Assessing Climate-Related Financial Risk: Guide to Implementation of Methods
Bibliographic record
Abstract
The Bank of Canada and the Office of the Superintendent of Financial Institutions completed a climate scenario analysis pilot project with the collaboration of six Canadian financial institutions. The project aimed to increase understanding of the financial sector’s potential exposure to risks in transitioning to a low-carbon economy and to help build the capabilities of authorities and financial institutions in assessing climate-related risks. To support the broader financial-sector community in building these capabilities, this report provides detail on the methodologies the pilot used to assess credit and market risks, which were informed by the financial impacts generated by the climate transition scenarios. The method to assess credit risk combined top-down and bottom-up approaches. Variables from the climate transition scenarios were first translated into sector-level financial impacts. The financial institutions then used these impacts to estimate the implications on credit outcomes through borrower-level assessments. Using the transition scenarios’ financial impacts, and the stressed credit outcomes, the project estimated a relationship between climate transition information and credit risk. This was used to calculate expected credit losses at the portfolio level. The method to assess market risk was solely top-down. Using the scenario analysis, the project used a dividend discount model to estimate sectoral equity revaluations, which it then applied to equity portfolio holdings.
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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 | 0.038 | 0.050 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.061 | 0.026 |
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".