Carbon Monitor, a near-real-time daily dataset of global CO 2  emission from fossil fuel and cement production

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Abstract

We constructed a near-real-time daily CO ₂ emission dataset, the Carbon Monitor, to monitor the variations in CO ₂ emissions from fossil fuel combustion and cement production since January 1, 2019, at the national level, with near-global coverage on a daily basis and the potential to be frequently updated. Daily CO ₂ emissions are estimated from a diverse range of activity data, including the hourly to daily electrical power generation data of 31 countries, monthly production data and production indices of industry processes of 62 countries/regions, and daily mobility data and mobility indices for the ground transportation of 416 cities worldwide. Individual flight location data and monthly data were utilized for aviation and maritime transportation sector estimates. In addition, monthly fuel consumption data corrected for the daily air temperature of 206 countries were used to estimate the emissions from commercial and residential buildings. This Carbon Monitor dataset manifests the dynamic nature of CO ₂ emissions through daily, weekly and seasonal variations as influenced by workdays and holidays, as well as by the unfolding impacts of the COVID-19 pandemic. The Carbon Monitor near-real-time CO ₂ emission dataset shows a 8.8% decline in CO ₂ emissions globally from January 1 ^st to June 30 ^th in 2020 when compared with the same period in 2019 and detects a regrowth of CO ₂ emissions by late April, which is mainly attributed to the recovery of economic activities in China and a partial easing of lockdowns in other countries. This daily updated CO ₂ emission dataset could offer a range of opportunities for related scientific research and policy making.

Abstract

Measurement(s)	carbon dioxide emission
Technology Type(s)	computational modeling technique
Factor Type(s)	geographic location • sector • temporal interval
Sample Characteristic - Environment	climate system
Sample Characteristic - Location	global

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12994058

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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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Global Carbon Budget 2018

Corinne Le Quéré, Robbie Andrew, Pierre Friedlingstein … (2018)

Abstract. Accurate assessment of anthropogenic carbon dioxide ( CO 2 ) 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 CO 2 emissions ( E FF ) are based on energy statistics and cement production data, while emissions from land use and land-use change ( E LUC ), mainly deforestation, are based on land use and land-use change data and bookkeeping models. Atmospheric CO 2 concentration is measured directly and its growth rate ( G ATM ) is computed from the annual changes in concentration. The ocean CO 2 sink ( S OCEAN ) and terrestrial CO 2 sink ( S LAND ) are estimated with global process models constrained by observations. The resulting carbon budget imbalance ( B IM ), 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 ±1 σ . For the last decade available (2008–2017), E FF was 9.4±0.5 GtC yr −1 , E LUC 1.5±0.7 GtC yr −1 , G ATM 4.7±0.02 GtC yr −1 , S OCEAN 2.4±0.5 GtC yr −1 , and S LAND 3.2±0.8 GtC yr −1 , with a budget imbalance B IM of 0.5 GtC yr −1 indicating overestimated emissions and/or underestimated sinks. For the year 2017 alone, the growth in E FF was about 1.6 % and emissions increased to 9.9±0.5 GtC yr −1 . Also for 2017, E LUC was 1.4±0.7 GtC yr −1 , G ATM was 4.6±0.2 GtC yr −1 , S OCEAN was 2.5±0.5 GtC yr −1 , and S LAND was 3.8±0.8 GtC yr −1 , with a B IM of 0.3 GtC. The global atmospheric CO 2 concentration reached 405.0±0.1 ppm averaged over 2017. For 2018, preliminary data for the first 6–9 months indicate a renewed growth in E FF of + 2.7 % (range of 1.8 % to 3.7 %) based on national emission projections for China, the US, 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. The analysis presented here shows that the mean and trend in the five components of the global carbon budget are consistently estimated over the period of 1959–2017, but discrepancies of up to 1 GtC yr −1 persist for the representation of semi-decadal variability in CO 2 fluxes. A detailed comparison among individual estimates and the introduction of a broad range of observations show (1) no consensus in the mean and trend in land-use change emissions, (2) a persistent low agreement among the different methods on the magnitude of the land CO 2 flux in the northern extra-tropics, and (3) an apparent underestimation of the CO 2 variability by ocean models, originating 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 the global carbon cycle compared with previous publications of this data set (Le Quéré et al., 2018, 2016, 2015a, b, 2014, 2013). All results presented here can be downloaded from https://doi.org/10.18160/GCP-2018 .

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Reduced carbon emission estimates from fossil fuel combustion and cement production in China.

Zhu Liu, Dabo Guan, Wei Wei … (2015)

Nearly three-quarters of the growth in global carbon emissions from the burning of fossil fuels and cement production between 2010 and 2012 occurred in China. Yet estimates of Chinese emissions remain subject to large uncertainty; inventories of China's total fossil fuel carbon emissions in 2008 differ by 0.3 gigatonnes of carbon, or 15 per cent. The primary sources of this uncertainty are conflicting estimates of energy consumption and emission factors, the latter being uncertain because of very few actual measurements representative of the mix of Chinese fuels. Here we re-evaluate China's carbon emissions using updated and harmonized energy consumption and clinker production data and two new and comprehensive sets of measured emission factors for Chinese coal. We find that total energy consumption in China was 10 per cent higher in 2000-2012 than the value reported by China's national statistics, that emission factors for Chinese coal are on average 40 per cent lower than the default values recommended by the Intergovernmental Panel on Climate Change, and that emissions from China's cement production are 45 per cent less than recent estimates. Altogether, our revised estimate of China's CO2 emissions from fossil fuel combustion and cement production is 2.49 gigatonnes of carbon (2 standard deviations = ±7.3 per cent) in 2013, which is 14 per cent lower than the emissions reported by other prominent inventories. Over the full period 2000 to 2013, our revised estimates are 2.9 gigatonnes of carbon less than previous estimates of China's cumulative carbon emissions. Our findings suggest that overestimation of China's emissions in 2000-2013 may be larger than China's estimated total forest sink in 1990-2007 (2.66 gigatonnes of carbon) or China's land carbon sink in 2000-2009 (2.6 gigatonnes of carbon).

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Author and article information

Contributors

Zhu Liu:

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

zhuliu@tsinghua.edu.cn

Philippe Ciais:

ORCID: http://orcid.org/0000-0001-8560-4943

philippe.ciais@lsce.ipsl.fr

Steven J. Davis:

ORCID: http://orcid.org/0000-0002-9338-0844

sjdavis@uci.edu

Journal

Journal ID (nlm-ta): Sci Data

Journal ID (iso-abbrev): Sci Data

Title: Scientific Data

Publisher: Nature Publishing Group UK (London )

ISSN (Electronic): 2052-4463

Publication date (Electronic): 9 November 2020

Publication date PMC-release: 9 November 2020

Publication date Collection: 2020

Volume: 7

Electronic Location Identifier: 392

Affiliations

[1 ]GRID grid.12527.33, ISNI 0000 0001 0662 3178, Department of Earth System Science, , Tsinghua University, ; Beijing, 100084 China

[2 ]GRID grid.460789.4, ISNI 0000 0004 4910 6535, Laboratoire des Sciences du Climat et de l’Environnement (LSCE/IPSL), , CEA-CNRS-UVSQ, Univ Paris-Saclay, ; Gif-sur-Yvette, France

[3 ]GRID grid.266093.8, ISNI 0000 0001 0668 7243, Department of Earth System Science, , University of California, Irvine, 3232 Croul Hall, ; Irvine, CA 92697-3100 USA

[4 ]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

[5 ]GRID grid.12527.33, ISNI 0000 0001 0662 3178, Department of Electrical Engineering, , Tsinghua University, ; Beijing, 100084 China

[6 ]GRID grid.462844.8, ISNI 0000 0001 2308 1657, Institute Pierre-Simon Laplace, , Sorbonne Université/CNRS, ; Paris, France

[7 ]GRID grid.12527.33, ISNI 0000 0001 0662 3178, School of Mathematical School, , Tsinghua University, ; Beijing, 100084 China

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

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

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

[11 ]GRID grid.11024.36, ISNI 0000000120977052, Université Paris-Dauphine, ; PSL Paris, France

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

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

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

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

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

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

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

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

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

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

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

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

Article

Publisher ID: 708

DOI: 10.1038/s41597-020-00708-7

PMC ID: 7653960

PubMed ID: 33168822

SO-VID: 942e8867-4c9c-4987-a426-fa2298e98395

License:

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/.

The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files associated with this article.

History

Date received : 10 June 2020

Date accepted : 17 September 2020

Funding

Funded by: FundRef https://doi.org/10.13039/501100001809, National Natural Science Foundation of China (National Science Foundation of China);

Award ID: 41921005

Award Recipient : Zhu Liu

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Keywords: climate-change policy,energy and society,climate-change mitigation

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Keywords: climate-change policy, energy and society, climate-change mitigation

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Carbon Monitor, a near-real-time daily dataset of global CO ₂ emission from fossil fuel and cement production

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Most cited references 25

Global Carbon Budget 2019

Global Carbon Budget 2018

Reduced carbon emission estimates from fossil fuel combustion and cement production in China.

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