The role of energy intensity, green energy transition, and environmental policy stringency on environmental sustainability in G7 countries
Bibliographic record
Abstract
Abstract The increase in energy intensity and energy depletion may lead to faster depletion of natural resources and increased environmental impacts. The green energy transition can improve environmental quality by reducing the pressure on natural resources and the carbon footprint. At this point, public environmental regulations are significant for environmental sustainability. On the one hand, the environmental policy stringency imposes high environmental taxes on polluting activities and, on the other hand, provides R&D support to clean technologies. This study examines the impact of energy intensity, energy depletion, green energy transition, and environmental policy stringency on load capacity factor in G7 countries from 1990–2020 using common correlated effects mean group and augmented mean group panel long run estimators. The study's robust results show that i) energy intensity has a negative impact on environmental sustainability in Germany, Italy, and the USA, ii) energy depletion has a negative impact on environmental sustainability in Canada and France, and iii) green energy transition has a positive impact on environmental sustainability in Japan. G7 countries must reverse the adverse effects of energy intensity and energy depletion by accelerating the transition to green energy. These countries with significant fiscal capacity should use environmental policy instruments that include environmental taxes. Graphical abstract
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".