Artificial intelligence, institutional environment, and corporate green transformation: Evidence from China's resource-based sector

Abstract

Resource-based enterprises (RBEs) face mounting pressure to achieve green transformation amid intensifying environmental regulations and volatile commodity markets. While artificial intelligence technology (AIT) emerges as a potential catalyst for sustainable development, its effectiveness in facilitating green transformation among RBEs remains unclear, particularly within varying institutional contexts. We examine whether AIT adoption facilitates green transformation in RBEs. Using a sample of 1105 Chinese listed RBEs from 2009 to 2022, we provide robust evidence AIT adoption significantly enhances green transformation of RBEs via increasing R&D investment, alleviating financing constraints, and optimizing human capital structure by replacing low-skilled workers with high-quality personnel. Contrary to conventional wisdom, we find that developed institutional environments paradoxically weaken AIT's positive impact on green transformation. Our cross-sectional results show that the positive impact of AIT is more pronounced for RBEs in manufacturing industries and those in Midwestern regions. Notably, the institutional environment's negative moderating effect varies across contexts that manufacturing RBEs demonstrate greater resilience to institutional constraints compared to non-manufacturing counterparts. Our findings provide novel insights into how artificial intelligence can drive environmental sustainability in resource-based sector while highlighting the critical role of institutional context, revealing instead that institutional development can create market-driven competitive dynamics that systematically crowd out environmental investments in favor of short-term profitability optimization.