Dear Chairwoman Nichols,

More than two years have passed since the California Air Resources Board (CARB) adopted resolution 10-49, which directed CARB staff to prepare amendments to the Low Carbon Fuel Standard (LCFS) by the spring of 2011. Among the amendments directed by the Board were carbon intensity (CI) revisions that would reflect “[u]pdates to the land use values for corn ethanol, sugarcane ethanol, and soy biodiesel, and other feedstocks…” Despite this clear directive and a substantial body of scientific evidence justifying changes, amendments revising the LCFS indirect land use change (ILUC) penalties still have not been proposed. Further, in the face of numerous updates to the lifecycle model that serves as the basis for CARB’s assignment of direct CI values, CARB staff continues to rely on outdated and obsolete data to assign direct CI values to default corn ethanol pathways. Indeed, new peer-reviewed, published research found a CI value of 62 g/MJ(grams of CO2-equivalent per mega joule) for average corn ethanol (including emissions from ILUC), compared to CARB’s obsolete value of 99.4 g/MJ for “average Midwest corn ethanol.” Thus, I am writing to again encourage CARB to honor its commitments to expeditiously revise the ILUC penalty factor assessed against corn ethanol and to utilize the “best available science” when determining direct CI values. Revising the direct and indirect CI values for corn ethanol would be much more than a mere academic exercise; rather, a continued failure to update these CI values will jeopardize the ability of regulated parties to reasonably comply with the LCFS program’s increasingly rigid CI standards in 2013, 2014 and beyond. Aside from two workshops held in mid-2011, CARB staff has been silent on the issue of updating ethanol direct and indirect CI values. It is unclear what, if any, progress has been made toward proposing regulatory amendments that would improve the ILUC values for corn ethanol and revise the direct CI estimates for default pathways. Throughout the LCFS rulemaking process, CARB repeatedly stated its intent to integrate lifecycle modeling improvements and new data as they became available. Indeed, in the LCFS Initial Statement of Reasons, the agency wrote, “…ARB has committed to determining the total direct and indirect emissions associated with production, distribution, and use of all fuels through conducting complete lifecycle analyses based on the best available science (emphasis added).”1 Further, ARB 1 California Air Resources Board, Staff Report: Initial Statement of Reasons, Proposed Regulation to Implement the Low Carbon Fuels Standard: Vol. I (March 5, 2009), Page IV-48 suggested, “The Board agrees that the issue of land use change impact estimation must be subject to ongoing evaluation and analysis…”2 and, “[t]he Board has also committed to an ongoing inquiry into the best indirect land use change estimation methodologies.”3 A substantial amount of research and lifecycle modeling has been conducted since CARB adopted the LCFS in 2009. Most of this research has been peer-reviewed and published, and much of the work has been conducted by researchers that CARB appointed in 2009 to its “Expert Work Group” on ILUC. As a result of the new research, the scientific understanding of corn ethanol’s direct lifecycle emissions and possible ILUC impacts has greatly improved. Yet today, nearly four years after CARB formally adopted the LCFS, not a single change has been made to the LCFS program’s ILUC penalty or direct CI values for default corn ethanol pathways. This letter briefly outlines some of the key advances in lifecycle analysis and ILUC modeling that have occurred since CARB formally adopted the LCFS, and discusses their relevance and applicability to the program. DIRECT EMISSIONS Version 1.8b of Argonne National Laboratory’s Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation (GREET) model served as the basis for CARB’s “CA-GREET” model that was used to assign direct CI values to the LCFS regulation’s corn ethanol pathways. Five new versions of Argonne’s GREET model, each with important revisions and data updates, have been released since CARB published its final analysis in March 2009. However, CARB has neglected to integrate any of the changes from Argonne into its own CA-GREET model. Argonne’s GREET1.8d, released in August 2010, significantly revised corn ethanol plant energy use based on an extensive peer-reviewed and published survey of ethanol plants conducted by the University of Illinois at Chicago.4 According to GREET1.8d (and the subsequent versions of GREET), dry mill ethanol plants producing dried distillers grains use, on average, 27,576 BTU/gallon of natural gas thermal energy. Dry mill plants producing wet distillers grains use 16,435 BTU/gallon on average. These values (based on real-world data) are considerably lower than CARB’s default pathway assumptions regarding natural gas use by dry mill ethanol plants. For instance, GREET1.8d’s default for a natural gas dry mill producing dried distillers grains is 15% lower than the value assumed by CARB. A comparison of the current Argonne GREET model’s ethanol plant energy use values to CARB’s CA-GREET assumptions is shown in Figures 1-3 of the attachment. Simply adopting GREET1.8d’s assumptions on ethanol plant thermal energy and electricity use would reduce the CI of CARB’s default corn ethanol pathways by approximately 8 g/MJ—a level that clearly surpasses CARB’s 5 g/MJ threshold of significance for Method 2 petitions. Better data and information are also available regarding emissions related to corn production. Specifically, newer data from the USDA National Agricultural Statistics Service is available regarding fertilizer use and energy use related to corn farming. This updated agricultural data is included in the 2 Calif. Air Resources Board, California’s Low Carbon Fuels Standard: Final Statement of Reasons (Dec. 2009), 638. 3 Ibid., 642 4 Mueller, S. (2010). 2008 National dry mill corn ethanol survey. Biotechnology Letters, 32, 1261-1264. Summary available at: current version of the Argonne GREET model. Further, new research by Clay et al. demonstrates that notill corn production systems (which account for about 30% of U.S. corn acres) can result in a large carbon sequestration credit (approximately 10-15 g/MJ) that is not reflected in CARB’s current analysis.5 In addition, research is under way to estimate the impacts on corn ethanol lifecycle GHG emissions of replacing a portion of traditional animal feed ingredients with corn stover that is harvested as a “co-product” of corn grain production. CARB staff has previously suggested that the availability of the “Method 2A/2B” pathway petition process obviates the need to update default direct CI values. On the contrary, corn ethanol producers are submitting Method 2A/2B petitions primarily because the “Look-up Table” default values badly misrepresent current average practices and efficiencies; the petition process is the only means available to secure a CI value that more appropriately reflects current industry norms. In this way, CARB’s failure to update its default corn ethanol direct CI values has not only created unnecessary cost and time burdens for ethanol producers (i.e., consultant fees for preparation of a typical Method 2 petition are in the range of $30,000-40,000), but it has also greatly increased CARB staff’s administrative burden and diverted resources away from other tasks (i.e., staff has indicated that substantial resources are dedicated to review of Method 2 petitions and replication of the applicants’ CA-GREET analyses). Updating the default corn ethanol pathways to reflect the current Argonne GREET default values would greatly alleviate these burdens on both corn ethanol producers and CARB staff. INDIRECT LAND USE CHANGE EMISSIONS While predictive ILUC analysis remains highly uncertain, the methods and data have substantially improved since CARB adopted the LCFS. These improvements have resulted in corn ethanol ILUC emissions estimates that are much lower than CARB’s estimate for the LCFS. The improved ILUC emissions estimates primarily result from better data and enhanced understanding of: the types of land most likely to be converted, the most likely location of predicted conversions, crop yields on newly converted lands, crop yield responses to changes in prices, carbon stocks and emissions from land conversion, the effects of animal feed co-products on land use, and crop switching/cross-commodity effects. New and improved methodologies for accounting for land use emissions over time (i.e., “time accounting”) have also been established.6 As the CARB “Expert Work Group” highlighted in 2010, important revisions have been made to Purdue University’s GTAP model, which was used by CARB to estimate ILUC for biofuel pathways under the LCFS. Specifically, improvements were made to the model’s energy elasticities, treatment of distillers grains, land conversion factors for new cropland, treatment of endogenous yield for cropland pasture, handling of cropland switching, and availability of cropland pasture and CRP. The result of these improvements was a reduction in estimated LUC emissions for corn ethanol from 30 g/MJ to 14.5- 18.2 g/MJ.7 Subsequent work by Purdue lowered corn ethanol LUC emissions further to 12.9-17 g/MJ.8 5 Clay, D., Schumacher, T., Clay, S., Chang, J., Gelderman, R., Carlson, C., Reitsma, K., & Janssen, L. (2012). Corn Yields and No-Tillage Affects Carbon Sequestration and Carbon Footprints. Agronomy Journal, 104, 763-770. It is notable that this study examines the carbon sequestration credit of no-tillage systems only; the emissions and sequestration impacts of other conservation tillage practices (e.g., strip-till, reduced till, etc.) have not been examined. 6 See, for example, Kloverpris, J. & Mueller, S. (2012). Baseline time accounting: Considering global land use dynamics when estimating the climate impact of indirect land use change caused by biofuels. Int J Life Cycle Assess, Sep. 11, 2012. 7 Tyner, W., Taheripour, F., Zhuang, Q., Birur, D., & Baldos, U. (2010). Land Use Changes and Consequent CO2 Emissions due to US Corn Ethanol Production: A Comprehensive Analysis, Final Report. Available at: Meanwhile, LUC modeling conducted in 2011 by the International Food Policy Research Institute (IFPRI) for the European Commission estimated corn ethanol LUC emissions at 10 g/MJ.9 In a report released in May 2012, researchers at Argonne National Laboratory and University of IllinoisChicago built upon Purdue’s recent GTAP work to develop a Carbon Calculator for Land Use Change from Biofuels Production (CCLUB) that is included in the newest version of the GREET model.10 The CCLUB estimates corn ethanol LUC emissions at 8-9.1 g/MJ. Most recently, Kim, Dale, and Ong estimated corn ethanol LUC emissions at 3.9-8.6 g/MJ using a new allocation method that more accurately assigns LUC emissions among the various drivers of conversion.11 Thus, based on newer data and improved methodologies, the independent estimates of corn ethanol LUC produced since the LCFS was finalized have generally trended in the range of 8-15 g/MJ. This compares to CARB’s ILUC estimate for corn ethanol of 30 g/MJ. A comparison of major LUC estimates for corn ethanol is presented in Figure 4 of the attachment. Based on controversial analysis commissioned by CARB and conducted by Yale University Prof. Steve Berry, CARB staff has indicated it may propose altering a key elasticity (the so-called “price-yield elasticity”) within the GTAP model for future ILUC revisions. Like Purdue University’s independent modeling on ILUC, CARB’s original corn ethanol ILUC estimate is based on a price-yield elasticity of 0.25. However, in its last public workshop regarding ILUC (held in September 2011), CARB staff indicated it was considering lowering the elasticity value to 0.10 or lower based on a “lack of research”to support the 0.25 value. CARB staff showed lowering this elasticity would effectively increase ILUC emissions. However, two recent papers on the subject of yield responsiveness to changes in price provide strong statistical support for retaining an elasticity value of 0.25 or higher. According to a paper published by economists at North Carolina State University and the University of Illinois-Chicago, “[i]n general, the aggregate models suggest a long-run price-yield elasticity of about 0.19-0.27, which is consistent with the 0.25 estimate currently used in the GTAP modeling framework.” 12 Further, the authors found, “[t]he long-run price-yield elasticities range from 0.15 to 0.43 at the state level. In that these states make up a significant proportion of total corn production in the US, these results may suggest adopting a relatively more elastic yield response to price when modeling land use and other economic factors in general equilibrium models (emphasis added).” These results are corroborated by a recent analysis conducted by an Iowa State University doctoral candidate. This analysis found a priceyield elasticity of 0.14-0.53 for corn, with a median value of 0.29.13 8 Taheripour, F. & Tyner, W. (2012). Induced land use emissions due to first and second generation biofuels and uncertainty in land use emissions factors. Agricultural & Applied Economics Association’s 2012 Annual Meeting, Seattle, Washington, August 12-14, 2012. Available at: 9 Laborde, D. (2011). Assessing the Land Use Change Consequences of European Biofuel Policies, Final Report. Available at: 10 Mueller, S., Dunn, J., & Wang, M. (2012). Carbon Calculator for Land Use Change from Biofuels Production (CCLUB): Users’ Manual and Technical Documentation. ANL/ESD/12-5. At: 11 Kim, S, Dale, B.E., & Ong, R.G. (2012). An alternative approach to indirect land use change: Allocating greenhouse gas effects among different uses of land. Biomass & Bioenergy, 46, 447-452. 12 Goodwin et al. (2012). Is Yield Endogenous to Price? An Empirical Evaluation of Inter- and Intra-Seasonal Corn Yield Response. Paper presented at Agricultural and Applied Economics Association 2012 Annual Meeting, August 12-14, 2012, Seattle, Washington. Available at: 13 Rosas Perez, J.F., Ph.D., (2012). Essays on the environmental effects of agricultural production. Iowa State University. 186 pages; 3539418. Available at: CONCLUSION When some of the abovementioned improvements are combined into one analysis, the results are striking. A recently published study by GREET model creator Michael Wang and others incorporated many of the aforementioned improvements regarding ethanol plant energy use, corn farming energy use, and land use change. Using the same basic analytical framework used by CARB in 2009, the research found a CI value of 62 g/MJ for average corn ethanol, including emissions from ILUC. 14 This value is 38% lower than CARB’s assumed CI value for “Midwest average” corn ethanol of 99.4 g/MJ. Clearly, given the data and modeling results that have become available since the LCFS was adopted, CARB has no scientific basis or rationale to maintain its current CI scores for corn ethanol. As noted above, failure to revise corn ethanol direct and indirect CI values in a timely matter will greatly complicate compliance with the LCFS in 2013, 2014 and beyond. In order to fully offset the deficits generated on CARBOB, the ethanol blended into E10 must possess a CI value of 83.6 g/MJ or lower in 2013. In 2014, ethanol must have a CI value of 76.7 g/MJ in order to fully offset CARBOB deficits. Because of CARB’s inflated direct and indirect CI values, most Midwest corn ethanol—even ethanol from many of the plants that secured new pathways via Method 2A/2B—will not be viable for compliance this year or next. The efficacy of the LCFS will be significantly jeopardized if regulated parties lose Midwest corn ethanol as a viable compliance option. Indeed, CARB data shows that ethanol has been used to generate approximately 90% of LCFS credits to date. We appreciate your consideration of this new information and again urge CARB to make good on its commitment to appropriately integrate the best available science into the LCFS regulation. We would greatly appreciate the opportunity to meet with you and your staff to discuss the new data and modeling results described above. Sincerely, Bob Dinneen Cc: Richard Corey Mike Waugh Floyd Vergara Jim Duffy John Courtis 14 Wang, M., et al (2012) Environ. Res. Lett. 7 045905 32,330 27,576 25,000 26,000 27,000 28,000 29,000 30,000 31,000 32,000 33,000 CA-GREET1.8b GREET1_2012 CARB (2009) Argonne (2012) BTU/gallon Figure 1. Corn Ethanol Dry Mill Natural Gas Thermal Energy Use (DDGS) CARB overstates thermal energy use by 15% 22,430 16,435 13,000 14,000 15,000 16,000 17,000 18,000 19,000 20,000 21,000 22,000 23,000 CA-GREET1.8b GREET1_2012 CARB (2009) Argonne (2012) BTU/gallon Figure 2. Corn Ethanol Dry Mill Natural Gas Thermal Energy Use (WDGS) CARB overstates thermal energy use by 27% 3,670 2,518 1,000 1,500 2,000 2,500 3,000 3,500 4,000 CA-GREET1.8b GREET1_2012 CARB (2009) Argonne (2012) BTU/gallon Figure 3. Corn Ethanol Dry Mill Electricity Use CARB overstates electricity use by 31% ATTACHMENT 104 63.3 30 28.4 27 10 9.1 11 14.5 3.9 0 10 20 30 40 50 60 70 80 90 100 110 Searchinger et al. [1] U.S. EPA [2] CA Air Resources Board [3] U.S. EPA [4] Hertel et al. [5] Tyner et al. [6] Laborde [7] GREET1_2012 CCLUB [8] Mueller & Kloverpris [9] Kim et al. [10] Feb-08 May-09 Dec-09 Mar-10 Mar-10 Jun-10 Oct-11 Jul-12 Aug-12 Nov-12 g CO2e/MJ Figure 4. CORN ETHANOL LUC EMISSIONS ESTIMATES, 2008-PRESENT 18 8.6 1. Searchinger, T., Heimlich, R., Houghton, R.A., Dong, F., Elobeid, A., & Fabiosa, J.(2008) Use of U.S. croplands for biofuels increases greenhouse gases through emissions from land-use change. Science, 319, 1238-1240. 2. U.S. EPA (2009). Notice of Proposed Rulemaking: Changes to Renewable Fuel Standard Program 3. California Air Resources Board (2009). California’s Low Carbon Fuel Standard, Final Statement of Reasons. 4. U.S. EPA (2010). Regulation of Fuels and Fuel Additives: Changes to Renewable Fuel Standard Program; Final Rule. 5. Hertel, T.W., Golub, A.A., Jones, A.D., O’Hare, M., Plevin, R.J., & Kammen, D.M. (2010). Effects of US Maize Ethanol on Global Land Use and Greenhouse Gas Emissions: Estimating Market-mediated Responses. BioScience, 60, 223-231. 6. Tyner, W., Taheripour, F., Zhuang, Q., Birur, D., & Baldos, U. (2010). Land Use Changes and Consequent CO2 Emissions due to US Corn Ethanol Production: A Comprehensive Analysis, Final Report. 7. Laborde, D. (2011). Assessing the Land Use Change Consequences of European Biofuel Policies, Final Report. 8. Mueller, S., Dunn, J., & Wang, M. (2012). Carbon Calculator for Land Use Change from Biofuels Production (CCLUB): Users’ Manual and Technical Documentation. ANL/ESD/12-5. 9. Kloverpris, J. & Mueller, S. (2012). Baseline time accounting: Considering global land use dynamics when estimating the climate impact of indirect land use change caused by biofuels. Int J Life Cycle Assess, published online. [Value shown is from Hertel et al., corrected for time accounting] 10. Kim, S, Dale, B.E., & Ong, R.G. (2012). An alternative approach to indirect land use change: Allocating greenhouse gas effects among different uses of land. Biomass & Bioenergy, 46, 447-452

January 17, 2013
Mary D. Nichols Chairwoman
California Air Resources
Board Headquarters Building
1001 “I” Street Sacramento, CA 95812