How do market players understand green bonds as different from infrastructure bonds?: An analysis of the perceptions of players in the green bond market
Bibliographic record
Abstract
To investigate the difference between green bonds and infrastructure bonds, 15 market participants including issuers, investors and verifiers, were interviewed in the UK, USA, Canada, France, Sweden, Belgium and Australia. These interviewees represent 38.6% of the global labelled green bond market which currently has total outstanding issuance of USD65.9 Billion. These semi-structured interviews provided insights into their understanding of the green bond market and the decision-making processes to be involved in the green bond market. The market participants referred to the governance structure and counterparty to differentiate between the two bond options, labelled green bonds and infrastructure bonds, instead of the projects being funded. The' participants believed that verification in the form of the Green Bond Principles (GBP) provided commonality and definitional certainty to the market. However, they also acknowledged that as a self- labelling mechanism, the GBP relied on the reputational credibility of the issuer. Participants understood green bonds to definitely provide a positive environmental impact and were seeking measurability from these, thus limiting the green bond projects to climate-related infrastructure. They believed that the green bond market was viable in the medium term with growth expected from the corporate and municipal issuers. Market participants supported the development of project specific accreditation to mitigate risk, particularly for corporate issuers. They reported that labelled green bonds were priced in line with similar issues from the same issuers, and initial considerations around liquidity and scale were not seen as limitations. This research looks at the attitudes of the labelled green bond market participants and seeks to revisit the findings of Wood and Grace (2011) in light of the significant growth of the labelled green bond market since their initial research
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".