Paper

{
  "raw_xml": "<entry>\n    <id>http://arxiv.org/abs/2603.18415v1</id>\n    <title>The Spillover Effects of Peer AI Rinsing on Corporate Green Innovation</title>\n    <updated>2026-03-19T02:22:08Z</updated>\n    <link href='https://arxiv.org/abs/2603.18415v1' rel='alternate' type='text/html'/>\n    <link href='https://arxiv.org/pdf/2603.18415v1' rel='related' title='pdf' type='application/pdf'/>\n    <summary>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.</summary>\n    <category scheme='http://arxiv.org/schemas/atom' term='cs.CY'/>\n    <category scheme='http://arxiv.org/schemas/atom' term='cs.AI'/>\n    <published>2026-03-19T02:22:08Z</published>\n    <arxiv:comment>Comments: 32 pages, 6 tables, empirical research on corporate finance &amp; digital economy, using Chinese A-share listed companies data (2006-2024), incorporating agent-based modelling simulations, suitable for finance/innovation economics journals</arxiv:comment>\n    <arxiv:primary_category term='cs.CY'/>\n    <author>\n      <name>Li Wenxiu</name>\n    </author>\n    <author>\n      <name>Wen Zhanjie</name>\n    </author>\n    <author>\n      <name>Xia Jiechang</name>\n    </author>\n    <author>\n      <name>Guo Jingqiao</name>\n    </author>\n  </entry>"
}