Abstract
This study examines how corporate carbon information disclosure (CID) influences debt contracting. We construct a comprehensive stakeholder-oriented evaluation system to assess CID quality, encompassing 26 s-level and 115 third-level indicators that capture the specificity, extensiveness, and equilibrium of disclosure. Drawing on 4080 firm-year observations of Chinese listed firms from 2017 to 2023, we apply machine learning techniques to extract and score carbon-related information from corporate reports. Our results show that higher-quality CID is associated with lower borrowing costs and longer debt maturity, with robust results verified by alternative measures, Tobit regression and sample adjustments. Heterogeneity analyses reveal that CID reduces costs primarily for non-high-carbon and low-financing-constraint firms, while it extends maturity for high-carbon and high-financing-constraint ones. Additional tests indicate that continuous disclosure is associated with stronger effects of CID. When accounting for ownership structure, we find that the negative association between CID and borrowing costs is more pronounced among state-owned enterprises (SOEs), whereas the positive association between CID and debt maturity is stronger among non-SOEs. Moreover, negative association between CID and borrowing costs is more evident in mixed-ownership firms than in non-mixed-ownership firms. Dynamic tests further suggest that the association between CID and debt contracting outcomes is primarily driven by current-period disclosure. Overall, our evidence demonstrates that high-quality carbon disclosure provides incremental, decision-useful information to creditors and serves as a key component of firms’ sustainable financing strategies.
| Original language | English |
|---|---|
| Article number | 102201 |
| Journal | Quarterly Review of Economics and Finance |
| Volume | 109 |
| DOIs | |
| State | Published - Sep 2026 |
Keywords
- Carbon information disclosure (CID)
- Cost of debt
- Debt contracting
- Debt maturity structure
- Machine learning
- Stakeholders’ demand
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