Sustainable management and design of the energy‐water‐food nexus using a mathematical programming approach
Bibliographic record
Abstract
Abstract Global primary and secondary resources are important for economic growth. Resource management and environment conservation are currently frequently discussed topics worldwide. In this study, a discrete optimization model formulation is presented for an integrated energy, water, and food (EWF) supply chain problem. The optimization model examines the temporal and spatial integration of the EWF supply chain elements to provide optimal infrastructure capacity expansion of essential commodities within the EWF system, and their corresponding periodic optimal supply for a given region. Furthermore, the model considers endogenous demand between the EWF elements that reflect the interdependency of nexus elements. A mixed integer linear programming model is developed to assist in the process of optimal infrastructure capacity expansion and operation of the EWF system. A case study is given to show the application of the proposed mathematical programming model. Several scenarios are assumed for the case study under different commodity prices and climate change conditions. In addition, diversification in the energy and agriculture sectors is examined by shifting from international refined sugar trading to bioethanol production. The results show economic gains of ~10% under the emergence of bioethanol production compared with the business‐as‐usual scenario. Production dynamic exits for the production of refined sugar, bioethanol, and power from sugarcane and bagasse resources over time in the considered sale price range for the refined sugar and bioethanol products.
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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 | 0.001 | 0.001 |
| 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 | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".