Which factors influence consumers’ selection of wood as a building material for houses?
Bibliographic record
Abstract
Construction and use of buildings is one of the highest users of global energy (34%) and one of the highest contributors to greenhouse gas emissions (37%). Using wood instead of carbon-intensive materials such as bricks reduces a building's embodied energy and is a more eco-friendly alternative. Since the quota of newly built wooden houses in Germany is still relatively low, gaining insights into the perspective of consumers is crucial. This study aims to investigate factors from a consumer perspective that influence the selection of wood as the primary building material for residential houses. Therefore, an online survey was conducted in Germany to gather data from individuals ( N = 510) who either bought or built a house in the last 5 years. By conducting a logistic regression, we have identified six influencing factors for the selection of wood. Positive views on wood's eco-friendliness and emphasis on renewable materials are key factors in choosing wood. Higher age and living in rural areas also increase the likelihood of selecting wood, while concerns about value stability and durability have negative effects. We conclude that increasing information activities, raising awareness about wood's ecological benefits, and dispelling prejudices can significantly impact its selection as a preferred building material.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".