Zeng Fanlong, Wang Jintao, Zeng Chaoyan
School of Foreign Studies, Yiwu Industrial and Commercial College, Jinhua, Zhejiang, China.
School of Finance, Shanxi Technology and Business University, Taiyuan, Shanxi, China.
PLoS One. 2025 Mar 6;20(3):e0316287. doi: 10.1371/journal.pone.0316287. eCollection 2025.
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.
准确预测和解释企业环境、社会和治理（ESG）漂绿行为对于提高信息透明度和增强监管有效性至关重要。本文通过引入一个优化的机器学习框架，解决了现有预测模型在超参数优化和可解释性方面的局限性。该框架集成了改进的猎人-猎物优化（IHPO）算法、极端梯度提升（XGBoost）模型和夏普利值加性解释（SHAP）理论，以预测和解释企业ESG漂绿行为。首先，通过广泛的文献综述和专家访谈，构建了一个全面的ESG漂绿预测数据集。然后，使用IHPO算法优化XGBoost模型的超参数，形成用于预测企业ESG漂绿行为的IHPO-XGBoost集成学习模型。最后，利用SHAP对模型的预测结果进行解释。结果表明，IHPO-XGBoost模型在预测企业ESG漂绿方面表现出色，R²、RMSE、MAE和调整后的R²值分别为0.9790、0.1376、0.1000和0.9785。与传统的HPO-XGBoost模型以及结合其他优化算法的XGBoost模型相比，IHPO-XGBoost模型展现出更优的整体性能。使用SHAP理论进行的可解释性分析突出了影响预测结果的关键特征，揭示了特征交互的具体贡献以及单个样本特征的影响。这些发现为监管机构和投资者更有效地识别和评估潜在的企业ESG漂绿行为提供了有价值的见解，从而提高监管效率和投资决策水平。
