Mandatory Versus Voluntary GHG Emissions Disclosures and Credit Risk
Bibliographic record
Abstract
The aim of this study is to examine the effect of GHG emission performance, as disclosed through voluntary versus mandatory channels, on credit risk (credit ratings and cost of debt). Two different channels are examined: voluntary disclosures made through the CDP and mandatory disclosures made through the EPA. Using a sample of US S&P 500 firms that have voluntarily/mandatorily disclosed their GHG emissions from 2010 to 2016, our results show that GHG emissions disclosures made through both channels have a negative effect on S&P credit ratings. These results imply that credit rating agencies incorporate GHG emissions in their credit assessment of a firm. However, our results show that only the GHG emissions mandatorily disclosed have a significant effect on cost of debt. These results imply that US lenders take into account, in their own lending decisions, only mandatory GHG emissions disclosures made through the EPA and not the voluntary ones made through the CDP. Additional analyses shows that these results are driven by firms in carbon intensive sectors and by firms with speculative grade ratings/high cost of debt. Overall, we conclude that credit market participants (credit rating agencies and creditors), as major stakeholders, make firms accountable for their carbon profile.
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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 | 0.006 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".