International Comparison of\nEnergy Efficiency Awards for\nAppliance Manufacturers and\nRetailers
Bibliographic record
Abstract
Enforcement of appliance standards and consumer trust in appliance labeling are important foundations of growing a more energy efficient economy.Product certification and verification increase compliance rates which in turn increase both energy savings and consumer trust.This paper will serve two purposes: 1) to review international practices for product certification and verification as they relate to the enforcement of standards and labeling programs in the U.S., E.U., Australia, Japan, Canada, and China; and 2) to make recommendations for China to implement improved certification processes related to their mandatory standards and labeling program such as to increase compliance rates and energy savings potential.Practices for product certification and verification vary across the world, with some programs focusing solely on either certification or verification (such as in Australia and Canada) and other programs focusing on both (such as ENERGY STAR in the U.S.).Accreditation practices for testing laboratories and certification bodies also vary, and some appliance standards and labeling programs are building databases to house all information on products and compliance.Costs are imposed on manufacturers and program administrators when either product certification or verification processes are implemented.When designing or refining standards and labeling programs, program administrators make a comparison (estimation or calculation) of the costs of non-compliance to the costs of various third party certification and verification processes.The costs of third party processes fall on manufacturers (often passed on to consumers) and administrators (often paid for with taxpayer money), while the costs of non-compliance fall on consumers (in lost savings), society (increased costs associated with energy and climate change), and some manufacturers (those who do not comply and go unpunished have an advantage over those that do comply).A standards and labeling program decision on which monitoring methods to use (certification and/or verification) are based on a number of factors including legal framework, cost and budget, human resources, number of products, number of manufacturers, whether the program is voluntary or mandatory, and other factors.For instance, when the U.S. Environmental Protection Agency (EPA) designed new certification and verification processes for its ENERGY STAR program, it tried to minimize costs for manufacturers and itself as the administrator.Recognizing that there would be new costs for any process involving a certification body and a third party testing laboratory, the EPA decided to allow witnessed manufacturer Table of Contents Executive Summary ....
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".