Role of market and nonmarket-based environmental policies, energy use, and income on environmental sustainability: The case of G7 countries
Bibliographic record
Abstract
Because the role of stringent environmental policies, energy use, and eco-friendly economic growth is highly critical in combating climate-related problems and preserving environmental quality, this study uncovers the incremental impact of aforementioned factors on load capacity factor (LCF) in G7 countries between 2000 and 2020 by performing a kernel-based regularized least squares (KRLS) model. The outcomes show that (i) gross domestic product (GDP) has only a supporting impact on LCF in the USA; (ii) market-based environmental policies are beneficial in Canada, France, Japan, and the USA; (iii) nonmarket-based environmental policies are helpful in France and USA; (iv) renewable energy use has positive support in Germany, Italy, Great Britain, and USA; (v) fossil energy use is harmful in all countries; (vi) the KRLS model has a high prediction performance; (vii) with regarding to G7 countries, the USA has the most positive condition. Thus, the study empirically highlights the average and pointwise incremental impact of the factors considered on LCF across countries and percentiles. Accordingly, the study discusses various policy options, such as mainly focusing on market-based environmental policies through making required regulations, considering also nonmarket-based environmental policies as a supportive mechanism, relying on further use of renewable energy through support packages and incentives, which should be taken into account in case of any additional measures application in the environmental area.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".