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

==== 6.7.1.1 SLCF Emissions and atmospheric abundances ====

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The trajectory of future SLCF emissions is driven by the evolution of socio-economic drivers described in [[IPCC:Wg1:Chapter:Chapter-1#1.6.1.1|Section 1.6.1.1]] but dedicated, SSP-specific, air pollution policy storylines can change the regional and global trends ( [[#Rao--2017|Rao et al., 2017]] ). Additionally, assumptions about urbanization ( [[#Jiang--2017|Jiang and O’Neill, 2017]] ) will affect the spatial distribution of emissions and consequently air quality.

Growing urbanization worldwide has strongly modified the spatial distribution and intensity of SLCF emissions. The effect and extent of urbanization on air pollution and other emissions species are captured within Integrated Assessment Models (IAMs) at varying levels of complexity. In most cases, models use a combination of proxies and assumptions of end-use efficiency and technological improvement assumptions to estimate emissions arising from rural-to-urban migration and population growth within cities, utilizing quantifications of urbanization for the SSPs ( [[#Jiang--2017|Jiang and O’Neill, 2017]] ). In addition, spatial patterns of future rural and urban population growth, migration, and decline have been quantified for the SSPs using a gravity model ( [[#Jiang--2017|Jiang and O’Neill, 2017]] ). However, linking these spatial patterns with IAM regional emissions pathways is still an ongoing area of study and has not yet been represented in spatial emissions estimates provided by IAMs ( [[#Riahi--2017|Riahi et al., 2017]] ; [[#Gidden--2019|Gidden et al., 2019]] ; [[#Feng--2020|Feng et al., 2020]] ). As described in [[#Feng--2020|Feng et al. (2020)]] , spatial emissions estimates derived for CMIP6 are largely a product of existing spatial patterns of population, but do not vary dynamically in future patterns. To the extent urbanization is accounted for in gridded emissions, IAM native region resolution (varying, for example, from 11 world regions to more than 30, depending on the model) provides urbanization-based dynamics. Despite the interest of studying the effect of well-planned, densely populated urban centres, which can help to maximize the benefits of agglomeration, by providing proximity to infrastructure and services, the opportunity for energy saving, and providing a frame for air-quality control, IAM realizations of SSPs are not sufficient to assess this effect. The opportunities and risks associated with this rapid urbanization for SLCF emissions and air quality are analysed in the Chapter 6 of the WGII report and [[IPCC:Wg1:Chapter:Chapter-8|Chapter 8]] of the WGIII report.

All the RCP trajectories started in 2005 and relied on the assumption that economic growth will bring rapid strengthening of air pollution legislation, effectively reducing emissions of non-methane SLCFs (e.g., [[#Chuwah--2013|Chuwah et al., 2013]] ). While in the long-term such trends are expected if more ambitious air pollution control goes on par with economic growth. The near-term developments, however, might be much more diverse across regions and species, as has been observed in the last three decades ( [[#Amann--2013|Amann et al., 2013]] ; [[#Rafaj--2014|Rafaj et al., 2014]] ; [[#Rafaj--2018|Rafaj and Amann, 2018]] ; [[#Ru--2018|Ru et al., 2018]] ), especially in several fast-growing economies, leading to the difference between CMIP6 historical estimates for the post-2000 period ( [[#Hoesly--2018|Hoesly et al., 2018]] ) and those used in RCPs (Figure 6.18). Since several SLCFs are also air pollutants, the narrow range of the RCP emissions trajectories in the future allowed for only limited analysis of near-future air quality (Amann et al. , 2013; Chuwah et al. , 2013; von Schneidemesser et al. , 2015) . However, the range of storylines in the SSPs lead to a wider range of assumed pollution-control policies in the SSPs ( [[#Rao--2017|Rao et al., 2017]] ; [[#Riahi--2017|Riahi et al., 2017]] ). In SSP1 and SSP5, strong air-quality policies are assumed to minimize the adverse effects of pollution on population and ecosystems. In SSP2, a medium pollution control, with lower than current policy targets, is considered. Only weak, regionally varied, air pollution policies are applied in the SSP3 and SSP4. Additional climate policies introduced to reach defined radiative forcing targets will also affect SLCF emissions. The SSP SLCF-emissions trajectories ( [[#Rao--2017|Rao et al., 2017]] ; [[#Gidden--2019|Gidden et al., 2019]] ) assume a long-term coupling of economic growth and specific emissions indicators, such as sectoral emission densities. The pace of change varies across regions and SSPs resulting in a wider range of future air pollutants evolution '''(''' Figure 6.18), reflecting the differences in assumed levels of air pollution controls across regions (Figure 6.19). At the end of the century, the range across the SSPs is about four times that of RCPs for SO <sub>2</sub> and NO <sub>x</sub> , two to four times for BC and NMVOCs, and up to three times for CO and OC, while indicating a slightly smaller range than RCPs for methane (Figure 6.18). The originally developed SSP scenarios ( [[#Rao--2017|Rao et al., 2017]] ) have been harmonized with the CMIP6 historical emissions ( [[#Hoesly--2018|Hoesly et al., 2018]] ) and include updated SO <sub>2</sub> emissions to account for the recent decline in China ( [[#Gidden--2019|Gidden et al., 2019]] ).

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'''Figure 6.18 |''' '''Global anthropogenic and biomass burning short-lived climate forcer (SLCF) and CO''' <sub>2</sub> '''emissions from 1850 to 2100 and HFC emissions from 1990 to 2100.''' Emissions for the Coupled Model Intercomparison Project Phase 6 (CMIP6) for the period 1850–2014 are based on [[#Hoesly--2018|Hoesly et al. (2018)]] and [[#van%20Marle--2017|van Marle et al. (2017)]] ; emissions for CMIP5 for the period 1850–2005 are from [[#Lamarque--2010|Lamarque et al. (2010)]] ; CO <sub>2</sub> emissions are from EDGAR database ( [[#Crippa--2020|Crippa et al., 2020]] ); methane (CH <sub>4</sub> ) and HFCs are from ( [[#Crippa--2019|Crippa et al., 2019]] ); and air pollutants are from EC-JRC / PBL (2020), [[#Höglund-Isaksson--2012|Höglund-Isaksson (2012)]] and [[#Klimont--2017a|Klimont et al. (2017a)]] for ECLIPSE. Projections originate from the Shared Socio-Economic Pathway (SSP) database ( [[#Riahi--2017|Riahi et al., 2017]] ; [[#Rogelj--2018a|Rogelj et al., 2018a]] ; [[#Gidden--2019|Gidden et al., 2019]] ); Representative Concentration Pathway (RCP) database ( [[#van%20Vuuren--2011|van Vuuren et al., 2011]] ); GAINS (CLE – current legislation baseline, KA – Kigali Amendment, MTFR – maximum technical mitigation potential) for HFCs ( [[#Purohit--2020|Purohit et al., 2020]] ; Velders et al. 2015); and, ECLIPSE ( [[#Stohl--2015|Stohl et al., 2015]] ). Further details on data sources and processing are available in the chapter data table (Table 6.SM.3).

All SSP scenarios (Figure 6.18), except SSP3-7.0, project decline in global total emissions for all SLCFs by the end of the 21st century, except for ammonia and for HFCs (more on HFCs below). Similar to RCPs, ammonia emissions continue to increase in most SSPs, except SSP1 and SSP2, accounting for the expected growth in food demand and a general lack of effective policies targeting agricultural emissions. Additionally, mitigation potential for NH <sub>3</sub> is generally smaller than for other species owing to fugitive and widely distributed sources ( [[#Pinder--2007|Pinder et al., 2007]] ; [[#Klimont--2015|Klimont and Winiwarter, 2015]] ; [[#Mohankumar%20Sajeev--2018|Mohankumar]] [[#Sajeev--2018|Sajeev et al., 2018]] ; [[#Sajeev--2018|Sajeev et al., 2018]] ). Most significant changes of SLCF emissions in the near and long-term compared to the present day are expected for SO <sub>2.</sub> This is due to ever more stringent (and enforced) legislation in China’s power sector, extended recently to industrial sources ( [[#Zheng--2018b|Zheng et al., 2018b]] ; [[#Tong--2020|Tong et al., 2020]] ), declining coal use in most SSPs, recently announced stricter emissions limits for the power sector in India, and reduction of the sulphur content of oil fuel used in international shipping from 2020 ( [[#IMO--2016|IMO, 2016]] ). For the lower forcing targets (e.g., SSP1-2.6), the SO <sub>2</sub> trajectories are similar to the RCPs resulting in over 50–90% decline by 2050 and 2100, respectively, while for the scenarios with no climate policies, the SSPs show a large spread even at the end of the century.

Until the mid-21st century, SSP3-7.0 and SSP5-8.5 scenarios project no reduction in NO <sub>x</sub> emissions at the global level with decline in most OECD countries and Eastern Asia, driven by existing legislation in power, industry and transportation (e.g., [[#Tong--2020|Tong et al., 2020]] ), and continued increase in the rest of the world (Figures 6.18 and 6.19). Towards the end of the century, similar trends continue in SSP3-7.0 while emissions in SSP5 decline strongly owing to faster technological progress and stronger air-quality action ( [[#Rao--2017|Rao et al., 2017]] ; [[#Riahi--2017|Riahi et al., 2017]] ). By 2100, the ‘Regional Rivalry’ (SSP3) scenario emissions of NO <sub>x</sub> (and most other SLCFs, except ammonia) are typically twice as high as the next highest SSP projection, both at the global (Figure 6.18) and regional levels (Figure 6.19). In emissions pathways consistent with Paris Agreement goals (SSP1-1.9 or SSP1-2.6; [[IPCC:Wg1:Chapter:Chapter-1#1.6.1|Section 1.6.1]] ), NO <sub>x</sub> drops, compared to 2015, by 50% in SSP1-2.6 and by 65% in SSP1-1.9 by 2050, is reduced by about 70% by 2100, resulting in global emissions levels comparable to the 1950s and below the RCP range. In these pathways considering strong climate change mitigation, similar reductions are projected at the regional level, except in Africa (less than 50% decline) due to its high share of biomass emissions as well as strong growth in population and fossil fuel use. The trends in anthropogenic and biomass-burning emissions for other ozone precursors (NMVOC, CO) are similar to that of NO <sub>x</sub> .

An additional scenario, based on the SSP3-7.0, has been designed specifically to assess the effect of a strong SLCF emissions abatement and is called SSP3-7.0-lowNTCF in the literature ( [[#Collins--2017|Collins et al., 2017]] ; [[#Gidden--2019|Gidden et al., 2019]] ). It has been applied in the modelling studies (e.g., AerChemMIP) with or without consideration of additional methane reduction and we refer here to these scenarios, respectively, as SSP3-7.0-lowSLCF-lowCH <sub>4</sub> or SSP3-7.0-lowSLCF-highCH <sub>4</sub> . In these scenarios, aerosols, their precursors, and non-methane tropospheric ozone precursors are mitigated by applying the same emissions factors as in SSP1-1.9.

For global methane emissions, the range is similar for SSPs and RCPs over the entire century (Figure 6.18), with highest projections in SSP3-7.0 (slightly below RCP8.5) estimating doubling of the current emissions and a reduction of about 75% by 2100 in scenarios consistent with 1.5°C to 2°C targets; similar to RCP2.6. At the regional level, the evolution of methane emissions in climate change mitigation scenarios is comparable to RCPs but there are significant differences for some regions with respect to high CO <sub>2</sub> emissions scenarios. In particular, projections for Eastern Asia differ significantly, the highest SSP3-7.0 is about half of the highest RCP by 2100 (Figure 6.19), which is due to much lower projections of coal use in China driven largely by efforts during the last decade to combat poor air quality. At the same time, the SSP scenarios without climate change mitigation project faster growth in methane emissions in Africa, the Middle East and Latin America (Figure 6.19) driven by developments in agriculture, the oil and gas sectors, and, especially in Africa, waste management. There are significant differences in the assessment and feasibility of rapid methane mitigation. [[#Höglund-Isaksson--2020|Höglund-Isaksson et al. (2020)]] review most recent studies and assess feasibility of rapid widespread mitigation, concluding that significant (over 50%) reductions are attainable but the feasibility of such reductions could be constrained in the short term due to locked capital. This might have implications for near-term evolution assumed in, for example, SSP1-1.9 or SSP3-lowSLCF-lowCH <sub>4</sub> , where emissions drop very quickly due to fast decarbonization and reductions in agriculture. Such high reduction potential in agriculture has been also assumed in other studies ( [[#Lucas--2007|Lucas et al., 2007]] ; [[#Harmsen--2020a|Harmsen et al., 2020a]] ) but is questioned by [[#Höglund-Isaksson--2020|Höglund-Isaksson et al. (2020)]] who indicate that widespread implementation (within decades) of policies bringing about institutional and behavioural changes would be important for transition towards very low methane emissions from livestock production.

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'''Figure 6.19 |''' '''Regional anthropogenic and biomass burning short-lived climate forcer (SLCF) emissions from 1850 to 2100.''' Emissions for the Coupled Model Intercomparison Project Phase 6 (CMIP6) for the period 1850–2014 are based on [[#Hoesly--2018|Hoesly et al. (2018)]] and [[#van%20Marle--2017|van Marle et al. (2017)]] and emissions for CMIP5 for the period 1850–2005 are from [[#Lamarque--2010|Lamarque et al. (2010)]] . Projections originate from the Shared Socio- economic Pathway (SSP) database ( [[#Riahi--2017|Riahi et al., 2017]] ; [[#Rogelj--2018a|Rogelj et al., 2018a]] ; [[#Gidden--2019|Gidden et al., 2019]] ) and Representative Concentration Pathway (RCP) database ( [[#van%20Vuuren--2011|van Vuuren et al., 2011]] ). Further details on data sources and processing are available in the chapter data table (Table 6.SM.3).

Global emissions of carbonaceous aerosols are projected to decline in all SSP scenarios (Figure 6.18) except SSP3-7.0. In that scenario, which also has much higher emissions than any of the RCPs, about half of the anthropogenic BC originates from cooking and heating on solid fuels, mostly in Asia and Africa (Figure 6.19), where only limited progress in access to clean energy is achieved. Slow progress in improving waste management, high coal use in energy and industry, and no further progress in controlling diesel engines in Asia, Africa and Latin America contributes most of the remaining emissions, resulting in about 90% of anthropogenic BC emitted in the non-OECD world by 2100 in SSP3-7.0. A similar picture emerges for OC but with greater contribution of the waste management sector and biomass burning, and lower impact of transportation and industry developments. Since scenarios compliant with Paris Agreement goals (SSP1-1.9 or SSP1-2.6; [[IPCC:Wg1:Chapter:Chapter-1#1.6.1|Section 1.6.1]] ) include widespread access to clean energy already by 2050, the global and regional emissions of BC decline by 70–75% by 2050 and 80% by 2100 relayive to 2015. The decline in the residential sector (about 90% by 2050 and over 95% by 2100) is accompanied by a strong reduction in transport (over 98%) and the decarbonization of the industry and energy sector. About 50% of remaining BC emissions in SSP1-1.9 or SSP1-2.6 are projected to originate from waste and open biomass burning of which open burning of waste represent a significant part. Some studies suggest this might be pessimistic as, for example, efficient waste management (consistent with SDG goals) could potentially eliminate the open burning of solid residues ( [[#Gómez-Sanabria--2018|Gómez-Sanabria et al., 2018]] ), which accounts for over 30% of BC emissions in SSP1-1.9 in 2050 or 2100.

The SSP scenarios draw on the HFC projections developed by [[#Velders--2015|Velders et al. (2015)]] considering, in climate change mitigation scenarios, the provisions of the Kigali Amendment (2016) to the Montreal Protocol leading to phase-down of HFCs (Section 6.6.3.2; [[#Papanastasiou--2018|Papanastasiou et al., 2018]] ). The SSP scenarios without climate change mitigation (e.g, SSP3-7.0, SSP5-8.5) show a range in HFC emissions of 3.2–5.3 GtCO <sub>2</sub> -eq yr <sup>–1</sup> in 2050 and about 4–7.2 GtCO <sub>2</sub> -eq yr <sup>–1</sup> by 2100 while in deep climate change mitigation scenarios (SSP1-1.9 and SSP1-2.6), consistent with the 1.5–2°C targets, they are expected to drop to 0.1–0.35 GtCO <sub>2</sub> -eq yr <sup>–1</sup> (Figure 6.18). In SSP1-1.9, the extent of reduction and its pace is more ambitious than current estimates of the effect of the fully implemented and enforced Kigali Amendment (Figure 6.18; [[#Höglund-Isaksson--2017|Höglund-Isaksson et al., 2017]] ; [[#Purohit--2017|Purohit and Höglund-Isaksson, 2017]] ). The best representation of the HFC emissions trajectories in the SSP framework compliant with the Kigali Amendment is the SSP1-2.6 and the baseline (including only pre-Kigali national legislation; Section 6.6.3) is best represented by SSP5-8.5 (Figure 6.18). However, since HFC emissions in SSPs were developed shortly after the Kigali Amendment had been agreed, none of these projections represents accurately the HFC emissions trajectory corresponding to the phase-out emissions levels agreed to in the Kigali Amendment ( [[#Meinshausen--2020|Meinshausen et al., 2020]] ), leading to ''medium confidence'' in the assessment of the benefits of the Kigali Amendment when using SSP projections for HFCs.

The SSP SLCF trajectories reflect the effect of recent legislation and the assumed evolution thereof in the longer term, however, they do not necessarily reflect the full mitigation potential for several SLCFs, within particular SSPs (Figure 6.18), that could be achieved with air-quality- or SDG-targeted policies (Amann et al. , 2013; Rogelj et al. , 2014a; Haines et al. , 2017; Klimont et al. , 2017b; [[#Rafaj--2018|Rafaj and Amann, 2018]] ; Shindell et al. , 2018; Tong et al. , 2020) . Such policies could bring more rapid mitigation of SLCFs, independent of the climate strategy (Section 6.6.3).

The projections of future SLCF abundances typically follow their emissions trajectories except for SLCFs that are formed from precursor reactions (e.g., tropospheric ozone) or are influenced by biogeochemical feedbacks (Sections 6.2.2 and 6.4.5). According to multi-model CMIP6 simulations, total column ozone (reflecting mostly stratospheric ozone) is projected to return to 1960s values by the middle of the 21st century under the SSP2-4.5, SSP3-7.0, SSP4-3.4, SSP4-6.0 and SSP5-8.5 scenarios ( [[#Keeble--2021|Keeble et al., 2021]] ). ESMs project increasing tropospheric ozone burden over the 2015–2100 period for the SSP3-7.0 scenario (Figure 6.4; [[#Griffiths--2021|Griffiths et al., 2021]] ), there is, however, a large spread in the magnitude of this increase reflecting structural uncertainties associated with the model representation of processes that influence tropospheric ozone. Sources of uncertainties in SLCF-abundance projections include scenario uncertainties, or parametric and structural uncertainties in the model representation of the processes affecting simulated abundances with implications for radiative forcing and air quality. The evolution of methane abundances in SSP scenarios, for example, is derived from integrated assessment models (IAMs) which do not include the effects from biogeochemical feedbacks (e.g., climate-driven changes in wetland emissions; [[#Meinshausen--2020|Meinshausen et al., 2020]] ) introducing uncertainty.

In summary, in SSPs, in addition to the socio-economic development and climate change mitigation policies shaping the GHG emissions trajectories, the SLCF emissions trajectories are also steered by varying levels of air pollution control originating from SSP narratives and independent from climate change mitigation. Consequently, SSPs span a wider range of SLCF emissions than considered in the RCPs, better covering the diversity of future options in air pollution management and SLCF-induced climate effects ( ''high confidence'' ). In addition to SSP-driven emissions, the future evolution of SLCFs abundance is also sensitive to chemical and biogeochemical feedbacks involving SLCFs, particularly natural emissions, whose magnitude and sign are poorly constrained.

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