The organic integration of food security and agricultural mechanization has become a challenge to realize a low-carbon economy, which helps promote carbon peaking and carbon neutralization. In this work, a simultaneous equation model has developed to analyze the relationship between food security, agricultural automation, and agricultural carbon emissions in China. The ordinary least square method was used to verify the method. The logarithmic mean Divisia index decomposition was used to decompose further the influencing factors of agricultural carbon emissions. Results show that the organic coupling of a low-carbon economy, food security, and agricultural mechanization positively affects environmental protection. In which, unit fertilizer usage and crop sown area have the greatest impact on carbon emission intensity, followed by agricultural diesel fuel and agricultural plastic film. It is worth noting that the bottom line of the grain sown area cannot be touched. It is a prerequisite for ensuring grain production. Finally, this paper presents suggestions based on China’s achievements, where the top-level design is crucial.

The rapid economic development and climate change have accelerated the changes in China’s food production and have a potential impact on food security. In this paper, the grain sown area from 2001 to 2019 was selected to analyze the spatio-temporal evolution and driving factors of China’s grain production through spatial autocorrelation analysis and geographically weighted regression. Our findings were as follows: (1) From the perspective of time characteristics, China’s grain production from 2001 to 2019 experienced four stages, rapid decline, rapid growth, steady growth, and slow decline, although with an overall upward trend. (2) From the perspective of spatial characteristics, the overall spatial pattern had a significant positive correlation. The high values were mainly concentrated in Shandong, Anhui, and Jilin and moved to the northeast China as time went on. (3) In terms of influencing factors, the positive impact of agricultural labor force on the grain production gradually decreased, showing a decreasing trend from southwest to northeast. The promotion of agricultural mechanization on the grain production increased year by year, with the spatial distribution characteristics of high in the northeast and low in the southwest. Besides, the coefficient of water resources endowment was negative, showing a spatial distribution pattern of high north and low south.

Many studies have shown that the rapid agricultural mechanization development in China led to substantial energy consumption and CO₂ emission growth. To better explain the mechanism behind the decoupling between economic growth and CO₂ emissions, this paper extends the logarithmic mean Divisia index (LMDI) and production-theoretical decomposition (PDA) considering agricultural decoupling from both structural and technical perspectives. The results reveal that (1) China’s agricultural decoupling performance was not ideal. Investment and investment efficiency were the most important factors influencing the decoupling status. The main decoupling obstacle was a higher investment in productivity rather than in energy conservation and carbon reduction. (2) The decoupling status and investment orientation of decoupling efforts among regions were different. Strong negative decoupling statuses frequently occurred in the eastern region, whose main disadvantage was high potential energy intensity. The decoupling status of the central region exhibited expansive features. The decoupling key is to invest more in energy-saving technology rather than in production. The western region changed from weak decoupling to expansive negative decoupling. Both output technology and energy-related factors should be the main investment targets. (3) Weak decoupling and expansive negative decoupling were the most common statuses among provinces. The influence mechanism of drivers exhibited a high spatial heterogeneity at the provincial level. Therefore, the study offered a convincing basis for local governments to formulate low-carbon agricultural development policies by identifying the main decoupling drivers.