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

“洗绿”的代价：利用共形机器学习进行算法验证与市场约束

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.

摘要： 尽管企业可持续发展要求日益增加，但金融市场对企业自报排放数据的系统性依赖，使其面临普遍的“洗绿”（greenwashing）风险。目前的文献主要依赖主观的ESG评级或文本情感分析，这在客观量化物理气候现实方面留下了一个关键的计量经济学空白。

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.

为了解决这种信息不对称，我们将美国证券交易委员会（SEC）的财务基本面数据与美国环保署（EPA）的设施级温室气体登记数据相结合，建立了一个具有数学保证的企业物理排放基准。通过利用梯度提升架构（Gradient Boosting）和蒙德里安共形预测（Mondrian Conformal Prediction），我们将自报数据与该算法基准之间的差距量化为一种新颖的“共形加权连续偏差”（CWCD）指标。

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).

通过横截面领先-滞后（lead-lag）计量经济学设计评估这种偏差，我们发现了一种稳健的市场约束机制：算法测算的排放偏差与随后的市场估值（托宾Q值）和运营盈利能力（资产收益率，ROA）之间存在显著的负相关关系。

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.

本研究为反驳“市场盲目性假说”提供了确凿证据，证明机构资本在定价时，不仅将环境欺诈视为道德失误，更将其视为企业根本性管理不善的先行指标。最终，这些发现为资产管理者和监管机构大规模部署算法审计基础设施提供了必要的定量依据。