Lin Mei

In green credit markets, information asymmetry and corporate greenwashing increasingly undermine the efficiency of resource allocation, while traditional assessment models relying on static, self-reported environmental data fail to impose effective constraints. To address this limitation, this paper develops a dynamic corporate carbon credit evaluation framework by integrating multiple sources of physical (hard) signals and embeds it into commercial banks’ credit management systems. Anchored in multi-source power-carbon signals (e.g., carbon intensity and compliance records), the framework integrates verifiable physical metrics with ESG disclosures via a Bayesian AHP–CRITIC weighting scheme to construct a dual-dimensional classification scheme (“Credit Rating–Green Label”). It further embeds carbon credit scores into dynamic adjustments to credit limits and differentiated interest rate pricing, forming an integrated risk management mechanism. Empirically, a stratified validation strategy is adopted. Analysis based on a sample of 3327 firms shows that the proposed framework achieves a classification consistency of 81.3%, significantly outperforming both a financial-only baseline model (46.8%) and models based on voluntary carbon disclosure (61.4%). Ablation studies further confirm that physical (hard) signal indicators contribute substantially to ranking stability. Moreover, panel regression analysis, based on 36,185 firm-year observations from 3327 firms over the period 2000–2023, demonstrates that carbon credit scores have robust predictive power for future financial distress. Overall, the proposed framework offers a sustainable, data-driven approach to green credit risk management.