June 1, 2012

Forecasting Adoption of Ultra-Low-Emission Vehicles Using the GHK Simulator and Bayes Estimates of a Multinomial Probit Model


Dr. Ricardo A. Daziano, PhD Economics, Assistant Professor, School of Civil and Environmental Engineering, Cornell University


Transportation is the largest consumer of oil products and second largest emitter of carbon dioxide (CO2); within the sector, road transportation dominates in both regards. Consumer shift to ultra-low-emission vehicles has been regarded as a way to promote sustainable personal transportation. Whereas new low-emission technologies – including battery electric vehicles – have clear benefits such as efficiency gains and emission reductions, there are several barriers preventing broad adoption. On the one hand, electric vehicles are much more expensive than standard gas vehicles with a similar build. On the other hand, consumers face reliability issues, namely limited and variable driving range, and lack of refueling stations.

Using stated-preference data on vehicle choice from a Germany-wide survey of potential light-duty-vehicle buyers using computer-assisted personal interviewing, in this paper we analyze market shares of different automotive technologies produced by a discrete choice model with flexible substitution among different fuel types. Effectively, we propose a methodology to use the estimates of a probit model to produce both market-share forecasts as well as Bayesian confidence intervals for the forecasted shares. These forecasts are simulated from the posterior distribution of a Bayesian model and account for uncertainty. Having better tools to address uncertainty is particularly relevant in the context of modeling consumer response to emerging energy-efficient technologies.

We define a base scenario of vehicle attributes that aims at representing an average of the current vehicle choice situation in Germany. Consumer response to qualitative changes in the base scenario is subsequently studied. Because limited fuel availability is a major obstacle to consumer adoption of low-emission vehicles, we analyze the specific effect of increasing the density of the network of service stations for charging electric vehicles as well as for refueling hydrogen-fueled vehicles. Our results indicate that if availability of charging is increased to its maximum, electric vehicles would experience a greater than three-fold increase in market penetration.

Biographical Sketch

Daziano joined the CEE faculty at Cornell University in January 2011, after finishing his PhD in economics at Laval University. His research focuses on theoretical and applied econometrics of consumer behavior, specifically on discrete choice models applied to technological innovation and transportation demand. Daziano’s specific empirical research interests include the analysis of pro-environmental preferences toward low-emission vehicles, modeling the adoption of sustainable travel behavior, estimating willingness-to-pay for renewable energy, and forecasting consumers’ response to environmentally-friendly energy sources.