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ILUC: Real World Results vs. Economic Theory

October 20, 2010


A soon-to-be published paper by experts at the Department of Energy (DOE) has determined that indirect land use change (ILUC) resulting from corn ethanol expansion over the past decade has likely been "minimal to zero."  The forthcoming paper, from DOE's Oak Ridge National Laboratory, used empirical data to reach its conclusions rather than the "black box" economic models employed by the U.S. Environmental Protection Agency (EPA) and California Air Resources Board (CARB) for recently implemented fuel regulations. Few, if any, renewable energy issues have been as hotly debated over the past several years as the ILUC theory. Based on highly uncertain results obtained through imperfect economic modeling, the recently implemented federal Renewable Fuels Standard (RFS2) and California Low Carbon Fuels Standard (LCFS) penalize crop-based biofuels (like ethanol from grain and switchgrass) for greenhouse gas emissions (GHG) that may or may not actually occur as a result of predicted land use changes. Many in the scientific community have argued that lawmakers and regulators hastily put the policy cart ahead of the scientific horse by codifying ILUC penalties. They say not nearly enough is known about ILUC and other possible indirect GHG effects to exclusively penalize biofuels in a regulatory context, while ignoring the possible indirect effects associated with using other fuels. While still imprecise and uncertain, the science of ILUC is progressing rapidly. Earlier this year, Purdue University researchers released a study showing that the predicted ILUC emissions associated with corn ethanol expansion were just 14% of the first published estimate and less than half of the estimates finalized by CARB and EPA for the recently implemented LCFS and RFS2. And while the academic and regulatory communities admittedly still cannot predict ILUC and other indirect effects with a level of certainty necessary for regulatory purposes, it is clear that possible ILUC effects are much less significant than initially postulated. In fact, projected ILUC impacts may be approaching insignificance. The forthcoming paper from Oak Ridge National Laboratory is adding to the mounting body of research that suggests ILUC associated with biofuels expansion in the U.S. is likely negligible. Oak Ridge researchers last week presented their research results to a CARB expert panel, stating that "...minimal to zero indirect land use change was induced by use of corn for ethanol over the last decade." (emphasis added)  The findings are based on a rigorous examination of real world data from the 2001-2008 time period, a span in which U.S. ethanol production more than quadrupled. The researchers found that "Empirical evidence does not support significant effects on U.S. commodity exports [and] other crops or cropland expansion in the U.S." Another recent paper, this one published in Environmental Science & Technology and authored by Bruce Dale and other researchers at Michigan State University, found that significantly larger volumes of biofuels can be produced without incurring ILUC. "Using less than 30% of total U.S. cropland, pasture, and range, 400 billion liters (106 billion gallons) of ethanol can be produced annually without decreasing domestic food production or agricultural exports. This approach also reduces U.S. greenhouse gas emissions by 670 Tg CO2-equivalent per year, or over 10% of total U.S. annual emissions, while increasing soil fertility and promoting biodiversity. Thus we can replace a large fraction of U.S. petroleum consumption without indirect land use change." (emphasis added)  Such a volume of ethanol would be equivalent to roughly 75% of current annual U.S. gasoline consumption. With each new credible study on ILUC and other potential indirect GHG effects, the scientific community's understanding of these extremely complex issues improves. Indeed, as Purdue Professor Wally Tyner (a leading figure in the field of ILUC) recently stated, "As with any issue, your first cut may not be the best, but when you get new data and new methods, you improve."