A Fuzzy Approach to Analyze Data Uncertainty in the Life Cycle Assessment of a Drinking Water System
Bibliographic record
Abstract
Water is an important element in the urban environment, and urban water distribution relies mostly on centralized systems. The life cycle assessment (LCA) of these systems refers to their environmental impacts throughout their life cycle and can have significant data uncertainties. Additionally, limited studies on drinking water treatment and distribution systems are available in the literature, especially those considering a sensitivity or uncertainty analysis. In this context, this study aims to evaluate the effect of the uncertainty in input data on the final environmental impacts of water treatment and distribution systems in the city of Penticton (CA). To analyze data uncertainty, this work implements a fuzzy method coupled with the weighted product method (WPM) to estimate life cycle impact. Additionally, a comparison between the life cycle impact and ReCiPe endpoint single score is presented to understand how sensitive those indicators are to data uncertainties. The results indicate that the most sensible life cycle stage is the water treatment and distribution use, with variations from 3 to 15%, mainly resulting from the chemical analysis in the use phase. Additionally, the WPM methodology presented a deviation from 60 to 378% when considering the individual life cycle stages. The total LCA presented higher robustness to the changes, with 1–8% variations in the midpoint categories. However, the WPM disparities ranged from 80 to 410% in the midpoint categories. Finally, the comparisons between the final aggregation scores show lower sensitivity in the SimaPro single-score indicator.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.001 | 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".