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Record W2185805916

International Comparison of Energy Efficiency Standard and Labels: Development Process and Implementation Phase

2008· article· en· W2185805916 on OpenAlexaboutno aff
Sho Hirayama, Hidetoshi Nakagami, Chiharu Murakoshi, Mikiko Nakamura

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicDiverse Scientific and Engineering Research
Canadian institutionsnot available
Fundersnot available
KeywordsEfficient energy useProcess (computing)ChinaDeveloping countryEuropean unionBusinessPromotion (chess)International standardInternational comparisonsEnvironmental economicsComputer scienceEconomic growthInternational tradePolitical scienceEngineeringEconomicsTelecommunications
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.027
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.143

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0070.012
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.025
GPT teacher head0.331
Teacher spread0.305 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2008
Admission routes1
Has abstractyes

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