Editing IPCC:AR6/WGIII/Chapter-3 (section)

=== Cross-Working Group Box 1 | Economic Benefits from Avoided Climate Mitigation Pathways ===

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'''Authors:''' Céline Guivarch (France), Steven Rose (the United States of America), Alaa Al Khourdajie (United Kingdom/Syria), Valentina Bosetti (Italy), Edward Byers (Austria/Ireland), Katherine Calvin (the United States of America), Tamma Carleton (the United States of America), Delavane Diaz (the United States of America), Laurent Drouet (France/Italy), Michael Grubb (United Kingdom), Tomoko Hasegawa (Japan), Alexandre C. Köberle (Brazil/United Kingdom), Elmar Kriegler (Germany), David McCollum (the United States of America), Aurélie Méjean (France), Brian O’Neill (the United States of America), Franziska Piontek (Germany), Julia Steinberger (United Kingdom/Switzerland), Massimo Tavoni (Italy)

Mitigation reduces the extent of climate change and its impacts on ecosystems, infrastructure, and livelihoods. This box summarises elements from the AR6 WGII report on aggregate climate change impacts and risks, putting them into the context of mitigation pathways. AR6 WGII provides an assessment of current lines of evidence regarding potential climate risks with future climate change, and therefore, the avoided risks from mitigating climate change. Regional and sectoral climate risks to physical and social systems are assessed (AR6 WGII Chapters 2–15). Over 100 of these are identified as Key Risks (KRs) and further synthesised by WGII [[IPCC:Wg3:Chapter:Chapter-16|Chapter 16]] into eight overarching Representative Key Risks (RKRs) relating to low-lying coastal systems; terrestrial and ocean ecosystems; critical physical infrastructure, networks and services; living standards; human health; food security; water security; and peace and mobility (AR6 WGII [[IPCC:Wg3:Chapter:Chapter-16#16.5.2|Section 16.5.2]] ). The RKR assessment finds that risks increase with global warming level, and also depend on socio-economic development conditions, which shape exposure and vulnerability, and adaptation opportunities and responses. ‘Reasons For Concern’, another WGII aggregate climate-impacts risk framing, are also assessed to increase with climate change, with increasing risk for unique and threatened systems, extreme weather events, distribution of impacts, global aggregate impacts, and large-scale singular events (AR6 WGII Chapter 16). For human systems, in general, the poor and disadvantaged are found to have greater exposure level and vulnerability for a given hazard. With some increase in global average warming from today expected regardless of mitigation efforts, human and natural systems will be exposed to new conditions and additional adaptation will be needed (AR6 WGII Chapter 18). The range of dates for when a specific warming level could be reached depends on future global emissions, with significant overlap of ranges across emissions scenarios due to climate system response uncertainties (AR6 WGI Tables 4.2 and 4.5). The speed at which the climate changes is relevant to adaptation timing, possibilities, and net impacts.

The AR6 WGII also assesses the growing literature estimating the global aggregate economic impacts of climate change and the social cost of carbon dioxide and other greenhouse gases (AR6 WGII Cross-Working Box ECONOMIC: Estimating Global Economic Impacts from Climate Change and the Social Cost of Carbon in AR6 WGII Chapter 16). The former represents aggregate estimates that inform assessment of the economic benefits of mitigation. This literature is characterised by significant variation in the estimates, including for today’s level of global warming, due primarily to fundamental differences in methods, but also differences in impacts included, representation of socio-economic exposure, consideration of adaptation, aggregation approach, and assumed persistence of damages. The AR6 WGII’s assessment identifies different approaches to quantification of aggregated economic impacts of climate change, including: physical modelling of impact processes, such as projected mortality rates from climate risks such as heat, vector- or waterborne diseases that are then monetised; structural economic modelling of impacts on production, consumption, and markets for economic sectors and regional economies; and statistical estimation of impacts based on observed historical responses to weather and climate. The AR6 WGII finds that variation in estimated global economic impacts increases with warming in all methodologies, indicating higher risk in terms of economic impacts at higher temperatures ( ''high confidence'' ). Many estimates are non-linear with marginal economic impacts increasing with temperature, although some show declining marginal economic impacts with temperature, and functional forms cannot be determined for all studies. The AR6 WGII’s assessment finds that the lack of comparability between methodologies does not allow for identification of robust ranges of global economic impact estimates ( ''high confidence'' ). Further, AR6 WGII identifies evaluating and reconciling differences in methodologies as a research priority for facilitating use of the different lines of evidence ( ''high confidence'' ). However, there are estimates that are higher than AR5, indicating that global aggregate economic impacts could be higher than previously estimated ( ''low confidence'' due to the lack of comparability across methodologies and lack of robustness of estimates) (AR6 WGII Cross-Working Box ECONOMIC).

Conceptually, the difference in aggregate economic impacts from climate change between two given temperature levels represents the aggregate economic benefits arising from avoided climate change impacts due to mitigation action. A subset of the studies whose estimates were evaluated by AR6 WGII (5 of 15) are used to derive illustrative estimates of aggregate economic benefits in 2100 arising from avoided climate change ( [[#Howard--2017|Howard and Sterner 2017]] ; [[#Burke--2018|Burke et al. 2018]] ; [[#Pretis--2018|Pretis et al. 2018]] ; [[#Kahn--2019|Kahn et al. 2019]] ; [[#Takakura--2019|Takakura et al. 2019]] ). [[#Burke--2018|Burke et al. (2018)]] , [[#Pretis--2018|Pretis et al. (2018)]] and [[#Kahn--2019|Kahn et al. (2019)]] are examples of statistical estimations of historical relationships between temperature and economic growth, whereas [[#Takakura--2019|Takakura et al. (2019)]] is an example of structural modelling, which evaluates selected impact channels (impacts on agriculture productivity, undernourishment, heat-related mortality, labour productivity, cooling/heating demand, hydro-electric and thermal power generation capacity and fluvial flooding) with a general equilibrium model. [[#Howard--2017|Howard and Sterner (2017)]] and [[#Rose--2017b|Rose et al. (2017b)]] estimate damage functions that can be used to compute the economic benefits of mitigation from avoiding a given temperature level for a lower one. [[#Howard--2017|Howard and Sterner (2017)]] estimate a damage function from a meta-analysis of aggregate economic impact studies, while [[#Rose--2017b|Rose et al. (2017b)]] derive global functions by temperature and socio-economic drivers from stylised aggregate cost-benefit-analysis (CBA) integrated assessment models (IAMs) using diagnostic experiments. Cross-Working Group Box 1, Figure 1 summarises the global aggregate economic benefits in 2100 of avoided climate change impacts from individual studies corresponding to shifting from a higher temperature category (above 3°C, below 3°C or below 2.5°C) to below 2°C, as well as from below 2°C to below 1.5°C. Benefits are positive and increase with the temperature gap for any given study, and this result is robust across socio-economic scenarios. The Figure provides evidence of a wide range of quantifications, and illustrates the important differences associated with methods. Panel a puts the studies used to calculate aggregate economic benefits arising from avoided impacts into the context of the broader set of studies assessed in WGII ( [[IPCC:Wg3:Chapter:Chapter-16#16.6.2|Section 16.6.2]] of this report, AR6 WGII Cross-Working Group Box ECONOMIC,). However, economic benefits in 2100 arising from avoided impacts cannot be directly computed from damage estimates across this broader set of studies, due to inconsistencies – different socio-economic assumptions, scenario designs, and counterfactual reference scenarios across studies. Furthermore, these types of estimates cannot be readily compared to mitigation cost estimates. The comparison would require a framework that ensures consistency in assumptions and dynamics and allows for consideration of benefits and costs along the entire pathway.

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'''Cross-Working Group Box 1, Figure 1 | Global aggregate economic benefits of mitigation from avoided climate change impacts in 2100 corresponding to shifting from a higher temperature category (4°C (3.75°C–4.25°C), 3°C (2.75°C–3.25°C), or above 2°C (2°C–2.5°C), to below 2°C (1.5°C–2°C), as well as from below 2°C to below 1.5°C (1°C–1.5°C)), from the five studies discussed in the text.''' Panel '''(a)''' is adapted from AR6 WGII Cross-Working Group Box ECONOMIC, Figure 1, showing global aggregate economic impact estimates (% global GDP loss relative to GDP without additional climate 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 coloured for the selected studies, which provide results for different temperature change levels. See the AR6 WGII AR6 WGII Cross-Working Group Box ECONOMIC for discussion 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 distinguishes the baseline scenarios. Temperature ranges are highlighted. HS17 estimates are based on their preferred model –50th percentile of non-catastrophic damage. Panel '''(b)''' shows the implied aggregate economic benefits in 2100 of a lower temperature increase. Economic benefits for point estimates are computed as a difference, while economic benefits from the curve HS17 are computed as ranges from the segment differences.

Aggregate benefits from avoided impacts expressed in GDP terms, as in Figure 1, do not encompass all avoided climate risks, adaptation possibilities, and do not represent their influence on well-being and welfare (AR6 WGII Cross-Working Group Box ECONOMIC). Methodological challenges for economic impact estimates include representing uncertainty and variability, capturing interactions and spillovers, considering distributional effects, representing micro- and macro-adaptation processes, specifying non-gradual damages and non-linearities, and improving understanding of potential long-run growth effects. In addition, the economic benefits aggregated at the global scale provide limited insights into regional heterogeneity. Global economic impact studies with regional estimates find large differences across regions in absolute and percentage terms, with developing and transitional economies typically more vulnerable. Furthermore, (avoided) impacts for poorer households and poorer countries can represent a smaller share in aggregate quantifications expressed in GDP terms or monetary terms, compared to their influence on well-being and welfare ( [[#Hallegatte--2020|Hallegatte et al. 2020]] ; [[#Markhvida--2020|Markhvida et al. 2020]] ). Finally, as noted by AR6 WGII, other lines of evidence regarding climate risks, beyond monetary estimates, should be considered in decision-making, including Key Risks and Reasons for Concern.

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