Analysis of the spatiotemporal evolution and drivers of agricultural carbon emissions: evidence from provincial-level regions of China

Analyzing the evolution characteristics of China's agricultural spatial patterns and identifying the influencing factors are of great significance for clarifying the sources of agricultural carbon emissions across regions and enhancing the efficiency of emission reduction. Using agricultural carbon emission data from 31 Chinese provinces (municipalities and autonomous regions) from 2000 to 2020, this study examines the spatial patterns of agricultural carbon emissions through methods, including the standard deviational ellipse, Gini coefficient, kernel density estimation, and spatial autocorrelation analysis. Furthermore, a dynamic Spatial Durbin model was employed to analyze the influencing factors. The results reveal that China's agricultural carbon emissions increased initially, followed by a decrease, reflecting an overall declining trend. The spatial distribution pattern tends to align along a northeast-southwest orientation. Significant disparities were observed, with the eastern region showing the greatest variation and pronounced differences existing between eastern and western China. Interregional differences were identified as the primary source of overall variation in agricultural carbon emissions. The key influencing factors included the value-added of the primary industry, total agricultural machinery power, chemical fertilizer application, rural electricity consumption, and crop sowing area. Among these, an increase in the value-added of the primary industry suppresses agricultural carbon emissions, whereas the other factors contribute to higher emissions. This study provides a scientific basis for optimizing China's agricultural carbon emission reduction strategies and formulating cross-regional collaborative mitigation plans, while also offering valuable insights for other developing countries at similar stages of development.