Optimal strategy of artificial intelligence on low-carbon energy transformation: Perspective from enterprise green technology innovation efficiency

Enterprise green technology innovation efficiency (GTIE) is a crucial driver of low-carbon energy transformation (LCET), with artificial intelligence (AI) emerging as a pivotal tool for augmenting enterprise GTIE. This study leverages data from the International Federation of Robotics and employs the Super-EBM-GML model to assess enterprise GTIE. By conducting an empirical analysis of annual data encompassing Chinese A-share listed enterprises from 2008 to 2022, this study uncovers the effects of AI on LCET through a comprehensive examination of both the heterogeneous environmental factors and internal regulatory mechanisms that affect enterprise GTIE. The heterogeneity analysis reveals that AI significantly boosts GTIE in competitive, high-tech, non-state-owned, and labor-intensive enterprises. However, AI has no positive effect on state-owned enterprises and even hampers technology-intensive enterprise GTIE. Furthermore, this study emphasizes that AI indirectly facilitates enterprise GTIE by alleviating financial constraints and bolstering research and development investments. Additionally, the threshold mechanism shows that human capital significantly amplifies the effect of AI on enterprise GTIE when a certain threshold is surpassed. However, the relationship between AI and income growth has an inverted Ushaped curve. This study provides valuable insights for devising optimal strategies for harnessing AI to support LCET from the perspective of enterprise GTIE, thereby offering valuable guidance for aligning LCET for effective energy management.