Unmasking Upstream Misrepresentation: A Multi-Tiered Diagnostic Construct for Measuring Greenwashing and Opaque Environmental Claims in Global Value Chains

• Adaan Ahsun

  Covenant University

  Author

The proliferation of corporate environmental claims in global value chains has outpaced the development of robust verification mechanisms, creating systemic vulnerabilities to greenwashing that undermine stakeholder trust and sustainable investment. Despite increasing regulatory scrutiny and the emergence of ESG disclosure frameworks, existing detection approaches remain fragmented, reactive, and ill-equipped to address sophisticated misrepresentation across multinational supply networks. This study addresses this gap by developing and validating a multi-tiered diagnostic construct for measuring greenwashing and opaque environmental claims in global value chains. Drawing on signaling theory and prospect theory, the research employs a design-based methodology combining retrospective analysis of 5031 corporate ESG reports across 26 industries with prospective simulation of a multi-agent diagnostic framework. The proposed construct integrates semantic deconstruction, adversarial fact-checking, and dynamic risk quantification, achieving 89.4% detection accuracy—a 25.7% improvement over conventional methods (p < 0.001). Key predictors of greenwashing risk include selective disclosure patterns, ESG metric manipulation, and symbolic environmentalism. The study contributes a replicable diagnostic framework with practical implications for regulators, investors, and supply chain administrators. It further identifies implementation barriers related to data accessibility, organizational capacity, and inter-jurisdictional regulatory misalignment. The findings provide actionable metrics for enhancing transparency and accountability in corporate environmental reporting.

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