An LLM-based agentic system for greenwashing detection

Environmental reporting has assumed growing importance in capital markets, particularly in shaping investors’ decision-making. However, the absence of universally accepted reporting standards and domain-specific assurance gives managers substantial discretion to present their sustainability performance in a deceptively favorable way, which is commonly called greenwashing. This risk is further compounded by investors’ limited access to robust data and specialized analytical expertise, which impairs their ability to detect greenwashing in environmental disclosures. Large Language Models (LLMs) with advanced reasoning capabilities and integrated web search functions offer a novel approach to addressing these challenges. This study proposes a framework for developing LLM-based agentic systems to detect greenwashing risks in environmental disclosures. Since greenwashing is not a binary outcome but a multi-dimensional phenomenon, this study introduces seven indicators to assess different facets of greenwashing risks. These indicators are further integrated into a dashboard tailored to the needs of various users. This study contributes to the greenwashing literature by establishing a comprehensive framework for constructing LLM-based agents to detect greenwashing and proposing a set of quantifiable greenwashing indicators. It also contributes to the accounting profession by providing a tool for real-time monitoring of greenwashing risks.