Spatial-temporal patterns and influencing factors of carbon emissions in different regions of China

China, as a vast nation, exhibits significant regional disparities in total carbon emissions (CEs). In order to promote the rational implementation of CEs reduction strategies, this study used the Dagum Gini coefficient method, exploratory spatial data analysis, kernel density estimation, traditional and spatial Markov chains, and the GTWR-STIRPAT model to investigate the spatial-temporal patterns and quantify influencing factors of CEs in various regions of China from 2005 to 2019. The results show that China's total CEs presented a clear upward trend from 2005 to 2019, and generally the regional characteristics of Eastern > Western > Central > Northeast; The Gini coefficient of China's overall CEs is still high, the difference is gradually expanding, and the CEs show positive spatial autocorrelation, with the eastern and central regions dominated by "High-High" cluster and the western region dominated by "Low-Low" cluster; The status of CEs from neighboring provinces has a strong effect on that from local provinces; Influencing factors of CEs indicate significant spatial-temporal variations. This study is based on the spatial differences, dynamic evolution characteristics and influencing factors of CEs in each region of China to formulate targeted CEs reduction measures, thereby complementing and enhancing extant policy frameworks.