The State of Climate Resilience and Water Governance in the City of Toronto: Advancing Adaptation at the Climate–Water Interface Through Baseline Assessment Research
Bibliographic record
Abstract
ABSTRACT The City of Toronto is Canada's largest city and the fourth largest in North America. In the last two decades, it has experienced significant climate‐related water issues, which have resulted in significant local flooding, decreasing water quality, and increasing climate change awareness. Similar to other jurisdictions across the world, the city has undertaken many climate resilience‐building efforts and actions across multiple areas. This article undertakes a review of current climate resilience actions and efforts in the city, focusing on the areas of climate science and modeling, climate and water policies, city governance, and community responses to water‐related climate hazards. The article develops and applies a new Community Climate Resilience Assessment Framework (CCRAF) to assess the research findings and evaluate the baseline state of climate change resilience in the city, particularly related to water governance challenges. Using the City of Toronto as an illustrative case study, this article demonstrates the importance of establishing a strong understanding of the current community, governance, and institutional contexts, as a means of identifying opportunities to increase climate and community resilience in cities. The results from the application of the CCRAF indicate current areas of strength in city resilience‐building efforts include its focus on emissions mitigation, numerous climate and community initiatives, resident support programs, and incentives. Potential areas for improvement include integrating fragmented governance structures, building capacity and designating resource allocations for climate–water resilience efforts, and enhancing engagement of marginalized groups at the climate–water interface. On the basis of these findings, the article highlights the importance of and need for additional focus on climate–water adaptation, deepening community engagement of diverse perspectives, and centering Indigenous knowledges and perspectives to support governance innovations and increase overall climate resilience. The article also outlines the importance of baseline research and how this new framework can be applied in other communities and transboundary regions in Canada and beyond.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".