Willingness to Pay for Electric Vehicles and Attributes- Estudo de Preferencia

Resource and Energy Economics 33 2011 686705 Contents lists available at ScienceDirect Resource and Energy Economics journal homepage wwwelseviercomlocateree Willingness to pay for electric vehicles and their attributes Michael K Hidrue a George R Parsons b Willett Kempton c Meryl P Gardner d a Department of Economics University of Delaware Delaware United States b School of Marine Science and Policy and Department of Economics University of Delaware United States c College of Earth Ocean and Environment Department of Electrical and Computer Engineering University of Delaware United States d Department of Business Administration University of Delaware United States ARTICLE INFO Article history Received 17 November 2010 Received in revised form 22 February 2011 Accepted 28 February 2011 Available online 8 March 2011 JEL classification Q42 Q51 Keywords Electric vehicles Stated preference Discrete choice ABSTRACT This article presents a stated preference study of electric vehicle choice using data from a national survey We used a choice experiment wherein 3029 respondents were asked to choose between their preferred gasoline vehicle and two electric versions of that preferred vehicle We estimated a latent class random utility model and used the results to estimate the willingness to pay for five electric vehicle attributes driving range charging time fuel cost saving pollution reduction and performance Driving range fuel cost savings and charging time led in importance to respondents Individuals were willing to pay wtp from 35 to 75 for a mile of added driving range with incremental wtp per mile decreasing at higher distances They were willing to pay from 425 to 3250 per hour reduction in charging time for a 50 mile charge Respondents capitalized about 5 years of fuel saving into the purchase price of an electric vehicle We simulated our model over a range of electric vehicle configurations and found that people with the highest values for electric vehicles were willing to pay a premium above their wtp for a gasoline vehicle that ranged from 6000 to 16000 for electric vehicles with the most desirable attributes At the same time our results suggest that battery cost must drop significantly before electric vehicles will find a mass market without subsidy 2011 Elsevier BV All rights reserved This research was supported by funding from the US Department of Energy Office of Electricity DEFC2608NT01905 Corresponding author Email addresses mkesseteudeledu MK Hidrue gparsonsudeledu GR Parsons willettudeledu W Kempton gardnermudeledu MP Gardner 09287655 see front matter 2011 Elsevier BV All rights reserved doi101016jreseneeco201102002 1 Introduction Concerns about climate change and energy security along with advances in battery technology have stimulated a renewed interest in electric vehicles The Obama administration has set a goal of one million plugin vehicles on the road by 2015 and has introduced laws and policies supporting this goal These include a multibillion dollar investment in automotive battery manufacturing tax credits and loans for plugin vehicle manufacturing and purchase and research initiatives Some states have adopted their own initiatives as well Encouraged by these actions along with advances in lithiumion battery technology and recent success stories for hybrid electric vehicles automakers have begun a major push to develop plugin battery vehicles Indeed all major automakers have RD programs for electric vehicles EVs and have indicated their intentions to begin mass production within the next few years1 We are interested in the potential consumer demand for electric vehicles and whether or not they might become economic To this end we used a stated choice experiment to estimate how much consumers are willing to pay for EVs with different design features We focused on pure electric vehicles rather than plugin hybrid electric vehicles Economic analyses of EVs to date have not been favorable largely due to high battery cost short driving range long charging times and limited recharging infrastructure However recent advances in technology suggest that driving range can be extended charging time shortened and battery cost lowered Also after a few years of mass production the unit cost for EVs like most new technologies is likely to fall The time seems right for another look at the economic potential for EVs The latest round of published studies which we discuss shortly were completed around the year 2000 We carried out a nationwide survey of potential car buyers in 2009 using a webbased instrument We offered respondents hypothetical electric versions of their preferred gasoline vehicle at varying prices and with varying attributes eg driving range and charging time Then using a latent class random utility model we estimated the demand for EVs We estimated a model with two latent classes labeled here as EVoriented and GVoriented drivers where GV is for gasoline vehicle Using parameter estimates from our model we then estimated respondents willingness to pay to switch from their preferred GV to several hypothetical EVs In a final section of this paper we compare the willingness to pay estimates with the estimated incremental cost of an EV over a GV based on battery cost projections Most demand studies for EVs to date like ours have used stated preference analysis in some form The earliest studies started in response to the 1970s oil crisis Beggs et al 1981 and Calfee 1985 are probably the best known Both targeted multicar households with driving and demographic characteristics likely to favor EVs Both found low market share for EVs and range anxiety as the primary concern for consumers Both also found significant preference heterogeneity Another wave of studies started in the early 1990s in response to Californias zeroemission vehicle mandate These studies tried to predict the potential demand for EVs in California Major among these were Bunch et al 1993 Brownstone et al 1996 2000 and Brownstone and Train 1999 There were also some similar studies outside California including Tompkins et al 1998 Ewing and Sarigollu 2000 and Dagsvike et al 2002 These studies differ from the earlier ones in at least four ways First they moved from targeting multicar households to targeting the entire population Second they included a measure of emission level as a standard vehicle attribute Third the choice set typically included other vehicle technologies such as concentrated natural gas hybrid electric methanol and ethanol as alternatives for conventional gasoline vehicles Finally they employed some form of survey customization different respondents receiving different choice options to increase the relevance of the choice task A common finding in these studies was that EVs have low likelihood of penetrating the market Limited driving range long charging time and high purchase price were identified as the main concerns for consumers They also found that people were willing to pay a significant amount to reduce emission and save on gas see Bunch et al 1993 Tompkins et al 1998 Ewing and Sarigollu 2000 Table 1 summarizes these past EV studies 1 Interest in electric vehicles is not new In 1900 nearly 40 of all cars were electric Thomas Edison experimented with electric vehicles and there was a notable surge in interest during the oil crisis in the 1970s For an interesting historical account of electric vehicles see Anderson and Anderson 2005 MK Hidrue et al Resource and Energy Economics 33 2011 686705 687 Our analysis builds on this body of work and contributes to the literature by using more recent data using a method that focuses respondents on EV attributes we offer respondents EV equivalents of their preferred GV to control for extraneous features estimating a latent class model and comparing willingness to pay wtp to incremental EV cost based on battery cost projections 2 Survey sampling and study design We used an internetbased survey developed between September 2008 and October 2009 During this period we designed and pretested the survey and made multiple improvements and adjustments based on three focus groups three pilot pretests and suggestions from presentations of our study design at two academic workshops2 The final version of the survey had four parts i background questions on car ownership and driving habits ii description of conventional EVs followed by two choice questions iii description of vehicletogrid EVs followed by two more choice questions and iv a series of attitudinal and demographic questions The survey included a brief cheap talk script intended to encourage realistic responses3 It also included debriefing questions to get respondents feedback regarding the relevance of each attribute in their choice and to ascertain the clarity and neutrality of the information provided on the survey The survey wording and questions were probably also improved due to some coauthors work with our EV policy and technology group that has been driving EVs and explaining EV Table 1 Summary of past EV studies Study Econometric model Number of choice sets attributes and levels List of attributes used Beggs et al 1981 Ranked logit 16 8 NA Price fuel cost range top speed number of seats warranty acceleration air conditioning Calfee 1985 Disaggregate MNL 30 5 NA Price operating cost range top speed number of seats Bunch et al 1993 MNL and Nested logit 5 7 4 Price fuel cost range acceleration fuel availability emission reduction dedicated versus multifuel capability Brownstone and Train 1999 Brownstone et al 2000 MNL and Mixed logit Joint SPRP Mixed logit 2 13 4 The two papers used the same datastudy Hence the list in the attribute column and the number of choice sets attributes and levels column are the same for both Price range home refueling time home refueling cost service station refueling time service station refueling cost service station availability acceleration top speed tailpipe emission vehicle size body type luggage space Ewing and Sarigollu 1998 2000 MNL 9 7 3 Price fuel cost repair and maintenance cost commuting time acceleration range charging time Dagsvike et al 2002 Ranked logit 15 4 NA Price fuel cost range top speed NA not available 2 Paper presentation at the Academy of Marketing Sciences Annual Workshop Marketing for a Better World May 2023 2009 and poster presentation at the Association of Environmental and Resource Economists Workshop Energy and the Environment June 1820 2009 3 The following script proceeded our choice questions Please treat each choice as though it were an actual purchase with real dollars on the line MK Hidrue et al Resource and Energy Economics 33 2011 686705 688 characteristics at demonstrations and conferences for the prior three years The vehicletogrid EV choice data from part iii are not analyzed in this paper4 The first stage of the survey covered the respondents current driving habits vehicle ownership and details on the vehicle they are most likely to purchase next The latter included the expected size type price and timing of purchase Next was a descriptive text on the similarities and differences of EVs and GVs Then respondents were asked two choice questions in a conjoint format A sample question is shown in Fig 1 In each of the two choice questions respondents were asked to consider three vehicles two EVs and one GV The GV was their preferred gasoline vehicle and was based on TDFIG Fig 1 Sample EV choice set in questionairre 4 VehicletoGrid V2G electric vehicles allow owners to sell their battery capacity to electric grid operators during times the vehicle is not being driven and thus have the potential of making EVs more economical Kempton and Tomic 2005 In the V2G choice questions we analyzed different V2G contract terms to establish their feasibility These data will be analyzed in a second paper MK Hidrue et al Resource and Energy Economics 33 2011 686705 689 the response they gave to a previous question on the type of vehicle they were most likely to purchase next it could be gasoline or a hybrid like a Toyota Prius The preferred GV and the amount of money the respondent planned to spend was mentioned in the preamble to the question reminding the respondent what he or she had reported previously Because the survey was webbased the text of questions could include values from or be adjusted based on prior answers In each threeway choice we treated the GV as the optout alternative The two EVs were described as electric versions of their preferred GV Respondents were told that other than the characteristics listed the EVs were identical to their preferred GV This allowed us in principle to control for all other design features of the vehicle interior and exterior amenities size look safety reliability and so forth This enabled us to focus on a key set of attributes of interest without the choice question becoming too complex The attributes and their levels are shown in Table 25 Most of the attributes are selfexplanatory and capture what we expected would matter to car buyers in comparing EVs and GVs driving range charging time fuel saving pollution reduction performance and price difference Price was defined as the amount the respondent would pay above the price of the respondents preferred GV This puts the focus on the tradeoff between the extra dollars being spent on an EV and the attributes one would receive in exchange Charging time was defined as the time needed to charge the battery for 50miles The average vehicle is driven less than 40milesday so this is a little more than a typical daily charging time to recharge or enough to extend a trip 50miles The electric refuel cost was defined in gasequivalent terms eg like 150 per gallon Table 2 Attributes and levels used in the choice experiment Attributes Levels Price relative to your preferred GV Same 1000 higher 2000 higher 3000 higher 4000 higher 8000 higher 16000 higher 24000 higher Driving range on full battery 75miles 150miles 200miles 300miles Time it takes to charge battery for 50miles of driving range 10min 1h 5h 10h Acceleration relative to your preferred GV 20 slower 5 slower 5 faster 20 faster Pollution relative to your preferred GV 95 lower 75 lower 50 lower 25 lower Fuel cost Like 050gal gas Like 100gal gas Like 150gal gas Like 200gal gas 5 A drawback of this strategy is that we miss substitution across vehicle types such as buying a new smaller EV instead of a new larger GV People may employ this type of substitution to lower the purchase price for an EV MK Hidrue et al Resource and Energy Economics 33 2011 686705 690 gas This pretested far better than the other measures we considered and was independent of miles driven by the respondent6 Pollution reduction was included as an indicator of the desire to buy more environmentally beneficial goods Finally acceleration was included as a proxy for performance differences between EVs and GVs We used SASs choice macro function Kuhfeld 2005 to generate the choice sets Given an a priori parameter vector b the algorithm for this macro searches for a design that minimizes the variance of the estimated parameters We used data from our last pretest to estimate the a priori parameters7 A total of 243 respondents participated in the pretest each answering two choice questions This gave us 486 observations that we used to estimate a simple multinomial logit model The parameter estimates from this model were then used as the a priori parameters in developing the final choice design The final design had 48 choice sets in 24 blocks and a Defficiency of 48 The blocks were randomly assigned to respondents during the survey The response options for our choice experiment include a yeasay correction shown as the last response at the bottom of Fig 1 We were concerned that respondents might choose an electric option to register their support for the concept of EVs even though they would not actually purchase an EV at the cost and configuration offered The yeasay option allowed people to say I like the idea of EVs registering favor with concept but not at these prices showing their real likelihood of purchase We conducted a treatment on this variable to see if it would indeed have any effect About onethird of the sample had the yeasay correction response included Table 3 shows the breakdown by responses to all our choice experiment questions There is a nice distribution across the response categories suggesting that our levels were offered over reasonable ranges about a 5050 split between EV and GV Also there appears to be very little yeasaying That is even with the additional response option the selection of EVs dropped by only 2 Our sample was selected to be representative of US residents over 17 years of age A qualifying question asked if they intended to spend more than 10000 the next time they purchase a vehicle We used the 10000 cutoff because we felt few people who planned to spend less than this would be in the nearterm market for EVs The number of completed surveys was 3029 The survey was administered by Survey Sampling International SSI and was collected so as to mimic the general population along the lines of income age education and population by region8 The computerbased questionnaire delivery allowed us to design our survey with skip patterns and questions tailored to respondentspecific data such as car type planned for next purchase Table 4 compares our sample to the national census Since we had SSI mimic the census we have nearly the same age distribution income distribution and population size by region as the census Our sample is also close to national Table 3 Distribution of choices among alternatives Alternatives Without yeasaying correction N1996 With yeasaying correction N1033 Electric vehicle1 235 233 Electric vehicle2 271 250 My preferred gasoline vehicle 494 236 My preferred gasoline vehicle although I like the idea of electric vehicles and some of the features here are ok I couldwould not buy these electric vehicles at these prices 281 Total 100 100 6 We also considered defining fuel savings as cost to fully charge the battery absolute fuel savings in dollars per year for EV versus GV or fuel cost savings per mile driven 7 We used a linear design to develop the choice sets for the pretest 8 Because of the way SSI administers the survey response rate calculations are not possible SSI dispatches the survey to its panel until the agreed number of completed surveys is obtained Since we do not know whether those who have not completed the survey at the time it was terminated are nonresponders or late responders calculating response rate is not meaningful MK Hidrue et al Resource and Energy Economics 33 2011 686705 691 statistics in number of vehicles per household and type of residence variables important to EV choice Our sample somewhat underrepresents men and less educated persons The latter is no doubt due to our prescreening exclusion of respondents purchasing cars less than 10000 Descriptive statistics for the variables used in our model are shown in Table 5 3 A latent class random utility model We estimated a latent class random utility model using the choice data described above see Swait 19949 The model allows us to group respondents into different preference classes based on individual characteristics and attitudinal responses It is easiest to discuss the model in two parts the choice model and then the class membership model The random utility portion is a discrete choice model in which respondents choose one of the three vehicles offered in our choice experiment two electric and one gasoline See the questions shown in Fig 1 Using each persons preferred GV as the optout alternative and letting the EV depend on the vehicle characteristics in our experiment gives the following random utilities for a given person on each choice occasion Table 4 Comparing sample and census data Variable Sample Census Male 430 487 Age distribution 1824 120 129 2544 394 363 4564 347 339 6584 138 144 85 or above 017 25 Educational achievement High school incomplete 20 157 High school complete 392 300 Some college 217 293 BA or higher 367 250 Household income distribution Less than 10000 4 72 1000014999 33 55 1500024999 102 106 2500034999 13 106 3500049999 191 142 5000074999 225 188 7500099999 135 125 100000149999 103 122 150000199999 19 43 200000 or more 15 42 Type of residence House 728 692 Apartmentcondo 208 246 Mobile or other housing type 64 62 Number of vehicles in a household No vehicle 42 88 1 vehicle 34 334 2 vehicles 403 378 3 or more vehicles 215 200 Census Data Source US Census Bureau 2008 American Community Survey 9 We compared mixed logit and latent class models which is actually a mixed logit variant on the basis of estimated parameters nonnested test statistics and within sample prediction The latent class model provided better fit than the mixed logit model MK Hidrue et al Resource and Energy Economics 33 2011 686705 692 Ui ¼ b pD pi þ bxxi þ ei U0 ¼ e0 1 where i1 2 for the two EVs and i0 for the GV The vector xi includes all of the attributes used in the choice experiment driving range charging time pollution reduction performance and fuel cost saving Dpi is the price difference for the EV versus the GV Under the usual assumption of independent and identically distributed iid extreme value errors in 1 we have the following logit probability for vehicle choice for any given person LðbÞ ¼ d1expðbPD p1 þ bxx1Þ I þ d2expðbPD p2 þ bxx2Þ I þ d0 I 2 where d11 if the respondent chooses EV 1 d21 if the respondent chooses EV 2 d01 if the respondent chooses GV I ¼ 1 þ P2 i¼1 expðbPD pi þ bxxiÞ and bbp bx The latent class portion of the model allows for preference heterogeneity across the population The model assumes there are C preference groups classes where the number of groups is unknown Each group has its own set of random utilities with its own parameters bc in Eq 1 Class membership for each person is unknown The model assumes each person has some positive probability of membership in each preference group and assigns people probabilistically to each group as a function Table 5 Definition and descriptive statistics N3029 for variables used in LC model Either or mean is shown depending on whether the variable is dichotomous or not Variable Description in sample Mean SD Young 1 if 1835 years of age 0 otherwise 30 Middle age 1 if 3655 years of age 0 otherwise 43 Old 1 if 56 years of age or above 0 otherwise 27 Male 1 if male 0 otherwise 43 College 1 if completed a BA or higher degree 0 otherwise 37 Income Household income 2009 60357 42398 Car price Expected amount spent on next vehicle 23365 9607 Expected gasoline price Expected price of regular gasoline in 5 years nominal dollars 44 17 Multicar 1 if household owns 2 or more cars 0 otherwise 62 Hybrid 1 if household plans to buy a hybrid on next car purchase 0 otherwise 33 Outlet 1 if the respondent is very likely or somewhat likely to have a place to install an outlet charger at their home at the time of next vehicle purchase 0 otherwise 77 New goods 1 if respondent has a tendency to buy new products that come on the market 0 otherwise 57 Long drive 1 if respondent expects to drive more than 100milesday at least one day a month 0 otherwise 70 Small car 1 if respondent plans to buy small passenger car on next purchase 0 otherwise 17 Medium car 1 if respondent plans to buy medium or large passenger car on next purchase 0 otherwise 41 Large car 1 if respondent plans to buy an SUV pickuptruck or Van on next purchase 0 otherwise 42 Major green 1 if respondent reported making major change in life style and shopping habits in the past 5 years to help the environment 0 otherwise 23 Minor green 1 if respondent reported making minor change in life style and shopping habits in the past 5years to help the environment 0 otherwise 60 Not green 1 if respondent reported no change in life style and shopping habits in the past 5years to help the environment 0 otherwise 17 MK Hidrue et al Resource and Energy Economics 33 2011 686705 693 of individual characteristics The number of groups is determined statistically The probability of observing a respondent select a vehicle in our latent class model is Sαβ c1C expαcz c1C expαcz Lβc 3 where z vector of individual characteristics C is the number of latent classes ββ1 βc αα1 αc and one αc vector is arbitrarily set of zero for normalization The term expαczc1C expαcz is the probability of membership in class c Lβc is the logit probability from Eq 2 now defined for class c There are C sets of βc and C1 sets of αc Only C1 sets of the latent class parameters are identified The classes are said to be latent because respondents are not actually observed being the member of any given preference group In our interpretation of the model each person has a weighted class membership The weights are by class and are predicted by the model The parameters are estimated using maximum likelihood and the number of preference groups is determined using a Bayesian Information Criterion BIC Eq 3 is an entry in the likelihood function for each choice by each person The latent class LC model then captures preference heterogeneity by allowing different preference orderings over the vehicles with some classes having greater propensity for buying electric than others Shonkwiler and Shaw 2003 and Swait 2007 show that the LC model is not constrained by the iia property of the MNL model However as pointed out by Greene and Hensher 2003 the LC model assumes independence of multiple choices made by the same individual 2000 p 289 We computed two information criteria Bayesian and Akaki for each latent class model10 The Bayesian criterion selects a twoclass model while the Akaki criterion selects a fourclass model We decided to use the twoclass model The two preference classes had a clear interpretation one class was more likely to select EVs and the other more likely to stay with GVs We labeled our classes accordingly as EVoriented and GVoriented The number of preference classes identified in our study empirically confirms earlier suggestions made by Santini and Vyas 2005 Building on the intuition of diffusion models Santini and Vyas 2005 suggested using two sets of coefficients for predicting the adoption of alternative fuel vehicles What they refer to as an early group a group that includes early adopters and early buyers corresponds to our EV class However as can be seen from Table 6 our EV class also includes a much broader range of variables and probably runs deeper than just early adopters The parameter estimates and odds ratios for the class membership model are shown in Table 6 The parameters for the GVoriented class are normalized to zero so the estimated parameters refer to the EVoriented class They represent the impact of an attribute on the probability of being EVoriented For example the positive and significant parameter for young indicates younger respondents 1835 are more likely to be EVoriented than older respondents 56 and above The EVoriented weights probability of being in the EVoriented class ranged from as low as 6 to as high as 94 with a sample mean of 54 Table 6 shows that the following variables increase a respondents EVorientation with statistical significance Being younger or middle age Having a BA or higher degree Expecting higher gasoline prices in the next 5 years Having made a shopping or life style change to help the environment in the last 5 years Likely to buy a hybrid gasoline vehicle on their next purchase Having a place they could install an EV electrical outlet at home Likely to buy a small or mediumsized passenger car on next purchase Having a tendency to buy new products that come on to the market Taking at least one drive per month longer than 100 miles The first eight were expected The ninth taking one or more frequent long drives a month is counterintuitive We expected that people making more long drives would be less inclined to buy an EV due to limited driving range and slow refueling This result which we also saw in some of our pretests may come from an interest in saving fuel People traveling longer distances pay more for fuel and stand to save more from EVs The odds ratios shown in Table 6 give the relative odds of a person being in one class versus the other for a given attribute For example the odds ratio of 13 for a middleaged driver indicates that a person between 35 and 56 is 13 times more likely to be EVoriented than a person over 56 The largest odds ratios are 33 for having a place for an electric outlet where they park 29 for people who have recently made a major change in their life style to help the environment and 23 for being a likely purchaser of a hybrid gasoline vehicle The finding on hybrids suggests that EVs will compete with hybrids more than with conventional gasoline vehicles Contrary to expectations income and being a multicar household both reduced the likelihood of being in the EV class rather than increasing it although without statistical significance Analysts have assumed that multicar households are more amenable to EVs than single car households In fact the early EV market studies sampled only multicar households Beggs et al 1981 Calfee 1985 Kurani et al 1996 The logic for this stems from the fact that EVs have limited driving range and multicar households would not be constrained by this since they have a reserve car Our data provide no evidence to support this assumption Ewing and Sarigollu 1998 had a similar result 10 Following Swait 2007 these measures are defined as AIC 2LLβ K and BIC 2LLβ K logN where LLβ is log likelihood value at convergence K is the total number of parameters estimated and N is number of observations The class size that minimizes the BIC and AIC is the preferred class size Finally we tested for regional differences in preference for EVs We divided the United States into 10 regions California and Florida were each treated as their own region When we included only regional dummies in our latent class model California Florida and the Northeastern United States were most EVoriented the Western and Midwestern states most GVoriented However when the covariates shown in Table 6 are included in the model the regional differences largely vanish suggesting that it is the characteristics of people not where they are from that predicts class membership The regional results are not shown in our tables 42 Random utility model The vehicle attributes Dpi and xi used in the random utility portion of our model are shown in Table 2 The model is shown in Table 7 along with a multinomial logit version for comparison We assume price and fuel cost have a linear effect All other attributes are specified as categorical variables based on Wald and likelihood tests that showed nonlinear versions give a better fit For Table 7 the category exclusions or reference levels required for identification are the least favorable level in each case We also tested for potential interaction of vehicle attributes with several demographic variables Of those tested only the interaction between price of EV and the price for the respondents next vehicle was found to be significant This is the only interaction we included in the model11 Most of the parameters have expected signs Also the relative size of the parameters for the attributes specified as stepwise dummy variables perform as expected For example the coefficient estimates show a preference ordering for range that increases consistently with more miles This basic stepwise consistency holds for all attributes across the two classes Finally the coefficient on price is statistically significant and negative in all instances Vehicle price is clearly an important predictor of EV choice as one would expect The LC model has a higher likelihood than the MNL model and when tested is statistically preferred The LC model is also preferable to the MNL model because there is considerable heterogeneity in the data Also several of the parameters that are significant in the MNL model are only significant for one class in the LC model In a few cases the differences in the parameters across the two classes are sizable and significant A good example of this is fuel saving It is significant in the MNL model but significant only in the EVoriented class of the LC model The last three columns of Table 7 are implicit values for the attributes These values are computed by simply dividing the attribute coefficient estimate by the coefficient estimate of price within each class12 The third of these three columns is a probability weighted average for the two classes The coefficient estimate on the EV dummy variable a key variable defining our two classes indicates a wide separation in willingness to pay for EVs The value represents the premium a respondent would pay or compensation a respondent would ask for to switch from a GV to an EV version of hisher preferred vehicle with base level attributes ignoring any adjustment for fuel cost continuous variable in the model The EVoriented class would pay a premium of 2357 while the GVoriented class would ask for compensation of 22006 The weighted average is compensation of 7060 This is sensible given that the baselevel EV attributes were the least desirable 75miles range 10h to charge etc The compensation or premiums for differing EV types including adjustments for fuel cost are presented in the next section Another difference between the two classes is in the value of fuel saving The EVoriented is more fuel conscious than the GVoriented The EVoriented portion has a willingness to pay of 4853 for each 100gallon reduction in fuel cost equivalent The GVoriented portion has a willingness to pay of only 499 per 100gallon cost reduction a value based on a parameter that is not statistically different from zero This finding makes sense Respondents showing a greater interest in EV put more weight on fuel economy This is also consistent with our class membership model where the EV oriented expect higher gas prices and hence greater concern for fuel saving The weighted average 11 Among the interactions tested were range and annual miles driven range and multicar household range and driving more than 100miles a day fuel cost and annual miles driven fuel cost and expected gas price pollution and changes in life style 12 Since we include an interaction of price difference times expected vehicle purchase price we actually divide by an amount adjusted for expected price The results shown in the table are means for our sample MK Hidrue et al Resource and Energy Economics 33 2011 686705 696 Table 7 Random utility model and wtp estimates Tstat in parenthesis Parameters Implicit attribute valuesa MNL model Latent class model Latent class model GVoriented class EVoriented class GVoriented class EVoriented class Weighted average EV constant 25 123 746 49 054 43 22006 2357 7060 Yea saying tendency 028 45 025 11 037 46 Price relative to preferred GV 000 009 122 0339 30 0102 180 Price relative to GV x car price 000000 00007 27 00021 062 00012 56 Fuel cost gall 021 50 0169 072 035 98 499b 4853 2706 Driving range on full battery excluded category is 75 miles 150 miles 049 68 132 18 053 90 3894b 7349 5646 200 miles 077 113 194 27 092 159 5723 12757 9289 300 miles 100 136 26 37 128 192 7670 17748 12779 Charging time for 50 miles of driving range excluded category is 10 hours 5 h 019 28 16 29 007 13 4720 971b 2136 1 h 048 76 20 40 055 101 5900 7626 5858 10 min 067 107 22 42 080 149 6490 11093 8567 Pollution relative to preferred GV excluded category is 25 lower 50 lower 007 11 075 16 012 19 2212b 1664b 1935 75 lower 010 16 090 25 019 32 2655 2635 2645 95 lower 035 52 12 31 037 62 3540 5130 4346 Acceleration relative to preferred GV excluded category is 20 slower 5 slower 015 24 11 14 015 28 3245b 2080 2655 5 faster 036 52 197 24 033 53 5811 4576 5186 20 faster 055 80 22 25 059 96 6490 8181 7348 Log likelihood value 5356 4929 Sample size 6032 6058 a Yeasay correction turned on in all cases b Based on a statistically insignificant parameter at the 5 level of confidence value across the two classes is 2706 The average respondent appears to be capitalizing about 5 or 6 years of fuel savings into their vehicle purchase Assuming that a car is driven about 12000 milesyear at the US car average of 24 milesgallon each 100gallon reduction in cost is worth about 500 of fuel savings per year13 13 During our survey the retail price of regular gasoline was about 280 per gallon and electricity was at about 100 per gallon 625 kWh085 x 13kWh Assuming 4 kWh per mile for an electric sedan and 85 efficiency to fill up fuel savings would be about 900 per year for buying electric versus gasoline Considering the weighted results for the other EV attributes in Table 7 the driving range increments have the highest value followed by charging time performance and pollution reduction These are all relative to the baseline attribute values indicated in the table To the weighted average respondent increasing range from 75 to 150 miles is worth over 5600 Increasing it from 75 to 200 is worth over 9200 and from 75 to 300 miles over 12700 Note that the values increase at a decreasing rate The permile incremental values are 75mile 75150 mile range 73mile 150200 mile range and 35mile 200300 mile range For charging time on average respondents valued the initial improvement a reduction from 10 to 5 h at more than 2000 Going from 10 h to 1 h is worth nearly 6000 and going from 10 h to 10 min is worth about 8500 The perhour incremental values are 427h 105 h range 930h 5 1 h range and 3250h 1 h10 min range Improving vehicle performance from 20 slower to 5 slower than a persons preferred GV is worth about 2600 using the weighted values Increasing from 20 slower to 5 faster and to 20 faster are worth about 5100 and 7300 Better performance defined here as faster acceleration noticeably increases the value of an EV Finally pollution reduction has the lowest values of the attributes included With a 25 reduction over their preferred GV as a baseline and using the weighted values people valued a 50 pollution reduction at about 1900 a 75 reduction at about 2600 and a 95 reduction at over 4300 The incremental values for going to 50 are not statistically significant The EVoriented class has higher value for moving to 95 lower while the GVoriented has higher value for moving to 50 lower Both classes have similar value for moving to 75 lower 5 Willingness to pay for different EV configurations In this section we calculate respondents willingness to pay wtp for several combinations of electric car attributes more precisely for several differing electric versions of their preferred gasoline vehicle We then compare wtp with a simple projection of the added cost of producing electric versus gasoline vehicles Since future costs and EV configurations are imprecise projections from current costs trends and technology opportunities we will present a range of estimates We will also present a test of the model that estimates the wtp for an EV with attributes equivalent to the attributes of a GV We use these results to calibrate our estimates A persons wtp for an EV conditioned on being in class c is the amount of money that makes the person indifferent between an EV of a given configuration and a GV In our model that is the value of Δw that solves the following equation within a given class βp Δw βx xi εi ε0 or Δ w βx xi ε0 εi βp Since no person belongs entirely to one or the other class in our model and is instead part EVoriented and part GVoriented we use the following weighted average in our calculation for each respondent Δwweighted pev Δwev 1 pev Δwgv where pev is probability of being in the EVoriented class Boxall and Adamowicz 2002 and Wallmo and Edwards 2008 use this formulation Again in our model estimates for the probability of being EVoriented pev range from 6 to 94 We begin with the test of our model We constructed an EV that more or less mimics a contemporary GV Driving range is 300 miles charging time is 10 min pollution removal is 0 changed performance acceleration is the same and fuel cost is 280gal Fuel cost and pollution are the only attributes outside the range of our data in this simulation and neither is far outside the range In our survey the closest to 0 change in pollution offered was 25 reduction and the highest EV fuel cost offered was 200 We used a simple linear projection for these attributes to extrapolate to 0 change and 280gal We simulated the model only over the sample of respondents expecting gas prices to be in the range of 2 to 4 over the next 5 years If our model is a good predictor of the total value of an EV one would expect the wtp for this EV to be near zero at least for the median person That is if people bought EVs based only on their attributes buyers would be indifferent between an EV and GV with nearly equivalent attributes14 We have to be careful There will be some people who are willing to pay more and some less for an EV with nearly equivalent attributes to their preferred GV For example we included a set of questions leading up the choice experiment that asked people to indicate which attributes might matter to them in making an EV purchase The purpose was to get people thinking about the attributes of EVs before making a choice While being far from a commitment the results suggest what might drive preferences and what might lead to wtp for EVs diverging from wtp for like GVs For example 64 of the respondents indicated that lower dependence on foreign oil mattered a lot 47 reported that avoiding trips to the gas station mattered a lot and 30 reported that interesting new technology mattered a lot For these fractions of the sample at least this suggests wtps for EVs would be above a like GV Of course saying that certain attributes matter and actually being willing to pay for them can be quite different Also there is an obvious freerider problem with lower dependence on foreign oil If everyone else buys EV I can enjoy the security without having to pay myself If everyone behaves as such EV purchases for the purpose of lowering oil dependence would be limited even if many consider it important There will also be respondents who require compensation for an EV equivalent to their preferred GV There is the simple inertia of staying with what you know and some may not trust a new technology Approximately 33 of the sample said unfamiliar technology mattered a lot in thinking about buying an EV When we simulate the model for the test EV we find a median wtp of 3023 over a GV That is over half of the respondents are willing to pay more than 3000 extra for an EV As mentioned above this could be due to a desire to purchase an EV beyond its specific attributes due to conspicuous conservation or due to some lingering SP bias in our data To be on the conservative side we treated this as SP hypothetical bias and recalibrated our model to generate a wtp median value of zero for an EV with attributes comparable to a GV This amounted to adjusting the alternative specific constant on the two EVs in our model until the median wtp for the test vehicle is zero This more or less follows an approach suggested by Train 2009 pp 6667 in a somewhat different context and gives us a model with half of the sample willing to pay more for an EV equivalent to a GV and half willing to pay less The spread using the calibrated model for the middle 50 of the population from the 25th to the 75th percentile is 1816 to 3178 with a median value of 0 This model preserves the trade off among attributes in our model discussed in the previous section We considered six hypothetical EV configurations in our wtp estimation All configurations are within the range of our data Table 8 shows the assumed levels for each configuration where A is the least desirable and F is the most desirable Table 9 shows the wtp estimates for each While our six EV configurations are not real vehicles actual vehicles are likely to fall in our range of attribute combinations A through F For comparison Table 10 describes attributes of electric vehicles that are on sale available in prototype or announced for production and categorizes them as being closest to one of our six hypothetical EV configurations Fig 2 is a boxandwhisker plot of our calibrated wtp for the six configurations over our sample of respondents The bundles of EV attributes become more desirable as we move from left to right in the graph Thus the share of drivers willing to pay a premium increases as the attributes of the EV improve The median wtp for our six configurations using the calibrated model ranges from 12395 to 9625 For configuration B 75 mi5 h50 lower pollution5 slower1 gal the median wtp from the calibrated model is 8243 and the maximum over the sample is 4762 For configuration E 200 mi1 h50 lower pollution20 faster1 gal the median wtp is 6234 and maximum is 12820 So our wtp estimates as one would expect from the parameters estimated in our model are quite sensitive to the vehicles configuration of attributes Fuel economy and performance play a critical role in these wtp estimates not just whether the vehicle is an EV Consider configuration E Driving range 200 miles is worse than most GVs and charging time 1 hour for 50 miles is much longer than a 14 If this is not the case despite our efforts to purge the data of SP bias respondents giving values that diverge from their true values because there is no actual commitment to purchase some may remain Table 8 Attribute levels used to compose six hypothetical EV configurations EV scenario Range mi Charging time for 50 mi Pollution lower Acceleration Fuel cost Like gallon A 75 10 h 25 5 slower 1 B 75 5 h 50 5 slower 1 C 100 5 h 50 Same 1 D 150 1 h 50 5 faster 1 E 200 1 h 50 20 faster 1 F 300 1 h 75 20 faster 1 Table 9 Calibrated wtp for six hypothetical EV configurations 2009 dollars EV scenario Min Q1 Median Q3 Max A 19224 14695 12395 10241 6919 B 12597 9709 8243 6874 4762 C 9971 7075 5606 4234 2117 D 4714 523 1604 3598 6671 E 1974 3467 6234 8823 12820 F 526 6556 9625 12497 16930 gasoline fill up The other attributes fuel economy performance and pollution reduction are better than a GV When we estimate wtp for configuration E using 280gal gasoline equivalent so there is no fuel saving over a conventional gasoline vehicle the median wtp in the calibrated model falls from 6234 to 2439 When we change performance to the same level of a gasoline vehicle fuel economy reset to 100gal the median wtp is 3419 And when fuel economy and performance are both set to levels comparable to a gasoline vehicle wtp is 375 Fuel economy and performance are clearly important drivers of overall vehicle wtp Now we consider the added production cost of an electric versus gasoline vehicle and compare it to our wtp estimates for our six configurations Our intention here is not to conduct a rigorous cost analysis rather it is to make a rough approximation for comparative purposes As an approximation we consider only the incremental cost of the battery This is because the electric motor drive electronics and charger are a little less expensive than the gasoline engine fuel and exhaust systems Thus to a first approximation the cost differential between GV and EV is primarily the cost of the battery The Department of Energys current cost estimates for its near term automotive battery goals are 1000kWh DOE stated current cost 500kWh DOE goal for 2012 300kWh DOE goal for 2014 The second and third are goals established by the DOE as part of their Energy Storage RD program Howell 2009 A recent interim technical assessment report by EPA Department of Transportation and California Air Board 2010 has similar per kWh cost projections for 2012 and 2015 Several industry sources also indicate that the above DOE goals and rate of change are approximately correct as does an analysis of new EV offerings We assume an EV fuel efficiency of 1 kWh for 4 miles of driving eg 250 Whmile The Nissan Leaf for example has a 24 kWh battery size and an advertised driving range of 100 miles This translates to 4 mileskWh The Tesla Roadster has a 56 kWh battery and a driving range of around 220 miles and For example Tesla Automotive currently sells their 56 kWh battery pack for 36000 or 642kWh The Nissan Leaf with a 24kWh battery has a retail price of 32000 if we say this is 18000 above a comparable gasoline car and the increment is attributed to the battery pack it represents 1800024 kWh or 750kWh for a 2010 model wwwnissanusacom Table 10 Battery size driving range charging time and price of some current EVs Vehicle Battery Range mi Charging time empty to full battery Charging time for 50 miles Expected date of release Closest vehicle configuration for Table 9 Estimate of current base price BMW Mini E 35 kWh lithium ion 156 mi 3 h at 240 V48 A 58 min Limited trial since 2009 D 850mo lease incl insurance Coda Sedan 34 kWh 90120 mi 6 h at 240 V 2535 h Launch slated for late 2011 C 40000 Ford Focus EV 23 kWh lithium ion 75 mi 68 h at 230 V 45 h B 35000 AC Propulsion eBox 35 kWh 120 mi 2 h at 240 V 50 min On sale since 2007 by custom order D NA Mitsubishi iMiEV 16 kWh 80 mi 7 h at 220 V 45 h On sale in Japan B 47000 Nissan LEAF 24 kWh 100 mi city driving 8 h at 220 V 4 h On sale since December 2010 C 33000 Smart Fortwo ED 165 kWh lithium ion 85 mi 8 hrs at 230 V 4 h On sale in EU A 19000 Tesla Model S 42 kWh standard 160 mi base model 35 h at 220 V70 A 80 charge in 45 min at 440 V 115 h Deliveries scheduled to begin in 2012 D 57000 Tesla Roadster 56 kWh lithium cobalt 220 mi combined cityHY 35 h 50 min On sale since 2009 EF 109000 Think City 245 kWh lithium ion batteries 112 mi for the US market 8 h at 110 V 35 h On sale in EU initial deliveries to US December 2010 B 38000 Volvo Electric C30 24 kWh 932 mi 8 h at 230 V 16 A 45 h 1000 vehicle consumer test in Fall 2011 B NA Source Josie Garthwaite 2010 Battle of the Batteries Comparing Electric Car Range Charge Times on Gigacom posted June 8 2010 httpearth2techcom20100608battleofthebatteriescomparingelectriccarrangechargetimes corrected and augmented from our own testing calculations and communications with EV industry a When data were available time required for a midstate of charge 50 miles is used when not available full charge time is proportionally reduced to 50 miles Fast charge with DC equipment is not included as this infrastructure is not yet available this translates to 39mileskWh These checks show 4mileskWh is reasonable for sedansized vehicles The three solid lines in Fig 3 show the incremental cost per vehicle for each configuration using the three DOE battery cost estimates Incremental costs range from 75000 for a driving range of 300miles at current battery costs to 5625 for a range of 75miles if battery costs drop to 300kWh The two dashed lines are our estimated wtp for each configuration for the noncalibrated and TDFIG Fig 2 Boxwhisker plot of calibrated wtp for the six vehicle configuarations AF shown in Table 8 MK Hidrue et al Resource and Energy Economics 33 2011 686705 702 calibrated versions of our model The lines are for the person in our sample with the maximum wtp see Fig 2 for the full range of wtp below this line The plots show a wide disparity between current battery costs and wtp Current costs as stated by DOE are in every instance above maximum wtp However at the DOE projected cost of 300kWh the gap closes considerably and in some instances falls below the uncalibrated wtp suggesting EVs might be economic at lower costs To get a sense of where the market is today see the rightmost column of Table 10 TDFIG Fig 3 Maximum wtp values dotted lines and estimated incremental vehicle costs solid lines for the six vehicle configurations MK Hidrue et al Resource and Energy Economics 33 2011 686705 703 There are a number of factors that could alter the position of either the cost or wtp lines in Fig 2 First there is the roughness of our cost estimates as discussed above Second our cost projections ignore technological developments for other aspects of EV production and the potential for savings through mass production of EVs and components Third we are assuming the cost of electricity stays at a level that keeps EV fuel costs at a 100gallon equivalent Fourth we are not analyzing issues related to the life and disposal of the battery Fifth gasoline prices may rise or fall in a way unanticipated by our respondents Sixth if EVs make inroads in the market infrastructure for charging at work shopping centers and so forth are likely to be more accessible Although we asked respondents to assume such infrastructure existed it is not obvious that they did Seventh there is the prospect of vehicletogrid EVs producing revenue for drivers Kempton and Tomic 2005 making EVs more attractive to buyers Eighth the makers of GVs and other alternative fuel vehicles will not be dormant they may introduce very small more fuelefficient vehicles to reduce the gap in costper mile Finally it is interesting to note that current US energy policy subsidizes the purchase of EVs with a tax credit of up to 7500vehicle depending in part on battery size A few states supplement this subsidy California for example adds 3000 for a total of 10500 Our analysis suggests that 7500 is sufficient to close the gap between wtp and vehicle cost for the DOEprojected 300kWh case in Fig 316 That subsidy appears to be sufficient to stimulate market activity given current and near future US costs of gasoline electricity and EV batteries Without the subsidy our wtp analysis suggests that nearterm purchase of EVs in the US would likely be limited 6 Conclusions Our analysis adds new insights into the demand for electric vehicles and confirms some earlier findings We found that a persons propensity to buy an electric vehicle increases with youth education green life style believing gas prices will rise significantly in the future and living in a place where a plug is easily accessible at home It also increases if a person has a tendency to buy a small or medium sized vehicle andor is likely to be in the market for a hybrid vehicle for their next car purchase Surprisingly income and owning multiple cars were not important We also found that people were driven more by expected fuel savings than by a desire to be green or help the environment A reduction of one dollar per gallon of gas was worth about 2700 or five years of fuel cost saving Our analysis also confirmed some findings of earlier studies We found that range anxiety long charging time and high purchase price remain consumers main concerns about electric vehicles For example we find that individuals value driving range at about 35 to 75 per mile and charging time at about 425 to 3250 per hour Given the large push in favor of electric vehicles and the sizable investment of resources required to make such a transition it is important to understand the market for EVs It is surprising how little has been done on this front given the interest in the technology Our analysis provides some guidance for both product attributes and consumer characteristics Producers for example can gauge their own cost estimates for attributes like range or charging time against our wtp estimates for the same to judge where cost cutting is needed For example the wtp for a faster recharge 5646 wtp to reduce 50miles recharge from 10h to 1h is a new finding of direct design relevance In particular one competing class of charger design achieves this charging time reduction by means of integrating the charging system into the drive system and does so at low marginal cost Also the current focus of RD on improved batteries for more range makes sense based on our findings Our results may also be used to target specific populations in marketing For example younger and educated populations are a good target but income is probably less important than one might expect From a policy perspective we found that despite the high premium some consumers are willing to pay for electric vehicles battery costs need to drop considerably if EVs are to be competitive without subsidy at current US gasoline prices At the same time we found that the current federal tax credit of 16 Since vehicle cost exceeds unsubsidized wtp in our analysis this subsidy is essentially passed on to the manufacturers of EVs since it will produce little or no reduction in the price of EVs on the market MK Hidrue et al Resource and Energy Economics 33 2011 686705 704 7500 is likely to be sufficient to close the gap between costs and wtp if battery costs decline to 300 kWh the cost level projected for 2014 by DOE References Anderson CD Anderson J 2005 Electric and Hybrid Cars A History McFarland Co London Beggs S Cardell S Hausman J 1981 Assessing the Potential Demand for Electric Cars Journal of Econometrics 16 119 Boxall PC Adamowicz WL 2002 Understanding heterogeneous preferences in random utility models a latent class approach Environmental and Resource Economics 23 4 421446 Brownstone D Bunch DS Train K 2000 Joint mixed logit models of stated and revealed preferences for alternativefuel vehicles Transportation Research Part B 34 315338 Brownstone D Train K 1999 Forecasting new product penetration with flexible substitution patterns Journal of Econo metrics 89 12 109129 Brownstone D Bunch DS Golob TF Ren W 1996 A transaction choice model for forecasting demand for alternative fuel vehicles Research in Transportation Economics 4 87129 Bunch DS Bradley M Golob TF Kitamura R Occhiuzzo GP 1993 Demand for cleanfuel vehicles in California a discrete choice stated preference pilot project Transportation Research Part A 27 3 237253 Calfee JE 1985 Estimating the demand for electric automobiles using disaggregated probabilistic choice analysis Transporta tion Research B Methodological 19 4 287301 Dagsvike JK Wetterwald DG Wennemo T Aaberge R 2002 Potential demand for alternative fuel vehicles Transportation Research Part B Methodological 36 361384 EPA NHTSA California Air Resource Board September 2010 Interim Joint Technical Assessment Report LightDuty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Economy Standards for Model Years 20172025 Ewing G Emine S 2000 Assessing consumer preference for cleanfuel vehicles a discrete choice experiment Journal of Public Policy and Marketing 19 1 106118 Ewing G Emine S 1998 Car fueltype choice under travel demand management and economic incentives Transport Research D 3 6 429444 Greene WH Hensher DA 2003 A latent class model for discrete choice analysis contrasts with mixed logit model Transport Research Part B Methodological 37 8 681698 Howell D 2009 Annual Merit Review Energy Storage RD Overview httpwww1eereenergygovvehiclesandfuelspdfs meritreview2009energystoragees0howellpdf Kempton W Tomic J 2005 Vehicle to grid fundamentals calculating capacity and net revenue Journal of Power Sources 144 1 268279 Kuhfeld WF 2005 Marketing Research Methods in SAS Experimental Design Choice Conjoint and Graphic Techniques SAS 9 1 edition TS722 Kurani KS Turrentine T Sperling D 1996 Testing electric vehicle demand in hybrid households using reflexive survey Transportation Research part D Transport and Environment 1 2 131150 Louviere JJ Hensher DA Swait JD 2000 Stated Choice Methods Analysis and Applications Cambridge University Press Cambridge Santini DJ Vyas AD 2005 Suggestions for New Vehicle Choice Model Simulating Advanced Vehicle Introduction Decisions AVID Structure and Coefficients Center for Transportation Research Energy Systems Division Argonne National Laboratory ANLESD051 Shonkwiler SJ Shaw DW 2003 A finite mixture approach to analyzing income effects in random utility models reservoir recreation along the Colombia River In Hanley ND Shaw DW Wright RE Eds The New Economics of Outdoor Recreation Edward Elgar Northampton pp 268278 Swait J 2007 Advanced choice models In Kanninen BJ Ed Valuing Environmental Amenities Using Stated Choice Studies Springer Dordrecht pp 229329 Swait J 1994 A Structural equation model of latent segmentation and product choice for crosssectional revealed preference choice data Journal of Retailing and Consumer Service 1 2 7789 Tompkins M Bunch DS Santini D Bradley M Vyas A Poyer D 1998 Determinants of alternative fuel vehicle choice in the Continental United States Journal of Transportation Research Board 1641 130138 Train KE 2009 Discrete Choice Methods with Simulation Cambrige University Press UK Wallmo K Edwards SF 2008 Estimating nonmarket values of marine protected areas a latent class modeling approach Marine Resource Economics 23 3 301323 MK Hidrue et al Resource and Energy Economics 33 2011 686705 705