Collaborative Management of Water‐Energy‐Food‐Ecosystems Nexus in Central Asia Under Uncertainty
Bibliographic record
Abstract
Abstract Collaborative management of the water‐energy‐food‐ecosystems (WEFE) nexus can contribute significantly to sustainable development. However, multiple decision‐making levels with diverse preferences and multiple uncertainties in different forms pose intractable challenges to the management process. In this study, a novel optimization method named as multi‐level chance‐constrained fuzzy programming (MCFP) is developed to jointly manage the WEFE nexus. MCFP has advantages in evaluating trade‐offs among multiple competitive decision makers, solving decentralized planning problems with hierarchical structure, and tackling uncertainties expressed as randomness and vagueness. MCFP is then applied to the WEFE nexus in Central Asia, where five countries, 43 states, six water sources, and eight water users are involved over a long‐term planning horizon (2021–2050). A set of scenarios are designed to reflect decision‐making preferences based on different irrigation efficiencies, food, ecological and electricity demands as well as constraint‐violation probability and system credibility levels. The major findings are: (a) the proportion of agricultural water allocation would reduce to 45.4%–56.6% by 2050 to save more water for ensuring ecological restoration and energy supply; and (b) in order to balance water demands and support regional sustainable development, policymakers should sacrifice some of the benefits, set strict arable land limits for cereal crops, improve irrigation efficiency through adopting drip and sprinkler irrigation, and avoid the effects of the irrigation efficiency paradox. The findings are helpful for policymakers in gaining insight into the interrelationships of water, energy, food and ecosystems as well as making decisions for collaborative management of the WEFE nexus system.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".