New paradigms on how to achieve zero food waste in future cities: Optimizing food use by waste prevention and valorization
Bibliographic record
Abstract
Cities currently manage uneaten food and other food system based biowaste quite inefficiently. The organic compound, despite its high nutriment value, is only to a small extent recycled and returned to farm soil and therefore, does not contribute to closing ecological nutrient cycles and to supporting sustainable food production [1]. In the US, over 97% of food waste is estimated to be buried in landfills [2]. Forkes has shown for Toronto that only 4.7% at most of food waste nitrogen (including sewage waste) was recovered and/or recycled [3]. For Paris and its suburbs, a similar estimate has been obtained, and this share of nitrogen food waste recycling has been in steep decline in the course of two centuries, from 40% to close to 5% estimated for today [4]. One study has analyzed the nutrient balance (N, P) for Bangkok Province [5]. These studies mainly focus on food waste and sewage waste management from a nutrient recycling point of view. Furthermore, food waste related resource use and environmental pollution are highlighted as no longer acceptable in the context of global warming and increasing pressure on the planet’s limited boundaries [6], [7]. According to an analysis from the Waste & Resources Action Program (WRAP), prevention of 1 ton of food waste can yield in carbon equivalent savings of 3090 kg when food from manufacture or retail is redistributed to people. But savings are much lower when food from manufacture is redistributed as animal feed (220 kg eq CO2/ ton food) or used for anaerobic digestion (162 kg eq CO2/ ton food). This analysis illustrates from a climate point of view priority for food waste prevention over food waste valorization. The problem of food waste is crucial: the FAO estimates that one third of world food production is lost or wasted. In industrialized countries, food waste amounts to close to 300 kg/cap/year in North America or Europe – and more than two third of it occurring at distribution, catering and in-home consumption [8]. A “preparatory study on food waste across the EU 27 Member States” estimates annual food waste generation in the EU27 at approximately 89 million tons, or 179 kg per capita (without agriculture) [9]. Households (42%) and manufacturing (39%) have been identi-fied as the most important food waste producers, followed behind by food services/caterers (14%) and retail/wholesale (5%). The high share of food waste occurrence close to consumption, cities’ dense population and the accumulation of waste in periurban areas, together with the numerous socio-technical initiatives coming from both urban citizens and stakeholders are all factors that place cities as important players. Although food waste in cities in Asia, Africa and South America is relatively lower at the downstream stages of supply chains, the fast growing population and changing habits towards urban diets nevertheless raise the question also for these sets on how to optimize food use in cities. The world population is going to become more and more urban, being expected to make up 66% of the world population by 2050 compared to 30% in 1950. Ongoing population growth together with urbanization is expected to increase the urban population pre-dominantly in Asia and in Africa. Today, the most urbanized regions include Northern America (82%), Latin America and the Caribbean (80%) and Europe (73%), but all regions in the world are projected to urbanize further [10]. Our study analyses the specific link between food waste and cities in a zero waste perspective in the future. By using a foresight approach we suggest to identify and discuss key prevention and valorization measures, to pinpoint knowledge gaps on the specific character of food waste in cities and to bring up relevant questions for research.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".