What does resilience mean for urban water services?
Bibliographic record
Abstract
Disasters and climate change impacts, as well as increased water demand, pose serious risks to the provision of sustainable urban water services, e.g., drinking water, sanitation, and safe drainage, especially in cities.These challenges call for a transition toward improved water management, including considerations of "resilience."However, because the resilience concept has multidisciplinary origins it is open to multiple interpretations, which poses a challenge to understanding and operationalizing the concept.We explore how resilience thinking can be translated into urban water practice to develop the conceptual understanding of transitions toward sustainability.The study is based on a literature review, interviews with water experts, as well as four case studies in South Africa, India, Sweden, and the Philippines.We identify seven key principles or attributes of urban water resilience and the related transition process.We find that resilience building needs to discern between and manage three levels (i.e., socioeconomic, external hazard considerations, and larger social-ecological systems) to be sustainable.In addition, we find that human agency is a strong driver of transition processes, with a certain level of risk awareness and risk perception providing one threshold and a certain capacity for action to implement measures and reorganize in response to risks being another.The difficulty of achieving "knowledge to action" derives from the multiple challenges of crossing these two types of identified thresholds.To address long-term trends or stressors, we find an important role for social learning to ensure that the carrying capacity of urban water services is not exceeded or unwanted consequences are created (e.g., long-term trends like salinization and water depletion).We conclude that the resilience term and related concepts add value to understanding and addressing the dynamic dimension of urban water transitions if the key principles identified in this study are considered.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".