Umamaheswararao Mogili
The rapid growth of e-commerce and digital marketing has significantly increased the number of environmental claims made by companies regarding the sustainability of their products. While many organizations promote environmentally friendly initiatives, a large number of these claims lack verifiable evidence and are often misleading. This practice, commonly known as greenwashing, creates confusion among consumers and undermines trust in genuine sustainability efforts. Identifying deceptive environmental claims has therefore become an important challenge in modern digital commerce. In this research, we present EcoLens GRIE (Greenwashing Risk Intelligence Engine), an Artificial Intelligence-based system designed to detect misleading environmental claims using Natural Language Processing and machine learning techniques. The proposed system analyzes sustainability claims from product descriptions, marketing materials, and online listings to evaluate their credibility and transparency. A transformer-based zero-shot classification model is used to identify patterns associated with common greenwashing practices such as vague language, hidden trade-offs, and absence of supporting evidence. The system computes multiple analytical metrics including deception probability, transparency index, credibility score, and environmental confidence. These metrics are combined to generate a comprehensive sustainability intelligence report for consumers. The EcoLens platform is implemented using a FastAPI backend integrated with transformer-based NLP models and a React-based interactive dashboard for visualization of results. Experimental analysis demonstrates that the proposed system can effectively identify misleading sustainability claims and provide real-time risk assessment. The system not only assists consumers in making informed purchasing decisions but also encourages companies to adopt transparent environmental communication practices.