Can artificial intelligence improve energy-environmental performance in traditional manufacturing: The roles of innovation-driven and supply chain coordination

With the rapid development of artificial intelligence (AI), traditional production modes and resource allocation patterns are undergoing profound transformations. From the perspectives of innovation-driven and supply chain coordination, this study develops a micro-level theoretical framework to elucidate the underlying mechanisms through which AI affects firms’ energy–environmental performance (EEP). Using panel data of Chinese A-share listed traditional manufacturing firms, this study measures AI adoption through text analysis of annual reports and employs a two-way fixed effects model for empirical examination. The results show that AI significantly improves EEP; however, its marginal effect exhibits a diminishing trend, and an energy rebound effect emerges at the early stage of adoption. Mechanism analysis reveals that, on the one hand, AI enhances total factor productivity through innovation-driven channels, thereby reducing energy consumption and emissions per unit of output. On the other hand, AI improves supply chain collaboration, optimizing resource allocation and reducing operational losses, which in turn mitigates negative environmental externalities. In addition, supply chain finance (SCF) further strengthens the positive impact of AI on EEP. Heterogeneity analysis indicates that the effect is more pronounced in firms with technically skilled executives, in heavily polluting industries, and in regions characterized by stringent environmental regulations or more developed factor markets. This paper systematically clarifies the theoretical mechanisms and boundary conditions under which AI enhances EEP, providing new empirical evidence for the green transformation of traditional industries in the AI era.