Digital Twin-Driven Hybrid Fuzzy Bayesian Simulation Framework for Dynamic Green Credit Risk Assessment in Manufacturing Enterprises

Abstract

Green credit risk assessment for manufacturing enterprises is a dynamic and complex problem affected by operational states, environmental uncertainty, financial conditions, information quality, and interactive risk evolution. Traditional assessment methods depend heavily on low-frequency financial and disclosure data and therefore have limited ability to capture early risk signals arising from production operations, energy consumption, environmental performance, and supply-chain disturbances. In addition, conventional machine-learning models provide limited interpretability for causal dependence and uncertain risk propagation. This paper proposes a digital twin-driven hybrid fuzzy dynamic Bayesian network (DT-FDBN) framework for dynamic green credit risk assessment and scenario simulation in manufacturing enterprises. The framework integrates multi-source information covering financial solvency, production and equipment operation, energy consumption and emissions, green transition, environmental disclosure and information credibility, and the external environment into a unified enterprise digital-twin representation. Fuzzy evidence transformation is used to represent incomplete and imprecise observations, while credibility correction and group-level sensor reliability reduce the influence of unreliable digital-twin evidence. The DT-FDBN further models hierarchical risk dependence, temporal evolution, and probabilistic uncertainty propagation, supporting dynamic inference, key-factor diagnosis, multi-period prediction, and intervention-scenario simulation. Based on a semi-synthetic dataset containing 2,160 enterprise-month observations from 60 manufacturing enterprises, the proposed framework achieves an accuracy of 0.881, recall of 0.893, F1-score of 0.869, AUC of 0.943, and Brier score of 0.079. It outperforms XGBoost and conventional fuzzy dynamic Bayesian networks in overall predictive and calibration performance and identifies operational and environmental deterioration earlier than financial-only assessment. The proposed framework provides an interpretable and dynamically adaptive modeling paradigm for manufacturing green credit risk management.