Dear Ms. Wind,

The Renewable Fuels Association (RFA) appreciates the opportunity to provide comments in response to the Department of Environmental Quality’s (DEQ) proposed rule for Phase 2 of the Oregon Clean Fuels Program (CFP). RFA is a national trade association representing the domestic ethanol industry. Our membership includes ethanol producers and marketers, vendors to the ethanol industry, agricultural organizations, and other groups dedicated to the continued expansion and promotion of fuel ethanol.

RFA has generally supported science-based policies and regulations designed to reduce the carbon intensity (CI) of our transportation fuels. In fact, ethanol producers have often cited Phase 1 of Oregon’s Clean Fuels Standard and British Columbia’s Low Carbon Fuel Requirement Regulation as examples of “Low Carbon Fuels Policies Done Right,” as both programs have, to date, used a consistent and level playing field for CI scoring of all fuels (i.e., only verifiable direct emissions have been included).

Thus, the ethanol industry was surprised and highly disappointed to see that the Phase 2 proposed rule includes subjective penalty factors for hypothetical indirect land use changes (ILUC) for select biofuels, but no indirect effect penalty factors for any other fuel types. Inclusion of highly uncertain and prescriptive ILUC factors creates an asymmetrical and discriminatory framework for the CFP.

As detailed in the attached comments, we strongly recommend that DEQ exclude indirect effects from the CFP’s carbon intensity scoring framework until such time as there is broad scientific agreement on the best methodology for estimating the indirect effects for all fuels. Further, even if it was appropriate to include ILUC factors for biofuels in the CI scoring framework, DEQ is proposing to use factors that have been shown to be grossly exaggerated and based on outdated information and data. In addition, a careful review of U.S. and global land use trends over the past decade provides no evidence that the types of “biofuels-induced” land use changes predicted by economic models have in fact occurred. Thus, RFA believes DEQ should conduct analysis using empirical data to ascertain whether, and to what extent, biofuels expansion has actually caused ILUC and related emissions before rushing to adopt ILUC factors.

Additionally, we are concerned by certain elements of the proposed rule related to direct CI values, the proposed petroleum baseline, and other issues. These matters are addressed more fully in the attached comments.

The success of the Oregon CFP ultimately depends on having strong support and backing from affected stakeholder groups. The U.S. ethanol industry will continue to support performance-based low carbon fuel programs that are grounded in the principles of fairness, sound science, and consistent analytical boundaries. However, introducing concepts that lack scientific integrity and balance into the regulatory framework (i.e., ILUC for biofuels but no indirect effects for other fuels) only creates stakeholder division and controversy. Again, we urge DEQ to exclude indirect effects from CI scoring in the Phase 2 CFP rule.

Thank you again for the opportunity to comment on the proposal.


Geoff Cooper
Senior Vice President


The Oregon Department of Environmental Quality (DEQ) released its proposed rule for Phase 2 of the Oregon Clean Fuels Program (CFP) on October 1, 2014. The Renewable Fuels Association (RFA) offers the following comments in response to the proposal.


a. DEQ should exclude indirect effects from the program’s carbon intensity scoring framework until such time as there is broad scientific agreement on the best methodology for estimating the indirect effects for all fuels.

The ethanol industry has generally supported LCFS and CFP programs that are based on fair and symmetrical carbon intensity (CI) scoring principles. In fact, ethanol producers have cited British Columbia’s Low Carbon Fuel Requirement Regulation and the first phase of Oregon’s CFP as examples of “LCFS Policies Done Right,” as both programs have, to date, based CI scoring for all fuels on verifiable direct emissions only.

Given Oregon DEQ’s pragmatic approach to CI scoring during Phase 1 of the program, the ethanol industry was surprised and disappointed to see that the Phase 2 proposed rule includes indirect land use change (ILUC) penalty factors for certain biofuels, but no indirect effect penalty factors for any other fuel types. Not only does inclusion of ILUC factors create an asymmetrical and discriminatory framework for the CFP, but it also appears to go against the recommendations of DEQ’s CFP Advisory Committee.

Six years after the concept of ILUC emissions was introduced in Searchinger et al., there is still no scientific consensus on the best methods for estimating ILUC or other indirect effects. While published estimates of ILUC emissions have trended downward over the past six years, the latest estimates still exhibit a wide range and high level of uncertainty. Further, the use of uncertain and subjective ILUC penalty factors for regulatory purposes (e.g., CI scoring under the California LCFS) remains highly controversial and polarizing. Indeed, as a result of California Air Resources Board’s (CARB) decision in 2009 to include ILUC factors, the California LCFS lost the support of important industry, academic, and political stakeholders. By proposing to include indirect effect penalties only for biofuels, Oregon runs the risk of similarly losing the backing of key stakeholder groups at a critical juncture for the program.

b. As a matter of fairness and consistency, DEQ should not assess penalties for indirect effects against only one class of fuels. If DEQ includes ILUC for biofuels, it must also include indirect emissions associated with all other regulated fuels (including baseline petroleum).


As stated above, DEQ should exclude penalties for indirect effects until there is broad consensus on how best to estimate such effects for all fuels. The principles of lifecycle analysis require that consistent analytical boundaries are used when evaluating and comparing the attributes of various competing products. Thus, if DEQ decides to penalize biofuels for predicted ILUC emissions, it must also include penalty factors for other fuels based on their potential to induce additional emissions through indirect economic effects at the resource margin. It is inarguable that all forms of energy have associated indirect economic effects, many of which have implications for the fuel’s lifecycle carbon intensity. The challenge for policymakers and regulators is isolating and quantifying those effects in a manner that is scientifically defensible and driven by consensus-based methodologies.

Despite requests from leading experts that CARB and the U.S. Environmental Protection Agency undertake such analysis, there remains a substantial void of research on the potential indirect effects of transportation fuels other than crop-based biofuels. Indeed, in its January 2011 final report, the CARB- appointed Expert Work Group identified a number of potential indirect emissions sources from other fuels and recommended that CARB should, in the short-term:

…conduct analysis, including but not limited to economic modeling, of the impact of the marginal barrel of oil[,]…the marginal supply of natural gas[,]…the potential market-mediated effect on electric power markets of using increased quantities of natural gas in the transportation sector[,]…reevaluation of marginal electricity[,]…[and] the impact of petroleum substitutes on refinery operations.1

To our knowledge, CARB has disregarded this recommendation to date. However, the scant body of existing research on indirect effects for other fuels does indicate the potential for significant indirect emissions that are not being captured in DEQ’s proposed lookup table. For example, Liska and Perrin (2010) estimate that assigning military emissions related to protecting access to Persian Gulf oil would result in a CI value increase of 8.1 grams CO2e/megajoule (g/MJ) (a roughly 8.5 percent increase in the overall CI value of gasoline and diesel fuel derived from Persian Gulf oil under the California LCFS).2 Similarly, Unnasch et al. (2009) identified a number of direct and indirect emissions sources that are excluded from most lifecycle analyses of petroleum-based fuels. According to the study:

…to the extent that economic effects are considered a part of the life cycle analysis of alternative fuels, as is the case with iLUC for biofuels, their effect vis-à-vis petroleum is also of interest. The effect of changes in petroleum

1 Heirigs et al. (2011). Air Resources Board Expert Working Group. Final Recommendations: Subgroup on Indirect Effects of Other Fuels. Available at: alternative-modeling.pdf
2 A.J. Liska & R.K. Perrin (2010). Securing Foreign Oil: A Case for Including Military Operations in the Climate Change Impact of Fuels. Environment 52:4 (July/August 2010), pp. 9–22. Available at:


supply and price will affect global goods, their movement, and the use of resources and their related GHG emissions.3

Similarly, the indirect effects of emerging alternative fuels/vehicles have been omitted from most lifecycle analyses because those effects are not well understood and have not been rigorously scrutinized. However, where research does exist on these effects, potentially significant indirect (and overlooked direct) effects are revealed. For example, the limited research available on the direct and consequential effects of increased reliance on electric vehicles (EVs) shows that widespread use of EVs could be worse for the climate than continued reliance on gasoline and diesel. According to Hawkins et al. (2012), when the impacts of EV production, battery production, battery disposal, and expansion of electricity demand are properly included in lifecycle emissions inventories, some EV pathways perform worse than gasoline and diesel. The authors concluded:

EVs are poised to link the personal transportation sector together with the electricity, the electronic, and the metal industry sectors in an unprecedented way. Therefore the developments of these sectors must be jointly and consistently addressed in order for EVs to contribute positively to pollution mitigation efforts.4

Further, Tahil (2007) found that resource constraints for certain rare earth minerals used in EV battery production will present economic and environmental challenges not currently considered in lifecycle analyses of EV emissions. Tahil writes:

Analysis of Lithium’s geological resource base shows that there is insufficient economically recoverable Lithium available in the Earth’s crust to sustain Electric Vehicle manufacture in the volumes required…Depletion rates would exceed current oil depletion rates and switch dependency from one diminishing resource to another. Concentration of supply would create new geopolitical tensions, not reduce them.5

Clearly, all fuels have associated indirect effects. If DEQ opts to include ILUC penalties for biofuels, it must also analyze and include economically-derived indirect effects penalties for all other fuels as well.

3 Unnasch. S., et al. (2009). Assessment of Life Cycle GHG Emissions Associated with Petroleum Fuels; Life Cycle Associates Report LCA-6004-3P. 2009. Prepared for New Fuels Alliance. Available at:
4 Hawkins, T. R., et al. (2013). Comparative Environmental Life Cycle Assessment of Conventional and Electric Vehicles. Journal of Industrial Ecology, 17: 53–64. doi: 10.1111/j.1530-9290.2012.00532.x.

5 Tahil, W. (2007) Meridian International Research. The Trouble with Lithium: Implications of Future PHEV Production for Lithium Demand. Available at:


c. Even if it was appropriate to include ILUC factors for biofuels in the CI scoring framework, DEQ is proposing to use factors that have been shown to be grossly exaggerated and based on outdated information and data.

Table #3 (“Oregon Carbon Intensity Lookup Table for Gasoline and Gasoline Substitutes”) of the proposal assigns an ILUC penalty of 30 g/MJ to all corn ethanol pathways. It appears DEQ has simply “copied and pasted” the lookup table from CARB’s LCFS regulation without performing any due diligence or modeling to determine whether the CARB factors are accurate or appropriate. Because CI values are the engine that drives the CFP program, and because ILUC factors have been the subject of intense scientific debate and scrutiny, it is critical that DEQ conduct its own evaluation of indirect effects before rashly inserting CARB’s values into the regulation.

In any event, CARB will soon be proposing revisions to its ILUC factors for the California LCFS. While there are still many significant problems with CARB’s draft revisions to its ILUC penalties, it should be noted that the preliminary work presented by CARB shows a reduction of ILUC for corn ethanol from 30 g/MJ to 21.6-25 g/MJ.6 However, CARB’s preliminary revised ILUC values for corn ethanol still remain well outside the range of recent estimates from Argonne National Laboratory (developer of the GREET model), Purdue University, the European Commission, Michigan State University, Oak Ridge National Laboratory and others. In a March 2014 letter to CARB Chair Mary Nichols (Attachment A), 14 leading bioenergy researchers (including five members of CARB’s Expert Work Group on ILUC) wrote:

Many of us continue to believe the use of point-estimate ILUC factors is inappropriate for the purposes of regulation. However, to the extent that CARB continues to rely upon the use of ILUC factors in calculating CI scores for the LCFS, we believe the Board should be familiar with the most recent independent modeling results. In general, our recent work—and analyses conducted by other experts in the field—indicates that CARB’s existing CI factors significantly overestimate the GHG emissions associated with potential ILUCs resulting from corn ethanol expansion. Analyses conducted since CARB adopted the LCFS in 2009 show that potential ILUC emissions associated with corn ethanol are more likely in the range of 6-15 grams per megajoule of CO2 equivalent (g/MJ), compared to CARB’s estimate of 30 g/MJ (emphasis added).

6 Comments submitted by RFA outline a number of technical concerns with CARB’s preliminary ILUC analyses presented at public workshops in March and September 2014. RFA comments are available at:; (with Growth Energy); and


If DEQ remains committed to including ILUC factors in the CFP lookup table (which we strongly oppose for the reasons stated in previous sections), the penalty factors should at least be based on the best available science and data from the leading experts and institutions in the field. We do not believe DEQ should rely entirely on CARB for estimating potential ILUC emissions.

d. Before deciding whether to include ILUC penalties for biofuels, DEQ should conduct analysis using empirical data to determine whether, and to what extent, biofuels expansion caused ILUC and related emissions.

CARB’s ILUC analysis, upon which DEQ’s proposed ILUC penalty factor is based, is meant to simulate the land use effects of biofuels growth from 2001 to 2015. Obviously, the time period in question is drawing to a close. Thus, empirical land use data is available for most of the simulation period. Any objective scientist would find it prudent to examine the real-world data to determine whether predictive model results agreed with actual observed outcomes.

Certainly, it is difficult to disentangle the real-world impact of biofuels expansion from the effects of other factors on actual global land use—but that does not mean regulatory agencies considering ILUC factors shouldn’t at least attempt to ground-truth predictive results against real-world data. For example, CARB’s ILUC analysis predicts that biofuels growth from 2001 to 2015 would cause roughly 100,000 hectares of forest to be converted to cropland in the U.S. However, empirical data from the U.S. Department of Agriculture and U.N. Food & Agriculture Organization show no loss of forestland in the U.S. during that period; instead, U.S. forestland has grown by approximately 7 million hectares.

The principles of good policymaking and sound scientific analysis require that model predictions be validated when possible. Indeed, other predictive models utilized by CARB and other agencies for other regulatory purposes have been validated and results have been verified. One potential means of validating CARB’s analysis would be to “back-cast” the new dynamic version of the GTAP model. We encourage DEQ to conduct such an exercise.


a. The California LCFS direct CI values, which serve as the basis for DEQ’s proposed values, are currently in the process of being revised by CARB based on updated versions of the GREET model. Final revised values for the California LCFS have not yet been proposed for adoption. Thus, DEQ is proposing direct CI scores based on CARB values that may change substantially in the near term.

As noted above, CARB is in the process of revising both direct and indirect CI values for corn ethanol and other fuels. The revisions are the result of CARB’s migration to the latest version of the GREET model maintained by Argonne National Laboratory. The newest Argonne version of GREET contains important updates and corrects several flawed assumptions from previous versions of the model. Yet, the DEQ


proposal uses direct CI values based on the 2008 version of CA-GREET1.8b and CARB’s original lookup table, which is soon to be outdated and irrelevant.

We understand DEQ has developed an Oregon-specific version of GREET (called “OR-GREET”) based on the outdated 2008 CA-GREET1.8b. Thus, it is surprising that corn ethanol CI values in Table #3 of the proposal are identical to CARB’s soon-to-be revised CI values. It seems impossible to us that the Oregon CI values would be identical to California’s CI values, based on differences in transportation modes and distances alone.

Nonetheless, if DEQ remains committed to using CARB values for direct CI scores (which we would discourage), we believe DEQ should use values from the CA-GREET2.0 model under development. Preferably, however, DEQ would not rely on CARB’s direct CI analysis for regulated fuel pathways and would conduct its own lifecycle modeling using Argonne’s GREET1_2014 or a new version of OR-GREET based on Argonne’s GREET1_2014.


a. DEQ should revisit its lifecycle analysis of baseline gasoline and diesel fuels. The baseline values appear incorrect when the sources of Oregon’s gasoline are considered, and when DEQ’s baseline values are compared to baseline values for other programs.

DEQ’s proposed CI value for baseline gasoline appears inappropriately low, and we were unable to locate any DEQ analyses that show how the baseline values were derived. DEQ reports that roughly 90% of the gasoline consumed in Oregon comes from refineries in the Puget Sound area, and that 80-90% of the crude oil processed by those refineries comes from Alaska’s North Slope by pipeline and tanker.7 The balance of the crude oil processed by the refineries comes primarily from Alberta via pipeline.8

The Oil Production Greenhouse Gas Emissions Estimator (OPGEE) model developed by Stanford University contains CI values for recovery of various crude oil sources, including oil from Alaska’s North Slope and various fields in Canada. When OPGEE values for these oil sources are combined with GREET model values for other phases of the gasoline lifecycle, we see that the direct CI of baseline gasoline used in Oregon is more likely in the range of 96-113 g/MJ, compared to the 89.4 g/MJ in the DEQ proposed rule (see table below). A weighted average (80% Alaska NS and 20% Canada) results in a CI score for baseline Oregon gasoline of 99.9-103.3 g/MJ. If DEQ ultimately decides to include indirect effects in its CI scoring framework, the total CI for baseline gasoline will be even higher. We strongly encourage DEQ to re-estimate the CI of Oregon baseline gasoline using OPGEE and GREET1_2014.

7 Wallace, R. (2009) Oregon Department of Environmental Quality. Motor Fuel & Distillate in Oregon: Quantity, Sources & Distribution. Available at: 8 Ibid.


Approx. CI of Oregon Gasoline Produced from Alaskan NS and Canadian Crude Oil (in g CO2e/MJ)

Alaska North Slope


Crude Recovery [1]



Crude Transport [2]



Crude Refining [3]



Gasoline Transport/Distribution [2]



Combustion [3]



TOTAL Direct



Indirect Effects

? [TBD]

? [TBD]

TOTAL Direct + Indirect



[1] OPGEE Model. Stanford University [2] GREET1_2014
[3] CARB (based on CA-GREET)


Table #6 (“Oregon Energy Densities of Fuels”) of the proposal incorrectly shows the energy content of “denatured ethanol” as 80.53 MJ/gallon. The 80.53 MJ/gallon figure actually refers to the energy content of undenatured ethanol. CARB made this same error in its original LCFS regulation, but later amended the value to 81.51 MJ/gallon to accurately reflect the energy content of denatured fuel ethanol. The difference of roughly 1 MJ/gallon may at first seem trivial, but when spread across hundreds of millions of gallons of fuel, the discrepancy has important implications for credit and deficit generation. We recommend that DEQ correct this error.


We are encouraged by the provisions of OAR 340-253-0450, which allow individual biofuel producers to secure approval of unique carbon intensity values. This process will be particularly important if DEQ finalizes the lookup table in the proposal, as it appears to overstate the direct CI of the most common corn ethanol pathways. However, the experience of the Method 2A/2B process in California has demonstrated that reviewing and approving petitions for individual CI scores can be a resource intensive process. In order to ensure the efficient and timely approval of petitions for individual CI values, we strongly recommend that DEQ prioritize this review process when allocating staff resources for management of the CFP program.


Appendix A

Letter from scientists and researchers to CARB Chair Mary Nichols regarding advancements in ILUC analysis

March 6, 2014

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

Dear Chairwoman Nichols,

We, the undersigned scientists and researchers, are writing to encourage the California Air Resources Board (CARB) to strongly consider recent developments in the analysis of indirect land use change (ILUC) when contemplating potential amendments to the Low Carbon Fuel Standard (LCFS). We understand CARB is considering potential changes to the LCFS regulation’s current carbon intensity (CI) values, and that these possible adjustments are the subject of an upcoming stakeholder workshop on March 11.

Many of us were members of the CARB-appointed expert work group, which convened in 2010 for the purposes of critically reviewing CARB’s ILUC analysis, identifying data gaps and areas in need of additional analysis, and recommending improvements. Upon completion of a year-long deliberative process, the work group recommended that CARB should revise its ILUC estimates using the latest version of Purdue University’s GTAP model. Further, many of us have independently conducted additional data analysis and ILUC modeling in the years following the conclusion of CARB’s expert work group process. In many cases, the findings from our research have been subjected to peer-review and published in the scientific literature.

While ILUC analysis continues to suffer from a relatively high degree of systematic and data uncertainty, the quality of both the models and input data chosen for use by CARB have substantially improved since the Board formally adopted the LCFS. These improvements have resulted in corn ethanol ILUC emissions estimates that are much lower than CARB’s current estimates for the LCFS. The improved ILUC emissions estimates result from the availability of more robust data and enhanced understanding of: 1) the types of land most likely to be converted; 2) the likely location of predicted conversions; 3) crop yields on newly converted lands; 4) crop yield responses to changes in prices; 5) carbon stocks and emissions from land conversion; 6) the feedback effects of animal feed co-products on land use; and 7) crop switching, double-cropping, and cross- commodity effects. Alternative methodologies for accounting for land use change emissions over time (i.e., “time accounting”) have also been established.

Many of us continue to believe the use of point-estimate ILUC factors is inappropriate for the purposes of regulation. However, to the extent that CARB continues to rely upon the use of ILUC factors in calculating CI scores for the LCFS, we believe the Board should be familiar with the most recent independent modeling results. In general, our recent work—and analyses conducted by other experts in the field—indicates that CARB’s existing CI factors significantly overestimate the GHG emissions associated with potential ILUCs resulting from corn ethanol expansion. Analyses conducted since CARB adopted the LCFS in 2009 show that potential ILUC emissions associated with corn ethanol are more likely in the range of 6-15 grams per megajoule of CO2 equivalent (g/MJ), compared to CARB’s estimate of 30 g/MJ. A bibliography of relevant corn ethanol ILUC studies conducted in recent years is provided in the attachment.

Nearly three and a half years have passed since the Board adopted resolution 10-49, which directed CARB staff to prepare amendments to the LCFS by the spring of 2011. Among the amendments directed by the Board were CI revisions that would reflect “[u]pdates to the land use values for corn ethanol, sugarcane ethanol, and soy biodiesel, and other feedstocks…” Given this directive and CARB’s commitment to using the “best available science” to “determin[e] the total direct and indirect emissions associated with…all fuels,”[1] we believe CARB staff should give serious consideration to immediately adopting a lower ILUC factor for corn ethanol based on the studies included in the attachment.


Steffen Mueller, PhD
Principal Research Economist Energy Resources Center University of Illinois at Chicago CARB Expert Work Group Member

Blake A. Simmons, PhD Vice-President
Deconstruction Division
DOE Joint BioEnergy Institute Sandia National Laboratories CARB Expert Work Group Member

[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

Jesper Kløverpris, PhD Sustainability Manager Novozymes A/S
CARB Expert Work Group Member

Richard G. Nelson, PhD
Enersol Resources Inc.
(Former Associate Professor at Kansas State University) CARB Expert Work Group Member

Mark D. Stowers, PhD
Vice President and Head
Global Research and Development HM.Clause
CARB Expert Work Group Member

Harvey W. Blanch, PhD
Merck Professor of Biochemical Engineering Department of Chemical & Biomolecular Engineering University of California Berkeley

Jay D. Keasling, PhD
University of California, Berkeley
Lawrence Berkeley National Laboratory Director, DOE Joint BioEnergy Institute Synthetic Biology Engineering Research Center

Bruce E. Dale, PhD
University Distinguished Professor
Department of Chemical Engineering and Materials Science DOE Great Lakes Bioenergy Research Center
Michigan State University

C. Gregg Carlson, PhD
South Dakota State University Professor, Plant Science

David E. Clay, PhD
South Dakota State University
Professor, Plant Science and Director South Dakota Drought Center

Timothy Donohue, PhD
University of Wisconsin-Madison
Professor of Bacteriology
Director, DOE Great Lakes Bioenergy Research Center

Seungdo Kim, PhD
Associate Professor
Department of Chemical Engineering and Materials Science Michigan State University

Jon Magnuson, PhD
Director of Fungal Biotechnology DOE Joint BioEnergy Institute

Stefan Unnasch Managing Director
Life Cycle Associates, LLC

Bibliography of Recent Studies on Ethanol Carbon Intensity and Indirect Land Use Change

Clay, D.E.; Chang, J; Clay, S.A.; Stone, J.; Gelderman, R.H.; Carlson, G.C.; Reitsma, K.; Jones, M.; Janssen, L.; Schumacher, T. corn Yield and No-Tillage Affects Carbon Sequestration and Carbon Footprints. Agron. J. 2012, 104, 763-770, DOI:10.2134/agronj2011.0353

Dunn, J. B.; Mueller, S.; Kwon, H.-Y.; Wang, M. Q. Land-use change and greenhouse gas emissions from corn and cellulosic ethanol. Biotechnol. Biofuels 2013, 6 (51), 1−13, DOI: 10.1186/1754-6834-6-51.

Dunn, J. B.; Mueller, S.; Kwon, H.-Y.; Wander, M.; Wang, M. Carbon Calculator for Land Use Change from Biofuels Production (CCLUB) Manual, ANL/ESD-13/8, 2013.

Kim, S.; Dale, B.E.; Ong, R.G. An alternative approach to indirect land use change: Allocating greenhouse gas effects among different uses of land. Biomass and Bioenergy, 2012, 46, 447-452, DOI: 10.1016/j.biombioe.2012.07.015.

Kim, S.; Dale, B.E. Indirect land use change for biofuels: Testing predictions and improving analytical methodologies. Biomass and Bioenergy, 2011, DOI: 10.1016/j.biombioe.2011.04.039

Kløverpris, J. H.; Mueller, S. 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 2013, 18 (2), 319−330, DOI: 10.1007/s11367-012-0488- 6.

Oladosu, G; Kline, K. A dynamic simulation of the ILUC effects of biofuel use in the USA. Energy Policy 2013, 61, 1127-1139, DOI: 10.1016/j.enpol.2013.06.124.

Oladosu, G.; Kline, K.; Leiby, P.; Uria-Martinez, R.; Davis, M.; Downing, M.; Eaton, L. Global economic effects of USA biofuel policy and the potential contribution from advanced biofuels. Future Sci. Biofuels 2012, 3, 703–723.

Oladosu, G.; Kline, Keith; Uria-Martinez, R.; Eaton, L. Sources of corn for ethanol production in the United States: a decomposition analysis of the empirical data. Biofuels, Bioproducts and Biorefining 2011, 5 (6), 640-653, DOI: 10.1002/bbb.305.

Elliott, J; Sharma, B.; Best, N.; Glotter, M.; Dunn. J.; Foster, I.; Miguez, F.; Mueller, S.; Wang, M. A Spatial Modeling Framework to Evaluate Domestic Biofuel-Induced Potential Land Use Changes and Emissions. Environmental Science & Technology 2014, 48 (4), 2488-2496.

Taheripour, F.; Tyner, W. E. Induced land use emissions due to first and second generation biofuels and uncertainty in land use emission factors. Econ. Res. Int. 2013, 1−12.

Tyner, W. E.; Taheripour, F.; Zhuang, Q.; Birur, D.; Baldos, U. Land Use Changes and Consequent CO2 Emissions Due to US Corn Ethanol Production: A Comprehensive Analysis; Purdue University: West Lafayette, IN, 2010;

Unnasch. S.; Boland, S. Carbon Intensity of Marginal Petroleum and Corn Ethanol Fuels. Life Cycle Associates Report LCA.6075.83.2014, 2014, Prepared for Renewable Fuels Association.

Wang, M.; Han, J.; Dunn, J. B.; Cai, H.; Elgowainy, A. Well-to-wheels energy use and greenhouse gas emissions of ethanol from corn, sugarcane and cellulosic biomass for US use. Environ. Res. Lett. 2012, 7, 1−13, DOI: 10.1088/1748-9326/7/4/045905.

Wang, M.Q.; Han,J.; Haq,Z.; Tyner, W.E.; Wu,M.; Elgowainy, A. Energy and greenhouse gas emission effects of corn and cellulosic ethanol with technology improvements and land use changes. Biomass and Bioenergy 2011, 35 (5), 1885-1896, DOI: 10.1016/j.biombioe.2011.01.028.

November 7, 2014

Cory Ann Wind
Oregon Department of Environmental Quality 811 SW 6th Ave.
Portland, OR 97204-1390

RE: Comments of the Renewable Fuels Association (RFA) in Regard to Proposed Rule for Phase 2 of the Oregon Clean Fuels Program