Adoption of energy efficiency innovations in new UK housing
Bibliographic record
Abstract
The UK is committed to increasing housing—the Department for Communities and Local Government has set a target to provide three million more homes in England by 2020. The housing sector is responsible for over a quarter of the nation's total carbon emissions and new targets require all new homes to be zero carbon by 2016. This is a challenging objective and will require developers to adopt energy efficiency innovations more widely. Yet change in the house building industry has been slow and building regulations in England and Wales lag behind energy standards in other European countries. This paper considers the house building industry as a complex socio-technical system made up of many actors who both act together and constrain each other's actions. Through interviews with commercial developers, local and central government bodies, architectural consultancies and housing associations, barriers relating to these actors' willingness, motivation and capacity for change in introducing energy-efficient measures into new build housing are identified. A series of policy responses are proposed to overcome these barriers and help suggest strategies to drive improved energy performance in UK new build homes. In order to provide a real context to explore the implications of these recommendations, the paper considers how such responses may be integrated into a sustainable new town development. It is concluded that to stimulate innovation, all parts of the sociotechnical system need to be influenced by all the mechanisms available to the UK government.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".