Can Artificial Intelligence Enhance Corporate Green Productivity? Evidence from Chinese Listed Firms

Abstract

Artificial Intelligence (AI) has emerged as a transformative force in the global economy, yet its contribution to environmentally sustainable productivity growth remains insufficiently understood. Using 33,017 firm-year observations from 4079 Chinese A-share listed firms during 2015–2024, this study examines the relationship between AI adoption and corporate green total factor productivity (GTFP). We construct a text-based proxy for AI adoption by applying a machine learning-generated dictionary to the management discussion and analysis (MD&A) sections of annual reports. We construct the GTFP proxy using the slacks-based measure, the Malmquist–Luenberger (SBM-ML) index, which incorporates undesirable outputs. The results show a significant positive relationship between AI adoption and GTFP. This relationship remains robust across a series of robustness checks, including alternative specifications, PSM-matched sample analysis, instrumental variable estimation, and exogenous shock design. Further analysis identifies R&D intensity as an important transmission channel. The relationship is stronger among firms facing tighter financing constraints, non-polluting industries, and non-state-owned enterprises. The positive association between AI adoption and firm value further indicates that its economic relevance may extend beyond environmental efficiency in the long term. These findings highlight the potential of AI-enabled innovation to advance green productivity and sustainable corporate development in emerging economies.