International Comparison of Energy Efficiency Standard and Labels: Development Process and Implementation Phase
Bibliographic record
Abstract
Energy-efficiency standards and labeling programs for household appliances, equipment and lighting have been adopted not only in developed countries but also in the developing countries. They are contributing greatly to the achievement of energy conservation. Many countries have mandatory minimum energy-efficiency standards and labeling programs. Meanwhile some countries have voluntary programs. Japan has established and adopted her own Top-Runner program. Although the key elements of standard and labeling programs are available on the web or documents, there are few sources that document comprehensively the development process and implementation phase of standard and labeling programs. For many countries, in particular the developing Asian nations whose energy demand is expected to experience continues rapid growth in the near future, it is one of the crucial issues of energy efficiency promotion to provide policymakers with comprehensive information on implementation phase of standard programs. We surveyed the development process and implementation phase of standards and labeling programs in the U.S.A., Canada, the European Union (European Council, UK, Germany and France), China, Korea and Japan. We obtained information about designing, developing, implementing, enforcing, monitoring and maintaining standards and labeling programs. In this paper, we present an international comparison framework of the standards-setting processes and labeling implementation in these countries. This comparative analysis will help policymakers to introduce and revise energy efficiency standards and labeling programs.
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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 | 0.027 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".