The impact of new digital infrastructure on agriculture green development: evidence from China - Zhao, Yanru; Yang, Chenyu; Khan, Sarwar (Frontiers in Environmental Economics, 2025)
IntroductionNew digital infrastructure (NDI) is a key driver for agricultural green development (AGD). However, its underlying mechanisms and heterogeneous impacts remain to be explored. This study focuses on agricultural green total factor productivity (AGTFP) in China, aiming to elucidate the direct effects of NDI on AGD, its mediating pathways, and geographical heterogeneity, thereby providing theoretical evidence and policy implications for the green transformation of agriculture.MethodsBased on panel data from 30 provinces in China from 2006 to 2022, this study employs the global Malmquist-Luenberger (GML) index and the slack-based measure (SBM) model to measure AGTFP and constructs a comprehensive NDI indicator using principal component analysis. The study systematically examines the direct impact of NDI on AGD, the mediating effects of technological and land factors, and the moderating role of labor quality through fixed-effects models, instrumental variable regression, and moderated-mediation effect models. Heterogeneity analysis is also conducted through subgroup regression to explore geographical and institutional differences.ResultsNDI significantly enhances AGTFP, a conclusion that is robust to endogeneity and sensitivity tests. technology level and farmland quality are the dual mediating pathways through which NDI drives AGD. Labor quality amplifies the green empowerment effect of NDI by strengthening the synergistic effects of technological and land factors. Heterogeneity analysis shows that the effect of NDI is more pronounced in ecologically fragile areas in the northwest, as well as in regions with low marketization and strict environmental regulations.DiscussionNDI promotes the green transformation of agriculture by optimizing resource utilization efficiency and driving technological innovation. However, it requires matching with improved labor quality and region-specific policies. It is recommended to prioritize the deployment of intelligent monitoring facilities in ecologically fragile areas to compensate for institutional shortcomings through digital technology; and to promote precision agronomic systems in intensive agricultural areas to reduce resource dependence and accelerate the diffusion of green technologies through market mechanisms. The limitations of this study include the omission of spatial spillover effects and potential biases in the calculation of agricultural value-added. Future research could further explore these aspects by incorporating spatial econometric models.