Urban food systems: socio-technological innovation for cities to tackle the zero waste challenge
Bibliographic record
Abstract
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.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".