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Enregistrement W7057162369

An investigation of the effects of large houses on occupant behaviour and resource-use in New Zealand

2017· article· en· W7057162369 sur OpenAlexaboutno aff

Notice bibliographique

RevueResearchArchive–Te Puna Rangahau (Victoria University of Wellington) · 2017
Typearticle
Langueen
DomaineEngineering
ThématiqueMagnetic Field Sensors Techniques
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésBedroomOccupancyLiving spaceMeaning (existential)Living roomMeasure (data warehouse)Space (punctuation)Quarter (Canadian coin)Energy (signal processing)Standard of living
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

According to Statistics New Zealand the average size of new New Zealand houses almost doubled from 1974‐2011 at the same time that occupancy reduced, meaning fewer people live in larger houses. Features of large houses are extra bedrooms, specialised rooms (e.g. study, media room), more than one living space, several bathrooms (including en‐suites), and double/triple garages. This contrasts with what is defined in this thesis as the “core house”, which is a house (or part of a house) consisting of a living room, a dining room, a kitchen, and a bedroom for each occupant (assuming couples share a bedroom). Based on this, houses with more space than the appropriate core house for each household are considered as living in some level of large housing.\n\nLiving in larger houses than necessary means use of more natural resources in terms of construction materials, operating energy and the additional furniture and appliances needed. This study, therefore, aimed to measure resource‐use efficiency in different sized houses and rooms found in NZ houses to show the significance of human decisions on housing energy use. To do this, it used a life‐cycle energy approach to measure resource‐use and reveal the long term environmental impact of house size decision. A 100 year cycle was used to cover typical human lifespan.\n\nUsing grounded theory, the research developed into four studies:\n\n1‐ An observation of the features of New Zealand houses: Houses advertised for sale in TradeMe website were studied to show the features of New Zealand houses and types of furniture and appliances people keep in their houses.\n\n2‐ Study 1: Based on the observation study, a questionnaire was prepared for a pilot study of 7 households living in small and large houses asking about occupants, type/number of rooms and types/number/location of furniture/appliances in their house. Each occupant also reported where he/she spend his/her time at home indoor for 14 consecutive days. This study revealed any problems with the preliminary questionnaire and also set strategy for the large time‐use survey.\n\n3‐ Study 2: Based on the results of study 1, an online questionnaire based survey was undertaken by families with 4 or fewer members living in NZ owner‐occupied houses. The questionnaire asked for information about family members, type/number of spaces in their home, furniture and its location and the time spent in each room of the house, outdoors, and out of home by each occupant over one day. This survey provided a reliable data set about the features of New Zealand owner‐occupied houses and their occupants, the type an number of furniture items, appliances and tools in them and where/for how long each household member spent his/her daily time in the house.\n\n4‐ Floor plan study: To get a better understanding of the size of rooms in NZ houses, a floor plan study of 287 houses was performed. Floor plans were redrawn in AutoCAD and the floor area of each room and the whole house were extracted for mapping with house size in SPSS.\n\nResults of the time‐use study indicate New Zealanders on average spend 15.94 hours/day at home indoor and house size does not affect this. On average 54.7% of this is spent in usual bedrooms, 29.9% in the usual living room, dining room and kitchen, and use of other rooms including bathrooms accounts for 15.4% of time at home indoors.\n\nUsing a life cycle analysis approach, selecting to live in a house with 3 extra rooms, a single person, couple, couple with one child and couple with two children will use 66%, 66%, 75% and 66% more energy for housing over 100 years. By combining time‐use and energy use results, a sample person living in a house with no extra rooms for their whole life will have a housing energy of 1.59GJ/hour which increases to 2.68GJ/hour by living in a house with 3 extra rooms. Based on resources for construction, refurbishment and heating and the time occupants spend in each room over the life the house, for each hour of using a master bedroom New Zealanders use 0.9MJ, and this increases to 9.3MJ for an hour of using a study and 5.1MJ for a play room.\n\nThis research suggests more public awareness is needed regarding the role of human behaviour in achieving a sustainable architecture and perhaps it is time for governments to control use of natural resources by restricting house sizes where applicable.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,023
Score d'incertitude au seuil0,852

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,012
Tête enseignante GPT0,232
Écart entre enseignants0,220 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2017
Routes d'admission1
Résumé présentoui

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