Comparative life cycle assessment of excreta management systems through composting and biomethanization: Case of a low-income tropical country
Bibliographic record
Abstract
Low-income tropical regions, such as Haiti, grapple with environmental issues stemming from inadequate sanitation infrastructure for fecal sludge management. This study scrutinizes on-site sanitation systems in these regions, evaluating their environmental impacts and pinpointing improvement opportunities. The focus is specifically on systems integrating excreta valorization through composting and/or anaerobic digestion. Each system encompasses toilet access, evacuation, and sludge treatment. A comparative life cycle assessment was undertaken, with the functional unit managing one ton of excreta in Haiti over a year. Six scenarios representing autonomous sanitation systems were devised by combining three toilet types (container-based toilets (CBTs), ventilated improved pit (VIP) latrines, and flush toilets (WC)) with two sludge treatment processes (composting and biomethanization). Biodigester-based systems exhibited 1.05 times higher sanitary impacts and 1.03 times higher ecosystem impacts than those with composters. Among toilet types, CBTs had the lowest impacts, followed by VIP latrines, with WCs having the highest impacts. On average, WC scenarios were 3.85 times more impactful than VIP latrines and 4.04 times more impactful than those with CBTs regarding human health impact. Critical variables identified include the use of toilet paper, wood shavings, greenhouse gas emissions, and construction materials.
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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 | 0.001 |
| 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) | 0.001 | 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".