Modeling the causal linkages between nuclear energy, renewable energy and economic growth in developed and developing countries
Bibliographic record
Abstract
This article investigates the causal relationship between two types of energy variables and economic growth using dynamic simultaneous-equation panel data models for 17 developed and developing countries. Our results indicate that there is a unidirectional causality running from nuclear consumption to economic growth in Belgium and Spain, while a unidirectional causality running from economic growth to nuclear consumption is supported in Bulgaria, Canada, Netherlands, and Sweden. A bidirectional relationship appears in Argentina, Brazil, France, Pakistan, and the USA, while no causality exists in Finland, Hungary, India, Japan, Switzerland, and the U.K. Second, the results for the second nexus among renewable energy and economic growth show that there is a unidirectional causality running from renewable consumption to economic growth in Hungary, India, Japan, Netherlands, and Sweden, while there exist a unidirectional running from economic growth to renewable consumption in Argentina, Spain, and Switzerland. A bidirectional relationship is supported in Belgium, Bulgaria, Canada, France, Pakistan, and the USA, while no causality exists in Brazil, Finland, and Switzerland. Third, we find the existence of a bidirectional causality between nuclear consumption and economic; and a unidirectional causality running from economic growth to renewable energy consumption for the global panel.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".