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.

In the scope of a future-oriented collaboration in the field of industrial production, the Fraunhofer-Gesellschaft is cooperating with the VSB – Technical University of Ostrava (VSB-TUO). The partners research and develop the potential offered by energy management technologies, artificial intelligence (AI) and intelligent production in industry. The collaboration provides production companies with innovative solutions, which they can in turn use to develop innovative and sustainable solutions for reducing greenhouse gas emissions. This builds on over five years of successful collaboration between the Fraunhofer Institute for Machine Tools and Forming Technology IWU, the Fraunhofer Institute for Chemical Technology ICT and the VSB – Technical University of Ostrava (VSB-TUO). The ambitious venture "Fraunhofer Innovation Platform for Applied Artificial Intelligence for Materials & Manufacturing at VSB – Technical University of Ostrava FIP-AI@VSB-TUO" commenced operation on June 1, 2021.

Very often in the context of AI, it is mentioned that enormous amounts of data are required in order to work with it in the first place. Very complex models have to be programmed and the success of a project is often associated with many unpredictabilities and risks. However, as a general rule, this is completely wrong. This article is all about giving you a perspective on how to handle situations of data scarcity and the possibilities to consider in this context. Of course, there are complex projects that place extreme demands on the amount of data in order to achieve effective results, but usually this has to do with poor planning or a deliberately high willingness to experiment.

According to a new analysis by Inkwood Research, the global market for collaborative robots is on track to generate a net revenue of about $9.27 billion by 2025. Many so-called cobots cost around $30K. There's increasing demand for small, flexible robotic platforms in numerous industries. Other factors responsible for the surging market and stiffening competition include widening applications of collaborative robots, falling sensor and platform prices, and heavy investments by robotics companies over the past decade in research & development.

The internet of things, artificial intelligence, networked production, smart homes - these are the magic words of digital transformation. While the big technology companies are already equipping their products and production with artificial intelligence - all parts of the chain of values added are to supply data in the future -, German medium-sized companies are not succumbing to its spell. Scientists of Karlsruhe Institute of Technology (KIT) help companies implement Industry 4.0. Most of the small and medium-sized companies consider the increasing use of digital technology an opportunity, but where to start? According to a survey of Germany's digital association Bitcom, 90% of the small and medium-sized companies consider digital transformation an opportunity.

As new technologies and trends emerge in the marketplace, small and medium-sized companies must look out for ways they too can benefit from improvements and advancements. Startups have demonstrated time and time again in recent years that the company willing to put new technology to use to solve an old problem (Netflix, Uber, Airbnb, etc.) are the companies that will succeed -- no matter how big or small they are when they begin. These technologies mean that work that once might have been too time consuming or expensive for a small company to do becomes quick and relatively inexpensive. Companies that can't afford a dedicated customer service representative can outsource much of that work to a chatbot that can answer simple customer service questions. While a very small business might not be able to employ the latest in robotics in-house, the advent of more automated manufacturing will make manufacturing more affordable and small runs of products more achievable. This will open up production possibilities for many small businesses.