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

D EEPG REEN: E FFECTIVE LLM-D RIVEN G REEN-WASHING M ONITORING S YSTEM D ESIGNED FOR E MPIRICAL T ESTING --E VIDENCE FROM C HINA Congluo Xu Business School Sichuan University Chengdu, 610065 Y u Miao School of Economics Sichuan University Chengdu, 610065 Yiling Xiao Business School Sichuan University Chengdu, 610065 Chengmengjia Lin Business School Sichuan University Chengdu, 610065 April 11, 2025 A BSTRACT This paper proposes DeepGreen, an Large Language Model Driven (LLM-Driven) system for detecting corporate green-washing behaviour. Utilizing dual-layer LLM analysis, DeepGreen preliminar-ily 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. K eywords Green-washing Monitoring Large Language Models Financial Statement Analysis Unstructured Data Analysis 1 Introduction Amid intensifying global focus on sustainable development and environmental protection, the phenomenon of corporate "green-washing" has emerged as a contentious issue. "Green-washing" typically refers to those companies exaggerating or misrepresenting their environmental protection efforts in promotional materials, while their actual practices fail to meet sustainable development standards [1]. However, a more elusive challenge lies in "general green-washing", which involves subtler tactics that distort perceptions by repeatedly invoking terms such as "carbon peak" or "green development" without substantive evidence [2]. The elusiveness of general green-washing stems from its exploitation of human psychology and information processing mechanisms.