Home time: an exploration of the time dimensions of the eco-renovations of housing, Energy and People: Futures, complexity and challenges
Bibliographic record
Abstract
Over a quarter of all UK energy is used within the housing stock, while carbon emissions from all sectors of the economy are required to be reduced by 80 % by 2050. There is increasing recognition that eco-renovation of the existing housing stock will be vital, but many questions about achieving this remain. This paper focuses on the time dimensions of eco-renovation of housing. Firstly, various current definitions of eco-renovation are outlined and compared. Then, two distinct types of eco-renovation, which differ primarily in their time profile, are discussed. Eco-renovation activity is considered within the context of other housing-related time scales, including: frequency and timing of repairs and home improvements; the life time of energy efficiency measures; and patterns of home ownership. The characteristics, advantages and disadvantages of an „over-time‟ approach to eco-renovation are contrasted with the „one-off ‟ approach. Amendments to the current definitions of eco-renovation are proposed, linking the definition of ecorenovation more closely with an over-time approach and explicitly integrating the role of occupants. Specific topics which require additional research are identified. In conclusion, preliminary research suggests that „over-time ‟ eco-renovation could 1 make a significant contribution to national carbon savings targets, if appropriate measurement and evaluation tools were in place.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".