AI Based Analysis of Environmental Sustainability Metrics in Annual Reports

Abstract

This research introduces a novel, cross-disciplinary methodology that applies artificial intelligence to analyze environmental sustainability metrics within corporate
annual 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 environmental
posture. We formulate the problem not as a simple classification task, but as a
multi-dimensional inference problem to assess the coherence, specificity, and comparability of sustainability metrics across industries. Our model, the Sustainability
Metric Inference and Linkage Engine (SMILE), is trained and validated on a unique
corpus of 5,000 annual reports (2000-2004) across five high-impact sectors. Results
demonstrate 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 unclassified
categories of sustainability reporting strategies, including ’Selective Amplification’
and ’Contextual Obfuscation.’ The findings provide a new, computational lens for
stakeholders to evaluate corporate environmental accountability, moving beyond
binary 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 tool
for enhanced transparency and a novel framework for future research in automated
sustainability audit.