Editing IPCC:AR6/WGII/Chapter-18 (section)

==== 18.2.5.2 Mitigation ====

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Mitigation, including greenhouse gas emissions reductions, avoidance, and removal and sequestration, as well as management of other climate forcing factors (WGIII AR6), is a key element of addressing climate risk and pursuing CRD. There are numerous individual and system mitigation options throughout the economy and within human and natural systems ( ''very high confidence'' ) (Chapter 16; [[#18.5|Section 18.5]] ). Limiting global average warming has been found to reduce climate risks ( [[#IPCC--2018a|IPCC, 2018a]] ; [[#IPCC--2019b|IPCC, 2019b]] ), and limiting global average warming to any temperature level has also been found to be associated with broad ranges of potential global emissions pathways that represent future uncertainty in the evolution of socioeconomic, technological, market and physical systems ( ''very high confidence'' ) ( [[#Rose--2018|Rose and Scott, 2018]] ; [[#Rose--2020|Rose and Scott, 2020]] ). Pathways consistent with limiting warming to 2°C and below have been found to require significant deployment of mitigation options spanning energy, land use and societal transformation ((Lecocq et al., 2022; Riahi et al., 2022); [[#18.3|Section 18.3]] ). and substantial economic, energy, land use, policy and societal transformation (Lecocq et al., 2022; Riahi et al., 2022). Such emissions pathways would represent deviations from current trends that raise issues about their feasibility and therefore plausibility ( [[#Rose--2018|Rose and Scott, 2018]] ; [[#Rose--2020|Rose and Scott, 2020]] ).

The technical and economic challenge of limiting warming has been found to increase nonlinearly with greater ambition, fewer mitigation options, less than global cooperative policy designs and delayed mitigation action ((Riahi et al., 2022); Table 18.2). Table 18.2 provides a high-level summary of pathway characteristic ranges based on the WGIII AR6 assessment. Global pathways find large regional differences in mitigation potential, as well as the degree of regional nonlinearity with greater mitigation ambition. These represent opportunities for mitigation, but how this effort and cost would be facilitated and distributed respectively is a policy question.

Table 18.2 illustrates that greater climate ambition implies more aggressive emissions reductions in each region, and earlier regional peaking of emissions (if they have not peaked to date). Near-term regional emissions increases are possible, even for 1.5°C compatible pathways, but significantly lower emissions than today are shown in all regions by 2050. Increases in total regional energy consumption and fossil energy are observed for many pathways, even in the most ambitious where energy consumption growth is potentially slower compared with less ambitious pathways. By 2050, regional fossil energy declines, but is not eliminated in any region. Regional growth in electricity use is substantial in all pathways, even the most ambitious, with the growth continuing and accelerating with time and regional dependence on electricity (share of total energy consumption) also growing significantly. The broad ranges are an indication of uncertainty and risk for regional transitions, noting that full uncertainty is likely broader than what is captured by emissions scenario databases ( [[#Rose--2018|Rose and Scott, 2018]] ; [[#Rose--2020|Rose and Scott, 2020]] ). Among other things, pathways commonly assume idealised climate policies with immediate implementation, and model infeasibilities (i.e., models unable to solve) increase with climate ambition and pessimism about mitigation technologies (e.g., Clarke et al., 2014; [[#Bauer--2018|Bauer et al., 2018]] ; [[#Rogelj--2018|Rogelj et al., 2018]] ; [[#Muratori--2020|Muratori et al., 2020]] ), highlighting the increasing challenge and potential for actual infeasibility with lower global warming targets. Together, Table 18.2 provides insights into the increasingly demanding system and development transitions associated with lower global warming levels, as well as some of the low-carbon transition uncertainties and risks (see also Figure 18.5).

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'''Figure 18.5 |''' '''Regional implications of climate mitigation pathways in 2050 for different global mean peak temperature outcomes (during the century) for various development and sustainable development proxy variables.''' Each row reports results for a different variable for each of the five global regions (columns) used by WGIII, and SDG associated with each variable is noted. Blue dots represent individual emissions scenario results from each of the respective WGIII climate outcome scenario categories, with red bars the median results. All results are changes (percentage or fraction) relative to each WGIII scenario’s reference scenario. In some circumstances the reference case emissions are below those from the scenario consistent with a global warming level, which can produce results that appear counter-intuitive (e.g., increases in GDP or consumption). Data sample sizes vary substantially across temperature levels for a given variable and across variables due to model infeasibilities and model differences in reporting. Model infeasibilities, in particular, result in significantly fewer data points for 1.5°C compatible emissions pathways compared to 2°C pathways (i.e., models are more often unable to solve for a 1.5°C consistent pathway, than a 2°C pathway, with a given set of assumptions). Food/feed crop price results were not available for 1.5°C and 4°C warming levels. Sample sizes for each variable and warming level respectively—1.5°C, 2°C, 3°C, and 4°C—are as follows (and apply to all regions): GDP (n = 2, 93, 29, 12); Consumption (2, 93, 30, 13); Black Carbon (2, 100, 39, 16), NOx (2, 100, 39, 15), SO2 (2, 100, 39, 16), price food/feed crops (0, 44, 23, 0); price electricity (2, 94, 38, 15); price natural gas (10, 86, 44, 10). The sample sizes are very small for the 1.5°C and 4°C results; therefore, the medians for these warming levels are statistically unreliable, which should be considered in comparing across warming levels. Individual values in the samples exceed y-axis’ ranges in a few cases: black carbon 2°C Latin America minimum equals 0.08, food/feed price change 3°C minimums in Asia, Latin America, Middle East/Africa, OECD, and Reforming Economies equal respectively -33%, -28%, -28%, -29%, and -29%, natural gas price change 2°C maximums in Asia, Latin America, Middle East/Africa, OECD, and Reforming Economies equal respectively 962%, 1240%, 2768%, 917%, and 3588%. Figure developed from the WGIII AR6 scenarios database, with scenarios filtered according to WGIII exclusions and regional vetting.

Past assessment has evaluated representative mitigation strategies in terms of economic, technological, institutional, socio-cultural, environmental/ecological and geophysical viability, as well as relationships to SDGs ( [[#de%20Coninck--2018|de Coninck et al., 2018]] ). The strategies assessment analysis has been updated for AR6 (Cross-Chapter Box FEASIB). These assessments identify types of barriers that could affect an option’s feasibility. Among other things, this work finds that, other than public transport and non-motorised transport, every other mitigation option evaluated had at least one feasibility dimension that represented a barrier or obstacle. The barriers also imply that there are trade-offs in these feasibility dimensions to consider. The assessment of mitigation option-sustainable development relationships identifies related literature and derives aggregate characterisations. Concerns about the potential sustainable development implications of some mitigation technologies may be motivation for precluding the use of some mitigation options. For instance, the potential food security and environmental quality implications of bioenergy have received significant attention in the literature (e.g., [[#Smith--2013|Smith et al., 2013]] ). However, constraining or precluding the use of bioenergy without or with CCS could have significant implications for the cost of pursuing ambitious climate goals, and potentially the attainability of those goals (e.g., Clarke et al., 2014; [[#Bauer--2018|Bauer et al., 2018]] ; [[#Rogelj--2018|Rogelj et al., 2018]] ; [[#Muratori--2020|Muratori et al., 2020]] ). Bioenergy is not unique in this regard. Social, environmental, and sustainability concerns have also been raised about the large-scale deployment of many low-carbon technologies, for example, REDD+, wind, solar, nuclear, fossil with CCS and batteries. See WGIII [[IPCC:Wg2:Chapter:Chapter-3|Chapter 3]] (Riahi et al., 2022) for examples of the potential implications of limiting or precluding different low-carbon technologies.

Overall, as with adaptation options, insights from this aggregate feasibility and sustainable development mapping work are high level and difficult to apply to a specific mitigation context. The feasibility, ranking and sustainable development implications of mitigation options, as well as the list of options themselves, for a given location will vary from location to location, with different criteria and weighting of criteria that reflect the relevant social priorities and differences in markets, technology options and policies for managing risks and trade-offs. Integrated evaluation of criteria and options is needed here as well, that accounts for the relevant geographic context and interactions between options, systems and implications.

Analyses of the potential implications of mitigation on sustainable development has various strands of literature—studies exploring general greenhouse gas mitigation feedbacks to society, assessments of mitigation implications on specific societal objectives other than climate and literature evaluating mitigation implications specifically for sustainable development objectives (Denton et al., 2022; Lecocq et al., 2022; Riahi et al., 2022). In general, mitigation alters development opportunities by constraining the emissions future society can produce, which affects markets, resource allocation, economic structure, income distribution, consumers and the environment (besides climate) ( ''very high confidence'' ). Examples of general development feedbacks from mitigation include estimated price changes, macroeconomic costs, and low carbon energy and land system transformations ( [[#Fisher--2007|Fisher et al., 2007]] ; Clarke et al., 2014; [[#Popp--2014|Popp et al., 2014]] ; [[#Rose--2014|Rose et al., 2014]] ; [[#Weyant--2014|Weyant and Kriegler, 2014]] ; [[#Bauer--2018|Bauer et al., 2018]] ; [[#Rogelj--2018|Rogelj et al., 2018]] ). Examples of mitigation implications for other specific variables of societal interest include evaluating potential effects on air pollutant emissions, crop prices, water and land use change (e.g., [[#McCollum--2018b|McCollum et al., 2018b]] ; [[#Roy--2018|Roy et al., 2018]] ), while the literature evaluating mitigation implications specifically for sustainable development objectives includes evaluations on energy access, food security and income equality (e.g., [[#Roy--2018|Roy et al., 2018]] ; [[#Arneth--2019|Arneth et al., 2019]] ; [[#Mbow--2019|Mbow et al., 2019]] ). Proxy indicators are frequently used to represent whether there might be implications for a sustainable development objective. For example, changes in energy prices are used as a proxy for effects on energy security (e.g., [[#Roy--2018|Roy et al., 2018]] ). This is common with aggregate modelling studies, such as those associated with global or regional emissions scenarios and energy systems.

Figure 18.5, derived from WGIII scenarios data, illustrates estimated relationships between mitigation and various sustainable development proxy variables for different global regions. Figure 18.5 illustrates synergies and trade-offs with mitigation, as well as regional heterogeneity, that can intensify with the level of climate ambition—synergies in air pollutants, such as black carbon, NOx and SO 2 ; and trade-offs in overall economic development, household consumption, food crop prices and energy prices for electricity and natural gas. For comparison, recent IPCC assessments also observed similar synergies and trade-offs but did not directly make comparisons regarding overall development nor evaluate potential climates above 2°C ( [[#Rogelj--2018|Rogelj et al., 2018]] ; [[#Roy--2018|Roy et al., 2018]] ; [[#Mbow--2019|Mbow et al., 2019]] ). Regional nonlinearity in the economic costs of mitigation with greater climate ambition (i.e., costs rising at an increasing rate with lower warming goals) can be significant within individual models ( [[#Rose--2018|Rose and Scott, 2018]] ; [[#Rose--2020|Rose and Scott, 2020]] ). Figure 18.5 also illustrates transition risks in the potential for significant synergistic and trade-off implications with, for instance, potentially large regional commodity price implications and household consumption losses, as well as more significant air pollution benefits. Note that the 1.5°C results in Figure 18.5 (and Table 18.2) are biased by model infeasibilities. Many models are unable to solve, especially with less optimistic assumptions, resulting in small sample sizes and a different representation of models compared to the 2°C and higher results.

Results such as those in Figure 18.5 illustrate that mitigation–development trade-offs are inevitable and need to be considered and addressed. For instance, Roy (2018) found that although limiting warming to 1.5°C would make it markedly easier to achieve most of the UN’s SDGs, none of the 1.5°C pathways assessed achieved all of the SDGs. A similar conclusion follows from the results in Figure 18.5 based on WGIII AR6 scenarios.. A newer literature is developing, evaluating the potential for managing SDG trade-offs. Results like those in Figure 18.5 provide insights regarding some of the types of strategy sets to consider. [[#Roy--2018|Roy et al. (2018)]] discuss the potential for policies that address distributional implications, such as payments, food support and revenue recycling, as well as education, retraining and technology outreach, subsidies or prioritisation. Recent studies have begun to estimate potential payments to offset trade-offs, such as related to food, water and energy access (e.g., [[#McCollum--2018a|McCollum et al., 2018a]] ). These analyses estimate investments to address specific trade-offs; however, with mitigation redirecting resources away from other productive activities, there is a need to also evaluate the aggregate economy-wide, distributional and welfare effects, including the redistribution effects of managing sustainable development trade-offs.

There are a wide range of mitigation options and systems to consider, with assessment suggesting that a diverse portfolio is practical for pursing climate policy ambitions. However, local context will impact mitigation choices, with unique sustainable development priorities, available mitigation options, sustainable development synergies and trade-offs, and policy design and implementation possibilities.

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