2100 (Hohne et al. 2021)."


id: 1ead1d5b378d59a42add0e04a905ab18
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 2269
content:
"Figure 3.11 shows the possible consequences of the different scenario \r\ncategories for global mean temperature calculated using a reduced \r\ncomplexity model (RCM) calibrated to the IPCC AR6 WGI assessment \r\n(see Annex III.II.2.5 of this report and Cross-Chapter Box 7.1 in \r\nAR6 WGI report). For the C5-C7 categories (containing most of the \r\nreference and current policy scenarios), the global mean temperature \r\nis expected to increase throughout the century (and further increase \r\nwill happen after 2100 for C6 and C7). While warming would more \r\nlikely than not be in the range from 2.2degC to 3.5degC, warming up to 5degC \r\ncannot be excluded. The highest emissions scenarios in the literature \r\ncombine assumptions about rapid long-term economic growth \r\nand pervasive climate policy failures, leading to a reversal of some \r\nrecent trends (Box 3.3)"


id: 288b1c459d588178091641871be0ef1f
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 3798
content:
" For the categories C1-C4, a peak in global \r\nmean temperature is reached mid-century for most scenarios in the \r\ndatabase, followed by a small (C3/C4) or more considerable decline \r\n(C1/C2). There is a clear distinction between the scenarios with no or "


id: 4629bc8a34f9340b942733daefc03870
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 8421
content:
"At the time the Representative Concentration Pathways (RCPs) were published, they included three scenarios that could represent \r\nemission developments in the absence of climate policy: RCP4.5, RCP6 and RCP8.5, described as, respectively, low, medium and high\u0002end scenarios in the absence of strong climate policy (van Vuuren et al. 2011). RCP8.5 was described as representative of the top 5% \r\nscenarios in the literature. The SSPs-based set of scenarios covered the RCP forcing levels, adding a new low scenario (at 1.9 W m-2). \r\nHausfather and Peters (2020) pointed out that since 2011, the rapid development of renewable energy technologies and emerging \r\nclimate policy have made it considerably less likely that emissions could end up as high as RCP8.5. Still, emission trends in developing \r\ncountries track RCP8.5 Pedersen et al. (2020), and high land-use emissions could imply that emissions would continue to do so in the \r\nfuture, even at the global scale (Schwalm et al. 2020)"


id: 5cf2a9e3c680aebe4c68e09d35c586c6
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 12140
content:
" Other factors resulting in high emissions include higher population or economic \r\ngrowth as included in the SSPs (Section 3.3.1) or rapid development of new energy services. Climate projections of RCP8.5 can also \r\nresult from strong feedbacks of climate change on (natural) emission sources and high climate sensitivity (AR6 WGI Chapter 7), and \r\ntherefore their median climate impacts might also materialise while following a lower emission path (e.g., Hausfather and Betts 2020). \r\nThe discussion also relates to a more fundamental discussion on assigning likelihoods to scenarios, which is extremely difficult given \r\nthe deep uncertainty and direct relationship with human choice. However, it would help to appreciate certain projections (e.g., Ho \r\net al. 2019). All in all, this means that high-end scenarios have become considerably less likely since AR5 but cannot be ruled out. It \r\nis important to realise that RCP8.5 and SSP5-8"


id: e7694f36564aed8f1339741ca3814da6
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
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vector_id: 33901
content:
"5 do not represent a typical 'business-as-usual' projection but are only useful as high\u0002end, high-risk scenarios. Reference emission scenarios (without additional climate policy) typically end up in the C5-C7 categories \r\nincluded in this assessment."


id: c64711e094590cd24a7f3ed0b0532205
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 28770
content:
"For accounting of emissions and the substitution of different gases as \r\npart of a  mitigation strategy, typically, emission metrics are used to \r\ncompare the climate impact of different gases. Most policies currently \r\nuse Global Warming Potentials (GWPs) with a 100-year time horizon \r\nas this is also mandated for emissions reporting in the Paris Rulebook \r\n(for a  wider discussion of GHG metrics, see Box 2.1 in Chapter  2 of \r\nthis report, and AR6 WGI, Chapter 7, Section 7.6). Alternative metrics \r\nhave also been proposed, such as those using a shorter or longer time \r\nhorizon, or those that focus directly on the consequences of reaching \r\na certain temperature target (Global Temperature Change Potential - \r\nGTP), allowing a more direct comparison with cumulative CO2 emissions \r\n(Allen et al. 2016; Lynch et al. 2020) or focusing on damages (Global \r\nDamage Potential) (an overview is given in Chapter 2, and Cross-Chapter \r\nBox 3 in Chapter 3)"


id: f8a2d56f6d067f6d5c0d4ffef049198c
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 36631
content:
" Depending on the metric, the value attributed to \r\nreducing short-lived forcers such as methane can be lower in the near \r\nterm (e.g., in the case of GTP) or higher (GWP with a short reference \r\nperiod). For most metrics, however, the impact on mitigation strategies is \r\nrelatively small, among others, due to the marginal abatement cost curve \r\nof methane (low costs for low-to-medium mitigation levels; expensive \r\nfor high levels). The timing of reductions across different gases impacts \r\nwarming and the co-benefits (Harmsen et al. 2016; Cain et al. 2019). \r\nNearly all scenarios in the literature use GWP-100 in cost-optimisation, \r\nreflecting the existing policy approach; the use of GWP-100 deviates \r\nfrom cost-optimal mitigation pathways by at most a few percent for \r\ntemperature goals that limit warming to 2degC (>67%) or lower (Box 2.1)."


id: 45df93fd6da34907b7ab7885e6958393
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 8382
content:
"The trajectory of future CO2 emissions plays a critical role in mitigation, \r\ngiven CO2 long-term impact and dominance in total greenhouse gas \r\nforcing. As shown in Figure 3.12, CO2 dominates total greenhouse \r\ngas emissions in the high-emissions scenarios but is also reduced \r\nmost, going from scenarios in the highest to lower categories. In C4 \r\nand below, most scenarios exhibit net negative CO2 emissions in the \r\nsecond half of the century compensating for some of the residual \r\nemissions of non-CO2 gases as well as reducing overall warming \r\nfrom an intermediate peak. Still, early emission reductions and further \r\nreductions in non-CO2 emissions can also lead to scenarios without \r\nnet negative emissions in 2100, even in C1 and C3 (shown for the \r\n85-95th percentile)"


id: 37cc63667e635fe5023dab8b0dbfc940
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 6153
content:
" In C1, avoidance of significant overshoot implies \r\nthat immediate gross reductions are more relevant than long-term \r\nnet negative emissions (explaining the lower number than in C2) but \r\ncarbon dioxide removal (CDR) is still playing a role in compensating \r\nfor remaining positive emissions in hard-to-abate sectors."


id: 641a11d463bab961a98004c390fb26ae
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 13316
content:
"CH4 and N2O emissions are also reduced from C7 to C1, but this mostly \r\noccurs between C7 and C5. The main reason is the characteristics \r\nof abatement potential: technical measures can significantly reduce \r\nCH4 and N2O emissions at relatively low costs to about 50% of the \r\ncurrent levels (e.g., by reducing CH4 leaks from fossil fuel production \r\nand transport, reducing landfill emissions gazing, land management \r\nand introducing measures related to manure management, see also \r\nChapter 7 and 11). However, technical potential estimates become \r\nexhausted even if the stringency of mitigation is increased (Harmsen \r\net al. 2019a,b; Hoglund-Isaksson et al. 2020). Therefore, further \r\nreduction may come from changes in activity levels, such as switching \r\nto a less meat-intensive diet, therefore reducing livestock (Stehfest \r\net al. 2009; Willett et al. 2019; Ivanova et al. 2020) (Chapter 7)"


id: 2c606e5ca1ddc53d265965e123a469c3
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 4413
content:
" Other \r\nnon-CO2 GHG emissions (halogenated gases) are reduced to low \r\nlevels for scenarios below 2.5degC."


id: 59016017109acdbf00beaeead064d0ea
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 11495
content:
"The dominating role of CO2 and its long lifetime in the atmosphere \r\nand some critical characteristics of the Earth System implies that \r\nthere is a  strong relationship between cumulative CO2 emissions \r\nand temperature outcomes (Allen et al. 2009; Matthews et al. 2009; \r\nMeinshausen et al. 2009; MacDougall and Friedlingstein 2015). This \r\nis illustrated in Figure 3.13, which plots the cumulative CO2 emissions \r\nagainst the projected outcome for global mean temperature, both until \r\npeak temperature and through to end of century (or 2100). The deviations \r\nfrom a linear relationship in Figure 3.13 are mostly caused by different \r\nnon-CO2 emission and forcing levels (see also Rogelj et al. 2015b). This \r\nmeans that reducing non-CO2 emissions can play an important role \r\nin limiting peak warming: the smaller the residual non-CO2 warming, \r\nthe larger the carbon budget. This impact on carbon budgets can be \r\nsubstantial for stringent warming limits. For 1"


id: bf088551d0d840eaa2752b4c423e8bae
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 27607
content:
"5degC pathways, variations \r\nin non-CO2 warming across different emission scenarios have been \r\nfound to vary the remaining carbon budget by approximately 220 GtCO2 \r\n(AR6 WGI Chapter 5, Section 5.5.2.2). In addition to reaching net zero \r\nCO2 emissions, a strong reduction in methane emissions is the most \r\ncritical component in non-CO2 mitigation to keep the Paris climate goals \r\nin reach (Collins et al. 2018; van Vuuren et al. 2018) (see also AR6 WGI, \r\nChapters 5, 6 and 7). It should be noted that the temperature categories \r\n(C1-C7) generally aligned with the horizontal axis, except for the end\u0002of-century values for C1 and C2 that coincide."


id: a8dc70d93422681056cd18ea888db26b
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 24094
content:
"Short-lived climate forcers (SLCFs) also play an important role in climate \r\nchange, certainly for short-term changes (AR6 WGI, Figure  SPM.2) \r\n(Shindell et al. 2012). These forcers consist of (i) substances contributing \r\nto warming, such as methane, black carbon and tropospheric ozone, \r\nand (ii) substances contributing to cooling (other aerosols, such as \r\nrelated to sulphur emissions). Most SLCFs are also air pollutants, and \r\nreducing their emissions provides additional co-benefits (Shindell et al. \r\n2017a,b; Hanaoka and Masui 2020). In the case of the first group, \r\nemission reduction thus leads to both air pollution and climate benefits. \r\nFor the second, group there is a possible trade-off (Shindell and Smith \r\n2019; Lund et al. 2020). As aerosol emissions are mostly associated \r\nwith fossil fuel combustion, the benefits of reducing CO2 could, in \r\nthe short term, be reduced as a result of lower aerosol cooling"


id: 76df90afef580a0ebe5bcad459fffa43
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 16280
content:
" There \r\nhas been an active discussion on the exact climate contribution of \r\nSLCF-focused policies in the literature. This discussion partly emerged \r\nfrom different assumptions on possible reductions in the absence of \r\nambitious climate policy and the uncertain global climate benefit from \r\naerosol (black carbon) (Rogelj et al. 2014). The latter is now assessed to \r\nbe smaller than originally thought (Takemura and Suzuki 2019; Smith \r\net al. 2020b) (see also AR6 WGI Section 6.4). Reducing SLCF emissions \r\nis critical to meet long-term climate goals and might help reduce \r\nthe rate of climate change in the short term. Deep SLCF emission \r\nreductions also increase the remaining carbon budget for a specific \r\ntemperature goal (Rogelj et al. 2015a; Reisinger et al. 2021) (Box 3.4). \r\nA more detailed discussion can be found in AR6 WGI Chapters 5 and 6."


id: 841312318992e5d1a0fe8b513aa32705
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 18315
content:
"The AR6 WGI estimated the remaining carbon budgets from their assessment of (i) the transient climate response to cumulative \r\nemissions of carbon dioxide (TCRE), and estimates of (ii) the historical human-induced warming, (iii) the temperature change after \r\nreaching net zero CO2 emissions, (iv) the contribution of future non-CO2 warming (derived from the emissions scenarios assessed in \r\nthe Special Report on 1.5degC Warming using WGI-calibrated emulators), and (v) the Earth System feedbacks (AR6 WGI Chapter 5.5, \r\nBox 5.2). For a given warming level, AR6 WGI assessed the remaining carbon budget from the beginning of 2020 onwards. These are \r\n650/500/400 GtCO2 for limiting warming to 1.5degC with 33%/50%/ 67% chance and 1350/1150 GtCO2 for limiting warming to 2degC \r\nwith 50%/67% chance. The estimates are subject to considerable uncertainty related to historical warming, future non-CO2 forcing, \r\nand poorly quantified climate feedbacks"


id: 20fa84cfbaeddd4ea3f49efd7501a2d6
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 2620
content:
" For instance, variation in non-CO2 emissions across scenarios are estimated to either increase \r\nor decrease the remaining carbon budget estimates by 220 GtCO2. The estimates of the remaining carbon budget assume that non\u0002CO2 emissions are reduced consistently with the tight temperature targets for which the budgets are estimated."


id: ba0c79ad3722e0630023091e01fc8ec3
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 26812
content:
"Estimates of the remaining carbon budgets thus vary with the assumed level of non-CO2 emissions, which are a function of policies \r\nand technology development. The linear relationship used in the AR6 WGI assessment between peak temperature and the warming \r\nas a result of non-CO2 emissions (based on the SR1.5 data) is shown in the right panel of Figure 2 (dashed line). In the AR6 WGIII \r\napproach, the non-CO2 warming for each single scenario is based on the individual scenario characteristics. This is shown in the same \r\nfigure by plotting the outcomes of scenario outcomes of a range of models (dots). The lines show the fitted data for individual models, \r\nemphasising the clear differences across models and the relationship with peak warming (policy level). In some scenarios, stringent \r\nnon-CO2 emission reductions provide an option to reach more stringent climate goals with the same carbon budget"


id: 3f26a2dd0855a8cf7f7a86b6f8fa7f54
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 7325
content:
" This is especially \r\nthe case for scenarios with a very low non-CO2 warming, for instance, as a result of methane reductions through diet change. The \r\nleft panel shows how these differences impact estimates of the remaining carbon budget. While the AR6 scenarios database includes \r\na broad range of non-CO2 emission projections the overall range is still very consistent with the WGI relationship and the estimated \r\nuncertainty with a +-220 GtCO2 range (see also Figure 5 in Annex III.II.2.5.1)."


id: 29879a441f15192329a617984b844fc
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 3970
content:
"Overall, the slight differences between the cumulative emissions in AR6 WGIII and the carbon budget in AR6 WGI are because the \r\nnon-CO2 warming in the WGIII scenarios is slightly lower than in the SR1.5 scenarios that are used for the budget estimates in WGI \r\n(Annex III.2.5.1). In addition, improved consistency with Cross-Chapter Box 7.1 in Chapter 7, AR6 WGI results in a non-CO2-induced \r\ntemperature difference of about about 0.05K between the assessments. Recalculating the remaining carbon budget using the WGI \r\nmethodology combined with the full AR6 WGIII scenario database results in a reduction of the estimated remaining 1.5degC carbon \r\nbudget by about 100 GtCO2 (-20%), and a reduction of about 40 GtCO2 (-3%) for 2degC. Accounting also for the categorisation effect, \r\nthe difference between the WGI and WGIII estimates is found to be small and well within the uncertainty range (Figure 1)"


id: 8f8b96a4acdd66c0d946c60e8a3c4390
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 20131
content:
" This means \r\nthat the cumulative CO2 emissions presented in WGIII and the WGI carbon budgets are highly consistent."


id: dd87545ac38052caed44a98922a63b64
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 32354
content:
"Box 3.4, Figure 2 | (a) Differences in regressions of the relationship between peak surface temperature and associated cumulative CO2 emissions \r\nfrom 2020 derived from scenarios of eight integrated assessment model frameworks. The coloured lines show the regression at median for scenarios of \r\nthe eight modelling frameworks, each with more than 20 scenarios in the database and a detailed land-use representation. The red dotted lines indicate the non-CO2\r\nuncertainty range of AR6 WGI Chapter 5 (+-220 GtCO2), here visualised around the median of the eight model framework lines. Carbon budgets from 2020 until 1.5degC \r\n(0.43K above 2010-2019 levels) and 2.0degC (0.93K above 2010-2019 levels) are shown for minimum and maximum model estimates at the median, rounded to the \r\nnearest 10 GtCO2. Panel (b) shows the relationship between the estimated non-CO2 warming in mitigation scenarios that reach net zero and the associated peak \r\nsurface temperature outcomes"


id: b0398ee25dcfa9cd5a0055fda33b48e4
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 25249
content:
" The coloured lines show the regression at median for scenarios of the eight modelling frameworks with more than 20 scenarios in the \r\ndatabase and a detailed land-use representation. The black dashed line indicates the non-CO2 relationship based on the scenarios and climate emulator setup as was \r\nassessed in AR6 WGI Chapter 5."


id: 9e7025b9577e3940c48144082accc816
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 22459
content:
"systems. For the energy systems, these negative emissions originate \r\nfrom bioenergy with carbon capture and storage (BECCS), while \r\nfor AFOLU, they originate from reforestation and afforestation. For \r\nC3-C5, reforestation has a  larger CDR contribution than BECCS, \r\nmostly due to considerably lower costs (Rochedo et al. 2018). For C1 \r\nand C2, the tight carbon budgets imply in many scenarios more CDR \r\nuse (Riahi et al. 2021). Please note that net negative emissions are not \r\nso relevant for peak-temperature targets, and thus the C1 category, \r\nbut CDR can still be used to offset the remaining positive emissions \r\n(Riahi et al. 2021). While positive CO2 emissions from fossil fuels are \r\nsignificantly reduced, inertia and hard-to-abate sectors imply that \r\nin many C1-C3 scenarios, around 800-1000 GtCO2 of net positive \r\ncumulative CO2 emissions remain"


id: 6b7ce09b96dde373096755ed5db3e6f9
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 14527
content:
" This is consistent with literature \r\nestimates that current infrastructure is associated with 650 GtCO2\r\n(best estimate) if operated until the end of its lifetime (Tong et al. \r\n2019). These numbers are considerably above the estimated carbon \r\nbudgets for  1.5degC estimated in AR6 WGI, hence explaining CDR \r\nreliance (either to offset emissions immediately or later in time)."


id: 14a5969c53ef7b5388503741e3ec6ca
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 752
content:
"Creating net negative emissions can thus be an important part of \r\na mitigation strategy to offset remaining emissions or compensate \r\nfor emissions earlier in time. As indicated above, there are different \r\nways to potentially achieve this, including reforestation and \r\nafforestation and BECCS (as often covered in IAMs) but also soil \r\ncarbon enhancement, direct air carbon capture and storage (DACCS) \r\nand ocean alkalinisation (Chapter 12). Except for reforestation, these \r\noptions have not been tested at large scale and often require more R&D. \r\nMoreover, the reliance on CDR in scenarios has been discussed given \r\npossible consequences of land use related to biodiversity loss and \r\nfood security (BECCS and afforestation), the reliance on uncertain \r\nstorage potentials (BECCS and DACCS), water use (BECCS), energy \r\nuse (DACCS), the risks of possible temperature overshoot and the \r\nconsequences for meeting Sustainable Development Goals (SDGs) \r\n(Anderson and Peters 2016; Smith et al"


id: 21e2dc5a5014db681b146858d35fd316
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 2773
content:
" 2016; Venton 2016; Peters \r\nand Geden 2017; van Vuuren et al. 2017; Honegger et al. 2021). In \r\nthe case of BECCS, it should be noted that bioenergy typically is \r\nassociated with early-on positive CO2 emissions and net negative \r\neffects are only achieved in time (carbon debt), and its potential is \r\nlimited (Cherubini et al. 2013; Hanssen et al. 2020); most IAMs have \r\nonly a very limited representation of these time dynamics. Several \r\nscenarios have therefore explored how reliance on net negative CO2 \r\nemissions can be reduced or even avoided by alternative emission \r\nstrategies (Grubler et al. 2018; van Vuuren et al. 2018) or early \r\nreductions by more stringent emission reduction in the short term \r\n(Rogelj et al. 2019b; Riahi et al. 2021). A more in-depth discussion \r\nof land-based mitigation options can be found in Chapter 7"


id: 86c087417fc86a42ef5881b94973d8ef
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 18735
content:
" It needs \r\nto be emphasised that even in strategies with net negative CO2 \r\nemissions, the emission reduction via more conventional mitigation \r\nmeasures (efficiency improvement, decarbonisation of energy supply) \r\nis much larger than the CDR contribution (Tsutsui et al. 2020)."


id: 60c615420b89ead1469f1f4ea730de4e
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 12797
content:
"Figure  3.14 shows that there is a  relationship between the \r\ntemperature target, the cumulative CO2 emissions budget, and the net \r\nzero year for CO2 emissions (panel a) and the sum of greenhouse \r\ngases (panel b) for the scenarios published in the literature. In other \r\nwords, the temperature targets from the Paris Agreement can, to \r\nsome degree, be translated into a  net-zero emission year (Tanaka \r\nand  O'Neill 2018). There is, however, a  considerable spread. In \r\naddition to the factors influencing the emission budget (AR6 WGI \r\nand Section  3.3.2.2), this is influenced by the emission trajectory \r\nuntil net zero is reached, decisions related to temperature overshoot \r\nand non-CO2 emissions (especially for the moment CO2 reaches net \r\nzero emissions). Scenarios with limited or no net negative emissions \r\nand rapid near-term emission reductions can allow small positive \r\nemissions (e.g., in hard-to-abate-sectors)"


id: e02db40137d6c79e7580f1fdcd8365f8
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 32762
content:
" They may therefore have \r\na later year that net zero CO2 emissions are achieved. High emissions \r\nin the short term, in contrast, require an early net zero year."


id: ed45e0cee02628885268db61e4861e52
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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vector_id: 34807
content:
"For the scenarios in the C1 category (limit warming to 1.5degC (>50% \r\nwith no or limited overshoot, the net zero year for CO2 emissions \r\nis typically around 2035-2070. For scenarios in C3 (limiting \r\nwarming to 2degC (>67%)), CO2 emissions reach net zero around \r\nafter 2050. Similarly, also the years for net zero GHG emissions \r\ncan be calculated (see Fig 3.14b. The GHG net zero emissions year \r\nis typically around 10-40 years later than the carbon neutrality. \r\nResidual non-CO2 emissions at the time of reaching net zero CO2\r\nrange between 5-11 GtCO2-eq in pathways that limit warming to \r\n2degC (>67%) or lower. In pathways limiting warming to 2degC (>67%), \r\nmethane is reduced by around 19% (3-46%) in 2030 and 46% (29-\r\n64%) in 2050, and in pathways limiting warming to 1.5degC (>50%) \r\nwith no or limited overshoot by around 34% (21-57%) in 2030 and \r\na  similar 51% (35-70%) in 2050"


id: 76186712e65262612eff831d20d22c1
created_at: 2023-03-16 14:02:42
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vector_id: 16183
content:
" Emissions-reduction potentials \r\nassumed in the pathways become largely exhausted when limiting \r\nwarming to 2degC (>50%). N2O emissions are reduced too, but similar \r\nto CH4, emission reductions saturate for stringent climate goals. \r\nIn the mitigation pathways, the emissions of cooling aerosols are \r\nreduced due to reduced use of fossil fuels. The overall impact on non\u0002CO2-related warming combines these factors."


id: 729dd03e02b42acdde0ad318c503bde4
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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vector_id: 15647
content:
"In addition to the constraints on change in global mean temperature, \r\nthe Paris Agreement also calls for reaching a  balance of sources \r\nand sinks of GHG emissions (Art. 4). Different interpretations of the \r\nconcept related to balance have been published (Rogelj et al. 2015c; \r\nFuglestvedt et al. 2018). Key concepts include that of net zero CO2\r\nemissions (anthropogenic CO2 sources and sinks equal zero) and net \r\nzero greenhouse gas emissions (see Annex I: Glossary, and Box 3.3). \r\nThe same notion can be used for all GHG emissions, but here \r\nranges also depend on the use of equivalence metrics (Box  2.1). \r\nMoreover, it should be noted that while reaching net zero CO2\r\nemissions typically coincides with the peak in temperature increase; \r\nnet zero GHG emissions (based on GWP-100) imply a  decrease in \r\nglobal temperature (Riahi et al. 2021) and net zero GHG emissions \r\ntypically require negative CO2 emissions to compensate for the \r\nremaining emissions from other GHGs"


id: d701e1f3542e201cddd270a823e87841
created_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 31333
content:
" Many countries have started \r\nto formulate climate policy in the year that net zero emissions (either \r\nCO2 or all greenhouse gases) are reached - although, at the moment, \r\nformulations are often still vague (Rogelj et al. 2021). There has \r\nbeen increased attention on the timing of net zero emissions in the \r\nscientific literature and ways to achieve it."


id: 65d7b77f3db9de434663da29b80a7582
created_at: 2023-03-16 14:02:42
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vector_id: 13607
content:
"In cost-optimal scenarios, regions will mostly achieve net zero \r\nemissions as a function of options for emission reduction, CDR, and \r\nexpected baseline emission growth (van Soest et al. 2021b). This \r\ntypically implies relatively early net zero emission years in scenarios \r\nfor the Latin America region and relatively late net zero years for \r\nAsia and Africa (and average values for OECD countries). However, an \r\nallocation based on equity principles (such as responsibility, capability \r\nand equality) might result in different net zero years, based on the \r\nprinciples applied - with often earlier net zero years for the OECD \r\n(Fyson et al. 2020; van Soest et al. 2021b). Therefore, the emission \r\ntrajectory until net zero emissions is a critical determinant of future \r\nwarming (Section 3.5). The more CO2 is emitted until 2030, the less \r\nCO2 can be emitted after that to stay below a warming limit (Riahi \r\net al. 2015)"


id: 6d8e5a3e4afbf32a1a9ceffd40d7f252
created_at: 2023-03-16 14:02:42
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content:
"Authors: Elmar Kriegler (Germany), Alaa Al Khourdajie (United Kingdom/Syria), Edward Byers (Austria/Ireland), Katherine Calvin (the \r\nUnited States of America), Leon Clarke (the United States of America), Annette Cowie (Australia), Navroz Dubash (India), Jae Edmonds \r\n(the United States of America), Jan  S. Fuglestvedt (Norway), Oliver Geden (Germany), Giacomo Grassi (Italy/European Union), \r\nAnders Hammer Stromman (Norway), Frank Jotzo (Australia), Alexandre Koberle (Brazil/United Kingdom), Franck Lecocq (France), \r\nYun Seng Lim (Malaysia), Eric Masanet (the United States of America), Toshihiko Masui (Japan), Catherine Mitchell (United Kingdom), \r\nGert-Jan Nabuurs (the Netherlands), Anthony Patt (the United States of America/Switzerland), Glen  P. Peters (Norway/Australia), \r\nAndy  Reisinger (New Zealand), Keywan Riahi (Austria), Joeri Rogelj (United Kingdom/Belgium), Yamina Saheb (France/Algeria), \r\nJim Skea (United Kingdom), Detlef P"


id: f404db4d35d37d9f6a0129d9341924cc
created_at: 2023-03-16 14:02:42
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vector_id: 35900
content:
"Net zero CO2 emissions are reached between 2050-2055 (2035-2070) in global emissions pathways limiting warming \r\nto 1.5degC (>50%) with no or limited overshoot, and between 2070-2075 (2055-...) in pathways limiting warming to 2degC \r\n(>67%) as reported in the AR6 scenarios database (median five-year interval and 5-95th percentile ranges).5\r\n The variation of \r\nnon-CO2 emissions in 1.5degC-2degC pathways varies the available remaining carbon budget which can move the time of reaching net \r\nzero CO2 in these pathways forward or backward.6\r\n The shape of the CO2 emissions reduction profile also affects the time of reaching \r\nnet zero CO2 (Figure 1c in this Cross-Chapter Box). Global emission pathways that more than halve CO2 emissions from 2020 to 2030 \r\ncan follow this rapid reduction by a more gradual decline towards net zero CO2 and still limit warming to 1.5degC with no or limited \r\novershoot, reaching the point of net zero after 2050. The literature since SR1"


id: 168d0b67b52cb3d22dcdbdbdfb288765
created_at: 2023-03-16 14:02:42
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content:
"5 included a larger fraction of such pathways than were \r\navailable at the time of SR1.5. This is the primary reason for the small backward shift in the median estimate of reaching global net \r\nzero CO2 emissions in 1.5degC pathways collected in the AR6 scenario database compared to SR1.5. This does not mean that the world \r\nis assessed to have more time to rapidly reduce current emissions levels compared to SR1.5. The assessment of emissions reductions \r\nby 2030 and 2040 in pathways limiting warming to 1.5degC (>50%) with no or limited overshoot has not changed substantially. It only \r\nmeans that the exact timing of reaching net zero CO2 after a steep decline of CO2 emissions until 2030 and 2040 can show some \r\nvariation, and the SR1.5 median value of 2050 is still close to the middle of the current range (Figure 1c in this Cross-Chapter Box)."


id: 490cc361a2a2a8ab5a344d1cd8366d61
created_at: 2023-03-16 14:02:42
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content:
"Global net zero GHG emissions measured in terms of GWP-100 are reached between 2095 and 2100 (2050-...)10 in \r\nemission pathways limiting warming to 1.5degC (>50%) with no or limited overshoot (median and 5-95th percentile).\r\nAround 50% of pathways limiting warming to 1.5degC (>50%) with no or limited overshoot and 70% of pathways limiting warming \r\nto 2degC (>67%) do not reach net zero GHG emissions in terms of GWP-100 before 2100. These pathways tend to show less reduction \r\nin warming after the peak than pathways that reach net zero GHG emissions. For the subset of pathways that reach net zero GHG \r\nemissions before 2100, including around 90% of pathways that return warming to 1.5degC after a high overshoot (>0"


id: fe7939055b5d1ef5521737e603009c94
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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vector_id: 37576
content:
"1degC) by 2100, \r\nthe time lag between reaching net zero CO2 and net zero GHG is 12-14 (7-39) years and the amount of net negative CO2 emissions \r\ndeployed to balance non-CO2 emissions at the time of net zero GHG is around -7 (-10 to -4) GtCO2 (range of medians and lowest \r\n5th to highest 95 percentile across the four scenario classes that limit median warming to 2degC or lower) (Section 3.3 and Table 3.2)."


id: eb98aea5536d930f672b7ca8841b4105
created_at: 2023-03-16 14:02:42
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content:
"The timing of net zero CO2 or GHG emissions may differ across regions and sectors. Achieving net zero emissions globally \r\nimplies that some sectors and regions must reach net zero CO2 or GHG ahead of the time of global net zero CO2 or GHG if \r\nothers reach it later. Similarly, some sectors and regions would need to achieve net negative CO2 or GHG emissions to compensate \r\nfor continued emissions by other sectors and regions after the global net zero year. Differences in the timing to reach net zero \r\nemissions between sectors and regions depend on multiple factors, including the potential of countries and sectors to reduce GHG \r\nemissions and undertake carbon dioxide removal (CDR), the associated costs, and the availability of policy mechanisms to balance \r\nemissions and removals between sectors and countries (Fyson et al. 2020; Strefler et al. 2021a; van Soest et al. 2021b)"


id: 73946e28febc6f74ddb5327234a493a8
created_at: 2023-03-16 14:02:42
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vector_id: 15806
content:
" A lack of such \r\nmechanisms could lead to higher global costs to reach net zero emissions globally, but less interdependencies and institutional needs \r\n(Fajardy and Mac Dowell 2020). Sectors will reach net zero CO2 and GHG emissions at different times if they are aiming for such \r\ntargets with sector-specific policies or as part of an economy-wide net zero emissions strategy integrating emissions reductions and \r\nremovals across sectors. In the latter case, sectors with large potential for achieving net negative emissions would go beyond net zero \r\nto balance residual emissions from sectors with low potential, which in turn would take more time compared to the case of sector\u0002specific action"


id: 1331496cdbfd6095758954ce7cc15415
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
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vector_id: 531
content:
" Global pathways project global AFOLU emissions to reach global net zero CO2 the earliest, around 2030 to 2035 in \r\npathways to limit warming to 2degC (>67%) or lower, by rapid reduction of deforestation and enhancing carbon sinks on land, although \r\nnet zero GHG emissions from global AFOLU are typically reached 30 years later, if at all. The ability of global AFOLU CO2 emissions to \r\nreach net zero as early as in the 2030s in modelled pathways hinges on optimistic assumptions about the ability to establish global \r\ncost-effective mechanisms to balance emissions reductions and removals across regions and sectors. These assumptions have been \r\nchallenged in the literature and the Special Report on Climate Change and Land (IPCC SRCCL)."


id: 212aa492f49ca4a50cab26dcf9d03572
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 2653
content:
"The accounting of anthropogenic carbon dioxide removal on land matters for the evaluation of net zero CO2 and net zero \r\nGHG strategies. Due to the use of different approaches between national inventories and global models, the current net CO2 emissions \r\nare lower by 5.5 GtCO2, and cumulative net CO2 emissions in modelled 1.5degC-2degC pathways would be lower by 104-170 GtCO2, if carbon \r\ndioxide removals on land are accounted based on national GHG inventories. National GHG inventories typically consider a much larger \r\narea of managed forest than global models, and on this area additionally consider the fluxes due to human-induced global environmental \r\nchange (indirect effects) to be anthropogenic, while global models consider these fluxes to be natural. Both approaches capture the same \r\nland fluxes, only the accounting of anthropogenic vs natural emissions is different"


id: 380b58240c01eae93cd2e4a2c880a5af
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 6191
content:
" Methods to convert estimates from global models \r\nto the accounting scheme of national GHG inventories will improve the use of emission pathways from global models as benchmarks \r\nagainst which collective progress is assessed. (Section 7.2.2.5)."


id: 878425911dfd344515b69d62ed6f910a
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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vector_id: 18856
content:
"Table  3.2 summarises the key characteristics for all temperature \r\ncategories in terms of cumulative CO2 emissions, near-term \r\nemission reductions, and the years of peak emission and net \r\nzero CO2 and GHG emissions. The table shows again that many \r\npathways in the literature limit global warming to 2degC (>67%) \r\nor limit warming to  1.5degC (>50%) with no or limited overshoot \r\ncompared to pre-industrial levels. Cumulative net CO2 emissions \r\nfrom the year 2020 until the time of net zero CO2 in pathways \r\nthat limit warming to 1.5degC (>50%) with no or limited overshoot \r\nare 510 (330-710) GtCO2 and in pathways that limit warming \r\nto 2degC (>67%), 890 (640-1160) GtCO2 (see also Cross-Chapter \r\nBox 3 in this chapter). Mitigation pathways that limit warming to \r\n2degC (>67%) compared to pre-industrial levels are associated with \r\nnet global GHG emissions of 44 (32-55) GtCO2-eq yr-1 by 2030 \r\nand 20 (13-26) GtCO2-eq yr-1 in 2050"


id: cb705ee554ebcbcc38520bff51cd578a
created_at: 2023-03-16 14:02:42
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content:
" These correspond to GHG \r\nemissions reductions of 21% (1-42%) by 2030, and 64% (53-77%) \r\nby 2050 relative to 2019 emission levels. Pathways that limit \r\nglobal warming to  1.5degC (>50%) with no or limited overshoot \r\nrequire a  further acceleration in the pace of the transformation, \r\nwith GHG emissions reductions of 43% (34-60%) by 2030 and \r\n84% (73-98%) in 2050 relative to modelled 2019 emission levels. \r\nThe likelihood of limiting warming to below  1.5degC (>50%) with \r\nno or limited overshoot of the most stringent mitigation pathways \r\nin the literature (C1) has declined since SR1.5. This is because \r\nemissions have risen since 2010 by about 9 GtCO2 yr-1, resulting \r\nin relatively higher near-term emissions of the AR6 pathways by \r\n2030 and slightly later dates for reaching net zero CO2 emissions \r\ncompared to SR1.5."


id: f84134a1bbf2975652abde3f08aca1f1
created_at: 2023-03-16 14:02:42
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content:
" Subsequently, the 5th and \r\n95th percentile values across all pathways for each emulator are calculated. The coolest and warmest outcomes (i.e., the lowest p5 of two emulators, and the highest p95, \r\nrespectively) are shown in square brackets. These ranges therefore cover both the uncertainty of the emissions pathways as well as the climate emulators' uncertainty."


id: fdc4e4a33d1e4de1ec1b18ddf49de913
created_at: 2023-03-16 14:02:42
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content:
"i\r\n Emissions milestones are provided for five-year intervals in order to be consistent with the underlying five-year time-step data of the modelled pathways. Peak emissions \r\n(CO2 and GHGs) are assessed for five-year reporting intervals starting in 2020. The interval 2020-2025 signifies that projected emissions peak as soon as possible between \r\n2020 and at latest before 2025. The upper five-year interval refers to the median interval within which the emissions peak or reach net zero. Ranges in square brackets \r\nunderneath refer to the range across the pathways, comprising the lower bound of the 5th percentile five-year interval and the upper bound of the 95th percentile five-year \r\ninterval. Numbers in round brackets signify the fraction of pathways that reach specific milestones.\r\nj\r\n Percentiles reported across all pathways in that category include those that do not reach net zero before 2100 (fraction of pathways reaching net zero is given in round \r\nbrackets)"


id: a8ede0584b141b522a8f241a67ca16c7
created_at: 2023-03-16 14:02:42
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vector_id: 24104
content:
"The share of renewables is around 30-70% in the scenarios that \r\nlimit warming to 2degC (>67%) and clearly above 40% for scenarios \r\nthat limit warming   1.5degC (>50%) (panel  c). Scenarios have been \r\npublished with 100% renewable energy systems even at a  global \r\nscale, partly reflecting the rapid progress made for these technologies \r\nin the last decade (Creutzig et al. 2017; Jacobson et al. 2018; Breyer \r\nand Jefferson 2020). These scenarios do not show in the graph due to \r\na lack of information from non-energy sources. There is a debate in \r\nthe literature on whether it is possible to achieve a 100% renewable \r\nenergy system by 2050 (Brook et al. 2018). This critically depends on \r\nassumptions made on future system integration, system flexibility, \r\nstorage options, consequences for material demand and the ability \r\nto supply high-temperature functions and specific mobility functions \r\nwith renewable energy"


id: d16068156f307225e070ae559bd612c5
created_at: 2023-03-16 14:02:42
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vector_id: 30474
content:
" The range of studies published showing \r\n100% renewable energy systems show that it is possible to design \r\nsuch systems in the context of energy system models (Hong et al. "


id: 547b1a4452cd9d178a94ce5d31031d8
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 10750
content:
"At the moment, climate change impact on mitigation potential is hardly \r\nconsidered in model-based scenarios. While a detailed overview of \r\nclimate impacts is provided in IPCC AR6 WGII and Section 3.6 discusses \r\nthe economic consequences, here we concentrate on the implications \r\nfor mitigation potential. Climate change directly impacts the carbon \r\nbudget via all kinds of feedbacks - which is included in the ranges \r\nprovided for the carbon budget (e.g., 300-900 GtCO2 for 17th-83rd \r\npercentile for not exceeding 1.5degC; see AR6 WGI Chapter 5, 2021). \r\nClimate change, however, alters the production and consumption \r\nof energy (Section 6.5). An overview of the literature is provided by \r\nYalew et al. (2020). In terms of supply, impacts could influence the \r\ncooling capacity of thermal plants, the potential and predictability of \r\nrenewable energy, and energy infrastructure (van Vliet et al. 2016; \r\nTurner et al. 2017; Cronin et al. 2018a; Lucena et al. 2018; Yalew \r\net al"


id: a73bf1ac3970baf2bfdff4b45684f74b
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 23823
content:
" 2020; Gernaat et al. 2021). Although the outcomes of these \r\nstudies differ, they seem to suggest that although impacts might be \r\nrelatively small at the global scale, they could be substantial at the \r\nregional scale (increasing or decreasing potential). Climate change \r\ncan also impact energy demand, with rising temperatures resulting \r\nin decreases in heating demand and increases in cooling demand \r\n(Isaac and van Vuuren 2009; Zhou et al. 2014; Labriet et al. 2015; \r\nMcFarland et al. 2015; Auffhammer et al. 2017; Clarke et al. 2018; \r\nvan Ruijven et al. 2019; Yalew et al. 2020). As expected, the increase \r\nin cooling demand dominates the impact in warm regions and \r\ndecreases in heating demand in cold regions (Isaac and van Vuuren \r\n2009; Zhou et al. 2014; Clarke et al. 2018). Globally, most studies \r\nshow a net increase in energy demand at the end of the century due \r\nto climate impacts (Isaac and van Vuuren 2009; Clarke et al. 2018; \r\nvan Ruijven et al"


id: 4a0087f4e4c7a0baad9e364e994e09e4
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 9050
content:
" 2019); however, one study shows a net decrease \r\n(Labriet et al. 2015). Only a  few studies quantify the combined \r\nimpacts of climate change on energy supply and energy demand \r\n(McFarland et al. 2015; Mima and Criqui 2015; Emodi et al. 2019; "


id: e5331946d5a17563e18936c38396e4a8
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 33555
content:
"Climate change can impact the potential for AFOLU mitigation \r\naction by altering terrestrial carbon uptake, crop yields and \r\nbioenergy potential (Chapter  7). Carbon sequestration in forests \r\nmay be positively or adversely affected by climate change \r\nand  CO2 fertilisation. On the one hand, elevated CO2 levels and \r\nhigher temperatures could enhance tree growth rates, carbon \r\nsequestration, and timber and biomass production (Beach et al. \r\n2015; Kim et al. 2017; Anderegg et al. 2020). On the other hand, \r\nclimate change could lead to greater frequency and intensity of \r\ndisturbance events in forests, such as fires, prolonged droughts, \r\nstorms, pests and diseases (Kim et al. 2017; Anderegg et al. 2020). \r\nThe impact of climate change on crop yields could also indirectly \r\nimpact the availability of land for mitigation and AFOLU emissions \r\n(Calvin et al. 2013; Bajzelj and Richards 2014; Kyle et al. 2014; Beach \r\net al. 2015; Meijl et al. 2018)"


id: bd44ac805b94af64ab1ad9bf7948af85
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 27341
content:
" The impact is, however, uncertain, as \r\ndiscussed in AR6 WGII Chapter 5. A few studies estimate the effect \r\nof climate impacts on AFOLU on mitigation, finding increases in \r\ncarbon prices or mitigation costs by 1-6% in most scenarios (Calvin \r\net al. 2013; Kyle et al. 2014)."


id: b5a805310d1751f8b1af85891f44c55d
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 26111
content:
"This section describes the role of sectors in long-term emissions \r\npathways (Table  3.3). We discuss both sectoral aspects of IAM \r\npathways and some insights from sectoral studies. Sectoral studies \r\ntypically include more detail and additional mitigation options \r\ncompared to IAMs. However, sectoral studies miss potential \r\nfeedbacks and cross-sectoral linkages that are captured by IAMs. \r\nAdditionally, since IAMs include all emissions sources, these models \r\ncan be used to identify pathways to particular climate goals. In such \r\npathways, emissions are balanced across sectors typically based \r\non relative marginal abatement costs; as a result, some sectors are \r\nsources and some are sinks at the time of net zero CO2 emissions. \r\nFor these reasons, the mitigation observed in each sector in an IAM \r\nmay differ from the potential in sectoral studies"


id: 5bc58b8f56237c2f5798f7c90b42f6f8
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 11950
content:
" Given the strengths \r\nand limitations of each type of model, IAMs and sectoral models are \r\ncomplementary, providing different perspectives."


id: d0a98e1fcf3eb1fc120aa8d614504106
created_at: 2023-03-16 14:02:42
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vector_id: 30359
content:
"A small number of mitigation pathways include stringent demand\u0002side mitigation, including changes in thermostat set points \r\n(van Sluisveld et al. 2016; van Vuuren et al. 2018), more efficient or \r\nsmarter  appliances (van Sluisveld et al. 2016; Grubler et al. 2018; \r\nNapp  et al. 2019), increased recycling or reduced industrial goods \r\n(Liu  et al. 2018; van  Sluisveld et al. 2016; Grubler et al. 2018; \r\nvan  de  Ven et al. 2018; Napp et al. 2019), telework and travel \r\navoidance (Grubler et al. 2018; van de Ven et al. 2018), shifts to public \r\ntransit (van  Sluisveld et al. 2016; Grubler et al. 2018; van Vuuren \r\net al. 2018), reductions in food waste (van de Ven et al. 2018) and \r\nless meat-intensive diets (Liu et al. 2018; van de Ven et al. 2018; \r\nvan Vuuren et al. 2018). These pathways show reduced dependence \r\non CDR and reduced pressure on land (Grubler et al. 2018; Rogelj et al. \r\n2018a; van de Ven et al. 2018; van Vuuren et al. 2018) (Section 5.3.3)"


id: f55274cefc65dba1d3dee5af3db8d178
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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vector_id: 36117
content:
" \r\nHowever, the representation of these demand-side mitigation \r\noptions in IAMs is limited, with most models excluding the costs of \r\nsuch changes (van Sluisveld et al. 2016), using stylised assumptions \r\nto represent them (van den Berg et al. 2019), and excluding rebound \r\neffects (Krey et al. 2019; Brockway et al. 2021). Furthermore, there \r\nare questions about the achievability of such pathways, including \r\nwhether the behavioural changes included are feasible (Azevedo \r\net al. 2021) and the extent to which development and demand can \r\nbe decoupled (Steckel et al. 2013; Brockway et al. 2021; Keysser and \r\nLenzen 2021; Semieniuk et al. 2021)."


id: 30a996e655677a6fe3ed6f4f9ec6da9e
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 5078
content:
"So, while pathways indicate some flexibility in emissions \r\nreductions across sectors, all pathways involve substantial CO2\r\nemissions reductions in all sectors and regions (high confidence) \r\n(Luderer et al. 2018; Rogelj et al. 2018a,b; Mejean et al. 2019; \r\nAzevedo et al. 2021). Projected CO2 emissions reductions between \r\n2019 and 2050 in  1.5degC (>50%) pathways with no or limited \r\novershoot are around 77% for energy demand, with a  5-95% \r\nrange of 31-96%,12 115% for energy supply (90-167%), and \r\n148% for AFOLU (94-387%). In pathways that limit warming to \r\n2degC (>67%), projected CO2 emissions are reduced between 2019 \r\nand 2050 by around 49% for energy demand, 97% for energy \r\nsupply, and 136% for AFOLU (Sections 3.4.2-3.4.6). Almost 75% \r\nof GHG reductions at the time of net zero GHG are from the \r\nenergy system, 13% are from AFOLU CO2, and 13% from non-CO2\r\n(Figure 3.21). These reductions are achieved through a variety of \r\nsectoral strategies, illustrated in Figure  3"


id: 3d52db64a36537e62bc3d793ff6cc6bb
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 7007
content:
"21 (Figure  3.21b), and \r\ndescribed in Sections 3.4.2 to 3.4.7; the primary strategies include \r\ndeclines in fossil energy, increases in low-carbon energy use, and \r\nCDR to address residual emissions."


id: d997db3e35311a9784e57c4d71596a41
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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vector_id: 31746
content:
"In the context of mitigation pathways, only a  few studies have \r\nexamined solar radiation modification (SRM), typically focusing on \r\nStratospheric Aerosol Injection (Arinoa et al. 2016; Emmerling and \r\nTavoni 2018a,b; Heutel et al. 2018; Helwegen et al. 2019; Rickels \r\net al. 2020; Belaia et al. 2021). These studies find that substantial \r\nmitigation is required to limit warming to a given level, even if SRM \r\nis available (Moreno-Cruz and Smulders 2017; Emmerling and Tavoni \r\n2018b; Belaia et al. 2021). SRM may reduce some climate impacts, \r\nreduce peak temperatures, lower mitigation costs, and extend the \r\ntime available to achieve mitigation; however, SRM does not address \r\nocean acidification and may involve risks to crop yields, economies, \r\nhuman health, or ecosystems (AR6 WGII Chapter 16; AR6 WGI TS and \r\nChapter 5; SR1.5 SPM; and Cross-Working Group Box 4 in Chapter 14 \r\nof this report)"


id: 6326fc88663bbaeac120368db7c68882
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 13167
content:
" There are also significant uncertainties surrounding \r\nSRM, including uncertainties on the costs and risks, which can \r\nsubstantially alter the amount of SRM used in modelled pathways \r\n(Tavoni et al. 2017; Heutel et al. 2018; IPCC 2018; Helwegen \r\net al. 2019; NASEM 2021). Furthermore, the degree of international \r\ncooperation can influence the amount of SRM deployed in scenarios, \r\nwith uncoordinated action resulting in larger SRM deployment and \r\nconsequently larger risks/impacts from SRM (Emmerling and Tavoni \r\n2018a). Bridging research and governance involves consideration \r\nof the full range of societal choices and ramifications (Sugiyama \r\net al. 2018). More information on SRM, including the caveats, risks, \r\nuncertainties, and governance issues is found in AR6 WGI Chapter 4; \r\nAR6 WGIII Chapter 14; and Cross-Working Group Box 4 in Chapter 14 \r\nof this report."


id: eb79d9a46750bf1d2103c762c014061
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 34547
content:
"The inclusion of CDR options, like BECCS, can affect the timing of \r\nemissions mitigation in IAM scenarios, that is, delays in mitigations \r\nactions are compensated by net negative emissions in the second half \r\nof the century. However, studies with limited net negative emissions \r\nin the long term require very rapid declines in emissions in the near \r\nterm (van Vuuren et al. 2017). Especially in forest-based systems, \r\nincreased harvesting of forests can perturb the carbon balance of \r\nforestry systems, increasing emissions for some period; the duration \r\nof this period of increased emissions, preceding net emissions \r\nreductions, can be very variable (Mitchell et al. 2012; Lamers and \r\nJunginger 2013; Roder et al. 2019; Hanssen et al. 2020; Cowie et al. \r\n2021). However, the factors contributing to differences in recovery \r\ntime are known (Mitchell et al. 2012; Zanchi et al. 2012; Lamers and \r\nJunginger 2013; Laganiere et al. 2017; Roder et al. 2019)"


id: d7deed155367e5b7b6b365110cef7c69
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 31489
content:
" Some studies \r\nthat consider market-mediated effects find that an increased demand \r\nfor biomass from forests can provide incentives to maintain existing \r\nforests and potentially to expand forest areas, providing additional \r\ncarbon sequestration as well as additional biomass (Dwivedi et al. \r\n2014; Kim et al. 2018; Baker et al. 2019; Favero et al. 2020). However, \r\nthese responses are uncertain and likely to vary geographically."


id: 954ed3583f11a17319877bd5aa18e7b9
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 20986
content:
"Pathways with very high biomass production for energy use typically \r\ninclude very high carbon prices in the energy system (Popp et al. \r\n2017; Rogelj et al. 2018b), little or no land policy (Calvin et al. 2014b), \r\na high discount rate (Emmerling et al. 2019), and limited non-BECCS \r\nCDR options (e.g.,  afforestation, DACCS) (Chen and Tavoni 2013; \r\nCalvin et al. 2014b; Marcucci et al. 2017; Realmonte et al. 2019; \r\nFuhrman et al. 2020). Higher levels of bioenergy consumption are \r\nlikely to involve trade-offs with mitigation in other sectors, notably \r\nin construction (i.e., wood for material and structural products) and \r\nAFOLU (carbon stocks and future carbon sequestration), as well as \r\ntrade-offs with sustainability (Section  3.7) and feasibility concerns \r\n(Section 3.8). Not all of these trade-offs are fully represented in all \r\nIAMs"


id: 541baaae7ac0a517852762d39c6e0f56
created_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 10699
content:
" Based on sectoral studies, the technical potential for bioenergy, \r\nwhen constraints for food security and environmental considerations \r\nare included, are 5-50 EJ yr-1 and 50-250 EJ yr-1 in 2050 for residues \r\nand dedicated biomass production systems, respectively (Chapter 7). \r\nBioenergy deployment in IAMs is within the range of these potentials, "


id: b1396280558a20c805cf93b0fb2709d9
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 25389
content:
"Without mitigation, energy consumption and supply emissions \r\ncontinue to rise (high confidence) (Kriegler et al. 2016; Bauer et al. \r\n2017; Riahi et al. 2017; Mcjeon et al. 2021) (Section 6.7). While the \r\nshare of renewable energy continues to grow in reference scenarios, \r\nfossil fuel accounts for the largest share of primary energy (Bauer \r\net al. 2017; Price and Keppo 2017; Riahi et al. 2017). In scenarios \r\nthat limit warming to 2degC or lower, transition of the energy-supply \r\nsector to a  low- or no-carbon system is rapid (Rogelj et al. 2016, \r\n2018b; Grubler et al. 2018; Luderer et al. 2018; van Vuuren et al. \r\n2018). CO2 emissions from energy supply reach net zero around \r\n2041 (2033-2057) in pathways limiting warming to 1.5degC (>50%) \r\nwith no or limited overshoot and around 2053 (2040-2066) in \r\npathways that limit warming to 2degC (>67%). Emissions reductions \r\ncontinue, with emissions reaching -7.1 GtCO2 yr-1  (-15 to \r\n-2"


id: 76768d9e4f7739addb959d3582731778
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 16231
content:
"2020; Urge-Vorsatz et al. 2020). For example, energy demand is \r\ndriven by economic development that fulfills basic needs (Mastrucci \r\net al. 2019; Rao et al. 2019a), but also drives up floor space in \r\ngeneral (Daioglou et al. 2012; Levesque et al. 2018; Mastrucci et al. \r\n2021) and ownership of energy-intensive appliances such as air \r\nconditioners (Isaac and van Vuuren 2009; Colelli and Cian 2020; \r\nPoblete-Cazenave et al. 2021). These dynamics are heterogeneous \r\nand lead to differences in energy demand and emission mitigation \r\npotential across urban/rural buildings and income levels (Krey et al. \r\n2012; Poblete-Cazenave et al. 2021). Mitigation scenarios rely on \r\nfuel switching and technology (Knobloch et al. 2017; Dagnachew \r\net al. 2020), efficiency improvement in building envelopes \r\n(Levesque et al. 2018; Edelenbosch et al. 2021) and behavioural \r\nchanges (van  Sluisveld et al. 2016; Niamir et al. 2018, 2020)"


id: ae074b8316fab46689543cb025386a42
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 24915
content:
"The relative contribution of demand-side reduction, energy- efficiency \r\nimprovements, fuel switching, and decarbonisation of fuels, varyies \r\nby model, level of mitigation, mitigation options available, and \r\nunderlying socio-economic pathway (Longden 2014; Wise et al. 2017; \r\nYeh et al. 2017; Luderer et al. 2018; Yeh et al. 2017; Edelenbosch \r\net al. 2020; Muller-Casseres et al. 2021a,b). IAMs typically rely on \r\ntechnology-focused measures like energy- efficiency improvements \r\nand fuel switching to reduce carbon emissions (Pietzcker et al. 2014; \r\nEdelenbosch et al. 2017a; Yeh et al. 2017; Zhang et al. 2018a,b; Rogelj \r\net al. 2018b; Zhang et al. 2018a,b; Sharmina et al. 2020). Many \r\nmitigation pathways show electrification of the transport system \r\n(Luderer et al. 2018; Pietzcker et al. 2014; Longden 2014; Luderer \r\net al. 2018; Zhang et al"


id: 291d1f8cc49b60b0b9d406388c07351b
created_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 3899
content:
" 2018a); however, without decarboniszation \r\nof the electricity system, transport electrification can increase \r\ntotal energy system emissions (Zhang and Fujimori 2020). A small \r\nnumber of pathways include demand-side mitigation measures in \r\nthe transport sector; these studies show reduced carbon prices and \r\nreduced dependence on CDR (Grubler et al. 2018; Mejean et al. \r\n2019; van de Ven et al. 2018; Zhang et al. 2018c; Mejean et al. 2019) \r\n(Section 3.4.1)."


id: 8175a9761b733dbcb82e3878e8408ed8
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 17897
content:
"As a result of these missing options and differences in how mitigation \r\nis implemented, IAMs tend to show less mitigation than the potential \r\nfrom national transport/energy models (Wachsmuth and Duscha \r\n2019; Gota et al. 2019; Yeh et al. 2017; Gota et al. 2019; Wachsmuth \r\nand Duscha 2019; Edelenbosch et al. 2020). For the transport sector \r\nas a whole, studies suggest a mitigation potential of 4--5 GtCO2 per \r\nyear in 2030 (Edelenbosch et al. 2020) with complete decarbonization \r\ndecarbonisation possible by 2050 (Gota et al. 2019; Wachsmuth and \r\nDuscha 2019). However, in the scenarios assessed in this chapter that \r\nlimit warming to below 1.5degC (>50%) with no or limited overshoot, \r\ntransport sector CO2 emissions are reduced by only 59% (28-% \r\nto 81%) in 2050 compared to 2015. IAM pathways also show less \r\nelectrification than the potential from other studies; pathways that \r\nlimit warming to 1"


id: 8dd10975280320ac7fa55c0d6011f2b
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 19838
content:
"5degC with no or limited overshoot show a median \r\nof 25% (7- to 43%) of final energy from electricity in 2050, while the \r\nIEA NZE scenario includes 45% (IEA 2021a)."


id: f53caf12a15d573b822e0d3edb63accb
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 36098
content:
"Limiting warming to likely 2degC (>67%) or lower can result in \r\nlarge-scale transformation of the land surface (high confidence) \r\n(Popp et al. 2017; Rogelj et al. 2018a,b; Brown et al. 2019; Roe \r\net al. 2019). The scale of land transformation depends, inter alia, \r\non the temperature goal and the mitigation options included (Popp \r\net al. 2017; Rogelj et al. 2018a; IPCC 2019a). Pathways with more \r\ndemand-side mitigation options show less land transformation \r\nthan those with more limited options (Grubler et al. 2018; van \r\nVuuren et al. 2018; IPCC 2019a). Most of these pathways show \r\nincreases in forest cover, with an increase of 322 million ha \r\n(-67 to 890 million ha) in 2050 in pathways that limit warming \r\nto  1.5degC (>50%) with no or limited overshoot, whereas bottom\u0002up models portray an economic potential of 300-500 million ha of \r\nadditional forest (Chapter 7)"


id: b94d3f18d09d81049ddfd5ed13047240
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 26695
content:
" Many IAM pathways also include large \r\namounts of energy cropland area, to supply biomass for bioenergy and \r\nBECCS, with 199 (56-482) million ha in 2050 in pathways that limit \r\nwarming to 1.5degC (>50%) with no or limited overshoot. Large land \r\ntransformations, such as afforestation/reforestation and widespread \r\nplanting of energy crops, can have implications for biodiversity and \r\nsustainable development (Sections 3.7, 7.7.4 and 12.5)."


id: 4d1334136e7ca4815ede345134d380b9
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 9550
content:
"Some AFOLU mitigation options can enhance vegetation and \r\nsoil carbon stocks such as reforestation, restoration of degraded \r\necosystems, protection of ecosystems with high carbon stocks and \r\nchanges to agricultural land management to increase soil carbon \r\n(high confidence) (Griscom et al. 2017; de Coninck et al. 2018; Fuss \r\net al. 2018; Smith et al. 2019) (AR6 WGIII Chapter 7). The time scales \r\nassociated with these options indicate that carbon sinks in terrestrial \r\nvegetation and soil systems can be maintained or enhanced so as to \r\ncontribute towards long-term mitigation (high confidence); however, \r\nmany AFOLU mitigation options do not continue to sequester \r\ncarbon indefinitely (Fuss et al. 2018; de Coninck et al. 2018; IPCC \r\n2019a) (AR6 WGIII Chapter  7)"


id: 6a7ac71c576a9c3d1c9db1a7432d03b6
created_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 14364
content:
" In the very long term (the latter \r\npart of the century and beyond), it will become more challenging \r\nto continue to enhance vegetation and soil carbon stocks, so that \r\nthe associated carbon sinks could diminish or even become sources \r\n(high confidence) (de  Coninck et al. 2018; IPCC 2019a) (AR6 WGI \r\nChapter 5). Sustainable forest management, including harvest and "


id: 4cb660e3a11a0cac64e3040e8fe811e3
created_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 9495
content:
"Very few studies and pathways include other CDR options (Table 3.5). \r\nPathways with DACCS include potentially large removal from DACCS \r\n(up to 37 GtCO2 yr-1 in 2100) in the second half of the century \r\n(Chen and Tavoni 2013; Marcucci et al. 2017; Realmonte et al. 2019; \r\nFuhrman et al. 2020, 2021; Shayegh et al. 2021; Akimoto et al. 2021) \r\nand reduced cost of mitigation (Bistline and Blanford 2021; Strefler \r\net al. 2021a). At large scales, the use of DACCS has substantial \r\nimplications for energy use, emissions, land, and water; substituting \r\nDACCS for BECCS results in increased energy usage, but reduced \r\nland-use change and water withdrawals (Fuhrman et al., 2020, \r\n2021) (Chapter 12.3.2; AR6 WGI Chapter 5)"


id: c27c8f62feb0b878f1e1dfa8eb1c55be
created_at: 2023-03-16 14:02:42
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vector_id: 28188
content:
" The level of deployment \r\nof DACCS is sensitive to the rate at which it can be scaled up, the \r\nclimate goal or carbon budget, the underlying socio-economic \r\nscenario, the availability of other decarbonisation options, the cost \r\nof DACCS and other mitigation options, and the strength of carbon\u0002cycle feedbacks (Chen and Tavoni 2013; Fuss et al. 2013; Honegger \r\nand Reiner 2018; Realmonte et al. 2019; Fuhrman et al. 2020; Bistline \r\nand Blanford 2021; Fuhrman et al. 2021; Strefler et al. 2021a) (AR6 \r\nWGI Chapter  5). Since DACCS consumes energy, its effectiveness \r\ndepends on the type of energy used; the use of fossil fuels would \r\nreduce its sequestration efficiency (Creutzig et al. 2019; NASEM 2019; \r\nBabacan et al. 2020). Studies with additional CDR options in addition \r\nto DACCS (e.g., enhanced weathering, BECCS, afforestation, biochar, \r\nand soil carbon sequestration) find that CO2 removal is spread across "


id: bf8f154839e94b30b29dddc6d12c841f
created_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 27697
content:
"Anthropogenic land CO2 emissions and removals in IAM pathways \r\ncannot be directly compared with those reported in national \r\nGHG inventories (high confidence)  (Grassi et al. 2018, 2021) \r\n(Section  7.2).   Due to differences in  definitions  for the area  of \r\nmanaged forests and which emissions and removals are considered \r\nanthropogenic, the reported anthropogenic land CO2  emissions \r\nand removals differ by about  5.5 GtCO2  yr-1 between IAMs, \r\nwhich rely on bookkeeping approaches  (e.g.,  Houghton and \r\nNassikas 2017), and national GHG inventories (Grassi et al. \r\n2021). Such  differences  in  definitions  can alter the  reported  time \r\nat which  anthropogenic  net zero  CO2 emissions  are  reached  for \r\na  given emission scenario. Using national inventories would lead \r\nto an earlier reported time of net zero (van Soest et al. 2021b) or \r\nto lower calculated cumulative emissions until the time of net zero \r\n(Grassi et al. 2021) as compared to IAM pathways"


id: d1e82ee134bb6cdcbf6d2777788f5a90
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 30550
content:
" The numerical \r\ndifferences are purely due to differences in the conventions applied \r\nfor reporting  the  anthropogenic  emissions and do not have any \r\nimplications for the underlying land-use changes or mitigation \r\nmeasures in the pathways. Grassi et al. (Grassi et al. 2021) \r\noffer a  methodology for adjusting to reconcile these differences \r\nand enable a more accurate assessment of the collective progress \r\nachieved under the Paris Agreement (Chapter 7 and Cross-Chapter \r\nBox 6 in Chapter 7)."


id: 3d998719188783db930f37d2ae1b0d4
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 7045
content:
"The assessment in this section shows that if mitigation ambitions \r\nin NDCs announced prior to COP262,18 are followed until 2030, \r\nleading to estimated emissions of 47-57 GtCO2-eq in 203019\r\n(Section  4.2.2), it is no longer possible to limit warming to  1.5degC \r\n(>50%) with no or limited overshoot (high confidence). Instead, it \r\nwould entail high overshoot (typically >0.1degC) and reliance on net \r\nnegative CO2 emissions with uncertain potential to return warming \r\nto 1.5degC (>50%) by the end of the century. It would also strongly \r\nincrease mitigation challenges to limit warming to 2degC (>67%) \r\n(high confidence). GHG emissions reductions would need to abruptly \r\nincrease after 2030 to an annual average rate of  1.4-2.0 GtCO2-\r\neq during the period 2030-2050, around 70% higher than in \r\nmitigation pathways assuming immediate action1\r\n to limit warming \r\nto 2degC (>67%)"


id: 933406c57c0aec73122d68a109967148
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 20673
content:
" The higher post-2030 reduction rates would have \r\nto be obtained in an environment of continued buildup of fossil fuel \r\ninfrastructure and less development of low-carbon alternatives until \r\n2030. A  lock-in to fossil fuel-intensive production systems (carbon \r\nlock-in) will increase the societal, economic and political strain of \r\na rapid low-carbon transition after 2030 (high confidence)."


id: 7341bb9b25fc12ed4a4cd095ede109d3
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 15747
content:
"The relationship between warming limits and near-term action is \r\nillustrated in Figure 3.29, using a set of 1.5degC-2degC scenarios with \r\ndifferent levels of near-term action, overshoot and non-CO2 warming \r\ncontribution from a recent study (Riahi et al. 2021). In general, the \r\nmore CO2 is emitted until 2030, the less CO2 can be emitted thereafter \r\nto stay within a remaining carbon budget and below a warming limit. \r\nScenarios with immediate action to observe the warming limit give \r\nthe longest time to exhaust the associated remaining carbon budget \r\nand reach net zero CO2 emissions (see light blue lines in Figure 3.29 \r\nand Cross-Chapter Box 3 in this chapter). In comparison, following \r\nprojected NDC emissions until 2030 would imply a more pronounced \r\ndrop in emissions from 2030 levels to net zero to make up for the \r\nadditional near-term emissions (see orange lines in Figure  3.29)"


id: 39489249b0597fae49caaea0acba82cd
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 6389
content:
" \r\nIf  such a  drop does not occur, the remaining carbon budget is \r\nexceeded and net negative CO2 emissions are required to return \r\nglobal mean temperature below the warming limit (see black lines in \r\nFigure 3.29) (Clarke et al. 2014; Fuss et al. 2014; Rogelj et al. 2018a)."


id: 4b1743a095ba3131f2f49fafca33a9f8
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
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vector_id: 9234
content:
"The section builds on previous assessments in the IPCC's Fifth \r\nAssessment Report (Clarke et al. 2014) and the IPCC Special Report \r\non 1.5degC Warming (Rogelj et al. 2018a). The literature assessed in these \r\ntwo reports has focused on delayed action until 2030 in the context of \r\nlimiting warming to 2degC (den Elzen et al. 2010; van Vuuren and Riahi \r\n2011; Luderer et al. 2013, 2016; Rogelj et al. 2013a; Kriegler et al. 2015; \r\nRiahi et al. 2015) and 1.5degC (Rogelj et al. 2013b; Luderer et al. 2018; \r\nStrefler et al. 2018). Here we provide an update of these assessments \r\ndrawing on the most recent literature on global mitigation pathways. \r\nNew studies have focused, inter alia, on  constraining near-term \r\ndevelopments by peak warming limits (Rogelj et al. 2019b; Riahi et al. \r\n2021; Strefler et al. 2021b) and updating assumptions about near- and\r\nmedium-term emissions developments based on national plans \r\nand long-term strategies (Roelfsema et al. 2020) (Section 4.2)"


id: c3085590f55338bfeef38c7c0742ae07
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 28266
content:
" Several \r\nstudies have explored new types of pathways with accelerated action \r\nbridging between current policy plans and the goal of limiting warming \r\nbelow 2degC (Kriegler et al. 2018a; van Soest et al. 2021a) and looked at \r\nhybrid international policy regimes to phase in global collective action \r\n(Bauer et al. 2020)."


id: 75c8aff6c949a130a7c5ed717019d10b
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 16127
content:
"The relationship between warming limits and near-term action is \r\nalso affected by the warming contribution of non-CO2 greenhouse \r\ngases and other short-lived climate forcers (Section  3.3; AR6 WGI \r\nSection  6.7). The estimated budget values for limiting warming \r\nto  1.5degC-2degC already assume stringent reductions in non-CO2\r\ngreenhouse gases and non-CO2 climate forcing as found in 1.5degC-2degC \r\npathways (Section 3.3 and Cross-Working Group Box 1 in this chapter; \r\nAR6 WGI Section  5.5 and Box  5.2 in Chapter  5). Further variations \r\nin non-CO2 warming observed across 1.5degC-2degC pathways can vary \r\nthe median estimate for the remaining carbon budget by 220 GtCO2\r\n(AR6 WGI Section 5.5). In 1.5degC-2degC pathways, the non-CO2 warming \r\ncontribution differs strongly between the near, medium and long term. \r\nChanges to the atmospheric composition of short-lived climate forcers \r\n(SLCFs) dominate the warming response in the near term (AR6 WGI \r\nSection 6.7)"


id: d38bb5fb8242f8304623e73e212bae67
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 30812
content:
" CO2 reductions are combined with strong reductions in air \r\npollutant emissions due to rapid reduction in fossil fuel combustion and \r\nin some cases the assumption of stringent air quality policies (Rao et al. \r\n2017b; Smith et al. 2020c). As air pollutants exert a net-cooling effect, "


id: aef41ca12db3ed5d5ae8013dd5ba435b
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 25062
content:
"Figure 3.29 | Illustration of emissions and climate response in four mitigation pathways with different assumptions about near-term policy developments, \r\nglobal warming limit and non-CO2 warming contribution drawn from Riahi et al. (2021). Shown are (a) CO2 emissions trajectories, (b) cumulative CO2 emissions, \r\n(c) effective non-CO2 radiative forcing, and (d) the resulting estimate of the 67th percentile of global mean temperature response relative to 1850-1900. Light blue lines \r\nshow a scenario that acts immediately on a remaining carbon budget of 900 GtCO2 from 2020 without allowing net negative CO2 emissions (i.e., temporary budget overshoot) \r\n(COFFEE 1.1, Scenario EN_NPi2020_900). Orange and black lines show scenarios drawn from the same model that follow the NDCs until 2030 and thereafter introduce action \r\nto stay within the same budget - in one case excluding net negative CO2 emissions like before (orange lines; COFFEE 1.1"


id: 14b65300b3a703e42b053637614b06f4
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 768
content:
", Scenario EN-INDCi2030_900) and in the other \r\nallowing for a temporary overshoot of the carbon budget until 2100 (black lines; COFFEE 1.1., Scenario EN-INDCi2030_900f). Light blue lines describe a scenario following the \r\nNDCs until 2030, and then aiming for a higher budget of 2300 GtCO2 without overshoot (AIM/CGE 2.2, Scenario EN-INDCi2030_1200). It is drawn from another model which \r\nprojects a lower anthropogenic non-CO2 forcing contribution and therefore achieves about the same temperature outcome as the other two non-overshoot scenarios despite \r\nthe higher CO2 budget. Grey funnels include the trajectories from all scenarios that limit warming to 2degC (>67%) (category C3). Historical CO2 emissions until 2019 are from \r\nChapter SM.2.1 EDGAR v6.0."


id: 52431ec2fee59de995a84425c2eea231
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 10397
content:
"integrated assessment modelling of global mitigation pathways \r\naccounts for technology inertia (Bertram et al. 2015a; Mercure et al. \r\n2018) and technology innovation and diffusion (Wilson et al. 2013; \r\nvan Sluisveld et al. 2018a; Luderer et al. 2021), there are limitations \r\nin capturing socio-technical and political drivers of innovation, \r\ndiffusion and transition processes (Gambhir et al. 2019; Kohler et al. \r\n2019; Hirt et al. 2020; Keppo et al. 2021). Mitigation pathways show \r\na  wide range of transition speeds that have been interrogated in \r\nthe context of socio-technical inertia (Gambhir et al. 2017; Kefford \r\net al. 2018; Kriegler et al. 2018a; Brutschin et al. 2021) vs accelerating \r\ntechnological change and self-enforcing socio-economic developments \r\n(Creutzig et al. 2017; Zenghelis 2019) (Section 3.8)"


id: e05f3b64de4e3e117a3489a482f81e11
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 32802
content:
" Diagnostic analysis \r\nof detailed IAMs found a lag of 8-20 years between the convergence \r\nof emissions pricing and the convergence of emissions response after \r\na period of differentiated emission prices (Harmsen et al. 2021). This \r\nprovides a  measure of the inertia to changing policy signals in the \r\nmodel response. It is about half the time scale of 20-40 years observed \r\nfor major energy transitions (Grubb et al. 2021). Hence, the mitigation \r\npathways assessed here capture socio-technical inertia in reducing \r\nemissions, but the limited modelling of socio-political factors may alter \r\nthe extent and persistence of this inertia."


id: 4d14dfa873a79632dc0756cddaa34254
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 9552
content:
"The assessed emission ranges from implementing the unconditional \r\n(unconditional and conditional) elements of NDCs announced prior to \r\nCOP26 implies an emissions gap to cost-effective mitigation pathways \r\nof 19-26 (16-23) GtCO2-eq in 2030 for limiting warming to 1.5degC \r\n(>50%) with no or limited overshoot and 10-16 (6-14) GtCO2-eq in \r\n2030 for limiting warming to 2degC (>67%) (Cross-Chapter Box 4 in \r\nChapter 4). The emissions gap gives rise to a number of mitigation \r\nchallenges (Kriegler et al. 2013a, 2018a,b; Luderer et al. 2013, 2018; \r\nRogelj et al. 2013a; Fawcett et al. 2015; Riahi et al. 2015; Fujimori \r\net al. 2016b; Strefler et al. 2018; Winning et al. 2019; SEI et al"


id: 1eb8eecbfb5e59cdfa99b2c6eeddfb21
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 2280
content:
" 2020; \r\nUNEP 2020): (i) larger transitional challenges post-2030 to still \r\nremain under the warming limit, in particular higher CO2 emissions \r\nreduction rates and technology transition rates required during \r\n2030-2050; (ii)  larger lock-in into carbon-intensive infrastructure \r\nand increased risk of stranded fossil fuel assets (Section  3.5.2.2); \r\nand (iii) larger reliance on CDR to reach net zero CO2 more rapidly \r\nand compensate excess emissions in the second half of the century \r\n(Section  3.5.2.1). All these factors exacerbate the socio-economic \r\nstrain of implementing the transition, leading to an increased risk \r\nof overshooting the warming and a  higher risk of climate change \r\nimpacts (Drouet et al. 2021)."


id: 2c5953046655c77b519a34d54ee71cf8
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 4409
content:
"The implications of near-term climate action for long-term climate \r\noutcomes can be explored by comparing mitigation pathways with \r\ndifferent near-term emissions developments aiming for the same \r\nclimate target (Riahi et al. 2015; Vrontisi et al. 2018; Roelfsema \r\net al. 2020). A particular example is the comparison of cost-effective \r\npathways with immediate action to limit warming to 1.5degC-2degC with \r\nmitigation pathways pursuing more moderate mitigation action \r\nuntil 2030. After the adoption of the Paris Agreement, near-term \r\naction was often modelled to reflect conditional and unconditional \r\nelements of originally submitted NDCs (2015-2019) (Fawcett et al. \r\n2015; Fujimori et al. 2016a; Kriegler et al. 2018a; Vrontisi et al. \r\n2018; Roelfsema et al. 2020). The most recent modelling studies \r\nalso include submission of updated NDCs or announcements of \r\nplanned updates in the first half of 2021 (Network for Greening \r\nthe Financial System 2021; Riahi et al. 2021)"


id: 621663248a6c8a43fc19f4c8e3188c15
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 12988
content:
" Emissions levels \r\nunder NDCs announced prior to COP26 are assessed to range \r\nbetween 47-57 GtCO2-eq in 2030 (Section  4.2.2). This assessed \r\nrange corresponds well to 2030 emissions levels in 2degC mitigation \r\npathways in the literature that are designed to follow the original \r\nor updated NDCs until 2030.20 For the 139 scenarios of this kind \r\nthat are collected in the AR6 scenario database and that still \r\nlimit warming to 2degC (>67%), the 2030 emissions range is 53 \r\n(45-58) GtCO2-eq (based on native model reporting) and 52.5 \r\n(47-56.5) GtCO2-eq, respectively (based on harmonised emissions \r\ndata for climate assessment (Annex III.2.5.1); median and 5-95th \r\npercentile). This close match allows a  robust assessment of the \r\nimplications of implementing NDCs announced prior to COP26 for "


id: 42e827b8c171d47ff431cc1c0348593a
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 7918
content:
"The challenges are illustrated in Table 3.6 and Figure 3.30, surveying \r\nglobal mitigation pathways in the literature that were collected in \r\nthe AR6 scenarios database. There is a clear trend of increasing peak \r\nwarming with increasing 2030 GHG emission levels (Figure 3.30a,b). \r\nIn particular, there is no mitigation pathway designed to follow the \r\nNDCs until 2030 that can limit warming to 1.5degC (>50%) with no or \r\nlimited overshoot. Our assessment confirms the finding of the IPCC\r\nSpecial Report on Global Warming of  1.5degC (Rogelj et al. 2018) for \r\nthe case of NDCs announced prior to COP26 that pathways following \r\nthe NDCs until 2030 'would not limit global warming to 1.5degC, even if \r\nsupplemented by very challenging increases in the scale and ambition \r\nof emissions reductions after 2030' (SR1.5 SPM). This assessment is now \r\nmore robust than in SR1.5 as it is based on a larger set of 1"


id: 8dcc7f42fbc0f2a372f3e7942de4416d
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 19834
content:
"5degC-2degC \r\npathways with better representation of current trends and plans \r\ncovering a  wider range of post-2030 emissions developments. In \r\nparticular, a  recent multi-model study limiting peak cumulative CO2\r\nemissions for a wide range of carbon budgets and immediate vs NDC\u0002type action until 2030 established a feasibility frontier for the existence \r\nof such pathways across participating models (Riahi et al. 2021)."


id: 52cdfa6b2f846edba3e3d2a330b28b45
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 10483
content:
"Figure 3.30 | Relationship between level of global GHG emissions in 2030 and selected indicators as listed in the panel titles for scenarios collected \r\nin the AR6 scenario database. Emissions data based on harmonised emissions used for the climate assessment. All scenarios that limit warming to 2degC (>67%) or lower \r\nare coloured blue or red (see p67 peak warming in panel (b)). The large majority of blue-coloured scenarios act immediately on the temperature target, while red-coloured \r\nscenarios depict all those that were designed to follow the NDCs or lesser action until 2030 and orange-coloured scenarios comprise a small set of pathways with additional \r\nregulatory action beyond NDCs (Section 3.5.3). Grey-coloured scenarios do not limit warming to 2degC (>67%) due to temporary overshoot or towards the end of the century. \r\nLarge markers denote the five Illustrative Mitigation Pathways (IMPs) (legend in Panel (h); Section 3.2)"


id: 91afc9e8285f7203331bde93b6c6143d
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 20461
content:
" Shaded yellow areas depict the estimated range of 2030 emissions from \r\nNDCs announced prior to COP26 (Section 4.2.2). Dotted lines are inserted in some panels to highlight trends in the dependency of selected output variables on 2030 GHG \r\nemissions levels (Section 3.5.2)."


id: b5c0cded8457343bc8fb062fd7dd35f
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 26124
content:
"Table 3.6 | Comparison of key scenario characteristics for five scenario classes (see Table 3.2): (i) immediate action to limit warming to 1.5degC (>50%) with \r\nno or limited overshoot, (ii) near team action following the NDCs until 2030 and returning warming to below 1.5degC (>50%) by 2100 after a high overshoot, \r\n(iii) immediate action to limit warming to 2degC (>67%), (iv) near term action following the NDCs until 2030 followed by post-2030 action to limit warming to \r\n2degC (>67%). Also shown are the characteristics for (v) the combined class of all scenarios that limit warming to 2degC (>67%). The classes (ii) and (iv) comprise the large majority of \r\nscenarios indicated by red dots, and the classes (i) and (iii) comprise the scenarios depicted by blue dots in Figure 3.30. Shown are median and interquartile ranges (in brackets) for \r\nselected global indicators"


id: 1a591837d858fc5d3f5a3b2af2d2b6c3
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 1615
content:
" Emissions ranges are based on harmonized emissions data for the climate assessment with the exception of land use CO2 emissions for which uncertainty \r\nin historic estimates is large. Numbers are rounded to the nearest 5, with the exception of cumulative CCS, BECCS, and net negative CO2 emissions rounded to the nearest 10."


id: fc6eff9ba52f8980a1396cde0c3231ca
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 37224
content:
"The 2030 emissions levels in the NDCS announced prior to COP26 \r\nalso tighten the remaining space to limit warming to 2degC (>67%). As \r\nshown in Figure 3.30b, the 67th percentile of peak warming reaches \r\nvalues above 1.7degC in pathways with 2030 emissions levels in this \r\nrange. To still limit warming to 2degC (>67%), the global post-2030 GHG \r\nemission reduction rates would need to be abruptly raised in 2030 \r\nfrom 0-0.7 GtCO2-eq yr-1 to an average of 1.4-2.0 GtCO2-eq yr-1\r\nduring the period 2030-2050 (Figure  3.30c), around 70% higher \r\nthan in immediate mitigation pathways confirming findings in \r\nthe literature (Winning et al. 2019). Their average reduction rate \r\nof 0.6-1.4 GtCO2 yr-1 would already be unprecedented at the global \r\nscale and, with a  few exceptions, national scale for an extended \r\nperiod of time (Riahi et al. 2015). For comparison, the impact of \r\nCOVID-19 on the global economy is projected to have lead to \r\na decline of around 2"


id: 2c7c8d8c31fc763a67233029dc6c5ff7
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 4431
content:
"5-3 GtCO2 of global CO2 emissions from fossil \r\nfuels and industry in 2020 (Friedlingstein et al. 2020) (Section 2.2)."


id: 6f35501678be99cbe5299be62905025e
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 15123
content:
"If near- to medium-term emissions developments deplete the \r\nremaining carbon budget, the associated warming limit will be \r\novershot. Some pathways that return warming to 1.5degC (>50%) by \r\nthe end of the century show mid-century overshoots of up to 1.8degC \r\nmedian warming. The overshoot tends to be higher, the higher the \r\n2030 emissions. Mitigation pathways with 2030 emissions levels in \r\nthe NDCS announced prior to COP26 consistently overshoot 1.5degC \r\nby  0.15degC-0.3degC. This leads to higher risks from climate change \r\nimpacts during the time of overshoot compared to pathways that limit \r\nwarming to 1.5degC (>50%) with no or limited overshoot (Schleussner \r\net al. 2016a; Mengel et al. 2018; Hofmann et al. 2019; Lenton et al. \r\n2019; Tachiiri et al. 2019; Drouet et al. 2021)"


id: 102dc8c3c56c90c414bbabeaae48889d
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 33
content:
" Furthermore, even if \r\nwarming is reversed by net negative emissions, other climate changes \r\nsuch as sea level rise would continue in their current direction for \r\ndecades to millennia (AR6 WGI Sections 4.6 and 5.6)."


id: 7ab9d2973224981638dce0fcd9b5a4d3
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 16888
content:
"The increased post-2030 transition challenge in mitigation pathways \r\nwith moderate near-term action is also reflected in the timing of \r\nreaching net zero CO2 emissions (Figure  3.30d and Cross-Chapter \r\nBox 3 in this chapter). As 2030 emission levels and the cumulated \r\nCO2 emissions until 2030 increase, the remaining time for dropping \r\nto net zero CO2 and staying within the remaining carbon budget \r\nshortens (Figure  3.29). This gives rise to an inverted  v-shape of \r\nthe lower bound on the year of reaching net zero as a function of \r\n2030 emissions levels. Reaching low emissions in 2030 facilitates \r\nreaching net zero early (left leg of the inverted v), but staying high \r\nuntil 2030 also requires reaching net zero CO2 faster to compensate \r\nfor higher emissions early on (right leg of the inverted v)"


id: af24379ac64b1180428acbb2065e0da4
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 25080
content:
" Overall, \r\nthere is a considerable spread of the timing of net zero CO2 for any \r\n2030 emissions level due to variation in the timing of spending the \r\nremaining carbon budget and the non-CO2 warming contribution \r\n(Cross-Chapter Box 3 in this chapter)."


id: 4a905849dfda8c9723675d365f9df1ef
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 9132
content:
"Returning warming to lower levels requires net negative CO2\r\nemissions in the second half of the century (Clarke et al. 2014; \r\nFuss et al. 2014; Rogelj et al. 2018a). The amount of net negative \r\nCO2 emissions in pathways limiting warming to 1.5degC-2degC climate \r\ngoals varies widely, with some pathways not deploying net negative \r\nCO2 emissions at all and others deploying up to -600 to -800 GtCO2. \r\nThe amount of net negative CO2 emissions tends to increase with \r\n2030 emissions levels (Figure 3.30e and Table 3.6). Studies confirmed \r\nthe ability of net negative CO2 emissions to reduce warming, but \r\npointed to path dependencies in the storage of carbon and heat \r\nin the Earth System and the need for further research particularly \r\nfor cases of high overshoot (Zickfeld et al. 2016, 2021; Keller et al. \r\n2018a,b; Tokarska et al. 2019)"


id: 5312f255e18ac33bbddceb20c9c1d0ad
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 10536
content:
" The AR6 WGI assessed the reduction \r\nin global surface temperature to be approximately linearly related to \r\ncumulative CO2 removal and, with lower confidence, that the amount \r\nof cooling per unit CO2 removed is approximately independent of the \r\nrate and amount of removal (AR6 WGI TS.3.3.2). Still there remains \r\nlarge uncertainty about a potential asymmetry between the warming \r\nresponse to CO2 emissions and the cooling response to net negative \r\nCO2 emissions (Zickfeld et al. 2021). It was also shown that warming \r\ncan adversely affect the efficacy of carbon dioxide removal measures \r\nand hence the ability to achieve net negative CO2 emissions \r\n(Boysen et al. 2016)."


id: 616ed7d67771bd7e60ac882b5d2aa0a8
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 12900
content:
"There is also a  profound impact on the underlying transition of \r\nenergy and land use (Figure  3.30f-h and Table  3.6). Scenarios \r\nfollowing NDCs until 2030 show a much smaller reduction in fossil \r\nfuel use, a slower growth in renewable energy use, and a  smaller \r\nreduction in CO2 and CH4 land-use emissions in 2030 compared to \r\nimmediate action scenarios. This is then followed by a much faster \r\nreduction of land-use emissions and fossil fuels, and a larger increase \r\nof nuclear energy, bioenergy and non-biomass renewable energy \r\nduring the medium term in order to get close to the levels of the \r\nimmediate action pathways in 2050. This is combined with a larger \r\namount of net negative CO2 emissions that are used to compensate \r\nthe additional emissions before 2030"


id: ebd64070c37d6e61c1686eba9b358f80
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vector_id: 34608
content:
" The faster transition during \r\n2030-2050 is taking place from a greater investment in fossil fuel \r\ninfrastructure and lower deployment of low-carbon alternatives in \r\n2030, adding to the socio-economic challenges to realise the higher \r\ntransition rates (Section 3.5.2.2). Therefore, these pathways also show \r\nhigher mitigation costs, particularly during the period 2030-2050, \r\nthan immediate action scenarios (Section  3.6.1 and Figure  3.34d) \r\n(Liu et al. 2016; Kriegler et al. 2018a; Vrontisi et al. 2018). Given \r\nthese circumstances and the fact the modelling of socio-political and \r\ninstitutional constraints is limited in Integrated Assessment Models \r\n(IAMs) (Gambhir et al. 2019; Kohler et al. 2019; Hirt et al. 2020; Keppo \r\net al. 2021), the feasibility of realising these scenarios is assessed to "


id: ae0771ed426e31812ddf6f5b62ec4ec8
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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vector_id: 24916
content:
"et al. 2018; Hilaire et al. 2019; Jia et al. 2019) (Section 12.3). CDR \r\ntherefore cannot be deployed arbitrarily to compensate any degree \r\nof overshoot. A fraction of models was not able to compute pathways \r\nthat would follow the mitigation ambition in unconditional and \r\nconditional NDCs until 2030 and return warming to below 1.5degC by \r\n2100 (Luderer et al. 2018; Roelfsema et al. 2020; Riahi et al. 2021). \r\nThere exists a  three-way trade-off between near-term emissions \r\ndevelopments until 2030, transitional challenges during 2030-50, \r\nand long-term CDR deployment post-2050 (Sanderson et al. 2016; \r\nHolz et al. 2018; Strefler et al. 2018). For example, Strefler et al"


id: e87fdcacf87d6422b308e2159d9ab15f
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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vector_id: 34085
content:
" (2018) \r\nfind that if CO2 emission levels stay at around 40 GtCO2 until 2030, \r\nwithin the range of what is projected for NDCs announced prior \r\nto COP26, rather than being halved to 20 GtCO2 until 2030, CDR \r\ndeployment in the second half of the century would have to increase \r\nby 50-100%, depending on whether the 2030-2050 CO2 emissions \r\nreduction rate is doubled from 6% to 12% or kept at 6% yr-1. This \r\nthree-way trade-off has also been identified at the national level \r\n(Pan et al. 2020)."


id: b1a188d1e27dea286e6e118369c0ee5f
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 25446
content:
"A systematic map of the literature on carbon lock-in has synthesized \r\nquantification of stranded assets in the mitigation pathways \r\nliterature, and showed that (i) coal power plants are the most \r\nexposed to risk of  becoming stranded, (ii) delayed mitigation \r\naction increases stranded assets, and (iii) sectoral distribution and \r\nthe amount of  stranded assets differ between countries (Fisch\u0002Romito et al. 2021). There is high agreement that existing fossil fuel \r\ninfrastructure would need to be retired earlier than historically, used \r\nless, or retrofitted with CCS, to stay within the remaining carbon \r\nbudgets of limiting warming to 1.5degC or 2degC (Johnson et al. 2016; \r\nKefford et al. 2018; Pfeiffer et al. 2018; Cui et al. 2019; Fofrich et al. \r\n2020; Rogelj et al. 2018a). Studies estimate that cumulative early \r\nretired power plant capacities by 2060 can be up to 600 GW for gas \r\nand 1700 GW for coal (Iyer et al. 2015a; Kefford et al"


id: 68c636743c1b499167873770ad8a9ed2
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 14076
content:
" 2018), that \r\nonly 42% of the total capital stock of both operating and planned \r\ncoal-fired powers plants can be utilised to be compatible with the \r\n2degC target (Pfeiffer et al. 2018), and that coal-fired power plants in \r\nscenarios consistent with keeping global warming below 2degC or 1.5degC \r\nretire one to three decades earlier than historically has been the case \r\n(Cui et al. 2019; Fofrich et al. 2020). After coal, electricity production \r\nbased on gas is also projected to be phased out, with some capacity \r\nremaining as back-up (van Soest et al. 2017a). Kefford et al. (2018) \r\nfind USD541 billion worth of stranded fossil fuel power plants could \r\nbe created by 2060, with China and India the most exposed."


id: 9222bb6bd39195e91309b908a1d07a11
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 20513
content:
"In addition to enabling a  temporary budget overshoot by net \r\nnegative CO2 emissions in the second half of the century, CDR can \r\nalso be used to compensate  - on an annual basis  - residual CO2\r\nemissions from sources that are difficult to eliminate and to reach \r\nnet zero CO2 emissions more rapidly if deployed before this point \r\n(Kriegler et al. 2013b; Rogelj et al. 2018a). This explains its continued \r\ndeployment in pathways that exclude overshoot and net negative \r\nCO2 emissions (Riahi et al. 2021). However, given the time scales that \r\nwould likely be needed to ramp-up CDR to gigatonne scale (Nemet \r\net al. 2018), it can be expected to only make a limited contribution to \r\nreaching net zero CO2 as fast as possible. In the vast majority (95%) \r\nof 1.5degC-2degC mitigation pathways assessed in this report, cumulative \r\nCDR deployment did not exceed 100 GtCO2 until mid-century"


id: 984dd693ec0e908e83b3eb92ac5c7a34
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 21474
content:
" This \r\nadds to the risk of excessively relying on CDR to compensate for \r\nweak mitigation action until 2030 by either facilitating massive \r\nnet CO2 emissions reduction rates during 2030-2050 or allowing \r\na high temporary overshoot of 1.5degC until the end of the century. If \r\ninternational burden-sharing considerations are taken into account, \r\nthe CDR penalty for weak action could increase further, in particular \r\nfor developed countries (Fyson et al. 2020). Further assessment \r\nof CDR deployment in  1.5degC-2degC mitigation pathways is found \r\nin Section 3.4.7."


id: c7237f4a0e30438269409292bb4ede8a
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 28917
content:
"Some publications have suggested that stranded long-lived assets \r\nmay be even more important outside of the power sector. While \r\nstranded power sector assets by 2050 could reach up to USD1.8 trillion \r\nin scenarios consistent with a 2degC target, Saygin et al. (2019) found \r\na range of USD5-11 trillion in the buildings sector. Muldoon-Smith \r\nand Greenhalgh (2019) have even estimated a potential value at risk \r\nfor global real estate assets up to USD21 trillion. More broadly, the set \r\nof economic activities that are potentially affected by a low-carbon \r\ntransition is wide and includes also energy-intensive industries, \r\ntransport and housing, as reflected in the concept of climate policy \r\nrelevant sectors introduced in Battiston et al. (2017). The sectoral \r\ndistribution and amount of stranded assets differ across countries \r\n(Fisch-Romito et al. 2021)"


id: a6d9d7f80bcd837218d8f3b05f36d104
created_at: 2023-03-16 14:02:42
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vector_id: 23769
content:
" Capital for fossil fuel production and \r\ndistribution represents a larger share of potentially stranded assets in \r\nfossil fuel-producing countries such as the United States and Russia. \r\nElectricity generation would be a larger share of total stranded assets \r\nin emerging countries because this capital is relatively new compared \r\nto its operational lifetime. Conversely, buildings could represent \r\na  larger part of stranded capital in more developed countries and \r\nregions such as the USA, EU or even Russia because of high market \r\nvalue and low turnover rate."


id: f24007f41142e4f9d58e9f261eb0529a
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 35645
content:
"2019; SEI et al. 2020). Pathways following NDCs announced prior \r\nto COP26 until 2030 do not show a significant reduction of coal, oil \r\nand gas use (Figure 3.30f-h and Table 3.6) compared to immediate \r\naction pathways. Stranded coal power assets are evaluated to be \r\nhigher by a factor of two to three if action is strengthened after 2030 \r\nrather than now (Iyer et al. 2015b; Cui et al. 2019). There is high \r\nagreement that the later climate policies are implemented, the higher \r\nthe expected stranded assets and the societal, economic and political \r\nstrain of strengthening action. Associated price increases for carbon\u0002intensive goods and transitional macro-economic costs have been \r\nfound to scale with the emissions gap in 2030 (Kriegler et al. 2013a). \r\nAt the aggregate level of the whole global economy, Rozenberg et al. \r\n(2015) showed that each year of delaying the start of mitigation \r\ndecreases the required CO2 intensity of new production by 20-50 \r\ngCO2 per USD"


id: 93132c16928806a4649549a06404122e
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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vector_id: 20653
content:
" Carbon lock-in can have a long-lasting effect on future \r\nemissions trajectories after 2030. Luderer et al. (2018) compared \r\ncost-effective pathways with immediate action to limit warming \r\nto  1.5degC-2degC with pathways following the NDCs until 2030 and \r\nadopting the pricing policy of the cost-effective pathways thereafter, \r\nand found that the majority of additional CO2 emissions from carbon \r\nlock-in occur after 2030, reaching a cumulative amount of 290 (160-\r\n330) GtCO2 by 2100 (Section 2.7.2). Early action and avoidance of \r\ninvestments in new carbon-intensive assets can minimise these risks."


id: 3f425c9a685f1a418236246f81b23e77
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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vector_id: 7346
content:
"The large literature on accelerating climate action was assessed in \r\nthe IPCC Special Report on Global Warming of  1.5degC (de Coninck \r\net al. 2018) and is taken up in this report primarily in Chapters 4, 13, \r\nand 14. Accelerating climate action and facilitating transformational \r\nchange requires a perspective on socio-technical transitions (Geels \r\net al. 2016a; Geels et al. 2016b; Geels 2020), a portfolio of policy \r\ninstruments to manage technological and environmental change \r\n(Fischer and Newell 2008; Goulder and Parry 2008; Acemoglu et al. \r\n2012, 2016), a  notion of path dependency and policy sequencing \r\n(Pierson 2000; Meckling et al. 2017; Pahle et al. 2018) and the \r\nevolvement of polycentric governance layers of institutions and \r\nnorms in support of the transformation (Dietz et al. 2003; Leach et al. \r\n2007; Messner 2015). This subsection is focused on an assessment \r\nof the emerging quantitative literature on global accelerated action \r\npathways towards 1"


id: 1771f131bf3c577460843dcb9120b60
created_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 1194
content:
"5degC-2degC, which to a large extent abstracts from \r\nthe underlying processes and uses a number of stylised approaches \r\nto generate these pathways. A representative of accelerated action \r\npathways has been identified as one of the Illustrative Mitigation \r\nPathways (IMPs) in this assessment (IMP-GS, Figure 3.31)."


id: 8adb528253cc3aa5104f8d156ddfaa30
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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vector_id: 19383
content:
"Figure 3.31 | Comparison of (i) pathways with immediate action to limit warming to 2degC (>67%) (Immediate, light blue), (ii) pathways following the \r\nNDCs until 2030 and limiting warming to 2degC (>67%) thereafter (NDC; orange), and (iii) pathways accelerating near-term action until 2030 beyond NDC \r\nambition levels and limiting warming to 2degC (>67%) thereafter (accelerated) for selected indicators as listed in the panel titles, based on pathways from \r\nvan Soest et al. (2021a). Low-carbon electricity comprises renewable and nuclear power. Indicator ranges are shown as box plots (full range, interquartile range, and median) \r\nfor the years 2030, 2050 and 2100 (absolute values) and for the periods 2020-2030, 2030-2050 (change indicators). Ranges are based on nine models participating in van \r\nSoest et al. (2021a) with only seven models reporting emissions and climate results and eight models reporting carbon prices"


id: 2765e238465bedde9c5a86f5215588ce
created_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 3601
content:
" The purple dot denotes the Illustrative Mitigation \r\nPathway GS that was part of the study by van Soest et al."


id: d611d6c4e32b07cd99b2e73afbb87d2f
created_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 31196
content:
"about 20% compared to a  current policy scenario. These findings \r\nwere found in good agreement with a  recent model comparison \r\nstudy based on results from nine integrated assessment models \r\n(IAMs) (van Soest et al. 2021a). Based on these three studies, \r\nimplementing accelerated action in terms of a  global roll out of \r\nregulatory and moderate pricing policies is assessed to lead to \r\nglobal GHG emissions of 48 (38-52) GtCO2-eq in 2030 (median \r\nand 5-95th percentile based on 10 distinct modelled pathways). This \r\ncloses the implementation gap for the NDCs, and in addition falls \r\nbelow the emissions range implied by implementing unconditional \r\nand conditional elements of NDCs by  2-9 GtCO2-eq. However, it \r\ndoes not close the emissions gap to immediate action pathways \r\nthat limit warming to 2degC (>67%), and, based on our assessment in \r\nSection 3.5.2, emission levels above 40 GtCO2-eq in 2030 still have \r\na very low prospect for limiting warming to 1"


id: 44a95eb7cd7eb6ac7fd6598bb08d20db
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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vector_id: 8226
content:
"The most common approach relies on strengthening regulatory policies \r\nbeyond current policy trends, also motivated by the finding that such \r\npolicies have so far been employed more often than comprehensive \r\ncarbon pricing (Kriegler et al. 2018a; Roelfsema et al. 2018; Fekete et al. \r\n2021; IEA 2021a; van Soest et al. 2021a). Some studies have focused \r\non generic regulatory policies such as low-carbon support policies, \r\nfossil fuel-sunset policies, and resource-efficiency policies (Bertram \r\net al. 2015b; Hatfield-Dodds et al. 2017). Bertram et al. (2015b) found \r\nthat a moderate carbon price combined with a coal moratorium and \r\nambitious low-carbon support policies can limit efficiency losses until \r\n2030 if emissions pricing is raised thereafter to limit warming to 2degC. \r\nThey also showed that all three components are needed to achieve this \r\noutcome. Hatfield-Dodds et al"


id: 6a73b6f5e5f7ba3e81ed081227bbf62f
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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vector_id: 14356
content:
" (2017) found that resource efficiency \r\ncan lower 2050 emissions by an additional 15-20% while boosting \r\nnear-term economic growth. The International Energy Agency (IEA \r\n2021a) developed a detailed net zero scenario for the global energy \r\nsector characterised by a  rapid phase-out of fossil fuels, a  massive \r\nclean energy and electrification push, and the stabilisation of energy \r\ndemand, leading to 10 GtCO2 lower emissions from energy use in 2030 \r\nthan in a scenario following the announced pledges."


id: a306bb6a400cf9fda84db86cf109ce34
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 23215
content:
"Figure  3.31 shows the intermediate position of accelerated action \r\npathways derived by van Soest et al. (2021a) between pathways \r\nthat follow the NDCs until 2030 and immediate action pathways \r\nlimiting warming to 2degC (>67%). Accelerated action is able to reduce \r\nthe abrupt shifts in emissions, fossil fuel use and low-carbon power \r\ngeneration in 2030 and also limits peak warming more effectively \r\nthan NDC pathways. But primarily due to the moderate carbon price \r\nassumptions (Figure 3.31b), the reductions in emissions and particular \r\nfossil fuel use are markedly smaller than what would be obtained in \r\nthe case of immediate action. The assessment shows that accelerated \r\naction until 2030 can have significant benefits in terms of reducing the \r\nmitigation challenges from following the NDCs until 2030. But putting \r\na significant value on GHG emissions reductions globally remains a key \r\nelement of moving onto  1.5degC-2degC pathways"


id: 12a9a097adb0dfaa1ea0be258f2d8eac
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 437
content:
" The vast majority of \r\npathways that limit warming to 2degC (>67%) or lower, independently \r\nof their differences in near-term emission developments, converge to \r\na global mitigation regime putting a significant value on GHG emission \r\nreductions in all regions and sectors."


id: 1ad1bd13d76cf6a7b23e27c0d7321b2d
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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vector_id: 1691
content:
"The Paris Agreement has spurred the formulation of NDCs for 2030 \r\nand mid-century strategies around the world (cf. Chapter 4). This is \r\ngiving researchers a  rich empirical basis to formulate accelerated \r\npolicy packages taking national decarbonisation pathways as \r\na starting point (Graichen et al. 2017; Jiang et al. 2017; van Soest \r\net al. 2017b; Waisman et al. 2019). The concept is to identify good \r\npractice policies that had demonstrable impact on pushing low\u0002carbon options or reducing emissions in a  country or region and \r\nthen consider a wider roll out of these policies taking into account \r\nregional specificities (den Elzen et al. 2015; Fekete et al. 2015, 2021; \r\nKriegler et al. 2018a; Kuramochi et al. 2018; Roelfsema et al. 2018). \r\nA challenge for this approach is to account for the fact that policy \r\neffectiveness varies with different political environments in different \r\ngeographies"


id: 7a14941d1552f57ffe1c7b704c2670a0
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 16779
content:
" As a result, a global roll out of good practice policies to \r\nclose the emissions gap will still be an idealised benchmark, but it is \r\nuseful to understand how much could be gained from it."


id: bbf7be5d7724fe4622b624b28a182fd3
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 27137
content:
"The marginal abatement cost of carbon, also called carbon price, is determined by the mitigation target under consideration: it \r\ndescribes the cost of reducing the last unit of emissions to reach the target at a given point in time. Total macroeconomic mitigation \r\ncosts (or gains) aggregate the economy-wide impacts of investments in low-carbon solutions and structural changes away from \r\nemitting activities. The total macroeconomic effects of mitigation pathways are reported in terms of variations in economic output \r\nor consumption levels, measured against a  reference scenario, also called baseline, at various points in time or discounted over \r\na given time period. Depending on the study, the reference scenario reflects specific assumptions about patterns of socio-economic \r\ndevelopment and assumes either no-climate policies or the climate policies in place or planned at the time the study was carried \r\nout"


id: 967350c7fe67a645bd498f60012e88f3
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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vector_id: 21190
content:
" When available in the AR6 scenarios database, this second type of reference scenario, with trends from implemented policies \r\nuntil the end of 2020, has been chosen for computation of mitigation costs. In the vast majority of studies that have produced the \r\nbody of work on the cost of mitigation assessed here, and in particular in all studies that have submitted global scenarios to the AR6 \r\nscenarios database except (Schultes et al. 2021), the feedbacks of climate change impacts on the economic development pathways \r\nare not accounted for. This omission of climate impacts leads to overly optimistic economic projections in the reference scenarios, in \r\nparticular in reference scenarios with no or limited mitigation action where the extent of global warming is the greatest"


id: 29eaf6a5f0a390622b5704faa569c31b
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 4027
content:
" Mitigation \r\ncost estimates computed against no or limited policy reference scenarios therefore omit economic benefits brought by avoided climate \r\nchange impact along mitigation pathways, and should be interpreted with care (Grant et al. 2020). When aggregate economic benefits \r\nfrom avoided climate change impacts are accounted for, mitigation is a welfare-enhancing strategy (Section 3.6.2)."


id: ddd88cf5fd328508e564e47c4cb95b10
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 32400
content:
"If GDP or consumption in mitigation pathways are below the reference scenario levels, they are reported as losses or macroeconomic \r\ncosts. Such cost estimates give an indication of how economic activity slows relative to the reference scenario; they do not necessarily \r\ndescribe, in absolute terms, a reduction of economic output or consumption levels relative to previous years along the pathway. Aggregate \r\nmitigation costs depend strongly on the modelling framework used and the assumptions about the reference scenario against which \r\nmitigation costs are measured, in particular whether the reference scenario is, or not, on the efficiency frontier of the economy. If the \r\neconomy is assumed to be at the efficiency frontier in the reference scenario, mitigation inevitably leads to actual costs, at least in \r\nthe short-run until the production frontier evolves with technical and structural change"


id: 562b175c834e9cb721ccbc8edd074127
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 11045
content:
" Starting from a reference scenario that is not \r\non the efficiency frontier opens the possibility to simultaneously reduce emissions and obtain macroeconomic gains, depending on the \r\ndesign and implementation of mitigation policies. A number of factors can result in reference scenarios below the efficiency frontier, for \r\ninstance distorting labour taxes and/or fossil fuel subsidies, misallocation or under-utilisation of production factors such as involuntary \r\nunemployment, imperfect information or non-rational behaviours. Although these factors are pervasive, the modelling frameworks used \r\nto construct mitigation pathways are often limited in their ability to represent them (Koberle et al. 2021)."


id: 9fc7fc14d6dcde2509b989d68fdd735b
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 22677
content:
"Estimates for the marginal abatement cost of carbon in mitigation \r\npathways vary widely, depending on the modelling framework used \r\nand socio-economic, technological and policy assumptions. However, \r\nit is robust across modelling frameworks that the marginal abatement \r\ncost of carbon increases for lower temperature categories, with \r\na higher increase in the short term than in the longer term (Figure 3.32, \r\nleft panel) (high confidence). The marginal abatement cost of carbon \r\nincreases non-linearly with the decrease of CO2 emissions level, but \r\nthe uncertainty in the range of estimates also increases (Figure 3.33). \r\nMitigation pathways with low-energy consumption patterns exhibit \r\nlower carbon values (Mejean et al. 2019; Meyer et al. 2021). In the \r\ncontext of the COVID-19 pandemic recovery, Kikstra et al. (2021a) also \r\nshow that a  low-energy-demand recovery scenario reduces carbon \r\nprices for a 1"


id: 3c986e57f89c5a98c4749b570812470e
created_at: 2023-03-16 14:02:42
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vector_id: 6903
content:
"5degC-consistent pathway by 19% compared to a scenario \r\nwith energy demand trends restored to pre-pandemic levels."


id: c1d70531e2b93ed3dd21e6832e73a508
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 28077
content:
"below the efficiency frontier, based on non-equilibrium economic \r\ntheory, and assume that mitigation is undertaken in such a  way \r\nthat green investments do not crowd out investment in other parts \r\nof the economy - and therefore offers an economic stimulus. In the \r\ncontext of the recovery from the COVID-19 crisis, it is estimated that \r\na green investment push would initially boost the economy while also \r\nreducing GHG emissions (IMF 2020; Pollitt et al. 2021). Conversely, \r\nseveral studies find that only a  GDP non-growth/degrowth or post\u0002growth approach enable reaching climate stabilisation below 2degC \r\n(Hardt and O'Neill 2017; D'Alessandro et al. 2020; Hickel and Kallis \r\n2020; Nieto et al. 2020), or to minimise the risks of reliance on high \r\nenergy-GDP decoupling, large-scale CDR and large-scale renewable \r\nenergy deployment (Keysser and Lenzen 2021)"


id: b349cd384c18901e593c3f62ed1c08aa
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vector_id: 25721
content:
" Similarly, feedbacks of \r\nfinancial system risk amplifying shocks induced by mitigation policy \r\nand lead to a higher impact on economic activity (Stolbova et al. 2018)."


id: 44019da56bcb899e9c37167ab16d9a50
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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vector_id: 8111
content:
"Aggregate economic activity and consumption levels in mitigation \r\npathways are primarily determined by socio-economic development \r\npathways but are also influenced by the stringency of the mitigation \r\ngoal and the policy choices to reach the goal (high confidence). \r\nMitigation pathways in temperature categories C1 and C2 entail \r\nlosses in global consumption with respect to their baselines  - not \r\nincluding benefits of avoided climate change impacts nor co-benefits \r\nor co-harms of mitigation action - that correspond to an annualised \r\nreduction of consumption growth by 0.04 (median value) (interquartile \r\nrange [0.02-0.06]) percentage points over the century. For pathways in \r\ntemperature categories C3 and C4 this reduction in global consumption \r\ngrowth is  0.03 (median value) (interquartile range [0.01-0.05]) \r\npercentage points over the century"


id: 3442a999bd20aa747507df6e9e157255
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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vector_id: 5632
content:
" In the majority of studies that \r\nfocus on the economic effects of mitigation without accounting for \r\nclimate damages, global economic growth and consumption growth \r\nis reduced compared to baseline scenarios (that omit damages from \r\nclimate change), but mitigation pathways do not represent an absolute \r\ndecrease of economic activity level (Figure 3.34b,c)."


id: 5a8de33d5e9225e1508e982c030bcf8b
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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vector_id: 11762
content:
"Mitigation pathways with early emissions reductions represent higher \r\nmitigation costs in the short-run but bring long-term gains for the \r\neconomy compared to delayed transition pathways (high confidence). \r\nPathways with earlier mitigation action bring higher long-term GDP \r\nthan pathways reaching the same end-of-century temperature with \r\nweaker early action (Figure 3.34d). Comparing counterfactual history \r\nscenarios, Sanderson and  O'Neill (2020) also find that delayed \r\nmitigation action leads to higher peak costs. Rogelj et al. (2019b) \r\nand Riahi et al. (2021) also show that pathways with earlier timing \r\nof net zero CO2 lead to higher transition costs but lower long-term \r\nmitigation costs, due to dynamic effects arising from lock-in avoidance \r\nand learning effects. For example, Riahi et al"


id: 111f0178b13c19da6d1db51644f4334f
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 174
content:
"(2021) find that for a 2degC \r\ntarget, the GDP losses (compared to a  reference scenario without \r\nimpacts from climate change) in 2100 are 5-70% lower in pathways \r\nthat avoid net negative CO2 emissions and temperature overshoot \r\nthan in pathways with overshoot. Accounting also for climate change \r\ndamage, van der Wijst et al. (2021a) show that avoiding net negative \r\nemissions leads to a small increase in total discounted mitigation costs \r\nover 2020-2100, between 5% and 14% in their medium assumptions, \r\nbut does not increase mitigation costs when damages are high and \r\nwhen using a low discount rate, and becomes economically attractive \r\nif damages are not fully reversible. The modelled cost-optimal balance \r\nof mitigation action over time strongly depends on the discount rate \r\nused to compute or evaluate mitigation pathways: lower discount \r\nrates favour earlier mitigation, reducing both temperature overshoot \r\nand reliance on net negative carbon emissions (Emmerling et al. "


id: d6dbdff1a67eecebf9c5512e21a9f291
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 31312
content:
"The economic repercussions of mitigation policies vary across \r\ncountries (Aldy et al. 2016; Hof et al. 2017): regional variations \r\nexist in institutions, economic and technological development, and \r\nmitigation opportunities. For a globally uniform carbon price, carbon\u0002intensive and energy-exporting countries bear the highest economic \r\ncosts because of a  deeper transformation of their economies and \r\nof trade losses in the fossil markets (Stern et al. 2012; Tavoni et al. \r\n2015; Bohringer et al. 2021). This finding is confirmed in Figure 3.35. \r\nSince carbon-intensive countries are often poorer, uniform global \r\ncarbon prices raise equity concerns (Tavoni et al. 2015). On the other \r\nhand, the climate economic benefits of mitigating climate change \r\nwill be larger in poorer countries (Cross-Working Group Box 1 in this \r\nchapter). This reduces policy regressivity but does not eliminate it \r\n(Taconet et al. 2020; Gazzotti et al. 2021)"


id: 7816eebba268aa01fc752319823e7f87
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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vector_id: 16458
content:
" Together with co-benefits, \r\nsuch as health benefits of improved air quality, the economic benefits \r\nof mitigating climate change are likely to outweigh mitigation costs \r\nin many regions (Li et al. 2018, 2019; Scovronick et al. 2021)."


id: 233cfdb041e068214bc2a675101f9a1e
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 2993
content:
"Mitigation reduces the extent of climate change and its impacts on ecosystems, infrastructure, and livelihoods. This box summarises \r\nelements from the AR6 WGII report on aggregate climate change impacts and risks, putting them into the context of mitigation \r\npathways. AR6 WGII provides an assessment of current lines of evidence regarding potential climate risks with future climate change, \r\nand therefore, the avoided risks from mitigating climate change. Regional and sectoral climate risks to physical and social systems are \r\nassessed (AR6 WGII Chapters 2-15). Over 100 of these are identified as Key Risks (KRs) and further synthesised by WGII Chapter 16 \r\ninto eight overarching Representative Key Risks (RKRs) relating to low-lying coastal systems; terrestrial and ocean ecosystems; critical \r\nphysical infrastructure, networks and services; living standards; human health; food security; water security; and peace and mobility \r\n(AR6 WGII Section  16.5.2)"


id: 9477437545fdb674217c985e24ae1c56
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 20871
content:
" The RKR assessment finds that risks increase with global warming level, and also depend on socio\u0002economic development conditions, which shape exposure and vulnerability, and adaptation opportunities and responses. 'Reasons \r\nFor Concern', another WGII aggregate climate-impacts risk framing, are also assessed to increase with climate change, with increasing \r\nrisk for unique and threatened systems, extreme weather events, distribution of impacts, global aggregate impacts, and large-scale \r\nsingular events (AR6 WGII Chapter  16). For human systems, in general, the poor and disadvantaged are found to have greater \r\nexposure level and vulnerability for a given hazard. With some increase in global average warming from today expected regardless \r\nof mitigation efforts, human and natural systems will be exposed to new conditions and additional adaptation will be needed (AR6 \r\nWGII Chapter 18)"


id: 3c93c39dd861e0e88892ba76a53123ee
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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content:
" The range of dates for when a specific warming level could be reached depends on future global emissions, with \r\nsignificant overlap of ranges across emissions scenarios due to climate system response uncertainties (AR6 WGI Tables 4.2 and 4.5). \r\nThe speed at which the climate changes is relevant to adaptation timing, possibilities, and net impacts."


id: 18beed2012a3a4547fb50d89b5420eeb
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 1391
content:
"The AR6 WGII also assesses the growing literature estimating the global aggregate economic impacts of climate change and the social \r\ncost of carbon dioxide and other greenhouse gases (AR6 WGII Cross-Working Box ECONOMIC: Estimating Global Economic Impacts \r\nfrom Climate Change and the Social Cost of Carbon in AR6 WGII Chapter  16). The former represents aggregate estimates that \r\ninform assessment of the economic benefits of mitigation. This literature is characterised by significant variation in the estimates, \r\nincluding for today's level of global warming, due primarily to fundamental differences in methods, but also differences in impacts \r\nincluded, representation of socio-economic exposure, consideration of adaptation, aggregation approach, and assumed persistence \r\nof damages"


id: 9eaf2e3b0fb5b815c6fb149f704a2e6f
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 22504
content:
" The AR6 WGII's assessment identifies different approaches to quantification of aggregated economic impacts of climate \r\nchange, including: physical modelling of impact processes, such as projected mortality rates from climate risks such as heat, vector- or \r\nwaterborne diseases that are then monetised; structural economic modelling of impacts on production, consumption, and markets for \r\neconomic sectors and regional economies; and statistical estimation of impacts based on observed historical responses to weather \r\nand climate. The AR6 WGII finds that variation in estimated global economic impacts increases with warming in all methodologies, \r\nindicating higher risk in terms of economic impacts at higher temperatures (high confidence). Many estimates are non-linear with \r\nmarginal economic impacts increasing with temperature, although some show declining marginal economic impacts with temperature, \r\nand functional forms cannot be determined for all studies"


id: 587fd78d59c67f15d18379c93bd51499
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 11422
content:
" The AR6 WGII's assessment finds that the lack of comparability between \r\nmethodologies does not allow for identification of robust ranges of global economic impact estimates (high confidence). Further, AR6 \r\nWGII identifies evaluating and reconciling differences in methodologies as a research priority for facilitating use of the different lines \r\nof evidence (high confidence). However, there are estimates that are higher than AR5, indicating that global aggregate economic \r\nimpacts could be higher than previously estimated (low confidence due to the lack of comparability across methodologies and lack of \r\nrobustness of estimates) (AR6 WGII Cross-Working Box ECONOMIC)."


id: 1ec7d7202055fdb0ea9dbddba800ed63
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 2287
content:
"Cross-Working Group Box  1, Figure  1 | Global aggregate economic benefits of mitigation from avoided climate change impacts in 2100 \r\ncorresponding to shifting from a higher temperature category (4degC (3.75degC-4.25degC), 3degC (2.75degC-3.25degC), or above 2degC (2degC-2.5degC), to below \r\n2degC (1.5degC-2degC), as well as from below 2degC to below 1.5degC (1degC-1.5degC)), from the five studies discussed in the text. Panel (a) is adapted from AR6 \r\nWGII Cross-Working Group Box ECONOMIC, Figure 1, showing global aggregate economic impact estimates (% global GDP loss relative to GDP without additional \r\nclimate change) by temperature change level. All estimates are shown in grey. Estimates used for the computation of estimated benefits in 2100 in panel (b) are \r\ncoloured for the selected studies, which provide results for different temperature change levels"


id: e29e0f0baa84a8401f9c36f398928c15
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 33160
content:
" See the AR6 WGII AR6 WGII Cross-Working Group Box ECONOMIC for \r\ndiscussion and assessment of the estimates in panel (a) and the differences in methodologies. For B18 and T19, median estimates in the cluster are considered. Shape \r\ndistinguishes the baseline scenarios. Temperature ranges are highlighted. HS17 estimates are based on their preferred model -50th percentile of non-catastrophic \r\ndamage. Panel (b) shows the implied aggregate economic benefits in 2100 of a lower temperature increase. Economic benefits for point estimates are computed as \r\na difference, while economic benefits from the curve HS17 are computed as ranges from the segment differences."


id: 21488da287dc23885a9638f769236c16
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 2673
content:
"from avoided climate change (Howard and Sterner 2017; Burke et al. 2018; Pretis et al. 2018; Kahn et al. 2019; Takakura et al. 2019). \r\nBurke et al. (2018), Pretis et al. (2018) and Kahn et al. (2019) are examples of statistical estimations of historical relationships between \r\ntemperature and economic growth, whereas Takakura et al. (2019) is an example of structural modelling, which evaluates selected \r\nimpact channels (impacts on agriculture productivity, undernourishment, heat-related mortality, labour productivity, cooling/heating \r\ndemand, hydro-electric and thermal power generation capacity and fluvial flooding) with a general equilibrium model. Howard and \r\nSterner (2017) and Rose et al. (2017b) estimate damage functions that can be used to compute the economic benefits of mitigation \r\nfrom avoiding a given temperature level for a lower one. Howard and Sterner (2017) estimate a damage function from a meta\u0002analysis of aggregate economic impact studies, while Rose et al"


id: 14f45e918da64eef5a6b9e1562f3b3fd
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 803
content:
" (2017b) derive global functions by temperature and socio-economic \r\ndrivers from stylised aggregate cost-benefit-analysis (CBA) integrated assessment models (IAMs) using diagnostic experiments. Cross\u0002Working Group Box 1, Figure 1 summarises the global aggregate economic benefits in 2100 of avoided climate change impacts from \r\nindividual studies corresponding to shifting from a higher temperature category (above 3degC, below 3degC or below 2.5degC) to below 2degC, \r\nas well as from below 2degC to below 1.5degC. Benefits are positive and increase with the temperature gap for any given study, and this \r\nresult is robust across socio-economic scenarios. The Figure provides evidence of a wide range of quantifications, and illustrates the \r\nimportant differences associated with methods. Panel a puts the studies used to calculate aggregate economic benefits arising from \r\navoided impacts into the context of the broader set of studies assessed in WGII (Section 16.6"


id: 668395341c49cfea6a704cf85295f0e0
created_at: 2023-03-16 14:02:42
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vector_id: 13716
content:
"2 of this report, AR6 WGII Cross-Working \r\nGroup Box ECONOMIC,). However, economic benefits in 2100 arising from avoided impacts cannot be directly computed from damage \r\nestimates across this broader set of studies, due to inconsistencies - different socio-economic assumptions, scenario designs, and \r\ncounterfactual reference scenarios across studies. Furthermore, these types of estimates cannot be readily compared to mitigation \r\ncost estimates. The comparison would require a framework that ensures consistency in assumptions and dynamics and allows for \r\nconsideration of benefits and costs along the entire pathway."


id: b95e383d21768da9842c61f6da83ebb4
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 26707
content:
"Cost-benefit analyses (CBA) aim to balance all costs and benefits \r\nin a  unified framework (Nordhaus, 2008). Estimates of economic \r\nbenefits from avoided climate change impacts depend on the types \r\nof damages accounted for, the assumed exposure and vulnerability \r\nto these damages as well as the adaptation capacity, which in \r\nturn are based on the development pathway assumed (Cross\u0002Working Group Box 1 in this chapter). CBA IAMs raised criticism, in \r\nparticular for omitting elements of dynamic realism, such as inertia, \r\ninduced innovation and path dependence, in their representation of \r\nmitigation (Grubb et al. 2021), and for underestimating damages \r\nfrom climate change, missing non-monetary damages, the uncertain \r\nand heterogeneous nature of damages and the risk of catastrophic \r\ndamages (Stern 2013, 2016; Diaz and Moore 2017; NASEM 2017; \r\nPindyck 2017; Stoerk et al. 2018; Stern and Stiglitz 2021)"


id: 4c2f6a45bb39395fd08e42ee6754d754
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 9408
content:
" Emerging \r\nliterature has started to address those gaps, and integrated into cost\u0002benefit frameworks the account of heterogeneity of climate damage \r\nand inequality (Dennig et al. 2015; Budolfson et al. 2017; Fleurbaey \r\net al. 2019; Kornek et al. 2021), damages with higher persistence, \r\nincluding damages on capital and growth (Moyer et al. 2014; Dietz \r\nand Stern 2015; Moore and Diaz 2015; Guivarch and Pottier 2018; \r\nRicke et al. 2018; Piontek et al. 2019), risks of tipping points (Cai et al. \r\n2015, 2016; Lontzek et al. 2015; Lemoine and Traeger 2016; van der \r\nPloeg and de Zeeuw 2018; Cai and Lontzek 2019; Nordhaus 2019; \r\nYumashev et al. 2019; Taconet et al. 2021) and damages to natural \r\ncapital and non-market goods (Tol 1994; Sterner and Persson 2008; \r\nBastien-Olvera and Moore 2020; Drupp and Hansel 2021)."


id: cb2e4c9cd97bf2e57a78ecb5fed2c07c
created_at: 2023-03-16 14:02:42
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vector_id: 29525
content:
"Lontzek 2019), in the consideration of damages to natural capital \r\nand non-market goods (Bastien-Olvera and Moore 2020) or in the \r\ncombination of updated representations of carbon cycle and climate \r\nmodules, updated damage estimates and/or updated representations \r\nof economic and mitigation dynamics (Dietz and Stern 2015; Hansel \r\net al. 2020; Wei et al. 2020; van der Wijst et al. 2021b). In the studies \r\ncited above that perform a sensitivity analysis, this result is found to \r\nbe robust to a wide range of assumptions on social preferences (in \r\nparticular, on inequality aversion and pure rate-of-time preference) \r\nand holds except if assumptions of economic damages from climate \r\nchange are in the lower end of available estimates and the pure rate\u0002of-time preference is in the higher range of values usually considered \r\n(typically above 1.5%)"


id: db5fd731c007175b81b63f7a3a4c5982
created_at: 2023-03-16 14:02:42
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content:
" However, although such pathways bring net \r\nbenefits over time (in terms of aggregate discounted present value), \r\nthey involve distributional consequences and transition costs (Brown \r\net al. 2020; Brown and Saunders 2020) (Sections 3.6.1.2 and 3.6.4)."


id: 6970dfff5f61f1b5a4afc322001f258e
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 14184
content:
"Each of these factors, when accounted for in a  CBA framework, \r\ntends to increase the welfare benefit of mitigation, thus leading \r\nto stabilisation at a  lower temperature in optimal mitigation \r\npathways. The limitations in CBA modelling frameworks remain \r\nsignificant, their ability to represent all damages incomplete, and \r\nthe uncertainty in estimates remains large. However, emerging \r\nevidence suggests that, even without accounting for co-benefits \r\nof mitigation on other sustainable development dimensions (see \r\nSection 3.6.3 for further details about on co-benefits), global benefits \r\nof pathways that limit warming to 2degC outweigh global mitigation \r\ncosts over the 21st century: depending on the study, the reason for \r\nthis result lies in assumptions of economic damages from climate \r\nchange in the higher end of available estimates (Moore and Diaz \r\n2015; Ueckerdt et al. 2019; Brown and Saunders 2020; Glanemann \r\net al"


id: 46ed088dbd6c96d6d90ad6941b205036
created_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 8544
content:
"Systematic reviews of the literature on co-benefits and trade-offs \r\nfrom mitigation actions have shown that only a  small portion of \r\narticles provide economic quantifications (Deng et al. 2017; Karlsson \r\net al. 2020). Most economic quantifications use monetary valuation \r\napproaches. Improved air quality, and associated health effects, \r\nare the co-benefit category dominating the literature (Markandya \r\net al. 2018; Vandyck et al. 2018; Scovronick et al. 2019; Howard et al. \r\n2020; Karlsson et al. 2020b; Rauner et al. 2020a,b), but some studies \r\ncover other categories, including health effects from diet change \r\n(Springmann et al. 2016b) and biodiversity impacts (Rauner et al. \r\n2020a). Regarding health effects from air quality improvement and \r\nfrom diet change, co-benefits are shown to be of the same order of \r\nmagnitude as mitigation costs (Thompson et al. 2014; Springmann \r\net al. 2016a,b; Markandya et al. 2018; Scovronick et al. 2019b; \r\nHoward et al. 2020; Rauner et al"


id: 268912c3bd48828ef7dcb4684dc5f462
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 3469
content:
" 2020a,b; Liu et al. 2021; Yang \r\net al. 2021). Co-benefits from improved air quality are concentrated \r\nsooner in time than economic benefits from avoided climate change \r\nimpacts (Karlsson et al. 2020), such that when accounting both \r\nfor positive health impacts from reduced air pollution and for the \r\nnegative climate effect of reduced cooling aerosols, optimal GHG \r\nmitigation pathways exhibit immediate and continual net economic \r\nbenefits (Scovronick et al. 2019a). However, AR6 WGI Chapter  6 \r\n(Section 6.7.3) shows a delay in air pollution reduction benefits when \r\nthey come from climate change mitigation policies compared with air \r\npollution reduction policies."


id: c9654d7fce63d8bc999cefd3b9c0cde0
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 29248
content:
"Mitigation affects work through multiple channels, which impacts \r\ngeographies, sectors and skill categories differently (Fankhaeser \r\net al. 2008; Bowen et al. 2018; Malerba and Wiebe 2021). Aggregate \r\nemployment impacts of mitigation pathways mainly depend on \r\nthe aggregate macroeconomic effect of mitigation (Sections  3.6.1 \r\nand  3.6.2) and of mitigation policy design and implementation \r\n(Freire-Gonzalez 2018) (Section 4.2.6.3). Most studies that quantify \r\noverall employment implications of mitigation policies are conducted \r\nat the national or regional scales (Section 4.2.6.3), or sectoral scales \r\n(e.g., see Chapter 6 for energy sector jobs)"


id: f3d430c4202260e6a962ca927c9f75a9
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 35879
content:
" The evidence is limited \r\nat the multinational or global scale, but studies generally find \r\nsmall differences in aggregate employment in mitigation pathways \r\ncompared to baselines: the sign of the difference depends on the \r\nassumptions and modelling frameworks used and the policy design \r\ntested, with some studies or policy design cases leading to small \r\nincreases in employment (Chateau and Saint-Martin 2013; Pollitt \r\net al. 2015; Barker et al. 2016; Garcia-Casals et al. 2019; Fujimori \r\net al. 2020a; Vrontisi et al. 2020; Malerba and Wiebe 2021) and other \r\nstudies or policy design cases leading to small decreases (Chateau \r\nand Saint-Martin 2013; Vandyck et al. 2016). The small variations in \r\naggregate employment hide substantial reallocation of jobs across \r\nsectors, with jobs creation in some sectors and jobs destruction in \r\nothers"


id: fdb654f0d6bacc0ea1fcdf3a085b5e2c
created_at: 2023-03-16 14:02:42
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vector_id: 37435
content:
" Mitigation action through thermal renovation of buildings, \r\ninstallation and maintenance of low-carbon generation, and the \r\nexpansion of public transit lead to job creation, while jobs are lost \r\nin fossil fuel extraction, energy supply and energy-intensive sectors \r\nin mitigation pathways (von Stechow et al. 2015, 2016; Barker et al. \r\n2016; Fuso Nerini et al. 2018; Perrier and Quirion 2018; Pollitt and \r\nMercure 2018; Dominish et al. 2019; Garcia-Casals et al. 2019). In \r\nthe energy sector, job losses in the fossil fuel sector are found to \r\nbe compensated by gains in wind and solar jobs, leading to a net \r\nincrease in energy sector jobs in 2050 in a  mitigation pathway \r\ncompatible with stabilisation of the temperature increase below \r\n2degC (Pai et al. 2021). Employment effects also differ by geographies, \r\nwith energy-importing regions benefiting from net job creations but \r\nenergy-exporting regions experiencing very small gains or suffering "


id: b0a9a5e8b9f425ef490cfa07fd33e912
created_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 25314
content:
"A few studies consider both mitigation policies' distributional impacts \r\nand avoided climate change impacts on inequalities along mitigation \r\npathways. Rezai et al. (2018) find that unmitigated climate change \r\nimpacts increase inequality, whereas mitigation has the potential \r\nto reverse this effect. Considering uncertainty in socio-economic \r\nassumptions, emission pathways, mitigation costs, temperature \r\nresponse, and climate damage, Taconet et al. (2020) show that the \r\nuncertainties associated with socio-economic assumptions and \r\ndamage estimates are the main drivers of future inequalities between \r\ncountries and that in most cases mitigation policies reduce future \r\ninequalities between countries. Gazzotti et al. (2021) show that \r\ninequality persists in 2degC-consistent pathways due to regressivity \r\nof residual climate damages"


id: 8dcdf9207f73e890ccdf4dbebeba9371
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 19837
content:
" However, the evidence on mitigation \r\npathways' implications for global inequality and poverty remains \r\nlimited, and the modelling frameworks used have limited ability to \r\nfully represent the different dimensions of inequality and poverty and \r\nall the mechanisms by which mitigation affects inequality and poverty \r\n(Rao et al. 2017a; Emmerling and Tavoni 2021; Jafino et al. 2021)."


id: a594d9b5e14df952b4bbfb186a69411d
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 23623
content:
"Mitigation policies can have important distributive effects between \r\nand within countries, either reducing or increasing economic \r\ninequality and poverty, depending on policy instruments' design \r\nand implementation (see Section  3.6.1.2 for an assessment of \r\nthe distribution of mitigation costs across regions in mitigation \r\npathways; Sections 3.7 and 4.2.2.6, and Box 3.6 for an assessment of \r\nthe fairness and ambition of NDCs; and Section 4.5 for an assessment \r\nof national mitigation pathways along the criteria of equity, including \r\nJust Transition, as well as Section 17.4.5 for equity in a Just Transition). \r\nFor instance, emissions taxation has important distributive effects, \r\nboth between and within income groups (Cronin et al. 2018b; Klenert \r\net al. 2018; Pizer and Sexton 2019; Douenne 2020; Steckel et al. \r\n2021). These effects are more significant in some sectors, such as \r\ntransport, and depend on country-specific consumption structures \r\n(Dorband et al"


id: f075e29c9c7bc0a7377e94885ccdb971
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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vector_id: 35329
content:
" 2019; Fullerton and Muehlegger 2019; Ohlendorf \r\net al. 2021). However, revenues from emissions taxation can be used \r\nto lessen their regressive distributional impacts or even turn the \r\npolicy into a  progressive policy reducing inequality and/or leading \r\nto gains for lower-income households (Cameron et al. 2016; Jakob \r\nand Steckel 2016; Fremstad and Paul 2019; Fujimori et al. 2020b; \r\nBohringer et al. 2021; Budolfson et al. 2021; Soergel et al. 2021b; \r\nSteckel et al. 2021). Mitigation policies may affect the poorest \r\nthrough effects on energy and food prices (Hasegawa et al. 2015; \r\nFujimori et al. 2019). Markkanen and Anger-Kraavi (2019) and Lamb \r\net al. (2020) synthesize evidence from the existing literature on social \r\nco-impacts of climate change mitigation policy and their implications \r\nfor inequality"


id: 53064f703c8436f368580a493225ea96
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 10518
content:
" They show that most policies can compound or \r\nlessen inequalities depending on contextual factors, policy design \r\nand policy implementation, but that negative inequality impacts of \r\nclimate policies can be mitigated (and possibly even prevented), \r\nwhen distributive and procedural justice are taken into consideration \r\nin all stages of policymaking, including policy planning, development \r\nand implementation, and when focusing on the carbon intensity \r\nof lifestyles, sufficiency and equity, well-being and decent living \r\nstandards for all (Section 13.6)."


id: 2bcc654802c9f3aa8b88fdb3ba1532c9
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 4318
content:
"This section covers the long-term interconnection of sustainable \r\ndevelopment and mitigation, taking forward the holistic vision of \r\nsustainable development described in the SDGs (Brandi 2015; Leal \r\nFilho et al. 2018). Recent studies have explored the aggregated \r\nimpact of mitigation for multiple sustainable-development \r\ndimensions (Hasegawa et al. 2014; Bertram et al. 2018; Fuso Nerini \r\net al. 2018; Grubler et al. 2018; McCollum et al. 2018b; Soergel et al. \r\n2021a; van Vuuren et al. 2019). For instance, Figure  3.38 shows \r\nselected mitigation co-benefits and trade-offs based on a  subset \r\nof models and scenarios, since so far many IAMs do not have \r\na comprehensive coverage of SDGs (Rao et al. 2017a; van Soest et al. \r\n2019). Figure 3.38 shows that mitigation likely leads to increased \r\nforest cover (SDG 15  - life on land) and reduced mortality from \r\nambient PM2.5 pollution (SDG 3  - good health and well-being) \r\ncompared to reference scenarios"


id: caafc8cd9141f07fe7b628b23c8783b2
created_at: 2023-03-16 14:02:42
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vector_id: 29448
content:
"Figure 3.39 | Sustainable development effects of mitigation to 1.5degC. Panel (a): benefits of mitigation from avoided impacts. Panel (b): sustainability co-benefits \r\nand trade-offs of narrow mitigation policies (averaged over multiple models). Panel (c): sustainability co-benefits and trade-offs of mitigation policies integrating Sustainable \r\nDevelopment Goals. Scale: 0% means no change compared to 3degC (left) or current policies (middle and right). Blue values correspond to proportional improvements, red values \r\nto proportional worsening. Note: only the left panel considers climate impacts on sustainable development; the middle and right panels do not. 'Res' C&P' stands for Responsible \r\nConsumption and Production (SDG 12). Data are from Byers et al. (2018) (left), SP/Soergel et al. (2021a) (right). Methods used in middle panel: for biodiversity, Ohashi et al. \r\n(2019); for ecotoxicity and eutrophication, Arvesen et al. (2018) and Pehl et al"


id: 270b17fcce9c91fbf8a68b4df542575a
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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vector_id: 3540
content:
" (2017); for energy access, Cameron et al. (2016). 'Energy services' on the right is a measure of \r\nuseful energy in buildings and transport. 'Food prices' and 'Risk of hunger' in the middle panel are the same as in Figure 3.38."


id: 782b2a57b40f9503cb31516df0962155
created_at: 2023-03-16 14:02:42
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vector_id: 16473
content:
"There is high confidence (medium evidence, high agreement) that the eradication of extreme poverty and universal access to energy \r\ncan be achieved without resulting in significant GHG emissions (Tait and Winkler 2012; Chakravarty and Tavoni 2013; Pachauri et al. \r\n2013; Pachauri 2014; Rao 2014; Hubacek et al. 2017b; Poblete-Cazenave et al. 2021). There is also high agreement in the literature \r\nthat a focus on well-being and decent living standards for all can reduce disparities in access to basic needs for services concurrently \r\nwith climate mitigation (Section 5.2). Mitigation pathways in which national redistribution of carbon-pricing revenues is combined with \r\ninternational climate finance, achieve poverty reduction globally (Fujimori et al. 2020b; Soergel et al. 2021b). Carbon-pricing revenues \r\nin mitigation pathways consistent with limiting temperature increase to 2degC could also contribute to finance investment needs for \r\nbasic infrastructure (Jakob et al"


id: 82f461d5735dcb1c2d05a7c482b891d
created_at: 2023-03-16 14:02:42
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vector_id: 18131
content:
"Several studies conclude that reaching higher income levels globally, beyond exiting extreme poverty, and achieving more qualitative \r\nsocial objectives and well-being, are associated with higher emissions (Ribas et al. 2017, 2019; Hubacek et al. 2017b; Fischetti 2018; \r\nScherer et al. 2018). Studies give divergent results on the effect of economic inequality reduction on emissions, with either an increase \r\nor a decrease in emissions (Berthe and Elie 2015; Lamb and Rao 2015; Grunewald et al. 2017; Hubacek et al. 2017a,b; Jorgenson et al. \r\n2017; Knight et al. 2017; Mader 2018; Rao and Min 2018; Liu et al. 2019; Sager 2019; Balezentis et al. 2020; Liobikiene 2020; Liobikiene \r\nand Rimkuviene 2020; Liu et al. 2020b; Millward-Hopkins and Oswald 2021). However, the absolute effect of economic inequality \r\nreduction on emissions remains moderate, under the assumptions tested"


id: 6e7384d0b1364acaa907bbe5116cac
created_at: 2023-03-16 14:02:42
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vector_id: 15002
content:
" For instance, Sager (2019) finds that a full redistribution \r\nof income leading to equality among US households in a counterfactual scenario for 2009 would raise emissions by 2.3%; and Rao \r\nand Min (2018) limit to 8% the maximum plausible increase in emissions that would accompany the reduction of the global Gini \r\ncoefficient from its current level of 0.55 to a level of 0.3 by 2050. Similarly, reduced income inequality would lead to a global energy\u0002demand increase of 7% (Oswald et al. 2021). Reconciling mitigation and inequality reduction objectives requires policies that take into \r\naccount both objectives at all stages of policymaking (Markkanen and Anger-Kraavi 2019), including focusing on the carbon intensity \r\nof lifestyles (Scherer et al. 2018), attention to sufficiency and equity (Fischetti 2018), and targeting the consumption of the richest and \r\nhighest-emitting households (Otto et al. 2019)."


id: c775536560ee502a90dbf394d83460ac
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 28966
content:
"Mitigating climate change while ensuring that food security is \r\nnot adversely affected requires a range of different strategies and \r\ninterventions (high confidence). Fujimori et al. (2018) explore possible \r\neconomic solutions to these unintended impacts of mitigation \r\n(e.g.,  agricultural subsidies, food aid, and domestic reallocation of \r\nincome) with an additional small (<0.1%) change in global GDP. \r\nTargeted food-security support is needed to shield impoverished and \r\nvulnerable people from the risk of hunger that could be caused by the \r\neconomic effects of policies narrowly focussed on climate objectives. \r\nIntroducing more biofuels and careful selection of bioenergy \r\nfeedstocks could also reduce negative impacts (FAO, IFAD, UNICEF, \r\nWFP and WHO, 2017)"


id: 75ab265f91b4619bd8d3ef7bdfe3294b
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vector_id: 16104
content:
" Reconciling bioenergy demands with food and \r\nbiodiversity, as well as competition for land and water, will require \r\nchanges in food systems  - agricultural intensification, open trade, \r\nless consumption of animal products and reduced food losses - and \r\nadvanced biotechnologies (Henry et al. 2018; Xu et al. 2019)."


id: be7a718d5491ab8dd769091dbe6b587
created_at: 2023-03-16 14:02:42
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vector_id: 27518
content:
"The effects of mitigation on water demand depends on the mitigation \r\ntechnologies deployed (high confidence) (Chaturvedi et al. 2013a,b; \r\nHanasaki et al. 2013; Kyle et al. 2013; Hejazi et al. 2014; Bonsch et al. \r\n2016; Jakob and Steckel 2016; Mouratiadou et al. 2016; Fujimori \r\net al. 2017; Maizi et al. 2017; Bijl et al. 2018; Cui et al. 2018; Graham \r\net al. 2018; Parkinson et al. 2019). Some mitigation options could \r\nincrease water consumption (volume removed and not returned) \r\nwhile decreasing withdrawals (total volume of water removed, \r\nsome of which may be returned) (Kyle et al. 2013; Fricko et al. 2016; \r\nMouratiadou et al. 2016; Parkinson et al. 2019). Bioenergy and \r\nBECCS can increase water withdrawals and water consumption (high \r\nconfidence) (Chaturvedi et al. 2013a; Kyle et al. 2013; Hejazi et al. \r\n2014; Bonsch et al. 2016; Jakob and Steckel 2016; Mouratiadou et al. \r\n2016; Fujimori et al. 2017; Maizi et al. 2017; Seferian et al. 2018; \r\nYamagata et al"


id: 66079db11e53dc1339cbbd52f1c12382
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 13641
content:
" 2018; Parkinson et al. 2019) (AR6 WGII Chapter 4). \r\nDACCS (Fuhrman et al. 2020) and CCS (Kyle et al. 2013; Fujimori "


id: 97c471a8cc9ac292cb5f3b6140eb501f
created_at: 2023-03-16 14:02:42
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content_type: text
index: climate_gpt_only_giec
vector_id: 21400
content:
"The additional energy demand to meet the basic cooling requirement \r\nin the Global South is estimated to be much larger than the \r\nelectricity needed to provide basic residential energy services \r\nuniversally via clean and affordable energy, as defined by SDG 7 (IEA \r\n2019; Mastrucci et al. 2019) (high confidence). If conventional air\u0002conditioning systems are widely deployed to provide cooling, energy \r\nuse could rise significantly (van Ruijven et al. 2019; Bezerra et al. \r\n2021; Falchetta and Mistry 2021), thus creating a positive feedback \r\nfurther increasing cooling demand. However, the overall emissions \r\nare barely altered by the changing energy demand composition with \r\nreductions in heating demand occurring simultaneously (Isaac and \r\nvan Vuuren 2009; Labriet et al. 2015; McFarland et al. 2015; Clarke \r\net al. 2018). Some mitigation scenarios show price increases of clean \r\ncooking fuels, slowing the transition to clean cooking fuels (SDG 7"


id: 2bcad9464ee8dd1a3cec5b09655727ca
created_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 4317
content:
"1) \r\nand leaving a billion people in 2050 still reliant on solid fuels in South \r\nAsia (Cameron et al. 2016)."


id: 11a0e8fd99894269522042761c33f25b
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
index: climate_gpt_only_giec
vector_id: 264
content:
"Climate change alters the production of energy through changes \r\nin temperature (hydropower, fossil fuel, nuclear, solar, bioenergy, \r\ntransmission and pipelines), precipitation (hydropower, fossil fuel, \r\nnuclear and bioenergy), windiness (wind and wave), and cloudiness \r\n(solar) (high confidence). Increases in temperature reduce efficiencies \r\nof thermal power plants (e.g.,  fossil fuel and nuclear plants) with \r\nair-cooled condensers by  0.4-0.7% per degC increase in ambient \r\ntemperature (Cronin et al. 2018a; Simioni and Schaeffer 2019; \r\nYalew, S.G. et al. 2020). Potentials and costs for renewable energy \r\ntechnologies are also affected by climate change, though with \r\nconsiderable regional variation and uncertainty (Gernaat et al. 2021). \r\nBiofuel yields could increase or decrease depending on the level of \r\nwarming, changes in precipitation, and the effect of CO2 fertilisation \r\n(Calvin et al. 2013; Kyle et al. 2014; Gernaat et al. 2021)"


id: 54130bce0f296b1f101746e0321a691
created_at: 2023-03-16 14:02:42
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content_type: text
index: climate_gpt_only_giec
vector_id: 10694
content:
" Coastal \r\nenergy facilities could potentially be impacted by sea level rise \r\n(Brown et al. 2014)."


id: ad90cd0ec5d5220442ff49900d77b6b2
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 24850
content:
"Cost-benefit analyses for climate mitigation in urban settings that do \r\nnot account for health may underestimate the potential cost savings \r\nand benefits (Hess et al. 2020). The net health benefits of controlling \r\nair pollution as part of climate mitigation efforts could reach trillions \r\nof dollars annually, depending on the air quality policies adopted \r\nglobally (Markandya et al. 2018; Scovronick et al. 2019b). Air pollution \r\nreductions resulting from meeting the Paris Agreement targets \r\nwere estimated to provide health co-benefits-to-mitigation ratios \r\nof between 1.4 and 2.5 (Markandya et al. 2018). In Asia, the benefit of \r\nair pollution reduction through mitigation measures was estimated to \r\nreduce premature mortality by 0.79 million, with an associated health \r\nbenefit of USD2.8 trillion versus mitigation costs of USD840 billion, \r\nequating to 6% and 2% of GDP, respectively (Xie et al. 2018). Similarly, \r\nstabilising radiative forcing to  3"


id: d3200cf2aafe04b9d435fa96705fd372
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vector_id: 30741
content:
"4 W  m-2 in South Korea could \r\ncost USD1.3-8.5 billion in 2050 and could lead to a USD23.5 billion \r\ncost reduction from the combined benefits of avoided premature \r\nmortality, health expenditures, and lost work hours (Kim et al. 2020). \r\nThe health co-benefits related to physical exercise and reduced air \r\npollution largely offset the costs of implementing low-CO2-emitting \r\nurban mobility strategies in three Austrian cities (Wolkinger et al. 2018)."


id: e7e11ad623e02c764e9ae836a05469f7
created_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 33977
content:
"Climate change mitigation actions to reduce or slow negative impacts \r\non ecosystems are likely to support the achievement of SDGs 2, 3, 6, \r\n12, 14 and 15. Some studies show that stringent and constant GHG \r\nmitigation practices bring a net benefit to global biodiversity even \r\nif land-based mitigation measures are also adopted (Ohashi et al. \r\n2019), as opposed to delayed action which would require much more \r\nwidespread use of BECCS. Scenarios based on demand reductions \r\nof energy and land-based production are expected to avoid many \r\nsuch consequences, due to their minimised reliance on BECCS (Conijn \r\net al. 2018; Grubler et al. 2018; Bowles et al. 2019; Soergel et al. \r\n2021a). Stringent mitigation that includes reductions in demand for \r\nanimal-based foods and food waste could also relieve pressures on \r\nland use and biodiversity (high confidence), both directly by reducing \r\nagricultural land requirements (Leclere et al"


id: aa84b761dac48b50c2c4ff607f60aac0
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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vector_id: 24360
content:
"Effectively responding to climate change and achieving sustainable \r\ndevelopment requires overcoming a series of challenges to transition \r\naway from fossil-based economies. Feasibility can be defined in many \r\nways (Chapter 1). The political science literature (Majone 1975a,b; \r\nGilabert and Lawford-Smith 2012) distinguishes the feasibility of \r\n'what' (i.e., emission reduction strategies), 'when and where' (i.e., in \r\nthe year 2050, globally) and 'whom' (i.e.,  cities). It distinguishes \r\ndesirability from political feasibility (von Stechow et al. 2015): the \r\nformer represents a normative assessment of the compatibility with \r\nsocietal goals (i.e., SDGs), while the latter evaluates the plausibility of \r\nwhat can be attained given the prevailing context of transformation \r\n(Nielsen et al. 2020). Feasibility concerns are context and time \r\ndependent and malleable: enabling conditions can help overcome \r\nthem"


id: 39cb0c9069d27edcfc773aded6085a71
created_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 6471
content:
" For example, public support for carbon taxes has been hard to \r\nsecure but appropriate policy design and household rebates can help \r\ndissipate opposition (Murray and Rivers 2015; Carattini et al. 2019)."


id: 882249139e7f7daee2ed222fd35e1148
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 18939
content:
"Most of the existing literature has focused on the technological \r\ndimensions, given the technology focus of models and the ease of \r\ncomparison. The literature points to varied findings. Some suggest that \r\nscenarios envision technological progress consistent with historical \r\nbenchmarks (Wilson et al. 2013; Loftus  et al. 2015). Others that \r\nscenarios exceed historically observed rates of low-carbon technology \r\ndeployment and of energy demand transformation globally (van der \r\nZwaan et al. 2013; Napp et al. 2017; Cherp et al. 2021; Semieniuk \r\net al. 2021), but not for all countries (Cherp et al. 2021). The reason for \r\nthese discrepancies depends on the unit of analysis and the indicators \r\nused. Comparing different kinds of historical indicators, (van Sluisveld \r\net al"


id: 6023c8ebee5c486846b7103b0a30e783
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 12701
content:
" 2015) find that indicators that look into the absolute change of \r\nenergy systems remain within the range of historical growth frontiers \r\nfor the next decade, but increase to unprecedented levels before mid\u0002century. Expert assessments provide another way of benchmarking \r\nscenarios, though they have shown to be systematically biased \r\n(Wiser et al. 2021) and to underperform empirical methods (Meng \r\net al. 2021). van Sluisveld et al. (2018a) find that scenarios and \r\nexperts align for baseline scenarios but differ for low-carbon ones. \r\nScenarios rely more on conventional technologies based on existing \r\ninfrastructure (such as nuclear and CCS) than what is forecasted by \r\nexperts. Overall, the technology assessment of the feasibility space \r\nhighlights that Paris-compliant transformations would have few \r\nprecedents, but not zero (Cherp et al. 2021)."


id: 4fb952e5efc5f45664317a5286eb7f2f
created_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 9979
content:
"be comprehensively understood. It is of vital importance that the \r\nprocess of technical change must be considered in its institutional \r\nand social context so as to ascertain potential transition barriers \r\nwhich in turn provide an indication of pathway feasibility. In order \r\nto address the multitudinous challenges associated with low\u0002carbon transition feasibility and governance, it has been opined \r\nthat the robustness of evaluating pathways may be improved by the \r\nbridging of differing quantitative-qualitative analytical approaches \r\n(Haxeltine et al. 2008; Foxon et al. 2010; Hughes 2013; Wangel et al. \r\n2013; Li et al. 2015; Turnheim et al. 2015; Geels et al. 2016a,b, 2020; \r\nMoallemi et al. 2017; De Cian et al. 2020; Li and Strachan 2019)"


id: 90b4a1b76703f302d6497f55e4097cdf
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 20303
content:
" The \r\nrationale for such analytical bridging is to rectify the issue that in \r\nisolation each disciplinary approach can only generate a fragmented \r\ncomprehension of the transition pathway with the consequence \r\nbeing an incomplete identification of associated challenges in terms \r\nof feasibility. Concerning low-carbon transition pathways generated \r\nby IAMs, it has been argued that a comprehensive analysis should \r\ninclude social scientific enquiry (Geels et al. 2016a, 2020; van Sluisveld \r\net al. 2018b). The normative analysis of IAM pathways assists in the \r\ngeneration of a vision or the formulation of a general plan with this \r\nbeing complemented by socio-technical transition theory (Geels et al. \r\n2016a). Such an approach thereby allowing for the socio-political \r\nfeasibility and the social acceptance and legitimacy of low-carbon \r\noptions to be considered. Combining computer models and the multi\u0002level perspective can help identify 'transition bottlenecks' (Geels \r\net al. 2020)"


id: f995a5e59c47d30bceb87d756b7d00d4
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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content:
" Similarly, increased resolution of integrated assessment \r\nmodels' actors has led to more realistic narratives of transition in terms \r\nof granularity and behaviour (McCollum et al. 2017; van  Sluisveld \r\net al. 2018b). Increased data availability of actual behaviour from \r\nsmart technology lowers the barriers to representing behavioural \r\nchange in computer simulations, and thus better represents crucial \r\ndemand-side transformations (Creutzig et al. 2018). Increasing the \r\nmodel resolution is a meaningful way forward. However, integrating \r\na much broader combination of real-life aspects and dynamics into \r\nmodels could lead to an increased complexity that could restrict them \r\nto smaller fields of applications (De Cian et al. 2020)."


id: ffa83fae0ed8c22919f168515e4ba23a
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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index: climate_gpt_only_giec
vector_id: 37765
content:
"In Figure  3.43, we show the results of applying the methodology \r\nof Brutschin et al. (2021) to the AR6 scenarios database. The \r\ncharts highlight the dynamic nature of feasibility risks, which are \r\nmostly concentrated in the decades before mid-century except \r\nfor geophysical risks driven by CO2 removals later in the century. \r\nDifferent dimensions pose differentiated challenges: for example, \r\ninstitutional feasibility challenges appear to be the most relevant, in \r\nline with the qualitative literature. Thus, feasibility concerns might be \r\nparticularly relevant in countries with weaker institutional capacity. \r\nFigure 3.43 also highlights the key roles of policy and technology as \r\nenabling factors. In particular (panel b), internationally coordinated \r\nand immediate emission reductions allow to smooth out feasibility \r\nconcerns and reduce long-term challenges compared to delayed \r\npolicy action, as a  result of a  more gradual transition and lower \r\nrequirements of CO2 removals"


id: 5f0ee30a78365c8d2d139fdfba8816c6
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
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vector_id: 12501
content:
" For the same climate objective, \r\ndifferent Illustrative Mitigation Pathways entail somewhat different \r\ndegrees and distributions of implementation challenges (panel c)."


id: 3721cd858034d9ddd99520d3d8fc5e42
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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vector_id: 6059
content:
"One of the most relevant factors affecting mitigation pathways and \r\ntheir feasibility is the rate and kind of socio-economic development. \r\nFor example, certain socio-economic trends and assumptions \r\nabout policy effectiveness preclude achieving stringent mitigation \r\nfutures (Rogelj et al. 2018b). The risk of failure increases markedly \r\nin high-growth, unequal and/or energy-intensive worlds such as \r\nthose characterised by the shared socio-economic pathways SSP3, \r\nSSP4 and SSP5. On the other hand, socio-economic development \r\nconducive to mitigation relieves the energy sector transformation \r\nfrom relying on large-scale technology development: for example, \r\nthe amount of biomass with CCS in SSP1 is one third of that in SSP5. \r\nThe reason why socio-economic trends matter so much is that they \r\nboth affect the CO2 emissions in counterfactual scenarios as well \r\nas the mitigation capacity (Riahi et al. 2017; Rogelj et al. 2018b)"


id: 519598726bf55d0e7b4290755e6c3877
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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content:
" \r\nEconomic growth assumptions are the most important determinant \r\nof scenario emissions (Marangoni et al. 2017). Degrowth and post\u0002growth scenarios have been suggested as valuable alternatives to \r\nbe considered (Hickel et al. 2021; Keysser and Lenzen 2021), though \r\nsubstantial challenges remain regarding political feasibility (Keysser \r\nand Lenzen 2021)."


id: 57acc2d3b38886f69b739a212646c8f8
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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content:
"The AR6 scenarios database contains 3131 scenarios of which \r\n2425 with global scope were considered by this chapter, generated \r\nby almost 100 different model versions, from more than 50 model \r\nfamilies. Of the 1686 vetted scenarios, 1202 provided sufficient \r\ninformation for a climate categorisation. Around 46% of the pathways \r\nare consistent with an end-of-century temperature of at least likely\r\nlimiting warming to below 2degC (>67%). There are many ways of \r\nconstructing scenarios that limit warming to a particular level and \r\nthe choice of scenario construction has implications for the timing of \r\nboth net zero CO2 and GHG emissions and the deployment of CDR \r\n(Emmerling et al. 2019; Rogelj et al. 2019b; Johansson et al. 2020). \r\nThe AR6 scenarios database includes scenarios where temperature \r\nis temporarily exceeded (40% of all scenarios in the database \r\nhave median temperature in 2100 that is 0.1degC lower than median \r\npeak temperature)"


id: ec1e4544a4f6ff23da3002e1ad16c87c
created_at: 2023-03-16 14:02:42
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vector_id: 34647
content:
" Climate stabilisation scenarios are typically \r\nimplemented by assuming a carbon price rising at a particular rate \r\nper year, though that rate varies across model, scenario, and time \r\nperiod. Standard scenarios assume a  global single carbon price to \r\nminimise policy costs. Cost-minimising pathways can be reconciled \r\nwith equity considerations through posterior international transfers. \r\nMany scenarios extrapolate current policies and include non-market, \r\nregulatory instruments such as technology mandates."


id: c4c1ee00326cfa3b245ec63729b6f289
created_at: 2023-03-16 14:02:42
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vector_id: 28528
content:
"The type of policy instrument assumed to drive the decarbonisation \r\nprocess also plays a vital role for determining feasibility. The majority \r\nof scenarios exploring climate stabilisation pathways in the past have \r\nfocused on uniform carbon pricing as the most efficient instrument \r\nto regulate emissions. However, carbon taxation raises political \r\nchallenges (Beiser-McGrath and Bernauer 2019) (Chapters  13 and \r\n14). Carbon pricing will transfer economic surplus from consumers \r\nand producers to the government. Losses for producers will be highly \r\nconcentrated in those industries possessing fixed or durable assets \r\nwith 'high asset specificity' (Murphy 2002; Dolphin et al. 2020). These \r\nsectors have opposed climate jurisdictions (Jenkins 2014). Citizens \r\nare sensitive to rising energy prices, though revenue recycling can \r\nbe used to increase support (Carattini et al. 2019)"


id: 479412a3d15c6198e769abd2a68719ee
created_at: 2023-03-16 14:02:42
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content:
" A recent model \r\ncomparison project confirms findings from the extant literature: \r\nusing revenues to reduce pre-existing capital or, to a lesser extent, \r\nlabour taxes, reduces policy costs and eases distributional concerns \r\n(Barron et al. 2018; Mcfarland et al. 2018)."


id: 6c9879a2c87addef22054a82d92a00d1
created_at: 2023-03-16 14:02:42
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content:
"The models assessed in this chapter differ in their sectoral coverage \r\nand the level of complexity in each sector. Models tend to have more \r\ndetail in their representation of energy supply and transportation, \r\nthan they do for industry (Section 3.4 and Annex III.I). Some models \r\ninclude detailed land-use models, while others exclude land models \r\nentirely and use supply curves to represent bioenergy potential \r\n(Bauer et al. 2018a). IAMs do not include all mitigation options \r\navailable in the literature (Rogelj et al. 2018b; Smith et al. 2019). \r\nFor example, most IAM pathways exclude many granular demand\u0002side mitigation options and land-based mitigation options found in \r\nmore detailed sectoral models; additionally, only a  few pathways \r\ninclude CDR options beyond afforestation/reforestation and BECCS. \r\nSection 3.4 and Chapter 12 include some results and comparisons \r\nto non-IAM models (e.g.,  bottom-up studies and detailed sectoral \r\nmodels)"


id: 330586e08837eaa7094ac061c7455027
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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content:
" These sectoral studies often include a  more complete set \r\nof mitigation options but exclude feedbacks and linkages across \r\nsectors which may alter the mitigation potential of a given sector. \r\nThere is an increasing focus in IAM studies on SDGs (Section 3.7), \r\nwith some studies reporting the implications of mitigation pathways \r\non SDGs (e.g.,  Bennich et al. 2020) and others using achieving \r\nSDGs as a constraint on the scenario itself (van Vuuren et al. 2015; \r\nSoergel et al. 2021a). However, IAMs are still limited in the SDGs they \r\nrepresent, often focusing on energy, water, air pollution and land. \r\nOn the economic side, the majority of the models report information \r\non marginal costs (i.e.,  carbon price). Only a  subset provides full \r\neconomic implications measured by either economic activity or \r\nwelfare. Also often missing, is detail about economic inequality \r\nwithin countries or large aggregate regions."


id: 6579e075301fe90d4b3321897794561c
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
content_type: text
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vector_id: 13536
content:
"Yes. Achieving net zero CO2 emissions and sustaining them into the future is sufficient to stabilise the CO2-induced warming signal \r\nwhich scales with the cumulative net amount of CO2 emissions. At the same time, the warming signal of non-CO2 GHGs can be \r\nstabilised or reduced by declining emissions that lead to stable or slightly declining concentrations in the atmosphere. For short-lived \r\nGHGs with atmospheric lifetimes of less than 20 years, this is achieved when residual emissions are reduced to levels that are lower \r\nthan the natural removal of these gases in the atmosphere. Taken together, mitigation pathways that bring CO2 emissions to net \r\nzero and sustain it, while strongly reducing non-CO2 GHGs to levels that stabilise or decline their aggregate warming contribution, \r\nwill stabilise warming without using net negative CO2 emissions and with positive overall GHG emissions when aggregated using \r\nGWP-100. A considerable fraction of pathways that limit warming to 1"


id: 30e418a606506a0165e5365360db9c78
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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vector_id: 5113
content:
"5degC (>50%) with no or limited overshoot and limit warming \r\nto 2degC (>67%), respectively, do not or only marginally (<10 GtCO2 cumulative until 2100) deploy net negative CO2 emissions (26% \r\nand 46%, respectively) and do not reach net zero GHG emissions by the end of the century (48% and 70%, respectively). This is no \r\nlonger the case in pathways that return warming to 1.5degC (>50%) after a high overshoot (typically >0.1degC). All of these pathways \r\ndeploy net negative emissions on the order of 360 (60-680) GtCO2 (median and 5-95th percentile) and 87% achieve net negative \r\nGHGs emissions in AR6 GWP-100 before the end of the century. Hence, global net negative CO2 emissions, and net zero or net \r\nnegative GHG emissions, are only needed to decline, not to stabilise global warming"


id: 2ab00686c97fdd077b134c831db08a0f
created_at: 2023-03-16 14:02:42
updated_at: 2023-03-16 14:02:42
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vector_id: 4139
content:
" The deployment of carbon dioxide removal \r\n(CDR) is distinct from the deployment of net negative CO2 emissions, because it is also used to neutralise residual CO2 emissions to \r\nachieve and sustain net zero CO2 emissions. CDR deployment can be considerable in pathways without net negative emissions and \r\nall pathways limiting warming to 1.5degC use it to some extent."


