Near-real-time monitoring of global CO 2  emissions reveals the effects of the COVID-19 pandemic

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Abstract

The COVID-19 pandemic is impacting human activities, and in turn energy use and carbon dioxide (CO ₂) emissions. Here we present daily estimates of country-level CO ₂ emissions for different sectors based on near-real-time activity data. The key result is an abrupt 8.8% decrease in global CO ₂ emissions (−1551 Mt CO ₂) 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.

Abstract

The COVID-19 pandemic has stopped many human activities, which has had significant impact on emissions of greenhouse gases. Here, the authors present daily estimates of country-level CO ₂ emissions for different economic sectors and show that there has been a 8.8% decrease in global CO2 emissions in the first half of 2020.

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Most cited references 16

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

Corinne Le Quéré, Robert B. Jackson, Matthew Jones … (2020)

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Global Carbon Budget 2019

Pierre Friedlingstein, Matthew Jones, Michael O&apos;Sullivan … (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

Bo Zheng, Dan Tong, Meng Li … (2018)

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

Contributors

Zhu Liu:

ORCID: http://orcid.org/0000-0002-8968-7050

zhuliu@tsinghua.edu.cn

Journal

Journal ID (nlm-ta): Nat Commun

Journal ID (iso-abbrev): Nat Commun

Title: Nature Communications

Publisher: Nature Publishing Group UK (London )

ISSN (Electronic): 2041-1723

Publication date (Electronic): 14 October 2020

Publication date PMC-release: 14 October 2020

Publication date Collection: 2020

Volume: 11

Electronic Location Identifier: 5172

Affiliations

[1 ]GRID grid.12527.33, ISNI 0000 0001 0662 3178, Department of Earth System Science, , Tsinghua University, ; Beijing, China

[2 ]GRID grid.457340.1, ISNI 0000 0001 0584 9722, Laboratoire des Sciences du Climat et de l’Environnement LSCE, CEA CNRS UVSQ, Centre d’Etudes Orme de Merisiers, ; Gif-sur-Yvette, France

[3 ]GRID grid.29857.31, ISNI 0000 0001 2097 4281, Department of Meteorology and Atmospheric Science, , The Pennsylvania State University, ; University Park, PA USA

[4 ]GRID grid.266093.8, ISNI 0000 0001 0668 7243, Department of Earth System Science, , University of California, Irvine, ; 3232 Croul Hall, Irvine, CA USA

[5 ]GRID grid.20861.3d, ISNI 0000000107068890, Division of Geological and Planetary Sciences, , California Institute of Technology, ; Pasadena, CA USA

[6 ]GRID grid.20861.3d, ISNI 0000000107068890, Jet Propulsion Laboratory, , California Institute of Technology, ; Pasadena, CA USA

[7 ]GRID grid.260478.f, 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

[8 ]GRID grid.9227.e, ISNI 0000000119573309, Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, , Chinese Academy of Sciences, ; Beijing, China

[9 ]GRID grid.9227.e, ISNI 0000000119573309, Climate Change Research Center, Institute of Atmospheric Physics, , Chinese Academy of Sciences, ; Beijing, China

[10 ]GRID grid.9227.e, ISNI 0000000119573309, Key Laboratory of Middle Atmosphere and Global Environment Observation, Institute of Atmospheric Physics, , Chinese Academy of Sciences, ; Beijing, China

[11 ]GRID grid.12527.33, ISNI 0000 0001 0662 3178, School of Environment, , Tsinghua University, ; Beijing, China

[12 ]GRID grid.12527.33, ISNI 0000 0001 0662 3178, School of Mathematical School, , Tsinghua University, ; Beijing, China

[13 ]GRID grid.12527.33, ISNI 0000 0001 0662 3178, Department of Mathematical Sciences, , Tsinghua University, ; Beijing, China

[14 ]Center of Hubei Cooperative Innovation for Emissions Trading System, Wuhan, China

[15 ]GRID grid.218292.2, ISNI 0000 0000 8571 108X, Faculty of Management and Economics, , Kunming University of Science and Technology, ; 13 Kunming, China

[16 ]GRID grid.27476.30, ISNI 0000 0001 0943 978X, Economic Research Centre of Nagoya University, Furo-cho, Chikusa-ku, ; Nagoya, Japan

[17 ]GRID grid.423115.0, ISNI 0000 0000 9000 8794, Institut Pierre-Simon Laplace, Sorbonne Université / CNRS, ; Paris, France

[18 ]GRID grid.11024.36, ISNI 0000000120977052, Université Paris Dauphine, Place du Maréchal de Lattre de Tassigny, ; 75016 Paris, France

[19 ]GRID grid.140139.e, ISNI 0000 0001 0746 5933, Center for Global Environmental Research, , National Institute for Environmental Studies, ; Tsukuba, Japan

[20 ]GRID grid.43555.32, ISNI 0000 0000 8841 6246, Center for Energy and Environmental Policy Research, , Beijing Institute of Technology, ; Beijing, China

[21 ]GRID grid.12527.33, ISNI 0000 0001 0662 3178, 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

[22 ]GRID grid.27255.37, ISNI 0000 0004 1761 1174, Institute of Blue and Green Development Shandong University, ; Weihai, China

[23 ]GRID grid.20513.35, ISNI 0000 0004 1789 9964, School of Environment, Beijing Normal University, ; Beijing, China

[24 ]GRID grid.9227.e, ISNI 0000000119573309, Institute of Applied Ecology, , Chinese Academy of Sciences, ; Shenyang, China

[25 ]GRID grid.41156.37, ISNI 0000 0001 2314 964X, State Key Laboratory of Pollution Control and Resource Reuse, , School of the Environment, Nanjing University, ; Nanjing, China

[26 ]GRID grid.12955.3a, ISNI 0000 0001 2264 7233, Key Laboratory of Wetland Ecology of Ministry of Education, , College of Ecology and the Environment, Xiamen University, ; Xiamen, China

[27 ]GRID grid.47840.3f, ISNI 0000 0001 2181 7878, Energy and Resources Group and Goldman School of Public Policy, , University of California, ; Berkeley, CA USA

[28 ]GRID grid.4556.2, ISNI 0000 0004 0493 9031, Potsdam Institute for Climate Impact Research, ; Potsdam, Germany

Author information

Zhu Liu http://orcid.org/0000-0002-8968-7050

Philippe Ciais http://orcid.org/0000-0001-8560-4943

Zhu Deng http://orcid.org/0000-0002-6409-9578

Ruixue Lei http://orcid.org/0000-0001-6226-7075

Steven J. Davis http://orcid.org/0000-0002-9338-0844

Sha Feng http://orcid.org/0000-0002-2376-0868

Bo Zheng http://orcid.org/0000-0001-8344-3445

Xinyu Dou http://orcid.org/0000-0001-7783-6971

Biqing Zhu http://orcid.org/0000-0001-9360-6797

Rui Guo http://orcid.org/0000-0001-5536-0669

Piyu Ke http://orcid.org/0000-0003-0353-6313

Taochun Sun http://orcid.org/0000-0003-3640-6005

Xu Yue http://orcid.org/0000-0002-8861-8192

Jinjun Xue http://orcid.org/0000-0002-6794-1361

Eulalie Boucher http://orcid.org/0000-0002-6070-2544

Frédéric Chevallier http://orcid.org/0000-0002-4327-3813

Katsumasa Tanaka http://orcid.org/0000-0001-9601-6442

Yimin Wei http://orcid.org/0000-0002-8476-7334

François-Marie Bréon http://orcid.org/0000-0003-2128-739X

Yonglong Lu http://orcid.org/0000-0002-3338-4210

Qiang Zhang http://orcid.org/0000-0002-8376-131X

Peng Gong http://orcid.org/0000-0003-1513-3765

Daniel M. Kammen http://orcid.org/0000-0003-2984-7777

Article

Publisher ID: 18922

DOI: 10.1038/s41467-020-18922-7

PMC ID: 7560733

PubMed ID: 33057164

SO-VID: 2aa23775-5df6-4878-a2d3-af59fc05789f

License:

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