Title: Agricultural carbon emission intensity (ACEI) in China from 1997 to 2022: Spatiotemporal differences, convergence, and its influence factors
ABSTRACT: Exploring the spatiotemporal differences and convergence mechanisms of agricultural carbon emissionsin China can contribute to global agricultural carbon emission reduction. Taking 31 provinces, municipalities, and autonomous regions of China (excluding Hong Kong, Macao, and Taiwan due to data unavailability) as the study area, this paper estimated the agricultural carbon emission intensity (ACEI) during 1997–2022 and analyzed its spatiotemporal characteristics, decomposed its regional differences by using the Dagum Gini coefficient, and explored its convergence mechanism by adopting the spatial β-convergence and the Spatial Durbin Model (SDM). The result showed that China’s ACEI has shown a downward trend during 1997–2022, with an average annual reduction rate of 4.71%, and the inter-regional difference continued to widen, with the inter-regional difference of 66.17%. The absolute β-convergence speed was lower than the conditional β-convergence speed, indicating that regional heterogeneity in agricultural production significantly accelerates spatial convergence. This effect was particularly pronounced in the central region, where favorable agricultural conditions contribute to the fastest convergence rate. In this process, social support and environmental regulation, application and innovation of agricultural technologies, as well as agricultural structure and development potential in different regions (eastern, central, and western regions) exhibited distinct spatiotemporal effects. These findings suggest that the hierarchical spatial pattern of ACEI in China results from the interaction of multiple systems, including the environmental, social, economic, and governmental dimensions.
Keywords: Agricultural carbon emission intensity; (ACEI);Dagum Gini; coefficient Absolute and; conditional; β-convergence; Spatial Durbin Model(SDM); Spatial convergence
