Decentralized management of urban food waste: A proof of concept with neighborhood-scale vermicomposting in Montreal, Canada
Notice bibliographique
Résumé
With growing urban populations, the management of organic waste in cities is becoming increasingly challenging. A large fraction of food waste is currently landfilled, where its decomposition leads to greenhouse gas emissions. Although composting is becoming more common in Canada, the conventional approach for collecting and managing municipal organic waste has typically been to construct large, centralized treatment facilities, which can be costly, time intensive, and may have negative environmental and social impacts for surrounding communities. Furthermore, due to logistical constraints, some industrial, commercial and institutional buildings either do not separate the organic fraction of their waste or are gaps in existing municipal organic waste collection. I investigate the potential for decentralized (neighborhood block level) urban organic waste collection and treatment in small- to medium-scale vermicomposting facilities. Vermicomposting is the process of breaking down organic waste with the use earthworms, which is quicker than conventional composting and yields a more valuable end-product. By using spatial and systems modelling, I examine the efficacy for such an approach in different urban and suburban neighborhoods across the densely populated Island of Montreal, Canada, focusing on food waste sources that are presently unrecovered or overlooked in Montreal’s municipal waste collection (i.e., industrial-commercial-institutional, ICI, and large residential buildings). First, I estimate the potential magnitude and spatial distribution of unrecovered food waste across the Island of Montreal by spatially disaggregating existing city-wide food waste values by source type and their discrete locations using a geographic information system (GIS). The identified 10,882 source locations generate ~141,351 tonnes of potentially recoverable food waste annually, or about 120% of the total amount of organic waste recovered by the City of Montreal in the circa 2020-2021 period. Key ‘hot spots’ of recoverable food waste are mainly in high-population density central neighborhoods with clusters of residential buildings and restaurants, as well also throughout the Island in areas with single concentrated sources (e.g., a supermarket or hospital). Second, I create a systems model of a hypothetical vermicomposting operation to examine the economic feasibility and carbon offset potential depending on locating that facility in different representative types of neighborhoods (by gradients of population density and land value). I then discuss tradeoffs between food waste availability and rental rates when determining which areas would be best suited for local food waste management with vermicomposting. Based on my systems modelling of facilities located in different neighborhood types, I conclude that decentralized vermicomposting for urban food waste management can be both profitable and reduce carbon emissions compared to landfilling. My study is therefore a proof-of-concept test of the potential of decentralized vermicomposting to divert urban organic waste streams, serving as the basis for the implementation of novel paradigms in urban organic waste management. Such an alternative, decentralized approach to organic waste treatment could complement existing waste management infrastructure, with co-benefits of reducing transport distances, added flexibility, potentially reduced operations including careful consideration of potential end-users of worm castings, such as urban and peri-urban agricultural costs, and allowing for nutrient recycling within urban neighborhoods. However, achieving this would require collaboration among various stakeholders, producers.
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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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».