Towards Equitable and Resilient Climate Planning: How Can Scarborough Adapt? Lessons Learned from New Orleans
Bibliographic record
Abstract
My paper outlines three recommendations—tangible solutions— for more resilient climate justice planning in Scarborough, a large community of the city of Toronto, Ontario. Scarborough is facing challenges of a lower-income population and a higher risk of climate emergencies than other residents of Toronto. My research focused on New Orleans as an example of a place that struggles with equitable and resilient climate adaptation planning. Hurricane Katrina and its aftermath have given scholars and planners significant lessons about climate adaptation planning. Social equity and climate change are not well integrated in planning scholarship and my recommendations are intended to better integrate these important issues. The planning issue addressed in this paper is how to focus on climate change for equity-seeking groups, and how critical this is in achieving meaningful solutions for at-risk groups faced by climate change. The research questions are: 1. In what ways can climate change planning be more equitable and resilient? 2. How can plans, processes, and interventions support equitable and resilient climate change planning? The key debates in this planning issue are 1. How best to undertake climate change adaptation planning in the suburbs, and 2.How to create more equitable climate adaptation plans. This paper is focused on recommendations for just climate adaptation planning for Toronto, and long-term sustainable community development strategies. Through writing this paper, I have sought to understand more deeply how climate adaptation plans and planning can build capacity for climate action simultaneously at both the municipal and higher levels of government. The effects of climate change significantly and disproportionately affect vulnerable groups such as racialized communities and low-income groups who are less able to deal with extreme heat and cold, drought, and flooding. Vulnerable populations are the canaries in the coal mine in terms of climate change, as we already are seeing through displacement and other impacts. My three recommendations are to 1. Facilitate inclusive and meaningful community-based planning, 2. Plan and develop inclusive social infrastructure, and 3. Implement integrated and localized plans; but my overarching question remaining is the governance in the climate planning process, and how local government structures may be the main barrier to local municipal efforts.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.010 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".