DeepGreen Detects Corporate Greenwashing Using LLMs
This paper proposes DeepGreen, a dual-stage LLM-driven system to detect corporate greenwashing in 9,369 A-share annual reports published between 2021 and 2023. Validation shows high reliability and ablation experiments indicate Retrieval-Augmented Generation (RAG) reduces hallucinations versus longer input windows; empirical IV, PSM, and placebo tests find DeepGreen’s greenwashing signals positively associate with environmental penalties. Findings suggest green investors, firm size, and green assets can weaken this penalty correlation, informing targeted ESG oversight.
Key Points
- 1Introduces DeepGreen, dual-stage LLM system detecting greenwashing across 9,369 A-share annual reports (2021–2023)
- 2Uses RAG to reduce hallucinations, improving extraction reliability compared to simply lengthening input windows
- 3Demonstrates greenwashing signals predict environmental penalties; enables targeted regulator oversight and monitoring prioritization
Scoring Rationale
High methodological novelty and broad industry relevance, but limited by single-source arXiv preprint without peer-reviewed validation.
Sources
Public references used for this report.
Practice with real FinTech & Trading data
90 SQL & Python problems · 15 industry datasets
250 free problems · No credit card
See all FinTech & Trading problems
