(untitled) (gjstudies, 2024)
This research introduces a novel, hybrid machine learning framework specificallydesigned to detect and quantify greenwashing—the practice of making misleadingenvironmental claims—within the narrative sections of corporate financial reports.While existing literature primarily focuses on sentiment analysis or keyword spotting for sustainability reporting, our approach uniquely integrates three distinctmethodologies to capture the nuanced, often obfuscated nature of greenwashing.First, we employ a transformer-based language model fine-tuned on a purposebuilt corpus of verified greenwashing and legitimate sustainability disclosures toperform deep semantic analysis, moving beyond surface-level features. Second,we implement a novel coherence scoring mechanism that measures the alignmentbetween environmental claims made in the front-of-report narratives and the quantitative environmental performance data presented in appendices or supplementaryreports, identifying strategic decoupling. Third, we develop a temporal inconsistency detector using recurrent neural networks to flag claims that contradict acompany’s own historical environmental disclosures. We validate our frameworkon a manually annotated dataset of 500 annual reports from the SP 500 between1995 and 2004, achieving a detection accuracy of 91.7% and a precision of 88.3%in identifying materially misleading statements, significantly outperforming baseline keyword-matching and sentiment analysis models. Our findings reveal thatgreenwashing is not merely a function of exaggerated positive sentiment but ischaracterized by specific rhetorical patterns, strategic vagueness, and measurabledisconnects between narrative and data. This work provides auditors, regulators,and investors with a powerful, automated tool for enhanced scrutiny of corporateenvironmental communications and establishes a new methodological paradigm forcomputational analysis of corporate discourse.