Factors Influencing the Green Bond Market Expansion: Evidence from a Multi-Dimensional Analysis
Bibliographic record
Abstract
Expansion of green bond markets as an appropriate way to lower environmental pollution is one of the most debatable issues among scholars. However, the expansion of this market is not a simple matter and depends on several factors. The main purpose of this study is to carry out a multi-dimensional analysis using the analytic hierarchy process (AHP) method to find and prioritize factors influencing the development of green bond markets. As a case, we do our analysis for Vietnam that, since the last years, has been trying to expand green bond market as an effective investment channel to finance low-carbon projects. The main findings revealed that legal infrastructure, official interest rate of green bonds, and economic stability are the most important factors directly affecting green bond market expansion. Therefore, economic and legal requirements should be addressed by policy makers. As major policy implications, we recommend an affordable price of green bonds and improvement of economic and financial stability to accelerate the development of green bond markets.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".