Strategic green roof placement in Toronto to maximize benefits while incorporating citizen preferences
Bibliographic record
Abstract
Green roofs provide a broad range of ecosystem services, including air quality regulation, temperature regulation, flood regulation, and habitat provision. Through strategic placement they can address multiple urban challenges. However, city governments often do not engage in strategic spatial and fail to consider citizen preferences. To address this, we developed an approach for strategic placement of green roofs, based on the four mentioned ecosystem services for Toronto, Canada. We designed an approach based on biophysical information, which we further enriched with data on citizen preferences for different ecosystem services. We used literature-derived criteria and spatial analysis to identify optimal green roof placement locations for each ecosystem service separately, and the four services combined. A citizen survey was conducted (n = 402) to rank ecosystem services based on preferences. Mean rankings were used for a citizen-weighted model for strategic green roof placements. Our approach identified key priority areas for Toronto, while also highlighting that a significant portion of the city’s rooftops are sub-optimal for maximizing the benefits of all four ecosystem. However, by focusing on turning roofs in hotspot areas (0.2–1.2 % of total roof space) into green roofs, the four ecosystem services could be enhanced simultaneously. Results showed large spatial variations in priority areas between individual ecosystem services. However, comparing results across the biophysical and citizen weighted approach for all ecosystem services combined indicated similar priority areas, suggesting citizen support for prioritisation related to biophysical needs. Moreover, citizen involvement in governance practices can foster transparency, inclusivity, and satisfaction in strategic planning of green roofs. The methods employed in this study can be adapted to other cities worldwide, enabling more strategic, participatory approaches to implementing green roofs to enhance multiple ecosystem services where they are valued most. • We developed a strategic green roof planning approach to enhance ecosystem services. • We assessed differences between a purely biophysical and a citizen-weighted approach. • Green roof priority areas differ depending on the ecosystem service considered. • Our approach identifies up to 1.2 % of roofs as priority areas for green roofs. • Citizen preferences for green roof priority areas overlap with biophysical prioritization.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".