The notion of "residential footprint" consists in aggregating the environmental impacts of the dwelling and the environmental impacts of the mobility induced by the home location, over their entire life cycle. This presentation proposes a review of 61 scientific literature documents which aims to present the state-of-the-art in assessing the residential footprint, in a perspective of evaluating the sustainability of residences in the Montreal and Quebec City metropolitan areas. First, the methods to conduct an assessment of the environmental sustainability of residences, as well as data requirements, are outlined. Second, the results of the studies are analyzed and compared. The studies highlight the importance of simultaneously considering housing and daily mobility, both of which account for a significant share of the residential footprint, but also of assessing and differentiating between direct and indirect impacts. They also make it possible to identify the explanatory factors for the different levels of impact, which is essential for implementing equitable measures to reduce households' environmental footprint. Finally, the current gaps in the literature, such as data limitation, incomplete assessment of daily mobility and lack of simultaneous consideration of environmental and monetary impacts, are discussed to guide future work on households' residential footprint.
This study assesses transportation-related emissions across Edmonton's land use zones using a bottom-up urban vehicular emission inventory, with the goal of exploring potential Low Emission Zones (LEZs). LEZs have been successfully implemented in cities worldwide, including London, Santa Monica, and Beijing, and are also in place in Toronto's North York area through restricted road actions. Given Edmonton's Climate Resilience Planning and Development Action Plan on low carbon transportation, LEZs represent a viable pathway for further exploration.
Edmonton's Zoning Bylaw Geographical Data includes heavy and medium-sized industrial areas adjacent to residential neighborhoods, which contributes to the formation of emission hotspots. Beside establishing the zone-based mobile source emissions, the study will evaluate impact of LEZ common policies, for both groups of heavy-duty vehicles (HDVs) and light-duty vehicles (LDVs), by calculating the spatial and temporal distributions of GHG and (Criteria Air Contaminants) CACs emissions which can help identifying the action zones. This model is coupled with a spatial visualization tool, to support understanding the impact of traffic restrictions on neighborhoods.
In Canada, road freight transportation contributes 84% of GHG emissions within the freight transportation sector (De Bruycker, 2023). In cities, freight transport is also a significant source of noise emissions and road accidents. Cargo bikes have emerged as a promising alternative for urban deliveries, offering environmental benefits and cost-effective deliveries. Studies have shown that cargo bikes outperform vans in dense urban areas, excelling in delivery speed and reducing operational expenses (Nocerino et al., 2016; Conway et al., 2017; Sheth et al., 2019). However, research has predominantly focused on large cities, despite medium-sized cities exhibiting characteristics favorable to cargo bike use.
This study proposes a novel method to evaluate the feasibility of cargo bikes for freight deliveries in medium-sized cities. The approach leverages open data to analyze key factors such as land use, elevation, weather conditions, population density, and bike lane networks. To address diverse urban contexts, the analysis incorporates scenario-based modeling using dominant land-use patterns and land-use entropy. The findings support the strategic adoption of cargo bikes as realistic and efficient strategy for sustainable urban freight solutions in medium-sized cities.
Full Professor, Polytechnique Montréal and CIRRELT
Martin Trépanier is a civil engineer and professor at the department of mathematics and industrial engineering of École Polytechnique de Montréal, an engineering school affiliated to the Université de Montréal. He is the titular of the Chair in the transformation of transportation... Read More →
As urban areas strive to reduce greenhouse gas emissions, bike-sharing systems emerge as a promising solution to decrease private vehicle reliance. This study presents a robust methodology to quantify the emission reduction potential of bike-sharing systems in Vancouver, focusing on three core steps: trip purpose classification, mode substitution simulation, and emission factor estimation. Trip purposes were classified using k-means clustering, distinguishing between commuting and leisure trips. Mode substitution was then simulated using a graph neural network trained on trip diary data, allowing for accurate predictions of traditional modes replaced by bike-sharing, such as cars, transit, and walking. Finally, emission reductions were quantified using MOVES, a comprehensive emission inventory model, accounting for road types, fleet composition, and temporal variations.
The mode substitution analysis revealed that bike-sharing systems predominantly replace car trips, with 54% of trips previously made by car, while 14% were from transit and 32% were walking trips. The results indicate significant environmental benefits, with each bike saving approximately 119 kg of CO2, 1.26 kg of CO, 0.08 kg of NOx, and 1.24 g of PM2.5 annually. These findings highlight the effectiveness of bike-sharing systems in reducing urban transportation emissions and emphasize their role as a cost-effective decarbonization strategy.
This study aims to map on-road transportation emissions in various communities within the City of Surrey, British Columbia, using GIS and geospatial analysis techniques. Transportation is a significant contributor to air pollution and greenhouse gas emissions, making it crucial to analyze its spatial distribution to inform sustainable urban planning. The methodology employs methods to process and visualize emission data from multiple sources, including vehicle types, fuel usage, and road network characteristics. By integrating geospatial datasets, we associate transportation emissions with specific community boundaries, providing a detailed spatial representation of pollutant emissions and identify emission hotspots. Results highlight spatial disparities in emissions, with high-density traffic areas showing elevated levels of pollutants like nitrogen oxides (NOx) and particulate matter (PM). These findings offer insights into community exposure to transportation-related air pollution, which is crucial for targeted mitigation strategies and mobilizing equity.
