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      Co-benefits of CO 2 emission reduction from China’s clean air actions between 2013-2020

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          Abstract

          Climate change mitigation measures can yield substantial air quality improvements while emerging clean air measures in developing countries can also lead to CO 2 emission mitigation co-benefits by affecting the local energy system. Here, we evaluate the effect of China’s stringent clean air actions on its energy use and CO 2 emissions from 2013-2020. We find that widespread phase-out and upgrades of outdated, polluting, and inefficient combustion facilities during clean air actions have promoted the transformation of the country’s energy system. The co-benefits of China’s clean air measures far outweigh the additional CO 2 emissions of end-of-pipe devices, realizing a net accumulative reduction of 2.43 Gt CO 2 from 2013-2020, exceeding the accumulated CO 2 emission increase in China (2.03 Gt CO 2) during the same period. Our study indicates that China’s efforts to tackle air pollution induce considerable climate benefit, and measures with remarkable CO 2 reduction co-benefits deserve further attention in future policy design.

          Abstract

          China’s clean air action stimulated a net accumulative reduction of 2.43 Gt CO 2 emission from 2013-2020. Phase-out and upgrades of outdated, polluting, and inefficient combustion facilities have promoted the transition of the country’s energy system.

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          Global Carbon Budget 2019

          Abstract. Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere – the “global carbon budget” – is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFF) are based on energy statistics and cement production data, while emissions from land use change (ELUC), mainly deforestation, are based on land use and land use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) and terrestrial CO2 sink (SLAND) are estimated with global process models constrained by observations. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the last decade available (2009–2018), EFF was 9.5±0.5 GtC yr−1, ELUC 1.5±0.7 GtC yr−1, GATM 4.9±0.02 GtC yr−1 (2.3±0.01 ppm yr−1), SOCEAN 2.5±0.6 GtC yr−1, and SLAND 3.2±0.6 GtC yr−1, with a budget imbalance BIM of 0.4 GtC yr−1 indicating overestimated emissions and/or underestimated sinks. For the year 2018 alone, the growth in EFF was about 2.1 % and fossil emissions increased to 10.0±0.5 GtC yr−1, reaching 10 GtC yr−1 for the first time in history, ELUC was 1.5±0.7 GtC yr−1, for total anthropogenic CO2 emissions of 11.5±0.9 GtC yr−1 (42.5±3.3 GtCO2). Also for 2018, GATM was 5.1±0.2 GtC yr−1 (2.4±0.1 ppm yr−1), SOCEAN was 2.6±0.6 GtC yr−1, and SLAND was 3.5±0.7 GtC yr−1, with a BIM of 0.3 GtC. The global atmospheric CO2 concentration reached 407.38±0.1 ppm averaged over 2018. For 2019, preliminary data for the first 6–10 months indicate a reduced growth in EFF of +0.6 % (range of −0.2 % to 1.5 %) based on national emissions projections for China, the USA, the EU, and India and projections of gross domestic product corrected for recent changes in the carbon intensity of the economy for the rest of the world. Overall, the mean and trend in the five components of the global carbon budget are consistently estimated over the period 1959–2018, but discrepancies of up to 1 GtC yr−1 persist for the representation of semi-decadal variability in CO2 fluxes. A detailed comparison among individual estimates and the introduction of a broad range of observations shows (1) no consensus in the mean and trend in land use change emissions over the last decade, (2) a persistent low agreement between the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) an apparent underestimation of the CO2 variability by ocean models outside the tropics. This living data update documents changes in the methods and data sets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this data set (Le Quéré et al., 2018a, b, 2016, 2015a, b, 2014, 2013). The data generated by this work are available at https://doi.org/10.18160/gcp-2019 (Friedlingstein et al., 2019).
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            Trends in China's anthropogenic emissions since 2010 as the consequence of clean air actions

            Abstract. To tackle the problem of severe air pollution, China has implemented active clean air policies in recent years. As a consequence, the emissions of major air pollutants have decreased and the air quality has substantially improved. Here, we quantified China's anthropogenic emission trends from 2010 to 2017 and identified the major driving forces of these trends by using a combination of bottom-up emission inventory and index decomposition analysis (IDA) approaches. The relative change rates of China's anthropogenic emissions during 2010–2017 are estimated as follows: −62 % for SO 2 , −17 % for NO x , +11 % for nonmethane volatile organic compounds (NMVOCs), +1 % for NH 3 , −27 % for CO, −38 % for PM 10 , −35 % for PM 2.5 , −27 % for BC, −35 % for OC, and +16 % for CO 2 . The IDA results suggest that emission control measures are the main drivers of this reduction, in which the pollution controls on power plants and industries are the most effective mitigation measures. The emission reduction rates markedly accelerated after the year 2013, confirming the effectiveness of China's Clean Air Action that was implemented since 2013. We estimated that during 2013–2017, China's anthropogenic emissions decreased by 59 % for SO 2 , 21 % for NO x , 23 % for CO, 36 % for PM 10 , 33 % for PM 2.5 , 28 % for BC, and 32 % for OC. NMVOC emissions increased and NH 3 emissions remained stable during 2010–2017, representing the absence of effective mitigation measures for NMVOCs and NH 3 in current policies. The relative contributions of different sectors to emissions have significantly changed after several years' implementation of clean air policies, indicating that it is paramount to introduce new policies to enable further emission reductions in the future.
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              Drivers of improved PM 2.5 air quality in China from 2013 to 2017

              Significance The high frequency of haze pollution in China has attracted broad attention and triggered, in 2013, the promulgation of the toughest-ever clean air policy in the country. In this study, we quantified the air quality and health benefits from specific clean air actions by combining a chemical transport model with a detailed emission inventory. As tremendous efforts and resources are needed for mitigating emissions from various sources, evaluation of the effectiveness of these measures can provide crucial information for developing air quality policies in China as well as in other developing and highly polluting countries. Based on measure-specific analysis, our results bear out several important implications for designing future clean air policies.
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                Author and article information

                Contributors
                qiangzhang@tsinghua.edu.cn
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                27 August 2022
                27 August 2022
                2022
                : 13
                : 5061
                Affiliations
                [1 ]GRID grid.12527.33, ISNI 0000 0001 0662 3178, School of Environment, , Tsinghua University, ; Beijing, China
                [2 ]GRID grid.12527.33, ISNI 0000 0001 0662 3178, Department of Earth System Science, , Tsinghua University, ; Beijing, China
                [3 ]GRID grid.12527.33, ISNI 0000 0001 0662 3178, Institute of Environment and Ecology, Tsinghua Shenzhen International Graduate School, , Tsinghua University, ; Shenzhen, China
                [4 ]GRID grid.464275.6, ISNI 0000 0001 1998 1150, Center of Air Quality Simulation and System Analysis, , Chinese Academy of Environmental Planning, ; Beijing, China
                [5 ]GRID grid.12527.33, ISNI 0000 0001 0662 3178, Institute for Carbon Neutrality, , Tsinghua University, ; Beijing, China
                Author information
                http://orcid.org/0000-0001-8344-3445
                http://orcid.org/0000-0002-3429-5754
                http://orcid.org/0000-0003-3787-0707
                http://orcid.org/0000-0002-7629-8208
                http://orcid.org/0000-0002-1605-8448
                http://orcid.org/0000-0003-3773-3403
                http://orcid.org/0000-0001-6006-9323
                http://orcid.org/0000-0002-8376-131X
                Article
                32656
                10.1038/s41467-022-32656-8
                9419635
                36030262
                82a448b5-74e1-48fd-a524-1542ab8b11de
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 15 August 2021
                : 9 August 2022
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001809, National Natural Science Foundation of China (National Science Foundation of China);
                Award ID: 41625020 and 41921005
                Award Recipient :
                Categories
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                © The Author(s) 2022

                Uncategorized
                environmental impact,climate-change policy
                Uncategorized
                environmental impact, climate-change policy

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