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Global daily CO2 emissions from 1970 to 2024

  • Tao Li
  • , Lixing Wang
  • , Zihan Qiu
  • , Philippe Ciais
  • , Steven J. Davis
  • , Zhu Deng
  • , Yufei Zhao
  • , Glen P. Peters
  • , Piyu Ke
  • , Matthew W. Jones
  • , Robbie M. Andrew
  • , Ye Hao
  • , Taochun Sun
  • , Xiaoting Huang
  • , Robert B. Jackson
  • , Pierre Friedlingstein
  • , Chenxi Lu
  • , Duo Cui
  • , Zhu Liu
  • Tsinghua University
  • Université Paris-Saclay
  • Stanford University
  • University of Hong Kong
  • School of Earth Sciences, Zhejiang University
  • Center for International Climate Research (CICERO)
  • University of East Anglia
  • China Agricultural University
  • University of Exeter
  • TU Berlin
  • International Research Center of Big Data for Sustainable Development Goals
  • State Key Laboratory of Hydro Science and Engineering
  • Yellow River Laboratory (Henan)

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article number605
JournalScientific Data
Volume13
Issue number1
DOIs
Publication statusPublished - 1 Dec 2026

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