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161023-1 Report Abstract

The Light-Duty-Vehicle Fleet’s Evolution: Anticipating PHEV Adoption and Greenhouse Gas Emissions Across the U.S. Fleet

Binny Paul, Kara Kockleman and Sashank Musti, University of Texas at Austin, May 2011, 195 pp. (161023-1)

The first part of this report relies on stated and revealed preference survey results across a sample of U.S. households to first ascertain vehicle acquisition, disposal, and use patterns, and then simulate these for a synthetic population over time. Results include predictions of future U.S. household-fleet composition, use, and greenhouse gas (GHG) emissions under nine different scenarios, including variations in fuel and plug-in-electric-vehicle (PHEV) prices, new-vehicle feebate policies, and land-use-density settings. This work highlights the impacts of various directions consumers may head with such vehicles. For example, twenty-five-year simulations at gas prices at $7 per gallon resulted in the second highest market share predictions (16.30%) for PHEVs, HEVs, and Smart Cars (combined) — and the greatest GHG-emissions reductions. The strciter feebate policy (pivot point at 30 mpg and fee or rebate rate of $400 per mpg) – coupled with gasoline at $5 per gallon – resulted in the highest market share (16.37%) for PHEVs, HEVs, and Smart Cars, but not as much GHG emissions reduction as the $7 gas price scenario. Excepting the low PHEV price and two feebate policy simulations, all other scenarios predicted a lower fleet VMT. While plug-in vehicles are now hitting the market, their adoption and widespread use will depend on thoughtful marketing, competitive pricing, government incentives, reliable driving-range reports, and adequate charging infrastructure.

The second part of this report relies on data from the U.S. Consumer Expenditure Survey (CEX) to estimate the welfare impacts of carbon taxes and household-level capping of emissions (with carbon-credit trading allowed). A translog utility framework was calibrated and then used to anticipate household expenditures across nine consumer goods categories, including vehicle usage and vehicle expenses. An input-output model was used to estimate the impact of carbon pricing on goods prices, and a vehicle choice model determined vehicle type preferences, along with each household’s effective travel costs. Behaviors were predicted under two carbon tax scenarios ($50 per ton and $100 per ton of CO2-equivalents) and four cap-and-trade scenarios (10-ton and 15-ton cap per person per year with trading allowed at $50 per ton and $100 per ton carbon price). Carbon taxes were found to relatively regressive than a cap-and-trade setting (in terms of taxes paid per dollar of expenditure), but a tax-revenue redistribution can be used to offset this regressivity. In the absence of substitution opportunities (within each of the nine expenditure categories), these results represent highly conservative (worst-case) results, but they illuminate the behavioral response trends while providing a rigorous framework for future work.


Keywords: Vehicle Choice, Fleet Evolution, Vehicle Ownership, Greenhouse Gas (GHG) emissions, Plug-In Hybrid Electric Vehicles (PHEVs), Climate Change Policy, Stated Preference, Opinion Survey, Microsimulation


ENTIRE REPORT (Adobe Acrobat File – 1.4 MB)