1 

Chapter 2: Lifecycle GHG Analysis 

2.1 Chapter Overview 

 This chapter describes each component of the 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 and 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 modeling approach. 

o Section 2.4.2 focuses on domestic agricultural sector GHG emissions impacts, 

including our evaluation of changes in agricultural inputs and livestock 

production. 

o Section 2.4.3 discusses international agricultural impacts. 

o Section 2.4.4 explains EPA's assessment of GHG emissions impacts from biofuelinduced domestic and international land conversions, including our quantification 

of uncertainty in international land conversion GHG emissions impacts. 

o Section 2.4.5 describes our accounting for 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 renewable fuel tailpipe emissions. 

o Section 2.4.10 discusses other potential indirect 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 lifecycle GHG emissions results, including 

sensitivity analyses. 

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

• Section 2.8 concludes the chapter with a discussion of the effects of the RFS2 on global 

temperature and sea level. 
2 

2.2 Background for Estimating Fuel Lifecycle Greenhouse Gas Emissions 

2.2.1 Lifecycle Analysis for 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 feedstock, feedstock transportation, fuel production, fuel 

transportation and distribution, and tailpipe emissions. Use of a lifecycle approach to analyze 

different transportation fuels requires modeling and evaluation of many different input factors. 

Lifecycle assessments can be divided into two major methodological categories: 

attributional and consequential.1

 

An attributional approach to GHG emissions accounting 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 typically describes changes in GHG emissions levels 

from affected processes, which are identified by linking causes with effects. 

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

The term ‘lifecycle greenhouse gas emissions’ means the aggregate quantity of 

greenhouse gas emissions (including direct emissions and significant indirect 

emissions such as significant emissions from land use changes), as determined by 

the Administrator, related to the full fuel 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 significant 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 influence this assessment, including the secondary or 

indirect impacts of expanded biofuels use. 

Furthermore, independent of the statutory language the Agency believes it is important to 

include secondary, indirect, or consequential impacts of biofuel use, specifically: 
3 

− Capturing secondary market driven agricultural sector impacts, such as changes in 

other crop patterns and livestock production as a response to changing prices in 

biofuel feedstocks. 

− Production of co-products from biofuel production requires some type of 

allocation, either splitting emissions of fuel production between fuel and coproducts or examining the use of co-products 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 equivalent amount of corn and soybean meal to these 

co-products are subtracted from the lifecycle assessment. This 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 indirectly 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 internationally. Another example is corn 

production increases resulting in less rice production and lower CH4 emissions. 

− Consideration of specific policies and interaction between different fuel volumes 

could have very distinct impacts especially in the agricultural sector. 

The lifecycle methodology developed for the RFS2 rulemaking analysis included the use 

of economic models to perform a consequential 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-LCA 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 the U.S. economy to determine the 

full economy wide impacts of producing a product or service. 

 Mark Delucchi at the Institute of Transportation Studies of the University of California 

Davis has developed the Lifecycle Emissions Model (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, taking 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 production. Commercial agricultural operations make 

many of their decisions based on an economic assessment of profit maximization. Assessment 

of the interactions throughout the agricultural sector 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., changes in acres by crop type) and would not provide 

the types of outputs needed for a thorough GHG analysis. 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 outputs of each are used and combined 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), developed by Texas A&M University and others. 

FASOM is a partial equilibrium economic 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 CO2, methane, and N2O from most agricultural activities and tracks carbon 

sequestration and carbon losses 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 allows 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 the different crops produced due to increased 

biofuel production. FASOM output also includes changes 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 output changes in fertilizer, energy use, and 

livestock are combined with GHG emission factors from those sources to generate biofuel 

lifecycle impacts. The GHG emission factors for fuel and fertilizer production come from the 

Greenhouse gases, Regulated Emissions, and Energy use in Transportation (GREET) spreadsheet 
5 

analysis tool developed by Argonne National Laboratories, and livestock GHG emission factors 

are from IPCC guidance. 

GREET includes the GHG emissions associated with the production and combustion of 

fossil fuels (diesel fuel, gasoline, natural gas, coal, etc.). GREET also estimates the GHG 

emissions associated with electricity production required for agriculture 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 fertilizer, herbicides, pesticides, 

etc. GREET has been under development for several 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 sector 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 N2O emissions from fertilizer application, we used 

the CENTURY and DAYCENT models, developed by Colorado State University. The 

DAYCENT model simulates plant-soil systems and is capable of simulating detailed daily soil 

water and temperature dynamics and trace gas fluxes (CH4, N2O, NOx and N2). 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 N2O 

emissions from different crop and land use changes were combined with FASOM output to 

generate overall domestic N2O emissions. 

FASOM output also provides changes in total 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, developed 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 run 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 Agricultural Policy, analyze farm bill proposals since 

1984, and evaluate the impact of biofuel development 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 changes in crop acres and livestock production 

by type and by country globally. Unlike FASOM, the FAPRI output did not include changes in 

fertilizer or energy use or have land type interactions 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 based on the same basic approach used 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 estimate 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 frame 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 and modes are used to develop the amount and type 

of energy used for transport which are combined 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 energy use is combined with emissions factors 

from GREET to develop GHG impacts of the biofuel production process 

To test the robustness of the FASOM, FAPRI and Winrock results, we also examined 

biofuel land use change impacts with the Global Trade Analysis Project (GTAP) model, a multiregion, multi-sector, computable general equilibrium model that estimates changes in world 

agricultural production. Maintained through Purdue University, GTAP projects international 

land use change based on the economics of land conversion, rather than using the historical data 

approach applied by FAPRI/Winrock. GTAP is designed to project changes in international land 

use as a result of the change in U.S. biofuel policies, based on the relative 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 generally 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 models 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 lifecycle analysis, the Agency has employed a 

collaborative, transparent, and science-based approach. EPA’s lifecycle methodology, as 

developed for the RFS2 proposal, required breaking 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 this 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 development of this final rule, EPA held hundreds of 

meetings with stakeholders, including government, academia, industry, and non-profit 

organizations, to gather expert technical input. Our work was also informed heavily by 

consultation with other federal agencies. For example, we have relied on the expert advice of 

USDA and DOE, as well as incorporating the most 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 helped inform EPA’s methodology. As described 

below, formal technical exchanges and an independent, formal peer review of the methodology 
8 

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 specifically on lifecycle analysis to assure full 

understanding of the analyses conducted, the issues addressed, and the options discussed. The 

workshop featured EPA presentations on each component of the methodology as well as 

presentations and discussions by stakeholders from the renewable fuel community, federal 

agencies, universities, and environmental groups. The Agency also took advantage of 

opportunities to meet in the field with key, affected 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 agricultural and biofuel production practices. 

As indicated in the proposal, our lifecycle results were particularly impacted by 

assumptions about land use patterns 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 productivity, areas of crop expansion, and regional 

differences in costs of crop production and land 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, independent 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, third-party contractors to select highly qualified 

peer reviewers. The reviewers selected are leading experts in their respective fields, including 

lifecycle assessment, economic modeling, remote sensing imagery, biofuel technologies, soil 

science, agricultural economics, and climate science. They were asked to evaluate four key 

components of EPA’s methodology: (1) land use modeling, specifically the use of satellite data 

and EPA’s proposed land conversion GHG emission factors; (2) methods to account for the 

variable timing of GHG emissions; (3) GHG emissions 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 recommendations 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 tools and approaches for this work. However, the review also 

recognized that no existing model comprehensively simulates the direct and indirect effects of 
9 

biofuel production both domestically and internationally, 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 technical exchanges, and the scientific 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 other 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 ModuleA

• 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 FAOStat6

• 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 USDA10, NREL11,12,13 and 

Energy Resources Center14

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 2010 
11 

Furthermore, in the proposal, we asked for 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, supported 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 increased 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 ways for this rulemaking. Fuel-specific GHG 

reductions are used to develop threshold determinations for specific fuels. Lifecycle analysis is 

also used to determine the overall impact of the 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 impacts of specific fuels and 

fuel pathways for comparison with thresholds as defined in the legislation. Obligated parties will 

be required to use mandated quantities 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 here 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 conventional petroleum based fuels and renewable 

fuels. 

− Results are combined to quantify the emission 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 associated 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 practical 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 the Energy Information Agency’s Annual Energy 

Outlook (AEO) for 2007 (which took into account the 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 individual biofuel, we analyzed the incremental impact 

of increasing the volume of that fuel to the total mix of biofuels needed to meet the EISA 

requirements while holding volumes of other fuels 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 specific gallon of fuel and tracking inputs and outputs across different 

lifecycle stages, we determined the overall aggregate impacts across sections of the economy in 

response to a given volume change in the amount of biofuel produced. We then normalize those 
13 

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 for 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 different 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 sections 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 was then ascribed to all biofuel produced across 

facilities using that same basic technology. 

We focus our final rule analyses on 2022 results for two main reasons. First, it would 

require an extremely complex assessment and administratively difficult implementation program 

to track how biofuel production might 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 required 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 implementation of technology changes and updates that were 

made to improve or modeling efforts. For example, the inclusion of price induced yield 

increases and the efficiency gains of DDGS replacement 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 modeled 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 time. In doing this we have held the other fuel 

volumes constant at their mandated levels in order to best approximate 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 soybean (and other vegetable) oils, and biodiesel 

produced using waste oils as feedstock. For waste oils, we note that no land use changes are 

included in the FASOM assessment, because any land use impacts are attributed to the original 
14 

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 produced outside the U.S. We addressed 

imported ethanol by analyzing the difference 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 projected 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 model used for this analysis did not include 

switchgrass or similar energy crops, and could only 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 byproducts (e.g., MSW, rendered fats and waste oils 

and corn stover feedstock), we assumed no land use 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 the land. Table 2.3-1 shows the different fuel 

scenarios considered. 

Table 2.3-1. Fuel Volume Scenarios 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 system boundaries in lifecycle analysis. By determining a common set of 

system boundaries, different fuel types can then be validly compared. As described in the previous 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 diagram 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 were compared based on delivery 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 Table 2.3-2 are the emissions and energy use 

associated with each operation as well as upstream 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. Electricity 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 boundaries that include operation-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 production of the equipment or vehicles. Furthermore, 

we include the emissions from the operations of biofuel production plants and petroleum 
16 

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 renewable fuels, conventional renewable fuel, 

advanced biofuel, cellulosic biofuel, and biomass-based diesel. The categories of fuel are 

defined in part based on their GHG emissions. For example for cellulosic biofuel: 

The term ‘cellulosic biofuel’ means renewable fuel derived 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 determine the lifecycle GHG emissions of different 

biofuel feedstock and fuel pathways for determination 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 greenhouse gas emissions are further defined as: 

The term ‘lifecycle greenhouse gas emissions’ means the aggregate quantity of 

greenhouse gas emissions (including direct emissions and significant indirect emissions 

such as significant emissions from land use changes), as determined by the 

Administrator, related to the full fuel 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.B

This definition forms the basis of defining the system boundaries for the biofuels 

lifecycle analysis. As the language specifically mandates that lifecycle GHG emissions include 

“direct emissions and significant indirect emissions such as significant emissions from land use 

changes” the system boundaries modeled include indirect 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 for crude oil used to make gasoline or diesel 

sold or distributed as transportation fuel, and not the marginal crude oil that will be displaced by 

renewable fuel. More discussion on the petroleum fuel baseline and potential impact of 

considering indirect impacts on the petroleum 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 anthropogenically emitted gas that is determined by the 

Administrator, after notice and comment, to contribute to global warming. 

EISA also specifies that the mass values for 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 CH4 is much higher than that of CO2, 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 ‘CO2-equivalent’ basis as global warming potentials (GWPs). The 

GWPs used in this analysis were developed by the UN Intergovernmental Panel on Climate 

Change (IPCC) as listed in their Second Assessment Report, and are shown in Table 2.3-3. 

Second assessment report values are used to be consistent with current standards for international 

reporting of GHG emissions. 

Table 2.3-3. 

100 Year Global Warming Potentials for Greenhouse Gases 

Greenhouse Gas GWP 

CO2 1 

CH4 21 

N2O 310 

Greenhouse gases are measured in terms of CO2-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 CH4, N2O, and CO2, yields the 

total effective GHG impact. Other GHGs like HFCs, PFCs and SF6 are not released in 

significant amounts over the lifecycle of renewable or petroleum fuels, and are therefore not 

tracked in this analysis. Other non-GHG climate impacts like albedo (light reflectance), land 

surface roughness, hydrologic and energy flux, and loss of forest aerosols, while potentially an 
18 

important aspect of climate impacts associated with land use change, are currently outside the 

scope of this analysis. 

Other environmental flows besides GHG emissions are also considered in our analysis for 

this rulemaking. Criteria and toxic air pollutants 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 used to collect and integrate those data into the 

analysis. The quality of data used can be characterized by how well the geographic, technical 

and temporal aspects of the data match the goals and scope of the analysis in question. 

The quality of the data used in this analysis was classified based on its geographic, 

technical and temporal relevance to the 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 included here, as are the international emissions 

associated with producing imported ethanol. Data for agricultural sector impacts include 

both U.S. and international defaults. Agricultural commodity production in other 

countries was based on data specific to those 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 representing 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 represent 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. For 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 pathways as discussed in section 2.4.7. 

Temporal coverage – this analysis considered impacts in 2022. Therefore we modeled 

future data; we projected ethanol production 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 improvements in yields and cropping patterns. 
19 

2.3.2.5 Addressing Uncertainty 

The peer review, the public comments we have received, and the analysis conducted for 

the proposal and updated here for the final rule, indicate 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 interactions 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 these market interactions are not considered. 

However, it is also clear that there are significant uncertainties associated with these 

estimates, particularly with 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 inherent uncertainty of the economic modeling. 

Therefore, working closely with other government agencies as well as incorporating 

feedback from experts who commented on the rule, we have quantified 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 sensitive to a number of key assumptions (e.g., 

current and future corn yields). The indirect, international emissions are the component of our 

analysis with the highest level of uncertainty and have particularly significant impact on our 

overall assessment results. For example, identifying 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 international 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 main 
20 

comments we received on our analysis. While the 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 uncertainty 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, making accurate predictions of many of these 

factors are not relevant to our analysis. For example different assumptions 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 absolute values and projections for 

crops, etc. in the model projections are not as important as the difference the model is projecting 

due to an increase in biofuels production. This 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 and 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 determined based on the results 

of the economic models that provide the total amount of new land needed. The results of the 

economic models were combined with recent satellite data to predict 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 methodological 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 short 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 uncertainty was to conduct sensitivity analysis with 

various methodology choices, as presented in section 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 production inputs (energy use for tractors, etc.), 

and agricultural sector GHG emissions (livestock, soil N2O, etc.). These factors are applied on 
21 

top of economic modeling to determine 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 developing uncertainty profiles and ranges 

around these other input factors, we focused on reducing the uncertainty through updates to 

improve our data and modeling. For example, 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 information 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 the use of the FASOM model to determine 

domestic agriculture sector-wide impacts of increased biofuel production, and the FAPRI model 

to determine international changes in crop production and total crop. Agricultural sector GHG 

emissions are estimated by FASOM, and FAPRI results were converted to GHG emissions based 

on GREET defaults and IPCC emission factors. Biofuel process energy use and associated GHG 

emissions were based on process models for the different pathways 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 similar volumes of the specific fuel in question, while other fuel 

volumes were held constant to isolate 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 production were calculated by taking the 

difference in total GHG emissions from the two scenarios considered. Per gallon, or per million 

British Thermal Units (mmBTU) impacts were calculated 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 represent 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 average 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 economic model of the U.S. forest and agricultural sectors. As 

discussed in section 2.2, FASOM accounts for changes in GHG emissions from most agricultural 

activities, including the total amount of fertilizer, chemicals, gasoline, diesel and electricity used 

on farms for the entire domestic agricultural sector. It also captures changes in the soil 
23 

management, livestock production and U.S. agricultural exports. More detail on the FASOM 

model can be found in Chapter 5 of the RIA. For 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 acres 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

Case

Millions 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 Domestic 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 Ethanol

Acres per 1000 gallons (EtOH Equiv)

Other Crops

Cotton

Hay

Oats

Sorghum

Rice

Wheat

Sw itchgrass

Soybeans

Corn + Silage

Soy-based biodiesel production induced a large increase in harvested soybean acres, 

largely due to the low yield of soy-based biodiesel in terms of gallons produced per acre. Cotton 

was the only other crop that increased substantially 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 ethanol production increased, switchgrass was the only field crop to gain 

acreage, with the exception of a small increase in corn and sugarbeet 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 scenario, 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 crop 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, and do not specifically calculate input or yield 

changes that could result from the use of marginal croplands or altered 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. Below 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 alternative 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 regional variation in the 

inputs used per acre, which suggests that total input use (and the associated 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 average nitrogen fertilizer use by crop in 2022 for 

non-irrigated production without 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 (soybeans 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 Cotton

Nitrogen 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 include 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 Cotton

Diesel 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 Cotton

Gasoline Use (gal per acre)

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

FASOM crop budgets include electricity and 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, overall natural gas use in each scenario 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 Irrigated 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 Cotton

Electricity 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 1,400.8
30 

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. agricultural sector will change with increased feedstocks used for biofuel 

production. The results for total agricultural sector inputs of the different fuel scenarios considered are 

shown in 

Table 2.4-5 through 

Table 2.4-8. 

Table 2.4-5. Change in Domestic Agricultural 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 Agricultural 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 Inputsin the Switchgrass Ethanol Scenario, 2022 
32 

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 defaults for GHG emissions from production of 

fertilizer and chemicals to calculate GHG emissions changes. Fuel use emissions included both 

the upstream emissions associated with production of the fuel as well as combustion emissions, 

also from GREET. Emissions from electricity production represented average U.S. grid 

electricity production. 

In addition to the GHG emissions associated with fertilizer and chemical production, and 

fuel production and use, there are several other non-fossil fuel combustion related GHG sources 

of emissions from the agricultural sector that would be impacted by the increased use of corn for 

ethanol and associated changes to the agricultural 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 agricultural sector is releases of nitrous oxide (N2O) emissions. N2O 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. N2O can be released either directly or indirectly 

through N leaching offsite. 
33 

Figure 2.4-7 highlights some of the major sources of agricultural N2O emissions. 

Figure 2.4-7. Agricultural Sources of N2O Emissions 

Crutzen et al.20 show that, as long as it includes both direct and indirect emissions, topdown accounting for N2O emissions are not inconsistent with the IPCC bottom –up approach to 

N2O accounting. Since the publication of the NPRM, the N2O emission factors in FASOM have 

been updated with the DAYCENT/CENTURY model by Colorado State University (CSU) to 

more accurately estimate direct and indirect N2O emissions in cropland. The FASOM modeling 

captures both direct N2O emissions from fertilizer application and N-fixing crops and indirect 

emissions from leaching, volitization well as from crop residue emissions and residue burning, 
34 

capturing all sources of N2O emissions and reflecting the most recent available science. This 

section discusses the changes made using the 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 N2O emissions and N2O 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 N2O Emissions by Crop (Non-Irrigated, No Residue Harvesting) 

(kgCO2e per acre) 
35 

0

100

200

300

400

500

600

700

N2O 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 N2O 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, including mineral N fertilizer, organic amendments, 

symbiotic N fixation, asymbiotic N fixation, crop residue N, and mineralization of soil organic 

matter. 

CSU used the DAYCENT/CENTURY model to simulate 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 regression equations with coefficients for N2O 

estimation that vary by region, crop type, irrigation status, and crop residue treatment. Each of 

the 63 FASOM regions were assigned the coefficients for its respective super-region. The 

regression equations were incorporated into FASOM to calculate N2O emissions per acre 

according to region, crop, irrigation status, and crop residue treatment. 

FASOM estimates N2O 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 assumes that 1% of N residing in crop residues that remain 

on the field are emitted as N2O emissions, following IPCC guidelines. 

Field burning of crop residues is not considered a net source of CO2, because the carbon 

released to the atmosphere as CO2 during burning is assumed to be reabsorbed during the next 

growing season. Field burning of crop residues, however, does emit N2O and CH4, 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 fields are burned each year, which results in 

N2O emissions as well as CH4 emissions. Using the IPCC default value for burned residue, 

FASOM assumes that, on average, 0.7% of N contained in the burned residue is emitted as N2O. 

FASOM predicts minor reductions in GHG emissions from residue burning under the full RFS2 

policy due to reductions in crop production with residues that are typically burned. In addition, 

CH4 emissions are calculated based on the average methane emissions per acre; however, CH4

emissions are typically quite small relative to the other emissions tracked in FASOM. 

2.4.2.3 Domestic Rice Production Emissions 

Methane (CH4) emissions associated with rice production are also a source of noncombustion GHG emissions from the domestic agricultural 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, CH4 is 

produced through anaerobic decomposition of soil organic matter by methanogenic bacteria. 

Some of this CH4 is transported from the soil to the atmosphere through the rice plants via 

diffusive transport. Minor amounts of CH4 also escape from the soil via diffusion and bubbling 

through floodwaters. 

FASOM assumes that all rice produced in the United States is grown in flooded fields 

and emits CH4. 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 CH4 emissions from rice cultivation. Thus, 

changes in CH4 emissions from rice cultivation result only from changes in the acreage planted 

to rice in the model. 

Methane emissions per acre are calculated based on regional emissions factors 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 Emissions 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 sugarcane production or irrigated hybrid poplar, switchgrass, or 

willow production in FASOM. 

As with other sources of emissions different management methods and other factors 

(such as soil type and amounts of fertilization) will impact CH4 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 management. Enteric fermentation produces CH4

emissions as a by-product of normal digestive processes in animals. During digestion, microbes 

resident in an animal’s digestive system ferment food consumed by the animal. The amount of 

CH4 produced and excreted by an individual animal depends primarily upon the animal’s 

digestive system, and the amount and type of feed it consumes. Ruminant animals (e.g., cattle, 

buffalo, sheep, goats, and camels) are the major emitters of CH4 because of their unique 

digestive system. 

FASOM projects changes in CH4 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 CH4 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 livestock herds as shown in Table 2.4-11. 
38 

Table 2.4-11. Change in Domestic Livestock Herd Size by Scenario, 2022 

Corn Ethanol Soy-based 

Biodiesel 

Livestock Corn Stover Ethanol Switchgrass Ethanol 

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 numbers increase under the corn ethanol, corn stover 

ethanol, and switchgrass ethanol scenarios. Cattle are ruminants, and therefore, increase in cattle 

number results in increased CH4 emissions (Table 2.4-12). Cattle number decreases under the 

soy-based biodiesel scenario, resulting in decreased methane emissions due to enteric 

fermentation. 

Table 2.4-12. 

Change in Domestic Livestock Emissions by Scenario, 2022 

Corn Ethanol Soybean 

Biodiesel 

Corn Stover 

Ethanol 

Switchgrass 

Ethanol 

Enteric CH4 Emissions 

(‘000 tons CO2e) 21 -128 1,129 338 

Manure CH4 and N2O 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 methane 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 regarding the efficiency of DGS as an animal feed in 

our agricultural sector modeling, we have also 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 CH4 (-3,381 g CO2e/mmBTU ethanol) per head of cattle and cows that 

were fed with DGS. The reduction in CH4 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 lifecycle GHG emissions for corn ethanol compared to the proposal 

assumptions. 

The management of livestock manure can also produce anthropogenic CH4 and N2O 

emissions. CH4 is produced by the anaerobic decomposition of manure. N2O is produced 

through the nitrification and denitrification of the organic nitrogen in livestock manure and urine. 

The type of manure management methods impacts the quantity of GHG emissions emitted. 

FASOM bases manure management emissions calculations on emissions factors for livestock 

types and livestock management methods as reported in the EPA GHG inventory report for 

1990–2003.23 Manure management emissions are projected to decrease as a result of lower 

livestock herd values. 
39 

Under the corn ethanol and soybean biodiesel 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 Results (Excluding Land Use Change) 

Table 2.4-13 provides a summary of FASOM projections for total GHG emissions 

impacts for the domestic agricultural sector for each fuel pathway scenario analyzed. Land use 

change impacts are discussed in section 2.4.4. 

Table 2.4-13. 

Domestic Agriculture GHG Emission 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 inputs increase and thereby increase GHG emissions. 

No one domestic agricultural sector emission source (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 domestic agricultural sector is due to the indirect effects and demand changes, 

specifically demand changes in U.S. exports. For example, the sources of corn used in ethanol 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 offset by reductions in other crop acres as shown in 

Figure 2.4-10. Some of the corn used for ethanol 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 domestic agricultural sector impacts are only a portion 

of the total impacts due to increased ethanol production 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 Impacts into International Impacts 

In order to estimate the impact on international agricultural sector GHG emissions, the 

FAPRI model was run with the same domestic biofuel volume scenarios, with the exception of 

cellulosic ethanol, as was run in the FASOM model for the domestic agriculture sector analysis. 

In the FAPRI models, links between the U.S. and international models are made through 
41 

commodity prices and net trade equations. In general, for each commodity sector, the economic 

relationship that quantity supplied 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 equation. 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 close the model. The models specify behavioral equations for 

production, use, stocks, and trade between countries/regions. The models solve for representative 

world prices by equating excess supply and demand 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 changes in world prices that change in worldwide 

commodity production and trade is determined. 

 When analyzing the impact of the RFS2 biofuel 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. For example, as discussed above, the corn ethanol 

biofuel requirement places an additional demand for corn used for ethanol, and this corn comes 

not just from additional production, but also from decreases in other uses including exports. In 

addition, as corn production expands, it places pressure in terms of relative demand on other 

crops in a particular region in the U.S., which in turn affects their prices 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 Changes 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 Ethanol

Tons 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 biofuel will have the greatest impact on U.S. 

exports of that biofuel’s feedstock. For instance, 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 two 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 ethanol 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 basis 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, production, 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 FAPRI 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 the 
43 

world price for that commodity. This, in turn, will have an impact on the demand for that 

commodity worldwide, and affect production and trade levels in other countries. Additional 

information on the changes in the world price for commodities and the coordination of 

assumptions between the FASOM and FAPRI models can be found in Chapter 5 of the RIA. 

2.4.3 Evaluation of International Agricultural GHG Emissions Impacts 

For this analysis we used the FAPRI model to estimate the impacts on international crop 

production due to changes in biofuel production. These results were used to generate GHG 

emissions from the international agricultural sector, 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 provide 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 type and country. We, 

therefore, determined international fertilizer and energy use based 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 Industry Association (IFA) report, 

Assessment of Fertilizer Use by Crop 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 changes 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 fertilizer application. Historical fertilizer application rates for nitrogen, 

phosphorus, and potassium were updated using more recent data available from the IFA report as 

recommended by expert reviewers 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 altered its survey methodology since 2004.27 

The IFA dataset covers 23 countries (considering 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 periods: 2006 or 2006/07, and 2007 or 2007/08 to account 

for seasonal variations in crop production. Fertilizer application rates were calculated by 

dividing IFA total consumption values by FAOStat 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 region/crop combinations not covered by the IFA 

dataset. In addition, Feristat data is preferred to calculate fertilizer consumption for “rest of the 

world” regions since the dataset provides for a greater number of countries and greater detail for 
44 

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 FAO 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 EU

Nitrogen 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 country 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 Mecedo (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 change 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 N2O Emissions 

For international N2O 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 N2O Emissions from Synthetic Fertilizer and Crop Residues 
46 

Direct N2O 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 N2O-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 NH3–N + NOx–N) / kg N applied 

 EF for N lost through volatilization = 0.010 kg N2O–N / (kg NH3–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 N2O 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 suggested 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 N2O emissions from sugarcane production based on 

perennial grass as a proxy. Perennial grass is chosen as a proxy based on input from N2O 

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 Regions 
47 

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

corn

soybean

sugarcane

corn 

soybean

corn

soybean

corncorn

Brazil Argentina China EU Mexico

N2O 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 projected by FAPRI, we estimated the total 

change in N2O emissions for each fuel scenarios, as shown in Table 2.4-16. 

Table 2.4-16. 

International Crop Change in N2O 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 purpose (providing data by end use and fuel type for 

over 130 countries representing the major energy users of the world). We collected data from 

IEA on total CO2 emissions from agricultural fuel combustion by country.33 We also collected 
48 

IEA data on agricultural electricity and fuel use by country, which was combined with emissions 

factors to estimate country-level GHG emissions from agricultural electricity and fuel use. 

Historical trends were used to project chemical and energy use in 2022. These total GHG 

emissions were only combustion related, 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 average 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 acreage changes projected by FAPRI to determine 

the change in GHG emissions from foreign agricultural energy use for each fuel scenario. 

In the case of Brazilian sugarcane, we had country 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 included in this calculation, as consistent with 

other renewable fuel pathways. Table 2.4-17 shows 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 energy use estimated in 2020 for agricultural 

operations, harvesting, other activities and seed production would be similar to that in 2022, and 

have adopted these estimates for our GHG calculations 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 agricultural energy use GHG emissions for the 

different fuel scenarios. 

Table 2.4-18. 

International Change in Agricultural Energy Use GHG Emissions by Scenario, 2022 

(kg CO2e / mmBTU) 

 Corn 

Ethanol 

Soybean 

Biodiesel 

Switchgrass 

Ethanol 

Brazilian 

Sugarcane 
49 

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 factors 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 emissions 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 cropping regime: irrigated, rainfed lowland, upland 

and deepwater by country. Rice cultivation season lengths were available from the International 

Rice Research Institute (IRRI).36

2.4.3.5 International Crop Residue Burning Emissions 

International crop residue burning on the field, 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 leaves and tops (with 50% moisture content) or 140 

kg of dry leaves and tops, which we assume for 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. See 
50 

Figure 2.4-13 for the phase out schedule for trash burning practices. 

Figure 2.4-13. Phase Out Schedule for Trash Burning Practices 

According to Brazil’s Sugarcane Research Center (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. 20-21 
51 

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 for areas where mechanical areas are possible 

with existing technologies (over 85% of existing 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 Sao 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-South, and Sao Paulo consists of a considerable portion of that 

production, the following situation in Table 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-field burning from two pollutants, methane 

(CH4) and nitrous oxide (N2O). Carbon dioxide (CO2) emissions were not taken into account 

because the CO2 is considered to be taken from the air during sugarcane growth. 

Table 2.4-21. Emission Factors of Open-Field Burning of Sugarcane Leaves and Tops37

Pollutant g/kg of dry leaves and tops burned

CH4 2.7 

CO 92 

N2O 0.07 

NOx 2.5 

PM10 7.8 

PM2.5 3.9 

SOx 0.4 

VOC 7.0 

2.4.3.6 International Livestock GHG Emissions 

Similar to domestic livestock impacts associated 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, therefore, livestock GHG impacts were based on 
52 

activity data provided by the FAPRI model (e.g., number and type 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 -30.92 
53 

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 impacts of livestock changes were calculated by applying regional default 

factors for enteric fermentation CH4 emissions by livestock type. These factors are shown in 

Table 2.4-23. 

Table 2.4-23. Enteric Fermentation Emission Factors

Enteric Fermentation 

(kg CH4/head/year) Dairy 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 default factors for manure management CH4 and N2O emissions 

by livestock type. Manure management CH4 emission factors are shown in Table 2.4-24. 
54 

Manure management N2O values were based on default IPCC nitrogen produced per livestock 

type and IPCC default manure management 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 used 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 final 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, fertilizer and other chemicals. The farm inputs category also includes 

energy used in crop production processes. Land 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 -920
55 

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 impacts from that land conversion? 

5. How do we account for the variable timing 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 answering these questions about land use change. 

We used the FASOM model to project land conversions in the United States. FASOM was 

designed to simulate domestic land use interactions and land use change GHG impacts. We used 

the FAPRI model to project international cropland 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 pasture. 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 expansion 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 projected land conversions we applied GHG emissions 

factors prepared by Winrock following IPCC guidelines. 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 quantification of GHG emissions from international 

land conversions, we focused on two areas: uncertainty in the MODIS satellite data used to 

determine the types of land affected (e.g., forest or grassland), and uncertainty 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 the uncertainty in every data input based on the quality, quantity, 

resolution and variability in the underlying data sources. Correlation groups were assigned based 

on the sources of underlying data. Finally, the total uncertainty in international land use change 

GHG emissions was quantified a Monte Carlo analysis following Tier 2 IPCC guidelines. 

 

This section describes the data sources and methods used for the analysis summarized 

above, with key results illustrated throughout. 

2.4.4.1 Evaluation of Domestic Land Conversion GHG Emissions Impacts 
56 

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 modeling of land conversion and related 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 assessment of land use in the 

U.S. One of these updates includes the incorporation of the forestry component of the FASOM 

model. Running both the forestry and agriculture 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 products and prices in each respective sector. 

In addition, FASOM also includes a representation of seven major land use categories, 

including cropland, cropland pasture, forestland, forest pasture, rangeland, developed 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 livestock, 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). Cropland pasture is managed pasture 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 idled or used for production may vary, rangeland 

may not be used for any other purpose than for animal grazing. For each of these categories, 

FASOM accounts for how much is actively used 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 LaboratoryE

 that demonstrate higher 

replacement rates than what was used in the analysis 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.pdf 
57 

a byproduct of corn ethanol production, are relatively 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 the FASOM model can be found in Chapter 5. 

For the corn ethanol scenario, 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 other 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 switchgrass, 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 crop in each fuel-specific volume scenario. 

Table 2.4-26. 

Change in total area of domestic cropland 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 0.06 
58 

Figure 2.4-14. Normalized Domestic Crop 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 Ethanol

Acres 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 livestock 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 acres, 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 million acres. With an increase in switchgrass 

ethanol, cropland increases by 2.2 million acres, cropland 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 uses with an increase in corn stover ethanol 

production. Specifically, cropland increases by 0.07 million acres, cropland pasture increases by 
59 

0.05 million acres, forest pasture acres do not change, 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 from the program and converted to cropland for 

production, all leaving the assumed minimum limit of 32 million acres, in accordance with the 

2008 Farm Bill. The number of acres in rangeland 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 assumed to be of higher value than all other 

land categories, and FASOM assumes that the amount of developed land increases at a steady 

rate over time, and does not vary with changes in demand for biofuel. 

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

-3

-2

-1

0

1

2

3

Corn Ethanol Soy Biodiesel Sw itchgrass Ethanol Corn Stover Ethanol

Millions 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 are 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 change in soil carbon due to change in crop 

production acres and in crop type. In addition, FASOM’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. With the addition of the forestry module for the final rulemaking, 
60 

we have used FASOM to model changes in soil carbon and biomass carbon due to land use 

conversion between cropland, pasture, forestland, and developed land. 

 In addition to quantifying GHG emissions and sinks, FASOM distinguishes the unique 

time dynamics and accounting issues of carbon sequestration options. These include issues such 

as saturation of carbon sequestration over time (i.e., carbon sequestration in a particular sink 

reaches an equilibrium such that carbon storage is maintained, but is no longer increasing), 

potential reversibility of carbon benefits (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 products. Sequestration refers to storage of the GHGs for more than one 

year. As a consequence, the sequestration definition 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. Carbon sequestration is also modeled within cropland soils, 

pastureland soils, soils in idled lands, timberland soils, and harvested 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 housing, shopping centers, and roads are tracked in 

the model. 

 In the subsections below, we detail FASOM 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 agricultural soil carbon due to land conversion and 

changes in crop patterns. The FASOM GHG factors 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, land 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 and results in reduced soil carbon with an 

associated release of CO2 into the atmosphere. The use of tillage alternatives that 

reduce soil disturbance and therefore reduce oxidation of soil organic matter will 
61 

increase soil carbon sequestration. Reduced tillage 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 increase soil carbon sequestration compared to 

dryland cropland. 

 Relative abundance of grasslands. Generally, pastureland and CRP land experience 

less soil disturbance than actively tilled croplands and store more carbon per acre. 

Thus, changes in the distribution of land between pastureland, cropland, and land in 

the CRP will affect agricultural 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 are 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 over 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 sequestration towards equilibrium levels is not 

constant over time, FASOM yields 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 alternative. 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 N2O 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 sequestered 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 reduction in carbon sequestration, although some of 

the carbon that was in the harvested trees will continue to be stored in forest products for some 

time afterward. If harvested stands 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 forestland. It accounts for carbon storage in forest 

products and emission streams from these products 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 periodic 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 volume, (2) the ratio of growing stock volume to merchantable 

volume, and (3) and parameters of a forest volume-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 must 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 timber stands for each timber stand strata (e.g., 

a stand giving location, forest type, management intensity 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 model parameters vary by species and region and 

are obtained from42,43 and Smith et al. (2003).44 

Carbon in live and standing dead trees is calculated 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/forest type designations. Forest 
63 

land area data reported by the RPA assessment46 are used to calculate the appropriate weights. 

Birdsey’s assumption that the mass of wood is approximately 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 

Birdsey48,49 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 forests are regenerated immediately after harvest. 53 

As a result, FASOM assumes soil carbon on a reforested 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 value for that land/region combination reported by 

the Century Model, which was developed 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 largest 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 Heath56 to estimate forest floor carbon mass. The model’s definition of 

forest floor excludes coarse woody debris materials; that is, pieces of down dead wood with a 

diameter of at least 7.5 cm that are not attached to trees.57 In order to account for this material, 

coarse woody debris is assumed to be a fixed 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 smallest 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 Sinks59 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 (the 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 for logs coming from an aggregate softwood and 

hardwood stand. They exclude carbon in logging residue left onsite. Logging residue is tracked 

separately in the forest floor carbon pool described above. 

Harvested logs removed from site are converted into three types of outputs through 

primary manufacturing processes: wood and paper products, mill residues, and fuel wood (See 

Table 2.4-27 for a list of products tracked by FASOM). The fate of each of these outputs is 

discussed below. 

Table 2.4-27. Wood and paper products 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 over time, and FASOM tracks the fate of 

product carbon with two carbon pools: carbon remaining in-product and carbon leaving the 

product (Figure 2.4-17). Carbon that leaves the product ultimately is emitted or permanently 

sequestered in landfills. 
65 

Figure 2.4-17. Carbon Disposition after Timber Harvest61

FASOM determines the fraction of carbon that remains 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 Nicholson63 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 Manufacturing 
66 

goes to landfills (Figure 2.4-18). The remainder of the carbon leaving the wood and paper 

product pools is emitted as CO2 into the atmosphere. 

Table 2.4-28. Half-life for Forest Products in End Uses64

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 Disposition65

Total Carbon in Wood and 

Paper Products 

Emission

Carbon

remaining in

product

Landfill 

Landfill

(Permanent) 

Landfill

(Temporary) 

Carbon

leaving

product 
67 

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 and paper products. This 

carbon is tracked using the appropriate product category as described 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 wood 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 the 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 agricultural use, soil carbon levels are consistent with 

DAYCENT/CENTRUY model data on agricultural soil 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 related to land use changes are not only a function of the difference 

between the land use change and management practices 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-specific 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 (CO2 and N2O from 

cropland, pastureland, CRP land) and forestland (CO2 from biomass, soil, and forest products) 

between the years 2000 and 2022 for the control and 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 CO2 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 Land 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 efficiency of renewable fuel co-products over 

time, supply and demand in the livestock sector, and many other significant variables. More 

details about the FAPRI model and our assumptions are provided in RIA Chapter 5. 

2.4.4.2.2 Area and Location of International Cropland Conversions 
69 

 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 incremental increase in renewable fuel production in 

a given scenario and year, on an energy-content 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 tropics releases substantially more carbon than 

deforestation in drier regions. The FAPRI model 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 the 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 scenarios where corn ethanol 

production increases in the United States, we project 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 Scenario, 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 Scenario, 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 International Pasture Land Conversions 

In addition to considering international changes in crop area, our analysis also accounts 

for changes in pasture area, i.e., land used for livestock 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 uses pasture. Furthermore, more land is used 

globally for pasture than for crop production.68 The new , more detailed, 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 competition 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 response 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 assumption 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 amount of land used for livestock grazing. As a 

result of this analytical improvement, in countries 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 countries where livestock numbers 

increase, more land is needed for pasture, which can be added on abandoned cropland or unused 

grassland, or it can result in deforestation. This new methodology 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 for 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 livestock 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 in 
74 

Table 2.4-31. Expert judgment was used to adjust unusually low regional stocking rates. 

For example, we removed serious outliers from the "CIS, Other" FAPRI region.K Specifically 

Kazakhstan and Turkmenistan were removed from this calculation because these countries 

include vast stretches of desert pasture used for rotational sheep grazing. Removing these 

outliers provided a more realistic estimate of how cattle population changes would affect land 

use in this region. For other regions that had unreasonably 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 considered, 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 livestock population changes to have a large impact 

on pasture area in these regions. 

Table 2.4-30. Animal Unit Equivalents (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 adjusting 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 expect 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 0.41 
76 

 As described above, pasture intensification was modeled endogenously 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 intensification 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 intensification factor of 10% 

in regions where livestock populations increased. 

Table 2.4-32 shows total and normalized 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 beef and dairy production (which require pasture) 

declined more than swine and poultry production (which do not require pasture), the pasutre area 

changes were larger. 2.4.3.6 included international livestock production results for each 

scenario, 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, pasture 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 Area 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 Cropland and Pasture Land Conversions 
78 

As discussed above, in the proposed rule we made a broad assumption 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 regions where crop area increased. In the final 

rule analysis we used a more sophisticated approach that captures a wider range of potential 

interactions between crop and pasture areas. For example, in regions were pasture decreases, 

this land is available for crop expansion. Therefore, 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 for 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 International Land Use Change by Renewable Fuel 

(ha / billion BTU) 

-10

-5

0

5

10

15

Corn Ethanol

Soy Biodiesel

Sugarcane Ethanol

Switchgrass Ethanol

Corn Ethanol

Soy Biodiesel

Sugarcane Ethanol

Switchgrass Ethanol

Corn Ethanol

Soy Biodiesel

Sugarcane Ethanol

Switchgrass 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 assumed that pasture area could not decline. As 

discussed in the next sections, this had a large impact on the types of land 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 Final
80 

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 International Land Conversions 

 As explained in the previous section, the FAPRI international models were used to 

project changes in the area of land used for cropland and pasture in 54 regions. In this section 
81 

we describe the two-step procedure that was used to determine in more detail the types and 

locations of land conversions: 

1. The FAPRI output was disaggregated into 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) were evaluated with MODIS Version 5 satellite 

data from 2001-2007. 

2.4.4.2.5.1 Determination of International Land Conversion Categories 

 Based on the FAPRI model results, we determined the conversions between annual crops, 

perennial crops, pasture land, and natural ecosystems 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: annual 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 decreases, annual crops expand onto the land 

previously used for perennial crops. 

2. Pasture and crop area interact with each other, 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 FAPRI projections were disaggregated into 12 

land conversion categories, where natural eco-systems 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 results 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 opposite direction. For example, in the Ntrl to 

Annl (i.e., Natural Ecosystems to Annual Crops) column, a positive 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 annual 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 Conversion 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 Conversion 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 scheme. For the final rule analysis we used 

the higher resolution, and more recent, MODIS Version 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 uncertainty in the satellite data as quantified by 

NASA. The MODIS data set is routinely validated by NASA’s MODIS land validation team.P 

NASA uses several validation techniques for quality 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 procedures are summarized below and discussed in 

more detail in a technical report by ICF International, Inc., available 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 understand 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 procedures is available from the NASA Goddard Space 

Flight Center website, http://landval.gsfc.nasa.gov/ 
83 

labelled a series of test sites 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 all 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 sample. The producer’s accuracy is the 

probability that a pixel truly belonging to class 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 Programme (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 cover classes presented in confusion matrix. 

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

EPA Land Cover 

IGBP Land Cover Class Classes 

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 Excluded 
84 

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 matrix 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 systematic biases. The matrix includes data from 39,470 

training 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 class 2) in the matrix, we 

see that of the 8,384 sites that were classified by the satellite as forest (see the satellite total row) 

7,763 of these sites (see the diagonal in row 2) were 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 for 
85 

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 classes 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 presents 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 fairly 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 with 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 ACMcup = CMup /CMxp, where CMup are the values in the input aggregated 

confusion matrix and CMxp = ∑uCMup where u is the user’s land use and p is the 

producer’s land useQ

o The coefficients from the resulting matrix sum to 1.0 

• Next, the number of producer sites is scaled similarly to the approach above and the 

producer’s estimate is recalculatedR

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

o ALUci = ∑pBCMcyip * AMcpx 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 ALUcf = ∑pBCMcyfp * AMcxp 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 (ALUci < 

AMcix) 

o We scale the land use change to all land uses down from that land use category 

based on the percentage decrease 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 2nd to last step, let’s assume that, based on the confusion matrix 

adjustment procedure described above, the adjusted 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 that 3.5, 2.1, and 1.4 million hectares of increased 

cropland, forestland, and grassland have been accounted 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 originally, it is now 0.7 million hectares. If the 

Cropland, Forestland, and Grassland 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 use 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, forestland, and grassland respectively. 

 The approach for the adjustments in land use 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, instead 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 land use change data from 2001-2007. We also used 

this approach to determine, within each country/region, the location of land use changes, i.e. the 

Administrative Units where conversions would occur. For example, in each region we looked at 

the types of land converted to cropland during this time period. 

To determine the types of land converted to pasture, 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 and savanna, which we know are used for livestock 

grazing, but it provides no information about the share of grassland and savanna 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 included/approximated these important land conversion 
87 

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 used 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 reversions, i.e. the Administrative Units where 

reversions would occur. 

The contributing land use change categories and 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. 
88 

Table 2.4-38. 

Contributing Land Use Change Categories and Bases of Weighting Factors for Agricultural Land Use Change Categories 

Agricultural Land 

Use Change 

Category 

Basis of 

Weighting 

Factor 

Land Use Types and Land Use Change 

Categories, used to calculate each Country 

and Admin Unit’s Contribution to the 

Regional Total 

Share of Grassland or Savanna of 

Total Grassland and Savanna, used 

to Apportion Pasture Changes 

Land Use Change Categories Used to Apportion 

Changes to Initial or Final Land Use (Regional Shares 

Unless Noted) and to Calculate Weighted Average 

Emissions 

Perennial Crops Annual Crops to 2007 Land Use Cropland - Croplands to Perennial Annual Crops Perennial Crops to 2007 Land Use 

Grasslands to Perennial - Grasslands to Perennial 

Cropland - Perennial to Croplands 

Crops Pasture to Perennial 

to 2007 Change - 2001 Land Use 

Perennial to Grasslands - Perennial to Grasslands 

Savanna to Perennial - Savanna to Perennial 

Pasture Perennial Crops to 

to 2007 Change - 2001 Land Use 

Grasslands to Croplands - Grasslands to Croplands 

Perennial to Savanna - Perennial to Savanna 

Crops Pasture to Annual 

to 2007 Change - 2001 Land Use 

Croplands to Grasslands - Croplands to Grasslands 

Savanna to Croplands - Savanna to Croplands 

Pasture Annual Crops to 

to 2007 Change - 2001 Land Use 

Wetland to Croplands - Wetland to Croplands Shrubland to Croplands - Shrubland to Croplands Savanna to Croplands - Savanna to Croplands Mixed to Croplands - Mixed to Croplands Grasslands to Croplands - Grasslands to Croplands Forestland to Croplands - Forestland to Croplands 

Croplands to Savanna - Croplands to Savanna 

to Annual Crops Natural Ecosystems 

to 2007 Change - 2001 Land Use 

Croplands - Croplands to Savanna (at the Country/Admin Level) Croplands - Croplands to Mixed (at the Country/Admin Level) Croplands - Croplands to Grasslands (at the Country/Admin Level) Croplands - Croplands to Forestland (at the Country/Admin Level) 

Barren to Croplands - Barren to Croplands 

Natural Ecosystems Annual Crops to 2007 Land Use 

Wetland to Perennial - Wetland to Perennial Shrubland to Perennial - Shrubland to Perennial Savanna to Perennial - Savanna to Perennial Mixed to Perennial - Mixed to Perennial Grasslands to Perennial - Grasslands to Perennial Forestland to Perennial - Forestland to Perennial 

Croplands - Croplands to Shrubland (at the Country/Admin Level) 

to Perennial Crops Natural Ecosystems 

to 2007 Change - 2001 Land Use 

Croplands - Perennial to Savanna Croplands - Perennial to Mixed Croplands - Perennial to Grasslands Croplands - Perennial to Forestland 

Barren to Perennial - Barren to Perennial 

Natural Ecosystems Perennial Crops to 2007 Land Use 

Forestland to Croplands Forestland to Grasslands 

Croplands - Perennial to Shrubland 

to Pasture Natural Ecosystems 

Change - 2001 Land Use 

Grasslands to Croplands Administrative Unit 

Grassland Share, by Country and 

Grasslands to Grasslands 
89 

Agricultural Land 

Use Change 

Category 

Basis of 

Weighting 

Factor 

Land Use Types and Land Use Change 

Categories, used to calculate each Country 

and Admin Unit’s Contribution to the 

Regional Total 

Share of Grassland or Savanna of 

Total Grassland and Savanna, used 

to Apportion Pasture Changes 

Land Use Change Categories Used to Apportion 

Changes to Initial or Final Land Use (Regional Shares 

Unless Noted) and to Calculate Weighted Average 

Emissions 

Wetland to Croplands Wetland to Savanna Shrubland to Croplands Shrubland to Savanna Savanna to Croplands Savanna to Savanna Mixed to Croplands Mixed to Savanna Grasslands to Croplands Grasslands to Savanna Forestland to Croplands Forestland to Savanna Barren to Croplands Barren to Grasslands Wetland to Croplands Wetland to Grasslands Shrubland to Croplands Shrubland to Grasslands Savanna to Croplands Savanna to Grasslands Mixed to Croplands Mixed to Grasslands to 2007 

Barren to Croplands 

Administrative Unit Savanna Share, by Country and 

Barren to Savanna 

Cropland Grassland to Forestland (at the Country/Admin Level) Cropland Grassland to Grassland (at the Country/Admin Level) Cropland Grassland to Savanna (at the Country/Admin Level) Cropland Grassland to Mixed (at the Country/Admin Level) Cropland 

Administrative Unit Grassland Share, by Country and 

Grassland to Shrubland (at the Country/Admin Level) 

Cropland Savanna to Forestland (at the Country/Admin Level) Cropland Savanna to Grassland (at the Country/Admin Level) Cropland Savanna to Savanna (at the Country/Admin Level) Cropland Savanna to Mixed (at the Country/Admin Level) 

Ecosystems Pasture to Natural 2007 Land UseU

Cropland 

Administrative Unit Savanna Share, by Country and 

Savanna to Shrubland (at the Country/Admin Level) 

U The model actual uses the three land uses twice, once to represent the replacement of grasslands and the other to represent the replacement of savanna 
90 

Table 2.4-39 includes the regional shares of land types converted to cropland and pasture 

based on the original Version 5 MODIS data. Table 2.4-40 shows the same data after it was 

corrected using the confusion matrix data and the 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 tendency of MODIS to confuse these land types with 

each other and with cropland. As a result, the share 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 Land Conversion GHG Emissions Impacts 

Land use change emissions factors were calculated by the non-profit organization 

Winrock International following 2006 IPCC Agriculture 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. Based on the reviewers recommendations a number of 

important improvements were made, including incorporation 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 rule, 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 significant agricultural producers. This section 

describes the methods used to estimate GHG emissions from international land use change, with 

a focus on updates since the proposed rule. More details are available in a technical document 

by Winrock available on the public docket.79

2.4.4.2.6.1 Data Sources and Methods for International 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 mineral soils, emissions from peat drainage on 

peat soils cleared for agriculture, foregone forest sequestration, and non-CO2 emissions (CH4, 

N2O) 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. Updates to various components of the final emission 

factor are described below. 

2.4.4.2.6.1.1 Data Sources and Methods for International Forest Carbon Stock Estimates 
94 

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 (Ruesch and Gibbs 2008). We used regional and/or 

country-level maps where available, and the global Ruesch and Gibbs (2008) data product was 

used only to fill in gaps where no other information on forest carbon stocks was available. 81

Our analysis of forest carbon stocks was improved by incorporating several new data 

sources. Most notable is the inclusion of a new 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- and 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 heights measured by spaceborne lidar, and a 

suite of satellite imagery products to derive a spatially refined map of aboveground forest carbon 

at a 1-km grid cell resolution. Belowground carbon 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- and 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). Preliminary 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 for 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 approximately 50% of the ground and lidar biomass data 
95 

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 spatially-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) dataset filled in the data gap for Canada and 

many Eastern European countries.86,87,88

Figure 2.4-33. Aboveground biomass carbon stocks in Northern forests. 

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

Apart from the new data sources described 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 in 
96 

Figure 2.4-34. 

Figure 2.4-34. 

Data sources used for estimating forest carbon stocks in updated emission factor analysis 
97 

Note: Nabuurs et al. and Nabuurs et al. 2003 are the same data source. 

In all cases where belowground biomass was not estimated, belowground biomass was 

added to aboveground biomass estimates using an equation from Mokany et al. (2006).90 (This 

equation represents an update to the default belowground biomass values given in the IPCC 

Guidelines.) Forest carbon stock values per country 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 International Cropland Carbon Stock Estimates 
98 

In the proposed rule emission factor analysis, all cropland conversion was assumed to be 

conversion to annual cropland. In the updated analysis, emission 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 after one year of growth 

were estimated as 15 t CO2e/ha. 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 after one year of growth were assumed 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 sugarcane was derived from estimates of carbon stocks in 

sugarcane in aboveground biomass (17 t C ha-1 or 62 t CO2, Amaral et al. 2008)91 and in 

belowground biomass (7 t C/ha or 26 t CO2/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 CO2/ha. 

All biomass accumulated after Year 1 would have been harvested 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 plantations may have a longterm average carbon stock higher than that at Year 1, but the land use after 30 years is highly uncertain and 

there is no guarantee of future rotations. Therefore, the average carbon 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 showing an example of carbon stock growth for 

perennial crops with different rotation lengths. 

Figure 2.4-36. Perennial Crop Carbon Stocks Over Time
99 

2.4.4.2.6.1.3 Data Sources and Methods for International Grassland, Savanna 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 vegetation gradient from campo limpo (pure 

grassland), campo sujo (a savanna with a sparse 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 stocks in Brazil were estimated as the average biomass value 

reported for campo sujo and grassland carbon stocks in Brazil were estimated as the average 

value reported for campo limpo. 

To maintain a consistent approach, 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 Guidelines. 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 ecological zone present within each country. Carbon 

stocks of savanna and shrubland land cover types in all countries except Brazil were estimated 

using a proportional approach based on the Brazil dataset, which indicates an increasing trend in 

carbon stocks from grassland to savanna to shrubland 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 grassland 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 estimates for each country and administrative unit 

(t CO2e/ha) 

< 5

6 - 10

11 - 15

16 - 20

21 - 25

26 - 30

> 30
100 

2.4.4.2.6.1.4 Data Sources and Methods for International Wetland, Barren and Mixed Carbon 

Stock Estimates 

In line with recommendations for the expert peer reviewers, the updated analysis 

included land cover change to/from the wetland and barren land cover categories, and therefore 

emission factors were estimated for these conversions. According to the IGBP land cover 

description, the permanent wetlands category can consist of herbaceous and/or woody 

vegetation. However, after confirming that Indonesian peat swamp forests (a type of permanent 

forested wetland) are classified as forest and not wetland in the MODIS land cover maps, the 

carbon stocks of permanent wetlands in a given country or administrative unit were calculated as 

the average of carbon stocks in shrubland and grassland land cover categories. Carbon stocks on 

barren lands were assumed to be zero. In accordance with the IGBP land cover definitions, 

mixed carbon stocks were calculated as the average of forest, shrubland, grassland and cropland 

carbon stocks. 

2.4.4.2.6.2 Evaluation of Changes in Biomass Carbon Stocks from International Land 

Conversions 

Initial changes in biomass carbon stocks on land 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 = ∑ − • iCCONVERSION BAFTER BBEFORE CF i i ( )

where: 

ΔCCONVERSION = initial change in biomass carbon stocks on land converted to 

 another land category, tonnes C ha-1

 yr

-1 

AFTERi B = biomass stocks on land type i immediately after the conversion, 

 tonnes d.m. ha-1 

BEFOREi B = biomass stocks on land type I before the conversion, tonnes d.m. 

 ha-1 

CF = carbon fraction of dry matter, 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 production. In general, it was assumed that clearing 

with fire takes place in tropical regions. Several 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 in 
101 

isolation would not allow a distinction between fire 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 conversion) 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 analysis exceeded the time available. Therefore, we 

maintained the approach used in the proposed rule analysis, whereby expert judgment was used 

to determine the regions where fire is commonly 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 countries where fire is used commonly as a land 

clearing practice for conversion to agriculture, non-CO2 emissions were estimated using 

emission factors in Table 2.5 and 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-CO2 emissions from land clearing with fire were estimated as: 

3 10− = • • • • Lfire A M B Cf Gef Where: 

L fire = amount of greenhouse gas emissions from fire, MT of each GHG (i.e., 

 CH4, N2O) 

A = area burnt, ha 

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

Cf = combustion factor, dimensionless 

Gef = emission factor, g kg-1 dry matter burnt 
102 

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 International Soil Carbon Stocks 

For the initial analysis, soil carbon stocks were estimated using the FAO/UNESCO Soil 

Map of the World. In March 2009, a new soil database was released (Harmonized Soil Map of 

the World v.1.1) with 1-km resolution grid cells and therefore this improved dataset was used for 

the updated analysis.97 Attribute values of the database did not include average soil carbon 

stocks, but values were included 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 – assumed 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 m3

) 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., after conversion) are based on long-term 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 CO2e/ha) 

< 50

51 - 100

101 - 150

151 - 242

243 - 500

501 - 750

> 750

Note: estimates were derived from the Harmonized World Soil Database v. 1.1 
103 

2.4.4.2.6.4.1 Evaluation of Changes in Soil Carbon Stocks from International Land Conversions 

Identical to the approach used in the proposed rule analysis, changes in soil carbon stocks 

on land converted to cropland were calculated based on Section 5.3.3.4 of the IPCC AFOLU. 

Soil carbon stocks after conversion to cropland were based on specific soil stock change factors 

for land use, management and inputs (FLU, FMG, FI, 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 soil 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

( ) ( ) SOCRe f FLU ,before FMG,before FI,before SOCRe f FLU ,after FMG,after FI,after SOC • • • − • • • Δ =

where: 

ΔSOC = average annual change in carbon stocks in top 30 cm of soil; t C 

ha-1 yr-1 

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

FLU = land use factor before or after conversion 

FMG = management factor before or after conversion 

FI = input factor before or after conversion 

As default values for stock change factors (FLU, FMG, FI) 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 shrubland, savanna, perennial cropland) except conversion to 

cropland. Full tillage and medium inputs were assumed in all scenarios 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 International Harvested Wood Products 

In the updated analysis, we addressed the potential significance of the harvested wood 

product pool and concluded that the amount of carbon 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 products was not incorporated into our updated 

emission factors. 

We reached this conclusion as follows: the proportion of extracted timber that ends up in 

long-lived (>5 yr) wood products was estimated using 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 roundwood by the total carbon in commodity uses 

>5 yr. We did this for the developing and developed world and calculated percentages of 53% 

and 60%, respectively. The country-level percentages were generally lower than the aggregated 
104 

values. Winjum et al. (1998) also estimates inherited emissions from 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 wood 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 timber 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 Resources Assessment. Of the countries analyzed, the 

country with the highest reported extraction rate was Indonesia (50 m3

/ha). This value was much 

higher compared to countries in Africa and Asia, which weren’t much higher than about 20 

m3

/ha and often less. 

For the 50 m3

/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 m3

/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 above for Indonesia), the emission 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 CO2 ha-1, or approximately 1-2%. 

This result of 11 t CO2/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 wood 
105 

products is stored forever. After taking into account the inherited emissions that emanate from 

the oxidation (i.e., burning and decay) of wood products that were produced from harvests 

during previous years (retirement rate, see Table 3), the Indonesia value of carbon stored in 

wood products decreases even further to 2 t C/ha, or 7 t CO2e/ha. 

Finally, Winjum et al. (1998) states that for 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 timber 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 reviewers 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 sequestration in tropical 

forests. 

Lewis et al. (2009) published long-term aboveground carbon sequestration 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 lower 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 carbon 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 values 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. These 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-growth tropical forests as well. 
106 

2.4.4.2.6.7 Evaluation of International Land Reversion 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 biomass carbon stocks occur in Year 1 (analogous 

to the stock change approach used to estimate 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 factors assumed that all biomass carbon stock changes occurred in 

Year 1 (i.e., IPCC stock change approach), forest 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 back 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 foregone carbon sequestration value, then the annual 

carbon sequestration rate for reversion factors was assumed to be 1/20th 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 below cover all of the types 

of land conversions considered, but they do not cover 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 production 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, Roraima, 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 the data inputs are the same for all of the 

Administrative Units. 
108 

Table 2.4-44. Data inputs for non-CO2 emissions 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 emissions (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 continues 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 0.00 301.96 
109 

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 for 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 results 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 international 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 largest 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 fuels analyzed, our modeling found that sugarcane 

ethanol causes the least amount of land use change emissions. This was due largely to our 

projection that sugarcane crops would expand onto grasslands in South and Southeast Brazil, 

which results in a net sequestration because sugarcane sequesters more biomass carbon than the 

grasslands it would replace. 

Figure 2.4-41. International land use change 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 projections 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 sequestrations 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 emissions 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 2.08 
112 

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 calculated 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 International 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 specified probability. For example, if the area of forest land converted to 

cropland (mean value) is 100 ha, with a 95% confidence 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 validation data was used to adjust/correct systematic errors 

in the MODIS land cover classifications from 2001 to 2007. These adjustments were based on 

the producer's accuracies for each land cover class 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 calculated 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 simplification 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 variable 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 referred to as the producer’s estimate, and the 

 

V Our simplified procedures very closely reproduced the producer 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 sourceW 

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 deviation 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 standard errors for MODIS Collection 5 classes 

and EPA classes based 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 (RVpy), for each of 

the land uses. The pseudo random values are distributed 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 confusion matrix and create an adjusted confusion 

matrix, BCM, with coefficients BCMcyup as follows: 

o BCMcypp = ACMcpp * (1+RVpy) 

o Scale the remaining coefficients in the column so that the total of the column does 

not change 

 BCMcyup = ACMcup * β

 Where β = (1-BCMcypp)/(1-ACMcpp) when ACMcpp < 1 and 0 otherwise 

o For every iteration, we repeated the steps 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 times 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 intervals 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 aggregated 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 sequester similar amounts of carbon (i.e., 

savanna, shrubland and mixed land). Finally, we assume that 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 uncertainty 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 stocks, 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 own uncertainty. In this section 

we describe the uncertainty estimates for each input parameter and the Monte Carlo analysis 

used to combine all of the individual input parameter uncertainties. 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 uncertainty analysis was a rigorous assessment, 

following IPCC guidelines, of the uncertainty in all of the input parameters used to calculate the 

land use change emissions factors. Winrock generated 95% confidence intervals for every data 

input based on the quality, quantity, resolution and 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 value. 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 validation 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%, Southeast Asia: 69%). Because we used the new pantropical carbon 

stock map to derive estimates 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 Tier 1 aboveground biomass estimates. Biomass 

ranges are reported by continent and ecological zone, with extremely wide ranges in many cases 
118 

(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 was derived using some Tier 2 (country-level) 

information, we assumed the uncertainty of the Brown et al. (2001) product to be lower than 

uncertainty for Tier 1 IPCC values and therefore assigned Asia biomass carbon stocks an 

uncertainty value of 50%. This was done to reflect the somewhat better prediction of biomass 

over IPCC Tier 1 default values. 

Some data sources provided accuracy information 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 estimated for biomass carbon estimates in Eastern 

Europe and Canada (33%; Myneni et al. 2001), China (36%, Piao 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 developed by Reusch and 

Gibbs (2008), as this was developed 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 information presented in Cairns et al. (1997) to add in 

the uncertainty related to estimating belowground biomass. The relationship in Cairns et al. 

(1997) relates belowground biomass to aboveground biomass, and the equation has an adjusted 

R2

 value of 0.83. Therefore, we assumed the 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 Uncertainty 
119 

Uncertainty around carbon stocks in annual croplands and grasslands was assumed to be 

75%, based on default error margins reported in IPCC Table 5.9 and 6.4, respectively. In the 

absence of any uncertainty information for savanna, shrubland, perennial cropland and wetlands, 

uncertainty in carbon stock estimates for these other 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 continuum of grasslands, savanna and shrublands. 

The uncertainty for these land cover types was estimated 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 resolution 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 stocks 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, and input factors that 

relate to how the soil is managed. 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 Guidelines 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 stocks 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 Forest Sequestration Input Parameter Uncertainty 

Uncertainty values for foregone forest carbon sequestration were derived from standard 

errors reported in the original data sources. Uncertainty 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 Uncertainty 
120 

Fire emissions were calculated in the updated 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 calculated 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 information on standard errors reported on combustion 

factors in de Castro and Kaufmann (1998). Uncertainties in combustion factors ranged from 42% 

to 69%. IPCC defaults were used to estimate uncertainty in the CH4 and N2O emission factors 

and ranged from 59% to 78% depending 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 propagation (IPCC Tier 1) or (2) Monte Carlo simulation (IPCC Tier 2). 

We followed the Tier 2 approach. 

One of the inputs required for the Monte Carlo uncertainty model is an estimate of the 

degree of correlation among different variables – 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 various 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 groups 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 (Mokany et al. 2006), therefore we assumed that belowground 

biomass is perfectly correlated to aboveground biomass. 
121 

Although the World Harmonized Soil Database was used to estimate soil carbon stocks in 

the updated analysis, the dataset was developed by compiling different data sources together, and 

therefore soil carbon stocks are not correlated across 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 proportion of each country falling within each 

ecological zone. Correlation groups for these land cover 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 because 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. Perennial 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 equivalent to that of sugarcane). 

Correlation groups for lost forest sequestration are delineated by data sources, described 

above, such that lost forest sequestration 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 treated the data inputs as variables and assumed that 

each variable's uncertainty distribution is normal with a standard deviation equal to the 
122 

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 Carlo model first generates pseudo random values 

for the variables above using the mean, calculated standard deviations, and pseudo random 

values for a Normal (0,1) distribution. Note that the resulting values after applying the mean and 

standard deviations are all constrained to be greater than 0. 

 The Monte Carlo model then used these pseudo random variables to calculate 

intermediate emissions variables for each country 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 uncertainty in our emissions factor estimates to total uncertainty by 

land conversion category and region. The uncertainty from emissions 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 intervals 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 97.5th percentile of the distribution minus the 2.5th

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 International 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 changes and, for each of these 300 cases, generated 

50 iterations where it varied the pseudo-random values 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 uncertainties derived from the confusion 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 use 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 aggregated emissions factors. The model then 
124 

used the land use change to calculate weighted average emission factors for each of the 54 

FAPRI regions and each of the 12 land conversion 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 emissions. 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 renewable 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 land 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 Ethanol
125 

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 extraction, conversion, and combustion 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 production 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 the conversion and combustion of biofuels 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 had 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

700,000

2022203020402050

GHG 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 benefit relative to the petroleum product 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 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). 
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 payback period is not sufficient for our analysis, we 

have developed methods for capturing the stream of emissions and benefits over time. For our 

analytical purposes, it is important for us to determine 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 the 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 emissions are less certain and more 

difficult to value, so the analysis should be confined 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 gasses 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 emissions to be converted into a metric whose 

underpinnings rest on principals of economic valuation. A more literal interpretation of EISA is 

that EPA should consider only physical GHG emissions. Second, even if the principle of tying 

GHG emissions to economic valuation approaches were 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 proxy for economic damages. Also, there is a lack of consensus 

as to the appropriate discount rate to apply to GHG lifecycle emissions streams through time. 

Finally, since EPA has decided to base threshold 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 feedstock from the field to the biofuel facility 

and transporting co-products from the biofuel facility to the point of use were included in this 

analysis. The GREET default of truck transportation 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 assumptions for DGS transport were 14% shipped by 

rail 800 miles, 2% shipped by barge 520 miles, and 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 GREET defaults for other commodities shipped by 

those transportation modes. Default GREET assumptions were also used for cellulosic ethanol 

feedstock transport. Crop residues, 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 sugarcane 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 used the projections available for the 2020 

scenario given in Table 2.4-54, along with a revised truck payload assumption based on 

comments we received, to estimate GREET inputs for truck payload (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. 106
130 

2.4.7 Biofuel Processing 

GHG emissions from renewable fuel production 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 the NPRM. The emission factors for the different 

fuel types are from GREET and were based on assumed carbon contents of the different process 

fuels. The emissions from producing electricity in the U.S. were also taken from GREET and 

represent average U.S. grid electricity production emissions. The emissions from combustion of 

biomass fuel source are not assumed to increase net atmospheric CO2 levels. Therefore, CO2

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 different feedstocks and fuel pathways. 

2.4.7.1 Corn Ethanol 

One of the key sources of information on energy 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 pathways process energy use for the final rule. 

We also updated corn ethanol production energy use for different technologies in the final rule 

based on feedback from industry technology providers as part of the public comment period. 

The main difference between proposal and final corn 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 from corn are dry milling and wet milling. 

In the dry milling process, the entire corn kernel 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 animal feed (dried distillers grains with 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 more 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 used for ethanol production as modeled by the 

FASOM and FAPRI models was based 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 traditional lifecycle analyses, the energy consumed and 

emissions generated by a renewable fuel plant must 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 sector 
131 

modeling described above. DGS are considered a partial replacement for corn and other animal 

feed and thus reduce the need to make up for the corn production that went into ethanol 

production. Since FASOM takes the production 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 was the treatment of the 

co-products DGS. One of the main energy drivers 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. Therefore, this category is also broken out as a 

separate category in the following section. See 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 data on energy used in ethanol production. The 

energy use at dry mill plants was based on ASPEN 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. 
132 

Table 2.4-55. 2022 Energy Use at Ethanol Plants w/CHP (BTU/gal) 

Type Technology NG Use Coal Use Biomass Use Purchased Elec 

Corn Ethanol – Dry Mill NG Base Plant (dry DDGS) 28,660 2,251 

w/ CHP (dry DDGS) 30,898 512 

 w/ CHP and Fractionation (dry DDGS) 25,854 1,512 

w/ CHP, Fractionation and Membrane Seperation (dry DDGS) 21,354 1,682 

 w/ CHP, Fractionation, Membrane Seperation, and Raw Starch Hydrolysis (dry DDGS) 16,568 1,682 

Base Plant (wet DGS) 17,081 2,251 

 w/ CHP (wet DGS) 19,320 512 

w/ CHP and Fractionation (wet DGS) 17,285 1,512 

 w/ CHP, Fractionation and Membrane Seperation (wet DGS) 12,785 1,682 

w/ CHP, Fractionation, Membrane Seperation, and Raw Starch Hydrolysis (wet DGS) 9,932 1,682 

Corn Ethanol – Dry Mill Coal Base Plant (dry DGS) 35,824 2,694  w/ CHP (dry DGS) 39,407 205 

w/ CHP and Fractionation (dry DGS) 33,102 986 

 w/ CHP, Fractionation and Membrane Seperation (dry DGS) 27,477 1,191 

w/ CHP, Fractionation, Membrane Seperation, and Raw Starch Hydrolysis (dry DGS) 21,495 1,191 

 Base Plant (wet DGS) 21,351 2,694 

w/ CHP (wet DGS) 24,934 205 

 w/ CHP and Fractionation (wet DGS) 22,390 986 

w/ CHP, Fractionation and Membrane Seperation (wet DGS) 16,766 1,191 

Corn Ethanol – Dry Mill Biomass  w/ CHP, Fractionation, Membrane Seperation, and Raw Starch Hydrolysis (wet DGS) 13,200 1,191 

2022 Base Plant (dry DGS) 35,824 2,694 

 2022 Base Plant w/ CHP (dry DGS) 39,407 205 

2022 Base Plant w/ CHP and Fractionation (dry DGS) 33,102 986 

 2022 Base Plant w/ CHP, Fractionation and Membrane Seperation (dry DGS) 27,477 1,191 

2022 Base Plant w/ CHP, Fractionation, Membrane Seperation, and Raw Starch Hydrolysis (dry DGS) 21,495 1,191 

 2022 Base Plant (wet DGS) 21,351 2,694 

2022 Base Plant w/ CHP (wet DGS) 24,934 205 

 2022 Base Plant w/ CHP and Fractionation (wet DGS) 22,390 986 

2022 Base Plant w/ CHP, Fractionation and Membrane Seperation (wet DGS) 16,766 1,191 

Corn Ethanol – Wet Mill  2022 Base Plant w/ CHP, Fractionation, Membrane Seperation, and Raw Starch Hydrolysis (wet DGS) 13,200 1,191 

Plant with NG 45,950 

 Plant with coal 45,950 

Plant with biomass 45,950 
133 

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 corn ethanol plants can be used 

as an additional biodiesel feedstock source. Dry mill corn ethanol plants have two different 

technological methods to withdraw corn oil 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 withdraws 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 fractionation, 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 scenario of converting corn starch into butanol. 

The production of corn was assumed to be the same as for ethanol production and based on the 

agricultural sector modeling described in the previous sections. However, the results were scaled 

based on the yield of butanol produced. Corn ethanol was assumed to have a processing yield of 

2.7 gal/bu and an energy content of 76,000 Btu/gal which results in an overall energy yield of 

206,280 Btu/bu. Corn butanol has a slightly lower 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 estimate GHG emissions we used the average 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 Production 
134 

We developed our lifecycle analysis of the algae pathways primarily based on Aspen 

modeling provided by the National Renewable 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 lipid 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 sections 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 production configurations for algae. Nevertheless, we 

believe that the assumptions and 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 data 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/m2

/day (op), 63 g/m2/day (PBR); lipid content = 25% 

(corresponds to a reasonable but still challenging target for the near future) 

Aggressive case: algae yield = 40 g/m2

/day (op), 100 g/m2/day (PBR); lipid content = 50% 

(assumes identification of a strain with near optimal growth rates and lipid content) 

Maximum case: algae yield = 60 g/m2

/day (op), 150 g/m2/day (PBR); lipid content = 60% 

(represents the near theoretical maximum 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 next 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 biodiesel and soy-based biodiesel distribution. 

Algae require several inputs, including water, land, nutrients, and in most cases, light to 

sustain growth. The following Table 2.4-56 summarizes 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 generated in conventional wastewater 

treatment processes. 

 

AA Some fresh makeup water is assumed to replace evaporative 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 avoided and thus emissions from land-use change. 

Due to higher cell densities, the use of photobioreactors can lower land use in comparison to 

open pond systems. The scenarios assume 330 operating 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 phosphate (DAP). The nutrients required to aid 

anaerobic digestion are primarily caustic with some phosphoric acid, urea and micronutrients. 

Algae also consume CO2 during cultivation. The scenarios assume that part of the CO2 is 

recycled within the process from anaerobic digestion of spent biomass and part of the CO2 is 

delivered from offsite flue gas, e.g. from power plant. Scenarios assume that CO2 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 CO2 was chosen where instead of transporting the entire flue 

gas material, the CO2 is scrubbed out and transported under 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 dewatered 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 concentrate 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 remainder consists of carbohydrates, proteins, nutrients, 

and ash), usually referred to as the 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 solvent extraction, supercritical 

fluid extraction, and mechanical extraction. Algal extraction under both op and PBR cases was 

assumed here to be carried out using mechanical 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 technology. Other extraction techniques discussed 

include solvent extraction, supercritical fluid extraction, osmotic shock, and sonication. 

 The lipids are assumed to be recovered via 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 plant. 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 electricity 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 developed by USDA and used by EPA in the cost modeling of 

soybean oil biodiesel including crushing, as discussed in Chapter 4. Soybean crushing was 

modeled assuming yields of 11.2 lbs soybean oil/bu soybeans and energy use of 14,532 BTU of 

natural gas and 2,843 BTU of purchased electricity 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 volumes would take into account GHG emissions 

reductions from a decrease in the production of other 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 processing energy on a process model developed by 

USDA-ARS to simulate biodiesel production from the Fatty Acid Methyl Ester (FAME) 

transesterification process. In this process vegetable oil (triglyceride) 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 biodiesel transesterification was modeled assuming 

yields of one kilogram of biodiesel from a kilogram of soybean oil and energy use of 4,381 BTU 

of natural gas and 361 BTU of electricity 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 reference 
138 

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 yellow grease is higher than for soybean oil 

biodiesel production. The energy use for yellow grease biodiesel production was assumed to be 

1.7 times the energy used for soybean oil biodiesel and yields of 0.94 kilograms of biodiesel 

from a kilogram of yellow grease. 

GHG emissions from other biodiesel 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 raw 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

CO2 g/g 0.401 0.966 0.923 1.011 

CH4 g/g 0.003 0.002 0 0 

N2O 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 production. Our proposal analysis did not assume 

any credit for this glycerin product. We have included 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 saturated in 2022 and that glycerin produced from 

biodiesel would not displace any 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 updated 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) 
139 

we have expanded the analysis for the FRM to also include thermochemical processes (MixedAlcohols route and Fischer-Tropsch to diesel route) 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 fermentation 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 worked 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-future (2010) and future (2015 and 2022). 

Table 2.4-59 shows the energy use required for 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 (mainly lignin) of incoming biomass being burned 

for electricity production. The process is assumed to generate excess electricity per gallon of 

ethanol produced. 

Table 2.4-59. 2022 Energy Use at Cellulosic 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 the 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 defaults for electricity production and subtracted 

from the lifecycle results of cellulosic 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 electricity 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 electricity 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 comment that there are differences in the types 

of process fuel e.g. used in the dehydration process 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 configured 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 ethanol 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 CH4 and N2O from 

biomass combustion, as shown in Table 2.4-60. 
141 

Table 2.4-60. Emissions per mmBTU Bagasse BurnedGG

Pollutant g/mmBTU bagasse burned

CH4 31.65 

N2O 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 for Chemicals and LubricantsHH

2005/2006

Scenario 

2020

Energy Use (Btu/gal); 100% 

residual oil 798 766

We further assumed a 10% allocation 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 ethanol 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 over 4% water in ethanol, requiring a second step in the 

process to remove water in order to obtain fuel grade anhydrous ethanol (>99.3 wt%).II The 

most important ethanol dehydration techniques used in the world industry include azeotropic 

distillation, dehydration on molecular sieves, and more recently, pervaporation or vapor 

permeation.JJ Azeotropic distillation uses a third component, 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 are built with molecular sieve dehydrators. 

Pervaporation is still a fairly new technology, however, there is potential for energy consumption 

savings, thus making the technology 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 United 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 Required for Dehydration 

BTU/gallon of 

anhydrous 

Source/Details 

4,000 Swain 

2,830-5665 VaneLL 

4,500 KawaitkowskiMM

4186-5931 CBEPGNN; Fuel 

Oil (primarily 

diesel) 

5156-5210 CBEPGOO; 

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 comments 
143 

As noted in Chapter 1, the majority of ethanol imported into the U.S. may preferentially 

come through the Caribbean Basin Initiative countries due to favorable economic 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. Fuels used to run the dehydration process include 

bagasse, natural gas, #2 distillate (diesel fuel), and #6 oil (bunker fuel).PP

For the final rule, we have assumed an average 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 pathway for the Caribbean Basin countries. We 

evaluated the pathway based on the type of fuel used 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 calculated 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 generation efficiency of 30% (the current Brazil industrial 

average) and the energy consumption for dehydration 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 use of bagasse to generate enough steam and 

electricity to supply the whole mill energy demand 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 increase 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, however, 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 2007108

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 Operating Margin average fuel mix 

for electricity generation 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 electricity 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, etc.). This section summarizes the assumptions used 

in this analysis to represent the transport of biodiesel, and domestic and imported ethanol. A 

recent release of GREET110 was utilized to estimate the GHG emissions based on these 

assumptions. 

2.4.8.1 Biodiesel 

For biodiesel transport, GREET default values 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 production or import facilities to petroleum blending terminals by 

domestic truck, marine, and rail distribution systems. We used ORNL’s transportation 

projections for 2022 under the EISA policy scenario 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 assumptions of 100% shipped by truck a distance of 30 

miles. 
147 

 

Table 2.4-68. Corn and Cellulosic Ethanol Assumptions 

for Transport from Plant to Terminal 

Mode % Distance 

(miles) 

Barge 12% 336 

Rail 77% 629 

Truck 17% 68 

Local Truck112 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 2009113 to determine the fraction of ethanol shipped to different 

U.S. destinations from Brazil the five contributing 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 attributing 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 Distances for Sugarcane Ethanol Transport from 

Brazil and CBI Countries to the United StatesQQ

 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 CO2 emissions for ethanol and biomass-based diesel were based on the 

carbon content of the fuel. However, over the full lifecycle of the fuel, the CO2 emitted from 

biomass-based fuels combustion does not increase atmospheric CO2 concentrations, assuming 

the biogenic carbon emitted is offset by the uptake of CO2 resulting from the growth of new 

biomass. As a result, CO2 emissions from biomass-based fuels combustion are not included in 

their lifecycle emissions results. Net carbon fluxes from changes in biogenic carbon reservoirs 

in wooded or crop lands are accounted for separately in the land use change analysis as outlined 

in the agricultural sector modeling above. 

When calculating combustion GHG emissions, however, the CH4 and N2O emitted 

during biomass-based fuels combustion are included in the analysis. Unlike CO2 emissions, the 

combustion of biomass-based fuels does result in net additions of CH4 and N2O to the 

atmosphere. Therefore, combustion CH4 and N2O emissions are included in the lifecycle GHG 

emissions results for biomass-based fuels. 

Combustion related CH4 and N2O emissions for biomass-based 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 for Bio and Petroleum Based Fuels 

CO2 CH4 N2O 

Fuel Type (g/mmBTU) (g/mmBTU) (g/mmBTU) 

Ethanol 75,250 269 611 

Biodiesel 81,044 11 689 
150 

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 production. 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 explicitly 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 biofuels 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 triggered 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 product 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 CO2 impact was a 34,736 grams CO2 decrease for each mmbtu increase in 

renewable fuels over the baseline, or -34,736 grams CO2/mmbtu. Reduced consumption of 

gasoline, diesel, and still gas (for refining) resulted in an overall decrease in emissions of 86.6 

mmt CO2. This decrease in emissions was partially offset by natural gas consumption for 

production of renewable fuels and by increased coal consumption for power generation, yielding 

an overall decrease in the domestic energy sector of 61.5 mmt CO2. 

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 calculations of renewable fuel 

threshold analysis or for the overall rule impacts because of double counting issues regarding 

GHG emissions sources. 

The final rule EPA-NEMS analysis eliminated some of the problems with the proposal 

modeling by considering a larger increase in biofuels consumption and by not specifically trying 
151 

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 difficult 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 global model that was originally developed for 

addressing agricultural trade issues among countries. An advantage of GE models such as 

GTAP is that they take into account how changes in U.S. biofuel policies affect world prices, 

output, and trading patterns for a wide variety of commodities that extend beyond the 

agricultural sector. The GTAP data base is peer 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, global 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 for substitution between energy commodities115. 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 Standard. Current research is ongoing to add 

additional detail on the biofuels market, some of which is described below. 
152 

2.4.11.1.1 Partial Equilibrium versus General Equilibrium Modeling 

 Although we have used the partial equilibrium (PE) models FASOM and FAPRI as the 

primary tools for evaluating whether individual biofuels meet the GHG thresholds, as part of the 

peer review process, we explicitly requested input 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 reviewers discussed some of the advantages 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 given the increased production of biofuels from 

agricultural products. Higher crude oil prices and policies to increase demand for biofuels have 

increased the linkages between these two markets, and increased biofuel 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 consequences 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 about 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 the ideal modeling framework. However, as 

described in other sections, there is currently no single model that captures all of the necessary 

aspects of lifecycle GHG emissions. In their current state of development, GE models including 

GTAP are not yet adequate for determining whether biofuels meet greenhouse gas emission 

thresholds for the following reasons. 

 First, most GE models do not contain the level 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, simplifications 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 information 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 resource implications. Furthermore, the GTAP 

model does not yet contain cellulosic 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 against historical data from 2001 through 2006 and the 

resulting 2006 baseline is used for biofuels policy analyses. Due to its static nature, the GTAP 

 

RR See Kløverpris, Wenzel and Nielson (2008) 
153 

biofuels model is not able to project the global 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 continue 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 crop land as a result of increasing biofuel 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, pasture, 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 information on timberland land rent 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 model 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 between 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 value 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 competition for land and other inputs 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.asp 
154 

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 using their updated variation of the model, the 

programming code is generally 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 model the production and substitution of oilseed meal 

as a byproduct of biodiesel production, which provides 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 modeling 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, the 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 acre 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 elasticity for our analysis of corn ethanol and 

biodiesel. Second, we adjusted the 2006 baseline ethanol and biodiesel production levels. Our 

modified version of the CARB model used in this study has 4.25 BG of corn-ethanol and 0.14 

BG of biodiesel in the 2006 baseline. Finally, since our baseline included a crude oil price shock 

from $25/barrel in 2001 to $60/barrel in 2006, our biofuel scenarios started with a $60 oil price 

laden economy versus $25 in the 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 increase in corn ethanol over the 2006 updated baseline 

level of 4.25 BG. Similarly, our soybean biodiesel 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 aggregates 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 biofuel 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 are 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 results assume all land that is not crop or 

pasture is forest. However, the FAPRI results allow land that is not crop or pasture to come from 

a variety of other non agricultural land such as grassland, savanna, shrubland and wetlands. The 

disaggregation of FAPRI “non ag land” is described in more detail in section 2.4.4.2.5. 

 Although the global aggregated results are similar, 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 the 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

6

GTAP

FAPRI

GTAP

FAPRI

GTAP

FAPRI

GTAP

FAPRI

GTAP

FAPRI

GTAP

FAPRI

GTAP

FAPRI

GTAP

FAPRI

GTAP

FAPRI

GTAP

FAPRI

GTAP

FAPRI

GTAP

FAPRI

Africa &

Middle

East

Asia Brazil Canada E.

Europe

India Oceania Other L.

America

ROW US W.

Europe

World

Ha/Billion BT

U

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 in 
157 

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, which requires additional grazing land to complement the use of 

soybean meal for beef production. In contrast, the FAPRI model assumes that increased 

biodiesel production will lead to a decrease in U.S. beef production, since the relative price of 

non-grazing animals (e.g., poultry and pork) will decrease more than the price of beef as a result 

of lower soybean prices. As a result, U.S. beef production and pasture land 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

15

GTAP-BYP

FAPRI

GTAP-BYP

FAPRI

GTAP-BYP

FAPRI

GTAP-BYP

FAPRI

GTAP-BYP

FAPRI

GTAP-BYP

FAPRI

GTAP-BYP

FAPRI

GTAP-BYP

FAPRI

GTAP-BYP

FAPRI

GTAP-BYP

FAPRI

GTAP-BYP

FAPRI

GTAP-BYP

FAPRI

Africa &

Middle

East

Asia Brazil Canada E.

Europe

India Oceania Other L.

America

ROW US W.

Europe

World

Ha/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 from 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 equilibrium conditions using a large number of 

draws from the underlying distribution of potential parameter values. However, Monte Carlo 
158 

analysis is not generally practical for a large CGE model. Instead, previous researchers have 

performed a SSA with Gaussian Quadrature numerical integration. This methodology uses a 

small number of draws from the distribution of random variables to provide a robust range of 

results that can be used to develop a confidence 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 across 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 transformation 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 transformation of land supply is a measure 

of how easily land can be converted between land 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 land 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 important 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 acres. 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 change due to U.S. corn-ethanol 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

EU27

BRAZIL

JAPAN

CHIHKG

INDIA

LAEEX

RoLAC

EEFSUEX

RoE

MEASTNAEX

SSAEX

RoAFR

SASIAEEX

RoHIA

RoASIA

Oceania

Million Acres

Figure 2.4-49. Pasture cover change due to U.S. corn-ethanol 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 Biodiesel 
161 

 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, most 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 production (million acres) 

3.214

-0.50

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

USA

EU27

BRAZIL

CAN

JAPAN

CHIHKG

INDIA

C_C_Amer

S_o_Amer

E_Asia

Mala_Indo

R_SE_Asia

R_S_Asia

Russia

Oth_CEE_CIS

Oth_Europe

MEAS_NAfr

S_S_AFR

Oceania

GLOBAL

Ch in Crop Cover

Figure 2.4-52. Pasture cover change due to U.S. biodiesel 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 data set with 30-m resolution provided similar 

results. 

Based on resources and data availability, three regions were chosen for analysis: Brazil, 

India and the Indonesian island of Sumatra. Brazil was chosen because it was, and remains, the 

country with the largest agricultural land use response 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 analyze with higher resolution data because it is a 

major agricultural producer with crops and land cover types distinct from what is found in Brazil. 

Finally, Indonesia was chosen because it is a major 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 mosaics (i.e., compilations) 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, the 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 V5CCC

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) ArchiveDDD LP DAAC Data PoolEEE

 

 By calibrating the land cover classification definitions to those used by MODIS, we 

converted the true color Landsat images into corresponding 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_products_table/land_cover/yearly_l3_global_500_m/mcd12q1 DDDhttp://eros.usgs.gov/#/Find_Data/Products_and_Data_Available/TriDecadal_Global_Landsat_Orthorectified_Ov

erview 

EEE https://lpdaac.usgs.gov/lpdaac/get_data/data_pool 
165 

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 data found deforestation 

rates somewhere in between the original and corrected MODIS data (see Section 2.4.4 for an 

explanation of the MODIS correction process). Validation 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 the land types converted to cropland. The Indonesia results 

are omitted because of the cloud cover issues mentioned previously. More details about the 

Landsat data analysis are provided in a technical 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 Region 
167 

 Region 

 Data Source Brazil: 

Amazon 

Biome 

Brazil: 

CentralWest 

Cerrados 

Brazil: 

Northeast 

Coast 

Brazil: 

NorthNortheast 

Cerrados 

Brazil: 

South 

Brazil: 

Southeast 

India

Forest Landsat 50% 6% 0% 1% 15% 2% 3%

MODIS-Corrected 54% 11% 11% 15% 13% 10% 10%

Grassland Landsat 8% 19% 8% 12% 14% 10% 5%

MODIS-Corrected 8% 26% 19% 16% 23% 18% 21%

Mixed Landsat 11% 20% 28% 14% 55% 53% 27%

MODIS-Corrected 15% 20% 19% 10% 28% 30% 30%

Savanna Landsat 26% 55% 54% 67% 14% 34% 49%

MODIS-Corrected 20% 36% 41% 49% 29% 36% 19%

Shrubland Landsat 2% 0% 9% 5% 1% 1% 14%

MODIS-Corrected 2% 6% 8% 9% 6% 6% 17%

Wetlands Landsat 2% 0% 0% 1% 1% 0% 0%

MODIS-Corrected 1% 0% 0% 0% 0% 0% 1%

Barren Landsat 0% 0% 1% 0% 0% 0% 2%

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 established 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 diesel (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 utilized the 2009 analysis performed by the National 

Energy Technology Laboratory (NETL), “Development of Baseline Data and Analysis of Life 

Cycle Greenhouse Gas Emissions of Petroleum-Based Fuels”, which was specifically 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 petroleum 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).” Furthermore, 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 Energy Independence and Security Act of 2007 

(EISA 2007), Title II, Subtitle A, Sec. 201.” Specific 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 (NGLs), and unfinished oils as feedstocks to U.S. 

petroleum refineries and to foreign refineries producing 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, and unfinished oils to domestic 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.pdf
169 

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 profiles 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 oil extraction emissions are more difficult to 

estimate, as the U.S. imports both conventional oil and oil sands from Canada. To estimate 

emissions from Canadian conventional crude extraction, the U.S. conventional crude extraction 

profile was utilized, while incorporating Canada-specific data on venting and flaring rates.GGG 

For Canadian oil sands, extraction emission rates were derived using 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 were grouped into a category 

termed “other”, which consisted of imports from 31 countries. Due to the complexity and 

uncertainty associated with developing estimates for each of these countries, extraction 

emissions for this group were assumed to be the average of the conventional crude extraction 

emissions from the other importers for which specific 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 oilsHHH were assumed to be the 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 the 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 countries 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, the 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 oil was extracted in the same country in which it 

was refined, so extraction emissions could be estimated from PE International extraction profiles. 

In other cases, crude oil was imported from one country for refining in another, and the crude 

import mix was not entirely clear. For most of these countries, PE International’s GaBi 4 Life 

Cycle Assessment SoftwareIII was utilized to provide estimates of extraction emissions. 

This still left a handful of countries for which there was no method to estimate extraction 

emissions. For these cases, “surrogate” profiles were used. For instance, for European countries 

for which a country-specific profile was not available, the EU-15 or EU-25 extraction profile 

was utilized. For South Korea, it is known that the 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 emissions were estimated to be the foreign 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 requiring 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 product 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.com/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 transport within the exporting country, ocean tanker transport to the 

U.S., and domestic crude oil transport to refineries via a combination of pipeline, water carrier, 

rail, and truck.” All crude is assumed to be transported by pipeline 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 distance from the foreign port to the U.S receiving 

port was estimated for the top ten countries from which crude oil was imported. 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 transportation 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 pipeline from well to refinery was assumed. For countries which 

imported crude, refined it into liquid fuels, and exported 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 that 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 the 
172 

petroleum refinery is not included in the model, since 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 estimation accounts for the following: 

• Acquisition of fuels 

o Indirect emissions associated with purchased power and steam 

o Emissions associated with the acquisition 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 CO2 process emissions from steam methane reforming (SMR) 

o Fuel combustion and upstream emissions associated 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 organized into a refinery 

emissions pool and a hydrogen emissions pool and subsequently allocated between the various 

refinery products. There are no individual assignments of energy sources to unit operations or 

refinery products.” 

To determine the GHG emissions from the refining 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 operations was noted by NETL as a data limitation 

to the study. 
173 

2.5.5 Fuel Transport and Distribution 

“Product transport includes transport of imported liquid fuels from the exporting nations 

to the U.S. as well as domestic transport of both imported fuels and domestically produced liquid 

fuels.” Foreign transport consists of tanker and/or pipeline transport of imported products to 

U.S. ports. The products are assumed to be shipped 10 miles by pipeline to a port or the U.S. 

border. Specific port-to-port travel distances were 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 CO2 emission factors for gasoline and diesel to reflect revisions in the 

factors made by EPA, which were used in the September 28, 2009 proposed rule to establish 

GHG standards for light-duty vehicles. We have also updated the CO2 emissions factors for 

ethanol and biodiesel to be consistent 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 CH4

and N2O emission factors used for the proposed rule, which were based on EPA MOVES model 

run results. 

Table 2.5-7. Tailpipe emissions for relevant fuels (g/mmBTU) 

Fuel Type CO2 CH4 N2O 

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 determine if the value was significant enough to be included in 

our petroleum baseline calculation. 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 forest for oil sands production. 

Jordaan et al. (2009) estimated the land use change 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 m2

/m3 synthetic crude oil (SCO) for surface mining and 0.11 m2/m3 SCO for insitu. These intensity values were based on dividing the area of land disturbance by the total 

volume of SCO produced over the lifetime of the project. Jordaan also calculated values for land 
174 

use change associated with upgrading of oil sands and for the extraction of natural gas utilized 

for oil sands production. However, we restricted 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 established 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 carbon was lost from conversion, which gave an 

overall value of 278.25 grams CO2/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 CO2/bbl for surface mining and 

487 (310-708) grams/bbl for in-situ. These values were considerably lower (approximately 

98%) than the oil sands extraction estimates determined by NETL and used in our petroleum 

baseline (81,000-122,000 g CO2/bbl). 

On a gasoline basis, the land-use change values were 380 (298-570) g CO2/mmbtu for 

surface mining and 99 (63-145) g CO2/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 CO2

emissions, and, for in-situ, it was 0.06%. Although 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 shown 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 CO2eq/mmBTU and 

97,006 grams CO2eq/mmBTU was obtained. 

Table 2.5-8. Gasoline and diesel baseline well-to-tank GHG emissions (g/mmbtu of fuel)

CO2 CH4 N2O CO2eq.

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 natural 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 assumptions. 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 discussed 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 they 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 influence whether a particular renewable 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 corn 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 costs, most facilities will 

sell a portion of their co-product DGS prior to drying thus reducing energy consumption and 
176 

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 ethanol 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 the range of results is a 21% reduction in GHG 

emissions compared to the gasoline 2005 baseline. 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% dry, 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-dioxide equivalent GHG emissions per million 

British Thermal Units of fuel produced (gCO2e/mmBTU). Figure 2.6-2 includes our mean 
177 

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. For the petroleum baseline, the fuel production stage 

includes emissions from extraction, transport, refining and distribution of petroleum 

transportation fuel. Petroleum tailpipe emissions include CO2 and non-CO2 gases emitted from 

fuel combustion. 

Figure 2.6-2. Results for a New Natural Gas Fired Corn Ethanol Plant by Lifecycle Stage 

Average 2022 plant: natural gas, 63% dry, 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 NG

GHG 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, technologies 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 Biomass

GHG 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 different 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 Results 
180 

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 butanol 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 production 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% dry, 37% wet DGS (w/ fractionation) 

-60 -50 -40 -30 -20 -10 0

Percentage Change in Biofuel GHG Lifecycle Emissions Compared to Petroleum Fuel
181 

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 average for gasoline. Lifecycle emissions are 

normalized per energy unit of fuel produced and presented 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 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 2.6-5. 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 NG

GHG 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 Results 
182 

Figure 2.6-6 shows the percent change in the typical 2022 soybean biodiesel lifecycle 

GHG emissions compared to the petroleum diesel fuel 2005 baseline. 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 represented by the dashed line at -50 on the graph. The results for 

soybean biodiesel are that the midpoint of the range of results is a 57% reduction in GHG 

emissions compared to the diesel fuel baseline. 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 Biodiesel 
183 

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 average for diesel fuel. Lifecycle emissions 

are normalized per energy unit of fuel produced and presented in grams of carbon-dioxide 

equivalent GHG emissions per million British 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 Biodiesel 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 Biodiesel

GHG 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 compared to the 2005 baseline average for diesel fuel. 

Results are shown for the different cases of production 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 Stage 
185 

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 Case

GHG 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 gasoline 2005 baseline. These results assume the 

ethanol is produced and dehydrated in Brazil prior 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-axis. 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 collection 
186 

-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 burning 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 sugarcane 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-dioxide 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 uncertainty in the types of land use changes and 

the magnitude of resulting GHG emissions. 

Figure 2.6-10. Results for Sugarcane Ethanol by Lifecycle Stage 
187 

With and without residue collection and CBI 

-91% -89%

-61% -59%

-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. CBI

GHG 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 ethanol scenarios with residue 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 collection 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 distilled 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 the 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 biochemical 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-axis. 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 the 
188 

gasoline baseline. The 95% confidence interval around that midpoint ranges from 102% 

reduction to a 117% reduction compared to the gasoline baseline. 

Figure 2.6-11. Distribution of Results for Switchgrass Biochemical Ethanol 

Average 2022 plant: biochemical process producing ethanol, excess 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 different cellulosic feedstock to ethanol production 

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-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 Cellulosic Ethanol by Lifecycle Stage 
189 

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 -

Thermochemical

GHG 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 presented 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 Diesel

GHG 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 excess electricity production from the 

lignin generated from the biochemical process. The corn stover 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 several 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 Rate 
191 

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 influence whether a particular renewable 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 immediate GHG emissions, followed 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 graphically 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. Although 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

800,000

2022203020402050

GHG 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 drying 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 GHG 
192 

benefits of corn ethanol compared to gasoline 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 fuels that result in land use change, soybean 

biodiesel, sugarcane ethanol, and switchgrass biofuels. Furthermore, the uncertainty in the land 

use change emissions results in a range of payback periods depending on the range of land use 

change emissions. Table 2.6-2 shows 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 threshold 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 24 
193 

Switchgrass Diesel 

(F-T diesel) 

60 7 14 26 

The payback period concept helps to demonstrate 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 discussed 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 over 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 Results 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%

20222030204020502060207020802090210021102120

Lifecycle 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 economic 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 USDA’s method of projecting future crop yields. 

During the public comment process we learned that numerous technical advancements-- 

including better farm practices, seed hybridization and genetic modification--have led to more 

rapid gains in yields since 1995. In addition, commenters, including many leading seed 

companies, provided data supporting more rapid improvements in future yields. For example, 

commenters pointed to recent advancements in seed development (including genetic 

modification) and the general accumulation of knowledge of how to develop and bring to market 

seed varieties—factors that would allow for a greater rate of development of seed varieties 

requiring fewer inputs such as fertilizer and pest management applications. 

Therefore, in coordination with USDA experts, EPA has developed for this final rule a 

high yield case scenario of 230 bushels/acre for 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. Figure 2.6-16 shows the historic 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

Year

Yield (bu/acre)

85 - 09 Trend

95 - 09 Trend

Historic Projected

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. For international yields, it is reasonable to 

assume the same percent increases from the baseline yield assumptions could occur as we are 
195 

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 advantage of the seed varieties' 

potential. For example, seeds can be more readily developed to perform well in the particular 

regions of these countries and can be coupled with 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 advantages 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 average 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 yield 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% dry, 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 High 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 ethanol plant for the base and high yield scenario 
196 

compared to the 2005 baseline average for gasoline. Lifecycle emissions are normalized per 

energy unit of fuel produced and presented 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% dry, 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 Yield

GHG 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 scenario 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 international 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 Base 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 High 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 emissions are normalized per energy unit of fuel 
198 

produced and presented in grams of carbon-dioxide equivalent GHG emissions per million 

British Thermal Units of fuel produced (gCO2e/mmBTU). 

Figure 2.6-20. High and Base Yield 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 Yield

GHG 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 Emissions Results of Rulemaking 

Volumes Compared to AEO Projected Volumes 

Our analysis of the overall GHG emission impacts of this proposed rulemaking was 

performed in parallel with the lifecycle analysis performed to develop the individual fuel 

thresholds described in previous sections. The same system boundaries apply such that this 

analysis includes the effects of three main areas: a) emissions related to the production of 

biofuels, including the growing of feedstock (corn, 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 gasoline and diesel fuel that is replaced by use of biofuels; and c) 

difference in tailpipe combustion of the renewable 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 reference 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. This scenario provided a point of comparison for 

assessing the impacts of the RFS2 standard volumes 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 impacts 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 fuel 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 and configurations for the 2022 renewable fuel 

production representing the mix of plants and feedstock 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. 
200 

Table 2.7-1. Types of Plants and Volumes Considered in 2022 

Plant Configuration and Energy Used (Btu/gal) Volume (Bgal)

NG Use Coal Use

Bi

Use

omass Diesel Fuel

Use

Purchased

Elec Sold Elec

Reference 

Case Case Difference

Policy 

Corn Ethanol – Dry Mill NG - Base Plant (dry DDGS) 25,672 2,165 4.2 5.2 1.0

 - Integrated Biogas System (wet DGS) 7,285 231 0.6 0.7 0.1 - Integrated Biogas System (dry DGS) 11,459 231 0.9 1.2 0.2 - Base Plant (wet DGS) 16,320 2,165 2.5 3.1 0.6

Corn 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 0.0

Corn 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 0.2

Corn Ethanol – Wet Mill - Plant with NG 45,950 0.0 0.02 0.02

 - Plant with coal 45,950 1.4 1.4 0.0

Cellulosic 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 0.7

Cellulosic Ethanol – Thermochemical - Switchgrass feedstock 100,543 177 0.0 1.5 1.5

 - Corn stover feedstock 95,369 177 0.0 1.0 1.0

Biodiesel - Soybean oil feedstock 18,913 3,205 0.4 1.4 1.1

 - Yellow grease / tallow feedstock 21,051 494 0.0 0.2 0.2

Renewable Diesel - Yellow grease / tallow feedstock 838 0.0 0.2 0.2Sugarcane Ethanol - CBI - Marginal Elec 2,592 84,241 2,606 -7,287 0.6 2.2 1.6Cellulosic Diesel – F-T - Farmed trees feedstock 198,429 327 13 0.0 6.5 6.5
201 

The upstream feedstock production and processing impacts for each of the different fuel 

technologies were modeled based on the same assumptions used in determining the per fuel 

lifecycle GHG results described 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 threshold values are required by EISA to be based 

on a 2005 petroleum fuel baseline. For this analysis of the overall impacts of the rule we 

considered the crude oil and finished product 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 gasoline 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 2022120. EIA projects 

that roughly 64% of total Canadian crude oil production will be oil sand production in 2022, and 

that roughly 40% of total Venezuelan crude oil production will be heavy crude production in 

2022. EIA also has assumptions on how much crude 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 and 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 represented 7.6% and heavy oil represented 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 represent 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 considered 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 conventional gasoline to E10. For diesel highway 

vehicles, emissions of N20 and CH4 are almost 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 limited 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 CH4122) and summing the 

products. The results are summarized in Table 2.7-2 below. 

Table 2.7-2. Tailpipe Nitrous Oxide 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 however, 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 application. This effect is somewhat offset by 

reductions due to lower livestock production and reductions in rice methane. 

As with domestic agriculture impacts, the international 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. 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 production both domestically and internationally to 

meet the biofuel demand in the U.S., and land use change emissions associated with converting 

land into crop production. 

Other portions of the biofuel lifecycle include fuel production and feedstock and fuel 

transport. We project reductions in GHG emissions from the renewable fuel production portion 
203 

of the lifecycle due to the generation of electricity 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 fluxes from changes in biogenic carbon reservoirs 

in wooded or crop lands associated with land use change are accounted for in the domestic and 

international agriculture impacts shown in upstream 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 production (crude oil extraction 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 release 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 GHG 
204 

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 metric 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 reduction benefits of 150 million 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 emissions 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% discount 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 annualized 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 impacts 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 oil to meet the additional demand were Middle 

Eastern countries. Using well-to-tank GHG values for crude extracted from various countries123, 

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-products 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 marginally 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 for 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 price, 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 CO2 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 global 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 modelKKK

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 estimated emissions reductions associated with the 

proposed rule, a simple climate model such as MAGICC is reasonable for estimating the 

climate response. 

 

KKKMiniCAM 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 development 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, NOx, VOCs), the halocarbons (e.g. HCFCs, 

HFCs, PFCs) and sulfur dioxide (SO2). MAGICC emulates 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 IPCC 

Emissions 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 emissions changes for the final rule to a 

MiniCAM baseline emissions scenario.MMM Specifically, the CO2, N2O, and CH4 annual 

emission changes from 2022-2052 from section 2.7 were 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 Projected Global Mean Surface Temperature 

and Global Mean Sea Level Rise from Baseline 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 historical 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-CO2 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 Projected Global Mean Surface Temperature 

and Global Mean Sea Level Rise from Baseline 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 Projected Global Mean Surface Temperature 

and Global Mean Sea Level Rise from Baseline 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 -0.001 
209 

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 appears 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 CO2-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_products_table/land_cover/yearly_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 “N2O 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 

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102 Macedo (2008) “Greenhouse gases emissions in the production and use of ethanol from sugarcane in 

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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 

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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 

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