Amid the coordinated advancement of the digital economy and rural revitalization, Digital Rural Construction (DRC) has increasingly emerged as a critical catalyst for agricultural modernization and sustainable development. Faced with dual challenges of land resource constraints and agricultural green transformation, improving the Agricultural Land Green Utilization Efficiency (ALGUE) has become essential for achieving high-quality agricultural development. Based on panel data from 29 Chinese provinces from 2012 to 2023, this study employs the super-efficiency SBM model to quantify ALGUE. A comprehensive four-dimensional evaluation system—encompassing digital infrastructure, service capacity, human capital quality, and practical application—is constructed, and the entropy method is used to measure the level of digital rural construction. By applying two-way fixed effects models, mediation analysis, and heterogeneity tests, this study systematically examines the impact of digital rural construction on ALGUE and its underlying transmission pathways. The results demonstrate that: (1) Digital rural construction significantly enhances ALGUE, and this finding remains robust under multiple sensitivity checks. (2) Pronounced heterogeneity exists in two dimensions: the promotion effect is stronger in economically developed regions and in regions with higher agricultural mechanization intensity, while it is weaker in less developed and low-mechanization regions. (3) Mechanism analysis reveals that digital rural construction promotes ALGUE through two channels. The first involves accelerating the transition of the primary industry toward intelligent and high-value-added models, thereby optimizing resource allocation and reducing environmental pressure. The second operates by fostering regional economic growth in an inverted U-shaped nonlinear pattern that supports agricultural green transformation. By integrating DRC and ALGUE into a unified framework, this study identifies two mediating channels and reveals heterogeneity across economic development levels and agricultural structures. These findings provide empirical support and policy implications for digitally driven green agricultural development.
Wan et al. (2026) studied this question.