Green living envelopes for food and energy production in cities
Bibliographic record
Abstract
This research explores the potential benefits derived from a proposed green intervention which combines living envelopes (green roofs and green façades) and green energy envelopes (photovoltaic and thermal panels), as a means of addressing the concept of carbon neutral cities.It proposes to take advantage of the environmental contributions that living envelopes provide, in terms of food production and the reduction of energy demand; as well as the energy produced through green energy envelopes such as photovoltaic and thermal panels.This green living envelopes intervention is applied to a specific site of downtown Vancouver, Canada.The research explores the contribution of such a green intervention.It analyses existing conditions of the site in terms of different building types and uses as well as their current energy consumption and CO 2 emissions.It then proposes to incorporate living envelopes such as green roofs and façades, as well as green energy envelopes by applying the proposed Vancouver Green Factor.Achieved findings from such a green intervention shows that the total energy consumed by buildings by the greening of roofs and façades would be reduced by 17%.In addition, energy produced through photovoltaic and thermal panels is enough to cover 16% of the energy demand.Moreover, by using green roofs as food producers, 54% of the vegetable demand of the people living in the selected site would be covered, further contributing to a reduction of 4% of the total food production.This translates into a reduction of 45% of CO 2 emissions produced by the selected site.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".