Assessing the Connection between Nuclear and Renewable Energy on Ecological Footprint within the EKC Framework: Implications for Sustainable Policy in Leading Nuclear Energy-producing Countries
Bibliographic record
Abstract
Numerous Sustainable Development Goals (SDGs) set by the United Nations can't be completed without switching to alternative energy sources. Countries need to find common ground between energy security, affordability, accessibility, and environmental sustainability if they are to lay the groundwork for competition and success. Considering the connection between economic growth, nuclear power generation, renewable energy consumption, and non-renewable energy usage, this study examines the ecological footprints of leading nuclear energy-producing countries ( the United States, France, China, Russia, Japan, South Korea, Canada, the Ukraine, the United Kingdom, and Germany) from 1990 to 2020. In order to perform such a thorough empirical investigation, this study makes use of advanced econometric methods. According to the long-term cointegration study, environmental quality is negatively impacted by economic growth and the use of non-renewable energy, while it is positively impacted by the square of economic growth, the use of nuclear energy, and the use of renewable energy. The study found that the ecological footprint is directly correlated with both nuclear power and economic growth. Meanwhile, both renewable and non-renewable energy sources were found to have an effect on the ecological footprint in a causal manner. The findings of this study emphasize the significance of the world's major nuclear energy producing countries harmonizing their energy policies and developing a common energy strategy that includes equitable distribution of key components of the global nuclear energy sector.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".