The superefficiency direction distance function and total factor energy efficiency: Evidence from the comprehensive and progressive agreement for trans‐pacific partnership (CPTPP) member countries
Bibliographic record
Abstract
Abstract This study used the superefficiency direction distance function and total factor energy efficiency to evaluate the changes in energy efficiency and total factor energy efficiency of 11 member countries of the Comprehensive and Progressive Agreement for Trans‐Pacific Partnership (CPTPP) from 2013 to 2017. Based on the results, we consider the policy implications for CPTPP members, and suggest approaches to improve the direction and range of the difference variable for aalyzing energy efficiency performance. In the selection of variables, input variables include labor force, energy consumption, and capital formation; undesirable output variables include the domestic production gross, life expectancy, and PM2.5. The results show that Canada, Japan, and Mexico have the best performance in terms of energy superefficiency and total factor energy efficiency; in contrast, Chile, Malaysia, Peru, and Vietnam would benefit from improved energy efficiency. Energy efficiency can be improved by reducing labor and final energy consumption, increasing gross domestic product, and reducing PM2.5. However, simultaneously improving all three factors is challenging. Countries should first focus on appropriately adjusting energy policies, actively developing new energy use, improving production technology, avoiding waste, reducing PM2.5, and accelerating urban development to achieve optimal energy efficiency.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".