Asymmetric impact of climate change on banking system stability in selected sub-Saharan economies
Bibliographic record
Abstract
This study is an empirical examination of the asymmetric impact of climate change on banking system stability in selected sub-Saharan economies. The study leverages a quantitative research approach through a panel non-linear ARDL framework and data from 29 selected economies, which spans 1996-2017. Findings from the study reveal that in the long term, partial sums of temperature have an insignificant relationship with banking system stability. Further, partial sums of precipitation are negatively related to banking system stability in the long term. The finding indicates that both positive and negative precipitation do harm banking system stability in the selected sub-Saharan economies. Moreover, we find that partial sums of greenhouse gas emissions had an insignificant relationship with banking system stability. In addition, we conclude that greenhouse gases (positive and negative) do not impair banking system stability in selected sub-Saharan economies in the long term. We surmise from the findings that both positive and negative climate change indexes are harmful to banking system stability. On the short-term asymmetric impact, we discover that partial sums of temperature, precipitation, and climate change index do not harm banking system stability. However, negative greenhouse gas emissions are pernicious to the banking system’s stability in the short term. Conversely, positive greenhouse gas levels were statistically insignificant. The study recommends that central banks and monetary authorities in Sub-Saharan Africa design green banking policies to push the climate change agenda in the banking sector.
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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 | 0.003 |
| 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".