Where to Expand Green Infrastructure to Support Equitable Climate Change Adaptation in the City of Toronto?
Bibliographic record
Abstract
Green Infrastructure (GI) is a potential tool to help cities adapt to climate change. In particular, GI can help moderate high summer temperatures and reduce urban flooding, both of which are expected to become more common with on-going climate change. However, GI is not evenly distributed in many cities suggesting that some neighborhoods are more vulnerable to climate change impacts. Additionally, marginalized communities often lack the resources needed to reduce existing vulnerabilities. This study explores the question: where should GI initiatives be focused to support equitable climate change adaptation in the City of Toronto (Ontario, Canada)? We address this question by applying a GI Equity Index that includes built environment and socio-economic factors to identify neighborhoods’ level of need for GI to support climate change adaptation. The spatial location of high need neighborhoods, and their particular characteristics, are examined. Our results highlighted the spatial clustering of very high and low need neighborhoods in Toronto. Three types of neighborhoods were identified as most in need: (1) those vulnerable due to very limited existing GI, (2) those vulnerable due to socio-economic characteristics, and (3) those that lack GI and have marginalized populations based on socio-economic measures. On the other hand, neighborhoods identified as least in need based on the index were relatively uniform in character: all had abundant tree canopy and residents who were high income, highly educated, and disproportionately white. These results highlight the importance of considering both built environment and social vulnerability to support an equitable distribution of GI for climate change adaptation, and that varied opportunities and challenges exist related to increasing GI in different types of vulnerable neighborhoods.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".