Water-energy-food nexus as a new approach for watershed resources management: a review
Bibliographic record
Abstract
The Water-Energy-Food nexus (WEF) has been initially introduced in the international community as an adaptive management approach in response to climate change. This study aims to review and analyze the existing literature on WEF nexus approach at different scales and to suggest supplementary ideas for better applicability of WEF nexus framework in integrated watershed management. In terms of geographical distribution, the study covers Asia (Central, South, Southeast, and East), Australia, Africa (North, South, and East), North America (USA, Mexico, and Canada), South America (Brazil), Europe (UK, Italy, Germany, Spain, Sweden, and Greece), and Oceania.For this, 203 articles and documents were found dealing with WEF nexus. Interest over time in WEF nexus was examined from 2011 to 2019 in these regions. The review showed 10 articles had a close linkage with water–food, 49 with water-energy, 119 with water-energy-food, six with water-food-energy-ecosystems, five with water-energy-land-food, three with food-energy-environment, three with water-soil-waste and eight with climate. We propose ecosystem services and other important commodities like soil be considered in future nexus relevant studies. Towards this, the soil-water-energy-food (SWEF) nexus is introduced as a useful approach towards higher sustainability and adaptive management at the watershed scale.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.009 | 0.008 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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; both teacher heads agree on what is shown here.
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".