Impact of Pipe Material Selection on Greenhouse Gas Mitigation in Water Networks under Uncertain Discount Rates and Carbon Prices
Bibliographic record
Abstract
The magnitude and long-term uncertainty of a carbon tax in Canada could have implications for water distribution system capital planning decisions that are made today. The objective of this paper is to assess the impact of discount rate and carbon price uncertainty on municipal water distribution infrastructure investments in Canada. A non-dominated sorting genetic algorithm (NSGA-II) is coupled with the hydraulic solver EPANET2 in a single-objective optimization approach to identify network expansion solutions that minimize: i) capital cost of installing new and parallel pipes and of cleaning and lining existing pipes; ii) operation cost of electricity for pumping water; and iii) carbon cost levied on electricity used for pumping water. The optimization approach is used in a parametric analysis to examine the impact of cement-mortar ductile iron (DCLI) and polyvinyl chloride (PVC) pipe materials on reducing greenhouse gas (GHG) emissions in the all-pipes water network in Amherstview, Ontario, Canada. The Amherstview water distribution system was optimized for a range of discount rates and carbon prices reflective of possible climate change abatement strategies in Canada over the next 50-years. Preliminary results demonstrate that the discount rate and carbon price trajectories investigated had no significant influence on the total mass of GHGs emitted in the Amherstview system. Pipe material selection was also found to minimally affect the amount of GHG emitted in the Amherstview system. Insensitivity of GHG emissions in the Amherstview system to changes in discount rate and carbon tax may be attributable to a number of factors including, the use of a time-declining GHG emission intensity factor, the lack of available Canadian pipe embodied energy data and the exclusion of tank and pump sizing from the water distribution system expansion problem. Further research is needed to confirm these findings of the Amherstview study.
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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 | 0.000 |
| 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".