Spatial and temporal evolution patterns and spatial spillover effects of carbon emissions in China in the context of digital economy.

Global warming caused by carbon emissions has become a significant concern for countries worldwide. This study thoroughly examines the spatiotemporal patterns and spatial spillover effects of carbon emissions in China. This research employs kernel density estimation, Moran's index, and the standard deviation ellipse model to analyse the spatiotemporal evolution of carbon emissions in China while utilizing the spatial Durbin model to explore the spatial spillover effects of the digital economy on carbon emissions. The subsequent findings are derived from the following: (1) China's carbon emissions are characterized by substantial spatial and temporal agglomeration. Low carbon emissions are in the eastern littoral regions, while high carbon emissions are concentrated in the inland areas, such as the northwest. The local Moran index suggests that high-high and low-low clustering patterns characterize China's carbon emissions. (2) The spatial trends and evolutionary characteristics of carbon emissions in China are readily apparent. During the sample period, the carbon emission level in the east and west was significantly higher than in the central region, and the gap between the areas was progressively narrowing. The results of the standard deviation ellipse indicate that China's carbon emissions are undergoing a substantial discrete phenomenon in their spatial distribution. (3) Digital economies reduce carbon emissions, have regional spillover effects, and reveal geographical variance across eastern, central, and western regions. This study offers quantitative evidence for integrated nationwide and regional emission reduction and carbon mitigation strategies, as well as for region-specific emission reduction programs.