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Forecasting Market Shares of Environment-Friendly Vehicles under Different Market Scenarios

  • Received : 2013.01.10
  • Accepted : 2013.03.05
  • Published : 2013.03.31

Abstract

The purpose of this study is to estimate consumer preferences on hybrid cars and electric cars by employing a choice experiment reflecting the various market conditions, such as different projected market shares of green vehicles and $CO_2$ emission regulations. Depending on different market scenarios, we examine as to which attribute and individual characteristic affect the preferences of potential consumers on green vehicles and further, forecast the potential market shares of green cars. The primary results, estimated by a conditional logit and panel probit models, indicate that sales price, fuel cost, maximum speed, emission of air pollutants, fuel economy, and distance between fuel stations can significantly affect consumer's choice of environment-friendly cars. The second finding is that the unique features of electric cars might better appeal to consumers as the market conditions for electric cars are improved. Third, education, age, and gender can significantly affect individual preferences. Finally, as the market conditions become more favorable toward green cars, the forecasted market shares of hybrid and electric vehicles will increase up to 67% and 14%.

Keywords

References

  1. Adamowicz, W., P. Boxall, M. Williams, and J. Louviere, 1995. Stated preference approaches for measuring passive use values: choice experiments versus contingent valuation, Staff paper 95-03, Department of Rural Economy, University of Alberta, Canada.
  2. Axsen, J., D. C. Moutain, and M. Jaccard, 2009. Combining stated and revealed choice research to simulate the neighbor effect: The case of hybrid-electric vehicle. Resource and Energy Economics 31, 221-238. https://doi.org/10.1016/j.reseneeco.2009.02.001
  3. Batt, C. and J. Katz, 1997. A conjoint model of enhanced voice mail services: Implications for new service development and forecasting. Telecommunication Policy 21, 743-760. https://doi.org/10.1016/S0308-5961(97)00044-X
  4. Brownstone, D., D. S. Bunch, and K. Train, 2000. Joint mixed logit models of stated and revealed preferences for alternative-fuel vehicles. Transportation Research Part B 34, 315-338. https://doi.org/10.1016/S0191-2615(99)00031-4
  5. Bunch, D. S., M. Bradley, T. F. Golob, Kitamura, R., and G. P. Occhiuzzo, 1993. Demand for clean-fuel vehicles in California: A discrete choice stated preference pilot project. Transportation Research Part A 27(3), 237-253. https://doi.org/10.1016/0191-2615(93)90033-7
  6. Champ, P. A., K. J. Boyle, and T. C. Brown, 2003. A Primer on nonmarket valuation. Kluwer Academic Publishers. Netherlands.
  7. Ewing, G., S. Emine, 2000. Assessing consumer preference for clean-fuel vehicles: A discrete choice experiment. Journal of Public Policy and Marketing 19(1), 106-118. https://doi.org/10.1509/jppm.19.1.106.16946
  8. Greene, D., P. Patterson, M. Singh, and J. Li, 2005. Fee bates, rebates and gas-guzzler taxes: A study of incentives for increased fuel economy. Energy Policy 33(6), 757. https://doi.org/10.1016/j.enpol.2003.10.003
  9. Greene, W. H. 2008. Econometric Analysis. Pearson International Edition.
  10. Hanley, N., D. MacMillan, R. Wright, C. Bullock, I. Simpson, D. Parrisson, and B. Crabtree, 1998. Contingent valuation versus choice experiments, estimating the benefits of environmentally sensitive areas of Scotland. Journal of Agricultural Economics 49(1), 1-15. https://doi.org/10.1111/j.1477-9552.1998.tb01248.x
  11. Hensher, D. A. 1994. Stated preference analysis of travel choices: the state of the practice. Transportation, vol. 21, pp. 107-133. https://doi.org/10.1007/BF01098788
  12. Hidrue, M. K., G. R. Parsons, W. Kempton, and M. P. Gardner, 2011. Willingness to pay for electric vehicles and their attributes. Resource and energy economics 33, 686-705. https://doi.org/10.1016/j.reseneeco.2011.02.002
  13. Holmes, T. P., W. L. Adamowicz, 2003. Attribute-based methods in : Champ, P. A., Boyle, K. J., Brown, T. C. (Eds.), A Primer on nonmarket valuation. Kluwer Academic Publishers, Netherlands.
  14. Horne, M., M. Jaccard, and K. Tiedemann, 2005. Improving behavioral realism in hybrid energy-economy models using discrete choice studies of personal transportation decisions. Energy Economics 27 (1), 59. https://doi.org/10.1016/j.eneco.2004.11.003
  15. KAMA (Korea Automobile Manufacturers Association). 2010, Trend and forecast of HEV global sales, http://www.kama.or.kr(accessed at December 10, 2010).
  16. Korean Statistical Information Service. 2008. Korean Census 2007, http://kosis.kr/ abroad/abroad_01List.jsp?parentId=A. (accessed October 10, 2012).
  17. Mau, P., J. Eyzaguirre, and M. Jaccard, 2008. The "neighbor effect": Simulating dynamics in consumer preferences for new behicle technologies. Ecological Economics 68 (1), 504-516. https://doi.org/10.1016/j.ecolecon.2008.05.007
  18. McFadden, D. 1981. Econometric models of probabilistic choice, in: Manski, C., McFadden, D. (Eds.), Structural analysis of discrete data with econometric applications, Cambridge, MA: MIT Press.
  19. Ministry of Environment, 2010, Promotion plan for electric vehicles in the public sector document for public announcement. Republic of Korea.
  20. Slothuus, U., M. L. Larsen, and P. Junker, 2002. The contingent ranking method: A feasible and valid method when eliciting preference for health care?. Social Science and Medicine 54, 1601-1609. https://doi.org/10.1016/S0277-9536(01)00139-3
  21. Train, K. E. 2009, Discrete choice methods with simulation, 2nd edition, Cambridge University Press.
  22. Ulsan Technopolis, 2010, Comparison of Vehicle Performance Between EFVs and GVs. An internal document. Ulsan, Republic of Korea.
  23. Potoglou, D., P. S. Kanaroglou, 2007. Household demand and willingness to pay for clean vehicles. Transportation Research part D 12, 264-274. https://doi.org/10.1016/j.trd.2007.03.001
  24. Salant, P., D. A. Dillman, 1994. How to conduct your own survey. New York, NY: John Wiley & Sons, Inc.
  25. Santini, D. J. and A. D. Vyas, 2005. Suggestions for a New Vehicle Choice Model Simulating Advanced vehicle Introduction Decisions (AVID): Structure and Coefficients, Argonne National Laboratory Report ANL/ESD/05-1.
  26. STATA, 2009, STATA Longitudinal data/Panel data Reference Manual, Release 11.

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