Environmental sustainability opportunity and socio-economic cost analyses of phosphorus recovery from sewage sludge
Bibliographic record
Abstract
Although phosphorus (P) recovery and management from sewage sludge are practiced in North America and Europe, such practices are not yet to be implemented in China. Here, we evaluated the environmental sustainability opportunity and socio-economic costs of recovering P from sewage sludge by replacing the current-day treatments (CT; sludge treatment and landfill) and P chemical fertilizer application (CF) in China using life cycle assessment and life cycle costing methods. Three potential P recovery scenarios (PR1‒PR3: struvite, vivianite, and treated sludge) and corresponding current-day scenarios (CT1‒CT3 and CF) were considered. Results indicated that PR1 and PR2 have smaller environmental impacts than the current-day scenarios, whereas PR3 has larger impacts in most categories. PR3 has the lowest net costs (sum of internal costs and benefits, 39.1–54.7 CNY per kg P), whereas PR2 has the lowest external costs (366.8 CNY per kg P). Societal costs for production and land use of 1 kg P by P recovery from sewage sludge (e.g., ∼527 CNY for PR1) are much higher than those of P chemical fertilizers (∼20 CNY for CF). However, considering the costs in the current-day treatments (e.g., ∼524 CNY for CT1), societal costs of P recovery scenarios are close to or slightly lower than those of current-day scenarios. Among the three P recovery scenarios, we found that recovering struvite as P fertilizer has the highest societal feasibility. This study will provide valuable information for improved sewage sludge management and will help promote the sustainable supply of P in China.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".