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

=== 6.2.1 Anthropogenic Sources ===

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Estimates of global anthropogenic (human-caused) SLCF emissions and their historical evolution that were used in AR5 (CMIP5; [[#Lamarque--2010|Lamarque et al., 2010]] ) have been revised for use in CMIP6 ( [[#Hoesly--2018|Hoesly et al., 2018]] ). The update considered new data and assessment of the impact of environmental policies, primarily regarding air pollution control (R. Wang et al. , 2014; S.X. Wang et al. , 2014; Montzka et al. , 2015; Crippa et al. , 2016; Turnock et al. , 2016; Klimont et al. , 2017a; Zanatta et al. , 2017; Prinn et al. , 2018) . Additionally, [[#Hoesly--2018|Hoesly et al. (2018)]] have extended estimates of anthropogenic emissions back to 1750 and developed an updated and new set of spatial proxies allowing for more differentiated (source sector-wise) gridding of emissions ( [[#Feng--2020|Feng et al., 2020]] ). The CMIP6 emissions inventory has been developed with the Community Emissions Data System (CEDS) that improves upon existing inventories with a more consistent and reproducible methodology, similar to approaches used in, for example, the EDGAR database ( [[#Crippa--2016|Crippa et al., 2016]] ) and the GAINS model ( [[#Amann--2011|Amann et al., 2011]] ; [[#Klimont--2017a|Klimont et al., 2017a]] ; [[#Höglund-Isaksson--2020|Höglund-Isaksson et al., 2020]] ) where emissions of all compounds are consistently estimated using the same emissions drivers and propagating individual components (activity data and emissions factors) separately to capture fuel and technology trends affecting emissions trajectories over time. This contrasts with the approach used to establish historical emissions for CMIP5 where different datasets available at the time were combined. The CMIP6 exercise is based on the first release of the CEDS emissions dataset (version 2017-05-18, sometimes referred to hereafter as CMIP6 emissions) whose main features regarding SLCFs are described hereafter. The CEDS has been and will be regularly updated and extended; the recent update of the CEDS ( [[#Hoesly--2019|Hoesly et al., 2019]] ) and consequences for this Assessment is discussed when necessary. Some details on how SLCF emissions have been represented in scenarios used by IPCC assessments can be found in [[IPCC:Wg1:Chapter:Chapter-1|Chapter 1]] ( [[IPCC:Wg1:Chapter:Chapter-1#1.6.1|Section 1.6.1]] and Cross-Chapter Box 1.4 and in Section 6.7.1.1).

For most of the SLCF species, the global and regional anthropogenic emissions trends developed for CMIP6 for the period 1850–2000 are not substantially different from those used in CMIP5 (Figures 6.18 and 6.19) despite the different method used to derive them. Hoesly et al. (2018, CEDS) developed independent time series capturing trends in fuel use, technology and level of control, whereas CMIP5 combined different emissions datasets. However, for the period after 1990, the CMIP6 dataset shows for all species, except for SO <sub>2</sub> , CO, and (since 2011) for NO <sub>x</sub> , a different trend than CMIP5 (i.e., continued strong growth of emissions driven primarily by developments in Asia (Figure 6.19)). The unprecedented growth of emissions from Eastern and Southern Asia since 2000 changed the global landscape of emissions, making Asia the dominant SLCF source region (Figures 6.3 and 6.19). The Representative Concentration Pathways (RCP) scenarios used in AR5 started from the year 2000 ( [[#van%20Vuuren--2011|van Vuuren et al., 2011]] ) and did not capture the SLCF emissions which actually occurred until 2015. The CEDS inventory ( [[#Hoesly--2018|Hoesly et al., 2018]] ) includes improved representation of these trends and the estimate for 2014. These findings have been largely supported by several independent emissions inventory studies and remote-sensing data analysis. However, for the last decade the decline of Asian emissions of SO <sub>2</sub> and NO <sub>x</sub> appears underestimated while growth of BC and OC emissions in Asia and Africa seems overestimated in CMIP6, compared to most recent regional evaluations ( [[#Klimont--2017a|Klimont et al., 2017a]] ; [[#Zheng--2018b|Zheng et al., 2018b]] ; [[#Elguindi--2020|Elguindi et al., 2020]] ; [[#Kanaya--2020|Kanaya et al., 2020]] ; [[#McDuffie--2020|McDuffie et al., 2020]] ), which are largely considered in the updated release of the CEDS ( [[#Hoesly--2019|Hoesly et al., 2019]] ). Consequently, global CMIP6 anthropogenic emissions for 2014 are likely overestimated by about 10% for SO <sub>2</sub> and NO <sub>x</sub> and by about 15% for BC and OC.

For SO <sub>2</sub> , independent emissions inventories and observational evidence show that on a global scale strong growth of Asian emissions has been countered by reduction in North America and Europe (Reis et al. , 2012; Amann et al. , 2013; Crippa et al. , 2016; Aas et al. , 2019) . However, Chinese emissions declined by nearly 70% between about 2006 and 2017 ( ''high confidence'' ) (Silver et al. , 2018; Zheng et al. , 2018b; Mortier et al. , 2020; Tong et al. , 2020) . The estimated reduction in China contrasts with continuing strong growth of SO <sub>2</sub> emissions in Southern Asia (Figure 6.19). In 2014, over 80% of anthropogenic SO <sub>2</sub> emissions originated from power plants and industry, with Asian sources contributing more than 50% of the total (Figure 6.3).

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'''Figure 6.3 |''' '''Relative regional and sectoral contributions to the present day (year 2014) anthropogenic emissions of short-lived climate forcers (SLCFs).''' Emissions data are from the Community Emissions Data System (CEDS; [[#Hoesly--2018|Hoesly et al., 2018]] ). Emissions are aggregated into the following sectors: fossil fuel production and distribution (coal mining, oil and gas production, upstream gas flaring, gas distribution networks), fossil fuel combustion for energy (power plants), residential and commercial (fossil and biofuel use for cooking and heating), industry (combustion and production processes, solvent-use losses from production and end use), transport (road and off-road vehicles), shipping (including international shipping), aviation (including international aviation), agriculture (livestock and crop production), waste management (solid waste, including landfills and open trash burning, residential and industrial waste water), and other. Further details on data sources and processing are available in the chapter data table (Table 6.SM.3).

Global emissions of NO <sub>x</sub> have been growing in spite of the successful reduction of emissions in North America, Europe, Japan and Korea ( [[#Crippa--2016|Crippa et al., 2016]] ; [[#Turnock--2016|Turnock et al., 2016]] ; [[#Miyazaki--2017|Miyazaki et al., 2017]] ; [[#Jiang--2018|Jiang et al., 2018]] ), partly driven by continuous efforts to strengthen the emissions standards for road vehicles in most countries (Figures 6.18 and 6.19). In many regions, an increase in vehicle fleet as well as non-compliance with emissions standards (Anenberg et al. , 2017, 2019; Jonson et al. , 2017; Jiang et al. , 2018) , growing aviation ( [[#Grewe--2019|Grewe et al., 2019]] ; [[#Lee--2021|Lee et al., 2021]] ) and demand for energy, and consequently a large number of new fossil fuel power plants, have more than compensated for these reductions. Since about 2011, global NO <sub>x</sub> emissions appear to have stabilized or slightly declined ( ''medium confidence'' ) but the global rate of decline has been underestimated in the CEDS, as recent data suggest that emissions reductions in China were larger than included in the CEDS (Figure 6.19 and [[#Hoesly--2018|Hoesly et al., 2018]] ). Recent bottom-up emissions estimates ( [[#Zheng--2018b|Zheng et al., 2018b]] ) largely confirm what has been shown in satellite data (F. [[#Liu--2016|]] [[#Liu--2016|Liu et al., 2016]] ; [[#Miyazaki--2017|Miyazaki et al., 2017]] ; [[#Silver--2018|Silver et al., 2018]] ): a strong decline of NO <sub>2</sub> column over eastern China ( ''high confidence'' ) (Section 6.3.3.1). At a global level, the estimated CEDS CO emissions trends are comparable to NO <sub>x</sub> , which has been confirmed by several inverse modelling studies (Section 6.3.3.2). The transport sector (including international shipping and aviation) was the largest anthropogenic source of NO <sub>x</sub> (about 50% of the total) and also contributed over 25% of CO emissions in 2014; Asia represented 50% and North America and Europe about 20% of global total NO <sub>x</sub> and CO emissions (Figure 6.3).

Oil production-distribution and transport sectors have dominated anthropogenic NMVOC emissions for most of the 20th century ( [[#Hoesly--2018|Hoesly et al., 2018]] ) and still represent a large share (Figure 6.3). Efforts to control transport emissions (i.e., increasing stringency of vehicle emissions limits) were largely offset by the fast growth of emissions from chemical industries and solvent use, as well as from fossil fuel production and distribution, resulting in continued growth of global anthropogenic NMVOC emissions since 1900 ( ''high confidence'' ) (Figure 6.18). Since AR5, there is ''high confidence'' that motor vehicle NMVOC emissions have sharply declined in North America and Europe in the last decades ( [[#Rossabi--2018|Rossabi and Helmig, 2018]] ), for example, by about an order of magnitude in major US cities since 1990 ( [[#Bishop--2018|Bishop and Haugen, 2018]] ; [[#McDonald--2018|McDonald et al., 2018]] ). Increasing (since 2008) oil- and gas-extraction activities in North America lead to a strong growth of NMVOC emissions ( ''high confidence'' ) as shown by analysis of ethane column data ( [[#Franco--2016|Franco et al., 2016]] ), but absolute emission amounts remain uncertain ( [[#Pétron--2014|Pétron et al., 2014]] ; [[#Tzompa-Sosa--2019|Tzompa-Sosa et al., 2019]] ). In Eastern Asia, there is ''medium confidence'' in a decreasing trend of motor vehicle emissions, suggested by ambient measurements in Beijing since 2002 ( [[#Wang--2015|Wang et al., 2015]] ) and by bottom-up estimates ( [[#Zheng--2018b|Zheng et al., 2018b]] ), and a decrease in residential heating emissions due to declining coal and biofuel use since 2005 ( [[#Zheng--2018b|Zheng et al., 2018b]] ; [[#Li--2019|]] [[#Li--2019|M Li et al., 2019]] ). However, total anthropogenic NMVOC emissions have increased steadily in China since the mid-20th century, largely due to the growing importance of the solvent-use and industrial sectors ( ''medium evidence'' , ''high agreement'' ) (Sun et al. , 2018; Zheng et al. , 2018b; M. Li et al. , 2019) . Resulting changes in the NMVOC speciated emissions might be underestimated in the current regional and global inventories. For example, in the USA, a recent study suggested an emergent shift in urban NMVOC sources from transportation to chemical products (i.e., household chemicals, personal care products, solvents, etc.), which is not in accordance with emissions inventories currently used ( [[#McDonald--2018|McDonald et al., 2018]] ). In many European regions and cities, wood burning has been increasingly used for residential heating, partly for economic reasons and because it is considered CO <sub>2</sub> -neutral ( [[#Athanasopoulou--2017|Athanasopoulou et al., 2017]] ); in situ measurements in several cities, including Paris, suggest that wood burning explains up to half of the NMVOC emissions during winter ( [[#Kaltsonoudis--2016|Kaltsonoudis et al., 2016]] ; [[#Languille--2020|Languille et al., 2020]] ). Due to the vast heterogeneity of sources and components of NMVOCs, uncertainty in regional emissions and trends is higher than for most other components.

Emissions of carbonaceous aerosols (BC, OC) have been steadily increasing and their emissions have almost doubled since 1950 ( ''medium confidence'' ) ( [[#Hoesly--2018|Hoesly et al., 2018]] ). Before 1950, North America and Europe contributed about half of the global total but successful introduction of diesel particulate filters on road vehicles ( [[#Fiebig--2014|Fiebig et al., 2014]] ; [[#Robinson--2015|Robinson et al., 2015]] ; [[#Klimont--2017a|Klimont et al., 2017a]] ) and declining reliance on solid fuels for heating brought in large reductions ( ''high confidence'' ) (Figure 6.19). Currently, global carbonaceous aerosol emissions originate primarily from Asia and Africa ( [[#Bond--2013|Bond et al., 2013]] ; [[#Hoesly--2018|Hoesly et al., 2018]] ; [[#Elguindi--2020|Elguindi et al., 2020]] ; [[#McDuffie--2020|McDuffie et al., 2020]] ), representing about 80% of the global total ( ''high confidence'' ) (Figure 6.3). Consideration, in CMIP6, of emissions from kerosene lamps and gas flaring, revised estimates for open burning of waste, regional coal consumption, and new estimates for Russia ( [[#Stohl--2013|Stohl et al., 2013]] ; [[#Huang--2015|Huang et al., 2015]] ; [[#Huang--2016|Huang and Fu, 2016]] ; [[#Kholod--2016|Kholod et al., 2016]] ; [[#Conrad--2017|Conrad and Johnson, 2017]] ; [[#Evans--2017|Evans et al., 2017]] ; [[#Klimont--2017a|Klimont et al., 2017a]] ) resulted in over 15% higher global emissions of OC and BC than in the CMIP5 estimates for the first decade of the 21st century (Figure 6.18). However, the continued increase of BC emissions over Eastern Asia after 2005, estimated in CMIP6 (Figure 6.19), has been questioned recently as a steady decline of BC concentrations was measured in the air masses flowing out from the east coast of China ( [[#Kanaya--2020|Kanaya et al., 2020]] ), which has been also estimated in recent regional bottom-up and top-down inventories (Zheng et al. , 2018a; Elguindi et al. , 2020; McDuffie et al. , 2020) . Since AR5, confidence in emissions estimates and trends in North America and Europe has increased, but high uncertainties remain for Asia and Africa, despite their major contribution to global emissions. The size distribution of emitted species, of importance for climate and health impacts, remains uncertain and the CEDS inventory does not provide such information. Overall, a factor two uncertainty in global estimates of BC and OC emissions remains, with post-2005 emissions overestimated in Asia ( ''high confidence'' ) and Africa ( ''medium confidence'' ).

Bottom-up global emissions estimates of methane ( [[#Lamarque--2010|Lamarque et al., 2010]] ; [[#Hoesly--2018|Hoesly et al., 2018]] ; [[#Janssens-Maenhout--2019|Janssens-Maenhout et al., 2019]] ; [[#Höglund-Isaksson--2020|Höglund-Isaksson et al., 2020]] ) for the last two decades are higher than top-down assessments (e.g., [[#Saunois--2016|Saunois et al., 2016]] , 2020) but trends from the two methods are similar and indicate continued growth ( ''high confidence'' ). Larger discrepancies exist at the sectoral and regional levels, notably for coal mining ( [[#Peng--2016|Peng et al., 2016]] ; [[#Miller--2019|Miller et al., 2019]] ) and the oil and gas sector due to the growth of unconventional production and higher loss estimates [[IPCC:Wg1:Chapter:Chapter-5#5.2.2|Section 5.2.2]] ; Franco et al. , 2016; Alvarez et al. , 2018; Dalsøren et al. , 2018).

Agricultural production (livestock and mineral nitrogen fertilizer application) is the primary source of ammonia in the atmosphere with more than half of present-day emissions originating in Asia (Hoesly et al. , 2018; Figure 6.3, [[#EC-JRC/PBL--2020|EC-JRC/PBL, 2020]] ; Vira et al. , 2020) . NH <sub>3</sub> emissions are estimated to have grown strongly since 1850, especially since 1950, driven by continuously increasing livestock production, widespread application of mineral nitrogen fertilizers, and lack of action to control ammonia ( ''high confidence'' ) (Erisman et al. , 2008; Riddick et al. , 2016; Hoesly et al. , 2018; Fowler et al. , 2020) . The trends estimated in CMIP5 and CMIP6 are similar, while in absolute terms, CMIP6 has somewhat higher emissions as it includes emissions from wastewater and human waste that were largely missing in CMIP5 ( [[#Hoesly--2018|Hoesly et al., 2018]] ). CMIP6 has improved spatial and temporal distribution of emissions ( [[#Lamarque--2013a|Lamarque et al., 2013a]] ) relying on the EDGAR v4.3 database and [[#Paulot--2014|Paulot et al. (2014)]] , but important uncertainties remain for regionally specific temporal patterns (Riddick et al. , 2016; Liu et al. , 2019; Feng et al. , 2020; Vira et al. , 2020) . The continuing increase in global NH <sub>3</sub> emissions is driven primarily by growing livestock and crop production in Asia while emissions in the USA and Europe remain about constant or have slightly declined in the last decade ( [[#Hoesly--2018|Hoesly et al., 2018]] ). Recent satellite and ground observations support trends estimated in CMIP6 dataset (Section 6.3.3.4).

To summarize, there are significant differences in spatial and temporal patterns of SLCF emissions across global regions (Figure 6.18). Until the 1950s, the majority of SLCF emissions were associated with fossil fuel use (SO <sub>2</sub> , NO <sub>x</sub> , NMVOCs, CO) and about half of BC and OC originated from North America and Europe ( [[#Lamarque--2010|Lamarque et al., 2010]] ; [[#Hoesly--2018|Hoesly et al., 2018]] ). Since the 1990s a large redistribution of emissions was associated with strong economic growth in Asia and declining emissions in North America and Europe due to air-quality legislation and the declining capacity of energy-intensive industry; currently more than 50% of anthropogenic emissions of each SLCF species (including methane and NH <sub>3</sub> ) originates from Asia (Figure 6.3; Amann et al. , 2013; Bond et al. , 2013; Fiore et al. , 2015; Crippa et al. , 2016, 2018; Klimont et al. , 2017a; Hoesly et al. , 2018) . The dominance of Asia for SLCF emissions is corroborated by growing remote-sensing capacity that has been providing an independent evaluation of estimated pollution trends in the last decade (Duncan et al. , 2013; Lamsal et al. , 2015; Luo et al. , 2015; Fioletov et al. , 2016; Geddes et al. , 2016; Irie et al. , 2016; Krotkov et al. , 2016; Wen et al. , 2018) .

Since AR5, the quality and completeness of activity and emission-factor data and applied methodology, including spatial allocation together with independent satellite-derived observations, have improved, raising confidence in methods used to derive emissions. There is ''high confidence'' in the sign of global trends of SLCF emissions until the year 2000. However, only ''medium confidence'' for the rate of change in the two last decades, owing primarily to uncertainties in the actual application of reduction technologies in fast-growing economies of Asia. At a regional level, bottom-up derived SLCF emissions trends, and magnitudes in regions with strong economic growth and changing air-quality regulation, are highly uncertain but can be better constrained with top-down methods (Section 6.3). For most SLCF species, there is ''high confidence'' in trends and magnitudes for affluent countries from the Organisation for Economic Co- operation and Development (OECD) regions where accurate and detailed information about drivers of emissions exists; ''medium confidence'' is assessed for regional emissions of NH <sub>3</sub> , methane and NMVOC.

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