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Appendices for “Do I Want to Buy It Now? A Vehicle Replacement Model Considering Strategic Consumer Behavior”Hyunhong Choi a and Yoonmo Koo b, Appendix A. Attribute and attribute levels for the choice experiment and designing choice situationsThis section provides detailed explanation about how attribute and attribute levels suggested in Table 1 is selected and how choice situations for the conjoint analysis are constructed. The first attribute is the operating method of the vehicle. This attribute explains which powertrain and type of fuel operates the vehicle. There are four attributes: gasoline, diesel, hybrid, and electric. Gasoline and diesel each represents traditional internal combustion engine vehicles (ICEVs) using each as its fuel. Hybrid represents hybrid electric vehicles (HEVs), which use both fossil fuel and electricity for driving. Specifically, HEV here means gasoline hybrid electric vehicles (GHEVs), which require refueling only for gasoline and electricity generated while driving is used to assist the powertrain to enhance fuel efficiency (e.g. Toyota Prius). Other types of hybrid electric vehicles exist, plug-in hybrid electric vehicles (PHEV) or diesel hybrid electric vehicles (DHEV). However, these vehicles have an extremely small market share (PHEV) or no sales (DHEV) in the South Korean market. Therefore, we excluded them for our analysis and only considered GHEVs as HEVs. Finally, electric refers to pure battery electric vehicles (BEVs) which only use electricity for driving.Next, the availability of charging infrastructure is a second key attribute. To improve respondents’ readability and understanding, attribute levels are defined in relative percentage, assuming availability of the current gasoline fuel charging station as 100% (Choi et al., 2018; Hong et al., 2012). We can use this attribute to observe change in vehicle adoption behavior as charging infrastructure for BEVs are expanded. For attribute levels, three levels are assumed; minimum value of 20% referring to current number of EV charging stations compared to number of gas stations (Korea Environment Corporation, 2018; Korea Oil Station Association, 2016), maximum value of 100%, and median value of 60%. The third key attribute is the class of the vehicle. Previous studies usually focused only on operating method of vehicles, and did not consider various vehicle classes (Choi et al., 2018; Byun et al., 2017). However, considering the class of a vehicle is quite important since it significantly effects consumer’s vehicle adoption behavior (Higgins et al., 2017). Moreover, vehicles with different class have different fuel economy and in turn, have different GHG emissions. This implies consideration of vehicle class can be especially important for environmental analysis for road transport sector. However, we cannot consider all classes exist in the real market as attributes in our choice experiment. Therefore, we defined four class groups that are distinctly differentiated and set each as an attribute level. The first group is the economy car class. Economy cars are small hatchback vehicles with low displacement. Although they enjoy policy incentives such as designated parking spaces or toll free discounts, regulations on size and engine displacement are strict for these vehicles. This distinguishes this class from other classes and it is, therefore, considered separately in our analysis. The second group is the compact/mid-size class. This class represents the popular intermediate sedan vehicles that have been a flagship product of the Korean auto market for many years. The compact and mid-size classes are combined since we assume that these two classes are perceived similarly by consumers. The third group is the full-size class, which has a similar appearance as the compact/mid-size class but larger and more luxurious. We separated this class since the class is likely to be perceived quite differently from the intermediate class (Higgins et al., 2017). The final group is the SUV; specifically, we refer to small and mid-sized SUVs. The class is attracting increasing attention and is clearly differentiated by its appearance. The fourth key attribute is vehicle fuel cost. To compare different fuel economy units (km/L, km/kWh) of vehicles with different operating methods, we used the fuel cost unit (KRW/km) for the analysis. We considered three attribute levels assuming that the current fuel cost of high-performance BEVs is approximately 50 KRW/km (0.04 USD/km), and the current fuel cost of low-performance gasoline vehicles is approximately 150 KRW/km (0.13 USD/km). We defined each value as a minimum and maximum attribute level and define the median level as 100 KRW/km (0.09 USD/km). Finally, the fifth attribute is the vehicle purchase price. We assumed three attribute levels for vehicle purchase price: high, normal, and low. We uses these three levels to construct hypothetical alternatives. Then, we used a baseline price for each vehicle type based on its operating method and vehicle class (16 types according to four operating methods and four vehicle classes) to provide specific value for each hypothetical alternative in the choice experiment. We derive the baseline price for each vehicle type by conducting a simple regression analysis using vehicle characteristics (operating method, class) and the market price of vehicle models sold on the market. Table A-1 shows the specific values used.Table A-1. Base price used for each vehicle type in choice experiment 1 (unit: USD)GasolineDieselHybridElectricEconomy13,267 15,036 17,690 33,610 Compact/mid-size17,690 19,459 22,112 38,917 Full-size26,534 31,841 34,495 53,069 SUV20,343 22,112 23,881 44,224 For a high (low) attribute level, the price was 30% higher (lower) than the baseline price. For a normal attribute level, the baseline price was applied. We used this design because we did not want to provide unrealistic alternatives to respondents. For example, the cheapest vehicle type considered in this study was the gasoline economy car with a market price of approximately USD 13,000. On the other hand, the most expensive vehicle type considered was the electric full-size vehicle with a market price that is expected to be over three to four times that of the gasoline economy car. In this situation, if we use a fixed price attribute level, the alternatives will be too unrealistic (for example, an electric full-size vehicle priced at USD 13,000 or a gasoline economy vehicle priced at USD 53,000). However, when constructing choice alternatives using these attributes, the second attribute, the availability of charging infrastructure, only applies to BEVs since gasoline and hybrid vehicles use gasoline fuel, and almost all gas stations in South Korea also sell diesel fuel. Therefore, although the constructed alternatives were hypothetical, providing gasoline, hybrid, or diesel vehicles with 20% or 60% infrastructure is unrealistic and may confuse respondents. Therefore, in this study, we combined the first and second attributes (operating method and availability of charging facilities) and considered a single attribute with six attribute levels when conducting an orthogonality test to construct independent choice alternatives. We used SPSS 23 for the orthogonality test and derived 32 independent alternatives. These alternatives were grouped by four to construct 16 choice sets with two alternatives for gasoline, hybrid, or diesel operating methods and two alternatives for the electric operating method. Sixteen choice sets were divided into two types of questionnaire with eight choice sets each (type A and B), and these questionnaires were randomly distributed to the respondents. Appendix B. Parameter estimates for other modelsAppendix B-1. Parameter Estimates for Model 1 and Model 2Model specification:ItemDescriptionUtility functionObservations used2664 (follow-up questions not included)Parameter Estimates:Mean estimate95% conf. int.Operating method(base: hybrid)Gasoline-1.082**-0.777 -1.418 Diesel-1.567**-1.299 -1.867 Electric-0.0310.682 -0.631 Vehicle class(base: economy)Compact/mid-size3.427**3.782 3.059 Full-size3.725**4.171 3.292 SUV4.186**4.698 3.690 Infrastructure (ln(%))2.308**3.188 1.424 Fuel cost (100 KRW/km)-1.433**-0.969 -1.913 Purchase price (10 million KRW)-1.821**-1.599 -2.059 **: significant in 99% confidence levelAppendix B-2. Parameter Estimates for Model 3Model Specification:ItemDescriptionUtility functionObservations used5328 (follow-up questions included)Parameter Estimates:Mean estimate95% conf. int.Operating method(base: hybrid)Gasoline-0.617**-0.406-0.824Diesel-1.008**-0.837-1.199Electric-1.033**-0.772-1.301Vehicle class(base: economy)Compact/mid-size2.257**2.532.027Full-size2.33**2.6192.084SUV2.4**2.6732.148Infrastructure (ln(%))0.701**1.0550.477Fuel cost (100 KRW/km)-0.076**-0.026-0.15Purchase price (10 million KRW)-0.994**-0.906-1.084Vehicle age (ln(years+1))-2.101**-1.836-2.383**: significant in 99% confidence levelAppendix B-3. Mean switching cost by operating type for three type of models (unit: USD)Switching situationModel 1 and Model 2Model 3Model 4FromToGasolineHybrid-5,255 -5,493 -4,078 DieselHybrid-7,609 -8,969 -5,822 GasolineElectric-5,103 3,702 4,841 DieselElectric-7,458 226 3,097 HybridElectric151 9,195 14,206 Note: Mean switching cost was derived by calculating the differences in mean marginal willingness to pay (MWTP) between different operating methods. In case of Model 4, mean MWTP of preference for SQ for operating method was also added.Appendix C. More detailed information about the base policy scenarioIn this section, we provide information on the base policy scenario used for the simulation analysis. The scope of the analysis is year 2018 to 2030 while data is prepared until 2033 to consider three-year forward-looking behavior. First, we need information about possible alternatives, which is the vehicles currently being sold in the market. Previous studies usually define a single representative vehicle by vehicle type of interest (Byun et al., 2017; Choi et al., 2018; Qian and Spooramanien, 2015) or chose a specific representative model for each vehicle type of interest (Helveston et al., 2015). However, this approach is valid only if the availability of each vehicle type is the same, or at least similar. However, the number of BEV and HEV models is currently small compared to ICEVs, and some vehicle classes do not have BEV or HEV models at all. In this case, consumers may not buy BEV or HEV models because of the limited choice. Thus, the market share of these vehicles may be overestimated if this aspect is not considered. Therefore, in this study, we incorporate multiple vehicle models available for purchase for each vehicle type based on the market situation (Woo, 2016).We selected 25 popular domestic vehicle models that sold more than 10,000 units between May 2017 and April 2018 in South Korea for the analysis. However, each model has one to three specific models with different operating types (e.g., Hyundai Grandeur Gasoline, Hyundai Grandeur Diesel, and Hyundai Grandeur Hybrid). This brings the total number of models (considering the operating method) to 50. Then, we collected fuel economy and purchase price information for these models. However, these 50 models were composed of 22 gasoline models, 18 diesel models, seven hybrid models, and only three electric models. Leading global auto manufacturers announced that they will expand the number of HEV and BEV models (CNBC, 2017; Volkswagen AG, 2018); therefore, we assume that the number of models for HEVs and BEVs will be extended to the same extent as gasoline fuel vehicles in the near future (Woo, 2016). Specifically, we assume that four BEV or HEV models are added each year until 2025. Therefore, from 2025, 78 vehicle models exist in the market. Table C-1 and C-2 gives specific information on the models chosen for the analysis and model expansion scenario for BEVs and HEVs. Table C-1. Vehicle models considered for the analysis (base year)RankSalesMakeModelClassOperating methodGasolineDieselHybridElectric1123,761HyundaiGrandeurFull-size1110280,502KIASorentoMid-size SUV1100379,506HyundaiSonataMid-size1110479,489 HyundaiAvanteCompact1100566,653 KIAMorningEconomy1000665,143 HyundaiSantafeMid-size SUV1100749,528 SsangyongTivoliCompact SUV1100845,037 HyundaiTussanCompact SUV1100941,991 KIAK5Mid-size11101041,381 ChevroletSparkEconomy10001141,319 KIAK7Mid-size11101240,385 HyundaiSportageCompact SUV11001339,112 GenesisG80Full-size Luxury11001437,983 HyundaiKonaCompact SUV11011534,546 KIAK3Compact11001631,455 Renault-SamsungSM6Mid-size11001726,516 Renault-SamsungQM6Mid-size SUV11001824,683 KIARayEconomy10001924,139 ChevroletMalibumid-sized10102023,521 KIANiroCompact SUV00112115,848 KIAStonicCompact SUV11002213,671 HyundaiIoniqCompact00112312,917 ChevroletTraxCompact SUV11002411,824 GenesisEQ900Full-size Luxury10002511,347Renault-SamsungQM3Compact SUV0100Total221873Table C-2. Model expansion for hybrid and electric modelsOperating methodVehicle class20182019202020212022202320242025Hybridcompact/mid-size44445566Full-size22334444SUV12356789Electriccompact/mid-size12333456Full-size01122334SUV23456789Total models in the market5054586266707478Next, we need information on the price of each type of fuel to convert fuel economy (km/L, km/kWh) to fuel cost (KRW/km). We used the average price of gasoline and diesel fuel (gasoline: 1491.3 KRW/L, diesel: 1282.53 KRW/L) for the year 2017. For the electricity used for BEVs, we used the price announced by the Ministry of Environment (Ministry of Environment, 2018). According to the Ministry of Environment, the cost of electricity for EVs was 173.8 KRW/kWh from 2017 to 2019 but will be 313.1KRW/kWh after 2019. However, the fuel cost and purchase price data collected are only for the base year (2018), and vehicle fuel economy and purchase price is expected to change in the future due to technological innovation. In this study, we used the projection of the Energy Information Administration (EIA, 2015) to determine future vehicles’ fuel economy and purchase price change by vehicle operating method. First, the fuel economy of ICEVs (gasoline, diesel), HEVs, and BEVs are assumed to be improved by 35%, 25%, and 10%, respectively, in 10 years (compounded annual growth rate (CAGR) of 3.39%, 2.51%, and 1.06% respectively). For the purchase price of vehicles, only the BEV purchase price is assumed to decrease 20% in 10 years (CAGR -2.45%), and the purchase price of vehicles with other operating methods is fixed for the whole timeline.Moreover, as of 2018, the Korean government provides incentives, such as purchase subsidies and tax exemptions, for BEVs and HEVs. However, the incentives for HEVs (approximately USD 300 in 2018) are only provided until 2018, which implies that HEVs must compete with ICEVs without any policy incentives (in terms of purchase price) starting in 2019. In case of BEVs, incentives are expected to continue, but the amount of incentives is expected to decrease. In 2018, the sum of all monetary incentives for purchasing (about USD 20,000) a BEV decreased by approximately 10% compared to 2017 (Environmentally Friendly Vehicle Integrated Information System, 2018). Therefore, this study assumes that the subsidy for BEVs decreases by 10% every year. Table C-3 provides an outline of the base policy scenario. Table C-3. Outline of the base policy scenarioFuel economyPurchase priceSubsidy and tax exemptionsGasoline35% improvement for 10 years(CAGR 3.39%)FixedNoneDieselHybrid25% improvement for 10 years(CAGR 2.51%)Abolished after 2018Electric10% improvement for 10 years(CAGR 1.06%)20% decrease for 10 years(CAGR -2.45%)Decrease 10% every yearFinally, the charging infrastructure for BEVs is assumed to be 15% in 2018 according to a comparison of the number of EV quick charging stations in April 2018 with the number of gas stations (Korea Environment Corporation, 2018; Korea Oil Station Association, 2016). Then, we assumed that this value reaches 100% by 2027 (CAGR of 23.47%).Appendix D. Vehicle replacement rate by forward-looking duration (Model 4)Note: results for 5 year forward-looking is provided until 2028, since data is only prepared for 2018-2033Appendix E. Simulation results not presented in the manuscriptThis section provides simulation results not presented in the main text of the manuscript, due to space constraints. Figures include annual sales share (Figure E-1) and stock share (Figure E-2) forecast sorted by vehicle class for the base policy scenario. Figure E-1. Annual sales share forecasts for the base policy scenario by vehicle classNote: the y-axis represents annual sales share (unit: %) and x axis represents yearFigure E-2. Stock share forecasts for the base policy scenario by vehicle classNote: the y-axis represents stock share (unit: %) and x axis represents yearAppendix F. Annual sales share forecast of BEVs for two scenarios for each model This section provides simulation results for annual sales share of BEVs for two scenarios (base policy vs. early abolishment of incentives in 2025) for each model (Figure F-1). Figure 9 in the main text is drawn using difference between the results of two scenarios for each model, for 2018-2025.Figure F-1. Change in annual sales share for BEVs for each modelReferences for AppendicesByun, H., Shin, J., & Lee, C.-Y. (2018). Using a discrete choice experiment to predict the penetration possibility of environmentally friendly vehicles. Energy, 144, 312–321.Choi, H., Shin, J., & Woo, J. (2018). Effect of electricity generation mix on battery electric vehicle adoption and its environmental impact. Energy Policy, 121, 13–BC. (2017). Toyota says all its cars will have an electric or hybrid option by 2025. CNBC. (2015). Annaul Energy Outlook 2015 with projections to 2040. Energy Information Administration (EIA). Washington DC, USA.Environmentally Friendly Vehicle Integrated Information System (2018). Subsidies for environmentally friendly vehicles. Environmentally Friendly Vehicle Integrated Information System (In Korean) (Accessed: 2018-03-01)Helveston, J. P., Liu, Y., Feit, E. M., Fuchs, E., Klampfl, E., & Michalek, J. J. (2015). Will subsidies drive electric vehicle adoption? Measuring consumer preferences in the US and China. Transportation Research Part A: Policy and Practice, 73, 96-112.Higgins, C. D., Mohamed, M., & Ferguson, M. R. (2017). Size matters: How vehicle body type affects consumer preferences for electric vehicles. Transportation Research Part A: Policy and Practice, 100, 182–201.Hoen, A., & Koetse, M. J. (2014). A choice experiment on alternative fuel vehicle preferences of private car owners in the Netherlands. Transportation Research Part A: Policy and Practice, 61, 199–215. Hong, J., Koo, Y., Jeong, G., & Lee, J. (2012). Ex-ante evaluation of profitability and government's subsidy policy on vehicle-to-grid system. Energy policy, 42, 95-104.Korea Environment Corporation (2018). Electric Vehicle Charging Information. Korea Environment Corporation. (In Korean) (Accessed: 2018-04-11)Korea Oil Station Association. (2016). Monthly Oil Station Report. Korea Oil Station Association (KOSA). (In Korean)Ministry of Environment (2018). Graded government subsidy for electric vehicles. Ministry of Environment (MOE). Qian, L., & Soopramanien, D. (2015). Incorporating heterogeneity to forecast the demand of new products in emerging markets: Green cars in China. Technological Forecasting and Social Change, 91, 33–46.Volkswagen AG. (2018). Volkswagen Group to expand production of electric vehicles worldwide on a massive scale. Volkswagen AG.., J. (2016). Identifying Structural Heterogeneity in Consumer Demand for New Technology (Doctoral dissertation, Seoul National University). ................
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