Investigating a Deep Energy Retrofit Package for Nova Scotia's Affordable Housing Sector
Bibliographic record
Abstract
The construction industry is a significant contributor of greenhouse gas emissions in developed countries around the world. The building sector has adopted new ideas and initiatives in recent years to help tackle climate change, but the move toward net zero has been gradual. This is particularly troublesome in countries with cold climates, like Canada, where fossil fuels are used extensively for space heating. This study investigated an optimal deep energy retrofit package for an affordable housing building owned by the Housing Trust of Nova Scotia to determine its constructibility, effectiveness, financial feasibility, as well as scalability and repeatability for buildings of similar archetype. This project was intended to act as a proof-of-concept to address much needed deep energy upgrades to Canada's existing building stock, with particular focus on affordable housing. The objective was to create an exterior deep energy retrofit package that ensures high performance, minimal disruption to occupants and fosters a "pride of place" for its residents. The results indicated a 95% reduction in space heating demand and projected thermal comfort improvement by including a cooling system. Upgrades to water use equipment for the entire building decreased the water system energy demand by 60% when compared to the baseline model. Similarly, the energy use intensity for the proposed retrofit shows a significant reduction from both the baseline and the ENERGY STAR benchmark for multifamily residential buildings, with an energy use intensity (EUI) of 62 kWh/m². Furthermore, the annual greenhouse gas emissions reduced from 57 kgCO₂/year to 2.47 kgCO₂/year, illustrating the significance that deep energy retrofits have on contributing to the achievement of federal and provincial GHG emission targets. Additionally, the methodology investigated in this research implies that buildings of the same archetype are easily repeatable and scalable. Some design details of the panels require consideration with buildings that have more complex geometry and building components, such as balconies. Overall, the deep energy retrofit package has a high likelihood of replication and application to similar buildings.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".