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<p>While artificial intelligence (AI) is transforming corporate operations, its organizational pathways to improve environmental performance remain underexplored. This study addresses this critical gap by examining how AI enhances corporate environmental behavior (CEB) through internal governance mechanisms rather than just technical features. Grounded in the Resource-Based View (RBV), we propose that AI serves as a strategic resource whose environmental value is realized through two complementary governance pathways: enhanced internal control systems and strengthened supply chain integration. Using panel data from 968 Chinese listed firms (2012–2022) and a two-way fixed effects model with robustness checks, we examine how AI enhances CEB through internal governance mechanisms. We reveal three key findings: (Ayoub et al. 2017) AI adoption increases CEB by 38.29% (<i>p</i>&lt; 0.01), measured through green patent applications; (Antoncic 2020)this effect is positively moderated by both supply chain integration (including customer and supplier integration) and internal control quality; and (Ashraf 2024) the impacts are most pronounced for state-owned enterprises (SOEs), large firms, and energy-intensive industries.We extend RBV theory by revealing AI’s environmental value as a VRIN(valuable, rare, inimitable, non-substitutable) resource depends on organizational governance, bridging digital transformation and environmental governance research. The results demonstrate that governance-enhanced AI delivers the strongest environmental benefits, particularly in state-owned and energy-intensive firms, suggesting policymakers should target subsidies at AI applications that strengthen internal governance mechanisms while firms align AI investments with governance capacity-building. The study offers the first empirical evidence that AI’s environmental returns depend critically on internal governance quality, challenging prevailing technological-deterministic views in sustainable operations research.</p>

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The impact of artificial intelligence on corporate environmental behavior: the mechanism of internal control and supply chain integration

  • 期刊论文

  • 发表日期：

    2025年9月19日

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  • Zhengxia He,
  • Hongnan Tu,
  • Jianming Wang,
  • Changshuai Cao

摘要

While artificial intelligence (AI) is transforming corporate operations, its organizational pathways to improve environmental performance remain underexplored. This study addresses this critical gap by examining how AI enhances corporate environmental behavior (CEB) through internal governance mechanisms rather than just technical features. Grounded in the Resource-Based View (RBV), we propose that AI serves as a strategic resource whose environmental value is realized through two complementary governance pathways: enhanced internal control systems and strengthened supply chain integration. Using panel data from 968 Chinese listed firms (2012–2022) and a two-way fixed effects model with robustness checks, we examine how AI enhances CEB through internal governance mechanisms. We reveal three key findings: (Ayoub et al. 2017) AI adoption increases CEB by 38.29% (p< 0.01), measured through green patent applications; (Antoncic 2020)this effect is positively moderated by both supply chain integration (including customer and supplier integration) and internal control quality; and (Ashraf 2024) the impacts are most pronounced for state-owned enterprises (SOEs), large firms, and energy-intensive industries.We extend RBV theory by revealing AI’s environmental value as a VRIN(valuable, rare, inimitable, non-substitutable) resource depends on organizational governance, bridging digital transformation and environmental governance research. The results demonstrate that governance-enhanced AI delivers the strongest environmental benefits, particularly in state-owned and energy-intensive firms, suggesting policymakers should target subsidies at AI applications that strengthen internal governance mechanisms while firms align AI investments with governance capacity-building. The study offers the first empirical evidence that AI’s environmental returns depend critically on internal governance quality, challenging prevailing technological-deterministic views in sustainable operations research.