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

== 3.6 Economics of Long-term Mitigation and Development Pathways, Including Mitigation Costs and Benefits ==

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A complete appraisal of economic effects and welfare effects at different temperature levels would include the macroeconomic impacts of investments in low-carbon solutions and structural change away from emitting activities, co-benefits and adverse side effects of mitigation, (avoided) climate damages, as well as (reduced) adaptation costs, with high temporal, spatial and social heterogeneity using a harmonised framework. If no such complete appraisal in a harmonised framework exists, key elements are emerging from the literature, and assessed in the following subsections: on aggregated economy-wide global mitigation costs ( [[#3.6.1|Section 3.6.1]] ), on the economic benefits of avoiding climate impacts ( [[#3.6.2|Section 3.6.2]] ), on economic benefits and costs associated with mitigation co-benefits and co-harms ( [[#3.6.3|Section 3.6.3]] ) and on the distribution of economic implications between economic sectors and actors ( [[#3.6.4|Section 3.6.4]] ).

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=== 3.6.1 Economy-wide Implications of Mitigation ===

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==== 3.6.1.1 Global Economic Effects of Mitigation and Carbon Values in Mitigation Pathways ====

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Estimates for the marginal abatement cost of carbon in mitigation pathways vary widely, depending on the modelling framework used and socio-economic, technological and policy assumptions. However, it is robust across modelling frameworks that the marginal abatement cost of carbon increases for lower temperature categories, with a higher increase in the short term than in the longer term (Figure 3.32, left panel) ( ''high confidence'' ). The marginal abatement cost of carbon increases non-linearly with the decrease of CO 2 emissions level, but the uncertainty in the range of estimates also increases (Figure 3.33). Mitigation pathways with low‐energy consumption patterns exhibit lower carbon values ( [[#Méjean--2019|Méjean et al. 2019]] ; [[#Meyer--2021|Meyer et al. 2021]] ). In the context of the COVID-19 pandemic recovery, [[#Kikstra--2021a|Kikstra et al. (2021a)]] also show that a low-energy-demand recovery scenario reduces carbon prices for a 1.5°C-consistent pathway by 19% compared to a scenario with energy demand trends restored to pre-pandemic levels.

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'''Figure 3.33 | Marginal abatement cost of carbon with respect to CO''' 2 '''emissions for mitigation pathways with immediate global mitigation action, in 2030 (a) and 2050 (b).'''

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'''Figure 3.32 | Marginal abatement cost of carbon in 2030, 2050 and 2100 for mitigation pathways with immediate global mitigation action (a), and ratio in 2050 between pathways that correspond to NDCs announced prior to COP26 in 2030 and strengthen action after 2030 and pathways with immediate global mitigation action, for C3 and C4 temperature categories (b)''' .

For optimisation modelling frameworks, the time profile of marginal abatement costs of carbon depends on the discount rate, with lower discount rates implying higher carbon values in the short term but lower values in the long term ( [[#Emmerling--2019|Emmerling et al. 2019]] ) (see also ‘Discounting’ in Annex I: Glossary, and Annex III.I.2). In that case, the discount rate also influences the shape of the emissions trajectory, with low discount rates implying more emissions reduction in the short term and, for low-temperature categories, limiting CDR and temperature overshoot.

Pathways that correspond to NDCs announced prior to COP26 in 2030 and strengthen action after 2030 imply higher marginal abatement costs of carbon in the longer run than pathways with stronger immediate global mitigation action (Figure 3.32b) ( ''hi'' ''gh confidence'' ).

Aggregate economic activity and consumption levels in mitigation pathways are primarily determined by socio-economic development pathways but are also influenced by the stringency of the mitigation goal and the policy choices to reach the goal ( ''high confidence'' ). Mitigation pathways in temperature categories C1 and C2 entail losses in global consumption with respect to their baselines – not including benefits of avoided climate change impacts nor co-benefits or co-harms of mitigation action – that correspond to an annualised reduction of consumption growth by 0.04 (median value) (interquartile range [0.02–0.06]) percentage points over the century. For pathways in temperature categories C3 and C4 this reduction in global consumption growth is 0.03 (median value) (interquartile range [0.01–0.05]) percentage points over the century. In the majority of studies that focus on the economic effects of mitigation without accounting for climate damages, global economic growth and consumption growth is reduced compared to baseline scenarios (that omit damages from climate change), but mitigation pathways do not represent an absolute decrease of economic activity level (Figure 3.34b,c).

However, the possibility for increased economic activity following mitigation action, and conversely the risk of large negative economic effects, are not excluded. Some studies find that mitigation increases the speed of economic growth compared to baseline scenarios ( [[#Pollitt--2018|Pollitt and Mercure 2018]] ; [[#Mercure--2019|Mercure et al. 2019]] ). These studies are based on a macroeconomic modelling framework that represent baselines below the efficiency frontier, based on non-equilibrium economic theory, and assume that mitigation is undertaken in such a way that green investments do not crowd out investment in other parts of the economy – and therefore offers an economic stimulus. In the context of the recovery from the COVID-19 crisis, it is estimated that a green investment push would initially boost the economy while also reducing GHG emissions ( [[#IMF--2020|IMF 2020]] ; [[#Pollitt--2021|Pollitt et al. 2021]] ). Conversely, several studies find that only a GDP non-growth/degrowth or post-growth approach enable reaching climate stabilisation below 2°C ( [[#Hardt--2017|Hardt and O’Neill 2017]] ; [[#D’Alessandro--2020|D’Alessandro et al. 2020]] ; [[#Hickel--2020|Hickel and Kallis 2020]] ; [[#Nieto--2020|Nieto et al. 2020]] ), or to minimise the risks of reliance on high energy-GDP decoupling, large-scale CDR and large-scale renewable energy deployment ( [[#Keyßer--2021|Keyßer and Lenzen 2021]] ). Similarly, feedbacks of financial system risk amplifying shocks induced by mitigation policy and lead to a higher impact on economic activity ( [[#Stolbova--2018|Stolbova et al. 2018]] ).

Mitigation costs increase with the stringency of mitigation ( [[#Hof--2017|Hof et al. 2017]] ; [[#Vrontisi--2018|Vrontisi et al. 2018]] ) (Figure 3.34b,c), but are reduced when energy demand is moderated through energy efficiency and lifestyle changes ( [[#Fujimori--2014|Fujimori et al. 2014]] ; [[#Bibas--2015|Bibas et al. 2015]] ; [[#Liu--2018|Liu et al. 2018]] ; [[#Méjean--2019|Méjean et al. 2019]] ), when sustainable transport policies are implemented (Zhang et al. 2018c), and when international technology cooperation is fostered ( [[#Schultes--2018|Schultes et al. 2018]] ; [[#Paroussos--2019|Paroussos et al. 2019]] ). Mitigation costs also depend on assumptions on availability and costs of technologies (Clarke et al. 2014; [[#Bosetti--2015|Bosetti et al. 2015]] ; [[#Dessens--2016|Dessens et al. 2016]] ; [[#Creutzig--2018|Creutzig et al. 2018]] ; [[#Napp--2019|Napp et al. 2019]] ; [[#Giannousakis--2021|Giannousakis et al. 2021]] ), on the representation of innovation dynamics in modelling frameworks ( [[#Hoekstra--2017|Hoekstra et al. 2017]] ; [[#Rengs--2020|Rengs et al. 2020]] ) (Chapter 16), as well as the representation of investment dynamics and financing mechanisms ( [[#Iyer--2015c|Iyer et al. 2015c]] ; [[#Mercure--2019|Mercure et al. 2019]] ; [[#Battiston--2021|Battiston et al. 2021]] ). In particular, endogenous and induced innovation reduce technology costs over time, create path dependencies and reduce the macroeconomic cost of reaching a mitigation target ( [[IPCC:Wg3:Chapter:Chapter-1#1.7.1.2|Section 1.7.1.2]] ). Mitigation costs also depend on socio-economic assumptions ( [[#Hof--2017|Hof et al. 2017]] ; [[#van%20Vuuren--2020|van Vuuren et al. 2020]] ).

Mitigation pathways with early emissions reductions represent higher mitigation costs in the short-run but bring long-term gains for the economy compared to delayed transition pathways ( ''high confidence'' ). Pathways with earlier mitigation action bring higher long-term GDP than pathways reaching the same end-of-century temperature with weaker early action (Figure 3.34d). Comparing counterfactual history scenarios, [[#Sanderson--2020|Sanderson and O’Neill (2020)]] also find that delayed mitigation action leads to higher peak costs. [[#Rogelj--2019b|Rogelj et al. (2019b)]] and [[#Riahi--2021|Riahi et al. (2021)]] also show that pathways with earlier timing of net zero CO 2 lead to higher transition costs but lower long-term mitigation costs, due to dynamic effects arising from lock-in avoidance and learning effects. For example, Riahi et al.(2021) find that for a 2°C target, the GDP losses (compared to a reference scenario without impacts from climate change) in 2100 are 5–70% lower in pathways that avoid net negative CO 2 emissions and temperature overshoot than in pathways with overshoot. Accounting also for climate change damage, [[#van%20der%20Wijst--2021a|van der Wijst et al. (2021a)]] show that avoiding net negative emissions leads to a small increase in total discounted mitigation costs over 2020–2100, between 5% and 14% in their medium assumptions, but does not increase mitigation costs when damages are high and when using a low discount rate, and becomes economically attractive if damages are not fully reversible. The modelled cost-optimal balance of mitigation action over time strongly depends on the discount rate used to compute or evaluate mitigation pathways: lower discount rates favour earlier mitigation, reducing both temperature overshoot and reliance on net negative carbon emissions ( [[#Emmerling--2019|Emmerling et al. 2019]] ; [[#Riahi--2021|Riahi et al. 2021]] ). Mitigation pathways with weak early action corresponding to NDCs announced prior to COP26 in 2030 and strengthening action after 2030 to reach end-of-century temperature targets imply limited mitigation costs in 2030, compared to immediate global action pathways, but faster increase in costs post-2030, with implications for intergenerational equity (Aldy et al. 2016; [[#Liu--2016|Liu et al. 2016]] ; [[#Vrontisi--2018|Vrontisi et al. 2018]] ). Emissions trading policies reduce global aggregate mitigation costs, in particular in the context of achieving NDCs ( [[#Fujimori--2015|Fujimori et al. 2015]] , 2016a; [[#Böhringer--2021|Böhringer et al. 2021]] ; [[#Edmonds--2021|Edmonds et al. 2021]] ), and change the distribution of mitigation costs between regions and countries ( [[#3.6.1|Section 3.6.1]] .2).

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==== 3.6.1.2 Regional Mitigation Costs and Effort-sharing Regimes ====

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The economic repercussions of mitigation policies vary across countries (Aldy et al. 2016; [[#Hof--2017|Hof et al. 2017]] ): regional variations exist in institutions, economic and technological development, and mitigation opportunities. For a globally uniform carbon price, carbon-intensive and energy-exporting countries bear the highest economic costs because of a deeper transformation of their economies and of trade losses in the fossil markets ( [[#Stern--2012|Stern et al. 2012]] ; [[#Tavoni--2015|Tavoni et al. 2015]] ; [[#Böhringer--2021|Böhringer et al. 2021]] ). This finding is confirmed in Figure 3.35. Since carbon-intensive countries are often poorer, uniform global carbon prices raise equity concerns ( [[#Tavoni--2015|Tavoni et al. 2015]] ). On the other hand, the climate economic benefits of mitigating climate change will be larger in poorer countries (Cross-Working Group Box 1 in this chapter). This reduces policy regressivity but does not eliminate it ( [[#Taconet--2020|Taconet et al. 2020]] ; [[#Gazzotti--2021|Gazzotti et al. 2021]] ). Together with co-benefits, such as health benefits of improved air quality, the economic benefits of mitigating climate change are likely to outweigh mitigation costs in many regions ( [[#Li--2018|Li et al. 2018]] , 2019; [[#Scovronick--2021|Scovronick et al. 2021]] ).

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'''Figure 3.35 | a: regional mitigation costs in the year 2050 (expressed as GDP losses between mitigation scenarios and corresponding baselines, not accounting for climate change damages), under the assumption of immediate global action with uniform global carbon pricing and no international transfers, by climate categories for the 2°C (&gt;67%) and 1.''' '''5°C (&gt;50%) (with and without overshoot) categories. Right panel:''' policy costs in 2050 (as in panel a) for 2°C (&gt;67%) climate category C3 for scenario pairs that represent either immediate global action (‘immediate’) or delayed global action (‘delayed’) with weaker action in the short term, strengthening to reach the same end-of-century temperature target.

Regional policy costs depend on the evaluation framework ( [[#Budolfson--2021|Budolfson et al. 2021]] ), policy design, including revenue recycling, and on international coordination, especially among trade partners. By fostering technological change and finance, climate cooperation can generate economic benefits, both in large developing economies such as China and India ( [[#Paroussos--2019|Paroussos et al. 2019]] ) and industrialised regions such as Europe ( [[#Vrontisi--2020|Vrontisi et al. 2020]] ). International coordination is a major driver of regional policy costs. Delayed participation in global mitigation efforts raises participation costs, especially in carbon-intensive economies (Figure 3.35a. Trading systems and transfers can deliver cost savings and improve equity ( [[#Rose--2017a|Rose et al. 2017a]] ). On the other hand, measures that reduce imports of energy-intensive goods such as carbon-border tax adjustment may imply costs outside of the policy jurisdiction and have international equity repercussions, depending on how they are designed ( [[#Böhringer--2012|Böhringer et al. 2012]] , 2017; [[#Cosbey--2019|Cosbey et al. 2019]] ) ( [[IPCC:Wg3:Chapter:Chapter-13#13.6.6|Section 13.6.6]] ).

An equitable global emission-trading scheme would require very large international financial transfers, in the order of several hundred billion USD per year ( [[#Tavoni--2015|Tavoni et al. 2015]] ; [[#Bauer--2020|Bauer et al. 2020]] ; [[#van%20den%20Berg--2020|van den Berg et al. 2020]] ). The magnitude of transfers depends on the stringency of the climate goals and on the burden-sharing principle. Some interpretations of equitable burden sharing compliant with the Paris Agreement leads to negative carbon allowances for developed countries and some developing countries by mid-century ( [[#van%20den%20Berg--2020|van den Berg et al. 2020]] ), more stringent than cost-optimal pathways. International transfers also depend on the underlying socio-economic development ( [[#Leimbach--2019|Leimbach and Giannousakis 2019]] ), as these drive the mitigation costs of meeting the Paris Agreement ( [[#Rogelj--2018|Rogelj et al. 2018]] b). By contrast, achieving equity without international markets would result in a large discrepancy in regional carbon prices, up to a factor of 100 ( [[#Bauer--2020|Bauer et al. 2020]] ). The efficiency-sovereignty trade-off can be partly resolved by allowing for limited differentiation of regional carbon prices: moderate financial transfers substantially reduce inefficiencies by narrowing the carbon price spread ( [[#Bauer--2020|Bauer et al. 2020]] ).

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==== 3.6.1.3 Investments in Mitigation Pathways ====

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Figures 3.36 and 3.37 show increased investment needs in the energy sector in lower temperature categories, and a major shift away from fossil fuel generation and extraction towards electricity, including for system enhancements for electricity transmission, distribution and storage, and low-carbon technologies. Investment needs in the electricity sector are 2.3 trillion USD2015 yr –1 over 2023–2050 on average for C1 pathways, 2 trillion USD for C2 pathways, 1.7 trillion USD for C3, 1.2 trillion USD for C4 and 0.9–1.1 billion USD for C5/C6/C7 (mean values for pathways in each temperature category). The regional pattern of power sector investments broadly mirrors the global picture. However, the bulk of investment requirements are in medium- and low-income regions. These results from the AR6 scenarios database corroborate the findings from [[#McCollum--2018a|McCollum et al. (2018a)]] , [[#Zhou--2019|Zhou et al. (2019)]] and [[#Bertram--2021|Bertram et al. (2021)]] .

In the context of the COVID-19 pandemic recovery, [[#Kikstra--2021a|Kikstra et al. (2021a)]] show that a low-energy-demand recovery scenario reduces energy investments required until 2030 for a 1.5°C consistent pathway by 9% (corresponding to reducing total required energy investment by USD1.8 trillion) compared to a scenario with energy demand trends restored to pre-pandemic levels.

Few studies extend the scope of the investment needs quantification beyond the energy sector. [[#Fisch-Romito--2019|Fisch-Romito and Guivarch (2019)]] and [[#Ó%20Broin--2017|Ó Broin and Guivarch (2017)]] assess investment needs for transportation infrastructures and find lower investment needs in low-carbon pathways, due to a reduction in transport activity and a shift towards less road construction, compared to high-carbon pathways. [[#Rozenberg--2019|Rozenberg and Fay (2019)]] estimate the funding needs to close the service gaps in water and sanitation, transportation, electricity, irrigation, and flood protection in thousands of scenarios, showing that infrastructure investment paths compatible with full decarbonisation in the second half of the century need not cost more than more-polluting alternatives. Investment needs are estimated between 2% to 8% of GDP, depending on the quality and quantity of services targeted, the timing of investments, construction costs, and complementary policies.

[[IPCC:Wg3:Chapter:Chapter-15|Chapter 15]] also reports investment requirements in global mitigation pathways in the near term, compares them to recent investment trends, and assesses financing issues.

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'''Figure 3.36''' '''| Global average yearly investments from 2023–2052 for''' '''nine electricity supply subcomponents and for extraction of fossil fuels (in''' '''billion''' '''USD2015), in pathways by temperature categories.''' T&amp;D: transmission and distribution of electricity. Bars show the median values (number of pathways at the bottom), and whiskers show the interquartile ranges.

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'''Figure 3.37 | Average yearly investments from 2023–2052 for the four subcomponents of the energy system representing the larger amounts (in''' '''billion''' '''USD2015), by aggregate regions, in pathways by temperature categories.''' T&amp;D: transmissions and distribution of electricity. Extr.: extraction of fossil fuels. Bars show the median values (number of pathways at the bottom), and whiskers show the interquartile ranges. For definition of regional classifications used see Annex II Table 1.

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=== Box 3.5 | Concepts and Modelling Frameworks Used for Quantifying Macroeconomic Effects of Mitigation ===

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Most studies that have developed mitigation pathways have used a cost-effectiveness analysis (CEA) framework, which aim to compare the costs of different mitigation strategies designed to meet a given climate change mitigation goal (e.g., an emission-reduction target or a temperature stabilisation target) but does not represent economic impacts from climate change itself, nor the associated economic benefits of avoided impacts. Other studies use modelling frameworks that represent the feedback of damages from climate change on the economy in a cost-benefit analysis (CBA) approach, which balances mitigation costs and benefits. This second type of study is represented in [[#3.6.2|Section 3.6.2]] .

The marginal abatement cost of carbon, also called carbon price, is determined by the mitigation target under consideration: it describes the cost of reducing the last unit of emissions to reach the target at a given point in time. Total macroeconomic mitigation costs (or gains) aggregate the economy-wide impacts of investments in low-carbon solutions and structural changes away from emitting activities. The total macroeconomic effects of mitigation pathways are reported in terms of variations in economic output or consumption levels, measured against a reference scenario, also called baseline, at various points in time or discounted over a given time period. Depending on the study, the reference scenario reflects specific assumptions about patterns of socio-economic development and assumes either no-climate policies or the climate policies in place or planned at the time the study was carried out. When available in the AR6 scenarios database, this second type of reference scenario, with trends from implemented policies until the end of 2020, has been chosen for computation of mitigation costs. In the vast majority of studies that have produced the body of work on the cost of mitigation assessed here, and in particular in all studies that have submitted global scenarios to the AR6 scenarios database except ( [[#Schultes--2021|Schultes et al. 2021]] ), the feedbacks of climate change impacts on the economic development pathways are not accounted for. This omission of climate impacts leads to overly optimistic economic projections in the reference scenarios, in particular in reference scenarios with no or limited mitigation action where the extent of global warming is the greatest. Mitigation cost estimates computed against no or limited policy reference scenarios therefore omit economic benefits brought by avoided climate change impact along mitigation pathways, and should be interpreted with care ( [[#Grant--2020|Grant et al. 2020]] ). When aggregate economic benefits from avoided climate change impacts are accounted for, mitigation is a welfare-enhancing strategy ( [[#3.6.2|Section 3.6.2]] ).

If GDP or consumption in mitigation pathways are below the reference scenario levels, they are reported as losses or macroeconomic costs. Such cost estimates give an indication of how economic activity slows relative to the reference scenario; they do not necessarily describe, in absolute terms, a reduction of economic output or consumption levels relative to previous years along the pathway. Aggregate mitigation costs depend strongly on the modelling framework used and the assumptions about the reference scenario against which mitigation costs are measured, in particular whether the reference scenario is, or not, on the efficiency frontier of the economy. If the economy is assumed to be at the efficiency frontier in the reference scenario, mitigation inevitably leads to actual costs, at least in the short-run until the production frontier evolves with technical and structural change. Starting from a reference scenario that is not on the efficiency frontier opens the possibility to simultaneously reduce emissions and obtain macroeconomic gains, depending on the design and implementation of mitigation policies. A number of factors can result in reference scenarios below the efficiency frontier, for instance distorting labour taxes and/or fossil fuel subsidies, misallocation or under-utilisation of production factors such as involuntary unemployment, imperfect information or non-rational behaviours. Although these factors are pervasive, the modelling frameworks used to construct mitigation pathways are often limited in their ability to represent them ( [[#Köberle--2021|Köberle et al. 2021]] ).

The absolute level of economic activity and welfare also strongly depends on the socio-economic pathway assumptions regarding, ''inter alia'' , evolutions in demography, productivity, education levels, inequality, and technical change and innovation. The GDP or consumption indicators reported in the database of scenarios, and synthesized below, represent the absolute level of aggregate economic activity or consumption but do not reflect welfare and well-being ( [[#Roberts--2020|Roberts et al. 2020]] ), that notably depend on human-needs satisfaction, distribution within society and inequality ( [[#3.6.4|Section 3.6.4]] ).

[[IPCC:Wg3:Chapter:Chapter-1|Chapter 1]] and Annex III.I give further details of the economic concepts and modelling frameworks, including their limitations, used in this report, respectively.

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=== 3.6.2 Benefits of Avoiding Climate Change Impacts ===

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Cost-benefit analyses (CBA) aim to balance all costs and benefits in a unified framework (Nordhaus, 2008). Estimates of economic benefits from avoided climate change impacts depend on the types of damages accounted for, the assumed exposure and vulnerability to these damages as well as the adaptation capacity, which in turn are based on the development pathway assumed (Cross-Working Group Box 1 in this chapter). CBA IAMs raised criticism, in particular for omitting elements of dynamic realism, such as inertia, induced innovation and path dependence, in their representation of mitigation ( [[#Grubb--2021|Grubb et al. 2021]] ), and for underestimating damages from climate change, missing non-monetary damages, the uncertain and heterogeneous nature of damages and the risk of catastrophic damages ( [[#Stern--2013|Stern 2013]] , 2016; [[#Diaz--2017|Diaz and Moore 2017]] ; [[#NASEM--2017|NASEM 2017]] ; [[#Pindyck--2017|Pindyck 2017]] ; [[#Stoerk--2018|Stoerk et al. 2018]] ; [[#Stern--2021|Stern and Stiglitz 2021]] ). Emerging literature has started to address those gaps, and integrated into cost-benefit frameworks the account of heterogeneity of climate damage and inequality ( [[#Dennig--2015|Dennig et al. 2015]] ; [[#Budolfson--2017|Budolfson et al. 2017]] ; [[#Fleurbaey--2019|Fleurbaey et al. 2019]] ; [[#Kornek--2021|Kornek et al. 2021]] ), damages with higher persistence, including damages on capital and growth ( [[#Moyer--2014|Moyer et al. 2014]] ; [[#Dietz--2015|Dietz and Stern 2015]] ; [[#Moore--2015|Moore and Diaz 2015]] ; [[#Guivarch--2018|Guivarch and Pottier 2018]] ; [[#Ricke--2018|Ricke et al. 2018]] ; [[#Piontek--2019|Piontek et al. 2019]] ), risks of tipping points ( [[#Cai--2015|Cai et al. 2015]] , 2016; [[#Lontzek--2015|Lontzek et al. 2015]] ; [[#Lemoine--2016|Lemoine and Traeger 2016]] ; [[#van%20der%20Ploeg--2018|van der Ploeg and de Zeeuw 2018]] ; [[#Cai--2019|Cai and Lontzek 2019]] ; [[#Nordhaus--2019|Nordhaus 2019]] ; [[#Yumashev--2019|Yumashev et al. 2019]] ; [[#Taconet--2021|Taconet et al. 2021]] ) and damages to natural capital and non-market goods ( [[#Tol--1994|Tol 1994]] ; [[#Sterner--2008|Sterner and Persson 2008]] ; [[#Bastien-Olvera--2020|Bastien-Olvera and Moore 2020]] ; [[#Drupp--2021|Drupp and Hänsel 2021]] ).

Each of these factors, when accounted for in a CBA framework, tends to increase the welfare benefit of mitigation, thus leading to stabilisation at a lower temperature in optimal mitigation pathways. The limitations in CBA modelling frameworks remain significant, their ability to represent all damages incomplete, and the uncertainty in estimates remains large. However, emerging evidence suggests that, even without accounting for co-benefits of mitigation on other sustainable development dimensions (see [[#3.6.3|Section 3.6.3]] for further details about on co-benefits), global benefits of pathways that limit warming to 2°C outweigh global mitigation costs over the 21st century: depending on the study, the reason for this result lies in assumptions of economic damages from climate change in the higher end of available estimates ( [[#Moore--2015|Moore and Diaz 2015]] ; [[#Ueckerdt--2019|Ueckerdt et al. 2019]] ; [[#Brown--2020|Brown and Saunders 2020]] ; [[#Glanemann--2020|Glanemann et al. 2020]] ), in the introduction of risks of tipping points ( [[#Cai--2019|Cai and Lontzek 2019]] ), in the consideration of damages to natural capital and non-market goods ( [[#Bastien-Olvera--2020|Bastien-Olvera and Moore 2020]] ) or in the combination of updated representations of carbon cycle and climate modules, updated damage estimates and/or updated representations of economic and mitigation dynamics ( [[#Dietz--2015|Dietz and Stern 2015]] ; [[#Hänsel--2020|Hänsel et al. 2020]] ; [[#Wei--2020|Wei et al. 2020]] ; [[#van%20der%20Wijst--2021b|van der Wijst et al. 2021b]] ). In the studies cited above that perform a sensitivity analysis, this result is found to be robust to a wide range of assumptions on social preferences (in particular, on inequality aversion and pure rate-of-time preference) and holds except if assumptions of economic damages from climate change are in the lower end of available estimates and the pure rate-of-time preference is in the higher range of values usually considered (typically above 1.5%). However, although such pathways bring net benefits over time (in terms of aggregate discounted present value), they involve distributional consequences and transition costs ( [[#Brown--2020|Brown et al. 2020]] ; [[#Brown--2020|Brown and Saunders 2020]] ) (Sections 3.6.1.2 and 3.6.4).

The standard discounted utilitarian framework dominates CBA, thus often limiting the analysis to the question of discounting. CBA can be expanded to accommodate a wider variety of ethical values to assess mitigation pathways ( [[#Fleurbaey--2019|Fleurbaey et al. 2019]] ). The role of ethical values with regard to inequality and the situation of the worse off (Adler et al. 2017), risk ( [[#van%20den%20Bergh--2014|van den Bergh and Botzen 2014]] ; [[#Drouet--2015|Drouet et al. 2015]] ), and population size ( [[#Scovronick--2017|Scovronick et al. 2017]] ; [[#Méjean--2020|Méjean et al. 2020]] ) has been explored. In most of these studies, the optimal climate policy is found to be more stringent than the one obtained using a standard discounted utilitarian criterion.

Comparing economic costs and benefits of mitigation raises a number of methodological and fundamental difficulties. Monetising the full range of climate change impacts is extremely hard, if not impossible (AR6 WGII Chapter 16), as is aggregating costs and benefits over time and across individuals when values are heterogeneous (Chapter 1; AR5 WGIII Chapter 3). Other approaches should thus be considered in supplement for decision-making ( [[IPCC:Wg3:Chapter:Chapter-1|Chapter 1]] and [[IPCC:Wg3:Chapter:Chapter-1#1.7|Section 1.7]] ), in particular cost-effectiveness approaches that analyse how to achieve a defined mitigation objective at least cost or while also reaching other societal goals ( [[#Koomey--2013|Koomey 2013]] ; [[#Kaufman--2020|Kaufman et al. 2020]] ; [[#Köberle--2021|Köberle et al. 2021]] ; [[#Stern--2021|Stern and Stiglitz 2021]] ). In cost-effectiveness studies too, incorporating benefits from avoided climate damages influences the results and leads to more stringent mitigation in the short term ( [[#Drouet--2021|Drouet et al. 2021]] ; [[#Schultes--2021|Schultes et al. 2021]] ).

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=== 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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[[File:6041fe6cd5708620fd974af53e25ea46 IPCC_AR6_WGIII_CWGBox_3_Figure_1_left.png]]

'''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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=== 3.6.3 Aggregate Economic Implication of Mitigation Co-benefits and Trade-offs ===

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Mitigation actions have co-benefits and trade-offs with other sustainable development dimensions ( [[#3.7|Section 3.7]] ) beyond climate change, which imply welfare effects and economic effects, as well as other implications beyond the economic dimension. The majority of quantifications of mitigation costs and benefits synthesized in Sections 3.6.1 and 3.6.2 do not account for these economic benefits and costs associated with co-benefits and trade-offs along mitigation pathways.

Systematic reviews of the literature on co-benefits and trade-offs from mitigation actions have shown that only a small portion of articles provide economic quantifications ( [[#Deng--2017|Deng et al. 2017]] ; [[#Karlsson--2020|Karlsson et al. 2020]] ). Most economic quantifications use monetary valuation approaches. Improved air quality, and associated health effects, are the co-benefit category dominating the literature ( [[#Markandya--2018|Markandya et al. 2018]] ; [[#Vandyck--2018|Vandyck et al. 2018]] ; Scovronick et al. 2019; [[#Howard--2020|Howard et al. 2020]] ; [[#Karlsson--2020|Karlsson et al. 2020]] b; [[#Rauner--2020a|Rauner et al. 2020a]] ,b), but some studies cover other categories, including health effects from diet change ( [[#Springmann--2016b|Springmann et al. 2016b]] ) and biodiversity impacts ( [[#Rauner--2020a|Rauner et al. 2020a]] ). Regarding health effects from air quality improvement and from diet change, co-benefits are shown to be of the same order of magnitude as mitigation costs ( [[#Thompson--2014|Thompson et al. 2014]] ; [[#Springmann--2016a|Springmann et al. 2016a]] ,b; [[#Markandya--2018|Markandya et al. 2018]] ; [[#Scovronick--2019b|Scovronick et al. 2019b]] ; [[#Howard--2020|Howard et al. 2020]] ; [[#Rauner--2020a|Rauner et al. 2020a]] ,b; [[#Liu--2021|Liu et al. 2021]] ; [[#Yang--2021|Yang et al. 2021]] ). Co-benefits from improved air quality are concentrated sooner in time than economic benefits from avoided climate change impacts ( [[#Karlsson--2020|Karlsson et al. 2020]] ), such that when accounting both for positive health impacts from reduced air pollution and for the negative climate effect of reduced cooling aerosols, optimal GHG mitigation pathways exhibit immediate and continual net economic benefits ( [[#Scovronick--2019a|Scovronick et al. 2019a]] ). However, AR6 WGI [[IPCC:Wg3:Chapter:Chapter-6|Chapter 6]] ( [[IPCC:Wg3:Chapter:Chapter-6#6.7.3|Section 6.7.3]] ) shows a delay in air pollution reduction benefits when they come from climate change mitigation policies compared with air pollution reduction policies.

Achieving co-benefits is not automatic but results from coordinated policies and implementation strategies (Clarke et al. 2014; [[#McCollum--2018a|McCollum et al. 2018a]] ). Similarly, avoiding trade-offs requires targeted policies ( [[#van%20Vuuren--2015|van Vuuren et al. 2015]] ; [[#Bertram--2018|Bertram et al. 2018]] ). There is limited evidence of such pathways, but the evidence shows that mitigation pathways designed to reach multiple Sustainable Development Goals instead of focusing exclusively on emissions reductions, result in limited additional costs compared to the increased benefits ( [[#Cameron--2016|Cameron et al. 2016]] ; [[#McCollum--2018b|McCollum et al. 2018b]] ; [[#Fujimori--2020a|Fujimori et al. 2020a]] ; [[#Sognnaes--2021|Sognnaes et al. 2021]] ).

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=== 3.6.4 Structural Change, Employment and Distributional Issues Along Mitigation Pathways ===

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Beyond aggregate effects at the economy-wide level, mitigation pathways have heterogeneous economic implications for different sectors and different actors. Climate-related factors are only one driver of the future structure of the economy, of the future of employment, and of future inequality trends, as overarching trends in demographics, technological change (innovation, automation, etc.), education and institutions will be prominent drivers. For instance, [[#Rao--2019b|Rao et al. (2019b)]] and [[#Benveniste--2021|Benveniste et al. (2021)]] have shown that income inequality projections for the 21st century vary significantly, depending on socio-economic assumptions related to demography, education levels, social public spending and migrations. However, the sections below focus on climate-related factors, both climate-mitigation actions themselves and the climate change impacts avoided along mitigation pathways, effects on structural change, including employment, and distributional effects.

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==== 3.6.4.1 Economic Structural Change and Employment in Long-term Mitigation Pathways ====

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Mitigation pathways entail transformation of the energy sector, with structural change away from fossil energy and towards low-carbon energy ( [[#3.3|Section 3.3]] ), as well as broader economic structural change, including industrial restructuring and reductions in carbon-intensive activities in parallel to extensions in low-carbon activities.

Mitigation affects work through multiple channels, which impacts geographies, sectors and skill categories differently ( [[#Fankhaeser--2008|Fankhaeser et al. 2008]] ; [[#Bowen--2018|Bowen et al. 2018]] ; [[#Malerba--2021|Malerba and Wiebe 2021]] ). Aggregate employment impacts of mitigation pathways mainly depend on the aggregate macroeconomic effect of mitigation (Sections 3.6.1 and 3.6.2) and of mitigation policy design and implementation ( [[#Freire-González--2018|Freire-González 2018]] ) ( [[IPCC:Wg3:Chapter:Chapter-4#4.2.6.3|Section 4.2.6.3]] ). Most studies that quantify overall employment implications of mitigation policies are conducted at the national or regional scales ( [[IPCC:Wg3:Chapter:Chapter-4#4.2.6.3|Section 4.2.6.3]] ), or sectoral scales (e.g., see [[IPCC:Wg3:Chapter:Chapter-6|Chapter 6]] for energy sector jobs). The evidence is limited at the multinational or global scale, but studies generally find small differences in aggregate employment in mitigation pathways compared to baselines: the sign of the difference depends on the assumptions and modelling frameworks used and the policy design tested, with some studies or policy design cases leading to small increases in employment ( [[#Chateau--2013|Chateau and Saint-Martin 2013]] ; [[#Pollitt--2015|Pollitt et al. 2015]] ; [[#Barker--2016|Barker et al. 2016]] ; [[#Garcia-Casals--2019|Garcia-Casals et al. 2019]] ; [[#Fujimori--2020a|Fujimori et al. 2020a]] ; [[#Vrontisi--2020|Vrontisi et al. 2020]] ; [[#Malerba--2021|Malerba and Wiebe 2021]] ) and other studies or policy design cases leading to small decreases ( [[#Chateau--2013|Chateau and Saint-Martin 2013]] ; [[#Vandyck--2016|Vandyck et al. 2016]] ). The small variations in aggregate employment hide substantial reallocation of jobs across sectors, with jobs creation in some sectors and jobs destruction in others. Mitigation action through thermal renovation of buildings, installation and maintenance of low-carbon generation, and the expansion of public transit lead to job creation, while jobs are lost in fossil fuel extraction, energy supply and energy-intensive sectors in mitigation pathways ( [[#von%20Stechow--2015|von Stechow et al. 2015]] , 2016; [[#Barker--2016|Barker et al. 2016]] ; [[#Fuso%20Nerini--2018|Fuso Nerini et al. 2018]] ; [[#Perrier--2018|Perrier and Quirion 2018]] ; [[#Pollitt--2018|Pollitt and Mercure 2018]] ; [[#Dominish--2019|Dominish et al. 2019]] ; [[#Garcia-Casals--2019|Garcia-Casals et al. 2019]] ). In the energy sector, job losses in the fossil fuel sector are found to be compensated by gains in wind and solar jobs, leading to a net increase in energy sector jobs in 2050 in a mitigation pathway compatible with stabilisation of the temperature increase below 2°C ( [[#Pai--2021|Pai et al. 2021]] ). Employment effects also differ by geographies, with energy-importing regions benefiting from net job creations but energy-exporting regions experiencing very small gains or suffering from net job destruction ( [[#Barker--2016|Barker et al. 2016]] ; [[#Pollitt--2018|Pollitt and Mercure 2018]] ; [[#Garcia-Casals--2019|Garcia-Casals et al. 2019]] ; [[#Malerba--2021|Malerba and Wiebe 2021]] ). Coal phase-out raises acute issues of just transition for the coal-dependent countries ( [[#Spencer--2018|Spencer et al. 2018]] ; [[#Jakob--2020|Jakob et al. 2020]] ) ( [[IPCC:Wg3:Chapter:Chapter-4#4.5|Section 4.5]] and Box 6.2).

Mitigation action also affects employment through avoided climate change impacts. Mitigation reduces the risks to human health and associated impacts on labour and helps protect workers from the occupational health and safety hazards imposed by climate change ( [[#Kjellstrom--2016|Kjellstrom et al. 2016]] , 2018, 2019; [[#Levi--2018|Levi et al. 2018]] ; [[#Day--2019|Day et al. 2019]] ) (AR6 WGII Chapter 16).

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==== 3.6.4.2 Distributional Implications of Long-term Mitigation Pathways ====

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Mitigation policies can have important distributive effects between and within countries, either reducing or increasing economic inequality and poverty, depending on policy instruments’ design and implementation (see [[#3.6.1|Section 3.6.1]] .2 for an assessment of the distribution of mitigation costs across regions in mitigation pathways; Sections 3.7 and 4.2.2.6, and Box 3.6 for an assessment of the fairness and ambition of NDCs; and [[IPCC:Wg3:Chapter:Chapter-4#4.5|Section 4.5]] for an assessment of national mitigation pathways along the criteria of equity, including Just Transition, as well as [[IPCC:Wg3:Chapter:Chapter-17#17.4.5|Section 17.4.5]] for equity in a Just Transition). For instance, emissions taxation has important distributive effects, both between and within income groups ( [[#Cronin--2018b|Cronin et al. 2018b]] ; [[#Klenert--2018|Klenert et al. 2018]] ; [[#Pizer--2019|Pizer and Sexton 2019]] ; [[#Douenne--2020|Douenne 2020]] ; [[#Steckel--2021|Steckel et al. 2021]] ). These effects are more significant in some sectors, such as transport, and depend on country-specific consumption structures ( [[#Dorband--2019|Dorband et al. 2019]] ; [[#Fullerton--2019|Fullerton and Muehlegger 2019]] ; [[#Ohlendorf--2021|Ohlendorf et al. 2021]] ). However, revenues from emissions taxation can be used to lessen their regressive distributional impacts or even turn the policy into a progressive policy reducing inequality and/or leading to gains for lower-income households ( [[#Cameron--2016|Cameron et al. 2016]] ; [[#Jakob--2016|Jakob and Steckel 2016]] ; [[#Fremstad--2019|Fremstad and Paul 2019]] ; [[#Fujimori--2020b|Fujimori et al. 2020b]] ; [[#Böhringer--2021|Böhringer et al. 2021]] ; [[#Budolfson--2021|Budolfson et al. 2021]] ; [[#Soergel--2021b|Soergel et al. 2021b]] ; [[#Steckel--2021|Steckel et al. 2021]] ). Mitigation policies may affect the poorest through effects on energy and food prices ( [[#Hasegawa--2015|Hasegawa et al. 2015]] ; [[#Fujimori--2019|Fujimori et al. 2019]] ). [[#Markkanen--2019|Markkanen and Anger-Kraavi (2019)]] and [[#Lamb--2020|Lamb et al. (2020)]] synthesize evidence from the existing literature on social co-impacts of climate change mitigation policy and their implications for inequality. They show that most policies can compound or lessen inequalities depending on contextual factors, policy design and policy implementation, but that negative inequality impacts of climate policies can be mitigated (and possibly even prevented), when distributive and procedural justice are taken into consideration in all stages of policymaking, including policy planning, development and implementation, and when focusing on the carbon intensity of lifestyles, sufficiency and equity, well-being and decent living standards for all ( [[IPCC:Wg3:Chapter:Chapter-13#13.6|Section 13.6]] ).

Mitigation pathways also affect economic inequalities between and within countries, and poverty, through the reduction of climate change impacts that fall more heavily on low-income countries, communities and households, and exacerbate poverty (AR6 WGII Chapters 8 and 16). Higher levels of warming are projected to generate higher inequality between countries as well as within them (AR6 WGII Chapter 16). Through avoiding impacts, mitigation thus reduces economic inequalities and poverty ( ''hig'' ''h confidence'' ).

A few studies consider both mitigation policies’ distributional impacts and avoided climate change impacts on inequalities along mitigation pathways. [[#Rezai--2018|Rezai et al. (2018)]] find that unmitigated climate change impacts increase inequality, whereas mitigation has the potential to reverse this effect. Considering uncertainty in socio-economic assumptions, emission pathways, mitigation costs, temperature response, and climate damage, [[#Taconet--2020|Taconet et al. (2020)]] show that the uncertainties associated with socio-economic assumptions and damage estimates are the main drivers of future inequalities between countries and that in most cases mitigation policies reduce future inequalities between countries. [[#Gazzotti--2021|Gazzotti et al. (2021)]] show that inequality persists in 2°C-consistent pathways due to regressivity of residual climate damages. However, the evidence on mitigation pathways’ implications for global inequality and poverty remains limited, and the modelling frameworks used have limited ability to fully represent the different dimensions of inequality and poverty and all the mechanisms by which mitigation affects inequality and poverty ( [[#Rao--2017a|Rao et al. 2017a]] ; [[#Emmerling--2021|Emmerling and Tavoni 2021]] ; [[#Jafino--2021|Jafino et al. 2021]] ).

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