Creditors at the Gate: Effects of Selective Environmental Disclosure on the Cost of Debt
Bibliographic record
Abstract
ABSTRACT Research Question/Issue What is the impact of selective environmental disclosure, also known as greenwashing, on firms' credit risk profiles? Can the superior information and monitoring abilities of private lenders serve as environmental governance mechanisms to promote the adoption of ESG best practices by firms? Research Findings/Insights Through detailed examination of private debt contracts and environmental disclosure practices, we reveal that private lenders impose financial penalties on firms with poor environmental records, manifesting as higher spreads and loan‐related fees. Additionally, our analysis demonstrates that greenwashing, or misleading environmental transparency, results in increased debt financing costs for firms. Moreover, lenders may adopt lenient nonprice terms to mitigate the impact of higher loan costs on firms engaged in selective environmental disclosure. This intricate contract design allows lenders to extract appropriate returns without hindering firms' access to external financing. Theoretical/Academic Implications Our findings underscore the significance of private creditors in enhancing environmental disclosure standards within the corporate sphere. Additionally, our evidence emphasizes the importance of integrating firms' environmental impact into theoretical and empirical credit risk models. Practitioner/Policy Implications The intricate contract structures of bank loans can effectively address the informational risks associated with selective disclosure, without impeding firms' access to external financing. Hence, this financing mechanism holds the potential to enhance the ESG performance of firms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.105 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".