Techno-economic evaluation of decentralized community composting as a management model to valorize organic matter in small and medium-sized towns
Bibliographic record
Abstract
Abstract Decentralized community composting presents a viable Techno-economic alternative to centralized industrial systems for managing 100% of the organic fraction of municipal solid waste (MSW) in rural municipalities. This study, conducted in Catalonia, Spain, evaluated a system capable of processing 90 t/y of organic matter under six scenarios, varying by mixing method (manual or mechanized) and the number of compost transfers. Mechanized mixing without transfers emerged as the most efficient approach, reducing processing time by 40% and labor demand by 50%, with annual operating costs of 15,141 €/year—14,325 €/year lower than manual methods. Payback was achieved in 10 years, supported by a canon return of 165–194 €/composter (5–7% discount rates). The resulting compost met Class A standards under Royal Decree 506/2013, ensuring high quality. This model aligns with European regulations, addressing 41% of Europe’s organic waste, while promoting a circular economy through localized waste valorization. Mechanization optimizes resource use, reduces costs, and enhances sustainability, offering a scalable solution for small-to-medium municipalities. Graphical abstract
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".