Incorporating pro-environmental preferences towards green automobile technologies through a Bayesian hybrid choice model

Daziano, Bolduc, 2013, in Transportmetrica A: Transport Science

doi:10.1080/18128602.2010.524173

Location	Canada
Population	General
Sample size	1877
Factor analysis type	nan, nan rotation
Stepwise regression	no
Removal of insignificant variables	no
Reviewed by	2020-04-13 00:00:00

Abstract

Using stated data on both vehicle purchase decisions and environmental concerns, we develop, implement and apply the MCMC Gibbs sampler for Bayesian estimation of a Hybrid Choice Model (HCM). Whereas classical estimation of HCMs is fairly complex, we verify the feasibility of the Bayesian estimator as well as the HCM capacity to adapt to practical situations. We show that the Bayesian approach for HCMs is methodologically easier to implement than simulated maximum likelihood because the inclusion of latent variables translates into adding independent ordinary regressions; we also find that, using the Bayesian estimates, forecasting and deriving confidence intervals for willingness to pay measures is straightforward. Our empirical results coincide with a priori expectations, namely that environmentally-conscious consumers are willing to pay more for low-emission vehicles. The model outperforms standard discrete choice models because it not only incorporates pro-environmental preferences but also provides tools to build a profile of environmentally-conscious consumers.

Factors

Models