Unveiling the Effects of Digital Transformation on Agribusiness Green Innovation in China: An Explainable Machine Learning-Based Approach

Digital transformation is a key driver of green innovation in agribusiness. While the positive impact of digital transformation on firm innovation has been well documented, its multidimensional nature and heterogeneous associations on agribusiness green innovation remain underexplored. This study deconstructs digital transformation into five business dimensions and two structural features, using a sample of 155 Chinese A-share listed agricultural companies from 2011 to 2021. By combining an explainable machine learning framework integrating Bayesian-optimized XGBoost and SHAP, we identify individual and interaction predictive effects of each feature on green innovation measured by green patent applications. The results reveal correlational evidence that governance digitalization is the dominant predictive driver of agricultural green innovation, followed by institutional digitalization. Merely expanding the scope of digital transformation delivers no substantial improvements in green-patent-based innovation outputs. Different digital dimensions present notable heterogeneous nonlinear correlations with distinct threshold characteristics. We further find significant synergistic interaction linkages across digital dimensions, where coordinated multi-dimensional digital development is critical to fully unlocking the green innovation potential of digital transformation. These findings provide insights for agribusiness managers and policymakers to prioritize digital investment and facilitate low-carbon transition.