Market Transformation for Clothes Dryers: Lessons Learned from the European Experience
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The European residential clothes dryer market is undergoing a transformation driven by highly efficient heat pump dryer technology. In 2012, over 80 residential heat pump dryer models from 18 different manufacturers were available on the European market 1 . Additionally, Switzerland implemented a new minimum energy performance standard (MEPS) that effectively allowed only heat pump dryers to be sold in that country. The Super Efficient Dryer Initiative (SEDI) was formed in the US to support improvements in dryer energy efficiency based on the European experience and to bring together utility energy efficiency programme providers, dryer manufactures, government agencies and other stakeholders to repeat the European success in the US and Canada. Heat pump dryers have substantial energy saving potential in North America. Recent testing indicates that European heat pump dryers are 50-60% more energy efficient than existing North American conventional electric dryers 2 . In 2012, SEDI supported the US Environmental Protection Agency (EPA) decision to offer an ENERGY STAR Emerging Technology Award (ETA) for efficient dryers. The ETA for Advanced Clothes Dryers is designed to support the introduction of efficient technology through recognition and promotion 3 . Several manufacturers are now ready to introduce a significantly more energy efficient clothes dryer into the North American market, and the announcement of an ETA recipient is expected soon. This paper describes the actions dryer stakeholders have taken on both sides of the Atlantic to promote efficiency and identifies lessons from the European market transformation experience that can be applied in North America.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it