Improving the Thermal Environment of Abuja’s Affordable Housing Through Passive Design Solutions
Bibliographic record
Abstract
West Africa is increasingly becoming more vulnerable to extreme heat due to climate change intensification with forecasts predicting hazardous heat days to double by 2060 affecting all societal classes and life sectors. This study examines the relationship between urbanisation, energy-efficient building design, and government guidelines within the Nigerian context. The review of the current national building codes and energy efficiency regulations revealed an alarming gap regarding the abandonment of basic sustainable design practices when addressing the needs of low-income housing. Validated simulations were used to assess the thermal performance of six distinct residential prototypes for low- and middle-income mass housing, which were previously developed by the government and are still used today as development blueprints. The effectiveness of incorporating passive design solutions into the selected prototypes was examined, providing insights into their thermal performance and practical recommendations for improving occupants’ comfort. The findings highlight the value of utilising a combination of passive design methods to achieve occupant thermal comfort, suggesting a reduction of up to 20% in the frequency of thermal discomfort during the hottest period of the year. The study advocates for more comprehensive guidelines to facilitate sustainable housing design that prioritises low-cost passive approaches to enhance indoor comfort and reduce reliance on conventional energy sources, ultimately fostering resilience in the face of climate change.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".