AI-Based Detection of Greenwashing to En-hance Environmental Accountability in Sus-tainability Reporting

As environmental concerns intensify globally, the integrity of corporate sustainability reporting has come under scrutiny. This study presents a machine learning-based method to identify greenwashing tendencies in corporate sustainability disclosures, with the aim of reinforcing environmental accountability and supporting technological innovation in eco-engineering. Drawing on 165 sustainability reports from companies listed on the Indonesia Stock Exchange, we apply text mining techniques using the Loughran-McDonald Financial Dictionary alongside the Myšková-Hájek CSR lexicon. The findings reveal distinct linguistic patterns that may serve as indicators of misleading environmental claims. Reports characterized by greater complexity and a higher presence of socially oriented language are more likely to reflect stronger CSR engagement. Conversely, a greater frequency of constraining terms, modal expressions, and negative sentiment words often corresponds with questionable reporting integrity. Among the models tested, Gradient Boosting delivered the most reliable classification performance (AUC: 0.89). This approach offers an emerging solution for sustainability auditors and environmental engineers seeking to enhance oversight of corporate environmental commitments. The research also contributes to the broader goals of SDG 12 (Responsible Consumption and Production) and SDG 13 (Climate Action), suggesting a practical path forward for integrating AI technologies in green governance systems.