Rethinking food loss and waste to promote sustainable resource use and climate change mitigation in agri-food systems: A review
Bibliographic record
Abstract
The sustainable agri-food system is an important sector recognized for promoting the United Nations' Sustainable Development Goals on food security, resource conservation and climate change mitigation. However, the increasing food loss and waste (FLW) along the supply chains has continued to hinder these goals. This study evaluates the trend of FLW research from 1975 to 2022 and how it promotes the achievement of resource and environmental sustainability in agri-food systems. The salient research themes and hotspots that are of interest to researchers were identified. Bibliometric and network analyses were carried out on scholarly research articles from the Scopus database using bibliometrix and VOSviewer. Furthermore, the content analysis was conducted on the selected highly influential articles containing relevant data to understand the role of FLW in promoting sustainable agri-food systems. The results showed disaggregate and unbalanced research distribution on the impacts of FLW among the countries, with China and the United States having the highest contributions. The identified major research themes relating to sustainable agri-food systems are food waste and sustainable systems, food waste management and food waste impact assessment. Moreover, the circular economy was found to be a relatively new approach being explored in agri-food systems to promote FLW reduction and ensure sustainability of resource use. This study highlights the critical role of the impact of FLW in addressing the grand challenge of food security, resource use efficiency and environmental sustainability.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".