Spatial Heterogeneity and Clustering of County-Level Carbon Emissions in China

At present, China is the world's largest carbon emitter and has also made significant efforts in energy conservation and emission reduction. This study utilized the EDGAR dataset of remote-sensing image inversion to investigate the spatial heterogeneity and clustering patterns of carbon emissions across 2184 counties in China through a data-driven approach. By analyzing the impact of socioeconomic factors on carbon emissions with the Spatial Clustering Autoregressive Panel (SCARP) model, significant regional variations were uncovered. The results reveal significant differences in carbon emission drivers between resource-dependent regions and economically developed areas. For instance, regions with heavy industries, such as Inner Mongolia and Xinjiang, exhibit higher carbon emissions, underscoring the need for policies focused on industrial restructuring and clean energy adoption. In contrast, economically advanced regions such as the Yangtze River Delta and Pearl River Delta show slower emission growth, indicating the potential for further reductions through green technology innovations and energy efficiency improvements. These findings highlight the necessity of regionally tailored carbon reduction strategies, offering policymakers a precise framework to address the specific socioeconomic and industrial characteristics of different regions in China.