Adapting the 15-Minute City to North America: A Framework for Neighborhood Clusters with Urban Agriculture and Green Mobility
Bibliographic record
Abstract
To reduce GHG emissions from food miles and enhance urban food security, this study develops and evaluates an integrated framework combining three strategies: the 15-minute city concept, urban agriculture, and a renewable-energy-powered green transportation (GT) system. The goal is to create a scalable, holistic approach to sustainable food production and distribution within neighborhoods. A Food Production and Transportation Framework is proposed, modeling vegetable cultivation across rooftops, facades, and lot spaces, with optimized allocations based on a tailored Food Production Schedule. The harvested produce is distributed via GT powered by sidewalk-integrated photovoltaics (PVs). Results demonstrate that using 15% of roof, facade, and lot spaces yields an achieved annual food self-sufficiency of 100%. The transportation system operates with a single GT unit powered by 98 m2 of sidewalk PVs, reducing CO2 emissions by 98% from the base case. Economic analysis indicates a payback period of 2.8 years, with the cost of PV-generated electricity estimated at C$0.92/kWh. This framework highlights that 0.19 units of local food production offset one unit of CO2 emissions. This integrated approach advances multiple UN Sustainable Development Goals (SDGs), including SDG 2 (Zero Hunger), SDG 7 (Affordable and Clean Energy), SDG 11 (Sustainable Cities and Communities), and SDG 13 (Climate Action).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".