Spatiotemporal evolution and driving factors of China’s agricultural carbon emissions

Relevant research on China’s agricultural carbon emission reduction is of great practical significance for optimizing the agricultural structure, improving agricultural production efficiency, and promoting sustainable agricultural development. Based on data from the planting and animal husbandry sectors, this study utilized the IPCC carbon accounting method to calculate the total amount and intensity of agricultural carbon emissions in the selected 30 provinces in China (excluding Hongkong of China, Macau of China, Taiwan of Chine, and Xizang of China) from 2010 to 2021. The following five sources of agricultural carbon emissions were considered: rice cultivation, land tillage, agricultural material inputs, energy use, and animal husbandry. The Dagum Gini coefficient regional decomposition and equal-weighted Gini coefficient structural decomposition methods were used to analyze the regional and structural differences. This study also discussed the spatial correlation of agricultural carbon emission intensity using the global Moran’s Index method. Furthermore, influencing factor decomposition was conducted based on the Logarithmic Mean Divisia Index (LMDI). This method was employed to explore the factors driving agricultural carbon emissions by analyzing the effects of agricultural production technology, agricultural industry structure, agricultural development scale, agricultural employment structure, and labor force scale on agricultural carbon emissions. The results indicate the following. 1) Compared to 2010, the national agricultural carbon emissions decreased in 2021, with minor changes in the overall rankings. 2) According to the division of main grain-producing areas, main sales areas, and production and sales balance areas, the Gini coefficient regional decomposition revealed that a decrease in Gini coefficient in 2021 compared to 2010, differences in carbon emissions intensity increased in 2020 and 2021. This may be due to the COVID-19 pandemic. This study analyzed regional differences in agricultural carbon emission intensity and concluded that the greatest disparity existed among main producing areas, while the smallest disparity was found among main sales areas. This study analyzed the magnitude of regional disparity contribution rates, finding that the contribution rate of super-variation density was the highest, followed by the intra-regional disparity contribution rate, with the inter-regional disparity contribution rate being the lowest. The structural decomposition results showed that rice cultivation contributed the most to carbon emission intensity, accounting for over 70%, leading to higher carbon emission levels in provinces with larger rice cultivation areas, such as Jiangxi Province. The contribution rate of carbon emissions from animal husbandry to agricultural carbon emission intensity ranked second, accounting for between 17% and 27%. This indicated that both rice cultivation and animal husbandry play significant roles in carbon emissions inequality. The spatial correlation of carbon emissions intensity was weak. 3) The key factors inhibiting agricultural carbon emissions include agricultural production technology and agricultural employment structure. This study provided insight into the characteristics and factors influencing agricultural carbon emissions. This study aimed to assist provinces in conducting in-depth analyses of their agricultural development characteristics. These provinces should formulate different strategies for agricultural carbon reduction, thereby promoting carbon reduction and sustainable agricultural development.ent.