The Impact of Green Bonds and Energy Use on Carbon Dioxide Emissions: Evidence from 17 Financially Developed Countries (2014–2023)
Bibliographic record
Abstract
This study investigates how green bond issuance, energy use, renewable energy, and economic growth relate to per capita CO2 emissions in 17 financially developed countries that are active in green bond markets over the period 2014–2023. We construct an annual panel for Australia, Austria, Canada, Mainland China, Finland, France, Germany, Italy, Japan, Luxembourg, New Zealand, Norway, Spain, Sweden, the United Kingdom, and the United States, and apply panel-corrected standard errors (PCSEs) together with Method of Moments Quantile Regression (MMQR). Diagnostic tests based on Pesaran’s CIPS unit root and Westerlund’s cointegration procedures indicate that the variables are I(1) and cointegrated, while Pesaran-type dependence and slope heterogeneity tests justify the use of robust panel methods. The PCSE results show that total energy consumption is the strongest factor associated with higher emissions, renewable energy consumption is consistently associated with lower emissions, economic growth is positively linked to emissions, and green bond issuance is associated with lower emissions, although the magnitude of this relationship is modest. MMQR estimates reveal that these relationships are heterogeneous across the CO2 distribution. Green bonds are associated with lower emissions only in low-emission country–years, while this association becomes statistically weak at higher quantiles. Renewable energy is linked to lower emissions across all quantiles, with stronger associations in the lower part of the distribution, and the growth–emissions relationship weakens at the top, consistent with an Environmental Kuznets Curve pattern. These findings suggest that expanding renewables and improving the carbon content of energy use remain central for decarbonization, while green bonds may support emission reductions, particularly in cleaner, institutionally advanced economies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".