ORIGINAL RESEARCH article
Front. Clim.
Sec. Climate and Economics
Volume 7 - 2025 | doi: 10.3389/fclim.2025.1649791
This article is part of the Research TopicDynamics of Land Use and Carbon Emissions in the Context of Carbon Neutrality and Carbon Peaking, Volume IIView all 4 articles
Temporal and spatial evolution of interprovincial CO2 emissions and driving factors based on a spatial-temporal geo-weighted regression mode
Provisionally accepted
- 1The School of Arts and Design, Xi’an University, Xi’an, China
- 2Henan Agricultural University School of Landscape Architecture and Art, Zhengzhou, China
- 3College of Landscape Architecture and Art, Northwest A&F University, Yangling, China
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Abstract: To support China's "dual carbon" goals (peak by 2030, neutrality by 2060), this study quantifies the spatio-temporal evolution of provincial CO₂ emissions and identifies regional decarbonization pathways. Combining spatial autocorrelation analysis, spatio-temporal geographically weighted regression (GTWR/SGTWR), and agglomerative hierarchical clustering with dynamic time warping (DTW-AHC), we analyze emission patterns across 30 Chinese provinces. In a comprehensive view, Emissions show weakening aggregation post-2015, with northern regions exhibiting persistently higher carbon intensity due to fossil fuel dependency. Energy and transportation dominate post-2008 emissions (>70%), while food/water emissions decline post-2016 due to technological advances. Four distinct emission clusters: Rapid Growth (Integrate renewable corridors with urban green infrastructure), Resource-Dependent (Prioritize ecological restoration and CCUS), Typical Growth (Accelerate green industrial transitions) and Low-Carbon Exemplar. This framework provides actionable insights for spatially targeted carbon governance, emphasizing regional diversification in achieving national climate goals.
Keywords: Carbon emission clustering, spatial autocorrelation, Dynamic Time Warping, Regional decarbonization, Industrial structure
Received: 20 Jun 2025; Accepted: 22 Sep 2025.
Copyright: © 2025 Xiaolu, Shihan, Jiayi, Jun and Xiaoyan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Ma Xiaoyan, maxiaoyan@nwafu.edu.cn
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