Impact on Corn Prices from Reduced Biofuel Mandates
Press reports indicate that the Environmental Protection Agency may significantly reduce ethanol mandates to levels that can easily be met. To gain insight into what this decision implies about the price of corn we use a new model of the corn and RIN markets to project corn and ethanol prices and quantities through the 2019 marketing year under two ethanol mandate scenarios. The first scenario is the status quo where mandates that can be met with corn ethanol increase to 14.4 billion gallons in 2014 and 15 billion gallons in 2015 and thereafter. Mandates at this level can only be met using E85 so also included in this scenario is 5,000 new locations where E85 can be purchased. The second scenario holds mandates at 13 billion gallons, a level that can be met with E10. The price of corn is higher by between 5 and 6 percent-about 25 cents per bushel-in the higher mandate scenario. RIN prices are close to zero most of the time in the lower mandate scenario and average between 50 and 60 cents in the higher mandate scenario. Though the corn price difference is economically meaningful to corn farmers and livestock feeders, it is small compared to the price swings that the market has experienced since 2006. This modest change in corn prices from alternative mandate levels suggests that the level of mandate should be determined more by consideration of broad policy objectives rather than the impact on the price of corn. Of key importance to the advanced biofuel industry is whether policy will support the expansion of biofuels consumption by creating incentives to invest in flex cars and fueling stations that will facilitate expanded consumption of low-carbon ethanol. Consideration of the costs and benefits of creating these incentives as part of a national energy policy is of greater long-run importance than the impact of mandates on the price of corn.
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- Christophe Gouel, 2013. "Comparing Numerical Methods for Solving the Competitive Storage Model," Computational Economics, Society for Computational Economics, vol. 41(2), pages 267-295, February.
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