Generative Artificial Intelligence Disclosure and Corporate Energy Efficiency: Staggered Difference-in-Differences Evidence from Chinese Listed Firms

Abstract

Generative artificial intelligence (GenAI) is diffusing faster than any recent technology, yet its net effect on energy use remains unsettled: the systems that enable smarter energy management are themselves energy-intensive. Using 22,143 firm-year observations on 3177 Chinese A-share listed firms over 2014–2023, this study measures corporate energy efficiency as a total-factor slacks-based measure score with undesirable output and dates GenAI disclosure from the fiscal year of a firm’s first annual-report mention, giving a staggered difference-in-differences design. Disclosure is followed by an increase in the efficiency score of between 6% and 9% of the sample mean, which is stable under parallel-trend, placebo and heterogeneity-robust checks and three alternative frontiers. Disclosing firms subsequently record more green innovation and report deeper digital transformation and more intensive use of data as a production factor. The post-disclosure association is larger where environmental regulation is tighter and digital infrastructure denser, and it is statistically significant among heavy-polluting and state-owned firms. In exploratory estimates, a firm’s book-measured artificial-intelligence capital intensity is separately associated with lower energy efficiency.