Decentralized management of urban food waste: A proof of concept with neighborhood-scale vermicomposting in Montreal, Canada
Bibliographic record
Abstract
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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".