The Determinants of Green Bond Issuance in Indonesia: An Analysis of Sustainable Financial Instruments
Bibliographic record
Abstract
Green Bonds (GBs) have emerged as one of the most prominent innovations in sustainable finance instruments in recent times, necessitating an understanding of the factors determining their issuance. However, empirical literature on the factors driving GB issuance in Indonesia is limited. This study aims to investigate the impact of bond characteristics and macroeconomic factors on Government and Corporate Bond issuance from 2018 to 2023 using a random-effects panel regression model. The results confirm that all factors, except economic growth, have a significant effect on GB issuance; however, the impact of some factors differs between government-issued GBs and corporate-issued GBs. Among them, the green stock market and exchange rate have a positive effect on Corporate GB issuance, but the opposite is true for Government GB issuance. Furthermore, increases in interest rates and coupon rates encourage more government GB issuance but have the opposite effect on Corporate GB issuance. Our results contribute to the literature on sustainable finance, providing policymakers, issuers, and investors with valuable practical insights to encourage the development of the 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".