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<p>As extreme temperature events become increasingly frequent, there is a growing need for daily CO<sub>2</sub> 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 CO<sub>2</sub> emissions dataset for 2019–2024. We then applied machine-learning models and degree-day methods to disaggregate non-residential and residential monthly CO<sub>2</sub> emissions for 1970–2018 to a daily basis. The historical dataset was then merged with the 2019–2024 dataset to produce a global daily CO<sub>2</sub> 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.</p>

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Global daily CO2 emissions from 1970 to 2024

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月24日

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  • 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

摘要

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.