Pathways to sustainability: Deconstructing AI's impact on green supply chain transformation

Against the backdrop of global efforts to address economic slowdown, environmental degradation, and social inequality, how artificial intelligence (AI) can drive the transformation of green supply chains has become a core issue in achieving sustainable development. Based on microdata from China's A-share listed companies (2013-2023), this study investigates the spillover effects of AI applications within supply chain networks, specifically examining how downstream customers' AI adoption influences the green transformation of midstream suppliers. The findings reveal that this catalytic effect is primarily realized through three key mediating pathways: (1) by alleviating financing constraints through enhancing the transparency and verifiability of corporate environmental information, thereby guiding capital markets to reinforce green preferences; (2) by improving operational efficiency through optimizing demand forecasting, energy management, and supply chain collaboration; and (3) By reducing the risk of supply chain disruptions for supplier enterprises, it encourages companies to undergo green transformation.Heterogeneity analysis indicates that these effects vary significantly with enterprise-customer relationship characteristics, ownership type, and industry asset structure.This research not only provides a comprehensive theoretical framework for understanding how digital technologies reshape green finance and supply chain management through multiple mechanisms but also offers empirical evidence for policymakers and enterprises to formulate differentiated, risk-aware strategies.