Does nuclear energy consumption mitigate carbon emissions in leading countries by nuclear power consumption? Evidence from quantile causality approach
Bibliographic record
Abstract
Nuclear energy has sparked international attention as one of the most important strategies for reducing emissions thanks to its ability to provide low-carbon power. Based on this interesting fact, the current research explores the effect of nuclear energy on CO 2 emissions in the leading countries by nuclear power consumption using a quarterly dataset from 1990 to 2019. The study employs the quantile-on-quantile (QQ) estimator, which accounts for both non-parametric and conventional analyses and enhances the provision of unbiased and consistent estimates. In addition, the Granger causality in quantiles approach is adopted to assess the causality in quantiles between the variables of investigation. The outcomes from the QQ estimator reveals that in the majority of the quantiles, nuclear energy contributes to decreased degradation of the environment in the USA, France, Russia, South Korea, Canada, Ukraine, Germany, and Sweden. Contrawise, the feedbacks from Spain and China expose that Nuclear Energy Consumption (NUC) contributes to the deterioration of the environment. Moreover, the outcomes of the causality test disclose that nuclear energy and CO 2 emissions can predict each other in the majority of the quantiles. The findings above provide profound ramifications for policymakers planning nuclear energy and CO 2 -emission policies towards achieving sustainable environment in the sample countries and beyond..
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".