Modelling Four Neighbourhood-Scale Urban Forest Scenarios for 2050: Vancouver, Canada
Bibliographic record
Abstract
Abstract Background Urban forests are increasingly recognized as important tools in climate change mitigation and adaptation, prompting many cities to set tree canopy cover targets. However, current gaps in knowledge include understanding relationships and the feasibility of maximizing benefits between urban greening and other climate actions, such as densification. This study offers a data-driven and manageable framework for assessing current and anticipated future urban forestry conditions using spatial tree and built-form models. Methods We spatially modelled 4 planting scenarios for increasing tree canopy cover by 2050 in a densifying neighbourhood in Vancouver, Canada, with low (< 10%) existing tree canopy. Results Based on mortality assumptions, we aged out and replaced 1,853 to 2,445 trees since 2020. We added 6,079 to 11,726 trees across the 4 scenarios (10,228 to 15,823 total), increasing canopy cover from 7% in 2020 to a maximum of 16% by 2050. Despite rigorous tree planting, we were unable to achieve a 30% canopy cover target at neighbourhood scale. Tree replacement due to mortality was a major contributor to decreased canopy cover and volume in future scenarios. The 31% to 34% reduction in future canopy cover due to the replacement of aged-out trees was driven by changes on private parcels. Conclusion Our systematic framework for generating and spatially modelling trees in a simulated future neighbourhood provides an opportunity for iteratively assessing multiple potential tree planting configurations. Future work for this project includes investigating social-ecological, outdoor shading, and building energy implications of various modelled urban forest strategies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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".