Sustainable Housing: Understanding the Barriers to Adopting Net Zero Energy Homes in Ontario, Canada
Bibliographic record
Abstract
Buildings in Canada account for a significant amount of greenhouse gas (GHG) emissions and net zero energy building technology has been identified as part of the solution. This study presents a conceptual model identifying barriers to the adoption of net zero energy housing and tests it by administering a survey to 271 participants in a net zero energy housing demonstration project in Toronto, Canada. Using multivariate correlation and multi-linear regression analyses this study finds that of all the innovation adoption variables it was the construction and design quality that was the most significant contributor to the adoption of a net zero energy home by a potential home owner. This study found that the (a) extra cost compared to a conventional home, b) lack of knowledge about the technology associated with a net zero energy home or (c) not knowing someone who owned a net zero energy home were not significant barriers to accepting net zero energy homes. Our results suggest that policy-makers should promote the diffusion of net zero energy home technology by encouraging housing developers to include net zero energy homes in their collection of model homes, with an emphasis on quality design and construction. Furthermore, engaging in trust building initiatives such as education and knowledge about the technology, its related energy cost savings, and the environmental benefits would contribute to a greater acceptance of net zero energy homes.
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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 | 0.002 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".