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.

At the Goodwood festival of speed near Chichester, the crowds gathered at the hill-climb circuit to watch the world's fastest cars roar past, as they do every year. But not far from the high-octane action, there was a new, and quieter, attraction: a display of the latest electric vehicles, from the £28,000 Mini Electric to the £2m Lotus Evija hypercar. Even here, at one of the biggest events in Britain's petrolhead calendar, it's clear the days of the internal combustion engine are numbered. As countries strive to meet stringent carbon-emission targets, and vehicle-makers phase out combustion engines, 145m electric vehicles are predicted to be on the roads within a decade, up from 11m last year. The car batteries they require, along with storage batteries for solar and wind power, have sent demand for metals soaring, taking mining firms to the bottom of the sea in the hunt for those metals.

When the 300-foot Maersk Launcher docked in San Diego early Monday morning, it unloaded a cargo of hardened black blobs scooped from the bottom of the sea. The blobs are not rocks, but naturally-occurring metallic nodules that could one day yield metal deposits of cobalt, manganese, and nickel--not to mention scarce rare earth minerals. As worldwide demand rises for electric vehicle batteries and wind turbines, along with next generation technologies and weapon systems, demand for these metals has taken off. And the seabed is a prime target for those mining operations. Of course, it's no small feat to bring these potato-sized nodules from the bottom of the remote Pacific Ocean, and then sail them to a processing plant where the metals can be extracted.