Life-cycle assessment of common water main materials in water distribution networks
Bibliographic record
Abstract
This paper examines the economy-wide environmental impacts linked to the manufacturing of PVC and ductile iron (DI) pipes, steel tanks, and to the generation of electricity for pumping in water distribution network optimization. The non-dominated sorting genetic algorithm (NSGA-II) is used to generate Pareto-optimal solutions of the benchmark ‘Anytown’ network expansion problem. Selected Pareto-optimal solutions of the ‘Anytown’ network are evaluated with an economic input–output life-cycle assessment (EIO-LCA) and 14 environmental measures on air emissions, non-renewable energy use and environmental releases. The major findings suggest that DI and PVC pipe manufacturing and electricity generation activities (for pumping) have higher environmental impacts than steel tank manufacturing and construction activities in the ‘Anytown’ network. The EIO-LCA suggests that DI pipe manufacturing is linked to: (i) carbon monoxide emissions from truck transportation and wholesale trade and (ii) land and underground toxic releases from metal mining activities. PVC pipe manufacturing is linked to: (i) carbon monoxide emissions from truck transportation, (ii) toxic air releases from the plastics material and resin manufacturing sector, (iii) land and underground toxic releases from metal mining and resin manufacturing, and (iv) natural gas use for plastics material and resin manufacturing.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".