Temporal and spatial evolution of interprovincial CO2 emissions and driving factors based on a spatial-temporal geo-weighted regression mode

Introduction To 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 CO 2 emissions across 30 Chinese provinces. As regional disparities significantly influence national climate strategies, a detailed provincial-level analysis is essential for effective policy-making. Methods We 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. Results The findings reveal that provincial CO 2 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. Discussion Tailored 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.