Mapping Spatiotemporal Dynamic Changes in Urban CO2 Emissions in China by Using the Machine Learning Method and Geospatial Big Data - Guo, Wei; Li, Yongxing; Cui, Ximin; Zhao, Xuesheng; Teng, Yongjia; Rienow, Andreas (Remote sensing (Basel, Switzerland), 2025)
Accurately and objectively evaluating the spatiotemporal dynamic changes in CO2 emissions is significant for human sustainable development. However, traditional CO2 emissions estimates, typically derived from national or provincial energy statistics, often lack spatial information. To develop a more accurate spatiotemporal model for estimating CO2 emissions, this research innovatively incorporates nighttime light data, vegetation cover data, land use data, and geographic big data into the study of pixel-level urban CO2 emissions estimation in China. The proposed method significantly improves the precision of CO2 emissions estimation, achieving an average accuracy of 83.76%. This study reveals that the type of decoupling varies according to different scales, with more negative decoupling occurring in northern cities. Factors such as the per capita GDP and urbanization contribute to the increase in CO2 emissions, while the structure of industry and energy consumption play a crucial role in reducing them. The findings in this study could potentially be used to develop tailored carbon reduction strategies for different spatial scales in China.