MétaCan
Menu
Back to cohort
Record W7111976966

On the relationship between building energy efficiency, aesthetic features and marketability:Toward a novel policy for energy demand reduction

2019· article· en· W7111976966 on OpenAlexaff

Bibliographic record

VenueRePEc: Research Papers in Economics · 2019
Typearticle
Languageen
FieldEngineering
TopicSustainable Building Design and Assessment
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsInefficiencyEnergy (signal processing)Energy demandMultidisciplinary approachFace (sociological concept)Market failureEfficient energy use
DOInot available

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.407
Threshold uncertainty score0.532

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.026
GPT teacher head0.292
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueRePEc: Research Papers in EconomicsSame topicSustainable Building Design and AssessmentFrench-language works237,207