Mapping Spatiotemporal Dynamic Changes in Urban CO<sub>2</sub> Emissions in China by Using the Machine Learning Method and Geospatial Big Data

Mapping Spatiotemporal Dynamic Changes in Urban CO<sub>2</sub> Emissions in China by Using the Machine Learning Method and Geospatial Big Data

Accurately and objectively evaluating the spatiotemporal dynamic changes in CO<sub>2</sub> emissions is significant for human sustainable development. However, traditional CO<sub>2</sub> emissions estimates, typically derived from national or provincial energy statistics, oft...

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Bibliographic Details
Main Authors: Wei Guo, Yongxing Li, Ximin Cui, Xuesheng Zhao, Yongjia Teng, Andreas Rienow
Format: Article
Language:English
Published: MDPI AG 2025-02-01
Series:Remote Sensing
Subjects:
nighttime light
CO<sub>2</sub> emissions
points of interest
decoupling type
machine learning
Online Access:https://www.mdpi.com/2072-4292/17/4/611
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