Traditional financial distress early-warning models mostly rely on lagged structured financial indicators, which fail to capture the potential credit risks associated with the climate transition of green enterprises. Taking A-share listed green companies from 2015 to 2024 as research samples, this paper centers on the core research question of whether mandatory climate narratives in annual reports can deliver incremental risk warning information beyond accounting indicators. Based on textual data from annual reports, this study constructs a corporate climate resilience indicator by integrating word frequency statistics and sentiment analysis. Two data-partitioning schemes (random sampling and time-series extrapolation) are adopted to compare the predictive performance of four ensemble learning models. Extended tests are further conducted via SHAP values, partial dependence plots, polynomial Logit regression, interaction effect regression and grouped regression. The results indicate that the climate resilience indicator carries incremental information supplementary to financial indicators and possesses predictive power for financial distress. XGBoost demonstrates optimal adaptability to the hybrid feature framework, combining financial data and climate textual features. The climate resilience indicator exerts synergistic effects with financial variables and presents a non-linear statistical correlation with default probability. This study verifies that climate narratives disclosed in annual reports can serve as valid early-warning signals for credit risks. The conclusions provide empirical evidence for financial risk control, corporate disclosure management and the formulation of climate regulatory policies.
Niu et al. (Mon,) studied this question.