An optimized machine learning framework for predicting and interpreting corporate ESG greenwashing behavior

Fanlong Zeng, Jintao Wang and Chaoyan Zeng

PLOS ONE, 2025, vol. 20, issue 3, 1-25

Abstract: The accurate prediction and interpretation of corporate Environmental, Social, and Governance (ESG) greenwashing behavior is crucial for enhancing information transparency and improving regulatory effectiveness. This paper addresses the limitations in hyperparameter optimization and interpretability of existing prediction models by introducing an optimized machine learning framework. The framework integrates an Improved Hunter-Prey Optimization (IHPO) algorithm, an eXtreme Gradient Boosting (XGBoost) model, and SHapley Additive exPlanations (SHAP) theory to predict and interpret corporate ESG greenwashing behavior. Initially, a comprehensive ESG greenwashing prediction dataset was developed through an extensive literature review and expert interviews. The IHPO algorithm was then employed to optimize the hyperparameters of the XGBoost model, forming an IHPO-XGBoost ensemble learning model for predicting corporate ESG greenwashing behavior. Finally, SHAP was used to interpret the model’s prediction outcomes. The results demonstrate that the IHPO-XGBoost model achieves outstanding performance in predicting corporate ESG greenwashing, with R², RMSE, MAE, and adjusted R² values of 0.9790, 0.1376, 0.1000, and 0.9785, respectively. Compared to traditional HPO-XGBoost models and XGBoost models combined with other optimization algorithms, the IHPO-XGBoost model exhibits superior overall performance. The interpretability analysis using SHAP theory highlights the key features influencing the prediction outcomes, revealing the specific contributions of feature interactions and the impacts of individual sample features. The findings provide valuable insights for regulators and investors to more effectively identify and assess potential corporate ESG greenwashing behavior, thereby enhancing regulatory efficiency and investment decision-making.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0316287

DOI: 10.1371/journal.pone.0316287

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