Nature’s role in residential development: Identifying leverage points for climate change planning in Ontario, Canada
Bibliographic record
Abstract
Cities are establishing climate change related targets to mitigate the effects of climate change and adapt to its consequences. Natural features, such as trees and wetlands, can help communities reach their mitigation goals by storing carbon while also providing co-benefits that build resilience to climate change impacts. However, nature-based solutions for addressing climate change are not widely recognized in current development practices. To better understand this limitation, we interviewed nine municipal planners and eight private developers across Ontario, Canada, to assess how stakeholders in residential development consider natural features and climate change in their decisions. Our findings demonstrate that natural features, particularly in the natural heritage system, receive substantial attention in residential development decisions, but that climate change is rarely an explicit factor in those decisions. We anticipate that if the climate change benefits of natural features were explicitly quantified, this could impact the decisions of key stakeholders and support the design of alternative development forms. Our findings also suggest that policy changes, green development standards, cross-sector collaboration, and reliable ecosystem services data could all serve as significant leverage points for communities to support the implementation of nature-based solutions for climate change. Future research should investigate the effectiveness of green development standards and how tools that quantify ecosystem services could be incorporated into the development process to identify effective pathways for implementing nature-based solutions. • City planners and developers gauge natural features early in development applications. • Climate change is rarely explicitly factored into decisions on natural features. • Quantifying the benefits of natural features could help positively steer development. • Green development standards could guide the implementation of nature-based solutions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".