Forecasting the demand for electric cars in Morocco

Author(s): Aziza Chachdiab, Bouchra Rahmounia, Jamal Zahia, Badr Ikkenb
a Faculty of Juridical, Economic and Social Sciences, Km 3 route de Casablanca, B.P784, Settat, Morocco
b Research Institute for Solar Energy and New Energies IRESEN, 16 Amir Sidi Mohamed Street Souissi, Rabat, Morocco
International Journal of Smart Grid and Clean Energy, vol. 8, no. 2, March 2019: pp. 191-200
ISSN: 2315-4462 (Print)
ISSN: 2373-3594 (Online)
Digital Object Identifier: 10.12720/sgce.8.2.191-200

Abstract: In the face of the transition from existing transportation methods to electric vehicles, new economic models are indispensable in order to understand and anticipate the effect of this new sector on the automotive market. This document aims to develop three points related to the prediction of demand for electric vehicles in Morocco as a new technology not yet widespread in the country: conducting the survey, estimating the model, and forecasting. The structure of the model includes several significant variables. In order to study the determinants of future purchase of an electric car (EC), a multivariate logistic regression analysis was carried out by considering as a dependent variable the probability of purchase and as explanatory variables the respondent’s socio-demographic characteristics, daily travel mode, and perception of ECs. The choice of the multiple logistic regression method was based on the fact that the dependent variable is a qualitative binomial with two modalities, yes or no. The results show that demand depends significantly on the age of the potential buyers of EC, their daily mileage travelled, as well as the owners of the classic cars that will be the most demanded of this type of vehicle. The findings will also be of interest to decision makers who wish to develop effective measures to stimulate this new sector.

Keywords: Electric car, multiple logistic regressions, forecast analysis
Full Paper.pdf
Copyright © 2016 International Journal of Smart Grid and Clean Energy, All Rights Reserved