摘要：公共数据开放是数字化治理的重要制度安排，为抑制企业“漂绿”行为提供了新的治理路径。本文基于城市公共数据开放平台上线的准自然实验，以2009—2022年沪深A股上市公司为研究样本，运用双重机器学习方法系统评估公共数据开放对企业“漂绿”行为的治理效应。研究结果表明，公共数据开放能够显著抑制企业的“漂绿”行为。机制分析显示，公共数据开放通过增大规制性压力、强化规范性压力和加剧模仿性压力三条路径形成对企业的持续约束，从而抑制企业“漂绿”行为。异质性分析显示，公共数据开放的治理效应具有显著的对象差异与制度环境依赖特征，其抑制作用在高污染行业、国有企业和短期主义倾向较强的企业中更为明显，并在市场化程度较高的地区表现得更为显著。本研究为通过公共数据开放提升环境治理效能和助力经济高质量发展提供了重要的经验证据。
关键词：公共数据开放；企业漂绿；双重机器学习；制度压力；环境治理
The Impact of Public Data Openness on Corporate Greenwashing: Causal Inference Based on Double Machine Learning
Zhou Yang, Fang Kai, Yuan Shuqiang, Li Haowu
Abstract：Public data openness is an important institutional arrangement in digital governance and provides a new pathway for curbing corporate greenwashing. Exploiting the launch of city-level public data open platforms as a quasi-natural experiment, this paper uses a sample of Chinese A-share listed firms from 2009 to 2022 and applies the double machine learning approach to estimate the governance effect of public data openness on corporate greenwashing. The results show that public data openness significantly restrains corporate greenwashing. Mechanism analyses indicate that public data openness constrains corporate greenwashing by intensifying regulatory pressure, strengthening normative pressure, and amplifying mimetic pressure, thereby reducing corporate greenwashing. Heterogeneity analyses further reveal that the governance effect of public data openness exhibits pronounced firm-level differences and institutional-environment dependence: the inhibitory effect is more evident among heavily polluting firms, state-owned enterprises, and firms characterized by stronger managerial short-termism, and is significantly stronger in regions with a higher degree of marketization. This study provides important empirical evidence for improving environmental governance and promoting high-quality economic development through public data openness.
Keywords：Public Data Openness; Corporate Greenwashing; Double Machine Learning; Institutional Pressure; Environmental Governance
