Strengthening Climate Resilience Through Urban Policy: A Mixed-Method Framework with Case Study Insights
Bibliographic record
Abstract
While climate resilience is a growing priority in urban planning, limited attention has been given to the procedural and governance mechanisms needed to effectively integrate resilience into policy development. This study presents a comprehensive policy analysis aimed at enhancing climate resilience, using the city of Kamloops, Canada, as a case study. A policy evaluation framework was developed, encompassing four dimensions and 20 indicators, to assess 11 policies and bylaws in Kamloops. The evaluation yielded a moderate score of 0.559 out of 1, revealing both existing strengths and critical gaps in the city’s climate resilience strategies. Key challenges identified include policy inflexibility, the absence of clear climate adaptation goals, insufficient emphasis on education and research, the lack of long-term projections and risk assessments, and implementation gaps such as unclear timelines, responsibilities, and funding mechanisms. To validate these findings, interviews with city staff from multiple departments provided further insights into governance barriers and opportunities for policy enhancement. Beyond Kamloops, this study offers a scalable and adaptable framework for cities worldwide seeking to integrate resilience into their urban planning policies. By addressing governance and procedural challenges, cities can strengthen their capacity to mitigate climate risks, enhance sustainability, and build long-term urban resilience.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".