Energy Efficiency and Global Warming Potential in the Residential Sector: Comparative Evaluation of Canada and Saudi Arabia
Bibliographic record
Abstract
In the Canadian building sector, residential housing has been identified as the largest contributor to greenhouse gas (GHG) emissions. Similarly, high-income countries, such as Saudi Arabia, are also excessively investing to meet their residential demands. Although developed countries are considered to have more sustainable development practices, factors such as occupancy per household and floor area of a single-family residence can contribute to greater energy consumption and GHG emissions per capita. To develop global sustainable strategies, there is a need to evaluate the global warming potential (GWP) on a household basis by considering different lifestyles and climatic conditions in different parts of the world. In this study, a methodical framework was developed to compare an average Canadian single-family detached house (CSDH) with an average Saudi Arabian villa (SAV) (with similar living standards) based on their energy consumption and associated GWP. Demographic and environmental data were collected from the literature and relevant organizations. To accommodate regional variations in construction practices and climatic conditions, two different types of houses in five different cities of both the countries were analyzed. The study found that the overall GWP of a SAV is approximately 25% higher than that of a CSDH because of the larger floor area. However, comparison on a per-person (occupant) basis revealed that the SAV produces 43% less GHG emissions than the CSDH. The results of this study will assist in formulating sustainable development policies in the residential sector and provides a rationale for both Canada and Saudi Arabia to adopt sustainable development strategies.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".