Information Asymmetry and Greenwashing in the Green Bond Market
Bibliographic record
Abstract
Disclosure and transparency are two critical components in the green financing sector, especially the green bond segment. Compared to green instruments like green credit, green bond issuances facilitate information dissemination and reduce information asymmetry. Still, concerns stemming from numerous macro-level and firm-level factors impede market advancement. Investors are restrained from green bond financing owing to a fear of potential greenwashing. The nascency of the market, resulting in inadequate disclosure regimes and measurement challenges, exacerbates the problem. Can we find a solution to tackle the dilemma of greenwashing and information asymmetry using emerging, sophisticated technologies? Assessing the major theoretical underpinnings, this chapter presents a comprehensive landscape of how technologies like distributed ledger technologies, blockchain, the internet of things, artificial intelligence, machine learning, and the like fit into the green debt market. While following a theoretical approach, collating research, and the green bond market developments, the authors initiate an investigation into how technology can manage disclosure biases. The assessment signifies the role of technology, specifically FinTech, blockchain, and AI technologies, in spotting greenwashing and information asymmetry.
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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 | 0.001 | 0.002 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".