Review of White Roofing Materials and Emerging Economies with Focus on Energy Performance Cost-Benefit, Maintenance, and Consumer Indifference
Bibliographic record
Abstract
This article performed a comprehensive review of the different state-of-the-art of roofing technologies and roofing materials and their impact on the urban heat island (UHI) and energy consumption of buildings. The building roofs are the main sources of undesirable heat for buildings, especially in warm climates. This paper discusses the use and application of white roofing material in emerging economies. The use of white roofing material is a suggestion because of its cooling, evaporative and efficiency characteristics compared to traditional black roofing materials. Many research studies have shown that the darker roofing surfaces that are prevalent in many urban areas actually can increase temperature by 1 to 3 degrees Celsius to the environment surrounding these urban areas. Additionally, improved temperature control and heat reflection also work to reduce the energy requirements for the interior spaces of the structures that have white roofing surfaces. The white or lighter colored roofs tend to reflect a part of the solar radiation that strikes the roof’s surface. Consequently, one might believe that white roofing material would be commonplace and especially so within emerging economies. Yet, this is hardly the case at all. This paper examines the issue of white roofing materials in emerging economies from a dual perspective. The dual perspective includes the technical details of white roofing material and its impact on lowering the interior temperature of the affected structures, which consequently reduces hours of indoor thermal discomfort and use of air conditioners in indoor spaces. The other element in this study, however, involves the marketing aspect of white roofing material. This includes its adoption, acceptance and cost-benefit in emerging economies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".