Towards sustainability for water-agriculture-energy-ecosystems nexus with interconnected uncertainty: A vine copula-based fixed-mix stochastic programming method
Bibliographic record
Abstract
The collaborative management of the water-agriculture-energy-ecosystems (WAEE) nexus is of great significance for promoting sustainable development; however, some parameters associated with interconnected uncertainty (i.e., joint-random uncertainty) existing in the WAEE nexus can introduce additional complexity and intensify the conflict-laden issue of water allocation among agriculture, energy and ecosystems. This study develops a vine copula-based fixed-mix stochastic programming (VCFSP) method that can capture joint violation risks among multiple random variables and handle uncertainties represented as probability distributions in the WAEE nexus. A VCFSP-WAEE model is then formulated for synergistically managing the WAEE nexus of the Kaidu-Kongque River Basin in northwest China, where 96 scenarios involving different utilization rates of brackish water and joint violation probabilities are examined. Results reveal that, with the increase of brackish water utilization rate: (i) the total amount of water (allocated to agriculture, energy and ecosystems) would rise from 13.5 × 10 9 m 3 to 16.0 × 10 9 m 3 during 2046–2050, while the amount of conventional water resources would decrease by 209.4 × 10 6 m 3 ; (ii) the agricultural cultivated area would increase by 0.4 × 10 6 ha, and the food yield would increase by 4.7 × 10 9 kg at the end of planning period. In order to alleviate the pressure of local surface water and groundwater shortages, it is suggested to enhance the utilization efficiency of unconventional water resources and develop low-cost desalination technologies. Compared with the current situation, during the planning period, the proportion of agricultural water allocation would show a general decrease trend (from 73.2 % to 55.9 %), while the proportion of ecological water allocation would present an increase trend (from 6.5 % to 17.7 %). This discloses that increasing the utilization of unconventional water (especially brackish water) is beneficial for increasing ecological water supply, thereby improving the eco-environment in arid regions. • VCFSP is developed to plan WAEE nexus system under interconnected uncertainties. • Vine copula is introduced into fixed-mix stochastic programming for the first time. • VCFSP can capture joint violation risk among multiple random variates. • Use of unconventional water can alleviate water scarcity and ensure food security. • The share of agricultural water allocation would show a general decrease trend.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".