A review of life cycle impacts of different pathways for converting food waste into livestock feed
Bibliographic record
Abstract
The concept of direct valorization of food waste to livestock feed has garnered increasing attention as a potential solution to reduce the significant environmental impacts of both food waste disposal and livestock production. Much of that interest lies in the arena of Life Cycle Assessment (LCA) since it is the preferred approach to evaluate the systems-level environmental performance of agri-food systems. It remains unclear, however, which specific substrate types and technologies should be prioritized for detailed LCA scrutiny under various regional and socio-economic contexts. This review aims to contribute towards filling this knowledge gap by synthesizing existing literature on the topic, identifying areas of improvement, and suggesting priority future research directions. Towards that end, 27 relevant research articles found through a keyword search and shortlisted using the PRISMA method and specific exclusion criteria were reviewed. The results showed that mixed food waste from households, supermarkets, and restaurants was the most studied substrate. A majority (56 %) of the studies either investigated a theoretical assumption-based system or did not include any information at all regarding the technology readiness of their studied system. Six of the seven commercial-scale facilities found in the review were from Asia, reflecting the supportive regulatory environments in this context. The majority of the studies identifying monogastrics as the target species indicated the valorized feed to be incomplete and requiring conventional feed supplementation. The review highlighted the substrate drying process as the largest contributor to the life cycle environmental impacts of food waste to feed valorization, accounting for up to 94 % of Global Warming Potential (GWP) burdens. Specific hotspot mitigation measures were discussed, such as adopting renewable energy sources, energy recycling, and more efficient substrate drying technology. Several LCA-specific issues were also highlighted in this review, such as ambiguity regarding the selection of impact categories, reporting single score results, or using uncommon units. Based on these findings, the review highlights the need for more LCA studies on commercial scale food waste to feed valorization systems, the need to further explore the impacts of avoided landfill emissions, and conduct more holistic assessments taking into account infrastructure availability and techno-economic viability.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".