Zhaofa Sun

Amid the accelerating global transition toward a low-carbon and intelligent economy, the issues of resource misallocation and mounting environmental pressures in agriculture have become increasingly prominent, posing significant bottlenecks to the modernization of the sector. As a novel factor of production, agricultural and rural big data theoretically offer new avenues for facilitating a green transformation in agriculture. However, institutional constraints have hindered its full potential. Drawing on provincial panel data from 2011 to 2022, this study treats the big data policy pilot as a quasi-natural experiment and employs a difference-in-differences (DID) approach to comprehensively analyze its mechanisms and actual effects on high-quality agricultural development. An indicator system encompassing five dimensions—innovation, coordination, greenness, openness, and sharing—is constructed, and the entropy method is used to measure the level of high-quality agricultural development. Multiple empirical strategies, including parallel trend tests, are utilized to ensure the robustness of the findings. The results indicate that high-quality agricultural development exhibits significant regional gradients and periodic leaps. The implementation of the big data policy in 2016 marked a crucial turning point, yielding a significant positive effect on agricultural development. Notably, pronounced heterogeneity exists regarding regional distribution, major grain-producing areas, and development stages. The policy’s impact primarily operates through pathways of openness and sharing, although some mechanisms remain to be improved. Accordingly, this paper recommends differentiated regional policies and enhanced targeted support, thereby providing theoretical and practical policy guidance for optimizing big data policy design, promoting high-quality agricultural development, and advancing rural revitalization. For policymakers, these findings clarify the priorities for differentiated interventions and offer empirical evidence for optimizing the spatial allocation of big data policy pilots and strengthening open and shared development mechanisms. This, in turn, can improve the precision of agricultural policy and accelerate the green transformation and revitalization of rural areas. Compared to existing literature, the distinct contribution of this study lies in its pioneering use of big data policy pilots as a quasi-natural experiment. The research systematically constructs a multidimensional indicator system to measure high-quality agricultural development, elucidates the heterogeneous effects and specific pathways of policy intervention, and addresses gaps in the empirical assessment and mechanism analysis of agricultural big data policies.

As digital technologies advance rapidly, their applications to environmental governance are becoming increasingly widespread. Against this backdrop, we focus on the critical area of water pollution control. Using a fixed-effects model, we empirically examine the impact of digital monitoring and administration on corporate water pollution emissions and the mechanisms involved. The findings are as follows: (1) The digital monitoring of water pollution is significantly positively correlated with corporate water pollution emissions, indicating that while digital monitoring enhances pollution detection capabilities, it has not yet effectively curbed pollution emissions. (2) Digital administration significantly reduces corporate water pollution emissions. (3) Digital administration positively moderates the relationship between corporate technological innovation and water pollution, whereas the moderating effect of digital monitoring is not significant. (4) The heterogeneity analysis reveals that the pollution-reduction effects of digital monitoring and digital administration vary significantly with respect to the corporate ownership structure and industry competitiveness. This study provides theoretical foundations and practical insights for improving digital environmental regulation policies.