The Impact of Climate Change on Financial Stability in South Africa
Bibliographic record
Abstract
This study investigates the dynamic relationships between climate change and financial stability in South Africa by employing a Bayesian vector autoregression model (BVAR). Using data from 1991 to 2022, we examine the impact of carbon emissions, adjusted savings, renewable energy consumption, lending interest rates, and unemployment on financial stability. Our findings indicate that carbon emissions, adjusted savings damaged by carbon dioxide emissions, renewable energy consumption, and unemployment significantly erode financial stability. Impulse response functions reveal that shocks to carbon emissions, lending interest rates, and unemployment have lasting effects on financial stability. Forecast error variance decomposition analysis shows that external factors, particularly carbon emissions and lending interest rates, increasingly drive uncertainty in forecasting financial stability over time. The study’s results support the Financial Instability Hypothesis and the Diamond–Dybvig model, highlighting the importance of considering climate-related risks in financial stability analysis. The findings have significant implications for policymakers and financial regulators seeking to promote financial stability and mitigate climate-related risks in South Africa.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".