Integrating urban forestry into compact, climate resilient cities : policies and pathways
Bibliographic record
Abstract
Cities are leading contributors to climate change while also suffering severe climate change impacts. In response, many cities have introduced climate change policies to guide the planning and implementation of local climate action. While most municipal climate action is focused on densification, building energy use and transportation, there is increasing recognition and integration of urban forests into cities’ climate change policies. However, the integration of urban forests may conflict with other climate measures (e.g., efforts to build compact communities), creating barriers to effective policy implementation. There is still limited understanding of the alignment and interactions among municipal policies relating to urban forestry, climate action, and climate-focused urban planning. This dissertation aims to address this research gap by conducting the following three connected studies. The first study examined the extent of alignment and integration between climate change policies and urban forestry policies in the 20 largest Canadian cities. Key policy gaps and discrepancies were identified across and within cities, especially in the use of terminologies, stakeholder engagement, maintenance priorities and practices. The second study aimed to identify key synergies and trade-offs in the policy-implementation-outcome spectrum of municipal policies relating to climate action, urban planning, and urban forestry through conducting semi-structured interviews with experts from Metro Vancouver, Canada. The findings highlighted significant trade-offs and synergies among policies, implementation processes and outcomes, especially in the areas of (re)development and infrastructure. The final study aimed to explore plausible urban forestry policy intervention solutions related to themes identified in the previous interviews. Modelled scenarios of urban form and urban forests in a hypothetical densifying urban neighbourhood were analyzed. Crucial urban greening opportunities on public and private land to better integrate urban forests into a densifying city for climate and livability benefits were identified and their relative impacts were discussed. The dissertation highlights important inherent gaps and conflicts among climate change, urban planning and urban forestry policies in cities that create significant barriers to successful outcomes. The findings can provide policy-makers, practitioners and researchers with useful evidence and insights on feasible pathways to make our cities more compact, green, climate-resilient, and livable.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.012 | 0.015 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| 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".