Money at risk: climate change and performance of Canadian banking sector
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it