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Evolution of the household vehicle fleet: Anticipating fleet composition, PHEV adoption and GHG emissions in Austin, Texas

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  • Musti, Sashank
  • Kockelman, Kara M.

Abstract

In today's world of volatile fuel prices and climate concerns, there is little study on the relationship between vehicle ownership patterns and attitudes toward vehicle cost (including fuel prices and feebates) and vehicle technologies. This work provides new data on ownership decisions and owner preferences under various scenarios, coupled with calibrated models to microsimulate Austin's personal-fleet evolution. Opinion survey results suggest that most Austinites (63%, population-corrected share) support a feebate policy to favor more fuel efficient vehicles. Top purchase criteria are price, type/class, and fuel economy. Most (56%) respondents also indicated that they would consider purchasing a Plug-in Hybrid Electric Vehicle (PHEV) if it were to cost $6000 more than its conventional, gasoline-powered counterpart. And many respond strongly to signals on the external (health and climate) costs of a vehicle's emissions, more strongly than they respond to information on fuel cost savings. Twenty five-year simulations of Austin's household vehicle fleet suggest that, under all scenarios modeled, Austin's vehicle usage levels (measured in total vehicle miles traveled or VMT) are predicted to increase overall, along with average vehicle ownership levels (both per household and per capita). Under a feebate, HEVs, PHEVs and Smart Cars are estimated to represent 25% of the fleet's VMT by simulation year 25; this scenario is predicted to raise total regional VMT slightly (just 2.32%, by simulation year 25), relative to the trend scenario, while reducing CO2 emissions only slightly (by 5.62%, relative to trend). Doubling the trend-case gas price to $5/gallon is simulated to reduce the year-25 vehicle use levels by 24% and CO2 emissions by 30% (relative to trend). Two- and three-vehicle households are simulated to be the highest adopters of HEVs and PHEVs across all scenarios. The combined share of vans, pickup trucks, sport utility vehicles (SUVs), and cross-over utility vehicles (CUVs) is lowest under the feebate scenario, at 35% (versus 47% in Austin's current household fleet). Feebate-policy receipts are forecasted to exceed rebates in each simulation year. In the longer term, gas price dynamics, tax incentives, feebates and purchase prices along with new technologies, government-industry partnerships, and more accurate information on range and recharging times (which increase customer confidence in EV technologies) should have added effects on energy dependence and greenhouse gas emissions.

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  • Musti, Sashank & Kockelman, Kara M., 2011. "Evolution of the household vehicle fleet: Anticipating fleet composition, PHEV adoption and GHG emissions in Austin, Texas," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(8), pages 707-720, October.
  • Handle: RePEc:eee:transa:v:45:y:2011:i:8:p:707-720
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    References listed on IDEAS

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    1. Dubin, Jeffrey A & McFadden, Daniel L, 1984. "An Econometric Analysis of Residential Electric Appliance Holdings and Consumption," Econometrica, Econometric Society, vol. 52(2), pages 345-362, March.
    2. Small, K.A. & Kazimi, C., 1994. "On the Costs of Air Pollution from Motor Vehicules," Papers 94-95-3, California Irvine - School of Social Sciences.
    3. Kenneth A. Small & Kurt Van Dender, 2007. "Fuel Efficiency and Motor Vehicle Travel: The Declining Rebound Effect," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 25-52.
    4. Puller, Steven L. & Greening, Lorna A., 1999. "Household adjustment to gasoline price change: an analysis using 9 years of US survey data," Energy Economics, Elsevier, vol. 21(1), pages 37-52, February.
    5. Molly Espey & Santosh Nair, 2005. "Automobile Fuel Economy: What Is It Worth?," Contemporary Economic Policy, Western Economic Association International, vol. 23(3), pages 317-323, July.
    6. Mannering, Fred & Winston, Clifford & Starkey, William, 2002. "An exploratory analysis of automobile leasing by US households," Journal of Urban Economics, Elsevier, vol. 52(1), pages 154-176, July.
    7. Fang, Hao Audrey, 2008. "A discrete-continuous model of households' vehicle choice and usage, with an application to the effects of residential density," Transportation Research Part B: Methodological, Elsevier, vol. 42(9), pages 736-758, November.
    8. Choo, Sangho & Mokhtarian, Patricia L., 2004. "What type of vehicle do people drive? The role of attitude and lifestyle in influencing vehicle type choice," Transportation Research Part A: Policy and Practice, Elsevier, vol. 38(3), pages 201-222, March.
    9. Kurani, Kenneth S & Turrentine, Tom, 2004. "Automobile Buyer Decisions about Fuel Economy and Fuel Efficiency," Institute of Transportation Studies, Working Paper Series qt6zq891d1, Institute of Transportation Studies, UC Davis.
    10. Bhat, Chandra R. & Sen, Sudeshna & Eluru, Naveen, 2009. "The impact of demographics, built environment attributes, vehicle characteristics, and gasoline prices on household vehicle holdings and use," Transportation Research Part B: Methodological, Elsevier, vol. 43(1), pages 1-18, January.
    11. Kenneth Train, 1980. "A Structured Logit Model of Auto Ownership and Mode Choice," Review of Economic Studies, Oxford University Press, vol. 47(2), pages 357-370.
    12. Berkovec, James & Rust, John, 1985. "A nested logit model of automobile holdings for one vehicle households," Transportation Research Part B: Methodological, Elsevier, vol. 19(4), pages 275-285, August.
    13. Gallagher, Kelly Sims & Muehlegger, Erich, 2011. "Giving green to get green? Incentives and consumer adoption of hybrid vehicle technology," Journal of Environmental Economics and Management, Elsevier, vol. 61(1), pages 1-15, January.
    14. Kurani, Ken & Turrentine, Thomas, 2004. "Automobile Buyer Decisions about Fuel Economy and Fuel Efficiency," Institute of Transportation Studies, Working Paper Series qt5hh5k3j3, Institute of Transportation Studies, UC Davis.
    15. Berkovec, James, 1985. "Forecasting automobile demand using disaggregate choice models," Transportation Research Part B: Methodological, Elsevier, vol. 19(4), pages 315-329, August.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Javid, Roxana J. & Nejat, Ali, 2017. "A comprehensive model of regional electric vehicle adoption and penetration," Transport Policy, Elsevier, vol. 54(C), pages 30-42.
    2. Schwanen, Tim & Banister, David & Anable, Jillian, 2011. "Scientific research about climate change mitigation in transport: A critical review," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(10), pages 993-1006.
    3. Larson, Paul D. & Viáfara, Jairo & Parsons, Robert V. & Elias, Arne, 2014. "Consumer attitudes about electric cars: Pricing analysis and policy implications," Transportation Research Part A: Policy and Practice, Elsevier, vol. 69(C), pages 299-314.
    4. Mahlia, T.M.I. & Tohno, S. & Tezuka, T., 2013. "International experience on incentive program in support of fuel economy standards and labelling for motor vehicle: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 25(C), pages 18-33.
    5. Campbell, Amy R. & Ryley, Tim & Thring, Rob, 2012. "Identifying the early adopters of alternative fuel vehicles: A case study of Birmingham, United Kingdom," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(8), pages 1318-1327.
    6. Khan, Mobashwir & Kockelman, Kara M., 2012. "Predicting the market potential of plug-in electric vehicles using multiday GPS data," Energy Policy, Elsevier, vol. 46(C), pages 225-233.
    7. Bansal, Prateek & Kockelman, Kara M., 2017. "Forecasting Americans’ long-term adoption of connected and autonomous vehicle technologies," Transportation Research Part A: Policy and Practice, Elsevier, vol. 95(C), pages 49-63.
    8. Bishop, Justin D.K. & Martin, Niall P.D. & Boies, Adam M., 2016. "Quantifying the role of vehicle size, powertrain technology, activity and consumer behaviour on new UK passenger vehicle fleet energy use and emissions under different policy objectives," Applied Energy, Elsevier, vol. 180(C), pages 196-212.
    9. Whitehead, Jake & Franklin, Joel P. & Washington, Simon, 2014. "The impact of a congestion pricing exemption on the demand for new energy efficient vehicles in Stockholm," Transportation Research Part A: Policy and Practice, Elsevier, vol. 70(C), pages 24-40.
    10. Krupa, Joseph S. & Rizzo, Donna M. & Eppstein, Margaret J. & Brad Lanute, D. & Gaalema, Diann E. & Lakkaraju, Kiran & Warrender, Christina E., 2014. "Analysis of a consumer survey on plug-in hybrid electric vehicles," Transportation Research Part A: Policy and Practice, Elsevier, vol. 64(C), pages 14-31.
    11. 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.
    12. Brand, Christian & Anable, Jillian & Tran, Martino, 2013. "Accelerating the transformation to a low carbon passenger transport system: The role of car purchase taxes, feebates, road taxes and scrappage incentives in the UK," Transportation Research Part A: Policy and Practice, Elsevier, vol. 49(C), pages 132-148.
    13. Harris, Chioke B. & Webber, Michael E., 2014. "An empirically-validated methodology to simulate electricity demand for electric vehicle charging," Applied Energy, Elsevier, vol. 126(C), pages 172-181.
    14. Cartenì, Armando & Cascetta, Ennio & de Luca, Stefano, 2016. "A random utility model for park & carsharing services and the pure preference for electric vehicles," Transport Policy, Elsevier, vol. 48(C), pages 49-59.
    15. Tanaka, Makoto & Ida, Takanori & Murakami, Kayo & Friedman, Lee, 2014. "Consumers’ willingness to pay for alternative fuel vehicles: A comparative discrete choice analysis between the US and Japan," Transportation Research Part A: Policy and Practice, Elsevier, vol. 70(C), pages 194-209.
    16. Hirte, Georg & Tscharaktschiew, Stefan, 2013. "The optimal subsidy on electric vehicles in German metropolitan areas: A spatial general equilibrium analysis," Energy Economics, Elsevier, vol. 40(C), pages 515-528.
    17. repec:eee:enepol:v:110:y:2017:i:c:p:447-460 is not listed on IDEAS
    18. repec:eee:enepol:v:107:y:2017:i:c:p:561-572 is not listed on IDEAS
    19. Hackbarth, André & Madlener, Reinhard, 2016. "Willingness-to-pay for alternative fuel vehicle characteristics: A stated choice study for Germany," Transportation Research Part A: Policy and Practice, Elsevier, vol. 85(C), pages 89-111.
    20. Chen Wang & Ricardo Daziano, 2015. "On the problem of measuring discount rates in intertemporal transportation choices," Transportation, Springer, vol. 42(6), pages 1019-1038, November.
    21. Gardner, Lauren M. & Duell, Melissa & Waller, S. Travis, 2013. "A framework for evaluating the role of electric vehicles in transportation network infrastructure under travel demand variability," Transportation Research Part A: Policy and Practice, Elsevier, vol. 49(C), pages 76-90.
    22. Fontes, T. & Pereira, S.R., 2014. "Impact assessment of road fleet transitions on emissions: The case study of a medium European size country," Energy Policy, Elsevier, vol. 72(C), pages 175-185.
    23. repec:eee:transa:v:100:y:2017:i:c:p:182-201 is not listed on IDEAS
    24. Whitehead, Jake & Franklin, Joel P. & Washington, Simon, 2015. "Transitioning to energy efficient vehicles: An analysis of the potential rebound effects and subsequent impact upon emissions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 74(C), pages 250-267.
    25. Spickermann, Alexander & Grienitz, Volker & von der Gracht, Heiko A., 2014. "Heading towards a multimodal city of the future?," Technological Forecasting and Social Change, Elsevier, vol. 89(C), pages 201-221.

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