Mandatory or voluntary approaches to green roof implementation: a comparative study among some global cities
Bibliographic record
Abstract
Green roofs can deliver multiple environmental and social benefits by reducing the urban heat island effect, reducing building energy use and greenhouse gas emissions, improving air quality, providing habitat for biodiversity and access to the biophilia effect. Green roofs provide these benefits to differing degrees in different climate zones globally. Despite known benefits, uptake of green roofs has been slow. Different cities, globally, adopt various policies and programmes to increase their green roofs; the question is which approach is best? This research used an in-depth review, site visits and qualitative methods, to determine whether mandatory or voluntary approaches produced greater uptake. Green roof policies and practices from selected global cities, London, Toronto, Singapore, Rotterdam and Stockholm, Sydney and Melbourne were examined. Singapore’s voluntary approach led to the greater uptake of green roofs. The mandatory approach taken by Toronto, with financial grants provided meaningful outcomes. London and Rotterdam implemented useful voluntary programmes, and Stockholm required more time to evaluate the effectiveness of its voluntary approaches in increasing green roofs. A voluntary approach for retrofit and a mandatory approach for new build developments are suggested as recommendations for Australian cities. Given the increases in green roofs internationally, similar increases can occur in Melbourne and Sydney in Australia, and these findings may be transferable to other global cities investigating different approaches to the increased adoption of retrofitted green roofs.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".