Bibliographic record
Abstract
This dissertation examines whether the issuers of high-quality green bonds differentiate themselves by bonding with two reputable organizations that impose high green bond transparency standards -- the Climate Bonds Initiative (CBI) and the Luxembourg Green Exchange (LGX), and whether the bonding mechanism effectively mitigates investors' greenwashing concerns. I document evidence that is consistent with a separating equilibrium. Specifically, I find that the bonding issuers (i.e., issuers that obtain CBI certifications for their green bonds or list their green bonds on LGX) demonstrate higher post-issuance transparency and a larger reduction in carbon emissions while also receiving a larger green premium than the non-bonding issuers. Further analyses document differences between the CBI and LGX regimes. The CBI regime, which requires certification against a green taxonomy but allows for private communication, is more effective at screening issuers that achieve larger carbon reduction, whereas the LGX regime, which accepts broad categories of green projects but requires public disclosure on a centralized platform, is more effective at screening issuers that provide higher post-issuance transparency. Interestingly, only the LGX-listed green bonds receive a significant green premium. Moreover, green premium is more concentrated in the LGX-listed green bonds with high ex-ante transparency commitment. Furthermore, in the secondary market, green bonds from issuers that end up providing high post-issuance transparency tend to have higher liquidity. The CBI-certified or LGX-listed green bonds with high post-issuance transparency tend to have even larger liquidity benefits. Taken together, consistent with prior survey evidence in Chiang (2017) and Sangiorgi and Schopohl (2021), the findings in this dissertation corroborate that high post-issuance transparency may effectively mitigate investors’ greenwashing concerns, facilitate low-cost environmental financing, and bolster market liquidity.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".