A Machine Learning-Based Precision Index Correlating Corporate Sustainability Reporting with Real-Time Satellite Emissions Data

• Abi cit

  Lautech

  Author

Corporate sustainability reporting has become mandatory for large enterprises under the EU Corporate Sustainability Reporting Directive (CSRD), yet a persistent gap exists between self-reported emissions data and independently observed atmospheric measurements. This study addresses that gap by developing a machine learning-based Precision Index that correlates corporate sustainability disclosures with real-time satellite emissions observations. The research employed a design-based methodology integrating three data streams: greenhouse gas reporting program data from the U.S. Environmental Protection Agency, MethaneSAT satellite concentration and point-source emissions data, and corporate sustainability reports filed under the CSRD framework. A gradient boosting classifier was trained on extracted text features and matched satellite observations to predict disclosure-reality alignment, achieving 89.4% classification accuracy with a macro F1-score of 0.89. Feature importance analysis identified satellite-observed methane intensity, reported Scope 1 emissions, and flaring intensity as the strongest predictors of misalignment. The study contributes a replicable framework for regulatory oversight and advances greenwashing detection theory by operationalizing Okeke and Rahim’s (2026) triangulated construct in a predictive computational context. The findings have immediate implications for auditors, regulators, and corporate sustainability officers seeking to validate disclosure accuracy.

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