The effect of greenhouse gas emissions on cost of debt: Evidence from Canadian firms
Bibliographic record
Abstract
Abstract The aim of this paper is to investigate the relation between greenhouse gas (GHG) emissions and cost of debt and to estimate the cost that lenders are imputing to GHG emissions. Data on GHG emissions were hand‐collected from Carbon Disclosure Project reports, whereas data on the cost of debt and other financial data were obtained from Bloomberg Professional database. Using a sample of Canadian firms, the results show that GHG emissions increase firms' cost of debt. In other words, for each additional tonne of GHG emissions, the cost of debt increases on average by 11–15%. These results imply that creditors incorporate firms' GHG emissions into their lending decisions and they penalize the polluting firms. This could encourage firms to reduce and manage their GHG emissions because there is a cost associated with these emissions. This study is one of the first to examine the relationship between GHG emissions and the cost of debt.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".