China's county-level monthly CO emissions during 2013-2021.

Journal: Scientific data
Published Date: Jul 14, 2025

Abstract

The top-down method is widely used to estimate China's CO emissions at the county level. However, studies have relied on a single indicator of regional total nighttime light brightness as an instrumental variable for prediction, leading to the assumption that there is a positive correlation between CO emissions and total nighttime light brightness in all regions within the same province. This assumption overlooks other heterogeneous relationships and does not correspond to reality. Therefore, this study constructed a dataset of potential feature variables based on multisource data (improved and calibrated nighttime light data, urban and rural human settlement data, and socioeconomic indicator data based on statistical yearbooks). After the main feature variables were identified, a hybrid regression algorithm combining deep neural networks and CatBoost was constructed to generate instrumental variable for predicting CO emissions. Compared with the total nighttime brightness, it has a stronger linear relationship with CO emissions. Using the top-down algorithm, we estimated China's monthly CO emissions at the county level from 2013 to 2021. This dataset provides a solid foundation for predicting the achievement of China's county-level "dual carbon" strategy. The methods used in this study can be generalized to other global regions.

Authors

  • Ming Gao
    State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China.
  • Chaofan Tu
    School of Computer Science, Chengdu University, Chengdu, China.
  • Miaomiao Liu
    College of Food Science and Engineering, Northwest A&F University, Yangling Shaanxi 712100, China.
  • Jiandong Chen
    College of Physical Education, Beihua University, Jilin, China.
  • Xingyu Chen
    Department of Infectious Diseases, Wenzhou Central Hospital, Wenzhou, China.
  • Hong Zou
    School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou, 510006, People's Republic of China.
  • Thomas Shiu Tong
    Department of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong, China.
  • Long Chen
    Department of Critical Care Medicine, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
  • Shuke Fu
    School of Law & Business, Wuhan Institute of Technology, Wuhan, China.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals