TY - JOUR
T1 - Global daily CO2 emissions from 1970 to 2024
AU - Li, Tao
AU - Wang, Lixing
AU - Qiu, Zihan
AU - Ciais, Philippe
AU - Davis, Steven J.
AU - Deng, Zhu
AU - Zhao, Yufei
AU - Peters, Glen P.
AU - Ke, Piyu
AU - Jones, Matthew W.
AU - Andrew, Robbie M.
AU - Hao, Ye
AU - Sun, Taochun
AU - Huang, Xiaoting
AU - Jackson, Robert B.
AU - Friedlingstein, Pierre
AU - Lu, Chenxi
AU - Cui, Duo
AU - Liu, Zhu
N1 - Publisher Copyright:
© The Author(s) 2026.
PY - 2026/12/1
Y1 - 2026/12/1
N2 - As extreme temperature events become increasingly frequent, there is a growing need for daily CO2 emissions data to quantify their impacts. However, such data are available only from 2019 onward. To address this gap, we compiled over two million near-real-time observations of electricity generation, traffic activity, natural gas consumption or heating degree days (HDD), and industrial output since 2019, and used these high-frequency data to construct a daily CO2 emissions dataset for 2019–2024. We then applied machine-learning models and degree-day methods to disaggregate non-residential and residential monthly CO2 emissions for 1970–2018 to a daily basis. The historical dataset was then merged with the 2019–2024 dataset to produce a global daily CO2 emissions dataset spanning 1970 to 2024 for 14 countries and regions, covering four sectors: power, industry, residential, and transport (including ground transport and aviation). The resulting long-term dataset will enable robust analyses of extreme-temperature impacts on emissions and enhance the accuracy of chemical transport model inversions of carbon fluxes.
AB - As extreme temperature events become increasingly frequent, there is a growing need for daily CO2 emissions data to quantify their impacts. However, such data are available only from 2019 onward. To address this gap, we compiled over two million near-real-time observations of electricity generation, traffic activity, natural gas consumption or heating degree days (HDD), and industrial output since 2019, and used these high-frequency data to construct a daily CO2 emissions dataset for 2019–2024. We then applied machine-learning models and degree-day methods to disaggregate non-residential and residential monthly CO2 emissions for 1970–2018 to a daily basis. The historical dataset was then merged with the 2019–2024 dataset to produce a global daily CO2 emissions dataset spanning 1970 to 2024 for 14 countries and regions, covering four sectors: power, industry, residential, and transport (including ground transport and aviation). The resulting long-term dataset will enable robust analyses of extreme-temperature impacts on emissions and enhance the accuracy of chemical transport model inversions of carbon fluxes.
UR - https://www.scopus.com/pages/publications/105035903747
U2 - 10.1038/s41597-026-06621-9
DO - 10.1038/s41597-026-06621-9
M3 - Article
C2 - 41735339
AN - SCOPUS:105035903747
SN - 2052-4463
VL - 13
JO - Scientific Data
JF - Scientific Data
IS - 1
M1 - 605
ER -