Motivated by the emerging adoption of Large Language Models (LLMs) in economics and management research, this paper investigates whether LLMs can reliably identify corporate greenwashing narratives and, more importantly, whether and how the greenwashing signals extracted from textual disclosures can be used to empirically identify causal effects. To this end, this paper proposes DeepGreen, a dual-stage LLM-Driven system for detecting potential corporate greenwashing in annual reports. Applied to 9369 A-share annual reports published between 2021 and 2023, DeepGreen attains high reliability in random-sample validation at both stages. Ablation experiment shows that Retrieval-Augmented Generation (RAG) reduces hallucinations, as compared to simply lengthening the input window. Empirical tests indicate that "greenwashing" captured by DeepGreen can effectively reveal a positive relationship between greenwashing and environmental penalties, and IV, PSM, Placebo test, which enhance the robustness and causal effects of the empirical evidence. Further study suggests that the presence and number of green investors can weaken the positive correlation between greenwashing and penalties. Heterogeneity analysis shows that the positive relationship between "greenwashing - penalty" is less significant in large-sized corporations and corporations that have accumulated green assets, indicating that these green assets may be exploited as a credibility shield for greenwashing. Our findings demonstrate that LLMs can standardize ESG oversight by early warning and direct regulators' scarce attention toward the subsets of corporations where monitoring is more warranted.

翻译：本文受大语言模型在经济学与管理学研究中日益广泛应用的启发，探讨了LLMs能否可靠识别企业漂绿叙事，更重要的是，从文本披露中提取的漂绿信号能否以及如何用于实证识别因果效应。为此，本文提出了DeepGreen，一个用于检测年报中潜在企业漂绿的双阶段LLM驱动系统。应用于2021年至2023年间发布的9369份A股年报，DeepGreen在两个阶段的随机样本验证中均表现出高可靠性。消融实验表明，与简单地延长输入窗口相比，检索增强生成技术减少了幻觉。实证检验表明，DeepGreen捕捉到的“漂绿”能有效揭示漂绿与环境处罚之间的正向关系，而工具变量法、倾向得分匹配和安慰剂检验则增强了实证证据的稳健性与因果效应。进一步研究表明，绿色投资者的存在及其数量会削弱漂绿与处罚之间的正相关性。异质性分析显示，“漂绿-处罚”的正向关系在大型企业和已积累绿色资产的企业中显著性较低，表明这些绿色资产可能被用作漂绿行为的信誉屏障。我们的研究结果表明，LLMs能够通过预警来标准化ESG监管，并将监管者有限的注意力引导至更需要监控的企业子集上。