An investigation of the effects of large houses on occupant behaviour and resource-use in New Zealand
Bibliographic record
Abstract
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".