Can Nuclear Energy Stimulates Economic Growth? Evidence from Highly Industrialised Countries
Bibliographic record
Abstract
This paper attempts to examine the causal relationship between nuclear energy consumption and economic growth for four industrialised countries; the US, Canada, Japan, and France, between 1965 to 2010. In a multivariate framework that accounts for other key determinants such that of oil demand and price, a modified version of the Granger causality test developed by Toda and Yamamoto (1995) is applied. Results show that there is one-way causality from nuclear energy consumption to economic growth in Japan denoting that an energy conservation policy that aims to minimise nuclear energy consumption may adversely affect economic growth. Oppositely, increasing real GDP causes additional nuclear energy consumption in France. In the US and Canada, there is evidence that support the neutrality hypothesis. Looking at the other investigated channels, the level of real oil prices seems to have a vital role in deriving the demand for nuclear power in three out of four countries. There is also a causal linkage between oil and nuclear energy consumption in the US, Japan, and France, suggesting that the uncertainty surrounding the global oil market plays a key role in determining the demand for nuclear energy. This means that the policies in these countries should endeavor to overcome the constrains on nuclear energy consumption to face any un-expected hikes in oil prices, which may adversely affect economic growth in such oil importing countries.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".