Key points are not available for this paper at this time.
Artificial intelligence (AI) technology is gradually integrating into the entire process of green supply chain management (GSCM), providing a systematic solution for enterprises to improve productivity and performance. This paper focuses on Chinese manufacturing enterprises, aiming to explore the multi-factor synergistic mechanism influencing differences in GSCM levels from a temporal perspective under the drive of AI. Based on 2019–2023 panel data of enterprises, this paper innovatively integrates the random forest algorithm with dynamic qualitative comparative analysis (QCA) to reveal the configurational effects of technological, organizational, and environmental factors in enterprises’ GSCM practices. The findings demonstrate that no single factor is a necessary condition for enterprises to implement GSCM; configurational analysis identifies two driving models: “AI technology innovation-driven (Configuration 1 and Configuration 2)” and “strategic resource-driven (Configuration 3)”; Configuration 1 combines research and development (R Configuration 2 couples R Configuration 3 combines AI technology adoption and green awareness among executives, supported by the necessary funding capacity and government subsidies. Additionally, inter-group analysis reveals no significant temporal effect among configurations but shows phased evolutionary characteristics. This paper has thoroughly explored the complex paths for enhancing GSCM of manufactory enterprises under the influence of AI. It is recommended that the government refine and strengthen targeted subsidy policies to better support the adoption and integration of AI in advancing GSCM within the manufacturing sector. Concurrently, manufacturers must align technology, organizational structure, and external factors, specifically through core AI technology improvements, enhanced executive green awareness, and the mobilization of government and external funding. These advancements have led to high-level GSCM within enterprises, allowing them to achieve high-quality and sustainable development.
Cao et al. (Sun,) studied this question.