Artificial intelligence and the sustainable development of agricultural enterprises: a total factor productivity perspective

Introduction From the perspective of sustainable agricultural development, the adoption of artificial intelligence (AI) not only improves factor allocation efficiency but also constitutes a critical economic foundation for efficiency-driven sustainable growth in agriculture by optimizing resource utilization and strengthening risk-management capacity. Methods Using panel data on agricultural firms listed on the Shanghai and Shenzhen A-share markets from 2007 to 2023, this study applies a multidimensional fixed-effects model to estimate the impact of AI on firms’ total factor productivity (TFP). Results The empirical results demonstrate that AI significantly enhances TFP. However, mechanism analysis reveals a structural divergence in transmission pathways: while AI fosters productivity growth mainly by optimizing labor structures and facilitating inter-firm resource sharing, it has yet to significantly promote university-industry collaborative R&D capabilities. Heterogeneity analysis further indicates that these productivity gains are more pronounced among firms in their growth stage and in regions facing higher natural risks. Discussion Overall, the expanding use of AI is reshaping agricultural production systems and has emerged as a key driver of high-quality development in the sector. Within an efficiency-driven sustainability framework, continued advances in AI are expected to play a pivotal role in achieving a dynamic alignment among the objectives of efficiency, environmental performance, and long-term sustainability in agriculture.