Abstract: In recent years, the concept of sustainable development, based on environmental, social, and governance (ESG) principles, has gained global consensus. However, corporate greenwashing has gradually emerged. Greenwashing refers to firms intentionally concealing poor environmental performances, presenting favorable environmental information, or using vague language to mask a lack of substantive environmental actions. The hidden and intricate nature of enterprises' greenwashing heightens its governance challenges. In an era of coordinated digital and green transformation, digital technologies have become an essential tool for governing corporate greenwashing. The existing literature has not clearly examined their real effect on corporate greenwashing. Therefore, investigating the effect of big data technologies on corporate greenwashing and its underlying mechanism becomes crucial. Using A-share enterprises listed on China's Shanghai and Shenzhen Stock Exchanges from 2012 to 2022 as the research sample, this paper crawls big data keywords in enterprises' annual reports to construct the indicator measuring the enterprise's big data technologies and applies OLS regressions to examine the effect of big data technologies on corporate greenwashing and its mechanism.
It is found that big data technologies significantly inhibit corporate greenwashing. Mechanism analysis suggests that big data technologies inhibit corporate greenwashing by improving corporate green performances and reducing information asymmetry. Heterogeneity analysis shows that the above inhibitory effect is more prominent in state-owned enterprises, enterprises in highly competitive markets, or enterprises located in the northern region. Our empirical findings yield the following implications. Firstly, firms should fully leverage big data to enhance their environmental performance. They should also establish robust oversight mechanisms for environmental responsibility that involve both employees and the board of directors as well as implement green accounting practices. Secondly, the government and its affiliates should introduce policy incentives to encourage firms to genuinely meet their environmental responsibilities through big data. Additionally, the government should strengthen regulatory constraints on corporate greenwashing and improve oversight by external market entities. Finally, when developing relevant policies, it is crucial to pay attention to the asymmetrical effects of big data technologies on greenwashing in different enterprises.
The contributions of the study are as follows: First, it enriches the literature on governing corporate greenwashing from the perspective of big data technologies. We construct the big data technology indicators by defining some keywords related to big data. The empirical result shows that big data technologies have a significant suppression effect on corporate greenwashing, which adds to the research on factors influencing corporate greenwashing. Second, this paper expands the literature on how big data technologies affect enterprises' green development from the perspective of greenwashing. Although existing studies have examined the effects of big data on green development through qualitative analysis or from a macro perspective, research on how the use of big data technologies affects firms' green speculative behavior remains limited. This study addresses this gap by investigating the effects of big data technologies on corporate greenwashing, which contributes to the understanding of the economic consequences of big data technologies. Third, this paper clarifies the underlying mechanisms through which big data technologies affect corporate greenwashing. It explores how big data technologies inhibit corporate greenwashing through the channels of green performances and information asymmetry. It also performs heterogeneity analysis based on enterprises' ownership structures, the degree of market competition, and the differences between northern and southern areas. This analysis provides better insights into how big data technologies inhibit greenwashing.

Key words: Big Data, Greenwashing, Green Performance, Information Asymmetry, Textual Analysis

摘要： 在“数字+绿色”双转型时代,大数据赋能如何影响企业漂绿行为成为重要的研究议题。本文通过对A股上市企业年报的文本分析,构建企业层面大数据赋能的度量指标,考察大数据赋能对企业漂绿行为的影响及作用机制。实证结果发现,大数据赋能显著抑制企业漂绿行为;机制分析表明,大数据赋能通过提高企业绿色业绩和缓解企业信息不对称抑制企业漂绿行为;异质性分析表明,大数据赋能对企业漂绿行为的抑制效应在国有企业、市场竞争程度较高或北方地区企业中更为显著。研究结论对推动企业应用大数据技术实现漂绿治理的政策设计提供经验证据,也为企业实现数字与绿色双转型提供决策参考。

关键词: 大数据, 漂绿, 绿色业绩, 信息不对称, 文本分析