Assessing the feasibility of implementing the Sponge City concept as a climate change response in mid-sized Ontario cities: A case study in the City of Kingston, Ontario
Bibliographic record
Abstract
An increase of extreme weather events is expected to occur within the context of climate change and evidence has proven that climate change will increase the intensity and frequency of rainfall, raising concerns regarding flooding. As cities grow and climate change intensifies, urban planners must integrate resilient and adaptive solutions to combat this risk. The Sponge City (SPC) concept is a systemic water management approach which uses natural-based solutions by promoting green infrastructure, sustainable drainage systems, as well as protecting, and enhancing natural landscapes. SPC aims to control water quality and quantity and increase urban resilience in cities and is widely practiced in China; however, it is not commonly used in western planning. Therefore, this study exams the feasibility of implementing SPC concept in mid-sized Ontario cities by using the City of Kingston as a case study. This study investigates the effectiveness and benefits of SPC along with approaches for SCP implementation for application within the City of Kingston. Further, local stakeholders’ familiarity with SPC concepts are explored. This study used a literature review, policy analysis, semi-structured interviews and GIS mapping as primary methods to achieve research objectives. Findings from this study suggest that SPC is an effective strategy for flood control as well as bringing social and environment benefits to cities. Findings indicated that stakeholders have a good understanding of SPC concepts, and many stakeholders recognized SPC is similar to Low Impact Development (LID) which is used in Kingston for flood mitigation. Findings also highlighted that while SPC principles can enhance urban resilience, there are gaps present in public involvement, as well as physical challenges. Lastly through mapping, areas with combined sewers, high built-up and impervious surfaces, low vegetation, and close to the existing floodplain are identified as the most optimal area SPC implementation. This study also provides recommendations for integrating SPC concepts into current urban planning practices in Kingston based on learnings from international case studies, data collected through interview results, and policy analysis. This research provides insights for SPC concepts, and ways of integrating SPC concepts into municipal planning thus contributing to sustainable and resilient urban development.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".