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      Near-real-time monitoring of global CO2 emissions reveals the effects of the COVID-19 pandemic.

      1 , 2 , 3 , 4 , 5 , 4 , 2 , 3 , 3 , 3 , 3 , 3 , 3 , 3 , 3 , 6 , 7 , 8 , 9 , 10 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 2 , 2 , 20 , 21 , 22 , 22 , 23 , 24 , 25 , 26 , 2 , 27 , 3 , 3 , 3 , 28 , 12 , 3 , 29
      Nature communications
      Springer Science and Business Media LLC

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          Abstract

          The COVID-19 pandemic is impacting human activities, and in turn energy use and carbon dioxide (CO2) emissions. Here we present daily estimates of country-level CO2 emissions for different sectors based on near-real-time activity data. The key result is an abrupt 8.8% decrease in global CO2 emissions (-1551 Mt CO2) in the first half of 2020 compared to the same period in 2019. The magnitude of this decrease is larger than during previous economic downturns or World War II. The timing of emissions decreases corresponds to lockdown measures in each country. By July 1st, the pandemic's effects on global emissions diminished as lockdown restrictions relaxed and some economic activities restarted, especially in China and several European countries, but substantial differences persist between countries, with continuing emission declines in the U.S. where coronavirus cases are still increasing substantially.

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          Most cited references16

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          Temporary reduction in daily global CO2 emissions during the COVID-19 forced confinement

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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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                Author and article information

                Journal
                Nat Commun
                Nature communications
                Springer Science and Business Media LLC
                2041-1723
                2041-1723
                October 14 2020
                : 11
                : 1
                Affiliations
                [1 ] Department of Earth System Science, Tsinghua University, Beijing, China. zhuliu@tsinghua.edu.cn.
                [2 ] Laboratoire des Sciences du Climat et de l'Environnement LSCE, CEA CNRS UVSQ, Centre d'Etudes Orme de Merisiers, Gif-sur-Yvette, France.
                [3 ] Department of Earth System Science, Tsinghua University, Beijing, China.
                [4 ] Department of Meteorology and Atmospheric Science, The Pennsylvania State University, University Park, PA, USA.
                [5 ] Department of Earth System Science, University of California, Irvine, 3232, Croul Hall, Irvine, CA, USA.
                [6 ] Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA, USA.
                [7 ] Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA.
                [8 ] Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing, China.
                [9 ] Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China.
                [10 ] Climate Change Research Center, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China.
                [11 ] Key Laboratory of Middle Atmosphere and Global Environment Observation, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China.
                [12 ] School of Environment, Tsinghua University, Beijing, China.
                [13 ] School of Mathematical School, Tsinghua University, Beijing, China.
                [14 ] Department of Mathematical Sciences, Tsinghua University, Beijing, China.
                [15 ] Center of Hubei Cooperative Innovation for Emissions Trading System, Wuhan, China.
                [16 ] Faculty of Management and Economics, Kunming University of Science and Technology, 13, Kunming, China.
                [17 ] Economic Research Centre of Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Japan.
                [18 ] Institut Pierre-Simon Laplace, Sorbonne Université / CNRS, Paris, France.
                [19 ] Université Paris Dauphine, Place du Maréchal de Lattre de Tassigny, 75016, Paris, France.
                [20 ] Center for Global Environmental Research, National Institute for Environmental Studies, Tsukuba, Japan.
                [21 ] Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing, China.
                [22 ] Department of Electrical Engineering, the State Key Lab of Control and Simulation of Power Systems and Generation Equipment, Institute for National Governance and Global Governance, Tsinghua University, Beijing, China.
                [23 ] Institute of Blue and Green Development Shandong University, Weihai, China.
                [24 ] School of Environment, Beijing Normal University, Beijing, China.
                [25 ] Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, China.
                [26 ] State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, China.
                [27 ] Key Laboratory of Wetland Ecology of Ministry of Education, College of Ecology and the Environment, Xiamen University, Xiamen, China.
                [28 ] Energy and Resources Group and Goldman School of Public Policy, University of California, Berkeley, CA, USA.
                [29 ] Potsdam Institute for Climate Impact Research, Potsdam, Germany.
                Article
                10.1038/s41467-020-18922-7
                10.1038/s41467-020-18922-7
                7560733
                33057164
                2aa23775-5df6-4878-a2d3-af59fc05789f
                History

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