Artificial Intelligence and Firms’ Environmental Cost Pressures: Mechanisms, Spillover Effects, and Optimization Pathways

Against the backdrop of increasingly stringent global environmental constraints and rising environmental cost pressures on businesses, artificial intelligence offers a new approach to green cost-reduction and transformation. However, due to constraints such as transformation costs, technological compatibility, and industry standards, the extent to which it can effectively reduce costs and empower businesses remains uncertain. Based on this, this paper uses panel data from Chinese A-share listed companies on the Shanghai and Shenzhen stock exchanges from 2018 to 2024 as a sample to systematically empirically examine the impact, transmission mechanisms, boundary conditions, and spatial spillover characteristics of AI on corporate environmental cost pressures. The study finds that AI can significantly alleviate corporate environmental cost pressures, a conclusion that remains robust after multiple robustness and endogeneity tests. Moderating effects indicate that corporate willingness to engage in green governance and the regional digital regulatory environment can positively reinforce its cost-reduction effects. At the mechanism level, AI can indirectly reduce corporate environmental costs through two pathways: promoting green technological innovation and optimizing the allocation of production factors. Further research confirms that AI exhibits distinct positive spatial spillover effects, which can help regional firms achieve coordinated reductions in environmental costs. This paper enriches the theoretical framework of corporate environmental cost governance from a digital empowerment perspective, providing empirical references and practical insights for corporate green digital transformation, the refinement of government digital-green support policies, and low-carbon development in emerging economies.