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

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.

[1]  J. Burton,et al.  Birds and climate change , 1995 .

[2]  Ye Feng,et al.  Vehicle choices, miles driven, and pollution policies , 2005 .

[3]  Kara M. Kockelman,et al.  Behavioral distinctions : the use of light-duty trucks and passenger cars , 2000 .

[4]  Jeffrey A. Dubin,et al.  An Econometric Analysis of Residential Electric Appliance Holdings and Consumption , 1984 .

[5]  Eric J. Miller,et al.  Empirical Investigation of Household Vehicle Type Choice Decisions , 2003 .

[6]  Kenneth A. Small,et al.  On the Costs of Air Pollution from Motor Vehicles , 2018, Controlling Automobile Air Pollution.

[7]  Greenhouse Gas Emissions from a Typical Passenger Vehicle , 2005 .

[8]  K. Kockelman,et al.  Household Vehicle Ownership by Vehicle Type: Application of a Multivariate Negative Binomial Model , 2002 .

[9]  Thomas S Turrentine,et al.  Symbolism In Early Markets For Hybrid Electric Vehicles , 2007 .

[10]  K. S. Gallagher,et al.  Giving Green to Get Green: Incentives and Consumer Adoption of Hybrid Vehicle Technology , 2008 .

[11]  F. Mannering,et al.  A DYNAMIC EMPIRICAL ANALYSIS OF HOUSEHOLD VEHICLE OWNERSHIP AND UTILIZATION , 1985 .

[12]  Eric J. Miller,et al.  Dynamic Modeling of Household Automobile Transactions , 2003 .

[13]  John Rust,et al.  A nested logit model of automobile holdings for one vehicle households , 1985 .

[14]  Chandra R. Bhat,et al.  The Impact of Demographics, Built Environment Attributes, Vehicle Characteristics, and Gasoline Prices on Household Vehicle Holdings and Use , 2009 .

[15]  Nathan H. Miller,et al.  Automobile Prices, Gasoline Prices, and Consumer Demand for Fuel Economy , 2008 .

[16]  M. J. Hutzler,et al.  Emissions of greenhouse gases in the United States , 1995 .

[17]  Martin Lanzendorf,et al.  Impact of Life-Course Events on Car Ownership , 2006 .

[18]  Kara M. Kockelman,et al.  Microsimulation of Household and Firm Behaviors: Anticipation of Greenhouse Gas Emissions for Austin, Texas , 2009 .

[19]  H. Fang,et al.  A discrete–continuous model of households’ vehicle choice and usage, with an application to the effects of residential density , 2008 .

[20]  Patricia L. Mokhtarian,et al.  What type of vehicle do people drive? The role of attitude and lifestyle in influencing vehicle type choice - eScholarship , 2004 .

[21]  T. Markel,et al.  Plug-In HEV Vehicle Design Options and Expectations , 2006 .

[22]  Richard Barney Carlson,et al.  Deriving in-use PHEV fuel economy predictions from standardized test cycle results , 2009, 2009 IEEE Vehicle Power and Propulsion Conference.

[23]  Pavlos S. Kanaroglou,et al.  Household demand and willingness to pay for clean vehicles , 2007 .

[24]  Thomas S Turrentine,et al.  Automobile Buyer Decisions about Fuel Economy and Fuel Efficiency , 2004 .

[25]  K. Kockelman,et al.  Forecasting Greenhouse Gas Emissions from Urban Regions: Microsimulation of Land Use and Transport Patterns in Austin, Texas , 2013 .

[26]  Kara M. Kockelman,et al.  Quantifying External Costs of Vehicle Use: Evidence from America’s Top-Selling Light-Duty Models , 2008 .

[27]  Kara M. Kockelman,et al.  Micro-Simulation Models of Urban Regions: Anticipating Greenhouse Gas Emissions from Transport and Housing in Austin, Texas , 2009 .

[28]  F. Mannering,et al.  An exploratory analysis of automobile leasing by US households , 2002 .

[29]  Thomas S Turrentine,et al.  Driving Plug-In Hybrid Electric Vehicles: Reports from U.S. Drivers of HEVs converted to PHEVs, circa 2006-07 , 2008 .

[30]  Kara M. Kockelman,et al.  Household Energy Use and Travel: Opportunities for Behavioral Change , 2010 .

[31]  K. Small,et al.  Fuel Efficiency and Motor Vehicle Travel: The Declining Rebound Effect , 2007, Controlling Automobile Air Pollution.

[32]  Tony Markel,et al.  Plug-In Hybrid Electric Vehicle Energy Storage System Design , 2006 .

[33]  Kara M. Kockelman,et al.  Microsimulation of Household and Firm Behaviors: Coupled Models of Land Use and Travel Demand in Austin, Texas , 2007 .

[34]  Molly Espey,et al.  Automobile Fuel Economy: What is it Worth? , 2005 .

[35]  Jonn Axsen,et al.  The Early U.S. Market for PHEVs: Anticipating Consumer Awareness, Recharge Potential, Design Priorities and Energy Impacts , 2008 .

[36]  J. Berkovec,et al.  Forecasting automobile demand using disaggregate choice models , 1985 .

[37]  Kenneth Train,et al.  A disaggregate model of auto-type choice , 1979 .

[38]  D. Greene,et al.  Energy efficiency and consumption — the rebound effect — a survey , 2000 .

[39]  K. Train A Structured Logit Model of Auto Ownership and Mode Choice , 1980 .

[40]  Tony Markel,et al.  Cost-Benefit Analysis of Plug-In Hybrid Electric Vehicle Technology , 2007 .

[41]  Greg Spitz,et al.  Internet Access , 2010 .

[42]  Rene Roy,et al.  La distribution du revenu entre les divers biens , 1947 .

[43]  Toshiyuki Yamamoto,et al.  Accessibility and Auto Use in a Motorized Metropolis , 1999 .

[44]  Matthew J. Roorda,et al.  Toronto Area Car Ownership Study: A Retrospective Interview and Its Applications , 2000 .

[45]  C. Manski,et al.  An empirical analysis of household choice among motor vehicles , 1980 .

[46]  Lorna A. Greening,et al.  Household adjustment to gasoline price change: an analysis using 9 years of US survey data , 1999 .