(untitled) (gjstudies, 2026)
This research introduces a novel, cross-disciplinary methodology that applies artificial intelligence to analyze environmental sustainability metrics within corporateannual reports, a domain traditionally dominated by manual, qualitative assessment. We propose an unconventional hybrid framework that combines transformerbased natural language processing with graph neural networks to extract, quantify,and contextualize environmental disclosures. Unlike prior work focused on keyword frequency or simple sentiment, our approach models the semantic relationships between sustainability claims, financial performance indicators, and regulatory contexts, constructing a dynamic knowledge graph of corporate environmentalposture. We formulate the problem not as a simple classification task, but as amulti-dimensional inference problem to assess the coherence, specificity, and comparability of sustainability metrics across industries. Our model, the SustainabilityMetric Inference and Linkage Engine (SMILE), is trained and validated on a uniquecorpus of 5,000 annual reports (2000-2004) across five high-impact sectors. Resultsdemonstrate SMILE’s ability to identify significant discrepancies between quantitative environmental targets and qualitative narrative, a pattern we term ’metricnarrative dissonance.’ Furthermore, the analysis reveals previously unclassifiedcategories of sustainability reporting strategies, including ’Selective Amplification’and ’Contextual Obfuscation.’ The findings provide a new, computational lens forstakeholders to evaluate corporate environmental accountability, moving beyondbinary greenwashing detection to a nuanced understanding of reporting quality.This work contributes original insights at the intersection of AI, computational linguistics, and environmental, social, and governance (ESG) analysis, offering a toolfor enhanced transparency and a novel framework for future research in automatedsustainability audit.