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ORIGINAL RESEARCH
Spatiotemporal Characteristics and Driving
Factors of Provincial-Level Carbon Dioxide
Emissions in China Based on Multi-Source
Remote Sensing Data
1
School of Business Administration, Northeastern University, Shenyang, 110819, China
2
School of Economics, Northeastern University at Qinhuangdao, Qinhuangdao, 066004, China
3
School of Humanities and Law, Northeastern University, Shenyang, 110819, China
4
School of Management, Shenyang University of Technology, Shenyang, 110870, China
5
School of Geography and Tourism, Huizhou University, Huizhou, 516007, China
Submission date: 2026-01-04
Final revision date: 2026-02-11
Acceptance date: 2026-02-16
Online publication date: 2026-04-02
Corresponding author
KEYWORDS
carbon dioxide emissionsspatiotemporal heterogeneitydriving factorsmulti-source remote sensing datacarbon emission reduction strategies
TOPICS
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.
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| ISSN: | 3072-1962 |
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