Appendix E
Assessing the Sustainability of Biofuels: Metrics, Models, and Tools for Evaluating the Impact of Biofuels

Background Paper for the National Academies’ Workshop: “Expanding Biofuel Production: Sustainability and the Transition to Advanced Biofuels”


June 23-24, 2009

Madison, WI


Chris Tessum, Adam Boies, Jason Hill, and Julian D. Marshall University of Minnesota

INTRODUCTION

This background paper explains and discusses concepts and issues related to the sustainability of biofuels, including the definition of sustainability in general and as related to biofuel production, the proposed and implemented regulatory frameworks aimed at labeling and controlling the sustainability of biofuel production, and the software tools available to quantify various aspects of sustainability.

SUSTAINABILITY FRAMEWORKS

The use of the term “sustainability” is so widespread in the discussion of anthropogenic impacts on our planet that its meaning in several contexts, including biofuels, is ill defined. Multiple sustainability frameworks are available, most or all of which are applicable to biofuel production:

The Triple Bottom Line. This framework, also called the “3 E’s” (envi-



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Appendix E Assessing the Sustainability of Biofuels: Metrics, Models, and Tools for Evaluating the Impact of Biofuels Background Paper for the National Academies’ Workshop: “Expanding Biofuel Production: Sustainability and the Transition to Advanced Biofuels” June 23-24, 2009 Madison, WI Chris Tessum, Adam Boies, Jason Hill, and Julian D. Marshall University of Minnesota INTRODUCTION This background paper explains and discusses concepts and issues related to the sustainability of biofuels, including the definition of sustainability in general and as related to biofuel production, the proposed and implemented regulatory frameworks aimed at labeling and controlling the sustainability of biofuel production, and the software tools available to quantify various aspects of sustainability. SUSTAINABILITY FRAMEWORKS The use of the term “sustainability” is so widespread in the discussion of anthropogenic impacts on our planet that its meaning in several contexts, includ - ing biofuels, is ill defined. Multiple sustainability frameworks are available, most or all of which are applicable to biofuel production: The Triple Bottom Line. This framework, also called the “3 E’s” (envi- 

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8 APPENDIX E ronment, equity, economy), holds that to be sustainable an organization must consider the environmental and social aspects of its actions as well as economic returns. The Natural Step. The Natural Step defines a sustainable society as one in which “nature is not subject to systematically increasing (1) concentrations of substances extracted from the earth’s crust, (2) concentrations of substances pro - duced by society, or (3) degradation by physical means; and, in that society, (4) human needs are met worldwide” (Nattrass and Altomare, 1999). The Ecological Footprint. Here, sustainability, defined as “living within the regenerative capacity of the biosphere” (Rees and Wackernagel, 1994; Wacker- nagel et al., 2002), involves comparing the amount of land required to produce food and other goods for, and to absorb wastes from, society to the amount of land available. Wackernagel et al. (2002) calculated that human demand may have been in excess of the Earth’s regenerative capacity since the 1980s, and is currently 20 percent above capacity. Graedel and Klee’s Sustainable Emissions and Resource Usage. Graedel and Klee (2002) quantify a sustainable activity in the following steps: (1) Establish the available supply or limit of the chosen resource or product. (2) Choose a time period over which the use of the resource or creation of the product cannot exceed the supply or limit (e.g., 50 years). (3) Account for recapture (e.g., recycling, se - questration). (4) Using this information, derive the maximum acceptable rate of use or production and compare it to the current rate. If the current rate is higher than the maximum rate, it is unsustainable. Marshall-Toffel Sustainability Hierarchy. Marshall and Toffel (2005) review previous frameworks, and then prioritize sustainability goals into a four-level hierarchy, starting with the most important as follows: “(1) Actions that, if con- tinued at the current forecasted rate, endanger the survival of humans. (2) Actions that significantly reduce life expectancy or other basic health indicators. (3) Ac - tions that may cause species extinction or violate human rights. (4) Actions that reduce quality of life or are inconsistent with other values, beliefs, or aesthetic preferences.” DISCUSSION As Marshall and Toffel (2005) highlight, each of the frameworks listed above has strengths and weaknesses. For example, the Triple Bottom Line provides a method for corporations to increase their sustainability, but critics point out that it is arbitrary to stop at three constraints when other factors (e.g., ethics) are also important. The social and environmental performance of a company can also be difficult to quantify. A strength of the Natural Step is that it presents quantifiable indicators for sustainability. However, it does not address the relative importance of specific criteria. Also, it is difficult to relate the Natural Step criteria to physical effects

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9 APPENDIX E of unsustainability. For example, a decrease in tropospheric carbon dioxide (CO2) concentration from 380 to 360 parts per million (ppm) could be considered sustainable by the definition above, yet those concentrations are still elevated above the historical average (280 ppm), and would likely continue anthropogenic climate change. The Ecological Footprint can illustrate the relative sustainability of different practices by calculating how the footprint would change if all of society adopted a given practice. However, the data required to calculate an Ecological Footprint are difficult to obtain, and difficult to update to account for improvements in technology. The Graedel and Klee method is novel because it introduces the need for a time scale of sustainability, and the idea that nonrenewable resource use can be sustainable up to a certain rate. The method is only applicable to single resources or products, so the application of this method to a suite of resources, products, and limitations, such as those necessary for biofuel production, would require multiple analyses. The sustainability hierarchy (Marshall and Toffel, 2005) attempts to combine and prioritize aspects of the frameworks listed above it. The first three levels of the hierarchy are readily quantifiable within the current scope of scientific in - quiry. (Marshall and Toffel argue that the fourth level should not be included in the definition of sustainability because values, beliefs, and aesthetic preferences vary among people and cultures and change over time.) Because the sustainabil - ity hierarchy encompasses goals from the frameworks reviewed here, it will be used to review the strengths and weaknesses of the extant principles for biofuel sustainability. It should be noted that whether level 1, 2, or 3 applies to a certain situation depends on the severity of the situation, among other factors. For in - stance, severe levels of emissions of greenhouse gases could cause the extinction of the human species (level 1), while less severe emission levels may only reduce human life expectancy (level 2). In this report, sustainability principles are split into two categories: levels 1-3 versus level 4 from the hierarchy. BIOFUEL SUSTAINABILITY PRINCIPLES, CRITERIA, AND INDICATORS To determine whether individual instances of biofuel production are sustain - able within the general frameworks above, principles, criteria, and indicators have been developed. Principles are general tenets that adapt the sustainability frameworks discussed above for biofuel production. Criteria are conditions to be met to achieve these tenets. Indicators are measurable tests to determine whether individual farms, producers, or companies are meeting the criteria. Some examples of frameworks for principles, criteria, and indicators involving the United States follow.

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0 APPENDIX E Principles Roundtable on Sustainable Biofuels Standard The Roundtable on Sustainable Biofuels (RSB) standard is an international, multiparty attempt to define the requirements for sustainable biofuel produc - tion. The current version of the standard is version zero. Table 1 summarizes the RSB principles and the applicable levels of the Marshall-Toffel Sustainability Hierarchy. Principles 3, 4, 6, 7, 8, 9, and 10 of the RSB standard all relate directly to either human health or survival, human rights, or species extinction; therefore, therefore, they are included in the first three levels of the sustainability hier- archy. Principles 1, 2, 5, and 12 relate to quality of life or values and beliefs; therefore, they fall into the fourth level of the hierarchy. Principle 11, cost- effectiveness, does not fit within the hierarchy, but it is clearly a consideration for any biofuel. x Sustainability Principles The Energy Independence and Security Act of 2007 (EISA) established a U.S. goal to derive 25 percent of U.S. energy use from renewable sources by 2025. The related action plan specified sustainability as one of the main require - ments for successful realization of the Act. It defined sustainability as “…[To] conserve, enhance, and protect natural resources and be economically viable, en - vironmentally sound, and socially acceptable.” To encourage sustainable biomass production, EISA developed the principles summarized in Table 2. TABLE 1 Summary of Roundtable for Sustainable Biofuels Sustainability Principles and Applicable Levels of the Marshall-Toffel Sustainability Hierarchy Hierarchy Principles Levels 1-3 Level 4 1 Obey all local laws and international treaties. X 2 Consider all relevant stakeholders. X 3 Reduce greenhouse gas emissions relative to fossil fuels. X 4 Obey all human rights and worker rights. X 5 Contribute to rural development. X 6 Do not impair food security. X 7 Avoid negative impacts on biodiversity, ecosystems, and X areas of high conservation value. 8 Seek to improve soil health and minimize degradation. X 9 Optimize water use, minimize contamination and depletion, X and respect water rights. 10 Minimize air pollution. X 11 Must be produced cost-effectively. 12 Do not violate land rights. X

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 APPENDIX E TABLE 2 Summary of the 25x25 Sustainability Principles and Applicable Levels of the Marshall-Toffel Sustainability Hierarchy Hierarchy Principles Levels 1-3 Level 4 1 Producers and consumers should have equal access to renewable energy markets, products, and infrastructure. 2 Renewable energy production should maintain or improve X air quality. 3 Renewable energy production should maintain or improve X biodiversity. 4 Renewable energy production should bolster the local X economic foundation and quality of life. 5 Renewable energy production should be energy efficient X and conserve natural resources. 6 Renewable energy production should reduce greenhouse gas X emissions compared to fossil fuels. 7 If invasive species are used, appropriate safeguards should X be implemented. 8 Renewable energy production should have market parity with fossil fuels. 9 All regions of the nation should have the opportunity to participate in renewable energy development and use. 10 If renewable energy is produced on private land it should improve the health and productivity of these lands. 11 Renewable energy production on public lands should be sustainable and contribute to the long-term health and mission of the land. 12 Renewable energy production should incorporate the best X available erosion management properties. 13 Renewable energy production should maintain or enhance X soil quality. 14 Renewable energy production should respect areas with X important conservation, historic, and social value. 15 New technologies should be implemented with care to X avoid negative consequences. 16 Renewable energy production should maintain or improve X water quality. 17 Renewable energy production should maximize water X conservation. 18 Renewable energy production should maintain or enhance X wildlife habitat health and productivity. Principles 2, 3, 5, 6, 7, 12, 13, 15, 16, 17, and 18 of the 25x25 action plan all relate directly to either human health or survival, human rights, or species extinction; therefore, they are included in the first three levels of the sustainabil- ity hierarchy. Principles 4 and 14 relate to quality of life or values and beliefs; therefore, they fall into the fourth level of the hierarchy. Principles 1, 8, and 9 relate to economic competition and do not fit within the sustainability hierarchy. Principles 10 and 11 state that the some of the principles laid for all lands in other

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 APPENDIX E points should also apply to public and private lands. Since a general framework such as this is usually assumed to apply to both public and private lands, restating this in two added principles with slightly differing wording is redundant from the standpoint of sustainability. United Nations Sustainable Bioenergy Framework for Decision Makers UN-Energy, a collaborative framework of the United Nations (UN) bodies that contribute to energy solutions, provides a set of principles to draw attention to the “issues that need further attention, analysis, and valuation, so that appro - priate trade-offs can be made and both the energy needs of people met and local and global environment adequately protected.” The UN framework reports that principles should be created around the issues in Table 3. Principles 1, 3, 5, 8, and 9 of the UN framework all relate directly to either human health or survival, human rights, or species extinction; therefore, they are included in the first three levels of the Marshall-Toffel Sustainability Hierarchy. Principles 2, 4, 6, and 7 all relate to the economic situation of developing nations. In some cases, the job and economic status of the citizens of developing nations would have a significant impact on the health and survival of those citizens. In those cases, these principles would also be included within the first three levels of the hierarchy. Otherwise they would fall under level four. Criteria and Indicators Multiple criteria and indicators are available for biofuels, although the pub - lications that directly apply to the United States are still in draft form. The Inter- TABLE 3 Summary of the United Nations Sustainable Bioenergy Framework and Applicable Levels of the Marshall-Toffel Sustainability Hierarchy Hierarchy Issues Levels 1-3 Level 4 1 Ability of modern bioenergy to provide energy services to X the poor. 2 Implications for agro-industrial development and job ? X creation. 3 Health and gender implications of modern bioenergy. X 4 Implications for the structure of agriculture. ? X 5 Implications for food security. X 6 Implications for government budget. ? X 7 Implications for trade, foreign exchange balances, and ? X energy security. 8 Impacts on biodiversity and natural resource management. X 9 Implications for climate change. X

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3 APPENDIX E American Development Bank developed a Biofuels Sustainability Scorecard.1 The two draft sets of criteria and indicators being developed in the United States are the California Low-Carbon Fuel Standard and the U.S. Renewable Fuel Stan- dard (RFS). The Council on Sustainable Biomass Production also has a draft stan- dard that focuses on dedicated fuel crops, crop residues, purpose-grown wood, and forestry residues in North America. And the Global BioEnergy Partnership is developing a set of sustainability criteria and indicators. Delzeit and Holm-Müller (2009) published a general guide to developing criteria and indicators, which evolved from their work in developing criteria and indicators for Brazilian bioethanol certification. They found that, in general: (1) sustainability criteria should be grounded in theory, important to stakeholders, and verifiable at a reasonable cost; (2) some criteria that are highly important cannot be included as indicators because of low verifiability; (3) it is difficult to develop a reliable indicator for greenhouse gas reduction; and (4) “Land Conver- sion Burden” multipliers can be assigned to account for land-use change. European Frameworks Some European sustainability frameworks that are complete with principles, criteria, and indicators are the European Union’s Biofuel Directive and Fuel Quality Directive (European Commission, 2008), the United Kingdom’s Re- newable Transport Fuel Obligation (RTFO, 2007), the Netherlands’ framework (Cramer et al., 2006, 2007) and World Wildlife Fund Germany (Fritsche et al., 2006). These frameworks are not discussed in depth here. Certification Schemes Certification schemes have been developed to solve what is called the Principal-Agent Problem: where potential consumers of biomass (principals) have little or no information about the production characteristics of the products they buy, although those characteristics may be important to the consumer. In the case of biofuels, certification of a brand allows customers to know that the fuel they are buying was produced with a certain amount of sustainability. Delzeit and Holm-Müller (2009) give an example of the certification process for Brazilian bioethanol. Some limitations of certification are potential conflicts with World Trade Organization rules and free trade agreements and the possibility that cer- tification schemes may be used as nontariff trade barriers, requiring standards of practice for production that developing countries do not have the resources to adhere to. 1 For more information, see http://www.iadb.org/scorecard.

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 APPENDIX E TOOLS FOR THE QUANTIFYING ECONOMIC AND ENVIRONMENTAL IMPACTS The preceding sections have provided sustainability definitions and princi - ples. Although the related criteria and indicators are not discussed in detail in this paper, determining whether a particular biofuel production pathway meets sus - tainability criteria typically requires quantitative analysis. The software models discussed in this section are among those that can be used for this analysis. Here, the models are divided into greenhouse gas and non-greenhouse gas software. Greenhouse Gas Software Life-cycle analysis (LCA) is the process of examining a product or good from cradle to grave, accounting for all of the inputs and outputs during the pro - duction, use, and disposal of the product. In the context of transportation fuels, LCA is primarily focused on accounting for the processes that are involved in resource production, refining, transportation, storage, and use of the fuels. Initially, life-cycle analyses focused on determining the total amount of energy required to produce a fuel. As concerns of climate change have increased and the life-cycle energy of fuels was better understood, much of the attention has shifted to focus on greenhouse gas emissions from fuels. Within the last several decades, numerous software packages have been developed that contain databases of relevant fuel life-cycle data and frameworks for accounting for greenhouse gas emissions. The following sections outline several software pack - ages that calculate fuel life-cycle emissions and/or use LCA emissions as a part of their framework. GREET The greenhouse gases, regulated emissions, and energy use in transportation (GREET) model was developed by Argonne National Laboratories to calculate the full life-cycle emissions and energy use from the transportation sector. The model is among the most reviewed of the U.S. models and has been used in many peer-reviewed studies (Farrell et al., 2006; Farrell and Sperling, 2007; Wang et al., 2007; Hill et al., 2009). The model is composed of two separate spreadsheet- based modules that calculate the emissions associated with the well-to-wheels production of fuels (current model 1.8C) and the vehicle production and disposal cycle (current model 2.7). GREET calculates the energy consumption (with delineated fossil fuel and petroleum consumption) for the entire fuel life cycle. The current model contains more than 100 distinct fuel life cycles. For each fuel pathway the model calcu- lates emissions of five criteria pollutants (volatile organic compounds, carbon monoxide, nitrogen oxides, particulate matter with diameters of 10 micrometers or less, and sulfur oxides) and three greenhouse gases (CO2, methane [CH4], and

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 APPENDIX E nitrous oxide [N2O]), along with to the total energy consumption. The GREET model converts all greenhouse gas emissions to carbon dioxide equivalent (CO2e) emissions based on the Intergovernmental Panel on Climate Change’s (IPCC’s) global warming potential, which normalizes the radiating forcing of the gases over a 100-year period. In addition to calculating life-cycle emissions of fuels, GREET calculates life-cycle emissions associated with the production and dis - posal of six different vehicle configurations based on the same vehicle platform. Typical results indicated that emissions from vehicle production and disposal make up ~10 percent of total vehicle use emissions (Wang, 1999). While GREET accurately accounts for the direct emissions from fuel pro - duction, some of the model’s indirect emissions calculations need further work. GREET likely underestimates the emissions that result from direct land-use changes and does not calculate any emissions from indirect land-use changes (Farrell and Sperling, 2007). These indirect land use emissions have been shown to be a significant portion of the fuel’s lifecycle and may ultimately determine whether ethanol has lower net emissions than gasoline (Fargione et al., 2008; Searchinger et al., 2008). Additionally, co-products created as a part of the fuel production are not well accounted for within GREET. GREET accounts for co- product credits by having set displacement coefficients. It likely overestimates the amount of credits because the true amount of goods that are displaced is a result of market forces, which can only be captured by economic modeling (Far- rell and Sperling, 2007). To deal with the uncertainty of input data, GREET includes a stochastic modeling package that defines probability distributions of critical inputs. Un - fortunately, as GREET inputs come from government data, academic literature, and stakeholder input, the probability distributions of the data are rarely known. While using unknown probability distributions to calculate confidence intervals for fuels’ emissions can lead to a false confidence in the results, the stochastic modeling package is useful for determining critical parameters that affect emis - sions. In addition to stochastic modeling, GREET includes a time series feature that allows for projections of future energy use and emissions for the production of fuels. However, results are highly speculative as they are largely influenced by assumptions about the future.2 EBAMM The Energy Resources Group Biofuel Analysis Meta-Model (EBAMM) was developed by the University of California (UC), Berkeley as a model for com- paring LCA software. EBAMM was originally developed to compare studies of total energy use of corn ethanol production. Though it only contains inputs for the production of gasoline, corn ethanol, and cellulosic ethanol, the model con - 2 For more information, refer to http://www.transportation.anl.go/modeling_simulation/GREET/.

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6 APPENDIX E sists of inputs from a range of sources and therefore produces a range of values for energy consumption and greenhouse gas emissions. EBAMM does not track non-greenhouse gas pollutants, such as particulate emissions or volatile organic compounds. EBAMM takes as inputs the results of six previous ethanol studies and com - pares them for a common set of boundary conditions and assumptions. Results from a study using EBAMM indicate that ethanol requires much less petroleum than gasoline to produce but is nearly equivalent in terms of greenhouse gas emis- sions (Farrell et al., 2006). A second study using EBAMM showed that biomass was better used to reduce total greenhouse gas emissions by displacing coal in co-fired burners to generate electricity than by displacing gasoline by produc - ing ethanol. Results also indicated that electricity could provide more vehicle miles per hectare when converted to electricity than when converted to ethanol (Campbell et al., 2009).3 Peek/Poke and MOUSE Lifecycle Associates, a private environmental consulting firm, has created two add-on packages for the GREET model. Peek/Poke and Matrix Organization Using Specific Energy (MOUSE) software packages work with GREET to pro- duce or process company-specific fuel life cycles, rather than industrial averages. Peek/Poke serves as a driver for GREET, allowing the user to introduce input data into the software and run simulations without having to modify the GREET code directly. The model first “pokes” the user-defined inputs into the GREET model via Visual Basic macros. Then the software runs the GREET simulation and “peeks” at the results by outputting them from the GREET report. The MOUSE software works with GREET results to provide accurate ac- counting of mixed fuels that are not contained within GREET. MOUSE contains a matrix of GREET-calculated fuel life-cycle emissions and allows users to determine emissions for mixtures of fuel types, such as E85 (85 percent ethanol in diesel fuel). The software is designed to help blenders and fuel producers calculate emissions of fuel mixtures that are specific to their processes, composi - tions, and regions.4 BEACCON The Biofuels Emissions and Cost Connection (BEACCON) model was de- veloped by Richard Plevin at UC Berkeley to calculate the costs of greenhouse gas reductions from ethanol. To create an economic cost model for ethanol production, BEACCON combines the operating and maintenance costs of corn 3 For more information, refer to http://rael.berkeley.edu/EBAMM/. 4 For more information, refer to http://www.lifecycleassociates.com/.html.

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 APPENDIX E farming and ethanol refining with corresponding emissions from GREET into a single spreadsheet. By combining ethanol production costs with emissions, BE - ACCON allows users to model the effect of carbon pricing policies on ethanol prices. BEACCON has been used to model the change in the price of ethanol as a result of a charge per unit of life-cycle CO2 emissions, a charge per unit of direct biorefinery emissions only, and a low-carbon fuel standard (Plevin and Muel - ler, 2008). Results from the study indicated that costs largely depended on the refinery fuel choice, with natural gas plants incurring low or negative additional costs and coal plants incurring higher costs. Currently the model only contains economic data for ethanol; further development would be required to expand the analysis to other fuels.5 LEM The Lifecycle Emissions Model (LEM) was developed by Mark Delucchi at UC Davis. While the LEM has been used for a variety of studies and is the basis for the GHGenius model (see below) the model itself has not been published in any peer-reviewed journals, so its validity remains unverified. The LEM is not publicly available for independent use, but the model results along with critical inputs are available in a series of reports (Delucchi, 2003, 2004, 2005). The LEM calculates emissions for fuels from the largest numbers of coun- tries of the LCA models and includes inputs for 30 different countries. The available data for different countries vary in accuracy and completeness, and the model is most complete for use within the United States (Delucchi, 2003). The model is spreadsheet based and currently calculates emissions for 28 fuel pathways and over 20 different vehicles, including passenger vehicles, buses, scooters, bicycles, heavy rail, light rail, diesel trains, and cargo ships. The LEM calculates emissions for 12 pollutants, more than any other LCA model. It con - tains historical data that allow for results to be calculated for any target year from 1970 to 2050. The historical data also allow the LEM to make predictions about future fuels based on historical data using the model’s dynamic capabilities (Delucchi, 2003). Of all the models, the LEM has the most complete treatment of land-use change. Results from the LEM indicate that the largest sources of cultivation and land-use emissions are: changes in soil carbon and biomass carbon due to cultivation; changes in soil and biomass carbon due to fertilization of off-site eco- systems by all nitrogen input; N2O emissions from fertilizer use, crop-residues, and biological fixation; and emissions of oxides of nitrogen (Farrell and Sperling, 2007). As a result of more complete inclusion of land-use emissions, the LEM- 5 For more information, refer to http://plein.berkeley.edu/biofuels/.

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30 APPENDIX E calculating region- and plant-specific emission, the model requires further refine - ment before widespread use is warranted.8 CONCAWE The joint European study of life-cycle fuel emissions was conducted by the European Council for Automotive R&D, the European Commission Joint Re - search Centre, and Conservation of Clean Air and Water in Europe (CONCAWE). The study includes over 40 liquid fuels and multiple electricity production path - ways, all of which focus on fuels as derived for the European market. The CON- CAWE model is not publicly available, but results and inputs are detailed in a series of online reports (Armstrong et al., 2002). The model builds on previous studies dating back to 1995 and includes updated inputs primarily from government agencies and academic literature (Armstrong et al., 2002). The study includes fuel pathways not seen in models fo - cusing on North American fuels, particularly those derived from rapeseed, sugar beets, and wheat. The model is distinct from American models, in that it does not include significant portions of crude oil sources from Canadian oil sands, as it projects that Europe will continue to use sweet crude through 2030. Although the CONCAWE model does not include a detailed account of emissions as a result of direct or indirect land use-changes, creators of the model acknowledge that land-use changes are significant (Farrell and Sperling, 2007). Results from the model indicate that emissions from conventional petroleum products have slightly lower emissions in Europe than the United States, however it is not clear whether this indicates a true difference in emissions or is a result of variations in the models. Comparison of CONCAWE biofuels emissions to the emissions of the American models is difficult since the CONCAWE model does not provide corn ethanol pathways and American models do not typically include European type biofuels. The model is currently being updated to incorporate new data and preliminary results have been posted on the CONCAWE website (see links below).9 U.S. EPA The U.S. Environmental Protection Agency (EPA) recently proposed changes to the RFS, which included guidelines that establish standards for evaluating fuel life-cycle greenhouse gas emissions. As a part of the RFS framework, qualifying biofuels are required to reduce life-cycle emissions by defined percentages rela - tive to the traditional fossil fuels that they replace. Life-cycle emissions for both traditional and biomass-derived fuels will include all direct and indirect emis - 8 For more information, refer to http://www.bess.unl.edu/. 9 For more information, refer to www.concawe.org and http://ies.jrc.ec.europa.eu/WTW.

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3 APPENDIX E sions, including land-use changes. To determine the full life cycle of fuels, EPA is using a combination of fuel, agricultural, and economic models. The EPA guidelines indicate that the new standard used a combination of models to determine life-cycle emissions, including GREET, Texas A&M University’s Forestry and Agricultural Sector Optimization Model, Iowa State University’s Food and Agricultural Policy Research Institute’s (FAPRI’s) inter- national agricultural models, and the Winrock International database (U.S. EPA, 2009). The rulemaking process is still ongoing, and EPA is seeking input for the rule. Despite the uncertain nature of the final rule, the EPA has indicated several important aspects of the framework that the final rule will contain. For example, EPA has decided that the overall fuel life cycle will include greenhouse gas emissions released both domestically and internationally as a result of U.S. fuel consumption. The full life-cycle emissions from fuels are evaluated according to their incremental increase in production volume to comply with the 2022 RFS re - quirements, rather than focusing on a specific gallon of fuel. EPA’s analysis does not distinguish emissions within a given feedstock—i.e. all corn production will have the same average value of emissions associated with the life cycle, regard- less of where and how it is grown. EPA also states that the uncertainty of aspects of the fuel life cycle does not warrant their exclusion from the model—i.e. inter- national land use and nitrogen cycles (U.S. EPA, 2009). Significant controversy remains about how land-use changes will be incorporated in the final rule and what time frame for evaluating payback horizon will be used to evaluate land-use changes (Grunwald, 2009).10 Swiss Life-Cycle Assessment of Energy Products The Swiss government developed a method for evaluating fuels to determine the full energy, greenhouse gas, and environmental costs of transportation fuels used in Switzerland, The method uses a life-cycle assessment model based on input data from Ecoinvent 1.3 to determine the overall energy consumption and greenhouse gas emissions of the fuels. The method also evaluates fuels on their overall environmental impacts using Eco-Indicator 99 and Environmental Impact Points, UBP 06. The Swiss method also reports the impact of fuels with two met - rics, greenhouse gas emissions and overall environmental impacts, which include damage to human health, damage to ecosystems, and depletion of nonrenewable resources (Zah et al., 2007). Findings from the Zah et al. study indicate that while most fuels derived from biomass have reduced emissions when compared to petroleum-derived fuels, they often have overall environmental impacts that are significantly more severe (Zah, 2007). In particular, the study highlights how fuels produced from crop mono - 10 For more information, refer to http://www.epa.go/otaq/renewablefuels/index.htm.

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3 APPENDIX E cultures (such as corn ethanol) have substantially higher environmental costs in terms of eutrophication, acidification, and land occupation and transformation. Limitations of the Swiss method stem from the use of an old dataset (2004) and combination of dissimilar environmental impacts into a single metric that reflects subjective rather than universal environmental values.11 LEAP The Long-range Energy Alternatives Planning System (LEAP) is a software program developed by the Stockholm Environmental Institute for conducting long-range energy and emissions planning. LEAP is not an LCA model of fuel production. Rather, it provides a framework for analyzing fuel emissions as they relate to vehicle efficiency and use. LEAP provides baseline tailpipe emissions for different fuels that can easily be augmented to include total fuel life-cycle emissions when used in conjunction with fuel LCA software. LEAP incorporates fuel emissions with other key components of the trans- portation sector to solve for regional, state, or national emissions from the trans - portation sector. By including such components as vehicle mix and turnover rates, it is possible to determine how fast specific fuels, such as E85, can penetrate the market. A recent analysis using the LEAP framework within Minnesota examined how separate policies aimed at reducing vehicle fuel consumption, life-cycle fuel emissions, and vehicle miles traveled combine to reduce overall emissions (Boies et al., 2008). In addition to tracking GHG emissions, LEAP accounts for regulated pollutants and can be modified to accounts for other critical factors, such as water use.12 Energy Choice Simulator The Energy Choice Simulator was developed by the Great Plains Institute and the University of Minnesota to model the effect of various fuel policies on the price, quantity, and emissions from the transportation fuels sector. The Energy Choice Simulator is a Web-based tool that draws on outputs from GREET to cal - culate the change in full life-cycle greenhouse gas emissions based on changes to future policies. In addition to greenhouse gas emissions, the simulator tracks the same regulated pollutants that are included in GREET. The Energy Choice Simulator currently includes data for 12 states in the Mid- west, including life-cycle fuel emissions, information on vehicle fleet makeup, vehicle turnover rate, and current and proposed policies. The Web-based model allows users to test assumptions about future policies and compare them to a base case scenario. Policies that are included are individual or regional state taxes 11 For more information, refer to http://www.bioenergywiki.net/images/8/80/Empa_Bioenergie_ ExecSumm_engl.pdf. http://www.sciencemag.org/cgi/content/short/39/89/3. 12 For more information, refer to www.energycommunity.org.

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33 APPENDIX E and subsidies, low-carbon fuel standards, and efficiency mandates. The simula - tor is currently under development and is expected to be available in late 2009. (Warner, 2009).13 Non-GHG Software Agricultural Models POLYSYS—The Policy Analysis System (POLYSYS) is an economic simu- lation modeling system of the U.S. agricultural sector. POLYSYS incorporates agricultural planning decisions in each of 305 U.S. agricultural statistical districts and national averages for crop demands and prices as well as livestock sup - ply and demand. Using the agricultural data, POLYSYS estimates agricultural production response, resource use, price, income, and environmental impacts of projected changes from an agricultural baseline. POLYSYS is able to model the first- and second-generation biofuel crops of corn, soybeans, sugarcane, switch - grass, hybrid poplars, and hybrid willows, among other crops. POLYSYS is a partial-equilibrium model, meaning that it considers the interrelatedness of the agricultural sector with some other sectors, but not all sectors. For instance, it can model the interdependence of biofuel production and prices with livestock feed production and prices, but it cannot model the interdependence of biofuel production and prices with oil production prices. POLYSYS uses a baseline approach, meaning that it simulates a deviated path from a published agricultural baseline. This approach allows for quick turn - around and relatively few data requirements, as the majority of the simulation work has already been done to generate the baseline. POLYSYS baseline data are available from the U.S. Department of Agriculture (USDA), FAPRI, and the U.S. Congressional Budget Office. It can be coupled with IMPLAN (Impact Analysis for Planning) to model income data and with EPIC (Environmental Policy Inte- grated Climate) to model environmental impacts (both discussed below). If EPIC is used, POLYSYS uses data from the USDA STATSGO and GRASS databases and selection criteria from the USDA Natural Resources Conservation Service to identify dominant soils. For simulations involving crop rotations and cropping practices, data can be obtained from the USDA Cropping Practices Survey, and for simulations involving enterprise or rotation budgets, data can be obtained from the Agricultural Policy Analysis Center Budgeting System. POLYSYS has been used to estimate the potential U.S. biomass feedstock supply (Ugarte et al., 2000; Walsh et al., 2003) and the economic and agricul - tural sector impacts of a potential increase in demand for biodiesel (Ugarte et al., 1999). It has also been used along with REAP (discussed below) to quantify the 13 For more information, refer to http://forio.com/simulation/mga/index and http://forio.com/wiki/ mga/index.php/Main_Page.

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3 APPENDIX E environmental and economic impacts of increased U.S. biofuel feedstock produc - tion (BRDI 2008). POLYSYS allows a detailed simulation of land-use change effects within the continental United States. The POLYSYS model has some limitations. Since POLYSYS simulations are anchored to a baseline, the accuracy of all results is dependent on the accuracy of the baseline. POLYSYS is a deterministic, not stochastic, model, and as such is not able to calculate probability distributions of different outcomes. As men - tioned above, the model cannot simulate the interdependency of the energy and agricultural sectors. Therefore, it is necessary to model the increase in bioenergy production in the form of a mandate, rather than in reaction to energy prices. POLYSYS also cannot simulate forestland, and thus cannot model biomass pro - duction from forest residue. Since POLYSYS only models the continental United States, it cannot simulate international land-use changes. 14 REAP—The Regional Environment and Agricultural Programming Model (REAP) is a partial-equilibrium agricultural model, similar to POLYSYS. REAP simulates how changes in policy, demand, and production technology affect the regional supply of crops and livestock, commodity prices, crop management be - havior and the use of production inputs, farm income, and environmental indica - tors. Similar to POLYSYS, REAP’s results are relative to a baseline projection, and REAP uses EPIC (discussed below) to simulate biophysical indicators. REAP also shares some limitations with POLYSYS. The accuracy of its results is constrained by the accuracy of the baseline, and REAP cannot calcu - late stochastic distributions, simulate interdependency between the energy and agricultural sectors, simulate forest land, or model land-use changes outside of the continental United States. REAP is different from POLYSYS in that it is a static framework: it assesses changes in market and other conditions for a given point in time. While POLY- SYS can show the impact path of a certain event over time relative to a baseline, REAP can only calculate a snapshot equilibrium. REAP also only has information for first-generation biofuel crops of corn, soybeans, and sugarcane. It is not able to model second-generation biofuel crops. The data requirements for the 50 U.S. agricultural regions modeled by REAP are crop yields, input requirements, costs, and returns. The data are provided by USDA’s Agricultural Resource Management Survey and the EPIC model. REAP has been used along with POLYSYS by the Biomass Research and Development Initiative (BRDI, 2008).15 14 For more information, refer to Ugarte and Ray (2000) and http://www.agpolicy.org/polysys. html. 15 For more information refer to BRDI (2008) and http://www.ers.usda.go/Publications/TB96/.

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3 APPENDIX E Enironmental Models EPIC—The Environmental Policy Integrated Climate (EPIC) model was developed by USDA to simulate the impact of agricultural management strate - gies on agricultural production and soil and water resources. EPIC takes soil, weather, and management information as inputs and outputs crop yields, erosion, and chemical discharges to the environment. EPIC is used within POLYSYS and REAP to calculate biophysical properties on a field-by-field basis. The version of EPIC included in REAP and POLYSYS is limited by its use of historical weather data. Farm chemical runoff and erosion occur disproportion- ately during extreme weather events. Since climate instability and the frequency of extreme weather events are projected to increase in the future, the use of historical weather data may decrease the accuracy of runoff and erosion predic - tions (BRDI, 2008). However, the standalone version of EPIC has been used to simulate the effects of global climate change on crops (Gassman et al., 2005). EPIC is only able to simulate the properties of a single field. Therefore, to be able to model the impact of an agricultural simulation on a watershed, EPIC would need to be linked to SPARROW (described below). This is yet to be accomplished. SPARROW—The U.S. Geological Survey’s (USGS’s) SPAtially Referenced Regression On Watershed Attributes (SPARROW) model links water quality with constituent sources. SPARROW uses USGS land-use and land-cover data and USDA data on animal nutrients and cropland area. It tracks the transport of nitro - gen from atmospheric deposition, nitrogen and phosphate from agricultural fertil- izer, and nutrients from urban and other runoff as they are transported to streams and downstream receiving waters. It also tracks the attenuation of these nutrients by natural processes as they are transported from land and downstream. SPARROW has stochastic capabilities to predict the uncertainty embedded in its simulations. SPARROW can predict water quality in both small watersheds and large river drainages. From a policy standpoint, SPARROW can be used to predict the changes in water quality due to management actions or changes in land use. SPARROW also has limitations. Due to data limitations, it cannot account for effects of land management or conservation practices, manure application, or urban contaminants (i.e., sewer overflows). SPARROW’s mean load spatial distributions are disproportionately influenced by high-flow data; therefore, the mean spatial distributions are more indicative of high-flow seasons than of other times of the year. SPARROW predicts long-term averages, not short-term values, and is more accurate across broad regions than in single catchments. 16 16 For more information, refer to http://water.usgs.go/nawqa/sparrow/.

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36 APPENDIX E Economic Models RIMS II—The Bureau of Economic Analysis (BEA) Regional Input-Output Modeling System (RIMS II) is a tool for estimating the indirect impacts of changes in a local economy. RIMS II acts as a multiplier: users provide the initial effects in output, earnings, or employment of a change, such as closing an army base or opening an ethanol plant, and RIMS calculates the total impact on output, earnings, or employment over a region of specified size that is at least one county. The RIMS multipliers use data from BEA’s national input-output table of 500 industries and BEA’s regional accounts. As models discussed so far, the RIMS multipliers have limitations. Although studies have found that RIMS gives similar results to more complex input-output models,17 RIMS is only recommended for use with small-scale changes. The national-scale ramping up of biofuel production is beyond the scope of this tool. Also, as reported by Swenson (2007) and Low and Isserman (2009), RIMS II does not have an appropriate category for biofuels. They fall under the larger category of “organic chemical industry,” which does not have sufficiently similar characteristics to those of biofuel production plants.18 IMPLAN—The Impact Analysis and Planning (IMPLAN) model, developed by the Minnesota IMPLAN Group, is also an input-output model, but is more complex than RIMS II. Like RIMS II, IMPLAN models the total regional eco- nomic effect of a given change, but IMPLAN splits the additional effects beyond the initial action into two categories: indirect and induced. Indirect effects are changes in interindustry transactions, or basically the supply and distribution chains of the affected entity. Induced effects are the changed spending habits in the local economy. IMPLAN can also disaggregate impacts into sectors of the economy. IMPLAN requires data from the U.S. system of national accounts, which are collected by the U.S. Department of Commerce’s Bureau of Labor Statistics and other federal and state government agencies. Even when using a more sophisticated tool such as IMPLAN, it is possible to misportray the number of local jobs created by increased biofuel produc - tion. As explained in Swenson (2007) and Low and Isserman (2009), the corn produced for ethanol is sometimes misclassified as new production, while in reality virtually all of the production is pre-existing and simply diverted from other uses. This misclassification by itself can cause a 200 percent overestimate in the number of jobs created (Swenson, 2007). Also, increased profits from the price premiums given by ethanol plants do not necessarily stay in the hands of the farmers. Rather, if the farmers do not own their land, excess profits go to the landlords in the form of increased rent, many of whom do not reside locally (Low and Isserman, 2009). Also, IMPLAN assumes that soybean production is more 17 See http://www.bea.go/regional/rims/brfdesc.cfm. 18 For more information refer to Swenson (2007) and http://www.bea.go/regional/rims/brfdesc. cfm.

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3 APPENDIX E labor intensive than corn production, which is not true on a local level. Farmers who decide to plant soybeans in a given year instead of corn typically do not hire extra workers to do so (Low and Isserman, 2009). Air Quality Models Examples of models that could be used to estimate the impacts of biofuels on air quality are CAMx,19 CMAQ,20 and GATOR-GCMM (Jacobson, 2001). These models incorporate emissions, meteorology, and photochemical reactions. Applications of these models to biofuels include Jacobson (2007), Hill et al. (2009), and Morris et al. (2003) SUMMARY This document has reviewed the proposed and extant frameworks to explore and label the sustainability of biofuel production and the software tools available to quantify different aspects of that sustainability. Frameworks of sustainability have been discussed and, while no one definition is universally applicable, the Marshall and Toffel Sustainability Hierarchy was used to evaluate sustainability principles in the context of biofuels. Also discussed were criteria, indicators, and certification schemes for biofuels. The study discussed a variety of tools for deter- mining various aspects of the sustainability of biofuels, including greenhouse gas, environmental, economic, and air quality models. Of the greenhouse gas models, GREET was found to be the most widely used and most comprehensive, while other models, such as GHGenius, were found to have better treatment of land-use effects. The agricultural/economic models POLYSYS and REAP were similar in structure, with POLYSYS having the advantage of being able to calculate the impact path of a decision over time. The economics model IMPLAN was found to be more accurate than RIMS II. Overall, it was found that carefully consider- ing the inputs to any economic model is important in obtaining accurate results. Several studies have modeled the air quality impacts of biofuel production, but work in this area is still preliminary. A U.S. Department of Energy and USDA report offers a more thorough discussion of research frontiers (U.S. DOE and USDA, 2009). REFERENCES Armstrong, A. P., et al. April 2002. Energy and Greenhouse Gas Balance of Biofuels for Europe—An Update. Report No. 2/02. Brussels: CONCAWE Ad Hoc Group on Alternative Fuels. Available at www.concawe.be. 19 For more information, refer to http://www.camx.com/. 20 For more information, refer to http://www.cmascenter.org/.

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38 APPENDIX E Boies, A., et al. June 2008. Reducing Greenhouse Gas Emissions from Transportation Sources in Minnesota. CTS 08-10. Minneapolis, MN: University of Minnesota, Center for Transportation Studies. Available at http://www.cts.umn.edu/Research/Featured/GreenhouseGas/. BRDI (Biomass Research and Development Initiative). 2008. Increasing Feedstock Production for Biofuels: Economic Driers, Enironmental Implications, and the Role of Research. Bio- mass Research and Development Board. Available at http://www.eere.energy.go/biomass/ publications.html. Cramer, J., et al. 2006. Criteria for Sustainable Biomass Production: Final Report from the Project Group “Sustainable Production of Biomass.” Available at www.globalproblems-globalsolutions -files.org/unf_website/PDF/criteria_sustainable_biomass_prod.pdf [Accessed June 6, 2009]. Cramer, J., et al. 2007. Testing Framework for Sustainable Biomass: Final Report from the Project Group “Sustainable Production of Biomass.” Available at http://www.lowcp.org.uk/assets/ reports/00-Cramer-FinalReport_EN.pdf [Accessed June 6, 2009]. Campbell, J. E., et al. 2009. Greater transportation energy and GHG offsets from bioelectricity than ethanol. Science 324(5930):1055-1057. Available at http://www.sciencemag.org/cgi/content/ abstract/6888. Delzeit, R., and K. Holm-Müller. 2009. Steps to discern sustainability criteria for a certification scheme of bioethanol in Brazil: approach and difficulties. Energy 34(5):662-668. Delucchi, M. A. 2002. “Overview of the Lifecycle Emissions Model (LEM).” University of Califor- nia, Davis. Available at www.its.ucdais.edu/publications/00/UCD-ITS-RR-0-0.pdf. Delucchi, M. A. 2003. A Lifecycle Emissions Model (LEM): Lifecycle Emissions from Transportation Fuels, Motor Vehicles, Transportation Modes, Electricity Use, Heating and Cooking Fuels, and Materials. Institute of Transportation Studies, University of California, Davis. Available at http://escholarship.org/uc/item/9r8sbb. Delucchi, M. A. 2004. Conceptual and Methodological Issues in Lifecycle Analysis of Transportation Fuels. Research Report UCD-ITS-RR-03-17. Institute of Transportation Studies, University of California, Davis. Available at http://pubs.its.ucdais.edu/publication_detail.php?id=03. Delucchi, M. A. 2005. A Multi-Country Analysis of Lifecycle Emissions from Transportation Fuels and Motor Vehicles. Research Report UCD-ITS-RR-05-10. University of California, Davis. Available at http://pubs.its.ucdais.edu/publication_detail.php?id=. EC (European Commission). 2008. European Parliament legislative resolution of 17 December 2008 on the proposal for a directive of the European Parliament and of the Council amending Directive 98/70/EC as regards the specification of petrol, diesel and gas-oil and introducing a mechanism to monitor and reduce greenhouse gas emissions from the use of road transport fuels and amending Council Directive 1999/32/EC, as regards the specification of fuel used by inland waterway vessels and repealing Directive 93/12/EEC. U.S. EPA (Environmental Protection Agency). 2009. Regulation of Fuels and Fuel Additives: Modi - fication to Renewable Fuel Standard Program; Proposed Rule, p. 74. Available at http://www. epa.go/OMS/renewablefuels/. Fargione, J., et al. 2008. Land clearing and the biofuel carbon debt. Science 319(5867):1235-1238. Available at http://www.sciencemag.org/cgi/content/abstract/. Farrell, A. E., and D. Sperling. 2007. A Low-Carbon Fuel Standard for California, Part : Technical Analysis. Research Report UCD-ITS-RR-07-07. Institute of Transportation Studies, University of California, Davis. Available at http://pubs.its.ucdais.edu/publication_detail.php?id=08. Farrell, A. E., et al. 2006. Ethanol can contribute to energy and environmental goals. Science 311(5760):506-508. Available at http://www.sciencemag.org/cgi/content/abstract/3/60/ 06. Fritsche, U. R., et al. 2006. Sustainability Standards for Bioenergy. Frankfurt am Main, Germany: WWF Germany. Available at www.wwf.de/…/fm…/Sustainability_Standards_for_Bioenergy. pdf.

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39 APPENDIX E Gassman, P. W., et al. 2005. Historical Deelopment and Applications of the EPIC and APEX Models. Center for Agricultural and Rural Development, Iowa State University. Available at http://ideas. repec.org/p/ias/cpaper/0-wp39.html. Graedel, T. E. and R. J. Klee. 2002. Getting serious about sustainability. Enironmental Science & Technology 36(4):523-529. Available at http://pubs.acs.org/doi/abs/0.0/es00606. Grunwald, M. 2009. Stress-testing biofuels: how the game was rigged. Time May 12 Available at http://www.time.com/time/health/article/0,899,899,00.html [Accessed June 4, 2009]. Hill, J., et al. 2009. Climate change and health costs of air emissions from biofuels and gasoline. Proceedings of the National Academy of Sciences of the United States of America 106(6):2077- 2082. Available at http://www.pnas.org/content/early/009/0/0/088306.abstract. Jacobson, M. Z. 2001. GATOR-GCMM: global- through urban-scale air pollution and weather fore - cast model 1. Model design and treatment of subgrid soil, vegetation, roads, rooftops, water, sea ice, and snow. Journal of Geophysical Research 106(D6):5385-5401. Available at www.stanford. edu/group/efmh/jacobson/GATORGCMM0.pdf. Jacobson, M. Z. 2007. Effects of ethanol (E85) versus gasoline vehicles on cancer and mortality in the United States. Enironmental Science and Technology 41(11):4150-4157. Available at http://pubs.acs.org/doi/abs/0.0/es0608. Liska, A. J., et al. 2008. “BESS: Biofuel Energy Systems Simulator. A Model for Life-Cycle Energy & Emissions Analysis of Corn-Ethanol Biofuel Production Systems.” Available at http://www. bess.unl.edu [Accessed June 2, 2009]. Liska, A. J., et al. 2009. Improvements in life cycle energy efficiency and greenhouse gas emissions of corn-ethanol. Journal of Industrial Ecology 13(1):58-74. Available at http://www3.interscience. wiley.com/journal/666/abstract?CRETRY=&SRETRY=0. Low, S. A., and A. M. Isserman. 2009. Ethanol and the local economy: industry trends, location fac - tors, economic impacts, and risks. Economic Deelopment Quarterly 23(1):71-88. Available at http://edq.sagepub.com/cgi/content/abstract/3//. Marshall, J. D., and M. W. Toffel. 2005. Framing the elusive concept of sustainability: a sustainability hierarchy. Enironmental Science & Technology 39(3):673-682. Available at http://pubs.acs. org/doi/abs/0.0/es0039k. Mulkey, D., and A. W. Hodges. 2004. Using IMPLAN to assess local economic impacts. University of Florida IFAS Extension. Available at http://edis.ifas.ufl.edu/FE68. Morris, R. E., et al. 2003. Impact of Biodiesel Fuels on Air Quality and Human Health: Summary Report. NREL/SR-540-33793. Golden, CO: National Renewable Energy Laboratory. Available at www.nrel.go/docs/fy03osti/3393.pdf. Nattrass, B., and M. Altomare. 1999. The Natural Step for Business: Wealth, Ecology and the Eolu- tionary Corporation. Gabriola Island, British Columbia: New Society Publishers. Available at http://www.newsociety.com/bookid/366. Plevin, R. 2009. Modeling corn ethanol and climate: a critical comparison of the BESS and GREET models. Journal of Industrial Ecology 13(4):495-507. Available at http://www3.interscience. wiley.com/journal/6699/abstract. Plevin, R. J., and S. Mueller. 2008. The effect of CO2 regulations on the cost of corn ethanol pro- duction. Enironmental Research Letters 3:024003. Available at www.iop.org/EJ/article/8- 936/3//…/erl8__0003.pdf. Rees, W., and M. Wackernagel. 1994. Our Ecological Footprint: Reducing Human Impact on the Earth. Gabriola Island, British Columbia: New Society Publishers. Available at http://www. newsociety.com/bookid/3663. RTFO (Renewable Transport Fuel Obligation). 2007. Carbon and Sustainability Reporting Within the Renewable Transport Fuel Obligation: Requirements and Guidance. Draft Government Recom - mendation to RTFO Administrator, Department for Transport. Available at http://www.dft.go. uk/pgr/roads/enironment/rtfo/ [Accessed June 1, 2009]. Scharlemann, J. P. W., and W. F. Laurance. 2008. Environmental science: how green are biofuels? Sci- ence 319(5859):43-44. Available at http://www.sciencemag.org/cgi/content/short/39/89/3.

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0 APPENDIX E Searchinger, T., et al. 2008. Use of U.S. croplands for biofuels increases greenhouse gases through emissions from land-use changes. Science 19(5867):1238-1240. Available at http://www. sciencemag.org/cgi/content/abstract/86. Swenson, D. 2007. “Understanding Biofuels Economic Impact Claims.” Department of Economics Staff Report, Iowa State University. Available at www.econ.iastate.edu/research/webpapers/pa- per_90.pdf. Ugarte, D., et al. 2000. The Economic Impacts of Bioenergy Crop Production on U.S. Agriculture. Oak Ridge, TN: Oak Ridge National Laboratory. Available at www.usda.go/oce/reports/en- ergy/AER86Bi.pdf. Ugarte, D. G., and D. E. Ray. 2000. Biomass and bioenergy applications of the POLYSYS modeling framework. Biomass and Bioenergy 18(4):291-308. Ugarte, D. G., et al. 1999. Assessment of Biodiesel Production Potential in the Southeast: Final Report. Southeast Regional Biomass Energy Program. U.S. DOE and USDA (U.S. Department of Energy and U.S. Department of Agriculture. 2009. Sustain- ability of Biofuels: Future Research Opportunities. Report from the October 2008 Workshop. Available at http://genomicsgtl.energy.go/biofuels/sustainability/ [Accessed May 14, 2009]. Wackernagel, M., et al. 2002. Tracking the ecological overshoot of the human economy. Proceedings of the National Academy of Sciences of the United States of America 99(14):9266-9271. Avail- able at www.pnas.org/content/99//966.full.pdf. Walsh, M., et al. 2003. Bioenergy crop production in the United States: potential quantities, land use changes, and economic impacts on the agricultural sector. Enironmental and Resource Economics 24(4):313-333. Available at http://ideas.repec.org/a/kap/enreec/y003ip33- 333.html. Wang, M. 1999. GREET .—Transportation Fuel Cycle Model. Volume : Appendices of Data and Results. Argonne, IL: Argonne National Laboratory, p. 208. Available at http://www. transportation.anl.go/modeling_simulation/GREET/publications.html. Wang, M., et al. 2007. “Life-cycle energy and greenhouse gas emission impacts of different corn ethanol plant types.” Enironmental Research Letters (2): 024001. Warner, E. S. 2009. Evaluating Life Cycle Assessment (LCA) Models for Use in a Low Carbon Fuel Standard Policy. M.S. thesis. St. Paul, MN: University of Minnesota. Available at conserancy. umn.edu/bitstream/08//Warner,%0Ethan.pdf. Zah, R., et al. 2007. Lifecycle Assessment of Energy Products: Enironmental Assessment of Biofuels. Bern, Switzerland: Swiss Federal Office for Energy, Environment and Agriculture.