Thermal performance assessment of cool roofs on supermarkets through case analysis in 13 cities
Bibliographic record
Abstract
Purpose This paper aims to study the use of cool roof technology to avoid unnecessary energy consumption in supermarkets. This will allow to reduce and even cancel the heat absorbed by the roofs, transferring it to the buildings and thus, creating more sustainable cities. Design/methodology/approach Thirteen real supermarkets with cool roofs were analysed in Australia, Canada, the USA and Spain. An analysis of so many supermarkets located in different parts of the world with different climatic zones has allowed an inductive analysis, obtaining real data of energy consumption associated with the air conditioning installations for a year with and without implementing the cool roof technology. Findings The paper provides insights on how the use of cool roof managed to reduce the need for energy for heating, ventilating and air conditioning by between 3.5 and 38%. Additionally, this technology reduces the annual generation of carbon dioxide (CO2) emissions per square meter of supermarket up to 2.7 kgCO2/m2. It could be an economical technology to apply in new and old buildings with a period of average economic recovery of four years. Research limitations/implications Because of the chosen research approach, the research results may be generalisable. Therefore, researchers are encouraged to test proposals in construction with other uses. Practical implications The paper includes economic and environmental implications for the development of cool roof technology and smooths the way for its implementation to increase energy efficiency in commercial buildings. Originality/value This paper is an innovative contribution to the application of cool roof technology as a source of energy savings in commercial construction through the analysis of supermarkets located in different countries with different climate zones. This will help other researchers to advance in this field and facilitate the implementation of the technology.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".