The Incremental Predictive Value of Corporate Carbon Performance Indicators for Financial Distress: Evidence from Carbon-Intensive Chinese A-Share Firms

The low-carbon transition has strengthened the link between corporate carbon performance and financial risk. This study examines whether carbon performance improves the out-of-sample prediction of financial distress beyond conventional financial indicators and whether its incremental value varies with financing constraints. Using panel data from Chinese A-share firms in eight carbon-intensive industries over 2012–2025, we compile 40 candidate financial indicators and 34 candidate carbon performance indicators covering emissions performance, carbon governance and low-carbon transition, and carbon disclosure. Financial indicators are selected using LASSO, while a corporate carbon performance index is constructed following data-quality screening and multicollinearity diagnostics. Defining financial distress as an ST/*ST designation two years ahead, we estimate two nested Logit models using a chronological training–test split and compare their out-of-sample performance through company-clustered paired bootstrap tests. Adding the carbon performance index significantly improves model discrimination and the accuracy of probability predictions. The results remain robust to Firth Logit estimation and changes in industry coverage. The improvement is concentrated among firms with stronger financing constraints and becomes insignificant at a three-year prediction horizon. These findings suggest that multidimensional carbon performance provides a complementary short-term risk signal for predicting financial distress among carbon-intensive firms.