Pathways for FEW nexus collaboration in U.S. city resilience planning
Bibliographic record
Abstract
The food-energy-water (FEW) nexus has been argued as an approach to improve system resilience and sustainability theoretically. However, there is limited empirical understanding of which governance factors lead to FEW nexus collaboration in practice. The purpose of this study is to investigate the conditions associated with FEW nexus collaboration in cities in resilience planning: does it arise from risk of resource insecurity, pre-existing governance mechanisms, or both? The study analyzed the 22 cities in the United States that are part of the Resilient Cities Network using fuzzy-set Qualitative Comparative Analysis. The results show that food, energy, and water insecurity are not sufficient to explain FEW nexus collaboration in resilience planning. However, the results do show that FEW nexus collaboration is present in resilience planning in (a) cities that do experience water insecurity and employ two of three investigated governance conditions—policy coherence, stakeholder participation, or institutional support—or (b) that employ all three governance conditions, regardless of whether or not they experience water insecurity. It concludes that the risk of resource insecurity alone is not sufficient to explain cities’ implementation of FEW nexus collaborations and provides policy recommendations for increased FEW nexus collaboration in practice.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".