Digital Economy, Green Innovation, and Agricultural Carbon Emission Reduction: Spillover Effects and Analyses of Mechanisms

Against the backdrop of the global imperative for carbon neutrality, in this study, we systematically assessed the roles of spatial spillover and underlying mechanisms along with threshold characteristics of the digital economy on agricultural carbon emissions as related to green innovation. Using provincial panel data from China, as obtained over the period from 2013 to 2022, we determined agricultural carbon emissions as measured using the emission coefficient method and constructed a comprehensive digital economy index via the entropy weight method. An array of econometric models, including linear regression, the Spatial Durbin Model (SDM), mediation effect models, and panel threshold models were employed to examine both direct and indirect pathways, spatial interactions, and nonlinear moderating effects of digital economy. The results indicate that the following findings: (1) The digital economy significantly reduces agricultural carbon emissions, with a coefficient of approximately –2.051 in the baseline model. (2) Green innovation serves as a key mediator. The mediation effect analysis revealed that green innovation has a mediation effect value of 1.896 in the digital economy’s carbon reduction effect. (3) Significant negative spatial spillovers were observed upon reducing neighboring regions’ digital development of local emissions, with indirect effects ranging from –1.434 to –2.708 under different spatial matrices. (4) Urbanization rates exhibit a dual-threshold effect (73.38% and 74.79%), with the carbon reduction effect of the digital economy showing a notable strengthening when these rates extend beyond these thresholds. Heterogeneity analysis reveals a stronger effect in western China (coefficient: –6.079), attributable to higher marginal returns from digitalization as compared with that observed in less developed regions. Limitations associated with this study include the use of provincial-level data which may mask sub-regional heterogeneity, reliance on green patent counts as a proxy for green innovation output, and omissions of effects of exogenous policy programs such as the “Dual Carbon” policy. Future research would markedly benefit from micro-level data and more dynamic tests of the mechanisms involved.