Sustainability of Rooftop Technologies in Cold Climates: Comparative Life Cycle Assessment of White Roofs, Green Roofs, and Photovoltaic Panels
Bibliographic record
Abstract
Summary Sustainable building rooftop technologies, such as white roofs, green roofs, and photovoltaic(s) (PV) panels, are becoming increasingly implemented as a result of their associated environmental benefits. Studies of these rooftop technologies are often located in hot climates and do not assess their full environmental consequences. Further, current studies tend to focus on one technology and often do not evaluate the full range of technology options using a systematic framework with common assumptions and boundaries. This article evaluates the environmental performance on a life cycle basis of white roofs, green roofs, and roof‐mounted PV in the cold Canadian climate. Solar PV demonstrates the highest environmental performance in all impact categories considered (see complete list in Results section) and is the preferred option from an environmental perspective. Green roofs result in beneficial environmental impacts, although much less significant than those obtained with PV, and are the only rooftop technology that reduces both heating and cooling energy use. The environmental performance of white roofs in cold climates is strongly affected by the heating penalty (i.e., the increase in heating energy use resulting from the high solar reflectance). Although white roofs have been proven an outstanding option in warmer climates, in cold climates, net negative environmental impacts lead to white roof technology not being recommended for general applications in cold climates. A sensitivity analysis shows that the conclusions in this study provide robust insights across Canada and cold climates in general.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".