First Tier: Diffusion of Electric Vehicles in the Target Market
Although a study of EV demand was published more than 30 years ago (Beggs et al. 1981), many studies on the diffusion of alternative fuel vehicles have been conducted in the last 10-20 years. There are two strands of the literature that are relevant to forecasting the market penetration of EVs:
- • The first characterizes consumer-level decision; contributions here are mainly based on discrete choice models, often based on stated preferences surveys (Dagsvik et al. 2002; Achtnicht 2009).
- • The second uses a diffusion model to predict the uptake of new technologies by the market. In this strand, contributions are mainly based on Bass Diffusion Theory (Bass 1969).
We implemented the first tier in accordance with the Bass Diffusion Model, since it has been widely recognized as one of the theoretical cornerstones of the literature relevant to diffusion of innovation (Cao and Mokhtarian 2004; Becker et al. 2009). The Bass Diffusion Model requires three inputs to forecast the annual number of adopters of a new technology: (1) potential market (M), that is, the number of potential adopters of the technology;
- (2) coefficient of innovation (p), the likelihood that somebody who is not yet using the product will start using it because of mass media coverage or other external factors; and
- (3) coefficient of imitation (q), the likelihood that somebody who is not yet using the product will start using it because of word of mouth or other influences from those already using the product.
These parameters may be estimated by the time series of sales of EVs or by adopting the "guessing by analogy" method. Through this method, the parameters are estimated using the time series of sales of another technology whose diffusion process we suppose will be the same as the one that EVs will follow. The analogs used to estimate the parameters of the Bass model are the methane gas vehicle and the natural gas vehicle. In fact, these two
The three tiers of modeling and simulation building the core of the DSS.
technologies have many similarities to the one we intend to study, for example, category of users, purpose of use, and reliance on the presence of a network of power distributors.
The choice of one method over the other gives the user the possibility of analyzing different diffusion scenarios. However, some preliminary analyses conducted showed that the adoption of the guessing by analogy method is to be preferred in the case of full EVs. This is due to the still-limited penetration of EVs as a percentage of the total market and to the current low annual growth rates.