Environmental, economic, and social sustainability of urban water systems: a critical review using a life-cycle-based approach
Bibliographic record
Abstract
The increasing number of studies covering different life cycle sustainability assessment (LCSA) tools and urban water systems (UWSs) emphasize the need to synthesize current research. While LCSA studies focus on an integrated approach considering the life cycle assessment (LCA), Life Cycle Costing (LCC), and social life cycle assessment (S-LCA) methodologies, these tools are typically applied separately, disregarding the trade-offs amongst economic, social, and environmental impacts. In this context, this review aims to critically analyze the literature on LCSA tools to enhance the integrated application in the future. Furthermore, we aim to identify technological trends, current challenges, and future research directions to improve sustainability. The ProKnow-C methodology was applied using a combination of four keyword sets and three databases. We selected 72 relevant papers that were analyzed in detail. Results demonstrate that authors apply different boundaries when using different LCSA tools, and lack of data was also a common issue. Furthermore, papers lack system description in the scope definition, leading to a biased interpretation of results. Another important issue was the functional unit selection, which did not represent the complexity of UWSs, lacking important details such as water loss, water quality, and population served. Water treatment is the most researched process in UWSs, and stormwater systems (collection or treatment) are rarely included, representing only 25% of the analyzed literature. In conclusion, the application of LCSA tools faces fundamental challenges, such as data quality and availability. Concerning the engineering improvements, future works should use LCSA data to support design and technology development, also focusing on human behaviour and frugal technology alternatives. Finally, new paradigms need to be developed and applied to enhance sustainability and integrate UWS.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".