Martino Tran

Associate Professor

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Modeling electric vehicle adoption in new multi-unit residential buildings in British Columbia (2021)

British Columbia’s (BC) transportation sector is the second largest contributor to the provinces’ GHG emissions. A transition towards a more electrified transportation sector could potentially see reductions in GHG emissions and reliance on fossil fuels. BC’s clean energy sources also adds to the benefits of widespread EV usage. However, potential EV owners in BC are faced with various barriers to EV ownership. Inadequate and inconvenient access to charging points at a vehicle owner’s home is one of the main barriers to EV ownership. This barrier is usually encountered by EV owners living in existing multi-unit residential buildings (MURBs). The lack of charging points is attributed to high retrofit costs when installing charging points in existing buildings. Recognizing this challenge, the City of Vancouver’s (COV) recent policy requires all new multi-unit residential buildings be 100% electric vehicle ready. All parking stalls for residents of new MURBs must now be equipped with an energized charging outlet. This research aims to assess and evaluate the impact of COV’s 100% electric vehicle ready policy on electric vehicle adoption by residents of new MURBs. This research adopted a system dynamics methodology to construct qualitative and quantitative models of elements in the system that influences electric vehicle adoption in new MURBs. System elements and their interrelations are first conceptually represented in a causal loop diagram. Three key leverage points influencing EV adoption were identified from this model: policies, market size and vehicle purchase price. Next, a stock and flow diagram is constructed incorporating these leverage points to enable a quantitative and temporal analysis of the system’s output and behaviour. Using the stock and flow diagram in the scenario analysis, an exploration of the system’s behaviour was analyzed over a period of 30 years. Six scenarios were designed by varying the values of key leverage points. Results from the scenario analysis indicate that an EV’s purchase price had the most influence on EV purchases. The contribution of this research is the development of models that provide quantitative insight on possible future uptake of EVs by residents of new MURBs under different conditions.

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