Evaluation of the Environmental Protection Agency Treatment of Life Cycle Assessment in the Renewable Fuel Standard Rulemaking.

LCA.8009.21PES.2009 January 2010 Evaluation of the Environmental Protection Agency Treatment of Life Cycle Assessment in the Renewable Fuel Standard Rulemaking Executive Summary Prepared by Stefan Unnasch, Brent Riffel, Larry Waterland, Life Cycle Associates Christopher Loreti, The Loreti Group for American Petroleum Institute 1220 I Street, NW Washington, DC 20005 Life Cycle Associates, LLC

DISCLAIMER This report was prepared by Life Cycle Associates, LLC and The Loreti Group for the American Petroleum Institute (API). Life Cycle Associates and The Loreti Group are not liable to any third parties who might make use of this work. No warranty or representation, express or implied, is made with respect to the accuracy, completeness, and/or usefulness of information contained in this report. Finally, no liability is assumed with respect to the use of, or for damages resulting from the use of, any information, method or process disclosed in this report. In accepting this report, the reader agrees to these terms. ACKNOWLEDGEMENT Life Cycle Associates, LLC performed this study under contract 2007-103154 for API, with The Loreti Group subcontracted to review petroleum fuels pathways. API s Project Manager was David Lax. Contact Information: Stefan Unnasch Life Cycle Associates, LLC unnasch@lifecycleassociates.com

1 Background In December of 2007, President Bush signed the Energy Independence and Security Act (EISA) into law. One of the provisions of this law mandates a substantial increase in the use of renewable transportation fuels in the U.S. by 2022, a provision referred to as the Renewable Fuels Standard (RFS). The U.S. Environmental Protection Agency (EPA) is charged with developing the regulations for the RFS, and is responsible for the life cycle analysis (LCA) of both baseline petroleum fuels and renewable fuels, another requirement of EISA. 2 Objectives The objective of this review is to evaluate the U.S. Environmental Protection Agency s EPA s treatment of key issues involving the assessment of greenhouse gas (GHG) emissions 1 for alternative and petroleum transportation fuels as described in the Draft Regulatory Impact Assessment (DRIA) and supporting documentation for the RFS2 Notice of Proposed Rulemaking (NPRM) released in May of 2009. EPA used a variety of agricultural and process engineering models and spreadsheet analysis tools, including the Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation (GREET), Forest and Agricultural Sector Optimization Model (FASOM) and Food and Agricultural Policy Research Institute (FAPRI), to analyze life cycle impacts of petroleum and renewable fuels. This review focused on assessing EPA s application of these tools and the conclusion resulting from their application as stated in the DRIA. EPA states that the LCA discussed in the DRIA is a consequential LCA instead of the attributional LCA relied upon in the RIA for the RFS1 rule adopted in 2007. An attributional LCA is one that accounts for flows/impacts of pollutants, resources, and exchanges among processes within a chosen temporal window. Whereas a consequential LCA is one that attempts to account for flows/impacts that are caused beyond the immediate system in response to a change to the system. Thus, for example, in the consequential LCA, the effects of the changes in the fuel production system between the baseline and the with biofuel cases are as important as the emissions associated with each individual fuel. 3 Key Findings This review identifies a number of issues and shortcomings that are briefly summarized in Table 1, focusing on biofuels production, petroleum production, and LCA modeling respectively. EPA s treatment of biofuels reflects the most optimistic view of production technologies and provides an excessive credit for co-products compared to the likely environmental benefit and credits applied in other studies. The analysis of petroleum relies on a combination of studies and models and should be updated to reflect available data in a consistent manner. EPA used FASOM and FAPRI to asses emissions associated with land use change (LUC) and GREET for direct emissions. Although the models have limitations, they are detailed greenhouse gas modeling tools that can yield useful output with appropriate data 1 Up to the point of use; EPA performed a well-to-tank (WTT) assessment in which the end product is the finished fuel at a vehicle fueling station, but does not include the emissions from the vehicle fuel combustion. 2 Life Cycle Associates, LLC

Table 1. Review of EPA DRIA - Key Findings Topic Biofuels Technology Feasibility Co-products Electric Power Representation of EISA Impact Petroleum Oil production Petroleum Refining LCA Modeling Choice of models FASOM Analysis Direct LUC N 2 O Emissions FAPRI Analysis Food Effects Other Indirect Effects Time Horizon Uncertainty Comment Optimistic assumptions make biofuels look very feasible. EPA represents idealized nth plant technology. Actual production costs will likely be much higher in near term. Thus, assumptions on suitable feedstock and potential volumes need to be reexamined. DGS and glycerin production will double with corn ethanol and biodiesel production. The risk of glutting the market is not considered. Co-product credits are assumed for export power for cellulosic ethanol. However, power production is not required as part of the ethanol process. Use of credits in carbon trading mechanisms should be reflected in a consequential LCA. Analysis of corn ethanol effectively segregates existing and low efficiency plants and attributes all improved plants to the rule. Analysis can be refined to reflect appropriate data. More appropriate data can be used for energy consumption and flaring and venting emissions for imported crude oil in 2005, eliminating need to estimate emissions based on domestic production. EPA used incorrect assumptions to model the production of diesel and gasoline The GREET model uses a rule-of-thumb approach to allocating energy among refinery products; a more rigorous method is needed. Emissions from hydrogen production associated with petroleum refining appear to be understated and need to be reexamined. Unsupported domestic crude oil energy content was used for refining calculations Used available tools. Idealized model assumptions and constraints. FASOM, FAPRI, and GREET are available tools that model life cycle GHG emissions most completely. This approach results in lower GHG emissions than straightforward substitution analysis for LUC. FASOM model was constrained for zero change in forestry, maximum 10% conversion from pasture to crop, and 2008 Farm Bill limits on CRP conversion, which effectively cap U.S. LUC. Soil carbon storage from stover is attributed to cellulosic ethanol. Perpetual carbon storage assumed for stover and switch grass. N 2 O emissions are a large GHG source from biofuels. The uncertainty and variability in factors affecting N 2 O should be examined. Unsatisfactory GHG agreement between FASOM and FAPRI. Soil carbon release is based on Winrock analysis that covers only 60% of countries predicted by FAPRI for land conversion. FAPRI U.S. land constraints are not incorporated into FASOM. Economic models do not hold food production constant, thus providing a GHG benefit for producing less food. EPA plans to model rebound effect but ignores transportation logistics and demand for coal based fertilizer. 100 year time horizon is inconsistent with future land use. No uncertainty analysis, only sensitivity case studies. 3 Life Cycle Associates, LLC

inputs. A key challenge is in identifying input assumptions that are appropriate for these complex models. 4 Fuel Pathways Evaluated 4.1 Biofuel Pathways EPA considers a mix of biofuel conversion technologies to comply with the EISA requirements. These fuels include both existing biofuels such as corn ethanol, as well as a range of fuels that are under development including cellulosic ethanol from a variety of materials and other fuel pathways. 4.1.1. Biofuel Production Pathways EPA s calculation of the life cycle GHG emissions is based on the performance of fuel technologies described in the DRIA. Energy and material inputs provide the basis for the calculation of direct emissions. The GREET model that EPA relied upon for the basis of their analysis performs most of these calculations internally and provides an adjustment for coproducts. EPA calculates the GHG emissions for a range of corn ethanol technology options. The energy requirements for specific corn ethanol plant technologies in the DRIA are consistent with surveys and process modeling. However, EPA focuses on a projection of new technologies, which would need to be built or retrofitted in 10 years. In fact, most of the ethanol capacity required to meet EISA requirements has either been built or has started construction covering over 12 billion gallons of capacity. EPA s analysis of 2022 technologies effectively cherry picks the best ethanol plant options and attributes these to EISA. Improvements to ethanol plants could occur both to the existing capacity as well as any new capacity built to comply with the rule. A better approach would be to rate ethanol plants according to their performance and only categorize plants that fall within certain feedstock or energy use parameter ranges as is the approach for the California Air Resources Board s (ARB s) low carbon fuel standard (LCFS). EPA should determine what process performance is required to achieve a 20% reduction in GHG emissions rather than a-priori assuming capital intensive technology improvements. The DRIA examines a number of cellulosic ethanol pathways. Enzymatic hydrolysis processes are examined in great detail. Other options include gasification, acid hydrolysis, and others. EPA does not investigate the material inputs for these options or their life cycle GHG emissions. EPA examines a number of biodiesel pathways. These include soy biodiesel and biodiesel from tallow and other oils. Their life cycle analysis of tallow and other waste oils is inconsistent because the system boundaries are ignored. The alternative use of tallow needs to be considered since the material is not disposed in a landfill. Tallow is used as both animal feed and boiler fuel. A consequential LCA needs to consider its alternative use. 4 Life Cycle Associates, LLC

4.1.2. Co-Products Co-products from biofuels production have a significant impact on the LCA of fuels with different attribution methods leading to quite different results for any given product or process. Some biofuel pathways generate excess electricity through the combustion of biomass residue to meet onsite heat and electricity needs. EPA used FASOM to calculate the net change in agricultural sector electricity consumption associated with each of the pathways analyzed, and these amounts were then combined with GREET default GHG emission factors for fertilizer and pesticide production to calculate GHG emissions changes. However, this approach does not appropriately reflect the environmental impacts of power generation associated with biofuels facilities because it employs a credit based on displaced fossil power. This credit could also be sold as a renewable power credit thus double counting the impact under RFS2. Ethanol produced using the dry-milling process results in co-product distillers dried grains and solubles (DDGS), which can be used for animal feed. Projected annual corn ethanol production under EISA would result in the coproduction of substantial quantities of DDGS. But, if the animal feed market cannot use all the DDGS produced, the material could be burned as an energy source instead. Thus, EPA s analysis needs to take into account the challenges in effectively selling excess DDGS, which it currently does not. Biodiesel is typically produced by reacting fat or oil feedstocks with a base and methanol in a transesterification reaction to produce a methyl ester and glycerin. In addition, biodiesel production from soy oil, a significant vegetable oil feedstock results in another co-product, soybean meal. The treatment of biodiesel co-products (soybean meal and glycerin) in GREET does not appropriately partition the emissions associated with glycerin production and the treatment in EPA s analysis is unclear. Several errors in the GREET allocation factor approach compound to yield excessively high glycerin energy content of the glycerin co-product. This effect alone understates GHG emissions of biodiesel production, resulting in lower well to tank emissions than would be calculated by other approaches. 4.1.3. Biofuels Key Recommendations EPA should take several steps to present a more accurate picture of the life cycle GHG impact of biofuels. First the cost and viability of the near term transition to biofuels is oversimplified. Secondly, the aggregation of technologies and analysis for 2022 technologies attributes improvements and co-product power that are not related to the rule. Finally, the GREET model should be updated to reflect numerous data and calculation issues summarized below and discussed in the main report. To address the viability of the rule, EPA should examine more carefully the near term costs of cellulosic ethanol and the risks and contingencies associated with a build-up of new technology that is as rapid as the prior expansion in corn ethanol. Alternative scenarios for sugar cane ethanol also need to be examined. For example, EPA s analysis does not examine the use of residual oil to dewater ethanol in the Caribbean, which accounts for most of the 5 Life Cycle Associates, LLC

Brazilian ethanol imported to the U.S. today. EPA should rate ethanol plants on actual performance and develop ratings for ethanol plants that fall into technology categories similar to the approach taken by ARB. EPA could determine what energy inputs are required to meet a 20% GHG reduction rather than predicting the future mix of ethanol plant technologies. And, several issues with GREET calculations should be addressed, most notably, the fossil fuel carbon in biodiesel should be counted and allocation methods for biodiesel should be revised. 4.2 Petroleum Fuels Pathways The petroleum fuel cycle includes energy inputs and emissions related to the production of crude oil, transport of crude oil to refineries, refining of the oil, and distribution of the finished products. This analysis focused on the crude oil recovery and refining steps as they require the greatest amounts of energy and have the greatest emissions. EPA used GREET for conducting its analyses of the petroleum fuels pathways. This analysis reviewed how EPA applied the model as well as the petroleum fuels pathways within the model itself. 4.2.1. Crude Oil Supply and GHG Emissions In applying GREET, EPA adjusted the GHG emissions for the production of heavy oil and Venezuela extra heavy oil by applying scaling factors to the GREET GHG emissions for the production of conventional crude oil. EPA s basis for making these adjustments is questionable, however, for several reasons. For example, EPA did not use the emissions value for conventional oil in GREET to derive the scaling factors. Instead, an average of other imported oils from another literature source was used. However, there is no supporting data for any of the emissions intensity values cited in this source. An additional problem with the EPA s use of the scaling factor is that the GREET model already contains an adjustment factor to account for additional venting and flaring associated with imported oil. GREET itself contains a number questionable assumptions about the energy and emissions associated with crude oil production. Notably the use of 98% efficient extraction is difficult to validate. GREET estimates that energy consumption to produce foreign crude oil and the venting of methane other than that in associated gas is the same as for domestic crude oil. For associated gas venting and flaring, GREET is based on the assumption that emissions from imported oil are twice the domestic level. No data are provided to support either of these assumptions. 4.2.2. Petroleum Refining and GHG Emissions In conducting its analysis, the EPA applied the GREET model to the quantities of different types of gasoline and diesel fuel consumed in the base year 2005. EPA should incorporate more appropriate estimates of the actual gasoline and diesel fuel produced for the 2005 baseline and the correct allocation of diesel fuel production among the different sulfur content categories. Within GREET, the refinery energy efficiency is a key parameter in calculating emissions. Crude oil is by far the largest input to a refinery, however GREET uses an understated energy content for 2005 crudes. In addition, the revised GREET refinery efficiency may underestimate the amount of natural gas required to make the hydrogen consumed at 6 Life Cycle Associates, LLC

refineries. And, GREET employs an outdated allocation of the energy to the distribution of refinery products. Better data on refinery operation should be used to distribute energy inputs and emissions to refined products. 4.2.3. Petroleum Fuel Recommendations EPA should revisit the adjustments it makes to the imports of heavy oil and Venezuela extra heavy crude oil and provide a rigorous justification for the selected adjustments used. EPA should also explain how the correction factors it applies to these different crude oils interact with the correction factors GREET already applies to all types of imported crude oil. EPA should re-run the GREET model using the correct proportion of gasoline and diesel fuels sold in 2005 using the correct EIA data. EPA should also re-run the GREET model for diesel fuel, to correct a mistake in modeling diesel fuel production in GREET as if it were ultra-lowsulfur diesel (as defined by the EIA) when in fact virtually no such fuel was sold in 2005. 5 Life Cycle Modeling EPA performs life cycle analyses for a variety of renewable fuel pathways taking into account both direct energy inputs and emissions and land-use impacts. The use of crops as feedstocks for fuel production results in the conversion of land and the consequent release of carbon dioxide. However, the land use conversion (LUC) impacts associated with biofuels production cannot readily be described via a simple substitution analysis. 5.1 GREET Model and Direct Emission Impacts EPA used GREET to determine energy and emissions values for the processes employed in the various stages of the life cycles of selected fuel pathways. These model results were combined with FASOM results estimating fertilizer use, changes in electric power, and land use emissions. This complicated set of model interactions allows the use of FASOM s more detailed assessment of land impacts. While the advantages of this approach are not examined or sufficiently justified in the DRIA, the results are directly comparable to analogous analyses conducted by ARB. An important concern is that EPA does not clearly explain how emission factors or intermediate life cycle results from GREET are aligned with those used in FAPRI and FASOM. EPA provides no straightforward way to compare the energy and GHG intensity of various processes in the fuel cycle. For example, the impacts of various life cycle parameters on results and related errors in the DRIA are buried in a layer of models and need to be clearly presented. In addition, GREET appear to omit a portion of fuel cycle emissions for fertilizer production and assumes only natural gas based while the use of coal based fertilizer is growing. 5.2 Direct Land Use Impacts The EPA s analysis shows a significant reduction in GHG emissions for the feed stocks used in several biofuels pathways, particularly those associated with the build-up of root material 7 Life Cycle Associates, LLC

in the soil. However, the life cycle GHG impact of stored crop roots remains uncertain. While certain cropping systems may store additional carbon, the same carbon can be released by conversion to a different crop. Also, any potential benefits of increased soil carbon storage associated with the adoption of no-till cropping practices can potentially be offset by increases in soil N 2 O emissions in some types of soils. Assumptions regarding crop rotation and/or crop conversion are difficult to determine from the FASOM and FAPRI modeling performed. This subject requires further documentation. Agricultural N 2 O emissions include both direct and indirect emissions. Soil N 2 O emissions are probably the largest source of GHG emissions associated with the growing of energy crop feedstocks, but probably the least well quantified at the regional scale. EPA s treatment of N 2 O emissions appears to be consistent with Intergovernmental Panel on Climate Change (IPCC) requirements; however it is difficult to verify. The analysis on U.S. emissions is embedded in FASOM and determining an aggregate N 2 O factor (% of nitrogen applied as fertilizer to biofuels) is not straightforward. International N 2 O emissions are based on nitrogen fertilizer application rates and again it is difficult to verify the level of nitrogen application for each crop type. Since N 2 O emissions are one of the largest emission sources from biofuels production, the uncertainty in these emissions needs to be examined more carefully. 5.3 FASOM and FAPRI Land Use Analysis EPA uses the FASOM and FAPRI agricultural models to estimate the cropland affected by biofuel production. The analysis performed depends upon the supply and demand for land, crops, and products including animal feed and beef. The net effect depends on key economic inputs that are exogenous to the agricultural models used. EPA s analysis should make these input assumptions more transparent and provide more illustrations of the sensitivity of model results to these inputs. Several other issues are apparent from the analysis and are discussed in the main report. 5.4 Other Indirect Effects EPA has examined a variety of direct and indirect effects associated with renewable fuels. The analysis of LUC impacts includes an assessment of the economic impacts of agricultural commodities which are addressed by FASOM and FAPRI. However, a variety of other indirect effects are not examined in the DRIA. For example, a significant impact of biofuels expansion is the shift in crop exports, global shipping, and bunker fuel consumption. Changes in the transport of U.S. and Brazilian exports seem as meaningful as the subtle macroeconomic shifts examined by FAPRI and FASOM. In addition, expansion of biofuels use will have a significant impact of food consumption and fertilizer manufacture. EPA s analysis maintains a constant supply of fuel, but food supplies are allowed to vary in the agro economic analysis. Clearly food price rationing will induce less consumption and potential GHG savings, but should this effect be a credit for biofuels? The effect of price rationing on the analysis should also be examined as was done for the California LCFS. Another critical aspect of biofuels life cycle analysis is related to fertilizer production. The direct emissions from fertilizer production are estimated in GREET and are presumably comparable to those in FASOM. However, EPA s consequential LCA does not take into 8 Life Cycle Associates, LLC

account the growth in fertilizer demand associated with biofuels agriculture. If EPA s analysis is based only on the natural gas factors in GREET for ammonia fertilizer production, these should be revised to reflect an appropriate marginal mix, consistent with the other consequential analyses in the DRIA. 5.5 Uncertainty Analysis EPA performs a variety of sensitivity analyses that examine different feedstock scenarios and other life cycle model inputs. The EPA s sensitivity analysis examines the effect on model results of perturbations in individual model parameters. Because parameters are perturbed one-by-one, this is a local analysis that does not consider the impact of multiple, simultaneous parameter perturbations. However, EPA does little to assess the uncertainty in its analysis. While the difficulty in performing uncertainly analysis is well understood, EPA could do much more to quantify the uncertainty in its modeling results. For example, data on parameters such as agricultural inputs, soil carbon release, energy use and many other parameters could provide the basis for a true uncertainty analysis. 5.6 Life Cycle Modeling Key Recommendations EPA has investigated the life cycle GHG emissions of renewable and petroleum fuels in a detailed manner using the modeling tools that are available and feasible at present. The complex mix of life cycle and agro-economic models and databases employed by EPA captures the major factors that affect GHG emissions. EPA used FASOM and FAPRI to assess emissions associated LUC, and GREET for direct emissions. Although the models have limitations, they are detailed greenhouse gas modeling tools that can yield useful output with appropriate data inputs. A key challenge is in identifying input assumptions that are appropriate for these complex models and EPA should review and better justify the input assumptions adopted. 6 Summary of Recommendations This review identifies a number of issues and shortcomings focusing on biofuels production, petroleum production, and LCA modeling respectively that EPA should address. These are summarized in the following. 6.1 Biofuels Production EPA should take several steps to present a more accurate picture of the life cycle GHG impact of biofuels. These include: The cost and viability of the near term transition to biofuels is oversimplified. EPA should: - Examine more carefully the near term costs of cellulosic ethanol and the risks and contingencies associated with a build-up of new technology that is as rapid as the prior expansion in corn ethanol - Examine alternative scenarios for sugar cane ethanol including the use of residual oil to dewater ethanol in the Caribbean, which accounts for most of the Brazilian ethanol imported to the U.S. today 9 Life Cycle Associates, LLC

- Rate ethanol plants on actual performance and develop ratings for ethanol plants that fall into technology categories similar to the approach taken by ARB - Determine what energy inputs are required to meet a 20% GHG reduction rather than predicting the future mix of ethanol plant technologies The aggregation of technologies and the analysis for 2022 technologies attributes improvements and co-product power that are not related to the rule. EPA s treatment of biofuels reflects the most optimistic view of production technologies and provides an excessive credit for co-products compared to the likely environmental benefit and credits applied in other studies. EPA should adjust this treatment to align more closely to that employed in these other studies The GREET model should be updated to reflect numerous data and calculation issues discussed in the main report. Most notably, the fossil fuel carbon in biodiesel should be counted and allocation methods for biodiesel should be revised. 6.2 Petroleum Fuels Production The analysis of petroleum relies on a combination of studies and models that should be updated to reflect available data in a consistent manner. Specifically EPA should: Revisit the adjustments it makes to the imports of heavy oil and Venezuela extra heavy crude oil and provide a rigorous justification for the selected adjustments used Explain how the correction factors it applies to these different crude oils interact with the correction factors GREET already applies to all types of imported crude oil Re-run the GREET model using the correct proportion of gasoline and diesel fuels sold in 2005 using the correct EIA data Re-run the GREET model for diesel fuel, to correct a mistake in modeling diesel fuel production in GREET as if it were ultra-low-sulfur diesel (as defined by the EIA) when in fact virtually no such fuel was sold in 2005 6.3 Life Cycle Modeling EPA used FASOM and FAPRI to assess emissions associated LUC, and GREET for direct emissions. Although the models have limitations, they are detailed greenhouse gas modeling tools that can yield useful output with appropriate data inputs. Given the importance of specifying appropriate data inputs, EPA should review and better justify the input assumptions adopted. 10 Life Cycle Associates, LLC