On the relationship between building energy efficiency, aesthetic features and marketability:Toward a novel policy for energy demand reduction
Bibliographic record
Abstract
Despite significant progresses in development of energy-efficient buildings (EEBs), energy demand in building sector is still drastically increasing. This paradox is conceptualized in this study as Inefficiency of Increased Building Energy Efficiency. Marketability failure of EEBs and inefficiency in integrated design approach are the main causes of this paradox. Compared to merely focusing on the energy-efficiency enhancement, increasing the number of EEBs with a better marketability via enhancement of their aesthetic features is proposed as a novel approach for energy demand reduction in the building sector. This article aims to first investigate the current stage of EEBs’ adoption and the associated market barriers, and then to propose a multidisciplinary design approach to scrutinize the role of aesthetic features on buildings’ marketability for development of effective policies. Conducted comprehensive survey among real-estate agencies across 26 UK cities reveals a negative correlation between the energy-efficiency and housing marketability. Moreover, house price and aesthetic features are understood as the most dominant parameters that impact individuals' buying decision. Any extra initial cost of EEBs over 3% is likely to face market resistance. Furthermore, strong empirical evidences have been found to confirm that the proposed approach has a substantial potential to increase the EEBs’ number.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".