Consultancy Centres and Pop-Ups as Local Authority Policy Instruments to Stimulate Adoption of Energy Efficiency by Homeowners
Bibliographic record
Abstract
The housing sector is responsible for a more than a quarter of the total final energy consumption in the EU. As the majority (70%) of the EU-housing stock is owner occupied and largely consists of single family dwellings it is understandable that many countries focus their energy saving policies on homeowners. Complementary to the national policy frameworks, regional and local authorities implement locally based policy instruments targeting specific groups and individual homeowners. In order to enlarge the effectiveness of their policy instruments and to reach the energy saving goals, frontrunner local authorities in particular are searching for ways to reach homeowners. Consultancy centres and pop-ups can be a way to make individual homeowners more aware about their energy use and stimulate them to apply low carbon technologies. The research results not only show that a wide range of business models are available to develop, structure and organise these consultation centres and pop-ups, but also that they indeed could play an important role in accelerating the energy performance of owner occupied housing. Through a pop-up or consultancy centre, public and private parties can join their forces to reach, stimulate and support the individual needs and wishes of homeowners during their customer journey to realise an energy efficient dwelling.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.004 |
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".