Toward low-carbon agriculture: measurement and driver analysis of agricultural carbon emissions in Sichuan province, China

Introduction Agricultural carbon emission reduction is the meaning of realizing the goal of double carbon, and Sichuan province, as one of the main grain producing areas in China, it is urgent to realize agricultural carbon reduction. Methods Based on the data of 18 cities in Sichuan province from 2000 to 2022, this paper calculates the total agricultural carbon emission and carbon emission intensity in Sichuan province by using IPCC guidelines, and measures its temporal, spatial evolution trend and regional differences, and further evaluates the driving factors by using fixed effect model. Results The results show that: (1) The total quantity of agricultural carbon emissions in Sichuan province has increased, but the carbon intensity has decreased, among which agricultural carbon emissions caused by agricultural land planting and residents’ life are the main carbon sources; (2) The regional differences of agricultural carbon emissions in Sichuan province are narrowing, among which the gap between groups is the root of the regional differences of agricultural carbon emissions, which shows that the agricultural carbon emissions in eastern Sichuan and western Sichuan, eastern Sichuan and southern Sichuan, western Sichuan and southern Sichuan, are quite different; (3) Agricultural carbon emissions in Sichuan province are characterized by agglomeration and spatial spillover, mainly showing a High-High agglomeration mode, but a few cities have changed their agglomeration modes; (4) The agricultural carbon intensity in Sichuan province is influenced by multiple factors. Population density, industrial structure, social wealth, agricultural mechanization and technological progress have negative effects on agricultural carbon intensity, while macro-control has increased agricultural carbon intensity. Discussion In this study, a complete accounting system for agricultural carbon emissions was established, and a series of statistical methods were used to analyze and obtain insightful results. It is a useful exploration of low-carbon agricultural models in the context of climate change. The results of this paper have important implications for the green development of agriculture in Sichuan province.