Ambitious deep energy retrofits of buildings to accelerate the 1.5°C energy transition in Canada
Bibliographic record
Abstract
Scientists advise limiting global warming to 1.5°C with substantial actions by 2030. Our viewpoint argues that climate response strategies in Canada have underemphasized and underestimated the potential contribution deep energy retrofits can make to greenhouse gas (GHG) emissions reductions, leading to inadequate responses in the building sector, and that Canada can (and should) be ambitious with building retrofits over the next decade. GHG savings from building retrofits can be realized more quickly than GHG reductions from other sectors, and either deliver net cost savings or are cost‐effective when compared to other mitigation measures. Retrofits can also provide social and economic benefits, such as improved health and comfort, and lower energy costs. This paper reviews energy use and building retrofits in Canada and argues the following should be implemented: (1) focus innovation on deep energy retrofit processes, not singular retrofit actions; (2) maximize both social and environmental benefits; (3) improve data gathering and availability for analysis and delivery; (4) innovate for a process of decisions and to avoid “dropouts” during the retrofit process; and (5) focus innovation on business models that maximize benefits.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".