Urban food systems: socio-technological innovation for cities to tackle the zero waste challenge
Notice bibliographique
Résumé
Cities currently manage food waste in a quite inefficient manner: Food waste and food industry co and by products, despite their high nutriment value, are only to a small ex-tent recycled and returned to farm soil and therefore, does not contribute to closing nu-trient cycles and to supporting sustainable food production. Food waste related resource use and environmental pollution can no longer be justified in the context of global warming and increasing pressure on the planet’s limited boundaries. Cities today are acting as laboratories for socio-technological innovations in food waste prevention and valorization, yet coherent concepts and strategies involving the different actors are miss-ing. This work aims to: i) review high potential socio-technological innovations in food waste prevention and valorization ii) extract research questions contributing to fostering and accompanying cities’ breakthrough strategies towards zero waste sustainable food sys-tems, specific to different urban settings worldwide (covering both industrialized and unindustrialized areas). Twenty experts related to disciplines of industrial ecology, urban metabolism, urban farming, aquaculture systems, waste recovery, food science, law, eth-ics, system innovation and foresight studies were organized as a working group follow-ing a foresight study approach. Expert panel and literature review have shown that in-novative approaches in urban food waste prevention and management are abundantly experimented in a lot of cities worldwide (for example in Canada, the USA, UK, France and other European countries). They use manifold tools (regulation, technology, social innovation etc.) both in food waste prevention and valorization. Food system actors involved are as different as business and catering companies, civil society, NGOs and municipalities. Experts have identified 9 main categories for socio-technological innova-tions: 1) Education of public and training of professionals 2) Simplification of supply chain specifications, 3) Collaborative use of data, flow monitoring and smart sensors, 4) Regulation, taxation and financial tools, 5) Gradual withdrawal of food from market, selling off, stock clearance, on-site processing and donations, 6) Breakthrough manufac-turing and packaging technologies, 7) Urban practices such as shared gardens, swapping and food give-and-take, 8) Biomass valorization and biorefinery, 9) Good Samaritan law and distribution of responsibility between stakeholders. These nine innovative approaches are discussed on the base of their expected high im-pact potential and transferability. Most of them are new, tested small-scale and have not yet been subject of in-depth analysis of performances, forces and drawbacks. Techno-logical and cultural challenges remain to be overcome, for example the analysis of “big data” to support alignment of supply and demand, the mutual share of information and joint planning of food supply, and societal acceptance of new technologies. Overall, data on food waste flows in cities are challenging to obtain. In a next step we are going to run fieldwork in four cities (Dakar, Chicago, Antananarivo and Montpellier) to con-tribute to closing this data gap and to progressing on the urban metabolism approach applied to food systems.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
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 | 0,006 | 0,003 |
| 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 | 0,001 | 0,000 |
| Communication savante | 0,000 | 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 ».