The Relationship Between Nuclear Energy Consumption And Economic Performance: An Empiric Analysis on Selected Countries
Bibliographic record
Abstract
Energy production is important for the development of developing countries. The future of nuclear energy depends on the people living in that country gaining their tolerance and being able to continue this tolerance in a safe way. This is seen as an important condition, especially for industrialized countries. In developing countries, even if nuclear energy is not used in industrial terms, it is used in medical terms, radioactive examination and various fields. The aim of the study is to determine the relationship between nuclear energy consumption and economic performance in the countries that are ranked first in nuclear energy consumption by econometric analyses and to make recommendations to policy decision makers. For this purpose, it has been investigated whether there is a long or short term relationship between nuclear energy consumption and economic performance in countries (Germany, the United States, the United Kingdom, China, France, South Korea, India, Japan, Canada, Russia and Ukraine) that are in the first place in nuclear energy consumption in the world by using panel data methods. In the analysis, Pooled Mean Group Estimator (PMGE), Mean Group Estimator (MGE) and Dynamic Fixed Effects (DFE) methods are used using the normalized annual data for the period 1997-2017 compiled from the World Bank Development Indicators and BP World Energy Statistics Reports. As a result of the econometric analysis conducted through the Stata program, it has been concluded that nuclear energy consumption does not have an impact on economic performance in the short term, whereas nuclear energy consumption in other countries, except Japan, has an impact on economic performance in the long term. In the panel data method, it will be important for economic performance and efficiency to be followed by policy decision makers considering the findings from the analysis provided for each country separately.
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 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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".