1 

## Chapter 2: Lifecycle GHG Analysis

2.1 Chapter Overview

This chapter describes each component of th e analysis undertaken by EPA as part of the RFS2 rulemaking to determine lifecycle GHG emissions impacts for renewable and petroleumbased transportation fuels. The chapter is organized as follows:

• Section 2.2 provides background about lifecycle analysis for RFS2 and key modeling updates EPA has made since the proposed rule.

• Section 2.3 lays out the goals a nd scope of our analysis.

• Section 2.4 provides a detailed explanation of each component in EPA’s lifecycle analysis of renewable fuels.

o Section 2.4.1 summarizes the Agency’s overall biofuel m odeling approach.

o Section 2.4.2 focuses on domestic agricu ltural sector GHG emissions impacts, including our evaluation of changes in agricultural inputs and livestock production.

o Section 2.4.3 discusses interna tional agricultural impacts.

o Section 2.4.4 explains EPA's assessment ofGHG emissions impacts from biofuelinduced domestic and international land c onversions, including our quantification of uncertainty in international la nd conversion GHG emissions impacts.

o Section 2.4.5 describes our accounting fo r lifecycle GHG emissions over time.

o Section 2.4.6 explains EPA’s analysis of biofuel feedstock transport.

o Section 2.4.7 discusses energy use and GHG emissions from biofuel processing.

o Section 2.4.8 includes our updated analysis of fuel transport and distribution.

o Section 2.4.9 covers renewabl e fuel tailpipe emissions.

o Section 2.4.10 discusses other potential indi rect impacts from biofuel production.

o Section 2.4.11describes other modeling approaches that EPA considered for lifecycle GHG analysis.

• Section 2.5 presents EPA’s analysis of baseline gasoline and diesel lifecycle GHG emissions for comparison with biofuels.

• Section 2.6 discusses the fuel-specific lif ecycle GHG emissions results, including sensitivity analyses.

• Section 2.7 includes our analysis of the ove rall GHG impacts of the rulemaking volumes.

• Section 2.8 concludes the chapter with a disc ussion of the effects of the RFS2 on global temperature and sea level.2 

### 2.2 Background for Estimati ng Fuel Lifecycle Greenhouse Gas Emissions

2.2.1 Lifecycle Analysis fo r the RFS2 Proposal

Lifecycle modeling of transportation fuels, often referred to as fuel cycle or well-towheel analysis, assesses the net impacts of a fuel throughout each stage of its production and use including production / extraction of the feedstoc k, feedstock transportation, fuel production, fuel transportation and distribution, a nd tailpipe emissions. Use of a lifecycle approach to analyze different transportation fuels requi res modeling and evaluation of many different input factors.

Lifecycle assessments can be divided in to two major methodological categories: attributional and consequential. 1

An attributional approach to GHG emissions accountin g in products provides information about the GHG emitted directly by a product and its life cycle. The product system includes processes that are directly linked to the product by material, energy flows or services following a supply-chain logic.

A consequential approach to GHG emissions accounting in products provides information about the GHG emitted, directly or indirectly, as a consequence of changes in demand for the product. This approach typicall y describes changes in GHG emissions levels from affected processes, which are identified by linking causes with effects.

The definition of lifecycle greenhouse gas emi ssions established by Congress states that:

The term ‘lifecycle greenhouse gas emi ssions’ means the aggregate quantity of greenhouse gas emissions (including dir ect emissions and significant indirect emissions such as significant emissions from land use changes), as determined by the Administrator, related to the full fu el lifecycle, including all stages of fuel and feedstock production and distribution, from feedstock generation or extraction through the distribution and delivery and use of the finished fuel to the ultimate consumer, where the mass values for all greenhouse gases are adjusted to account for their relative global warming potential. 2

This definition and specifically the clause “(including direct emissions and significant indirect emissions such as signific ant emissions from land use changes)” requires the Agency to consider a consequential lifecycle analyses and to develop a methodology that accounts for all of the important factors that may significantly influe nce this assessment, including the secondary or indirect impacts of expanded biofuels use.

Furthermore, independent of the statutory lang uage the Agency believe s it is important to include secondary, indirect, or consequen tial impacts of biofuel use, specifically:3 − Capturing secondary market driven agricultu ral sector impacts, such as changes in other crop patterns and livest ock production as a response to changing prices in biofuel feedstocks.

− Production of co-products from biofue l production requires some type of allocation, either splitting e missions of fuel production between fuel and coproducts or examining the use of co-produc ts in other markets. For example in the case of corn ethanol, the co-product of ethanol production is a feed product that is assumed to replace the use of corn and soybean meal. Therefore, the emissions of producing an e quivalent amount of corn and soybean meal to these co-products are subtracted from the lifecyc le assessment. Th is requires modeling of the co-product economic markets.

− To the extent that they are included in attributional lifecycle analyses, land use impacts are typically confined to direct impacts, e.g., land converted to produce corn directly used for ethanol production. This does not capture effects of land converted to produce crops that are indi rectly impacted by increased biofuel production. One specific example of this is increased corn ethanol production in the U.S. could lead to decreased crop exports resulting in increased crop production and land use impacts internati onally. Another example is corn production increases resulting in le ss rice production and lower CH 4 emissions.

− Consideration of specific po licies and interaction between different fuel volumes could have very distinct impacts especi ally in the agricultural sector.

The lifecycle methodology developed for the R FS2 rulemaking analysis included the use of economic models to perform a conseq uential type of lifecycle analysis.

The consequential approach of incorporating economic models into a lifecycle assessment is not a new concept. Most notably the Economic Input-Output Lifecycle Assessment (EIO-LCA) method has been employed in the past. The EIO-LCA method estimates the materials and energy resources required for, and the environmental emissions resulting from, activities in the overall economy. The EIO-LC A method was theorized and developed by economist Wassily Leontief in the 1970s based on his earlier input-output work from the 1930s for which he received the Nobel Prize in Economics. Researchers at the Green Design Institute of Carnegie Mellon University operationalized this method in the mid-1990s, once sufficient computing power was widely available to perform the large-scale matrix manipulations required in real-time. This work relies on static input-output tables of th e U.S. economy to determine the full economy wide impacts of produc ing a product or service.

Mark Delucchi at the Institute of Transpor tation Studies of the University of California Davis has developed the Lifecycle Emissions Mode l (LEM) that looks at transportation fuels. He has also highlighted the need to look at market impacts when considering biofuel production and specifically to consider land use changes. 3 There have also been several studies examining the consequential or economic-based life cycle assessment including several focusing on the agricultural sector.4 Currently, no single model captures all of the complex interactions associated with estimating lifecycle GHG emissions for biofuels, ta king into account the "significant indirect emissions such as significant emissions from land use change" required by EISA. For example, some lifecycle analysis tools typically used in the past focused on process modeling—the energy and resultant emissions associated with the direct production of a fuel at a petroleum refinery or biofuel production facility. But this is only one component in the production of the fuel. Clearly in the case of biofuels, impacts from and on the agricultural sector are important, because this sector produces feedstock for biofuel produc tion. Commercial agricu ltural operations make many of their decisions based on an economic assessment of profit ma ximization. Assessment of the interactions throughout the agricultural se ctor requires an analysis of the commodity markets using economic models. However, existing economy wide general equilibrium economic models are not detailed enough, on their own, to capture the specific agricultural sector interactions critical to our analysis (e.g., chan ges in acres by crop type) and would not provide the types of outputs needed for a thorough GHG anal ysis. As a result, EPA has used a set of tools that are best suited for each specific component of the analysis to create a more comprehensive estimate of GHG emissions. Where no direct links between the different models exist, specific components and out puts of each are used and combin ed to provide an analytical framework and the composite lifecycle assessment results.

To estimate the changes in the domestic agricultural sector (e.g., changes in crop acres resulting from increased demand for biofuel feedstock or changes in the number of livestock due to higher corn prices) and their associated emissions, we used the Forestry and Agricultural Sector Optimization Model (FASOM), develope d by Texas A&M University and others. FASOM is a partial equilibrium ec onomic model of the U.S. forest and agricultural sectors. EPA selected the FASOM model for this analysis for several reasons. FASOM is a comprehensive forestry and agricultural sector model that tracks over 2,000 production possibilities for field crops, livestock, and biofuels for private lands in the contiguous United States. It accounts for changes in CO 2 , methane, and N 2 O from most agricultural activities and tracks carbon sequestration and carbon lo sses over time. Another advantage of FASOM is that it captures the impacts of all crop production, not just biofuel feedstock. Thus, as compared to some earlier assessments of lifecycle emission, using FASOM a llows us to determine secondary agricultural sector impacts, such as crop shifting and reduced demand due to higher prices. It also captures changes in the livestock market (e.g., smaller herd sizes that result from higher feed costs) and U.S. export changes. FASOM also has been used by EPA to consider U.S. forest and agricultural sector GHG mitigation options. 4

The output of the FASOM analysis includes changes in total domestic agricultural sector fertilizer and energy use. These are calculated based on the inputs required for all the different crops modeled and changes in the amounts of th e different crops produced due to increased biofuel production. FASOM output also includes ch anges in the number and type of livestock produced. These changes are due to the changes in animal feed prices and make-up due to the increase in biofuel production. The FASOM out put changes in fertilizer, energy use, and livestock are combined with GHG emission fact ors from those sources to generate biofuel lifecycle impacts. The GHG emission factors fo r fuel and fertilizer production come from the Greenhouse gases, Regulated Emissions, and Energy use in Transportation (GREET) spreadsheet5 analysis tool developed by Argonne National Laboratories, and livestock GHG emission factors are from IPCC guidance.

GREET includes the GHG emissi ons associated with the production and combustion of fossil fuels (diesel fuel, gasoline, natural ga s, coal, etc.). GREET also estimates the GHG emissions associated with elect ricity production required for ag riculture and biofuel production. For the agricultural sector, we also relied upon GREET to provide GHG emissions associated with the production and transport of agricultural inputs such as fe rtilizer, herbicides, pesticides, etc. GREET has been under development for se veral years and has undergone extensive peer review through multiple updates. Of the available data sources of information on lifecycle GHG emissions of fossil energy and agricultural s ector inputs consumed, we believe that GREET offers the most comprehensive treatment of emissions from the covered sources. GREET version 1.8c was the primary version used in this analysis.

To estimate the domestic impacts of N 2 O emissions from fertilizer application, we used the CENTURY and DAYCENT models, develope d by Colorado State University. The DAYCENT model simulates plant-soil systems and is capable of simula ting detailed daily soil water and temperature dynamic s and trace gas fluxes (CH 4 , N 2 O, NOx and N 2 ). The CENTURY model is a generalized plant-soil ecosystem model that simulates plant production, soil carbon dynamics, soil nutrient dynamics, and soil water and temperature. Model results for N 2 O emissions from different crop and land use chan ges were combined with FASOM output to generate overall domestic N 2 O emissions.

FASOM output also provides changes in tota l land use required for agriculture and land use shifting between crops, and internations with pasture, and forestry. This output is combined with emission factors from land use change to generate domestic land use change GHG emissions from increased biofuel production.

To estimate the impacts of biofuels feedstock production on international agricultural and livestock production, we used the integrated Food and Agricultural Policy and Research Institute (FAPRI) international models, de veloped by Iowa State University and the University of Missouri. These models capture the biological , technical, and economic relationships among key variables within a particular commodity and across commodities. FAPRI is a worldwide agricultural sector economic model that was r un by the Center for Agricultural and Rural Development (CARD) at Iowa State University on behalf of EPA. The FAPRI models have been previously employed to examine the impacts of World Trade Organization proposals, changes in the European Union’s Common Agricu ltural Policy, analyze farm bill proposals since 1984, and evaluate the impact of biofuel develo pment in the United States. In addition, the FAPRI models have been used by the USDA Office of Chief Economist, Congress, and the World Bank to examine agricultural impacts from government policy changes, market developments, and land use shifts.

The output of the FAPRI model included chan ges in crop acres and livestock production by type and by country globally. Unlike FASO M, the FAPRI output did not include changes in fertilizer or energy use or have land type inter actions built in. These were developed outside the FAPRI model and combined with the FAPRI output to generate GHG emission impacts.6 Crop input data by crop and country were developed and combined with the FAPRI output crop acreage change data to generate overall changes in fertilizer and energy use. These fertilizer and energy changes along with the FAPRI output livestock changes were then converted to GHG emissions base d on the same basic approach us ed for domestic sources, which involves combining with emission factors from GREET and IPCC.

The FAPRI model does predict how much crop land will change in other countries but does not predict what type of land such as forest or pasture will be affected. We used data analyses provided by Winrock International to es timate what land types will be converted into crop land in each country and the GHG emissions associated with the land conversions. Working with Winrock, we used recent satellite data to analyze recent land use changes around the world that have resulted from the social, economic, and political forces that drive land use. In our assessment, we are assuming that these recent drivers of land use change will remain in relative affect through our 2022 modeling time fram e such that the recent trends in land use change are indicative of land use changes like to result in 2022 due to biofuel production. we combined the recent land use change patterns with various estimates of carbon stocks associated with different types of land at the state level. This international land use assessment is an important consideration in our lifecycle GHG assessment and is explained in more detail later in section 2.4.4 in this chapter.

Additional modeling and data sources used to determine the GHG emissions of other stages in the biofuel lifecycle include studies and data on the distance and modes of transport needed to ship feedstocks from the field to the biofuel processing facility and the finished biofuel from the facility to end use. These distances a nd modes are used to develop the amount and type of energy used for transport which are co mbined with GREET factors to generate GHG emissions.

We also calculate energy use needed in the biofuel processing facility from industry sources, reports, and process modeling. This en ergy use is combined with emissions factors from GREET to develop GHG impacts of the biofuel production process

To test the robustness of the FASOM, FAPR I and Winrock results, we also examinedbiofuel land use change impacts with the Global Trade Analysis Project (GTAP) model, a multiregion, multi-sector, computable general equilib rium model that estimates changes in world agricultural production. Maintained through Pur due University, GTAP pr ojects international land use change based on the econo mics of land conversion, rather th an using the historical data approach applied by FAPRI/Winroc k. GTAP is designed to project changes in international land use as a result of the change in U.S. biofuel policies, based on the rela tive land use values of cropland, forest, and pastureland. The GTAP design has the advantage of explicitly modeling the competition between different land types due to a change in policy. As further discussed in section 2.4.11, the GTAP model results were genera lly consistent with our FAPRI/satellite data analysis, in particular supporting the significant impact on international land use.

Figure 2.2-1 graphically shows the different m odels used and what parts of the lifecycle they are used to represent.7 Figure 2.2-1 System Boundaries and Models Used

2.2.2 Updates for this Final Rulemaking

Throughout the development of EPA’s lifecyc le analysis, the Agency has employed a collaborative, transparent, a nd science-based approach. EP A’s lifecycle methodology, as developed for the RFS2 proposal, required breaki ng new scientific ground and using analytical tools in new ways. The work was generally recognized as state of the art and an advance in lifecycle modeling, specifically regarding the indirect impacts of biofuels.

However, the complexity and uncertainty inherent in this work made it extremely important that we seek the advice and input of a broad group of experts and stakeholders. In order to maximize stakeholder outreach opportunities, the comment period for the proposed rule was extended to 120 days. In addition to th is formal comment period, EPA made multiple efforts to solicit public and expert feedback on our approach. Beginning early in the NPRM process and continuing throughout the developm ent of this final rule, EPA held hundreds of meetings with stakeholders, including g overnment, academia, industry, and non-profit organizations, to gather expert technical input . Our work was also informed heavily by consultation with other federal agencies. For ex ample, we have relied on the expert advice of USDA and DOE, as well as incorporating the mo st recent inputs and models provided by these Agencies. Dialogue with the State of California and the European Union on their parallel, ongoing efforts in GHG lifecycle analysis also help ed inform EPA’s methodology. As described below, formal technical exchanges and an indepe ndent, formal peer review of the methodology8 were also significant components of the Agency’s outreach. A key result of our outreach effort has been awareness of new studies and data that have been incorporated into our final rule analysis.

Technology Exchanges : Immediately following publication of the proposed rule, EPA held a two-day public workshop focused specifi cally on lifecycle analysis to assure full understanding of the analyses conducted, the issu es addressed, and the op tions discussed. The workshop featured EPA presen tations on each component of the methodology as well as presentations and discussions by stakeholders from the renewable fuel community, federal agencies, universities, and environmental gr oups. The Agency also took advantage of opportunities to meet in the field with key, affect ed stakeholders. For example, the Agency was able to twice participate in meetings and tours in Iowa hosted by the local renewable fuel and agricultural community. As described in this section, one of the many outcomes of these meetings was an improved understanding of agri cultural and biofuel pr oduction practices.

As indicated in the proposal, our lifecycle results were particularly impacted by assumptions about land use patte rns and emissions in Brazil. During the public comment process we were able to update and refine these assumptions, including the incorporation of new, improved sources of data based on Brazil-specific data and programs. In addition, the Agency received more recent trends on Brazilian crop prod uctivity, areas of crop expansion, and regional differences in costs of crop production and la nd availability. Lastly, we received new information on the effectiveness of current efforts to curb deforestation allowing the Agency to better predict this impact through 2022.

Peer Review: To ensure the Agency made its decisions for this final rule on the best science available, EPA conducted a formal, independent peer review of key components of the analysis. The reviews were conducted following the Office of Management and Budget’s peer review guidance that ensures consistent, indepe ndent government-wide implementation of peer review, and according to EPA's longstanding and rigorous peer review policies. In accordance with these guidelines, EPA used independent, thir d-party contractors to se lect highly qualified peer reviewers. The reviewers selected are leading experts in their respective fields, including lifecycle assessment, economic modeling, remote sensing image ry, biofuel technologies, soil science, agricultural economics, and climate science. They were asked to evaluate four key components of EPA’s methodology: (1) land use m odeling, specifically the use of satellite data and EPA’s proposed land conve rsion GHG emission factors; (2 ) methods to account for the variable timing of GHG emissions; (3) GHG emi ssions from foreign crop production (both the modeling and data used); and (4) how the models EPA relied upon are used together to provide overall lifecycle estimates. The full peer review records, including all of the charge questions and peer reviewer responses, are available in the public docket for this rulemaking.

The advice and information received through this peer review are reflected throughout this chapter. The reviewers also provided recomme ndations that have helped to inform the larger methodological decisions presented in this final rule. For example, the reviewers in general supported the importance of assessing indirect land use change and determined that in general EPA used the best available tool s and approaches for this work. However, the review also recognized that no existing model comprehensivel y simulates the direct and indirect effects of9 biofuel production both domestically and internati onally, and therefore model development is still evolving. The uncertainty associated with estimating indirect impacts and the difficulty in developing precise results also were reflected in the comments. In the long term, this peer review will help focus EPA’s ongoing lifecycle analysis work as well as our future interactions with the National Academy of Science and other experts.

Altogether, the many and extensive public comments we received to the rule docket, the numerous meetings, workshops and t echnical exchanges, and the scie ntific peer review have all been instrumental to EPA’s ability to advance our analysis between proposal and final and to develop the methodological and regulatory approach described in this section.

Based on peer review results as well as othe r comments received we have made several updates to our modeling since the NPRM analysis as shown in Table 2.2-1.

.10 Table 2.2-1. Key Lifecycle Modeling Updates Update Source Updates to Domestic Agricultural Sector Modeling :

• Incorporated the FASOM forestry module • Updated FASOM Forestry component

• Added new land classifications: cropland, cropland-pasture, rangeland, forest-pasture, forest, CRP, developed land • U.S. land cover databases

• Reflected new data on projected switchgrass yields • New data from PNNL on switchgrass yields

• Updated N2O / soil carbon emissions factors • DAYCENT/CENTURY model updates by Colorado State University

• Updated emission factors for farm input production • New version of GREET (version 1.8c)

Updates to International Agricultural Sector Modeling:

• Incorporated a Brazil module into the international model framework

o Regional crop and pasture modeling • FAPRI Brazil Module A

• Added price induced yield changes (e.g., long term elasticity for the Corn Belt in the U.S. 0.07) • FAPRI 2010 U.S. And World Agricultural Outlook

• Updated international agricultural GHG emission estimates • International Fertilizer Industry Assoc. (2009) 5 and pesticide consumption from FAOStat 6

• Updated Brazil sugarcane production based on recent studies • Macedo (2008) 7

Updates to Biofuel Processing in Both Domestic and International Agricultural Sector Modeling:

• Built in corn fractionation pathways (with coproduct markets, etc.) • USDA?

• Adjusted DGS co-product replacement rates

o Reflected studies that indicate more efficient use of co-product • Empirical studies by Argonne Laboratory and University of Minnesota: Arora, Wu and Wang (2008) 8 and Shurson (2009) 9

• Added biodiesel glycerin co-product credit • Based on data from NBB and GREET

• Updated process energy use • New studies by USDA 10 , NREL 11,12,13 and Energy Resources Center 14 Updates to Land Use Change Modeling:

• Used more recent / longer time coverage / higher resolution satellite data - 2001-2007 • MODIS V5 (2009) 15

a. Augmented satellite data with region specific data where available (e.g., data from Brazil on pasture intensification) • FAPRI Brazil module

• New soil carbon data • Harmonized World Soil Databased (2009) 16

• New studies monitoring long-term forest growth rates • Lewis et al. (2009) 17 and Phillips et al. (2008) 18

Petroleum Baseline Updates:

• Updated 2005 petroleum baseline • DOE/NETL (2009) 19

A

Iowa State University working with Brazilian experts developed this module which has been incorporated into the FAPRI 2010 U.S. And World Agricultural Outlook, released date early 201011 Furthermore, in the proposal, we asked fo r comment on whether and how to conduct an uncertainty analysis to help quantify the magnitude of this uncertainty and its relative impact on the resulting lifecycle emissions estimates. The results of the peer review, and the feedback we have received from the comment process, supporte d the value of conducting such an analysis. Therefore, working closely with other government agencies as well as incorporating feedback from experts who commented on the rule, one of the main changes we made since the proposal was that we have quantified the uncertainty associated with specifically the international indirect land use change emissions associated with in creased biofuel production. More discussion of treatment of uncertainty is found in section 2.4.4.2.8.12 

### 2.3 Goals and Scope of This Analysis

Lifecycle analysis is used in several wa ys for this rulemaking. Fuel-specific GHG reductions are used to develop thre shold determinations for specific fuels. Lifecycle analysis is also used to determine the overall impact of th e rulemaking on GHG emissions worldwide. The first step was to establish the goals and scope for this analysis, as summarized below.

2.3.1 Goal

The RFS2 rulemaking involves determining lifecycle GHG impac ts of specific fuels and fuel pathways for comparison with thresholds as defined in the legislati on. Obligated parties will be required to use mandated qua ntities of renewable fuels, but only fuels that meet the GHG thresholds can qualify under the program. (Fuels produced at grandfathered facilities are exempt from these GHG threshold requirements.) The lifecycle GHG reductions represent the GHG differences between renewable fuels relative to the petroleum-based gasoline and diesel that they displace. The lifecycle methodology described he re is used to determine the GHG displacement values for different renewable fuels to be compared to the thresholds. Therefore this analysis will provide:

− Amount of GHG emissions (on a mass basis) per amount of fuel produced (on an energy content basis) for both conventi onal petroleum based fuels and renewable fuels.

− Results are combined to quantify the emissi on change per energy unit (i.e., per BTU) of renewable fuel compared to that for the conventional fuel replaced.

2.3.2 Scope

2.3.2.1 Scenario Analysis

To quantify the lifecycle GHG emissions associ ated with the increase in renewable fuel mandated by EISA, we needed to compare the impacts of renewable fuels with EISA to a reference case without EISA. Since it is not prac tical or workable to conduct such an analysis and come up with factors for every year, to carry out this analysis we chose to look at the final year of the RFS2 standards when they are fully phased in. For our reference case we assumed a “business as usual” volume of a particular renewable fuel based on what would likely be in the fuel pool in 2022 without EISA as predicted by th e Energy Information Agency’s Annual Energy Outlook (AEO) for 2007 (which took into account th e economic and policy factors in existence in 2007 before EISA). For our control case we assumed the higher volumes of renewable fuels as mandated by EISA for 2022. For each individua l biofuel, we analyzed the incremental impact of increasing the volume of that fuel to the to tal mix of biofuels needed to meet the EISA requirements while holding volumes of other fu els constant. Any changes between now and 2022 in factors such as crop yields, energy costs, or production plant efficiencies, both domestically and internationally, are reflected in both scenarios. Rather than focus on the impacts associated with a specifi c gallon of fuel and tracking i nputs and outputs across different lifecycle stages, we determined the overall aggr egate impacts across sections of the economy in response to a given volume change in the amount of biofuel produced. We then normalize those13 impacts to a gallon of fuel by dividing total impacts over the given volume change. In the case of overall rule impacts, we analyze the change in reference vs. control case volumes for all fuels together and take the absolute GHG results (e.g ., do not normalize the overall rule impacts).

We did not calculate the emission impacts fo r each gallon of fuel based upon its unique production characteristics which could vary widely across the nation (e.g., a gallon of ethanol produced using corn grown in Iowa may have diff erent direct lifecycle emissions impacts than a gallon of ethanol produced at an identical facility in Nebraska using corn grown in Nebraska due to regional differences in agricultural practices. However, on a lifecycle basis, considering the indirect impacts in the context of the entire corn market they are not different). Rather, we determined the overall aggregate impacts across se ctions of the economy in response to a given volume change in the amount of biofuel produced . In the case of agricultural impacts, we assessed the impact on the entire U.S. agricultural system that would result from expanded demand for biofuel feedstock. We then normalized those impacts to a gallon of fuel by dividing total impacts over the renewable fuel volume change between our business as usual case and the EISA volumes. Similarly, we estimated the typical emissions impact of a type of biofuel production facility (e.g., a plant that uses the dry mill process to turn corn starch into ethanol). The emissions assessment from a typical facility wa s then ascribed to all biofuel produced across facilities using that sa me basic technology.

We focus our final rule analyses on 2022 resu lts for two main reasons. First, it would require an extremely complex assessment and administratively difficult implementation program to track how biofuel production mig ht continuously change from month to month or year to year. Instead, it seems appropriate that each biofuel be assessed a level of GHG performance that is constant over the implementation of this rule, allowing fuel providers to anticipate how these GHG performance assessments should affect their production plans. Second, it is appropriate to focus on 2022, the final year of ramp up in the requir ed volumes of renewable fuel as this year. Assessment in this year allows the complete fuel volumes specified in EISA to be incorporated. This also allows for the complete implementa tion of technology changes and updates that were made to improve or modeling efforts. For ex ample, the inclusion of price induced yield increases and the efficiency gains of DDGS repl acement are phased in over time. Furthermore, these changes are in part driven by the changes in earlier years of increased biofuel use.

Several of the lifecycle emission impacts for one fuel are interrelated with those of another fuel, in particular the land-use changes. For our analysis of the overall GHG impacts of the program (discussed in section 2.7), we mode led all of the fuel changes simultaneously to determine the land-use impact. However, from that analysis it is not possible to differentiate the contribution of the land-use change to one fuel vs. another. As a result, for this analysis we had to model the impacts of just one fuel change at a ti me. In doing this we have held the other fuel volumes constant at their mandate d levels in order to best appr oximate the impacts a single fuel change would have in the context of the full RFS2 standard volumes.

We used the same approach to determine the lifecycle GHG emissions for corn ethanol, cellulosic ethanol, biodiesel produced from soyb ean (and other vegetable) oils, and biodiesel produced using waste oils as feedstock. For wa ste oils, we note that no land use changes are included in the FASOM assessment, because any land use impacts are attributed to the original14 purpose of the feedstock (e.g.., the use of the vegetable oil for cooking or the production of animals for their meat), rather than the biofuel produced from the recovered waste material.

FASOM does not model feedstocks for fuels pr oduced outside the U.S. We addressed imported ethanol by analyzing the differen ce in total GHG emissions based on two 2022 scenarios using only the results from FAPRI modeling runs: (1) the business as usual reference case volume of 0.6 Bgal and (2) an RFS2 project ed volume of 2.2 Bgal of imported sugarcane ethanol.

Current models present some challenges in estimating GHG lifecycle emissions for cellulosic biofuels. For example, the FAPRI m odel used for this analysis did not include switchgrass or similar energy crops, and could onl y use corn stover or other food crop residues as feedstock in predicting cellulosic biofuel impacts. To overcome this limitation we ran the FASOM model with a switchgrass scenario to generate domestic land use and crop change results. We then applied these domestic crop changes by region to the FAPRI model to generate the international land use change and crop shifting due to the domestic impacts predicted by the FASOM switchgrass scenario.

For biofuels made from wastes and byproduct s (e.g., MSW, rendered fats and waste oils and corn stover feedstock), we assumed no land us e changes, because these biofuel feedstocks do not compete for domestic crop acreage. For corn stover, we analyzed only the change in domestic GHG emission resulting from an increase in fertilizer replacement application rates to compensate for the removal of stover from th e land. Table 2.3-1 shows the different fuel scenarios considered.

Table 2.3-1. Fuel Volume Scen arios Considered in This Analysis (Billions of Gallons) Biofuel Low Volume High Volume Change
Corn Ethanol 12.3 15.0 2.7 Switchgrass Cellulosic Ethanol 0 7.9 7.9 Corn Residue Cellulosic Ethanol 0 4.9 4.9 Imported Sugarcane Ethanol 0.6 2.2 1.6 Soybean Oil Biodiesel 0.1 0.6 0.5

2.3.2.2 System Boundaries

It is important to establish clear sy stem boundaries in lifecycle analysis. By determining a common set of system boundaries, different fuel types can then be validly compared. As described in the p revious section, we have assessed the direct and indirect GHG impacts in each stage of the full fuel lifecycle for biofuels and petroleum fuels.

Figure 2.3-1 provides a simplified diagra m describing the system studied.15 Figure 2.3-1. Simplified Lifecycle System Diagram

International Agricultural

Sector Domestic Agricultural

Sector Conventional Petroleum Fuel

Production

Food / Feed Uses Transportation Sector Fuel Demand Biofuel Production

The different fuel volume scenarios w ere compared based on de livery of the same functions, in this case providing for both the agricultural sector market and transportation fuels markets. Within the overall system shown in

Figure 2.3-1,the unit process listed in Table 2.3-2 will be considered.

Table 2.3-2. Unit Processes Considered

Biofuel Petroleum-Based Fuel Feedstock Agriculture Crude Oil Extraction

Feedstock Transport Crude Oil Transport Feedstock Processing & Biofuel Production Refining

Biofuel Transport and Distribution Fuel Transport and Distribution Biofuel Tailpipe Emissions Fuel Tailpipe Emissions

Included in each unit process shown in Tabl e 2.3-2 are the emissions and energy use associated with each operation as well as ups tream components that feed into them. For example, the feedstock agriculture stage includes emissions from fuel used in tractors as well as from producing and transporting the fertilizer used in the field. Electrici ty production emissions are included in almost all of the stages shown. For direct impacts, as was the case in the proposal analysis, this results in system boundari es that include operati on-related activities, but not infrastructure-related activities. As such, while we do include the emissions associated with the operation of farm equipment and trucks used for feedstock / fuel transportation we do not include the emissions associated with the producti on of the equipment or vehicles. Furthermore, we include the emissions from the operations of biofuel production pl ants and petroleum16 refineries but we do not include emissions from producing the material used to construct the facilities.

In determining what indirect impacts to include in the system boundaries of this analysis we focus on the goal and scope of the analysis as specified by the statutory language in EISA.

The Act specifies different categories of re newable fuels, conventional renewable fuel, advanced biofuel, cellulosic bi ofuel, and biomass-based diesel . The categories of fuel are defined in part based on their GHG emissi ons. For example for cellulosic biofuel:

The term ‘cellulosic biofue l’ means renewable fuel de rived from any cellulose, hemicellulose, or lignin that is derived from renewable biomass and that has lifecycle greenhouse gas emissions, as determined by the Administrator, that are at least 60 percent less than the baseline lifecycle greenhouse gas emissions.

So, the main goal of this analysis is to de termine the lifecycle GHG emissions of different biofuel feedstock and fuel pathways for deter mination of compliance against the GHG thresholds as defined and mandated in the Act. More specifically the language stipulates that the analysis compares biofuel “lifecycle greenhouse gas emissions” against the “baseline lifecycle greenhouse gas emissions”.

Biofuel lifecycle greenho use gas emissions are further defined as:

The term ‘lifecycle greenhouse gas emi ssions’ means the aggregate quantity of greenhouse gas emissions (incl uding direct emissions and sign ificant indirect emissions such as significant emissions from l and use changes), as determined by the Administrator, related to the full fuel lifecycle, including all stages of fuel and feedstock production and distribution, from feedsto ck generation or extraction through the distribution and delivery and use of the finished fuel to the ultimate consumer, where the mass values for all greenhouse gases are adjus ted to account for their relative global warming potential. B

This definition forms the basis of defini ng the system boundaries for the biofuels lifecycle analysis. As the language specifically mandates that lifecycle GHG emissions include

“ direct emissions and significant i ndirect emissions such as sign ificant emissions from land use changes” the system boundaries modeled include indi rect impacts as determined through our economic modeling discussed in section 2.4.

EISA defines baseline lifecycle greenhouse gas emissions as:

The term ‘baseline lifecycle greenhouse gas emissions’ means the average lifecycle greenhouse gas emissions, as determined by the Administrator, after notice and opportunity for comment, for gasoline or diesel (whichever is being replaced by the renewable fuel) sold or distributed as transportation fuel in 2005.

B Clean Air Act Section 211(o)(1).17 Therefore, the petroleum production component of the system boundaries is specifically mandated by EISA to be based on the 2005 average fo r crude oil used to make gasoline or diesel sold or distributed as transporta tion fuel, and not the marginal crude oil that will be displaced by renewable fuel. More discussion on the petr oleum fuel baseline and potential impact of considering indirect impacts on the petroleu m baseline are discussed in section 2.5.

2.3.2.3 Environmental Flows Considered

The lifecycle analysis discussed here evaluates the impacts of increased renewable fuel use on greenhouse gas emissions. EISA specifies a definition of greenhouse gases to include in the analysis:

The term ‘greenhouse gas’ means carbon dioxide, hydrofluorocarbons, methane, nitrous oxide, perfluorocarbons, sulfur hexafluoride. The Administrator may include any other anthropoge nically emitted gas that is determined by the Administrator, after notice and comment, to contribute to global warming.

EISA also specifies that the mass values fo r all greenhouse gases are adjusted to account for their relative global warming potential.

The relative global warming contribution of emissions of various greenhouse gases is dependant on their radiative forcing, atmospheric lifetime, and other considerations. For example, on a mass basis, the radiative forcing of CH 4 is much higher than that of CO 2 , but its effective atmospheric residence time is much lower. The relative warming impacts of various greenhouse gases, taking into account factors such as atmospheric lifetime and direct warming effects, are reported on a ‘CO 2 -equivalent’ basis as global wa rming potentials (GWPs). The GWPs used in this analysis were developed by the UN Intergovernmental Panel on Climate Change (IPCC) as listed in their Second Asse ssment Report, and are shown in Table 2.3-3. Second assessment report values are used to be cons istent with current stan dards for international reporting of GHG emissions.

Table 2.3-3. 100 Year Global Warming Potent ials for Greenhouse Gases Greenhouse Gas GWP CO 2 1 CH 4 21 N 2 O 310

Greenhouse gases are measured in terms of CO 2 -equivalent emissions (CO2e), which result from multiplying the GWP for each of the three pollutants shown in the above table by the mass of emissions for each pollutant. The sum of impacts for CH 4 , N 2 O, and CO 2 , yields the total effective GHG impact. Other GHGs like HFCs, PFCs and SF6 are not released in significant amounts over the lifecycle of renewabl e or petroleum fuels, and are therefore not tracked in this analysis. Other non-GHG clima te impacts like albedo (lig ht reflectance), land surface roughness, hydrologic and energy flux, a nd loss of forest aerosols, while potentially an18 important aspect of climate impacts associated with land use change, are currently outside the scope of this analysis.

Other environmental flows besi des GHG emissions are also cons idered in our analysis for this rulemaking. Criteria and toxic air polluta nts are modeled and results are described in Chapter 3 of the RIA. Water use and impacts are also considered and are described in Chapter 6 of the RIA.

2.3.2.4 Data Quality

Lifecycle analysis is a data intensive process and the results are affected by data quality. Data quality may be defined by specific characteristics that describe both quantitative and qualitative aspects of data, as well as the methods us ed to collect and integrate those data into the analysis. The quality of data used can be char acterized by how well the geographic, technical and temporal aspects of the da ta match the goals and scope of the analysis in question.

The quality of the data used in this analys is was classified based on its geographic, technical and temporal relevance to th e goals of the study as follows:

Geographic coverage – this analysis was conducted without any regard to the geographic attributes of where emissions or energy use occurs. The benefits of this proposed rule represent global reductions in GHG emissions and energy use, not just those occurring in the U.S. For example, the savings associated with reducing overseas crude oil extraction and refining are include d here, as are the international emissions associated with producing imported ethanol. Da ta for agricultural sector impacts include both U.S. and international defaults. Agricultural commodity production in other countries was based on data specific to thos e areas (e.g., fertilizer production in other countries). Land use change was specifically modeled in different countries; impacts of land use change were based on factors represen ting sub-country level land characteristics, and for areas where data was not available averages were used.

Technology coverage – this analysis models industries that do not exist yet – cellulosic ethanol and renewable diesel for example. Therefore assumptions based on existing information and modeling were made to repres ent these industries rather than relying on existing facility data. Even for industries that currently exist there is expected to be a range of technology development over time. Fo r this analysis we have made our best projections for what the industry may look like by 2022. There is expected to be considerable variation in the technologies used, for example combined heat and power and corn oil fractionation in a dry mill ethanol plant. To account for this we have looked at different fuel technology pathwa ys as discussed in section 2.4.7.

Temporal coverage – this analysis considered impacts in 2022. Therefore we modeled future data; we projected ethanol pr oduction in 2022 based on process models – consistent with cost analysis used in this rulemaking. For example, this assumed that future plants will be more energy efficient then current plants. Agricultural sector models also represented 2022 values including improve ments in yields and cropping patterns.19 2.3.2.5 Addressing Uncertainty

The peer review, the public comments we ha ve received, and the analysis conducted for the proposal and updated here for the final rule, i ndicate that it is important to take into account indirect emissions when looking at lifecycle emissions from biofuels. It is clear that, especially when considering commodity feedstocks, including the market interacti ons of biofuel demand on feedstock and agricultural markets is a more accurate representation of the impacts of an increase in biofuels production on GHG emissions than if thes e market interactions are not considered.

However, it is also clear that there are significant uncertainties associated with these estimates, particularly wi th regard to indirect land use change and the use of economic models to project future market interactions. Reviewers highlighted the uncertainty associated with our lifecycle GHG analysis and pointed to the inhe rent uncertainty of the economic modeling.

Therefore, working closely with other gove rnment agencies as well as incorporating feedback from experts who commented on the rule, we have quantif ied the uncertainty associated with specifically the international indirect land use change emissions associated with increased biofuel production. There are four main areas of uncertainty in our modeling approach:

• Economic Modeling Inputs

• Types of Land Converted and GHG Emission Factors

• Methodology Choices

• Other GHG Factors and Input Data

Although there is uncertainty in all portions of the lifecycle modeling, we focused our uncertainty analysis on the factors that are the most uncertain and have the biggest impact on the results. For example, the energy and GHG emissions used by a natural gas-fired ethanol plant to produce one gallon of ethanol can be calculated through direct observations, though this will vary somewhat between individual facilities. The indirect domestic emissions are also fairly well understood, however these results are sensiti ve to a number of key assumptions (e.g., current and future corn yields). The indirect, international e missions are the component of our analysis with the highest level of uncertainty and have particularly significant impact on our overall assessment results. For example, iden tifying what type of land is converted internationally and the emissions associated with this land conversion are critical issues that have a large impact on the GHG emissions estimates.

Therefore, we focused our efforts on the inte rnational indirect land use change emissions and worked to manage the uncertainty around those impacts in three ways: (1) getting the best information possible and updating our analysis to narrow the uncertainty, (2) performing sensitivity analysis around key factors to test the impact on the results, and (3) establishing reasonable ranges of uncertainty and using probability distributions within these ranges in threshold assessment. The following sections outline how we have incorporated these three approaches into our analysis.

Economic Modeling Inputs : The use of economic models and the uncertainty of those models to accurately predict future agricultural sector scenarios was one of the main20 comments we received on our analysis. While th e comments and specifically the peer review supported our need to use economic models to incorporate and measure indirect impacts of biofuel production they also highlighted the uncert ainty with that modeling approach, especially in projecting out to the future.

However, it is important to note that while many factors impact the certainty in predicting total land used for crop production, maki ng accurate predictions of many of these factors are not relevant to our analysis. Fo r example different assu mptions about economic growth rates, weather, and exchange rates will all impact future agricultural projections including amount of land use for crops. However, we are interested only in the difference between two biofuel scenarios holding all other changes constant. So the absolu te values and projections for crops, etc. in the model projections are not as im portant as the difference the model is projecting due to an increase in biofuels production. Th is limits the uncertainty of using the economic models for our analysis.

The main factors impacting the economic modeling and land use results due to biofuels are overall crop / commodity demand and yields (and the responsiveness of these parameters to price changes). To examine the impact of changes in yield on the overall biofuel lifecycle GHG results, we have made two main changes in the economic modeling used for the proposal. In order to update our analysis a nd reduce uncertainty we have included a price induced yield impact, as discussed in RIA Chapter 5. Furthermore we also include a sensitivity analysis of a high yield scenario to test the impact of higher yields on the results, as discussed in section 2.6.2.

Types of Land Converted and Land Conversion GHG Emissions Factors : The international indirect land use change impacts of biofuels were determin ed based on the results of the economic models that provi de the total amount of new la nd needed. The results of the economic models were combined w ith recent satellite data to pred ict the types of land converted to meet the increased land demand. GHG emissions factors were then applied to the type of land to calculate GHG emissions from land use change. As this is one of the areas of greatest uncertainty we specifically incorporated an approach to quantify the uncertainty in our satellite data and GHG emissions estimates and incorporated these results into our analysis.

Methodology Choices : A main underlying methodolog ical decision that impacts the overall lifecycle GHG results is how to deal with the timing of emissions. This is manifested in two main ways, the first is how to deal with s hort term land use change emissions versus ongoing benefits of the use of biofuels, and the second is what timeframe to consider the analysis for. The main approach for addressing this uncerta inty was to conduct sensitivity analysis with various methodology choices, as presented in s ection 2.6.2.1. For example, we used a 30-year time period for our lifecycle analysis, but we also present results with different time periods, as well as the payback periods for each fuel, which is a metric that does not require the analyst to choose a specific time period.

Other GHG Factors and Input Data : Non-economic modeling inputs and assumptions impact overall GHG results, for example crop produc tion inputs (energy use for tractors, etc.), and agricultural sector GHG emissions (livestock, soil N2O, etc.). These factors are applied on21 top of economic modeling to de termine mainly the non-land use GHG impacts of agriculture. While there is some uncertainty inherent in the factors, most of them do not have a significant impact on the overall results.

For the final rule analysis, instead of de veloping uncertainty profiles and ranges around these other input factors, we focused on reducing the uncertainty through updates to improve our data and modeling. For exampl e, N2O emissions from soil as part of crop production is a key component of agricultural sector GHG emissions so we focused on updating our analysis to include the most up to date info rmation on this source of emissions. We also had our analysis of international agricultural sector GHG emissions peer reviewed and have updated our analysis in response to the peer review comments.22 

### 2.4 Biofuels Analysis

2.4.1 Modeling Approach

As mentioned in section 2.2, our methodology includes the use of agricultural sector economic models. Our methodology involves th e use of the FASOM model to determine domestic agriculture sector-wide impacts of incr eased biofuel producti on, and the FAPRI model to determine international changes in crop pr oduction and total crop. Agricultural sector GHG emissions are estimated by FASOM, and FAPRI re sults were converted to GHG emissions based on GREET defaults and IPCC emission factors. Bi ofuel process energy use and associated GHG emissions were based on process models for the different pathwa ys considered. Feedstock and co-product transportation GHG emissions were based on GREET defaults.

The agricultural sector models were used to determine the impacts associated with biofuels production by comparing two similar scenarios in both models. Both agricultural sector models were run with two simila r volumes of the speci fic fuel in question, while other fuel volumes were held constant to isol ate the fuel-specific impacts. Table 2.4-1 shows the 2022 fuel volumes modeled in FASOM in order to isolate the incremental impacts of each type of renewable fuel. Section 2.3 includes more discussion of the fuel volume scenarios.

Table 2.4-1. 2022 Fuel Volumes Modeled with FASOM (Billions of Gallons)

Control Case Biodiesel Only Case Corn Ethanol Only Case Corn Stover Ethanol Only Case Switchgrass Ethanol Only Case Soybean Biodiesel 0.6 0.1 0.6 0.6 0.6 Corn Ethanol 15.0 15.0 12. 3 15.0 15.0 Corn Stover Ethanol 4.9 4.9 4.9 0.0 4.9 Switchgrass Ethanol 7.9 7.9 7.9 7.9 0.0

The total impacts from changes in biofuel pr oduction were calculated by taking the difference in total GHG emissions from the two s cenarios considered. Per gallon, or per million British Thermal Units (mmBTU) impacts were cal culated by dividing the total GHG emission changes by the increase in volume of fuel represented in the scenarios. Therefore, the results presented in this proposed rulemaking repres ent the per mmBTU “average marginal” impact of the change in fuel volumes considered. In other words, the GHG impacts were estimated for a marginal increase in fuel production, and the aver age impact of a marginal gallon was calculated.

2.4.2 Domestic Agriculture

GHG emissions from the domestic agricultural sector were estimated with the FASOM model, a partial equilibrium econo mic model of the U.S. forest a nd agricultural sectors. As discussed in section 2.2, FASOM accounts for ch anges in GHG emissions from most agricultural activities, including the total amount of fertilizer, chemicals, gasolin e, diesel and electricity used on farms for the entire domestic agricultural sector. It also captures changes in the soil23 management, livestock production and U.S. agri cultural exports. More detail on the FASOM model can be found in Chapter 5 of the RIA. Fo r all figures and tables in section 2.4.2, we report results for the biochemical pathway under the “Corn Stover Ethanol” and “Switchgrass Ethanol” scenarios.

Figure 2.4-1 shows the total harvested crop ac res in the different fuel-specific pathway scenarios. The projected changes in total harvested acres are modest, because we modeled the incremental difference in renewable fuel volumes between the scenarios.

Figure 2.4-1. FASOM Projected Domestic Harvested Acres, 2022

0 50 100 150 200 250 300 350

Reference Case Control Case Corn Only Case Biodiesel Only Case Sw itchgrass Only Case Corn Res Only CaseMillions of Acres Other

Sw itchGrass Sugarcane Sugarbeet Soybeans Sorghum Silage Rye

Rice

Oats Hay

Wheat

Cotton

Corn Barley

Figure 2.4-2 includes the projected changes in harvested crop acres by field crop for the fuel volume cases considered (acreage changes are normalized per thousand gallons of renewable fuel production). In the corn ethanol scenario, corn acreage increased; area planted with soybeans, wheat, switchgrass, sorghum and rice decreased; and harvested acres of other crops were practically unchanged. As anticipated, soybean acreage decreased the most when corn ethanol production increased, because corn and soybeans are often in direct competition for fertile land.24 Figure 2.4-2. Normalized Changes in Dome stic Cropland by Crop, 2022 (acres per thousand gallons of renewable fuel)

-4 -3 -2 -1 0 1 2 3 4 5 6

Corn Ethanol Soy Biodiesel Sw itchgrass Ethanol Corn Residue EthanolAcres per 1000 gallons (EtOH Equiv)

Other Crops

Cotton Hay

Oats Sorghum

Rice

Wheat Sw itchgrass Soybeans Corn + Silage

Soy-based biodiesel production induced a la rge increase in harvested soybean acres, largely due to the low yield of soy-based biodiesel in terms of gallons pr oduced per acre. Cotton was the only other crop that increased substa ntially along with biodiesel production. The competition between corn and soybeans was evident again, as corn acreage saw a steep decline. However, switchgrass acres declined by nearly the same amount as corn, showing the relative competition between switchgrass and soybeans. Wheat, rice, barley, sorghum and rye also declined when biodiesel volumes increased.

In the scenario where switchgrass etha nol production increased, switchgra ss was the only field crop to gain

acreage, with the excep tion of a small increase in corn and su garbeet acres. New switchgrass plantings displaced a wide variety of other crops (25 Figure 2.4-2). As discussed more in RIA Chapter 5, the FASOM runs for the proposed rule project that switchgrass will primarily be grown in Kansas, Missouri, Texas, Oklahoma, and Arkansas

Production of ethanol from corn residue had a very small effect on the acreage of other crops. This was expected because corn stover production does not displace other crop production, as corn stover is a residual product of corn cultivation. FASOM did project minor amounts of crop shifting in the corn stover scenar io, because using corn stover for ethanol can increase the profitability of corn production in certain regions, with subsequent impacts. The effects of corn stover harvesting on agricultural inputs, such as the need to use more fertilizer after stover removal, are discussed below.

2.4.2.1 Domestic Crop Inputs

FASOM utilizes data about crop inputs to build crop budgets for field crops across 11 market regions and 63 sub-regions. FASOM cr op budgets include data on yields, fertilizer, chemicals, and energy use needed to grow crops in each of the different regions. The crop budgets are based on USDA historic data and are also projected into the future. The crop budgets represent an average for each region, a nd do not specifically calc ulate input or yield changes that could result from the use of margin al croplands or altere d crop rotation patterns (e.g., continuous corn production). C Table 2.4-2 defines the 11 market regions in FASOM. RIA Chapter 5 includes a detailed discussion of the FASOM crop budgets, including assumptions about crop yields and yield growth rates. Be low we provide a summary of some of the key FASOM assumptions that were used to estimate domestic agricultural GHG emissions.

Table 2.4-2. Definitions of 11 Market Regions in FASOM
Key Market Region Production Region (States/Subregions)

NE Northeast Connecticut, Delaware, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, Vermont, West Virginia
LS Lake States Michigan, Minnesota, Wisconsin
CB Corn Belt All regions in Illinois, Indiana, Iowa, Missouri, Ohio (IllinoisN, IllinoisS, IndianaN, IndianaS, IowaW, IowaCent, IowaNE, IowaS, OhioNW, OhioS, OhioNE)

GP Great Plains Kansas, Nebraska, North Dakota, South Dakota
SE Southeast Virginia, North Carolina, South Carolina, Georgia, Florida
SC South Central Alabama, Arkansas, Kentucky, Louisiana, Mississippi, Tennessee, Eastern Texas

C FASOM does not explicitly model the selection of altern ative crop rotations. Because the model operates in 5- year time steps, it has not generally been applied to shorter-term decisions such as changes in rotation patterns. Rather, the model data implicitly reflect average conditions for crop production (e.g., yields, input use, etc.) associated with historical rotation patterns on a regional level.26 Key Market Region Production Region (States/Subregions)

SW Southwest (agriculture only) Oklahoma, All of Texas but the Eastern Part (Texas High Plains, Texas Rolling Plains, Texas Central Blacklands, Texas Edwards Plateau, Texas Coastal Bend, Texas South, Texas Trans Pecos)
RM Rocky Mountains Arizona, Colorado, Idaho, Montana, Nevada, New Mexico, Utah, Wyoming
PSW Pacific Southwest All regions in California (CaliforniaN, CaliforniaS)

PNWE Pacific Northwest— East side (agriculture only) Oregon and Washington, east of the Cascade mountain range

PNWW Pacific Northwest— West side (forestry only) Oregon and Washington, west of the Cascade mountain range

The crop budgets included in the FASOM model include data on input use that varies by crop, management practices, and region. There is often considerable regi onal variation in the inputs used per acre, which suggests that total input use (and the associ ated GHG emissions and other environmental impacts) will be affected as biofuel production causes crop shifting and alters crop management practices. For example, nitrogen fertilizer use is an important factor for lifecycle GHG analysis because of GHG emissions from fertilizer production and use. Figure 2.4-3 includes FASOM assumptions about averag e nitrogen fertilizer use by crop in 2022 for non-irrigated production wit hout residue harvesting. Regions that have a zero nitrogen fertilizer use rate are not included in the averages. Figure 2.4-3 illustrates the relative fertilizer intensity of major crops. Corn, hay and silage are relatively fertilizer-intense crops; whereas soybeans require less than 10 pounds of nitrogen per acre (s oybeans naturally fix nitrogen in the soil as they grow).

Figure 2.4-3. FASOM Average Nitrogen Fertilizer Use by Crop, 2022 Non-Irrigated, No Residue Harvesting (lbs per acre)27 0 20 40 60 80 100 120 140 160 180

Corn Soybeans Switchgrass Wheat (hard red winter) Rice (irrigated) Sorghum Oats Hay CottonNitrogen Fertzilier Use (lbs per acre)

Note: The range indicates the regions with the highest and lowest average nitrogen fertilizer use rates.

Mechanized agriculture requires many forms of energy including diesel, gasoline, natural gas and electricity. The FASOM crop budgets in clude detailed energy use information by crop and region. Figure 2.4-4 includes FASOM assumptions for average diesel use by crop in 2022, for non-irrigated production without residue harvesting.

Figure 2.4-4. FASOM Average Diesel Use by Crop, 2022 Non-Irrigated, No Residue Harvesting (gallons per acre)

0 5 10 15 20 25

Corn Soybeans Switchgrass Wheat (hard red winter) Rice (irrigated) Sorghum Oats Hay CottonDiesel Use (gal per acre)

Note: The range indicates the regions with the highest and lowest average diesel use rates.28 Figure 2.4-5 shows FASOM assumptions for average gasoline use by crop in 2022, for non-irrigated production without residue harvesting. The FASOM crop budgets do not include gasoline use for switchgrass production.

Figure 2.4-5 FASOM Average Gasoline Use by Crop, 2022 Non-Irrigated, No Residue Harvesting (gallons per acre)

0 1 2 3 4 5

Corn Soybeans Switchgrass Wheat (hard red winter) Rice (irrigated) Sorghum Oats Hay CottonGasoline Use (gal per acre)

Note: The range indicates the regions with the highest and lowest average gasoline use rates.

FASOM crop budgets include electricity a nd natural gas use for irrigation water pumping. Rice and sugarbeets are the only crops assumed to use natural gas for water pumping (See Table 2.4-3.). Therefore, ov erall natural gas use in each s cenario is dependent on changes in these crops. For the rest of the irrigated crops that have private energy use for water pumping, electricity is the assumed energy source, with the largest electricity consumption in the Great Plains region (See Figure 2.4-6).

Table 2.4-3. Natural Gas Usage for Irri gated Crop by Region, 2022 (1000 cu ft/acre)
Crop CB GP LS NE PNWE PSW RM SC SE SW

Rice 23.3 NA NA NA NA 0.0 NA 20.1 NA 0.0 Sugarbeet 26.1 9.7 26.1 NA 3.8 0.0 0.0 NA NA 0.0

Figure 2.4-6. FASOM Electricity Use by Crop, 2022 Irrigated, No Residue Harvesting (kWh per acre)29 0 2 4 6 8 10 12 14

Corn Soybeans Wheat, Hard Red Winter Sorghum Oats Hay CottonElectricity Use (kWh per acre)

Note: The range indicates the regions with the highest and lowest average gasoline use rates.

Energy use for grain drying is calculated in FASOM based on assumptions that removing 10 percentage points of moisture from 100 bushels of grain requires 17.5 gallons of propane and 9 kWh of electricity. Thus, energy use per acre is calculated as the number of percentage points of moisture to be removed multiplied by the yield per acre and the energy use per percentage point and yield unit for each crop that is dried. Emissions are then calculated based on assumed emissions factors per unit of energy use by energy type. Table 2.4-4 shows the average emissions associated with grain drying that are used in FASOM. Drying rice is a relatively energy intensive process, as reflected in the grain drying GHG emissions per acre. Emissions from grain drying are included in the overall domestic agricultural GHG emissions estimates.

Table 2.4-4. FASOM Average Carbon Dioxide Emissions from Grain Drying by Region (kgCO2e / acre)
Crop CB GP LS NE PNWE PSW RM SC SE SW
Dryland

Corn 161.4 135.9 202.2 160.5 NA NA 66.1 24.5 43.8 15.2 Sorghum 99.4 22.3 NA 54.3 NA 17.7 NA NA NA NA Soybeans 26.0 7.0 24.1 14.3 NA NA NA NA NA NA
Wheat, Durham NA 5.1 23.4 NA NA NA NA NA NA NA Wheat, Hard Red Spring NA 6.7 25.4 NA 9.1 NA NA NA NA NA
Wheat, Hard Red Winter 51.3 11.1 51.6 34.5 NA 11.6 NA NA NA NA
Wheat, Soft White NA NA NA NA NA NA 11.6 NA NA NA Irrigated

Corn NA 185.1 NA NA 132.6 121.6 103.2 21.0 NA 30.7
Rice 1,216.6 NA NA NA NA 1,667.3 NA 1,254.8 NA 1,400.830 Crop CB GP LS NE PNWE PSW RM SC SE SW
Sorghum NA 33.0 NA NA NA NA NA NA NA NA Soybeans NA 10.3 NA NA NA NA NA NA NA NA
Wheat, Durham NA 11.3 NA NA NA 21.0 NA NA NA NA Wheat, Hard Red Spring NA 10.2 NA NA NA NA 17.6 NA NA NA
Wheat, Hard Red Winter NA 15.4 NA NA NA 22.6 NA NA NA NA
Wheat, Soft White NA NA NA NA NA NA 18.3 NA NA NA

Based on input data for each individual crop and the associated costs of production and projected prices, the

model predicts how the total U.S. agr icultural sector will change with in creased feedstocks used for biofuel

production. The results for total ag ricultural sector inputs of the di fferent fuel scenar ios considered are shown in

Table 2.4-5 through Table 2.4-8.

Table 2.4-5. Change in Domestic Agricultur al Inputs under Corn Ethanol Scenario, 2022

Units per mmBTU Corn Ethanol Only Scenario Control Scenario Difference Percent Change
Total N use Pounds 136.6 138.8 2.1 1.5% Total P2O5 use Pounds 31.2 31.7 0.5 1.5% Total K2O use Pounds 38.8 39.5 0.7 1.9%
Total Lime Use Pounds 104.2 104.7 0.5 0.5%31 Units per mmBTU Corn Ethanol Only Scenario Control Scenario Difference Percent Change
Herbicide Use Pounds 1.9 2.0 0.0 2.2% Pesticide Use Pounds 0.4 0.4 0.0 2.8%
Total Diesel Fuel use Gal 14.3 14.2 -0.1 -0.5%
Total Gasoline use Gal 1.7 1.7 0.0 -0.9% Total Electricity Use kWh 1.0 1.0 0.0 0.3% Total Natural Gas Use BTU 248,002 234,746 -13,257 -5.6%

Table 2.4-6. Change in Domestic Agricultur al Inputs in the Soy Biodiesel Scenario, 2022
Units Per mmBTU Soy Biodiesel Only Scenario Control Scenario Difference Percent Change
Total N use Pounds 437.1 435.3 -1.8 -0.4% Total P2O5 use Pounds 99.2 99.4 0.2 0.2% Total K2O use Pounds 123.3 124.0 0.7 0.6%
Total Lime Use Pounds 325.6 328.5 2.9 0.9%
Herbicide Use Pounds 6.2 6.2 0.0 0.3% Pesticide Use Pounds 1.4 1.4 0.0 1.5%
Total Diesel Fuel use Gal 45.0 44.7 -0.4 -0.9%
Total Gasoline use Gal 5.3 5.4 0.1 1.6% Total Electricity Use kWh 3.2 3.2 0.0 1.4% Total Natural Gas Use BTU 833,308 736,362 -96,946 -13.2%

Table 2.4-7. Change in Domestic Agricultural Inputs in the Corn Stover Ethanol Scenario, 2022
Units

per mmBTU Corn Stover Only Ethanol Scenario Control Scenario Difference Percent Change
Total N use Pounds 74.6 75.8 1.2 1.5% Total P2O5 use Pounds 16.8 17.3 0.5 3.1% Total K2O use Pounds 19.3 21.6 2.3 10.8%
Total Lime Use Pounds 57.3 57.2 -0.1 -0.2%
Herbicide Use Pounds 1.1 1.1 0.0 0.6% Pesticide Use Pounds 0.2 0.2 0.0 0.4%
Total Diesel Fuel use Gal 7.8 7.8 -0.1 -0.8%
Total Gasoline use Gal 0.9 0.9 0.0 0.0% Total Electricity Use kWh 0.6 0.6 0.0 0.0% Total Natural Gas Use BTU 133,037 128,201 -4,836 -3.8%

Table 2.4-8. Change in Domestic Agricultural Input sin the Switchgrass Ethanol Scenario, 202232 Units

per mmBTU Switchgrass Ethanol Only Scenario Control Scenario Difference Percent Change
Total N use Pounds 44.5 45.9 1.4 3.1% Total P2O5 use Pounds 9.8 10.5 0.7 6.2% Total K2O use Pounds 12.5 13.1 0.6 4.3%
Total Lime Use Pounds 34.7 34.7 0.0 -0.1%
Herbicide Use Pounds 0.7 0.7 0.0 -1.1% Pesticide Use Pounds 0.1 0.1 0.0 -3.0%
Total Diesel Fuel use Gal 4.8 4.7 -0.1 -1.1%
Total Gasoline use Gal 0.6 0.6 0.0 -4.2% Total Electricity Use kWh 0.3 0.3 0.0 -0.2% Total Natural Gas Use BTU 90,890 77,690 -13,200 -17.0%

The amounts shown in

Table 2.4-5 through Table 2.4-8 were combined with GREET defa ults for GHG emissions from production of fertilizer and chemicals to calcu late GHG emissions changes. Fu el use emissions included both the upstream emissions associated with production of the fuel as well as combustion emissions, also from GREET. Emissions fro m electricity production repr esented average U.S. grid electricity production.

In addition to the GHG emissions associated with fertilizer and ch emical production, and fuel production and use, there are several other non-fossil fuel combustion related GHG sources of emissions from the agricultural sector that woul d be impacted by the increased use of corn for ethanol and associated changes to the agricu ltural sector. FASOM provides directly the GHG emissions from these additional sources.

2.4.2.2 Domestic Nitrous Oxide Emissions

An important GHG impact from the agricultu ral sector is releases of nitrous oxide (N 2 O) emissions. N 2 O can

be released from a number of different N-input sources including inorganic fertilizer, nitrogen fixing crops (e.g., soybeans), crop residues, and manure management. N 2 O can be released either directly or indirectly through N leaching offsite.33 Figure 2.4-7 highlights some of the major sources of agricultural N 2 O emissions.

Figure 2.4-7. Agricultural Sources of N 2 O Emissions

Crutzen et al. 20 show that, as long as it includes bot h direct and indire ct emissions, topdown accounting for N 2 O emissions are not inconsistent with the IPCC bottom –up approach to N 2 O accounting. Since the publication of the NPRM, the N 2 O emission factors in FASOM have been updated with the DAYCENT/CENTURY mode l by Colorado State University (CSU) to more accurately estimate direct and indirect N 2 O emissions in cropland. The FASOM modeling captures both direct N 2 O emissions from fertilizer application and N-fixing crops and indirect emissions from leaching, volitization well as fro m crop residue emissions and residue burning,34 capturing all sources of N 2 O emissions and reflecting the most recent available science. This section discusses the changes made using th e CSU DAYCENT/CENTURY work. It details the direct and indirect emissions from synthetic fertilizer, N fixing crops, and crop residue.

Figure 2.4-8 summarizes FASOM average direct and indirect N2O emissions per acre by crop. Livestock N 2 O emissions and N 2 O emissions associated with international agriculture are discussed in more detail in Section 2.4.2.3 and 2.4.3, respectively.

Figure 2.4-8. FASOM Average N 2 O Emissions by Crop (Non-Irri gated, No Residue Harvesting) (kgCO 2 e per acre)35 0 100 200 300 400 500 600 700N2O Emissions (kgCO2eq per acre) Residue Burning

Crop Residues

Leaching

Volatilization

Direct -- Fertilizer Application

EPA worked with CSU to use the DAYCENT/CENTURY model to refine FASOM accounting of direct N 2 O emissions from fertilizer application and indirect emissions associated with nitrogen leaching, volitization, and surface runoff. Specifically, DACENT simulations account for all N inputs to agricultural soils, incl uding mineral N fertilizer, organic amendments, symbiotic N fixation, asymbiotic N fixation, crop residue N, and mineraliz ation of soil organic
matter.

CSU used the DAYCENT/CENTURY model to s imulate a suite of domestic U.S. land use and crop management in the 11 FASOM market regions (Table 2.4-2). The DAYCENT/CENTURY simulations provided regre ssion equations with coefficients for N 2 O estimation that vary by region, crop type, irrigatio n status, and crop residue treatment. Each of the 63 FASOM regions were assigned the coeffi cients for its respective super-region. The regression equations were incorporated into FASOM to calculate N 2 O emissions per acre according to region, crop, irrigation st atus, and crop residue treatment.

FASOM estimates N 2 O emissions from crop residues and residue burning using IPCC guidelines, taking into account variation across regions in

• N content by crop based on yield,

• residue-to-crop ratio,

• percent dry matter,

• percentage of rice area burned in each state,

• burn and combustion efficiency,

• percent of residue burned by crop,36 For crop residue emissions, FASOM a ssumes that 1% of N residing in crop residues that remain on the field are emitted as N 2 O emissions, following IPCC guidelines.

Field burning of crop residues is not considered a net source of CO 2 , because the carbon released to the atmosphere as CO 2 during burning is assumed to be reabsorbed during the next growing season. Field burning of crop residues, however, does emit N 2 O and CH 4 , which are released during combustion. Field burning is not a common method of agricultural residue disposal in the United States. The primary crop types whose residues are typically burned in the United States are wheat, rice, sugarcane, corn, barley, soybeans, and peanuts.

FASOM assumes that a certain fraction of fiel ds are burned each year, which results in N 2 O emissions as well as CH 4 emissions. Using the IPCC de fault value for burned residue, FASOM assumes that, on average, 0.7% of N co ntained in the burned residue is emitted as N 2 O. FASOM predicts minor reductions in GHG emissi ons from residue burning under the full RFS2 policy due to reductions in crop production with re sidues that are typically burned. In addition, CH 4 emissions are calculated based on the average methane emissions per acre; however, CH 4 emissions are typically quite small relative to the other emissions tracked in FASOM.

2.4.2.3 Domestic Rice Production Emissions

Methane (CH 4 ) emissions associated with rice production are also a source of noncombustion GHG emissions from the domestic agricu ltural sector. When rice fields are flooded, aerobic decomposition of organic material gradually depletes most of the oxygen present in the soil, causing anaerobic soil conditions. Once the environment becomes anaerobic, CH 4 is produced through anaerobic decomposition of soil organic matter by methanogenic bacteria. Some of this CH 4 is transported from the soil to the atmosphere through the rice plants via diffusive transport. Minor amounts of CH 4 also escape from the soil via diffusion and bubbling through floodwaters.

FASOM assumes that all rice produced in the Un ited States is grown in flooded fields and emits CH 4 . Although there are potentially changes in water and soil management practices that could be implemented to reduce methane emissions, FASOM assumes that reduction of rice acreage is the only available method for reducing CH 4 emissions from rice cultivation. Thus, changes in CH 4 emissions from rice cultivation result only from changes in the acreage planted to rice in the model.

Methane emissions per acre are calcul ated based on regional emissions fa ctors per acre calculated for each region based on 2001 data from the EPA GHG inventory for 1990–2003 (see

Table 2.4-9). 21 The model then calculates emissions from rice production based on emissions factors for each region and the distribution of rice acreage in the model solution.37 Table 2.4-9. FASOM Average Methane Emission s from Irrigated Rice Cultivation by Region (kg CO2e / acre)
Crop CB GP LS NE PNWE PSW RM SC SE SW
Rice 1,826.1 NA NA NA NA 1,783.4 NA 2,249.2 NA 4,375.0 Note: NA indicates not applicable, i.e., those crops were not cultivated under that irrigation status in that FASOM region. In addition, there is no dryland rice or sugar cane production or irrigated hybrid poplar, switchgrass, or willow production in FASOM.

As with other sources of emissions differe nt management methods and other factors (such as soil type and amounts of fertilization) will impact CH 4 emissions from rice production. With the exception of corn stover ethanol, FASOM projects that rice methane emissions will decrease for all fuel pathways analyzed due to decreased domestic rice acreage (Table 2.4-10).

Table 2.4-10

. Change in Domestic Rice Emissions by Scenario, 2022 Corn Ethanol Soybean Biodiesel Corn Stover Ethanol Switchgrass Ethanol Rice Methane Emissions (‘000 tons CO2e) -42 -506 159 -938

2.4.2.4 Domestic Livestock Emissions

Livestock production and management also contribute significant non-combustion GHG emissions from the agricultural sector. GHG emissions from livestock come from two main sources: enteric fermentation and manure mana gement. Enteric fermentation produces CH 4 emissions as a by-product of normal digestive pr ocesses in animals. Du ring digestion, microbes resident in an animal’s digestive system fermen t food consumed by the animal. The amount of CH 4 produced and excreted by an individual animal depends primari ly upon the animal’s digestive system, and the amount and type of f eed it consumes. Ruminant animals (e.g., cattle, buffalo, sheep, goats, and camels) are the major emitters of CH 4 because of their unique digestive system.

FASOM projects changes in CH 4 emissions associated livestock enteric fermentation due to change in livestock herd number. Changes in production of crops used for feeds, such as corn or soybeans, can impact feed prices which, in turn, drive livestock production and demand. Enteric fermentation emissions from livestock are calculated based on the number of each livestock type and on the average emissions per head. Average emissions per head are based on 2001 emissions values by livestock type and the number of livestock in each livestock category reported in the EPA GHG inventory report for 1990–2003. 22 There are emissions mitigation options included within the FASOM model, but these options do not enter the market in the absence of incentives for reducing CH 4 emissions. Thus, enteric fermentation emissions are affected only by the number of animals in each livestock category in this model. The FASOM model generally predicts reductions in lives tock herds as shown in Table 2.4-11.38 Table 2.4-11. Change in Domestic Li vestock Herd Size by Scenario, 2022 Corn Ethanol Soy-based Biodiesel Corn Stover Ethanol Switchgrass Ethanol Livestock Type mmHead % change mmHead % change mmHead % change mmHead % change Dairy -0.02 -0.31% -0.01 -0.17% 0.00 -0.01% -0.02 -0.36% Beef 0.09 0.14% -0.11 -0.18% 0.95 1.56% 0.21 0.34% Poultry -58.84 -0.79% -58.84 -0.79% -58.84 -0.79% -58.84 -0.79% Swine -0.22 -0.17% 0.24 0.19% 9.15 7.27% 7.80 6.20%
Enteric fermentation emissions increase across fuel pathway scenarios with the exception of the soybean biodiesel scenario. Cattle numbe rs increase under the corn ethanol, corn stover ethanol, and switchgrass ethanol scenarios. Cattle are ruminants, and therefore, increase in cattle number results in increased CH 4 emissions (Table 2.4-12). Ca ttle number decreases under the soy-based biodiesel scenario, resulting in decreased methane emissions due to enteric fermentation.

Table 2.4-12

. Change in Domestic Livestoc k Emissions by Scenario, 2022 Corn Ethanol Soybean Biodiesel Corn Stover Ethanol Switchgrass Ethanol Enteric CH 4 Emissions (‘000 tons CO2e) 21 -128 1,129 338 Manure CH 4 and N 2 O Emissions (‘000 tons CO2e) -94 -5 2,194 1,751 Total Livestock Emissions (‘000 tons CO2e) -73 -133 3,322 2,089

Use of DGS has been shown to decrease me thane produced from enteric fermentation if replacing corn as animal feed. This is due to the fact that the DGS are a more efficient feed source. Consistent with our assumptions regardin g the efficiency of DGS as an animal feed in our agricultural sector modeling, we have al so included the enteric fermentation methane reductions of DGS use in our final rule analysis . Based on default factors in GREET, the model assumed a decrease in CH 4 (-3,381 g CO2e/mmBTU ethanol) per head of cattle and cows that were fed with DGS. The reduction in CH 4 is based on the same Argonne report used to determine DGS feed replacement efficiency (discussed in RIA Chapter 5). This assumption resulted in a reduction in the lif ecycle GHG emissions for corn et hanol compared to the proposal assumptions.

The management of livestock manure can also produce anthropogenic CH 4 and N 2 O emissions. CH 4 is produced by the anaerobic decomposition of manure. N 2 O is produced through the nitrification and denitrification of the organic nitrogen in livestock manure and urine. The type of manure management methods imp acts the quantity of GHG emissions emitted. FASOM bases manure management emissions calculations on emi ssions factors for livestock types and livestock management methods as reported in the EPA GHG inventory report for 1990–2003. 23 Manure management emissi ons are projected to decrease as a result of lower livestock herd values.39 Under the corn ethanol and soybean bi odiesel scenario, manure-associated GHG emissions slightly decrease. Under the corn stover and switchgrass scenarios, swine production markedly increases (Table 2.4-12).

2.4.2.5 Domestic Agriculture Sector Re sults (Excluding Land Use Change)

Table 2.4-13 provides a summary of FASO M projections for total GHG emissions impacts for the domestic agricultural sector for ea ch fuel pathway scenario analyzed. Land use change impacts are discussed in section 2.4.4.

Table 2.4-13. Domestic Agriculture GHG Emi ssion Changes by Scenario, 2022 (g CO2e/mmBTU)
Emission Source Corn Ethanol Soybean Biodiesel Corn Stover Ethanol Switchgrass Ethanol Fuel and Feedstock Transport 4,265 3,461 2,418 2,808 Farm Inputs 10,313 6,482 2,770 4,890 Livestock (Manure and Enteric Fermentation) -3,746 -2,100 9,086 3,462 Rice Methane -209 -7,950 434 -1,555 Total Domestic Agriculture 10,623 -107 14,708 9,605

With the exception of soybean biodiesel, FASOM projects that increased biofuel production in 2022 in the scenarios analyzed will lead to increased GHG emissions in the domestic agricultural sector, excluding land use change. With increased volumes of each biofuel, fuel and feed transport and farm i nputs increase and thereby increase GHG emissions. No one domestic agricultural sector emission sour ce (excluding land use change) emerges as the specific driver of GHG emissions across all fuel pathway scenarios. Rather, emission sources act with varying degrees of importance in each scenario.

Overall the small impact in the domest ic agricultural sector is due to th e indirect effects and demand changes, specifically demand changes in U.S. expo rts. For example, the sources of co rn used in ethano l production in the FASOM model are shown in

Figure 2.4-9. Some of the additional corn comes from increased corn production; however, the increase in corn acres is mostly offs et by reductions in other crop acres as shown in Figure 2.4-10. Some of the corn used for etha nol comes from decreased corn used for feed. During the corn ethanol production process, one of the byproducts produced are distillers grains with solubles (DGS). DGS can be used as a feed source for beef cattle, dairy cows, swine and poultry, and partially offsets the use of corn directly as feed.40 Figure 2.4-9

.

Sources of Corn for Ethanol

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Other Exports

Feed Use

Net Production

However, as seen from

Figure 2.4-9, one of the sources of corn for ethanol production is projected to come from reductions in corn exports. Therefore, the domes tic agricultural sector impacts are only a portion of the total impacts due to increased ethanol produc tion in the U.S. The change in corn and other crop exports will have impacts on the international agricultural sector that need to be accounted for when determining lifecycle GHG impacts of biofuel production in the U.S.

2.4.2.5 Translation of Domestic Impact s into International Impacts

In order to estimate the impact on interna tional agricultural sector GHG emissions, the FAPRI model was run with the same domestic bi ofuel volume scenarios, with the exception of cellulosic ethanol, as was run in the FASOM model fo r the domestic agriculture sector analysis. In the FAPRI models, links between the U.S. and international models are made through41 commodity prices and net trade equations. In ge neral, for each commodity sector, the economic relationship that quantity supp lied equals quantity demanded is achieved through a marketclearing price for the commodity. In each country domestic prices are modeled as a function of the world price using a price transmission equatio n. Since econometric models for each sector can be linked, changes in one commodity sector will impact the other sectors.

The model for each commodity consists of a number of countries/regions, including a rest-of-the-world aggregate to cl ose the model. The models sp ecify behavioral equations for production, use, stocks, and trade between countries /regions. The models solve for representative world prices by equating excess supply and dema nd across countries. Using price transmission equations, the domestic price for each country is linked with the representative world price through exchange rates. It is through change s in world prices that change in worldwide commodity production and trade is determined.

When analyzing the impact of the RFS2 biof uel requirements in the U.S., there are two primary domestic effects that directly affect a commodity’s worldwide use and trade: change in exports, and changes in domestic U.S. prices. Fo r example, as discussed above, the corn ethanol biofuel requirement places an a dditional demand for corn used for ethanol, and this corn comes not just from additional production, but also from d ecreases in other uses including exports. In addition, as corn production expands, it places pr essure in terms of relative demand on other crops in a particular region in the U.S., which in turn affects their pri ces and use (including exports). As the level of exports from the U.S. of a particular commodity decreases, other countries will adjust their production and trade to satisfy the demand for that commodity. Figure 2.4-10 shows the change in U.S. exports per by major commodity per thousand gallons of biofuel, as projected by FASOM.

Figure 2.4-10. Normalized Change s in U.S. Exports by Crop, 2022 (acres per thousand gallons of renewable fuel)42 -5.0 -4.0 -3.0 -2.0 -1.0 0.0 1.0

Corn Ethanol Soy Biodiesel Sw itchgrass Ethanol Imported EthanolTons per Thousand Gallons of Biofuel (Ethanol Equiv)

Wheat Sorghum

Rice

Cotton Barley

DG Soybean Meal Soybean Oil Soybeans

Corn

As expected, an increase of a particular bi ofuel will have the greatest impact on U.S. exports of that biofuel’s feedstock. For instan ce, with an increase of one thousand gallons of corn ethanol, corn exports decrease by four tons , and with an increase of one thousand gallons of soy biodiesel, soybean oil exports decrease by tw o tons. Increases in corn ethanol and soy biodiesel will not only affect the crop area and export levels of its primary feedstock (corn and soybean oil, respectively), but it will also affect other crops as increased demand for these commodities change the relative demand, and therefore production and use, between different commodities.

Although switchgrass and other cellulosic etha nol sources are not explicitly modeled in FAPRI, the changes in acres for various crops as a result from an increase in switchgrass ethanol as modeled by FASOM were applied to FAPRI on a regional ba sis in the U.S. This provides a reasonable approximation of the effects of an increase of switchgrass acres in the FAPRI model, and the affect it has on other crop area, pr oduction, prices and trade for other crops.

In addition, we have modeled the impact of increased production of Brazilian sugarcane ethanol for use in the U.S. market. The FA PRI model has been used to determine the international impacts of Brazilian sugarcane ethanol production. The increase in Brazilian sugarcane ethanol production is assumed to have no impacts on domestic U.S. agriculture emissions.

As well as the change in U.S. exports, the FAPRI model relies on price transmission equations, therefore changes in the U.S. price for a commodity will have a direct impact on the43 world price for that commodity. This, in tu rn, will have an impact on the demand for that commodity worldwide, and affect production and tr ade levels in other c ountries. Additional information on the changes in the world price for commodities and the coordination of assumptions between the FASOM a nd FAPRI models can be found in Chapter 5 of the RIA.

2.4.3 Evaluation of International Agri cultural GHG Emissions Impacts

For this analysis we used the FAPRI model to estimate the impacts on international crop production due to changes in bi ofuel production. These results were used to generate GHG emissions from the international agricultural se ctor, similar to what was done to determine domestic agricultural GHG emission changes.

2.4.3.1 International Agricultural Inputs

The FAPRI model does not directly provi de an assessment of the GHG impacts of changes in international agricultural practices (e. g., changes in fertilizer load and energy use). However, it does predict changes in crop area and production by crop ty pe and country. We, therefore, determined internati onal fertilizer and energy use base d on data collected by the Food and Agriculture Organization (FAO) of the United Nations and the International Energy Agency (IEA). For the final rule, we have also incorporated more up-to-date fertilizer consumption statistics provided by a recent International Fertilizer Industr y Association (IFA) report, Assessment of Fertilizer Use by Cr op at the Global Level, 2006/07 – 2007/08. 24 For more details refer to the memorandum to EPA from ICF International. 25 Where country and crop specific energy use data was available (in the case of Brazilian sugarcane), we used that data as further discussed in Section 2.4.3.3.

We took the FAPRI provided activity data on ch anges in crop acres, by crop and country, and multiplied by regional fertilizer use rate factors (kg per ha) to determine the global impacts of biofuel production on fer tilizer application. Historical fertiliz er application rates for nitrogen, phosphorus, and potassium were update d using more recent data available from the IFA report as recommended by expert review ers during our peer review. 26 IFA data are preferred over FAO’s Feristat data because estimates are more current and years of available data are consistent across all countries. Also, FAO has alte red its survey methodology since 2004. 27

The IFA dataset covers 23 countries (consid ering the European Union-27 as one country) and 11 crop groups including: wheat, rice, corn, other coarse grains, soybean, palm oil, other oilseeds, cotton, sugar crops, fruits and vegetables , and other crops. IFA consumption data were averaged over the two reported time peri ods: 2006 or 2006/07, and 2007 or 2007/08 to account for seasonal variations in crop production. Fer tilizer application rates were calculated by dividing IFA total consumption values by FAOS tat agricultural area harvested data from the FAOStat database. 28

The FAO Feristat dataset was also updated to the most recent version since the proposal. Feristat data are used for country/crop and re gion/crop combinations not covered by the IFA dataset. In addition, Feristat da ta is preferred to calculate fer tilizer consumption for “rest of the world” regions since the dataset provides for a gr eater number of countries and greater detail for44 a variety of crops. Feristat fertilizer application rates (kg per ha) are calculated by dividing total Feristat fertilizer consumption by Feristat agricultural area fertilized.

Figure 2.4-11 compares the nitrogen fertilizer application rates for major corn produces around the word as determined with IFA and FA O data, with the U.S. as a reference for comparison.

Figure 2.4-11. Nitrogen Application Rates for Corn in Select Regions

0 20 40 60 80 100 120 140 160 180

US Brazil Argentina China Mexico EUNitrogen Fertzilier Use (kg per ha)

Herbicide and pesticide use data have been updated using the most current data available from FAO’s FAOStat dataset for pesticide consumption. 29 FAO’s pesticide consumption dataset did not provide values for China, and thus data was used from the U.S. Department of Agriculture’s (USDA) Economic Research Service (ERS). 30

We acknowledge that there may be other count ry and crop specific sources of fertilizer data available for example for Brazilian sugarcane in addition to the IFA data, however, it was not available in time for incorporation in the final rule analyses and furthermore the data used provides a consistent dataset for all crops and countries. 31 This is an area for future research for any future analysis.

IFA does not collect data on lime use, however for the final rule we include lime use for sugarcane based on data form Mece do (2008), estimated at 333.3 kg/ha.

We then used GREET factors for emissions from production of agricultural chemicals to estimate the upstream GHG impacts of fertilizer and chemical production to calculate total impacts for each fuel scenario with the exception of lime where we used data from Mecedo to represent lime production in Brazil. Table 2.4-14 provides the total chan ge in fertilizer and chemical use for the different fuel scenarios, per mmBTU renewable fuel.

Table 2.4-14. International Change in Fertilizer and Chemical Use by Scenario, 2022 (kg/mmBTU)45 Input Corn Ethanol Soy-Based Biodiesel Switchgrass Biochemical Ethanol Sugarcane Ethanol N Application 0.3683 0.0526 0.0774 0.4451 P Application 0.1780 0.6216 0.1302 0.1520 K Application 0.1245 0.6288 0.1179 0.5735 Herbicide Application 0.0006 0.0021 0.0006 0.0006 Pesticide Application 0.0009 0.0024 0.0008 0.0008

2.4.3.2 International N 2 O Emissions

For international N 2 O emissions we considered both direct and indirect emissions from synthetic fertilizer application, crop residue N, and manure management. Manure management emissions are discussed in the following section. Direct and indirect emissions from synthetic fertilizer application and crop residues were calculated based on IPCC guidance as shown in

Table 2.4-15. 32

Table 2.4-15. Calculations of N 2 O Emissions from Synthetic Fertilizer and Crop Residues46 Direct N 2 O Emissions Direct Emissions (Overall Equation) Equation

Emissions = (N additions to soils from mineral fertilizer + N additions to soils from crop residues) × EF EF for N additions from mineral fertilizer and crop residues = 0.01 kg N 2 O-N / kg N added N Additions from Mineral Fertilizers

N additions to soils from mineral fertilizers = kg fertilizer N applied to soils (i.e., change in fertilizer N applications from Table 2.4-14) N Additions from Crop Residues

N additions to soils from crop residues = above-ground residue dry matter × Crop Area × [N content of aboveground residues + ratio of belowground residues to aboveground biomass × N content of belowground residues] where, Above-ground residue dry matter and N additions to soils from crop residues = Taken from IPCC default values by crop Indirect N2O Emissions Note that for indirect emissions, the calculation of N applied to soils from fertilizers or crop residues is the same as for direct emissions Indirect Emissions from Volatilization Equation

Emissions = N additions to soils from mineral fertilizers × N lost through volatilization × EF N lost (from synthetic fertilizer additions) through volatilization = 0.1 (kg NH 3 –N + NOx–N) / kg N applied EF for N lost through volatilization = 0.010 kg N 2 O–N / (kg NH 3 –N + NOX–N volatilised) Indirect Emissions from Leaching/Runoff Equation

Emissions = (N additions to soils from mineral fertilizers + N additions to soils from crop residues) × N lost through leaching or runoff × EF N lost through leaching/runoff (from all N sources) = 0.3 N losses by leaching or runoff / kg N addition EF for N lost through leaching/runoff = 0.0075 kg N20-N / kg N leaching or runoff

The proposal did not include N 2 O emissions from the Direct and Indirect Emissions from Crop Residues for cotton, palm oil, rapeseed, sugar beet, sugarcane, or sunflower. These were not included for these crops because default crop-specific IPCC factors used in the calculation were not available.

Comments from our peer review process sugges ted that we include proxy emissions from these crops based on similar crop types that do have default factors. Therefore, for our final rule analysis we have included crop residue N 2 O emissions from sugarcane production based on perennial grass as a proxy. Perennial gra ss is chosen as a proxy based on input from N 2 O modeling experts. Emissions for cotton, palm oil, rapeseed, sugar beet, and sunflower were also included based on root crops, other as a proxy.

Figure 2.4-12 summarizes N2O emissions by crop for a sample of crops and countries by the four categories of N2O emissions. Figure 2.4-12. Sources of N2O Emissions by Crop for Select Regions47 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000cornsoybeansugarcanecornsoybeancornsoybeancorncorn

Brazil Argentina China EU MexicoN2O Emissions (kg CO2-eq/ha)

Indirect Crop Res N Direct Crop Res N
Indirect Fertilizer N
Direct Fertilizer N

Based on the equations in

Table 2.4-15 and the crop production changes proj ected by FAPRI, we estimated the total change in N 2 O emissions for each fuel scenarios, as shown in Table 2.4-16.

Table 2.4-16

. International Crop Change in N 2 O Emissions in 2022 from Different Fuel Scenarios (kg CO2e/mmBTU) Corn Ethanol Soybean Biodiesel Switchgrass Ethanol Brazilian Sugarcane Etanol Direct and Indirect N2O Emissions 3.38 2.09 0.95 29.25

2.4.3.3 International Fuel Combustion Emissions

In terms of evaluating international agriculture energy use data, we continued to use IEA data as these are the best available for this purpos e (providing data by end use and fuel type for over 130 countries representing the major energy us ers of the world). We collected data from IEA on total CO 2 emissions from agricultural fuel combustion by country. 33 We also collected48 IEA data on agricultural electricity and fuel use by country, which was combined with emissions factors to estimate country-level GHG emissions from agricultural electrici ty and fuel use. Historical trends were used to project chemical and energy use in 2022. These total GHG emissions were only combustion re lated, so we scaled them to represent full lifecycle GHG emissions from fuel production, based on the ratio of combustion to full lifecycle GHG emissions from U.S. fuel and electricity use. Country-level GHG emissions from agricultural energy use were then divided by the area of agricultural land in each country, from the FAOSTAT land area database to derive a per acre GHG emissions factor from agricultural energy use by country. Our estimates use aver age energy consumption and GHG emissions per acre for all crops in each foreign country. We multiplied these agricultural energy consumption emissions factors by the country-level crop acr eage changes projected by FAPRI to determine the change in GHG emissions from foreign agricu ltural energy use for each fuel scenario.

In the case of Brazilian sugarcane, we had c ountry and crop specific data available to estimate agricultural energy use. 34 For sugarcane farming, energy use includes the diesel fuel used to power farming equipment and energy use for sugarcane preparation. The energy used to perform other activities and small services during productive operation was also included. Energy embedded in farming equipment was not in cluded in this calculation, as consistent with other renewable fuel pathways. Table 2.4-17 s hows how diesel consumption is expected to increase in the future mainly due to increased use of diesel consumption with the growth of mechanical harvesting and trash recovery.

Table 2.4-17. Energy Use (BTU/MT sugarcane) 35 Activity 2002 2005/2006 2020 Ag Operations 15544 12606 14028 Harvesting 20568 31562 44453 Other Activities 36491 42462 Seeds 5592 5592 6256 Total 41704 86251 107198

For the final rule we assumed the ener gy use estimated in 2020 for agricultural operations, harvesting, other ac tivities and seed production would be similar to that in 2022, and have adopted these estimates for our GHG calcul ations for the case where 40% of the trash (sugarcane leaves and tops) are assumed to be collected as predicted in literature. When trash is not assumed to be collected, we used 2005/2006 energy use data to account for the fact that less energy use would be required as the collection of trash is not needed. We assume the energy use is from 100% diesel.

Table 2.4-18 provides the total change in agri cultural energy use GHG emissions for the different fuel scenarios.

Table 2.4-18. International Change in Ag ricultural Energy Use GHG Emissions by Scenario, 2022 (kg CO2e / mmBTU) Corn Ethanol Soybean Biodiesel Switchgrass Ethanol Brazilian Sugarcane49 Ethanol Agricultural Sector Energy Use GHG Emissions 1.7 1.88 -0.16 5.14

2.4.3.4 International Rice Methane Emissions

To estimate rice emission impacts internationally, we used the FAPRI model to predict changes in international rice production as a result of the increase in biofuels demand in the U.S. Since FAPRI does not have GHG emissions fact ors built into the model, we applied IPCC default factors by country.

Calculating emissions from rice cultivation, per the IPCC 2006 guidelines, requires the following data: area of rice harvested, an emi ssions factor, and planting to harvesting season length. Area of rice harvested by country was provided by the FAPRI results. The default IPCC emission factors were used scaled for each cr opping regime: irrigated, rainfed lowland, upland and deepwater by country. Rice cultivation season lengths were availabl e from the International Rice Research Institute (IRRI). 36

2.4.3.5 International Crop Resi due Burning Emissions

International crop residue burning on the fi eld, and specifically changes in residue burning emissions could occur due to changes in U.S. biofuel policy. We specifically included for the final rule analysis an estimate of sugarcane field burning and mechanical harvesting emissions. We also incorporate emissions from land clearing for crop production as discussed in section 2.4.4.

Sugarcane leaves and tops are typically burned in the field before and after harvest. Per metric ton of sugarcane, there is 280 kg of leav es and tops (with 50% moisture content) or 140 kg of dry leaves and tops, which we assume fo r modeling purposes. In the case where trash is collected, it was assumed that 40% (i.e. 56 kg dry leaves and tops per metric ton of cane would be recovered to be used as fuel at the mill).

Current trends in Brazil are moving from burned cane with manual harvesting to unburned cane with mechanical harvesting. This change is related to the gradual reduction of cane trash pre-burning at both Federal and State levels in Brazil. See50 Figure 2.4-13 for the phase out schedule for trash burning practices.

Figure 2.4-13. Phase Out Schedul e for Trash Burning Practices

According to Brazil’s Sugarcane Research Cent er (CTC), about 47.5% of all sugarcane in Brazil is already mechanically harvested, and 35.3 % of all sugarcane in Brazil is mechanically harvested without being burned in the field. D These percentages have increased since 2002, see Table 2.4-19.

Table 2.4-19. Sugar Cane Harvest –2002 Situation Type of harvest Sao Paulo (%) Center-South (%) Manual 63.8 65.2 Mechanical 36.2 34.8

D UNICA. (August 2009) Submission of comments document # EPA-HQ-OAR-2005-0161-1761.1, pg. 20-2151 Burned sugar cane 75.0 79.1 Unburned sugar cane 25.0 20.9

UNICA states that in 2008 about half of the sugarcane fields in Sao Paulo were mechanically harvested, up from 36.2% in 2002. In the future, Sao Paulo state law requires that sugarcane field burning be phased-out by 2021 fo r areas where mechani cal areas are possible with existing technologies (over 85% of exis ting sugarcane fields) and where mechanical harvesting may not be possible, 30% will be required to phase-out burning. This implies that by 2022 considering all areas in Sa o Paulo about 90% of sugarcane fields could be unburned.

Sao Paulo currently accounts for 60% of all national production of sugarcane, with the Center-South producing about 89% of all sugarcane. Considering that 89% of Brazilian Production occurs in the Center-S outh, and Sao Paulo consists of a considerable portion of that production, the following situation in Tabl e 2.4-20 as assumed for Brazil in 2022.

Table 2.4-20. Sugarcane Harvest – Projected for 2022 Type of harvest Brazil (%) Manual 0 Mechanical 100 Burned sugar cane 10 Unburned sugar cane 90

We took into account emissions from open-fiel d burning from two pollutants, methane (CH 4 ) and nitrous oxide (N 2 O). Carbon dioxide (CO 2 ) emissions were not taken into account because the CO 2 is considered to be taken from the air during sugarcane growth.

Table 2.4-21. Emission Factors of Open-Fie ld Burning of Sugarcane Leaves and Tops 37 Pollutant g/kg of dry leaves and tops burned CH 4 2.7 CO 92 N 2 O 0.07 NO x 2.5 PM 10 7.8 PM 2.5 3.9 SO x 0.4 VOC 7.0

2.4.3.6 International Livest ock GHG Emissions

Similar to domestic livestock impacts associat ed with an increase in biofuel production, internationally the FAPRI model predicts changes in livestock production due to changes in feed prices. The GHG impacts of these livestock changes, enteric fermentation and manure management GHG emissions, were included in our analysis. Unlike FASOM, the FAPRI model does not have GHG emissions built in and, th erefore, livestock GHG impacts were based on52 activity data provided by the FA PRI model (e.g., number and ty pe of livestock by country) multiplied by IPCC default factors for GHG emissions.

Table 2.4-22 shows the changes in livestock predicted by the FAPRI model in 2022 for each of the fuel scenarios considered.

Table 2.4-22. Foreign Livestock Changes by Region and Renewable Fuel, 2022 (head / billion BTU)
Corn Ethanol Dairy Beef Swine Sheep Poultry Canada 0.00 0.05 -0.17 0.00 1.37 Western Europe 0.00 -0.07 0.12 0.02 1.58 Eastern Europe 0.00 -0.83 0.01 0.00 17.72 Oceania -0.02 0.11 0.01 0.07 3.53 Latin America -0.15 3.44 0.48 0.00 -0.46 Asia -0.09 0.17 -0.04 -1.19 -1.53 Africa and Middle East -0.03 -0.45 0.32 0.00 -3.01 Indian Subcontinent 0.00 0.12 0.02 0.00 -3.66 Soy-Based Biodiesel Dairy Beef Swine Sheep Poultry Canada 0.02 0.27 1.62 0.00 -8.07 Western Europe -0.01 0.57 -1.16 -0.45 29.30 Eastern Europe 0.00 -0.59 -0.44 0.00 -114.88 Oceania 0.05 0.03 0.11 -0.20 -30.9253 Latin America 0.45 -8.38 0.18 0.00 -0.06 Asia 0.33 0.92 0.60 6.67 -55.37 Africa and Middle East 0.01 2.11 0.82 0.00 2.65 Indian Subcontinent 0.00 0.27 0.06 0.00 -34.31 Sugarcane Ethanol Dairy Beef Swine Sheep Poultry Canada 0.00 0.00 -0.10 0.00 0.47 Western Europe 0.00 -0.01 0.07 0.01 -0.32 Eastern Europe 0.00 -0.12 0.02 0.00 4.56 Oceania 0.00 0.01 -0.01 0.00 0.96 Latin America -0.03 0.22 -0.08 0.00 -0.09 Asia -0.01 -0.01 -0.14 -0.32 0.13 Africa and Middle East -0.01 0.00 -0.06 0.00 -0.58 Indian Subcontinent 0.00 -0.01 0.00 0.00 -0.43 Switchgrass Ethanol (Biochemical) Dairy Beef Swine Sheep Poultry Canada 0.00 0.00 -0.11 0.00 0.72 Western Europe 0.00 -0.01 0.06 0.02 -0.34 Eastern Europe 0.00 -0.12 0.05 0.00 6.36 Oceania 0.00 0.02 0.00 0.00 1.10 Latin America -0.03 0.04 0.07 0.00 -0.11 Asia -0.02 0.00 0.13 -0.47 0.48 Africa and Middle East -0.01 0.05 0.03 0.00 -0.77 Indian Subcontinent 0.00 -0.01 0.00 0.00 -0.44

The enteric fermentation GHG impact s of livestock changes were calculat ed by applying regional default factors for enteri c fermentation CH 4 emissions by livestock type. These factors are shown in

Table 2.4-23.

Table 2.4-23. Enteric Fermen tation Emission Factors
Enteric Fermentation (kg CH4/head/year) Da iry Cattle Swine Sheep North America 121 53 1.5 8 Western Europe 109 57 1.5 8 Eastern Europe 89 58 1.5 8 Oceania 81 60 1 5
Latin America 63 56 1 5
Asia 61 47 1 5
Africa and Middle East 40 31 1 5
Indian Subcontinent 51 27 1 5

Manure management GHG impacts of livestock changes for each fuel scenario were calculated by applying regional defa ult factors for manure management CH 4 and N 2 O emissions by livestock type. Manure management CH 4 emission factors are shown in Table 2.4-24.54 Manure management N 2 O values were based on default IP CC nitrogen produced per livestock type and IPCC default manure ma nagement practices by region.

Table 2.4-24. Manure Management Methane Emission Factors Manure Management (kg CH4/head - year) Dairy Cattle Swine Sheep Poultry North America 78 2 23.5 0.28 0.02 Western Europe 51 15 15.5 0.28 0.02 Eastern Europe 27 13 6.5 0.28 0.02 Oceania 29 2 18 0.15 0.02
Latin America 1 1 1 0.15 0.02
Asia 18 1 4 0.15 0.02
Africa and Middle East 1.5 1 2 0.15 0.02
Indian Subcontinent 5 2 4 0.15 0.02

Based on the peer review of the methodology us ed for the proposal it was determined that the calculations for manure management did not include emissions from soil application. These emissions were included for our fi nal rule analysis but do not cause a significant change in the livestock GHG emission results.

2.4.3.7 International Agriculture Sector Results (Excluding Land Use Change)

Table 2.4-25 provides an overview of the total GHG emissions impacts from the international agricultural sector based on the results of the FAPRI modeling. As discussed above, emissions from farm inputs include the production, transport and fate of agricultural inputs including pesticide, fertiliz er and other chemicals. The fa rm inputs category also includes energy used in crop production processes. La nd use change impacts are discussed in section 2.4.4.

Table 2.4-25

. Foreign Agriculture GHG Emission Changes in 2022 from Different Fuel Scenarios (g CO2e/mmBTU) Corn Ethanol Soy Biodiesel Sugarcane Ethanol Switchgrass Ethanol Farm Inputs 6,601 5,402 37,884 1,310 Livestock Production 3,458 -6,436 -128 -245 Rice Methane 2,089 2,180 485 -92055 2.4.4 Land Conversion GHG Emissions Impacts

Our lifecycle GHG estimates include emissions from domestic and international land use conversions induced by increased renewable fuels consumption in the United States. To estimate land conversions GHG emissions we answered six key questions:

1. How much land is converted? 2. Where does land conversions occur? 3. What types of land are converted? 4. What are the GHG emissions impact s from that land conversion? 5. How do we account for the variable timin g of land conversion GHG releases? 6. What is the level of uncertainty in our land conversions GHG emissions estimates?

This section describes our approach for answ ering these questions about land use change. We used the FASOM model to project land c onversions in the United States. FASOM was designed to simulate domestic land use interac tions and land use change GHG impacts. We used the FAPRI model to project international cropl and expansion in response to increased United States biofuel consumption. We used the FAPRI international models to project changes in the area of land used for crop production and pastur e. FAPRI does not, however, project which types of land would be cleared to make room for additional agricultural land uses, or where within in each country or region agricultural ex pansion would likely take place. To fill this information gap we used MODIS satellite data provided by Winrock International, Inc. (from now on referred to as Winrock), that shows recent land use change patterns from 2001 to 2007. To determine the GHG impacts of the projec ted land conversions we applied GHG emissions factors prepared by Winrock following IPCC guid elines. To account for the variable timing of land use change GHG impacts, we annualized land use change GHG impacts over 30 years (with a 0% discount rate).

To quantify the uncertainty in our quantif ication of GHG emissions from international land conversions, we focused on two areas: uncer tainty in the MODIS sa tellite data used to determine the types of land affected (e.g., forest or grassland), and uncer tainty in the our land conversion GHG emissions factors (i.e., the GHG emissions per unit area of land conversion). To reduce and quantify the uncertainty in the MODIS satellite data we utilized extensive data validation efforts by NASA, which we used to correct systematic errors in the MODIS data set and to quantify the remaining uncertainty. To quantify the uncertainty in land conversion GHG emissions factors, we evaluated th e uncertainty in every data input based on the quality, quantity, resolution and variability in the underlying data so urces. Correlation group s were assigned based on the sources of underlying data. Finally, the to tal uncertainty in international land use change GHG emissions was quantified a Monte Carlo anal ysis following Tier 2 IPCC guidelines.

This section describes the data sources a nd methods used for the analysis summarized above, with key results illustrated throughout.

2.4.4.1 Evaluation of Domestic Land Conversion GHG Emissions Impacts56 We used FASOM to project U.S. land use change under each fuel-specific pathway scenario due to the increase in respective renewable fuels and then the change in GHG emissions that result from the changes in land use. FASOM was designed to simulate land use interactions to predict the types of land converted in the U.S. (See RIA Chapter 5 for more details). In this section we discuss FASOM mode ling of land conversion and rela ted GHG emissions as well as final calculations for GHG emissions on a per mmBTU basis for each fuel-specific pathway scenario.

2.4.4.1.1 Area and Location of Domestic Land Conversions

How land is used in FASOM is determined through the relative profits from various activities. This varies not only between crops, but also between different land uses, such as pasture for livestock production. A number of updates have been made to the FASOM model since the analysis for the Proposal in order to have a more complete assess ment of land use in the U.S. One of these updates includes the incorpor ation of the forestry component of the FASOM model. Running both the forestry and agricu lture components of the model for the final rulemaking analysis shows the interaction between these two sectors as they compete for land in various regions, as well as the effect on produc ts and prices in each respective sector.

In addition, FASOM also includes a represen tation of seven major land use categories, including cropland, cropland pastur e, forestland, forest pasture, rangeland, deve loped land, and acres enrolled in the Conservation Reserve Program (CRP). These categories are based on the USDA National Agriculture Statistics Service (NASS), and enable the FASOM model to explicitly link the interaction between livestoc k, pasture land, cropland, and forest land, as well as have a detailed accounting of acres in the U.S. across different land uses. Cropland is actively managed cropland, used for both traditional crops (e .g., corn and soybeans) and dedicated energy crops (e.g., switchgrass). Cropl and pasture is managed past ure land used for livestock production, but which can also be converted to cropland production. Forestland contains a number of sub-categories, tracking the number of acres both newly and continually harvested (reforested), the number of acres harvested and converted from other land uses (afforested), as well as the amount of forest acres on public land. Forest pasture is unmanaged pasture land with varying amounts of tree cover that can be used to raise livestock. A portion of this land may be used for timber harvest. Rangeland is unmanaged land that can be used for livestock grazing production. While the amount of rangeland idle d or used for production may vary, rangeland may not be used for any other purpose than for an imal grazing. For each of these categories, FASOM accounts for how much is actively us ed in production, and how much idled, in a particular time period.

Another update to the FASOM model is the distillers grains and solubles (DGS) replacement rates for corn and soybean meal in animal feed. These replacement rates are based on recent research published by Argonne National Laboratory E that demonstrate higher replacement rates than what was used in the anal ysis for the Proposal. This means that DGS, as

E Salil Arora, May Wu, and Michael Wang, “Update of Distillers Grains Displacement Ratios for Corn Ethanol LifeCycle Analysis,” September 2008. See http://www.transportation.anl.gov/pdfs/AF/527.pdf57 a byproduct of corn ethanol production, are relative ly more efficient compared to the Proposal’s analysis, and results in less corn and soybean meal needed for animal feed. This, in turn, results in less corn and soybean production needed for use in animal feed relative to the Proposal.

Further discussion of changes made to th e FASOM model can be found in Chapter 5.

For the corn ethanol scenar io, the FASOM model estimates that total cropland area used for production increases by 1.4 million acres in 2022. This is a result of an increase of 3.7 million acres of corn, a decrease of 1.3 million acres in soybeans, as well as changes in ot her crop acres. Similarly, total cropland area increases

by 1.9 million acres in the soybean biodiesel scenario, which consists of an increase of 3.5 million acres of soybeans, decreases of 0.6 million acres each of corn and sw itchgrass, as well as a variety of other changes. In the switchgrass ethanol scenario, total cropland acres increases by 4.2 million acres, including an increase of

12.5 million acres of switchgrass, a decrease of 4.3 million acres of soybeans, a 1.4 million acre decrease of wheat acres, a decrease of 1 million acres of hay, as well as decreases in a variety of other crops. Table 2.4-26 summarizes the change in total cropland acres used in production , both total and normalized by changes in biofuel volume, and

Figure 2.4-14 shows the changes for each cr op in each fuel-speci fic volume scenario.

Table 2.4-26. Change in total area of domestic cropla nd used for production by scenario, in 2022 Scenario Total Cropland Increase (million acres) Normalized Cropland Increase (acres per thousand gallons, ethanol equivalent) Corn Ethanol 1.4 0.12 Soybean Biodiesel 1.9 0.39 Switchgrass Ethanol 4.2 0.04 Corn Stover Ethanol 0.6 0.0658 Figure 2.4-14. Normalized Domestic Cr op Acreage Changes by Scenario, 2022 (acres per thousand gallons, ethanol equivalent)

-4 -3 -2 -1 0 1 2 3 4 5 6

Corn Ethanol Soy Biodiesel Sw itchgrass Ethanol Corn Residue EthanolAcres per 1000 gallons (EtOH Equiv)

Other Crops

Cotton Hay

Oats Sorghum

Rice

Wheat Sw itchgrass Soybeans Corn + Silage

2.4.4.1.2 Types of Domestic Land Conversions

Based on relative demand for crop and lives tock production changes that result from increased demand for biofuels, there are direct effects on land used for crop and livestock production, as well as indirect effects on other land types in the U.S. For instance, in 2022, as demand for corn ethanol increases in the corn ethanol scenario, total cropland (used for production and idled) increases by 0.9 million acr es, total cropland pasture decreases by 0.9 million acres, total forest pasture increases by 0.2 million acres, forestland decreases by 0.03 million acres. As soybean biodiesel increases (in the biodiesel scenario), total cropland increases by 1.2 million acres, cropland pasture decreases by 1.0 million acres, forest pasture increases by 0.2 million acres, and forestland decreases by 0.2 milli on acres. With an increase in switchgrass ethanol, cropland increases by 2.2 million acres, cr opland pasture decreases by 1.5 million acres, forest pasture increases by 0.6 million acres, and forestland decreases by 0.6 million acres.

In the corn stover scenario, an increase in ethanol from corn stover does not directly result in crop acre changes, merely an increase in the harvesting of residue from existing corn acres. However, an increased demand for ethanol from corn stover does inherently give more value per acre of corn with residue removal. Based on corn residue removal possibilities by region, there are relatively small changes to land us es with an increase in corn stover ethanol production. Specifically, cropland increases by 0.07 million acres, cropland pasture increases by59 0.05 million acres, forest pasture acres do not ch ange, and forestland acres decrease by 0.2 million acres.

The number of acres enrolled in CRP does not vary between volume scenarios because in each scenario, the maximum amount is taken fro m the program and converted to cropland for production, all leaving the assu med minimum limit of 32 million acres, in accordance with the 2008 Farm Bill. The number of acres in rangel and does not vary because rangeland acres are not suitable for any other use than unmanaged land for livestock production. The only change in rangeland is whether or not a certain number of acres are actively used for production, or whether they remain idle. Lastly , developed land is assu med to be of higher value than all other land categories, and FASOM assumes that the am ount of developed land in creases at a steady rate over time, and does not vary with changes in demand for biofuel.

Figure 2.4-15. Change in Do mestic Land Use by Type, 2022

-3 -2 -1 0 1 2 3

Corn Ethanol Soy Biodiesel Sw itchgrass Ethanol Corn Stover EthanolMillions of Acres Developed Land Rangeland

CRP

Forestland

Forest Pasture Cropland Pasture Cropland

Note: Some of these land use categories are not used in GHG emission calculations

2.4.4.1.3 Quantification of GHG Emissions from Domestic Land Conversions

Domestic land use change GHG emissions ar e based on outputs of the FASOM model. FASOM models the changes in GHG emissions and sequestration due to changes in land management. FASOM explicitly models ch ange in soil carbon due to change in crop production acres and in crop type. In addition, FA SOM’s forestry module models the change in above-ground biomass and below-ground biomass carbon stock and soil carbon in the forestry sector due to land conversion. W ith the addition of the forestry module for the final rulemaking,60 we have used FASOM to model changes in so il carbon and biomass carbon due to land use conversion between cropland, pastur e, forestland, and developed land.

In addition to quantifying GHG emissions and sinks, FASOM distinguishes the unique time dynamics and accounting issues of carbon seque stration options. These include issues such as saturation of carbon sequest ration over time (i.e., carbon seques tration in a particular sink reaches an equilibrium such that carbon storage is maintained, but is no longer increasing), potential reversibility of carbon be nefits (e.g., due to changes in tillage, forest harvests, wildfires), and fate of carbon stored in products after forest harvest.

GHGs, generally in the form of carbon, can be sequestered in soils, standing trees, other vegetation, and wood produc ts. Sequestration refers to storage of the GHGs for more than one year. As a consequence, the sequestration definiti on used in the model for standing vegetation is limited to carbon storage in trees, understory, and litter within both forests and plantations of woody biofuel feedstocks (poplar and willow) but excludes, for instance, carbon stored in annually cultivated crops. Ca rbon sequestration is also m odeled within cropland soils, pastureland soils, soils in idled lands, timber land soils, and harveste d wood products. In addition, changes in sequestration for lands that move out of forestry and agricultural production into some form of developed usage such as hous ing, shopping centers, and roads are tracked in the model.

In the subsections below, we detail FA SOM accounting of carbon stock changes from agricultural land and forestry land. We also describe EPA’s use of FASOM GHG accounting to project the changes in GHG emissions associated with domestic land use change for each renewable fuel-specific pathways for the year 2022.

2.4.4.1.4 Domestic Agricultural Soil GHG Accounting

FASOM models the change in agricultura l soil carbon due to land conversion and changes in crop patterns. The FASOM GHG fact ors for agricultural land conversion are based on factors for different crops, management practices, and land conversion effects (e.g., converting pasture to crop production). As EPA committed in the NPRM, the FASOM agricultural land GHG emission factors were updated with new DAYCENT/CENTURY model runs to reflect the most recent science available.

Agricultural soil carbon sequestration depends on management activities that influence carbon storage per acre. Baseline carbon storage is estimated from the baseline distribution of land across tillage practices, irrigation status, la nd use, and cropping patterns, assuming carbon sequestration rates are equal to those at equilibrium.

 Intensity of agricultural tillage . Agricultural soils have traditionally been tilled; however, tillage breaks up soil aggregates and increases the exposure of soil organic matter to oxygen, which speeds oxidation a nd results in reduced soil carbon with an associated release of CO 2 into the atmosphere. The use of tillage alternatives that reduce soil disturbance and therefore reduce oxidation of soil organic matter will61 increase soil carbon sequestration. Reduced tilla ge practices also leave crop residues on the soil, thereby potentially increasing carbon inputs.

 Irrigation status . The DAYCENT/CENTURY model shows differences in soil carbon sequestration per acre for a given region between irrigated and dryland cropland systems. F For irrigated sites, the increased yields are expected to increase biological activity and therefore increas e soil carbon sequestration compared to dryland cropland.

 Relative abundance of grasslands . Generally, pasturela nd and CRP land experience less soil disturbance than actively tilled cr oplands and store more carbon per acre. Thus, changes in the distribution of land between pastureland, cropland, and land in the CRP will affect agricultu ral soil carbon sequestration.

 Mix of annuals versus perennials . Perennial crops are not tilled on an annual basis typically show a reduction in soil disturbance relative to actively tilled annual crops. By definition in FASOM, perennial crops such as switchgrass, hybrid poplar, and willow are produced under zero tillage.

Changes in agricultural soil carbon due to changes in tillage, irrigation status, or land use are generally assumed to take place over a number of years as the soil carbon levels adjust to a new equilibrium. In FASOM, soil carbon levels ar e assumed to reach a new equilibrium after 25 years, although almost 94% of the adjustment takes place within 15 years (see Figure 2.4-16). G

Figure 2.4-16. Percentage Adjustment ove r Time to New Soil Carbon Equilibrium Following Change in Land Use or Management

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Year 0 Year 5 Year 10 Year 15 Year 20 Year 25% Adjustment over Time

F All pastureland and CRP land in FASOM are assumed to be produced in dryland systems.
G There is an immediate jump in carbon storage in year 0 due to changing tillage, irrigation, and/or land use that depends on the initial state and the new state. The dynamics discussed and shown in Figure A-5 refer to the change over time from the initial state under new management/land use conditions to the equilibrium for that state.62 Because movement of soil carbon sequestrati on towards equilibrium levels is not constant over time, FASOM yiel ds non-uniform changes in soil carbon consistent with the generally accepted scientific finding that carbon sequestered in an ecosystem approaches steadystate equilibrium under any management alternat ive. The rate of change in carbon storage decreases over time and eventually reaches zero at the new equilibrium (saturation) (See Figure 2.4-16). 38 Soil carbon per acre may increase or decrease depending on the land use change or change in land management taking place.

FASOM also estimates N 2 O emissions from cropland and pastureland due to land use change based on Colorado State University DAYCENT/CENTURY models. See section 2.4.2.2 for a full discussion.

2.4.4.1.5 Evaluation of GHG Emissions Impacts from Domestic Forests

One of the largest carbon pools is carbon sequester ed in forests. Carbon is stored not just in the live and standing dead trees, but also in understory, forest floor and coarse woody debris, and forest soil. Harvesting timber causes a re duction in carbon sequestra tion, although some of the carbon that was in the harvested trees will conti nue to be stored in fo rest products for some time afterward. If harvested sta nds are replanted, then there is little loss in forest soil carbon, and carbon sequestration in trees planted in that stand will increase over time.

The FASOM model estimates change in carbon stock of above-ground and below-ground biomass in continuous and afforesting forestla nd. It accounts for carbon storage in forest products and emission streams from these produc ts over time. It also takes into account changing management practices (e.g., harvest cycles). Land converted from forestry to agricultural or other uses, however, will have a much greater permanent reduction in carbon sequestration. We summarize the forest carbon accounting procedures used in FASOM below. 39

Forest carbon accounting in FASOM follows the FORCARB model developed by the U.S. Forest Service and used in the peri odic aggregate assessments of forest carbon sequestration. Tree carbon is the largest forest carbon pool and is modeled as a function of three factors: (1) merchantable vol ume, (2) the ratio of growing stock volume to merchantable volume, and (3) and parameters of a forest vol ume-to-biomass model developed by U.S. Forest Service researchers. 40 Harvest age is allowed to vary; thus, the growth of existing and regenerated/afforested stands mu st be modeled. Timber growth and yield data are included for existing stands, reforested stands , and afforested lands that track the volume of wood in each unharvested stand, which, in turn, is used in computing forest carbon sequestration. These data indicate the wood volume per acre in unharvested timb er stands for each timber stand strata (e.g., a stand giving location, forest type, management in tensity class) by age cohort. The data used are derived largely from the U.S. Forest Service RPA modeling system. 41 Merchantable volume, by age, on each representative stand is obtained from the timber growth and yield tables included in FASOM. The volume factors and biomass mode l parameters vary by species and region and are obtained from 42,43 and Smith et al. (2003). 44

Carbon in live and standing dead trees is cal culated using the parameters of the forest volume-to-biomass model equations for live and dead tree mass densities (above- and belowground) in Smith et al., 45 weighted for the FASOM region/fore st type designations. Forest63 land area data reported by the RPA assessment 46 are used to calculate the appropriate weights. Birdsey’s assumption that the mass of wood is ap proximately 50% carbon is used to derive the associated levels of carbon. 47

Soil carbon is the second-largest pool of carbon. Treatment of soil carbon follows Birdsey 48,49 and recent work by Heath, Birdsey, and Williams. 50 FASOM computes soil carbon profiles using soil carbon data over time from Birdsey. 51,52 As Heath, Birdsey, and Williams noted, little change in soil carbon occurs if fore sts are regenerated immediately after harvest. 53 As a result, FASOM assumes soil carbon on a reforest ed stand remains at a steady-state value. Currently, the age that this value is reached is assumed to be the minimum harvest age for FASOM region/forest type. This assumption is generally consistent with the ages at which steady-state levels of soil carbon are achieved in Birdsey. 54,55 Afforested land coming from crop or pasture use start with the initial soil carbon va lue for that land/region combination reported by the Century Model, which was develo ped by Colorado State University. H The land then accumulates carbon until reaching the steady-state value for forests of the type planted in the region afforestation takes place (where steady state is assumed to be reached at the minimum harvest age in FASOM for that region/forest type).

Forest floor carbon constitutes the third la rgest carbon storage pool, but is much smaller than tree or soil carbon pools. FASOM bases its forest floor carbon estimates on the model developed by Smith and Heath 56 to estimate forest floor carbon ma ss. The model’s definition of forest floor excludes coarse woody debris materi als; that is, pieces of down dead wood with a diameter of at least 7.5 cm th at are not attached to trees. 57 In order to account for this material, coarse woody debris is assumed to be a fixe d fraction of live tree carbon based on ratios of coarse woody debris carbon to live tree carbon. 58 This value is then added to the forest floor carbon values generated by Smith and Heath’s forest floor model. The model for net accumulation of forest floor carbon is a continuous and increasing function of age, although the rate of accumulation eventually approaches zero (i .e., forest floor carbon reaches a steady state).

Understory vegetation comprises the sma llest component of total carbon stock and includes all live vegetation except trees larger than seedlings. FASOM makes the assumption that understory carbon is a fixed fraction of live tree carbon and uses published ratios reported in U.S. EPA Inventory of U.S. GHG Emissions and Sinks 59 as the basis for these calculations. When timber is harvested, FASOM tracks the fate of the carbon that had been sequestered on the harvested land.

H The current version of the CENTURY agroecosystem model simulates carbon, nitrogen, phosphorus, and sulfur dynamics through an annual cycle over time scales and centuries and millennia. CENTURY is capable of modeling a wide range of cropping system rotations and tillage practices for analysis of the effects of management and climate on agroecosystem productivity and sustainability. The model has undergone numerous enhancements since the original version developed in Parton, W.J., D.S. Schimel, C.V. Cole, and D.S. Ojima. 1987. Analysis of factors controlling soil organic matter levels in Great Plains grasslands. Soil Science Society of America Journal 51:1173- 1179.64 Figure 2.4-17 summarizes the disposition of carbon following harvest. To calculate carbon in harvested logs, cubic feet of roundwood (t he units in which timber is quantified in the model) is converted into metric tons of carbon using factors reported in Skog and Nicholson. 60 These factors vary by region and are reported fo r logs coming from an aggregate softwood and hardwood stand. They exclude carbon in logging re sidue left onsite. Logging residue is tracked separately in the forest floor carbon pool described above.

Harvested logs removed from site are conve rted into three types of outputs through primary manufacturing processes: wood and paper products, mill re sidues, and fuel wood (See Table 2.4-27 for a list of products tracked by FA SOM). The fate of each of these outputs is discussed below.

Table 2.4-27. Wood and paper pr oducts tracked by FASOM Product softwood sawlogs for export hardwood sawlogs for export

softwood lumber softwood plywood

oriented strand board

hardwood lumber hardwood plywood softwood miscellaneous products hardwood miscellaneous products softwood used in non-OSB reconstituted panel hardwood used in non-OSB reconstituted panel softwood pulpwood hardwood pulpwood

The distribution of product carbon changes ov er time, and FASOM tracks the fate of product carbon with two carbon pools: carbon re maining in-product and carbon leaving the product (Figure 2.4-17). Carbon that leaves th e product ultimately is emitted or permanently sequestered in landfills.65 Figure 2.4-17. Carbon Dispos ition after Timber Harvest 61

FASOM determines the fraction of carbon thatremains in products using specified halflife values for a set of end-use categories (Table 2.4-28). 62 The half-life represents the time it takes for approximately half of the product to decompose. Skog and Nicholson 63 assumed that 67% of carbon leaving the wood product pool and 34% of carbon leaving the paper product pool Carbon Disposition After Harvest

Harvested Logs Removed from site

Primary FASOM Manufacturing

Wood and Paper Products (i = 1,...,13)

Mill Residue

Fuelwood Logging Residue Down Dead Wood (Coarse Woody Debris) Accounted for in on-site module

Wood and Paper Product Disposition over Time

Emission Fuel Offset Emission In-product or Landfill

Secondary Manufacturing66 goes to landfills (Figure 2.4-18). The remai nder of the carbon leaving the wood and paper product pools is emitted as CO 2 into the atmosphere.

Table 2.4-28. Half-life for Forest Products in End Uses 64

End Use or Product Half-Life in Years Paper 2 New residential construction Single family 100 Multifamily 70 Mobile homes 12 Residential upkeep & improvement 30 New nonresidential construction All ex. railroads 67 Railroad ties 12 Railcar repair 12 Manufacturing Household furniture 30 Commercial furniture 30 Other products 12 Shipping Wooden containers 6 Pallets 6 Dunnage etc. 6 Other uses for lumber and panels 12 Uses for other industrial timber products 12 Exports 12

Figure 2.4-18. Wood and Paper Product Carbon Disposition 65

Total Carbon in Wood and Paper Products

Emission

Carbon remaining in product Landfill

Landfill (Permanent) Landfill (Temporary) Carbon leaving product67 FASOM tracks the fate of mill residue using two different pools. The first is for mill residue that is used as an intermediate input in the production of wood a nd paper products. This carbon is tracked using the appropr iate product category as descri bed above. The second pool is for carbon in mill residue that is burned for fuel . The fraction burned in each region based on Smith et al. 66 It was assumed that one-third of mill residue burned is used to offset fossil fuels. Harvested fuel logs and the associated carbon are used as to produce energy at mills. For fuel wood, FASOM assumes that 100% of fuel w ood burned in the sawtimber and pulpwood production process is used to offset fossil fuels.

In FASOM, land used in forestry can move to agriculture or developed use, resulting in a dynamic change in carbon storage levels on th e previously forested land. When land moves from forestry to agricultural use or developed use, FASOM tracks carbon in residual forest floor carbon and in soil carbon. FASOM’s model of forest floor decay is based on the average forest floor of mature forests and regional averages for decay rates, as described in Smith and Heath. 67 When forested land is converted to agricultura l use, soil carbon levels are consistent with DAYCENT/CENTRUY model data on agricultural so il carbon for the appropriate category of agricultural land and do not vary over time. When forest is converted to developed land uses, FASOM assumes that soil carbon levels are consistent with the steady-state value of the minimum harvest age.

2.4.4.1.6 Aggregate GHG Emissions Impacts from Domestic Land Conversions in 2022

In the FASOM model the difference in GHG emissions and sequestrations from agricultural and forest lands rela ted to land use changes are not only a function of the difference between the land use change and management prac tices in 2022 under two scenarios, but are also dependent on previous changes in land use and practices under the two scenarios. For instance, different land use patterns under two scenarios may result in differing harvesting cycles.

Ideally, an emissions comparison of land use patterns in two scenarios would capture the changes and associated emissions that lead to the 2022 land use status in both scenarios. Because FASOM generates GHG emissions estimates associated with land use change for every five year period over the time horizon of the model run, EPA was able to calculate the cumulative GHG emissions change for each fuel-sp ecific pathway and for the RFS2 policy. We then annualized the cumulative change.

To calculate the annualized cumulative GHG emissions due to land use change for a specific fuel, we first summed all emissions associated with agricultural land (CO 2 and N 2 O from cropland, pastureland, CRP land) and forestland (CO 2 from biomass, soil, and forest products) between the years 2000 and 2022 for the control a nd fuel-specific scenarios. Emissions from soil, decaying biomass, and forest products can occur over several years or decades. FASOM tracks such emission streams over time. We included in the cumulative GHG emissions from land use change all emission streams due to changes that occurred between 2000 and 2022 for the thirty year time horizon (See section 2.4.5) after 2022.68 We report these results as CO 2 equivalent mass and then normalize the results on an mmBtu basis (Figure 2.4-19).

Figure 2.4-19. Change in GHG Emissions Due to Domestic La nd Use Change by Scenario, 2022, Annualized Over 30 Years

2.4.4.2 International Land Conversion GHG Emissions Impacts

2.4.4.2.1 Area and Location of International Land Conversions

We used the FAPRI international agricultural models to determine the amount of international land use change resulting from the renewable fuel volumes mandated by RFS2. The FAPRI model provides a dynamic projection of how policy or economic shocks will affect international agricultural commodity markets, and the resulting area of land used to produce agricultural goods. FAPRI accounts for several key factors that affect the amount/area of international land use change: crop yield growth rates over time, price-induced crop yield changes, crop yields on marginal/new land, the ef ficiency of renewable fuel co-products over time, supply and demand in the livestock sector, and many other significant variables. More details about the FAPRI mode l and our assumptions are provided in RIA Chapter 5.

2.4.4.2.2 Area and Location of Interna tional Cropland Conversions69 To determine the area of land use change caused by increased consumption of each of the renewable fuels analyzed (i.e., corn ethanol , soy-based biodiesel, sugarcane ethanol and switchgrass ethanol) we used the FAPRI model to simulate the scenarios outlined in Table 2.3-1. By varying only one type of renewable fuel in each scenario we isolated the impacts for each fuel type. The land use change results are the difference between each scenario and the control case, and are normalized by dividing by the incr emental increase in renewable fuel production in a given scenario and year, on an energy-conten t basis. Table 2.4-29 shows foreign (i.e. not including the United States) crop area changes in thousands of harvested hectares (000s ha), and the normalized changes in hectares per billion British Thermal Units (ha/ billion BTU), for each of the scenarios considered. I Note that we focus on the change in land use between scenarios in 2022. J

Table 2.4-29. Changes in International Crop Area Harvested by Renewable Fuel, 2022

Scenario International Crop Area Change (000s ha) Normalized Crop Area Change (ha / billion BTU)

Corn Ethanol 789 3.94 Soy-Based Biodiesel 678 10.65 Sugarcane Ethanol 430 4.38 Switchgrass Ethanol 1,358 2.25

The location of land use changes is a critical factor in the determination of land use change GHG impacts, because the GHG impacts of land conversions varies substantially by region. For example, deforestation in the tr opics releases substantially more carbon than deforestation in drier regions. The FAPRI mode l allocates crop area changes across 54 regions based on a number of factors, including existing trade patterns, regional costs of production, and the potential for agricultural expansion in each region. Normalized crop area changes by region and renewable fuel are shown in Figure 2.4-20, with 12 aggregated regions for purposes of illustration. Once again, land use changes in th e United States are excluded from the figure, except for the case of sugarcane ethanol.

Figure 2.4-20. Normalized Harvested Crop Area Changes by Renewable Fuel, 2022 (ha / billion BTU)

I The sugarcane ethanol scenario includes land use changes in the United States as projected by FAPRI. For all of the other renewable fuels, domestic land use changes were determined with FASOM as described in the previous section.
J We assumed 76,000 BTU/gallon of ethanol; 115,000 BTU/gallon of biodiesel and 2.471 acres/hectare.70 -2 0 2 4 6 8 10 12

Africa & Middle

East Asia Oceania Brazil Canada Eastern Europe &

Russia India Other Latin America Rest of World United States Western Europe World Soy Biodiesel

Corn Ethanol

Sugarcane Ethanol

Switchgrass Ethanol

The projected net changes in crop area are the result of many factors, including shifting among different types of crops in each region. For example, for scenar ios where corn ethanol production increases in the United States, we pr oject a domestic shift from soybean production to corn production, and a shift toward oilseeds production in other countries to fill the gap in lost U.S. output. The following figures illustrate projected changes in harvested area by crop type and region for each renewable fuel scenario. All results are from the FAPRI model, with changes in the United States are included for illustrative purposes.

Figure 2.4-21. Harvested Crop Area Changes by Crop and Region Corn Ethanol Scenario, 2022 (000s ha)

-1,000 -500

0 500 1,000 1,500 2,000

Africa & Middle East Asia Brazil Canada Eastern Europe India Oceania Other Latin
America Rest of World US Western Europe World Corn Soybeans Rapeseed
Wheat Sugarcane

Rice
Palm Sorghum

Cotton

Oats
Sunflower Seed Sugar Beet

Peanut Dry Beans Barley Hay All Crops

Figure 2.4-22. Harvested Crop Area Changes by Crop and Region Soy-Based Biodiesel S cenario, 2022 (000s ha)71 -400 -200

0 200 400 600 800 1,000 1,200

Africa & Middle East Asia Brazil Canada Eastern Europe India Oceania Other Latin
America Rest of World US Western Europe World Corn Soybeans Rapeseed
Wheat Sugarcane

Rice
Palm Sorghum

Cotton

Oats
Sunflower Seed Sugar Beet

Peanut Dry Beans Barley Hay All Crops

Figure 2.4-23. Harvested Crop Area Changes by Crop and Region Sugarcane Ethanol Scenario, 2022 (000s ha)

-300 -200 -100

0 100 200 300 400 500 600 700

Africa & Middle East Asia Brazil Canada Eastern Europe India Oceania Other Latin
America Rest of World US Western Europe World Corn Soybeans Rapeseed
Wheat Sugarcane

Rice
Palm Sorghum

Cotton

Oats
Sunflower Seed Sugar Beet

Peanut Dry Beans Barley Hay All Crops

Figure 2.4-24. Harvested Crop Area Changes by Crop and Region Switchgrass Ethanol Sc enario, 2022 (000s ha)72 -3,500 -3,000 -2,500 -2,000 -1,500 -1,000 -500

0 500 1,000 1,500

Africa & Middle East Asia Brazil Canada Eastern Europe India Oceania Other Latin
America Rest of World US Western Europe World Corn Soybeans Rapeseed
Wheat Sugarcane

Rice
Palm Sorghum

Cotton

Oats
Sunflower Seed Sugar Beet

Peanut Dry Beans Barley Hay All Crops

Note: Switchgrass harvested area is not included in the figure.

2.4.4.2.3 Area and Location of Internati onal Pasture Land Conversions

In addition to considering international chan ges in crop area, our analysis also accounts for changes in pasture area, i.e., land used for liv estock grazing. Accounting for pasture area is essential to understand the land use change impacts of renewable fuels, because renewable fuel production can affect the livestock sector which us es pasture. Furthermore, more land is used globally for pasture than for crop production. 68 The new , more detaile d, representation of Brazil in the FAPRI model (see RIA Chapter 5 for more details) explicitly accounts for changes in pasture area, therefore, accounting for the co mpetition between crop and pasture land uses. Furthermore, the FAPRI Brazil module allows for livestock intensification, i.e., the increasing the heads of cattle per unit area of land in re sponse to higher commodity prices, increased demand for land, or other reasons. In addition to modifying how pasture is treated in Brazil, we also improved the methodology for calculating pasture area changes in other countries.

In the proposed rule, we made a broad assumpti on that the total land area used for pasture would stay constant in each country or region. Thus, in the proposed rule, we assumed that any crop expansion onto pasture would necessarily require an equal amount of pasture to be replaced on forest or shrubland. For the final rule we relaxed this assumption, and we now account for changes in pasture area resulting from livestock fluctuations and therefore capture the link between livestock and land used for grazing. Based on regional pasture stocking rates (i.e., livestock per hectare), we now calculate the amou nt of land used for livestock grazing. As a result of this analytical improvement, in countri es where we project decreased livestock numbers we also project less land needed for pasture. Therefore, unneeded pasture areas are available for cropland or allowed to revert to their natural state. In c ountries where livestock numbers increase, more land is needed for pasture, wh ich can be added on abandoned cropland or unused grassland, or it can result in deforestation. This new methodol ogy provides a more realistic assessment of land use changes, especially in regions where livestock populations are changing significantly.73 A multi-step process was used to translate the FAPRI livestock projections to pasture area changes. First, the FAPRI projections fo r dairy cattle and beef cattle (i.e., all non-dairy cattle) and sheep were converted to animal unit equivalents (AUE) using IPCC data (see

Table 2.4-30). 69 Next, average stocking rates for each of the 54 FAPRI regions were determined with data on live stock populations from the UN Food and Agricultural Organization (FAO) 70 and data on pasture area measured with agricultural inventory and satellite-derived land cover data. 71 The FAO data set provides a globally consistent estimate of livestock units per country. The estimated stocking rates are listed in74 Table 2.4-31. Expert judgment was used to ad just unusually low regional stocking rates. For example, we removed serious outliers from the "CIS, Other" FAPRI region. K Specifically Kazakhstan and Turkmenistan were removed fr om this calculation because these countries include vast stretches of desert pasture used for rotationa l sheep grazing. Removing these outliers provided a more realis tic estimate of how cattle population changes would affect land use in this region. For other regions that had un reasonably low stocking rate factors, we set the stocking rates equal to a neighboring country with a more reasonable factor. L

Based on the data sources consid ered, some regions had very high stocking rates due in part to the use of intensive livestock operations , such as feedlots. We did not adjust these stocking rates because we would not expect liv estock population changes to have a large impact on pasture area in these regions.

Table 2.4-30. Animal Unit Equivalen ts (Livestock Units per Head) Region Dairy Beef Sheep
Canada 1.33 0.86 0.18 Western Europe 1.32 0.93 0.18 Eastern Europe 1.21 0.86 0.18 Oceania 1.10 0.73 0.10 Brazil 0.88 0.67 0.10 Other Latin America 0.88 0.67 0.10 Asia 0.77 0.70 0.10 Africa & Middle East 0.61 0.38 0.10 India 0.61 0.24 0.10 US 1.33 0.86 0.18 Rest of World 1.00 0.69 0.13 Source: IPCC Vol. 4, Ch.10

K

CIS, Other includes the following countries in the Commonwealth of Independent States: Armenia, Azerbajian, Belarus, Georgia, Kazakhstanm, Krygyzstan, Moldova, Tajikistan, Turkmenistan, Uzbekistan
L These adjustments were made after consulting experts at Iowa State University and USDA who suggested that, although the best available data was used, the calculated stocking rates in many regions were unreasonably low. One reason for this, and part of the justification for adju sting the stocking rates upward, is that the data used considered all pasture land globally, including areas (e.g., Kazakhstan) with extraordinarily low stocking rates. The adjustments help to account for the fact that we would ex pect biofuel-induced livestock changes to affect globally integrated livestock regions, and these regions would likely not exhibit very low stocking rates.75 Table 2.4-31. Pasture Stocking Rates by FAPRI Region (Livestock Units / Ha)
FAPRI Regions Stocking Rate Notes/Adjustments Algeria 0.50 equals Tunisia Argentina 0.41 Australia 0.41 equals world average Bangladesh 25.25 Brazil: Amazon Biome 0.95 Brazil: Central-West Cerrados 1.00 Brazil: Northeast Coast 0.87 Brazil: North-Northeast Cerrados 0.90 Brazil: South 1.62 Brazil: Southeast 0.94 from FAPRI Control Case, 2022

Canada 0.64 China 0.41 New Zealand 1.14 Colombia 0.60 Cuba 1.02 Egypt 0.44 equals Iraq
EU 1.45 Guatemala 0.74 India 9.22 Indonesia 4.11
Iran 0.22 Iraq 0.44 Ivory Coast 0.45 equals Guinea Japan 9.63 Malaysia 1.99 Mexico 0.45
Morocco 0.50 equals Tunisia Myanmar (Burma) 11.41 Nigeria 0.74 Africa, Other 0.33 Zambia, Chad and Botswana removed Asia, Other 0.34 Mongolia & Singapore removed CIS, Other 0.45 Kazakhstan & Turkmenistan removed Eastern Europe, Other 0.37 Latin America, Other 0.52 Bolivia removed Middle East, Other 0.30 Pakistan 3.64 Paraguay 0.35
Peru 0.37 Philippines 17.16 Rest of World 0.41 equals world average Russia 0.41 equals China South Africa 0.33 equals Africa, Other South Korea 35.14 Taiwan 0.41 equals China Thailand 17.01 Tunisia 0.50 Turkey 0.61 Ukraine 0.49 Uruguay 0.67 United States 0.46 Uzbekistan 0.45 equals CIS, Other Venezuela 0.63 Vietnam 8.95 Western Africa 1.89 World 0.4176 As described above, pasture intensification was modeled e ndogenously in Brazil by the FAPRI model. In the FAPRI model, pasture intensification was a function of many factors, including livestock and crop prices, and competition for land between grazing and crop production uses. In general, the FAPRI results produced pasture intensif ication elasticities of 5- 10% (i.e., the % change in pasture intensification resulting from a % change in livestock population). For regions outside of Brazil we used a simple pasture intensif ication factor of 10% in regions where livestock populations increased.

Table 2.4-32 shows total and nor malized international pasture area changes for each of the scenarios considered. M The pasture area results are largely driven by the relative changes in the livestock markets. In scenarios where b eef and dairy production (which require pasture) declined more than swine and poultry production (w hich do not require pasture), the pasutre area changes were larger. 2.4.3.6 included intern ational livestock production results for eachscenario, and RIA Chapter 5 discusses the determining factors for these changes, such as coproduct and livestock feed ration efficiencies.

Table 2.4-32. Changes in International Pasture Area by Renewable Fuel, 2022

Scenario International Pasture Area Change (000s ha) Normalized Pasture Area Change (ha / billion BTU)
Corn Ethanol -446 -2.23 Soy-Based Biodiesel -268 -4.20 Sugarcane Ethanol -164 -1.67 Switchgrass Ethanol -580 -0.96 Note: Only the Sugarcane ethanol scenario results include United States land use changes.

The location of pasture area changes are very important when analyzing these results. As the above table shows, we projected that global pasture area would decrease in all of our renewable fuel scenarios. However, past ure area changes resulted in positive net GHG emissions in some cases because of the location of the resulting land conversions. For example, in the corn ethanol scenario we projected a significant increase in pasture area in the Brazilian Amazon, which causes large GHG emissions. Figure

Figure 2.4-25 illustrates pasture area changes by region. Figure 2.4-26 includes pasture area changes by region in Brazil.

M The sugarcane ethanol scenario includes land use changes in the United States as projected by FAPRI. For all of the other renewable fuels, domestic land use changes were determined with FASOM as described in the previous section. Thus, in Table 2.4-32 only the sugarcane ethanol scenario includes United States land use change results.77 Figure 2.4-25. Normalized Pasture A rea Changes by Renewable Fuel, 2022 (ha / billion BTU)

-10 -8 -6 -4 -2 0 2 4

Africa & Middle

East Asia Oceania Brazil Canada Eastern Europe & Russia India Other
Latin America Rest of World United
States Western Europe World Soy Biodiesel

Corn Ethanol

Sugarcane Ethanol

Switchgrass Ethanol

Figure 2.4-26. Normalized Pasture Area Changes in Brazil by Renewable Fuel, 2022 (ha / billion BTU)

-10

-8 -6 -4 -2 0 2

Amazon
Biome CentralWest Cerrados Northeast
Coast NorthNortheast Cerrados South Southeast Brazil Corn Ethanol Soy Biodiesel Sugarcane Ethanol Switchgrass Ethanol

2.4.4.2.4 Area and Location of International Cr opland and Pasture Land Conversions78 As discussed above, in the proposed rule we made a broad assumpti on that the total land area used for pasture would stay constant in each country or region. Thus, in the proposed rule, we assumed pasture area could not decrease in re gions where crop area increased. In the final rule analysis we used a more sophisticated a pproach that captures a wider range of potential interactions between crop and pa sture areas. For example, in regions were pasture decreases, this land is available for crop expansion. Theref ore, it is important to look at both the crop and pasture area changes together to understand the land use change GHG emissions impacts. Figure 2.4-27 shows the crop and pasture area changes fo r each scenario. Brazil is broken out as a separate region because, as the figure shows, it is the most important country in terms of its response in livestock production and pasture area.

Figure 2.4-27. Normalized Internationa l Land Use Change by Renewable Fuel (ha / billion BTU)

-10 -5 0 5 10 15Corn EthanolSoy BiodieselSugarcane EthanolSwitchgrass EthanolCorn EthanolSoy BiodieselSugarcane EthanolSwitchgrass EthanolCorn EthanolSoy BiodieselSugarcane EthanolSwitchgrass Ethanol

Brazil Rest of World World Pasture
Crop

Total

Finally, the following figures compare the proposed rule land use change projections to the land use change results in the final rule. Notice that in the final rule pasture area decreases in many regions, whereas in the proposed rule we as sumed that pasture area could not decline. As discussed in the next sections, this had a larg e impact on the types of la nd conversions projected, and on the resulting GHG emissions impacts.79 Figure 2.4-28. Proposed Rule and Final Rule Comparison Normalized International Land Use Changes Corn Ethanol, 2022 (ha / billion BTU)

-4 -2 0 2 4 6 8 10 12 14 16

Proposal Final Proposal Final Proposal Final

Brazil Rest of World World Pasture Proposal
Crop Proposal

Pasture Final
Crop Final

Figure 2.4-29. Proposed Rule and Final Rule Comparison Normalized International Land Use Changes Soy-Based Biodiesel, 2022 (ha / billion BTU)

-15 -10 -5 0 5 10 15 20

Proposal Final Proposal Final Proposal Final

Brazil Rest of World World Pasture Proposal
Crop Proposal

Pasture Final
Crop Final80 Figure 2.4-30. Proposed Rule and Final Rule Comparison Normalized International Land Use Changes

Sugarcane Ethanol, 2022 (ha / billion BTU)

-4 -2 0 2 4 6 8 10 12 14 16

Proposal Final Proposal Final Proposal Final

Brazil Rest of World World Pasture Proposal

Crop Proposal

Pasture Final

Crop Final

Figure 2.4-31. Proposed Rule and Final Rule Comparison Normalized International Land Use Changes Switchgrass Ethanol, 2022 (ha / billion BTU)

-2 0 2 4 6 8 10

Proposal Final Proposal Final Proposal Final

Brazil Rest of World World Pasture Proposal

Crop Proposal

Pasture Final

Crop Final

2.4.4.2.5 Evaluation of the Types of In ternational Land Conversions

As explained in the previous section, the FAPRI international models were used to project changes in the area of land used for cropl and and pasture in 54 regions. In this section81 we describe the two-step procedure that was us ed to determine in more detail the types and locations of land conversions:

1. The FAPRI output was disaggregated in to 12 land conversion categories. 2. The land cover types affected (e.g., forest or grassland) and the location of land conversions (i.e., by Administrative Unit) we re evaluated with MO DIS Version 5 satellite data from 2001-2007.

2.4.4.2.5.1 Determination of Internationa l Land Conversion Categories

Based on the FAPRI model results, we deter mined the conversions between annual crops, perennial crops, pasture land, and natural ecosystem s in each of the 54 FAPRI regions. First, the FAPRI land use change projections (both positive and negative changes in area) were broken into three categories for each region: a nnual crops, perennial crops and pasture. N We used a rule-based approach to determine the interaction of these three agricultural land uses with natural eco-systems in each region. These rules are summarized below in order of priority:

1. Annual and perennial crop areas interact with each other, e.g., where annual crop area increases and perennial crop area decrea ses, annual crops expand onto the land previously used for perennial crops. 2. Pasture and crop area interact with each othe r, e.g., where pasture area decreases and crop area increases, crops expand onto the land previously used for pasture. 3. Changes in the total area of land used for agriculture affect previously non-agricultural

areas.

Following the 3 rules listed above, the FAPR I projections were disaggregated into 12 land conversion categories, where natural eco-sys tems include forests, grasslands, savannas, shrublands, wetlands and barren land:

• Annual Crops to/from Perennial Crops

• Pasture to/from Perennial Crops

• Pasture to/from Annual Crops

• Natural Ecosystems to/from Annual Crops

• Natural Ecosystems to/from Perennial Crops

• Natural Ecosystems to/from Pasture Table 2.4-33 illustrates the results of this process with the results in Argentina for each scenario. O The FAPRI Results columns show the projected change in area for annual crops (Annl), perennial crops (Prnnl) and pasture (Pstr). Positive numbers indicate expansion and negative numbers indicate contraction. These resu lts were translated into the 6 Land Conversion columns, where positive numbers indicate conversion in the direction shown in the header row, and negative numbers indicate a conversion in the oppos ite direction. For example, in the Ntrl to Annl (i.e., Natural Ecosystems to Annual Crops) column, a pos itive number indicates conversion

N The perennial crops included in the FAPRI model are sugarcane and palm oil.
O The results for all 54 FAPRI regions are included in the public docket.82 of natural ecosystems to annua l crop production. A negative number in the Ntrl to Annl column indicates reversion of annual crops back to natural ecosystems.

Table 2.4-33. Argentina Land Conversio n Categories by Renewable Fuel, 2022 (Ha / billion BTU)

FAPRI Results Land Conversions

Scenario Annl Crops Prnnl Crops Pstr Annl to Prnnl Pstr to Prnnl Pstr to Annl Ntrl to Annl Ntrl to Prrnl Ntrl to Pstr

Corn Ethanol 0.01 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 Soy Biodiesel 0.26 0.00 -0.48 0.00 0.00 0.26 0.00 0.00 -0.22 Sugarcane Ethanol -0.18 0.01 0.00 0.01 0.00 0.00 -0.17 0.00 0.00 Switchgrass Ethanol 0.52 0.00 0.06 0.00 0.00 0.00 0.51 0.00 0.06

2.4.4.2.5.2 Evaluation of International Land Conve rsion Patterns with Satellite Data

In the proposed rule analysis land use change patterns were estimated at the national scale (and sub-national scale for key countries) using 1-km resolution MODIS products for the years 2001-2004. 72 MODIS land cover products were chosen originally due to their global, multi-year coverage, low cost and homogenous classification sc heme. For the final rule analysis we used the higher resolution, and more recent, MODIS Ve rsion 5 (MODIS V5) land cover dataset which covers the years 2001-2007 with 500-meter resolution.

To assess the accuracy/uncertainty in our use of MODIS satellite data we performed a Monte Carlo analysis based on the underlying uncer tainty in the satellite data as quantified by NASA. The MODIS data set is routinely va lidated by NASA’s MODIS land validation team. P NASA uses several validation techniques for qu ality assurance and to develop uncertainty information for its products. NASA’s primary validation technique includes comparing the satellite classifications to data collected through field and aircraft surveys, and other satellite data sensors. The accuracy of the MODIS V5 land cover product was assessed over a significant set of international locations, including roughly 1,900 sample site clusters covering close to 150 million square kilometers. The results of these validation efforts are summarized in a “confusion matrix” which compares the satellite’s land classifications with the actual land types observed on the ground. 73 We used this information to assess and correct the accuracy and systematic biases in the published MODIS data. Our analytical pr ocedures are summarized below and discussed in more detail in a technical report by ICF Inte rnational, Inc., availa ble on the public docket. 74 The full Monte Carlo model, with all data inputs and results, is also publicly available.

The key data source that allow us to unders tand the accuracy of the MODIS V5 product is a confusion matrix published by researchers that work as part of the MODIS land validation team. As explained by Dr. Mark Friedl:

The confusion matrix is a commonly used tool for assessment of accuracy for land cover classifications. The matrix scores how the classification process has

P More information about the MODIS Land Validation pr ocedures is available from the NASA Goddard Space Flight Center website, http://landval.gsfc.nasa.gov/83 labelled a series of test site s or test pixels at which the correct land cover label is known. Typically, the true class label is displayed across rows, while the actual mapped class is displayed in columns. The diagonal of the confusion matrix displays the number of sites or pixels for which the true class and the mapped class agree. The overall accuracy of the entire sample is then the sum of the diagonal elements divided by the total of a ll sites or pixels. For individual classes, the marginal totals of the matrix can easily be used to estimate the producer’s accuracy and user’s accuracy from the sa mple. The producer’s accuracy is the probability that a pixel truly belonging to cl ass i is also mapped as class i, while the user’s accuracy is the probability that a pixel mapped as class i is truly of class i. 75

The MODIS V5 confusion matrix includes 17 land use/ land cover categories developed by the International Geosphere-Biosphere Prog ramme (IGBP). As shown in Table 2.4-34, we aggregated the confusion matrix data to match the 10 land categories used in our analysis. The resulting aggregate confusion matrix is shown below in Table 2.4-36.

Table 2.4-35 is a number key for the land cove r classes presented in confusion matrix.

Table 2.4-34. Aggregation of IGBP land c over classes into EPA land cover classes

EPA Land Cover Classes IGBP Land Cover Class Proposed Rule Final Rule Evergreen Needleleaf Forest Forest Evergreen Broadleaf Forest Forest Deciduous Needleleaf Forest Forest Deciduous Broadleaf Forest Forest Mixed Forest Forest Forest Closed Shrubland Shrubland Shrubland Open Shrubland Shrubland Shrubland Woody Savanna Savanna Savanna Savanna Savanna Savanna Grasslands Grassland Grassland Permanent Wetlands Excluded Wetland Cropland Cropland Cropland Cropland/Nat Veg Mosaic Excluded Mixed Barren/Sparse Excluded Barren Snow and Ice Excluded Excluded84 Water Excluded Excluded

Table 2.4-35. EPA land cover class number key Number Key EPA Land Cover Class 1 Annual Crops 2 Forest 3 Grassland 4 Mixed 5 Savanna 6 Shrubland 7 Wetland 8 Barren 9 Perennial Crops 10 Excluded

Table 2.4-36. MODIS Version 5 confusion matr ix with aggregated EPA land cover classes Satellite Classification Label Training Site Label 1 2 3 4 5 6 7 8 10 Training Total
1 6,963 0 118 84 77 73 60 2 127 7,504 2 25 7,763 5 42 564 52 482 1 5 8,939 3 414 3 1,938 26 279 570 40 77 111 3,458 4 498 103 22 402 264 69 105 0 0 1,463
5 300 422 172 102 2,331 275 233 16 0 3,851 6 148 34 341 10 279 3,135 71 111 8 4,137 7 19 59 5 0 6 0 2,406 0 0 2,495 8 4 0 14 0 27 334 0 4,802 1 5,182 10 0 0 13 0 0 1 12 4 2,411 2,441 Satellite Total 8, 371 8,384 2,628 666 3,827 4,509 3,409 5,013 2,663 39,470 Note: values for perennial crops (land class 9) were assigned with a procedure described in the ICF report. 76

The confusion matrix contains information about the accuracy of the satellite data which can be used statistically to correct systema tic biases. The matrix includes data from 39,470training sites where the MODIS land team validated the satellite classification labels with on-theground training site surveys. For example, if we look at forest (land cla ss 2) in the matrix, we see that of the 8,384 sites that were classified by the satellite as fo rest (see the satellite total row) 7,763 of these sites (see the diagonal in row 2) we re correctly classified. The quotient of these figures (i.e. 7,763/8,384 = 92.6%) gives us what it is known as the producer’s accuracy for85 forest. The user's accuracy for forest, 86.8%, can also be calculated by using the training site total for forest in the denominator (i.e., 7,763/8,939 = 86.8%).

Furthermore, we can determine which land cl asses forestlands tended to be misclassified as (i.e., confused with), which classes tended to be misclassified as forests, and the probability of each specific misclassification. Table 4 presen ts the producer's accuracy matrix for MODIS Version 5 using EPA's aggregated land classes. Each value in Table 2.4-37 gives the probability that a pixel reported as land cover R is actually land cover A, where R is the reported land class listed in the columns and A is the actual land cover listed in the rows. For example, the intersection of column 2 and row 5 shows that there was a 5.0% probability that a pixel reported as forest (land class 2) was actually savanna (land class 5).

Table 2.4-37. MODIS Version 5 producer’s accuracy matrix with aggregated EPA land cover classes Reported
Actual 1 2 3 4 5 6 7 8 10

1 83.2% 0.0% 4.5% 12.6% 2.0% 1.6% 1.8% 0.0% 4.8%
2 0.3% 92.6% 0.2% 6.3% 14.7% 1.2% 14.1% 0.0% 0.2%
3 4.9% 0.0% 73.7% 3.9% 7.3% 12.6% 1.2% 1.5% 4.2%
4 5.9% 1.2% 0.8% 60.4% 6.9% 1.5% 3.1% 0.0% 0.0%

5 3.6% 5.0% 6.5% 15.3% 60.9% 6.1% 6.8% 0.3% 0.0%
6 1.8% 0.4% 13.0% 1.5% 7.3% 69.5% 2.1% 2.2% 0.3%

7 0.2% 0.7% 0.2% 0.0% 0.2% 0.0% 70.6% 0.0% 0.0%
8 0.0% 0.0% 0.5% 0.0% 0.7% 7.4% 0.0% 95.8% 0.0%
10 0.0% 0.0% 0.5% 0.0% 0.0% 0.0% 0.4% 0.1% 90.5%

100.0% 100.0% 100.0% 100.0% 100. 0% 100.0% 100.0% 100.0% 100.0%

Using the information in Table 2.4-37, it is fair ly straight-forward to adjust/correct the reported land use data to provide a much better estimate of the actual land use during this time period. The MODIS data was corrected w ith the following multi-step process:

• First, the confusion matrix for each country and administrative unit is scaled for each year so that the share of reported land use of total land use matches the adjusted MODIS estimates share.

o ACM cup = CM up /CM xp , where CM up are the values in the input aggregated confusion matrix and CM xp = ∑ u CM up where u is the user’s land use and p is the producer’s land use Q

o The coefficients from the resulting matrix sum to 1.0

• Next, the number of producer sites is scal ed similarly to the approach above and the producer’s estimate is recalculated R

• The actual land use in 2001 and 2007 is then given by the following equations S

o ALU ci = ∑ p BCM cyip * AM cpx for y = 2001 and where i is the initial land use

Q x is a placeholder that indicates that the matrix presents total land use.
R

In fact, the producer’s estimate does not change.
S x is a placeholder that indicates that the matrix presents total land use.86 o ALU cf = ∑ p BCM cyfp * AM cxp for y = 2007 and where f is the final land use

• The reported land use change is then calculated first for 2001

o We find a land use category where the total land use for 2001 decreased (ALU ci < AM cix )

o We scale the land use change to all land uses down from that land use category based on the percentage d ecrease in total land use

o We allocate the remaining land use change to the remaining land uses in 2001 based on the share of the increase in land use that increased

• We then use a similar process to adjust the 2007 land use

As an example of the 2 nd to last step, let’s assume that, based on the confusion matrix adjustment procedure described above, the adju sted land use for savanna has decreased in 2001 from the producer values by 30%, from 21 to 14 million hectares and that the adjusted land use for cropland and forestland, and grassland increase by 5, 3, and 2 million hectares respectively and all other land uses decrease or do not change. The adjusted land use change from Savanna to all 10 land uses is then scaled to be 70% of the original land use change from Savanna. The remaining 30% of the land use change is then allocated as coming from cropland, forestland, and grassland with the shares equal to 5/(5+3 +2) = 5/10 for cropland,3/10 for forestland, and 2/10 for grassland respectively. This means th at 3.5, 2.1, and 1.4 million hectares of increased cropland, forestland, and grassland have been acco unted for and the ratios to apply to the land use change from Savanna is 0.5*0.3, 0.3*0.3, and 0.2*0.3 respectively. If the Savanna to Cropland land use change was 1 million hectares or iginally, it is now 0.7 million hectares. If the Cropland, Forestland, and Grassla nd to Cropland land use change was 2, 0.1, 0.4 million hectares originally, they are 2.0 + 1.0 * 0.5 * 0.3, 0.1 + 1.0*0.3*0.3, and 0.4 + 1.0*0.2 or 2.15, .19, and .6 respectively. We then go to the next land us e with reductions in the adjusted land use for 2001 and repeat this process but in the allocation, we use (5-3.5), (3-2.1), and (2-1.4) million hectares for the allocation for cropland, fore stland, and grassland respectively.

The approach for the adjustments in land us e for 2007 is similar, but it scales and adds using (i) the change in land use in 2007, instead of change in land use in 2001, and (ii) the land use change from the 10 land uses to Savanna, inst ead of the change from Savanna to the 10 land

uses. The corrected satellite data was used to evaluate the types of land affected by the projected land conversions in each scenario. For agricultural expansions, the types of land affected were evaluated with the corrected la nd use change data from 2001-2007. We also used this approach to determine, within each country/r egion, the location of land use changes, i.e. the Administrative Units where conversions would occu r. For example, in each region we looked at the types of land converted to cr opland during this time period.

To determine the types of land converted to pa sture, we applied the land use change data for cropland. This was done primarily because the MODIS data set does not classify land used for pasture. MODIS does classify grasslands a nd savanna, which we know are used for livestock grazing, but it provides no information about the share of grassland and sa vanna used as pasture in each region. Thus, looking only at land types that were replaced by grassland or savanna would ignore the fact that pasture can expand onto grassland and savanna areas. By applying the cropland change data for pasture we include d/approximated these im portant land conversion87 possibilities. The justification for this approach was based, in part, on the assumption that pasture expansion is likely to affect similar land types as cropland. T

For land reversions, land cover in 2007 was us ed to estimate the land types that would likely grow back on abandoned agricultural lands in each region. For example, in a region that was 80% forest, we assume that 80% of abandoned agricultural land would grow back as forest. For land reversions this approach was preferable to using change data, because the time period covered by the MODIS satellite imagery was not long enough to determine the final land category following reversion, i.e., 30 years later. We also used this approach to determine, within each country/region, the location of land re versions, i.e. the Administrative Units where reversions would occur.

The contributing land use change categories a nd the bases of their weighting factors for each agricultural land use change are presented in Table 2.4-38.. The first column lists the 12 land conversion categories modeled. The middle column indicates the satellite data weighting approach used. The last column includes the resulting land conversions, which were weighted with the approach listed in the middle column. More details about the application of satellite data to weight land conversions is provided in a technical report by ICF International available on the public docket. 77

T This assumption is supported to some degree by Cardille and Foley (2003) who found that cropland and pasture expansion affected similar land types in the Brazilian Amazonia between 1980 and 1995.88Table 2.4-38.Contributing Land Use Change Categoriesand Bases of Weighting Factors for Agricultural Land Use Change CategoriesAgricultural LandUse ChangeCategoryBasis ofWeightingFactorLand Use Types and Land Use ChangeCategories, used to calculate each Countryand Admin Unit’s Contribution to theRegional TotalShare of Grassland or Savanna ofTotal Grassland and Savanna, usedto Apportion Pasture ChangesLand Use Change Categories Used to ApportionChanges to Initial or Final Land Use (Regional SharesUnless Noted) and to Calculate Weighted AverageEmissionsAnnual Crops toPerennial Crops2007 Land UseCropland - Croplands to PerennialPerennial Crops toAnnual Crops2007 Land UseCropland - Perennial to CroplandsGrasslands to Perennial - Grasslands to PerennialPasture to PerennialCropsLand UseChange - 2001to 2007Savanna to Perennial - Savanna to PerennialPerennial to Grasslands - Perennial to GrasslandsPerennial Crops toPastureLand UseChange - 2001to 2007Perennial to Savanna - Perennial to SavannaGrasslands to Croplands - Grasslands to CroplandsPasture to AnnualCropsLand UseChange - 2001to 2007Savanna to Croplands - Savanna to CroplandsCroplands to Grasslands - Croplands to GrasslandsAnnual Crops toPastureLand UseChange - 2001to 2007Croplands to Savanna - Croplands to SavannaForestland to Croplands - Forestland to CroplandsGrasslands to Croplands - Grasslands to CroplandsMixed to Croplands - Mixed to CroplandsSavanna to Croplands - Savanna to CroplandsShrubland to Croplands - Shrubland to CroplandsWetland to Croplands - Wetland to CroplandsNatural Ecosystemsto Annual CropsLand UseChange - 2001to 2007Barren to Croplands - Barren to CroplandsCroplands - Croplands to Forestland (at the Country/Admin Level)Croplands - Croplands to Grasslands (at the Country/Admin Level)Croplands - Croplands to Mixed (at the Country/Admin Level)Croplands - Croplands to Savanna (at the Country/Admin Level)Annual Crops toNatural Ecosystems2007 Land UseCroplands - Croplands to Shrubland (at the Country/Admin Level)Forestland to Perennial - Forestland to PerennialGrasslands to Perennial - Grasslands to PerennialMixed to Perennial - Mixed to PerennialSavanna to Perennial - Savanna to PerennialShrubland to Perennial - Shrubland to PerennialWetland to Perennial - Wetland to PerennialNatural Ecosystemsto Perennial CropsLand UseChange - 2001to 2007Barren to Perennial - Barren to PerennialCroplands - Perennial to ForestlandCroplands - Perennial to GrasslandsCroplands - Perennial to MixedCroplands - Perennial to SavannaPerennial Crops toNatural Ecosystems2007 Land UseCroplands - Perennial to ShrublandForestland to Croplands Forestland to GrasslandsNatural Ecosystemsto PastureLand UseChange - 2001Grasslands to CroplandsGrassland Share, by Country andAdministrative UnitGrasslands to Grasslands89Agricultural LandUse ChangeCategoryBasis ofWeightingFactorLand Use Types and Land Use ChangeCategories, used to calculate each Countryand Admin Unit’s Contribution to theRegional TotalShare of Grassland or Savanna ofTotal Grassland and Savanna, usedto Apportion Pasture ChangesLand Use Change Categories Used to ApportionChanges to Initial or Final Land Use (Regional SharesUnless Noted) and to Calculate Weighted AverageEmissionsMixed to Croplands Mixed to GrasslandsSavanna to Croplands Savanna to GrasslandsShrubland to Croplands Shrubland to GrasslandsWetland to Croplands Wetland to GrasslandsBarren to Croplands Barren to GrasslandsForestland to Croplands Forestland to SavannaGrasslands to Croplands Grasslands to SavannaMixed to Croplands Mixed to SavannaSavanna to Croplands Savanna to SavannaShrubland to Croplands Shrubland to SavannaWetland to Croplands Wetland to Savannato 2007Barren to CroplandsSavanna Share, by Country andAdministrative UnitBarren to SavannaCroplandGrassland to Forestland (at the Country/Admin Level)CroplandGrassland to Grassland (at the Country/Admin Level)CroplandGrassland to Savanna (at the Country/Admin Level)CroplandGrassland to Mixed (at the Country/Admin Level)CroplandGrassland Share, by Country andAdministrative UnitGrassland to Shrubland (at the Country/Admin Level)CroplandSavanna to Forestland (at the Country/Admin Level)CroplandSavanna to Grassland (at the Country/Admin Level)CroplandSavanna to Savanna (at the Country/Admin Level)CroplandSavanna to Mixed (at the Country/Admin Level)Pasture to NaturalEcosystems2007 Land UseUCroplandSavanna Share, by Country andAdministrative UnitSavanna to Shrubland (at the Country/Admin Level)UThe model actual uses the three land uses twice, once to represent the replacement of grasslands and the other to represent the replacement of savanna90 Table 2.4-39 includes the regional shares of la nd types converted to cropland and pasture based on the original Version 5 MODIS data. Table 2.4-40 shows the sa me data after it was corrected using the confusion matrix data and th e procedure described above. In many regions, the corrections significantly reduced the share of grassland, savanna and/ or mixed land converted to cropland. This was due, in part, to the te ndency of MODIS to confus e these land types with each other and with cropland. As a result, the shar e of forest affected by agricultural expansion increased for most of the regions analyzed. Table 2.4-41 shows the land type shares for agricultural reversion with the corrected data.

Table 2.4-39. Types of Land Converted to Cropland/Pasture by Region Original Version 5 MODIS Data, 2001-2007
FAPRI Region Forest Grassland Mixed Savanna Shrubland Wetland Barren Algeria 0% 12% 7% 8% 72% 0% 0% Argentina 11% 37% 20% 17% 13% 1% 0% Australia 1% 54% 2% 16% 27% 0% 0% Bangladesh 21% 6% 24% 24% 11% 13% 1% Brazil: Amazon Biome 15% 33% 12% 36% 4% 0% 0% Brazil: Central-West Cerrados 3% 30% 17% 49% 1% 0% 0% Brazil: Northeast Coast 0% 22% 15% 54% 9% 0% 0% Brazil: North-Northeast Cerrados 1% 32% 7% 53% 7% 0% 0% Brazil: South 5% 52% 22% 20% 0% 0% 0% Brazil: Southeast 1% 20% 43% 35% 1% 0% 0% Canada 2% 52% 5% 5% 32% 0% 3% China 1% 67% 6% 3% 15% 0% 7% New Zealand 30% 37% 6% 2% 24% 0% 0% Colombia 3% 74% 5% 14% 4% 0% 0% Cuba 2% 6% 74% 15% 2% 1% 0% Egypt 2% 4% 50% 4% 27% 0% 13%
EU 4% 37% 36% 8% 14% 0% 1% Guatemala 17% 3% 60% 18% 1% 0% 0% India 2% 12% 41% 23% 22% 0% 1% Indonesia 27% 5% 43% 22% 2% 2% 0%
Iran 0% 77% 1% 1% 16% 0% 4% Iraq 0% 53% 4% 2% 39% 0% 2% Ivory Coast 26% 6% 30% 30% 6% 2% 1% Japan 8% 9% 58% 15% 10% 0% 0% Malaysia 35% 4% 50% 5% 3% 3% 0% Mexico 2% 36% 17% 16% 29% 0% 0%
Morocco 0% 18% 4% 4% 72% 0% 2% Myanmar 7% 9% 46% 27% 10% 2% 0% Nigeria 2% 73% 12% 11% 2% 0% 0% Other Africa 0% 59% 8% 18% 11% 0% 3% Other Asia 0% 79% 2% 1% 6% 0% 11% Other CIS 0% 87% 2% 1% 4% 0% 5% Other Eastern Europe 1% 48% 38% 9% 4% 0% 0% Other Latin America 7% 40% 13% 19% 20% 0% 1% Other Middle East 0% 13% 13% 8% 54% 0% 11% Pakistan 0% 13% 29% 3% 51% 0% 4% Paraguay 8% 31% 22% 39% 0% 0% 0% Peru 1% 78% 2% 3% 15% 0% 0% Philippines 12% 2% 78% 4% 1% 3% 0% Rest of World 1% 54% 9% 22% 12% 0% 1% Russia 3% 50% 25% 6% 16% 0% 0% South Africa 1% 52% 12% 18% 17% 0% 0% South Korea 5% 5% 82% 6% 3% 0% 0% Taiwan 25% 6% 36% 15% 16% 1% 1%91 FAPRI Region Forest Grassland Mixed Savanna Shrubland Wetland Barren
Thailand 5% 10% 64% 15% 4% 1% 0% Tunisia 0% 8% 10% 3% 79% 0% 1% Turkey 0% 81% 3% 6% 9% 0% 0% Ukraine 2% 26% 59% 7% 6% 0% 0% Uruguay 2% 82% 13% 3% 0% 0% 0% US 0% 84% 5% 4% 7% 0% 0% Uzbekistan 0% 56% 3% 3% 20% 0% 18% Venezuela 1% 38% 8% 36% 16% 0% 0% Vietnam 16% 5% 55% 11% 4% 7% 1% Western Africa 2% 15% 34% 46% 3% 0% 0%

Table 2.4-40. Types of Land Converted to Cropland/Pasture by Region Corrected Version 5 MODIS Data, 2001-2007
FAPRI Region Forest Grassland Mixed Savanna Shrubland Wetland Barren Algeria 1% 16% 8% 10% 64% 0% 0% Argentina 12% 26% 27% 17% 14% 1% 3% Australia 6% 32% 11% 22% 25% 0% 4% Bangladesh 19% 21% 24% 20% 11% 5% 1% Brazil: Amazon Biome 54% 8% 15% 20% 2% 1% 0% Brazil: Central-West Cerrados 11% 26% 20% 36% 6% 0% 0% Brazil: Northeast Coast 11% 19% 19% 41% 8% 0% 1% Brazil: North-Northeast Cerrados 15% 16% 10% 49% 9% 0% 1% Brazil: South 13% 23% 28% 29% 6% 0% 0% Brazil: Southeast 10% 18% 30% 36% 6% 0% 0% Canada 8% 28% 13% 14% 31% 2% 4% China 6% 30% 23% 20% 17% 1% 3% New Zealand 28% 33% 15% 7% 15% 1% 1% Colombia 33% 9% 31% 18% 8% 1% 1% Cuba 9% 12% 49% 23% 7% 0% 0% Egypt 2% 20% 30% 8% 33% 0% 7%
EU 6% 25% 32% 21% 14% 1% 1% Guatemala 21% 7% 42% 24% 5% 1% 0% India 10% 21% 30% 19% 17% 1% 2% Indonesia 39% 5% 29% 22% 3% 2% 0%
Iran 2% 43% 5% 8% 33% 0% 9% Iraq 1% 37% 8% 8% 43% 0% 3% Ivory Coast 22% 8% 15% 46% 8% 0% 1% Japan 8% 9% 47% 23% 11% 1% 1% Malaysia 52% 3% 27% 13% 2% 2% 0% Mexico 10% 18% 27% 21% 21% 1% 2%
Morocco 2% 28% 7% 9% 50% 0% 4% Myanmar 14% 10% 34% 30% 9% 2% 1% Nigeria 11% 36% 19% 25% 9% 0% 1% Other Africa 10% 19% 14% 37% 13% 0% 6% Other Asia 4% 42% 15% 11% 19% 0% 9% Other CIS 1% 49% 17% 11% 18% 0% 3% Other Eastern Europe 6% 37% 31% 16% 8% 1% 1% Other Latin America 18% 13% 27% 26% 13% 1% 2% Other Middle East 2% 21% 11% 11% 32% 0% 23% Pakistan 3% 23% 28% 13% 31% 0% 2% Paraguay 17% 20% 22% 36% 5% 1% 0% Peru 45% 30% 4% 9% 10% 1% 1% Philippines 16% 5% 54% 19% 2% 3% 0% Rest of World 18% 13% 25% 27% 12% 1% 3% Russia 8% 20% 27% 20% 22% 1% 2% South Africa 5% 35% 19% 18% 20% 0% 3% South Korea 5% 11% 58% 20% 5% 1% 0%92 FAPRI Region Forest Grassland Mixed Savanna Shrubland Wetland Barren Taiwan 25% 8% 27% 21% 17% 1% 1% Thailand 12% 10% 48% 23% 5% 1% 0% Tunisia 3% 29% 12% 12% 43% 0% 1% Turkey 5% 45% 15% 10% 23% 0% 3% Ukraine 3% 31% 20% 32% 13% 2% 1% Uruguay 3% 57% 17% 11% 12% 0% 0% US 6% 36% 24% 18% 14% 1% 1% Uzbekistan 2% 34% 16% 15% 32% 0% 1% Venezuela 7% 13% 27% 43% 9% 0% 1% Vietnam 21% 8% 39% 20% 6% 5% 1% Western Africa 14% 12% 14% 50% 8% 0% 1%

Table 2.4-41. Types of Land That Replace Abandoned Cropland/Pasture by Region Corrected Version 5 MODIS Data, 2001-2007
FAPRI Region Forest Grassland Mixed Savanna Shrubland Algeria 3% 21% 3% 10% 64% Argentina 14% 21% 6% 13% 45% Australia 7% 20% 3% 19% 51% Bangladesh 32% 14% 20% 24% 9% Brazil: Amazon Biome 83% 1% 5% 10% 1% Brazil: Central-West Cerrados 24% 9% 11% 49% 6% Brazil: Northeast Coast 14% 10% 15% 54% 7% Brazil: North-Northeast Cerrados 20% 9% 11% 53% 7% Brazil: South 32% 17% 23% 23% 5% Brazil: Southeast 21% 7% 22% 44% 6% Canada 43% 11% 4% 15% 26% China 27% 34% 10% 16% 14% New Zealand 64% 14% 2% 6% 13% Colombia 64% 8% 11% 14% 4% Cuba 36% 7% 33% 21% 4% Egypt 2% 30% 8% 10% 50% EU 45% 11% 15% 17% 11% Guatemala 54% 3% 17% 23% 3% India 22% 12% 24% 29% 13% Indonesia 77% 1% 12% 9% 1% Iran 3% 36% 3% 8% 51% Iraq 2% 25% 3% 8% 62% Ivory Coast 32% 5% 20% 38% 4% Japan 77% 2% 8% 11% 2% Malaysia 82% 1% 10% 7% 1% Mexico 19% 17% 8% 24% 32% Morocco 2% 18% 4% 10% 66% Myanmar 59% 4% 12% 22% 3% Nigeria 17% 18% 25% 33% 7% Other Africa 24% 17% 8% 36% 15% Other Asia 13% 52% 5% 9% 22% Other CIS 5% 64% 4% 8% 19% Other Eastern Europe 42% 8% 29% 17% 4% Other Latin America 56% 7% 8% 15% 14% Other Middle East 1% 20% 2% 8% 68% Pakistan 5% 17% 8% 10% 60% Paraguay 41% 8% 14% 33% 5%
Peru 62% 17% 2% 7% 12% Philippines 54% 3% 30% 12% 1% Rest of World 44% 11% 9% 20% 16% Russia 43% 10% 6% 14% 27% South Africa 8% 22% 6% 23% 42%93 FAPRI Region Forest Grassland Mixed Savanna Shrubland South Korea 67% 3% 13% 13% 3% Taiwan 75% 3% 9% 10% 3% Thailand 32% 5% 34% 25% 3% Tunisia 3% 17% 5% 10% 65% Turkey 12% 47% 7% 16% 18% Ukraine 34% 14% 32% 16% 5% Uruguay 4% 67% 5% 11% 13%
US 27% 31% 14% 12% 16% Uzbekistan 2% 40% 3% 9% 46% Venezuela 55% 8% 11% 21% 4% Vietnam 48% 5% 21% 22% 4% Western Africa 14% 8% 17% 54% 7%

2.4.4.2.6 Quantification of International La nd Conversion GHG Emissions Impacts

Land use change emissions factors were calculated by the non-profit organization Winrock International following 2006 IPCC Agricu lture Forestry and Other Land Use (AFOLU) Guidelines. 78 Winrock’s staff is highly regarded for their years of experience and accomplishments in this field, including their work with the IPCC to develop the AFOLU Guidelines. Following publication of the proposed rule, we sponsored an expert peer review on this part of our lifecycle analysis. Base d on the reviewers recommendations a number of important improvements were made, including incorporati on of more recent and higher resolution data sets. Our analysis of land use change emissions factors has also been expanded to provide global coverage. For the proposed ru le, emissions factors were estimated for 5 land categories in 314 regions across 35 of the most important countries, with a weighted average applied to the rest of the world. Our analysis now includes 9 land categories in over 750 distinct regions across 160 countries covering all signif icant agricultural producers. This section describes the methods used to estimate GHG emissions from intern ational land use change, with a focus on updates since the proposed rule. More details are available in a technical document by Winrock availabl e on the public docket. 79

2.4.4.2.6.1 Data Sources and Methods for Intern ational Land Conversion GHG Emissions Factors

Emission factors were calculated using the IPCC equations explained in DRIA Chapter 2. 80 The emissions factors include the sum of changes in above- and belowground biomass carbon stocks, changes in soil carbon stocks on min eral soils, emissions from peat drainage on peat soils cleared for agriculture, fo regone forest sequestration, and non-CO 2 emissions (CH 4 , N 2 O) resulting from land clearing with fire where applicable. Methane emissions from rice cultivation were excluded from the updated emission factors, as these emissions are accounted for elsewhere in EPA’s lifecycle analysis. Upda tes to various components of the final emission factor are described below.

2.4.4.2.6.1.1 Data Sources and Methods for Intern ational Forest Ca rbon Stock Estimates94 Our emission factor analysis incorporated spatial maps of forest carbon stocks from several data sources. The region- specific maps were preferred due to the use of country-specific data (i.e., IPCC Tier 2 vs. Tier 1), and also because the only globally consistent carbon stock map available was derived using adjusted biome- level Tier 1 default values from IPCC rather than from country specific data sources (Rue sch and Gibbs 2008). We used regional and/or country-level maps where available, and the gl obal Ruesch and Gibbs ( 2008) data product was used only to fill in gaps where no other info rmation on forest carbon stocks was available. 81

Our analysis of forest carbon stocks was improved by incorpora ting several new data sources. Most notable is the inclusion of a ne w spatially explicit map of tropical forest carbon stocks. Winrock is working with Dr. Sassan Saatchi from NASA’s Jet Propulsion Laboratory to create a pantropical benchmark map of above- an d belowground forest carbon stocks for the year 2000 at 1-km resolution. 82 The methodology uses about 4,000 ground inventory plots of forest biomass, 150,000 biomass values estimated from heig hts measured by spaceborne lidar, and a suite of satellite ima gery products to derive a spatially refined map of aboveground forest carbon at a 1-km grid cell resolution. Belowground ca rbon is added to aboveground carbon using an equation from Mokany et al. (2006). 83 The estimates are directly comparable across countries and regions due to the consistency in the methodological approach (see Figure 2.4-32).

Figure 2.4-32. Map of forest carbon stocks in above- a nd belowground biomass for the tropics.

Note: from Saatchi et al. (in prep)

Preliminary results of Saatchi et al. for forest carbon stocks in Latin America and Africa were incorporated into the updated EPA analysis by clipping the map to MODIS forest cover in 2001 and calculating the area-weighted average forest carbon stock per country (and per administrative unit in key countries). Preliminar y results for Asia are now complete, but were not included in the updated EPA analysis due to timing considerations. Therefore, the original Brown et al. (2001) map was retained fo r forest carbon stock estimates in Asia. 84 The Saatchi et al. results represent a significant improvement over previous estimates; the maps were evaluated for accuracy using cross validation with approx imately 50% of the ground and lidar biomass data95 and resulted in an overall accuracy of 76% across the three regions (Latin America: 81%, Africa: 86%, Southeast Asia: 69%).

Myneni et al. (2001) also produced a spa tially-explicit map of woody biomass for Northern (i.e., boreal and temperate) forests ( Figure 2.4-33 ). 85 Although we used carbon stock values from other data sources for the United States, Russia and many countries of the European Union (Blackard et al. 2007, Houghton et al. 2007, Nabuurs et al. 2003, see

Figure 2.4-34), the Myneni et al. (2001) data set filled in the data gap for Canada and many Eastern European countries. 86,87,88

Figure 2.4-33. Aboveground biomass carb on stocks in Northern forests.

Note: reproduced from Figure 2 of Myneni et al. (2001).

Apart from the new data sources des cribed above, the data sources used to estimate forest carbon stocks in other regions remained unchanged in the updated analysis. 89 A summary of data sources used is shown in96 Figure 2.4-34.

Figure 2.4-34. Data sources used for estimating forest carbon stocks in updated emission factor analysis97 Note: Nabuurs et al. and Nabuurs et al. 2003 are the same data source.

In all cases where belowground biomass was not estimated, belo wground biomass was added to aboveground biomass estimates using an equation from Mokany et al. (2006). 90 (This equation represents an update to the defau lt belowground biomass values given in the IPCC Guidelines.) Forest carbon stock values per c ountry or administrative unit (for key countries) are shown in Figure 2.4-35.

Figure 2.4-35. Spatially averaged forest carbon stocks in above- and belowground biomass (tCO2e/ha)

< 50

51 - 100
101 - 150

151 - 300
301 - 500

501 - 750
> 750

2.4.4.2.6.1.2 Data Sources and Methods for Intern ational Cropland Ca rbon Stock Estimates98 In the proposed rule emission factor analysis , all cropland conversi on was assumed to be conversion to annual cropland. In the updated analysis, e mission factors were estimated separately for conversion to annual cropland and conversion to perennial cropland. Perennial cropland in Indonesia and Malaysia was assumed to be oil palm, while perennial cropland in all other countries was sugarcane. Carbon stocks in oil palm plantations afte r one year of growth were estimated as 15 t CO2e/h a. Table 5.3 of the 2006 IPCC Guidelines for AFOLU gives biomass stocks on oil palm plantation as 136 t/ha (68 t C/ha), and if this value is divided by an assumed 15-year growth period, a linear growth rate of 4 t C/ha/yr (15 t CO2e/ha/yr) was assumed. This value is also nearly identical to the average carbon stock in biomass after one year of growth averaged across all tropical climate regions and all perennial crop types, as given in Table 5.9 of the 2006 IPCC Guidelines.

Carbon stocks in sugarcane afte r one year of growth were a ssumed to be 44 t CO2e/ha. (Carbon stocks for long-lived tree species such as oil palm accumulate carbon more slowly in the early phases of growth.) The value for sugarcan e was derived from estimate s of carbon stocks in sugarcane in aboveground biomass (17 t C ha -1 or 62 t CO 2 , Amaral et al. 2008) 91 and in belowground biomass (7 t C/ha or 26 t CO 2/ ha, Smith et al. 2005) 92 for a total of 88 t CO2e/ha. We assumed a growth period of two years to achieve full carbon stocks, therefore the carbon stock in sugarcane after one year of growth was assumed to be 44 t CO 2 /ha.

All biomass accumulated after Year 1 would have been ha rvested over the course of 30 years in the case of both sugarcane and oil palm, leading to little net sequestration during the time period for which emission factors were estimated (30 years). Over the long term (e .g., 100 years), oil palm pl antations may have a longterm average carbon stock hi gher than that at Ye ar 1, but the land use after 30 years is highly uncertain and there is no guarantee of fu ture rotations. Therefore, the average carb on sequestration at any given time over 30 years was assumed to be the carbon stock in vegetation after one year of growth. Figure 2.4-36 illustrates this concept by show ing an example of carbon stock growth for perennial crops with different rotation lengths.

Figure 2.4-36. Perennial Crop Carbon Stocks Over Time99 2.4.4.2.6.1.3 Data Sources and Methods for Interna tional Grassland, Sava nna and Shrubland Carbon Stock Esimates

The approach for estimating carbon stocks in grassland, savanna and shrubland land cover categories was unchanged from the proposed rule, as there were no significant comments on this aspect of our analysis from the peer reviewers. Above- and belowground carbon stocks of grassland, savanna and shrublands in Brazil were estimated using values from de Castro and Kauffman (1998) 93 who report biomass along a vegeta tion gradient from campo limpo (pure grassland), campo sujo (a savanna with a sp arse presence of shrubs), campo cerrado (a dominance of shrubs with scattered trees and a grass understory), cerrado sensu stricto (a dominance of trees with scattered shrubs and a grass understory) and cerradão (a closed canopy forest) (Coutinho 1978, Eiten 1972, Goodland & Pollard 1973). 94,95,96 Shrubland carbon stocks in Brazil were estimated as the average of biomass values reported for cerrado aberto and cerrado denso. Savanna carbon stoc ks in Brazil were estimated as the average biomass value reported for campo sujo and gr assland carbon stocks in Brazil were estimated as the average value reported for campo limpo.

To maintain a consistent a pproach, for all countries except Brazil (explained in the paragraph above), carbon stocks in grasslands were estimated based on default biomass values given in Table 6.4 of the IPCC AFOLU Guidelin es. These default values are presented by ecological zone. Therefore, grassland C stocks within each country reflect the area-weighted value based on the proportions of each ecologi cal zone present within each country. Carbon stocks of savanna and shrubla nd land cover types in all countri es except Brazil were estimated using a proportional approach based on the Brazil da taset, which indicates an increasing trend in carbon stocks from grassland to savanna to shr ubland in a ratio of 1 to 1.8 to 3.4. These ratios were applied to other countries for estimating carbon stocks in savanna and shrubland based on the estimated carbon stocks of gr assland within each country.

Grassland carbon stock estimates for each country and administrative unit (for key countries) are shown in Figure 2.4-37.

Figure 2.4-37. Grassland carbon stock estimate s for each country and administrative unit (t CO2e/ha)

< 5
6 - 10
11 - 15
16 - 20
21 - 25

26 - 30
> 30100 2.4.4.2.6.1.4 Data Sources and Methods for Intern ational Wetland, Barre n and Mixed Carbon Stock Estimates

In line with recommendations for the expe rt peer reviewers, the updated analysis included land cover change to/from the wetland a nd barren land cover categories, and therefore emission factors were estimated for these c onversions. According to the IGBP land cover description, the permanent wetlands category can consist of herbaceous and/or woody vegetation. However, after confirming that Indones ian peat swamp forests (a type of permanent forested wetland) are classified as forest a nd not wetland in the MODIS land cover maps, the carbon stocks of permanent wetlands in a given count ry or administrative unit were calculated as the average of carbon stocks in shrubland and gr assland land cover categor ies. Carbon stocks on barren lands were assumed to be zero. In a ccordance with the IGBP land cover definitions, mixed carbon stocks were calculated as the averag e of forest, shrubland, grassland and cropland carbon stocks.

2.4.4.2.6.2 Evaluation of Changes in Biomass Ca rbon Stocks from International Land Conversions

Initial changes in biomass carbon stocks on la nd converted to another land category (e.g., from forest to cropland) were calculated the same way as in the proposed rule analysis, i.e. based on Equation 2.16 in the IPCC AFOLU: 

## ∑ •

− = Δ

i BEFORE AFTER CONVERSION CF B B C

i i ) (

where:

CONVERSION C Δ = initial change in biomass car bon stocks on land converted to another land category, tonnes C ha -1 yr -1

i AFTER B = biomass stocks on land type i immediately after the conversion, tonnes d.m. ha -1

i BEFORE B = biomass stocks on land type I before the conversion, tonnes d.m. ha -1
CF = carbon fraction of dry ma tter, tonne C (tonnes d.m.) -1
i = type of land use converted to another land-use category

2.4.4.2.6.3 Evaluation of GHG Emissions from International Land Clearing with Fire

In the proposed rule we used expert judgment to determine the regions where land is cleared with fire prior to conversion to crop produ ction. In general, it wa s assumed that clearing with fire takes place in tropical regions. Severa l of the expert peer reviewers suggested the use of fire products, such as those derived from MODIS and AVHRR sensors, to determine which regions typically use fire to clear land for another land use. However, the use of these products in101 isolation would not allow a distinction between fi re that occurs for land conversion versus fire that occurs due to wildfires, especially for temperate regions. Therefore, we considered an approach in which various fire maps could be overlain onto land cover change maps to determine fires that occurred on changed pixels (land conver sion) versus fires that occurred on pixels that remained in the same land cover category (e.g., forest fire, annual burning of cropland residues, etc.). However, the time needed to do this analys is exceeded the time available. Therefore, we maintained the approach used in the proposed ru le analysis, whereby expert judgment was used to determine the regions where fire is commonl y used when land is cleared for agricultural production. Figure 2.4-38 shows the countries where fire is assumed to occur as part of site preparation for crop production.

Figure 2.4-38. Countries that Clear with Fire in Preparation for Crop Production

As in the proposed rule analysis, in countri es where fire is used commonly as a land clearing practice for conversion to agriculture, non-CO 2 emissions were estimated using emission factors in Table 2.5 a nd Equation 2.27 of the IPCC AFOLU. Fire for land clearing was assumed to occur in all countries included in the analysis except China and Argentina.

Non-CO 2 emissions from land clearing with fire were estimated as:

3 10 − • • • • = ef f B fire G C M A L
Where:

fire L = amount of greenhouse gas emissions from fire, MT of each GHG (i.e., CH 4 , N 2 O) A = area burnt, ha

B M = mass of fuel available for combustion, MT ha -1

.

f C = combustion factor, dimensionless

ef G = emission factor, g kg -1 dry matter burnt102 The mass of fuel available for combustion was conservatively assumed to be equal to the above- and belowground biomass only; dead wood and litter pools were not included in the fuel load estimates. IPCC defaults were used for the forest combustion factor. Values from de Castro and Kaufmann (1998) were used for clearing other land cover types (grassland, savanna, shrubland).

2.4.4.2.6.4 Evaluation of Internati onal Soil Carbon Stocks

For the initial analysis, soil carbon stocks we re estimated using the FAO/UNESCO Soil Map of the World. In March 2009, a new soil da tabase was released (Harmonized Soil Map of the World v.1.1) with 1-km resolution grid cells and therefore this improve d dataset was used for the updated analysis. 97 Attribute values of the database did not include average soil carbon stocks, but values were include d instead for bulk density (g cm -3 ) and carbon content (%C) in both the top 30 cm and top meter of soil in each grid cell. Therefore, we calculated average soil carbon stocks in the top 30 cm of soil – assume d to be the depth to which soil carbon stocks would be affected when converted to agriculture – by multiplying the volume of soil in a given hectare (1 ha x 30 cm depth = 3,000 m 3 ) by the bulk density to calculate the mass of soil in a given hectare, then multiplied the soil mass by the carbon content to derive an average soil carbon stock value per hectare (t C ha -1 ). Soil carbon stocks estimated per country (and per administrative unit in key countries) are shown in Figure 2.4-39. The soil carbon stocks for annual cropland (i.e., afte r conversion) are based on long-t erm cultivated annual cropland with full tillage and medium inputs.

Figure 2.4-39. Soil carbon stocks in the top 30 cm of soil for each country and administrative unit (t CO 2 e/ha)

< 50
51 - 100
101 - 150
151 - 242

243 - 500
501 - 750

> 750 Note: estimates were derived from the Harmonized World Soil Database v. 1.1103 2.4.4.2.6.4.1 Evaluation of Changes in Soil Carbon Stoc ks from International Land Conversions

Identical to the approach used in the proposed rule analysis, changes in soil carbon stocks on land converted to cropland we re calculated based on Section 5.3.3.4 of the IPCC AFOLU. Soil carbon stocks after conversion to cropland were based on speci fic soil stock change factors for land use, management and inputs (F LU , F MG , F I , respectively) listed in Table 5.10 of the IPCC AFOLU. Stock change factors were selected for each land cover type (before and after conversion) and multiplied by reference so il carbon stocks. Following the IPCC AFOLU guidelines, the total difference in carbon stocks before and after conversion was averaged over 20 years. Thus the average annual change in soil carbon stocks due to land use conversion were calculated as:

20 ) ( ) (

, , , Re
, , , Re after I after MG after LU f before I before MG before LU f F F F SOC F F F SOC SOC • • •

− • • •

= Δ

where: SOC Δ = average annual change in carbon stocks in top 30 cm of soil; t C ha -1 yr -1

f SOC Re = reference carbon stocks in top 30 cm of soil; t C ha -1
F

LU = land use factor before or after conversion F

MG = management factor before or after conversion F

I = input factor before or after conversion

As default values for stock change factors ( F LU , F MG , F I ) are all one for forest soils and non-degraded grassland soils, soil carbon stocks were assumed to remain unchanged for all conversion types (conversion to sh rubland, savanna, perennial cropland) except conversion to cropland. Full tillage and medium inputs were assumed in all scen arios of cropland conversion. Consistent with IPPC default guidelines, soil carbon stock changes were spread equally over 20
years.

2.4.4.2.6.5 Accounting for Internationa l Harvested Wood Products

In the updated analysis, we addressed the potential significance of the harvested wood product pool and concluded that the amount of ca rbon stored in wood products long-term is immaterial for most regions of the world, especially when considering a timeframe of 30 years. Therefore, carbon storage in harvested wood prod ucts was not incorporated into our updated emission factors.

We reached this conclusion as follows: the pr oportion of extracted timber that ends up in long-lived (>5 yr) wood products was estimated us ing information presented in Winjum et al. (1998), who related harvesting and use of wood products to carbon impacts (Table 2). 98 The proportion of timber volume extracted ending up in long-lived wood products was calculated by dividing carbon in net production of industrial r oundwood by the total carbon in commodity uses >5 yr. We did this for the developing and de veloped world and calculated percentages of 53% and 60%, respectively. The country-level percenta ges were generally lower than the aggregated104 values. Winjum et al. (1998) also estimates inherited emissions fro m the retirement of past wood products, so we also estimated the proportion of roundwood production that is re-emitted into the atmosphere through the retirement of past w ood products. These values are reproduced in Table 2.4-42 below.

Table 2.4-42. Calculation of the proportion of extracted timber that goes to long-lived wood products and the proportion of extracted timb er as inherited emissions. (Tg C) All units are in Tg C. From Winjum et al. (1998). SWD=sawnwood, WBP=woodbase panels, OIR=other industrial roundwood and P&P=paper and paperboard.

SWD WBP OIR P&P Total Developing Brazil 23 4 1 1 1 7 4 30 17 India 9 4 0.1 1 1 6 3 67 33 Indonesia 12 2 0.1 1 0.42 3 1 25 8 Ivory Coast 1 0.05 0.03 0.2 0.01 0.3 0.2 30 20 Developed Canada 39 3 1 1 2 7 1 18 3 Finland 9 0.6 0.2 0.1 0.4 1.2 0.3 13 3 New Zealand 2.7 0.3 0.1 0.1 0.2 0.6 0.3 22 11 U.S.A. 102 23 8 3 23 57 17 56 17 Worldwide Developing 128 26 6 22 14 68 42 53 33 Developed 308 70 27 29 58 184 71 60 23 Total 436 96 33 51 72 252 113 58 26 Commodity use ≥ 5 yr # Industrial Roundwood Production* Category /

Country % HWP Inherited emissions % Inherited Emissions

* From Table 4 in Winjum et al. (1998).
# From Table 5 in Winjum et al. (1998).
Next, we analyzed per-hectare extraction volumes from 111 developing countries using data reported to FAO for the 2005 Forest Resour ces Assessment. Of the countries analyzed, the country with the highest reported extraction rate was Indonesia (50 m 3 /ha). This value was much higher compared to countries in Africa and Asia, which weren’t much higher than about 20

m 3 /ha and often less.

For the 50 m 3 /ha Indonesia case, we converted volume to biomass using an average conversion factor of 0.55 (Table 1 in Winjum et al. 1998, tropical aggregate), then converted biomass to carbon using a conversion factor of 0.5. Therefore, 50 m 3 /ha of extracted timber translates into 14 t C/ha. Assuming that 25% of this carbon ends up in long-lived (>5 yrs) wood products (i.e., the value calculated in Table 3 a bove for Indonesia), the em ission factor estimated for forest conversion after taking into account carbon storage in wood products would be reduced only by 3 t C ha -1 , or 11 t CO 2 ha -1 , or approximately 1-2%.

This result of 11 t CO 2 /ha stored in wood products from Indonesian harvests longer than five years is an overestimate. The calculation assumes that the carbon that ends up in these wood105 products is stored forever. Afte r taking into account the inher ited emissions that emanate from the oxidation (i.e., burning and decay) of w ood products that were produced from harvests during previous years (retirement rate, see Ta ble 3), the Indonesia value of carbon stored in wood products decreases even furthe r to 2 t C/ha, or 7 t CO2e/ha.

Finally, Winjum et al. (1998) states that fo r the oxidation fractions of 0.04, 0.08 and 0.10 (representing rates for woodbase panels, other industrial roundwood and paper/paperboard), the time period of oxidation would extend back 25, 12 and 10 yr from the base year, respectively, for tropical regions. Therefore, much of the timbe r harvested today and stored as wood products will be completely oxidized 25 years from now. Considering EPA is estimating 30-year emission factors, carbon storage in wood products is likely insignificant, even for temperate and boreal regions where oxidation rates are slower. The analysis outlined above is also representative of productive forestlands only, and it is unlikely that every hectare of forest that is cleared for another land use is stocked for timber production.

2.4.4.2.6.6 Evaluation of International Foregone Forest Sequestration

Forest sequestration rates were estimated in the proposed rule analysis using IPCC Tier 1 default values for native forests. These values are listed by ecological zone, so final rates in the initial analysis were calculated by weighting the ecological zone-based sequestration rates by the proportion of forest area in each ecological zone within a country or administrative unit (for key countries). The expert peer re viewers pointed out a number of recent papers that summarize long-term monitoring plots in old growth tropical forests across the tropics and suggested the use of these more recent datasets for estimating annual rates of carbon se questration in tropical forests.

Lewis et al. (2009) published long-term above ground carbon sequestra tion rates of 0.63 t C/ha/yr for African “closed canopy mature forests” (assumed moist or rain forest) based on longterm monitoring plots. 99 This is similar to the IPCC default rate for >20 yr old African tropical moist deciduous forests (0.65 t C/ha/yr) but lowe r than for >20 yr old African tropical rain forests (1.55 t C/ha/yr). Baker et al. (2004) also report an annual Amazonian C sequestration rate of 0.61 t C ha -1 yr -1 , which is lower than the IPCC default of 1.0 t C ha/yr for >20 yr old tropical moist deciduous forests and 1.55 t C/ha/yr for >20 yr old tropical rain forests of South America. After combining all standardized inventory data from Africa, tropical America and Asia together, Lewis et al. (2009) estimate car bon sequestration across all tropical intact old growth forests as 0.49 t C/ha/yr. We have used this estimate for foregone sequestration across the tropics in our updated analysis.

Myneni et al. (2001) and Nabuurs et al. also estimated the carbon sink of temperate and boreal forests in various countries, and these valu es were generally higher than sequestration in tropical forests, with rates of approximately 3- 4 t CO2e/ha/yr on average but extending up to 7-8 t CO2e/ha/yr in Norway and Switzerland. Thes e data reflect the long-term carbon sink capacity of forests, which have long been understood to be the case in temperate forests and have more recently been illustrated for old-gr owth tropical forests as well.106 2.4.4.2.6.7 Evaluation of International Land Re version Carbon Uptake Factors

In addition to estimating emission factors, reversion factors were developed to estimate the carbon accumulation in biomass and soils that occurs when managed cropland and pasture land is abandoned. All reversion factors (except reversion to forest) were estimated as the reverse of emission factors, whereby all increases in bi omass carbon stocks occur in Year 1 (analogous to the stock change approach used to estimat e emission factors) while changes in soil carbon stocks on abandoned cropland recovers to pre-land use change levels in 20 years (analogous to the soil emission factors, which were assumed to emit over 20 years). The only reversion factor to include soil carbon accumulation was reversion from abandoned cropland.

While most reversion factor s assumed that all biomass car bon stock changes occurred in Year 1 (i.e., IPCC stock change approach), fo rest reversion factors assumed that biomass accumulates every year over the entire 30-year time period. This was done to reflect forests’ slow but continual carbon sink capacity. Despite the fact that young (<20 year old) forests accumulate biomass more quickly than older (>20 year old) forests, the annual rate of carbon accumulation on abandoned croplands that revert b ack to forests was conservatively assumed to be equal to the foregone forest sequestration rate (estimated for the emission factor analysis). If the forest biomass carbon stock (estimated for the emission factor analysis, see Figure 4 above) was less than 20 times the assumed annual forego ne carbon sequestration value, then the annual carbon sequestration rate for reversi on factors was assumed to be 1/20 th of the initial forest carbon stock. Both of these assumptions provide a very conservative estimation of the carbon accumulation that occurs on abandoned land when it reverts to forest.

2.4.4.2.6.8 International Land Conversion GHG Emissions Factor Results

Our updated analysis includes land use change emissions factors for up to 42 different land conversions in over 750 regions across 160 countries, i.e. over 30,000 land conversion emissions factors. In this section we use the example of the Amazon region in Brazil to illustrate the emissions factors used in our analysis. The sample results shown belo w cover all of the types of land conversions considered, but they do not co ver all of the 750+ regions. For all of the emissions factors used in our analysis, including the data inputs, refer to the results spreadsheets available on the public docket.

The FAPRI model simulates agricultural produc tion in 6 regions in Brazil, including the Amazon Biome. The Amazon Biome region in FAPRI includes the following Administrative Units: Acre, Amapa, Amazonas, Para, Rondonia, Rora ima, and the northern part of Mato Grosso which is characterized by forest biome land cover.

Figure 2.4-40 illustrates the segment of Mato Grosso included in the Amazon region). The carbon stock data inputs for the Amazon region are shown in Table 2.4-43.107 Figure 2.4-40. Division of Mato Grosso into North and South regions

Table 2.4-43. Carbon stocks by land type and Administrative Unit in the Amazon Region (t CO2e/ha)
Administrative Units

Acre Amapa Amazonas North Mato Grosso Para Rondonia Roraima Forest, Above Ground 585 425 477 453 457 481 487 Forest, Below Ground 150 112 125 119 120 125 127 Grassland 40 40 40 40 40 40 40 Savanna 72 72 72 72 72 72 72 Shrubland 137 137 137 137 137 137 137 Mixed 232 183 199 192 193 200 202 Wetland 88 88 88 88 88 88 88 Annual 18 18 18 18 18 18 18 Perennial 44 44 44 44 44 44 44 Soil 131 154 231 213 174 115 145 Soil, Annual 63 74 111 102 84 55 70

We assume that land converted to cropland in the Amazon will be cleared with fire. Therefore, to determine non-CO2 emissions from fire in the Amazon we used the fire combustion data inputs in Table 2.4-44 where th e data inputs are the same for all of the Administrative Units.108 Table 2.4-44. Data inputs for non-CO2 em issions in the Amazon by land type

Land Cover Fire combustion factors (dimensionless) Fire CH4 emission factors (g/kg) Fire N2O emission factors (g/kg)

Forest 0.46 6.80 0.20 Grass 0.76 2.30 0.21 Shrub 0.72 2.30 0.21 Savanna 0.57 2.30 0.21 Wetland 0.70 2.30 0.21 Mixed 0.64 3.80 0.21

To show emissions estimates for all of the land conversions considered in our analysis, we will focus on the North Mato Grosso, i.e. the portion of Mato Grosso in the Amazon region. Table 2.4-45

. shows land use change GHG emissions, broken out by emissions category, for crop and pasture expansion in this region. We show emissions for conversion of land cover to annual cropland and perennial cropland. We also show emissions for conversion to pasture, which can be in the form of grassland or savanna. The values are presented as total GHG emissions, or, where specified, as annual emissi ons (yr-1). Note that negative values signify carbon uptake, whereas positive values denote GHG releases.

We also present the emissions results over time. For accounting purposes, emissions are allocated to either year zero (i.e., the year when land clearing takes place), years 1-19 or to years 20-80. This procedure is not intended to be a precise accounting of the timing of emissions releases, but it is sufficient to determine total emissions over the first 30 years following land conversion. Carbon emissions from the changes in biomass resulting from land conversion (i.e., biomass combustion or decay) are assigned to year zero. Non-CO2 emissions from fire combustion are also allocated to year zero. Lost forest sequestration con tinues indefinitely. The change in soil carbon is spread evenly over the first twenty years following conversion, i.e. ending in year 19. Thus, total emissions are presented for year zero, years 1 through 19, and years 20-80. We also present the total emissions over 30 years.

Table 2.4-45

. Land use change emissions factors for North Mato Grosso, Brazil (t CO2e/ha)

Start End Change in biomass Lost forest seques. yr-1 Change in soil yr-1 Total fire emis. Yr 0 emis. Yrs 1-19 emis. Yr-1 Yrs 20-80 emis. yr-1 30-yr emis.

Forest Annual 553.78 1.80 5.53 53.31 614.42 7.33 1.80 771.62
Shrub Annual 118.43 0.00 5.53 11.17 135.13 5.53 0.00 240.22

Savanna Annual 53.90 0.00 5.53 4.67 64.10 5.53 0.00 169.19
Grass Annual 21.63 0.00 5.53 3.42 30.59 5.53 0.00 135.68
Wetland Annual 70.03 0.00 5.53 7.01 82.58 5.53 0.00 187.67
Mixed Annual 173.46 0.00 5.53 17.88 196.87 5.53 0.00 301.96109 Start End Change in biomass Lost forest seques. yr-1 Change in soil yr-1 Total fire emis. Yr 0 emis. Yrs 1-19 emis. Yr-1 Yrs 20-80 emis. yr-1 30-yr emis.

Forest Perennial 528.12 1.80 0.00 0.00 529.91 1.80 1.80 582.02

Shrub Perennial 92.77 0.00 0.00 0.00 92.77 0.00 0.00 92.77
Savanna Perennial 28.23 0.00 0.00 0.00 28.23 0.00 0.00 28.23
Grass Perennial -4.03 0.00 0.00 0.00 -4.03 0.00 0.00 -4.03

Wetland Perennial 44.37 0.00 0.00 0.00 44.37 0.00 0.00 44.37 Mixed Perennial Crop 147.80 0.00 0.00 0.00 147.80 0.00 0.00 147.80

Forest Grass 532.15 1.80 0.00 0.00 533.95 1.80 1.80 586.05
Shrub Grass 96.80 0.00 0.00 0.00 96.80 0.00 0.00 96.80

Mixed Grass 151.83 0.00 0.00 0.00 151.83 0.00 0.00 151.83

Forest Savanna 499.88 1.80 0.00 0.00 501.68 1.80 1.80 553.78
Shrub Savanna 64.53 0.00 0.00 0.00 64.53 0.00 0.00 64.53
Mixed Savanna 119.56 0.00 0.00 0.00 119.56 0.00 0.00 119.56 Note: "Annual" refers to annual crops, and "Perennial" refers to perennial crops, i.e., sugarcane.

Table 2.4-46

. is similar to the preceding table, except that emissions factors are shown for crop and pasture abandonment, i.e. land reversion. For land reverting to forest, the change in biomass (i.e. plant growth) is an annual factor that continues fo r twenty years. After twenty years forests grow at the foregone sequestration rate, which is 1.80 tCO2e /ha/yr in the Amazon region. For land reverting to any other land type, the change in biomass is a total uptake that is allocated fully to year zero. That is why year zero uptake in Table 2.4-46

. is larger for reversion to shrubland than to forest, but the total forest uptake over thirty years is larger than reversion to shrubland. These time accounting procedures were designed to provide accurate estimates of emissions over 30 years. Soil carbon uptake is an annual factor that is constant for the first twenty years following conversion.

Table 2.4-46

. Land reversion factors for North Mato Grosso, Brazil (t CO2e/ha)

Start End Change in biomass Soil seques. yr-1 Yr 0 uptake Yr 1-19 uptake yr-1 Yr 20-80 uptake yr-1 30-yr uptake
Annual Forest -25.93 -5.53 -31.46 -31.46 -1.80 -647.18 Annual Shrub -118.43 -5.53 -123.96 -5.53 0.00 -229.05 Annual Savanna -53.90 -5.53 -59.43 -5.53 0.00 -164.52 Annual Grass -21.63 -5.53 -27.16 -5.53 0.00 -132.25 Annual Mixed -173.46 -5.53 -178.99 -5.53 0.00 -284.08 Perennial Forest -25.93 0. 00 -25.93 -25.93 -1.80 -536.56 Perennial Shrub -92.77 0.00 -92.77 0.00 0.00 -92.77 Perennial Savanna -28.23 0.00 -28.23 0.00 0.00 -28.23 Perennial Grass 4.03 0.00 4.03 0.00 0.00 4.03 Perennial Mixed -147.80 0.00 -147.80 0.00 0.00 -147.80 Grass Forest -25.93 0.00 -25.93 -25.93 -1.80 -536.56 Grass Shrub -96.80 0.00 -96.80 0.00 0.00 -96.80 Grass Mixed -151.83 0.00 -151.83 0.00 0.00 -151.83 Savanna Forest -25.93 0.00 -25.93 -25.93 -1.80 -536.56 Savanna Shrub -64.53 0.00 -64.53 0.00 0.00 -64.53 Savanna Mixed -119.56 0.00 -119.56 0.00 0.00 -119.56 Note: "Annual" refers to annual crops, and "Perennial" refers to perennial crops, i.e., sugarcane.110 All of the data and calculations for the re sults presented above for the Amazon region, and for all of the 750+ regions analyzed, are available in supporting material on the public docket for this rulemaking.

2.4.4.2.7 Aggregate International Land Conversion GHG Emissions Impact Results

Figure 2.4-41 presents the 2022 interna tional land use change GHG emissions by renewable fuel, with land use change emissions normalized by the increment of additional biofuel produced in each scenario and annualized over 30 years. The figure shows that, based on our modeling, soy-based biodiesel causes the larg est release of international land use change GHG emissions. The majority of international land use change emissions originate in Brazil in the corn ethanol and switchgrass ethanol scenarios. This is largely a consequence of projected pasture expansion in Brazil, and especially in the Amazon region where land clearing causes substantial GHG emissions. Of the renewable fu els analyzed, our modeling found that sugarcane ethanol causes the least amount of land use chan ge emissions. This was due largely to our projection that sugarcane crops would expand ont o grasslands in South and Southeast Brazil, which results in a net sequestr ation because sugarcane sequester s more biomass carbon than the grasslands it would replace.

Figure 2.4-41. International land use chan ge GHG emissions by renewable fuel, 2022 (kgCO2e/mmBTU)

5 37 43

22

10 32

-1 5 4 14

2 15

-5 0 5 10 15 20 25 30 35 40 45

Brazil Rest of World World Soy Biodiesel
Corn Ethanol Sugarcane Ethanol Switchgrass Ethanol

Brazil is a very prominent region in our pr ojections of where biofuel-induced land use changes would occur.111 Figure 2.4-42 shows the land use change emissions across the 6 regions of Brazil included in the FAPRI model. All of the renewable fuels analyzed, except for soy-based biodiesel, cause land use change emissions in the Amazon region. In the soy biodiesel scenario we project net sequestration in the Amazon as a result of reduced pasture area in that region. As discussed above, sugarcane ethanol results in net seque strations in South and Southeast Brazil.

Figure 2.4-42. Brazil land use change GHG emissions by renewable fuel, 2022 (kgCO2e/mmBTU)

-25 -20 -15 -10 -5 0 5 10 15 20 25

Amazon Biome Central-West Cerrados Northeast
Coast NorthNortheast Cerrados South Southeast Brazil Corn Ethanol Soy Biodiesel Sugarcane Ethanol Switchgrass Ethanol

Finally, for reference, Table 2.4-47. presents same results broken out by all 54 international regions in the FAPRI model.

Table 2.4-47. International land use change GHG emission s by renewable fuel and by region, 2022 (kgCO2e/mmBTU)
FAPRI Region Corn Ethanol Soy Biodiesel Sugarcane Ethanol Switchgrass Ethanol Algeria 0.02 0.02 0.01 0.01 Argentina -0.31 0.11 -0.52 0.15 Australia 0.52 0.00 0.00 0.18 Bangladesh -0.43 -0.56 -0.12 0.10 Brazil: Amazon Biome 12.83 -18.63 4.79 5.57 Brazil: Central-West Cerrados 4.09 8.52 0.76 3.47 Brazil: Northeast Coast 0.41 0.14 0.77 0.14 Brazil: North-Northeast Cerrados 0.86 5.33 0.52 1.47 Brazil: South 1.93 8.95 -4.20 2.08112 FAPRI Region Corn Ethanol Soy Biodiesel Sugarcane Ethanol Switchgrass Ethanol Brazil: Southeast 1.56 1.00 -3.81 0.80 Canada -0.04 0.73 -0.11 0.08 China 0.56 4.54 -0.03 0.46 New Zealand 0.05 0.60 0.01 0.03 Colombia 0.25 1.98 0.25 0.15 Cuba 0.05 0.10 0.14 0.01 Egypt -0.01 0.00 0.00 0.00 EU 0.47 1.68 0.30 0.29 Guatemala 0.22 0.17 0.11 0.06 India 0.84 2.30 0.47 -2.14 Indonesia 3.34 4.07 1.13 -0.13 Iran 0.09 0.22 0.05 0.06 Iraq 0.01 0.04 0.01 0.01 Ivory Coast 0.07 0.33 0.09 0.13 Japan 1.22 0.07 0.02 -0.74 Malaysia -0.11 2.98 0.03 0.04 Mexico 1.01 2.25 0.11 0.06 Morocco 0.04 0.05 0.02 0.03 Myanmar (Burma) -0.06 0.14 0.01 -0.01 Nigeria 0.76 0.58 0.19 0.32 Africa, Other 1.13 3.87 0.43 0.61 Asia, Other 0.12 0.34 0.00 -0.09 CIS, Other -1.50 -0.70 -0.13 -0.05 Eastern Europe, Other 0.02 0.14 0.02 0.03 Latin America, Other 0.49 2.27 0.26 0.21 Middle East, Other 0.00 0.05 0.01 0.02 Pakistan -0.07 0.39 0.06 0.14 Paraguay 0.03 -0.52 0.17 0.26 Peru -0.56 1.88 0.08 0.09 Philippines 1.25 1.26 0.51 0.34 Rest of World 1.04 2.73 0.32 0.29 Russia 0.01 0.31 0.09 0.12 South Africa 0.04 0.58 0.05 0.05 South Korea 0.00 0.02 0.00 0.01 Taiwan 0.00 0.04 0.00 0.00 Thailand 0.22 0.40 0.15 0.16 Tunisia 0.02 0.05 0.01 0.02 Turkey -0.10 0.11 0.03 0.02 Ukraine -0.13 0.18 0.01 0.02 Uruguay -0.03 0.37 0.03 0.05 United States* 1.05 Uzbekistan -0.47 -0.29 -0.06 -0.05 Venezuela -0.21 1.14 0.05 0.02 Vietnam 0.23 0.15 0.11 0.07 Western Africa 0.03 0.09 0.04 0.08 TOTAL 31.79 42.54 4.30 15.07 Note: land use change emissions in the United States were calcu lated by the FASOM model (see discussion above about domestic land use change), except for in the sugarcane ethanol scenario.

2.4.4.2.8 Uncertainty Assessment for Interna tional Land Conversion GHG Emissions Impacts

For the proposed RFS rule, EPA estimated uncertainty around its lifecycle GHG emission estimates by sensitivity analyses by which, for example, the upper bound of the emissions from international land use change was estimated by assuming that all crop expansion came from forest and the lower bound was estimated by assuming that all expansion came from idle grassland (also by assuming that no pasture replacement is necessary). For its updated analysis, we took a more rigorous approach towards estimating uncertainty.113 Uncertainty can be expressed as a percentage confidence interval relative to a mean value, with the confidence interval defined as a range that encloses the true value of an unknown parameter with a specifie d probability. For example, if the area of forest land converted to cropland (mean value) is 100 ha, with a 95% co nfidence interval ranging from 90 to 110 ha, we can say that the uncertainty around the estimate is ±10%.

The 95% confidence interval, which is the value typically used in the context of estimating GHG emissions and removals under the United Nations Framework Convention on Climate Change (UNFCCC), has a 95 percent probability of enclosing the true but unknown value of a given parameter.

The first step in our uncertainty analysis was to identify the potential sources of uncertainty. We focused on two key sources of uncertainty in international land use change GHG emissions: (1) Classification errors that arise from interpretation of satellite imagery to derive land cover maps (i.e., the types of land affected by land use change); (2) Errors in parameters used in emission factor estimates (i.e., the magnitude of GHG emissions per unit of land area converted).

When estimating the total uncertainty in land use change GHG emissions, the two sources of uncertainty listed above need to be considered together, which was done with a Monte Carlo simulation model that combined the total uncertainty in the satellite imagery and the emissions factor estimates. Each step of our uncertainty analysis is explained in this section.

2.4.4.2.8.1 Satellite Data Uncertainty Assessment

As discussed above, MODIS valida tion data was used to adjus t/correct systematic errors in the MODIS land cover classifications from 200 1 to 2007. These adjustments were based on the producer's accuracies for each land cover cla ss derived from the aggregated confusion matrix (see Table 2.4-37 above). To estimate the uncertainty in this procedure, we calculated the producer’s accuracy standard errors for each land category based on the number of training sites that were used to validate the satellite classifications. For example, based on the number of training sites that validated forest land, we ca lculated a standard error of +/- 1.2% around the 92.6% producer’s accuracy for forest. The most accurate approach to estimate the standard errors from the aggregated confusion matrix would be to reassign the detailed site and pixel data and then recalculate the standard error following Stehman (1997). 100 However, detailed data about the training site clusters were not available, so a simplifi cation of this procedure was used where we assumed that the number of pixels per site (or cluster) was constant for each producer land use, essentially representing the random vari able for the producer’s accuracy estimate as a binomial distribution: V

• We assumed that the number of pixels per site for the reported land use is constant.

• The number of reported sites in each land category was then estimated by using the producer’s accuracy estimate, sometimes referr ed to as the producer’s estimate, and the

V Our simplified procedures very closely reproduced the pro ducer accuracy standard errors reported in Friedl et al. (2010).114 producer’s standard error using the following equation derived from the equation for the standard deviation for a binomial distribution:

o N = e * (1-e)/(s+a) 2

o where,

 N is the number of sites

 e is the producer’s estimate

 s is the producer’s standard error reported as a percentage of the number of sites

 a is an adjustment factor (-0.0007397) used to account for round off and so the total number of sites estimated approximates the total number of sites report by the input source W

o This equation is derived by assuming that the assignment of sites to land categories is a binomial process (either assigned to the actual land category or not) which has a standard devia tion of (s+a)*N = (N *e*(1-e)) 0.5

• The number of sites for the 17 land categories were then aggregated to the 10 EPA land

uses

• The standard error as a percentage of the total number of reported sites for the 10 EPA land uses was then calculated using the equations = (N*e*(1-e))^0.5/N

The producers accuracy and standard errors were reduced significantly by aggregating the MODIS data from 17 land cover categories to 10 categories. As an example, the standard error for forest estimated with 17 land categories land was high because there were less training sites for each type of forest, and the producer estimate for each forest type was low because different types of forest were confused with each other. Table 2.4-48 compares the producers accuracy and standard errors for the 17 IGBP land categories to the producers accuracies and standard errors for EPA’s aggregated land cover classes.

Table 2.4-48. Producer accuracies and standa rd errors for MODIS Collection 5 classes and EPA classes base d on cross-validation
Producer's accuracy (%) Producer's accuracy (%) IGBP Land Cover Class PA Std. err. EPA Land Cover Class PA Std. err. Evergreen Needleleaf 89.8 2.3 Evergreen Broadleaf 92.6 2.4 Deciduous Needleleaf 67.3 10.9 Deciduous Broadleaf 68.9 6.2 Mixed Forest 76.2 5.7 Forest 92.6 1.2

Closed Shrubland 63.4 5.9 Open Shrubland 48.3 6.2 Shrubland 69.5 4.0
Woody Savanna 45.2 4.1 Savanna 22.6 4.4 Savanna 60.9 3.1

Grasslands 73.6 4.1 Grasslands 73.7 4.0 Permanent Wetlands 70.6 4.2 Wetlands 70.6 4.1 Annual Crops 83.2 2.7
Cropland 83.3 2.0 Perennial 83.2 8.6

W This equation is derived from the equation from the standard deviation for a binomial distribution.115 Producer's accuracy (%) Producer's accuracy (%) Crops Cropland/Nat Veg Mosaic 60.5 5.7 Mixed 60.4 5.6
Barren/Sparse 95.8 1.4 Barren 95.8 1.3 Snow and Ice 75.6 10.9
Water 96.6 1.9 Excluded 90.5 2.7

The MODIS data was adjusted/corrected based on the producer accuracies reported above, i.e., with the producer standard errors set to zero. To assess the uncertainty in our correction process, and thus the MODIS data, we simulated the uncertainty in the producer’s accuracy by generating 10 pseudo random values each for 2001 and for 2007 (RV py ), for each of the land uses. The pseudo random values are di stributed normally with a mean of zero and a standard deviation equal to the calculated producer standard error. The stochastic model uses these to adjust the reported land use in the conf usion matrix and create an adjusted confusion matrix, BCM, with coefficients BCM cyup as follows:

o BCM cypp = ACM cpp * (1+RV py )

o Scale the remaining coefficients in the column so that the total of the column does not change

 BCM cyup = ACM cup * β

 Where β = (1-BCM cypp )/(1-ACM cpp ) when ACM cpp < 1 and 0 otherwise

o For every iteration, we repeated the step s listed above in section 2.4.4.2.5.2 to calculate the land conversion shares in each region.

We repeated this stochastic procedure 300 tim es to generate the 95% confidence intervals for the share of land conversion types in each of the 54 FAPRI regions.

Table 2.4-49 30-Year Emissions Factor Satellite Data Uncertainty for Select Land Conversions (+/- 95% confidence inter vals as percent of mean)
FAPRI Region Natural to Annual Annual to Natural Natural to Pasture Pasture to Natural Algeria 12% 1% 16% 7% Argentina 1% 1% 5% 4% Australia 3% 1% 6% 3% Bangladesh 2% 1% 9% 5% Brazil: Amazon Biome 6% 1% 8% 1% Brazil: Central-West Cerrados 2% 1% 5% 3% Brazil: Northeast Coast 1% 1% 4% 5% Brazil: North-Northeast Cerrados 2% 1% 5% 4% Brazil: South 1% 1% 6% 3% Brazil: Southeast 1% 1% 5% 3% Canada 5% 1% 11% 2% China 2% 1% 5% 3% New Zealand 3% 1% 5% 1% Colombia 7% 1% 17% 1% Cuba 3% 1% 8% 2% Egypt 25% 6% 19% 14%
EU 4% 1% 11% 2% Guatemala 3% 1% 6% 2%116 FAPRI Region Natural to Annual Annual to Natural Natural to Pasture Pasture to Natural
India 2% 2% 4% 4% Indonesia 2% 1% 4% 2%
Iran 3% 1% 5% 2% Iraq 2% 3% 3% 4% Ivory Coast 3% 1% 5% 2% Japan 10% 1% 29% 1% Malaysia 2% 2% 3% 2% Mexico 1% 1% 4% 2%
Morocco 3% 1% 8% 4% Myanmar 3% 1% 6% 2% Nigeria 2% 1% 5% 3% Other Africa 2% 2% 7% 4% Other Asia 3% 1% 6% 2% Other CIS 6% 1% 10% 2% Other Eastern Europe 3% 1% 9% 2% Other Latin America 5% 1% 11% 2% Other Middle East 12% 3% 13% 5% Pakistan 3% 1% 5% 3% Paraguay 2% 1% 6% 2% Peru 9% 1% 13% 1% Philippines 2% 1% 4% 2% Rest of World 5% 3% 8% 4% Russia 3% 1% 9% 2% South Africa 2% 1% 6% 3% South Korea 2% 1% 7% 2% Taiwan 3% 1% 6% 2% Thailand 2% 1% 4% 2% Tunisia 3% 1% 8% 4% Turkey 5% 1% 15% 4% Ukraine 10% 2% 31% 4% Uruguay 0% 1% 5% 6% US 4% 1% 7% 2% Uzbekistan 11% 2% 15% 4% Venezuela 5% 1% 15% 2% Vietnam 2% 1% 3% 2% Western Africa 1% 1% 5% 5%

Table 2.4-49 reports relatively modest levels of uncertainty from the MODIS data. There are several potential explanations for this. First, by correcting for systematic errors in the MODIS data, based on NASA's extensive validation efforts, the uncertainty in the satellite data set is reduced substantially. Second, the aggreg ated land classes in our analysis reduced the uncertainty compared to the 17 MODIS land cover classes. Third, the greatest uncertainty in the satellite data is between land cover classes that seque ster similar amounts of carbon (i.e., savanna, shrubland and mi xed land). Finally, we assume th at recent land use change patterns accurately predict future patterns, and our uncertainty assessment does not quantify the potential uncertainty from this assumption.

The overall uncertainty in our land use change GHG emissions estimates also includes the uncertainty in the emissions factors per unit area of land use change. Before the final uncertainty estimates are presented, our evaluation of emissions factor uncer tainty is discussed in the next section.117 2.4.4.2.8.2 International Land Conversions GHG Emissions Factor Uncertainty Assessment

We assessed the uncertainties in our estimates of carbon st ocks, and consequently of carbon stock changes (i.e., the emission factors), for every land conversion included in our land use change modelling. The final emissions factors for each land conversion were derived from a combination of several different input parameters, each with its ow n uncertainty. In this section we describe the uncertainty estimates for each input parameter and the Monte Carlo analysis used to combine all of the indivi dual input parameter uncer tainties. At the end of this section we present the total emissions factor uncertainty, which considers spatial correlation across emissions factor errors. All of the uncertainty estimates, for each data input and region, are available on the public docket.

2.4.4.2.8.2.1 Evaluation of Input Parameter Uncertainty

The foundation of our emissions factor uncerta inty analysis was a rigorous assessment, following IPCC guidelines, of the uncertainty in a ll of the input parameters used to calculate the land use change emissions factors. Winrock gene rated 95% confidence intervals for every data input based on the quality, quantity, resolution an d variability in the underlying data sources. The estimation of uncertainty was difficult for some parameters due to the absence of quantitative error analyses in the source data. Therefore, where no uncertainty information was available for a given parameter, expert judgement was used to identify an uncertainty range, and the upper bound was assumed as the uncertainty va lue. This produced final emission factor uncertainty values that are likely overestimated and thus conservative.

2.4.4.2.8.2.1.1 Evaluation of Forest Carbon Stocks Input Parameter Uncertainty

Forest carbon stocks for countries in Latin America and Africa were estimated using the new pantropical carbon stock map of Saatchi et al. (in prep.). However, an accuracy assessment for this new map was not completed in time for our updated analysis. Therefore, although we used the new map to derive mean values, uncertainty around these mean values was estimated using accuracy information reported for a prior forest biomass product for Latin America derived using a similar methodological approach (Saatchi et al. 2007). The accuracy of the Saatchi et al. (2007) product was 81%. Therefore, we estimated the uncertainty in our forest carbon stock estimates for Latin America and Africa to be 19%. The new pantropical carbon stock map has now been assessed for accuracy using cross va lidation with approximately 50% of the ground and lidar biomass data and has an overall accuracy of 76% across the three regions (Latin America: 81%, Africa: 86%, Sout heast Asia: 69%). Because we used the new pantropical carbon stock map to derive es timates only for Latin America and Africa, the 19% uncertainty is likely an overestimate for Africa.

Forest biomass carbon stocks for countries in South Asia were estimated using a map developed by Brown et al. (2001), but no formal accuracy assessment was performed as part of their analysis. Therefore, we consulted Table 4.7 in the IPCC 2006 Guidelines for AFOLU to see what the reported uncertainty was around Ti er 1 aboveground biomass estimates. Biomass ranges are reported by continent and ecological zone, with extremely wide ranges in many cases118 (e.g., mean biomass of subtropical humid forests in Asia is 180 t ha -1 , with a range of 10 to 560 t ha -1 ). Because the Brown et al. (2001) map wa s derived using some Tier 2 (country-level) information, we assumed the un certainty of the Brown et al. (2001) product to be lower than uncertainty for Tier 1 IPCC values and theref ore assigned Asia biomass carbon stocks an uncertainty value of 50%. This was done to refl ect the somewhat better prediction of biomass over IPCC Tier 1 default values.

Some data sources provided accuracy inform ation in the original documentation, and therefore this information was used to assign uncertainty values to the country-level estimates derived for our analysis. Uncertainty was estim ated for biomass carbon estimates in Eastern Europe and Canada (33%; Myne ni et al. 2001), China (36%, Pi ao et al. 2005), Russia (40%, Houghton et al. 2007), various EU countries (11% , Nabuurs et al. 2003), and the United States (7-31% depending on state, Blackard et al. 2007) . For other EU countries in which biomass carbon stocks were estimated using FAO data, an uncertainty value of 50% was assumed. We assigned an uncertainty value of 80% to the global carbon stock map deve loped by Reusch and Gibbs (2008), as this was devel oped primarily from IPCC Tier 1 information. A summary of uncertainty values used for forest biomass carbon stocks is presented in Table 2.4-50.

In cases where data sources reported only aboveground biomass (or aboveground carbon) stocks only, we used regression equation informati on presented in Cairns et al. (1997) to add in the uncertainty related to estim ating belowground biomass. The relationship in Cairns et al. (1997) relates belowground bioma ss to aboveground biomass, and the equation has an adjusted R 2 value of 0.83. Therefore, we assumed th e uncertainty (the percent of variation in belowground biomass not explained by aboveground biomass to be 7%.

Table 2.4-50. Uncertainty values used for forest carbon stock estimates.
Data Source Uncertainty Value Justification Saatchi et al. (2007) 19% (aboveground), 17% (belowground) Accuracy assessment based on past product (Saatchi et al. 2007) Brown et al. (2001) 50% (above- and belowground combined) No formal accuracy assessment given; therefore expert opinion as 50% (some country-level data used therefore better than IPCC Tier 1) Blackard et al. (2007) 7 – 31% (aboveground), 17% (belowground) Pixel-level uncertainty values averaged per state Houghton et al. (2007) 40% (above- and belowground combined) Reported in original source Myneni et al. (2001) 27% (aboveground), 33% (belowground) Reported in original source Nabuurs et al. (2003) 11% (above- and belowground combined) Reported in original source Piao et al. (2005) 36% (above- and belowground combined) Reported in original source Reusch and Gibbs (2008) 80% (above- and belowground combined) IPCC Tier 1 FAO (2006) 50% (above- and belowground combined) No formal accuracy assessment given; therefore expert opinion as 50% (some country-level data used therefore better than IPCC Tier 1)

2.4.4.2.8.2.1.2 Evaluation of Cropland, Grassland, Savanna, Shrubland and Wetland Carbon Stocks Input Parameter Uncertainty119 Uncertainty around carbon stocks in annual cr oplands and grasslands was assumed to be 75%, based on default error margins reported in IP CC Table 5.9 and 6.4, respectively. In the absence of any uncertainty information for sava nna, shrubland, perenni al cropland and wetlands, uncertainty in carbon stock estimates for these ot her land cover categories was also estimated as 75% in keeping with the default uncertainty values presented in the IPCC Guidelines. The one exception to the 75% uncertainty assumption was Brazil, for which more precise information was available on the carbon stocks along a contin uum of grasslands, sava nna and shrublands. The uncertainty for these land cover types was es timated as 0.6%, 0.9% and 16%, respectively, derived from the standard errors reported in the original data source (de Castro and Kaufmann 1998). The higher uncertainty in shrubland carbon stocks is likely related to the comparatively large variation in tree cover (and therefore carbon stocks) in shrublands compared to grasslands and savannas. Uncertainty in the mixed land cover category was estimated as the average of the uncertainty in forest, crop, shrub and grass categories in keeping with the IGBP description of this land cover class.

2.4.4.2.8.2.1.3 Evaluation of Soil Carbon Input Parameter Uncertainty

The data source used to estimate initial (i.e ., reference) soil carbon stocks was changed from FAO/UNESCO’s Soil Map of the World to the newly released World Harmonized Soil Database for the updated analysis. This was done because the spatial reso lution of the new data product is much improved compared to the FAO map. However, neither data source reports information on accuracy or uncertainty. In the absence of any reliable information about the uncertainty of the estimates, we assumed an uncertainty value of 90%, which is the default error estimate for Tier 1 default soil organic carbon stoc ks in all soil-climate types (derived from Table 2.3 of the IPCC 2006 Guidelines).

Carbon stocks after land use conversion to cropland were calculated using IPCC Equation 2.25 as the initial soil carbon stock modified by land use, management, an d input factors that relate to how the soil is manage d. Default soil factors presented in Table 5.5 of the 2006 IPCC Guidelines were used, assuming conversion to long-term cultivated annual cropland under full tillage and medium inputs. The error margin for the land use factor was estimated using default values presented in Table 5.5 of the 2006 IPCC Gu idelines and ranged from 9 to 61% depending on temperature and moisture regime. After carbon stocks after land use conversion were estimated, changes in carbon stocks due to land conversion were calculated as the difference between initial and final carbon st ocks divided by an assumed transition period of 20 years during which soil emissions take place.

2.4.4.2.8.2.1.4 Evaluation of Foregone Fo rest Sequestration Input Parameter Uncertainty

Uncertainty values for foregone forest car bon sequestration were de rived from standard errors reported in the original data sources. Un certainty in carbon sequestration rates ranged from 20% to 50%, with higher uncertainty in tropical regions.

2.4.4.2.8.2.1.5 Evaluation of Clearing with Fire Input Parameter Uncertainty120 Fire emissions were calculated in the update d analysis when land is cleared for cropland in a region where fire is assumed to be used as a means of site preparation for the new land use. Fire emissions were ca lculated following IPCC Guidelines as the product of initial carbon stocks, a combustion factor (define) and a GHG emission factor (define). Combustion factors were estimated per land cover type using informati on on standard errors reported on combustion factors in de Castro and Kauf mann (1998). Uncertainties in co mbustion factors ranged from 42% to 69%. IPCC defaults were used to estimate unc ertainty in the CH4 and N2O emission factors and ranged from 59% to 78% depe nding on land cover type burned.

2.4.4.2.8.2.2 Monte Carlo Analysis of Combined Emissions Factor Uncertainty

The uncertainties in individual parameters of an emission factor can be combined using either (1) simple error propagati on (IPCC Tier 1) or (2) Monte Ca rlo simulation (IPCC Tier 2). We followed the Tier 2 approach.

One of the inputs required for the Monte Carlo uncertainty m odel is an estimate of the degree of correlation among different variable s – both the correlation of one variable across space as well as the correlation of one variable to any others used in the analysis. This is in contrast to the IPCC error propagation approach , which assumes no correlation. The assumed correlations among different data inputs used to calculate uncertainty in emission factors using the Monte Carlo approach are summarized below.

Forest carbon stocks were derived from va rious sources, and therefore estimates for certain countries are correlated, i.e. errors in estimates that came from the same data source are assumed to be correlated. The correlation gr oups for forest carbon stocks are shown in

Figure 2.4-34. above. All countries use the same equation that relates belowground biomass to aboveground biomass (M okany et al. 2006), therefore we assumed that belowground biomass is perfectly correlat ed to aboveground biomass.121 Although the World Harmonized Soil Database wa s used to estimate soil carbon stocks in the updated analysis, the dataset was developed by compiling differe nt data sources together, and therefore soil carbon stocks are not correlated acro ss all regions. The regional distribution of data sources is shown in Figure 2.4-43 below.

Figure 2.4-43. Correlation groups for soil carbon stocks

Note: different colors represent different correlation groups

Grassland carbon stocks were estimated using IPCC default carbon stock values and weighting the carbon stock based on the propor tion of each country falling within each ecological zone. Correlation groups for these land c over categories were determined by assuming that countries with the same dominant ecological zone were correlated (i .e., all use the same carbon stock value). In addition, carbon stocks of grassland, shrubland and savanna were all assumed to be correlated to each other becaus e we used a simple proportional approach to estimate the carbon stocks of savanna and shrubland (based on the grassland value).

All annual croplands have an assumed carbon stock of 5 t C ha -1 and therefore all regions are correlated for this input parameter. Pere nnial croplands in Indonesia and Malaysia are assumed to be oil palm, and are therefore these regions are correlated, while perennial croplands in the rest of the world are assumed to be in a different correlation group than Indonesia and Malaysia (and have carbon stocks e quivalent to that of sugarcane).

Correlation groups for lost fore st sequestration are delineated by data sources, described above, such that lost forest sequestra tion across the tropics is correlated.

To estimate the uncertainty in each land conversion emissions factor, a Monte Carlo analysis was completed using the uncertainty estimates and correlation groups specified for each data input. For the Monte Carlo analysis we treat ed the data inputs as va riables and assumed that each variable's uncertainty distribution is normal with a standard deviation equal to the122 uncertainty value times the mean divided by 100, converting the uncertainty range to a fraction then dividing by 2. X

To calculate emission factors, Monte Carl o model first generates pseudo random values for the variables above using the mean, calcu lated standard devia tions, and pseudo random values for a Normal (0,1) distribution. Note that the resulting values after applying the mean and standard deviations are all cons trained to be greater than 0.

The Monte Carlo model then used thes e pseudo random variables to calculate intermediate emissions variables for each count ry and administrative unit in each Monte Carlo iteration. 15,000 iterations were generated to calculate the mean and 95% confidence intervals for the land use change emissions factors. The uncertainty ranges from the emissions factor estimates, excluding the uncertainty from the MODIS data, are presented below for select land conversions and for all of the FAPRI regions.

Table 2.4-51 shows the contribution of uncertai nty in our emissions factor estimates to total uncertainty by

land conversion category and region. The uncertainty from emission s factors is generally larger than the uncertainty from our satellite data analysis (see Table 2.4-49 for uncertainty from the satellite data). The uncertainty ranges, as a percent of the mean, are very large in certain regions, such as Egypt, where the mean emissions factor estimates are very small.

Table 2.4-51

. Contribution of Emissions Factor Estimates to Uncertainty in the 30-yr Weighted Land Conversion Emissions Factors (+/- 95% confidence inter vals as percent of mean)
FAPRI Region Natural to Annual Annual to Natural Natural to Pasture Pasture to Natural Algeria 142% 143% 69% 69% Argentina 74% 80% 18% 27% Australia 59% 57% 43% 35% Bangladesh 65% 64% 39% 49% Brazil: Amazon Biome 23% 37% 19% 41% Brazil: Central-West Cerrados 45% 41% 28% 34% Brazil: Northeast Coast 48% 50% 28% 22% Brazil: North-Northeast Cerrados 39% 38% 25% 22% Brazil: South 56% 52% 33% 22% Brazil: Southeast 47% 40% 38% 24% Canada 58% 52% 32% 47% China 57% 43% 33% 29% New Zealand 60% 54% 75% 52% Colombia 55% 54% 20% 43% Cuba 53% 43% 34% 36% Egypt 595% 401% 42% 50% EU 56% 61% 38% 48% Guatemala 53% 47% 24% 36% India 57% 63% 38% 39% Indonesia 42% 46% 47% 49% Iran 81% 76% 70% 67%

X The uncertainty range is defined as the absolute value of the 97.5 th percentile of the distribution minus the 2.5 th percentile of the distribution, divided by the mean value of the distribution which is 2 standard deviations divided by the mean for a random variable with a normal distribution.123 FAPRI Region Natural to Annual Annual to Natural Natural to Pasture Pasture to Natural Iraq 57% 57% 74% 72% Ivory Coast 42% 53% 16% 49% Japan 54% 34% 43% 32% Malaysia 42% 44% 47% 50% Mexico 61% 56% 29% 38% Morocco 95% 92% 71% 74% Myanmar 54% 47% 54% 50% Nigeria 63% 68% 16% 37% Other Africa 52% 57% 19% 40% Other Asia 53% 48% 45% 38% Other CIS 53% 49% 56% 55% Other Eastern Europe 62% 48% 34% 39% Other Latin America 47% 40% 21% 35% Other Middle East 88% 87% 61% 63% Pakistan 81% 90% 76% 70% Paraguay 52% 50% 15% 28% Peru 34% 45% 15% 43% Philippines 54% 47% 53% 51% Rest of World 37% 37% 34% 32% Russia 60% 56% 40% 39% South Africa 46% 43% 32% 39% South Korea 56% 36% 23% 24% Taiwan 59% 65% 73% 77% Thailand 49% 48% 38% 37% Tunisia 119% 116% 58% 66% Turkey 72% 73% 51% 57% Ukraine 62% 48% 51% 52% Uruguay 83% 86% 47% 47% US 45% 43% 28% 41% Uzbekistan 50% 58% 59% 69% Venezuela 57% 47% 34% 48% Vietnam 44% 43% 42% 41% Western Africa 62% 64% 7% 23%

2.4.4.2.8.3 Evaluation of Total Uncertainty in In ternational Land Conversion GHG Emissions Impacts

Total uncertainty in land use change GHG emissions was estimated for every renewable fuel scenario by combining the satellite data and emissions factor uncertainty estimates. The Monte Carlo model generated 15,000 iterations by generating 300 cases where it varied the pseudo-random values for historic land used chan ges and, for each of these 300 cases, generated 50 iterations where it varied the pseudo-random valu es for the variables used to calculated the emissions factors.

Within each of the 300 land use cases, the model first took the reported land use change from MODIS and remapped it to the simulated land use change using the MODIS version 5 confusion matrix and the uncerta inties derived from the confusi on matrix. This provided land use in 2007 and land use change from 2001 to 2007 for up to 10x10=100 combinations of land use in 2001 and land use in 2007.

Next, the Monte Carlo model calculates emission factors for each of the 50 iterations within the land use case for each of the 42 land us e change possibilities (e.g., forest to cropland) for each country and administrative unit. The model calculated annual land use change emissions for up to 80 years, and also 30-year a ggregated emissions factors. The model then124 used the land use change to calculate weighted average emission factors for each of the 54 FAPRI regions and each of the 12 land conversi on options (e.g., annual crops to natural ecosystems). Finally, the model reports mean emissions as welll as 95% confidence ranges. It also produces the mean and uncertainty ranges for each of the FAPRI scenario results, i.e., for each renewable fuel type.

Figure 2.4-44 provides a graphical illustration of the total uncertainty ranges for international land use change emi ssions. The error bars in the figure present the low and high ends of the 95% confidence range for international land use change GHG emissions. Table 2.4-52 and Table 2.4-53 include the low and high ends of the 95% confidence range for land conversion GHG emissions for each region and rene wable fuel scenario. Note that given the nature of stochastic modeling, the total low and high ends of the range are not the sum of the regions.

Figure 2.4-44. International la nd use change GHG emissions by renewable fuel with 95% confident intervals, 2022 (kgCO2e/mmBTU)

-10

0 10 20 30 40 50 60 70 80

Brazil Rest of World World Soy Biodiesel
Corn Ethanol Sugarcane Ethanol Switchgrass Ethanol125 Table 2.4-52. Low end of the 95% confidence range for international land use change GHG emissions by renewable fuel, 2022 (kgCO2e/mmBTU)
FAPRI Region Corn Ethanol Soy Biodiesel Sugarcane Ethanol Switchgrass Ethanol Algeria 0.00 0.00 0.00 0.00 Argentina -0.46 -0.35 -0.94 0.05 Australia 0.28 -0.13 -0.02 0.10 Bangladesh -0.72 -0.95 -0.21 0.04 Brazil: Amazon Biome 10.48 -27.56 3.94 4.45 Brazil: Central-West Cerrados 2.67 0.62 0.26 1.76 Brazil: Northeast Coast 0.18 0.07 0.27 0.07 Brazil: North-Northeast Cerrados 0.49 2.19 0.37 0.87 Brazil: South 0.73 3.26 -7.83 0.79 Brazil: Southeast 0.69 -0.02 -9.71 0.41 Canada -0.11 0.50 -0.16 0.04 China 0.26 3.04 -0.17 0.11 New Zealand 0.01 0.16 0.00 0.01 Colombia 0.01 1.49 0.17 0.09 Cuba 0.02 0.06 0.09 0.00 Egypt -0.02 -0.01 -0.01 -0.01 EU 0.19 0.99 0.14 0.13 Guatemala 0.11 0.12 0.08 0.03 India 0.42 1.16 0.27 -3.52 Indonesia 1.97 2.42 0.67 -0.19 Iran 0.02 0.10 0.01 0.02 Iraq 0.00 0.02 0.00 0.01 Ivory Coast 0.04 0.22 0.06 0.08 Japan 0.55 0.03 0.01 -1.00 Malaysia -0.18 1.52 0.02 0.02 Mexico 0.36 1.55 0.01 -0.02 Morocco 0.01 0.01 0.00 0.01 Myanmar (Burma) -0.09 0.07 0.00 -0.02 Nigeria 0.34 0.34 0.09 0.15 Africa, Other 0.52 3.05 0.24 0.36 Asia, Other 0.06 0.20 0.00 -0.15 CIS, Other -2.43 -1.26 -0.24 -0.16 Eastern Europe, Other 0.00 0.09 0.01 0.02 Latin America, Other 0.23 1.71 0.18 0.13 Middle East, Other 0.00 0.02 0.00 0.01 Pakistan -0.15 0.13 0.02 0.04 Paraguay -0.03 -0.88 0.13 0.20 Peru -0.84 1.55 0.05 0.05 Philippines 0.62 0.63 0.26 0.17 Rest of World 0.66 1.81 0.21 0.19 Russia -0.01 0.13 0.04 0.05 South Africa -0.04 0.39 0.03 0.03 South Korea 0.00 0.01 0.00 0.00 Taiwan -0.01 0.01 0.00 0.00 Thailand 0.12 0.23 0.09 0.09 Tunisia 0.00 0.02 0.00 0.00 Turkey -0.18 0.05 0.01 0.01 Ukraine -0.21 0.04 0.01 0.01 Uruguay -0.05 0.20 0.01 0.02 United States 0.62 Uzbekistan -0.80 -0.51 -0.10 -0.08 Venezuela -0.38 0.73 0.02 0.01 Vietnam 0.13 0.09 0.07 0.04 Western Africa 0.01 0.04 0.02 0.04 TOTAL 23.45 21.37 -3.66 9.58 Note: given the nature of stochastic modeling, the total low and high 95% confidence ranges do not equal the sum of the regions

.126 Table 2.4-53. High end of the 95% confidence range for international land use change GHG emissions by renewable fuel, 2022 (kgCO2e/mmBTU)
FAPRI Region Corn Ethanol Soy Biodiesel Sugarcane Ethanol Switchgrass Ethanol Algeria 0.06 0.05 0.02 0.04 Argentina -0.18 0.64 -0.17 0.27 Australia 0.77 0.17 0.02 0.28 Bangladesh -0.18 -0.23 -0.05 0.17 Brazil: Amazon Biome 15.27 -6.87 5.68 6.80 Brazil: Central-West Cerrados 5.82 18.14 1.20 5.54 Brazil: Northeast Coast 0.69 0.23 1.27 0.22 Brazil: North-Northeast Cerrados 1.31 9.18 0.71 2.19 Brazil: South 3.40 15.85 -1.24 3.65 Brazil: Southeast 2.61 2.25 1.20 1.27 Canada 0.02 0.97 -0.06 0.12 China 0.91 6.05 0.14 0.89 New Zealand 0.08 1.05 0.02 0.05 Colombia 0.53 2.46 0.33 0.21 Cuba 0.08 0.13 0.19 0.01 Egypt 0.01 0.01 0.00 0.01 EU 0.78 2.43 0.48 0.47 Guatemala 0.35 0.22 0.14 0.10 India 1.41 3.83 0.72 -1.11 Indonesia 4.81 5.79 1.63 -0.07 Iran 0.17 0.35 0.10 0.11 Iraq 0.01 0.06 0.01 0.02 Ivory Coast 0.10 0.45 0.14 0.19 Japan 1.97 0.12 0.03 -0.48 Malaysia -0.05 4.49 0.05 0.06 Mexico 1.77 2.96 0.23 0.16 Morocco 0.09 0.10 0.05 0.07 Myanmar (Burma) -0.04 0.22 0.02 -0.01 Nigeria 1.30 0.89 0.33 0.55 Africa, Other 1.96 4.73 0.67 0.93 Asia, Other 0.21 0.49 0.00 -0.04 CIS, Other -0.58 -0.15 -0.01 0.06 Eastern Europe, Other 0.05 0.19 0.03 0.04 Latin America, Other 0.81 2.85 0.35 0.29 Middle East, Other 0.00 0.09 0.02 0.04 Pakistan -0.02 0.71 0.11 0.27 Paraguay 0.08 -0.07 0.21 0.33 Peru -0.23 2.21 0.12 0.13 Philippines 1.97 1.97 0.79 0.53 Rest of World 1.50 3.68 0.45 0.40 Russia 0.02 0.52 0.15 0.21 South Africa 0.13 0.78 0.07 0.07 South Korea 0.00 0.03 0.00 0.01 Taiwan 0.00 0.07 0.00 0.00 Thailand 0.34 0.59 0.22 0.24 Tunisia 0.06 0.08 0.02 0.04 Turkey -0.04 0.17 0.06 0.04 Ukraine -0.06 0.34 0.02 0.02 Uruguay -0.01 0.58 0.07 0.10 United States 1.03 Uzbekistan -0.15 -0.09 -0.02 -0.01 Venezuela -0.04 1.55 0.07 0.04 Vietnam 0.35 0.22 0.16 0.10 Western Africa 0.05 0.14 0.06 0.14 TOTAL 41.89 69.80 10.99 21.86 Note: given the nature of stochastic modeling, for the low and high 95% confidence ranges total emissions do not equal the sum of the regions.127 2.4.5 Accounting for Lifecycle GHG Emissions Over Time

When comparing the lifecycle GHG emissions associated with biofuels to those associated with gasoline or diesel emissions, it is critical to take into consideration the time profile associated with each fuel’s GHG’s emissions stream. With gasoline, a majority of the GHG emissions associated with ex traction, conversion, and combus tion are likely to be released over a short period of time (i.e., annually) as crude oil is converted into gasoline or diesel fuel which quickly pass to market.

In contrast, the GHG emissions from the produc tion of a typical biofuel (e.g., corn-based ethanol) may continue to occur over a long period of time. As with petroleum based fuels, GHG emissions are associated with th e conversion and combustion of biof uels in every year they are produced. In addition, GHG emissions could be released through time if new acres are needed to produce corn or other crops for biofuels. The GHG emissions associated with converting land into crop production would accumulate over time with the largest release occurring in the first few years due to clearing with fire or biomass decay. After the land is converted, moderate amounts of soil carbon would continue to be released for approximately 20 years. Furthermore, there would be foregone sequestration associated with the fact that the forest would have continued to sequester carbon ha d it not been cleared for approximately 80 years (See Figure 2.4-45.).

Figure 2.4-45. Timing of International Land Use Change Emissions in the Corn Ethanol Scenario (gCO2e / mmBTU)

0 100,000 200,000 300,000 400,000 500,000 600,000 700,0002022203020402050GHG Emissions (gCO2-eq / mmBtu)

International Land Use Change Emissions

Year 0 Emissions: •Clearing with fire •Biomass decay •Soil carbon emissions •Foregone forest sequestration

Years 1-19 Emissions: •Soil carbon emissions •Foregone forest sequestration
Years 20-79 Emissions: •Foregone forest sequestration

While biomass feedstocks grown each year on new cropland can be converted to biofuels that offer an annual GHG benef it relative to the petroleum produc t they replace, these benefits may be small compared to the upfront release of GHG emission. Depending on the specific biofuel in question, it can take ma ny years for the benefits of the biofuel to make up for the large initial releases of carbon that result from la nd conversion (e.g., the payback period).128 As required by EISA, our analysis must demonstrate whether biofuels reduce GHG emissions by the required amount relative to the 2005 petroleum baseline. A payback period alone cannot answer that question. Since the payb ack period is not sufficient for our analysis, we have developed methods for capturi ng the stream of emissions and benefits over time. For our analytical purposes, it is important for us to de termine how the time profiles of emission releases of different fuels compare. It is useful to have a unitary metric that allows for a direct comparison of biofuels compared to gasoline or diesel, which requires an accounting system for GHG emissions over time. When considering the time profile of GHG emissions, the two assumptions that have a significant impact on th e determination of whether a biofuel meets the emissions reduction threshold include: 1) the time period considered and 2) the discount rate we apply to future emissions. The proposed rule presented results using a 100-year time horizon and a 2% discount rate, as well as results with a 30-year time horizon and a 0% discount rate.

Based on input from the expert peer review and public comments, EPA has chosen to analyze lifecycle GHG emissions using a 30 year time period, over which emissions are not discounted, i.e., a zero discount rate is applied to future emissions.

The main reasons for why a short time period is appropriate: this time frame is the average life of a typical biofuel production facility; future emissi ons are less certain and more difficult to value, so the analysis should be confin ed insofar as possible to the foreseeable future; and a near-term time horizon is consistent with the latest climate science that indicates that relatively deep reductions of heat-trapping gasse s are needed to avoid catastrophic changes due to a warming climate.

EPA has decided not to discount (i.e., use a 0% discount rate) GHG emissions due to the many issues associated with applying an economic concept to a physical parameter. First, it is unclear whether EISA intended lifecycle GHG emi ssions to be converted into a metric whose underpinnings rest on principals of economic valuation. A more lite ral interpretation of EISA is that EPA should consider only physical GHG emissi ons. Second, even if the principle of tying GHG emissions to economic valuation approaches we re to be accepted, there would still be the problem that there is a lack of consensus in the scientific community about the best way to translate GHG emissions into a prox y for economic damages. Also, there is a lack of consensus as to the appropriate discount ra te to apply to GHG lifecycle emissions streams through time. Finally, since EPA has decided to base thres hold assessments of lifecycle GHG emissions on a 30 year time frame, the issue of whether to discount GHG emissions is not as significant as if the EPA had chosen the 100 year time frame to assess GHG emissions impacts.129 2.4.6 Feedstock Transport

The GHG impacts of transporting biofuel feedst ock from the field to the biofuel facility and transporting co-products from the biofuel facilit y to the point of use were included in this analysis. The GREET default of truck transportati on of 50 miles was used to represent corn and soybean transportation from farm to plant. This includes 10 miles from farm to stacks and 40 miles from stacks to plant. Transportation assu mptions for DGS transport were 14% shipped by rail 800 miles, 2% shipped by barge 520 miles, an d 86% shipped by truck 50 miles. The percent shipped by mode was from data provided by USDA and based on Association of American Railroads, Army Corps of Engineers, Commodity Freight Statistics, and industry estimates. The distances DGS were shipped were based on GR EET defaults for other commodities shipped by those transportation modes. Default GREET assump tions were also used for cellulosic ethanol feedstock transport. Crop residue s, switchgrass and forest wastes were all assumed to be shipped by truck from point of production to plant. Crop residue distance shipped was 30 miles, switchgrass distance was 40 miles, and forest waste was 75 miles. The GHG emissions from transport of these feedstocks and co-products are based on GREET default emission factors for each type of vehicle including capacity, fuel economy, and type of fuel used.

GHG emissions from the transport of sugarcan e from the field to the ethanol production facility also depend on distance and the type of truck used. The average one-way distance in 2002 was 20 km (12.4 mi) 101 . Over time, transport distance has increased to 23 km for 2005/2006, and is expected to be close to 30 km (18.6 mi) by 2020. 102 In terms of trends for logistics, there has been a replacement of single load trucks by trucks with lower specific fuel consumption and higher load capacities (3 to 4 wagons).

Table 2.4-54. Sugarcane Transportation Inputs
Parameter Units GREET Default GREET ISJ (2008) 103 2002 2005/ 2006 Scenario 2020

Transportation distance (oneway) Km 19.3 20 20 23 30

Truck diesel use efficiency Ml/(t*km) 27.7 14.8 20.4 19.1 16.1 Diesel consumption ml/tonne

cane 534 296 408 439 483

As we are projecting to a 2022 case, we us ed the projections available for the 2020 scenario given in Table 2.4-54, along with a revised truck payl oad assumption based on comments we received, to estim ate GREET inputs for truck payloa d (tons), fuel economy of the truck (mpg), and the average one-way distance from field to the mill (miles). Specifically, we assumed a truck payload of 42 tons, 104,105 fuel economy of 3.8 mpg, and an average distance of 19 miles. We incorporated these revised inputs into a recent release of the GREET model to estimate the GHG impacts of sugarcane transport. 106130 2.4.7 Biofuel Processing

GHG emissions from renewable fuel producti on were calculated by multiplying the BTUs of the different types of energy inputs at biofuel process plants by emissions factors for combustion of those fuel sources. The BTU of energy input was determined based on analysis of the industry and specific work done as part of th e NPRM. The emission f actors for the different fuel types are from GREET and were based on as sumed carbon contents of the different process fuels. The emissions from produc ing electricity in the U.S. we re also taken from GREET and represent average U.S. grid electricity producti on emissions. The emissions from combustion of biomass fuel source are not assumed to increase net atmospheric CO 2 levels. Therefore, CO 2 emissions from biomass combustion as a process fuel source are not included in the lifecycle GHG inventory of the biofuel production plant. The following sections outline the assumptions used to model biofuel production for differe nt feedstocks and fuel pathways.

2.4.7.1 Corn Ethanol

One of the key sources of information on en ergy use for corn ethanol production was a study from the University of Illinois at Chicago Energy Resource Center. Between proposal and final rule, the study was updated to reflect more recent data, therefore, we incorporated the results of the updated study in our corn ethanol pa thways process energy use for the final rule. We also updated corn ethanol production energy us e for different technologies in the final rule based on feedback from industry technology provide rs as part of the public comment period. The main difference between proposal and final co rn ethanol energy use values was a slight increase in energy use for the corn ethanol fractionation process, based on feedback from industry technology providers.

The two basic methods for producing ethanol fr om corn are dry milling and wet milling. In the dry milling process, the entire corn kern el is ground and fermented to produce ethanol. The remaining components of the corn are then either left wet if used in the near term or dried for longer term use as an imal feed (dried distillers grains w ith solubles, or DGS). In the wet milling process, the corn is soaked to separate the starch, used to make ethanol, from the other components of the corn kernel. Wet milling is mo re complicated and expensive than dry milling, but it produces more valuable products (ethanol plus corn syrup, corn oil, and corn gluten meal and feeds). The majority of ethanol plants in the United States are dry mill plants, which produce ethanol more simply and efficiently.

For this analysis the amount of corn us ed for ethanol production as modeled by the FASOM and FAPRI models was ba sed on yield assumptions built into those two models. Assumptions were ethanol yields of 2.71 gallons per bushel for dry mill plants and 2.5 gallons per bushel for wet mill plants (yields represents pure ethanol).

As mentioned above, in trad itional lifecycle analyses, the energy consumed and emissions generated by a renewable fuel plant mu st be allocated not only to the renewable, but also to each of the by-products. However, for corn ethanol production, this analysis accounts for the DGS and other co-products use directly in the FASOM and FAPRI agricultural sector131 modeling described above. DGS are considered a partial replacement for corn and other animal feed and thus reduce the need to make up fo r the corn production that went into ethanol production. Since FASOM takes the producti on and use of DGS into account, no further allocation was needed at the ethanol plant and all plant emissions are accounted for here.

In terms of the energy used at renewable fuel facilities, there is a lot of variation between plants based on the process type (e.g., wet vs. dry milling) and the type of fuel used (e.g., coal vs. natural gas). There can also be variation between the same type of plants using the same fuel source based on the age of the plant and types of processes included, etc. For our analysis we considered different pathways for corn ethanol production. Our focus was to differentiate between facilities based on the key differences between plants, namely the type of plant and the type of fuel used. One other key difference we modeled between plants wa s the treatment of the co-products DGS. One of the main energy driv ers of ethanol production is drying of the DGS. Plants that are co-located with feedlots have the ability to provide the co-product without drying. This has a big enough impact on overall results that we defined a specific category for wet vs. dry co-product. One additional factor that appears to have a significant impact on GHG emissions is corn oil fractionation from DGS. Ther efore, this category is also broken out as a separate category in the following section. S ee RIA Chapter 1.4 for a discussion of corn oil fractionation.

Furthermore, as our analysis was based on a future timeframe, we modeled future plant energy use to represent plants that would be built to meet requirements of increased ethanol production, as opposed to current or historic da ta on energy used in ethanol production. The energy use at dry mill plants was based on A SPEN models developed by USDA and updated to reflect changes in technology out to 2022 as described in RIA Chapter 1. The modeling provided energy use for the different types of dry mill ethanol plants as shown in Table 2.4-55.132Table 2.4-55. 2022 Energy Use at Ethanol Plants w/CHP (BTU/gal)TypeTechnologyNG UseCoal UseBiomass UsePurchased ElecCorn Ethanol – Dry Mill NG Base Plant (dry DDGS)28,660 2,251w/ CHP (dry DDGS)30,898512w/ CHP and Fractionation (dry DDGS) 25,854 1,512w/ CHP, Fractionation and Membrane Seperation (dry DDGS)21,3541,682w/ CHP, Fractionation, Membrane Seperation, and Raw Starch Hydrolysis (dry DDGS) 16,568 1,682Base Plant (wet DGS)17,0812,251w/ CHP (wet DGS)19,320 512w/ CHP and Fractionation (wet DGS)17,2851,512w/ CHP, Fractionation and Membrane Seperation (wet DGS) 12,785 1,682w/ CHP, Fractionation, Membrane Seperation, and Raw Starch Hydrolysis (wet DGS)9,9321,682Corn Ethanol – Dry Mill CoalBase Plant (dry DGS)35,8242,694w/ CHP (dry DGS)39,407 205w/ CHP and Fractionation (dry DGS)33,102986w/ CHP, Fractionation and Membrane Seperation (dry DGS) 27,477 1,191w/ CHP, Fractionation, Membrane Seperation, and Raw Starch Hydrolysis (dry DGS)21,4951,191Base Plant (wet DGS)21,351 2,694w/ CHP (wet DGS)24,934205w/ CHP and Fractionation (wet DGS) 22,390 986w/ CHP, Fractionation and Membrane Seperation (wet DGS)16,7661,191w/ CHP, Fractionation, Membrane Seperation, and Raw Starch Hydrolysis (wet DGS) 13,200 1,191Corn Ethanol – Dry Mill Biomass2022 Base Plant (dry DGS)35,8242,6942022 Base Plant w/ CHP (dry DGS) 39,407 2052022 Base Plant w/ CHP and Fractionation (dry DGS)33,1029862022 Base Plant w/ CHP, Fractionation and Membrane Seperation (dry DGS) 27,477 1,1912022 Base Plant w/ CHP, Fractionation, Membrane Seperation, and Raw Starch Hydrolysis (dry DGS)21,4951,1912022 Base Plant (wet DGS)21,351 2,6942022 Base Plant w/ CHP (wet DGS)24,9342052022 Base Plant w/ CHP and Fractionation (wet DGS) 22,390 9862022 Base Plant w/ CHP, Fractionation and Membrane Seperation (wet DGS)16,7661,1912022 Base Plant w/ CHP, Fractionation, Membrane Seperation, and Raw Starch Hydrolysis (wet DGS) 13,200 1,191Corn Ethanol – Wet MillPlant with NG45,950Plant with coal45,950Plant with biomass45,950133 In response to comments received, we included corn oil fractionation and extraction as a potential source of renewable fuels for this final rulemaking. Based on research of various corn ethanol plant technologies, corn oil as a co-product from dry mill co rn ethanol plants can be used as an additional biodiesel feedstock source. Dry mill corn ethanol plants have two different technological methods to withdraw corn o il during the ethanol production process. The fractionation process withdraws corn oil before the production of the DGS co-product. The resulting product is food-grade corn oil. The extraction process withdr aws corn oil after the production of the DGS co-product, resulting in corn oil that is only suitable for use as a biodiesel feedstock.

Based on cost projections outlined in Section 4, it is estimated that by 2022, 70% of dry mill ethanol plants will conduct extraction, 20% will conduct fractionati on, and that 10% will choose to do neither. These parameters have been incorporated into the FASOM and FAPRI models for the final rulemaking analysis, allowing for corn oil from extraction as a major biodiesel feedstock.

2.4.7.2 Corn Butanol

For the final rule analysis we included a scenar io of converting corn starch into butanol. The production of corn was assumed to be the same as for ethanol pr oduction and based on the agricultural sector modeling described in the previ ous sections. However, the results were scaled based on the yield of butanol produced. Corn etha nol was assumed to have a processing yield of 2.7 gal/bu and an energy content of 76,000 Btu/ga l which results in an overall energy yield of 206,280 Btu/bu. Corn butanol has a slightly lowe r processing yield of 2.12 gal/bu but a higher energy content of 99,827 Btu/gal for an overall energy yield of 212,153 Btu/bu. Therefore, on a per Btu produced basis corn butanol has slightly lower emissions compared to corn ethanol.

For process energy use we assumed the same types of technology as used for corn ethanol production. To estimat e GHG emissions we used the av erage 2022 mix of plants and technologies which includes fractionation and 63% dry DGS and 37% wet DGS using natural gas as an energy source. Average energy use was 26,496 Btu NG per gallon produced and 4,642 Btu purchased electricity per gallon.

2.4.7.3 Biodiesel (including Algae)

Three scenarios for biodiesel production were considered, one utilizing soybean oil as a feedstock, one using yellow grease, and the last using algae oil. All three were assumed to be converted to biodiesel through the Fatty Acid Methyl Ester (FAME) process. The emissions from soybean growing were estimated through the agricultural sector modeling described in the previous sections. This section discusses the modeling for the production of algal oil, producing soybean oil, and conversion of al oils into biodiesel.

2.4.7.3.1 Algae Oil Production134 We developed our lifecycle an alysis of the algae pathways primarily based on Aspen modeling provided by the National Re newable Energy Laboratory (NREL). Y As the algae industry is still in its nascent stages and there are potentially many variations to the processing of algae, e.g. methods used for harvesting and lip id extraction, byproduct utilization, etc., it is challenging to say which set of technologies and configurations may be the most successful in the future. A recent publication summarized some of the potential algae-biofuel production pathways being considered. Z

Two production pathways were evaluated at this time, one utilizing an open pond (op) system and the other a photobioreactor (PBR) system. More details on the assumptions used for those systems are described in the following s ections as well as in the technical memorandum from NREL. We view this assessment as a starting point for evaluating algae-to-biofuel pathways and not as the only or preferred producti on configurations for algae. Nevertheless, we believe that the assumptions a nd scenarios chosen to represent the production of algae by 2022 are reasonable given the expert opinion solicited. Over time we plan to evaluate different variations of these pathways and to update the da ta and analyses as the algae industry grows and commercializes.

NREL evaluated three cases: a base case, an aggressive case, and a maximum case for each of the algae production pathways, i.e. op and PBR. A brief summary of the cases evaluated are given below:

Base case: algae yield = 25 g/m 2 /day (op), 63 g/m 2 /day (PBR); lipid content = 25% (corresponds to a re asonable but still challenging target for the near future)

Aggressive case: algae yield = 40 g/m 2 /day (op), 100 g/m 2 /day (PBR); lipid content = 50% (assumes identification of a strain with near optimal growth rates and lipid content)

Maximum case: algae yield = 60 g/m 2 /day (op), 150 g/m 2 /day (PBR); lipid content = 60% (represents the near theoretical maximu m based on photosynthetic efficiencies)

For all cases: scale of facility = 10 MMgal/yr, 10% algae lost after production, 5% lipid lost in extraction

The production of algae-based biofuel consists of the following stages:

• Algae Cultivation

• Algae Harvesting

• Algae Oil Extraction and Recovery

• Algae Oil Transport to Biofuel Facility

• Algae Oil Conversion to Biofuel

• Biofuel Distribution

Y Davis, Ryan. November 2009. Techno-economic analysis of microalgae-derived biofuel production. National Renewable Energy Laboratory. Technical Memorandum to EPA.
Z Ryan, Catie, “Cultivating Clean Energy: The Promise of Algae Biofuels,” NRDC and Terrapin Bright Green, LLC. October 2009.135 The modeling completed by NREL covered the first three production steps. We assumed that the biofuel facility would be co-located ne xt to the algae oil production processes and thus transport emissions for the fourth step would be negligible. The last two steps were assumed to be similar to the conversion of soy oil to bi odiesel and soy-based bi odiesel distribution.

Algae require several inputs, including water, land, nutrients, and in most cases, light to sustain growth. The following Table 2.4-56 summ arizes these main inputs for the various open pond and photobioreactor scenarios:

Table 2.4-56. Inputs for Algae Cultivation Input Base Case Aggressive Case Max Case op PBR op PBR op PBR Water Use: Net water demand [MMgal/yr] 9,740 720 3,830 320 2,710 250

Net water demand [gal/gal lipid] 974 72 383 32 271 25

Land Use [acre]: Pond/PBR land size 4,743 1,897 1,482 593 823 329 Total plant land required 7,079 3,795 2,212 1,186 1,229 659

Nutrient Use [ton/yr]: Fertilizer for algae 23,920 23,880 12,000 11,980 10,010 10,000 Nutrients for anaerobic digester 2,960 3,000 1,440 1,460 1,190 1,200

CO2 Use: CO2 consumed [lb/lb algae produced] 2 2 2 2 2 2

Net CO2 used from offsite flue gas [ton/yr] 290,000 290,000 150,000 150,000 120,000 120,000

The scenarios assume water use is from low-value brackish or saline water pumped from an underground source rather than drawing from fresh water resources. AA In areas where water is limited, fresh water may not be available at a reasonable cost and therefore may affect the feasibility of the system. One factor that could further limit water consumption is by cultivating algae in nutrient-rich eutrophic or mixed waters (e.g. animal litter, tertiary wastewater, and agricultural or industrial runoffs). This in turn could limit the amount of nutrients purchased for algae cultivation. An additional benefit to the use of wastewater is that an algae process that treats wastewater displaces carbon that would have been generate d in conventional wastewater treatment processes.

AA

Some fresh makeup water is assumed to replace evapor ative losses in the cooling system for the PBR system.136 Algae cultivation is expected to be able to use non-arable lands. As such, the conversion of carbon-rich lands to agriculture can be avoi ded and thus emissions from land-use change. Due to higher cell densities, the use of photobi oreactors can lower land use in comparison to open pond systems. The scenarios assume 330 op erating days/year and a solar exposure of 12 hours/day which implies that a site location is chosen which receives high year-round solar
exposure.

The nutrients used in the process include fertilizers purchased for the algae and those needed to operate the anaerobic digesters. The fertilizers for the algae purchased are approximately 64% urea and 36% di-ammonium phospha te (DAP). The nutrients required to aid anaerobic digestion are primarily caustic with some phosphoric acid, urea and micronutrients.

Algae also consume CO 2 during cultivation. The scenarios assume that part of the CO 2 is recycled within the process from anaerobic di gestion of spent biomass and part of the CO 2 is delivered from offsite flue gas, e.g. from power plant. Scenarios assume that CO 2 is delivered from a distance of 1.5 miles from a power plant or other emissions source. For both the open pond and PBR “base case”, pure CO 2 was chosen where instead of transporting the entire flue gas material, the CO 2 is scrubbed out and transported unde r pressure to the facility.

Harvesting is necessary to recover biomass from the cultivation system. Commonly used techniques include flocculation, dissolved air flotation (DAF), centrifugation, microfiltration, and decantation. Wet biomass may also be de watered or dried. Dewatering decreases the moisture content by draining or mechanical means. Additional drying can follow using e.g. drum dryer, freeze dryer, spray dryer, rotary dryer, or by solar drying. Primary harvesting under our scenarios occurs using natural settling to conc entrate the algae from 0.05% to 1%. Secondary harvesting concentrates the algae to 10% via DAF using chitosan as a flocculant.

Oil from algae can be extracted through chemical or mechanical processes to separate the algal oil from the cell membrane. The TAGs (Triacylglycerides) are typically the main product which goes to biodiesel production. The remainde r consists of carbohydrates, proteins, nutrients, and ash), usually referred to as th e algal residue or spent biomass.

The extraction step is commonly regarded as the most speculative in terms of large-scale feasibility. BB Thus extraction is a critical area of research going forward to achieve practical algal lipid production. Some of the more common methods are so lvent extraction, supercritical fluid extraction, and mechanical extraction. Algal extraction under both op and PBR cases was assumed here to be carried out using mechani cal extraction via high-pressure homogenization to lyse algae cells. Homogenization was chosen because it is the closest to the necessary processing scale investigated given current t echnology. Other extraction techniques discussed include solvent extraction, supercritical flui d extraction, osmotic shock, and sonication.

The lipids are assumed to be recovered vi a phase separation in a clarifier tank which allows contents to settle into lipid, water and spent biomass.

BB Davis, Ryan. op sit.137 The spent biomass is assumed to be used in anaerobic digestion and power generation via gas turbine which provides power to run the plan t. The other method commonly discussed is its use as animal feed; however, this was not assumed under these scenarios. Table 2.4-57 summarizes the net annual electri city required (purchased from grid) for the cultivation, harvesting, oil extraction and recovery stages. We assumed that the average U.S. grid electricity is used.

Table 2.4-57. Net Annual Electricity Required (purchased from grid) [MM kwh/yr] for 10 MMgal/yr Lipid Production Base Case Aggressive Case Max Case Op PBR Op PBR op PBR 60.2 35.7 27.0 18.8 19.8 16.0

2.4.7.3.2 Soybean Oil Production

For the soybean oil scenario, the energy use and inputs for the biodiesel production process were based on a model de veloped by USDA and used by EPA in the cost modeling of soybean oil biodiesel including crushing, as di scussed in Chapter 4. Soybean crushing was modeled assuming yields of 11.2 lbs soybean o il/bu soybeans and energy use of 14,532 BTU of natural gas and 2,843 BTU of purchased electri city per gallon of biodiesel produced.

Similar to the case with corn ethanol co-products, we analyze the aggregate GHG emissions from soybean crushing and transesterification that occur as a result of increased demand for a particular biofuel. Therefore, any increase in soybean meal or soybean oil produced as a result of larger biodiesel vol umes would take into account GHG emissions reductions from a decrease in the production of ot her feed and vegetable oil substitutes from our FASOM modeling.

2.4.7.3.3 Conversion of Oil to Biofuel

For the proposal we based biodiesel processi ng energy on a process model developed by USDA-ARS to simulate biodies el production from the Fatty Acid Methyl Ester (FAME) transesterification pr ocess. In this process vegetable oil (t riglyceride) is reacted with an alcohol (e.g., methanol) and a catalyst (e.g., sodium hydroxide) to produce biodiesel and glycerin. During the comment period USDA updated their energy balance for biodiesel production to incorporate a different biodiesel dehydration process based on a system which has resulted in a decrease in energy requirements. Soybean biodi esel transesterification was modeled assuming yields of one kilogram of biodiesel from a kil ogram of soybean oil and energy use of 4,381 BTU of natural gas and 361 BTU of electric ity per gallon of biodiesel produced. CC

We assumed that the algae oil produced would be similar in quality as soy oil. Although it is possible that the algae oil may require an upgrading step such as degumming to remove phospholipids, this step was not included as there is no information at this time regarding the process logistics specific to algal-derived oil. Algae oil is also assumed to be converted to

CC Insert Biodiesel model reference138 biodiesel through the transesterification process with the same energy and material requirements of soybean oil.

For the yellow grease case, no soybean agriculture emissions or energy use was included. Soybean crushing natural gas use was included as a surrogate for yellow grease processing (purification, water removal, etc.). Also, due to additional processing requirements, the energy use associated with producing biodiesel from ye llow grease is higher than for soybean oil biodiesel production. The energy use for yello w grease biodiesel production was assumed to be 1.7 times the energy used for soybean oil biodies el and yields of 0.94 kilograms of biodiesel from a kilogram of yellow grease.

GHG emissions from other biodies el production raw material inputs were also included in the analysis. HCl, methanol, NaOCH3 and sodium hydroxide are used in the production of biodiesel and GHG emissions from producing the ra w material inputs were also added to the model. Table 2.4-58 shows the values that were used to convert raw material inputs into GHG emissions used in the anlaysis.

Table 2.4-58


## . Lifecycle Factors for Biodiesel Raw Material Production

Factor Unit Methanol Sodium Methoxide Sodium Hydroxide HCl
CO 2 g/g 0.401 0.966 0.923 1.011
CH 4 g/g 0.003 0.002 0 0
N 2 O g/g 3.9E-06 2.5E-06 0 0 Total Energy BTU/g 19.05 24.10 9.67 9.35

Glycerin is a co-product of biodiesel producti on. Our proposal analysis did not assume any credit for this glycerin product. We have in cluded for the final rule analysis that glycerin would displace residual oil as a fuel source on an energy equivalent basis. This is based on the assumption that the glycerin market would be sa turated in 2022 and that glycerin produced from biodiesel would not displace an y additional petroleum glycerin production. However, the biodiesel glycerin would not be a waste and a low value use would be to use the glycerin as a fuel source. The fuel source assumed to be replaced by the glycerin is residual oil.

2.4.7.4 Cellulosic Biofuel

For the cellulosic biofuel pathways, we upd ated our final rule energy consumption assumptions on process modeling also completed by NREL. For the NPRM, NREL estimated energy use for the biochemical enzymatic process to ethanol route in the near future (2010) and future (2015 and 2022). DD,EE,FF As there are multiple processing pathways for cellulosic biofuel,

DD Tao, Ling and Aden, Andy. November 2008. Techno-economic Modeling to Support the EPA Notice of Proposed Rulemaking (NOPR). National Renewable Energy Laboratory (NREL)
EE Aden, Andy. September 2009. Mixed Alcohols from Woody Biomass – 2010, 2015, 2022. National Renewable Energy Laboratory (NREL)
FF Davis, Ryan. August 2009. Techno-economic analysis of current technology for Fischer-Tropsch fuels. National Renewable Energy Laboratory (NREL)139we have expanded the analysis for the FRM to also include thermochemical processes (MixedAlcohols route and Fischer-Tropsch to diesel rout e) for plants which assume woody biomass as its feedstock.

Cellulosic ethanol can be produced through two main types of production processes, either fermentation or gasification. The ferm entation option is mainly used for homogeneous feedstock sources like farmed trees, switchgrass and corn stover whereas the gasification option is recommended for more heterogeneous sources like forestry waste. The Fischer-Tropsch process can also be used with heterogeneous sources to produce a diesel fuel substitute.

As discussed in RIA Chapter 1, we have wo rked with NREL to generate models of cellulosic ethanol and diesel fuel production. NREL has estimates for plants that were assumed to be in operation in the near-futur e (2010) and future (2015 and 2022).

Table 2.4-59 shows the energy use required fo r the different cellulosic ethanol and F-T diesel production processes. For the biochemical pathway, process energy is assumed to be generated through the unfermentable portion (mai nly lignin) of incoming biomass being burned for electricity production. The process is assume d to generate excess electricity per gallon of ethanol produced.

Table 2.4-59. 2022 Energy Use at Cellu losic Biofuel Plants (BTU/gal) Type Technology Biomass Use Diesel Fuel Use Purchased Elec. Sold Elec. Cellulosic Ethanol – Enzymatic Switchgrass feedstock & lignin used as fuel 61,001 -12,249 Corn stover feedstock & lignin used as fuel 61,001 -12,249 Forest waste feedstock & lignin used as fuel 64,220 -18,391 Cellulosic Ethanol – Thermochemical Switchgrass feedstock 90,935 177 Corn stover feedstock 90,935 177 Forest waste feedstock 90,935 177 Farmed trees feedstock 90,935 177 Cellulosic Diesel – F-T Switchgrass feedstock 168,220 327 17 Corn stover feedstock 168,220 327 17 Forest waste feedstock 168,220 327 17 Farmed trees feedstock 168,220 327 17

The benefit of electricity generation is the possibility of lowering greenhouse gas emissions by offsetting other forms of electricity production. This is captured in our analysis by assuming that the excess electricity produced by th e ethanol plant will offset U.S. grid electricity production. Therefore, GHG emissions from U.S. grid electricity are calculated for the amount of excess electricity produced based on GREET defa ults for electricity pr oduction and subtracted from the lifecycle results of ce llulosic ethanol production.140 2.4.7.5 Brazilian Sugarcane Ethanol

Under the imported sugarcane ethanol cases we updated process energy use assumptions to reflect anticipated increases in electricit y production for 2022 based on recent literature and comments to the proposal. One major change was assuming the potential use of trash (tops and leaves of sugarcane) collection in future facilities to generate additional electricity. The NPRM had only assumed the use of bagasse for electric ity generation. Based on comments received, we are also assuming marginal electricity production (i.e., natural gas) instead of average electricity mix in Brazil which is mainly hydroelectricity. This approach assumes surplus electricity will likely displace electricity which is normally dispatched last, in this case typically natural gas based electricity. The result of this change is a greater credit for displacing marginal grid electricity and thus a lower GHG emissions profile for imported sugarcane ethanol than that assumed in the NPRM. We also received public co mment that there are differences in the types of process fuel e.g. used in the dehydration pr ocess for ethanol. While using heavier fuels such as diesel or bunker fuel tends to increase the imported sugarcane ethanol emissions profile, the overall impact was small enough that lifecycle results did not change dramatically. We describe these changes in further detail below.

In Brazil, the majority of mills are c onfigured to produce both sugar and ethanol simultaneously. To simplify the lifecycle analysis , we assumed that a sugarcane ethanol mill is operated with 100% feed for etha nol production. In a sugarcane mill, sugarcane is cleaned, crushed, and the cane juice extracted. The juice is then treated to produce ethanol and/or sugar, depending on market demands. The stream for ethanol is fermented and distilled into hydrous ethanol. From there, there are two possibilities. Hydrous ethanol may be stored as the final product or dehydrated to anhydrous ethanol.

2.4.7.5.1 Sugarcane Ethanol Process Energy Consumption

In Brazil, the majority of energy used at the sugarcane ethanol facility is supplied by burning bagasse, the fiber material leftover after extracting cane juice. The bagasse is combusted in a boiler to produce steam and generate electricity to meet internal demands as well as export surplus electricity to the grid. A smaller portion of energy is required for chemical and lubricant use. We used a bagasse yield of 280 kg (with 50% moisture) per MT of sugarcane.

2.4.7.5.2 Bagasse Combustion Emissions

We used the IPCC guidelines (2006b) and average emission factors of CH 4 and N 2 O from biomass combustion, as shown in Table 2.4-60.141 Table 2.4-60. Emissions p er mmBTU Bagasse Burned GG Pollutant g/mmBTU bagasse burned CH 4 31.65 N 2 O 4.22

2.4.7.5.3 Chemical and Lubricant Use

We assumed that the chemicals and lubricants are similar to residual oil in terms of energy and emission profiles (see Table 2.4-61).

Table 2.4-61. Energy used fo r Chemicals and Lubricants HH

2005/2006 Scenario 2020 Energy Use (Btu/gal); 100% residual oil 798 766

We further assumed a 10% allocati on of residual oil to ethanol to account for lubricating oil that is used not as a combustion source but is lost during operation of the machinery in the production of ethanol.

2.4.7.5.4 Ethanol yields

Table 2.4-62 shows a summary of etha nol yields from several studies.

Table 2.4-62. Ethanol yields in Sugarcane Mills Year L/MT Source 1996-1997 79.5 Moreira & Goldemberg (1999) 2000 85.4 Assuncao (2000) 2001 78.58 UNICA-Carb Comments; 138.7 TRS/ton cane and 1.7651 kg TRS/L anhydrous 2005 85 OECD (2008) Avg. 2006-2008 84.68 UNICA-Carb Comments; 149.47 TRS/ton cane and 1.7651 kg TRS/L anhydrous Avg. in 2002 Best 86 91 Macedo et al. (2004) GREET default 2006 “2020” 86.3 92.3/129* Macedo et al. (2008) *Includes cellulosic ethanol 2015 100 Unicamp, as noted in OECD (2008)

GG Wang, Michael (2008) Life-cycle energy use and greenhouse gas emissions implications of Brazilian sugarcane ethanol simulated with the GREET model. International Sugar Journal 2008, Vol. 110, No. 1317.

HH Macedo (2008) “Greenhouse gases emissions in the production and use of ethanol from sugarcane in Brazil: The 2005/2006 averages and a prediction for 2020.” Biomass and Bioenergy.142 Year L/MT Source
2025 109

2.4.7.5.5 Ethanol dehydration

Standard distillation leaves ove r 4% water in ethanol, requi ring a second step in the process to remove water in order to obtai n fuel grade anhydrous ethanol (>99.3 wt%). II The most important ethanol dehydra tion techniques used in the wo rld industry include azeotropic distillation, dehydration on molecular sieves, and more recently, pervaporation or vapor permeation. JJ Azeotropic distillation uses a third com ponent, typically benzene or cyclohexane, to remove the final water from ethanol. Molecular sieves use an adsorbent with a strong affinity for water and little affinity for ethanol. This allows for separation of water from the ethanol product. Most new ethanol plants today ar e built with molecular sieve dehydrators. Pervaporation is still a fairly new technology, however, there is potential for energy consumption savings, thus making the technolog y attractive for newly built facilities. KK

Data was unavailable to determine the split of facilities using one type of dehydration process over the other for import into the Unite d States. However, we collected data on the amount of energy required to dehydrate a gallon of hydrous ethanol into anhydrous ethanol using primarily molecular sieve technology, see Table 2.4-63.

Table 2.4-63. Energy Requi red for Dehydration BTU/gallon of anhydrous Source/Details

4,000 Swain 2,830-5665 Vane LL 4,500 Kawaitkowski MM
4186-5931 CBEPG NN ; Fuel Oil (primarily diesel) 5156-5210 CBEPG OO ; Natural Gas

II Swain, R.L., Molecular sieve dehydrators. How they became the industry standard and how they work. Chapter 19, Alcohol Textbook.
JJ Kaminski, W. (2008) Renewable energy source- Dehydrated ethanol. Chemical Engineering Journal 135: 95-102.
KK

Roy, Suzanne. Fuel Production: Using membrane integration for plant expansion. Vaperma handout.
LL Vane, Leland. (2008). Separation technologies for the recovery and dehydration of alcohols from fermentation broths. Biofuels, Bioproducts, and Biorefining 2.6: 553-588.
MM Kawaitkowski, J.R., McAloon, A., Taylor, F., Johnston, D.B., Modeling the process and costs of fuel ethanol production by the corn dry grind process. Industrial Crops and Products 23 (2006) 288-296. A copy of the current USDA model can be obtained by contacting the corresponding author. Model was adjusted based on conversations with technology providers.
NN Fitch, George (2009) Caribbean Basin Ethanol Producers Group email correspondences, 10/16/2009 "Caribbean Basin Ethanol Dehydration Energy Usage and CO2 Emissions instead of just "comments "

. OO Fitch, George (2009) Caribbean Basin Ethanol Producers Group comments143 As noted in Chapter 1, the majority of ethanol imported into the U.S. may preferentially come through the Caribbean Basin Initiative countr ies due to favorable ec onomic conditions. As the public comments on our rule suggest, there are differences in the type of fuels burned to run the processes for dehydration. This depends on the location of the dehydration facility and the fuel choices available at those locations. Fuel s used to run the dehy dration process include bagasse, natural gas, #2 distillate (diesel fuel), and #6 oil (bunker fuel). PP

For the final rule, we have assumed an aver age energy consumption for dehydration from fuel oil use of 5059 BTU/gallon anhydrous produced and 5183 BTU/gallon anhydrous produced if natural gas is used.

We received comment to include a pathwa y for the Caribbean Basin countries. We evaluated the pathway based on the type of fuel us ed for dehydration, either from fuel oil or from natural gas. For the NPRM we had already evaluated the Brazilian direct pathway assuming dehydration used bagasse as a fuel. We calculat ed 1.)The additional emissions from burning fuel oil and natural gas instead of bagasse 2.)The emissions credit from not dehydrating in Brazil from bagasse (i.e. electricity is produced instead).

Assuming an electricity gene ration efficiency of 30% (the current Brazil industrial average) and the energy consumption for de hydration as 5059 BTU/gallon anhydrous and 5183 BTU/gallon anhydrous for fuel oil and natural gas, we calculated an electricity credit of 0.44 and 0.46 kWh/gallon anhydrous produced for fuel oil and natural gas, respectively. This electricity credit is assumed to displace electricity as it is produced in Brazil, i.e. marginal electricity produced from natural gas. See discussion of Brazilian electricity generation in the following section.

2.4.7.5.6 Electricity generation in Brazil

In Brazil, there has been an increasing us e of bagasse to generate enough steam and electricity to supply the whole mill energy dema nd while still producing electricity surpluses. Table 2.4-64 summarizes the current and anticipated electricity generated for Brazilian sugarcane facilities. As noted, this is highly dependent on the types of boilers used, and the collection of sugarcane leaves and tops (trash). Average cogeneration surplus for all sugarcane mills in Brazil was 10.5 kwh/MT cane in 2008, and could increas e above 100 kwh/MT cane with the utilization of trash.

For the final rule, we have chosen to the low (40 kwh/MT cane) and high (135 kwh/MT cane) scenarios.

Table 2.4-64. Electricity Surplus in Brazil under Various Conditions
Year Biomass Used Kwh/MT

cane Source/Details

2006 Bagasse w/ surplus leftover 9.2 CTC (2006); 10% mills use high press boilers, 90% 21 bar/300 C 2007 Bagasse 22.5/23 UNICA/MME/COGEN-SP/GREET default

PP Fitch, George (2009) Caribbean Basin Ethanol Producers Group comments.144 2008 Bagasse 10.5 Avg. for all UNICA members (124 mills) 2008 Bagasse 40 OECD (2008), one standard facility; 20% of 39 mills or 4% of all mills 2008 Bagasse 25.16 Avg. for 39 mills surveyed by UNICA Current Bagasse 0-10 Smeets (2008), Combustion, partial steam extraction turbine, 22 bar, -300C Current Bagasse 40-60 Smeets (2008), Combustion, partial steam extraction turbine, 80 bar, -480C

2012 Bagasse 65 COGEN-SP, Amounts contracted Near-term Bagasse 75 UNICA, Upgrading to high-pressure steam cycle generators, using all bagasse 2020+ Bagasse + 40% trash for cellulosic ethanol; Bagasse + 40 % trash for electricity 44

135 Macedo (2008)

Mills at 65 bar/480 C, CEST systems; process steam consumption ~340 kg steam/tonne cane

Longerterm Bagasse + 50% trash for electicity 67-100 Smeets (2008), Combustion, condensing steam turbine, 80 bar, -480C

2020+ Bagasse + 50% trash for electricity 135-200 Smeets (2008), Gasification, steam-injected gas turbine

2.4.7.5.6.1 Average Brazilian Grid Electricity versus Marginal Grid Electricity

We have factored in credit in our analyses for the excess electricity generated from the burning of bagasse and potentially trash in the future. This, howev er, is dependent on the type of electricity displaced. Several comments on our rule indicate that the cogeneration in Brazil should displace the marginal power supplier (i.e., thermoelectric power plants running on natural gas or heavy fuel oil) instead of average grid electricity (i.e., hydroelectricity). 107 See Table 2.4-65 for the Brazil average fuel mix in 2007.

Table 2.4-65 Brazilian average fuel mix for electricity generation in 2007 108 Fuel % Petroleum 2.83% Natural Gas 3.63% Coal 1.34% Biomass 3.47% Nuclear 2.54% Hydro 77.28%145 Others 8.94% Total 100.0%

We believe the use of marginal grid electricity instead of average electricity is reasonable given that 1.) We are crediting on the basis of displacement 2.) Electricity produced at the sugarcane ethanol facility is always dispatched when a mill is operating and this allows for reduction of the use of other thermal power plants . Table 2.4-66 shows the average fuel mix for Brazil’s operating margin in 2008.

Table 2.4-66. Brazilian Grid Operati ng Margin average fuel mix for electricity genera tion in December 2008. 109
Fuel % Petroleum 3.63% Natural Gas 60.24% Coal 14.37% Biomass 0.00% Nuclear 18.99% Hydro 1.11% Others 1.65% Total 100.0%

As natural gas is the predominant fuel use, we have chosen to assume that marginal electricity in Brazil will displace el ectricity derived from natural gas.146 2.4.8 Fuel Transport

The greenhouse gas impacts associated with the transportation and distribution of biofuels depend the average distance the fuel is transported from the plant to the retail location and the mode of transport (barge, rail, truck, et c.). This section summarizes the assumptions used in this analysis to represent the transport of biodiesel, and domestic and imported ethanol. A recent release of GREET 110 was utilized to estimate the GHG emissions based on these assumptions.

2.4.8.1 Biodiesel

For biodiesel transport, GREET default valu es were used to represent the average distances biodiesel is transported by barge, pipeline, rail, and truck from the plant to the terminal where it is blended with petroleum- based diesel fuel. The percentage of fuel transported by each mode was chosen to be consistent with the GHG cost analysis described in Chapter 4. These inputs are summarized in Table 2.4-67.

GREET default values were used to represent the transport of biodiesel from the terminal to the retail location. These defaults assume 100% of biodiesel shipped by truck a distance of 30 miles.

Table 2.4-67. Biodiesel Assumptions for Transport from Plant to Terminal Mode % Distance (miles) Barge 5% 520 Pipeline 0% 400 Rail 45% 800 Truck 50% 50

2.4.8.2 Corn and Cellulosic Ethanol

Oak Ridge National Laboratories (ORNL) 111 recently conducted a study that models the transportation of ethanol from pr oduction or import facilities to pe troleum blending terminals by domestic truck, marine, and ra il distribution systems. We used ORNL’s transportation projections for 2022 under the EISA policy scenar io to estimate the percentage of corn and cellulosic ethanol transported by each mode and the averaged distance traveled. These assumptions are summarized in Table 2.4-68. The ORNL study is described in more detail in Chapter 1.

Since the study did not address the transport of ethanol from the terminal to refueling station, we used the GREET default assumpti ons of 100% shipped by truck a distance of 30 miles.147 Table 2.4-68. Corn and Cellu losic Ethanol Assumptions for Transport from Plant to Terminal Mode % Distance (miles) Barge 12% 336 Rail 77% 629 Truck 17% 68 Local Truck 112 83% 6.5

2.4.8.3 Sugarcane Ethanol

This analysis accounts for the transportation of sugarcane ethanol within Brazil, en route to U.S. import facilities, and within the United States. GREET default values are used to represent the transport of ethanol from a production facility in Brazil to a Brazilian port. Specifically, we assumed that 50% of the ethanol is transported via pipeline and the other 50% by rail an average distance of 500 miles (for each mode).

The ethanol is then loaded onto ocean tankers for transport to the United States. As described in Chapter 1, we projected that 46% of imported ethanol in 2022 would be shipped directly from Brazil, while 54% would first be shipped to a country in the Caribbean Basin Initiative (CBI) and then to the United States. For the latter case, we assumed 20% would be imported from Costa Rica, 20% from El Salvador , 30% from Jamaica, 15% from Trinidad and Tobago, and 15% from the Virgin Islands (see Table 1.5-31).

Table 2.4-69 summarizes EPA estimates for the average distance ethanol is transported by ocean tanker for each of these paths. For these estimates, we used EIA data on fuel ethanol imports from 1993 to August 2009 113 to determine the fraction of ethanol shipped to different U.S. destinations from Brazil the five contri buting CBI countries. We estimated the average distance imported ethanol travels by ocean tanker, accounting for all of these paths, to be 7,348 miles.

We received comment that ocean tankers bringing ethanol fuel from Brazil to California return to Brazil empty is incorrectly attributin g emissions of an ocean tanker’s round trip to sugarcane ethanol. We, therefore, assume that emissions from back-haul are negligible for this analysis.148 Table 2.4-69. Average Ocean Tanker Distance s for Sugarcane Ethanol Transport from Brazil and CBI Countries to the United States QQ Average distance to U.S. import facilities (miles) Total distance, including distance from Brazil (miles)

Costa Rica 3375 10398 El Salvador 3691 11011 Jamaica 2466 7393 Trinidad & Tobago 2766 6590 Virgin Islands 1702 5919 Brazil (direct) 6141 6141

Within the United States, ORNL’s transportation projections were used to estimate the average distance sugarcane ethanol is transported from an import facility to a petroleum blending terminal and the percentage that travels by each mode. Table 2.4-70 summarizes transport assumptions for sugarcane ethanol from production facilities in Brazil to blending terminals in the United States. As with corn and cellulosic ethanol, we used the GREET default assumptions to represent the transport of sugarcane ethanol from the terminal to a refueling station. These assumptions were 100% shipped by truck a distance of 30 miles.

Table 2.4-70. Sugarcane Ethanol Assumptions for Transport from Plant to Terminal Mode % Distance (miles) Pipeline (in Brazil) 50% 500 Rail (in Brazil) 50% 500 Ocean Tanker 100% 7348 Barge (in U.S.) 12% 336 Rail (in U.S.) 77% 629 Truck (in U.S.) 17% 68 Local Truck (in U.S.) 83% 6.5

QQ Distances were calculated using www.distances.com . For Brazil and CBI Countries, the following representative ports were used: Santos in Brazil, Puntarenas in Costa Rica, Acajutla in El Salvador, Kingston in Jamaica; Port of Spain in Trinidad and Tobago, and St. Croix in the Virgin Islands.149 2.4.9 Biofuel Tailpipe Combustion

Combustion CO 2 emissions for ethanol and bioma ss-based diesel were based on the carbon content of the fuel. However, over the full lifecycle of the fuel, the CO 2 emitted from biomass-based fuels combustion does not increase atmospheric CO 2 concentrations, assuming the biogenic carbon emitted is offset by the uptake of CO 2 resulting from the growth of new biomass. As a result, CO 2 emissions from biomass-based fuel s combustion are not included in their lifecycle emissions results. Net carbon fl uxes from changes in biogenic carbon reservoirs in wooded or crop lands are accounted for separate ly in the land use change analysis as outlined in the agricultural sector modeling above.

When calculating combustion GHG emissions, however, the CH 4 and N 2 O emitted during biomass-based fuels combustion are included in the analysis. Unlike CO 2 emissions, the combustion of biomass-based fuels does result in net additions of CH 4 and N 2 O to the atmosphere. Therefore, combustion CH 4 and N 2 O emissions are included in the lifecycle GHG emissions results for biomass-based fuels.

Combustion related CH 4 and N 2 O emissions for biomass-ba sed fuels are based on EPA MOVES model results. The values used are shown in Table 2.4-71. CO2 emissions from biofuels are shown for illustrative reasons, but as mentioned above are not included in the analysis because they are assumed to be offset by carbon uptake from plant growth.

Table 2.4-71

. Tailpipe Combustion Emissions fo r Bio and Petroleum Based Fuels
CO2 CH4 N2O Fuel Type (g/mmBTU) (g/mmBTU) (g/mmBTU) Ethanol 75,250 269 611 Biodiesel 81,044 11 689150 2.4.10 Other Indirect Impacts

In the analysis of the proposed rulemaking the Agency conducted a study of the U.S. energy sector impacts of increased biofuel produ ction. Using an EPA version of the Energy Information Administration’s National Energy Modeling System (NEMS) we attempted to determine the effects of biofuel production energy use, for example increased natural gas use for corn ethanol production and the impact that has on natural gas and other fuel sources price and use. The EPA-NEMS is a modeling system that simulates the behavior of energy markets and their interactions with the U.S. economy by explic itly representing the economic decision-making involved in the production, conversion, and consumption of energy products.

There were several problems encountered with the modeling done for the proposal, mainly in trying to isolate the impacts of a specific fuel and of the specific impact of biofuel energy use so the results were not used in the analysis. However, we indicated that we would continue exploring this modeling for the final rule.

Therefore, for the final rule we created a scenario in EPA-NEMS to simulate the RFS2 volumes, reaching 31.8 billion gallons of biofue ls production in 2022. This scenario was compared to AEO 09, which estimated 13.8 billion gallons of biofuel production. This allowed us to see the energy system impacts of an increase in renewable fuels of 18 billion gallons.

The increase in renewable fuels supply triggere d a decrease in gasoline demand. This led to a 0.73 million barrel per day decrease in crude oil imports and a 0.18 million barrel per day decrease in refined product imports. As a result of declining demand, crude oil prices decreased from $117.11/barrel to $116.43/barrel and petroleum pr oduct prices also decreased slightly. In addition, prices for natural gas and electricity declined significantly. The only price increases were a $0.10/mmbtu increase in the price of motor gasoline and a $1.49/mmbtu increase in the price of E85.

The overall CO 2 impact was a 34,736 grams CO 2 decrease for each mmbtu increase in renewable fuels over the ba seline, or -34,736 grams CO 2 /mmbtu. Reduced consumption of gasoline, diesel, and still gas (for refining) resulted in an overall decrease in emissions of 86.6 mmt CO 2 . This decrease in emissions was partially offset by natural gas consumption for production of renewable fuels and by increased co al consumption for power generation, yielding an overall decrease in the domestic energy sector of 61.5 mmt CO 2
. The EPA-NEMS results were used in part to estimate the crude oil import reductions from the increased renewable fuel volumes mandated by this rulemaking, as discussed in Chapter 5. However, we have not used this analysis at this point in calcula tions of renewable fuel threshold analysis or for the overall rule imp acts because of double counting issues regarding GHG emissions sources.

The final rule EPA-NEMS analysis eliminat ed some of the problems with the proposal modeling by considering a larger increase in biof uels consumption and by not specifically trying151 to isolate the impacts of one type of fuel. However, there were still issues with how this analysis compares to the other lifecycle modeling work conducted for this rulemaking. The main issue is double counting between the EPA-NEMS analysis and our lifecycle work. Both account for renewable fuel production energy use, which is difficult to separate in the EPA-NEMS modeling (especially for purchased electricity). Both also account for gasoline and diesel fuel reduction, both end use and refining energy. This is also di fficult to back out of the EPA-NEMS modeling. Therefore, it is difficulty to isolate only the secondary or energy sector impacts that are not already covered elsewhere. There is also the issue that the EPA-NEMS model is only domestic and does not capture any potential international energy sector impacts. We will continue to study this modeling as part of any ongoing work on biofuel analysis.

2.4.11 Other Modeling Approaches Considered

2.4.11.1 Analysis with the GTAP Model

The Global Trade Analysis Project (GTAP) model is an economy-wide multi-region general equilibrium (GE) model coordinated by the Center for Global Trade Analysis at Purdue University. GTAP is a publicly available gl obal model that was orig inally developed for addressing agricultural trade issues among count ries. An advantage of GE models such as GTAP is that they take into account how change s in U.S. biofuel policies affect world prices, output, and trading patterns for a wide vari ety of commodities that extend beyond the agricultural sector. The GTAP data base is p eer reviewed and updated triannually. The GTAP databases and versions of the model are widely used internationally by a large modeling community. 114 Since its inception in 1993, GTAP has rapidly become a common "language" for many of those conducting global economic analysis. For example, the WTO and the World Bank co-sponsored two conferences on the so- called Millennium Round of Multilateral Trade talks in Geneva. Here, virtually all of the quantitative, gl obal economic analyses were based on the GTAP framework. The use of the GTAP data base and model has been increasing with the growing research interests in international trade policies, energy policies, and climate change policies. Because GTAP is publicly available, there are numerous versions of the GTAP-based model. However, the GTAP Center has a peer review process to control the quality of these applications.

GTAP Version 6 divides the global economy into 57 sectors and 87 regions, some of which have been aggregated in the results presented below for simplification. Over the past few years, several improvements have been made to the model. For example, a version of the model was developed to explicitly account fo r substitution between energy commodities 115 . Another version of the model was developed to explicitly model global competition among different land types (e.g., forest, agricultural land, pasture) and different qualities of land based on the relative value of the alternative land-uses. 116 More recently the above two frameworks were combined and modified to include biofuel substitutes for gasoline and diesel. 117 The California Air Resources Board (CARB) has utilized the GTAP model to assess biofuel land use impacts in its recent rulemaking on a Low Carbon Fuel Standa rd. Current research is ongoing to add additional detail on the biof uels market, some of wh ich is described below.152 2.4.11.1.1 Partial Equilibrium versus General Equilibrium Modeling

Although we have used the partial equilibrium (PE) mode ls FASOM and FAPRI as the primary tools for evaluating whethe r individual biofuels meet the GH G thresholds, as part of the peer review process, we explicitly requested i nput on whether GE models should be used. None of the commentors recommended using a GE model as the sole tool for estimating GHG emissions, although several reviewer s discussed some of the advant ages of GE models compared to PE models. For example, GTAP captures the interaction between different sectors of the economy. As discussed by the peer reviewers, the link between the agricultural and the energy markets has become increasingly important gi ven the increased production of biofuels from agricultural products. Higher crude oil prices and policies to increase de mand for biofuels have increased the linkages between these two market s, and increased biofue l production could have impacts on food security, international trade, and natural resources. These linkages can be captured in a sufficiently detailed GE model. The literature on economic modeling of biofuels suggest that for analyzing the long-term conseq uences on consumption, the GTAP model is a suitable economic tool to link energy and crop demand. RR

One of the major benefits of using the GTAP model is that it explicitly models land-use conversion decisions. GTAP is designed with the framework of predicting the amount and types of land needed in a region to meet demands for both food and fuel production. The GTAP framework also allows predictions to be made ab out the types of land available in each region to meet the needed demands, since it explicitly represents different land types within the mode.

In theory, a detailed GE model would be th e ideal modeling framework. However, as described in other sections, there is currently no single model that captur es all of the necessary aspects of lifecycle GHG emissions. In their curre nt state of development, GE models including GTAP are not yet adequate for determining wh ether biofuels meet greenhouse gas emission thresholds for the following reasons.

First, most GE models do not contain the leve l of detail in the agriculture sector required to determine acreage and production changes by crop by region. Because GE models must account for all sectors of the global economy, simplif ications have been made to capture many of the complex interactions. Therefore, some level of aggregation of regions, markets, and relationships is necessary. For example, GTAP contains only an aggregated “coarse grain” crop and does not contain informati on specific to corn acres and production. Similarly, GTAP contains only a generic oilseed crop and does not contain information about soybean-specific production and usage data. As a result, palm oil and soybean oil are treated identically, even though these two crops have very different re source implications. Furthermore, the GTAP model does not yet contain cellu losic feedstocks such as switchgrass or corn stover.

Second, GTAP is a static model that does not currently capture changes over time. GTAP Version 6, the version of the model used for this analysis, is based on a 2001 world economy. The model has been validated agains t historical data from 2001 through 2006 and the resulting 2006 baseline is used for biofuels policy an alyses. Due to its static nature, the GTAP

RR

See Kløverpris, Wenzel and Nielson (2008)153 biofuels model is not able to project the globa l economy through 2022, which is the timeframe of primary interest for this rulemaking. Since we expect trends such as increases in crop yields, oil prices, population growth, and GDP growth to con tinue in the future, it is essential that our modeling framework captures these dynamics.

Third, the GTAP model relies on differences in land rental rates to determine which lands will be converted to cr op land as a result of increasing bi ofuel demand. Land rents are the indicators of productivity in each agro-ecological zone (AEZ). In the GTAP data base, Lee et al

. (2009) determine land rents for cropland, pastur e, and forest based on the yearly economic activity in a given AEZ. SS By definition, land rents are largest in those AEZs where high value crops are grown. For determining land rents for the livestock sector, Lee et al . draw on the direct competition between these sectors with grazing land. For computing livestock sectors’ land rent, Lee et al. use the average coarse grain yield in each AEZ (as there is no ‘forage crop’ sector in the GTAP data base) and multiply it by the pasture land cover hectares. Finally, Lee et al. compute the forest land rents by using inform ation on timberland land re nt and timberland area offered by Sohngen et al. (2009). One of the major limitations of this methodology is that inaccessible forests do not generate any rents in principle. Therefore, inaccessible (unmanaged) forests do not have rental rates and are therefore not currently captured in the peer-reviewed versions of the GTAP model. This is important because it excludes agricultural expansion into unmanaged forests, such as certain tropical forest areas.

Fourth, although most of the behavioral parameters (e.g., international trade elasticities, agricultural factor supply) contained in GTAP are estimated econometrically, some of the key relationships are actually based on literature reviews, theory, and analyst judgment. TT In theory, all the relationships in the mode l could be based on regionally-specific empirical data, however in practice this is often not the case. For example, the elasticity of transformation (i.e., the measure of how easily land can be converted be tween forest, pasture, and crop land) is an important parameter in the GTAP model. However, the global value used for this parameter relies on a single study that is based on U.S. data. Ideally, this va lue would be based on empirical data that is specific to each region in the model, since this response is likely to be different in different parts of the world.

Given the relative advantages of PE and GE models, we opted to use the GTAP model to provide another estimate of the quantity and type of land conversion resulting from an increase in corn ethanol and biodiesel given the compe tition for land and other i nputs from other sectors of the economy. These results help to bracket the land use changes estimated by the FAPRI model.

SS See Lee, H.-L., T.W. Hertel, S. Rose and M. Avetisyan (2009) “An Integrated Global Land Use Data Base for CGE Analysis of Climate Change Policy Options.” Chapter-4 in T. Hertel, S. Rose and R. Tol (eds.) Economic Analysis of Land Use in Global Climate Policy, Routledge Publishing, London, New York, pp: 72-88. Also available as GTAP Working Paper # 42, Center for Global Trade Analysis, Purdue University, West Lafayette, IN, USA
TT See https://www.gtap.agecon.purdue.edu/models/cge_gtap_n.asp154 2.4.11.1.2 Comparison of GTAP and FAPRI Model Results

One of the advantages of the GTAP model is that it is an open source framework in which many different groups can conduct research simultaneously. As a result, there are many different “variations” of the GTAP model in existence, each of which are in a different state of peer-review. As researchers publish papers us ing their updated variation of the model, the programming code is generall y published so that others may benefit from these model enhancements. For our corn ethanol analysis, we used a slightly modified the version of the GTAP model that was extensively reviewed as part of the California Air Resources Board (CARB) for their Low Carbon Fuel Standard rulemaking. UU However, one of the criticisms of the CARB analysis was the treatment of biodiesel byproducts. New research by Taheripour et al has been recently conducted to explicitly mode l the production and substitution of oilseed meal as a byproduct of biodiesel production, which prov ides a more accurate representation of the soybean biodiesel market interactions. We have therefore used this variation of the GTAP model to conduct the soybean biodiesel analysis. VV

We made three revisions to the CARB m odeling inputs to make our corn ethanol and soybean biodiesel analysis more consistent. First, we changed the elasticity of crop yields with respect to area expansion. This parameter is a measure of how much crop yields will decrease as agriculture expands onto new land. In theory, th e most productive agricultural lands are already in use, therefore expanding production into more marginal lands will result in a decrease in average crop yields. CARB used a factor of 0.5 in its analysis, which implies that each new acre of land is only 50% as productive as an existing ac re of land. However, more recent analysis suggests that a value of 0.66 may be more appropriate, indicating that for every two acres of additional cropland needed, three acres of forest or pasture lands must be converted to new cropland. WW Therefore, we have used 0.66 as the elas ticity for our analysis of corn ethanol and biodiesel. Second, we adjusted the 2006 baseline ethanol and biodi esel production levels. Our modified version of the CARB model used in th is study has 4.25 BG of corn-ethanol and 0.14 BG of biodiesel in the 2006 baseline. Finally, si nce our baseline included a crude oil price shock from $25/barrel in 2001 to $60/barrel in 2006, our bi ofuel scenarios started with a $60 oil price laden economy versus $25 in th e CARB biofuel scenarios.

Because the GTAP model is static, it was not possible to analyze the exact same corn ethanol and soybean biodiesel scenarios in GTAP that we analyzed using the FAPRI model. Therefore, we analyzed a 2 billion gallon incr ease in corn ethanol over the 2006 updated baseline level of 4.25 BG. Similarly, our soybean biodi esel shock imposed a 1 BG increase in U.S. soybean biodiesel production over the 2006 updated baseline level of 0.14 BG. In order to compare the results of the GTAP model to the FAPRI model, we then “normalized” the land cover changes to obtain an acreage change per BTU of the biofuel shock. Other simplifications were also required. For example, GTAP aggreg ates regions differently than FAPRI, therefore we have summarized the results into larger regions for comparison purposes. Despite these

UU See http://www.arb.ca.gov/fuels/lcfs/030409lcfs_isor_vol1.pdf
VV This version of the model was in press at the time of this rulemaking. See Taheripour et al, (2009). As a result, the code was not available to use this variation of the GTAP model for the corn ethanol analysis, hence our use of the CARB model for the corn ethanol analysis.
WW See Taheripour et al, (2009)155 shortcomings and compromises in trying to compare results from GTAP and FAPRI, the relative impacts on land use we believe are informative in that GTAP confirms that there are significant impacts on international land use due biof uel production from food and feed crops.

2.4.11.1.3 Comparison of GTAP and FAPRI Corn Ethanol Results

Despite differences in the way the corn ethanol scenarios were implemented, the quantity of total acres converted to crop land projected by GTAP were similar in scale to the changes projected by the FAPRI results when normalized on a per BTU basis. However, the mean estimates for land converted to crops projected by GTAP were smaller than the changes predicted by FAPRI, which is most likely due to two differences in the modeling frameworks.

First, demand for other uses of land ar e explicitly captured in GTAP, which puts downward pressure on the amount of land that can be converted to crops from pasture or forest. Second, none of the peer-reviewed versions of GTAP currently contain unmanaged land, thereby omitting additional sources of land. In

Figure 2.4-46 and Figure 2.4-47, the GTAP re sults assume all land that is not crop or pasture is forest. However, the FAPRI results allo w land that is not crop or pasture to come from a variety of other non agricultural land such as grassland, savanna, shr ubland and wetlands. The disaggregation of FAPRI “non ag land” is descri bed in more detail in section 2.4.4.2.5.

Although the global aggregated results are si milar, the regional distribution of land cover change varies between the FAPRI and GTAP models. Both models predict similar changes in India, Oceana, non-Brazilian Latin America, and Africa/Middle East. However, the FAPRI model predicts significant increases in crop acres in Brazil and Asia, whereas the changes from the GTAP- model projects limited land use change in those regions. In contrast, the GTAP model projects more crop acre conversion in th e U.S. for corn ethanol scenarios. These differences are likely the result of contrasting international trade structures in the models. FAPRI includes more flexible agricultural trade patterns, and projects agricultural expansion in lower cost of production regions that show the greatest capacity for expansion. In contrast, GTAP tends to maintain existing trade patterns, so it is more likely to project changes in countries that are already major trading partners of the U.S.156 Figure 2.4-46. Changes in Land Cover from an increase in Corn Ethanol

2022 Change in Land Cover from Corn Ethanol

-6 -4 -2 0 2 4 6GTAPFAPRIGTAPFAPRIGTAPFAPRIGTAPFAPRIGTAPFAPRIGTAPFAPRIGTAPFAPRIGTAPFAPRIGTAPFAPRIGTAPFAPRIGTAPFAPRIGTAPFAPRI

Africa & Middle

East Asia Brazil Canada E. Europe India Oceania Other L. America ROW US W. Europe WorldHa/Billion BTU Pasture Crop Forest/Non Ag Land

2.4.11.1.4 Comparison of GTAP and FAPRI Soybean Biodiesel Results

In the soybean biodiesel analysis, the total increase in crop acres aggregated at the global level is similar in the FAPRI and GTAP results. As with the corn ethanol analysis, the regional distribution of these changes also varies. While both models predict similar impacts in Eastern Europe and India, once again the FAPRI model estimates much larger increases in crop acres in157 Brazil than the GTAP model. The GTAP model estimates a larger increase in crop acres in Africa and the Middle East and Canada compared to the FAPRI model. The changes in the U.S. are also different. Whereas the FAPRI model predicts some increase in crop and forest acres, the GTAP model predicts almost no increase in crop acres. Instead, the GTAP model estimates that there will be an increase in pasture land as a result of increasing soybean biodiesel in the U.S. These differences appear to be based on the fact that the GTAP model assumes the price of soybean meal will decrease significantly as a result of the increase in soybean crushing required to produce oil for biodiesel. GTAP projects that the decrease in soybean meal prices will lead to increased beef production, whic h requires additional grazing la nd to complement the use of soybean meal for beef production. In contra st, the FAPRI model assumes that increased biodiesel production will lead to a decrease in U.S. beef producti on, since the relative price of non-grazing animals (e.g., poultry and pork) will decr ease more than the price of beef as a result of lower soybean prices. As a result, U.S. be ef production and pasture la nd decreases in FAPRI and we believe this is a more rational outcome.

Figure 2.4-47. Changes in Land Cover from an increase in Soybean Biodiesel

2022 Change in Land Cover from Soybean Biodiesel

-15 -10

-5 0 5 10 15GTAP-BYPFAPRIGTAP-BYPFAPRIGTAP-BYPFAPRIGTAP-BYPFAPRIGTAP-BYPFAPRIGTAP-BYPFAPRIGTAP-BYPFAPRIGTAP-BYPFAPRIGTAP-BYPFAPRIGTAP-BYPFAPRIGTAP-BYPFAPRIGTAP-BYPFAPRI

Africa & Middle East Asia Brazil Canada E. Europe India Oceania Other L. America ROW US W. Europe WorldHa/Billion BTU

Pasture Crop Forest/Non Ag Land

2.4.11.1.5 Systematic Sensitivity Analysis with the GTAP Model

As mentioned above, there are several parameters that have a significant impact on the amount and type of land conversions resulting fro m an increase in biofuel demand. Due to uncertainty in the past and future values of these parameters, it is possible to use the GTAP model to perform a systematic sensitivity analysis (SSA). Traditional uncertainty analysis relies on a Monte Carlo simulation which solves for e quilibrium conditions using a large number of draws from the underlying distribution of potentia l parameter values. However, Monte Carlo158 analysis is not generally practical for a large CG E model. Instead, previous researchers have performed a SSA with Gaussian Quadrature numer ical integration. This methodology uses a small number of draws from the distribution of random variables to provi de a robust range of results that can be used to develop a co nfidence interval around the mean estimates. XX

In our analysis, the parameters that appear to have the largest impact on the results include the elasticity of crop yields, the elasticity of harvested acreage response, and the elasticity of transformation acro ss cropland, pasture, and forest land. The elasticity of crop yields, often referred to as “price induced yields ” is the measure of how much a particular crop yields will increase in response to an increase in the price of that crop. The larger the value of the elasticity, the more the increase in yields is expected to increase in response to higher prices. In our analysis, we used GTAP’s recommended mean value of 0.25, which indicates that a 1% increase in coarse grain prices leads to a 0.25% increase in coarse grain yields. YY For the SSA, the range of values analyzed was from a low end of 0 (i.e., yields do not respond to price changes) to a high end of 0.5. ZZ The elasticity of transformati on of crop land is a measure of how easily crop acres can be converted between types of crops. For example, the larger the value, the more easily coarse grain acres can be converted to oilseed acres in response to a change in land rental rates. For our analysis, we used a mean value of -0.5 with a lower bound of -0.1 and an upper bound of -1.0. AAA The elasticity of transforma tion of land supply is a measure of how easily land can be converted between la nd cover types (e.g., from forest to crop or pasture). The larger the value of this elasticity, the more land will be converted to different types of land cover in response to changes in relative la nd rental rates. For our SSA, we used a mean value of -0.2, with a lower range of -0.04 and an upper range of -0.36. BBB

2.4.11.1.5.1 GTAP Systematic Sensitivity Analysis for Corn Ethanol

As shown in Table 2.4-48, there is a wide range of potential values for the amount of crop cover changes by region. However, it is im portant to note that for almost all regions, the range of potential values does not cross the X-axis. Thus, we interpret these results to imply that there is a statistically significant change in crop acres in most of the GTAP regions as a result of the increase in corn ethanol. Similarly, as shown in Figure 2.4-49, the range in potential values of pasture cover does not generally cross the X-axis for most of the regions. We therefore conclude that the decrease in pasture acres is statistically significant in most regions as a result of the increase in corn ethanol. Finally, Figure 2.4-50 shows that the mean estimate for some regions show an increase in forest acres, while other regions show a decrease in forest ac res. Again, the confidence intervals around these estimates do not generally cross the X-axis, therefore we interpret these results to be statistically significant.

XX See 1998 Arndt and Pearson, 2009 Keeney and Hertel.
YY As discussed in RIA Chapter 5, our FAPRI projections include disaggregated price-induced yield elasticities that vary by region, crop and time period.
ZZ See 2009 Keeney & Hertel.
AAA These values are based on 2004 FAPRI documentation. [DILEEP, please verify]
BBB These values are based on the 2008 Ahmed, Hertel, and Lubowski paper.159 Figure 2.4-48. Crop cover chan ge due to U.S. corn-ethan ol production (million acres)

Corn Ethanol Change in Crop Cover With Confidence Intervals

-0.20 0.00 0.20 0.40 0.60 0.80 1.00

USA CAN E U27 BRAZIL JAPAN CHIH K G INDIA
L A EEX
R oLAC

E EF

SUE

X RoE MEAS T NAEX SSAEX
R oAFR SA

S IAEEX RoHIA RoASIA
O ceaniaMillion Acres

Figure 2.4-49. Pasture cover ch ange due to U.S. corn-ethan ol production (million acres)160 -1.146

-1.60 -1.40 -1.20 -1.00 -0.80 -0.60 -0.40 -0.20 0.00 0.20

Ch in Pasture Cover

Figure 2.4-50. Forest cover change due to U. S. corn-ethanol production (million acres)

-0.236

-0.40 -0.30 -0.20 -0.10 0.00 0.10 0.20

Ch in Forest Cover

2.4.11.1.5.2 GTAP Systematic Sensitivity Analysis for Soybean Biodiesel161 Similar to the corn ethanol results, the SSA for biodiesel generally shows that the land cover changes are statistically significant for the crop, pasture, and forest acre changes predicted by GTAP. As shown in the following figures, mo st of the confidence intervals do not cross the X-axis, therefore indicating that that the results are robust.

Figure 2.4-51. Crop cover change due to U.S. biodiesel prod uction (million acres)

3.214

-0.50 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50

US

A E U27

BRA Z IL CAN JA

P AN

CHI

H KG IN D IA C_C_A m e r S_o_Amer E_Asi a Mala_Indo R_SE _A

s i a R_S_Asia Rus s ia Oth_CE E _CIS

O

t h_E

urope

M

EA

S _NA

f r S_S_AFR Oce

a ni

a

G L OBAL Ch in Crop Cover

Figure 2.4-52. Pasture cover change due to U.S. biodie sel production (million acres)162 -2.50 -2.00 -1.50 -1.00 -0.50 0.00 0.50 1.00 1.50

Ch in Pasture Cover

Figure 2.4-53. Forest cover change due to U.S. biodiesel production (million acres)163 -0.80 -0.60 -0.40 -0.20 0.00 0.20 0.40

Ch in Forest Cover

2.4.11.2 Evaluation of International Land Conversions with Higher Resolution Satellite Data

EPA worked with remote sensing experts from Integrity Applications Inc. (IAI) to analyze higher resolution satellite imagery in regions that factored prominently into our land use change analysis. The purpose of this analysis was to compare the Version 5 MODIS imagery with 500-m resolution to an imagery data set with much higher resolution. As discussed below, we found that the higher resolution LandSat da ta set with 30-m reso lution provided similar results.

Based on resources and data availability, three regions were chosen for analysis: Brazil, India and the Indonesian island of Sumatra. Braz il was chosen because it was, and remains, the country with the largest agricultural land use respon se in our modeling of the indirect impacts of U.S. biofuel consumption. In the proposed rule analysis, India was the most important region in Asia. Based on modeling updates, the response in India was much smaller in our final rule analysis. However, it is still a good country to an alyze with higher resolu tion data because it is a major agricultural producer with crops and land cove r types distinct from what is found in Brazil. Finally, Indonesia was chosen because it is a majo r producer of palm oil in a region with peat soils. We narrowed our focus to the island of Sumatra because satellite imagery for Indonesia suffered from significant cloud cover problems.164 Our high resolution analysis relied on the Landsat Global Land Surveys for the years 2000 and 2005; these Global Land Surveys are mo saics (i.e., compilati ons) of multi-spectral digital images produced to represent the entire earth during the growing season of a specific year. For optimal comparison of land cover categories, th e MODIS data set was used as training data to classify the raw Landsat imagery. Table 2.4-72 summarizes the characteristics of the data used.

Table 2.4-72 . Characteristics of Satellite Data for High Resolution Analysis
Landsat MODIS Data use IAI: land use classification and change detection IAI: training data to classify raw Landsat data Winrock: land use classification and change detection Data source Landsat Global Land Survey MODIS V5 CCC

Years covered Effectively 2000 and 2005 2001, 2005 and 2007

Temporal resolution +/- 3 years; data acquired every 16 days Every one to two days

Spatial resolution 30-m 500-m

Public Availability US Geological Survey (USGS) Archive DDD LP DAAC Data Pool EEE

By calibrating the land cover classification definitions to those used by MODIS, we converted the true color Landsat images into co rresponding classification maps (see Figure 1). The color key for the classification map is provided below in Figure 2.4-54.

Figure 2.4-54. Example of a true color Landsat image (left) and the corresponding land cover classification map (right)

CCC The MODerate resolution Imagin Spectoradiometer (MODIS) land Products ( MCD12Q1) Land Cover Type Product (Land Cover Type 1), https://lpdaac.usgs.gov/lpdaac/products/modis_pr oducts_table/land_cover/y early_l3_globa l_500_m/mcd12q1
DDD http://eros.usgs.gov/#/Find_Data/Products_and_Data_Available/TriDecadal_Global_Landsat_Orthorectified_Ov erview
EEE https://lpdaac.usgs.gov /lpdaac/get_data/data_pool165 Figure 2.4-55. Land categories and corresponding colors

Classification maps from 2000 and 2005 were compared to find areas where land changed from one category to another. For comparison with EPA’s MODIS analysis, the resulting change matrices were aggregated by Administrative Unit and we analyzed the types of land converted to cropland. For most regions, the higher resolution da ta found deforestation rates somewhere in between the original and co rrected MODIS data (see Section 2.4.4 for an explanation of the MODIS correction process). Va lidation data similar to the confusion matrix used to correct the MODIS data set was not available for the Landsat data, therefore it is difficult to directly compare these results. However, in general, the results do suggest that the resolution of the Version 5 MODIS data is adequate (i.e., provides similar results as higher resolution imagery), especially after it has been corrected with data validation procedures. Figure 2.4-54.166 Figure 2.4-54 shows the share of deforestation from crop expansion in each region analyzed, and Table 2.4-73 includes all of th e land types converted to cropland. The Indonesia results are omitted because of the cloud cover issues me ntioned previously. More details about the Landsat data analysis are provided in a tec hnical report by IAI available on the docket. 118

Figure 2.4-56. Share of Deforestation from Crop Expansion Measured with MODIS and Landsat Satellite Imagery

0% 10% 20% 30% 40% 50% 60%

Brazil:
Amazon
Biome Brazil: Central-West

Cerrados Brazil: Northeast

Coast Brazil: NorthNortheast Cerrados Brazil: South Brazil: Southeast India MODIS-Original Landsat MODIS-Corrected

Table 2.4-73. Types of Land Converted to Cropland by Data Source and Region167 Region Data Source Brazil: Amazon Biome Brazil:CentralWest Cerrados Brazil: Northeast Coast Brazil:NorthNortheast Cerrados Brazil: South Brazil: Southeast India

Landsat 50% 6% 0% 1% 15% 2% 3% Forest MODIS-Corrected 54% 11% 11% 15% 13% 10% 10%
Landsat 8% 19% 8% 12% 14% 10% 5% Grassland MODIS-Corrected 8% 26% 19% 16% 23% 18% 21%
Landsat 11% 20% 28% 14% 55% 53% 27% Mixed MODIS-Corrected 15% 20% 19% 10% 28% 30% 30%
Landsat 26% 55% 54% 67% 14% 34% 49% Savanna MODIS-Corrected 20% 36% 41% 49% 29% 36% 19%
Landsat 2% 0% 9% 5% 1% 1% 14% Shrubland MODIS-Corrected 2% 6% 8% 9% 6% 6% 17%
Landsat 2% 0% 0% 1% 1% 0% 0% Wetlands MODIS-Corrected 1% 0% 0% 0% 0% 0% 1%
Landsat 0% 0% 1% 0% 0% 0% 2% Barren MODIS-Corrected 0% 0% 1% 1% 0% 0% 2%168 

### 2.5 Baseline Gasoline and Diesel Fuel

2.5.1 Background

Section 201 of the Energy Independence and Security Act (EISA) mandated that a baseline for gasoline and diesel fuel be establis hed against which renewable fuels were to be compared:

The term ‘baseline lifecycle greenhouse gas emissions’ means the average lifecycle greenhouse gas emissions, as determined by the Administrator, after notice and opportunity for comment, for gasoline or dies el (whichever is being replaced by the renewable fuel) sold or distributed as transportation fuel in 2005.

For the proposed rule, the Agency used the GREET model (Version 1.8b) to calculate the baseline GHG impacts of gasoline and diesel fuel production. However, we received numerous comments stating that GREET was not the best tool to use to calculate the petroleum baseline. Hence, to estimate the lifecycle GHG emissions associated with baseline gasoline and diesel transportation fuel for the final rule, we utiliz ed the 2009 analysis performed by the National Energy Technology Laboratory (NETL), “Developme nt of Baseline Data and Analysis of Life Cycle Greenhouse Gas Emissions of Petroleum-Base d Fuels”, which was speci fically directed at establishing this 2005 baseline. NETL stated that the goal of their study was to “determine the life cycle greenhouse gas emissions for liquid fuels (conventional gasoline, conventional diesel, and kerosene-based jet fuel) production from petr oleum as consumed in the U.S. in 2005 to allow comparisons with alternative transportation fuel options on the same basis (i.e., life cycle modeling assumptions, boundaries, and allocation procedures).” Furt hermore, NETL stated that “[t]he study goals and scope were aligned to meet the definition of “baseline lifecycle greenhouse gas emissions” as defined in the En ergy Independence and Security Act of 2007 (EISA 2007), Title II, Subtitle A, Sec. 201.” Spec ific detail on NETL’s analysis can be found in their report. FFF

2.5.2 Crude Oil Extraction

NETL determined the emissions associated with extraction and processing for crude oil and synthetic crude oil, natural gas liquids (NGL s), and unfinished oils as feedstocks to U.S. petroleum refineries and to foreign refineries pr oducing gasoline and diesel imported by the U.S. in 2005.

2.5.2.1 U.S. refineries

The input of crude oil, natural gas liquids, a nd unfinished oils to dom estic refineries was determined from EIA data and is summarized in Table 2.5-1 below.

FFF Available in the docket, EPA-HQ-OAR-2005-0161, or on the NETL website at http://www.netl.doe.gov/energyanalyses/pubs/NETL%20LCA%20Petroleum-Based%20Fuels%20Nov%202008.pdf169 Table 2.5-1. Feedstock inputs to U.S. refineries Feedstock Input (thousand bbl/day) Crude Oil 15,220 Natural Gas Liquids 432 Unfinished Oils (net) 569

The crude oil mix to U.S. refineries was also determined from EIA data and is reflected in Table 2.5-2.

Table 2.5-2. Crude oil imports to U.S. refineries U.S. Crude Oil Sources Production/Import as % of Refinery Crude Input (Year 2005, EIA) U.S. Crude Oil 33.8% Canada Crude Oil Canada Oil Sands 10.7%

Mexico Crude Oil 10.2% Saudi Arabia Crude Oil 9.4%

Venezuela Crude Oil 8.1% Nigeria Crude Oil 7.1% Iraq Crude Oil 3.4% Angola Crude Oil 3.0% Ecuador Crude Oil 1.8% Algeria Crude Oil 1.5% Kuwait Crude Oil 1.5% Other 9.5%

Country-specific crude oil extraction pr ofiles were obtained by NETL from PE International for all major oil exporters to the U.S. aside from Canada and are available in the docket, EPA-HQ-OAR-2005-0161. Canadian crude o il extraction emissions are more difficult to estimate, as the U.S. imports both conventiona l oil and oil sands from Canada. To estimate emissions from Canadian conventional crude extr action, the U.S. conven tional crude extraction profile was utilized, while incorporating Canada-specific data on venting and flaring rates. GGG For Canadian oil sands, extracti on emission rates were derived us ing emissions reported by two major oil sands producers. These estimated values for oil sands production were comparable to those found by Charpentier et al. (2009) 119 . 9.5% of oil imports we re grouped into a category termed “other”, which consisted of imports from 31 countries. Due to the complexity and uncertainty associated with developing estim ates for each of thes e countries, extraction emissions for this group were assumed to be th e average of the conventional crude extraction emissions from the other importers for which sp ecific extraction estimates were developed.

GGG The U.S. extraction profile was used as a surrogate for extraction of Canadian conventional crude oil, as most data sources do not separate out emissions for Canadian conventional crude production from oil sands.170 Extraction emissions for unfinished oils HHH were assumed to be th e same as for crude oil extraction, with the addition of emissions for an atmospheric/vacuum distillation step after extraction. Unfinished oils were assumed to be of the same import mix as crude oil. Emissions associated with NGLs extraction were estimated using Canadian data for upstream oil and gas operations

.

2.5.2.2 Foreign Refineries

Countries exporting gasoline and diesel to th e U.S. were determined from EIA data. Table 2.5-3 reflects the percentage that imports made up of total U.S. consumption of gasoline and diesel.

Table 2.5-3. Imports as a percentage of 2005 U.S. Consumption Product Percentage of U.S. Consumption Conventional Gasoline 12.7%

Conventional Diesel 5.2%

Canada and the Virgin Islands were the primary liquid fuel exporters to the U.S., so extraction emissions associated with fuels imported from those c ountries were estimated more rigorously. Canada consisted of 25% of the finished motor gasoline imported to the U.S. and 32% of the diesel imported, while the Virgin Islands accounted for 17% of the gasoline and 29% of the diesel. For both of these countries, th e crude oil import mix was known, so crude oil extraction emissions were estimated using the PE International extraction profiles.

The estimation method for other liquid fuel exporters to the U.S. depended on the origin of the crude oil utilized. In some cases, crude o il was extracted in the same country in which it was refined, so extraction emissions could be estim ated from PE International extraction profiles. In other cases, crude oil was imported from one c ountry for refining in another, and the crude import mix was not entirely clear. For most of th ese countries, PE International’s GaBi 4 Life Cycle Assessment Software III was utilized to provide estimates of extraction emissions.

This still left a handful of countries for wh ich there was no method to estimate extraction emissions. For these cases, “surrogate” profiles we re used. For instance, for European countries for which a country-specific profile was not ava ilable, the EU-15 or EU -25 extraction profile was utilized. For South Korea, it is known that th e source of crude oil is primarily Saudi Arabia, so the Saudi profile was utilized to estimate extraction emissions. For the remainder of countries, the extraction em issions were estimated to be the fore ign average of all crude profiles. In total, the foreign average profile was used for 9% of the gasoline crude oil mix and 12% of the diesel crude oil mix.

HHH “All oils requiring further processing, except those requ iring only mechanical blending. Unfinished oils are produced by partial refining of crude oil and include naphthas and lighter oils, kerosene and light gas oils, heavy gas oils, and residuum .” Department of Energy: U.S. Energy Information Administration. “Glossary” http://www.eia.doe.gov/glossary/glossary_u.htm.
III “[PE International’s] GaBi software allows all the GHG emissions of your product to be captured in a systematic and transparent way. Primary data specific to your produ ct can then be incorporated into your analyses and combined with secondary data on GHG emissions available from the GaBi databases.” PE International. Product Carbon Footprint. < http://www.pe-international.co m/consulting/carbon-footprint/product-carbon-footprint>171 2.5.3 Crude Oil Transport

For domestic refineries, the NETL report states that “[c]rude oil transport to U.S. refineries includes pipeline tran sport within the exporting country , ocean tanker transport to the U.S., and domestic crude oil trans port to refineries via a combination of pipeline, water carrier, rail, and truck.” All crude is assumed to be transported by pi peline 100 miles to the U.S. border or to a port for shipping to the U.S., with the energy intensity for pipeline transport assumed to be 260 Btu/ton-mile. Based on EIA data, the dist ance from the foreign port to the U.S receiving port was estimated for the top ten countries from which crude oil was im ported. For all other countries, the one-way travel distance was assumed to be 10,000 nautical miles.

Table 2.5-4. Travel distance for crude oil based on country of origin

Crude Oil Sources Import as % of Refinery Crude Input (Year 2005, EIA) Country-Specific Average One-Way

Travel Distance (nautical miles) Canada Waterborne 3.0% 675

Canada Pipeline 7.7% NA Mexico Crude Oil 10.2% 1,061 Saudi Arabia Crude Oil 9.4% 12,018

Venezuela Crude Oil 8.1% 1,789

Nigeria Crude Oil 7.1% 5,672

Iraq Crude Oil 3.4% 12,370 Angola Crude Oil 3.0% 6,736 Ecuador Crude Oil 1.8% 5,653 Algeria Crude Oil 1.5% 4,452 Kuwait Crude Oil 1.5% 12,526 Other 9.5% 10,000

Emissions arising from domestic transport of crude were estimated using the breakout of crude oil transportation modes for 2004, as illustrated in Table 2.5-5.

Table 2.5-5. Domestic transporta tion breakout for crude oil

Pipelines Water Carriers Motor Carriers Railroads

75.9% 23.7% 0.3% 0.1%

For foreign refineries where extraction and refining occurred in the same country, transport of 100 miles by pipelin e from well to refinery was a ssumed. For countries which imported crude, refined it into liquid fuels, and ex ported the liquid fuels to the U.S., the GaBi 4 Life Cycle Assessment Software gave estimates of the emissions associated with crude oil transport. For Canada, the Virgin Islands, and South Korea, crude oil transport distances by tanker were estimated, with the only exception th at crude exported from the U.S. to Canada traveled by pipeline. Transport of crude oil from the port of entry into the United States to the172 petroleum refinery is not included in the model, si nce an analysis of petroleum refinery locations indicated that most refineries are geographically located near the port of entry. The exclusion of this transport operation was determined to have a negligible effect on the final results.

Transport of unfinished oils was modeled in the same way as crude oil transport. Transport of NGLs was modeled in similar way to transport and distribution of petroleum products, which is described in section 2.5.5, “Fuel Transport and Distribution”.

2.5.4 Refining

NETL’s refining emissions estim ation accounts for the following:

• Acquisition of fuels

o Indirect emissions associated with purchased power and steam

o Emissions associated with the acquisiti on of coal and natural gas purchased and consumed at the refinery as fuels

o Emissions associated with production of fuels at the refinery which are subsequently consumed as fuels (i.e. still gas, petroleum coke)

• Combustion of fuels at the refinery

• Hydrogen production (on-site and off-site)

o Upstream emissions associated with natural gas feed

o CO 2 process emissions from steam methane reforming (SMR)

o Fuel combustion and upstream emissions a ssociated with natural gas fuel and indirect (electricity) emissions for off-site hydrogen production

• Flaring

• Venting and fugitive emissions

The NETL report indicates that, “The emissions above will be organi zed into a refinery emissions pool and a hydrogen em issions pool and subsequently allocated between the various refinery products. There are no individual assign ments of energy sources to unit operations or refinery products.”

To determine the GHG emissions from the refi ning of gasoline and diesel, NETL first determined the total refining emissions from fuels combustion, fuels acquisition, flaring, hydrogen production, and methane venting. For each of the refinery units, they then used the capacity/throughput, energy, hydrogen consumption, and contribution to the final product slate to allocate emissions to gasoline and diesel production.

A domestic refinery model was used as a surrogate for all foreign refinery operations. A review of foreign refinery models from PE Americas indicated that differences in boundary conditions and allocation procedures introduced greater uncertainty in the final results than using the domestic refinery model as a surrogate for foreign refinery operations. The use of the domestic refinery model for foreign refinery ope rations was noted by NETL as a data limitation to the study.173 2.5.5 Fuel Transport and Distribution

“Product transport includes transport of im ported liquid fuels from the exporting nations to the U.S. as well as domestic transport of both imported fuels and dom estically produced liquid fuels.” Foreign transport consists of tanker a nd/or pipeline transport of imported products to U.S. ports. The products are assumed to be shi pped 10 miles by pipeline to a port or the U.S. border. Specific port-to-port travel distances we re calculated for imports from Canada and the U.S. Virgin Islands. All other product imports were assumed to travel 5,000 nautical miles to the U.S. Emissions arising from domestic transport were estimated using the breakout of petroleum product transportation modes for 2004, as shown in Table 2.5-6.

Table 2.5-6. Domestic transportation breakout for petroleum products Pipelines Water Carriers Motor Carriers Railroads

59.8% 29.9% 6.3% 4.0%

2.5.6 Tailpipe Emissions

We updated the CO 2 emission factors for gasoline and di esel to reflect revisions in the factors made by EPA, which were used in th e September 28, 2009 proposed rule to establish GHG standards for light-duty vehicles . We have also updated the CO 2 emissions factors for ethanol and biodiesel to be cons istent with those used in the October 30, 2009 final rulemaking for the Mandatory GHG Reporting Rule. For the final rule, we have maintained the same CH 4 and N 2 O emission factors used for the proposed ru le, which were based on EPA MOVES model run results.

Table 2.5-7. Tailpipe emissions for relevant fuels (g/mmBTU) Fuel Type CO 2 CH 4 N 2 O

Gasoline 77,278 3 5

Diesel 78,308 1 2 Ethanol 75,885 13 2 Biodiesel 79,837 1 2

2.5.7 Land Use Change GHG Emissions

For the final rule, we performed an estimate of land use change emissions associated with oil extraction and production to dete rmine if the value was significant enough to be included in our petroleum baseline ca lculation. As oil sands production incurs a greater degree of land use change versus conventional crude oil production, we started with an estimate of emissions from the conversion of Alberta fore st for oil sands production.

Jordaan et al. (2009) estimated the land use ch ange intensity for oil sands surface mining and in-situ development, using data on project area and established reserves. They estimated an average of 0.42 m 2 /m 3 synthetic crude oil (SCO) for surface mining and 0.11 m 2 /m 3 SCO for insitu. These intensity values were based on divi ding the area of land disturbance by the total volume of SCO produced over the lifetime of the proj ect. Jordaan also calculated values for land174 use change associated with upgrading of oil sands and for the extraction of natural gas utilized for oil sands production. However, we restrict ed our calculation to consider only the land-use change associated with oil sands production to be consistent with the life cycle analysis methodology that we estab lished for renewable fuels. JJJ

We then utilized the Winrock database values to determine GHG emissions from land use change of Alberta forest, the assumed area where oil sand extraction would occur. Per IPCC Guidelines, we assumed that 20% of the soil ca rbon was lost from conversion, which gave an overall value of 278.25 grams CO 2 /hectare over a 30 year timeframe. We multiplied the land use change intensity and the GHG emissions from land-use change to yield a GHG intensity value. This calculation yielded values of 1,858 (1,460-2,787) grams CO 2 /bbl for surface mining and 487 (310-708) grams/bbl for in-situ. These valu es were considerably lower (approximately 98%) than the oil sands extraction estimates dete rmined by NETL and used in our petroleum baseline (81,000-122,000 g CO 2 /bbl).

On a gasoline basis, the land-use change values were 380 (298-570) g CO 2 /mmbtu for surface mining and 99 (63-145) g CO 2 /mmbtu for in-situ. Since oil sands only comprised about 5% of the crude oil mix to domestic refineries in 2005, these estimates were adjusted to determine their impact on the aggregate well-to-tank petroleum baseline totals. For surface mining, the oil sands land use change value was on the order of 0.1% of total well-to-tank CO 2 emissions, and, for in-situ, it was 0.06%. Alt hough these values likely represent the worst-case land use impact of petroleum extraction, they are still negligible in the total well-to-tank values and including them would not change the overall petroleum baseline values. We anticipate that future work will help to quantify these values, and we can evaluate the appropriateness of including a land use estimate in the future.

2.5.8 Petroleum Fuel 2005 Baseline Well-to-Tank GHG Emissions

The results for each of the lifecycle stages were combined to give a well-to-tank lifecycle GHG value for 2005 gasoline and diesel as show n in Table 2.5-8. Tailpipe combustion emissions for the two fuels are described in section 2.5.6. When combined with the tailpipe emissions values, a well-to-wheels result for gasoline of 98,205 grams CO 2 eq/mmBTU and 97,006 grams CO 2 eq/mmBTU was obtained.

Table 2.5-8. Gasoline and diesel baseline we ll-to-tank GHG emissions (g/mmbtu of fuel) CO 2 CH 4 N 2 O CO 2 eq. Gasoline 16,816 2,282 103 19,200

Diesel 15,838 2,066 94 17,998

JJJ In other words, when considering the GHG impacts of renewable fuels, we did not consider GHG emissions from land use change associated with infrastructure or natura l gas extracted and used for renewable fuels production.175 

### 2.6 Fuel-Specific Lifecycle Greenhouse Gas Emissions Results

In this section we present detailed lifecycle GHG analysis results, including the results of sensitivity and scenario analyses on key assump tions. As discussed above, to implement the EISA the crucial result that determines which renewable fuel pathways qualify for RFS2 credits is the percent reduction in lifecycle GHG emissions compared to the average lifecycle greenhouse gas emissions for gasoline or diesel sold or distributed as transportation fuel in 2005. To compare lifecycle GHG emissions from renewable fuels and petroleum, we present the grams of CO2-equivalent emissions per BTU of fuel produced (gCO2eq/mmBTU). The previous sections in this Chapter disc ussed our methodology for calculating lifecycle GHG emissions for each component of the renewable fuel lifecycle, and for the 2005 petroleum baseline. In this Section we present and compare the GHG emissions results for each of these components in the fuel lifecycle. We also discuss how key assumptions can change the GHG emissions from each component of the fuel lifecycle, and how th ey influence the final GHG percent reduction estimates.

In addition to estimating GHG emissions at every stage of the fuel lifecycle, EPA’s task in this rulemaking is to integrate the GHG emissions estimates from all stages of the lifecycle in order to estimate lifecycle GHG percent reductions for each renewable fuel pathway. We have considered a number of ways to meet this challenge, and have identified several key methodological issues that can in fluence whether a particular re newable fuel pathway meets the thresholds set forth in the EISA.

2.6.1 Renewable Fuel Lifecycle GHG Results

This section presents fuel specific lifecycle GHG results for the different renewable fuels compared to the petroleum baseline fuel replaced. Results are presented for the baseline set of assumptions including:

• Business as usual yields

• 30 year 0% discounted

• 2022 year for results

Sensitivity around these assumptions are presented in section 2.6.2. This section presents the results as a range based on the uncertainty analysis conducted around the land use change emissions.

2.6.1.1 Corn Ethanol Results

EPA analyzed the lifecycle GHG performance of a variety of ethanol from corn starch pathways. The results presented here are for an average natural gas fired dry mill plant in 2022. We predict approximately 90% of all plants will be producing co rn oil as a by-product either through a fractionation or extraction process; it is likely most if not all new plants will elect to include such technology. We also expect that, to lower their operating co sts, most facilities will sell a portion of their co-product DGS prior to dr ying thus reducing energy consumption and176 improving the efficiency and lifecycle GHG performance of the plant. The current national average plant sells approximately 37% of the DGS co-product prior to drying.

Figure 2.6-1 shows the percent change in the lifecycle GHG emissions compared to the petroleum gasoline baseline in 2022 for a corn et hanol dry mill plant using natural gas for its process energy source, drying the national average of 63% of the DGS it produces and employing corn oil fractionation technology. Lifecycle GHG emissions equivalent to the gasoline baseline are represented on the graph by the zero on the X-axis. The 20% reduction threshold is represented by the dashed line at -20% on the graph. The results for this corn ethanol scenario are that the midpoint of th e range of results is a 21% reduction in GHG emissions compared to the gasoline 2005 baselin e. The 95% confidence interval around that midpoint ranges from a 7% reduction to a 32% reduction compared to the gasoline baseline based on the uncertainty in the land use change assumptions.

Figure 2.6-1. Distribution of Results for a New Natural Gas Fired Corn Ethanol Plant Average 2022 plant: natural gas, 63% dr y, 37% wet DGS (w/ fractionation)

-50 -40 -30 -20 -10 0 10 Percentage Change in Biofuel GHG Lifecycle Emissions Compared to Petroleum Fuel

Figure 2.6-2 below includes lifecycle GHG emissions broken down by several stages of the lifecycle impacts for the typical corn ethanol depicted in Figure 2.6-1 compared to the 2005 baseline average for gasoline. Lifecycle emissions are normalized per energy unit of fuel produced and presented in grams of carbon-di oxide equivalent GHG emissions per million British Thermal Units of fuel produced (gCO2e /mmBTU). Figure 2.6-2 includes our mean177 estimate of international land use change emissions as well as the 95% confidence range from our uncertainty assessment, which accounts for uncer tainty in the types of land use changes and the magnitude of resulting GHG emissions. For the petroleum baseline, the fuel production stage includes emissions from extraction, transport, refining and distribution of petroleum transportation fuel. Petroleum tailpipe emi ssions include CO2 and non-CO2 gases emitted from fuel combustion.

Figure 2.6-2. Results for a New Natural Gas Fi red Corn Ethanol Plant by Lifecycle Stage Average 2022 plant: natural gas, 63% dr y, 37% wet DGS (w/ fractionation)

-21%

-20,000 0 20,000 40,000 60,000 80,000 100,000 120,000

2005 Gasoline Baseline 2022 Avg Dry Mill NGGHG Emissions (gCO2-eq / mmBtu) Fuel Production

International Land Use Change

International Farm Inputs and Fert N2O
Other (fuel and feedstock transport)
Domestic Land Use Change

International Livestock

International Rice Methane

Tailpipe

Domestic Rice Methane

Domestic Farm Inputs and Fert N2O

Domestic Livestock

20% Reduction Threshold

Net Emissions

We also looked at a number of different plat types, technolog ies and fuel types used.

Figure 2.6-3 shows the results for an average 2022 corn ethanol dry mill plant (fractionation and 63% dry DGS) but with different fuel sources, natural gas coal and biomass.178 Figure 2.6-3. Results for New Corn Ethanol Plants by Fuel Source and Lifecycle Stage Average 2022 plant: 63% dry, 37% wet DGS (w/ fractionation)

-21% -1%

-38%

-20,000 0 20,000 40,000 60,000 80,000 100,000 120,000

2005 Gasoline Baseline 2022 Avg Dry Mill NG 2022 Avg Dry Mill Coal 2022 Avg Dry Mill BiomassGHG Emissions (gCO2-eq / mmBtu) Fuel Production

International Land Use Change

International Farm Inputs and Fert N2O

Other (fuel and feedstock transport)

Domestic Land Use Change

International Livestock

International Rice Methane

Tailpipe

Domestic Rice Methane

Domestic Farm Inputs and Fert N2O

Domestic Livestock

20% Reduction Threshold

Net Emissions

Table 2.6-1 shows the results for all of the di fferent corn ethanol pathways considered.

Table 2.6-1. Results for New Corn Ethanol Plants by Type Plant Type Plant Technology Percent Change in Lifecycle GHG Emissions Time Horizon (years) 30 30 30 Discount Rate 0% 0% 0% Range Low Mean High Dry Mill NG Base Plant (dry DGS) -28% -17% -3% Dry Mill NG w/ CHP (dry DGS) -31% -20% -6% Dry Mill NG w/ Fractionation (dry DGS) -30% -18% -4% Dry Mill NG w/ CHP and Fractionation (dry DGS) -33% -22% -7% Dry Mill NG w/ Fractionation and Membrane Separation (dry DGS) -33% -22% -8% Dry Mill NG w/ CHP, Fractionation and Membrane Separation (dry DGS) -37% -25% -11% Dry Mill NG w/ Fractionation, Membrane Separation, and Raw Starch Hydrolysis (dry DGS) -38% -26% -12%

Dry Mill NG w/ CHP, Fractionation, Membrane Separation, and Raw Starch Hydrolysis (dry DGS) -41% -30% -15%

Dry Mill NG Base Plant (wet DGS) -39% -27% -13% Dry Mill NG w/ CHP (wet DGS) -42% -30% -16% Dry Mill NG w/ Fractionation (wet DGS) -38% -26% -12%179 Plant Type Plant Technology Percent Change in Lifecycle GHG Emissions Dry Mill NG w/ CHP and Fractionation (wet DGS) -41% -29% -15% Dry Mill NG w/ Fractionation and Membrane Separation (wet DGS) -41% -30% -16% Dry Mill NG w/ CHP, Fractionation and Membrane Separation (wet DGS) -44% -33% -19% Dry Mill NG w/ Fractionation, Membrane Separation, and Raw Starch Hydrolysis (wet DGS) -44% -33% -18%

Dry Mill NG w/ CHP, Fractionation, Membrane Separation, and Raw Starch Hydrolysis (wet DGS) -47% -36% -22%

Dry Mill Coal Base Plant (dry DGS) 1% 12% 26% Dry Mill Coal w/ CHP (dry DGS) -1% 10% 24% Dry Mill Coal w/ Fractionation (dry DGS) -7% 5% 19% Dry Mill Coal w/ CHP and Fractionation (dry DGS) -9% 3% 17% Dry Mill Coal w/ Fractionation and Membrane Separation (dry DGS) -14% -3% 11% Dry Mill Coal w/ CHP, Fractionation and Membrane Separation (dry DGS) -16% -5% 9% Dry Mill Coal w/ Fractionation, Membrane Separation, and Raw Starch Hydrolysis (dry DGS) -23% -12% 2%

Dry Mill Coal w/ CHP, Fractionation, Membrane Separation, and Raw Starch Hydrolysis (dry DGS) -25% -14% 0%

Dry Mill Coal Base Plant (wet DGS) -21% -10% 4% Dry Mill Coal w/ CHP (wet DGS) -23% -12% 2% Dry Mill Coal w/ Fractionation (wet DGS) -23% -11% 3% Dry Mill Coal w/ CHP and Fractionation (wet DGS) -25% -13% 1% Dry Mill Coal w/ Fractionation and Membrane Separation (wet DGS) -30% -19% -5% Dry Mill Coal w/ CHP, Fractionation and Membrane Separation (wet DGS) -32% -21% -7% Dry Mill Coal w/ Fractionation, Membrane Separation, and Raw Starch Hydrolysis (wet DGS) -36% -24% -10%

Dry Mill Coal w/ CHP, Fractionation, Membrane Separation, and Raw Starch Hydrolysis (wet DGS) -38% -26% -12%

Dry Mill Biomass Base Plant (dry DGS) -51% -40% -26%

Dry Mill Biomass w/ CHP (dry DGS) -59% -47% -33%

Dry Mill Biomass w/ Fractionation (dry DGS) -49% -38% -24%

Dry Mill Biomass w/ CHP and Fractionation (dry DGS) -57% -45% -31%

Dry Mill Biomass w/ Fractionation and Membrane Separation (dry DGS) -49% -38% -24%

Dry Mill Biomass w/ CHP, Fractionation and Membrane Separation (dry DGS) -56% -45% -31%

Dry Mill Biomass w/ Fractionation, Membrane Separation, and Raw Starch Hydrolysis (dry DGS) -49% -38% -24%

Dry Mill Biomass w/ CHP, Fractionation, Membrane Separation, and Raw Starch Hydrolysis (dry DGS) -57% -45% -31%

Dry Mill Biomass Base Plant (wet DGS) -52% -41% -27%

Dry Mill Biomass w/ CHP (wet DGS) -59% -48% -34%

Dry Mill Biomass w/ Fractionation (wet DGS) -50% -39% -25%

Dry Mill Biomass w/ CHP and Fractionation (wet DGS) -57% -46% -32%

Dry Mill Biomass w/ Fractionation and Membrane Separation (wet DGS) -50% -38% -24%

Dry Mill Biomass w/ CHP, Fractionation and Membrane Separation (wet DGS) -57% -45% -31%

Dry Mill Biomass w/ Fractionation, Membrane Separation, and Raw Starch Hydrolysis (wet DGS) -50% -38% -24%

Dry Mill Biomass w/ CHP, Fractionation, Membrane Separation, and Raw Starch Hydrolysis (wet DGS) -57% -46% -32%

Wet Mill with NG -19% -7% 7% Wet Mill with Coal 8% 19% 33% Wet Mill with Biomass -59% -48% -33%

2.6.1.2 Corn Butanol Results180 We analyzed corn butanol, similar to corn ethanol in terms of types of plants and technologies. Figure 2.6-4 shows the percent change in the lifecycle GHG emissions compared to the petroleum gasoline baseline in 2022 for a corn but anol dry mill plant using natural gas for its process energy source, drying the national average of 63% of the DGS it produces and employing corn oil fractionation technology. Lifecycle GHG emissions equivalent to the gasoline baseline are represented on the graph by the zero on the X-axis. The 20% reduction threshold is represented by the dashed line at -20 on the graph. The results for this corn butanol scenario are that the midpoint of the range of results is a 31% reduction in GHG emissions compared to the gasoline 2005 baseline. The 95% confidence interval around that midpoint ranges from a 20% reduction to a 40% reduction compared to the gasoline baseline based on the uncertainty in the land use change assumptions.

The butanol results in slightly greater GHG reductions compared to corn ethanol because of the greater energy content of the fuel. There is actually slightly more energy used in processing but there is a greater energy pr oduction output. Therefore, on a per mmBTU basis there are less GHG emissions produced across all the lifecycle.

Figure 2.6-4. Distribution of Results for a New Natural Gas Fired Corn Butanol Plant Average 2022 plant: natural gas, 63% dr y, 37% wet DGS (w/ fractionation)

-60 -50 -40 -30 -20 -10 0 Percentage Change in Biofuel GHG Lifecycle Emissions Compared to Petroleum Fuel181 Figure 2.6-5 below includes lifecycle GHG emissions broken down by several stages of the lifecycle impacts for the typical corn butanol plant depicted in

Figure 2.6-5 compared to the 2005 baseline aver age for gasoline. Lifecycle emissions are normalized per energy unit of fuel produced and pr esented in grams of carbon-dioxide equivalent GHG emissions per million British Thermal Units of fuel produced (gCO2e/mmBTU).

Figure 2.6-5 includes our mean estimate of intern ational land use change emissions as well as the 95% confidence range from our uncertainty asse ssment, which accounts for uncertainty in the types of land use changes and the magnitude of resulting GHG emissions.

Figure 2.6-5. Results for New Corn Butanol Plants by Lifecycle Stage Average 2022 plant: 63% dry, 37% wet DGS (w/ fractionation)

-31%

-20,000 0 20,000 40,000 60,000 80,000 100,000 120,000

2005 Gasoline Baseline 2022 Base Dry Mill NGGHG Emissions (gCO2-eq / mmBtu) Fuel Production

International Land Use Change

International Farm Inputs and Fert N2O
Other (fuel and feedstock transport)
Domestic Land Use Change

International Livestock

International Rice Methane

Tailpipe

Domestic Rice Methane

Domestic Farm Inputs and Fert N2O

Domestic Livestock

20% Reduction Threshold

Net Emissions

2.6.1.3 Biodiesel Results182 Figure 2.6-6 shows the percent change in the typical 2022 soybean biodiesel lifecycle GHG emissions compared to the petroleum diesel fuel 2005 base line. Lifecycle GHG emissions equivalent to the diesel fuel baseline are represented on the graph by the zero on the X-axis. The 50% reduction threshold is represen ted by the dashed line at -50 on the graph. The results for soybean biodiesel are that the midpoint of th e range of results is a 57% reduction in GHG emissions compared to the diesel fuel base line. The 95% confidence interval around that midpoint results in range of a 22% reduction to an 85% reduction compared to the diesel fuel 2005 baseline.

Figure 2.6-6. Distribution of Results Soybean Biodiesel183 Average 2022 plant; natural gas

-140 -120 -100 -80 -60 -40 -20 0 20 Percentage Change in Biofuel GHG Lifecycle Emissions Compared to Petroleum Fuel

Figure 2.6-7 below includes lifecycle GHG emissions broken down by several stages of the lifecycle impacts for the typical soybean biodiesel plant depicted in

Figure 2.6-6 compared to the 2005 baseline aver age for diesel fuel. Lifecycle emissions are normalized per energy unit of fuel produ ced and presented in grams of carbon-dioxide equivalent GHG emissions per million Br itish Thermal Units of fuel produced (gCO2e/mmBTU).184 Figure 2.6-7 includes the mean estimate of international land use change emissions as well as the 95% confidence range from our uncertainty assessment, which accounts for uncertainty in the types of land use changes and the magnitude of resulting GHG emissions.

Figure 2.6-7 also includes emissions from waste grease based biodiesel. The waste grease biodiesel does not have any agricultural or land use emissions and therefore only a point source estimate is shown for that pathway.

Figure 2.6-7. Results for Bi odiesel by Lifecycle Stage Soybean and Waste Grease Feedstock

-57%

-86%

-40,000 -20,000 0 20,000 40,000 60,000 80,000 100,000 120,000

2005 Diesel Fuel Baseline 2022 Soybean Biodiesel 2022 Waste Grease BiodieselGHG Emissions (gCO2-eq / mmBtu) Fuel Production

International Land Use Change

International Farm Inputs and Fert N2O

Other (fuel and feedstock transport)

Domestic Land Use Change

International Livestock

International Rice Methane

Tailpipe

Domestic Rice Methane

Domestic Farm Inputs and Fert N2O

Domestic Livestock

Net Emissions

50% Reduction Threshold

Figure 2.6-8 shows lifecycle GHG emissions broken down by several stages of the lifecycle impacts for algae oil to biodiesel compar ed to the 2005 baseline average for diesel fuel. Results are shown for the different cases of produc tion described in section 2.4.7.3.3. The algae oil biodiesel does not have any agricultural or land use emissions and therefore only a point source estimate is shown for each pathway.

Figure 2.6-8. Results for Algae Biodiesel by Lifecycle Stage185 Algae Oil Feedstock

0 20,000 40,000 60,000 80,000 100,000 120,000

2005 Diesel Fuel Open Pond PBR Open Pond PBR Open Pond PBR

Petroleum Baseline Base Case Aggressive Case Max CaseGHG Emissions (gCO2-eq / mmBtu) Fuel Production

Other (fuel and feedstock transport) Tailpipe

Fertilizer

50% Reduction Threshold

Net Emissions

2.6.1.4 Sugarcane Ethanol Results

Figure 2.6-9 shows the percent change in the average 2022 sugarcane ethanol lifecycle GHG emissions compared to the petroleum gaso line 2005 baseline. These results assume the ethanol is produced and dehydrated in Brazil prio r to being imported into the U.S. and that the residue is not collected. Lifecycle GHG emissions equivalent to the gasoline baseline are represented on the graph by the zero on the X-ax is. The 50% reduction threshold is represented by the dashed line at -50 on the graph. The results for this sugarcane ethanol scenario are that the midpoint of the range of results is a 61% reduction in GHG emissions compared to the gasoline baseline. The 95% confidence interval around that midpoint results in a range of a 52% reduction to a 71% reduction compared to the gasoline 2005 baseline.

Figure 2.6-9. Distribution of Results for Sugarcane Ethanol Average 2022 plant: no residue collection186 -90 -80 -70 -60 -50 -40 -30 -20 -10 0

Percentage Change in Biofuel GHG Lifecycle Emissions Compared to Petroleum Fuel

We also considered pathways assuming most crop residue of the leaves as well as stalks are collected (and therefore available for burnin g as process energy) or without the extra crop residue being neither collected nor burned as fuel. We also analyzed pathways assuming the ethanol is distilled in Brazil or alternatively being distilled in the Caribbean (“CBI”).

Figure 2.6-10 below includes lifecycle GHG emissions broken down by several stages of the lifecycle impacts for the difference suga rcane ethanol scenarios compared to the 2005 baseline average for gasoline. Lifecycle emissions are normalized per energy unit of fuel produced and presented in grams of carbon-di oxide equivalent GHG emissions per million British Thermal Units of fuel produced (gCO2e /mmBTU). Figure 2.6-10 includes the mean estimate of international land use change emissions as well as the 95% confidence range from our uncertainty assessment, which accounts for uncer tainty in the types of land use changes and the magnitude of resulting GHG emissions.

Figure 2.6-10. Results for Sugarca ne Ethanol by Lifecycle Stage187 With and without residue collection and CBI

-89% -91% -59% -61%

-60,000 -40,000 -20,000 0 20,000 40,000 60,000 80,000 100,000 120,000

2005 Gasoline Baseline No Residue Coll. No CBI No Residue Coll. CBI Residue Coll. No CBI Residue Coll. CBIGHG Emissions (gCO2-eq / mmBtu) Fuel Production

International Land Use Change

International Farm Inputs and Fert N2O
Other (fuel and feedstock transport)
Domestic Land Use Change

International Livestock

International Rice Methane

Tailpipe

Domestic Rice Methane

Domestic Farm Inputs and Fert N2O

Domestic Livestock

50% Reduction Threshold

Net Emissions

As Figure 2.6-10 indicates, the sugarcane et hanol scenarios with re sidue collection have greater GHG reductions compared to the no collection cases. For residue collection there is slightly more energy and emissions needed for crop production due to colle ction and transport of the residue. However, there are significantly more GHG savings at the plant due to more excess electricity production from burning the collected residues.

The CBI cases in which the ethanol is dis tilled in the Caribbean add slightly more GHG emissions from burning fossil fuels for dehydration. This is slightly offset by the additional excess electricity from the sugarcane ethanol plant that does not need to dehydrate the ethanol. Energy used for dehydration at th e ethanol plant could then be used to generate excess electricity that offsets grid electricity production, and results in GHG savings.

2.6.1.5 Cellulosic Biofuels Results

Figure 2.6-11 shows the percent change in the average lifecycle GHG emissions in 2022 for ethanol produced from switchgrass using the bi ochemical process compared to the petroleum gasoline 2005 baseline. Lifecycle GHG emissions equivalent to the gasoline baseline are represented on the graph by the zero on the X-ax is. The 60% reduction threshold is represented by the dashed line at -60 on the graph. The results for this switchgrass ethanol scenario are that the midpoint of the range of results is a 110% reduction in GHG emissions compared to the188 gasoline baseline. The 95% confidence in terval around that midpoint ranges from 102% reduction to a 117% reduction compar ed to the gasoline baseline.

Figure 2.6-11. Distribution of Results for Switchgrass Biochemical Ethanol Average 2022 plant: biochemical pr ocess producing ethanol, ex cess electricity production

-140 -120 -100 -80 -60 -40 -20 0

Percentage Change in Biofuel GHG Lifecycle Emissions Compared to Petroleum Fuel

We have also analyzed additional cellulosic biofuel pathways (i.e., thermochemical cellulosic ethanol and a BTL diesel pathway) as well as considered crop residues as a cellulosic feedstock. Figure 2.6-12 below includes lifecycle GHG emissions broken down by several stages of the lifecycle impacts for the diff erent cellulosic feedstock to ethanol production scenarios compared to the 2005 baseline averag e for gasoline. Lifecycle emissions are normalized per energy unit of fuel produced and pr esented in grams of carbon-dioxide equivalent GHG emissions per million British Thermal Units of fuel produced (gCO2e/mmBTU). Figure 2.6-12 includes the mean estimate of international land use change emissions as well as the 95% confidence range from our uncertainty assessment, which accounts for uncertainty in the types of land use changes and the magnitude of resulting GHG emissions. The residues to ethanol scenarios do not have any international land use emissions and therefore only a point source estimate is shown for those pathways.

Figure 2.6-12. Results for Cellulo sic Ethanol by Lifecycle Stage189 Biochemical and Thermochemical for Switchgrass and Corn Stover

-110% -72%

-92%

-129%

-60,000 -40,000 -20,000 0 20,000 40,000 60,000 80,000 100,000 120,000

2005 Gasoline Baseline Sw itchgrass - Biochemical Sw itchgrass - Thermochemical Corn Stover - Biochemical Corn Stover - ThermochemicalGHG Emissions (gCO2-eq / mmBtu) Fuel Production

International Land Use Change

International Farm Inputs and Fert N2O Other (fuel and feedstock transport) Domestic Land Use Change

International Livestock

International Rice Methane

Tailpipe

Domestic Rice Methane

Domestic Farm Inputs and Fert N2O

Domestic Livestock

60% Reduction Threshold

Net Emissions

Figure 2.6-13 below includes lifecycle GHG emissions broken down by several stages of the lifecycle impacts for the different cellulosic feedstock to F-T diesel fuel production scenarios compared to the 2005 baseline average for diesel fuel. Lifecycle emissions are normalized per energy unit of fuel produced and presente d in grams of carbon-dioxide equivalent GHG emissions per million British Thermal Units of fuel produced (gCO2e/mmBTU).190 Figure 2.6-13. Results for Cellulosic Diesel Fuel by Lifecycle Stage F-T Diesel Fuel for Switchgrass and Corn Stover

-71%

-91%

-20,000 0 20,000 40,000 60,000 80,000 100,000 120,000

2005 Diesel Fuel Baseline Sw itchgrass - F-T Diesel Corn Stover - F-T DieselGHG Emissions (gCO2-eq / mmBtu) Fuel Production

International Land Use Change

International Farm Inputs and Fert N2O Other (fuel and feedstock transport) Domestic Land Use Change

International Livestock

International Rice Methane

Tailpipe

Domestic Rice Methane

Domestic Farm Inputs and Fert N2O
Domestic Livestock

60% Reduction Threshold

Net Emissions

Biochemical ethanol production results in greater GHG savings compared to the thermochemical or F-T diesel fuel scenarios due to the exce ss electricity production from the lignin generated from the bioche mical process. The corn st over scenarios have less overall agricultural sector GHG emissions compared to the switchgrass scenario and do not have international land use change emissions and therefore greater GHG savings.

2.6.2 Sensitivity Analysis

This section presents the results or seve ral sensitivity analyses performed around the different main components of the lifecycle analysis . Some of the sensitivity analysis impact all fuels considered while some only impact specific fuels.

2.6.2.1 Timing and Discount Rate191 In addition to estimating GHG emissions at every stage of the fuel lifecycle, EPA’s task in this rulemaking is to integrate the GHG emissions estimates from all stages of the lifecycle in order to estimate lifecycle GHG percent reductions for each renewable fuel pathway. We have considered a number of ways to meet this challenge, and have identified several key methodological issues that can in fluence whether a particular re newable fuel pathway meets the thresholds set forth in the EISA. For example, one issue that deserves attention is the timing of lifecycle GHG emissions.

Section 2.4.5 explained that the lifecycle GHG emissions associated with biofuels can vary over time. Clearing forests, grasslands, and other types of land that sequester carbon, for crop production can results in GHG emissions for many years. As depicted in Figure 2.6-14, this type of land conversion produces large immedi ate GHG emissions, followe d by a lesser stream of emissions that can last for many years. Biomass feedstocks grown annually on new cropland can be converted to biofuels that offer a GHG benefit relative to the petroleum product they replace, but these benefits may be small compared to the upfront GHG emissions associated with land clearing to expand crop production. Depending on the specific biofuel in question, it can take many years for the benefits of the biofuel to make up for the large initial releases of carbon that result from land conversion (e.g., the payback period).

The payback period calculation, presented gra phically in Figure 2.6-14, represents the time it takes for the emissions savings from the production of biofuels to equal the potentially large initial emissions from land use changes. Alt hough we do not believe it is appropriate to use the payback period for RFS2 compliance purposes, this calculation helps to illustrate the importance of the time dimension of renewable fuel lifecycle GHG emissions.

Figure 2.6-14. Corn Ethanol Payback Period

-1,000,000 -800,000 -600,000 -400,000 -200,000 0 200,000 400,000 600,000 800,0002022203020402050GHG Emissions (gCO2-eq / mmBtu)

2005 Gasoline Baseline

Corn Ethanol Lifecycle GHG Emissions (2022 Average NG Dry Mill)

Cumulative Net Corn Ethanol Life Cycle GHG Emissions Year 0 Land Use •Clearing with fire •Biomass decay •Soil carbon emissions •Foregone forest sequestration Years 1-19 Land Use •Soil carbon emissions •Foregone forest sequestration Years 20-79 Land Use •Foregone forest sequestration

Payback ~14 years

Figure 2.6-14 shows the baseline lifecycle GHG emissions from the 2022 average corn ethanol natural gas fired dry mill with fractionation and dryi ng 63% of DGS, and from the 2005 gasoline baseline. In the first year, in this case 2022, corn ethanol lifecycle GHG emissions are more than five times greater than the gasoline it replaces. However, corn ethanol has ongoing GHG benefits in every subsequent year. It takes approximately 14 years for the annual GHG192 benefits of corn ethanol compared to gasolin e to pay back the initial GHG releases from land clearing. This tells us that unless we analyze the lifecycle GHG emissions of corn ethanol over more than 14 years, corn ethanol from this pathway will not achieve a reduction compared to gasoline. As we extend our analysis beyond 14 years we will see increasing GHG reductions associated with the use of corn ethanol.

The same is true for other renewable fuel s that result in land use change, soybean biodiesel, sugarcane ethanol, and switchgrass biofue ls. Furthermore, the uncertainty in the land use change emissions results in a range of pa yback periods depending on the range of land use change emissions. Table 2.6-2 s hows the different payback periods for the different biofuels and for the high and low range of land use change emissions.

Table 2.6-2. Payback Periods for Different Fuels Payback Period (years) Type of Biofuel Low Midpoint High Corn Ethanol (2022 average plant) 7 14 24 Soybean biodiesel 5 9 21 Sugarcane ethanol (no residue collection, no CBI) 1 2 4 Switchgrass Ethanol (biochemical) 0 0 1 Switchgrass Ethanol (thermochemical) 1 1 2 Switchgrass Diesel (F-T diesel) 1 1 2

The payback periods shown in Table 2.6-2 represent the time needed for the renewable fuels to break even in terms of GHG emissions compared to the petroleum fuel replaced. However, the threshold determinations needed for the rulemaking are based on the fuels reaching a percentage reduction compared to the petroleum fuels replaced. The threshold reduction time period is longer than just the breakeven point. Table 2.6-3 shows the th reshold requirements and time periods to reach those threshold reductions for each fuel.

Table 2.6-3. Threshold Periods for Different Fuels Threshold Reduction Threshold Period (years) Type of Biofuel % Low Midpoint High Corn Ethanol (2022 average plant) 20 15 28 43

Soybean biodiesel 50 10 24 50 Sugarcane ethanol (no residue collection, no CBI) 50 6 12 24

Switchgrass Ethanol (biochemical) 60 2 3 5

Switchgrass Ethanol (thermochemical) 60 7 12 24193 Switchgrass Diesel (F-T diesel) 60 7 14 26

The payback period concept helps to demons trate the importance of the choice of a discount rate and time horizon for this analysis. These factors are so important because of the variation in GHG emissions from renewable fuels over time, and the contrasting steady annual emissions from the petroleum baseline. For the final rule threshold determinations we rely on a 30 year time horizon and a 0% discount rate. A longer time horizon would result in greater benefits for biofuels, and a higher discount rate would result in lower GHG reductions.

Figure 2.6-15 includes lifecycle GHG results for the 2022 average corn ethanol produced in a natural gas-fired dry mill over a continuum of time horizons. The horizontal axis is the choice of time horizon. As disc ussed above, our results indicate that the payback period for an average 2022 corn ethanol pathway is approximately 14 years. With a zero percent discount (the blue line in Figure 2.6-15) corn ethanol reduces GHG emissions by 21 percent over 30 years, and reduces emissions by 8 percent and 28 percent ov er 20 and 40 years respectively. With higher discount rates, it takes longer for the future benefits of corn ethanol production to payback earlier land clearing emissions. When we use a discount rate greater than zero, future benefits are discounted, causing the curves in Figure 2.6-15 to flatten out over time. Results for the midpoint of land use change uncertainty are shown in Figure 2.6-15, the high and low land use change uncertainty results would shift the results. Low land use change results would shift the curves down, high land use change results would shift the curves up.

Figure 2.6-15. Lifecycle GHG Result s for 2022 Average Corn Ethanol (Percent Change from Gasoline with Different Discount Rates and Time Horizons)

-8%

-28% -3%

-15%

-21% 0%

-17% -21% -12%

-40% -20% 0% 20% 40%20222030204020502060207020802090210021102120Lifecycle GHG Percent Change From Gasoline Baseline Corn Ethanol Average 2022 Plant, 0% Discount Rate

Corn Ethanol Average 2022 Plant, 2% Discount Rate

Corn Ethanol Average 2022 Plant, 3% Discount Rate

20, 30 & 40 Year Results, r=0%

20, 30 & 40 Year Results, r=2%

20, 30 & 40 Year Results, r=3%

2.6.2.2 High Yield Scenario Results

There are many factors that go into the ec onomic modeling but the yield assumptions for different crops has one of the biggest impacts on land use and land use change. Therefore, for this analysis we ran a base yield case and a high yield sensitivity case.194 EPA’s base yield projections are derived from extrapolating through 2022 long-term historical U.S. corn yields from 1985 to 2009. This estimate, 183 bushels/acre for corn and 48 bushels/acre for soybeans, is consistent with US DA’s method of projecting future crop yields. During the public comment process we learne d that numerous technical advancements-- including better farm practices, seed hybridiza tion and genetic modification--have led to more rapid gains in yields since 1995. In additi on, commenters, including many leading seed companies, provided data supporting more rapid im provements in future yields. For example, commenters pointed to recent advancements in seed development (including genetic modification) and the general accu mulation of knowledge of how to develop and bring to market seed varieties—factors that would allow for a gr eater rate of development of seed varieties requiring fewer inputs such as fertilizer and pest mana gement applications.

Therefore, in coordination with USDA experts, EPA has developed for this final rule a high yield case scenario of 230 bushels/acre fo r corn and 60 bushels/acre for soybeans. These figures represent the 99% upper bound confidence limit of variability in historical U.S. yields. This high yield case represents a feasible high yield scenario for the purpose of a sensitivity test of the impact on the results of higher yields. Fi gure 2.6-16 shows the histor ic data and trends for U.S. corn yields.

Figure 2.6-16. U.S. Historic and Projected Corn Yields

Projected Corn Yields

183 194 230

0 50 100 150 200 250

1980 1985 1990 1995 2000 2005 2010 2015 2020 2025

YearYield (bu/acre)

85 - 09 Trend
95 - 09 Trend Historic Pro j ected

Feedback we received indicated that corn and soybean yields respond in tandem and that a high yield corn case would also imply a higher yield for soybeans as well. The high yield case is therefore based on higher yield corn and soybeans in the U.S. as well as in the major corn and soybean producing countries around the world. Fo r international yields, it is reasonable to assume the same percent increases from the baseline yield assumptions could occur as we are195 estimating for the U.S. Thus in the case of corn, 230 bushels per acre is approximately 25% higher than the U.S. baseline yield of 183 bushels per acre in 2022. This same 25% increase in yield can be expected for the top corn producers in the rest of the world by 2022, as justified improvements in seed varieties and, perhaps even more so than in the case of the U.S., improvements in farming practices which can take more full adva ntage of the seed varieties' potential. For example, seeds can be more read ily developed to perform well in the particular regions of these countries and can be coupled wi th much improved farming practices as farmers move away from historical practices such as saving seeds from their crop for use the next year and better understand the economic ad vantages of modern farming practices. So the high yield scenarios would not have the same absolute yield values in other countries as the U.S. but would have the same percent increase.

Figure 2.6-17 shows the results for the 2022 aver age corn ethanol plant with the base and high yield scenarios. The high yield scenario has a modest change in the overall GHG reductions of corn ethanol. With the high yi eld estimates the 2022 average corn ethanol plant reduces GHG emissions compared to the gasoline baseline by 23%, compared to reductions of 21% for the base case scenario.

Figure 2.6-17 Distribution of High and Base Yield Results for a New Natural Gas Fired Corn Ethanol Plant Average 2022 plant: natural gas, 63% dr y, 37% wet DGS (w/ fractionation)

-60 -50 -40 -30 -20 -10 0 10 Percentage Change in Biofuel GHG Lifecycle Emissions Compared to Petroleum Fuel 2022 Hi g h Yield 2022 Base Yield

Figure 2.6-18 includes lifecycle GHG emissions broken down by several stages of the lifecycle impacts for the 2022 average corn ethano l plant for the base and high yield scenario196 compared to the 2005 baseline average for gasoline. Lifecycle emissions are normalized per energy unit of fuel produced and presente d in grams of carbon-dioxide equivalent GHG emissions per million British Thermal Units of fuel produced (gCO2e/mmBTU).

Figure 2.6-18. High and Base Yield Results for a New Natural Gas Fired Corn Ethanol Plant by Lifecycle Stage Average 2022 plant: natural gas, 63% dr y, 37% wet DGS (w/ fractionation)

-21% -23%

-20,000 0 20,000 40,000 60,000 80,000 100,000 120,000

2005 Gasoline Baseline 2022 Avg Dry Mill NG - Base Yield 2023 Avg Dry Mill NG - High YieldGHG Emissions (gCO2-eq / mmBtu) Fuel Production

International Land Use Change

International Farm Inputs and Fert N2O

Other (fuel and feedstock transport)

Domestic Land Use Change

International Livestock

International Rice Methane

Tailpipe

Domestic Rice Methane

Domestic Farm Inputs and Fert N2O

Domestic Livestock

20% Reduction Threshold

Net Emissions

The main difference with the high yield sc enario is that as ethanol production expands there is less overall land use and crop shifting needed domestically, reflected by lower domestic Farm input impacts. However, there is actually a greater impact on livestock compared to the base case. There is a greater shifting to grazing livestock internationally which results in more pasture land needed and slightly higher in ternational land use change emissions.197 Figure 2.6-19 shows the results for soybean biodiesel with the base and high yield scenarios. The high yield scenario has a fairly significant change in the overall GHG reductions of soybean biodiesel. With the high yield estimates soybean biodiesel reduces GHG emissions compared to the diesel fuel baseline by 70%, compared to reductions of 57% for the base case yield scenario.

Figure 2.6-19. Distribution of High and Ba se Yield Results for Soybean Biodiesel

-140 -120 -100 -80 -60 -40 -20 0 20 Percentage Change in Biofuel GHG Lifecycle Emissions Compared to Petroleum Fuel 2022 Hi g h Yield 2022 Base Yield

Figure 2.6-20 includes lifecycle GHG emissions broken down by several stages of the lifecycle impacts for soybean biodiesel for the base and high yield scenario compared to the 2005 baseline average for diesel fuel. Lifecycle em issions are normalized per energy unit of fuel198 produced and presented in grams of carbon-di oxide equivalent GHG emissions per million British Thermal Units of fuel produced (gCO2e/mmBTU).

Figure 2.6-20. High and Base Yiel d Results for Soybean Biodiesel

-57%

-70%

-40,000 -20,000 0 20,000 40,000 60,000 80,000 100,000 120,000

2005 Diesel Baseline Soy Biodiesel - Base Yield Soy Biodiesel - High YieldGHG Emissions (gCO2-eq / mmBtu) Fuel Production

International Land Use Change

International Farm Inputs and Fert N2O
Other (fuel and feedstock transport)
Domestic Land Use Change

International Livestock

International Rice Methane

Tailpipe

Domestic Rice Methane

Domestic Farm Inputs and Fert N2O

Domestic Livestock

50% Reduction Threshold

Net Emissions

Similar to the corn ethanol high yield scenario for the soybean high yield case the biggest impact is on livestock changes compared to the base case. There is a greater shifting in the high yield case away from grazing livestock internationally which results in less pasture land needed and lower international land use change emissions.199 

### 2.7 Overall Lifecycle Greenhouse Gas Em issions Results of Rulemaking Volumes Compared to AEO Projected Volumes

Our analysis of the overall GHG emission im pacts of this proposed rulemaking was performed in parallel with the lifecycle analysis performed to develop the individual fuel thresholds described in previous sections. Th e same system boundaries apply such that this analysis includes the effects of three main ar eas: a) emissions rela ted to the production of biofuels, including the growing of feedstock (cor n, soybeans, etc.) with associated domestic and international land use change impacts, transport of feedstock to fuel production plants, fuel production, and distribution of finished fuel; b) emissions related to the extraction, production and distribution of petroleum gasolin e and diesel fuel that is repl aced by use of biofuels; and c) difference in tailpipe combustion of the re newable and petroleum based fuels.

Consistent with the fuel volume feasibility analysis and criteria pollutant emissions evaluation, our analysis of the GHG impacts of this proposed rulemaking was conducted by comparing the difference between a 2022 referenc e case and a 2022 control case with volumes of renewable fuels meeting the RFS2 mandate. Similar to what was done to calculate lifecycle thresholds for individual fuels we considered the change in 2022 of these two volume scenarios of renewable fuels to determine overall GHG impacts of the rule. The reference case for the GHG emission comparisons was taken from the AEO 2007 projected renewable fuel production levels for 2022 prior to enactment of EISA. Th is scenario provided a point of comparison for assessing the impacts of the RFS2 standard vol umes on GHG emissions. We ran these multi fuel scenarios through our FASOM and FAPRI models and applied the satellite data land use change assumptions to determine to overall GHG impacts of producing this increase in renewable fuels.

The main differences between this overall imp acts analysis and the analysis conducted to develop the threshold values for the individual fuels were that we analyzed the total change in renewable fuels in one scenario as opposed to looking at individual fu el impacts. When analyzing the impact of the 2022 EISA mandate, we also took into account the agricultural sector interactions necessary to produce the full complement of feedstock.

We also considered a mix of plant types a nd configurations for the 2022 renewable fuel production representing the mix of plants and feedst ock we project to be in use in 2022. Table 2.7-1 shows the types of plants considered and the volumes produced by each in the analysis for the references and control cases.200Table 2.7-1. Types of Plants and Volumes Considered in 2022Plant Configuration and Energy Used (Btu/gal)Volume (Bgal)NG Use Coal UseBiomassUseDieselFuelUsePurchasedElec Sold ElecReferenceCasePolicyCase DifferenceCorn Ethanol – Dry Mill NG - Base Plant (dry DDGS) 25,672 2,165 4.2 5.2 1.0- Base Plant (wet DGS) 16,320 2,165 2.5 3.1 0.6- Integrated Biogas System (dry DGS) 11,459 231 0.9 1.2 0.2- Integrated Biogas System (wet DGS) 7,285 231 0.6 0.7 0.1Corn Ethanol – Dry Mill Coal - Base Plant (dry DDGS) 34,773 231 0.3 0.3 0.1- Base Plant (wet DGS) 22,106 231 0.2 0.2 0.0Corn Ethanol – Dry Mill Biomass - Base Plant (dry DDGS) 33,147 1,679 1.5 1.8 0.4- Base Plant (wet DGS) 21,072 1,679 0.9 1.1 0.2Corn Ethanol – Wet Mill - Plant with NG 45,9500.0 0.02 0.02- Plant with coal 45,950 1.4 1.4 0.0Cellulosic Ethanol – Enzymatic - Switchgrass feedstock & lignin used as fuel 72,144 -12,249 0.0 1.5 1.5- Corn stover feedstock & lignin used as fuel 68,431 -12,249 0.2 1.0 0.7Cellulosic Ethanol – Thermochemical - Switchgrass feedstock 100,543 177 0.0 1.5 1.5- Corn stover feedstock 95,369 177 0.0 1.0 1.0Biodiesel - Soybean oil feedstock 18,913 3,205 0.4 1.4 1.1- Yellow grease / tallow feedstock 21,051 494 0.0 0.2 0.2Renewable Diesel - Yellow grease / tallow feedstock838 0.0 0.2 0.2Cellulosic Diesel – F-T - Farmed trees feedstock 198,429 327 13 0.0 6.5 6.5Sugarcane Ethanol - CBI - Marginal Elec 2,592 84,241 2,606 -7,287 0.6 2.2 1.6201 The upstream feedstock production and processing impacts for each of the different fuel technologies were modeled based on the same as sumptions used in determining the per fuel lifecycle GHG results describe d in previous sections.

For this overall impacts analysis we also used a different petroleum baseline fuel that is offset from renewable fuel use. The lifecycle th reshold values are required by EISA to be based on a 2005 petroleum fuel baseline. For this anal ysis of the overall impacts of the rule we considered the crude oil and finished produc t that would be replaced in 2022.

For this analysis we consider that 25% of displaced gasoline will be imported gasoline and 0% of displaced diesel fuel will be imported diesel fuel. For the types of gasoline displaced we assume 65% of the displaced gasoline will be conventional gasolin e and 35% will be RFG blendstock gasoline. We assume 100% of the displaced diesel fuel will be low sulfur diesel fuel.

In order to come up with GHG emissions for average crude oil used in producing gasoline and diesel fuel in 2022 we assumed 7.6% would be from tar sands and 3.8% would be from Venezuelan heavy crude. The basis for this was EIA projections for 2022 120 . EIA projects that roughly 64% of total Canadian crude oil production will be oil sa nd production in 2022, and that roughly 40% of total Ve nezuelan crude oil production w ill be heavy crude production in 2022. EIA also has assumptions on how much cr ude oil will be imported into the U.S. from Canada and Venezuela in 2022. We assumed the percentage of this imported Canadian and Venezuelan crude oil that would be oil sands a nd heavy oil was the same percentage of total production that is unconventional crude in those countries (~64% for Canada and ~41% for Venezuela). Based on the percent of Canadian and Venezuelan imports to total crude oil projected in 2022, oil sands repres ented 7.6% and heavy oil repres ented 3.8% of total crude oil

use.

For this analysis we did not assume any efficiency improvements at the petroleum refining portion of the gasoline and diesel fuel lifecycle. Therefore the same refining energy use and emissions was assumed that was used to repr esent the 2005 petroleum fuel baseline. One the one hand this may be overestimating energy use and emission from petroleum refining, however, this also does not factor in recent regulations that might increase energy use and emissions, such as increased desulfurization of both gasoline and diesel fuel.

Furthermore, the tailpipe emissions changes were determined based on the specific volumes and blends of fuel cons idered as opposed to looking at only the difference between the renewable fuel and petroleum fuel replaced. For highway vehicles, the impact of this rule on Methane (CH4) and Nitrous Oxide (N2O) emissions is primarily due to vehicles switching from gasoline to E85 fuel. Based on available data, we projected no change in N2O or CH4 emissions from highway vehicles that switched from conven tional gasoline to E10. For diesel highway vehicles, emissions of N20 and CH4 are almo st one hundred times less than emissions from gasoline vehicles, 121 thus diesels were omitted from this analysis.

To estimate the inventory-wide impact, we used MOVES to model CH4 and N20 for highway gasoline vehicles using reference case fuels. Because MOVES does not vary CH4 and N20 emissions by temperature or by gasoline fuel properties, the model was run at the annual,202 national level. FFV use was assumed to be limite d to light duty cars and light duty trucks. We multiplied the appropriate E85 factor by the emissions for that model year and then computed a weighted average of E85 and E10 emissions for both CH4 and N2O. In order to compare the results in a meaningful way, we also computed the CO2 equivalent by multiplying the tons for each pollutant by the Global Warming Potential (310 for N2O, 21 for CH4 122 ) and summing the products. The results are summa rized in Table 2.7-2 below.

Table 2.7-2. Tailpipe Nitrous Oxid e and Methane Emissions in 2022 Pollutant Reference Case Tons Control Case Tons Percent Change N2O 31,447 29,191 -7% CH4 50,683 61,853 22% LDGV & LDGT CO2 equiv. 10,812,803 10,348,003 -4% N2O 33,997 31,741 -7% CH4 55,277 66,447 20% All Gasoline Highway Vehicles CO2 equiv. 11,699,809 11,235,009 -4%

Given these many differences, it is clearly not possible to simply add up the individual lifecycle results determined in Section 2.8 multiplied by their respective volumes to assess the overall rule impacts. The two analyses are separate in that the overall rule impacts capture interactions between the different fuels but can not be broken out into per fuels impacts, while the threshold values represent impacts of specific fuels but do not account for all the interactions.

Overall rule impacts were determined for the different components of the lifecycle analysis as described in previous sections. The domestic agricultural sector impacts include changes in energy use GHG emissions and fertilizer / soil N2O emissions as well as changes in livestock and rice production GHG emissions.

Our analysis indicates that overall domestic agriculture emissions would increase. There is a relatively small increase in total domestic crop acres howe ver, there are additional inputs required to grow the biofuel feedstock crops. These additional inputs result in GHG emissions from production and from N2O releases from applica tion. This effect is somewhat offset by reductions due to lower li vestock production and reducti ons in rice methane.

As with domestic agriculture impacts, the international agricultural sector impacts include changes in energy us e GHG emissions and fertilizer / soil N2O emissions as well as changes in livestock and rice producti on GHG emissions. Increased crop production internationally resulted in increased fertilizer and fuel use emissions.

We estimate the largest overall agricultural sector impact is an increase in land use change impacts, reflecting the shift of crop pr oduction both domestically and internationally to meet the biofuel demand in the U.S., and land us e change emissions associated with converting land into crop production.

Other portions of the biofuel lifecycle incl ude fuel production and feedstock and fuel transport. We project reductions in GHG emi ssions from the renewable fuel production portion203 of the lifecycle due to the generation of elect ricity along with the increased production of cellulosic ethanol and diesel fuel.

CO2 produced in the combustion of biofuels is offset by the uptake of CO2 in the biomass crop used to produce the fuel, resulting in a significant net reduction of CO2 compared to fossil fuel tailpipe combustion. Net carbon fl uxes from changes in biogenic carbon reservoirs in wooded or crop lands associated with land us e change are accounted for in the domestic and international agriculture impacts shown in upstrea m impacts. In addition we assume biofuel use is offsetting petroleum fuel production which also results in GHG emissions reductions from reduced upstream emissions of petroleum fuel pr oduction (crude oil extr action and transport, refining, transport).

The results of the individual lifecycle stage results can be summed to determine the overall GHG impact of the proposed rulemaking. As discussed in previous sections on lifecycle GHG thresholds there is an initial one time re lease from land conversion and smaller ongoing releases but there are also ongoing benefits of using renewable fuels over time replacing petroleum fuel use. Figure 2.7-1 shows the GHG emissions impact of the change in fuel volumes considered over time.

Figure 2.7-1. GHG Impacts over time

-1,400,000,000 -1,200,000,000 -1,000,000,000

-800,000,000 -600,000,000 -400,000,000 -200,000,000 0 200,000,000 400,000,000

0 1 2 3 4 5 6 7 8 9 10 Time Since Initial Land Use Change (years)Net Total GHG Impacts (MT CO2-eq.)

Initial Land Use Change Impacts
Ongoing Land Use Change Impacts
Ongoing Benefits (w/o Land Use Change)

Net Base Cumulative Total Emissions Over Time
Net High Cumulative Total Emissions Over Time

Net Low Cumulative Total Emissions Over Time

Figure 2.7-1 shows the baseline estimates for land use change as well as the range of results based on the uncertainty in the international land use change modeling. The net GHG204 emissions over time are also shown as a range of results based on the uncertainty in the land use change emissions.

Based on the volume scenario considered the one time land use change impact results in a baseline estimate of 312.8 million me tric tons of CO2-eq. emissions with a range of 296.9 to 331.9 million metric tons CO2-eq. There are however, based on the biofuel use replacing petroleum fuels, annual reducti on benefits of 150 milli on metric tons of CO2-eq. emissions. This results in a less than two year payback period before the ongoing benefits of the biofuels use offsets the initial land use impacts.

The timing of the impact of land use change and ongoing renewable fuels benefits were discussed in the previous lifecycle results section. The issue is slightly different for this analysis since we are considering absolute tons of emi ssions and not determining a threshold comparison to petroleum fuels. However the results can be presented in a similar manner to our individual fuels analysis in that we can determine net benefits over a 30 year time period with no discounting. Assuming a 0% di scount rate over 30 years would result in an estimate of 4.15 billion tons of discounted GHG emission reductions.

Furthermore, for the calculations of the monetized GHG benefits we calculate an annualized NPV GHG reduction. This annualized value is based on converting a lump sum present value into its annualized equivalent. For this analysis we convert the NPV results into an annualized stream such that the NPV of the a nnualized emissions will equal the NPV of the emission stream over 30 years with a 0% discount rate. This results in an annualized emission reduction of 138.4 million metric tons of CO2-eq. emissions (ranging from 136.1 to 140.3 based on uncertainty in the land use change results).

However, there may be additional indirect im pacts associated with the production and use of petroleum-based fuels in the real world that are not completely captured by this analysis. For example, it is possible that renewable fuels may actually displace fuels at the margin which have higher GHG lifecycle emissions than the average (e .g., tar sands instead of conventional crude).

To examine the question of what type of marginal crude would be displaced by biofuels use, we performed an analysis using the Department of Energy’s Energy Technology Perspectives (ETP) model, which is a partial equilibrium model used to analyze the international energy system. For our analysis, we created a scenario that increased domestic gasoline demand, as we wanted to isolate the impacts of petroleum use. The scenario roughly represented the additional amount of gasoline that would be required if the RFS2 renewable fuel mandates were not in effect. Our results showed that the increased gasoline demand was primarily met through production of conventional crude oil, along with a small amount of oil sands/bitumen production. The primary exporters of conventional crude o il to meet the additional demand were Middle Eastern countries. Using well-to-tank GHG values for crude extracted from various countries 123

, we were able to determine an approximate “marginal petroleum baseline” by applying the factors to the countries where crude production increased. We found that the marginal baseline was, for an average gallon of gasoline, not statistically different than the average baseline value used in this final rulemaking. More details on this analysis can be found in the memo, “Petroleum Indirect Impacts Analysis ” at EPA-HQ-OAR-2005-0161.205 There may be other indirect impacts as well. For instance, we considered whether the displacement of petroleum fuels could also displace petroleum co-products, thus increasing the GHG reductions associated with biofuels use. When crude oil is refined to produce gasoline and diesel, petroleum co-products are also produced. Petroleum co-pr oducts include residual fuel oil and petroleum coke, which are utilized as fuels in the energy system. An increase in the demand for renewable fuels could also impact the energy system’s utilization of petroleum co-products due to the ripple effects of price impacts.

While it is difficult to predict how the energy system would be affected in such an event, we expect that an increase in domestic renewable fuels demand will lead to a decrease in domestic crude oil consumption due to lower demand for gasoline and diesel. However, a decrease in demand for gasoline and diesel is unlikely to significantly impact demand for petroleum co-products unless the price for these co -products is significantly affected. Refiners respond to demand for fuels, and they may choose to produce a larger percentage of petroleum co-products per barrel of crude then they had in the past in response to lower gasoline or diesel demand. This increased supply and possible lower refinery costs could translate into a slight decrease in co-product cost and therefore marginal ly impact demand. We have not modeled this demand increase or what its impact might be on total GHG emissions, but we expect that it would have a negligible GHG effect for the rule overall. Thus, we are assuming no change in petroleum co-products supply and no shift in the energy system as a result.

Increased renewable fuel use domestically is expected to also have the effect of lowering the world crude oil price and therefore increase international demand fo r petroleum-based fuels and increase GHG emissions. As stated above, we expect that an increase in domestic renewable fuels demand will lead to a decrease in domestic crude oil demand. This decrease in U.S. oil demand could cause a decline in the world oil pric e, which would spur increased oil consumption abroad. This increase in demand outside of the U. S. due to price changes would partially negate the decrease in GHG emissions domestically from reduced petroleum fuel demand due to biofuels. This impact of biofuels use on crude oil imports and world crude oil price is included in our Energy Security Analysis discussed in Chapter 5.206 

### 2.8 Effects of GHG Emission Reductions and Changes in Global Temperature and Sea Level

The reductions in CO 2 and other GHGs associated with this final rule will affect climate change projections. GHGs mix well in the atmosphere and have long atmospheric lifetimes, so changes in GHG emissions will affect future climate for decades to centuries. Two common indicators of climate change are global mean surface temperature and global mean sea level rise. This section estimates the response in global mean surface temperature and global mean sea level rise projections to the estimated net global GHG emissions reductions associated with this final rule (see section 2.7 for the estimated net reductions in gl obal emissions over time by GHG).

EPA estimated changes in projected global mean surface temperatures to 2050 using the MiniCAM (Mini Climate Assessment Model) integrated assessment model KKK coupled with the MAGICC (Model for the Assessment of Greenhouse-Gas Induced Climate Change) simple climate model. LLL MiniCAM was used to create the globally and temporally consistent set of climate relevant variables required for running MAGICC. MAGICC was then used to estimate the change in the global mean surface temperature over time. Given the magnitude of the estimatedemissions reductions associated with the proposed rule, a simple climate model such as MAGICC is reasonable for estimating the climate response.

KKK MiniCAM is a long-term, global integrated assessment model of energy, economy, agriculture and land use, that considers the sources of emissions of a suite of greenhouse gases (GHGs), emitted in 14 globally disaggregated global regions (i.e., U.S., Western Europe, China), the fate of emissions to the atmosphere, and the consequences of changing concentrations of greenhouse related gases for climate change. MiniCAM begins with a representation of demographic and economic developments in each region and combines these with assumptions about technology de velopment to describe an internally consistent representation of energy, agriculture, land-use, and economic developments that in turn shape global emissions. Brenkert A, S. Smith, S. Kim, and H. Pitcher, 2003: Model Documentation for the MiniCAM. PNNL-14337, Pacific Northwest National Laboratory, Richland, Washington. For a recent report and detailed description and discussion of MiniCAM, see Clarke, L., J. Edmonds, H. Jacoby, H. Pitcher, J.Reilly, R. Richels, 2007. Scenarios of Greenhouse Gas Emissions and Atmospheric Concentrations. Subreport 2.1A of Synthesis and Assessment Product 2.1 by the U.S. Climate Change Science Program and the Subcommittee on Global Change Research. Department of Energy, Office of Biological & Environmental Research, Washington, DC., USA, 154 pp.
LLL MAGICC consists of a suite of coupled gas-cycle, climate and ice-melt models integrated into a single framework. The framework allows the user to determine changes in GHG concentrations, global-mean surface air temperature and sea-level resulting from anthropogenic emissions of carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), reactive gases (e.g., CO, NO x , VOCs), the halocarbons (e.g. HCFCs, HFCs, PFCs) and sulfur dioxide (SO2). MAGICC emulat es the global-mean temperature responses of more sophisticated coupled Atmosphere/Ocean General Circulation Models (AOGCMs) with high accuracy. Wigley, T.M.L. and Raper, S.C.B. 1992. Implications for Climate and Sea-Level of Revised IPCCEmissions Scenarios Nature 357, 293-300. Raper, S.C.B., Wigley T.M.L. and Warrick R.A. 1996. in SeaLevel Rise and Coastal Subsidence: Causes, Consequences and Strategies J.D. Milliman, B.U. Haq, Eds., Kluwer Academic Publishers, Dordrecht, The Netherlands, pp. 11-45. Wigley, T.M.L. and Raper, S.C.B. 2002. Reasons for larger warming projections in the IPCC Third Assessment Report J. Climate 15, 2945-2952.207 EPA applied the estimated annual GHG emissi ons changes for the final rule to a MiniCAM baseline emissions scenario. MMM Specifically, the CO 2 , N 2 O, and CH 4 annual emission changes from 2022-2052 from section 2.7 we re applied as net reductions to this baseline scenario for each GHG.

The tables below provide our estimated reductions in projected global mean surface temperatures and mean sea level rise associated with the increase in renewable fuels in 2022 required by this final rule. We modeled three scenarios using different values for the estimated net global GHG reduction associated with this rule; we utilized the average, low, and high values for GHG emissions reduced, as presented in Section x.x. To capture some of the uncertainty in the climate system, we estimated the changes in projected temperatures and sea level across the most current Intergovernmental Panel on Climate Change (IPCC) range of climate sensitivities, 1.5°C to 6.0°C. NNN

Table 2.8-1. Estimated Reductions in Proj ected Global Mean Surface Temperature

and Global Mean Sea Level Rise from Base line for the Average Case for the Final Rule in 2020-2050 Climate Sensitivity
1.5 2 2.5 3 4.5 6 Year Change in global mean surface temperatures (degrees Celsius)
2020 0.000 0.000 0.000 0.000 0.000 0.000
2025 0.000 0.000 0.000 0.000 0.000 0.000
2030 0.000 0.000 0.000 0.000 0.000 0.000
2035 -0.001 -0.001 -0.001 -0.001 -0.001 -0.001
2040 -0.001 -0.001 -0.001 -0.001 -0.001 -0.001
2045 -0.001 -0.001 -0.001 -0.001 -0.002 -0.002
2050 -0.001 -0.001 -0.002 -0.002 -0.002 -0.002 Year Change in global mean sea level rise (centimeters)
2020 0.000 0.000 0.000 0.000 0.000 0.000
2025 0.000 0.000 0.000 0.000 0.000 0.000

MMM The reference scenario is the MiniCAM reference (no climate policy) scenario used as the basis for the Representative Concentration Pathway RCP4.5 using hi storical emissions until 2005. This scenario is used because it contains a comprehensive suite of greenhouse and pollutant gas emissions including carbonaceous aerosols. The four RCP scenarios will be used as common inputs into a variety of Earth System Models for inter-model comparisons leading to the IPCC AR5 (Moss et al. 2008). The MiniCAM RCP4.5 is based on the scenarios presented in Clarke et al. (2007) with non-CO 2 and pollutant gas emissions implemented as described in Smith and Wigley (2006). Base-year information has been updated to the latest available data for the RCP process.
NNN In IPCC reports, equilibrium climate sensitivity refers to the equilibrium change in the annual mean global surface temperature following a doubling of the atmospheric equivalent carbon dioxide concentration. The IPCC states that climate sensitivity is “likely” to be in the range of 2°C to 4.5°C and described 3°C as a "best estimate." The IPCC goes on to note that climate sensitivity is “very unlikely” to be less than 1.5°C and “values substantially higher than 4.5°C cannot be excluded.” IPCC WGI, 2007, Climate Change 2007 - The Physical Science Basis, Contribution of Working Group I to the Fourth Assessment Report of the IPCC, http://www.ipcc.ch/.208 2030 -0.001 -0.001 -0.001 -0.001 -0.001 -0.001
2035 -0.002 -0.002 -0.002 -0.003 -0.003 -0.003
2040 -0.003 -0.004 -0.004 -0.005 -0.005 -0.006
2045 -0.005 -0.006 -0.006 -0.007 -0.008 -0.009
2050 -0.006 -0.008 -0.009 -0.009 -0.011 -0.012

Table 2.8-2. Estimated Reductions in Proj ected Global Mean Surface Temperature and Global Mean Sea Level Rise from Base line for the Low Case for the Final Rule in 2020-2050 Climate Sensitivity
1.5 2 2.5 3 4.5 6 Year Change in global mean surface temperatures (degrees Celsius)
2020 0.000 0.000 0.000 0.000 0.000 0.000
2025 0.000 0.000 0.000 0.000 0.000 0.000
2030 0.000 0.000 0.000 0.000 0.000 0.000
2035 -0.001 -0.001 -0.001 -0.001 -0.001 -0.001
2040 -0.001 -0.001 -0.001 -0.001 -0.001 -0.001
2045 -0.001 -0.001 -0.001 -0.001 -0.002 -0.002
2050 -0.001 -0.001 -0.002 -0.002 -0.002 -0.002 Year Change in global mean sea level rise (centimeters)
2020 0.000 0.000 0.000 0.000 0.000 0.000
2025 0.000 0.000 0.000 0.000 0.000 0.000
2030 -0.001 -0.001 -0.001 -0.001 -0.001 -0.001
2035 -0.002 -0.002 -0.002 -0.003 -0.003 -0.003
2040 -0.003 -0.004 -0.004 -0.005 -0.005 -0.006
2045 -0.005 -0.006 -0.006 -0.007 -0.008 -0.009
2050 -0.006 -0.008 -0.009 -0.009 -0.011 -0.012

Table 2.8-3. Estimated Reductions in Proj ected Global Mean Surface Temperature and Global Mean Sea Level Rise from Base line for the High Case for the Final Rule in 2020-2050 Climate Sensitivity
1.5 2 2.5 3 4.5 6 Year Change in global mean surface temperatures (degrees Celsius)
2020 0.000 0.000 0.000 0.000 0.000 0.000
2025 0.000 0.000 0.000 0.000 0.000 0.000
2030 0.000 0.000 0.000 0.000 0.000 0.000
2035 -0.001 -0.001 -0.001 -0.001 -0.001 -0.001
2040 -0.001 -0.001 -0.001 -0.001 -0.001 -0.001209 2045 -0.001 -0.001 -0.001 -0.001 -0.002 -0.002
2050 -0.001 -0.001 -0.002 -0.002 -0.002 -0.002 Year Change in global mean sea level rise (centimeters)
2020 0.000 0.000 0.000 0.000 0.000 0.000
2025 0.000 0.000 0.000 0.000 0.000 0.000
2030 -0.001 -0.001 -0.001 -0.001 -0.001 -0.001
2035 -0.002 -0.002 -0.002 -0.003 -0.003 -0.003
2040 -0.003 -0.004 -0.004 -0.004 -0.005 -0.006
2045 -0.005 -0.006 -0.006 -0.007 -0.008 -0.008
2050 -0.006 -0.007 -0.008 -0.009 -0.011 -0.012

The results in table above show small reductions in the global mean surface temperature and sea level rise projections across all climate sensitivities. Overall, the reductions are small relative to the IPCC’s “best estimate” temperature increases by 2100 of 1.8oC to 4.0oC. OOO Although IPCC does not issue “best estimate” sea level rise projections, the model-based range across SRES scenarios is 18 to 59 cm by 2099. PPP While the distribution of potential temperatures in any particular year is shifting down, the shift is not uniform. The magnitude of the decrease is larger for higher climate sensitivities. The same pattern appears in the reductions in the sea level rise projections. For instance, in 2050, the reduction in projected temperature (for all cases) for climate sensitivities of 3 and 6 is approximately 50% and 99% greater than the reduction for a climate sensitivity of 1.5. The same pattern appe ars in the reductions for the sea level rise projections. QQQ

Thus, we can conclude that the impact of this final rule is to lower the risk of climate change, as the probabilities of temperature increase and sea level rise are reduced.

1 Product Life Cycle Accounting and Reporting Standard, Review Draft for Stakeholder Advisory Group, November 2009, World Resources Institute & World Business Council for Sustainable Development, 2009
2 Clean Air Act Section 211(o)(1)
3 Delucchi, M. 2005. Incorporating the Effect of Price Chanes on CO 2 -Equivalent Emissions from Alternative Fuel Lifecycles: Scoping the Issues. Working Paper UCD—ITS—RR—05—19. http://pubs.its.ucdavis.edu/publication_detail.php?id=62
4 http://www.epa.gov/sequestration/greenhouse_gas.html
5 Heffer, Patrick (2009). Assessment of Fertilizer Use by Crop at the Global Level, 2006/07 – 2007/08. International Fertilizer Industry Association. Paris, France.
6 http://faostat.fao.org/
7 Macedo (2008) “Greenhouse gases emissions in the production and use of ethanol from sugarcane in Brazil: The 2005/2006 averages and a prediction for 2020.” Biomass and Bioenergy.

OOO IPCC WGI, 2007.
PPP “Because understanding of some important effects driving sea level rise is too limited, this report does not assess the likelihood, nor provide a best estimate or an upper bound for sea level rise.” IPCC Synthesis Report, p. 45 QQQ In 2050, the reduction in projected sea level rise (for all cases) for climate sensitivities of 3 and 6 is approximately 45% and 86% greater than the reduction for a climate sensitivity of 1.5.210 8 Argonne Laboratory. 2008. Update of Distillers Grains Displacement Ratios for Corn Ethanol Life-Cycle Analysis by Arora, Wu, and Wang
9 Shurson, J. 2009. Analysis of Current Feeding Practices of Distiller’s Grains with Solubles in Livestock and Poultry Feed Relative to Land Use Credits Associated with Determining the Low Carbon Fuel Standard for Ethanol. Dr. Jerry Shurson, Professor, Department of Animal Science, University of Minnesota, March 25, 2009
10 Energy Life-Cycle Assessment of Soybean Biodiesel, A. Pradhan, D.S. Shrestha, A. McAloon, W. Yee, M. Haas, J.A. Duffield, H. Shapouri, USDA, Agricultural Economic Report Number 845, September 2009
11 Davis, Ryan. November 2009. Techno-economic analysis of microalgae-derived biofuel production. National Renewable Energy Laboratory (NREL)
12 Aden, Andy. September 2009. Mixed Alcohols from Woody Biomass – 2010, 2015, 2022. National Renewable Energy Laboratory (NREL)
13 Davis, Ryan. August 2009. Techno-economic analysis of current technology for Fischer-Tropsch fuels. National Renewable Energy Laboratory (NREL)
14 An Analysis Of Modern Corn Ethanol Technologies, February 16, 2009, Energy Resources Center, 1309 South Halsted Street, Chicago, IL 60607
15 The MODerate resolution Imagin Spectoradiometer (MODIS) land Products ( MCD12Q1) Land Cover Type Product (Land Cover Type 1), https://lpdaac.usgs.gov/lpdaac/products/modis_pr oducts_table/land_cover/y early_l3_global_500_m/mcd12 q1
16 FAO/IIASA/ISRIC/ISSCAS/JRC, 2009. Harmonized World Soil Database (version 1.1). FAO, Rome, Italy and IIASA, Laxenburg, Austria.
17 Lewis et al. 2009. Increasing carbon storage in intact African tropical forests. Nature. 457(19): 1003- 1006
18 Phillips et al. 2008. The changing Amazon forest. Phil. Trans. R. Soc. (363): 1819-1827
19 Department of Energy: National Energy Technology Laboratory. 2009. NETL: Petroleum-Based Fuels Life Cycle Greenhouse Gas Analysis - 2005 Baseline Model.
20 “N 2 O release from agro-biofuel production negates global warming reduction by replacing fossil fuels” P.J. Crutzen, A.R. Mosier, K.A. Smith, and W. Winiwarter, Atmospheric Chemistry and Physics, 8, 389– 395, 2008.
21 U.S. Environmental Protection Agency. 2005. Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2003 . EPA 430-R-05-003. Available at http://www.epa.gov/climatechange/emissions/downloads06/05CR.pdf.
22 U.S. Environmental Protection Agency. 2005. Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2003. EPA 430-R-05-003. Available at http://www.epa.gov/climatechange/emissions/downloads06/05CR.pdf.
23 U.S. Environmental Protection Agency. 2005. Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2003. EPA 430-R-05-003. Available at http://www.epa.gov/climatechange/emissions/downloads06/05CR.pdf.
24 Heffer, Patrick (2009). Assessment of Fertilizer Use by Crop at the Global Level, 2006/07 – 2007/08. International Fertilizer Industry Association. Paris, France.
25 Venezia, John et. al. Notable Revisions and Recalculations: International Agriculture GHG Emissions and GHG Metrics. Memorandum to EPA on September 30, 2009.
26 Ross and Associates Environmental Consulting, Ltd (2009). Peer Review of International Greenhouse Gas Emissions and Factors as provided to EPA to support its RFS2 rulemaking.
27 Heffer, Patrick (2009). Personal correspondence between Patrick Heffer, IFA and Erin Gray, ICF International. Sept. 25, 2009.
28 FAO (2009). FAOStat Production (Crops). Retrieved Sept. 25, 2009 from http://faostat.fao.org/site/567/DesktopDefault.aspx?PageID=567#ancor . Assumes FAO definitions for seed cotton, maize, sunflower seed, groundnuts, in shell, and rice, paddy equal cotton; corn; sunflower; peanut; and rice, respectively.
29 Updates last reported in April, 2009. http://faostat.fao.org/site/291/default.aspx
30 USDA Economic Research Service. China Agricultural and Economic Data: National Data Results. Retrieved from211 http://www.ers.usda.gov/Data/China/NationalResults.aspx?DataType=6&DataItem=160&StrDatatype=Agr icultural+inputs&ReportType=0 on Sept 15, 2009.
31 Faroni, C.E.; Penatti, C.P.; Donzelli, J.L.; Paes, L. A.D. Simulação CTC para o uso de fertilizantes pela cana-de-açúcar no ano de 2020. Relatório Técnico de Pesquisa e Desenvolvimento (Planilha Microsoft Excel). CTC – Centro de Tecnologia Canavieira, Piracicaba-SP, 2009.
32 2006 IPCC Guidelines for National Inventories, Chapter 11: N2) Emissions from Managed Soils, and CO2 Emissions from Lime and Urea Applications
33 http://wds.iea.org/wds/default.aspx
34 Macedo (2008) “Greenhouse gases emissions in the production and use of ethanol from sugarcane in Brazil: The 2005/2006 averages and a prediction for 2020.” Biomass and Bioenergy.
35 Converted from Macedo (2008) Table 9.
36 International Rice Research Institute. www.iri.org . Last accessed: October 15, 2008.
37 Wang, Michael (2008) Life-cycle energy use and greenhouse gas emissions implications of Brazilian sugarcane ethanol simulated with the GREET model. International Sugar Journal 2008, Vol. 110, No. 1317.
38 West, T.O., and W.M. Post. 2002. “Soil Organic Carbon Sequestration Rates by Tillage and Crop Rotation.” Soil Science Society of America Journal 66:1930-1936.
39 See Adams et al. (2005) for additional detail on the FASOM forest carbon accounting procedures.
40 Smith, J., L.S. Heath, and J. Jenkins. 2003. Forest Volume-to-Biomass Models and Estimates of Mass for Live and Standing Dead Trees of U.S. Forests. Newton Square, PA: USDA Forest Services.
41 Haynes, R.W. (Technical coordinator). 2003. An Analysis of the Timber Situation in the United States: 1952 to 2050 . General Technical Report PNW-GTR-560. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, 254 p.
42 Birdsey, R.A. 1996a. “Regional Estimates of Timber Volume and Forest Carbon for Fully Stocked Timberland, Average Management after Cropland and Pasture Revision to Forest.” In Forests and Global Change. Volume II: Forest Management O pportunities for Mitiga ting Carbon Emissions , D. Hair, and Neil R. Sampson, eds., Washington, DC: American Forests: 309-333.
43 Birdsey, R.A. 1996b. “Carbon Storage for Major Forest Types and Regions in the Conterminous United States.” In Forests and Global Change. Volume II: Forest m anagement opportunities fo r mitigating carbon emissions, Chapter 1, D. Hair and Neil R. Sampson, eds. Washington, DC: American Forests: 1-25.
44 Smith, J., L.S. Heath, and J. Jenkins. 2003. Forest Volume-to-Biomass Models and Estimates of Mass for Live and Standing Dead Trees of U.S. Forests. Newton Square, PA: USDA Forest Services.
45 Ibid.
46 Miles, P., U.S. Forest Service, electronic file 2002_RPA_Tables.xls to Brooks Depro, RTI International, July 30, 2003.
47 Birdsey, R.A. 1992. Changes in forest carbon storage from increasing forest area and timber growth. In: Sampson, R.N.; Dwight, H.; eds. Forest and Global Change, Volume 1: Opportunities for Increasing Forest Cover . Washington, DC: American Forests: 23-29.
48 Birdsey, R.A. 1996a. “Regional Estimates of Timber Volume and Forest Carbon for Fully Stocked Timberland, Average Management after Cropland and Pasture Revision to Forest.” In Forests and Global Change. Volume II: Forest Management O pportunities for Mitiga ting Carbon Emissions , D. Hair, and Neil R. Sampson, eds., Washington, DC: American Forests: 309-333.
49 Birdsey, R.A. 1996b. “Carbon Storage for Major Forest Types and Regions in the Conterminous United States.” In Forests and Global Change. Volume II: Forest m anagement opportunities fo r mitigating carbon emissions, Chapter 1, D. Hair and Neil R. Sampson, eds. Washington, DC: American Forests: 1-25.
50 Heath, L.S.; Birdsey, R.A.; Williams, D.W. 2002. “Methodology for estimating soil carbon for the forest carbon budget model of the United States, 2001.” Environmental Pollution , 116: 373-380.
51 Birdsey, R.A. 1996a. “Regional Estimates of Timber Volume and Forest Carbon for Fully Stocked Timberland, Average Management after Cropland and Pasture Revision to Forest.” In Forests and Global Change. Volume II: Forest Management O pportunities for Mitiga ting Carbon Emissions , D. Hair, and Neil R. Sampson, eds., Washington, DC: American Forests: 309-333.
52 Birdsey, R.A. 1996b. “Carbon Storage for Major Forest Types and Regions in the Conterminous United States.” In Forests and Global Change. Volume II: Forest m anagement opportunities fo r mitigating carbon emissions, Chapter 1, D. Hair and Neil R. Sampson, eds. Washington, DC: American Forests: 1-25.
53 Heath, L.S.; Birdsey, R.A.; Williams, D.W. 2002. “Methodology for estimating soil carbon for the forest carbon budget model of the United States, 2001.” Environmental Pollution , 116: 373-380.212 54 Birdsey, R.A. 1996a. “Regional Estimates of Timber Volume and Forest Carbon for Fully Stocked Timberland, Average Management after Cropland and Pasture Revision to Forest.” In Forests and Global Change. Volume II: Forest Management O pportunities for Mitiga ting Carbon Emissions , D. Hair, and Neil R. Sampson, eds., Washington, DC: American Forests: 309-333.
55 Birdsey, R.A. 1996b. “Carbon Storage for Major Forest Types and Regions in the Conterminous United States.” In Forests and Global Change. Volume II: Forest m anagement opportunities fo r mitigating carbon emissions, Chapter 1, D. Hair and Neil R. Sampson, eds. Washington, DC: American Forests: 1-25.
56 Smith, J., and L.S. Heath. 2002. A Model of Forest Floor Carbon Mass for United States Forest Types. Newton Square, PA: USDA Forest Service.
57 Smith, J., USDA Forest Service, email communication to Brian Murray, RTI International. August 11, 2004.
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65 Source: Adams, D., R. Alig, B.A. McCarl, and B.C. Murray. 2005. “FASOMGHG Conceptual Structure and Specification: Documentation.” Available at http://agecon2.tamu.edu/people/faculty/ mccarl-bruce/papers/1212FASOMGHG_doc.pdf.
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74 ICF International, Inc. 2009. Biofuels Stochastic International Land Use Lifecycle Analysis Model. Report submitted to U.S. EPA213 75 Friedl, Mark, Validation of the Consistent-Year V003 MODIS Land Cover Product, MODIS Land Team Internal PI Document, http://landval.gsfc.nasa.gov/ Results.php?TitleID=mod12_valsup1 , accessed December 2009
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79 Harris, NL, S. Grimland and S. Brown. 2009. Land Use Change and Emission Factors: Updates since Proposed RFS Rule. Report submitted to EPA.
80 U.S. EPA. 2009. Draft Regulatory Impact Analysis: Changes to Renewable Fuel Standard Program. EPA-420-D-09-001
81 Ruesch, A. and H.K. Gibbs. 2008. New IPCC Tier-1 Global Biomass Carbon Map For the Year 2000. Available online from the Carbon Dioxide Information Analysis Center [http://cdiac.ornl.gov], Oak Ridge National Laboratory, Oak Ridge, Tennessee.
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99 Lewis, S.L., G. Lopez-Gonzalez, B. Sonké, K. Affum Bafoe, T.R. Baker, L.O. Ojo, O.L. Phillips, J.M. Reitsma, L. White, J.A. Comiskey, et al. 2009. Increasing carbon storage in intact African tropical forests. Nature 457: 1003-1006.
100 Stehman, S. 1997, Estimating Standard Errors of Accuracy Assessment Statistics under Cluster Sampling, Remote Sensing of the Environment , (60): 258-269
101 Macedo, I.D.C., Leal M.R.L.V. and Seabra J.E.A. (2004) Assessment of greenhouse gas emission sin the production and use of fuel ethanol in Brazil. Prepared for the State of Sao Paulo, Brazil.

102 Macedo (2008) “Greenhouse gases emissions in the production and use of ethanol from sugarcane in Brazil: The 2005/2006 averages and a prediction for 2020.” Biomass and Bioenergy.

103 Wang, Michael (2008) Life-cycle energy use and greenhouse gas emissions implications of Brazilian sugarcane ethanol simulated with the GREET model. International Sugar Journal 2008, Vol. 110, No. 1317.

104 UNICA. (August 2009) Submission of comments document # EPA-HQ-OAR-2005-0161-1761.1

105 UNICA. (September 2009) Submission of comments document # EPA-HQ-OAR-2005-0161-2137.1

106 Version 1.8c of the GREET model, released in March, 2009, was utilized to estimate GHG emissions associated with sugarcane transport. Information on this release can be found at: http://www.transportation.anl.gov/modeling_simulation/GREET/
107 UNICA. (September 2009) Submission of comments document # EPA-HQ-OAR-2005-0161-2137.1, pg. 21-29
108 Government of Brazil, “Executive Summary-National Plan on Climate Change”, pg. 10.
109 UNICA. (September 2009) Submission of comments document # EPA-HQ-OAR-2005-0161-2137.1, pg. 28
110 Version 1.8c of the GREET model, released in March, 2009, was utilized for the fuel transportation analysis described in this section. Information on this release can be found at: http://www.transportation.anl.gov/modeling_simulation/GREET/ 111 Analysis of Fuel Ethanol Transportation Activity and Potential Distribution Constraints," prepared for EPA by Oak Ridge National Laboratory, March 2009.
112 The ORNL study includes a second transportation mode for trucks, called “Local Trucks”, which transport ethanol from dedicated ethanol terminals to bl ending terminals. Ethanol that travels directly from a refinery to a petroleum blending terminal would not be transported by local truck.
113 EIA “Company Level Imports Historical” Available at: http://www.eia.doe.gov/oil_gas/petroleum/data_publications/company_level_imports/cli_historical.html
114 https://www.gtap.agecon.purdue.edu
115 See Burniaux and Truong (2002)
116 See Lee et al. (2005).
117 See Birur et al . (2008).
118 Integrity Applications Incorporated. 2009. Renewable Fule Lifecycle Analysis – High Resolution Land Use Change Analysis, Final Report, BPA-09-03/GS-23F-0076M
119 Alex D. Charpentier, Joule A. Bergerson and Heather L. MacLean, “Understanding the Canadian Oil Sands Industry’s Greenhouse Gas Emissions,” Environmental Research Letters 4 (2009).
120 International Energy Outlook 2007, Report #:DOE/EIA-0484(2007), Release Date: May 2007, Tables G1 and G3.
121 U.S. EPA 2008, Inventory of U.S. Greenhouse Gas Emissions and Sinks (EPA 430-R-08-005), Tables 3-23 and3-24 http://www.epa.gov/climatechange/emissions/usinventoryreport.html
122 U.S. EPA 2008, Inventory of U.S. Greenhouse Gas Emissions and Sinks (EPA 430-R-08-005), Table ES-1 http://www.epa.gov/climatechange/emissions/usinventoryreport.html215 123 Department of Energy: National Energy Technology Laboratory. 2009. NETL: Petroleum-Based Fuels Life Cycle Greenhouse Gas Analysis - 2005 Baseline Model. www.netl.doe.gov/energy-analyses.