How does green finance asymmetrically affect greenhouse gas emissions? Evidence from the top-ten green bond issuer countries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The present study analyzes the asymmetric association between green finance and greenhouse gas emissions in the top ten countries that support green finance (China, Canada, France, Germany, Japan, the Netherlands, Spain, Sweden, the UK, and the US). Previous research employed panel data methods, resulting in consistent outcomes concerning the association between green finance and environmental quality, regardless of the fact that many countries did not generate such a relationship individually. The present study, however, uses the quantile-on-quantile technique, which enables us to assess time-series dependence in each country independently. We find that green finance enhances environmental quality by curtailing greenhouse gas emissions in most of the economies studied at specific quantiles of the data. Moreover, the level of asymmetry among our variables changes by country, focusing on the need for policy makers to pay particular attention in implementing green finance and environmental sustainability policies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 it