Promoting Agricultural Green Productivity Through Geographical Indication Certification: Mechanisms and Spatial Heterogeneity

Agricultural green transformation is important for achieving economic growth and environmental sustainability. Geographical indication agricultural products (GIAPs) have become a useful tool for improving agricultural green total factor productivity (AGTFP). However, the mechanisms through which GIAPs affect AGTFP and their spatial differences are not yet fully understood. This study uses a balanced panel of 290 Chinese cities from 2008 to 2024 and combines Double Machine Learning (DML), Geographically Weighted Random Forest (GWRF), and Shapley Additive explanations (SHAP) to examine the effects of GIAPs on AGTFP. The results show that GIAPs significantly enhance AGTFP, primarily through agricultural industry agglomeration and the downstream penetration of leading agricultural enterprises. The effects exhibit substantial spatial and category heterogeneity. GWRF-SHAP results further reveal a clear geographical gradient, with GIAPs exhibiting greater predictive importance and contribution in inland than coastal regions. These findings highlight the need for region- and product-specific strategies to leverage GIAPs for agricultural green transformation. This study provides new evidence on the green development effects of geographical indications and offers policy insights for promoting sustainable agricultural development.