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Combining stated and revealed choice research to simulate the neighbor effect: The case of hybrid-electric vehicles

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  • Axsen, Jonn
  • Mountain, Dean C.
  • Jaccard, Mark
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    Abstract

    According to intuition and theories of diffusion, consumer preferences develop along with technological change. However, most economic models designed for policy simulation unrealistically assume static preferences. To improve the behavioral realism of an energy-economy policy model, this study investigates the "neighbor effect," where a new technology becomes more desirable as its adoption becomes more widespread in the market. We measure this effect as a change in aggregated willingness to pay under different levels of technology penetration. Focusing on hybrid-electric vehicles, an online survey experiment collected stated preference (SP) data from 535 Canadian and 408 Californian vehicle owners under different hypothetical market conditions. Revealed preference (RP) data was collected from the same respondents by eliciting the year, make and model of recent vehicle purchases from regions with different degrees of HEV popularity: Canada with 0.17% new market share, and California with 3.0% new market share. We compare choice models estimated from RP data only with three joint SP-RP estimation techniques, each assigning a different weight to the influence of SP and RP data in coefficient estimates. Statistically, models allowing more RP influence outperform SP influenced models. However, results suggest that because the RP data in this study is afflicted by multicollinearity, techniques that allow more SP influence in the beta estimates while maintaining RP data for calibrating vehicle class constraints produce more realistic estimates of willingness to pay. Furthermore, SP influenced coefficient estimates also translate to more realistic behavioral parameters for CIMS, allowing more sensitivity to policy simulations.

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    Paper provided by Institute of Transportation Studies, UC Davis in its series Institute of Transportation Studies, Working Paper Series with number qt02n9j6cv.

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    Date of creation: 03 Mar 2009
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    Handle: RePEc:cdl:itsdav:qt02n9j6cv
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    Keywords: UCD-ITS-RP-09-19; Engineering;

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    1. Couper, Mick P. & Kapteyn, Arie & Schonlau, Matthias & Winter, Joachim, 2007. "Noncoverage and nonresponse in an Internet survey," Munich Reprints in Economics 20093, University of Munich, Department of Economics.
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    Cited by:

    1. Jérôme Massiani, 2013. "The use of Stated Preferences to forecast alternative fuel vehicles market diffusion: Comparisons with other methods and proposal for a Synthetic Utility Function," Working Papers 2013:12, Department of Economics, University of Venice "Ca' Foscari".
    2. Noll, Daniel & Dawes, Colleen & Rai, Varun, 2014. "Solar Community Organizations and active peer effects in the adoption of residential PV," Energy Policy, Elsevier, vol. 67(C), pages 330-343.
    3. Jean-Francois Mercure & Hector Pollitt & Unnada Chewpreecha & Pablo Salas & Aideen M. Foley & Philip B. Holden & Neil R. Edwards, 2013. "The dynamics of technology diffusion and the impacts of climate policy instruments in the decarbonisation of the global electricity sector," 4CMR Working Paper Series 006, University of Cambridge, Department of Land Economy, Cambridge Centre for Climate Change Mitigation Research.
    4. Giraudet, Louis-Gaëtan & Guivarch, Céline & Quirion, Philippe, 2012. "Exploring the potential for energy conservation in French households through hybrid modeling," Energy Economics, Elsevier, vol. 34(2), pages 426-445.
    5. Hackbarth, André & Madlener, Reinhard, 2011. "Consumer Preferences for Alternative Fuel Vehicles: A Discrete Choice Analysis," FCN Working Papers 20/2011, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    6. Driscoll, Áine & Lyons, Seán & Mariuzzo, Franco & Tol, Richard S.J., 2013. "Simulating demand for electric vehicles using revealed preference data," Energy Policy, Elsevier, vol. 62(C), pages 686-696.
    7. Axsen, Jonn & Kurani, Kenneth S., 2013. "Hybrid, plug-in hybrid, or electric—What do car buyers want?," Energy Policy, Elsevier, vol. 61(C), pages 532-543.
    8. Dimitropoulos, Alexandros & Rietveld, Piet & van Ommeren, Jos N., 2013. "Consumer valuation of changes in driving range: A meta-analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 55(C), pages 27-45.
    9. Takanori Ida & Kayo Murakami & Makoto Tanaka, 2012. "Keys to Smart Home Diffusion: A Stated Preference Analysis of Smart Meters, Photovoltaic Generation, and Electric/Hybrid Vehicles," Discussion papers e-11-011, Graduate School of Economics Project Center, Kyoto University.
    10. Axsen, Jonn & Orlebar, Caroline & Skippon, Stephen, 2013. "Social influence and consumer preference formation for pro-environmental technology: The case of a U.K. workplace electric-vehicle study," Ecological Economics, Elsevier, vol. 95(C), pages 96-107.
    11. Andreas Ziegler, 2010. "Individual Characteristics and Stated Preferences for Alternative Energy Sources and Propulsion Technologies in Vehicles: A Discrete Choice Analysis," CER-ETH Economics working paper series 10/125, CER-ETH - Center of Economic Research (CER-ETH) at ETH Zurich.
    12. Kloess, Maximilian & Müller, Andreas, 2011. "Simulating the impact of policy, energy prices and technological progress on the passenger car fleet in Austria--A model based analysis 2010-2050," Energy Policy, Elsevier, vol. 39(9), pages 5045-5062, September.
    13. repec:dgr:uvatin:20110133 is not listed on IDEAS
    14. Hackbarth, André & Madlener, Reinhard, 2013. "Willingness-to-Pay for Alternative Fuel Vehicle Characteristics: A Stated Choice Study for Germany," FCN Working Papers 20/2013, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    15. Axsen, Jonn & Kurani, Kenneth S., 2009. "Interpersonal Influence within Car Buyers’ Social Networks: Five Perspectives on Plug-in Hybrid Electric Vehicle Demonstration Participants," Institute of Transportation Studies, Working Paper Series qt35w7s3jp, Institute of Transportation Studies, UC Davis.
    16. Jérôme Massiani, 2013. "SP surveys for electric and alternative fuel vehicles: are we doing the right thing?," Working Papers 2013_01, Department of Economics, University of Venice "Ca' Foscari".
    17. Yao, Richard T. & Scarpa, Riccardo & Turner, James A. & Barnard, Tim D. & Rose, John M. & Palma, João H.N. & Harrison, Duncan R., 2014. "Valuing biodiversity enhancement in New Zealand's planted forests: Socioeconomic and spatial determinants of willingness-to-pay," Ecological Economics, Elsevier, vol. 98(C), pages 90-101.
    18. Ladenburg, Jacob & Lutzeyer, Sanja, 2012. "The economics of visual disamenity reductions of offshore wind farms—Review and suggestions from an emerging field," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(9), pages 6793-6802.
    19. McCoy, Daire & Lyons, Sean, 2014. "The diffusion of electric vehicles: An agent-based microsimulation," MPRA Paper 54560, University Library of Munich, Germany.
    20. repec:dgr:uvatin:2011133 is not listed on IDEAS
    21. Parsons, George R. & Hidrue, Michael K. & Kempton, Willett & Gardner, Meryl P., 2014. "Willingness to pay for vehicle-to-grid (V2G) electric vehicles and their contract terms," Energy Economics, Elsevier, vol. 42(C), pages 313-324.
    22. Ziegler, Andreas, 2012. "Individual characteristics and stated preferences for alternative energy sources and propulsion technologies in vehicles: A discrete choice analysis for Germany," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(8), pages 1372-1385.

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