Role of clean energy on energy efficiency and sustainable development: Evidence from top ten energy consumers
Bibliographic record
Abstract
This study estimates the threshold level of clean energy and investigate its implications on energy efficiency and income per capita from world’s top ten energy consumers for the period of 1960-2017. Countries include Brazil, Canada, China, Germany, India, Japan, Russia, South Korea, France and United States, accounting for more than 60 % of world’s primary energy consumption. Results show that the correct mix of clean energy drives overall energy efficiency in all countries. Further results confirm that threshold levels of clean energy exist in the relationship of energy efficiency and income per capita. In particular, we find that increasing the share of clean energy (below the threshold level) increases energy efficiency in Brazil, Germany, India, Russia and South Korea. The effects of clean energy above its threshold levels turns stronger in all countries except Brazil, Canada and Russia. This has been noted that the above three countries have witnessed sluggish growth in clean energy share particularly above the threshold levels. In the similar settings result show that below the threshold levels of clean energy, income per capita significantly increases with increasing the energy efficiency (decreasing energy intensity) from all countries except China, Japan and Korea. This might be due to the dominant effects of non-clean energy share in total primary energy consumption in the above countries. However in cases of China and Korea above the threshold levels of clean energy, the impact of energy efficiency turns more prominent in accelerating income per capita. The similar results have been observed in all other countries except Brazil where income per capita appears insignificant with energy intensity predominantly above the threshold levels of clean energy.
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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) | 0.000 | 0.000 |
| 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) | 0.002 | 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".