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Spatiotemporal Characteristics and Driving Factors of Provincial-Level Carbon Dioxide Emissions in China Based on Multi-Source Remote Sensing Data

Year: 2026
Type: article
Abstract: Provincial-level carbon reduction policies in China are pivotal to achieving the national goals of carbon peaking and carbon neutrality. Understanding the spatiotemporal characteristics and influencing factors of provincial carbon emissions is essential for formulating and implementing effective emission reduction strategies. This study employs multi-source remote sensing data and selects Liaoning Province, a region with representative carbon emissions, as the research area. Grid-scale emission characteristics are analyzed using the center-of-gravity shift model, standard deviation ellipse model, hotspot/coldspot analysis, and global spatial autocorrelation. The Multi-Scale Geographically Weighted Regression model is applied to examine the influencing factors. The results show: (1) carbon emissions increased continuously during the study period, although the growth rate declined significantly after 2010. The spatial distribution displayed a migration trend from the northeast toward the southwest. (2) The effects of various influencing factors on carbon emissions varied. Electricity consumption exhibited a strong positive correlation. The energy consumption structure showed a positive correlation in most years. Annual Gross Primary Productivity showed a significant negative correlation, and urbanization rate presented both positive and negative effects across different regions. These findings deepen the understanding of the spatiotemporal distribution characteristics and driving mechanisms and provide a valuable reference for developing emission reduction policies in high-emission regions of China.
DOI: 10.15244/peai/218181
Language: English
Open Access status: hybrid
OpenAlex ID: W7147347274
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Cites: 59
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Related to: 10