Turning Data into Sustainability: Agricultural Big Data Policy, Capital Composition, and Rural Eco-Environmental Sustainability in China

Rural environmental degradation is diffuse, weakly monitored, and hard to govern, and is among the most persistent obstacles to the Sustainable Development Goals (SDGs). Whether treating data as a factor of production relieves this constraint remains unsettled, as does what a rural economy must already possess for agricultural data to matter. Using a panel of 271 Chinese cities from 2010 to 2024 and the 2016 Agricultural and Rural Big Data Policy (ABDP) pilot as a quasi-natural experiment, this study estimates the policy effect on rural eco-environmental sustainability (RES) with a staggered difference-in-differences (DID) design. ABDP raises RES by 0.0015 index points, equal to 2.91% of the sample mean and 6.8 percentile points of its distribution. The estimate survives heterogeneity-robust estimators, city-specific trends, three treatments of cross-city spillovers, alternative index weightings, reweighting and matching, and controls for concurrent pilots. The size of the effect depends on the capital stock the policy meets: it falls as agricultural mechanization (AM) rises and becomes indistinguishable from zero at high mechanization, while it rises with digital capital (DC). Capital that fixes what is done in the field substitutes for information; capital that carries information to the field complements it. Gains concentrate in the production-environment dimension.