The Impact of Artificial Intelligence on Energy Conservation and Emission Reduction: Evidence From China's Listed Firms

Artificial intelligence (AI) plays an increasingly pivotal role in advancing sustainable economic development. While existing literature predominantly examines the environmental impact of AI technologies from national or sectoral perspectives, this study provides a micro‐level analysis of its effects on energy conservation and emission reduction (ECER) performance, utilizing a dataset of Chinese listed firms. We employ a large language model (LLM)‐based intelligent scoring system to capture firms' ECER performance from publicly available environmental disclosures, and construct two‐pronged measures of AI technological capabilities encompassing both innovation and adoption dimensions. The empirical analysis demonstrates that AI technologies significantly enhance ECER performance among Chinese listed firms, with results remaining robust to various alternative specifications and robustness tests. Mechanism analysis reveals that AI facilitates environmental improvements through the enhancement of productive efficiency and the promotion of green innovation. Heterogeneity analysis further indicates that AI‐driven environmental effects are more pronounced among state‐owned enterprises, mature‐stage firms, firms in polluting industries, sectors with lower competitive intensity, labor‐intensive and capital‐intensive industries, and firms located in cities with stringent environmental regulations. These findings offer novel firm‐level empirical evidence on AI's environmental implications, contributing to a more comprehensive understanding of the technology‐environment nexus in emerging economies and laying a theoretical foundation for targeted AI‐related environmental policy interventions.