Advancing urban water autonomy: A Social Life Cycle Assessment of rainwater harvesting systems in Mexico City
Bibliographic record
Abstract
This study conducts a Social Life Cycle Assessment (S-LCA ) of Rainwater Harvesting Systems (RWHS) in Mexico City to evaluate their social performance. Given the city's pressing water scarcity, RWHS have become critical for promoting water autonomy and sustainable urban development. The research integrates quantitative data from surveys and interviews with RWHS users and organizational employees, along with qualitative analysis using the Product Social Impact Assessment (PSIA) approach. This methodology allows for a thorough examination of socio-environmental dynamics influenced by RWHS adoption. Our findings show high acceptance of RWHS among users and highlight progressive labor practices, underscoring RWHS's potential to transform urban water management. This study, the first to evaluate this ecotechnology through an S-LCA, identifies the need for a multidimensional approach to understand socio-economic and environmental intersections with water systems. It also underscores NGOs' role in facilitating technology transfer and adoption in urban communities. Recommendations include extending the S-LCA methodology to cover the entire RWHS lifecycle and incorporating broader social science theories to deepen understanding of water sustainability interventions. The results offer new insights into RWHS assessment, emphasizing the complexities of deploying decentralized water technologies in a mega-city and laying groundwork for policy recommendations that support sustainable, equitable water access.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".