New paradigms on how to achieve zero food waste in future cities: Optimizing food use by waste prevention and valorization
Notice bibliographique
Résumé
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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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».