DeepGreen: Effective LLM-Driven Green-washing Monitoring System Designed for Empirical Testing -- Evidence from China

• URL: http://arxiv.org/abs/2504.07733v1
• Date: Thu, 10 Apr 2025 13:29:07 GMT
• Title: DeepGreen: Effective LLM-Driven Green-washing Monitoring System Designed for Empirical Testing -- Evidence from China
• Abstract summary: DeepGreen preliminarily identifies potential green keywords in financial statements and then assesses their implementation degree.<n>We extract 204 financial statements of 68 companies from A-share market over three years, comprising 89,893 words, and analyse them through DeepGreen.<n>Our analysis, supported by violin plots and K-means clustering, reveals insights and validates the variable against the Huazheng ESG rating.
• Score: 0.9999629695552195
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper proposes DeepGreen, an Large Language Model Driven (LLM-Driven) system for detecting corporate green-washing behaviour. Utilizing dual-layer LLM analysis, DeepGreen preliminarily identifies potential green keywords in financial statements and then assesses their implementation degree via iterative semantic analysis of LLM. A core variable GreenImplement is derived from the ratio from the two layers' output. We extract 204 financial statements of 68 companies from A-share market over three years, comprising 89,893 words, and analyse them through DeepGreen. Our analysis, supported by violin plots and K-means clustering, reveals insights and validates the variable against the Huazheng ESG rating. It offers a novel perspective for regulatory agencies and investors, serving as a proactive monitoring tool that complements traditional methods.Empirical tests show that green implementation can significantly boost the asset return rate of companies, but there is heterogeneity in scale. Small and medium-sized companies have limited contribution to asset return via green implementation, so there is a stronger motivation for green-washing.

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