©CopyrightJASSS

MaxwellBrown(2013)

CatchingthePHEVer:SimulatingElectricVehicleDiffusionwithanAgent-BasedMixedLogitModelofVehicleChoice

JournalofArtificialSocietiesandSocialSimulation 16(2)5<http://jasss.soc.surrey.ac.uk/16/2/5.html>

Received:06-Aug-2012Accepted:02-Nov-2012Published:31-Mar-2013

Abstract

ThisresearchdevelopsthenmergestwoseparatemodelstosimulateelectricvehiclediffusionthroughrecreationoftheBostonmetropolitanstatisticalareavehiclemarketplace.Thefirstmodelisamixed(randomparameters)logisticregressionappliedtodatafromtheUSDepartmentofTransportation's2009NationalHouseholdTravelSurvey.Thesecond,agent-basedmodelsimulatessocialnetworkinteractionsthroughwhichagents'vehiclechoicesetsareendogenouslydetermined.Parametersfromthefirstmodelareappliedtothechoicesetsdeterminedinthesecond.Resultsindicatethatelectricvehiclesasapercentagesofvehiclestockrangefrom1%to22%intheBostonmetropolitanstatisticalareaintheyear2030,percentagesbeinghighlydependentonscenariospecifications.Alowerpriceisthemainsourceofcompetitiveadvantageforvehiclesbutothercharacteristics,suchasvehicleclassificationandrange,aredemonstratedtoinfluenceconsumerchoice.Governmentfinancialincentiveavailabilityleadstogreatermarketsharesinthebeginningyearsandhelpstospreaddiffusioninlateryearsduetoanincreasedbaseofinitialadopters.AlthoughseenasapotentialhindrancetoEVdiffusion,batterycostscenarioshaverelativelysmallimpactsonEVdiffusionincomparisontopolicy,range,milespergallon(MPG),andvehiclemilestravelled(VMT)asapercentageofrangeassumptions.PessimisticrangeassumptionsdecreaseoverallPHEVandBEVpercentagesofvehiclestockby50%and30%,respectively,relativetotheEPA-estimatedrangescenarios.FuelcostscenariosdonotconsiderablyalterestimatedBEVandPHEVstockbutincreasetheratioofcarstocktolighttruckstockintheinternalcombustionengine(ICE)vehiclespectrum.Specifically,carsareestimatedat55%ofICEvehiclestockinthedefaultfuelpricescenariobutincreaseto62%ofICEvehiclestockinthehighworldoilpricescenario,withLTscoveringtheappropriatedifferences.

Keywords:ElectricVehicle,Diffusion,MixedLogit,VehicleChoice,NetworkEffects

Introduction

1.1 BeforetheModelT,engineswereprimarilydesignedtorunonsteamandthenethanolorbiodiesel(Hamlyn1967).HenryFord'swife,ClaraFord,droveanelectric-poweredvehicle;atthetime,electricvehiclesheldasmallbutsubstantial-enoughnichemarketwithwell-to-dourbanhousewives(HenryFordEstate2007;Romero2009).Thesevehicletypeswerequicklyphasedoutbythemass-producedinternalcombustionengine(ICE)vehicles.Overthepastcentury,automobileenthusiastshaveintroducedalternativefuelvehicles(AFVs)butnonehaveheldasubstantialshareintheUStransportationmarket.Untilrecently,electricvehicles(EVs)wereprimarilyputtogetherbytheend-usersfromkitssoldbythemanufacturer.Now,in2012,thelargestautomobilemanufacturersintheUShaveintroducedelectricvehiclesintotheirmodellineupandarepushingforwardwithresearchanddevelopment.

1.2 Proponentsofalternativefuelsarechallengingpetroleum'sdominanceinthetransportationsector.Realizingclimatechangeandenergysecuritybenefits,USgovernmentsatthestateandfederallevelhaveincentivizedAFVpurchasesinanattempttoincreasedeployment.Overthepastdecade,morethan20USstateshaveofferedfinancialincentivesofvarioustypesandtheUSfederalgovernmenthasofferedtaxwaiverstowardsthepurchaseofhybridelectricvehicles(HEVs),batteryelectricvehicles(BEVs),andplug-inhybridelectricvehicles(PHEVs).Additionally,energysecurityconcernsandtheeconomiceffectsofnationalpetroleumrelianceduringperiodsofsubstantialpetroleumpricefluctuationhavebecomemoresalientfollowingsignificantgasolinepriceincreasesin2008.

1.3 Thetwolargest,broadly-groupedbarrierscommonlycitedinEVdeploymentaretechnologicaldevelopmentandsocialacceptance.Theprimaryconcernwiththeformeristherangeofthevehicle,typicallyreferencedas"rangeanxiety,"aswellasprice.Thebasisforthelatterconcernisthatoverthispastcenturyvehicleshaveprimarilyrunongasolineand,eveniftheywerecost-competitive,EVsstillfaceanuphillbattleinbeingacceptedasaprimarymeansofpersonaltransportation.Thisstudymodelsthespreadingofindividuals'willingnesstoconsider(WtC)electricvehiclesEVsthroughanagent-basedmodelofvehicleinnovationdiffusion.Agents'socialinteractionsareusedtomodelthespreadofWtCandifanagenthasexceededitsWtCthreshold(i.e.caughtthe"PHEVer")itwillintroduceEVsintotheirvehiclechoicesets.

LiteratureReview

SystemandAgent-BasedModelsofVehicleChoiceandAFVDiffusion

2.1 Themodeldevelopedinthispaperdrawsfromkeystrengthsincludedinotherrecentlycreated,agent-basedvehiclechoicemodels(VCMs).Thedecision-makingcomponentofthismodelutilizessurveydatafromthe2009NationalHouseholdTravelSurvey(NHTS;USDOT2009)todeterminevehicleattributevaluation,similartootherpaststudies(deHaanandMueller2009;deHaan,Mueller,andScholz2009;Zhangetal.2011;Cuietal.2011).Inpreviousagent-basedVCMs,researchershaveutilizedavariantoflogisticregression(logit)decisionmakingmethods(deHaanandMueller2009;deHaan,Mueller,andScholz2009;Cuietal2011;Zhangetal2011),whileotherresearchersdevelopedmathematicalandsystems-basedestimatesofconsumerbehavior(StrubenandSterman2008,Sullivanetal2009;Eppsteinetal2011).Priorresearchhasprimarilyutilizedtwomethodstoincorporateagentattributesintotheirvehiclechoicemodels.Thefirstmethodcreatessyntheticagentsfrompopulationcharacteristics(Eppsteinetal.2011;Cuietal.2011).Thesecondmethodreplicates

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surveyrespondentsasagents(deHaanandMueller2009;deHaan,Mueller,andScholz2009;Zhangetal.2011).Governmentinterventionandmanufacturerresponsefunctionshavealsobeenincorporatedintoanagent-basedVCMframework(Zhangetal.2011).

2.2 Theprior-citedagent-basedVCMsgenerallyincludethevariablesoffuelsavingsandannualincomebut,unlikemostotherVCMs,alsoincludemediaand/orsocialinteractionsasinfluentialvariableswhichaffectconsumers'willingnesstoconsider(WtC)orpurchaseAFVs.Prioragent-basedVCMsmodelsocialinteractionssimulatedinaspatiallyexplicitenvironment(Eppsteinetal.2011;Cuietal.2011).ResearchershavesimulatedthresholdrangesforconsiderationofanAFV(Eppsteinetal.2011)whileothersendogenouslyincorporatedtheagents'socialnetworkconditionsintotheagents'utilityfunctions,derivedfromprimaryresearchandBayesianmixedlogitmethodologies(Zhangetal.2011).Inthemajorityofcitedstudies,aswellasothermodels,diffusionaspectsaremodeledusingthetheoryofhomophilywhich,initssimplestform,theorizesthatpeople'sbeliefstendtodrifttowardsthosethataresimilartothem(Schelling1978).

2.3 Eppsteinetal.(2011)createdauniqueattributeintheirsimulationswhichinvolvedathresholdtoconsiderAFVs;simulatedinteractionsamongagentsimpactedtheirconsiderationthresholdforEVs.ThethresholdisnormallydistributedaroundameanderivedfromsurveydataanalyzedbyCurtinetal.(2009).Theauthorsinitiatedtheenvironmentsuchthatinteractionsamongagentsaredonewithinasocialnetworkanddevelopaheuristicvaluationofmediaandsocialinfluences,determinedbyanagent'ssusceptibilitytoaswellastheassumedamountofmediacoverage.ThemodelpresentedheredrawsheavilyonEppsteinetal.'sconsumerinteractionframeworkwhileincorporatingmixedlogitparameterestimates.

2.4 StrubenandSterman(2008)developedaframeworkandmodelforAFVdiffusionbasedonconsumers'WtCAFVs.Intheirsimulations,WtCisaffectedbytotalexposuretotheplatformwhichisprimarilyinfluencedthroughthreemainchannels:marketing,socialexposurefromdriversofvehicles,andwordofmouthonvehiclesfromnon-drivers.Additionally,consumersgaugetheattractivenessofanAFVbasedonprice,performance,operatingcosts,safety,range,andecologicalimpactwhichmaintainadynamicrelationshipwithautomobileproducersandfossilfuelprices.ThemodelinthispaperseekstobeadaptableforseveraldifferentinstancesofinfluentialfactorsindicatedbyStrubenandSterman(2008)throughflexibilityinmodelassumptionsanddatasourcesrelatedtofuelcostsandvehiclecharacteristics.

SocialInfluenceasaFactorAffectingAFVConsumers

2.5 Drivingamoreenvironmentally-friendlyvehicleisgreatlyinfluencedbyindividuals'socialnetworks(Heffneretal.2005;KahnandVaughn2008;SextonandSexton2010).Theroleofpeerinfluenceonconsumerdecisionshasbeenobservedinmultiplepaststudies.BeardenandEtzel(1982)originatedthequantitativeassessmentofpeerinfluenceonconsumerdecisionsusinganestedrepeatedmeasuresdesign.Theirresultssuggestthatpeerinfluencehasastrongroleinconsumerdecision-makingprocesses,mostlyforthoseproductswhichareportrayedinpublicsettings.TheresultsofBeardenandEtzel'sanalysishavebeenrepeatedelsewhere(ChildersandRao1992;MakgosaandMohube2007).Similarly,evidencehasbeenfoundforover100differentstudiesinwhichhomophilywaspresent(McPhersonetal.2001).Blendingtheprimaryaspectsoftheaforementionedstudies,researchhasindicatedthatmediaandsocialnetworkscansignificantlyimpactconsumerdecisions(AralandWalker2011a;2011b)aswellastravelmodelchoice(Dugundjietal.2008;Dugundjietal.2011).

2.6 Consideringthatdrivingavehicleisapublicbehaviorandpeopletendtoidentifythemselvesandmakeastatementwiththeirvehicles(ChooandMokhtarain2004;SextonandSexton2010),peerinfluenceandsocialnetworkscanbededucedandhavebeenrecognizedasaninfluentialdeterminantofvehiclechoice.Realizingtheseinfluencesonvehiclechoice,studieshaveintroducedsocialandpsychological(Bolducetal.2008)aswellasneighboreffects(Mauetal.2008)intotheirVCMs.Thisstudymergeshomophilywithsocialnetworkeffectstoendogenouslydeterminethevehiclesavailableinconsumers'choicesets,theinclusionbeingspurredbyexceedingagents'WtCthresholdthroughsimulatedideadiffusion.

LogitModelsandSurveysofVehicleChoice

2.7 Researchershavedevelopedlogitmodelsforavarietyofpurposestoassessvehiclechoiceandconsumervaluationofvehiclefeatures.WhetheritisdeterminingthereasonbehindfallingmarketsharesofUSautomobilemanufacturers(TrainandWinston2007),examiningtheeffectsoftimeseriesvariationingasolinepriceexpectationsonthepricesandsharesofvehiclewithdifferentfueleconomyratings(AllcottandWozny2010),orgaugingwhetheraconsumerwillleaseorbuyavehicle(Dasgupta,Siddarth,andSilva-Risso2007),vehiclechoicemodelshavebeenusedforavarietyofdifferentapplicationsandpurposes.Thecoreideaisconsistentacrossallstudies:estimating,andincertaincasessimulating,consumerbehaviorandvaluationofvariousvehicleattributes.Theresearchpresentedheredevelopsasimilardiscretechoicestatisticalframeworkforvehiclechoicebutdeterminesthepowertypeofvehiclesavailableineachagent'schoicesetthroughsimulatedsocialnetworkinteractions.

2.8 SeveralVCMsarecapableofestimatingconsumervaluationoffueleconomyimprovements.Greene(2010)reviewedseveralpastconsumervaluationstudiesofvehiclesandfueleconomywithvariousmethodologies:mixedlogit,nestedlogit,statedandrevealedpreferencedata,hedonicprice,andpriceregression.Fueleconomyvaluationestimateswerefoundtoberatherinconsistentbetweenstudies;afindingsimilartotheUnitedStatesEnvironmentalProtectionAgency'sreviewoffueleconomyvaluation(USEPA2010).Greene'sanalysisandreviewindicatedthatconsumerswereestimatedtobewillingtopayfrom<1%upto400%ofdiscountedpresentvalueforfueleconomyimprovements.additionally,therewasnoevidentexplanationforthewidedifferencesamongtheestimates.

MethodsandModelOutline

MixedLogitModel

3.1 Foranoverviewofmixedlogitmethodology,seeTrain(2009).Mixedlogithastwobenefitstothepurposesoutlinedinthismodelandagent-basedmodelsingeneral.First,itpermitsaheterogeneoussetofconsumervaluationsofvehicleattributesbyallowingagentstoplaceuniqueemphases,drawnfromspecificdistributions,ontheobservablecharacteristicsinthechoiceset.Essentially,mixedlogitmodelsestimateboththemeanandspreadofanexplanatoryvariable'sinfluenceontheprobablyofthedependentvariable'soccurence.Second,afterestimationofparameters,thesimulationsarestraightforwardtoconductwithinanagent-basedframework.

3.2 Logitmodelsareoftenreferredtoasrandomutilitymodels(RUMs);themainpurposeofaRUMistoestimateandevaluateutilityfunctionsbasedondifferentproductattributes.WithinaRUMcontext,thismixedlogitmodel'sRUMrepresentationis:

(1)

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Where:Ui,j:UtilityofobservationiforvehiclejCj:Classofvehiclej(e.g.CUV,SUV,sedan,etc.)HPj:HorsepowerofvehiclejFPj:Foot-poundoftorqueofvehiclejSafetyj:Safetyratinginstarsofvehiclej

K:Incomecategory(low,middle,high)[1]Dk:DummyvariableforincomecategorykMSRPj:Manufacturer'sSuggestedRetailPriceofvehiclejDPGi:Dollarspergallonofgasolineforobservationiduringmonthofpurchase,bystateMPGj:EPAmilespergallonestimateofvehiclejVMTi:Averagedailyvehiclemilestravelledofobservationi

3.3 Explanatoryvariablesinthispaper'smixedlogitmodelarerepresentativebutnon-exhaustive(duetodatalimitations)ofthosefoundinthestudiesreviewedbyGreene(2010).Thedailygascost(DGC)variableistheproductofstate-levelpricepergallonofgasolineatthemonthofpurchasedividedbytherespective

vehicle'smilespergallonofgasoline(MPG)andtheaveragedailyvehiclemilestravelled.Manufacturersuggestedretailprice[2](MSRP)andgasolinepricesareadjustedtoJanuary2011dollarsusingtheconsumerpriceindex.Differentclassesofvehiclesareimplementedasdummyvariables;allclassdummyparameterestimatesareinreferencetotheconvertibleclassbaselineanddefinedbyWard's(2011).Thegeometricmeanoftorque,infoot-pounds,andhorsepowerparametersattheWards-measuredenginerotationsperminute(RPM)isusedasameasureofvehicleperformance;forsimplificationthiswillbereferredtoasthe'power'variable.Thisvariable'sformprovedbestafterthreeprevailingconditionswereobserved:

1. Includingbothparametersseparatelyinthesamemodelintroducedcollinearityissues.2. Theformprovidesasignificantlyhigheradditiontothelog-likelihoodestimateforeachmarketsectorthaneitherparameterindividually.3. Thegeometricmeanprovidesasignificantlyhigheradditiontothelog-likelihoodestimateforeachmarketsectorthanonlytheproductofthetwoterms

Safetyofvehiclesismeasuredastheaverageofoverallimpact-testsofstarsbasedontheUSDepartmentofTransportation(USDOT)safetyratings(USDOT

2012)[3].ParameterestimatesaredisplayedinTable1anddatasourcesareexplainedinfurtherdetailinthe'Data'section.Severalfunctionalformswereassessed;theanalysiswhichprovidedthegreatestaveragelog-likelihoodvaluesacrossgroupswaschosenasthefinalform.

Table1:MixedLogitRegressionParameterResults

ParameterEstimates[4]

Grouping Single,NoChildren Single,WithChildren Married,NoChildren Married,WithChildren RetiredSedan 2.433**** 0.981** 1.735** 1.653*** 1.618***CUV 3.069** 1.272*** 1.704**** 1.954** 1.447***Pickup 2.040**** -0.271*** 1.538** 1.308** 1.294*SUV 2.067** 0.554** 1.184**** 0.8509**** 0.467**Coupe 2.841*** 0.323* 0.769*** 1.223**** 0.537**Hatchback 2.136**** -0.483* -0.021* 0.465**** 0.101*Wagon 0.415*** 0.746** -0.125** 0.978*** -0.464***Van 1.725*** 1.261**** 0.834** 1.732*** 1.380****ln(Safety) 2.105** 2.958*** 2.583*** 3.25**** 2.852***ln(Power) 2.41*** 1.950** 2.521*** 1.99** 2.64***ln(MSRP_Low) -4.267*** -5.067** -4.233**** -4.853*** -3.991****ln(MSRP_Mid) -3.426** -4.396*** -3.509**** -4.299*** -3.525***ln(MSRP_High) -3.044*** -3.904** -3.474*** -4.274*** -3.177***ln(DGC_Low) -2.937** -2.126*** -2.592**** -2.769*** -2.678****ln(DGC_Mid) -2.556**** -2.942*** -2.825**** -2.726**** -2.359***ln(DGC_High) -3.29*** -1.993** -2.254*** -1.985*** -2.654****

StandardDeviationln(Safety) 0.985** 0.568* 0.795*** 0.689*** 0.897**ln(Power) 0.567*** 0.298*** 0.354*** 0.453** 0.378***ln(MSRP_Low) 0.614** 0.490** 0.693**** 0.772**** 0.595****ln(MSRP_Mid) 0.524** 0.596** 0.594**** 0.658*** 0.595***ln(MSRP_High) 0.673** 0.880* 0.792** 0.635** 0.839**ln(DGC_Low) 1.252** 1.013** 1.375*** 1.235*** 1.2943***ln(DGC_Mid) 1.341*** 1.198** 1.125*** 1.123*** 0.986***ln(DGC_High) 1.120*** 1.001** 1.135** 1.356*** 0.978****

NumberofObservations 2136 527 17978 2162 10432Log-Likelihood -8.5E+6 -1.7E+6 -5.4E+07 -3.3E+6 -1.4E+07

SocialActorsandBehaviors

3.4 EachmodelagentisparameterizedforeveryNHTSsurveyrespondentfromMetropolitanStatisticalArea(MSA)groupings[5](USDOT2009).Theagentsarelocatedintheirenvironmentbyregionandthentothefurthestextentpossible,downtotheMSAandthenfurtherplacedaccordinglybytherespondent-indicated

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populationdensityatthetract-levelcountysubdivisions(USCensusBureau2012).Locationattributesareimplementedtocontrolforpossiblecorrelationsamongagents'demographicsandtheirlocation.Theagentsarerandomlylocatedinanorbitalfashionaroundthecitycenterwithdistancedependentonthepopulationdensityrecordsinthe2009NHTS,theindividualswithhigherreporteddensitiesbeingclosertothecitycenter.

3.5 Theagentsareoriginallylimitedtoexploreoptionswithinthefueltypeclassoftheircurrently-ownedmodel[6]butwillexploreotheroptionsaftertheirindividualconsiderationthresholdhasbeenmetthroughsimulatedsocialinteractions.Interactionswithfourneighborsareimplementedtoincorporatethesimilaritieswithlatticeneighborhoodsmodels,bywhichtheideadiffusionequationisheavilyinfluenced.

3.6 Correspondingwiththebeliefsofpeerinfluenceonconsumerdecisionmakingandhomophily,aswellasEppsteinetal.'s(2011)vehicleconsiderationfunctions,interactionsmodeledbyMolofskyetal(1999),StrubenandSterman's(2008)implementationoffractionaldecay,andconsiderableinspirationfromHiebeler(forthcoming),consumers'willingnesstoexploreothervehicletypesintheirpurchasedecisionismodeledusingthefollowingequation:

(2)

Where:Ev,i,t:WillingnessparameterforagentiduringtimettoconsidervehicletypevF:Annualfractionaldecayratej:Otheragentswithintheagenti'ssocialnetworkJ:Totalnumberofagentswithwhichagentiinteractsv:Vehiclepowertype(Hybrid,Plug-inElectric)

(3)

W(j_i),t:Referencedasasimilarityindex[7]p:Agenttraitindex(age,income,highesteducationalattainment)Cp:Characteristicoftypepofagentiorj

3.7 WhenEv,i,texceedstherandomly-assignedheterogeneousthresholdforvehicleconsiderationtheconsumerwillconsider(throughmixedlogitderivedparameters)vehicleoptionsfromvehiclepowertype,v.Withinthemodel,theoptionexiststoallowforagentsonlytoconsiderBEVsifanothermemberofthehouseholdhasavehiclewhichisnotaBEV.Theheterogeneousthresholdisacrucialcomponentoftheanalysisandthusatargetofsensitivityanalysis.Last,iftheagentdoesnotpurchaseanAFVinthatyear,theirpresentWtCdecreasesatafractionaldecayrate,similartoStrubenandSterman(2008).

3.8 Figure1providesanoverviewandpseudo-codeofthedecisionmakingsequenceforeachagent.Orderisdeterminedbywhichagenthastheleastamountoftimeremaininginvehicleownership;tiesaresettledbycoinflips.ThetablefollowingFigure1providesalegendandinformationregardingthevariablespresentedinFigure1.

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Figure1.FlowChartofAgentDecision-Making

Variable/Index Agent(A)orGlobal(G)

Description

Neighbors A OtheragentsinsocialnetworkAttributes A Attributesofagentsi A Focalagentj A OtheragentTraitRatio A W(j|i)ininteractionequationSimIndex A NumeratorinsecondportionofinteractionequationVEH G ICE,Hybrid,orElectricT G YearDecayRate G FractionaldecayrateofWtCfrom(T-1)toTMonthsRemaining A MonthsremaininginvehicleownershipYearsToOwn G YearsofownershipofnewvehiclepurchasersHybridsSold G HybridssoldinyearTAllVehSold G AllvehiclessoldinyearTMaximumMarketShare G MaximumvehiclepenetrationratefromNEMSAllHybridsSold G AllhybridssolduptoyearTMAN G ManufacturerindexPopRatio G P'inincentiveavailabityequationNationalVehicleSales G Total,nationalvehiclessoldfrom2012toyearTIncentiveLimit G IncentivelimitonnumberofvehiclessoldMSRP G Manufacturers'suggestedretailpriceHybModel G IndexofhybridmodelsiH A Otheragentsini'shouseholdDailyVMT A SurveyreportedaveragedailyVMTRange G EstimatedrangeofBEVModel,givenassumptionKBEVsSold G NumberofBEVssoldinyearTAllBEVsSold G AllBEVssoldupuptoyearTBEVModel G IndexofBEVmodelsBatCost G BatterycostfromUKCCC'sbatterycostreport(2012)Class G CarorLightTruckChargerCost G CostofchargingequipmentPresentValueBatDep G PresentvalueofbatterydepreciationDiscRate G Discountrate,assumedat7%AllPHEVsSold G AllPHEVssolduptoyearTPHEVModel G IndexofPHEVModelsDGC A DailyfuelcostinyearTDPGGE G DollarspergallonofgasolineequivalentMPG G MilespergallonofgasolineequivalentMPGROC G Milespergallonrateofchange(ANL2012)DailyVMT A DailyvehiclemilestravelledVehChoice A Vehicleselectedwhichmaximizesi'sutilityUtility A UtilityfromvehiclemodelsChoiceSet A VehiclechoicesetofagentML_Parameters A/G Agent'smixedlogitparametersfromglobaldistributionsR G Numberofrepetitionsforutilitycalculations,Train(2009)

Data

4.1 Figure2summarizesthevariousdatasourcesandtheirrespectivepurposesineachmodel.

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Figure2.MapofDataFlow

4.2 Thelargestsourceofdatadetailingagentattributesisthe2009NHTS(USDOT2009).The2009NHTSisanationallyrepresentativesurveyoftravelbehaviorconductedfromApril2008throughApril2009.The2009NHTSsamplesizewas150,147households,includinganationalsampleof25,000householdsandseparatesamplesfromtwentystateDepartmentsofTransportationthattogetheradded125,147completedhouseholds.Thesetwosampleunitsbroughtthe2009NHTSsamplesizetoapproximately150,000householdsand300,000people.

4.3 Thesecondmainsourceofdata,relatedtovehiclecharacteristics,isWard'sAutomotiveInformationProducts(2011).Thesedataentaildetailedcharacteristicsofvehiclesproducedfrom2004-2011whichwereavailableforpurchaseintheUnitedStates.ThesedatawerepurchasedinJuly2011.TheNHTS2009doesnotsupplydetailedautomobileinformationintheirpubliclyavailabledatafilesbutNationalAutomobileSamplingSystem(NASS)codesareprovidedwhichareusedtomatcheachobservation'svehicletoitsrespectiveattributes.NASSvehiclecodesusedinthisanalysiswereextractedfromtheDepartmentofTransportation's(DOT)manual(NHTSA2011).Anotherrequirementtoproducethefinaldatasetwasthecollectionofsafetydatameasuredintheaveragestars.ThesedatawereretrievedthroughtheUSDOT(viaSafeCar.gov;USDOT2011).FuturevehiclespecificationsareexplainedinAPPENDIX2.

4.4 Althoughmultipledatasourcesareutilizedtoallowforassumptionsonvehicleattributes,itisnotentirelyrealistictoassumethatspecificmodeltypesusedtocalibratethemodel(e.g.a2012FordFocus;seeAPPENDIX2)willstillbearoundby2030.Insteadthemodel'sresultsshouldbeunderstoodandinterpretedthatthemarketsharesofamid-classvehiclewithattributessimilartosaidvehiclesislikelytogroworshrinkby2030comparedtoothervehiclescharacterizedthroughdataassumptionsrelatedtofuturevehicleattributes.

4.5 Thepriceofgasolineattheindividualstatelevelduringthemonthofpurchase(EnergyInformationAdministration2011),basedonthedifferencebetweenmonthwhenthesurveywastakenandstatedmonthsofownership,wasmatchedtoeachvehicleandhouseholdobservation.Costspermileanddailygascostswerethencomputedforeachvehiclechoiceavailabletotheconsumer.Surveyresearchindicatesthatvehicleconsumersnormallydonotorerroneouslycalculateannualfuelcosts(KuraniandTurrentine2004),thusthefuelcostsforthetimeatpurchaseareincludedanddailyVMTisusedtoscaleobservationsbasedontheiraverageamountofdriving.Averagegasolinepricesthroughout2011atthestatelevelareusedasthereferencepointsduringtheinitialtimeperiodandthenthegasolinepriceisadjustedaccordingtothe2012AnnualEnergyOutlook's(AEO;USEIA2012)marginalannualrateofchange.Thesevaluesremaininconstant2011dollarsthroughoutprojectedperiods.IntheUSDepartmentofEnergy's(USDOE)NationalEnergyModelingSystem(NEMS;USDOE2001),themarketsharesofanyvehicletypearecalculatedusingacombinationofvehicleandpopulationcharacteristicsaswellastechnologycostsandregulatorycostsimplementedonmanufacturerstoimprovefueleconomy.WithinNEMS,themaximumvaluesforalternativefuelcarandlighttruckmarketsharesaredependentonthepasttimeperiod'smarketshare,carvehiclemarketsbeingmoreresponsiveandflexiblethanthelighttruckmarketshares.Withinthispaper'smodel,thesedatadeterminethemaximumamountofAFVsavailablebyclassgivenextensivedemand;thisistoreflectproducers'responsesandabilitytoadjustmanufacturingschedules.AfutureextensionofthismodelwouldbetheinclusionofadynamicmanufacturerprofitmaximizationfunctionsimilartoZhangetal(2011).Thelimitsformaximummarketsharesarecomputedfollowingindividualagents'choices.

4.6 Survivabilityratesbyageschedulesareneededtoaccuratelyestimatevehicleexpirationrates.VehiclesurvivabilityratesaretakenfromtheUSDOTNationalHighwayTrafficSafetyAdministration's(NHTSA)2006technicalreport(NHTSA2006).

4.7 Tocomputethegramsofcarbon-dioxideequivalent(gCO2e)emissionsandsubsequentlyAverageFuelCarbonIntensity,thegCO2e/MJofelectricityaretakenfromtheGreenhouseGases,RegulatedEmissions,andEnergyUseinTransportation(GREET)Model(ArgonneNationalLaboratory2011).ThecarbonintensitiesforblendedmotorgasolinearecomputedusingvaluesfromtheTransportationRegulationandCreditTrading(TRACT)model(RubinandLeiby2012).

VehiclePurchaseIncentivePolicies

4.8 AstheresultssectionofthisanalysisfocusesontheStateofMassachusettsandmorespecificallytheMSAofBoston,onlythefederalincentivewillbeofferedonvehicles.ThefederalincentivesfromthePlug-InElectricVehicleCreditintheamountof$7,500areavailableuntilalimitof200,000vehiclesnationallysoldpermanufacturerisreached(IRC30andIRC30D;InternalRevenueService2011).Theincentiveisappliedinthemodeluntiltheproportionofvehiclesthathavebeenpurchasedofthatvehicletypeisgreaterthanorequaltothesameproportionrelativetotheestimatednationalvehiclefleetfromthe2012AEO(USEIA2012).Forexample,ifaspecificEVweretoreach20,000totalsalesandtheMSAhas200,000vehiclessoldalready,thentheamountassumedtohavebeensoldonanationallevelis10%oftheprojectednationalvehiclesalesuptothatyear.Essentially,if

(4)

thenincentivesarenotincludedonvehiclemanufacturerm'smodel(s).Where:

P:Populationadjustmentfactor[8]v:Vehiclessoldfrommanufacturermofmodelim:Indexofvehiclemanufacturersi:IndexofvehiclemodelsV:AllvehiclessoldinsimulatedmarketinyearyY:CurrentyearUSVy:AmountofvehiclessoldintheUSinyeary

4.9 SincethehybridandEVfederaltaxcreditsareavailableonaper-manufacturerbasis,thisrateisappliedonlytovehiclemanufacturersthathavenotexceededthelimitasofMay2012,asindicatedbyAdvancedFuelDataCenter(AFDC,2012).Additionally,thenationalamountofvehiclessold,asindicatedbyWards(2011),ofeachmanufacturer'sincentive-eligiblevehiclesisdeductedfromthefederalincentive'sthresholds.ThesameruleregardingproportionsofEVsispresentindeterminingavailabilityofhybridvehicletaxincentives.Althoughaphase-downperiodexistsfollowingtheexpirationofincentiveavailability,thescopeoftimeisarbitraryrelativemodeltimescale.

Results

5.1 Themodeliscapableofimportingagentcharacteristicsfromseveraldifferentmetropolitanstatisticalareas(MSAs).Thissetofsensitivityanalysisandresults

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focusesontheBostonMassachusettsMSAwhichhasagoodbalanceof:

1. Highsurveyresponserate2. Representativepopulationproportionstothosepresentinthemixedlogitparameterestimationdata3. Adequatenumberofagentstoreliablydemonstratecoreconceptsandmodeldynamicswithoutconstrainingresultproductionandanalysisthrough

limitsoncomputerresources4. ThelimitedamountofpubliclyavailablehigherpercentagebiofuelsandCNGstationsavailable(AFDC,2012)minimizespotentialconcurrenteffects

fromotheralternativetransportationfuels

5.2 Therangeofforecastyearsissetat2009-2030throughoutthisresultssection.Themodelcanforecastoutfurther(to2100)whilekeepingseveralvariables

constantatthe2030level[9],buttheadditionaltimerequiredtorunthesimulationsoutto2100andthedecliningreliabilityofsimulationsdeterredtheauthorfromextended-rangeforecasting.

SensitivityAnalysisofCoreModelParametersandAssumptions

5.3 Resultspresentedwithinthisandothersectionsaretheaverageoveraburn-inof250runs.Recognizingthatinitialconditionsimpactoutcomesofnetworkeddiscretechoicemodels(DugundjiandGulyás2013),agentlocationandWtCthresholdsareresetatthebeginningofeachsimulation.Asopposedtovariousscenarioresults,thissectionexploresthedifferentimplicationsthatunderlyingfundamentalmodelassumptionshaveonestimatedresults.

Table2:PotentialScenariosfortheVehicleMarketplaceModel

IteminSecondaryLegend

Translation PotentialValues

MeanWtC MeanWtCParameter EagerNeutralReluctant

WtCFDRate AnnualFractionalDecayRateofWtCValues 0.50.751

EconScen EconomicScenario Business-As-Usual(BAU)LowEconomicGrowth(LEG)HighEconomicGrowth(HEG)

PopRatio Populationratioforpolicyanalysis 0.512

BatScen Batterycostsscenario Business-As-Usual(BAU)ConservativeOptimistic

VMTasRangeLimit PercentageofdailyVMTwhichtheBEVsrangehastobeinordertoconsider

50%75%100%

MPG Miles-per-Gallon/GGE EPAMFR

Range RangeofBEVandPHEV C&DEPAMFR

FuelPrice[10] Gasolineandelectricitypricescenarios Business-As-Usual(BAU)HighWorldOilPrice(HWOP)

5.4 Adrivingassumptionbehindthevehicleconsiderationcomponentoftheagent-basedmodelisthedistributionofWtCthresholds.Inthefirstgraph,thepercentageoftheagentswhichhavesurpassedtheirpersonalthresholdisplottedovertimeforvariousthresholdmeansandaconstantfractionaldecayrateof0.75,indicatingthateachagent'sWtCis75%ofthepriorperiods.Thelowerlimitfractionaldecayrateissetat0.5,approximatelythedecayratewithwhichdecaybecomestoosevereandnooneconsidersEVs.Thus,adecayrateof1indicatesthatagents'WtCdoesnotdeclinebetweentimeperiods.Intheanalyses,thedecayrateisassumedat0.75andthustheresultingeager,neutral,andreluctantscenarios'percentagesofpopulationexceedingWtCthresholdsaredisplayedinFigure3.

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Figure3.ComparisonofMeanWtCwithDecayHeldConstant

5.5 Arecurring,andespeciallynoticeablewiththeeagerscenario,resultisthateventhoughtheWtCforEVsbeginsataslightlylowerpoint(sincehybridownersalreadyexist)itincreasesatanincreasingratefollowingthespreadinducedbyinitialadopters.Note:thebeginningpercentagesofpopulationexceedingthethresholdisgreaterthanzerosincetheleft-handtailofthenormaldistributionpassesovertheY-axiswhileWtCvaluesareassumedtorangebetween0and100.

5.6 AninterestingandimportantaspectregardingtheassumptionofmeanWtCistheamountofdiffusion,measuredinthisanalysisasthepercentageofvehiclestockofeachvehicletype.AsshowninFigure4,themeanWTCassumptionhasvaryingyetprofoundeffectsonthediffusionofdifferentvehicletypes.Inconjunctionwithaforementionedparameterassumptions,thevaluesdemonstratetheeffectsofthedifferentmeanWtCthresholds.Notably,thediffusionratesofchangeroughlyresembletheexponentialgrowthfunction,representativeofproductdiffusiontheory(Rogers1963).

Figure4.AFVStockPercentageswithDifferentMeanWtCThresholds

IncentivePolicyPopulationRatio

5.7 The2012AnnualEnergyOutlook(AEO;USDOE2012)estimatesgreatvariabilityinregionalusageofalternativefuels.GiventhatotherregionsareestimatedtohavedifferentusageratesofFFVs,CNGvehicles,andEVs,assumingaone-to-oneratioofnationalvehiclesalestoBostonMSAvehiclesalescouldhinderreliabilitybyskewingnationalEVconsumptionestimates.AratiogreaterthanoneindicatesthatalesseramountofthenationalEVincentivesareavailabletotheBostonMSAresidentsthanotherareas.BecauseofthemultipleconcurrentpoliciesrelatedtoAFVdeploymentandalternativefuelusage(e.g.theCaliforniaLowCarbonFuelStandardandtheRenewableFuelStandardrevisedundertheEnergyIndependenceandSecurityAct),theratioofBostonMSAvehiclestocktonationalvehiclestockbecomesafocusofincentivepolicystructuresensitivityanalysis.

5.8 ThemostpertinentresultfromtheincentivepolicystructureanalysisisthatthegreateravailabilityofincentivesinthebeginningperiodsaddstothebaseofinitialadopterswhichinturnincreasestheoverallspreadofWtCoverthemodeledperiods.Anincentive-dependentpopulationratiothatisbeneficialfortheBostonMSA(i.e.PopulationRatio<1)resultsinahigheroverallstockofevsbutpercentagesofvehiclestockbymodelhavevariedresponses.bothvehiclesestimatedtohavethehigheststocklevels,thefocusandvolt,incurrelativelylesserincreasesinestimatedvehiclestockwithincreasedincentives.themiev,givenitsunfavorablecategorizationofahatchback(basedonmixedlogitparameterestimates)butlowermsrp,incursthelargestrelativeincreasewithgreateravailabilityofincentivesasthevehicle'slowermsrpbeginstooutweighthegenerallyrelativedisutilityofbeingahatchback.fromobservingthecharacteristicsofbuyersbymodelofev,priuspurchasingisprimarilydonebydriverswithverylowvmtandlesseraversionstomsrp.giventhatthepriusandrav4arebothproducedbythesamemanufacturertheavailabilityofincentives,andthusestimatedpercentagesofvehiclestock,areinterdependent.

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EffectsofMPGandRangeAssumptions

5.9 Estimatesofrangehavebeenproducedfrommanufacturers(MFR),thegovernmentthroughtheEnvironmentalProtectionAgency(EPA),andCarandDriver(C&D).Additionally,MPGrateshavebeenmeasuredbymanufacturersaswellastheEPA.Inthissection,thesimulationsareconductedwiththesesixdifferentassumptionsontherangeandMPGofelectricvehicles.Resultsindicatethat,similartothesensitivityanalysisinvolvingthelimitofdailyVMTasrange,theestimatedrangeofBEVshasacriticalimpactonthedeploymentofelectric-onlyvehicles.Theoverallpercentagestockofvehiclesvariesbynearly1.2%fromtheoptimistic(MFR)tothepessimistic(C&D)estimatesofBEVrange.HybridvehiclesarealsoinfluencedbythevariousassumptionsofBEVsandPHEVs;whentherangeestimatescomefromthemostpessimisticsource,hybridvehicles'2030percentageofvehiclestockincreasesbyapproximately0.6%overtheothermostoptimisticrangeassumptionsscenarios.SimilartoBEVs,PHEVsasapercentageofvehiclestockaresignificantlyimpactedbythevariousrangeandMPGassumptionsyettheeffectismuchmoresubstantialbetweentheEPAandC&Dscenarioswhichentailanestimated1%decreaseinpercentageofvehiclestockin2030.

BatteryCharacteristics

5.10 AlthoughseenasapotentialhindrancetoEVdiffusion,thedifferentcharacteristicsofbatterycostshaverelativelysmallimpactsonEVdiffusionincomparisontopolicy,range,MPG,andVMTasrangeassumptions.Incomparisontotheotherprimaryfinancial-centricmodelmechanismofpolicypopulationratios,the2030estimatesforbatterycostsvarybyapproximately$1000-$2000whereasthefinancialincentivesreducetheMSRPby$7500inthebeginningyearswhenEVsarestillrelativelyexpensive,addingtothebaseofinitialadoptersandencouragingWtCdiffusion.

5.11 Eventheoptimisticbatteryscenarioscouldbesignificantlyoverestimatingthefuturecostsofbatteries.ThebatteriesandEVportionoftheAmericanRecoveryAct(USDOE2012)anticipatesthatinvestmentsinbatteriesaloneshouldhelplowerthecostofsomeelectriccarbatteriesbynearly70%bytheendof2015,withthelargestproportionofvehiclecostreductionsoccurringbetween2009and2013.Additionally,theAmericanRecoveryActanticipatesthattheexpectedlifetimeofatypicalEVbatterywillincreaseby350%inthesametimeperiod,allowingthebatterytolastuptoandpotentiallybeyondthevehicles'anticipatedlifetimesanddecreasingthepresentvalueofbatterydepreciationoverownershipperiods.TheUKCCCmodeledchangesinEVbatterycostsaremuchless

optimisticandwiththeAmericanRecoveryAct'sassumptions[11],totalEVdiffusionisincreasedbyapproximately0.85%overtheoptimisticscenariobytheUKCCC.Othervehiclecharacteristicsthemselveswhichwouldbeaffectedinthesescenariosaresimplifiedinthisportionoftheanalysisandwouldlikelyplayamajorroleinconsumerdecision-making.

HighWorldOilPrice(HWOP)Scenario

5.12 Forthisscenario,theratesofchangeofallfuelpriceswereadjustedusingtheHWOPdatafromtheAEO2012(USEIA2012).TheestimatedeffectthattheHWOPconditionshadonEVstockswasminimal,buttheeffectsontheICEportionsofvehiclestockofcarsandlighttrucks(LTs)areworthmentioning.ConsumersubstitutionofLTsforcarsincreasestheestimatedstockofcarsbyapproximately5.1%by2030intheHWOPscenarioovertheBAUscenario.Figure15displaystheCarandLTpercentagesofICEstockundertheBAUandHWOPscenarios.IntheHWOPscenario,thestockofcarsisexpeditedduetotherelativelylargegaspricemarginalratesofchangeestimatedinthefirstfewyearsofsimulations.FollowingtheearlygreaterdivergencefromtheBAUscenarioICEstockpercentages,theHWOPcarandtruckICEstockpercentagesadjustatlessintenseratesbutstillmorerapidlythanintheBAUscenario.

5.13 WithrespecttoFigure5,thereisaninnateinstitutionalandmarketplacelagassociatedwithshiftingstocksandthusgaspriceincreaseswillnotforceimmediateshiftsofvehiclecharacteristics.AlthoughitisintuitivelyexpectedwithrisinggaspricesthatLTpercentagesofvehiclestockwouldimmediatelyfall,agentswithhigheraversiontoDGCareseekinghigherMPGLTsaswellasalternative,non-ICEvehicleswhichmeetthesestandards.Additionally,survivabilityratesfromNHTSAfavorLTsslightlymorethancars.Forsomeagents,inthelateryears,withanincreaseddisutilityofincreasedDGCs,theyarewillingtosacrificethepurchaseofanLTcategorizedvehicleforacar.

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Figure5.PercentageofICEStockbyVehicleTypeandFuelPriceScenario[12]

MostOptimisticScenario

5.14 Inthisscenario,themostoptimalconditionsaresettoinvestigatetheupperboundonEVdiffusion;theseconditionsaretohaveoptimisticbatterycosts,eagerWtC,noWtCdecay,0.5PopulationRatio,andmanufacturer-estimatedMPGandrange.TheVMTasRangeLimitisleftat0.75sincethiscomponenthasbeenshowntonothaveaconsiderableimpactonBEVdiffusionoverthefull-rangescenariobutisnecessarytomaintainrealisminagents'BEVconsiderations.Whentheopposite(most-conservative)simulationsareconducted,percentagesofvehiclestockofBEVsandPHEVsapproachzeroandhybridvehicles'percentageofvehiclestockisestimatedatapproximately0.25%bytheyear2030.Inthemostoptimisticscenario,PHEVsandBEVsareestimatedtomaintain12.2%and8.4%ofvehiclestockin2030.

Discussion

6.1 Resultsfromthisanalysisarehighlyvariabledependingonmodelspecificationswiththeunderlyingcoreparametershavingthehighestinfluenceonresultantscenarios.Thisreflectsthewiderangeinpotentialmodelinputsaswellastheagent-basedmodel'sassumptions.RecentsurveyworkhasindicatedthatamajorityofconsumersareeitherlikelyormoderatelylikelytoconsiderthepurchaseofanAFV,withthetwomostdeterringaspectsbeingvehiclepricesandrefuelingoptions(Michniaketal.2012).Thus,itmaybemoreappropriatetoassumeaneagermeanWtC.Neutralscenarioresultsweredisplayedtoprovideconservativeestimatesandthemainintentionofthispaper'sresultssectionistodemonstratemodeldynamicsfromimposedscenarioassumptions.Todevelopthe'true'meanWtCdistribution,furtherpurpose-specificprimaryresearchisrequiredtoquantifyconsumers'willingnesstoexploreAFVoptions;thissourceofuncertaintyisthemodel'slargestshortcomingintermsofexternalvalidity.

6.2 HybridandPlug-inEV(PEV)percentagesofvehiclestockandmarketsharesaredemonstratedtobeinterdependent.WhenthecharacteristicsofPEVsare

improvedtomakesaidvehiclesmoredesirablewithrespecttoconsiderationassumptions[13]andmixedlogitderivedparameters,hybridvehiclesaredisplacedbythemoreattractivePEVs;thisdisplacementisespeciallyevidentinscenariosinvolvingrangeandMPGassumptions.Atthesametime,thismodeldoesnottakeintoaccounthowlearning-by-doinginthesupplychainbyPHEVsandBEVsassistingimprovementsinoneanother.Thatis,evenifPHEVsdisplaceBEVsinearlyyears,theyhelpdevelopandlowerthecostoftechnologythatcanassistBEVs.

6.3 Similartopaststudiesoffinancialpolicyefficacy(GallagherandMuehlegger2011),hybridvehiclepercentagesofvehiclestockareestimatedtoberesponsivetofinancialpolicyavailabilityassumptions.Inthesesimulations,hybridvehiclesareestimatedtobethemostresponsiveintermsofpercentageofvehiclestock.FinancialpolicyefficacyisnotparticularlyevidencedforthePHEVpercentagesofvehiclestockastheirhigherMSRPandlowerconsiderationratesinthebeginningyears,evenwiththeincentive,isamajordeterrenttodiffusion.EstimatesofBEVsasapercentageofvehiclestockrisesubstantiallymorethanPHEVsasapercentageofvehiclestocksincetheirgenerallylowerMSRPrelativetoPHEVsbecomesevenmoreattractivewhentheincentiveisapplied;thisalsohelpstoalleviatethedisadvantageouscategorizationoftheMiEVasahatchbackvehicle.ThisleadstohigheroveralldiffusionofPHEVsandBEVssincetheWtCisabletospreadatanincreasedratefollowingmoreinitialadoptersinthebeginningperiodsofsimulations.

6.4 VehicleselectionofBEVsandPHEVsgenerallyfavorthemodelswiththelowerMSRPandsedancategorization,sincethesearegiventhehigherrelativepart-worthestimatesfromthemixedlogitmodel.GreenautomobilecolumnistsarecurrentlydenotingtheRAV4EVasanichevehicledesignedforwealthyearly-adopters(Voelcker2012;Marchetti2012)butasbatterycharacteristicsimproveconsumerswithloweraversionstoMSRPandhigheraversionstoDGCareestimatedtopurchasethevehicleatsmallyetstillconsiderablerates.Givenitsrelativelylowvalue,sensitivityanalysiswasconductedonthePrius'rangebyadjustingittohypotheticalvalues;thevehiclebecomesmuchmorecompetitivewhenrangesareincreasedtotheaverageofitsPHEVcompetitors.Withinthecontextofthismodel'smixedlogitparametersthereisonlyasmallrangeofagentswillingtospendextramoneyonacarwithlessrange;generallyonlyconsumerswithhigherincomes,muchlessdailyVMT,andhighersafetyparameterpart-worthsareestimatedtopurchasethePriusPHEV.Thesehouseholdsweregenerallythemarried,higher-incomehouseholdswithlowdailyVMT.

6.5 IntheAEO2012,PEVsandAFVsareestimatedtoreachapproximately1.6%and19.81%ofvehiclestock,respectively.HigherestimatesofPEVsasapercentageofvehiclestockareanticipatedinmostscenariossincesubstitutionsofPEVsbyotherAFVsarenotincorporatedintothisanalysis.ThismodelreplicatesamarketplacewheretheonlyalternativeoptiontoanICEisanEVandthusconsumersareassumedtoseekalleviationfromhigherDGCthroughthissinglechannel,creatingthespreadbetweenthismodel'sandtheAEO2012'sestimatesofEVsasapercentageofvehiclestock.Inthemostoptimisticscenario,theEVsasapercentageofvehiclestocknearlymatchbutslightlyexceedtheAEO2012'sestimateofAFVsasvehiclestockin2030.

6.6 SimilartoKarplus(2011)butnotafocalobjectiveofthisanalysis,simulationsdemonstratethatUShouseholdsareestimatedtopurchasemorefuelefficientvehiclesinresponsetogasolinepriceincreases(evidencedbyFigure5).However,dissimilarinthesesimulationstoKarplus'analysisisthenon-distinctincomegroupingsofhouseholdsthatswitchtheirvehicleconsumptionbehavior.Theambiguousbehaviorbyhouseholdcategoricalgroupingslendsitselftotherandomnessinheritinthemixedlogitparameterswhichhaveoverlappingestimateddistributions.

6.7 Overall,ahighamountofvariationisestimatedwithrespecttoallscenarioassumptionsandresults.ThemostoptimisticscenarioestimatesEVsatapproximately22%of2030vehiclestock,muchlessthanmoreoptimisticestimates(Becker,Sidhu,andTenderich2009)butthelandscapeiscontinuallychangingonboththefossilfuelandEVsides.Althoughgasolinepriceshaveincreasedgreatlyoverthepastfewyears,thegrowingextractionratesofunconventionalsourcesofpetroleumintheBakkentightshaleformationandCanadianoilsandshavedramaticallyalteredtheperceptionsofthefutureintheNorthAmericanpetroleummarket.Ingeneral,EVproponentsareoptimisticofthefutureoftheelectricautomobilebutmightfaceanuphillbattleifmajorrisesinpetroleumpricesareaprerequisitetoEVdeployment.

Limitations

7.1 Throughoutthisanalysis,multipleassumptionswereimposedanddemonstratedtoalterresults.Thereisgreatuncertaintyinthefuturecharacteristicsofautomobilesand,althoughthismodelattemptstogrounditselfinstatisticsanddata,aninfinitenumberofdifferentoutcomesarepossible.Inthesesimulationsalone,thepermutationsofpossiblescenariosamountto8,748differentcombinationsofassumptions.Asinanyothermodel,itshouldbethoughtofasasimplifiedversionofrealityandtreatedasawell-informedthoughtexperiment.

7.2 AlargeassumptionisthatconsumersvaluethesevehiclesequallyastheywouldtheirICEcounterparts.Atthetimethisiswritten,EVtechnologieshavenotbeenaroundinsufficientquantitiestofullyassessconsumervaluationfunctionswithrevealedpreferencedata.Anotherapproachtodevelopingthemixedlogit

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parametersinthisstudywouldbetoperformprimaryresearchwithstatedpreferencedataacquiredthroughconjointanalysis,orwaitforAFVstobecomemoremainstreamvehiclesandthenre-assessthestatisticalportionofthisanalysis.Performingthistaskwouldprovideaninterestingandvaluableanalysisbutisoutsidecurrenttimeandresourceavailability.

7.3 Asmentioned,EVtechnologieshavenotbeenaroundforasubstantive-enoughamountoftime.Thiscreatesuncertaintyinthefuturecharacteristicsofvehicles.Thisanalysisincorporatedconstantproducerandbatterymanufacturerresponseswhereasamoredynamicdepictionwouldbeaprofit-maximizationresponseofvehicleandcomponentmanufacturers,similartoZhangetal(2011).

7.4 Paststudieshaveindicatedthatseveralothersocialfactorsinfluenceconsumeradoptionandperceptionsofvehicles(ChooandMokhtarain2004;TurrentineandKurani2007).IncorporatingnetworkinteractionsintoaVCMisdonetoincludetheseeffectsbutfurtherresearchisrequiredtoinvestigatehumaninteractionsinamathematicalframework.Themodelpresentedhereisasimplificationoftheseinteractionsandtheassumptionsappliedhaveasubstantialeffectonthemodel'soutcome.

Appendix

AdditionalMixedLogitModelAssumptions

8.1 Discretechoicemodelsrequiredatatobeformattedsothatasinglechoiceismadeamongalternatives.Withrespecttothenewvehiclemarketplace,therearemorepurchaseoptionspresentthancurrentstatisticalsoftwarepackagescanreasonablyhandle.Duetotheserestrictions,thevehicleconsumerwasoriginallydesignatedtohavechosenamong100differentrandomly-determined(withoutreplacement)vehicleoptionsfromthesameyeartheobservedvehiclewaspurchased.Afterseveralstatisticalrunsandbasedonconvergenceandestimationefficiency,thechoicesetwaslimitedto20randomly-includedchosenvehicles.Thefinalamountofpotentialoptionsavailableinthechoicesetalsopresentedassuranceinboththereliabilityandconsistencyofestimationresults.

8.2 Theprobabilitythataspecificvehicleisincorporatedintothechoicesetisbasedontheannualmarketshareofsaidvehicle.Forexample,in2004,theFordF150marketsharewas5.29%(Wards2011),meaningthatineachobservation'schoicesettheprobabilitythattheFordF150isincludedis5.29%.Theaccuracyofthemarketsharesandtheirprobabilitiestobeincludedistothenearest1e-4,anyvehiclebelowthisvaluewasnotincludedinobservations'choicesets.

8.3 ThesoftwarepackagechosenforestimationisStata/SE64-bitandtheprocedureutilizedis'mixlogit'(Hole2007).Weightsfromthe2009NHTSwereincorporatedasfrequencyweightsintheanalysis.Additionally,sincethelargeamountofobservationsandvariabilityinthedatapresentedchallengesforthestandardalgorithmtoreachconvergence,thestandardNewton-RaphsonalgorithmweresupplementedwiththesteepestascentmethoddevelopedbyGouldandScribney(1999)aswellasGould,Pitblado,andScribney(2003).Thisreplacementonlyimpactedthenumberofiterationsrequiredtoreachconvergenceandnottheresultingparameterestimatesormodelqualities.

8.4 Since"[t]heobjectivefunctionoptimizedtoestimatethecoefficientsof[mixedlogit]modelsisgenerallyhighlynonlinear,andthuspronetomultiple,localoptima"(Greene2010,p.12),post-optimizationalgorithmsensitivityanalysiswasperformed.Theproductoftheparametersfollowingconvergenceandrandomly-assignedvalueswereusedasstartingparametersintheoptimizationprocesstoinsurethealgorithmacquiredthesameconvergencevalues.Theparametersobtainedfollowingtheoriginaloptimizationweresubsequentlymultipliedbyrandomly-assignedvaluesfromthefollowingsets:(-1.1,1.1),(-1.5,1.5),and(-2,2).Followingthesemodifications,thealgorithmreachedconvergencewiththesameparameters;theamountofadditionaliterationsrequireddependedontheextentofthedistortionappliedtotheoriginalparameterset.

8.5 Inthismodel,distributionsareimposedusingthebestfitmethoddescribedbyTrainandSonnier(2004).First,themodelswererunwithzeroandthenonlyoneofeachexplanatoryvariablewithanimposedlog-normaldistribution.Ifmultipleexplanatoryvariablessolelyprovidedanincreaseinthelog-likelihoodvalue,theexplanatoryvariablewiththegreatestmarginalcontributiontowardsthelog-likelihoodfunctionwasintroducedasthefirstlog-normallydistributedexplanatoryvariable.Thesecondgreatestcontributingexplanatoryvariabletothelog-likelihoodvaluefromlog-normaldistributionspecificationisthenintroducedandthelog-likelihoodvaluecomparedtothepriormodel'slog-likelihoodestimation,andsoon.Intheanalyses,thehouseholdswithchildrenincurredthebestfitwhenonlythelog-normaldistributionisappliedtothesafetyparameter.Imposingthelog-normaldistributiononthepowerparameteronlyimprovedtheretiredhousehold'smodelfit.Noneofthegroupings'modelfitsimprovedwhenimposingthelog-normaldistributiononeithertheDGCorMSRPparameters.Theresultsforlog-normaldistributedparameterspresentedinTable1haveundergonethenecessaryconversioncalculations.

8.6 Observationsfromthe2009NHTSwithincompleteinformationforvaluesincludedinthisstudywereexcludedresultinginalossofapproximately5%oftheoriginalsample.Also,inordertotakeintoaccountrecentvehiclecharacteristics,onlynewvehiclespurchasedfrom2004tothedateofthesurveytakenwereincludedinthemixedlogitmodel.TheNHTS2009queriedneitherthevehicle'sconditionwhenpurchased(neworused)northepriceofthevehicleandthustwoassumptionsneedtobemade.First,thedateofpurchaseneededtobewithinthenewmodelyear'ssellingperiod,assumedtobeAugusttheprioryearof

releasetoDecemberofthevehicleyear.Second,theconsumerpaidtheinflation-adjustedmanufacturer'ssuggestedretailprice(MSRP)[14].Intheend,thedatasetusedinthemixedlogitmodelcontained33,235observationsofvehiclesmatchedwiththeirprimarydriversandrespectivehouseholds.

8.7 SinceNationalAutomotiveSamplingSystem(NASS)vehiclecodesdonotdifferentiatebetweenvehicletrimlevels,theobservations'vehicletrimlevelwasdeterminedbytheclosestmatchwithinvehiclemodelbasedonthereportedMPGlevelfromthe2009NHTS.

FutureVehicleSpecifications

8.8 Vehicleconsumerswillchooseamongthesubsetofvehiclesavailableforpurchaseinthe2011vehiclefleetbutlimitedtothosewhichmeetthesamemarketsharepercentagesasthoseincludedinthemixedlogitmodeldatasetconstruction.ThesevehiclesarefixedinspecificationsexceptfortheattributesofMPGwhichincreases,foreachvehicle,withrespecttotheannualmarginalratesofchange(ROC)ofeachvehicle'spowertype(ICE,BEV,PHEV,HEV)andclass(carandlighttruck)indicatedinArgonneNationalLaboratory'sVISIONmodel(ANL,2011).

ICE

8.9 AcaveatwiththesimulationsisthatthechoicesetfromwhichthemixedlogitderivedparameterswereestimatedissmallerthanthechoicesetavailabletoconsumerswithintheABMsimulations.

HEV

CarClass LightTruckClassChevroletCruzeEcoFordFusionHybrid

BMWActiveHybridX6CadillacEscaladeHybrid

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HondaCivicHybridHondaInsightHondaCR-ZandCR-ZEXHyundaiSonataHybridKiaOptimaHybridLexusCT200hand200hPremiumLexusGS450hLexusHS250hand250hPremiumLincolnMKZHybridMercedesBenzS500HybridNissanAltimaHybridToyotaCamryHybridToyotaPriusI-V

ChevroletSilverado1500SeriesHybridChevroletTahoeHybridFordEscapeHybridGMCSierra1500SeriesHybridGMCYukonHybridMercedesBenzML450HybridPorsche911CayenneSHybridToyotaHighlandHybridVolkswagenTouaregHybrid

PHEV

8.10 ChevroletVolt

ToyotaRAV4Electric[15],[16],[17]

ToyotaPrius[18]

BEV

8.11 NissanLeafFordFocusElectric

Mitsubishii(MiEV)[19],[20],[21]

TeslaModelS[22](with40,60,and85kWhbatterycapacities,atbaseMSRP)

RechargingSystemCosts,PHEVDGC,andBatteryCosts

8.12 FuelingstationavailabilityandrangeanxietyhavebeenasignificantlimitationwithconsiderationtothediffusionofBEVs(Tate,Harpster,andSavagian,2008).

Inthemodel,ifthehouseholdisconsideringanEV,thefixedpriceforalevel2chargingstationis$2000[23]andisincurredasafixcostaddedtotheMSRP.Toaccountforrangeanxiety,aconsumerwillonlyconsideranelectricvehicleiftheiraveragedailyvehiclemilestravelledislessthanorequaltoaspecifiedpercentageoftherangeoftheBEVbeingassessed.ThisspecifiedpercentageisexploredintheResultssection.

8.13 Agenti'sDGCforPHEVmodeljiscomputedas:

(5)

where:

(6)

DPGGEe:Dollarspergallonofgasolineequivalent[24]MPGej:MilespergallonofgasolineequivalentVMTi:AveragedailyvehiclemilestravelledofagentiDPGg:DollarspergallonofgasolineMPGgj:MilespergallonofthegasolineportionofPHEVj

8.14 BEVandPHEVbatteryrangeshavebeenevaluatedbythreedifferentgroups:manufacturers,government(EPA),andtheprivatesector(CarandDriver).Sincetherangeofthevehicleisaseriousconsiderationforbuyers,scenariosareimplementedwhichreducetheagents'rangeofeachBEVandPHEVmodelbydifferentspecifications.Manufacturers,theEPA,aswellasCarandDriverestimatethemaximum,median,andminimumofrangeestimates,respectively.IftheEPAhastestedthevehicle,thatreductiongoesintoplace.Ifnot,theaveragepercentagereductionofthatvehicletype'srangeisapplied.SinceCarandDriverhasonlyreviewedandsuppliedestimatesfortheNissanLeafandChevroletVolt(asofMay2012),thesamepercentagereductionswillbeappliedontherangeofsaidvehicles'class.Additionally,MPGisadjustablebetweenmanufacturerandEPAestimates,andaveragepercentagereductionsifnotavailablefromthepriorsource.

8.15 SincebatterydepreciationandcostsareabarriertoconsumeradoptionofEVs(Morales-Espana2010),thepriceofEVsisadjustedannuallytotherateofchange,byvehicleclass(carandlighttruck)andBEV/PHEVdesignation,accordingtothescheduleestimatedbytheUKCommissiononClimateChange's(CCC)reportregardingfuturebatteryspecifications(UKCCC2012).Thesedataaremanufacturerpricesandthusanassumedmarkupof10%isincluded.Additionally,sincethebatterymaintainsalargereplacementcost,thenetpresentvalueofthetotaldepreciatedvalueofthebatteryattimeofresaleisincludedatthetimeofpurchase.Thisisdonewithanassumedconstantfailurerateat10years.Inequationform:

(7)

Where:

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PVi:PresentvalueofbatterydepreciationofvehicleiT:Yearsofownership(pre-determinedattimeorpurchase)ΔCi:Annualchangeinbatteryvalue

r:Discountrate[25]

FFVUtilization

8.16 Althoughotherworkhasresearchedconsumers'choicesinusingE85fuels(GreeneandZhou2008),usageratesforE85arenotsimulatedinthisresearchendeavor.Eventhoughbiofuelsareanimportantcomponentofaless-carbonintensiveandmoreenergysecurefuture(LeibyandRubin2012),severallimitationsonbiofuelutilizationhavebeenontheindustrialstructure,feedstock,andfinancialcomponentsintheindustrialsphere(Rabago2008)whichareoutsideofthismodel'spurpose.Thisisapotentialmodelexpansioncurrentlybeingexplored.

Acknowledgements

Thisworkwouldnotbepossiblewithouttheguidanceandsupportofmyadvisor,Dr.JonathanRubin.ThesiscommitteemembersDr.GaryHuntandDr.TimothyWaringdeserveconsiderableappreciationforhelpingmecompletethistask.Additionally,Iamfortunateandthankfulforthefouranonymousreviewerswhogreatlyhelpedinrefiningthispaper.

Notes

1Rangesforannualincomeareasfollows:Low:≤$45,000Middle:$45,000<x<$125,000high:≥$125,000

2Gas-guzzlertaxisincludedforcarsmeetingthecriteria

3Methodsforestimatingsafetyratingschangedforyear2011vehicles;safetyratingsusedinthisanalysisarethosepresentduringvehiclechoicedecision-making

4Significancelevels:*:0.15**:0.10***:0.05****:0.01

5AlthoughpossibletoreplicatetheentireUSpopulationitisdiscouragedduetoextensivecomputingtimes

6IftheagentownsanEV,theywillstillconsiderthestatusquoofICEvehicles

7Thisvariablefunctionssimilarlytothe'susceptibility'variablefromEppsteinetal(2011)

8ThisparameterbecomesthefocusofincentivepolicystructuresensitivityanalysisintheResultssection.

9After2035,annualratesofchangeforgasolinepricesarefromVISION2011

10Becausethisscenarioonlychangesonce,itisnotnormallypresentedinthesecondarylegend

11Includesextended-lifeonthepresentvaluecalculationsofbatterydepreciationandbatterycostadjustmentsbutdoesnotincludeefficiencygainsfromenergydensityimprovementbenefits.

12Assumedgasolineheatingvalueof125,000BTU

13Forexample,rangeisincreasedandthusVMTasarangelimitisnotasbinding

14Allmonetaryvaluesinthismodelwereadjustedto2011dollarsusingtheConsumerPriceIndex(2011)

15ModelinformationtakenfromToyota(2012a)andEdmunds(2012)

16TargetLaunchDateofSpring2013(Edmunds,2012)

17Volumeassumedtobethesameasthe2011modelICEcounterparts

18ModelinformationtakenfromToyota(2012b)

19ModelinformationtakenfromMitsubishi(2012)andEdmunds(2012)

20TargetLaunchDateofFall2012

21Atthetimethiswaswritten,safetyinformationwasnotavailable;thereforetheratingfromtheEuropeanNewCarAssessmentProgramme(EuroNCAP)offourstarswasassumed(EuroNCAP,2012)

22Sourceforinformation:TeslaMotors(2012);currentlysetonlytobeavailableinCalifornia

23BasedonNissan'sestimatesforchargerandinstallation(Nissan,2012)

24Assumedgasolineheatingvalueof125,000BTU/gallon

25Assumeddiscountrateof7%

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