Towards a greener economy: The quest for nuclear energy technology budgeting
Bibliographic record
Abstract
The growing climate change debate necessitates innovation in nuclear energy for economic activities worldwide. Existing studies, however, are skeptical of considering nuclear energy as clean energy in addressing climate change mitigation, natural resources, and waste management. This context addresses whether nuclear innovation has the same or a different effect on the green economy, as this question remains unexplored in the literature. We examine the impact of nuclear energy technology budgeting on the green economy in selected OECD countries using both time series and panel data from 1977 to 2019. The empirical findings highlight the heterogeneous effects of the nuclear energy technology budget on the green economy. The results demonstrate a significant negative influence of the nuclear energy technology budget on the green economy for Austria, Germany, Italy, the Netherlands, Spain, Sweden, and the United Kingdom. These economies can potentially contribute significantly to the green economy due to their funds allocations towards nuclear energy technology. We also found that the budgets allocated to nuclear energy technology in Canada, Denmark, France, Japan, Norway, Portugal, Switzerland, and the United States have a negligible impact on the green economy. This suggests that these countries' failure to achieve green economies results from a nuclear energy technology budget.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".