Editing IPCC:AR6/WGI/Chapter-6 (section)

=== 6.6.3 Past and Current SLCF Reduction Policies and Future Mitigation Opportunities ===

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Several SLCF-emissions reduction strategies have been explored in the literature or are already pursued as part of environmental and development policies, including air quality, waste management, energy poverty and climate change. The effects of various policies and strategies have been addressed in a limited number of modelling studies with different objectives that range from assessment of specific policies and their regional effects (UNEP and WMO, 2011; [[#Shindell--2012|Shindell et al., 2012]] , 2017b; [[#AMAP--2015a|AMAP, 2015a]] , b; [[#Haines--2017|Haines et al., 2017]] ; UNEP and CCAC, 2018; [[#UNEP--2019|UNEP, 2019]] ; Harmsen et al. , 2020a ) to large-scale global-scenario studies with varying levels of SLCF control (e.g., [[#Sand--2016|Sand et al., 2016]] ; [[#Rogelj--2018b|Rogelj et al., 2018b]] ; [[#Shindell--2019|Shindell and Smith, 2019]] ). They could be grouped into:

* Projections of future SLCF emissions compatible with the climate change mitigation trajectories investigated in the climate model intercomparison projects (respective RCP or SSP scenarios): in such scenarios, when climate change mitigation is considered, it is associated with a strong decrease of CO <sub>2</sub> emissions, largely relying on fossil fuel-use reduction, along with proportional reductions in the co-emitted SLCFs from combustion and methane from the production and distribution of fossil fuels. Depending on the carbon price and climate change mitigation target, further reduction of methane from waste and agriculture will also be part of such scenarios. The limitations of RCP scenarios (where continuous strengthening of air-quality legislation was assumed resulting in lack of futures where global and regional air quality deteriorates) for the analysis of air quality and potential for mitigation of SLCFs have been discussed in literature (e.g., [[#Amann--2013|Amann et al., 2013]] ; [[#von%20Schneidemesser--2015|von Schneidemesser et al., 2015]] ). SSP scenarios consider various levels of air pollution control, in accordance with their socio-economic narrative, and thus cover a wider span of SLCF trajectories (Section 6.7). The economic cost of the implementation of these scenarios and their co-benefits on air quality and SDGs are assessed in the AR6 WGIII Report (Chapter 3).
* Projections of SLCF emissions assuming strong reduction of all air pollutants in the absence of climate change mitigation (e.g., the SSP3-lowSLCF scenario): the latter is an idealized simulation of a very ambitious air-quality policy where the maximum technical potential of existing end-of-pipe technologies is explored in the SSP3-7.0 scenario. Methane reduction can also be part of such sensitivity analysis, although methane reductions have not historically been motivated by air pollution concerns.
* Projection of emissions targeting air quality or other development priorities: anthropogenic emissions’ source-structure and the level of exposure to pollution and subsequent effects varies significantly from one region to another. Therefore, air-quality policies, regional climate impact concerns and development priorities, as well as the consequent level of mitigation of particular SLCF species, will differ regionally and source-wise with respect to the emissions sources, influence of inter-continental transport of pollution, and spatial physical heterogeneities (Lund et al. , 2014; [[#AMAP--2015a|AMAP, 2015a]] ; Sand et al. , 2016; Turnock et al. , 2016; Sofiev et al. , 2018; [[#WMO--2018|WMO, 2018]] ) . This is also the case at a finer local and regional scale where priorities and scope for SLCF mitigation will differ (e.g., [[#Amann--2017|Amann et al., 2017]] ; [[#UNEP--2019|UNEP, 2019]] ).
* Projections exploring mitigation potential for a particular source or SLCF: These studies focus on the assessment of SLCF reduction potential that can be realised with either existing and proven technologies or extend the scope to include transformational changes needed to achieve further reduction (e.g., UNEP and WMO, 2011; [[#Stohl--2015|Stohl et al., 2015]] ; [[#Velders--2015|Velders et al., 2015]] ; [[#Purohit--2017|Purohit and Höglund-Isaksson, 2017]] ; [[#Gómez-Sanabria--2018|Gómez-Sanabria et al., 2018]] ; [[#UNEP--2019|UNEP, 2019]] ; [[#Höglund-Isaksson--2020|Höglund-Isaksson et al., 2020]] ; [[#Purohit--2020|Purohit et al., 2020]] ) – several of these studies are subsequently used for parametrization of the models used to develop emissions scenarios (e.g., IAM models used in the IPCC process).

In the following subsections, we assess the SLCF mitigation and its effects as identified in regional and global studies evaluating past and current air quality and other SLCF regulations (Sections 6.6.3.1, 6.6.3.2 and 6.6.3.3). Development policies, independent from the CMIP6 assessment framework, including peer-reviewed studies and initiatives like the United Nations Environment Programme (UNEP), analysing win-win climate, air-quality and SDG-motivated strategies are discussed in Section 6.6.3.4. Note that sensitivity studies where impacts of complete removal of particular species are analysed (e.g., [[#Samset--2018|Samset et al., 2018]] ) are used sparingly in this assessment. While such analysis can be useful for assessing the effect of a zero-emissions commitment (Chapter 4.7.1.1), they do not correspond to a realistic SLCF-mitigation strategy with plausible pace of implementation and removal of co-emitted species ( [[#Shindell--2019|Shindell and Smith, 2019]] ). Discussion of climate and air-quality implications of SLCF reductions in SSP scenarios is provided in Section 6.7.

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==== 6.6.3.1 Climate Response to Past AQ Policies ====

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Air-quality policies emerged several decades ago focusing on emissions mitigation, first driven by local- then by regional-scale air-quality and ecosystem-damage concerns, that is, health impacts, acidification and eutrophication. They have made it possible to reduce or limit pollution exposure in many megacities or highly populated regions, for example, in Los Angeles, Mexico City and Houston in North America ( [[#Parrish--2011|Parrish et al., 2011]] ), Santiago in Chile ( [[#Gallardo--2018|Gallardo et al., 2018]] ), São Paulo in Brazil ( [[#Andrade--2017|Andrade et al., 2017]] ), Europe ( [[#Reis--2012|Reis et al., 2012]] ; [[#Crippa--2016|Crippa et al., 2016]] ; [[#Serrano--2019|Serrano et al., 2019]] ), and over Eastern Asia during the last decade ( [[#Silver--2018|Silver et al., 2018]] ; [[#Zheng--2018b|Zheng et al., 2018b]] ). However, very few studies have quantified the impact of these policies on climate. The AR5 concluded that air-quality control will have consequences on climate including strong regional variability, however, no estimates of impacts of specific air-quality policy were available. Since AR5, few studies have provided estimates of climate-relevant indicators affected by significant air pollutant burden changes due to air-quality policy in selected regions. [[#Turnock--2016|Turnock et al. (2016)]] estimated that the strong decrease in NO <sub>x</sub> , SO <sub>2</sub> and PM <sub>2.5</sub> emissions in Europe, induced by air-quality policies resulting in implementation of abatement measures since the 1970s, have caused a surface warming of +0.45°C ± 0.11°C and increase of precipitation +13 ± 0.8 mm yr <sup>–1</sup> over Europe, compared to the scenario without such policies. While the temperature increase is likely overestimated since the impact of the increase in ammonium nitrate was not considered in this study, the simulated European all-sky TOA radiative effect of the European air pollutant mitigation over the period 1970–2009 is 2.5 times the change in global mean CO <sub>2</sub> radiative forcing over the same period ( [[#Myhre--2013|Myhre et al., 2013]] ). Other studies found that the recent measures to reduce pollution over China have induced a decrease of aerosols and increase of ozone over east China (K. [[#Li--2019|]] [[#Li--2019|Li et al., 2019]] , 2020), resulting in an overall warming effect mainly due to the dominant effect of sulphate reductions in the period 2012–2017 ( [[#Dang--2019|Dang and Liao, 2019]] ).

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==== 6.6.3.2 Recently Decided SLCF-relevant Global Legislation ====

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International shipping emissions regulation: from January 2020, a new global standard, proposed by the International Maritime Organisation, limits the sulphur content in marine fuels to 0.5% against the previous 3.5% ( [[#IMO--2016|IMO, 2016]] ). This legislation is considered in the SSP5 and SSP2-4.5 and with a delay of few years in SSP3-lowSLCF, SSP1-1.9, and SSP1-2.6, and in other SSP-emissions scenarios achieved by the mid-21st century. This global measure aims to reduce the formation of sulphate (and consequently PM <sub>2.5</sub> ) and largely reduce the health exposure to PM <sub>2.5</sub> , especially over India, east China and coastal areas of Africa, and the Middle East ( [[#Sofiev--2018|Sofiev et al., 2018]] ). [[#Sofiev--2018|Sofiev et al. (2018)]] used a high spatial-and-temporal resolution chemistry climate model and estimated a net total ERF of +71 mW m <sup>–2</sup> Associated with this measure and due to lower direct aerosol cooling (+3.9 mW m <sup>–2</sup> ) and lower cloud albedo (+67 mW m <sup>–2</sup> ). This value, which correponds to an 80% decrease of the cooling effect of shipping induced by about 8 Tg of SO <sub>2</sub> of avoided emissions, is consistent with older estimates which considered similar reduction of emitted sulphur. However, there is considerable uncertainty in the indirect forcing since small changes in aerosols, acting as CCNs in a clean environment, can have disproportionally large effects on the radiative balance. Since sulphate is by far the largest component of the radiative forcing ( [[#Fuglestvedt--2008|Fuglestvedt et al., 2008]] ) and of surface temperature effect (Figure 6.16) due to ship emissions over a short time scale, limiting the co-emitted SLCFs can not offset the warming by sulphur reductions. The reduction of sulphur emissions from shipping is assessed to lead to a slight warming mainly due to aerosol–cloud interactions ( ''medium evidence'' , ''medium agreement'' ).

The Kigali Amendment ( [[#UNEP--2016|UNEP, 2016]] ): with the adoption of the Kigali Amendment to the Montreal Protocol ( [[#UN--1989|UN, 1989]] ) in 2016, parties agreed to the phase-down of HFCs, substances that are not ozone depleting but are climate-forcing agents ( [[#Papanastasiou--2018|Papanastasiou et al., 2018]] ). Baseline scenarios, in the absence of controls or only pre-Kigali national legislation, projected increased use and emissions of HFCs. All recent baseline projections are significantly higher than those used in the Representative Concentration Pathways (RCP) scenarios (Figure 6.18; [[#Meinshausen--2011|Meinshausen et al., 2011]] ). There is ''low confidence'' that the high baseline (assuming absence of controls, lack of technical progress and high growth) as developed by [[#Velders--2009|Velders et al. (2009)]] , resulting in additional warming of about 0.5°C by 2100 ( [[#Xu--2013|Xu et al., 2013]] ; [[#WMO--2018|WMO, 2018]] ), is plausible. Evolution of HFC emissions along the baselines consistent with [[#Velders--2009|Velders et al. (2009)]] and [[#Velders--2015|Velders et al. (2015)]] would result in a global average warming, due to HFCs, relative to 2000, of about 0.1°C–0.12°C by 2050 and 0.35°C–0.5°C and 0.28°C–0.44°C by 2100, respectively (Xu et al., 2013). The baseline implementation considered in SSP5-8.5 (Section 6.7.1.1) is comparable to the lower bound of projections by Velders et al. (2015; Figure 6.18) and several other studies ( [[#Gschrey--2011|Gschrey et al., 2011]] ; [[#Purohit--2017|Purohit and Höglund-Isaksson, 2017]] ; [[#EPA--2019|EPA, 2019]] ; [[#Purohit--2020|Purohit et al., 2020]] ) and result in additional warming of 0.15°C–0.3°C by 2100 ( ''medium confidence'' ) (Figure 6.22).

Efficient implementation of the Kigali Amendment and national and regional regulations has been projected to reduce global average warming in 2050 by 0.05°C–0.07°C ( [[#Klimont--2017b|Klimont et al., 2017b]] ; [[#WMO--2018|WMO, 2018]] ) and by 0.2°C–0.4°C in 2100 compared with the baseline (see Figure 2.20 of [[#WMO--2018|WMO, 2018]] ). Analysis of SSP scenarios based on an emulator (Section 6.7.3) shows a comparable mitigation potential of about 0.02°C–0.07°C in 2050 and about 0.1°C–0.3°C in 2100 (Figure 6.22, SSP5-8.5 versus SSP1-2.6). Furthermore, the energy efficiency improvements of cooling equipment alongside the transition to low-global-warming potential alternative refrigerants for refrigeration and air-conditioning equipment could potentially increase the climate benefits from the HFC phasedown under the Kigali Amendment (Shah et al. ,2015; Höglund-Isaksson et al. , 2017; [[#Purohit--2017|Purohit and Höglund-Isaksson, 2017]] ; [[#WMO--2018|WMO, 2018]] ) . [[#Purohit--2020|Purohit et al. (2020)]] estimated that depending on the expected rate of technological development, improving the energy efficiency of stationary cooling technologies and compliance with the Kigali Amendment could bring future global electricity savings of more than 20% of the world’s expected electricity consumption beyond 2050 or cumulative reduction of about 75–275 Gt CO <sub>2-</sub> eq over the period 2018–2100 ( ''medium confidence'' ). This could potentially double the climate benefits of the HFC phase-down of the Kigali Amendment as well as result in small air-quality improvements due to reduced air pollutant emissions from the power sector (i.e., 8–16% reduction of PM <sub>2.5</sub> , SO <sub>2</sub> and NO <sub>x</sub> ; [[#Purohit--2020|Purohit et al., 2020]] ).

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==== 6.6.3.3 Assessment of SLCF Mitigation Strategies and Opportunities ====

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There is a consensus in the literature that mitigation of SLCF emissions plays a central role in simultaneous mitigation of climate change, air quality and other development goals including SDG targets (UNEP and WMO, 2011; Shindell et al. , 2012, 2017b; Rogelj et al. , 2014b, 2018b; [[#AMAP--2015a|AMAP, 2015a]] ; Haines et al. , 2017; Klimont et al. , 2017b; McCollum et al. , 2018; Rafaj et al. , 2018; UNEP and CCAC, 2018; [[#UNEP--2019|UNEP, 2019]] ) . There is less agreement in the literature with respect to the actual mitigation potential (or its potential rate of implementation), necessary policies to trigger successful implementation, and resulting climate impacts. Most studies agree that climate policies, especially those aiming to keep warming below 1.5°C or 2°C, trigger large SLCF mitigation co-benefits, (e.g., [[#Rogelj--2014b|Rogelj et al., 2014b]] , 2018b), however, discussion of practical implementation of respective policies and SDGs has only started ( [[#Haines--2017|Haines et al., 2017]] ). Note that mitigation scenarios outside of the SSP framework are assessed here while those within the SSPs are assessed in Section 6.7.3.

Focusing on air quality, specifically addressing aerosols, by introducing the best available technology reducing PM <sub>2.5</sub> , SO <sub>2</sub> and NO <sub>x</sub> in most Asian countries within the 2030–2050 time frame (a strategy that has indeed shown reduction in PM <sub>2.5</sub> exposure in China) comes, in many regions, short of national regulatory PM <sub>2.5</sub> concentration standards (often set at 35 µg m <sup>–3</sup> for annual mean; [[#UNEP--2019|UNEP, 2019]] ). Similarly, global studies ( [[#Rafaj--2018|Rafaj et al., 2018]] ; [[#Amann--2020|Amann et al., 2020]] ) show that strengthening current air-quality policies, that address primarily aerosols and their precursors, will not enable the achievement of WHO air quality guidelines (annual average concentration of PM <sub>2.54</sub> below 10 µg m <sup>–3</sup> ) in many regions.

A multi-model study (four ESMs and six CTMs) found a consistent response to the removal of SO <sub>2</sub> emissions that resulted in a global mean surface temperature increase of 0.69°C (0.4°C–0.84°C). However, results are mixed for a global BC-focused deep SLCF reduction without SO <sub>2</sub> and methane mitigation which remain as in the baseline (see ECLIPSE in Figure 6.18). BC contributed about –0.022°C temperature reduction for the decade 2041–2050 based on the assumption that mitigation of the non-methane species contributed only about 10% of the global temperature reduction for the strategy where methane mitigation was also included (–0.22°C ± 0.07°C; [[#Stohl--2015|Stohl et al., 2015]] ). These results are consistent with studies analysing similar strategies using emulators (e.g., [[#Smith--2013|Smith and Mizrahi, 2013]] ; [[#Rogelj--2014b|Rogelj et al., 2014b]] ). [[#Stohl--2015|Stohl et al. (2015)]] also analysed the impact of BC-focused mitigation on air quality, estimating large-scale regional reduction in PM <sub>2.5</sub> mean concentration from about 2% in Europe to 20% over India for the decade 2041–2050.

Local response to global reduction can be higher than the global temperature response, particularly for regions subjected to rapid changes. Hence, mitigation of rapid warming in the Arctic has been subject to an increasing number of studies (Sand et al. , 2013b, 2016; Jiao et al. , 2014; [[#AMAP--2015a|AMAP, 2015a]] , b; Mahmood et al. , 2016; Christensen et al. , 2019) . Considering maximum technically feasible reductions (MTFR) for methane globally and an idealized strategy reducing key global anthropogenic sources of BC (about 80% reduction by 2030 and sustained thereafter) and precursors of ozone was estimated to jointly bring a reduction of Arctic warming, averaged over the 2041–2050 period, between 0.2°C and 0.6°C ( [[#AMAP--2015a|AMAP, 2015a]] ; [[#Sand--2016|Sand et al., 2016]] ). [[#Stohl--2015|Stohl et al. (2015)]] have estimated that a global SLCF mitigation strategy (excluding further reduction of SO <sub>2</sub> ) would lead to about twice as high a temperature reduction (–0.44 (–0.39 to –0.49) °C) in the Arctic than the global response to such mitigation.

While there is robust evidence that air-quality policies resulting in reductions of aerosols and ozone can be beneficial for human health but can lead to ‘disbenefits’ for near-term climate change, the existence of such trade-offs in response to climate change mitigation policies is less certain ( [[#Shindell--2019|Shindell and Smith, 2019]] ). Recent studies show that very ambitious but plausible gradual phasing out of fossil fuels in 1.5°C-compatible pathways with little or no overshoot, lead to a near-term future warming of less than 0.1°C, when considering associated emissions reduction of both warming and cooling species. This suggests that there may not be a strong conflict, at least at the global scale, between climate and air-quality benefits in the case of a worldwide transition to clean energy ( [[#Shindell--2019|Shindell and Smith, 2019]] ; [[#Smith--2019|Smith et al., 2019]] ). However, at the regional scale, the changes in spatially variable emissions and abundance changes might result in different responses, including implications for precipitation and monsoons (Chapter 8), especially over Southern Asia (e.g., [[#Wilcox--2020|Wilcox et al., 2020]] ).

Decarbonization of energy supply and end-use sectors is among key pillars of any ambitious climate change mitigation strategy and it would result in improved air quality owing to associated reduction of co-emitted SLCF emissions (e.g., [[#McCollum--2013|McCollum et al., 2013]] ; [[#Rogelj--2014b|Rogelj et al., 2014b]] ; [[#Braspenning%20Radu--2016|Braspenning Radu et al., 2016]] ; [[#Rao--2016|Rao et al., 2016]] ; [[#Stechow--2016|Stechow et al., 2016]] ; [[#Lelieveld--2019|Lelieveld et al., 2019]] ; [[#Shindell--2019|Shindell and Smith, 2019]] ). Regional studies ( [[#Lee--2016|Lee et al., 2016]] ; [[#Shindell--2016|Shindell et al., 2016]] ; [[#Chen--2018|Chen et al., 2018]] ; [[#Li--2018|Li et al., 2018]] ), where significant CO <sub>2</sub> reductions were assumed for 2030 and 2050, show consistently reduced of PM <sub>2.5</sub> and ozone concentrations resulting in important health benefits. However, these improvements are not sufficient to bring PM <sub>2.5</sub> levels in agreement with the WHO air-quality guidelines in several regions. [[#Amann--2020|Amann et al. (2020)]] and [[#UNEP--2019|UNEP (2019)]] highlight that only the combination of strong air-quality, development and climate policies, including societal transformations, could pave the way towards the achievement of such a target at a regional and global level.

At a global level, [[#Rao--2016|Rao et al. (2016)]] showed that climate policies, compatible with Copenhagen pledges and a long-term CO <sub>2</sub> target of 450 ppm, result in important air-quality benefits, reducing the share of the global population exposed to PM <sub>2.5</sub> levels above the WHO Tier 1 standard (35 µg m <sup>–3</sup> ) in 2030 from 21% to 5%. The impacts are similar to a strong air-quality policy but still leave large parts of population, especially in Asia and Africa, exposed to levels well above the WHO air quality guideline level of 10 µg m <sup>–3</sup> . The latter can be partly alleviated by combining such climate policy with strong air-quality policy. [[#Shindell--2018|Shindell et al. (2018)]] analysed more ambitious climate change mitigation scenarios than [[#Rao--2016|Rao et al. (2016)]] and highlighted the opportunities to improve air quality and avert societal effects associated with warmer climate by accelerated decarbonization strategies. Most climate change mitigation strategies compatible with limiting global warming to well below 2°C rely on future negative CO <sub>2</sub> emissions postponing immediate reduction. Alternatively, a faster decarbonization could allow the achievement of a 2°C goal without negative CO <sub>2</sub> emissions and, with currently known and effectively applied emissions-control technologies, this would also have immediate and significant air-quality benefits, reducing premature deaths worldwide ( [[#Shindell--2018|Shindell et al., 2018]] ). For a 2°C-compatible pathway, [[#Vandyck--2018|Vandyck et al. (2018)]] estimated 5% and 15% reduction in premature mortality due to PM <sub>2.5</sub> in 2030 and 2050, respectively, compared to reference scenarios.

There is robust evidence that reducing atmospheric methane will benefit climate and improve air quality through near-surface ozone reduction ( [[#Fiore--2015|Fiore et al., 2015]] ; [[#Shindell--2017a|Shindell et al., 2017a]] ) and wide agreement that strategies reducing methane offer larger (and less uncertain) climate benefits than policies addressing BC (e.g., Smith andMizrahi, 2013; [[#Rogelj--2014b|Rogelj et al., 2014b]] , 2018b ; [[#Stohl--2015|Stohl et al., 2015]] ; [[#Christensen--2019|Christensen et al., 2019]] ; [[#Shindell--2019|Shindell and Smith, 2019]] ). SR1.5 ( [[#Rogelj--2018b|Rogelj et al., 2018b]] ) highlighted the importance of methane mitigation in limiting warming to 1.5ºC in addition to net zero CO <sub>2</sub> emissions by 2050. Implementation of the identified maximum technically feasible reductions (MTFR) potential for methane globally, estimated at nearly 50% reduction (or 205 Tg CH <sub>4</sub> in 2050) of anthropogenic emissions from the baseline, would lead to a reduction in warming, calculated as the differences between the baseline and MTFR scenario, for the 2036–2050 period of about 0.20°C ± 0.02°C globally ( [[#AMAP--2015b|AMAP, 2015b]] ). Plausible levels of methane mitigation, achieved with proven technologies, can increase the feasibility of achieving the Paris Agreement goal through slightly slowing down the pace of CO <sub>2</sub> reductions (but not changing the final CO <sub>2</sub> reduction goal) while this benefit is enhanced by the indirect effects of methane mitigation on ozone levels ( [[#Collins--2018|Collins et al., 2018]] ). Adressing methane mitigation appears even more important in view of recently observed growth in atmospheric concentrations that is linked to increasing anthropogenic emissions ( [[IPCC:Wg1:Chapter:Chapter-5#5.2.2|Section 5.2.2]] ).

Neither ambitious climate change policy nor air-quality abatement policy can automatically yield co-benefits without integrated policies aimed at co-beneficial solutions ( [[#Zusman--2013|Zusman et al., 2013]] ; [[#Schmale--2014a|Schmale et al., 2014a]] ; [[#Melamed--2016|Melamed et al., 2016]] ), particularly in the energy generation and transport sectors (Rao et al. , 2013; Thompson et al. , 2016; Shindell et al. , 2018; Vandyck et al. , 2018) . Integrated policies are necessary to yield multiple benefits of mitigating climate change, improving air quality, protecting human health and achieving several SDGs.

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'''Box 6.2 | SLCF Mitigation and Sustainable Development Goals (SDG) Opportunities'''

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Striving to achieve air-quality and climate targets will bring significant SLCF reductions. These reductions contribute first and foremost to the attainment of SDGs targeting improved human health and sustainable cities (SDGs 3 and 11), specifically related to PM exposure (goals 3.9 and 11.6; [[#Lelieveld--2017|Lelieveld, 2017]] ; [[#Amann--2020|Amann et al., 2020]] ), but also access to affordable and clean energy, responsible consumption and production, and climate, as well as reducing nutrient losses and consequently protecting biodiversity (SDG 7, 12, 13, 14 and 15; [[#UNEP--2019|UNEP, 2019]] ; [[#Amann--2020|Amann et al., 2020]] ). Furthermore, declining SLCF emissions will result in reduced crop losses (SDG 2; zero hunger) due to decrease of ozone exposure ( [[#Feng--2009|Feng and Kobayashi, 2009]] ; [[#Ainsworth--2012|Ainsworth et al., 2012]] ; [[#Emberson--2018|Emberson et al., 2018]] ).

However, the design of suitable policies addressing these SDGs can be difficult because of the complexity of linking emissions to impacts on human health, ecosystems, equity, infrastructure and costs. Beyond the fact that several species are co-emitted, interlinkage between species, such as through atmospheric chemistry, can weaken the benefit of emissions reduction efforts. An illustration lies in the recent (2013–2017) reduction of aerosols over China ( [[#Silver--2018|Silver et al., 2018]] ; [[#Zheng--2018b|Zheng et al., 2018b]] ) resulting from the strategy to improve air quality (‘Clean Air Action’); this has successfully reduced the level of PM <sub>2.54</sub> but has led to a concurrent increase in surface ozone, partly due to declining heterogeneous interactions of ozone precursors with aerosols (K. [[#Li--2019|]] [[#Li--2019|Li et al., 2019]] ; [[#Yu--2019|Yu et al., 2019]] ). This side effect on ozone has been addressed since then by amending the legislation to target NMVOC sources, especially solvent use. Complex interactions between anthropogenic and biogenic volatile compounds are also at play and reduction of certain SLCFs could possibly promote new particle formation from organic vapours (e.g., [[#Lehtipalo--2018|Lehtipalo et al., 2018]] ). Finally, a recent example of this complexity is the mixed effects on ozone pollution induced by NO <sub>x</sub> decrease during the COVID-19 pandemic (Cross-Chapter Box 6.1). Thus, the climate and air pollution effects of policies depend strongly on the choice of regulated compounds and the degree of reduction. Such policies have to be informed by strong science support, including for example multi-model analyses such as HTAP (UNECE, 2010) and AMAP ( [[#AMAP--2015a|AMAP, 2015a]] , b), based on global and regional CCMs. This is essential to capture the complexity and inform the policy development process.

In addition, pursuing SDG objectives, apparently decoupled from air pollution, such as improved waste management, access to clean energy, or improved agricultural practices, would also stimulate and lead to mitigation of SLCFs. Amann et al. (2020) show that a global strategy to achieve the WHO air quality guidelines, cannot only rely on air pollution control but also on a combination of SDG-aligned policies. Such actions would include energy efficiency improvements, increased use of renewables, reduction of methane from waste management and agriculture, and CO <sub>2</sub> and methane due to lower fossil fuel consumption, resulting in climate co-benefits. Consideration of SDGs including local air-quality co-benefits, creates an opportunity to support and gain acceptance for ambitious climate change mitigation ( [[#Jakob--2016|Jakob and Steckel, 2016]] ; [[#Stechow--2016|Stechow et al., 2016]] ; [[#Vandyck--2018|Vandyck et al., 2018]] ). Such near-term policies targeting SDGs and air quality would enable longer-term transformations necessary to achieve climate goals (Chapter 17, WGIII).

In summary, there is ''high confidence'' that effective decarbonization strategies could lead to air-quality improvements but are not sufficient to achieve, in the near term, air-quality WHO guideline values set for fine particulate matter, especially in parts of Asia and in some highly polluted regions. Additional policies (e.g., access to clean energy, waste management) envisaged to attain SDGs bring complementary SLCF reduction ( ''high confidence)'' . Sustained methane mitigation, wherever it occurs, stands out as an option that combines near- and long-term gains on surface temperature ( ''high confidence'' ) and leads to an air pollution benefit by reducing ozone levels globally ( ''high confidence'' ) ''.''

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'''Cross-Chapter Box 6.1 | Implications of COVID-19 Restrictions for Emissions, Air Quality and Climate'''

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'''Coordinators:''' Astrid Kiendler-Scharr (Germany/Austria), John C. Fyfe (Canada)

'''Contributors:''' Josep G. Canadell (Australia), Sergio Henrique Faria (Spain/Brazil), Piers Forster (UK), Sandro Fuzzi (Italy), Nathan P. Gillett (Canada), Christopher Jones (UK), Zbigniew Klimont (Austria/Poland), Svitlana Krakovska (Ukraine), Prabir Patra (Japan/India), Joeri Rogelj (Austria/Belgium), Bjørn Samset (Norway), Sophie Szopa (France), Izuru Takayabu (Japan), Hua Zhang (China)

In response to the outbreak of COVID-19 (officially the severe acute respiratory syndrome–coronavirus 2 or SARS-CoV-2), which was declared a pandemic on March 11 2020 by the World Health Organization (WHO), regulations were imposed by many countries to contain the spread of COVID-19. Restrictions were implemented on the movement of people, such as closing borders or requiring the majority of population to stay at home, for periods of several months. This Cross-Chapter Box assesses the influence of the COVID-19 containment on short-lived climate forcers (SLCFs) and long-lived greenhouse gases (LLGHGs), and related implications for the climate. Note that this assessment was developed late in the AR6 WGI process and is based on the available emerging literature.

'''Emissions'''

Global fossil CO <sub>2</sub> emissions are estimated to have declined by 7% ( ''medium confidence'' ) in 2020 compared to 2019 emissions, with estimates ranging from 5.8% to 13.0% based on various combinations of data on energy production and consumption, economic activity and proxy activity data for emissions and their drivers (Forster et al. , 2020; Friedlingstein et al. , 2020; Le Quéré et al. , 2020; Liu et al. , 2020) . However, the concentration of atmospheric CO <sub>2</sub> continued to grow in 2020 compared to previous years ( [[#Dlugokencky--2021|Dlugokencky and Tans, 2021]] ). Given the large natural interannual variability of CO <sub>2</sub> ( [[IPCC:Wg1:Chapter:Chapter-5#5.2.1|Section 5.2.1]] ), and the small expected impact of emissions in the CO <sub>2</sub> growth rate, there were no observed changes in CO <sub>2</sub> concentration that could be attributed to COVID-19 containment ( ''medium confidence'' ) ( [[#Chevallier--2020|Chevallier et al., 2020]] ; [[#Tohjima--2020|Tohjima et al., 2020]] ).

Global daily CO <sub>2</sub> emissions from fossil fuel sources had a maximum decline of 17% in early April, compared with the mean 2019 levels, and coinciding with the global peak pandemic lockdown ( [[#Le%20Quéré--2020|Le Quéré et al., 2020]] ). The reductions in CO <sub>2</sub> emissions in 2020 were dominated by the drop in emissions from surface transport followed, in order of absolute emissions reductions, by industry, power and aviation ( [[#Le%20Quéré--2020|Le Quéré et al., 2020]] ; [[#Liu--2020|Liu et al., 2020]] ). Residential emissions showed little change ( [[#Liu--2020|Liu et al., 2020]] ) or rose slightly ( [[#Forster--2020|Forster et al., 2020]] ; [[#Le%20Quéré--2020|Le Quéré et al., 2020]] ). Aviation had the biggest relative drop in activity. CO <sub>2</sub> emissions due to land use (based on early and uncertain evidence on deforestation and forest fires) were higher than average in 2020 ( [[#Amador-Jiménez--2020|Amador-Jiménez et al., 2020]] ).

Using similar methodologies, [[#Forster--2020|Forster et al. (2020)]] assembled activity data and emissions estimates for other greenhouse gases and aerosols and their precursors. Anthropogenic NO <sub>x</sub> emissions, which are largely from the transport sector, are estimated to have decreased by a maximum of 35% in April ( ''medium confidence'' ). Species whose emissions are dominated by other sectors, such as methane and NH <sub>3</sub> from agriculture, saw smaller reductions.

'''Abundances and air quality'''

Owing to the short atmospheric lifetimes of SLCFs relevant to air quality, changes in their concentrations were detected within a few days after lockdowns had been implemented (e.g., Bauwens et al. , 2020; Venter et al. , 2020; Gkatzelis et al. , 2021; Shi et al. , 2021) . The COVID-19-driven economic slowdown has illustrated how complex the relationship is between emissions and air pollutant concentrations due to non-linearity in the atmospheric chemistry leading to secondary compound formation (Section 6.1, Box 6.1; [[#Kroll--2020|Kroll et al., 2020]] ).

Several studies have examined the effect of COVID-19 containment on air quality, showing that multi-year datasets with proper statistical/modelling analysis are required to discriminate the effects of meteorology from that of emissions reduction (Dhaka et al. , 2020; L. Li et al. , 2020; Wang et al. , 2020; Zhao et al. , 2020b) . Accounting for meteorological influences and with an increasing stringency index, the median observed change in NO <sub>2</sub> decreased from –13% to –48%, and in PM <sub>2.5</sub> decreased from –10% to –33%, whereas the median change in ozone increased from 0% to 4% ( [[#Gkatzelis--2021|Gkatzelis et al., 2021]] ). The latter can be explained by the decrease of NO emissions that titrate ozone in specific highly polluted areas, leading to the observed increase in surface ozone concentration in cities (Le et al. , 2020; Sicard et al. , 2020; Huang et al. , 2021).

The temporary decrease of PM <sub>2.5</sub> concentrations should be put in perspective of the sustained reduction (estimated at 30–70%), which could be achieved by implementing policies addressing air quality and climate change (Section 6.6.3). Such sustained reductions can lead to multiple benefits and simultaneously achieve several SDGs (Section 6.6.3). These policies would also result in reduction of ground-level ozone by up to 20% (Section 6.7.1.3).

Except for ozone, temporary improvement of air quality during lockdown periods was observed in most regions of the world ( ''high confidence'' ), resulting from a combination of interannual meteorological variability and the impact of COVID-19 containment measures ( ''high confidence'' ). Estimated air pollution reductions associated with lockdown periods are lower than what can be expected from integrated mitigation policy leading to lasting reductions ( ''medium confidence'' ).

'''Radiative forcings'''

COVID-19-related emissions changes primarily exerted effective radiative forcing (ERF) through reduced emissions rates of CO <sub>2</sub> and methane, altered abundance of SLCFs, notably ozone, NO <sub>2</sub> and aerosols, and through other changes in anthropogenic activities, notably a reduction in the formation of aviation-induced cirrus clouds.

[[#Forster--2020|Forster et al. (2020)]] combined the FaIR emulator (Cross-Chapter Box 7.1) with emissions changes for a range of species, relative to a continuation of Nationally Determined Contributions ( [[#Rogelj--2017|Rogelj et al., 2017]] ). They found a negative ERF from avoided CO <sub>2</sub> emissions that strengthens through 2020 to –0.01 W m <sup>–2</sup> . During the spring lockdown, they found a peak positive ERF of 0.1 W m <sup>–2</sup> from loss of aerosol-induced cooling, and a peak negative ERF of –0.04 W m <sup>–2</sup> from reductions in tropospheric ozone (from reduced photochemical production via NO <sub>x</sub> ). Overall, they estimated a net ERF of +0.05 W m <sup>–2</sup> for spring 2020, declining to +0.025 W m <sup>–2</sup> by the end of the year.

Cross-Chapter Box 6.1

[[File:e5663caf3f762ec596b8100c7e22007c IPCC_AR6_WGI_CCBox_6_1_Figure_1.png]]

'''Cross-Chapter Box 6.1, Figure 1'''

'''|'''

'''Emissions reductions and their effect on aerosols and climate in response to COVID-19.''' Estimated reductions in emissions of CO <sub>2</sub> , SO <sub>2</sub> and NO <sub>x</sub> are shown in panel '''(a)''' based on reconstructions using activity data (updated from [[#Forster--2020|Forster et al., 2020]] ). Eight Earth system models (ESMs) performed multiple ensemble simulations of the response to COVID-19 emissions reductions forced with these assumed emissions reductions up until August 2020 followed by a constant continuation near the August value to the end of 2020. Emissions reductions were applied relative to the SSP2-4.5 scenario. Panel '''(b)''' shows ESM-simulated AOD at 550nm (only seven models reported this variable). Panel '''(c)''' shows ESM-simulated GSAT anomalies during 2020; curves denote the ensemble mean result for each model with shading used for ±1 standard deviation for each model. ESM data from these simulations (‘ssp245-covid’) is archived on the Earth System Grid CMIP6 database. Uncertainty is represented using the simple approach: no overlay indicates regions with high model agreement, where ≥80% of models agree on sign of change; diagonal lines indicate regions with low model agreement, where &lt;80% of models agree on sign of change. For more information on the simple approach, please refer to the Cross-Chapter Box Atlas.1.
[[#Gettelman--2021|Gettelman et al. (2021)]] extended Forster et al.’s (2020) results using two ESMs, and found a spring peak aerosol-induced ERF ranging from 0.12 to 0.3 W m <sup>–2</sup> , depending on the aerosol parametrization. They also estimated an ERF of –0.04 W m <sup>–2</sup> from loss of contrail warming. Overall, they report a peak ERF of 0.04 to 0.2 W m <sup>–2</sup> , and a subsequent decline to around half the peak value. Two independent ESM studies [[#Weber--2020|Weber et al. (2020)]] and [[#Yang--2020|Yang et al. (2020)]] found consistent results in time evolution and component contributions but included fewer forcing components.

The available studies are in broad agreement on the sign and magnitude of contributions to ERF from COVID-19-related emissions changes during 2020. The range in peak global mean ERF in spring 2020 was [0.025 to 0.2] W m <sup>–2</sup> ( ''medium confidence'' ), composed of a positive forcing from aerosol–climate interactions of [0.1 to 0.3] W m <sup>–2</sup> , and negative forcings from CO <sub>2</sub> (–0.01 W m <sup>–2</sup> ), NO <sub>x</sub> (–0.04 W m <sup>–2</sup> ) and contrail cirrus (–0.04 W m <sup>–2</sup> ) ( ''limited evidence'' , ''medium agreement'' ). By the end of 2020, the ERF was at half the peak value ( ''medium confidence'' ).

'''Climate responses'''

Changes in atmospheric composition due to COVID-19 emissions reductions are not thought to have caused a detectable change in global temperature or rainfall in 2020 ( ''high confidence'' ). A large ensemble of Earth system model (ESM) simulations show an ensemble average reduction in Aerosol Optical Depth (AOD) in some regions, notably Eastern and Southern Asia ( [[#Fyfe--2021|Fyfe et al., 2021]] ). This result is supported by observational studies finding decreases in optical depth in 2020 ( [[#Gkatzelis--2021|Gkatzelis et al., 2021]] ; [[#Ming--2021|Ming et al., 2021]] ; [[#van%20Heerwaarden--2021|van Heerwaarden et al., 2021]] ), which may have contributed to observed increases in solar irradiance ( [[#van%20Heerwaarden--2021|van Heerwaarden et al., 2021]] ) or solar clear-sky reflection ( [[#Ming--2021|Ming et al., 2021]] ).

Cross-Chapter Box 6.1

Model simulations of the response to COVID-19 emissions reductions indicate a small warming of global surface air temperature (GSAT) due to a decrease in sulphate aerosols ( [[#Forster--2020|Forster et al., 2020]] ; [[#Fyfe--2021|Fyfe et al., 2021]] ), balanced by cooling due to an ozone decrease ( [[#Forster--2020|Forster et al., 2020]] ; [[#Weber--2020|Weber et al., 2020]] ), black carbon decrease ( [[#Weber--2020|Weber et al., 2020]] ) and CO <sub>2</sub> decrease. It is noted that observational studies report little SO <sub>2</sub> change, at least locally near the surface ( [[#Shi--2021|Shi et al., 2021]] ), and do not correlate with emissions inventory-based changes ( [[#Gkatzelis--2021|Gkatzelis et al., 2021]] ). One study suggests a small net warming while another using idealized simulations suggests a small cooling ( [[#Weber--2020|Weber et al., 2020]] ). Simulated GSAT and rainfall changes are unlikely to be detectable in observations ( ''high confidence'' ) ( [[#Samset--2020|Samset et al., 2020]] ; [[#Fyfe--2021|Fyfe et al., 2021]] ). Multi-model ESM simulations based on a realistic COVID-19 containment forcing scenario ( [[#Forster--2020|Forster et al., 2020]] ) indicate a model mean reduction in regional AOD but no discernible response in GSAT (Figure 1, Cross-Chapter Box 6.1).

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