The Spillover Effects of Peer AI Rinsing on Corporate Green Innovation

TL;DR

This study investigates how peer AI washing in corporate reports negatively impacts green innovation, revealing significant crowding-out effects that vary across firm types and suggesting targeted policy interventions.

Contribution

It introduces a large language model-based semantic analysis to quantify AI washing's impact on green innovation and proposes policy measures to mitigate this issue.

Findings

AI washing crowds out green innovation in firms.

Negative effects are stronger in private, SME, and competitive sectors.

Policy tools can improve market outcomes and reduce AI washing impacts.

Abstract

At a time when the phenomenon of 'AI washing' is quietly spreading, an increasing number of enterprises are using the label of artificial intelligence merely as a cosmetic embellishment in their annual reports, rather than as a genuine engine driving transformation. A test regarding the essence of innovation and the authenticity of information disclosure has arrived. This paper employs large language models to conduct semantic analysis on the text of annual reports from Chinese A-share listed companies from 2006 to 2024, systematically examining the impact of corporate AI washing behaviour on their green innovation. The research reveals that corporate AI washing exerts a significant crowding-out effect on green innovation, with this negative relationship transmitted through dual channels in both product and capital markets. Furthermore, this crowding-out effect exhibits heterogeneity across firms and industries, with private enterprises, small and medium-sized enterprises (SMEs), and firms in highly competitive sectors suffering more severe negative impacts from AI washing. Simulation results indicate that a combination of policy tools can effectively improve market equilibrium. Based on this, this paper proposes that the government should design targeted support tools to 'enhance market returns and alleviate financing constraints', adopt a differentiated regulatory strategy, and establish a disclosure mechanism combining 'professional identification and reputational sanctions' to curb such peer AI washing behaviour.