A Quantile Analysis of Energy efficiency, green energy investment, and energy innovation in most industrialized Nations
Bibliographic record
Abstract
The continuous use of fossil fuels to meet the energy demands of the industrialized nations has led to environmental degradation. As such, there has been a call for research, exploration, and the usage of alternative energy which is believed to improve the depleting quality of the environment. This study investigates the relationship between energy efficiency, green energy investment, and energy innovation in a panel of nine highly industrialized countries such as Canada, Japan, France, Spain, Germany, Switzerland, Italy, the United States of America, and the United Kingdom. Relying on the environmental Kuznets' hypothesis (EKC), we employ the quantile-on-quantile regression approach to the data obtained between 1980 and 2018. The empirical estimates validate the EKC hypothesis in most of the industrialized nations considered. The findings also reveal that the continuous use of non-renewable energy consumption aggravates emissions, while the use of renewable energy reduces the level of emissions in the environment. Therefore, energy efficiency leads to an increase in emissions in the first 3 quantiles and reduces emissions in the remaining quantiles. Also, energy innovation leads to a high amount of emissions. Finally, the study calls for increased investments in renewable energy as well as energy efficiency to ensure continuous improvement in the quality of the environment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".