Living skins: environmental benefits of green envelopes in the city context
Bibliographic record
Abstract
This research reports the potential environmental benefits derived from an overall intervention of living skins [green roofs and green façades] in the city core of Vancouver, Canada.These include the reduction in cooling and heating demand; reduction in stormwater runoff; improvement of air quality; enrichment of urban biodiversity and urban agriculture; reduction in the urban heat island effect; the contribution to carbon neutral architecture; and an assessment of different construction systems.It analyses the environmental behaviour of the selected site by applying the Seattle Green Factor.The research focuses on the energy performance of a typical residential building within the selected area, through the Energy 10 simulation program.It also analyses the reduction in stormwater runoff through the Curve Number Method; as well as the reduction of CO 2 emissions based on related research.Obtained data shows that the total energy used for cooling is reduced [100%] through the greening of roofs and façades, which means the 9% of the total energy demand by the studied building.It also shows that CO 2 emissions would decrease by 9%; and stormwater runoff would be reduced by 4%.The research compares these findings with previous related research on green roofs, façades and urban forests.Its findings suggest that these types of "living skins" interventions achieve better environmental performance in warm-dry climates where cooling energy demand is greater.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".