Net Zero Energy‐Ready Buildings: A Canadian Construction Perspective and Evaluation
Bibliographic record
Abstract
Abstract To attain net zero energy‐ready building (NZErB) status, various research efforts have focused on identifying potential strategies and creating stringent code compliances for builders. This review presents a comparative assessment of Canadian newly constructed, retrofitted, and potential retrofit buildings from the mid‐1900s to 1990, all aiming for NZErB status. 22 case studies from climate zones 5, 6, and 7a are evaluated, including 12 new constructions and 4 retrofitted, and 6 potential retrofit buildings. A life cycle assessment (LCA) analysis is conducted to understand the environmental impacts of different insulation materials. Additionally, this review highlights retrofitted buildings measures toward climate resilience, challenges inretrofitting, andstrategies for achieving high‐quality retrofits. The work concluded that 83.3% of new buildings achieved level 5 in thermal energy demand intensity (TEDI), while 70% of completed and potential retrofits reached level 5 in mechanical energy usage intensity (MEUI). Cellulose insulation showed the lowest global warming potential (GWP) at 12.07 kg CO₂‐e·m−3. By comparing the performance of new constructions with completed and potential retrofits, this review provides valuable insights into the feasibility and effectiveness of retrofitting older buildings to attain net zero energy readiness.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".