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As China’s new energy vehicle (NEV) industry shifts from scale expansion to sustainable competition, enterprise risk is increasingly shaped by price pressure, innovation investment, operational efficiency, and cash-flow quality. Conventional financial early warning models based on static accounting ratios are limited in capturing how such risks emerge and transmit within NEV business ecosystems. This study develops an AI-driven risk governance framework that combines a digital twin-inspired state representation, interpretable machine learning, Shapley additive explanations, and competitive scenario simulation. Using annual data from 2021 to 2025 for twelve listed Chinese NEV automakers, we construct forty-eight enterprise-year observations and predict next-period high-risk status from current-period financial, operational, and competitive state vectors. Logistic regression is used as a transparent benchmark, while XGBoost serves as the main nonlinear learner. The results show that NEV risk identification requires the joint consideration of profitability, R&D intensity, cash-flow quality, asset utilisation, and liquidity, rather than reliance on a single accounting indicator. Logistic regression provides stronger temporal stability, whereas XGBoost achieves higher recall and area under the receiver operating characteristic curve in cross-validation. SHAP results identify return on assets, R&D intensity, operating cash-flow ratio, fixed asset turnover, and current ratio as the leading contributors to model predictions. Scenario simulations reveal asymmetric resilience: low-risk firms can absorb moderate competitive shocks, while high-risk firms remain locked in elevated risk states. This study provides a practical decision-support framework for identifying risk drivers, evaluating competitive shocks, and improving risk governance in sustainable NEV business ecosystems.
Xia et al. (Wed,) studied this question.