Objective/Meaning This study aimed to explore the transmission mechanism of artificial intelligence enabling the agricultural green transformation, providing theoretical basis and practical reference for driving the agricultural green sustainable development and high-quality transformation.
Methods/Procedures By focusing on the transmission mechanism of artificial intelligence enabling the agricultural green transformation, the provincial panel data in China from 2011 to 2024 was selected as the samples, and an evaluation system was constructed from three aspects: intelligent technology penetration, breadth of intelligent agricultural application and agricultural innovation achievements, so as to measure the development level of artificial intelligence. By taking the agricultural carbon emissions as the undesirable output, and combining the non-radial SBM model and GML index, the green total factor productivity of agriculture in China’s provinces was measured, and the impact effect was tested through the spatial panel model.
Results/Conclusions The study found that: (1) There was a significant imbalance in the spatial distribution of provincial artificial intelligence application level and agricultural GTFP. (2) The artificial intelligence could not only significantly improve the local agricultural GTFP, but also show pronounced positive spatial spillover effects across regions. (3) The agglomeration level of agricultural production had a threshold effect on the effect of technology empowerment. As the degree of agglomeration crossed a specific threshold, the driving effect of artificial intelligence on agricultural green development was significantly enhanced. The research results provided important evidence for optimizing the industrial layout through artificial intelligence and realizing the regional coordinated green transformation.
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