Multi-Objective Rehabilitation Planning of Water Distribution Systems under Climate Change Mitigation Scenarios
Bibliographic record
Abstract
Discounting and carbon pricing are being touted as effective economic instruments to reduce the greenhouse gas emissions of energy-intensive industries. Since the water industry is a heavy consumer of electricity to pump water, it is one of the industries that could be affected by policy changes in discounting and carbon pricing. The aim of this paper is to formulate and solve the water distribution network rehabilitation problem under different carbon-abatement scenarios. A multi-objective optimization model is developed and combined with pipe aging, pipe break, and leak models to solve the real-world Amherstview water distribution network in eastern Ontario, Canada. Two different carbon-pricing trajectories and two discount rates are compared against a status quo scenario. The results indicate that low discount rates reduced greenhouse gas (GHG) emissions linked to pumping and had a significant impact on water loss reduction in the Amherstview water distribution network. Further analysis is required to conclusively establish these relationships.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".