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The 'neighbor effect': Simulating dynamics in consumer preferences for new vehicle technologies

  • Mau, Paulus
  • Eyzaguirre, Jimena
  • Jaccard, Mark
  • Collins-Dodd, Colleen
  • Tiedemann, Kenneth
Registered author(s):

    Understanding consumer behaviour is essential in designing policies that efficiently increase the uptake of clean technologies over the long-run. Expert opinion or qualitative market analyses have tended to be the sources of this information. However, greater scrutiny on governments increasingly demands the use of reliable and credible evidence to support policy decisions. While discrete choice research and modeling techniques have been applied to estimate consumer preferences for technologies, these methods often assume static preferences. This study builds on the application of discrete choice research and modeling to capture dynamics in consumer preferences. We estimate Canadians' preferences for new vehicle technologies under different market assumptions, using responses from two national surveys focused on hybrid gas-electric vehicles and hydrogen fuel cell vehicles. The results support the relevance of a range of vehicle attributes beyond the purchase price in shaping consumer preferences towards clean vehicle technologies. They also corroborate our hypothesis that the degree of market penetration of clean vehicle technologies is an influence on people's preferences ('the neighbor effect'). Finally, our results provide behavioural parameters for the energy-economy model CIMS, which we use here to show the importance of including consumer preference dynamics when setting policies to encourage the uptake of clean technologies.

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    Article provided by Elsevier in its journal Ecological Economics.

    Volume (Year): 68 (2008)
    Issue (Month): 1-2 (December)
    Pages: 504-516

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    Handle: RePEc:eee:ecolec:v:68:y:2008:i:1-2:p:504-516
    Contact details of provider: Web page: http://www.elsevier.com/locate/ecolecon

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    1. Brownstone, David & Train, Kenneth, 1998. "Forecasting new product penetration with flexible substitution patterns," Journal of Econometrics, Elsevier, vol. 89(1-2), pages 109-129, November.
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    3. Hensher, David & Louviere, Jordan & Swait, Joffre, 1998. "Combining sources of preference data," Journal of Econometrics, Elsevier, vol. 89(1-2), pages 197-221, November.
    4. Brownston, David & Bunch, David S. & Train, Kenneth, 1999. "Joint mixed logit models of stated and revealed preferences for alternative-fuel vehicles," University of California Transportation Center, Working Papers qt7rf7s3nx, University of California Transportation Center.
    5. Train, Kenneth, 1985. "Discount rates in consumers' energy-related decisions: A review of the literature," Energy, Elsevier, vol. 10(12), pages 1243-1253.
    6. Nic Rivers & Mark Jaccard, 2005. "Combining Top-Down and Bottom-Up Approaches to Energy-Economy Modeling Using Discrete Choice Methods," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 83-106.
    7. Bunch, David S. & Bradley, Mark & Golob, Thomas F. & Kitamura, Ryuichi & Occhiuzzo, Gareth P., 1993. "Demand for clean-fuel vehicles in California: A discrete-choice stated preference pilot project," Transportation Research Part A: Policy and Practice, Elsevier, vol. 27(3), pages 237-253, May.
    8. Mark K. Jaccard & John Nyboer & Crhis Bataille & Bryn Sadownik, 2003. "Modeling the Cost of Climate Policy: Distinguishing Between Alternative Cost Definitions and Long-Run Cost Dynamics," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 49-73.
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    12. Roberts, James A. & Bacon, Donald R., 1997. "Exploring the Subtle Relationships between Environmental Concern and Ecologically Conscious Consumer Behavior," Journal of Business Research, Elsevier, vol. 40(1), pages 79-89, September.
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