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

Abstract

Die Dis cus si on Pape rs die nen einer mög lichst schnel len Ver brei tung von neue ren For schungs arbei ten des ZEW. Die Bei trä ge lie gen in allei ni ger Ver ant wor tung der Auto ren und stel len nicht not wen di ger wei se die Mei nung des ZEW dar.Dis cus si on Papers are inten ded to make results of ZEW research prompt ly avai la ble to other eco no mists in order to encou ra ge dis cus si on and sug gesti ons for revi si ons. The aut hors are sole ly respon si ble for the con tents which do not neces sa ri ly repre sent the opi ni on of the ZEW. Non-technical SummaryIn the European Union, transport is the largest consumer of oil products and second largest emitter of carbon dioxide (CO 2 ); within the sector, road transport 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. Das Wichtigste in KürzeDer Transportsektor ist innerhalb der Europäischen Union der größte Konsument vonÖlprodukten und der zweitgrößte Emittent von Kohlenstoffdioxid (CO 2 ), wobei der Straßenverkehr in beiderlei Hinsicht dominierend ist. Die Verlagerung der Nachfrage hin zu besonders e...

Fuel for Inequality: Distributional Effects of Environmental Reforms on Private Transport

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“…For this estimation, we use data available from a choice experiment (Achtnicht, 2012 andDaziano andAchtnicht, 2013). Based on these data, we estimate the probabilities of choosing electrical and CNG vehicles by means of a conditional logit model.…”

“…For the vehicle technology choice, we use the ECOCAR data set which has been analysed by Achtnicht (2012) as well as Daziano and Achtnicht (2013). The survey was carried out between August 2007 and March 2008 considering potential car purchasers.…”

Modeling purchases of new cars: an analysis of the 2014 French market

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