In search of the water-wise megacity: Assessing current and future challenges for sustainable water management in the city of Toronto
Bibliographic record
Abstract
Increasing water challenges are threatening urban regions across the world. Cities in developed countries face the paradox of having universal basic water and sanitation services while experiencing water issues that are as daunting as in developing regions. Such is the case of the city of Toronto in Canada that appears as a region with privileged geographic, economic, and political conditions as well as abundant availability of freshwater. However, the city has historically faced a myriad of water issues that are likely to intensify in the future. Toronto experienced rapid urbanization in the last century which will continue in the upcoming years through population growth and infrastructure development. Climate change comprises another issue that will create extra pressure into the water system. Additionally, water management in the region has been presented as a complicated matter due to the high degree of fragmentation of related institutions, creating challenges of integration and coordination. However, a comprehensive assessment of the actual performance of the city’s water management is lacking. In this regard, the main objective of this research is to assess the sustainability of water resources management and the IWRM performance at the city level, as well as the governance capacity to cope with increasing water challenges. It also aims to identify the system’s strengths and weaknesses and enable the comparison with other urban areas in a qualitative and a quantitative manner. For these purposes, two assessment methods are considered. First, The FUNqyWATER-project serves as an introductory approach that aims to qualitatively assess the current demands for a wide range of human water use functions as well as the underlying drivers that may lead to changes in future demands. Second, The City Blueprint Approach aims to facilitate the assessment of IWRM by (i) identifying the external pressures and conditions that affects the water system, (ii) determining the current performance of IWRM and (iii) assessing the city’s governance capacity to cope with a set of key water challenges. As expected, the results reflect positive results in areas such as provision of basic drinking water and sanitation services, preparedness against water risks, and financing capabilities. However, many weak points were revealed that are not evident, such as insufficient tertiary water treatment, the lack of nutrient recovery or the limited influence on decision-making by the community. The results were compared with other cities around the world but especially with those in the North American region. The integration of the two assessment methods is a potential tool that might be replicated in other cities. Cross-city learning is crucial in the transition towards sustainability of water resources and sound water governance in urban areas.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".