Transforming Canada's Housing Stock: A Comprehensive Categorization of Sustainable Retrofit Strategies for Residential Building
Bibliographic record
Abstract
Single-family detached and attached homes constitute a significant portion of Canada's housing stock, many of which were constructed during the pre-sustainability era.Given their substantial energy demand, retrofitting these buildings is essential for meeting Canada's climate action goals.With a wide range of retrofit options available to reduce energy use, improve comfort, and enhance aesthetics, it is crucial to identify solutions that not only improve environmental performance but also generate positive economic and social impacts.Few resources currently classify residential building retrofits based on their primary focus.This study investigates retrofit strategies for Canada's single-family homes by evaluating their impact on building performance and categorizing innovative hybrid approaches that balance energy efficiency, cost-effectiveness, aesthetic appeal, and structural resilience.Our findings indicate that implementing hybrid retrofit strategies in Canada's residential sector requires an integrated approach addressing technical, financial, and regulatory challenges.Moreover, a structured classification of retrofit types, combined with robust stakeholder engagement beyond mere financial incentives, is essential to achieving significant improvements in energy efficiency, sustainability, and resilience.Ultimately, this research provides a framework for transforming existing residential buildings into energy-efficient, visually appealing, and regulation-compliant homes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".