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

Abstract In this article we develop, implement and apply a Markov chain Monte Carlo (MCMC) Gibbs sampler for Bayesian estimation of a hybrid choice model (HCM), using stated data on both vehicle purchase decisions and environmental concerns. Our study has two main contributions. The first is the feasibility of the Bayesian estimator we derive. Whereas classical estimation of HCMs is fairly complex, 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. The second is the capacity of HCMs to adapt to practical situations. 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. Keywords: hybrid choice modellatent variablespro-environmental preferencesdiscrete choiceGibbs sampling Acknowledgements The authors acknowledge the support of Natural Resources Canada. Special thanks to David Brownstone, Stef Proost, as well as other participants at the 2009 EAERE-FEEM-VIU European Summer School in Resources and Environmental Economics for the useful discussion on a previous version of this article. The authors also acknowledge the comments and suggestions of the anonymous reviewers as well as the help of Rolf Noyer in proof-reading the text. Additional informationNotes on contributorsRicardo A. Daziano Current address. School of Civil and Environmental Engineering, Cornell University, 220 Hollister Hall, Ithaca, NY 14853, USA. Email: daziano@cornell.edu