Increasing resilience through nudges in the urban water cycle: An integrative conceptual framework to support policy decision-making
Bibliographic record
Abstract
Relevant challenges associated with the urban water cycle must be overcome to meet the United Nations Sustainable Development Goals (SDGs) and improve resilience. Unlike previous studies that focused only on the provision of drinking water, we propose a framework that extends the use of the theory of nudges to all stages of the overall urban water cycle (drinking water and wastewater services), and to agents of influence (citizens, organizations, and governments) at different levels of decision making. The framework integrates four main drivers (the fourth water revolution, digitalization, decentralization, and climate change), which influence how customers, water utilities and regulators approach the challenges posed by the urban water cycle. The proposed framework, based on the theory of nudges first advanced by the Nobel Prize in behavioral economics Richard H. Thaler and Cass R. Sunstein (Thaler and Sunstein, 2009), serves as a reference for policymakers to define medium- and long-term strategies and policies for improving the sustainability and resilience of the urban water cycle. Finally, we provide new insights for further research on resilience approaches to the management of the urban water cycle as an element to support the more efficient formulation of policies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".