Temporal and spatial evolution of interprovincial CO2 emissions and driving factors based on a spatial-temporal geo-weighted regression mode - Chen, Xiaolu; Zhang, Shihan; Lin, Jiayi; Tian, Jun; Ma, Xiaoyan (Frontiers in Climate, 2025)
IntroductionTo support China’s “dual carbon” goals (carbon emission peak by 2030 and carbon neutrality by 2060), this study systematically investigates the spatio-temporal evolution and decarbonization pathways of CO2 emissions across 30 Chinese provinces. As regional disparities significantly influence national climate strategies, a detailed provincial-level analysis is essential for effective policy-making.MethodsWe integrate spatial autocorrelation analysis, spatio-temporal geographically weighted regression (GTWR/SGTWR), and agglomerative hierarchical clustering with dynamic time warping (DTW-AHC) to capture both spatial and temporal heterogeneities in emission patterns.ResultsThe findings reveal that provincial CO2 emissions exhibit weakening spatial aggregation after 2015, with northern provinces maintaining higher carbon intensity due to heavy reliance on fossil fuels. Energy consumption and transportation collectively account for over 70% of emissions growth after 2008, while emissions from food and water sectors decline after 2016, largely driven by technological advances. Four distinct emission clusters are identified: Rapid Growth, Resource-Dependent, Typical Growth, and Low-Carbon Exemplar.DiscussionTailored decarbonization strategies are proposed for each cluster: integrating renewable energy corridors with urban green infrastructure for Rapid Growth provinces; prioritizing ecological restoration and carbon capture, utilization, and storage (CCUS) in Resource-Dependent regions; accelerating green industrial transitions in Typical Growth provinces; and reinforcing existing low-carbon policies for Exemplar provinces. This research provides a spatially explicit framework for regionally differentiated carbon governance, supporting the achievement of China’s national climate targets.