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Record W1881577558

Can Nuclear Energy Stimulates Economic Growth? Evidence from Highly Industrialised Countries

2015· article· en· W1881577558 on OpenAlexaboutno aff
Hanan Naser

Bibliographic record

VenueDergiPark (Istanbul University) · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMarket Dynamics and Volatility
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsMacroeconomicsEnergy (signal processing)Natural resource economicsDevelopment economicsMonetary economicsPhysics
DOInot available

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.854
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.039
GPT teacher head0.190
Teacher spread0.151 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2015
Admission routes1
Has abstractyes

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