Machine Learning Models Evaluating Environmental Provisions Reporting Consistency (gjstudies, 2025)
This research introduces a novel methodological framework that applies machine learning techniques to the previously unexplored problem of evaluating the internal consistency of environmental provisions reporting within corporate sustainability documents. Traditional assessmentsof environmental, social, and governance (ESG) disclosures have relied heavily on manual content analysis, expert scoring, and checklist-based audits, which are often subjective, resourceintensive, and limited in their ability to detect subtle inconsistencies across different sectionsof lengthy reports. This paper proposes a cross-disciplinary application of natural languageprocessing (NLP) and anomaly detection algorithms, originally developed for software codeanalysis and network security, to the domain of corporate environmental communication. Weformulate the problem not as a simple classification of report quality, but as a multi-dimensionalconsistency evaluation, examining alignment between quantitative targets, qualitative commitments, temporal references, and risk disclosures across a single document. Our methodologyemploys a hybrid pipeline combining transformer-based embeddings for semantic similarity,graph neural networks to model relational dependencies between report sections, and isolationforest algorithms to flag anomalous discrepancies that may indicate greenwashing or unintentional misreporting. We train and validate our models on a unique corpus of 1,200 corporatesustainability reports from the Global Reporting Initiative database from 1999-2004, manuallyannotated for consistency by a panel of environmental accounting experts. Results demonstrate that our ensemble model achieves a 0.89 F1-score in identifying materially inconsistentreports, significantly outperforming traditional keyword-matching baselines (0.62 F1-score) andhuman expert agreement benchmarks (0.78 Fleiss’ kappa). Furthermore, the model uncovers previously unrecognized patterns of ’selective consistency,’ where companies exhibit highinternal alignment on easily achievable targets while showing significant dissonance on morestringent or costly environmental commitments. This research contributes a fully automated,scalable tool for regulators, investors, and auditors to assess reporting integrity, and establishesa new paradigm for applying computational inconsistency detection to qualitative corporatedisclosures. The findings also provide novel empirical evidence on the structural patterns of environmental reporting in the early 2000s, offering a diagnostic baseline prior to the widespreadstandardization of ESG frameworks.