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Exploring the drivers' side of the “blend wall”: U.S. consumer preferences for ethanol blend fuels

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  • Aguilar, Francisco X.
  • Cai, Zhen
  • Mohebalian, Phillip
  • Thompson, Wyatt

Abstract

Analysis of stated preferences from over 2300 U.S. respondents shows that general attitudes nationwide favor the use of ethanol as a motor fuel but a sizeable segment (~20%) indicated strong unwillingness to buy ethanol blend fuels. Results from a discrete choice experiment analyzed using mixed logit regressions show that, all else constant, price-per-gallon and miles-per-gallon dominated preferences for fuel attributes but ethanol content made the average consumer more likely to choose a blend fuel. Findings provide strong evidence of heterogeneity in preferences driven by attitudes but also affected by age and income. At a point of price per mile equivalence for ethanol and gasoline, in a market where gasoline, E20 and E85 were available with no regulatory, supply or technological constraints, E85 would dominate market share. In this case ethanol would account for 56% of volume of motor fuels consumed. Our results show a high level of consumer substitutability of gasoline with ethanol and willingness to choose high ethanol blend fuels – which could help expand ethanol use beyond the current regulatory and technological limits of the blend wall.

Suggested Citation

  • Aguilar, Francisco X. & Cai, Zhen & Mohebalian, Phillip & Thompson, Wyatt, 2015. "Exploring the drivers' side of the “blend wall”: U.S. consumer preferences for ethanol blend fuels," Energy Economics, Elsevier, vol. 49(C), pages 217-226.
    • Handle: RePEc:eee:eneeco:v:49:y:2015:i:c:p:217-226
    DOI: 10.1016/j.eneco.2015.01.019
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    Cited by:

    1. Pouliot, Sébastien & Babcock, Bruce A., 2017. "Feasibility of meeting increased biofuel mandates with E85," Energy Policy, Elsevier, vol. 101(C), pages 194-200.
    2. Sébastien Pouliot & Kenneth A Liao & Bruce A Babcock, 2018. "Estimating Willingness to Pay for E85 in the United States Using an Intercept Survey of Flex Motorists," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 100(5), pages 1486-1509.
    3. Mohebalian, Phillip M. & Aguilar, Francisco X., 2018. "Design of tropical forest conservation contracts considering risk of deforestation," Land Use Policy, Elsevier, vol. 70(C), pages 451-462.
    4. Ghoddusi, Hamed, 2017. "Blending under uncertainty: Real options analysis of ethanol plants and biofuels mandates," Energy Economics, Elsevier, vol. 61(C), pages 110-120.
    5. Whistance, Jarrett & Thompson, Wyatt & Meyer, Seth, 2017. "Interactions between California's Low Carbon Fuel Standard and the National Renewable Fuel Standard," Energy Policy, Elsevier, vol. 101(C), pages 447-455.
    6. Liao, Kenneth, 2016. "Essays on the demand for ethanol in the United States: willingness to pay for E85," ISU General Staff Papers 201601010800005988, Iowa State University, Department of Economics.
    7. Liao, Kenneth & Pouliot, Sébastien, 2016. "Estimates of the Demand for E85 Using Stated-Preference Data off Revealed-Preference Choices," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 236107, Agricultural and Applied Economics Association.
    8. Anthony PARIS & Pascal GASTINEAU & Pierre-Alexandre MAHIEU & Benoît CHEZE, 2020. "Citizen involvement in the energy transition: Highlighting the role played by the spatial heterogeneity of preferences in the public acceptance of biofuels," LEO Working Papers / DR LEO 2828, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.

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    More about this item

    Keywords

    Ethanol fuel blends; Choice-based model; Mixed effects logistic regression; Market share analysis; U.S.;
    All these keywords.

    JEL classification:

    • Q01 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Sustainable Development
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • Q21 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Demand and Supply; Prices
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions

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