Multiple Pathways of Rural Digital Intelligence Driving Agricultural Eco-Efficiency: A Dynamic QCA Analysis

The shift toward sustainable and efficient agricultural production has become a global imperative. Rural digital intelligence, which integrates advanced technologies into agricultural practices, emerges as a pivotal driver for advancing green transformation. Based on the technology–organization–environment (TOE) framework, this study explores how rural digital intelligence drives agricultural eco-efficiency. Drawing on panel data from 30 Chinese provinces (2013–2023), this study applies dynamic qualitative comparative analysis (QCA) to unravel the complex causal pathways influencing agricultural eco-efficiency. Key findings demonstrate that (1) no single element of rural digital intelligence suffices to improve agricultural eco-efficiency; the combination of various factors can affect agricultural eco-efficiency. (2) Four distinct pathways achieve high agricultural eco-efficiency, categorized into three archetypes: application-driven pathway, synergy-robust pathway, and policy-driven pathway. (3) Temporal analysis indicates time-dependent effects in these pathways, influenced by fragmented policy implementation and technological constraints. (4) Spatial heterogeneity is pronounced; western China primarily follows the application-driven pathway, while eastern China and central China align with the synergy-robust pathway. This research explores configurational pathways through which rural digital intelligence enhances agricultural eco-efficiency, offering theoretical and empirical foundations for regionally tailored sustainable agricultural policies.