Water–energy–food nexus Research: What can it tell us about governance and policy?
Bibliographic record
Abstract
There has been over a decade of research on the water–energy–food (WEF) nexus, which by definition focuses on improving governance across WEF sectors to address resource scarcity. The purpose of this paper is to identity the extent and types of governance and policy recommendations covered in the WEF studies. The systematic review methodology is used to assess peer-reviewed WEF studies based on the set of inclusion criteria published between 2011 and 2023. Out of 683 paper, 40 papers were selected and assessed their contributions to governance and policy. The results show that the included studies have strongly focused on quantitative representations of the WEF nexus (63 %), but they showed limited attention to specific governance and policy implications (17.5 %). To the extent that governance and policy were considered, the studies were framed around improving engagement with policymakers, academia and communities (32.5 %), improving coordination efforts (27.5 %) and suggesting technological solutions to manage WEF resources (35.0 %). The study concludes that WEF research holds significant potential to contribute to governance and policy development by testing innovative governance mechanisms, informing strategic policy decisions, shaping implementation strategies, and supporting the monitoring of policy outcomes to tackle resource scarcity and advance sustainability. • Objective: To examine how WEF studies contribute to governance and policy recommendations. • A systematic review of 40 peer-reviewed studies (from 683 screened) published (2011–2023). • Most studies (63 %) focused on quantitative modelling, with limited attention (17.5 %) to governance and policy. • Governance was addressed through stakeholder engagement (32.5 %), coordination (27.5 %), and technological solutions (35 %). • WEF research has strong potential to inform governance and policy through innovation, strategy, and impact monitoring .
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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 | 0.078 | 0.287 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.015 | 0.024 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.012 | 0.025 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".