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Pricing RINs and Corn in a Competitive Storage Model


  • Zhou, Wei
  • Babcock, Bruce A.


A rational expectations competitive storage model for U.S. corn and RIN (Renewable Identification Numbers) markets is built to study the impacts of different ethanol policy scenarios. The model considers corn use for ethanol, storage and all other uses in each period, accounting for two random variables: oil prices and corn yields. Borrowing and banking provisions of the Renewable Fuels Standard (RFS) mandate are also integrated into the model. We use the model to provide estimates of the impact on corn prices, corn plantings and ethanol production under two ethanol mandate scenarios for six marketing years from 2014/15. The first scenario is one in which corn ethanol mandates stay the same as required in the RFS and additional E85 stations are introduced that allow for compliance with higher mandates. The second scenario is one in which no investment occurs and the Environmental Protection Agency reduces the mandate to 13 billion gallons. We find that corn prices drop about 6 percent from reduced mandates or about 26 cents per bushel, while RIN prices drop from around 54 cents to nearly zero. The results suggest that meeting the more broad policy objectives of energy policy and not the price of corn or RINs should determine the level of ethanol mandates.

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  • Zhou, Wei & Babcock, Bruce A., 2014. "Pricing RINs and Corn in a Competitive Storage Model," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 170581, Agricultural and Applied Economics Association.
  • Handle: RePEc:ags:aaea14:170581
    DOI: 10.22004/ag.econ.170581

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    References listed on IDEAS

    1. Christophe Gouel, 2013. "Comparing Numerical Methods for Solving the Competitive Storage Model," Computational Economics, Springer;Society for Computational Economics, vol. 41(2), pages 267-295, February.
    2. Anderson, David P. & Anderson, John D. & Sawyer, Jason, 2008. "Impact of the Ethanol Boom on Livestock and Dairy Industries: What Are They Going to Eat?," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 40(2), pages 1-7, August.
    3. Hikaru Hanawa Peterson & William G. Tomek, 2005. "How much of commodity price behavior can a rational expectations storage model explain?," Agricultural Economics, International Association of Agricultural Economists, vol. 33(3), pages 289-303, November.
    4. Kenneth L. Judd, 1998. "Numerical Methods in Economics," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262100711, August.
    5. Mallory, Mindy L. & Irwin, Scott H. & Hayes, Dermot J., 2012. "How market efficiency and the theory of storage link corn and ethanol markets," Energy Economics, Elsevier, vol. 34(6), pages 2157-2166.
    6. Wyatt Thompson & Seth Meyer & Pat Westhoff, 2011. "What to Conclude About Biofuel Mandates from Evolving Prices for Renewable Identification Numbers?," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 93(2), pages 481-487.
    7. Rubin, Jonathan D., 1996. "A Model of Intertemporal Emission Trading, Banking, and Borrowing," Journal of Environmental Economics and Management, Elsevier, vol. 31(3), pages 269-286, November.
    8. Searchinger, Timothy & Heimlich, Ralph & Houghton, R. A. & Dong, Fengxia & Elobeid, Amani & Fabiosa, Jacinto F. & Tokgoz, Simla & Hayes, Dermot J. & Yu, Hun-Hsiang, 2008. "Use of U.S. Croplands for Biofuels Increases Greenhouse Gases Through Emissions from Land-Use Change," Staff General Research Papers Archive 12881, Iowa State University, Department of Economics.
    9. Michael J. Roberts & Wolfram Schlenker, 2013. "Identifying Supply and Demand Elasticities of Agricultural Commodities: Implications for the US Ethanol Mandate," American Economic Review, American Economic Association, vol. 103(6), pages 2265-2295, October.
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