From Mismatch to Smart Match: Artificial Intelligence and Land Resource Misallocation

Abstract

Optimizing the allocation of national land resources is a crucial measure for safeguarding food security and ecological security, as well as a key pathway for achieving urbanrural integration and coordinated regional development. Against the backdrop of the global digital transformation, artificial intelligence offers new opportunities for intelligent and refined land resource management. However, existing research has not sufficiently explored the impact of AI on land resource allocation. Using panel data of 265 Chinese cities from 2010 to 2023, this study investigates the effect of artificial intelligence level (AIL) on land resource misallocation (LRM) and its underlying mechanisms. The findings demonstrate the key conclusions: (1) AIL significantly reduces urban LRM. This result remains robust after adjusting the sample, accounting for province–time interactions, controlling for other policy effects, excluding outliers, and addressing endogeneity concerns. (2) Mechanism analysis indicates that AIL reduces urban LRM by enhancing government environmental concerns, promoting land transfer marketization and industrial structure upgrading. (3) Heterogeneity analysis indicates that the inhibitory effect of AIL on LRM is more pronounced in non-old industrial base cities, low-population-density cities and strong government intervention cities. These findings suggest that local governments should tailor AI research, development, and application strategies according to their cities’ resource endowments and developmental foundations. This study not only enriches empirical evidence on AI’s role in reducing LRM but also offers practical pathways and decision-making references for other countries to optimize territorial governance and achieve sustainable land resource utilization through digital and intelligent technologies.