Urban tree planning: Can MCDA-driven approaches help improve current practices? A Canadian case study
Bibliographic record
Abstract
Canadian cities are striving to address climate change by setting ambitious goals for tree planting and increasing urban canopy cover. However, urban tree planning is complex, involving multiple objectives like reducing heat islands, improving public health, and minimizing costs, while balancing the interests of various stakeholders. To manage this complexity, a spatial suitability model for tree planting was developed using GIS-MCDA. This model was co-created with stakeholders in Montreal, Canada, and aims to improve upon traditional urban tree planning methods by combining territorial opportunities and needs. A comparison was made between the model's recommendations for tree planting sites and the sites previously planned by municipal institutions in three Montreal boroughs. The analysis, which considered both the entire study area, and a subset comprised of public land, showed a significant discrepancy between the areas prioritized by the model and those selected by the boroughs, with little overlap between the two. This difference may stem from the model's broaderscale approach, while the boroughs focused on individual tree pits at a finer scale. Despite these differences, the framework provides an opportunity for municipal institutions to consider a wider range of planting sites and take into account a more complete set of decision criteria. By using this model, cities can optimize tree planting efforts to provide needed ecosystem services to the local populations.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".