Investigation of urban heat island and climate change and their combined impact on building cooling demand in the hot and humid climate of Qatar
Bibliographic record
Abstract
Urban Heat Island (UHI) and climate change are two critical factors affecting the energy demand of buildings. However, the previous literature often overlooked the concurrent impacts of these factors, which leads to an erroneous estimation of the current and future energy demand of buildings. To address this issue, this paper investigates the UHI and climate change and their combined impacts on the current and future cooling demands of high-rise residential buildings in the hot and humid climate of Qatar. The impacts of UHI and climate change on the climatic conditions of the Marina district of Lusail City, Qatar, are evaluated using Urban Weather Generator (UWG) and World Weather Generator (WWG) tools, respectively, for 2050 and 2080. A total of eight weather sources, two for 2020 and six for 2050 and 2080, are compared to the weather data collected from the established local weather stations in the city. Two important methods are adopted to elaborate on the combined impact of the UHI and climate change on building cooling demand. In the first method (M1), the future weather file obtained from the Open Weather Map (OM) is processed by UWG for the UHI impact analysis and then by WWG for the climate change impact analysis, while in the second method (M2), the future weather file is first processed by WWG, followed by UWG. According to the results, for the hot and humid climate of Qatar, the cooling energy consumption of the high-rise residential building increases by 19% and 33.5% for 2050 and 2080, respectively, by the first method, and by 20% and 34.4% for 2050 and 2080, respectively by the second method. Both methods yield fairly similar results on the combined impact of UHI and climate change on building cooling demand in hot and humid climates. The findings of this study highlight the importance and necessity of considering UHI and climate change impact in building energy simulations to estimate the present and future energy demand of buildings accurately.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".