Bibliographic record
Abstract
As green finance grows, green bonds have become an essential tool for funding sustainable projects. While many studies explore whether green bonds exhibit a “green premium,” existing literature reviews often lack depth, timeliness, and consistent methodology. This paper addresses these gaps by systematically reviewing 70 empirical studies on green premiums published up to 2025, making it the most comprehensive review to date. We organize the literature by region (Global, U.S., Europe, Asia Pacific), market segment, premium dimension, data source, and estimation method, offering a structured framework to analyze diverse findings. Our analysis reveals a consistent negative green premium of −12.44 bps on average across most markets, with European and Asian markets showing higher yield spreads than the U.S. Studies using more recent data report smaller premiums, and larger bond issues tend to have lower premiums. Despite variations in methods and data sources, the overall results are consistent. This paper provides an updated overview of green premium research and offers key insights for investors, issuers, and policymakers on green finance pricing and investment strategies.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".