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Extreme temperature events necessitate daily carbon dioxide (CO2) emissions data. This study constructs a long-term daily CO2 dataset from 1970-2024 using high-frequency activity and machine learning, filling a critical data gap.

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Area of Science:

  • Environmental Science
  • Climate Science
  • Data Science

Background:

  • Extreme temperature events are increasing in frequency globally.
  • Daily carbon dioxide (CO2) emissions data are crucial for impact assessment but are only available from 2019.
  • A significant data gap exists for historical daily CO2 emissions.

Purpose of the Study:

  • To create a comprehensive, long-term daily CO2 emissions dataset spanning 1970-2024.
  • To enable detailed analysis of extreme temperature event impacts on CO2 emissions.
  • To improve the accuracy of carbon flux estimations using chemical transport models.

Main Methods:

  • Compiled over two million near-real-time observations (electricity, traffic, gas, industry) from 2019-2024.
  • Developed a daily CO2 emissions dataset for 2019-2024 using high-frequency activity data.
  • Applied machine learning and degree-day methods to disaggregate monthly emissions (1970-2018) to a daily basis.

Main Results:

  • Generated a global daily CO2 emissions dataset for 14 countries/regions from 1970 to 2024.
  • The dataset covers four key sectors: power, industry, residential, and transport (including aviation).
  • The merged dataset provides unprecedented temporal resolution for emissions analysis.

Conclusions:

  • The new long-term daily CO2 emissions dataset addresses a critical need for climate impact research.
  • This dataset will enhance the understanding of extreme temperature effects on emissions.
  • Improved emissions data will refine carbon flux inversion models and climate change assessments.