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June 3, 2026Scientific Data1 citationsOpen Access

China’s City-level CO2 emissions from power sector between 2000 and 2019

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BCBoyang ChenMGMing GaoYLYu Liu

Key Points

  • The study aims to estimate city-level CO2 emissions from China's power sector between 2000 and 2019 to support effective climate policies.
  • Analyses conducted for 352 Chinese cities, covering the years 2000 to 2019.
  • Utilized a particle swarm optimization–back propagation algorithm to establish emission relationships.
  • City-level power generation estimates derived from provincial data and CO2 emissions factors.
  • Comprehensive emissions data provided for 352 cities, showing significant regional variations.
  • Established robust relationships between power generation and socioeconomic indicators.
  • Results support the need for tailored local policies to effectively reduce emissions.

Abstract

The power sector is a major contributor to global energy use and greenhouse gas emissions. In 2020, China’s power sector accounted for over 40% of national carbon emissions, underscoring the importance of its decarbonization for achieving global climate targets. Effective emission-reduction policies require detailed and reliable carbon data. Given China’s “top-down” target-setting system, accurate regional data are essential for designing differentiated local policies and evaluating their effectiveness. In this study, we systematically estimate CO2 emissions from the power sector in 352 Chinese cities from 2000 to 2019, providing broader spatial and temporal coverage than existing datasets. Using a particle swarm optimization–back propagation algorithm, we established relationships among provincial and prefectural power generation, socioeconomic indicators, and operating revenue for the electricity and heat production and supply sectors. City-level power generation was derived based on provincial training results, combined with the 2021 electricity CO2 emissions factor published by the Ministry of Ecology and Environment and the National Bureau of Statistics, providing a valuable foundation for regional low-carbon research and policymaking.

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Cite This Study

Chen et al. (2026) studied this question.

synapsesocial.com/papers/6a1fc42cdee9eb8c0dce5b2chttps://doi.org/10.1038/s41597-026-07484-w
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