A critical review of the impact of uncertainties on green bonds
Bibliographic record
Abstract
Green bonds are relatively new in the financial market compared to other financial securities but are useful in financing environmentally friendly projects. Just like other financial securities, green bonds are affected by various factors, such as economic policy uncertainty. Our aim of this paper was to conduct a systematic literature review of the impact of economic policy uncertainty on green bonds. We sought to do a thorough analysis of the existing literature on the assessment of the impact of economic policy uncertainty on green bonds and the techniques used in assessing the impact. Our findings showed that economic policy uncertainty had a strong impact on the green bond, with its intensity varying by location. This impact tended to be more pronounced in periods of heightened uncertainty. Also, our findings highlighted that the assessment of the impact of economic policy uncertainty on green bonds gained popularity in 2019, with China emerging as a prominent contributor. However, other countries, such as Finland, even though they had few published papers, their citations signified the production of quality papers in this field. Additionally, we found that the application of the quantile analysis method was utilized by many recent studies, which signified its importance in this field. Our findings highlighted the importance of considering appropriate techniques in assessing the impact of economic policy uncertainty on green bonds while taking into account the paper quality.
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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".