Deep Energy Retrofits in Housing for Low Income Household in BC and Manitoba: An Opportunity for Climate Mitigation and Social Equity
Bibliographic record
Abstract
The low-income and vulnerable populations in Canada often live in social housing buildings with poor energy and environmental indoor performance. Many social housing buildings in Canada need major repairs and would also benefit from deep energy retrofits (DER) that could make them climate resilient, and safe for occupancy. The objective of this research was to investigate the GHG emissions reduction, as well as the energy and cost saving potential of different retrofit approaches in BC and Manitoba. I collected electricity and natural gas consumption data for 30 buildings in BC which were subsequently narrowed down to 6 building sites based on their location and type of retrofit. I also collected data for 2 buildings in Manitoba. One building received an interior insulating spray foam application, and the other, exterior spray foam. My study shows that different retrofit approaches executed in BC yielded 18% to 39% energy savings and 27% to 99% GHG emissions reduction as a result of the electrification of one or both end-use systems for space and water heating, as well as building envelope upgrade. Significant energy savings and GHG emissions reduction were also realized in the two Manitoba buildings where building envelope enhancements were executed including the installation of high efficiency heat recovery ventilation systems.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".