Plant selection for green roofs and their impact on carbon sequestration and the building carbon footprint
Bibliographic record
Abstract
One of the most critical determinants of a green roof's performance is the type of vegetation planted on its surface. This study examines the factors influencing the choice of plant for green roofing, such as sunlight requirements, water requirements, and cold tolerance, in order to identify the preferred green roof plants for use in cold and dry climates such as Mashhad, Iran, and to provide a roadmap to assist decision making in this regard. For this purpose, initially, fifty different plant species were evaluated from four perspectives: (i) applicability in extensive green roofs, (ii) photosynthesis rate, (iii) availability, (iv) low cost. Then, green roofs with selected vegetation were used in a field test, and the amount of carbon uptake of each of them was measured over one year. Finally, by modeling these green roofs in Design Builder software, the reduction of building energy consumption was evaluated to comprehensively investigate the overall impact of green roofing on the carbon footprint of the building. This study found that the best plants for the climate experienced in the field test are Sedum acre, Frankenia thymifolia, and Vinca major, which enjoy good tolerance and performance characteristics and offer the best energy demand and carbon emission. Green roofs with these three plants could reduce a typical building's annual energy consumption by 8.5%, 8.0%, and 7.1%, respectively. After implementing a green roof with Sedum acre, Frankenia thymifolia, and Vinca major atop a 4-story building and measuring these plants' dry weight monthly over one year, the annual CO2 absorption of these plants through photosynthesis was estimated to be 0.14, 2.07, and 0.61 kg/m2. In addition to absorbing carbon through photosynthesis, the green roofs with these plants also reduced the building's CO2 emissions by 28.16, 26.48, and 23.44 kg/m2 respectively, by reducing the energy demand.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".