Assessment of the Relationship among Climate Change, Green Finance and Financial Stability: Evidence from Emerging and Developed Markets
Bibliographic record
Abstract
This study assesses the relationship among climate change, green finance, and financial stability annually from 2013 up to 2021 for 14 countries, focusing on emerging and developed markets. It first considers whether a country’s climate change impact financial stability, investigates whether green finance influences financial stability and how it affects climate change by using carbon dioxide emissions as proxy of climate change. Green finance has been measured by green of asset backed securities, green loans and bonds, while financial stability has been measured by Z-score. Using panel data, the findings indicate that there is a significantly negative effect of CO2 emissions on financial stability, but positive effects of green finance on financial stability in these markets, most notably through green loans. Also, this paper examines the relationship between green finance and climate change by using Kao Residual Cointegration test of countries. In the long run, green finance negatively affects carbon dioxide emissions. Furthermore, the empirical results of the robustness test of GMM are highly consistent with the main test. This study may be extended by conducting Further research to focus on the effect of CO2 emissions on financial markets with the role of financial deepening for 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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".