To label or not? A choice experiment testing whether labelled green bonds matter to retail investors
Bibliographic record
Abstract
Abstract Green bonds are an important sustainable finance tool that can help reorient financial flows and influence public policy in addressing climate action across the global financial markets. However, this market is still in its infancy for the retail investor segment, and it has not been sufficiently examined from a behavioural policy lens. We fill this research gap by examining whether labelling and environmental benefits framing of a green bond can influence retail investor decision-making. By employing 1105 Amazon Mechanical Turk workers across three choice scenarios, we test whether alignment of pro-environmental personal norms or having specific personal traits can have a mediating effect on their green bond preferences. Using a mix of quantitative analyses, we find that most retail investors are influenced by the presence of a ‘green label effect’. For most retail investors, we find that the presence of a green label matters more than the ‘greenness’ of a green bond or the higher financial return of a non-green bond. However, for a very small sub-set of our sample, the alignment of environmental performance-related framing with their pro-environmental personal norms, enables greater investment into enhanced performance green bonds, even at the cost of losing financial returns. Finally, personal traits like individual risk tolerance (high), or previous investment experience with investment products (bonds, stocks), gender (non-binary individuals) and those having employment experience with financial industry, are more likely to invest in a labelled green bond. Our findings have timely implications for sustainable finance public policy, as it relates to regulating the growth of such products through labelling schemes like green taxonomies as well as addressing greenwashing risks through improved regulatory oversight.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".