An optimized machine learning framework for predicting and interpreting corporate ESG greenwashing behavior.
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...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0316287 |
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| author | Fanlong Zeng Jintao Wang Chaoyan Zeng |
| author_facet | Fanlong Zeng Jintao Wang Chaoyan Zeng |
| author_sort | Fanlong Zeng |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-21174a8ca01d4ea5bacc9eee1e603219 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-21174a8ca01d4ea5bacc9eee1e6032192025-08-20T03:52:46ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01203e031628710.1371/journal.pone.0316287An optimized machine learning framework for predicting and interpreting corporate ESG greenwashing behavior.Fanlong ZengJintao WangChaoyan ZengThe 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.https://doi.org/10.1371/journal.pone.0316287 |
| spellingShingle | Fanlong Zeng Jintao Wang Chaoyan Zeng An optimized machine learning framework for predicting and interpreting corporate ESG greenwashing behavior. PLoS ONE |
| title | An optimized machine learning framework for predicting and interpreting corporate ESG greenwashing behavior. |
| title_full | An optimized machine learning framework for predicting and interpreting corporate ESG greenwashing behavior. |
| title_fullStr | An optimized machine learning framework for predicting and interpreting corporate ESG greenwashing behavior. |
| title_full_unstemmed | An optimized machine learning framework for predicting and interpreting corporate ESG greenwashing behavior. |
| title_short | An optimized machine learning framework for predicting and interpreting corporate ESG greenwashing behavior. |
| title_sort | optimized machine learning framework for predicting and interpreting corporate esg greenwashing behavior |
| url | https://doi.org/10.1371/journal.pone.0316287 |
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