An economic and global warming impact assessment of common sewage sludge treatment processes in North America
Bibliographic record
Abstract
This study details a probabilistic life cycle assessment model to evaluate the environmental (i.e., global warming potential) and economic impact of four common sewage sludge treatment methods (anaerobic digestion, incineration, composting and pyrolysis) coupled with their most common end-of-life scenarios for the North American context. The model is subsequently applied to a realistic case study where each technology is assessed over a 10-year analysis period based on data made available by a Canadian municipality. For the case study, pyrolysis and anaerobic digestion coupled with agricultural land application have an expected global warming impact at least 46% and 60% lower, respectively, than the alternative treatment methods. Conversely, composting and pyrolysis have an expected life cycle cost at least 32% and 27% lower, respectively, than the competing treatment alternatives. Composting is able to achieve its relatively low life-cycle costs through the affordability of the required capital investments; conversely, pyrolysis is able to reduce its life-cycle cost through the recovery of valuable resources such as energy, fertilizer, and fuel. These findings and the resulting tool from this work will aid decision-makers as they seek sustainable sewage sludge treatment strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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".