The Environmental Cost of AI Hype: Corporate AI Catering, Pollution Emissions, and Sustainability Governance

The long development cycle and high investment requirements of artificial intelligence (AI) conflict with the short-term incentives prevailing in capital markets, leading some firms to cater to market expectations through promotional rhetoric rather than substantive investment, possibly at the expense of long-term sustainability commitments. This study examines whether such AI catering affects corporate pollution and under which conditions. Using Chinese A-share listed firms with firm-level emission records from 2016 to 2024, we measure AI catering as AI disclosure in excess of the level predicted by observable substantive AI input and estimate its effect with firm and year fixed-effects models. AI catering is associated with higher pollution emission intensity, whereas substantive AI input is associated with lower intensity. Lower environmental investment and environmental disclosure quality account for part of this association. The association is stronger under formation conditions that make substantive AI adoption costly and weaker under detection and penalty conditions that make laxer pollution control easier to discover and more costly. This study contributes to research on corporate sustainability by revealing an organizational and governance cost of technology hype, distinct from AI’s direct environmental footprint, and suggests that regulators and investors should verify the substantive basis of corporate AI claims.