Catalyzing the growth of green bonds: a closer look at the drivers and barriers of the Canadian green bond market
Bibliographic record
Abstract
Purpose This paper aims to examine the Canadian financial sector’s reaction to opportunities and risks created by the green bond market in a low-carbon and climate-resilient (LCR) economy. Design/methodology/approach The authors used a concurrent mixed methodological approach that undertakes an online survey and semistructured interviews with critical green bond market stakeholders. Findings The most significant market driver in Canada is the reputational benefit for stakeholders, i.e. its ability to meet the high demand for sustainable finance and the marketing potential of its green credentials. The major market barriers are transactional costs, i.e. additional tracking required for reporting purposes, lack of market liquidity and identification of environmental impact or additionality. Canadian green bonds are also more likely to be evaluated on their green impact than their global market peers. Research limitations/implications Limitations of this study include its focus on Canada, which may exclude or not apply to drivers and barriers in other green bond markets. Practical implications The paper helps create an accounting-based conceptual framework for key motivations and barriers that affect financial decision-making regarding green bonds. Social implications The authors identify economic and policy-related barriers and drivers for green bonds, addressing the financing gap for the LCR economy. Originality/value To the best of the authors’ knowledge, this study is the first to identify and compare Canadian green bond market drivers and barriers and to examine relevant stakeholder- and policy-related approaches that can be targeted to scale this market effectively.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".