Determining the retrofit viability of Vancouver’s single-detached homes: an expert elicitation
Bibliographic record
Abstract
The reduction in energy and emissions from the building sector can come from improved standards for new construction and retrofits to existing buildings. The retrofit viability for single-detached homes in Vancouver, Canada, is examined in terms of the key drivers and barriers involving economic and social forces. Local experts considered the likelihood of retrofits occurring to several archetypal dwellings that were synthesized from local building data and homeowner characteristics. The survey results (n = 56) raised less known but potentially significant issues regarding energy-efficiency retrofits in Vancouver. Domestic fuel switching, from fossil fuel energy services to electricity, is likely the most desirable future mechanism for decarbonizing homes. However, many of the respondents identified that Vancouver’s real estate market has a significant negative influence on retrofitting due to high land values, which results in a high demolition rate of existing homes. Only 46% of responses returned a view that an existing home would remain standing by 2050. In addition, 41% of responses expressed a doubt that the dwelling, whether existing or new, would achieve carbon neutrality by 2050. Both issues confront the City of Vancouver’s current emissions reduction planning, which has targeted near-complete decarbonization of the residential building stock by 2050. Policy relevance New construction is expected to account for only 30% of the greenhouse gas emissions reduction in Vancouver’s building sector. The potential for deep retrofits of single detached houses appear to be unlikely due to current real estate market conditions involving several perceived disincentives, e.g. low financial payback, poor knowledge, transaction costs, and the opportunity cost of new construction. If the widespread retrofit of single detached houses is a goal for cities that have high land-to-building value ratios, then the alteration of current market conditions is necessary. A basket of coordinated policy measures can be deployed to counter current market forces and reduce the demolition of existing homes. Such measures could include retrofit and planning codes, energy labelling, innovative finance, and public education.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".