Mapping Spatiotemporal Dynamic Changes in Urban CO2 Emissions in China by Using the Machine Learning Method and Geospatial Big Data

• Published in: Remote sensing (Basel, Switzerland) Vol. 17; no. 4; p. 611
• Main Authors: Guo, Wei, Li, Yongxing, Cui, Ximin, Zhao, Xuesheng, Teng, Yongjia, Rienow, Andreas
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.02.2025
• Subjects: Accuracy; Big Data; Carbon; Carbon dioxide; Carbon dioxide emissions; CO2 emissions; Decoupling; decoupling type; Emissions; Energy consumption; Estimation; Fossil fuels; Global warming; Land use; Machine learning; Meteorological satellites; nighttime light; points of interest; Remote sensing; Spatial data; Spatiotemporal data; Sustainable development; Urbanization; Vegetation; Vegetation cover; China

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...