Planning an Energy–Water–Environment Nexus System in Coal-Dependent Regions under Uncertainties
Bibliographic record
Abstract
Energy, water, and environment are inextricably interwoven in the complex social and economic networks. This study proposes an optimization model for planning the energy–water–environment nexus system (EWENS) through incorporating the linear autoregressive integrated moving average model prediction model (ARIMA), Monte Carlo simulation, chance-constrained programming (CCP), and type-2 fuzzy programming (T2FP) into one general framework. This method effectively tackles type-2 fuzzy set and stochastic uncertainties. The proposed model can quantitatively explore the interconnections between water, energy, and environment systems and generate an optimized solution for EWENS. The proposed model was applied to a coal-dominated region of China, i.e., Inner Mongolia. Several findings and policy implications were obtained. First, the total water supply for energy-generating activities will range from 1368.10 × 106 m3 to 1370.62 × 106 m3, at the end of planning periods. Second, the electricity for water supply will range from 2164.07 × 106 kWh to 2167.65 × 106 kWh at the end of the planning periods, with a growth rate of 46.06–48.72%. Thirdly, lifecycle carbon dioxide emission (LCDE) is projected to range from 931.85 × 106 tons to 947.00 × 106 tons at the end of the planning periods. Wastewater and SO2, NOx, and particulate matter (PM) emissions are projected to be 42.72 × 103–43.45 × 103 tons, 183.07 × 103–186.23 × 103 tons, 712.38 × 103–724.73 × 103 tons, and 38.14 × 103–38.80 × 103 tons at the end of the planning periods. Fourthly, as the largest electricity-exporting city of China, Inner Mongolia’s electricity outflows will export 1435.78 × 106 m3 of virtual water to other regions, implying that Inner Mongolia is pumping its important water resource to support other regions’ electricity demands. Finally, high carbon mitigation levels can effectively optimize the electricity power mix, reduce consumption amounts of water and coal, and mitigate air pollutants, wastewater, and LCDE. The obtained results provide useful information for managers to develop a sustainability plan for the EWENS.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".