Evaluating the Effect of Climate Risk on Financial Fragility in Arab Countries
Bibliographic record
Abstract
This study explores the impact of climate risk on the financial fragility in Arab countries, which is partitioned into four categories pursuant to the level of income from 2007 to 2019. This has been performed using an aggregate banking stability index, as a measure of financial fragility in 18 countries, including (i.e. Algeria, Bahrain, Djibouti, Egypt, Iraq, Jordan, Kuwait, Lebanon, Libya, Mauritania, Morocco, Oman, Qatar, Saudi Arabia, Sudan, Tunisia, UAE, Yemen). The outcomes reveal that climate risk has significant positive effects on financial fragility. The findings show that importance of climate-related risk and some factors in explaining financial fragility, where broad money, domestic credit to private sector by banks, GDP growth, and income seem to have significant effects on financial fragility. Robustness test using alternative measures of financial fragility and changing estimation method to assure the reliance of study results. Both approaches confirm the previous findings. The study contributes to the literature by providing empirical evidence on the effect of climate-related risk on banking stability in Arab countries over 13 years and emphasizing that climate risk is source of risk for the financial system. The research insights of this contribution can inform policymakers and central banks to assess climate-related financial risks, highlighting the need for a better understanding of the impact of climate shocks on world financial system across countries.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".