AI Enabled Assessment of Greenwashing Risk in Tokenized ESG Assets

The growing use of blockchain-based tokenized Environmental, Social, and Governance (ESG) assets, sustainable finance has obtained new opportunities due to the ability of transparent and decentralized trading of sustainability-related financial instruments. The absence of standard ESG checking processes and regulatory controls has however heightened chances of greenwashing whereby organizations inflate or distort environmental or social performance to win over investors. This paper suggests an Artificial Intelligence-based Greenwashing Risk Assessment Model (AI-GRAM), which is aimed at assessing the plausibility of tokenized ESG assets. The suggested framework will combine machine learning classification and blockchain analytics to evaluate ESG disclosure, token metadata, and environmental performance indicator. The probability of sustainability misrepresentation in tokens of ESG is quantified by a Greenwashing Risk Score (GRS), which is mathematically constructed. The findings demonstrate that the proposed framework substantially increases the accuracy of detection of misleading ESG claims and the transparency level of the decentralized financial ecosystems. The suggested solution is capable of helping investors, regulators, and sustainability auditors evaluate ESG credibility and minimize the risks linked to greenwashing in tokenized sustainable finance markets.