A Diagnostic Model for Categorizing Cognitive and Structural Drivers of Corporate Environmental Misrepresentation

• Abbas Ahsun

  Texas University

  Author

Corporate environmental misrepresentation remains a persistent challenge to sustainability governance, yet existing diagnostic tools focus predominantly on classifying misrepresentation practices rather than explaining their underlying drivers. This study addresses that gap by developing and validating a diagnostic model that categorizes the cognitive and structural antecedents of corporate environmental misrepresentation. Drawing on a retrospective dataset of sustainability disclosures, ESG metric patterns, and organizational characteristics, the study employs a design-based research approach combining supervised machine learning classification with feature attribution analysis. The model integrates cognitive drivers—managerial overconfidence, temporal discounting, and moral disengagement—with structural drivers including disclosure discretion, ESG metric ambiguity, and governance weakness. Results demonstrate that the hybrid cognitive-structural model achieves 89.4% classification accuracy in distinguishing high-risk from low-risk misrepresentation profiles, substantially outperforming practice-only diagnostic baselines. Feature importance analysis reveals that structural drivers related to metric ambiguity and disclosure discretion exhibit the strongest predictive weight, while cognitive drivers provide incremental explanatory value for severity prediction. The findings advance a replicable diagnostic framework that moves beyond identifying misrepresentation to explaining its origins, offering regulators and assurance providers a triage instrument for targeted scrutiny. The study concludes that effective anti-greenwashing governance requires attention to both the organizational structures that enable misrepresentation and the cognitive processes that normalize it.

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Copyright (c) 2026 Abbas Ahsun (Author)

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