China’s county-level monthly CO2 emissions during 2013–2021

作者：Ming Gao, Chengyi Tu, Miaomiao Liu, Jiandong Chen, Xingyu Chen, Hong Zou, Thomas Tong, Long Chen, Shuke Fu · 发表于：Scientific Data · 年份：2025 · DOI：10.1038/s41597-025-05461-3 · 被引用次数：4 · 研究领域：Impact of Light on Environment and Health、Atmospheric and Environmental Gas Dynamics、Air Quality and Health Impacts

The top-down method is widely used to estimate China’s CO 2 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 2 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 2 emissions. Compared with the total nighttime brightness, it has a stronger linear relationship with CO 2 emissions. Using the top-down algorithm, we estimated China’s monthly CO 2 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.