<p>Agriculture holds a pivotal position in the economic fabric of every nation, yet concerns about agricultural carbon emission intensity (ACI) have become a major hurdle to achieving global economic sustainability. Focusing on 31 provincial-level regions in China, this study uses the Exploratory Spatio-temporal Data Analysis (ESTDA) and Panel Quantile Regression (PQR) model to analyze the spatio-temporal interaction characteristics and influencing factors of ACI in China from 2004 to 2023. The findings are as follows: (1) ACI showed an overall downward trend, and the spatial distribution pattern was characterized by “high in the western region and low along the southeastern coast”. Although the overall disparity tended to converge, some high-carbon-intensity regions exhibited extreme trends. ACI displayed clear spatial directionality, with the spatial center shifting steadily toward the northeast. (2) Regions in the northwest, northeast, and central-south parts exhibited strong local spatial structural dynamics, and the local spatial dependence of ACI in each region showed a nonlinear trend. Generally speaking, the spatial association pattern demonstrated a certain degree of inertia in spatial transfer, reflecting strong path dependence or spatial lock-in characteristics. (3) Optimization of industrial structure and improvement in agricultural mechanization will increase ACI, while economic development can effectively reduce it. The impact of urbanization on ACI exhibits a nonlinear pattern. The coordinated development of economic growth and urbanization significantly reduces ACI, with a stronger emission reduction observed in regions with low ACI. The optimization of industrial structure, when combined with urbanization and environmental regulation, contributes to significant emission reductions particularly in high-ACI areas. Similarly, the synergy between agricultural mechanization and urbanization effectively lowers emissions in low-ACI regions, though this effect diminishes in areas with higher ACI.</p>