The Price of Greenwashing: Algorithmic Verification and Market Discipline using Conformal Machine Learning
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Computer Science > Machine Learning
Title:The Price of Greenwashing: Algorithmic Verification and Market Discipline using Conformal Machine Learning
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.
| Subjects: | Machine Learning (cs.LG); Econometrics (econ.EM) |
| Cite as: | arXiv:2610.02225 [cs.LG] |
| (or arXiv:2610.02225v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.02225
arXiv-issued DOI via DataCite
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