Green credit risk assessment and prediction in China’s construction industry: based on the optuna-catboost model

Green credit, as a new credit model, not only shares the risk characteristics of traditional credit but also incorporates environmental risk factors into its analysis, making the assessment more complex. This article employs a modified KMV model to evaluate the risk of green credit in the construction industry. Utilizing Mann-Whitney U test and a feature ranking method based on learning models for indicator selection, the final indicators are made more objective and scientific. Subsequently, a green credit risk assessment system suitable for the Chinese construction industry is proposed. Finally, the Optuna-CatBoost (OptCAT) algorithm is applied to predict the risk of green credit in the construction industry, aiming to provide a methodological reference for the industry to mitigate green credit risks. Empirical results indicate that the modified KMV model provides an accurate measurement of green credit risk in the Chinese construction industry. Compared to default parameter (DP), random search (RS), grid search (GS), bayesian optimization (BO), and Hyperopt, Optuna demonstrates better optimization effects. When compared to support vector machine (SVM), random forest (RF), XGBoost, and LightGBM models, OptCAT exhibits the optimal predictive performance.