Food loss and waste reduction interventions: A scoping review
Bibliographic record
Abstract
Globally, one third of food produced goes to waste, which contributes to climate change, negatively impacts air and water resources, and can lead to environmental and human health risks. Mitigation efforts have surged in response to these staggering statistics on food loss and waste, including initiatives such as food rescue and upcycling programs. Circular economy practices are important for a sustainable future. Limited literature is available that compares different food rescue programs worldwide and synthesizes considerations for planning new interventions. This paper is a scoping review of peer-reviewed literature on programs and interventions for food rescue and food waste reduction that occur at the retail level. The search in Scopus and Web of Science yielded 400 records for studies published in the past 30 years. Analysis of 18 full-text reports showed diverse food programs from the United States, United Kingdom, Germany, Greece, New Zealand, Canada, Sri Lanka, and Israel. Studies were conducted in various settings, including restaurants, institutions, and retail food stores. The collection methods of rescued food varied according to the program’s capacity and included accepting donations, redistribution programs, and social enterprises. The results of these reports highlight some of the barriers that food rescue programs face, including logistical and workforce challenges, liability concerns, food availability, and financial restraints. Facilitators that promoted food rescue included the use of complementary technology, cooperative alliances, supportive policies, and favourable incentives. Report findings highlight the key role of volunteers, partnerships, and innovative technological solutions in advancing food rescue and waste reduction programs. Our research focuses on consolidating the lessons previously learned as a means of helping future food waste diversion programs overcome obstacles and improve operational efficiency. While food rescue is an important intermediary endeavour, addressing the root causes of wasted food and reducing inefficiencies in the current modern industrial food system is necessary to meaningfully reduce food waste at a global level.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".