Reforming Sustainability-Linked Bonds by Strengthening Investor Trust
Bibliographic record
Abstract
This paper explores the emergence of sustainability-linked bonds (SLBs) as an innovative instrument to finance sustainability objectives. SLBs are any type of bond instrument for which the financial characteristics vary depending on whether the issuer achieves predefined sustainability objectives. SLBs were launched in 2019, represent 7% of labeled bonds, and now exceed USD 250 billion. In the context of the growth of sustainable finance and concerns of greenwashing, this paper asks whether SLBs are an effective mechanism to attract sustainable finance. Drawing on a complete revision of the literature and interviews with practitioners, the findings highlight the potential of SLBs to contribute to sustainability financing, especially in hard-to-abate sectors. Recommendations include defining standardized KPIs based on a materiality assessment, requesting SPTs to be supported by science, and tailored step-up mechanisms. The academic literature and experts converge in their description of greenwashing risks posed by SLBs, their signaling effect, and the lack of sophistication in SLB pricing, in particular the optionality represented by step-ups. The literature differs from the practitioners’ perception on the existence of an issuance premium. Enhancing the design of SLBs represents an opportunity to add rigor to sustainable finance and better price externalities, where material topics have an explicit impact on the cost of funding.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".