Forecasting Electric Vehicle Adaption Using System Dynamics: A Case Study of Regina, Saskatchewan †

Abstract

The Zero Emission Vehicle (ZEV) mandate by Canada’s federal government is a significant initiative towards achieving net zero emissions by 2050. In this context, to quantify the evolution scale of ZEVs alongside charging pile, a system dynamics (SD)-based policy simulation has been adopted for the city of Regina, Saskatchewan. The vector autoregressive model (VAR) equation is used as an input equation in the SD model for predicting ZEV sales. For model validity, calibration of the data with an available historical dataset alongside a sensitivity analysis has been performed. The SD model with two consecutive scenarios has been simulated until 2036, and “policy 2” has been found to be adequate.