AI and green transformation: the mediating role of operational efficiency in Chinese manufacturing

Purpose Grounded in Resource Orchestration Theory and Upper Echelons Theory, this study aims to explore how artificial intelligence (AI) applications drive green transformation and reveal its underlying mechanisms. Design/methodology/approach We propose a framework in which AI enhances green transformation performance through improved operational efficiency, contingent on the dual moderating mechanisms of organizational agility and executives’ environmental attention. We employ a comprehensive panel dataset using Chinese manufacturing listed companies from 2012 to 2023 for empirical testing. Findings Empirical analysis reveals that AI significantly enhances firms’ green transformation, with operational efficiency serving as a mediator in this relationship. Organizational agility amplifies the effect of AI on operational efficiency, and executives’ environmental attention strengthens the effect of operational efficiency on green transformation performance. Both factors positively moderate the mediated pathway. Heterogeneity analysis shows stronger effects among older firms, larger firms, firms located in western regions or regions with lower environmental regulation intensity. Practical implications Findings offer practical insights for firms to enhance AI’s benefits by optimizing internal management mechanisms. Social implications Findings provide theoretical foundations for policymakers designing AI-enabled green manufacturing policy tools. Originality/value This study applies the resource orchestration perspective to the AI-green transformation context, identifying operational efficiency as a mediating mechanism and organizational agility and executives’ environmental attention as key moderating conditions. It enriches research perspectives on micromanagement mechanisms within the context of green transformation.