Adapting the 15-Minute City to North America: A Framework for Neighborhood Clusters with Urban Agriculture and Green Mobility

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).

1. Introduction

A typical North American urban neighborhood requires long hours driving using fossil fuel-based automobiles from home to work or to other amenities, resulting in substantial CO₂ emissions coupled with wasted time on roads. The Canadian transportation sector produced 156 metric tons of CO₂ equivalents, i.e., 22% of the total in 2022, 80% of which is generated from the road transport sector [1,2]. The World Bank reported that, by 2050, 70% of the world population is going to live in cities [3]. The United Nations reports that cities consume 75% of the world’s energy while emitting 70% of global greenhouse gases (GHG) [4]. The 2024 report, The State of Food Security and Nutrition in the World shows that nearly 720 million people are facing hunger issues, and this number is likely to grow as urbanization is continuously increasing [5]. In line with principal Sustainable Development Goals (SDG), this study contributes to SDG 2 (Zero Hunger), SDG 7 (Affordable and Clean Energy), SDG 11 (Sustainable Cities and Communities), and SDG 13 (Climate Action). These SDGs emphasize communities’ ability to achieve food security; ensure affordable and sustainable energy; make communities inclusive and sustainable; and strengthen resilience while developing strategies to alleviate climate change effects [6]. This necessitates development of a multi-disciplinary data-driven approach that focuses on sustainable urban planning by integrating urban agriculture (UA) and green transportation (GT) by creating a holistic framework to achieve SDGs [7,8].

Rooted in the time and proximity principles of chrono-urbanism, the concept of the 15-Minute City (FMC) reimagines traditional mono-functional urban centers as polycentric, human-scaled cities [9]. A study conducted in Calgary, Canada showed that the distance of business centers from residences had the highest impact on GHG emission from a transportation standpoint [10]. The FMC is structured around four key dimensions—proximity, diversity, density, and ubiquity [11]. The FMC looks at urban life planning instead of city planning and describes an urban environment where residents meet their essential needs within a short trip from home using carbon-free transportation or active modes of travel, i.e., walking and cycling [12,13]. Figure 1 delineates a conceptual diagram of a multi-functional neighborhood, such as an FMC, in the context of a Canadian neighborhood called West 5, located in London (ON), where it is assumed that amenities are within a 15 min distance via active modes of transportation from residences.

The design process of an energy-efficient neighborhood starts from optimizing its site layouts, road layouts, and partition of land [14]. The energy consumption and corresponding GHG emission of a neighborhood are greatly impacted by the functions served by the buildings operating in that neighborhood [15]. Mixed-used neighborhoods, also known as “live, work, play” communities, are considered more environmentally friendly than single-use neighborhoods [16]. The FMC includes six essential social functions or amenities—living, working, supplying, caring, learning, and enjoying/leisure—which are available within a 15 min walking or wheeling distance or 1 km radius [11,12,17]. This paper examines “supplying” function only with emphasis on transporting food items from farmland to the consumer’s residence. Food mile describes the distance that a particular food product crosses through the chain from the production point to the point of sale. Food travels between 2400 and 4000 km in North America [18]. GHG emissions from the food transport sector are reduced by decreasing large food miles by producing food items in unutilized spaces in the neighborhood, i.e., urban agriculture (UA) [19].

Adding vegetable production on building rooftops and facades imparts food self-sufficiency and decreases the energy demand of the building while mitigating climate change to some extent [20]. Food produced in urban scenarios is preferred by locals, and studies show that urban dwellers are increasingly finding satisfaction in such activities [21,22]. Vegetables grown in urban scenarios are perceived as safe and accumulate contaminants within the limit [22]. Efficient use of available space acts as a promising solution for improving sustainability and food security. Food Self-Sufficiency (FSS) refers to a household, neighborhood, or country’s ability to meet its food requirements by domestic-level production [23,24]. The city of London in the province of Ontario, Canada experiences a food insecurity rate of 13.9% and particularly lacks the production of fresh fruits and vegetables [25].

Considering field lettuce, analyzing its annual production in different regions of Canada shows that roughly 90% is produced in Quebec, indicating a large food mile and corresponding CO₂ emission [26]. Leafy vegetables are responsible for about 1/5th of total vegetable demand and hence making a neighborhood self-sufficient in terms of leafy vegetables is more attainable compared to other vegetables [27]. A study in Seoul found that using 30% of rooftop space and 50% of lot space for UA reduces CO₂ emission by 11,668.53 metric tons [19]. In North America most studies focus on rooftop agriculture (RA) involving crop yield, system design, city-scale production and policy development [28]. Another study describes the strong potential of UA in Montreal to meet the city’s vegetable demand. Even in regions of North America, it is possible to achieve food reliance and sufficiency by implementing large-scale UA [29]. Similarly, large metropolitan areas such as Berlin can meet its own vegetable demand, and also Barcelona can implement UA and meet 50% of its annual tomato demand [28,30].

Contemporary urban planners incorporate sustainable transportation powered by renewable energy as a tool for reducing CO₂ emissions and achieving SDGs [31]. A green transportation (GT) system is a transportation system that has a lesser number of negative impacts on health and the environment in comparison to existing transportation systems, and it not only increases energy sustainability but also reduces traffic congestion [32]. A study conducted in Calgary, Canada emphasized that Electric Vehicles (EV) are fruitful as long as they are powered by clean energy sources [10]. Pavement Integrated Photovoltaics (PIPVs) or sidewalk PVs are attracting attention in the USA, where power outages in traffic intersections has become an issue due to natural calamities [33]. A year-long study conducted in 2024 on embedded PV technology in sidewalks in San Antonio, Texas found that 100 W panels withstanding the movement of 83,500 pedestrians annually generates 3087.91 kW-h/m² [34].

This study introduces a novel, integrated framework that combines urban amenity proximity, UA, and GT into a single, scalable platform. While existing FMC research often addresses isolated elements or single transport modes, this work innovatively unites multiple strategies to holistically address the food–energy–mobility nexus at the neighborhood scale, responding to recent calls for more comprehensive, multimodal approaches [35,36]. This study strives to answer three research questions: (i) What sort of spatial distribution of food-supply-related amenities is required to make a neighborhood more walkable? (ii) What spaces in urban neighborhoods are suitable for urban agriculture that can contribute to achievement of FSS? (iii) What quantity of sidewalk PVs is required and what would be the related cost to operate an EV-based food transportation system in a neighborhood?

Building on the FMC concept, this study advances the idea of self-sufficient, energy-efficient neighborhoods by integrating spatial planning with sustainable mobility and resource distribution. As part of a larger research project examining the impact of localizing all essential amenities, such as food, education, health and services, within walkable neighborhood boundaries in North American car-dependent contexts, this paper focuses specifically on food systems. It assesses not only the spatial accessibility of grocery stores and farmers markets within a 15 min walking radius but also explores the role of low-emission distribution strategies, including EVs, in reducing travel-related energy use and GHG emissions. By addressing both spatial proximity and energy implications, this study extends the conventional FMC framework towards a model of neighborhood-level self-sufficiency supported by functional complementarity across adjacent urban zones. Studies on UA have concluded that cultivating vegetables in urban neighborhoods enhances food security while mitigating associated GHG emissions, thus contributing to the fulfillment of both SDG 2 and SDG 13 [20,37]. SDG 7 involves affordable and clean energy; this is achievable at a community level through integration of renewable energy to fulfill neighborhood energy demand and utilize the surplus power for other purposes [38]. Additionally, the design of multi-functional neighborhood systems such as FMCs enhances the sustainability of communities, which is related to SDG 11 [11].

The novelty of this study lies in conducting a comprehensive investigation depicting multi-functional neighborhood, UA, and GT strategies together and developing a combined framework that contributes towards achievement of SDGs 2, 7, 11, and 13. This work aligns with the vision of SDGs 2, 7, 11, and 13 by introducing UA to achieve food security, the inclusion of sidewalk PVs that ensures access to affordable and sustainable energy, the establishment of FMC for inclusiveness and sustainable society, and launching GT systems to combat climate change. The principal objective of this research is to investigate the potential for reducing CO₂ emissions associated with food miles through the integration of urban agriculture (UA) with neighborhoods. Secondary goals of this study are as follows: (i) proposing a neighborhood design in which urban agriculture (UA) is introduced by utilizing roofs, facades, and lot spaces to reach Food Self-Sufficiency (FSS) in order to reduce food miles and associated CO₂ emissions; (ii) introducing a sidewalk PV-based renewable energy source that powers a green transportation (GT) system, which in turn transports food items to the neighborhood food outlets; (iii) developing a Food Production and Transportation Framework (FPTF) depicting a holistic approach integrating UA and a GT system together.

2. Methodology

This study is based on an advanced high-performance community, West 5, located 11 km from the city center of London in the province of Ontario, Canada. It is located in a 6b climate zone where average winter temperatures lie between −20 and −18 °C [39,40]. It is the largest smart community in North America. West 5 strives to achieve a net zero energy status, with one of its goals to create an Electric Vehicle (EV)-friendly neighborhood [41]. Plans of West 5 conform to “The London Plan” framework, which endorses sustainable development [42]. This study considers West 5 as an ideal base case Neighborhood Unit (NU) consisting of an approximately 70-acre or 0.28 km² land area and capacity of 2000 dwellers. The Methodology Section describes the procedure for development of a neighborhood cluster (NC) that includes food supply amenities, urban agriculture (UA), and a green transportation (GT) system powered by photovoltaics (PV). Finally, this methodology outlines a Food Production and Transportation Framework (FPTF) that combines NC, UA, and GT and is measured and quantified through a Decision-Making Metric (DMM), making it globally applicable in urban neighborhoods. Figure 2 summarizes the methodology and proposes an FPTF. Details of the methodology are explained in the following section.

2.1. Methodology for Development of a Neighborhood Cluster Containing Features of a 15-Minute City

West 5 comprises 36 buildings with different geometries and functions. A representative drawing of a NU was created using Google Earth Pro 7.3.6.10201 (64-bit) and AutoCAD 2025 delineating locations and equivalent areas of buildings and roads of West 5. One NC was developed where 10 identical NUs were placed in a circle (radial road pattern), with amenities inside them. The NC has a radius of 1km, making it a 15-min walk from the perimeter to the center through the radial roads. The NC lies inside a 2 km × 2 km square, which includes green areas. The area of each NU and total area of the NC is 0.31 and 3.14 km², respectively. Each NU has a total roof area of 23.21% of its total lot area and the remaining portion is for roads and green areas. Buildings 31 and 32 (B31 and B32) of each NU are utilized for establishing outlets of non-residential amenities (Figure 3). This study proposes grocery stores (GS) at B31 or B32 of every even-numbered NU. B31 and B32 each have an area of 1013 m² and are located near the center of the NC, making them easily accessible for all NUs. One farmers market (FM) on an open green area with an area of 2500 m² is placed at every odd-numbered NU, and these FMs are at walkable (e.g., 1 km) distances from each other and connected with roads, sidewalks, and bike lanes.

2.2. Methodology for Implementation of UA and Determining Annual Production and Food Self-Sufficiency

A fixed percentage of roof area is considered for cultivation in each of the 10 NUs, with 10 kinds of vegetables for the open-air growing season (June to October). The roof area percentages usable for UA are standardized by local regulatory authorities. In the absence of such regulations in London, this study chose cultivation percentages based on the “City of Toronto Green Roof Bylaw”. Horizontal roofs (e.g., building numbers 1 to 16 and 19 to 32) are considered as green roofs while tilted roofs (e.g., building numbers 17, 18 and 33 to 36) are not. For the sample NU, 36 buildings with different roof areas were considered, where the roof slope < 17% [43], for rooftop vegetation. Buildings with roof slopes exceeded 17% were not considered but their facade areas were included in calculation for vegetable cultivation. South- and west-side facades at a percentage of 10%, 20%, or 30% were considered for farming. In line with the regulations of Ontario, it was assumed the total coverage or building construction area in a NU was 35%, while the remaining 65% of the NU was defined as lot space and left as green space and roads [44]. For this study, 5%, 10%, and 15% of lot space were considered as cultivation areas. Nine cases were generated, with percentages of roof area constant in all cases, but lot areas and facade areas considered variable. Figure 4 shows the total cultivation area for the 9 cases based on percentage usage for each space (e.g., roof, facade, and lot).

Ten vegetables were selected based on demand, growth cycle, weather tolerance and open-air farming method. It was assumed that, upon harvesting a vegetable, re-cultivation-related works take place, requiring a one-month interval which accounts for the re-preparation of the soil. Vegetable yield (kg/m²) and per capita annual consumption (kg/person-year) related data were collected from Statistics Canada. Equations (1) and (2) describe annual production and vegetable self-sufficiency (VSS) for the NC [45,46,47].

Annual production = Vegetable yield × Area × No. of cycle in a year

(1)

Vegetable self-sufficiency = Annual Production ÷ (Per capita annual consumption × No. of people)

(2)

A Food Production Schedule (FPS) was developed and implemented where cultivation areas of vegetables were re-assigned with respect to the level of sufficiency achieved. If a high-yield vegetable reached a self-sufficiency of more than one hundred percent while a low-yield vegetable lagged behind, then cultivation areas for high-yield vegetables were re-assigned to low-cultivation vegetables.

2.3. Methodology for Implementation of Green Transportation System Powered by PV

GT, i.e., EVs, was selected based on supply and demand, i.e., mass of vegetable production and load-carrying and battery capacities of EVs. CO₂ emission was reduced on 2 fronts—food miles (i.e., truck trip lengths from farmland to grocery stores) were eliminated and automobile trip lengths (residence to food outlets) were significantly decreased. GT-related features such as the number of EVs and their maximum load-carrying and battery capacities were determined. The battery range of the EVs was considered 80% of the full range because studies show that keeping the battery charged between 20% and 80% reduces battery capacity degradation [48].

EnergyDemand_GT = GT_n × Battery_capacity × {Trip_Length ÷ (RAN_GT × 80%)}

(3)

where EnergeyDemand_GT is the total daily energy demand of green transportation, GT_n is the number of GT units, Battery_capacity is the battery capacity of a selected GT unit, Trip_Length is the trip length from the drawing, and RAN_GT is the range of the selected GT. Here, the range of GT unit is multiplied by 80% to prevent battery capacity degradation.

GT units were charged with PV power, eliminating CO₂ emissions from last-mile transportation of the cultivated products. It was assumed that sidewalk PVs had a rating of 100 wp with 15% efficiency. Mean daily solar irradiation (kW-h/m²) for London is lowest in December, i.e., 1.23, and highest in July, i.e., 6.17, while the mean daily value for the whole year is 3.69 [49]. The dimensions of sidewalk PVs were 600 mm × 600 mm. London (ON) enjoys 286 sunny days per year and favorable annual average temperatures from an efficiency perspective [50,51].

PV_quantity = EnergyDemand_GT ÷ (Solar_insolation × Area_PV × Efficiency_PV)

(4)

PV_quantity is the number of required PVs, EnergyDemand_GT is from Equation (3), Solar_insolation is the mean daily solar irradiation per unit area (kW-h/m²), Area_PV is the area of one PV of selected model (m²), Efficiency_PV is the PV efficiency (percentage).

However, environmental factors that affect PV energy production, such as shading loss, unforeseen solar insolation variation, and PV efficiency loss from temperature fluctuations, are not considered in this study.

2.4. Methodology for Developing Food Production and Transportation Framework (FPTF)

This study adds to the literature on FMCs by introducing a quantifiable and scalable FPTF that measures the application of three strategies—amenity-inclusive NC, UA, and GT systems. FPTF includes a DMM that presents computable conditions to aid decision makers. The strategies mentioned in the FPTF are described through Figure 5.

Quantifiable conditions for the DMM mentioned in Figure 5 are described in

Table 1. Decision-Making Metric (DMM) for implementing Food Production and Transportation Framework (FPTF).

Strategy and DMM Condition	Decision	Framework Performance Coefficient (FPC)
(A) Neighborhood Cluster	If all conditions are met and all responses are positive, then implement the FPTF. Special Condition: If condition (B.i.) is not satisfied, then the FPTF can still be implemented by utilizing GT to transport the remaining food products (=annual demand − annual production) from outside sources to neighborhood food outlets.	Annual production of food (kg/year)/Annual reduction in CO2 emission due to FPTF (kg/year)
(A.i.) Availability of food outlets in neighborhood (Yes or No)
(A.ii.) Trip length from residence to food outlet ≤ comfortable walking distance (≈1 km) (Yes or No)
(B) Urban Agriculture (UA)
(B.i.) Annual production ≥ annual demand of food product (Yes or No)
(C) Green Transportation (GT)
(C.i.) Energy generation of sidewalk PV system ≥ energy required to operate GT system (Yes or No)
(C.ii.) Present value of sidewalk PV to power GT ≤ present value of fuel for traditional transportation system in NU without UA and GT system (Yes or No)

.

This study only looks into supply amenities (e.g., food), hence the Framework Performance Coefficient (FPC) is defined as the ratio of annual food production (kg/year) to annual CO₂ reduction (kg/year).

2.4.1. Methodology for FPTF: CO₂ Emission Reduction from Green Transportation

Annual CO₂ emissions decrease upon implementation of strategies causing trip length reductions. Annual CO₂ emissions for both the base case and new neighborhood cluster scenarios were computed in two steps—emission by trucks for transporting the food product from farm to food outlets and emission by automobiles owned by UA dwellers for purchasing food from food outlets. For the base case scenario, i.e., UA- and GT-less neighborhood, the trip length and corresponding CO₂ emission were calculated. This study takes the trip length for food trucks as 2400 km, which is the same for all of the chosen vegetables for UA [18]. The average CO₂ emission rate from refrigerated trucks carrying grocery products around Canada is 1073.09 g/km [52]

It was assumed that the rate of vegetable sales from store shelves was equal to the rate of replenishing the shelves. Therefore, the number of monthly trips by a neighborhood dweller to the grocery stores (e.g., 5.43 trips per month) represented the number of grocery delivery trips by food trucks [53]. This study assumed that food trucks exclusively carried the UA products.

Consecutively, automobile emissions were determined from the following data: the 2016 London (ON) Household Travel Survey (LHTS 2016) found that the number of daily trips per person for the Census Metropolitan Area (CMA) was 3.4 [54]. Another study from Dalhousie University stated that Canadians visit grocery stores 5.43 times a month [53,55]. Combining these two studies, it was estimated that 5.32% of the daily trips made by the NU dwellers were for grocery purposes. A trip length in the 90th percentile of trips according to the LHTS-2016 is 13 km [54]. This study takes a market scenario approach for calculating CO₂ emission rates. Based on the most-sold automobiles in Canada for the year 2024 and their CO₂ emission rates from NRCan, the average CO₂ emission rate by an automobile per trip was calculated [56].

2.4.2. Methodology for FPTF: Present Value of Project Cost and Payback Period

Implementation of GT to transport produce from UA offset the emissions from long truck miles for vegetables. Multi-functional neighborhoods that include food outlets reduce last-mile emission as compared to the base case scenario, resulting in significant reductions in trip length and, consequently, fuel cost. This study examined a project life of 20 years and discount rate of 5%. The base case scenario was analyzed for the project life span and the present value of fuel expense was determined. It is also assumed that refrigerated trucks are using diesel and private automobiles use premium gasoline. Canadian Light Duty Vehicles consume 8.6 L gasoline-equivalent for traveling every 100 km [57]. The cost of diesel and premium gasoline per liter in Ontario is C$1.316 and C$1.644, respectively (date: 12 May 2025) [58].

A year-long study on sidewalk PVs conducted in San Antonio, Texas found that the initial cost of PVs is US$1000/m² [34]. Considering that study, this study assumed the same initial cost and added 10% yearly maintenance and 25% rehabilitation costs every 5 years with respect to the initial cost. The total area of the sidewalk PVs was calculated for the power requirements of an EV and lowest average daily solar irradiation for the month of December. Expenses pertaining to sidewalk PVs, such as initial costs, annual maintenance costs, and rehabilitation costs, were computed and the present value of these expenses was determined.

PresentValue = AnnualCost/Σ{(1 + DiscountRate)^Year)}

(5)

The total cost of the project and the unit cost of power produced by PV were calculated and compared with the present value of fuel expenses over the same time period to determine the payback period.

3. Results

In line with the methodology mentioned above, this section describes the results for application of three strategies—a multi-functional neighborhood such as a 15-Minute City (FMC) with food outlets, urban agriculture (UA), and green transportation (GT) powered by locally generated renewable energy, as shown in Figure 5.

3.1. Results for Neighborhood Cluster Comprising Food Outlets: Trip Length

This study strived to reduce trip length by establishing food outlets in the neighborhood cluster (NC). Availability of one essential amenity, such as supplies, within proximity of residential units fulfills the proximity condition of a 15 min neighborhood system. From Figure 3a,b of the NC, it can be found that five grocery stores (GS) are placed 214 m apart while the five farmers markets (FM) are 952 m from each other. The travel time required for walking from one GS to another is 3 min and from one FM to another is less than 14 min. The FMs are located 749 m from the center, which can be traversed through on foot within 10 min. The average trip length between residences and food outlets (both GS and FM) is 292 m. The food outlets are easily walkable from any location inside the NC and the required travel time is less than 15 min. NCs comprising 10 Neighborhood Units (NUs) fulfill the condition of FMC from a supply amenity proximity dimension. Figure 3 delineates 10 NUs with a combined area of 3.14 km², making the density of food outlets 3.18 outlets per km².

3.2. Results for Urban Agriculture: Annual Production and Food Self-Sufficiency

Different combinations of cultivation areas failed to achieve food self-sufficiency (FSS) when one vegetable was cultivated in one NU. This proved true even for Case-9, which had the maximum cultivation area (Figure 3). Yearly vegetable yields and consumption were collected from Canadian government sources [59]. All the chosen vegetables had a self-sufficiency at 113% to thwart the annual 13% loss for open-air farming where an FSS achievement of 113% or more was equivalent to the achievement of sufficiency for ten vegetables [60].

Instead of increasing the total cultivation area, an efficient approach called Food Production Schedule (FPS) was adopted to optimize area usage by having multiple vegetables farmed in a single NU. These revised cases where multiple vegetables were cultivated in a single NU showed that FSS was achieved for Cases-3, 5, 6, 8, and 9 in one scenario or by combining two scenarios. Cases-1, 2, 4, and 7 did not achieve FSS. Figure 6 illustrates the optimal area utilization for cases where FSS was achieved.

Figure 6a–e illustrates 10 vegetable products—Beetroot (BR), Broccoli (BC), Carrots (C), Chinese Cabbage (CC), Cucumber (CU), Lettuce (LE), Oyster Mushroom (OM), Peas (P), Pumpkins and Squash (P and S), and Tomatoes (T)—which were chosen for production in 10 NUs. In the graph (Figure 6), N is a neighborhood unit, R is a rooftop area, F is a facade area, and L is a lot area (e.g., N-1R means neighborhood unit-1 rooftop area is cultivated for a specific vegetable). For example, Figure 6a shows that vegetable BR has achieved a Vegetable Self-Sufficiency (VSS) of 127% by utilizing only the roof area of NU-1 while vegetable BC reached VSS 113% by producing 8% from NU-1 facade, 42% from NU-1 lot space, 13% from NU-2 rooftop area, 8% from NU-2 facade, and 42% from NU-2 lot space. For all the vegetables in Cases-3, 5, 6, 8, and 9, an FSS of 113% or more indicates achievement of food self-sufficiency. Out of five successful cases (e.g., Cases-3, 5, 6, 8, and 9), Case-3 had the minimum cultivation area, with 13.8% of rooftop, 10% of façade, and 15% of lot area utilized, resulting in an annual production of 768.77 metric tons from a cumulative area of 41,611.84 m². Cultivation area, annual yield, and FSS for all the cases are described in

Table 2. Cultivation cases and their respective cultivation areas and production.

Case Scenarios for Vegetable Production	Vegetable Cultivation Area (%)			Cultivation Area (Square Meter)	Quantity of Annual Vegetable Production from One Neighborhood Cluster (Metric Ton/Year)	Status of FSS (Percentage)
	Roof Top Area *	Facade	Empty Lot Space
Case-1	Constant	10	5	23,198.64	625.10	Not Achieved (86.1%)
Case-2	Constant	10	10	32,405.24	784.03	Not Achieved (98%)
Case-3	Constant	10	15	41,611.84	768.77	Achieved (100%)
Case-4	Constant	20	5	28,392.54	664.35	Not Achieved (93%)
Case-5	Constant	20	10	37,599.14	890.142	Achieved (100%)
Case-6	Constant	20	15	46,805.74	919.66	Achieved (100%)
Case-7	Constant	30	5	33,586.44	823.78	Not Achieved (98%)
Case-8	Constant	30	10	42,793.04	876.47	Achieved (100%)
Case-9	Constant	30	15	51,999.64	897.03	Achieved (100%)

* Rooftop area (usable) is 8798.14 m².

.

With an annual production of 768.77, 890.142, 919.66, 876.47, and 897.03 metric tons of vegetables, Cases-3, 5, 6, 8 and 9, respectively, achieved 100% self-sufficiency. However, Case-3 utilized the minimum cultivation area of 41,611.84 m², which is used for initial investment calculations in the following sections.

3.3. Results for Implementation of Green Transportation System Powered by PV

A range of popular electric freight trucks and e-trucks/pickups available in the market were explored for this research work and their battery capacity and load-carrying capacity were examined in this study [61,62,63,64,65,66,67], [68] (p. 5), [69]. A design electric vehicle was chosen and its load-carrying capacity and battery capacity and range were used to calculate the annual power demand.

From Figure 3, multiple route plans were explored in this study and the route with the maximum trip length was chosen, in which the GT unit travels back and forth through the center of the radial roads and yields a daily trip length of 106 km.

Table 3. Annual power demand for green transportation (GT) and annual power generation by PV.

Description	Results
Required load carrying capacity of EV	2.55 Metric Ton/day
Design EV	Load carrying capacity	0.8 Metric Ton
	Battery capacity	92.5 kW-Hr
	Range	675 km
Daily trip length of design EV	106 km
Charging frequency	0.194 times/day
Annual power demand for design EV	6551.75 kW-Hr
Maximum PV area for minimum solar insolation	97.61 m2
Annual power generation by design PV	19,719.99 kW-Hr

describes the annual power demand vs. supply.

3.3.1. Results for Number of Trips for Food Truck and Automobile Trip Lengths and Annual CO₂ Emission

Results obtained from analyzing the NUs and NC are summarized in

Table 4. Annual grocery-purchase-related CO₂ emission per person.

Description	Results
Number of trips	3.4 trips/person/day
Total number of annual trips generated from 1 NU	2,482,000 trips/year
Annual grocery-purchase-oriented trips	% of trips	Number of annual trips
	3	74,460
	4	99,280
	5	124,100
	5.32	132,130
	6	148,920
	7	173,740
CO2 emission due to grocery purchase
Annual CO2 emission by food truck	167.81 Metric Ton/year
Annual CO2 emission by automobiles (MT)	% of trips	Annual CO2 emission
	3	227.70
	4	303.60
	5	379.50
	5.32	404.60
	6	455.40
	7	531.30
Base case annual CO2 emission due to grocery purchase for 1 NU (e.g., West 5)/NC	572.41 Metric Ton (1 NU)
Base case annual per capita CO2 emission	286.20 kg/person/year
15 min neighborhood annual CO2 emission due to grocery purchase for one NU	9.07 Metric Ton (1 NU)
15 min neighborhood annual per capita CO2 emission	4.54 kg/person/year
Percentage reduction upon implementation of three strategies	98.41%

. In line with the methodology described in Section 2.4.1, the rate of CO₂ emission by automobiles is 235.23 gm/km. Annual CO₂ emission for the base case scenario describes truck trip emissions coupled with automobile emissions, amounting to 572.41 metric tons for one NU consisting of 2000 dwellers. Multifunctional neighborhoods such as 15 min neighborhoods coupled with self-sufficient UA systems and GT strategies eliminate the truck trips and associated CO₂ emissions. However, due to the significant reduction in trip length from residence to food outlets, the dwellers have two options—walk or continue using automobiles, where the annual CO₂ emission decreases to 9.07 metric tons, a compelling 98.41% reduction from the base case scenario.

Figure 7 delineates that grocery-purchase-related last-mile automobile trips produce more CO₂ emissions than long haul food trucks. Elimination of truck trips by introduction of UA followed by neighborhood food outlet redistribution resulting in reductions in last-mile automobile emissions annually reduces CO₂ emission/person from 286.2 to 4.54 kg CO₂/person/year.

3.3.2. Results for Present Value of Project Costs and Payback Period

Considering the design EV from Table 3, the PV area and corresponding expenses were determined. Also, the fuel cost for the food trucks and automobiles were calculated for the project life period which was time sensitive information due to market scenarios. Comparison between the present cost of fuel (food truck and automobiles) vs. the cost of sidewalk PVs is summarized in

Table 5. Comparative cost analysis for fossil fuel vs. sidewalk PVs.

Description	Result
Design PV area	98 m2
Present value of fuel cost	C$3.21 million (100%)
Present value of fuel cost by trucks	C$0.22 million (6.88%)
Present value of fuel cost by automobiles	C$2.99 million (93.12%)
Present value of sidewalk PV system	C$0.37 million (100%)
Present value of initial cost	C$0.13 million (35.59%)
Present value of maintenance cost	C$0.16 million (44.35%)
Present value of rehabilitation cost	C$0.073 million (20.06%)
Fuel cost reduction with respect to PV cost over 20-year life period	88.44%
Cost per unit of power generated by sidewalk PVs	C$0.92/kW-Hr
Payback period	2.58 years

.

Introduction of multi-functional neighborhoods such as the FMC principles in the neighborhood scenario and establishing food outlets at walkable distances from residences decreased the grocery purchase oriented trip lengths and corresponding CO₂ emissions (Figure 8).

3.4. Results for Decision-Making Metric (DMM)

Combining results of supply amenity inclusive NC, UA, and GT strategies into a quantifiable DMM aids decision makers to measure the performance of the framework and select a strategy.

All the strategies produce positive DMM response in

Table 6. Results for Decision-Making Metric (DMM) regarding implementation of Food Production and Transportation Framework (FPTF).

Strategy and DMM Result	Decision	Framework Performance Coefficient (FPC)
(A) Neighborhood Cluster	All Positive. FPTF is acceptable.	0.19
(A.i.) Yes. 5 GS and 5 FM. (A.ii.) Yes. All distances < 1 km
(B) Urban Agriculture (UA)
(B.i.) Yes. FSS achieved. 768.77 metric ton/year
(C) Green Transportation (GT)
(C.i.) Yes. 19,719.9 ≥ 6551.75 (kW-h/ year) (C.ii.) Yes. C$0.37million ≤ C$3.205

. The FPC value is 0.19, which signifies that 0.19 units of food production in the neighborhood results in a 1 unit reduction in CO₂ emissions due to the elimination of transportation.

4. Discussion and Recommendations

This work builds on the 15-Minute City (FMC) concept by introducing a spatial-energy framework aimed at neighborhood scale self-sufficiency, with a current focus on food systems. The Food Production and Transportation Framework (FPTF) integrates urban agriculture (UA) and green transportation (GT) to reduce CO₂ emission from both local production and last-mile distribution. A Food Production Schedule (FPS) was used to optimize cultivation within the neighborhood cluster (NC) to meet vegetable demand.

Sidewalk integrated photovoltaic (PV) systems were modeled to power Electric Vehicles (EVs) with results confirming sufficient power generation to eliminate transport-related emissions for select produce. Establishing food outlets within walkable distances further reduced trip lengths and associated CO₂ emissions and energy consumption.

While this study focuses on food-related infrastructure, the approach is designed for future application to other essential amenities, enabling the planning of low-carbon, service-rich, and self-sufficient urban neighborhoods that contribute towards the achievement of SDG s2, 7, 11, and 13.

The observations from this study are as follows:

• A NC consisting of ten Neighborhood Units (NU) is hypothesized where amenities are available within a 15 min walking trip from residential units. Supply related amenities are placed at every NU of the NC, significantly reducing the trip length. The food outlets are placed at walking distances from residential units, which reduces automobile dependency and promotes active modes of transportation.
• A collaborative approach among NUs is warranted, where multiple vegetables are cultivated in one NU, resulting in Food Self-Sufficiency (FSS) achievement for the chosen vegetables. An area optimization schedule called FPS is developed, depicting multiple combinations of cultivation areas. The results demonstrated that achieving an average FSS of 100% for an entire NC is feasible with area usage as low as 13.8% for rooftops, 10% for facades (south and west side), and 15% for lot spaces. This finding highlights the importance of community-based strategic planning and scheduling for UA to achieve FSS for certain crops.
• Density of amenities for this study is 3.18 amenity outlets per km² of a NU, resulting in amenity availability at a walkable distance of less than 15 min for its dwellers. Spatial redistribution of amenities and placing them at walkable distances from the residential units resulted in reductions in long trip lengths and corresponding annual CO₂ emissions. Achieving FSS with chosen vegetables in the NC with an UA strategy also supported community-based local contributions for sustainable development.
• Production of resources and re-distribution of only one amenity in the neighborhood resulted in reductions of 98.41% of annual CO₂ emissions. Carbonless food distribution is achieved by operating GT units powered by sidewalk PVs. Investment affiliated to the PV has a payback period of less than 3 years in comparison to the fuel cost of regular fossil-fuel based operations.
• Decision-Making Metric (DMM) and Framework Performance Coefficient (FPC) analyze the Food Production and Transportation Framework (FPTF) and quantify the strategies. The DMM yields positive results for all the five criteria described in the methodology. Also, the FPC value is 0.19, indicating the effectiveness of the current set of strategies. This study designed DMM and FPC in a quantifiable manner, indicating that decision makers have the flexibility to modify the framework and compare among different neighborhood scenario, strategies etc.
• Three strategies examined in this study have the potential to contribute towards achievement of Sustainable Development Goals (SDGs) 2, 7, 11, and 13. Spatial re-distribution of amenities in urban neighborhood adds to the attainment of inclusive communities and decreased trip length, i.e., CO₂ emission (SDGs 11 and 13) [70] (p. 11), [71] (p. 13). Self-sufficiency achieved upon implementation of UA contributes towards reaching food security and resilience in terms of SDGs 2 and 13, respectively [71] (p. 13), [72] (p. 2). This study also observes that transporting the UA produce using GT units powered by sidewalk PVs promotes SDGs 7 and 13, where affordable and sustainable energy sources are contributing towards alleviation of climate change effects.

FPTF combines and quantifies three strategies—a NC with amenity outlets available at proximity, introduction of UA and a GT system engaged to transport harvested products. These three strategies collectively reduce a substantial amount of CO₂ emission and transport expenses. FPTF has a DMM that quantifies and measures the conditions and a FPC that quantifies the effectiveness of FPTF. The DMM and FPC are easily interpretable tools that help decision makers select their strategy.

With a larger goal to decrease CO₂ emissions from urban transportation-related activities, future studies ought to investigate other FMC amenities. This study only delved into vegetable cultivation but other important and nutritious agricultural products, such as dairy products, and their corresponding CO₂ emissions require further investigation. Vegetable storage methods and labor force management are other important sources of CO₂ emissions and cost escalation warrant further research work. Additional structural loads imposed by UA and safety measures for cultivation- and harvesting-related operations demand separate analyses.

Study Limitations and Roadmap for Future Work

This study considers a base case, West 5 (London, ON, Canada), and evaluates a modified multi-functional NC calibrated with 2016 City of London traffic data. This analysis does not model food production systems; therefore, the energy use and associated CO₂ emissions from food growing are outside the scope of this study. The study did not examine closed growing systems (controlled or uncontrolled) suitable for cold-climate neighborhoods. Although the initial investment required for an FMC in a North American context was not investigated in this study and the literature generally finds limited evidence of contamination in urban-grown vegetables or systematic consumer reluctance to purchase them, uncertainty remains regarding the cost of such a project and local community perceptions of product quality and their willingness to buy; this is a limitation and an avenue for future work.

Future studies should collect up-to-date traffic survey data from the target neighborhoods to characterize residents’ travel behavior (e.g., modal share and number of trips by purpose) under multi-functional neighborhoods. In parallel, holistic assessment of urban agriculture at the neighborhood scale is needed, integrating agronomic practices with energy use and associated CO₂ emissions. Finally, initial investment requirements for 15 min neighborhood and social acceptance research is warranted to examine residents’ perceptions of urban-grown produce, including perceived quality and willingness to purchase, to close this remaining evidence gap.

5. Concluding Remarks

This study demonstrates the potential of integrating local food outlets, urban agriculture, and green transportation (GT) in neighborhood clusters (NC) as a framework for redistributing amenities and reducing environmental impacts. Within the multifunctional neighborhood model, the analysis focused on food-supplying functions by cultivating vegetables on rooftops, facades, and lot spaces in a cluster of 10 units housing 2000 residents each. A Food Production Schedule (FPS) indicated that dedicating 13.8% of rooftop area, 10% of facades, and 15% of lot space is sufficient to achieve Food Self-Sufficiency (FSS) for selected crops. Harvested produce can be distributed through intra-cluster food outlets using a photovoltaic-powered GT system, significantly lowering food miles and CO₂ emissions.

To generalize the approach, a Food Production and Transportation Framework (FPTF) was developed, incorporating a Decision-Making Metric (DMM) and Framework Performance Coefficient (FPC) for assessing performance and replicability. Although the implementation cost equals 2.5 times the project-life fuel expenditure of the baseline scenario, the framework delivers substantial benefits, including a 98% reduction in CO₂ emissions, enhanced FSS, and shorter travel distances. These outcomes directly support Sustainable Development Goals (SDGs) 2, 7, 11, and 13. Moreover, locally produced vegetables are not only preferred by urban residents but also remain within acceptable safety limits for consumption, reinforcing the viability of neighborhood-scale food–energy–transport integration.

This study, situated in a Canadian context, applies open-air farming methods operational for six months of the year to demonstrate neighborhood-scale food self-sufficiency. Future work on controlled-environment agriculture tailored to cold climates will further strengthen its applicability and resilience under uncertain climatic conditions. The FPTF developed in this study offers planners, policymakers, and communities a scalable tool for integrating food and mobility strategies. By translating outcomes into practical indicators such as average trip length, the framework enables comparison and prioritization of options across both existing neighborhoods and new developments. Beyond reducing environmental impacts, the framework aligns with multiple SDGs, including enhancing food security through UA (SDGs 2 and 13), advancing sustainable energy supply (SDGs 7 and 13), and fostering inclusive, resilient, and sustainable neighborhoods (SDGs 11 and 13).