生态经济

论文基于2010—2023年沪深A股上市公司数据，采用多期双重差分法实证检验人工智能政策对企业ESG“漂绿”行为的影响。研究发现，人工智能先导区政策的实施显著抑制了企业ESG“漂绿”行为，且该效应经平行趋势与安慰剂检验验证，具备较强的因果识别力。机制分析显示，人工智能政策主要通过增加绿色创新产出、缓解信息不对称、提升企业信贷可得性、强化媒体与分析师关注等方式，从多个路径共同发挥治理作用。进一步的调节效应分析表明，管理层的绿色背景和高管绿色认知能显著增强政策对企业ESG“漂绿”行为的负向影响。异质性分析结果显示，政策在成熟期和衰退期企业、国有控股企业、高科技企业以及非污染行业中的抑制效果更为显著。此外，政策可有效抑制实质性与象征性“漂绿”，且对象征性“漂绿”的约束更强。

Based on the data of A-share listed companies in Shanghai and Shenzhen from 2010 to 2023， the paper empirically examines the impact of artificial intelligence policies on corporate ESG “greenwashing” behavior using a multi-period difference-in-differences approach. The research finds that the implementation of artificial intelligence pilot zone policies significantly curbs corporate “greenwashing” behavior， and this effect is verified by parallel trend and placebo tests， demonstrating strong causal identification power. Mechanism analysis reveals that artificial intelligence policies mainly exert governance effects through multiple pathways， including increasing green innovation output， alleviating information asymmetry， enhancing corporate credit availability， and intensifying media and analyst attention. Further analysis of moderating effects indicates that a green background of management and green awareness of executives can significantly strengthen the negative impact of policies on corporate “greenwashing” behavior. Heterogeneity analysis shows that the policy’s inhibitory effect is more pronounced in mature and declining enterprises， state-controlled enterprises， high-tech enterprises， and non-polluting industries. Additionally， the policy can effectively curb both substantive and symbolic “greenwashing”， with a stronger constraint on symbolic “greenwashing”.