Carbon Costs and Credit Risk in a Resource-Based Economy: Carbon Cost Impact on the Z-Score of Canadian TSX 260 Companies
Bibliographic record
Abstract
Climate risks and climate risk-related policies on carbon threaten the ability of economies to thrive and will impact the credit risk of many sectors, primarily high-emitting sectors. Higher credit risks will also affect lenders if their credit portfolios are exposed to climate change risks. The introduction of carbon pricing policies will exacerbate this threat in resource-based economies such as Canada. While some research exists on climate exposure and risks to lending portfolios, there is a knowledge gap on how carbon pricing impacts individual commercial credit risk. Consequently, this study analyzes the effect of different carbon pricing scenarios on Altman’s z-score. Using the Canadian TSX 260 data between 2010 and 2020 as a sample, this paper applied the costs of different carbon prices using the Canadian Government’s carbon price regime of $0 to $170 to analyze Altman’s z-score variables until 2030. The results suggest that carbon price will significantly impact the credit risk of companies in high-emitting industries, such as the energy sector. We conclude that climate policy exposure in the form of carbon costs will have a real impact on credit risk and that lenders must consider carbon emissions as part of their credit risk assessment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".