发表机构
UNSW; Rennes School of Business（新南威尔士大学; 雷恩商学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究融合SEC财务数据与EPA排放登记，利用梯度提升和Mondrian共形预测构建CWCD指标，证明算法排放散度与市场估值及盈利能力显著负相关，为市场纪律和算法审计提供量化依据。
AI 中文摘要
虽然企业可持续发展任务正在扩大，但对自我报告排放数据的系统性依赖使金融市场面临普遍的漂绿风险。现有文献严重依赖主观的ESG评级或文本情感分析，在客观量化实际气候现实方面留下了关键的计量经济学空白。为解决这一信息不对称问题，我们将美国证券交易委员会（SEC）的财务基本面数据与环保署（EPA）的设施级温室气体登记数据相融合，建立企业实际排放的数学上可保证的基线。利用梯度提升架构和Mondrian共形预测，我们将自我报告数据与该算法基线之间的差距量化为一种新颖的共形加权连续散度（CWCD）指标。通过横截面领先-滞后计量经济学设计评估该散度，我们揭示了市场纪律的稳健机制：算法排放散度与随后的市场估值（托宾Q）和运营盈利能力（ROA）呈严重的、统计上显著的负相关关系。本研究提供了反对市场盲目假说的决定性证据，证明机构资本积极地将环境欺骗定价，不仅视为道德失误，而且视为企业根本管理不善的领先指标。最终，这些发现为资产管理者与监管机构大规模部署算法审计基础设施提供了必要的量化依据。
英文摘要
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.
