Money at risk: climate change and performance of Canadian banking sector
Bibliographic record
Abstract
Purpose In the last few decades, the frequency and intensity of extreme weather events have increased in most parts of the world including Canada because of global warming. The global warming in Canada is about double the magnitude of global warming; therefore, policymakers are concerned about the potential significant impact of the weather catastrophes on the economy and financial sector. The purpose of this study is to explore the impact of weather catastrophes on the Canadian banking sector. Design/methodology/approach Using a sample of banking firms from Canada over the period 1988–2019, the present study estimates different econometric techniques to investigate the impact of weather catastrophes on the risk and performance of Canadian banks. Findings Analyses of the study do not find a significant impact of the weather catastrophes on the performance of the Canadian banks; however, it has helped banks to lower their risk level and improve stability due to proactive risk management. The findings of this study are not consistent with concerns of the policymakers about climate risk to the Canadian bank sector. More sector-specific research and policy initiatives are recommended to minimize the future financial risk of the increased frequency and intensity of natural disasters. Originality/value The study contributes to support the notion that the climate risk of banks is protected with insurance and reconstruction activities provide more banking opportunities.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".