Institutional Investors’ Willingness to Pay for Green Bonds: A Case for Shanghai
Bibliographic record
Abstract
The issuance of green bonds has been increasing since 2016 in China, and the number of papers covering the topic is growing. In previous studies on greenium, not much has been investigated from the institutional investors’ perspective. The study estimates the institutional investors’ level of greenium by surveying the institutional investors in Shanghai, China, from October 23 to 1 November 2021, using the double-bound dichotomous choice (DBDC) contingent valuation method (CVM). The study also analyzes the effects of variables that are known to be important for the green bond based on previous studies. The study identifies that there is a greenium level of 0.47%. Among the seven variables tested with logit regression models, the credit and currency of the bond had a positive effect on the greenium. The study provides helpful insights for issuers’ strategic planning and could be a stepping stone to increasing issuance not only for the Chinese green bond market but also for the global green bond market.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".