The double sustainability: The link between government debt and renewable energy
Bibliographic record
Abstract
This paper innovatively explores the relationship between a country’s government debt and the use of renewable energy. Incorporating key socio-economic and financial variables, critical to the United Nations SDG-7, we build a panel dataset for G7 countries from 1990-2021. Using cointegrating regression methods (FMOLS and DOLS), Quantile Regressions (QR) and pairwise panel causality tests, we find bidirectional causality between government debt and renewable energy consumption (REC). The empirical findings emphasize the important policy implications for sustainable economic development. Escalating government debt can hinder investment in renewable energy infrastructure, while increased renewable energy has a positive impact on government debt dynamics. Policymakers are encouraged to prioritize fiscal responsibility to secure resources for renewable energy investments. Moreover, incentivizing renewable energy deployment promotes long-term fiscal benefits and creates a positive feedback loop. In fact, a comprehensive understanding of the relationship between government finances and environmental sustainability is crucial for an optimal balance.
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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 | 0.004 | 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 | 0.001 | 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".