The Price of Greenwashing: Algorithmic Verification and Market Discipline using Conformal Machine Learning

arXiv:2610.02225v1 Announce Type: new Abstract: While corporate sustainability mandates are expanding, the systemic reliance on self-reported emissions data exposes financial markets to pervasive greenwashing. Current literature relies heavily on subjective ESG ratings or textual sentiment analysis, leaving a critical econometric gap in objectively quantifying physical climate realities. To resolve this information asymmetry, we fuse U.S. SEC financial fundamentals with facility-level EPA greenhouse gas registries to establish a mathematically guaranteed baseline of physical corporate emissions. Leveraging a gradient boosting architecture and Mondrian Conformal Prediction, we quantify the shortfall between self-reported data and this algorithmic baseline into a novel Conformal-Weighted Continuous Divergence (CWCD) metric. Evaluating this divergence via a cross-sectional lead-lag econometric design, we uncover a robust mechanism of market discipline: algorithmic emissions divergence exhibits a severe, statistically significant negative relationship with subsequent market valuation (Tobin's Q) and operational profitability (ROA). Providing definitive evidence against the market blindness hypothesis, this study proves that institutional capital actively prices environmental deception not merely as an ethical lapse, but as a leading indicator of fundamental corporate mismanagement. Ultimately, these findings provide the quantitative justification necessary for asset managers and regulators to deploy algorithmic auditing infrastructure at scale.

Exposes greenwashing, shows market punishment for deception

This is a narrow academic application of ML to improve corporate transparency, a modestly positive societal use case if validated. Evidence is a single unreviewed preprint with strong claims ('definitive evidence', 'proves') that exceed typical econometric certainty, warranting caution in magnitude and confidence.

This paper demonstrates an AI system capable of robustly identifying corporate greenwashing by comparing self-reported emissions with an algorithmic baseline. The system's output is shown to have a statistically significant negative relationship with market valuation and profitability. This promotes market transparency and accountability, offering a tool for regulators and investors to address deceptive environmental practices.

SOURCE     arXiv cs.LG (Machine Learning) (tier 1)
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DOCUMENT   8259fc44-aaa9-4af4-9adb-745bd9159e65
           https://arxiv.org/abs/2610.02225
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EVIDENCE   2 extracted excerpts
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MODEL RUN  3 runs, methodology 1.0.1
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SCORE      -92  (Strongly adverse)
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CONFIDENCE 72%