Review of water–energy–food nexus applications in the Global South
Bibliographic record
Abstract
The study reviewed the applications of the water-energy-food (WEF) nexus for knowledge generation and decision-making in the Global South. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocol identified 336 studies from the Web of Science and Scopus datasets. One hundred eighty-five articles applied WEF nexus tools to improve the understanding of WEF nexus interactions and to show the potential of nexus applications. The other articles (151) focused on nexus applications to guide planning and decision support for resource allocation and policy formulation. Environment, climate, ecosystems, land, and socio-economics were other popular nexus dimensions, while waste and economy were considered to a lesser extent. Limitations associated with nexus applications included unavailability of data, uncertainties from data sources, scale mismatch and bias. The inability of nexus tools to capture the complex realities of WEF interactions is hindering adoption, especially for policy formulations and investment planning. Data limitations could be solved using a sound scientific basis to correct uncertainties and substitute unavailable data. Data gaps can be bridged by engaging stakeholders, who can provide local and indigenous knowledge. Despite the limitations, applying nexus tools could be useful in guiding resource management. Limitations associated with nexus applications included - investment planning. Plausible pathways for operationalising the WEF nexus are discussed.
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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".