The impact of climate change on the resilience of banking systems in selected Sub-Saharan economies
Bibliographic record
Abstract
Climate change is seen as a peril to the overall financial system, yet this revelation is in its infant stage. On that note, this study investigates the impact of climate change shocks on banking system resilience in selected Sub-Saharan economies. The study relies on a quantitative research method by first employing a Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) (1,1) model to forecast the volatility series of the climate change variables. Further, the study applies the panel ARDL model to disseminate the long- and short-term associations between the obtained conditional variances of climate change parameters and banking system resilience within a time frame of 1996-2017 for 29 selected economies. The results show that banking systems in SSA are resilient to temperature shocks in the long-term. However, the study finds that the banking systems in SSA are not resilient to both precipitation and greenhouse gas shocks in the long-term. For the short-term impact assessment, the study finds that banking systems in SSA are resilient to only precipitation shocks. The study concludes that banking sectors in SSA should vigorously conduct stress-testing on climate-related financial risks and also design forward-looking strategies as well as climate change risk management procedures in the wake of climate change events.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".