A review of passive design strategies and their effect on thermal resilience in low-income households
Bibliographic record
Abstract
Climate change is causing more frequent and extreme weather events (e.g., heat waves and ice storms) that disproportionately impact low-income households with poor building conditions. Passive design strategies offer a promising solution to enhance building thermal resilience; however, little is known about their actual application and effectiveness in low-income households under different climate conditions. This paper presents a scoping literature review of 123 articles that evaluated passive design strategies applied to low-income housing contexts. The detailed article review shows that wall- and roof-related passive design strategies (e.g., insulation, sealing) are the most frequently studied and effective strategies, increasing comfort hours and energy savings with a wide variation of up to 24 % and 67 %, respectively. Combined strategies often outperformed individual strategies. In contrast, some studies reported unintended consequences following the adoption of passive design strategies, such as increases in energy demand reaching as high as 50 %. Such findings shed light on the need for design processes to consider multiple performance metrics, as well as behavioural and socio-economic factors that require more in-depth investigation, such as energy poverty and rebound effects. Detailed recommendations are finally provided to guide future research and applications on the topic, covering the need to (i) explore and combine underrepresented thermal resilience strategies and metrics, (ii) follow more standardized reporting practices, (iii) quantify costs and identify implementation barriers, and (iv) integrate participatory research methods to support technical assessments with contextual knowledge of the studied low-income households and communities.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".