Chapter 5: Economic Impacts and Benefits 

5.1 Agricultural Impacts 

5.1.1 Models Utilized 

EPA used a suite of tools to model the potential domestic and international impacts of the 

RFS2 renewable fuel volumes on the agricultural sector. The Forest and Agricultural Sector 

Optimization Model (FASOM), developed by Professor Bruce McCarl of Texas A&M 

University and others, provides detailed information on domestic agricultural and greenhouse gas 

impacts of renewable fuels. The Food and Agricultural Policy Research Institute (FAPRI) at 

Iowa State University and the University of Missouri-Columbia maintains a number of 

econometric models that are capable of providing detailed information on impacts on 

international agricultural markets from the wider use of renewable fuels in the U.S. 

FASOM is a long-term economic model of the U.S. agriculture sector that maximizes 

total revenues for producers while meeting the demands of consumers. Using a number of 

inputs, FASOM determines which crops, livestock, and processed agricultural products would be 

produced in the U.S. In each model simulation, crops compete for price sensitive inputs such as 

land and labor at the regional level. The cost of these and other inputs are used to determine the 

price and level of production of primary commodities (e.g., field crops, livestock, and biofuel 

products). FASOM also estimates prices using costs associated with the processing of primary 

commodities into secondary products (e.g., converting livestock to meat and dairy, crushing 

soybeans to soybean meal and oil). FASOM does not capture short-term fluctuations (i.e., 

month-to-month, annual) in prices and production, however, as it is designed to identify longterm trends.

 The FASOM model also contains a forestry component, which details forest acres across 

the U.S., as well as forestry products. Running both the forestry and agriculture components of 

the model would 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). More information on these land 

categories can be found below in Chapter 5.1.2. 

FASOM uses supply and demand curves for the 11 major U.S. domestic regions,A which 

are calibrated to historic price and production data. FASOM also includes detailed supply and 

 

A U.S. regions consist of the Pacific Northwest (West and East), Pacific Southwest, Rocky Mountains, Great Plains, 

Southwest, South Central, Corn Belt, Lake States, Southeast, and the Northeast. 
2 

demand data for corn, wheat, soybeans, rice and sorghum across 37 foreign regions.B

 FASOM 

contains transportation costs to all regions and then uses all of this information to determine U.S. 

exports to the point where prices are then equated in all markets. 

We chose to use FASOM to model the full potential impacts on the domestic agricultural 

and forestry sectors given higher renewable fuel volumes, in part because FASOM also provides 

detailed greenhouse gas information resulting from these changes (see Chapter 2 of this RIA for 

more information). FASOM is not able to agricultural sector changes internationally, however. 

Therefore, we are working with the FAPRI modeling system to better understand international 

agricultural impacts. Additional details on the FASOM model are included in the docket.C

The FAPRI models are a system of econometric models covering many agricultural 

commodities. These models capture the biological, technical, and economic relationships among 

key variables within a particular commodity and across commodities. They are based on 

historical data analysis, current academic research, and a reliance on accepted economic, 

agronomic, and biological relationships in agricultural production and markets. The 

international modeling system includes international grains, oilseeds, ethanol, sugar, and 

livestock models. In general, for each commodity sector, the economic relationship that supply 

equals demand is maintained by determining a market-clearing price for the commodity. In 

countries where domestic prices are not solved endogenously, these 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 other sectors. Elasticity 

values for supply and demand responses are based on econometric analysis and on consensus 

estimates. Additional details on the FAPRI models are included in the docket.D

Agricultural and trade policies for each commodity in a country are included in the 

models to the extent that they affect the supply and demand decisions of the economic agents. 

These policies include taxes on exports and imports, tariffs, tariff rate quotas, export subsidies, 

intervention prices, and set-aside rates. The FAPRI models assume that existing agricultural and 

trade policy variables will remain unchanged in the outlook period.

We recognize that there are inherent challenges in reconciling the results from two 

different models, however using two models provides a more complete and robust analysis than 

either model would be able to provide alone. As described in Chapter 5.1.3, we have attempted 

to align as many of the key assumptions as possible to get a consistent set of modeling results. 

However, there are structural differences in the models that make account for some of the 

difference in the model results. For example, since FASOM is a long-term econometric model, 

short-term spikes are smoothed out over the five year reporting period. In comparison, the 

 

B

 FASOM Foreign Regions include: the European Economic Community, North Central Europe, Southwest 

Europe, Eastern Europe, Adriatic, Eastern Mediterranean, Former Soviet Union, North Africa, East Africa, West 

Africa, South Africa, Red Sea, Iran, India, Taiwan, Japan, South Korea, North Korea, China, Bangladesh, Indonesia, 

Myanmar, Pakistan, Philippines, Thailand, Vietnam, West Asia, Southeast Asia, Australia, Caribbean, Eastern 

Mexico, Eastern South America, Western South America, Argentina, Brazil, Canada, Other. 

C

 Beach, Robert; McCarl, Bruce, Agricultural Impacts of the Energy Independence and Security Act: FASOM 

Results and Model Description, RTI International, October, 2008. D Technical Report: An Analysis of EPA Biofuel Scenarios with the FAPRI International Models, CARD Staff, 

November, 2008. 
3 

FAPRI model captures annual fluctuations that may include short-term supply and demand 

responses. Some of the discrepancies may be attributed to different underlying assumptions 

pertaining to elasticities of supply and demand for different commodities. These differences, in 

turn, affect projections of imports and exports, acreage shifting, and total consumption and 

production of various commodities. Some of the differences in results are described in more 

detail in the following sections. 

5.1.2 Model Modifications Since the RFS2 Proposal 

 Since the analysis for the RFS2 proposal was completed, a number of updates have been 

made to the FASOM and FAPRI models to reflect comments received and the availability of 

new data. The major changes to the agricultural modeling framework include adding priceinduced yields, updating cellulosic yields, updating distillers grains replacement rates of corn and 

soybean meal in animal feed, adding corn oil from extraction as a biodiesel fuel pathway, adding 

additional land categories in the FASOM model, and adding a detailed Brazil module to the 

FAPRI modeling system. 

5.1.2.1 Price-Induced Yields 

The FAPRI model includes elasticity factors for yields to respond to changes in prices 

over time both in the U.S. and internationally. As the price of corn increase, farmers, seed 

producers, and others involved in crop production have an additional incentive to improve yields. 

The price induced yield phenomenon is partially offset by the reduced yields that result from 

expanding on to new crop acres, which is often referred to as extensification. However, the 

price-induced yield impact is projected to be larger than the extensification effect. For example, 

in 2022 the price of corn increases by $0.10 (3.3 percent) in the U.S. In response, the average 

corn yield in 2022 increases by 0.4 bushels per acre (0.4 percent). In another example, in 2022, 

world corn prices increase by $0.12 per bushel (3.1 percent). As a result, corn yields in China 

increased from 101.9 bushels per acre in the Reference Case to 102.3 bushels per acre in the 

Control Case in 2022, a 0.3 percent improvement. Additional details on the methodology behind 

the estimation of price-induced yields can be found in the FAPRI Technical Report in the 

docket.E

 Additional international results can be found below in this chapter. 

5.1.2.2 Cellulosic Yields 

Based on new research conducted by the National Renewable Energy Laboratory 

(NREL), we have updated the rates of conversion for cellulosic feedstocks into ethanol in the 

FASOM model.F

 As a result of these changes, the gallons per ton yields for switchgrass and 

several other feedstocks increased from the values used in the RFS2 proposal, while the yields 

for corn residue and several other feedstocks decreased slightly from the values used in the 

proposal. For additional detail, please see Chapter XX. In addition, we also updated our 

switchgrass production yields based on new work conducted by the Pacific Northwest National 

Laboratory (PNNL). In the analysis for the RFS2 proposal, national average switchgrass yields 

 

E Technical Report: An Analysis of EPA Biofuel Scenarios with the FAPRI International Models, CARD staff, 

November, 2008. 

F

 Tao, Aden, Technoeconomic Modeling to Support the EPA Notice of Proposed Rulemaking (NOPR), Nov. 2008. 
4 

were 6.3 wet tons per acre in the Control Case in 2022. For the final rulemaking analysis, 

national average switchgrass yields are 7.8 wet tons per acre in the Control Case in 2022. For 

more information on switchgrass yields, please refer to the FASOM technical documentation.G

5.1.2.3 Distillers Grains Replacement Rates 

One of the byproduct of the dry mill ethanol processes is the creation of distillers grains 

with solubles (DGS). This byproduct is a common source of animal feed, and can be used to 

feed beef cattle, dairy cows, swine, and poultry. When DGS are used in feed, they can replace 

other sources of feed that would otherwise be used, such as corn and soybean meal. Based on 

research conducted by Argonne National Laboratory,H one pound of DGS can be used to replace 

1.196 pounds of total corn and soybean meal for various beef cattle and dairy cows due to the 

ability of this livestock to take advantage of the higher nutritional content of DGS per pound 

compared to corn and soybean meal. Current livestock production practices use 1 pound of DGS 

to replace 1 pound of a combination of corn and soybean meal. For our analysis, the 

replacement rates for corn and soybean meal increase steadily over time from a 1:1 replacement 

rate, to the maximum technological replacement rate of 1:1.196 in 2015 for beef cattle and dairy 

cows. A replacement rate of 1:1 is used for swine and poultry throughout the time period 

analyzed. Based on work by Shurson,I

 DGS produced in combination with the corn oil 

fractionation/extraction processes has different nutritional characteristics than traditional DGS 

containing higher levels of oil. According to this research, fractionated/extracted DGS replaces a 

slightly higher proportion of soybean meal rather than corn compared to traditional DGS 

(although the total displacement rate for both types of DGS is 1:1). We have therefore used 

these modified replacement rates of corn and soybean meal of fractionated/extracted DGS for 

swine and poultry than traditional DGS. The Shurson paper does not include changes in 

replacement rates of corn and soybean meal for beef cattle and dairy cows, so replacement rates 

are assumed to be the same for traditional DGS and fractionated/extracted DGS. Maximum 

inclusion rates for DGS in feed are based on the Argonne research. 

5.1.2.4 Corn Oil Extraction as Biodiesel Pathway 

For the RFS2 analysis, both FASOM and FAPRI explicitly model corn oil withdrawn 

from the extraction as a source for biodiesel. Based on engineering research (see Chapter 1.4) 

regarding expected technological adoption, it is estimated that 70 percent of dry mill ethanol 

plants will withdraw corn oil via extraction from distillers grains, resulting in corn oil that is nonfood grade and can only be used as a biodiesel source; 20 percent will withdraw corn oil via 

fractionation (prior to the creation of distillers grains), resulting in corn oil that is food-grade; 

and 10 percent will do neither extraction or fractionation. 

 

G RTI Technical Report 

H Salil Arora, May Wu, and Michael Wang, “Update of Distillers Grains Displacement Ratios for Corn Ethanol 

Life-Cycle Analysis,” September 2008. See http://www.transportation.anl.gov/pdfs/AF/527.pdf 

I

 Shurson, The Value of High-Protein Distillers Coproducts in Swine Feeds, Distillers Grains Quarterly, First 

Quarter 2006. 
5 

5.1.2.5 Detailed Land Use Categories 

Since the proposal, the FASOM model has been updated to include several additional 

land use categories covering the majority of the U.S. land base. These categories are based on 

the USDA National Agriculture Statistics Service (NASS) data. These land classifications 

enable the FASOM model to explicitly link the interaction between livestock, pasture land, 

cropland, and forest land. For each of these categories, FASOM accounts for how much is 

actively used in production, and how much idled, in a particular time period. A brief description 

of these categories is described below. Additional detail on the land categories are included in 

the technical report on the FASOM model included in the docket.J

• 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 for livestock production. 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. 

• Developed (urban) land is assumed to have an inherently higher value than land used for 

any other use, but the rate of urbanization does not change between the cases analyzed. 

• Conservation Reserve Program (CRP) refers to land that is voluntarily taken out of 

crop production. 

5.1.2.6 Brazil Module 

 In the FAPRI modeling system, all non-U.S. countries are analyzed at the national level, 

with the exception of Brazil. Due to the importance of Brazil in determining the international 

impacts of increased biofuel demand, including the increase in U.S. demand for imported 

ethanol, the FAPRI model was updated to include additional agricultural detail in Brazil. The 

FAPRI model now includes an integrated Brazil module that provides additional detail on 

agricultural land use in Brazil for six geographic regions: the Amazon Biome, Northeast, NorthNortheast Cerrados, Central-West Cerrados, Southeast, and the South. The Brazil module 

explicitly models the competition between cropland and pastureland used for livestock 

production in each region. In addition, the Brazil module allows for region-specific agriculture 

practices such as double cropping and livestock intensification in response to higher commodity 

prices. This level of detail allows for a more refined analysis of land use change and economic 

 

J

 RTI Technical Report 
6 

impacts in Brazil than a national-level analysis. For more detail on the Brazil module and its 

development, please refer to the FAPRI model technical report in the docket.K

Figure 5.1-1. 

Map of Brazil by Geographic Region in FAPRI 

5.1.3 Key Modeling Assumptions 

 To analyze the U.S. and international agriculture sectors impact of the RFS2 renewable 

fuel volumes, a number of key assumptions and input parameters were standardized in the 

FASOM and FAPRI models. These assumptions were developed with the input of other 

government agencies, such as USDA and DOE. As shown in Table 5.1-1, key assumptions 

include corn and soybean yields,L

 corn ethanol dry and wet mill plant processing energy use, 

corn ethanol yields, corn ethanol by-product use, estimated corn stover yields, domestic energy 

 

K CARD Technical Report 

L

 USDA Agricultural Projections to 2018 (OCE-2009-1), February, 2008. 
7 

prices,M and others. Additional details on the assumptions included in FASOMN and FAPRIO

are included in the docket. For other estimates of input parameters, we relied on external 

expertise, such as FAPRI; the Greenhouse gases, Regulated Emissions and Energy use in 

Transportation (GREET) model;P the Assessment System for Population Exposure Nationwide 

(ASPEN) model;Q and the Agriculture Resource Management Survey (ARMS).R 

 

M Energy prices in all cases are based on the April Release of the 2009 Annual Energy Outlook, published by the 

Energy Information Administration (EIA) within DOE. Prices used include Gasoline, Diesel, E85, Coal, and 

Electricity. See: http://www.eia.doe.gov/oiaf/servicerpt/stimulus/index.html 

N Beach, Robert; McCarl, Bruce, Agricultural Impacts of the Energy Independence and Security Act: FASOM 

Results and Model Description, RTI International, October, 2008. O Technical Report: An Analysis of EPA Biofuel Scenarios with the FAPRI International Models, CARD staff, 

November, 2008. 

P

 The GREET model is run by Argonne National Laboratory at the Department of Energy. GREET can simulate 

more than 100 fuel production pathways and more than 80 vehicle/fuel systems. 

Q ASPEN is a computer simulation model used to estimate toxic air pollutant concentrations is called the 

Assessment System for Population Exposure Nationwide, or. This model is based on the EPA’s Industrial Source 

Complex Long Term model (ISCLT) which simulates the behavior of the pollutants after they are emitted into the 

atmosphere. ASPEN uses estimates of toxic air pollutant emissions and meteorological data from National Weather 

Service Stations to estimate air toxics concentrations nationwide. R

 ARMS is sponsored by the Economic Research Service (ERS) and National Agricultural Statistics Service (NASS) 

at USDA, and provides observations of field-level farm practices, the economics of the farm business, and the 

characteristics of the American farm household. 
8 

Table 5.1-1. 

Agriculture Model Assumptions 

Assumption Notes 

Feedstock Production 

Prices for Gasoline, Diesel, E85, Coal, and Electricity 

used in all volume cases AEO 2009, April Release 

U.S. national average corn yields are approximately 

170 bu/acre in 2017 and 180 bu/acre in 2022 (a 1.6% 

annual increase over the baseline year) 

U.S. national average soybean yields are 

approximately 50 bu/acre in 2022 (a 0.4% annual 

increase) 

Consistent with USDA projections 

(http://www.ers.usda.gov/publications/oce081/) 

International corn yields increasing over time, for 

example: 

Argentina ~133 bu/acre in 2022 (a 0.8% annual 

increase) 

Brazil ~72 bu/acre in 2022 (a 1.1% annual increase) 

International soybean yields increasing over time, for 

example: 

Argentina ~46 bu/acre in 2022 (a 0.7% annual 

increase) 

Brazil ~46 bu/acre in 2022 (a 1.1% annual increase) 

Price-Induced Yields, for example: 

World Price of Corn increases by 3.1% from the 

Reference Case to the Control Case in 2022; 

Corn Yields in China increases by 0.3% from the 

Reference Case to the Control Case in 2022 

FAPRI Models 

High Yield sensitivity runs for corn and soybeans: 

For example, U.S. High Yields in 2022: 

Corn: approximately 230 bu/acre (27% higher than 

Base Yield) 

Soybeans: approximately 60 bu/acre (30% higher 

than Base Yield) 

Similar yield increases for top producers of corn 

(China, Mexico, EU, Argentina, Brazil) and soybeans 

(China, Argentina, Brazil) 

FASOM and FAPRI Models 

Represented as increases in technological rates 

of progress, no additional inputs required 

Corn residue removal rates of 50% are allowed for no 

till practices; 35% removal rate allowed for reduced till 

practices (no removal from conventional till) 

Derived from Graham et. al., Agronomy Journal, 

99:1–11 (2007). “Current and Potential U.S. Corn 

Stover Supplies.” and Perlack, R. D., L. L. 

Wright, A. F. Turhollow, R. L. Graham, B. J. 

Stokes, and D. C. Erbach. 2005. Biomass as 

Feedstock for a Bioenergy and Bioproducts 

Industry: the Technical Feasibility of a Billion-ton 

Annual Supply. Report prepared for the U.S. 

Department of Energy and the U.S. Department 

of Agriculture. 
9 

Updated Conversion Rates (gallons per dry ton) for all 

cellulosic ethanol feedstock sources. 

For example: 

Proposal Analysis: 

 Corn Reisude: 94.2 gallons/dry ton 

 Switchgrass: 78.9 gallons/dry ton 

Final Rule Analysis: 

 Corn Reisude: 92.3 gallons/dry ton 

 Switchgrass: 92.3 gallons/dry ton 

Based on NREL Research 

Tao, Aden, Technoeconomic Modeling to 

Support the EPA Notice of Proposed Rulemaking 

(NOPR), Nov. 2008. 

Switchgrass Yields by Region in the U.S. Based on preliminary PNNL Research 

Non-Food Grade (NFG) Corn Oil modeled in FASOM 

and FAPRI as a biodiesel feedstock. 

NFG Corn Oil is a byproduct of the extraction and 

fractionation processes of dry mill ethanol plants. 

Based on enginnering cost projections and 

expected rates of technology adoption. See 

Chapter XX of the RIA. 

By 2022: 70% of dry mill ethanol plants will 

conduct extraction, 20% will conduct 

fractionation, and 10% will do neither 

The Conservation Reserve Program (CRP) has a 

maximum limit of 32 million acres enrolled in the 

program at any given time 

2008 Farm Bill 

USDA baseline assumptions 

Fertilizer Use 

U.S. nitrogen application rate in corn is approximately 

136 lbs/acre in the corn belt in 2022 

U.S. phosphorous application rate in corn is 

approximately 28 lbs/acre in the corn belt in 2022 

Based on ARMS data, adjusted for differences in 

regions and irrigation practices 

For U.S. assume higher yields require no increase in 

fertilizer use 

Based on USDA baseline assumptions 

This holds for all farming rotations (e.g., corn / 

soybean and corn / corn) and land types (e.g., 

prime and marginal land); see below for stover 

removal impacts 

Nitrogen nutrient replacement application = 7 lbs/ton 

corn residue removed 

Phosphorous nutrient replacement application = 3.6 

lbs/ton corn residue removed 

These numbers come from the Argonne National 

Lab Report, Fuel Cycle Assessment of Selected 

Bioethanol Production Pathways in the United 

States. (November 7, 2006). (Used and cited by 

GREET) 

Processing 

1 bushel of corn produces 17 lbs of dried distillers 

grains (dry tons). 

1 pound of DGS substitutes 1.196 pounds of corn and 

soybean meal feed for beef cattle, dairy cows by 2015 

1 pound of DGS substitutes 1 pound of corn and 

soybean meal feed for swine and poultry, with 

adjustments for fractionated/extracted DGS 

www.ethanol.org

Argonne National Laboratory: Update of Distillers 

Grains Displacement Ratios for Corn Ethanol 

Life-Cycle Analysis,” September 2008 

Shurson: The Value of High-Protein Distillers 

Coproducts in Swine Feeds, Distillers Grains 

Quarterly, First Quarter 2006. 

Projected crop yields, both domestically and internationally, are an important factor in the 

analysis of increased renewable fuel volumes. The U.S. yields presented in Table 5.1-1 are 

based on the USDA projections through 2018 (the last year of the USDA baseline projections 

report) and then extrapolated out to 2022. The U.S. yields in this table represented the national 

weighted average yields, which vary by region. These yields are based on historical averages for 
10 

the region, which increase over time in both the AEO 2007 Reference Case and the EISA 

Control Case. FASOM assumes the rate of increase for corn yields are 1.6 percent per year. 

The international crop yields included in FAPRI are different for each country and for 

each crop. The FAPRI model bases on each country’s crop yield on historical trends and 

projects this technical rate of progress into the future. As described in the previous section, the 

FAPRI model also incorporates yield responses to changes in price and for expansion onto 

marginal lands. Examples of how yields vary by region and crop are included in Table 5.1-1. 

For the lifecycle analysis, sensitivity runs were conducted in both FASOM and FAPRI to 

observe the effects of higher yields for both corn and for soybeans. The assumption behind these 

high-yield runs is that the technological rate of progress over time is higher for corn and 

soybeans than compared to our “base yield” modeling efforts used for the rulemaking impacts. 

This increase in the technological rate of progress begins in 2012 in the FASOM model, and in 

2010 in the FAPRI model (the next future time period in each respective model). By 2012, Corn 

yields in the U.S. are 7.1 percent higher in the High Yield Control Case than in the Base Yield 

Control Case. By 2017, it is 18 percent higher, and by 2022 it is 30 percent higher. Similarly, 

soybean yields in the U.S. are 7.8 percent higher in 2012, 20 percent higher in 2017, and 31 

percent higher in the High Yield Control Case than in the Base Yield Control Case. In the 

FAPRI model, similar increases in technological rates of progress for corn were applied to other 

top corn producers (China, Mexico, the EU, Argentina, and Brazil), and likewise for other top 

soybean producers (China, Argentina, and Brazil). Results from these high yield sensitivity runs 

can be found in Chapter 2 of the RIA. Table 5.1-2 lists the yields for the top producers of corn 

and soybeans in the Control Case in 2022, both for the “base” results and for the “high-yield” 

results. For overall impacts of the “high-yield” sensitivity model runs, please refer to Chapter 2. 

Table 5.1-2. 

Corn and Soybean Yields of Top Producers in the Control Case in 2022 

with “Base Yield” and “High-Yield” Sensitivity Runs 

(bushels per acre) 

Corn 

Country Base Yield High Yield % Difference 

U.S. 181.2 231.6 27.9% 

China 102.3 129.9 27.0% 

Mexico 55.8 70.9 27.0% 

EU 110.5 140.4 27.0% 

Argentina 133.9 170.1 27.0% 

Brazil * 72.2 81.8 13.3% 

Soybeans 

Country Base Yield High Yield % Difference 

U.S. 46.0 60.5 31.4% 

China 29.8 38.9 30.4% 

Argentina 46.1 60.1 30.4% 

Brazil * 46.3 57.8 25.0% 

* Note: Yields in individual regions in Brazil are 27% higher than in the Base yield, similar to other countries. However, since some 

regions are more productive than others, the regional distribution of soybean production is different between the “Base” and “HighYield” model runs. This results in the overall yields of corn and soybeans in Brazil in the “High Yield” sensitivity run to not be the 

same percentage higher than the “Base” model run compared to other countries. 
11 

For cellulosic biofuels from corn residues, the current assumptions in FASOM for residue 

removal rates are based on the Graham et. al. paperS

 and the Perlack et al study.T This approach 

uses a maximum percent removal of residues on acres based on tillage practices.U Although we 

requested comment on whether a better metric is the minimum amount of mass that must remain 

on an acre of land to prevent runoff and maintain soil carbon levels, we did not receive sufficient 

data to incorporate this approach into our modeling framework.V 

FASOM assumes fertilizer application rates do not increase over time in proportion to the 

increase in yields (i.e., delinks fertilizer application rates and crop yield changes through time). 

The principal reason for this is USDA data that shows fertilizer application rates per acre 

remaining relatively steady for the past 30 years, during which time corn yields have increased 

approximately 70 percent.W However, when residues are removed from the field, some of the 

nutrients that are contained in the residue must be replaced through additional fertilizer use. For 

the analysis, we assumed that 7 additional pounds of nitrogen and 3.6 pounds of phosphorous 

must be applied per ton of corn stover residue removed. 

Lastly, there is a limit to how many acres crops can be planted on Conservation Reserve 

Program (CRP) land.X CRP is run by the Natural Resources Conservation Service at USDA. 

This program is designed to maintain Federal, State, and tribal environmental laws by making 

payments to farmers equivalent to the income otherwise earned from developing the land 

enrolled in the program. In the 2008 Farm Bill, the number of acres enrolled in the CRP was 

given a maximum limit of 32 million acres. Based on input from USDA, we assumed that 

USDA will increase payments to maintain 32 million acres in CRP through 2022. 

 

5.1.4 Volumes 

For the agricultural sector analysis, we modeled the AEO2007 Reference Case and the 

Control Case (i.e., EISA mandated) volumes described in Chapter 1.2. Where possible, we 

modeled the same volumes in both FASOM and FAPRI. However, some of the projected future 

sources of renewable fuels are not explicitly included in both models. For example, since 

FASOM is a domestic agriculture and forestry model, it cannot explicitly model U.S. biofuel 

imports and its effect on worldwide trade and land use as the FAPRI model does. In addition, 

the FAPRI model does not currently model cellulosic renewable fuel feedstock production. 

Therefore, the cellulosic renewable fuel analysis relies on results from the FASOM model.Y 

Neither of the two models used for this analysis — FASOM or FAPRI — include biofuel 

produced from domestic municipal solid waste (MSW). Thus, for the RFS2 analysis, this biofuel 

 

S

 See also http://www.cpnrd.org/Harvesting%20Stover.pdf 

T

 Available at http://feedstockreview.ornl.gov/pdf/billion_ton_vision.pdf. U Many site specific factors associated with the sustainable removal of residue (e.g., crop type, soil type, soil 

fertility, slope, and climate) affect which geographic regions are suitable for crop residue removal. Detailed 

modeling of these factors was beyond the scope of this analysis. 

V Wilhelm et. al Corn Stover to Sustain Soil Organic Carbon Further Constrains Biomass Supply, Ag Journal (2007) 

W Data from the National Agriculture Statistics Service (NASS) at USDA: http://www.nass.usda.gov/ X See: http://www.nrcs.usda.gov/programs/CRP/ Y The FAPRI model was used to estimate the indirect land use effects of additional switchgrass acres on other crops 

in the U.S., as shown by the FASOM model, and the resultant impacts on trade and land use worldwide. Please refer 

to Chapter XX of the RIA for more information. 
12 

was modeled outside of the agriculture sector. For more information on how MSW was modeled 

elsewhere in the RFS2 analysis, please see Chapters XX and XX. We estimate that 

approximately 2.3 Bgal of cellulosic renewable fuels will be produced from municipal solid 

waste in 2022. 

All the results presented in the following section are relative to the AEO 2007 Reference 

Case renewable fuel volumes, which include 12.3 Bgal of grain-based ethanol, 0.1 Bgal of 

biodiesel from soybean oil, 0.1 Bgal of biodiesel from waste oils and greases, 0.3 Bgal of 

cellulosic ethanol, and 0.6 Bgal of imported ethanol in 2022. The domestic figures are provided 

by FASOM and FAPRI, and all of the international numbers are provided by FAPRI. For a more 

detailed set of results of the agricultural sector impacts of the RFS2 volumes, see the analytical 

reports submitted by FASOM and FAPRI in the docket of this rule. 

For ethanol, we assumed 15 billion gallons (Bgal) of corn ethanol would be produced for 

use as transportation fuel in the U.S. by 2022 in the Control Case in both FASOM and FAPRI. 

FASOM modeled an increase of 13.5 Bgal of cellulosic renewable fuel from the Reference Case 

in 2022.Z

 To satisfy the cellulosic renewable fuel requirements, the FASOM model was allowed 

to choose how much cellulosic renewable fuel was produced from different feedstocks, taking 

account the various harvesting and processing costs and the income the agriculture and forestry 

sectors derive from each feedstock. FASOM projects that 7.9 Bgal of cellulosic renewable fuel 

will be produced from switchgrass in 2022, 4.9 Bgal from corn residue, 0.4 Bgal from sugarcane 

bagasse, and 0.1 billion gallons from forestry logging and milling residues. FAPRI modeled an 

increase of 1.6 Bgal from ethanol imported to the U.S. over the AEO2007 Reference Case level 

in 2022. 

For biodiesel, both FASOM and FAPRI modeled an increase of 0.5 Bgal of biodiesel 

produced from soybean oil above the AEO2007 Reference Case in 2022. FASOM and FAPRI 

also modeled an increase of 0.1 Bgal of biodiesel produced from various animal fats, waste oils, 

and greases to 0.4 Bgal in the Control Case in 2022. Similarly, FASOM and FAPRI modeled an 

increase of 0.6 Bgal of biodiesel from non-food grade corn oil. This non-food grade corn oil 

used for biodiesel production is a byproduct of dry mill ethanol plants that undertake the 

extraction processes. 

 

Z

 FASOM does not include renewable diesel or biomass to liquids as potential cellulosic pathways, therefore all 

cellulosic volumes were assumed to be ethanol. 
13 

Table 5.1-3. 

Biofuel Volumes Modeled in 2022 

(Billions of Gallons) 

Biofuel AEO2007 

Reference Case 

Control Case 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 

Sugarcane Bagasse Cellulosic Ethanol 0.2 0.6 0.4 

Forest Residue Cellulosic Ethanol 0 0.1 0.1 

Imported Ethanol 0.6 2.2 1.6 

Total Ethanol (FASOM) 12.5 28.7 16.2 

Total Ethanol (FAPRI) 13.2 17.5 4.3 

Soybean Oil Biodiesel 0.1 0.6 0.5 

Corn Oil NFG Biodiesel 0.0 0.6 0.6 

Biodiesel from Other Fats, Oils, Greases 0.3 0.4 0.1 

Total Biodiesel 0.4 1.7 1.3 

5.1.5 Domestic Agricultural Impacts 

 For this economic analysis, the FASOM model is utilized for all domestic agriculture 

impacts. Although the FAPRI models do not provide the same amount of detail on GHG 

emissions for the domestic agriculture impacts as the FASOM model, FAPRI does estimate some 

of the same outputs, such as national crop acres, prices, and exports. In this chapter, we present 

both the FASOM and FAPRI results to demonstrate the range of potential agricultural impacts. 

Presenting both sets of results allows for a useful comparison between the two models, reinforces 

the accuracy of our domestic analysis, and ensures consistency when analyzing the impacts of 

the RFS2 fuel volume requirements on the domestic and international agriculture markets. 

5.1.5.1 Commodity Prices

To meet the RFS2 renewable fuel volumes, there are a number of price effects on the 

agricultural commodities. For instance, FASOM estimates that the Control Case renewable fuel 

volumes result in an increase in the U.S. corn price of $0.27 per bushel (8.2 percent) above the 

Reference Case price in 2022. By 2022, FASOM projects that U.S. soybean prices increase by 

$1.02 per bushel (10.3 percent) above the Reference Case price. FASOM also projects the price 

of soybean oil increases by $183 per ton (37.9 percent) over the 2022 Reference Case price. In 

2022, FAPRI projects that the price of corn increases by $0.10 per bushel (3.3 percent), the price 

of soybeans increases by $0.07 per bushel (0.9 percent), and the price of soybean oil increases by 

$12.35 (1.6 percent) relative to the AEO2007 Reference Case. 

FASOM projects that the price of switchgrass increases by $20.12 per wet ton as a result 

of the Control Case renewable fuel volumes in 2022. Similarly, the price of corn residue 

increases by $29.48 per wet ton in 2022, relative to the AEO2007 reference case. FASOM also 

projects that the Control Case price of sugarcane bagasse increases by $23.27 per wet ton in 
14 

2022. By 2022, FASOM projects that hardwood and softwood logging residues are used to 

produce cellulosic ethanol, and their prices are $23.22 per wet ton and $18.37 per wet ton, 

respectively, in 2022 in the Control Case. These prices do not include the storage, handling, or 

delivery costs. Since the FAPRI models do not explicitly model cellulosic ethanol production 

from agriculture residues or dedicated energy crops, comparable price impacts are not available. 

Additional details on the changes in commodity prices are included in Table 5.1.4. 

The prices for byproducts of renewable fuel production are also affected by the increased 

demand for renewable fuels required by the RFS2 rule. Soybean meal, while not exclusively 

used for animal feed, is an important element of the feed market. In 2022, FASOM projects that 

the price of soybean meal decreases by $0.48 per ton (-0.1 percent) relative to the AEO2007 

Reference Case. In 2022, FASOM projects the price of fractionated/extracted DGS increases by 

$7.69 per ton (6.5 percent) relative to the AEO2007 Reference Case. FASOM projects that the 

price of traditional DGS, produced by corn ethanol plants that do not conduct fractionation or 

extraction of corn oil, increases by $7.94 per ton (6.8 percent) relative to the AEO2007 

Reference Case in 2022. In FAPRI, the price of soybean meal increases by $1.05 (0.5 percent), 

and the price of DGS increases by $3.52 per ton (3.9 percent) in 2022 relative to the AEO2007 

Reference Case. 
15 

Table 5.1-4. 

U.S. Commodity Prices in 2022 

 (2007$ per Unit) 

Biofuel Feedstocks 

Commodity (Unit) FASOM FAPRI 

AEO 2007 

Reference 

Case 

Control 

Case Change % Change

AEO 2007 

Reference 

Case 

Control 

Case Change % Change

Corn (bushel) $3.32 $3.60 $0.27 8.2% $2.96 $3.06 $0.10 3.3% 

Soybeans (bushel) $9.85 $10.87 $1.02 10.3% $8.12 $8.19 $0.07 0.9% 

Soybean Oil (ton) $483.10 $666.42 $183.32 38% $782.13 $794.48 $12.35 1.6% 

Switchgrass (wet ton) $20.73 $40.85 $20.12 97% N/A N/A N/A N/A 

Corn Residue (wet 

ton) $5.01 $34.49 $29.48 588% N/A N/A N/A N/A 

Bagasse (wet ton) $6.43 $29.70 $23.27 362% N/A N/A N/A N/A 

Hardwood Logging 

Residue (wet ton) $5.37 $23.22 $17.85 332% N/A N/A N/A N/A 

Softwood Logging 

Residue (wet ton) $9.37 $18.37 $8.99 96% N/A N/A N/A N/A 

Byproducts 

Commodity (Unit) FASOM FAPRI 

AEO 2007 

Reference 

Case 

Control 

Case Change % 

Change

AEO 2007 

Reference 

Case

Control 

Case Change % 

Change

Soybean Meal (ton) $402.11 $401.63 -$0.48 -0.1% $206.81 $207.87 $1.05 0.5% 

DGS Traditional (ton) $116.75 $124.69 $7.94 6.8% N/A N/A N/A N/A 

DGS 

Fractionated/Extracted 

(ton) 

$118.88 $126.57 $7.69 6.5% N/A N/A N/A N/A 

DGS (Overall, FAPRI) N/A N/A N/A N/A $90.60 $94.12 $3.52 3.9% 

5.1.5.2 Commodity Use Changes 

The increased demand for renewable fuels also affects the use of these feedstocks in other 

markets. This section will review the use of these commodities for biofuels, their levels of 

exports, and their use in the animal feed market. In 2022, FASOM projects an additional 1 

billion bushels of corn will be used for corn ethanol production (22 percent) relative to the 

Reference Case. FASOM also projects that in 2022, an additional 98 million wet tons of 

switchgrass, 60 million wet tons of corn residue, 6.1 million tons of sugarcane bagasse, and 1.7 

million tons of forestry residues will be used to produce cellulosic ethanol, relative to the 

AEO2007 Reference Case. 

FASOM estimates that an additional 2 million tons of soybean oil is used to produce 

soybean biodiesel in 2022, relative to the AEO2007 Reference Case. In addition, FASOM 

projects that an additional 17.5 million tons of non-food grade corn oil from the extraction 

process will be used for biodiesel production in 2022. 
16 

The increase in renewable fuel volumes required by the RFS2 also impacts U.S. exports. 

For instance, FASOM estimates that the amount of corn exported from the U.S. decreases by 188 

million bushels (-8.2 percent) in 2022, relative to the AEO2007 Reference Case. This change 

represents a decrease of $57 million (-0.8 percent) in the total value of corn exports in the 

FASOM model in 2022. In FAPRI, U.S. corn exports decrease by 407 million bushels (-15.7 

percent) in 2022. This change translates into a decrease in the total value of corn exports of $991 

million (-12.9 percent) in 2022. 

Similarly, as more soybean oil is used for biodiesel production, the amount of soybeans 

and soybean oil exported from the U.S. can be expected to be affected. In FASOM, soybean 

exports decrease by 135 million bushels (-13.6 percent) in 2022 relative to the AEO2007 

Reference Case. This change represents a decrease of $453 million (-4.6 percent) in the total 

value of U.S. soybean exports in 2022. In FAPRI, soybean exports decrease by 32 million 

bushels (-3 percent), relative to the AEO2007 Reference Case. This change represents a 

decrease in the total value of U.S. soybean exports of $185 million (-2.1 percent) in 2022. The 

FASOM model projects that U.S. soybean oil exports decrease by 1.2 million tons (-51 percent) 

in 2022 relative to the AEO2007 Reference Case. In the FAPRI model, soybean oil exports 

decrease by 0.3 million tons (-6.2 percent) in 2022 relative to the AEO2007 Reference Case. 

Table 5.1-5. 

U.S. Exports in 2022 

Exports (millions of units) 

Commodity FASOM FAPRI 

(Unit) AEO 2007 

Reference 

Case 

Control 

Case Change % 

Change 

AEO 2007 

Reference 

Case 

Control 

Case Change % 

Change

Corn (bushel) 2,281 2,093 -188 -8.2% 2,589 2,182 -407 -15.7% 

Soybeans 

(bushel) 993 858 -135 -13.6% 1,073 1,041 -32 -3.0% 

Soybean Oil (ton) -1.2 -51.2% -0.3 -6.2% 

Total Value of Exports (millions of 2007$) 

Commodity FASOM FAPRI 

AEO 2007 

Reference 

Case 

Control 

Case Change % 

Change 

AEO 2007 

Reference 

Case

Control 

Case Change % 

Change

Corn $7,585 $7,527 -$57 -0.8% $7,669 $6,679 -$991 -12.9% 

Soybeans $9,780 $9,327 -$453 -4.6% $8,709 $8,524 -$185 -2.1% 

 Higher U.S. demand for renewable fuels causes a decrease in the use of corn for U.S. 

livestock feed. The total amount of corn used for feed in FASOM decreases by 3.3 million tons 

(-2.5 percent) in 2022 relative to the AEO2007 Reference Case. Substitutes are available for 

corn as a feedstock, and this market is price sensitive. Several ethanol processing byproducts 

can be used to replace a portion of the corn used as feed, depending on the type of animal. DGS 

are a byproduct of the dry milling process, whereas gluten meal and gluten feed are byproducts 

of wet milling corn ethanol production. FASOM estimates that total DGS used in feed increases 

by 5.2 million tons (15.2 percent), gluten meal used in animal feed decreases by 0.1 million tons 

(-4.5 percent), and gluten feed use increases by 0.3 million tons (6.4 percent) in 2022 relative to 

the AEO2007 Reference Case. As DGS are used more in the feed market in the Control Case 
17 

than in the Reference Case, corn and soybean meal used in the feed market is replaced. Thus, 

soybean meal used in feed decreases by 0.04 million tons (-0.4 percent) in 2022 to 10.5 million 

tons in the Control Case. Overall, the total ethanol byproducts used in feed (DGS, gluten meal, 

and gluten feed) increase by 5.4 million tons (13.2 percent) in 2022 relative to the AEO2007 

Reference Case. 

Table 5.1-6. 

Animal Feed Sources in 2022 

(millions of tons) 

Feed Source FASOM FAPRI 

AEO 2007 

Reference 

Case 

Control 

Case Change % 

Change 

AEO 2007 

Reference 

Case 

Control 

Case Change % 

Change

Corn 134.4 131.1 -3.3 -2.5% 158.8 152.3 -6.5 -4.1% 

Soybean Meal 10.53 10.49 -0.04 -0.4% 39.4 38.7 -0.7 -1.7% 

DGS Total 34.1 39.3 5.2 15.2% 33.9 39.1 5.2 15.2% 

Gluten Meal 2.2 2.1 -0.1 -4.5% 1.0 1.1 0.1 7.5% 

Gluten Feed 4.5 4.8 0.3 6.4% 7.6 7.9 0.3 4.1% 

Total Ethanol 

Byproducts 40.7 46.1 5.4 13.2% 42.5 48.1 5.5 13.0% 

5.1.5.3 Changes in Crop Acres 

In order to meet the Control Case volumes of renewable fuels, FASOM estimates an 

increase of 3.6 million acres (4.6 percent) for harvested corn acres in 2022.AA Most of the new 

corn acres come from a reduction in existing crop acres, such as rice, wheat, barley, rye and hay. 

FASOM projects that rice acres decrease by 788 thousand acres (-20.6 percent), wheat acres 

decrease by 2.9 million acres (-4.9 percent), and hay acres decrease by 752 thousand acres (-1.5 

percent) in 2022 relative to the AEO2007 Reference Case. See Table 5.1-7 for additional 

changes in crop acres in the FASOM and FAPRI models. 

Although the RFS2 Control Case includes more soybean biodiesel than the AEO2007 

Reference Case, competing demands for land results in a decrease in U.S. harvested soybean 

acres. According to the FASOM model, harvested soybean acres decrease by approximately 1.4 

million acres (-2.1 percent) in 2022 relative to the Control Case. As described in the previous 

section, most of the additional soybeans needed for increased biodiesel production are diverted 

from exports. FAPRI also projects that the increased demand for biodiesel from soybean oil 

results chiefly in a reduction in soybean oil exports, rather than an increase in acres harvested. 

FAPRI projects that harvested soybean acres decrease by 0.9 million acres (-1.1 percent) in 2022 

relative to the AEO2007 Reference Case. 

As the demand for cellulosic renewable fuels increases, FASOM projects that most of the 

cellulosic biofuels will be derived from switchgrass. In 2022, switchgrass acres increase by 12.5 

 

AA FASOM estimates that total planted corn acres increase to 89.4 million acres in the Control Case from the 

Reference Case level of 84.6 million acres in 2017. Total planted acres increases to 87.1 million acres in the Control 

Case from the Reference Case level of 83.5 million acres in 2022. 
18 

million acres, relative to the AEO2007 Reference Case. The remainder of the cellulosic biofuel 

is produced from corn residue, forestry residues, and sugarcane bagasse. The FAPRI models do 

not explicitly model the production of cellulosic ethanol, nor does it explicitly model the 

feedstocks for cellulosic ethanol. Table 5.1-7 and Figure 5.1-2 shows the change in acres for all 

crops in the U.S. in 2022. 
19 

Table 5.1-7. 

U.S. Crop Acres in 2022 

(millions of acres) 

Crop FASOM FAPRI 

AEO 2007 

Reference 

Case 

Control 

Case Change % 

Change 

AEO 2007 

Reference 

Case 

Control 

Case Change % 

Change

Barley 9.7 8.5 -1.2 -12.4 2.96 3.0 0.04 1.2% 

Corn 77.9 81.5 3.6 4.6% 79.1 80.9 1.8 2.2% 

Cotton 11.3 11.1 -0.2 -1.7% 7.7 7.7 0.0 0.1% 

Wheat 59.0 56.0 -2.9 -4.9% 48.3 48.1 -0.2 -0.5% 

Hay 50.7 50.0 -0.8 -1.5% 60.8 60.8 0.0 -0.1% 

Oats 5.5 5.4 -0.2 -3.2% 1.1 1.1 0.0 -1.0% 

Rice 3.8 3.0 -0.8 -20.6% 2.6 2.6 0.0 0.0% 

Sorghum 8.7 8.5 -0.2 -1.8% 5.8 5.8 0.0 0.3% 

Soybeans 68.1 66.6 -1.4 -2.1% 78.3 77.4 -0.9 -1.1% 

Sugarbeet 1.3 1.2 -0.1 -7.8% 1.2 1.2 0.0 -0.3% 

Sugarcane 0.7 0.9 0.1 19.8% 0.8 0.8 0.0 0.2% 

Switchgrass 0.1 12.6 12.5 20,261% 0.0 0.0 0.0 N/A 

Other (FASOM)* 9.5 9.1 -0.4 -4.4% N/A N/A N/A N/A 

Other (FAPRI)** N/A N/A N/A N/A 4.7 4.7 0.0 0.1% 

Total 306.3 314.4 8.1 2.6% 288.7 289.4 0.7 0.2% 
20 

Figure 5.1-2. 

Estimated Change in U.S. Crop Acres 

Relative to the AEO 2007 Reference Case in 2022 

-4

-2

0

2

4

6

8

10

12

14

Barley

Corn

CottonWheat

Hay

Oats

Rice

Sorghum

Soybeans

Sugarbeet

Sugarcane

SwitchGrass

Other (FASOM)

Other (FAPRI)

Total

Millions of Acres

FASOM

FAPRI

As switchgrass and corn residue are the largest feedstocks of cellulosic ethanol, it is 

important to know which regions in the U.S. these feedstocks are the most competitive. Corn 

residue removal takes place chiefly in the Corn Belt region of the U.S. Based on the residue 

removal rates outlined in Table 5.1-1, corn acres with residue removal in the Control Case takes 

place mostly in Iowa (9 million acres) and Illinois (7.4 million acres). Switchgrass production, 

on the other hand, takes place mostly in the Southwest region of the U.S. This includes 2.7 

million acres in Kansas, 1.7 million acres each in Missouri and Texas, as well as 1.3 million 

acres in Oklahoma. To see the top ten producing states for corn residue and switchgrass, please 

refer to Figures 5.1-3 and 5.1-4 below. 
21 

Figure 5.1-3. 

Top Ten Producing States of Corn Acres with Residue Removal in 2022 

FASOM Control Case 

0

1

2

3

4

5

6

7

8

9

10

Iowa

Illinois

SouthDakota

Nebraska

NorthDakota

Missouri

Michigan

Indiana

Kansas

Ohio

Millions of Acres
22 

Figure 5.1-4. 

Top Ten Producing States of Switchgrass in 2022 

FASOM Control Case 

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Kansas

Missouri

Texas

OklahomaArkansas

Ohio

Kentucky

NorthCarolina

Tennessee

Louisiana

Wisconsin

Florida

Millions of Acres

5.1.5.4 Land Use Change 

Changes in these land categories are summarized in Table 5.1-8 below. In 2022, 

FASOM projects that total cropland increases by 3.1 million acres (1.0 percent) relative to the 

Reference Case. The increase in cropland is derived primarily from a combination of decreased 

idle cropland acres, a decrease in idled cropland pasture acres, and a decrease in forest acres. 

FAPRI does not explicitly model U.S. forest or pasture acres. 
23 

Table 5.1-8. 

Change in U.S. Major Land Use Categories in 2022 

Relative to the Reference Case 

(millions of acres) 

Land Category AEO 2007 

Reference Case 

Control Case Change % 

Change 

Cropland 311.7 314.8 3.1 1.0% 

Used for Production 306.3 314.4 8.1 2.6% 

Idled 5.4 0.4 -5.0 -93.0% 

Cropland Pasture 32.0 30.1 -1.9 -5.8% 

Used for Production 23.1 25.0 1.8 8.0% 

Idled 8.9 5.2 -3.7 -41.7% 

Forest Pasture 148.4 149.5 1.1 0.7% 

Used for Production 113.1 114.4 1.3 1.1% 

Idled 35.3 35.1 -0.2 -0.5% 

Forestland 344.5 343.3 -1.2 -0.3% 

Rangeland 578.8 578.8 0 0% 

Used for Production 522.6 516.8 -5.8 -1.1% 

Idled 56.3 62.0 5.7 10.2% 

CRP 32.0 32.0 0.0 0% 

Developed Land 35.0 35.0 0.0 0% 

5.1.5.5 Fertilizer Use 

As crop acres increase to meet the additional demand for corn and other crops for biofuel 

production, fertilizer use increases as a result. In 2022, FASOM estimates that nitrogen fertilizer 

use in the U.S. agricultural sector will increase by 1.5 billion pounds (5.7%) relative to the 

AEO2007 Reference Case levels. FASOM also projects that phosphorous fertilizer use will 

increase by 714 million pounds (12.7%) relative to the Reference Case level in 2022. The 

FAPRI model does not provide estimates for fertilizer use. 

Table 5.1-9. 

Change in U.S. Fertilizer Use 

Relative to the Reference Case 

(millions of pounds) 

Fertilizer AEO 2007 

Reference Case 

Control Case Change %Change 

Nitrogen 26,209 27,710 1,501 5.7% 

Phosphorous 5,614 6,328 714 12.7% 

5.1.5.6 Impact on U.S. Farm Income 

The increase in renewable fuel production provides a significant increase in net farm 

income to the U.S. agricultural sector. FASOM predicts that net U.S. farm income will increase 

by $13 billion dollars in 2022 (36 percent). 
24 

5.1.5.7 Impact on U.S. Food Prices 

Higher corn and soybean prices also result in higher meat prices. For example, in 2022, 

the average price for all meat production in the FASOM model increases by 0.1 percent. In 

FAPRI, the price of beef increases by $0.37 per hundredweight (0.4 percent) in 2022 to $95.84 

per hundredweight in the Control Case, and the price of pork increases by $0.77 per 

hundredweight (1.6 percent) in 2022 to $49.19 per hundredweight in the Control Case. 

Due to higher commodity prices, FASOM estimates that U.S. food costsBB would 

increase by roughly $10 per person per year by 2022, relative to the Reference Case.CC Total 

effective farm gate food costs would increase by $3.6 billion (0.2 percent) in 2022.DD To put 

these changes in perspective, average U.S. per capita food expenditures in 2007 were $3,778 or 

approximately 10 percent of personal disposable income. The total amount spent on food in the 

U.S. in 2007 was $1.14 trillion dollars.EE 

5.1.6 International Impacts 

The FAPRI models are utilized to assess the international impacts on trade, land use, and 

food consumption as a result of the RFS2 renewable fuel volume requirement in the U.S. In the 

FAPRI models, links between the U.S. and international models are made through 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 market-clearing price for 

the commodity. In other countries 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. 

5.1.6.1 Global Commodity Price Changes 

 

BB FASOM does not calculate changes in price to the consumer directly. The proxy for aggregate food price change 

is an indexed value of all food prices at the farm gate. It should be noted, however, that according to USDA, 

approximately 80% of consumer food expenditures are a result of handling after it leaves the farm (e.g., processing, 

packaging, storage, marketing, and distribution). These costs consist of a complex set of variables, and do not 

necessarily change in proportion to an increase in farm gate costs. In fact, these intermediate steps can absorb price 

increases to some extent, suggesting that only a portion of farm gate price changes are typically reflected at the retail 

level. See http://www.ers.usda.gov/publications/foodreview/septdec00/FRsept00e.pdf. 

CC These estimates are based on U.S. Census population projections of 331 million people in 2017 and 348 million 

people in 2022. See http://www.census.gov/population/www/projections/summarytables.html 

DD Farm Gate food prices refer to the prices that farmers are paid for their commodities. EE See www.ers.usda.gov/Briefing/CPIFoodAndExpenditures/Data/table15.htm. 
25 

As demand for renewable fuels in the U.S. increases, the FAPRI model projects that U.S. 

and world commodity prices will generally increase.FF FAPRI projects that the world price of 

corn increases by $0.12/bu (3.1 percent) relative to the AEO2007 Reference Case in 2022. 

Similarly, FAPRI projects that world soybean prices increase by $0.08/bu (0.8 percent) and the 

world soybean oil price increases by $13.22 per ton (1.5 percent) in 2022 relative to the 

AEO2007 Reference Case. 

Since increased biofuel demand in the U.S. also impacts the livestock market, in terms of 

land use (i.e., pasture) and the feed market, we expect prices to change as well. The world price 

for beef, which is based on U.S. prices, increases $7.34 per ton (0.4 percent) in 2022 to $1,917 

per ton in the Control Case. 

Table 5.1-10. 

Global Commodity Price 

Changes from RFS2 in 2022 

(2007$ per unit) 

Commodity (Unit) AEO 2007 

Reference Case 

Control Case Change % Change 

Corn (bushel) $3.76 $3.88 $0.12 3.1% 

Soybeans (bushel) $9.55 $9.63 $0.08 0.8% 

Soybean Oil (ton) $854.45 $867.67 $13.22 1.5% 

Beef (ton) $1,909.42 $1,916.76 $7.34 0.4% 

5.1.6.2 World Renewable Fuels Trade 

 As the U.S. increases its demand for renewable fuels, world trade markets for renewable 

fuels are also likely to be impacted. As described in Section 1.2, we estimate that in 2022, the 

U.S. will increase net imports of ethanol by 1.6 billion gallons (248%) relative to the AEO2007 

Reference Case. In response, FAPRI projects that Brazil will increase net exports by 1.5 billion 

gallons (37.8 percent) in 2022 relative to the Reference Case. However, since the U.S. demand 

for ethanol imports exceeds the increase in Brazilian net exports, FAPRI projects that other 

countries will reduce their net imports of ethanol. In 2022, FAPRI projects that China decreases 

net imports of ethanol by 7.9 million gallons (-5.3 percent), the European Union decreases net 

imports by 16.8 million gallons (-2.3 percent), India decreases net imports by 15.2 million 

gallons (-7.4 percent), Japan decreases net imports by 3.1 million gallons (-0.5 percent), and 

South Korea decreases net imports by 1.6 million gallons (-0.4 percent) relative to the AEO2007 

Reference Case. The rest of the world decreases net imports of ethanol by 0.7 million gallons (-

0.2 percent) in 2022 relative to the Reference Case. 

 

FF FAPRI U.S. and world prices are different based on XXX. 
26 

Figure 5.1-5. 

Ethanol Net Exports by Country in 2022 

-3

-2

-1

0

1

2

3

4

5

6

Brazil China Canada EU India Japan South Korea US ROW

Billions of Gallons

Reference Case

Control Case

 Since the world price of soybean oil increases (1.5 percent), whereas the world price of 

biodiesel decreases (-1.3 percent) due to the RFS2 renewable fuel volume requirements, it 

becomes relatively more profitable to increase net exports of soybean oil for major producers. 

This results in less soybean oil being used in the production of biodiesel and therefore a decrease 

in biodiesel net exports in some countries. Argentina decreases their biodiesel net exports by 1.3 

million gallons (-0.8 percent) to 152.4 million gallons, Brazil decreases their biodiesel net 

exports by 6.2 million gallons (-7.4 percent) to 77.4 million gallons in 2022. In response the EU 

increases their net exports of biodiesel by 8.6 million gallons (29.6 percent) to 37.8 million 

gallons in the Control Case. Additionally, Japan reduces its net imports of biodiesel by 0.5 

million gallons (-5.9 percent). 
27 

Figure 5.1-6. 

Biodiesel Net Exports by Country in 2022

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

Argentina Brazil Indonesia Malaysia US EU Japan ROW

Billions of Gallons

Reference Case

Control Case

5.1.6.3 International Crop Acre Changes

 Changes to the global commodity trade markets and world commodity prices result in 

changes in international land use. The FAPRI model provides international change in crop acres 

as a result of the RFS2 renewable fuel volumes. Internationally, Brazil has the largest increase in 

crop acres in 2022, followed by a subset of nations in Africa, Japan, the EU, Mexico, and 

Indonesia. As the U.S. increases its net imports of ethanol by 1.5 billion gallons in 2022, the 

major supplier of this increase in ethanol is Brazil which produces ethanol from sugarcane. The 

FAPRI model estimates that Brazil crop acres increase by 2.2 million acres (1.6 percent) relative 

to the AEO2007 Reference Case. The major crop contributing to this increase is sugarcane, 

which increases by 1.2 million acres (4.4 percent) in 2022. “Africa, Other” increases total crop 

acres by 0.20 million acres (0.25 percent), the large majority of which is corn, which increases 

by 0.19 million acres (0.32 percent). Japan increases its total crop acres by 0.14 million acres 

(3.3 percent), solely due to a 0.14 million acre increase in rice acres. The EU increases its crop 

acres as well, by 0.12 million acres (0.1 percent). This is a result of a 0.06 million acre increase 

in wheat (0.1 percent), a 0.04 million acre increase in barley (0.1 percent), and a 0.02 million 

acre increase in corn (0.1 percent). Mexico increases its crop acres by 0.11 million acres in 2022 

(0.4 percent), which is primarily due to a 0.14 million acre increase in corn (0.6 percent) and 

small decreases in other crops. Indonesia increases its total crop acres by 0.11 million acres in 
28 

2022 (0.2 percent), including a 0.09 million acre increase in corn (0.9 percent) and a 0.02 million 

acre increase in rice (0.1 percent). 

Figure 5.1-7. 

Change in World Crop Acres from 

the RFS2 Rule by Country in 2022 

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Brazil US Africa,

Other

Japan EU Mexico Indonesia Rest of

World

World

Millions of Acres

 In response to the increased U.S. demand for imported ethanol, which is largely satisfied 

by increases in Brazil exports of sugarcane ethanol, sugarcane acres increase in various regions 

in Brazil. For instance, FAPRI projects 2022 sugarcane acres will increase by 0.01 million acres 

(2.7 percent) in the Amazon Biome region, 0.13 million acres (3.8 percent) in the Central-West 

Cerrados region, 0.15 million acres (3.6 percent) in the Northeast Coast region, 0.02 million 

acres (2.7 percent) in the North-Northeast Cerrados region, 0.05 million acres (2.5 percent) in the 

South region, and 0.87 million acres (5.0 percent) in the Southeast region, relative to the 

AEO2007 Reference Case. 

 Area for other crops in Brazil, including corn and soybeans, are affected not only by the 

increase in sugarcane crop acres, but also by the changes in world price for each commodity. 

Overall, total crop area in Brazil increases by 2.2 million acres (1.6 percent) in 2022 relative to 

the AEO2007 Reference Case. This change is includes an increase of 0.2 million acres (1.8 

percent) in the Amazon Biome, an increase of 0.6 million acres (1.9 percent) in the Central-West 

Cerrados, an increase of 0.2 million acres (2.1 percent) in the Northeast Coast, an increase of 0.2 

million acres (1.6 percent) in the North-Northeast Cerrados, an increase of 0.2 million acres (0.4 
29 

percent) in the South, and an increase of 0.7 million acres (2.6 percent) in the Southeast, relative 

to the AEO2007 Reference Case in 2022. 

Table 5.1-11. 

Change in Brazil Sugarcane Acres and Total 

 Crop Area from RFS2 Rule by Region in 2022 

(millions of acres) 

Region Sugarcane Total Crops 

AEO 2007 

Reference 

Case 

Control 

Case 

Change % 

Change

AEO 2007 

Reference 

Case

Control 

Case

Change % 

Change

Amazon Biome 0.45 0.46 0.01 2.7% 12.0 12.2 0.2 1.8% 

Central-West 

Cerrados 3.38 3.51 0.13 3.8% 31.8 32.4 0.6 1.9% 

Northeast Coast 4.07 4.21 0.15 3.6% 11.1 11.3 0.2 2.1% 

North-Northeast 

Cerrados 0.64 0.66 0.02 2.7% 14.6 14.8 0.2 1.6% 

South 2.02 2.07 0.05 2.5% 39.9 40.1 0.2 0.4% 

Southeast 17.47 18.34 0.87 5.0% 26.3 27.0 0.7 2.6% 

Brazil, Total 28.04 29.27 1.23 4.4% 135.7 137.9 2.2 1.6% 

5.1.6.4 World Food Markets 

The increase in renewable fuel volumes associated with the RFS2 will also impact world 

food consumption patterns.GG Since major agricultural commodity prices increase globally, 

FAPRI projects that world consumption of food decreases by 2.5 million metric tons (-0.1 

percent) in 2022, relative to the AEO2007 Reference Case. This change is includes a decrease of 

consumption of dairy food products of 0.1 million metric tons (-0.03 percent), a decrease of 0.1 

million metric tons (-0.05 percent) of livestock, a decrease of 0.3 million metric tons of sugar (-

0.15 percent), a decrease of 0.4 million metric tons of grains (-0.12 percent) of grains, a decrease 

of 1.7 million metric tons (-4.5 percent) of vegetable oils, and an increase of 0.1 million metric 

tons of rice (0.03 percent), relative to the AEO2007 Reference Case in 2022. Wheat 

consumption levels do not change between the Reference Case and the Control Case. While 

FAPRI provides estimates of changes in world food consumption, estimating effects on global 

nutrition is beyond the scope of this analysis. 

 

GG The food commodities included in the FAPRI model include corn, wheat, sorghum, barley, soybeans, sugar, 

peanuts, oils, beef, pork, poultry, and dairy products. 
30 

Table 5.1-12. 

Change in World Food Consumption Relative to the Reference Case 

(millions of metric tons) 

Category AEO 2007 

Reference 

Case 

Control 

Case 

Change % Change 

Dairy 288.2 288.1 -0.1 -0.03% 

Livestock 301.4 301.3 -0.1 -0.05% 

Sugar 206.3 206.0 -0.3 -0.15% 

Wheat 605.9 605.9 0.0 0.00% 

Grains 314.5 314.1 -0.4 -0.12% 

Vegetable Oils 37.1 35.4 -1.7 -4.5% 

Rice 500.6 500.7 0.1 0.03% 

Total Food 2,258.4 2,256.0 -2.5 -0.1% 

5.2 Petroleum, Biofuels and Energy Security Impacts 

 Increasing usage of renewable fuels helps to reduce U.S. petroleum imports. A reduction 

of U.S. petroleum consumption and imports reduces both financial and strategic risks associated 

with a potential disruption in supply or a spike in cost of a particular energy source. This 

reduction in risks is a measure of improved U.S. energy security. In this section, we detail an 

updated methodology for estimating the energy security benefits of reduced U.S. oil imports 

which explicitly includes biofuels. Based upon this updated approach, we estimate the monetary 

value of the energy security benefits associated with the increased usage of renewable fuels in 

the U.S. required by the RFS2 rule. 

 

5.2.1 Implications of Reduced Petroleum Use on U.S. Imports 

In 2008, U.S. petroleum import expenditures represented 21 percent of total U.S. imports 

of all goods and services.HH In 2008, the U.S. imported 66 percent of the petroleum it consumed, 

and the transportation sector accounted for 70 percent of total U.S. petroleum consumption. This 

compares to approximately 37 percent of petroleum from imports and 55 percent consumption of 

petroleum in the transportation sector in 1975.II It is clear that petroleum imports have a 

significant impact on the U.S. economy. Requiring the wider use of renewable fuels in the U.S. 

is expected to lower U.S. petroleum imports. 

 For this rule, EPA estimated the reductions in U.S. petroleum imports using a modified 

version of the National Energy Modeling System (EPA-NEMS). EPA-NEMS is an energyeconomy modeling system of U.S. energy markets through the 2030 time period. EPA-NEMS 

projects U.S. production, imports, conversion, consumption, and prices of energy subject to 

assumptions on world energy markets, resource availability and costs, behavioral and 

 

HH Source: U.S. Bureau of Economic Analysis, U.S. International Transactions Accounts Data, as shown on June 24, 

2009. 

II Source: U.S. Department of Energy, Annual Energy Review 2008, Report No. DOE/EIA-0384(2008), Tables 5.1 

and 5.13c, June 26, 2009. 
31 

technological choice criteria, cost and performance characteristics of energy technologies, and 

demographics. For this analysis, the 2009 NEMS model was modified to use the 2007 (preEISA) Annual Energy Outlook (AEO) levels of biofuels in the Reference Case. These results 

were compared to our Control Case, which assumes the renewable fuel volumes required by 

EISA will be met by 2022. Details on how the EPA-NEMS model was adjusted to incorporate 

these volumes are included in the docket.JJ The reduction in U.S. oil imports projected by EPANEMS is roughly 0.9 million barrels per day (a 9.5 per cent reduction in 2022). It is estimated 

that U.S. oil production in 2022 declines by much less, just 0.01 million barrels per day. 

Using the EPA-NEMS model, we also calculated the change in expenditures in both U.S. 

petroleum and biofuel imports with the RFS2 rule and compared these with the U.S. trade 

position measured as U.S. net exports of all goods and services economy-wide. Changes in fuel 

expenditures were estimated by multiplying the changes in petroleum and biofuel net imports by 

the respective imported petroleum prices and wholesale ethanol price forecasts. In Table 5.2.1-1, 

the net expenditures in reduced petroleum imports and increased biofuel imports are compared to 

the total value of U.S. net exports of goods and services of the whole economy for 2022 as 

estimated by the EPA-NEMS model. We project that avoided expenditures on imported crude 

oil and petroleum products from the 2022 RFS2 volumes of renewable fuels would be roughly 

$41.5 billion. Taking into consideration imports of renewable fuels, the total avoided 

expenditures on imported transportation fuels are projected to be $37.2 billion in the RFS2 

control case. 

Table 5.2.1-1. 

Selected U.S. Exports and Imports in 2022 

(billions of 2007$) 

Category 

AEO 

2007 

Reference 

Case 

RFS2 

Control 

Case 

Change Percent 

Change 

Total U.S. Exports of Goods and 

Services 3,838 3831 7 +0.22%

Total U.S. Imports of Goods and 

Services 3,840 3833 7 +0.23%

Total U.S. Net Imports of Goods 

and Services 2 2 0 0%

Expenditures on Net Petroleum 

Imports 456 414 -41.5 -9.1%KK

Expenditures on Imported Ethanol 1 5.3 +4.3 +419%

Total Expenditures on 

Transportation Fuel Imports 457 420 -37.2 -8.1%

 

JJ See Onlocation Technical Report, dated XXX, Docket Number XXX. KK Note: the 9.1 per cent reduction included in this table is a change in the monetary value of the oil reductions, 

whereas the 9.5 per cent reduction in oil imports cited in the previous paragraph refers to the volumetric change in 

imports. 
32 

5.2.2 Background on U.S. Energy Security 

 U.S. energy security is broadly defined as protecting the U.S. economy against 

circumstances that threaten significant short- and long-term increases in energy costs. Most 

discussion of U.S. energy security revolves around the topic of the economic costs of U.S. 

dependence on oil imports, although energy security is also a function of the stability of overall 

fuel supply and the flexibility of demand. An important part of the problem stems from U.S. 

reliance on imported oil, and the global oil market is strongly influenced by potentially 

unfriendly and unstable sources. In addition, oil exporters have the ability to raise the price of 

oil by exerting monopoly power through the formation of a cartel, the Organization of Petroleum 

Exporting Countries (OPEC). Finally, these factors contribute to the vulnerability of the U.S. 

economy to episodic oil supply shocks and price spikes. In 2008, U.S. imports of crude oil were 

roughly $391 billion (2007$, see Figure 5.2.2-1). 

Figure 5.2.2-1. U.S. Expenditures on Crude Oil 

U.S. Expenditures on Crude Oil

0

100

200

300

400

500

600

700

1970 1975 1980 1985 1990 1995 2000 2005

Year

(2007$ Billions)

Domestic

Imported

Source: Annual Energy Reviews and AEO 2009. 

By requiring the wider use of renewable fuels, the RFS2 rule promotes diversification of 

transportation fuels in the U.S. and helps to improve the U.S.’s energy security. For the RFS2 

proposal, an “oil import premium” approach was utilized to identify those energy securityrelated impacts which are not reflected in the market price of oil, and which are expected to 

change in response to an incremental change in the level of U.S. oil imports. For this analysis, 
33 

the “oil import premium” approach was extended to explicitly consider the energy security 

implications of the expansion of renewable fuels required by the RFS2 rule. 

5.2.3 Methodology Used to Estimate U.S. Energy Security Benefits 

 In order to understand the energy security implications of reducing U.S. oil imports, EPA 

has worked with Oak Ridge National Laboratory (ORNL), which has developed approaches for 

evaluating the social costs and energy security implications of oil use. In a recent study entitled 

"The Energy Security Benefits of Reduced Oil Use, 2006-2015," completed in 2007 for the final 

RFS1 rulemaking, ORNL updated and applied the method used in the 1997 report "Oil Imports: 

An Assessment of Benefits and Costs", by Leiby, Jones, Curlee and Lee.LL,MM The updated 2007 

report was included as part of the record in the final RFS1 rulemaking, and revisions were made 

based on external comment and peer review. NN,OO 

 Significant factors that drive energy security costs have been changing over the last 

decade, including: projected world oil prices, current and anticipated levels of OPEC production, 

U.S. oil import levels, the estimated responsiveness of regional oil supplies and demands to 

price, and the likelihood of oil supply disruptions. For this analysis, oil prices and supply and 

demand energy balances from the EIA's AEO 2009 Reference Case were used. In many 

instances, the recent market trends and projections suggest reasons for greater concern about oil 

security costs, compared to the prior decade: higher oil prices; growing U.S. import levels; and a 

larger value-share of oil in GDP. To the extent that the U.S. economy has become more 

resilient, and less sensitive to oil shocks, or that improved macroeconomic policies have reduced 

the impact of oil shocks, there may be influences countervailing to the oil market trends. This 

possibility is considered in the security estimates, but recent macroeconomic disturbances 

indicate that greater future macroeconomic stability cannot be assured. The degree to which 

sharply higher oil prices contributed to, or exacerbated, the recent global recession has not yet 

been resolved.PP 

 

LL Leiby, Paul N., Donald W. Jones, T. Randall Curlee, and Russell Lee, Oil Imports: An Assessment of Benefits and 

Costs, ORNL-6851, Oak Ridge National Laboratory, November, 1997. MM The 1997 ORNL paper was cited and its results used in DOT/NHTSA’s rules establishing CAFE standards for 

2008 through 2011 model year light trucks. See DOT/NHTSA, Final Regulatory Impacts Analysis: Corporate 

Average Fuel Economy and CAFE Reform MY 2008-2011, March 2006. 

NNLeiby, Paul N. “Estimating the Energy Security Benefits of Reduced U.S. Oil Imports: Final Report”, 

ORNL/TM-2007/028, Oak Ridge National Laboratory, March, 2008. 

OO Updating the ORNL methodology to incorporate the comments from the Peer Reviewers, based on AEO2007, 

ORNL estimated that the total energy security benefits associated with a reduction of imported oil is $12.38/barrel, 

with a range of $6.88 - $18.52/barrel of imported oil reduced ($2006). When the same methods and assumptions are 

applied to the AOE2009 Reference outlook, comparable estimates for 2025 are $19.21/barrel, with a range of $10.8 

to $29.6/barrel. 

PP See Hamilton, J. D. 2009, “Causes and Consequences of the Oil Shock of 2007-08”, Brookings Papers on 

Economic Activity, 2009, or the congressional testimony of Yergin, D. "The Long Aftershock: Oil: Oil and Energy 

Security After the Price Collapse" Testimony to U.S. Congress, Hearings, Joint Economic Committee, Oil and the 

Economy: The Impact of Rising Global Demand on the U.S. Recovery. May 20, 2009. 
34 

 In order to understand the energy security implications of this rule, EPA used the Oil 

Security Metrics ModelQQ,RR (OSMM), developed and maintained by Oak Ridge National 

Laboratory. The OSMM estimates the U.S. energy security benefits from increased availability 

and use of renewable transportation fuels. The OSMM took as inputs the renewable fuel volumes 

that are required under EISA as well at the biofuel costs estimated in Chapter 4.4 of this RIA. In 

addition, it assumed EPA’s projections of flexible fueled vehicles and use of E85. In conducting 

this analysis, ORNL considered the full economic cost of importing petroleum into the U.S. The 

full economic cost of importing petroleum into the U.S. is defined for this analysis to include two 

components in addition to the purchase price of petroleum itself. These are: (1) the higher costs 

for oil imports resulting from the effect of U.S. import demand on the world oil price and OPEC 

market power (i.e., the "import demand" or "monopsony" costs); and (2) the risk of reductions in 

U.S. economic output and disruption of the U.S. economy caused by sudden disruptions in the 

supply of imported oil to the U.S. (i.e., “macroeconomic disruption/adjustment costs”). 

Analogously, this analysis for the RFS2 rule also considers the economic costs of importing 

biofuels to meet the RFS2 rule requirements, and the estimated disruption/adjustment costs to the 

economy of biofuel price volatility due to biofuel supply disruptions (e.g., droughts and floods, 

etc.). 

 This energy security analysis extends the prior “oil import premium” analysis by 

considering risk-shifting that might occur as the U.S. reduces its dependency on petroleum by 

increasing its use of biofuels. The analysis accounts for the energy security implications 

associated with biofuels, such as possible supply disruptions of ethanol made from corn or 

ethanol derived from cellulosic feedstocks such as switchgrass. The use of OSMM broadens our 

energy security analysis to incorporate estimates of overall motor fuel supply and demand 

flexibility and reliability, and the impacts of possible agricultural sector market disruptions. For 

example, the use of biofuels can modestly alter short and long run demand elasticities (i.e., 

flexibility) in the motor fuel market, with implications for robustness of the fuel system in the 

face of diverse supply shocks. 

 As in the “oil import premium” analysis for the RFS2 proposal, U.S. military costs are 

excluded from the analysis performed by ORNL because their attribution to particular missions 

or activities is difficult. Most military forces serve a broad range of security and foreign policy 

objectives. Attempts to attribute some share of U.S. military costs to oil imports are further 

challenged by the need to estimate how those costs might vary with incremental variations in 

 

QQ The OSMM methods are consistent with the recommended methodologies of the National Resource Council’s 

(NRC’s) (2005) Committee on Prospective Benefits of DOE’s Energy Efficiency and Fossil Energy R&D Programs. 

The OSMM defines and implements a method that makes use of the NRC’s typology of prospective benefits and 

methodological framework, satisfies the NRC’s criteria for prospective benefits evaluation, and permits 

measurement of prospective energy security benefits for policies and technologies related to oil. It has been used to 

estimate the prospective oil security benefits of Department of Energy’s Energy Efficiency and Renewable Energy 

R&D programs, and is also applicable to other strategies and policies aimed at changing the level and composition 

of U.S. petroleum demand. To evaluate the RFS2, the OSMM was modified to include supplies and demand of 

biofuels as well as petroleum. 

RR Greene D.L. and P.N. Leiby, 2006. The Oil Security Metrics Model: A Tool for Evaluating the Prospective Oil 

Security Benefits of DOE's Energy Efficiency and Renewable Energy R&D Programs, ORNL/TM-2006/505, Oak 

Ridge National Laboratory (ORNL), 2006. 
35 

U.S. oil consumption and imports. Similarly, the Strategic Petroleum Reserve (SPR) size and 

policy is assumed unchanged by the RFS2 rule. 

5.2.4 Effect of Oil Use on Long-Run Oil Price and U.S. Import Costs 

 The first component of the full economic costs of oil use in the U.S. follows from the 

effect of U.S. import demand on the world oil price over the long-run. Because the U.S. is a 

sufficiently large purchaser of foreign oil supplies, its purchases can affect the world oil price. 

This monopsony power means that increases in U.S. petroleum demand can cause the world 

price of crude oil to rise, and conversely, that reductions in U.S. petroleum demand can reduce 

the world price of crude oil. Thus, one benefit of decreasing U.S. oil purchases, due to the 

increased availability and use of other transportation fuels, is the potential decrease in the crude 

oil price paid for all crude oil purchased. 

 The demand or monopsony effect can be readily illustrated with an example. If the U.S. 

imports 10 million barrels per day at a world oil price of $50 per barrel, its total daily bill for oil 

imports is $500 million. If a decrease in U.S. imports to 9 million barrels per day causes the 

world oil price to drop to $49 per barrel, the daily U.S. oil import bill drops to $441 million (9 

million barrels times $49 per barrel). While the world oil price only declines $1, the resulting 

decrease in oil purchase payments of $59 million per day ($500 million minus $441 million) is 

equivalent to an incremental benefit of $59 per barrel of oil imports reduced, or $10 more than 

the newly-decreased world price of $49 per barrel. This additional $10 per barrel “import cost 

premium” or “monopsony” benefit represents the incremental external benefit to U.S. society as 

a whole for avoided import costs beyond the price paid for oil purchases. This additional benefit 

arises only to the extent that the reduction in U.S. oil imports affects the world oil price. 

 A similar rationale can be applied to estimate the monopsony disbenefits of the increased 

use of biofuels from the RFS2 rule. In the same way, but working in the opposite direction of 

the oil market, increased use of biofuels in the U.S. is expected to increase demand for domestic 

and imported biofuels, and to increase the world price of renewable fuels. This results in higher 

total costs of U.S. renewable fuel imports. While the total cost of renewable fuel imports under a 

policy like RFS2 rule will include the cost of the additional imports, the monopsony cost portion 

is the added amount paid for the imports that would have occurred without the RFS2 rule 

renewable fuel volumes. Thus, to look at the total monopsony impacts of the RFS2 rule 

renewable fuel volumes, two separate impacts need to be assessed. First, U.S. oil import 

reductions result in paying lower prices for all barrels of U.S. imported oil, providing 

monopsony benefits. Second, increased use of renewable fuels results in higher prices of U.S. 

imported renewable fuels, yielding monopsony disbenefits for renewable fuels. The total 

monopsony benefit is the combined sum of these separate market impacts. 

Table 5.2.4.1 shows the RFS2 Reference Case levels of U.S. oil and biofuel imports as 

well as the average change in oil prices and renewable fuel prices projected due to the RFS2 

volumes in 2022. The Reference Case renewable fuel imports are relatively modest compared 

to oil (roughly 0.015 billions of barrels of biofuel are imported versus 3.283 billion barrels of 

oil). Projected U.S. renewable fuel imports in 2022 are ethanol, principally made from sugar 

cane harvested in Brazil. In 2022, the estimated change in ethanol price due to the RFS2 
36 

renewable fuel volumes is $0.61/barrel, and the estimated reduction in the world oil price is 

$1.05/barrel. The monopsony effect is the change in costs of the quantities of fuel imported 

without the RFS2 renewable fuel volumes (i.e., the Reference Case fuel volumes). Since the 

change in biofuel price applies to a much smaller quantity of biofuels imports than U.S. oil 

imports, the monopsony disbenefit per barrel of increased biofuels use is much smaller, only 

$0.02/barrel, compared to the oil monopsony benefit, $7.88/barrel. Thus, including the impact of 

expanded biofuels use on biofuels imports and price yields a slightly lower estimate of the total 

monoposony benefits. 

Table 5.2.4-1. 

Determinates of Monopsony Benefits of the RFS2 Renewable Fuel Volumes 

vs AEO2007 Reference Case 

Fuel Reference Case 

Import Quantity 

(billions of barrels 

in 2022) 

Change in Price 

($ per barrel in 

2022) 

Monopsony 

Benefit 

($ billion in 

2022) 

Monopsony 

Benefit 

($/barrel of 

biofuel in 2022) 

Biofuels 0.015 0.61 -0.009 -0.02 

Oil 3.282 -1.06 3.476 7.88 

Total 7.86 

 This analysis of the import cost and monopsony effect is based on the net import levels of 

petroleum as projected by the EPA-NEMS, and is not sensitive to the mix of crude and product 

imports. It is possible that in the future, while the U.S. will import most of its crude oil and some 

petroleum products, it may be a net exporter of others, e.g. diesel fuel.SS However, oil security 

concerns stem from the total consumption and net import of all petroleum fuels, whose prices are 

all directly dependent on the volatile (and non-competitive) world crude oil market. For this 

analysis, the key issue is not the trade balance for particular petroleum products, but the net level 

of U.S. consumption and import of all petroleum, both crude and products. Reducing domestic 

gasoline or diesel fuel with renewable fuels use can reduce net imports of all petroleum, and net 

import costs, even if the U.S. remains a net exporter of some petroleum products. Consider the 

case of U.S. diesel fuel. Replacing U.S. diesel fuel consumption with renewable biodiesel, whose 

root supply volatility is largely independent of that of petroleum, can reduce the volatility of 

productive inputs to the macroeconomy, regardless of the trade balance in diesel fuel. 

5.2.5 Macroeconomic Dislocation Costs Associated with Oil and Biofuel Price Variability 

 Fluctuations in oil and biofuel prices are estimated to cause macroeconomic losses due to 

dislocations and adjustment costs. Macroeconomic losses during price shocks reflect both 

aggregate output losses and so called “allocative” losses. The former are a reduction in the level 

 

SS While at the time of the implementation of this rule, the U.S. exports some diesel fuel, it is not clear that this 

situation will long persist. Under EIA AEO2009, the U.S. is a net importer of refined products (as well as crude) 

throughout the forecast horizon. In particular, over the RFS2 horizon (2010-2022) diesel fuel consumption is 

expected to grow at 1.4 per cent per year (Table 11) while gasoline demand will decline at 1.0 per cent per year. 

Thus, under this outlook, crude refinery runs needed to meet gasoline supply will decline, while U.S. demand for the 

distillate/diesel cuts will grow. 
37 

of output that the U.S. economy can produce fully using its available resources; and the latter 

stem from temporary dislocation and underutilization of available resources due to the shock, 

such as labor unemployment and idle plant capacity. The aggregate output effect, a reduction in 

“potential” economic output, will last so long as the price is elevated. It depends on the extent 

and duration of any disruption in the world supply of oil, since these factors determine the 

magnitude of the resulting increase in prices for petroleum products, as well as whether and how 

rapidly these prices return to their pre-disruption levels. 

 In addition to the aggregate contraction, there are “allocative” or “adjustment” costs 

associated with dislocated energy markets. Because supply disruptions and resulting price 

increases occur suddenly, empirical evidence shows they impose additional costs on businesses 

and households which must seek to adjust their use of petroleum and other productive factors 

more rapidly than if the same price increase had occurred gradually. Opportunities for short run 

adjustments of energy use and other productive factors of the economy are limited and costly. 

Dislocational effects include the unemployment of workers and other resources during the time 

needed for their intersectoral or interregional reallocation, and pauses in capital investment due 

to uncertainty. These adjustments temporarily reduce the level of economic output that can be 

achieved even below the “potential” output level that would ultimately be reached once the 

economy’s adaptation to higher petroleum prices was complete. The additional costs imposed on 

businesses and households for making these adjustments reflect their limited ability to adjust 

prices, output levels, and their use of energy, labor and other inputs quickly and smoothly in 

response to rapid changes in prices for petroleum products. 

 In the prior “oil import premium” analysis undertaken for the RFS2 proposal, oil price 

shocks were estimated to have macroeconomic losses based on a single fixed elasticity of Gross 

Domestic Product (GDP) with respect to oil price. For this final RFS2 rule analysis in the 

OSMM, it is recognized that the dislocation portion of disruption costs depends not only on the 

magnitude of the price change, but on the changing importance of both oil and renewable fuels in 

the economy, as well as the degree to which the price movement is novel and disturbing.TT 

Thus, when a shock causes fuel prices to jump up and stay up, initially the dislocation is larger, 

and over time the economy adjusts to higher prices and the macroeconomic dislocation 

dissipates. To account for this, OSMM tracks the evolution of an “adjusted” oil price and 

renewable fuel price, which is based on a lagged partial adjustment process, essentially yielding 

a weighted average of past prices with geometrically declining weights. This weighted-lag 

adjusted price is constructed to represent the average price level to which the economy has 

already had time to adjust.UU It is deviations from this level that are dislocational and costly. 

For both oil price and renewable fuel price fluctuations, the macroeconomic dislocation cost is 

 

TT This attention to the degree to which the observed price is unusual or “novel” follows the work of Lee et al. (Lee, 

K.; Ni, S. & Ratti, R. “Oil shocks and the Macroeconomy: The Role of Price Variability” Energy Journal, 1995, 16, 

39-56, and Hamilton’s NOPI formulation (e.g. Hamilton, J. “What is an Oil Shock?”, Journal of Econometrics, 

Elsevier, 2003, 113, 363-398). Several works of Brown, Huntington, and Gately, for example, also consider the role 

of an “adjusted price” in the determination of supply and demand responses. 

UU The adjusted price is the geometric distributed (i.e., Koyck) lag average of all prior prices, to represent that the 

economy only partially adjusts to changing prices each year. The annual adjustment rate of this price, which 

corresponds to the assumed annual accommodation of new prices by the macroeconomy, is taken as 33 per cent. 

This is consistent with empirical evidence that the dislocational impact of energy price shocks extends more than 

one year, but is mostly complete after three years. 
38 

calculated by applying a GDP loss elasticity to the ratio of the current price to the adjusted price. 

Furthermore, the applied GDP elasticity varies with the value share of expenditure on the fuel, 

both for oil and renewable fuels, in the economy. The estimated GDP losses from renewable 

fuel price fluctuations are based on a GDP Adjustment CostVV elasticity with respect to oil 

prices, but rescaled in accordance with the ratio of renewable fuel expenditures to total oil 

expenditures. 

 One feature of the OSMM is its explicit treatment of renewable fuel volatility. Renewable 

fuel supply and hence, the price of renewable fuels, is also subject to disturbances, and the 

resulting production cost volatility is anticipated to impose some costs on the economy. While 

E85 and gasoline prices are often strongly correlated at the level of individual retail stations, this 

correlation reflects primarily the process of market substitution, and the expected phenomenon 

that ordinarily end-use prices for close substitutes will equilibrate and track one another. Further 

upstream (i.e., closer to the terminal and production plant gate), this price correlation diminishes. 

The historical prices of agricultural products that are likely to be used as feedstocks for biofuels 

are somewhat volatile, but almost completely uncorrelated with oil prices. For example, 

consider monthly price changes between crude oil and key agricultural crops that would be used 

as feedstocks for renewable fuels—sugar, corn, switchgrass, and softwood lumber—during the 

time period from January, 1990 to December, 2008 (Table 5.2.5-1 below). In the case considered 

here, wheat prices are used as a surrogate for switchgrass prices, since both crops are likely to be 

grown in similar agricultural areas in the U.S. (i.e., the Great Plains) and subject to similar 

weather patterns. These agricultural commodities have relatively low correlations with crude oil; 

3 percent, 5 percent, -1 percent and 1 percent, for sugar, wheat, corn and softwood lumber, 

respectively. 

Table 5.2.5-1. Cross Correlations of Monthly Price Changes of Crude Oil 

and Selected Key Renewable Fuel Feedstocks, January, 1990 to December, 2008 

 Crude Oil Sugar Wheat Corn Softwood 

Crude Oil 100% 3% 5% -1% 1% 

Sugar 100% 20% 10% 2% 

Wheat 100% 46% 23% 

Corn 100% -10% 

Softwood 100% 

Source: Leiby 2009, based on IMF/IFS database, Commodity Prices & Indices, Monthly, 1970 to December 2008. 

 From the standpoint of quantifying the macroeconomic/disruption of the increased use of 

renewable fuels, two factors are important. The first factor is an estimate of the variability in the 

supply of biofuels. The second factor is the change in biofuel production costs that stems from 

 

VV GDP Adjustment Costs from biofuel price fluctuations are based on applying the adjustment cost elasticity to the 

ratio of the current year price to the adjusted price. See Huntington (2005, “The Economic Consequences of Higher 

Crude Oil Prices,” Final Report EMF SR 9, Energy Modeling Forum, Stanford University, October, p. 43) notes 

“Economic theory suggests strongly that, in the absence of major threshold effects, the direct response of the GDP 

and price levels to oil price changes should be proportional to oil’s value share in total output.” In the OSMM, for 

both oil and biofuels, the adjustment cost elasticity varies from year to year according to the expenditure share in 

GDP. For biofuels it is given by the 1983 reference value for oil times the ratio of current biofuel expenditure share 

to 1983 oil expenditure share. 
39 

fundamental supply volatility at the feedstock level. This analysis represents renewable fuel 

supply volatility and risk based on historical variations in annual crop yields. Crop yields vary 

substantially from year to year based on growing conditions, including droughts and floods, and 

a variety of other factors.WW The supply risk of lower volumes of feedstock production due to a 

host of factors is assumed to be independent of oil prices. 

 Data on renewable fuel feedstock yield volatilities are presented in Table 5.2.5-2 below. 

Resulting estimates of renewable fuel cost volatility, based upon standard deviations from 

historical trends in biofuel feedstock yields, vary from 2.4 percent to 8.7 percent, with ethanol 

derived from sugar cane estimated to have the lowest volatility, and ethanol derived from corn 

estimated to have the highest volatility. These estimates of feedstock yield volatility are then 

used to estimate variations in feedstock costXX and implied variations in renewable fuel 

production costs at the plant gate.YY As one would expect, renewable fuel cost volatility 

increases with the volatility of feedstock supply, but decreases in cases where feedstocks 

comprise a lower percentage of total production cost (as is the case with cellulosic renewable 

fuel). By way of comparison, the historical volatility of world oil prices over the last twenty-five 

years is 28 percent, considerably higher than the estimated volatility of renewable fuels.ZZ

 

Table 5.2.5-2. 

Selected Key Renewable Fuel Feedstocks Annual Yield Volatility

Biofuel 

Feedstock 

Corn Soybeans Wheat Sugar Cane 

Historical Yield 

Volatility 

8.69% 6.81% 13.07% 2.37% 

Based on Years 1960-2008 1960-2008 1960-2008 1997-2008 

Source: OSSM Supporting Data, Leiby/ORNL 2009. Volatility is measured as the standard deviation of annual 

percentage deviation from historical trend yields. Yield data and volatility are from Bruce Babcock, CARD, Iowa 

State, November, 2009. 

The introduction of renewable fuels affects macroeconomic disruption costs from oil by 

reducing the oil-intensity of the economy, and by changing oil price movements slightly (since 

the addition of renewable fuels slightly alters the elasticity of demand for motor fuels). In 

addition, the introduction of renewable fuels affects the macroeconomic disruption costs by 

 

WW In applying historical yield and feedstock price variations to projected outcomes, it was recognized that two 

offsetting factors may cause future biofuel feedstock supply risk to differ from the past. These factors may offset 

each other: future drought risk may increase with climate change; yet some crops are also becoming increasingly 

drought resistant. 

XX Data on yield volatility were obtained from Bruce Babcock of CARD, Iowa State. Variations in yield were 

converted to estimated variations in feedstock cost based on elasticities from Thompson, W., Meyer, S. & Westhoff, 

P. “How Does Petroleum Price and Corn Yield Volatility Affect Ethanol Markets With and Without an Ethanol Use 

Mandate?” Energy Policy, Elsevier, 2009, 37, Pages 745-749. YY Biofuel production cost economics used in this analysis are based upon estimates from Tao L. and A. Aden 

2009, "The Economics of Current and Future Biofuels," In Vitro Cell. Dev.Biol. - Plant, 45:199-217. ZZ Volatility is calculated as the standard deviation of annual percentage price changes in the real price of imported 

crude oil, 1984-2009, U.S. EIA data. This result is robust over the choice of the starting year for the volatility 

calculation: 2000-2009: 30.0 percent; 29.2 percent; 1990-2009: 26.4 percent; 1980-2009: 27.2 percent; and 1970-

2009: 29.5 percent. 
40 

adding separate disruption costs associated with the independent volatility of renewable fuels 

supply. The magnitude of GDP dislocation losses for a given oil price change is calculated 

based on a summary parameter, “GDP elasticity.” That elasticity is adjusted from historical (i.e., 

early 1980’s) levels based on the oil expenditure share in GDP. In the Reference Case, from 

2010 to 2022, oil expenditures as a cost share of the U.S. economy vary from 2.5 percent in 2010 

to peak at 4.7 percent in 2017, while declining thereafter to 3.8 percent by 2020. By way of 

comparison, in 1983, the oil cost share was 4.6 percent (for which the assumed 1983 

macroeconomic adjustment elasticity is -0.041). Under the RFS2 control case, renewable fuel 

cost shares in the economy are quite small, growing from under 0.1 percent to about 0.2 percent, 

an order of magnitude lower than the oil cost shares. 

 The reduction in GDP adjustment losses due to oil shocks is a result of both the reduction 

in oil price and the slight decrease in oil share (about 0.2%). As mentioned above, the estimated 

volatility of crude oil price is greater than biofuel production costs under the RFS2 control case. 

This is understandable for two reasons: historical crude prices are more volatile than agricultural 

commodity prices that are projected to be the biofuel feedstocks; and biofuel production costs are 

less volatile than feedstock costs. The combination of these effects—lower costs shares of oil 

because of the introduction of higher levels of biofuels and less volatility in biofuel costs 

compared to oil—leads to the offsetting gains and losses in terms of macroeconomic dislocation 

costs, with the avoided losses from crude shocks being greater than the added losses from biofuel 

shocks. Estimates of the avoided macroeconomic dislocation benefits, in dollars per barrel of 

biofuel, are displayed in Table 5.2.5-3 below. 

Table 5.2.5-3. 

Avoided Macroeconomic Dislocations Benefits for the RFS2 Control Case 

vs. the AEO2007 Reference Case ($ per barrel of biofuel) 

Reduction in Dislocation Cost from Oil Shocks $7.08 

Reduction in Dislocation Cost from Biofuel Shocks -$0.52 

Avoided Macroeconomic Dislocation Benefits $6.56 

 This approach has implications for estimated GDP losses due to shock and price 

fluctuations. If the use of renewable fuels reduces the expenditure share of oil in the U.S. 

economy, then the sensitivity of the economy to oil shocks is also reduced, and estimated GDP 

adjustment losses from oil shocks are lower. These effects are all relatively modest, since the 

changes in the renewable fuel cost share are fairly modest. However, the overall importance of 

oil in the economy and the greater estimated volatility of oil prices leads to a significant gain per 

barrel of oil use avoided. The applicable GDP elasticity for renewable fuel price shocks is much 

smaller than that for oil, in proportion to the expenditure share of renewable fuels. It does, of 

course, grow over time with increased use renewable fuels. 

Although retail renewable fuel prices will move closely with petroleum prices, and to a 

lesser extent the renewable fuel plant-gate market prices and feedstock prices, there is still a 

macroeconomic benefit from replacing oil with renewable fuels. This is true for three reasons. 

First, the nature of the oil price increase and renewable fuel price increases are quite distinct. 
41 

When the price of oil increases from an oil supply shock, the resulting increase in renewable 

fuels has a very different origin–it is due to substitution toward renewable fuels, and thus reflects 

a demand response rather than a supply shock. While price change is commonly used as the 

summary measure of the shock, the underlying quantity changes cannot be forgotten as the root 

cause of the economic loss. Unlike oil, the quantity of renewable fuel supplied during an oil 

shock will increase, or at least remain little changed. Second, the short-run increase in the price 

of renewable fuel is no different from the induced price increase in all other substitutes for oil: 

energy or non-energy. Adding a new, independent supply source like renewable fuel is 

equivalent to adding any other alternative fuel, or even to adding a conservation alternative. The 

price of all of these alternatives will rise to some extent with oil prices, but this substitution 

effect is part of the solution to the disruption rather than part of the problem. Through 

substitution, biofuels can also dampen the oil price shock, a beneficial effect that is only partially 

represented in this analysis. Third, to the extent that renewable fuel prices rise with little change 

in production, the payments will be largely to domestic producers, as windfall gains rather than 

losses. 

5.2.6 Estimates of per Barrel and Total U.S. Energy Security Benefits 

 Table 5.2.6-1 below summarizes ORNL’s estimate of the energy security benefits 

associated with the RFS2 renewable fuel volumes. The components of the energy security 

benefit estimate are shown as well as the total U.S. energy security benefits associated with 

increased renewable fuel production in Table 5.2.6-2. 

Table 5.2.6-1. 

Energy Security Benefits 

(2007$/barrel of renewable fuel)

Effect Updated ORNL Study 

Monopsony (best estimate) 

(range) 

$7.86 

($5.37-$10.71) 

Macroeconomic Disruption (best estimate) 

(range) 

$6.56 

($0.94-$12.23) 

Total (best estimate) 

(range) 

$14.42 

($6.31-$22.95) 

 The literature on the energy security for the last two decades has routinely combined the 

monopsony and the macroeconomic disruption components when calculating the total value of 

the energy security premium. However, in the context of using a global value for the Social Cost 

of Carbon (SCC) the question arises: how should the energy security premium be used when 

some benefits from the rule, such as the benefits of reducing greenhouse gas emissions, are 

calculated at a global level? Monopsony benefits represent avoided payments by the U.S. to oil 

producers in foreign countries that result from a decrease in the world oil price as the U.S. 

decreases its consumption of imported oil (net of increased imported biofuel payments by the 

U.S.) Although there is clearly a benefit to the U.S. when considered from the domestic 

perspective, the decrease in price due to decreased demand in the U.S. also represents a loss to 

other countries. Given the redistributive nature of this effect, do the negative effects on other 

countries “net out” the positive impacts to the U.S.? If this is the case, then, the monopsony 
42 

portion of the energy security premium should be excluded from the net benefits calculation for 

the rule. Based on this reasoning, EPA's estimates of net benefits for this rule exclude the 

portion of energy security benefits stemming from the U.S. exercising its monopsony power in 

oil markets. Thus, EPA only includes the macroeconomic disruption/adjustment cost portion of 

the energy security premium. 

 However, even when the global value for greenhouse gas reduction benefits is used, a 

strong argument can be made that the monopsony benefits should be included in net benefits 

calculation for this rule. Maintaining the earth’s climate is a global public good and as such 

requires that a global perspective be taken on the benefits of GHG mitigation by all nations, 

including the U.S. The global SCC is used in these calculations, not because the global net 

benefits of the rule are being computed (they are not), but rather because in the context of a 

global public good, the global marginal benefit is the correct benefit against which domestic 

costs are to be compared. In other words, using the global SCC does not transform the 

calculation from a domestic (i.e., U.S.) to a global one. Rather, the domestic perspective is 

maintained while recognizing that the impacts from domestic GHG emissions are truly global in 

nature. 

 Energy security, on the other hand, is broadly defined as protecting the U.S. economy 

against circumstances that threaten significant short- and long-term increases in energy costs. 

Energy security is inherently a domestic benefit. However, the use of the domestic monopsony 

benefit is not necessarily in conflict with the use of the global SCC, because the global SCC 

represents the benefits against which the costs associated with our (i.e., the U.S.’s) domestic 

mitigation efforts should be judged. In addition, the U.S. values both maintaining the earth’s 

climate and providing for its own energy security. If this reasoning holds, the two benefits—the 

global benefits of reducing greenhouse gas emissions and the full energy security premium, 

including the monopsony benefits—should be counted in the net benefits estimates of the rule. 

In the final analysis, the Agency determined that the first argument is more compelling and 

therefore has determined that using only the macroeconomic disruption component of the energy 

security benefit is the appropriate metric for this rule. 

The energy security benefits of increasing the total renewable fuel volumes from the 

AEO 2007 Reference Case volumes of 13.56 billion gallons to the Primary Control Case 

volumes of 30.5 billion gallons are shown in Table 5.2.4-2. Total annual energy security 

benefits are estimated by multiplying the change in renewable fuel volumes (16.94 billion 

gallons or 403 million barrels) and the macroeconomic disruption/adjustment portion of the 

energy security premium ($6.56/barrel of renewable fuels). 

Table 5.2.4-2. 

Total Energy Security Benefits 

(billions of 2007$)

Year 2022

Benefits $2.6
43 

5.3. Benefits of Reducing GHG Emissions 

5.3.1 Introduction 

As discussed in Section 2, the increased volumes of renewable fuels mandated by the 

RFS2 standards are projected to reduce greenhouse gas emissions (GHG). This section presents 

estimates of the economic benefits that could be monetized for the reductions in GHG emissions 

projected due to the RFS2 renewable fuel volumes. The total benefit estimates were calculated 

by multiplying a marginal dollar value (i.e., cost per ton) of carbon emissions, also referred to as 

“social cost of carbon” (SCC), by the anticipated level of emissions reductions in tons. 

 The SCC values underlying the benefits estimates for this rule represent U.S. 

government-wide interim values for SCC. As discussed below, federal agencies will use these 

interim values to assess some of the economic benefits of GHG reductions while an interagency 

workgroup develops SCC values for use in the long-term. The interim values should not be 

viewed as an expectation about the results of the longer-term process. Although these values 

were not used in the NPRM, some commenters raised issues with these values and the 

methodology used to develop them in response to their publication elsewhere. Many of these 

issues are being examined by the interagency workgroup. 

 The rest of this section provides the basis for the interim SCC values, and the estimates of 

the total climate-related benefits of the RFS2 renewable fuel volumes that follow from these 

interim values. As discussed below, the interim dollar estimates of the SCC represent a partial 

accounting of climate change impacts. 

 In addition to the partial quantitative account presented in this section, a qualitative 

appraisal of climate-related impacts that are not fully captured in these values is published in 

other recent climate change analyses. For example, EPA’s final Endangerment and Cause or 

Contribute Findings for Greenhouse Gases under Section 202(a) of the Clean Air Act and the 

accompanying Technical Support Document (TSD) presents a summary of impacts and risks of 

climate change projected in the absence of actions to mitigate GHG emissions.AAA The TSD 

synthesizes major findings from the best available scientific assessments of the scientific 

literature that have gone through rigorous and transparent peer review, including the major 

assessment reports of both the Intergovernmental Panel on Climate Change (IPCC) and the U.S. 

Climate Change Science Program (CCSP). 

5.3.2 Derivation of Interim Social Cost of Carbon Values 

The “social cost of carbon” (SCC) is intended to be a monetary measure of the 

incremental damage resulting from carbon dioxide (CO2) emissions, including (but not limited 

to) net agricultural productivity loss, human health effects, property damages from sea level rise, 

and changes in ecosystem services. Any effort to quantify and to monetize the consequences 

associated with climate change will raise serious questions of science, economics, and ethics. But 

 

AAA See Federal Register /Vol.74, No.2398/Wednesday, December 16, 2009/Rules and Regulations at 

http://frwebgate4.access.gpo.gov/cgi-bin/PDFgate.cgi?WAISdocID=969788398047+0+2+0&WAISaction=retrieve 
44 

with full regard for the limits of both quantification and monetization of impacts, the SCC can be 

used to provide an estimate of the social benefits of reductions in GHG emissions. 

 

For at least three reasons, any particular figure will be contestable. First, scientific and 

economic knowledge about the impacts of climate change continues to grow. With new and 

better information about relevant questions, including the cost, burdens, and possibility of 

adaptation, current estimates will inevitably change over time. Second, some of the likely and 

potential damages from climate change—for example, the loss of endangered species—are 

generally not included in current SCC estimates. These omissions may turn out to be significant 

in the sense that they may mean that the best current estimates are too low. As noted by the IPCC 

Fourth Assessment Report, “It is very likely that globally aggregated figures underestimate the 

damage costs because they cannot include many non-quantifiable impacts.” Third, when 

economic efficiency criteria, under specific assumptions, are juxtaposed with ethical 

considerations, the outcome may be controversial. These ethical considerations, including those 

involving the treatment of future generations, should and will also play a role in judgments about 

the SCC (see in particular the discussion of the discount rate, below). 

To date, SCC estimates presented in recent regulatory documents have varied within and 

among agencies, including DOT, DOE, and EPA. For example, a regulation proposed by DOT in 

2008 assumed a value of $7 per metric ton CO2

BBB (2006$) for 2011 emission reductions (with a 

range of $0-14 for sensitivity analysis). One of the regulations proposed by DOE in 2009 used a 

range of $0-$20 (2007$). Both of these ranges were designed to reflect the value of damages to 

the United States resulting from carbon emissions, or the “domestic” SCC. In the final MY2011 

CAFE EIS, DOT used both a domestic SCC value of $2/t-CO2 and a global SCC value of $33/tCO2 (with sensitivity analysis at $80/t-CO2) (in 2006 dollars for 2007 emissions), increasing at 

2.4% per year thereafter. The final MY2011 CAFE rule also presented a range from $2 to $80/tCO2. 

In the May 2009 RFS2 Proposal leading to today’s final rule, EPA identified preliminary 

SCC estimates that spanned three orders of magnitude. EPA’s May 2009 proposal also presented 

preliminary global SCC estimates developed from a survey analysis of the peer reviewed 

literature (i.e., meta analysis). The global mean values from the meta analysis were $68 and 

$40/t-CO2 for discount rates of 2% and 3% respectively (in 2006 real dollars for 2007 

emissions).CCC 

Since publication of the May 2009 RFS2 proposal, a federal interagency working group 

has established a methodology for selecting a range of interim SCC estimates for use in 

regulatory analyses. Today’s final rule presents the methodology and the resulting interim set of 

SCC estimates, which reflect the Administration’s current understanding of the relevant 

literature. Recent federal regulatory documents have also presented the interim SCC estimates, 

 

BBB For the purposes of this discussion, we present all values of the SCC as the cost per metric ton of CO2 emissions. 

Some discussions of the SCC in the literature use an alternative presentation of a dollar per metric ton of carbon. 

The standard adjustment factor is 3.67, which means, for example, that a SCC of $10 per ton of CO2 would be 

equivalent to a cost of $36.70 for a ton of carbon emitted. Unless otherwise indicated, a “ton” refers to a metric ton. CCC 74 FR 25094 (May 26, 2009). 
45 

including a proposal to limit vehicle greenhouse gas emissions that requests public comment on 

the estimates and underlying methodology.DDD

It should be emphasized that the analysis here is preliminary. These interim estimates are 

being used for the short-term while an interagency group develops a more comprehensive 

characterization of the distribution of SCC values for future economic and regulatory analyses. 

The interim values should not be viewed as an expectation about the results of the longer-term 

process.

This process will allow the workgroup to explore questions raised in the May 2009 RFS2 

Proposal as they are relevant to the development of SCC values for use in the long-term. The 

workgroup may evaluate factors not currently captured in today’s estimates due to time 

constraints, such as the quantification of additional impact categories where possible and an 

uncertainty analysis. The Administration will seek comment on all of the scientific, economic, 

and ethical issues before establishing improved estimates for use in future rulemakings. 

The outcomes of the Administration’s process to develop interim values are judgments in 

favor of a) global rather than domestic values, b) an annual growth rate of 3%, and c) interim 

global SCC estimates for 2007 (in 2007 dollars) of $56, $34, $20, $10, and $5 per metric ton of 

CO2. The interim set of values is based on the following judgments. 

5.3.2.1 Global and Domestic Measures 

Because of the distinctive nature of the climate change problem, we present both a global 

SCC and a fraction of that value that represents impacts that may occur within the borders of the 

U.S. alone, or a “domestic” SCC, but fix our attention on the global measure. This approach 

represents a departure from past practices, which relied, for the most part, on domestic measures. 

As a matter of law, both global and domestic values are permissible; the relevant statutory 

provisions are ambiguous and allow selection of either measure.EEE 

Under OMB guidance, analysis from the domestic perspective is required, while analysis 

from the international perspective is optional. The domestic decisions of one nation are not 

typically based on a judgment about the effects of those decisions on other nations. But the 

climate change problem is highly unusual in the sense that it involves (a) a global public good in 

which (b) the emissions of one nation may inflict significant damages on other nations and (c) 

the United States is actively engaged in promoting an international agreement to reduce 

worldwide emissions. 

 

DDD Federal Register 40 CFR Parts 86 and 600, September 28, 2009 “Proposed Rulemaking To Establish LightDuty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Economy Standards; Proposed 

Rule”

EEE It is true that federal statutes are presumed not to have extraterritorial effect, in part to ensure that the laws of the 

United States respect the interests of foreign sovereigns. But use of a global measure for the SCC does not give 

extraterritorial effect to federal law and hence does not intrude on such interests.
46 

In these circumstances, we believe the global measure is preferred. Use of a global 

measure reflects the reality of the problem and is consistent with the continuing efforts of the 

United States to ensure that emissions reductions occur in many nations. 

Domestic SCC values are also presented. The development of a domestic SCC is greatly 

complicated by the relatively few region- or country-specific estimates of the SCC in the 

literature. One potential source of estimates comes from EPA’s ANPR Benefits TSD, using the 

FUND model. The resulting estimates suggest that the ratio of domestic to global benefits varies 

with key parameter assumptions. With a 3% discount rate, for example, the U.S. benefit is about 

6 percent of the global benefit of GHG reductions for the “central” (mean) FUND results, while, 

for the corresponding “high” estimates associated with higher climate sensitivity and lower 

global economic growth, the U.S. benefit is less than 4 percent of the global benefit. With a 2 

percent discount rate, the U.S. share is about 2-5 percent of the global estimate. 

Based on this available evidence, an interim domestic SCC value equal to 6 percent of the 

global damages is proposed. It is recognized that the 6 percent figure is approximate and highly 

speculative and alternative approaches will be explored before establishing improved values for 

future rulemakings. However, it should be noted that it is difficult to properly apportion global 

benefits to different regions, because not all the damages citizens of one country would be 

willing to pay to avoid will occur only within their own borders. For example, impacts outside 

U.S. border can have significant welfare implications for U.S. populations (e.g. tourism, disaster 

relief) and if not included, these omissions will lead to an underestimation of the “domestic” 

SCC. 

5.3.2.2 Filtering existing analyses 

There are numerous SCC estimates in the existing literature, and it is reasonable to make 

use of those estimates in order to produce a figure for current use. A starting point is provided by 

the meta-analysis in Richard Tol, 2008.FFF With that starting point, the Administration proposes 

to “filter” existing SCC estimates by using those that (1) are derived from peer-reviewed studies; 

(2) do not weight the monetized damages to one country more than those in other countries; (3) 

use a “business as usual” climate scenario; and (4) are based on the most recent published 

version of each of the three major integrated assessment models (IAMs): FUND, PAGE, and 

DICE. 

 Proposal (1) is based on the view that those studies that have been subject to peer review 

are more likely to be reliable than those that have not. Proposal (2) avoids treating the citizens of 

one nation (or different citizens within the US) differently on the basis of income considerations, 

which some may find controversial and in any event would complicate that analysis. Further it is 

consistent with the potential compensation tests of Kaldor (1939) and Hicks (1940), which form 

the conceptual foundations of benefit-cost analysis and use unweighted sums of willingness to 

pay. Finally, this is the approach used in rulemakings across a variety of settings and 

consequently keeps USG policy consistent across contexts. 

 

FFF Richard Tol, The Social Cost of Carbon: Trends, Outliers, and Catastrophes, Economics: The Open-Access, 

Open-Assessment E-Journal, Vol. 2, 2008-25. http://www.economics-ejournal.org/economics/journalarticles/2008-

25 (2008). 
47 

Proposal (3) stems from the judgment that as a general rule, the proper way to assess a 

policy decision is by comparing the implementation of the policy against a counterfactual state 

where the policy is not implemented. In addition, our expectation is that most policies to be 

evaluated using these interim SCC estimates will constitute small enough changes to the larger 

economy to safely assume that the marginal benefits of emissions reductions will not change 

between the baseline and policy scenarios. 

Proposal (4) is based on four complementary judgments. First, the FUND, PAGE, and 

DICE models now stand as the most comprehensive and reliable efforts to measure the economic 

damages from climate change. Second, the latest versions of the three IAMs are likely to reflect 

the most recent evidence and learning, and hence they are presumed to be superior to those that 

preceded them. However, it is acknowledged that the most recently published results do not 

necessarily repeat prior modeling exercises with an updated model, so valuable information may 

be lost, for instance, estimates of the SCC using specific climate sensitivities or economic 

scenarios. In addition, although some older model versions were used to produce estimates 

between 1996 and 2001, there have been no significant modeling paradigm changes since 1996. 

Third, any effort to choose among them, or to reject one in favor of the others, would be 

difficult to defend at the present time. In the absence of a clear reason to choose among them, it 

is reasonable to base the SCC on all of them. Fourth, in light of the uncertainties associated with 

the SCC, a range of values is more representative and the additional information offered by 

different models is important. 

5.3.2.3 Use a Model-weighted Average of the Estimates at Each Discount Rate 

At this time, a strong reason to prefer any of the three major IAMs (FUND, PAGE, and 

DICE) over the others has not been identified. Accordingly, to address the concern that certain 

models not be given unequal weight relative to the other models, the estimates are based on an 

equal weighting of the means of the estimates from each of the models. Among estimates that 

remain after applying the filter, we begin by taking the average of all estimates within a model. 

The estimated SCC is then calculated as the average of the three model-specific averages. This 

approach is used to ensure that models with a greater number of published results do not exert 

unequal weight on the interim SCC estimates. However, note that the resulting set of SCC 

estimates does not provide information about variability among or within models except in so far 

as they have different discounting assumptions. In the future interagency process to generate a 

more comprehensive distribution of SCC, we expect to exercise the available SCC models in a 

systematic manner such that the resulting distributions of SCC values may incorporate a wider 

range of uncertainties including discount rates, growth rates, climate sensitivities, and other 

important parameters. This may lead to changes in the span of SCC estimates that are relevant 

for policy analyses. 

5.3.2.4 Apply a 3 Percent Annual Growth Rate to the Chosen SCC Values 

SCC is expected to increase over time, because future emissions are expected to produce 

larger incremental damages as physical and economic systems become more stressed as the 

magnitude of climate change increases. Indeed, an implied growth rate in the SCC can be 
48 

produced by most of the models that estimate economic damages caused by increased GHG 

emissions in future years. But neither the rate itself nor the information necessary to derive its 

implied value is commonly reported. In light of the limited amount of debate thus far about the 

appropriate growth rate of the SCC, applying a rate of 3 percent per year seems appropriate at 

this stage. This value is consistent with the range recommended by IPCC (2007) and close to the 

latest published estimate (Hope 2008). 

5.3.2.5 Discount Rates 

For estimation of the benefits associated with the mitigation of climate change, one of the 

most complex issues involves the appropriate discount rate. OMB’s current guidance offers a 

detailed discussion of the relevant issues and calls for discount rates of 3 percent and 7 percent. It 

also permits a sensitivity analysis with low rates (1 – 3 percent) for intergenerational problems: 

“If your rule will have important intergenerational benefits or costs you might consider a further 

sensitivity analysis using a lower but positive discount rate in addition to calculating net benefits 

using discount rates of 3 and 7 percent.”GGG 

The choice of a discount rate, especially over long periods of time, raises highly 

contested and exceedingly difficult questions of science, economics, philosophy, and law. See, 

e.g., William Nordhaus, The Challenge of Global Warming (2008); Nicholas Stern, The 

Economics of Climate Change (2008); Discounting and Intergenerational Equity (Paul Portney 

and John Weyant eds. 1999). It is not clear that future generations would be willing to trade 

environmental quality for consumption at the same rate as the current generations. Under 

imaginable assumptions, decisions based on cost-benefit analysis with high discount rates might 

harm future generations – at least if investments are not made for the benefit of those 

generations. See Robert Lind, Analysis for Intergenerational Discounting, id. at 173, 176-177. It 

is also possible that the use of low discount rates for particular projects might itself harm future 

generations, by diverting resources from private or public sector investments with higher rates of 

return for future generations. In the context of climate change, questions of intergenerational 

equity are especially important. 

Because of the substantial length of time in which CO2 and other GHG emissions reside 

in the atmosphere, choosing a discount rate which is higher than the actual discount rate could 

result in irreversible changes in CO2 concentrations, and possibly irreversible climate changes 

(unless substantial reductions in short-lived climate forcing emissions are achieved). Even if 

these changes are reversible, delaying mitigation efforts could result in substantially higher costs 

of stabilizing CO2 concentrations. On the other hand, using too low a discount rate in benefitcost analysis may recommend some potentially economically unwarranted investments in 

mitigation. In many cases these investments could be discontinued with little long term economic 

disruptions. However, it is also possible that the use of low discount rates for particular projects 

might itself harm future generations, by ensuring that resources are not used in a way that would 

greatly benefit them. 

 

GGG See OMB Circular A-4, pp. 35-36, citing Portney and Weyant, eds. (1999), Discounting and Intergenerational 

Equity, Resources for the Future, Washington, DC. 
49 

Reasonable arguments support the use of a 3 percent discount rate. First, that rate is 

among the two figures suggested by OMB guidance, and hence it fits with existing national 

policy. Second, it is standard to base the discount rate on the compensation that people receive 

for delaying consumption, and the 3 percent is close to the risk-free rate of return, proxied by the 

return on long term inflation-adjusted U.S. Treasury Bonds, as of this writing. Although these 

rates are currently closer to 2.5 percent, the use of 3 percent provides an adjustment for the 

liquidity premium that is reflected in these bonds’ returns. However, this approach does not 

adjust for the significantly longer time horizon associated with climate change impacts. It also 

could be argued that the appropriate interest rate should be lower than 3 percent if the benefits of 

climate mitigation policies tend to be higher than expected in time periods when the returns to 

investments in rest of the economy are lower than normal. 

At the same time, others would argue that a 5 percent discount rate can be supported. The 

argument relies on several assumptions. First, this rate can be justified by reference to the level 

of compensation for delaying consumption, because it fits with market behavior with respect to 

individuals’ willingness to trade-off consumption across periods as measured by the estimated 

post-tax average real returns to risky private investments (e.g., the S&P 500). In the climate 

setting, the 5 percent discount rate may be preferable to the riskless rate because the benefits to 

mitigation are not known with certainty. In principal, the correct discount rate would reflect the 

variance in payoff from climate mitigation policy and the correlation between the payoffs of the 

policy and the broader economy.HHH

Second, 5 percent, and not 3 percent, is roughly consistent with estimates implied by 

inputs to the theoretically derived Ramsey equation presented below, which specifies the optimal 

time path for consumption. That equation specifies the optimal discount rate as the sum of two 

components. The first term (the product of the elasticity of the marginal utility of consumption 

and the growth rate of consumption) reflects the fact that consumption in the future is likely to be 

higher than consumption today, so diminishing marginal utility implies that the same monetary 

damage will cause a smaller reduction of utility in the future. Standard estimates of this term 

from the economics literature are in the range of 3 percent-5 percent.III The second component 

reflects the possibility that a lower weight should be placed on utility in the future, to account for 

social impatience or extinction risk, which is specified by a pure rate of time preference (PRTP). 

A common estimate of the PRTP is 2 percent, though some observers believe that a principle of 

intergenerational equity suggests that the PRTP should be close to zero. It follows that discount 

 

HHH Specifically, if the benefits of the policy are highly correlated with the returns from the broader economy, then 

the market rate should be used to discount the benefits. If the benefits are uncorrelated with the broader economy the 

long term government bond rate should be applied. Furthermore, if the benefits are negatively correlated with the 

broader economy, a rate less than that on long term government bonds should be used (Lind, 1982 pp. 89-90). 

III For example, see: Arrow KJ, Cline WR, Maler K–G, Munasinghe M, Squitieri R, Stiglitz JE. 1996. Intertemporal 

equity, discounting, and economic efficiency. Chapter 4 in Economic and Social Dimensions of Climate Change: 

Contribution of Working Group III to the Second Assessment Report, Summary for Policy Makers. Cambridge: 

Cambridge University Press; Dasgupta P. 2008. Discounting climate change. Journal of Risk and Uncertainty 

37:141–169; Hoel M, Sterner T. 2007. Discounting and relative prices. Climatic Change 84:265–280; Nordhaus 

WD. 2008. A Question of Balance: Weighing the Options on Global Warming Policies. New Haven, CT: Yale 

University Press; Stern N. 2008. The economics of climate change. The American Economic Review 98(2):1–37.. 
50 

rate of 5 percent is near the middle of the range of values that are able to be derived from the 

Ramsey equation.JJJ 

It is recognized that the arguments above – for use of market behavior and the Ramsey 

equation – face objections in the context of climate change, and of course there are alternative 

approaches. In light of climate change, it is possible that consumption in the future will not be 

higher than consumption today, and if so, the Ramsey equation will suggest a lower figure. The 

historical evidence is consistent with rising consumption over time. KKK 

Some critics note that using observed interest rates for inter-generational decisions 

imposes current preferences on future generations, which some economists say may not be 

appropriate. For generational equity, they argue that the discount rate should be below market 

rates to correct for market distortions and inefficiencies in intergenerational transfers of wealth 

(which are presumed to compensate future generations for damage), and to treat generations 

equitably based on ethical principles (see Broome 2008).LLL 

Additionally, some analyses attempt to deal with uncertainty with respect to interest rates 

over time. We explore below how this might be done.MMM 

5.3.2.6 Interim Social Cost of Carbon Estimates 

The application of the methodology outlined above yields interim estimates of the SCC 

that are reported in Table 5.3.2.6-1. These estimates are reported separately using 3 er cent and 5 

percent discount rates. The cells are empty in rows 10 and 11, because these studies did not 

report estimates of the SCC at a 3 percent discount rate. The model-weighted means are reported 

in the final or summary row; they are $34 per t-CO2 at a 3% discount rate and $5 per t-CO2 with 

a 5 percent discount rate. 

 

JJJ Sterner and Persson (2008) note that a consistent treatment of the marginal utility of consumption would require 

that if higher discount rates are justified by the diminishing marginal utility of consumption, e.g., a dollar of 

damages is worth less to future generations because they have greater income, then so-called equity weights should 

be used to account for the higher value that countries with lower income would place on a dollar of damages relative 

to the U.S. This is a consistent and logical outcome of application of the Ramsey framework. Because the 

distribution of climate change related damages is expected to be skewed towards developing nations with lower 

incomes, this can have significant implications for estimates of total global SCC if the Ramsey framework is used to 

derive discount rates. 

KKKHowever, because climate change impacts may be outside the bounds of historical evidence, predictions about 

future growth in consumption based on past experience may be inaccurate. 

LLL See Arrow, K.J., W.R. Cline, K-G Maler, M. Munasinghe, R. Squiteri, J.E.Stiglitz, 1996. "Intertemporal equity, 

discounting and economic efficiency," in Climate Change 1995: Economic and Social Dimensions of Climate 

Change, Contribution of Working Group III to the Second Assessment Report of the Intergovernmental Panel on 

Climate Change. See also Weitzman, M.L., 1999, in Portney P.R. and Weyant J.P. (eds.), Discounting and 

Intergenerational Equity, Resources for the Future, Washington, D.C. 

MMM Richard Newell and William Pizer, Discounting the distant future: how much do uncertain rates increase 

valuations? J. Environ. Econ. Manage. 46 (2003) 52-71. 
51 

Table 5.3.2.6-1. 

Global Social Cost of Carbon (SCC) Estimates ($/t-CO2 in 2007 (2007$)), Based on 3 

Percent and 5 Percent Discount Rates * 

 Model Study Climate Scenario 3% 5% 

1 FUND Anthoff et al. 2009 FUND default 6 -1 

2 FUND Anthoff et al. 2009 SRES A1b 1 -1 

3 FUND Anthoff et al. 2009 SRES A2 9 -1 

4 FUND Link and Tol 2004 No THC 12 3 

5 FUND Link and Tol 2004 THC continues 12 2 

6 FUND Guo et al. 2006 Constant PRTP 5 -1 

7 FUND Guo et al. 2006 Gollier discount 1 14 0 

8 FUND Guo et al. 2006 Gollier discount 2 7 -1 

 FUND Mean 8.47 0 

9 PAGE Wahba & Hope 2006 A2-scen 59 7 

10 PAGE Hope 2006 7 

11 DICE Nordhaus 2008 8 

Summary Model-weighted Mean 34 5 

*The sample includes all peer reviewed, non-equity-weighted estimates included in Tol (2008), Nordhaus (2008), 

Hope (2008), and Anthoff et al. (2009), that are based on the most recent published version of FUND, PAGE, or 

DICE and use business-as-usual climate scenarios.NNNOOO All values are based on the best available information 

from the underlying studies about the base year and year dollars, rather than the Tol (2008) assumption that all 

estimates included in his review are 1995 values in 1995$. All values were updated to 2007 using a 3% annual 

growth rate in the SCC, and adjusted for inflation using GDP deflator. 

In today’s rule, benefits of reducing GHG emissions have been estimated using global 

SCC values of $34 and $5 as these represent the estimates associated with the 3 percent and 5 

percent discount rates, respectively.PPP The 3 percent and 5 percent estimates have independent 

 

NNN Most of the estimates in Table 1 rely on climate scenarios developed by the Intergovernmental Panel on Climate 

Change (IPCC). The IPCC published a new set of scenarios in 2000 for use in the Third Assessment Report (Special 

Report on Emissions Scenarios - SRES). The SRES scenarios define four narrative storylines: A1, A2, B1 and B2, 

describing the relationships between the forces driving greenhouse gas and aerosol emissions and their evolution 

during the 21st century for large world regions and globally. Each storyline represents different demographic, social, 

economic, technological, and environmental developments that diverge in increasingly irreversible ways. The 

storylines are summarized in Nakicenovic et al., 2000 (see also http://sedac.ciesin.columbia.edu/ddc/sres/ ). 

Although they were intended to represent BAU scenarios, at this point in time the B1 and B2 storylines are widely 

viewed as representing policy cases rather than business-as-usual projections, estimates derived from these scenarios 

to be less appropriate for use in benefit-cost analysis. They are therefore excluded. 

OOO Guo et al. (2006) report estimates based on two Gollier discounting schemes. The Gollier discounting assumes 

complex specifications about individual utility functions and risk preferences. After various conditions are satisfied, 

declining social discount rates emerge. Gollier Discounting Scheme 1 employs a certainty-equivalent social rate of 

time preference (SRTP) derived by assuming the regional growth rate is equally likely to be 1% above or below the 

original forecast growth rate. Gollier Discounting Scheme 2 calculates a certainty-equivalent social rate of time 

preference (SRTP) using five possible growth rates, and applies the new SRTP instead of the original. Hope (2008)

conducts Monte Carlo analysis on the PRTP component of the discount rate. The PRTP is modeled as a triangular 

distribution with a min value of 1%/yr, a most likely value of 2 %/yr, and a max value of 3 %/yr. 

PPP It should be noted that reported discount rates may not be consistently derived across models or specific 

applications of models: while the discount rate may be identical, it may reflect different assumptions about the 

individual components of the Ramsey equation identified earlier. 
52 

appeal and at this time a clear preference for one over the other is not warranted. Thus, we have 

also included – and centered our current attention on – the average of the estimates associated 

with these discount rates, which is $20. (Based on the $20 global value, the approximate 

domestic fraction of these benefits would be $1.20 per metric ton of CO2 assuming that domestic 

benefits are 6% of the global benefits. 

The distinctions between sets of estimates generated using different discount rates are due 

only in part to discount rate differences, because the models and parameters used to generate the 

estimates in the sets associated with different discount rates also vary. 

It is true that there is uncertainty about interest rates over long time horizons. 

Recognizing that point, Newell and Pizer (2003) have made a careful effort to adjust for that 

uncertainty. The Newell-Pizer approach models discount rate uncertainty as something that 

evolves over time.QQQ This is a different way to model discount rate uncertainty than the 

approach outlined above, which assumes there is a single discount rate with equal probability of 

3 percent and 5 percent. 

 

Table 5.3.2.6-2 reports on the application of the Newell-Pizer adjustments. The precise 

numbers depend on the assumptions about the data generating process that governs interest rates. 

Columns (1a) and (1b) assume that “random walk” model best describes the data and uses 3 

percent and 5 percent discount rates, respectively. Columns (2a) and (2b) repeat this, except that 

it assumes a “mean-reverting” process. While the empirical evidence does not rule out a meanreverting model, Newell and Pizer find stronger empirical support for the random walk model. 

 

QQQ In contrast, an alternative approach based on Weitzman (2001) would assume that there is a constant discount 

rate that is uncertain and represented by a probability distribution. The Newell and Pizer, and Weitzman approaches 

are relatively recent contributions to the literature. . 
53 

Table 5.3.2.6-2. 

Global Social Cost of Carbon (SCC) Estimates ($ per metric ton CO2 in 2007 (2007$))*, 

Using Newell & Pizer (2003) Adjustment for Future Discount Rate Uncertainty** 

Randomwalk model Mean-reverting 

model 

 Model Study 

Climate 

Scenario 3% 5% 3% 5% 

 (1a) (1b) (2a) (2b) 

1 FUND Anthoff et al. 2009 FUND default 10 0 7 -1 

2 FUND Anthoff et al. 2009 SRES A1b 2 0 1 -1 

3 FUND Anthoff et al. 2009 SRES A2 15 0 10 -1 

4 FUND Link and Tol 2004 No THC 21 6 13 4 

5 FUND Link and Tol 2004 THC continues 21 4 13 2 

6 FUND Guo et al. 2006 Constant PRTP 9 0 6 -1 

7 FUND Guo et al. 2006 Gollier discount 1 14 0 14 0 

8 FUND Guo et al. 2006 Gollier discount 2 7 -1 7 -1 

 FUND Mean 12 1 9 0 

9 PAGE 

Wahba & Hope 

2006 A2-scen 100 13 65 8 

10 PAGE Hope 2006 13 8 

11 DICE Nordhaus 2008 15 9 

Summary 

Model-weighted 

Mean 56 10 37 6 

*The sample includes all peer reviewed, non-equity-weighted estimates included in Tol (2008), Nordhaus (2008), 

Hope (2008), and Anthoff et al. (2009), that are based on the most recent published version of FUND, PAGE, or 

DICE and use business-as-usual climate scenarios. All values are based on the best available information from the 

underlying studies about the base year and year dollars, rather than the Tol (2008) assumption that all estimates 

included in his review are 1995 values in 1995$. All values were updated to 2007 using a 3% annual growth rate in 

the SCC, and adjusted for inflation using GDP deflator. See the Notes to Table 1 for further details. 

**Assumes a starting discount rate of 3% or 5%. Newell and Pizer (2003) based adjustment factors are not applied 

to estimates from Guo et al. (2006) that use a different approach to account for discount rate uncertainty (rows 7-8). 

Note that the correction factor from Newell and Pizer is based on the DICE model. The proper adjustment may 

differ for other integrated assessment models that produce different time schedules of marginal damages. We would 

expect this difference to be minor. 

The resulting estimates of the social cost of carbon are necessarily greater. When the 

adjustments from the random walk model are applied, the estimates of the social cost of carbon 

are $10 and $56 per ton of CO2, with the 5 percent and 3 percent discount rates, respectively. 

The application of the mean-reverting adjustment yields estimates of $6 and $37. Relying on the 

random walk model, analyses are also conducted with the value of the SCC set at $10 and $56. 

5.3.2.7 Caveats 

There are at least four caveats to the approach outlined above. 

First, the impacts of climate change are expected to be widespread, diverse, and 

heterogeneous. In addition, the exact magnitude of these impacts is uncertain, because of the 

inherent randomness in the Earth’s atmospheric processes, the U.S. and global economies, and 
54 

the behaviors of current and future populations. The existing IAMs do not currently individually 

account for and assign value to all of the important physical and other impacts of climate change 

that are recognized in the climate change literature.RRR Therefore, as noted by the IPCC, SCC 

estimates are “very likely” underestimated.SSS In addition, the SCC approach also likely 

underestimates the value of GHG reductions because the marginal values apply only to CO2

emissions, which have different impacts than non-CO2 emissions because of variances in 

atmospheric lifetimes and radiative forcing.TTT Although it is likely that our capability to 

quantify and monetize impacts will improve with time, it is also likely that even in future 

applications, a number of potentially significant benefits categories will remain unmonetized. In 

order to more fully characterize of benefits of mitigation these non-monetized benefits should be 

discussed along with monetized benefits based on the SCC. 

Second, in the opposite direction, it is unlikely that the damage estimates adequately 

account for the directed technological change that climate change will cause. In particular, 

climate change will increase the return on investment to develop technologies that allow 

individuals to better cope with climate change. For example, it is likely that scientists will 

develop crops that are better able to withstand high temperatures. In this respect, the current 

estimates may overstate the likely quantified damages, though the costs associated with the 

investments in adaptive technologies must also be considered (technologies must also be 

included in the calculations, as the benefits should reflect net welfare changes to society). 

Third, there has been considerable recent discussion of the risk of catastrophic impacts 

and of how best to account for worst-case scenarios. Recent research by Weitzman (2009) 

specifies some conditions under which the possibility of catastrophe would undermine the use of 

IAMs and conventional cost-benefit analysis. This research requires further exploration before its 

generality is known and the proper way to incorporate it into regulatory reviews is understood. 

Fourth, it is also worth noting that the SCC estimates are only relevant for incremental 

policies relative to the projected baselines, which capture business-as-usual scenarios. To 

evaluate non-marginal changes, such as might occur if the U.S. acts in tandem with other 

nations, then it might be necessary to go beyond the simple expedient of using the SCC along the 

BAU path. This would require explicitly calculating the total benefits in a move from the BAU 

scenario to the policy scenario, without imposing the restriction that the marginal benefit remains 

constant over this range. 

5.3.2.8 Other Options 

 

RRRExamples of impacts that are difficult to monetize, and have generally not been included in SCC estimates, 

include risks from extreme weather (death, disease, agricultural damage, and other economic damage from droughts, 

floods and wildfires) and possible long-term catastrophic events, such as collapse of the West Antarctic ice sheet or 

the release of large amounts of methane from melting permafrost. 

SSS IPCC WGII. 2007. Climate Change 2007 - Impacts, Adaptation and Vulnerability Contribution of Working 

Group II to the Fourth Assessment Report of the IPCC. TTTRadiative forcing is the change in the balance between solar radiation entering the atmosphere and the Earth's 

radiation going out. On average, a positive radiative forcing tends to warm the surface of the Earth while negative 

forcing tends to cool the surface. Greenhouse gases have a positive radiative forcing because they absorb and emit 

heat. See http://www.epa.gov/climatechange/science/recentac.html for more general information about GHGs and 

climate science. 
55 

The Administration considered other interim SCC options in addition to the approach 

described above. Similar to May 2009 RFS2 Propoal, one alternative option was to bring in SCC 

estimates in studies published after 1995, rather than limiting the estimates to those in studies 

relying on the most recent published version of each of the three major integrated assessment 

models: PAGE, FUND, and DICE. Although some older model versions (and old versions of 

other models) were used to produce estimates between 1996 and 2001, there have been no 

significant modeling paradigm changes since 1996. Rather, improvements to PAGE, FUND, and 

DICE since 1996 have reflected incremental technical enhancements. 

Another option was to select a range of SCC values for separate discount rates. For 

example, sensitivity analysis could be conducted at the lowest and highest SCC values reported 

in the filtered set of estimates for each discount rate considered. If considering SCC estimates 

from studies published after 1995 and a discount rate of 2 percent, this option would result in a 

range of SCC values of $5/t-CO2 to $260/t-CO2 (2007 emissions in 2007 dollars); at a 3 percent 

discount rate, the range would be $0 to $58/ t-CO2. 

Finally, we considered that the use of certain key assumptions under the Ramsey 

framework, such as placing approximately equal weight on the welfare of current and future 

generations, would imply use of a 2% discount rate. The Newell and Pizer (2003) method 

applied to recent long-term risk free rates would likewise be approximately consistent with a rate 

of 2 percent.UUU 

5.3.2.9 Ongoing SCC Development 

As noted, this is an emphatically interim SCC value. The judgments herein will be 

subject to further scrutiny and exploration. 

5.3.3 Application of Interim SCC Estimates to GHG Emissions Reductions from this 

Final Rule 

While no single rule or action can independently achieve the deep worldwide emissions 

reductions necessary to halt and reverse the growth of GHGs, the combined effects of multiple 

strategies to reduce GHG emissions domestically and abroad could make a major difference in 

the climate change impacts experienced by future generations.VVV The projected net GHG 

emissions reductions associated with this final rule reflect an incremental change to projected 

total global emissions. Given that the climate response is projected to be a marginal change 

relative to the baseline climate, we estimate the marginal value of changes in climate change 

impacts over time and use this value to measure the monetized marginal benefits of the GHG 

emissions reductions projected for this rule. 

 

UUU Specifically, Newell and Pizer (2003) found that modeling of uncertainty in economic growth causes the 

effective discount rate to decline over time. When starting at a 4% discount rate, the effective discount rate is 2% at 

100 years and 1% at 200 years. 

VVV The Supreme Court recognized in Massachusetts v. EPA that a single action will not on its own achieve all 

needed GHG reductions, noting that “[a]gencies, like legislatures, do not generally resolve massive problems in one 

fell regulatory swoop.’’ See Massachusetts v. EPA, 549 U.S. at 524 (2007). 
56 

Accordingly, EPA has used the set of interim, global SCC values described above to 

estimate the benefits resulting from the renewable fuel volumes mandated by EISA. The interim 

SCC values, which reflect the Administration’s interim interpretation of the current literature, are 

$5, $10, $20, $34, and $56, in 2007 dollars, and are based on a CO2 emissions change of 1 metric 

ton in 2007. Table 5.3.3-1 presents the interim SCC values for the years 2007 and 2022 in 2007 

dollars. 

Table 5.3.3-1. Interim SCC Schedule (2007$ per metric tonne of CO2)

Year 5% 5% (NewellPizer)* Average SCC 

from 3% and 5% 

3% 3% (NewellPizer)* 

2007 $5 $10 $20 $34 $56 

2022 $8 $16 $30 $53 $88 

Note: The SCC values are dollar-year and emissions-year specific. These values are presented in 2007$, for 

individual year of emissions. To determine values for years not presented in the table, use a 3% growth rate. SCC 

values represent only a partial accounting for climate impacts. 

*SCC values are adjusted based on Newell and Pizer (2003) to account to future uncertainty in discount rates. 

Tables 5.3.3-2 through 5.3.3-4 provide, for the high, base, and low cases, the average 

annual GHG emissions reductions in 2022. The annualized emissions reductions are multiplied 

by the SCC estimates for 2022 from Table 5.3.3-1 to produce the average annual monetized 

benefit from the emissions reductions from the rule for CO2-equivalent GHGs. 

 

Table 5.3.3-2. 

Average Annual Emissions Reduction (Million Metric Tonnes CO2-e) and Monetized 

Benefits (Million 2007$) of RFS-2 Volumes in 2022, High Case 

 CO2 Non-CO2 GHG Total GHG 

Emissions 

Reductions 

148.525 -8.234 140.291 

5% $1,188 -$66 $1,122 

5% (Newell-Pizer) $2,376 -$132 $2,245 

Average SCC from 

3% and 5% $4,515 -$250 $4,265 

3% $7,842 -$435 $7,407 

3% (Newell-Pizer) $13,069 -$725 $12,344 
57 

Table 5.3.3-3. 

Average Annual Emissions Reduction (Million Metric Tonnes CO2-e) and Monetized 

Benefits (Million 2007$) of RFS-2 Volumes in 2022, Base Case 

 CO2 Non-CO2 GHG Total GHG 

Emissions 

Reductions 

146.645 -8.234 138.411 

5% $1,173 -$66 $1,107 

5% (Newell-Pizer) $2,346 -$132 $2,215 

Average SCC from 

3% and 5% $4,458 -$250 $4,208 

3% $7,743 -$435 $7,308 

3% (Newell-Pizer) $12,903 -$725 $12,179 

Table 5.3.3-4. 

Average Annual Emissions Reduction (Million Metric Tonnes CO2-e) and Monetized 

Benefits (Million 2007$) of RFS-2 Volumes in 2022, Low Case 

 CO2 Non-CO2 GHG Total GHG 

Emissions 

Reductions 

144.338 -8.234 136.104 

5% $1,155 -$66 $1,089 

5% (Newell-Pizer) $2,309 -$132 $2,178 

Average SCC from 

3% and 5% $4,388 -$250 $4,138 

3% $7,621 -$435 $7,186 

3% (Newell-Pizer) $12,700 -$725 $11,976 

5.4 Quantified and Monetized Co-pollutant Health and Environmental 

Impacts 

5.4.1 Overview 

This section describes EPA’s analysis of the co-pollutant health and environmental 

impacts that can be expected to occur as a result of the increase in renewable fuel use throughout 

the period from initial implementation through 2022. Although the purpose of this final rule is to 

implement the renewable fuel requirements established by the Energy Independence and 

Security Act (EISA) of 2007, the increased use of renewable fuels will also impact emissions of 

criteria and air toxic pollutants and their resultant ambient concentrations. The fuels changes 

detailed in Section 3.1 of the RIA will influence emissions of VOCs, PM, NOX, and SOX and air 

toxics and affect exhaust and evaporative emissions of these pollutants from vehicles and 

equipment. They will also affect emissions from upstream sources such as fuel production, 

storage, distribution and agricultural emissions. Any decrease or increase in ambient ozone, 

PM2.5, and air toxics associated with the increased use of renewable fuels will impact human 
58 

health in the form of a decrease or increase in the risk of incurring premature death and other 

serious human health effects, as well as other important public health and welfare effects. 

This analysis reflects the impact of the 2022 volumes compared with two different 

reference scenarios that include the use of renewable fuels: a 2022 baseline projection based on 

the RFS1-mandated volume of 7.5 billion gallons of renewable fuels, and a 2022 baseline 

projection based on the AEO 2007 volume of roughly 13.6 billion gallons of renewable fuels. 

Thus, the results represent the impact of an incremental increase in ethanol and other renewable 

fuels. We note that the air quality modeling results presented in this final rule do not constitute 

the “anti-backsliding” analysis required by Clean Air Act section 211(v). EPA will be analyzing 

air quality and health impacts of increased renewable fuel use through that study and will 

promulgate appropriate mitigation measures under section 211(v), separate from this final action. 

As can be seen in Section 3.4 of this RIA, there are both increased and decreased 

concentrations of ambient criteria pollutants and air toxics. Overall, we estimate that the final 

rule will lead to net criteria pollutant-related disbenefits. By 2022, the final RFS2 rule volumes 

relative to both reference case scenarios (RFS1 and AEO2007), are projected to adversely impact 

PM2.5 air quality over parts of the U.S., while some areas will experience decreases in ambient 

PM2.5. As described in Section 3.4, ambient PM2.5 is likely to increase as a result of emissions at 

biofuel production plants and from biofuel transport, both of which are more prevalent in the 

Midwest. PM concentrations are also likely to decrease in some areas. While the PM-related air 

quality impacts are relatively small, the impact of the change in national average PM2.5 exposure 

on human health results in net disbenefits (the increase in national population weighted annual 

average PM2.5 is 0.006 μg/m3 and 0.002 μg/m3 relative to the RFS1 and AEO2007 reference 

cases, respectively). 

The required renewable fuel volumes, relative to both reference scenarios, are also 

projected to adversely impact ozone air quality over much of the U.S., especially in the Midwest, 

Northeast and Southeast. These adverse impacts are likely due to increased upstream emissions 

of NOx in many areas that are NOx-limited (acting as a precursor to ozone formation). There 

are, however, ozone air quality improvements in some highly-populated areas that currently have 

poor air quality. This is likely due to VOC emission reductions at the tailpipe in urban areas that 

are VOC-limited (reducing VOC’s role as a precursor to ozone formation). Relative to the RFS1 

mandate reference case, the RFS2 volumes result in a small ozone-related disbenefit (population 

weighted maximum 8-hour average ozone increases by 0.177 ppb). Relative to the AEO2007 

reference case, the RFS2 volumes result in small ozone-related disbenefits (population weighted 

maximum 8-hour average ozone increases by 0.116 ppb). 

The analysis of national-level PM2.5- and ozone-related health and environmental impacts 

associated with the final rule is based on peer-reviewed studies of air quality and human health 

effects (see US EPA, 2006 and US EPA, 2008).1,2 We are also consistent with the benefits 

analysis methods that supported the recently proposed Portland Cement National Emissions 

Standards for Hazardous Air Pollutants (NESHAP) RIA (U.S. EPA, 2009a),3 the proposed NO2

primary NAAQS RIA (U.S. EPA, 2009b),4 and the proposed Category 3 Marine Diesel Engines 

RIA (U.S. EPA, 2009c).5

 To model the ozone and PM air quality impacts of the 2022 renewable 

fuel volumes, we used the Community Multiscale Air Quality (CMAQ) model (see Chapter 3.4). 
59 

The modeled ambient air quality data serves as an input to the Environmental Benefits Mapping 

and Analysis Program (BenMAP).WWW BenMAP is a computer program developed by the U.S. 

EPA that integrates a number of the modeling elements used in previous analyses (e.g., 

interpolation functions, population projections, health impact functions, valuation functions, 

analysis and pooling methods) to translate modeled air concentration estimates into health effects 

incidence estimates and monetized benefits estimates. 

Emissions and air quality modeling decisions were made early in the analytical process 

and as a result, there are a number of important limitations and uncertainties associated with the 

air quality modeling analysis that must be kept in mind when considering the results. A key 

limitation of the analysis is that it employed interim emission inventories, which were enhanced 

compared to what was described in the proposal, but did not include some of the later 

enhancements and corrections of the final emission inventories presented in this FRM (see 

Section 3.3 of this RIA). Most significantly, our modeling of the air quality impacts of RFS2 

relied upon interim inventories that assumed that ethanol will make up 34 of the 36 billion gallon 

renewable fuel mandate, that approximately 20 billion gallons of this ethanol will be in the form 

of E85, and that the use of E85 results in fewer emissions of direct PM2.5 from vehicles. The 

emission impacts, air quality results and benefits analysis would be different if, instead of E85, 

more non-ethanol biofuels are used or mid-level ethanol blends are approved and utilized. 

In fact, as explained in Chapter 1, our more recent analyses indicate that ethanol and E85 

volumes are likely to be significantly lower than what we assumed in the interim inventories. 

Furthermore, the final emission inventories do not include vehicle-related PM reductions 

associated with E85 use, as discussed in Chapters 3.1-3.3 of this RIA. There are additional, 

important limitations and uncertainties associated with the interim inventories that must be kept 

in mind when considering the results, which are described in more detail in Chapter 3.4. While it 

is difficult to describe the overall impact of these limitations and uncertainties on the quantified 

and monetized health impacts of the increased renewable fuel volumes without updating the air 

quality modeling analysis, we believe the results are still useful for describing potential nationallevel health impacts. 

Additionally, after the air quality modeling was completed, we discovered an error in the 

way that PM2.5 emissions from locomotive engines were allocated to counties in the inventory. 

The mismatched allocations between the reference and control scenarios resulted in PM2.5

emission changes that were too high in some counties and too low in others, by varying degrees. 

As a result, we did not present the modeling results for specific localized PM2.5 impacts in 

Section 3.4. However, because the error was random and offsetting, there was very little impact 

on national-level PM2.5 emissions. An analysis of the error's impact on the national emission 

inventories found that direct PM2.5 emissions were inflated by 8% relative to the AEO reference 

case and by 0.6% relative to the RFS1 reference case, leading to a small overestimation of 

national PM-related disbenefits. Note that this error did not impact other PM precursor 

inventories such as NOx and SO2. As a result, we have concluded that PM2.5 modeling results 

are still informative for national-level benefits assessment, particularly given that other 

uncertainties in the PM2.5 inventory (such as E85 usage, discussed below) have a more important 

(and offsetting) effect. 

 

WWW Information on BenMAP, including downloads of the software, can be found at http://www.epa.gov/ttn/ecas/ 

benmodels.html. 
60 

Table 5.4-1. 

Estimated 2022 Monetized PM-and Ozone-Related Health Impacts 

from the Mandated Renewable Fuel Volumesa

2022 Total Ozone and PM Benefits, RFS2 Compared to RFS1 Reference Casea

Premature Ozone 

Mortality Function 

Reference Total Benefits 

(Billions, 2007$, 3% 

Discount Rate)b,c 

Total Benefits 

(Billions, 2007$, 7% 

Discount Rate) b,c

Bell et al., 2004 Total: -$1.4 to -$2.8 

PM: -$0.92 to -$2.3 

Ozone: -$0.52 

Total: -$1.4 to -$2.6 

PM: -$0.84 to -$2.0 

Ozone: -$0.52 

Huang et al., 2005 Total: -$1.8 to -$3.1 

PM: -$0.92 to -$2.3 

Ozone: -$0.83 

Total: -$1.7 to -$2.9 

PM: -$0.84 to -$2.0 

Ozone: -$0.83 

Multi-city analyses 

Schwartz, 2005 Total: -$1.7 to -$3.0 

PM: -$0.92 to -$2.3 

Ozone: -$0.77 

Total: -$1.6 to -$2.8 

PM: -$0.84 to -$2.0 

Ozone: -$0.77 

Bell et al., 2005 Total: -$2.5 to -$3.8 

PM: -$0.92 to -$2.3 

Ozone: -$1.6 

Total: -$2.4 to -$3.6 

PM: -$0.84 to -$2.0 

Ozone: -$1.6 

Ito et al., 2005 Total: -$3.1 to -$4.5 

PM: -$0.92 to -$2.3 

Ozone: -$2.2 

Total: -$3.0 to -$4.2 

PM: -$0.84 to -$2.0 

Ozone: -$2.2 

Meta-analyses 

Levy et al., 2005 Total: -$3.1 to -$4.5 

PM: -$0.92 to -$2.3 

Ozone: -$2.2 

Total: -$3.1 to -$4.3 

PM: -$0.84 to -$2.0 

Ozone: -$2.2 

2022 Total Ozone and PM Benefits, RFS2 Compared to AEO Reference Casea

Premature Ozone 

Mortality Function 

Reference Total Benefits 

(Millions, 2007$, 3% 

Discount Rate)b,c 

Total Benefits 

(Millions, 2007$, 7% 

Discount Rate) b,c

Bell et al., 2004 Total: -$0.63 to -$1.0 

PM: -$0.29 to -$0.70 

Ozone: -$0.34 

Total: -$0.60 to -$0.98 

PM: -$0.26 to -$0.63 

Ozone: -$0.34 

Huang et al., 2005 Total: -$0.84 to -$1.3 

PM: -$0.29 to -$0.70 

Ozone: -$0.55 

Total: -$0.81 to -$1.2 

PM: -$0.26 to -$0.63 

Ozone: -$0.55 

Multi-city analyses 

Schwartz, 2005 Total: -$0.80 to -$1.2 

PM: -$0.29 to -$0.70 

Ozone: -$0.51 

Total: -$0.77 to -$1.1 

PM: -$0.26 to -$0.63 

Ozone: -$0.51 

Bell et al., 2005 Total: -$1.3 to -$1.8 

PM: -$0.29 to -$0.70 

Ozone: -$1.0 

Total: -$1.3 to -$1.7 

PM: -$0.26 to -$0.63 

Ozone: -$1.0 

Ito et al., 2005 Total: -$1.7 to -$2.2 

PM: -$0.29 to -$0.70 

Ozone: -$1.5 

Total: -$1.7 to -$2.1 

PM: -$0.26 to -$0.63 

Ozone: -$1.5 

Meta-analyses 

Levy et al., 2005 Total: -$1.8 to -$2.2 

PM: -$0.29 to -$0.70 

Ozone: -$1.5 

Total: -$1.7 to -$2.1 

PM: -$0.26 to -$0.63 

Ozone: -$1.5 a Total includes premature mortality-related and morbidity-related ozone and PM2.5 benefits. Range was developed 

by adding the estimate from the ozone premature mortality function to the estimate of PM2.5-related premature 

mortality derived from either the ACS study (Pope et al., 2002) or the Six-Cities study (Laden et al., 2006).

b

 Note that total benefits presented here do not include a number of unquantified benefits categories. A detailed 

listing of unquantified health and welfare effects is provided in Table 5.4-2. 
61 

c

 Results reflect the use of both a 3 and 7 percent discount rate, as recommended by EPA’s Guidelines for Preparing 

Economic Analyses and OMB Circular A-4. Results are rounded to two significant digits for ease of presentation 

and computation. 

The monetized estimates in Table 5.4-1 include all of the human health impacts we are 

able to quantify and monetize at this time. However, the full complement of human health and 

welfare effects associated with PM and ozone remain unquantified because of current limitations 

in methods or available data. We have not quantified a number of known or suspected health 

effects linked with ozone and PM for which appropriate health impact functions are not available 

or which do not provide easily interpretable outcomes (i.e., changes in heart rate variability). 

Additionally, we are unable to quantify a number of known welfare effects, including acid and 

particulate deposition damage to cultural monuments and other materials, and environmental 

impacts of eutrophication in coastal areas. These are listed in Table 5.4-2. 
62 

Table 5.4-2. 

Unquantified and Non-Monetized Potential Effects from the 

Mandated Renewable Fuel Volumes 

POLLUTANT/EFFECTS EFFECTS NOT INCLUDED IN ANALYSIS - CHANGES IN: 

Ozone Healtha Chronic respiratory damageb 

Premature aging of the lungsb 

Non-asthma respiratory emergency room visits 

Exposure to UVb (+/-)e

Ozone Welfare Yields for 

-commercial forests 

-some fruits and vegetables 

-non-commercial crops 

Damage to urban ornamental plants 

Impacts on recreational demand from damaged forest aesthetics 

Ecosystem functions 

Exposure to UVb (+/-)e 

PM Healthc Premature mortality - short term exposuresd

Low birth weight 

Pulmonary function 

Chronic respiratory diseases other than chronic bronchitis 

Non-asthma respiratory emergency room visits 

Exposure to UVb (+/-)e 

PM Welfare Residential and recreational visibility in non-Class I areas 

Soiling and materials damage 

Damage to ecosystem functions 

Exposure to UVb (+/-)e 

Nitrogen and Sulfate 

Deposition Welfare 

Commercial forests due to acidic sulfate and nitrate deposition 

Commercial freshwater fishing due to acidic deposition 

Recreation in terrestrial ecosystems due to acidic deposition 

Existence values for currently healthy ecosystems 

Commercial fishing, agriculture, and forests due to nitrogen deposition 

Recreation in estuarine ecosystems due to nitrogen deposition 

Ecosystem functions 

Passive fertilization 

CO Health Behavioral effects 

HC/Toxics Healthf Cancer (benzene, 1,3-butadiene, formaldehyde, acetaldehyde) 

Anemia (benzene) 

Disruption of production of blood components (benzene) 

Reduction in the number of blood platelets (benzene) 

Excessive bone marrow formation (benzene) 

Depression of lymphocyte counts (benzene) 

Reproductive and developmental effects (1,3-butadiene) 

Irritation of eyes and mucus membranes (formaldehyde) 

Respiratory irritation (formaldehyde) 

Asthma attacks in asthmatics (formaldehyde) 

Asthma-like symptoms in non-asthmatics (formaldehyde) 

Irritation of the eyes, skin, and respiratory tract (acetaldehyde) 

Upper respiratory tract irritation and congestion (acrolein) 

HC/Toxics Welfare Direct toxic effects to animals 

Bioaccumulation in the food chain 

Damage to ecosystem function 

Odor 
63 

a

 The public health impact of biological responses such as increased airway responsiveness to stimuli, inflammation 

in the lung, acute inflammation and respiratory cell damage, and increased susceptibility to respiratory infection are 

likely partially represented by our quantified endpoints. 

b

 The public health impact of effects such as chronic respiratory damage and premature aging of the lungs may be 

partially represented by quantified endpoints such as hospital admissions or premature mortality, but a number of 

other related health impacts, such as doctor visits and decreased athletic performance, remain unquantified. 

c

 In addition to primary economic endpoints, there are a number of biological responses that have been associated 

with PM health effects including morphological changes and altered host defense mechanisms. The public health 

impact of these biological responses may be partly represented by our quantified endpoints. 

d

 While some of the effects of short-term exposures are likely to be captured in the estimates, there may be 

premature mortality due to short-term exposure to PM not captured in the cohort studies used in this analysis. 

However, the PM mortality results derived from the expert elicitation do take into account premature mortality 

effects of short term exposures. 

e

 May result in benefits or disbenefits. 

f

 Many of the key hydrocarbons related to this rule are also hazardous air pollutants listed in the Clean Air Act. 

While there will be impacts associated with air toxic pollutant emission changes that 

result from the increased use of renewable fuels, we do not attempt to monetize those impacts. 

This is primarily because currently available tools and methods to assess air toxics risk from 

mobile sources at the national scale are not adequate for extrapolation to incidence estimations or 

benefits assessment. The best suite of tools and methods currently available for assessment at 

the national scale are those used in the National-Scale Air Toxics Assessment (NATA). The EPA 

Science Advisory Board specifically commented in their review of the 1996 NATA that these 

tools were not yet ready for use in a national-scale benefits analysis, because they did not 

consider the full distribution of exposure and risk, or address sub-chronic health effects.XXX 

While EPA has since improved the tools, there remain critical limitations for estimating 

incidence and assessing benefits of reducing mobile source air toxics. EPA continues to work to 

address these limitations; however, we did not have the methods and tools available for nationalscale application in time for the analysis of the final rule.YYY 

5.4.2 Quantified Human Health Impacts 

Tables 5.4-3 and 5.4-4 present the annual PM2.5 and ozone health impacts in the 48 

contiguous U.S. states associated with the RFS2 volumes relative to both the RFS1 and AEO 

reference cases for 2022. For each endpoint presented in Tables 5.4-3 and 5.4-4, we provide 

both the mean estimate and the 90% confidence interval. 

Using EPA’s preferred estimates, based on the ACS and Six-Cities studies and no 

threshold assumption in the model of mortality, we estimate that the RFS2 volumes will result in 

 

XXX Science Advisory Board. 2001. NATA – Evaluating the National-Scale Air Toxics Assessment for 1996 – an 

SAB Advisory. http://www.epa.gov/ttn/atw/sab/sabrev.html. 

YYY In April, 2009, EPA hosted a workshop on estimating the benefits or reducing hazardous air pollutants. This 

workshop built upon the work accomplished in the June 2000 Science Advisory Board/EPA Workshop on the 

Benefits of Reductions in Exposure to Hazardous Air Pollutants, which generated thoughtful discussion on 

approaches to estimating human health benefits from reductions in air toxics exposure, but no consensus was 

reached on methods that could be implemented in the near term for a broad selection of air toxics. Please visit 

http://epa.gov/air/toxicair/2009workshop.html for more information about the workshop and its associated materials. 
64 

between 110 and 270 cases of PM2.5-related premature deaths annually in 2022 when compared 

to the RFS1 reference case. When compared to the AEO reference scenario, we estimate that the 

RFS2 volumes will result in between 33 and 85 cases of avoided PM2.5-related premature deaths 

annually in 2022. As a sensitivity analysis, when the range of expert opinion is used we estimate 

that in 2022 the RFS2 volumes will result in between 34 and 360 PM-related premature 

mortalities when compared to the RFS1 reference case and between 11 and 110 PM-related 

premature mortalities when compared to the AEO reference case. 

The range of ozone impacts associated with the RFS2 volumes is based on changes in 

risk estimated using several sources of ozone-related mortality effect estimates. This analysis 

presents six alternative estimates for the association based upon different functions reported in 

the scientific literature, derived from both multi-city studies, such as the National Morbidity, 

Mortality, and Air Pollution Study (NMMAPS) (Bell et al., 2004, Huang et al., 2005, and 

Schwartz et al., 2005) and from a series of recent meta-analyses (Bell et al., 2005, Ito et al., 

2005, and Levy et al., 2005). This approach is not inconsistent with recommendations provided 

by the NRC in their recent report (NRC, 2008) on the estimation of ozone-related mortality risk, 

“The committee recommends that the greatest emphasis be placed on estimates from new 

systematic multicity analyses that use national databases of air pollution and mortality, such as in 

the NMMAPS, without excluding consideration of meta-analyses of previously published 

studies.” For ozone-related premature mortality, we estimate that national changes in ambient 

ozone will contribute to between 54 to 250 additional premature mortalities in 2022 as a result of 

the RFS2 volumes relative to the RFS1 scenario. When compared to the AEO reference 

scenario, we estimate that the RFS2 volumes will contribute to between 36 to 160 additional 

ozone-related premature mortalities in 2022. 

Following these tables, we also provide a more comprehensive presentation of the 

distributions of mortality-related incidence generated using the available information from 

empirical studies and expert elicitation associated with the RFS2 volumes compared to each 

reference scenario. Tables 5.4-5 and 5.4-6 present the distributions of PM2.5-related premature 

mortality based on the C-R distributions provided by each expert, as well as that from the dataderived health impact functions, based on the statistical error associated with the ACS study 

(Pope et al., 2002) and the Six-cities study (Laden et al., 2006). The 90% confidence interval for 

each separate estimate of PM-related mortality is also provided. 

When comparing the RFS2 fuel volume scenario to the RFS1 reference case, the effect 

estimates of nine of the twelve experts included in the elicitation panel fall within the 

empirically-derived range provided by the ACS and Six-Cities studies. Only one expert falls 

below this range, while two of the experts are above this range. This same relationship occurs 

when comparing the RFS2 fuel volume scenario to the AEO reference case. Although the overall 

range across experts is summarized in these tables, the full uncertainty in the estimates is 

reflected by the results for the full set of 12 experts. The twelve experts’ judgments as to the 

likely mean effect estimate are not evenly distributed across the range illustrated by arraying the 

highest and lowest expert means. 
65 

Table 5.4-3. 

Estimated PM2.5-Related Health Impacts Associated with the 

Mandated Renewable Fuel Volumesa

a Note that negative incidence expressed in this table reflects disbenefits; in other words, an increase in total 

aggregated national-level ozone-related health impacts. Incidence is rounded to two significant digits. Estimates 

represent incidence within the 48 contiguous United States. 

b PM-related adult mortality based upon the American Cancer Society (ACS) Cohort Study (Pope et al., 2002) and 

the Six-Cities Study (Laden et al., 2006). Note that these are two alternative estimates of adult mortality and should 

not be summed. PM-related infant mortality based upon a study by Woodruff, Grillo, and Schoendorf, (1997).ZZZ

c Respiratory hospital admissions for PM include admissions for chronic obstructive pulmonary disease (COPD), 

pneumonia and asthma. 

d Cardiovascular hospital admissions for PM include total cardiovascular and subcategories for ischemic heart 

disease, dysrhythmias, and heart failure.

 

ZZZ Woodruff, T.J., J. Grillo, and K.C. Schoendorf. 1997. “The Relationship Between Selected Causes of 

Postneonatal Infant Mortality and Particulate Air Pollution in the United States.” Environmental Health 

Perspectives 105(6):608-612. 

Health Effect 

2022 RFS2 Compared to 

RFS1 Reference Case 

(5th% - 95th%ile) 

2022 RFS2 

Compared to AEO Reference

Case 

(5th% - 95th%ile) 

Premature Mortality – Derived from Epidemiology 

Literatureb

 Adult, age 30+, ACS Cohort Study (Pope et al., 2002)

 Adult, age 25+, Six-Cities Study (Laden et al., 2006) 

 Infant, age <1 year (Woodruff et al., 1997) 

-110 

(-42 - -170) 

-270 

(-150 - -400) 

0 

(0 - -1) 

-33 

(-13 - -53) 

-85 

(-46 - -120) 

0 

(0 - -1) 

Chronic bronchitis (adult, age 26 and over) -65 

(-26 - -110) 

-19 

(-4 - -18) 

Non-fatal myocardial infarction (adult, age 18 and over -180 

(-65 - -290) 

-51 

(-19 - -84) 

Hospital admissions - respiratory (all ages)c -26 

(-25 - -26) 

-7 

(-5 - -8) 

Hospital admissions - cardiovascular (adults, age >18)d -55 

(-44 - -70) 

-12 

(-9 - -16) 

Emergency room visits for asthma (age 18 years and 

younger) 

-180 

(-110 - -260) 

-99 

(-58 - -140) 

Acute bronchitis, (children, age 8-12) -160 

(0 - -330) 

-50 

(0 - -100) 

Lower respiratory symptoms (children, age 7-14) -1,900 

(-910 - -2,900) 

-600 

(-290 - -910) 

Upper respiratory symptoms (asthmatic children, age 9-

18) 

-1,400 

(-450 - -2,400) 

-450 

(-140 - -750) 

Asthma exacerbation (asthmatic children, age 6-18) -1,700 

(-190 - -4,800) 

-540 

(-60 - -1,500) 

Work loss days -11,000 

(-10,000 - -13,000) 

-3,200 

(-2,800 - -3,700) 

Minor restricted activity days (adults age 18-65) -68,000 

(-57,000 - -78,000) 

-19,000 

(-16,000 - -22,000) 
66 

Table 5.4-4. 

Estimated Ozone-Related Health Impacts Associated with the 

Mandated Renewable Fuel Volumesa

a

 Note that negative incidence expressed in this table reflects disbenefits; in other words, an increase in total 

aggregated national-level ozone-related health impacts. Incidence is rounded to two significant digits. Estimates 

represent incidence within the 48 contiguous United States. Note that negative incidence estimates represent 

additional cases of an endpoint related to pollution increases associated with the rule. 

b

 Estimates of ozone-related premature mortality are based upon incidence estimates derived from several alternative 

studies: Bell et al. (2004); Huang et al. (2005); Schwartz (2005) ; Bell et al. (2005); Ito et al. (2005); Levy et al. 

(2005). The estimates of ozone-related premature mortality should therefore not be summed. c Respiratory hospital admissions for ozone include admissions for all respiratory causes and subcategories for 

COPD and pneumonia. 

Health Effect 

2022 RFS2 Compared to RFS1 

Reference Case 

(5th% - 95th%ile) 

2022 RFS2 Compared to AEO 

Reference Case 

(5th% - 95th%ile) 

Premature Mortality, All agesb 

Multi-City Analyses 

 Bell et al. (2004) – Non-accidental 

 Huang et al. (2005) – Cardiopulmonary 

 Schwartz (2005) – Non-accidental 

Meta-analyses:

 Bell et al. (2005) – All cause 

 Ito et al. (2005) – Non-accidental 

 Levy et al. (2005) – All cause 

-54 

(-17 - -92) 

-90 

(-31 - -149) 

-83 

(-24 - -140) 

-180 

(-80 - -270) 

-240 

(-140 - -350) 

-250 

(-170 - -330) 

-36 

(-10 - -62) 

-59 

(-18 - -100) 

-55 

(-13 - -97) 

-120 

(-49 - -180) 

-160 

(-90 - -230) 

-160 

(-110 - -220) 

Hospital admissions- respiratory causes (adult, 6

and older)c

-470 

(-20 - -860) 

-310 

(-5 - -580) 

Hospital admissions -respiratory causes (children

under 2) 

-83 

(-24 - -140) 

-190 

(-52 - -330) 

Emergency room visit for asthma (all ages) -260 

(0 - -740) 

-180 

(0 - -510) 

Minor restricted activity days (adults, age 18-65) -300,000 

(-110,000 - -500,000) 

-200,000 

(-59,000 - -340,000) 

School absence days -110,000 

(-35,000 - -180,000) 

-75,000 

(-19,000 - -120,000) 
67 

Table 5.4-5. Results of Application of Expert Elicitation: Annual Reductions in Premature 

Mortality in 2022 Associated with the Mandated Renewable Fuel Volumes

Source of Mortality 2022 RFS2 compared to the RFS1 reference case 

Estimate 

5th Percentile Mean 95th Percentile 

Pope et al. (2002) -42 -110 -170 

Laden et al. (2006) -150 -270 -400 

Expert A -53 -290 -530 

Expert B -11 -210 -480 

Expert C -39 -220 -470 

Expert D -32 -150 -250 

Expert E -180 -360 -550 

Expert F -130 -200 -260 

Expert G 0 -130 -240 

Expert H -1 -160 -380 

Expert I -34 -220 -390 

Expert J -52 -180 -390 

Expert K 9 -34 -180 

Expert L 4 -130 -310 
68 

Table 5.4-6. Results of Application of Expert Elicitation: Annual Reductions in Premature 

Mortality in 2022 Associated with the Mandated Renewable Fuel Volumes

Source of Mortality 2022 RFS2 compared to the AEO reference case 

Estimate 

5th Percentile Mean 95th Percentile 

5.4.3 Monetized Benefits 

Monetized values for each quantified health endpoint are presented in Table 5.4-7. For 

each endpoint presented in Table 5.4-7, we provide both the mean estimate and the 90% 

confidence interval. Total aggregate monetized benefits are presented in Tables 5.4-8 and 5.4-9 

using either a 3 percent or 7 percent discount rate, respectively. All of the monetary benefits are 

in constant-year 2007 dollars. 

In addition to omitted benefits categories such as air toxics and various welfare effects, 

not all known PM2.5- and ozone-related health and welfare effects could be quantified or 

monetized. The estimate of total monetized health impacts from the renewable fuel volumes are 

thus equal to the subset of monetized PM2.5- and ozone-related health impacts we are able to 

quantify plus the sum of the nonmonetized health and welfare impacts. 

Our estimate of total monetized disbenefits in 2022 for the RFS2 fuel volume scenario 

compared to the RFS1 reference case, using the ACS and Six-Cities PM mortality studies and the 

range of ozone mortality assumptions, is between $1.4 billion and $4.5 billion, assuming a 3 

percent discount rate, or between $1.4 billion and $4.3 billion, assuming a 7 percent discount 

rate. When compared to the AEO reference case, we estimate the monetized disbenefits to be 

between $0.63 billion and $2.2 billion, assuming a 3 percent discount rate, or between $0.60 

billion and $2.1 billion, assuming a 7 percent discount rate. The monetized impacts associated 

Pope et al. (2002) -13 -33 -53 

Laden et al. (2006) -46 -85 -120 

Expert A -17 -91 -170 

Expert B 7 -65 -160 

Expert C -12 -68 -150 

Expert D -10 -48 -79 

Expert E -57 -110 -170 

Expert F -35 -59 -69 

Expert G 0 -40 -74 

Expert H 0 -51 -120 

Expert I -11 -68 -121 

Expert J -16 -55 -122 

Expert K 0 -11 -54 

Expert L 8 -38 -101 
69 

with an increase in the risk of both ozone- and PM2.5-related premature mortality ranges between 

90 to 98 percent of total monetized health impacts, in part because we are unable to quantify a 

number of health and environmental impact categories (see Table 5.4-2). These unquantified 

impacts may be substantial, although their magnitude is highly uncertain. 

The next largest disbenefit is for increased incidence of PM-related chronic illness 

(chronic bronchitis and nonfatal heart attacks), although this value is more than an order of 

magnitude lower than for PM-related premature mortality. Hospital admissions for respiratory 

and cardiovascular causes, minor restricted activity days, and work loss days account for the 

majority of the remaining disbenefits. The remaining categories each account for a small 

percentage of total disbenefit. A comparison of the incidence table to the monetary disbenefits 

table reveals that there is not always a close correspondence between the number of incidences 

for a given endpoint and the monetary value associated with that endpoint. For example, there 

are over 100 times more work loss days than PM-related premature mortalities (based on the 

ACS study), yet work loss days account for only a very small fraction of total monetized 

disbenefits. This reflects the fact that many of the less severe health effects, while more 

common, are valued at a lower level than the more severe health effects. Also, some effects, 

such as hospital admissions, are valued using a proxy measure of willingness-to-pay (e.g., costof-illness). As such, the true value of these effects may be different than that reported here. 
70 

Table 5.4-7. 

Estimated Monetary Value of Health and Welfare Effect Incidence (in millions of 2007$) a,b

Adult, age 25+ - Six-cities study 

(Laden et al., 2006) 

 3% discount rate 

 7% discount rate 

-$2,200 

(-$29 - -$5,500) 

-$2,000 

(-$26 - -$5,000) 

-$680 

(-$90 - -$1,700) 

-$620 

(-$81 - -$1,600) 

Infant Mortality, <1 year – 

(Woodruff et al. 1997) 

-$4.0 

(-$3.0 - -$15) 

-$1.7 

(-$1.3 - -$6.7) 

Chronic bronchitis (adults, 26 and over) -$32 

(-$2.5 - -$110) 

-$9.4 

(-$0.72 - -$33) 

 2022 RFS2 

Compared to RFS1 

Reference Case 

2022 RFS2 Compared 

to AEO Reference Case

PM2.5-Related Health Effect Estimated Mean Value of Reductions 

(5th and 95th %ile) 

Premature Mortality – 

Derived from 

Epidemiology 

S di c d

Adult, age 30+ - ACS study 

(Pope et al., 2002) 

 3% discount rate 

 7% discount rate 

-$860 

(-$100 - -$2,300) 

-$770 

(-$91 - -$2,000) 

-$270 

(-$32 - -$700) 

-$240 

(-$28 - -$630) 
71 

a

 Negatives indicate a disbenefit, or an increase in health effect incidence. Monetary impacts are rounded to two 

significant digits for ease of presentation and computation. PM and ozone impacts are nationwide. 

b

 Monetary impacts adjusted to account for growth in real GDP per capita between 1990 and the analysis year 

(2022) 

Non-fatal acute myocardial infarctions 

 3% discount rate 

 7% discount rate 

-$23 

(-$4.1 - -$58) 

-$23 

(-$3.8 - -$58) 

-$6.6 

(-$1.0 - -$17) 

-$6.4 

(-$0.95 - -$16) 

Hospital admissions for respiratory causes -$0.39 

(-$0.19 - -$0.57 

-$0.11 

(-$0.06 - -$0.17) 

Hospital admissions for cardiovascular causes -$1.5 

(-$0.96 - -$2.1) 

-$0.33 

(-$0.20 - -$0.45) 

Emergency room visits for asthma -$0.07 

(-$0.04 - -$0.10) 

-$0.04 

(-$0.02 - -$0.06) 

Acute bronchitis (children, age 8–12) -$0.01 

($0 - -$0.03) 

-$0.004 

($0 - -$0.01) 

Lower respiratory symptoms (children, 7–14) -$0.04 

(-$0.01 - -$0.07) 

-$0.01 

(-$0.004 - -$0.02) 

Upper respiratory symptoms (asthma, 9–11) -$0.04 

(-$0.01 - -$0.10) 

-$0.01 

(-$0.004 - -$0.03) 

Asthma exacerbations -$0.09 

(-$0.009 - -$0.28) 

-$0.03 

(-$0.003 - -$0.09) 

Work loss days -$1.7 

(-$1.5 - -$1.9) 

-$0.49 

(-$0.42 - -$0.55) 

Minor restricted-activity days (MRADs) -$4.3 

(-$2.5 - -$6.2) 

-$1.2 

(-$0.69 - -$1.7) 

Ozone-related Health Effect 

Bell et al., 2004 -$480 

(-$51 - -$1,300) 

-$320 

(-$32 - -$880) 

Huang et al., 2005 -$800 

(-$90 - -$2,200) 

-$530 

(-$56 - -$1,400) 

Premature Mortality, All ages – 

Derived from Multi-city 

analyses

Schwartz, 2005 -$740 

(-$76 - -$2,000) 

-$490 

(-$48 - -$1,300) 

Bell et al., 2005 -$1,600 

(-$200 - -$4,000) 

-$1,000 

(-$130 - -$,700) 

Ito et al., 2005 -$2,200 

(-$290 - -$5,400) 

-$1,400 

(-$190 - -$3,600) 

Premature Mortality, All ages – 

Derived from Meta-analyses 

Levy et al., 2005 -$2,200 

(-$300 - -$5,300) 

-$1,400 

(-$200 - -$3,500) 

Hospital admissions- respiratory causes (adult, 65 and 

older) 

-$11 

(-$0.49 - -$20) 

-$7.4 

(-$0.13 - -$14) 

Hospital admissions- respiratory causes (children, under 2) -$3.0 

(-$1.0 - -$4,9) 

-$1.9 

(-$0.52 - -$3.3) 

Emergency room visit for asthma (all ages) -$0.10 

(-$0.009 - -$0.26) 

-$0.07 

(-$0.008 - -$0.18) 

Minor restricted activity days (adults, age 18-65) -$19 

(-$6.4 - -$35) 

-$13 

(-$3.6 - -$24) 

School absence days -$10 

(-$3.1 - -$16) 

-$6.7 

(-$1.7 - -$11) 
72 

c

 Valuation assumes discounting over the SAB recommended 20 year segmented lag structure. Results reflect the 

use of 3 percent and 7 percent discount rates consistent with EPA and OMB guidelines for preparing economic 

analyses. 

Table 5.4-8. Total Monetized Impacts Associated with the Mandated Renewable Fuel 

Volumes – 3% Discount Rate 

Total Ozone and PM Monetized Impacts (billions, 2007$) – 

PM Mortality Derived from the ACS and Six Cities Studies 

2022 RFS2 Compared to RFS1 Reference Case 2022 RFS2 Compared to AEO Reference Case 

Ozone 

Mortality 

Function 

Reference Mean Total 

Benefits 

Ozone 

Mortality 

Function 

Reference Mean Total 

Benefits 

Bell et al., 

2004 

-$1.4 to -$2.8 Bell et al., 

2004 

-$1.3 to -$1.8 

Huang et al., 

2005 

-$1.8 to -$3.1 Huang et al., 

2005 

-$0.84 to -$1.3 Multi-city 

Schwartz, 

2005 

-$1.7 to -$3.0 

Multi-city 

Schwartz, 

2005 

-$0.80 to -$1.2 

Bell et al., 

2005 

-$2.5 to -$3.8 Bell et al., 

2005 

-$1.3 to -$1.8 

Meta-analysis Ito et al., 2005 -$3.1 to -$4.5 Ito et al., 2005 -$1.7 to -$2.2 

Levy et al., 

2005 

-$3.1 to -$4.5 

Meta-analysis 

Levy et al., 

2005 

-$1.8 to -$2.2 

Total Ozone and PM Monetized Impacts (millions, 2007$) – 

PM Mortality Derived from Expert Elicitation (Lowest and Highest Estimate) 

2022 RFS2 Compared to RFS1 Reference Case 2022 RFS2 Compared to AEO Reference Case 

Ozone 

Mortality 

Function 

Reference Mean Total 

Benefits 

Ozone 

Mortality 

Function 

Reference Mean Total 

Benefits 

Bell et al., 

2004 

-$0.86 to -$3.5 Bell et al., 

2004 

-$0.45 to -$1.3 

Huang et al., 

2005 

-$1.2 to -$3.8 Huang et al., 

2005 

-$0.66 to -$1.5 Multi-city 

Schwartz, 

2005 

-$1.1 to -$3.8 

Multi-city 

Schwartz, 

2005 

-$0.62 to -$1.4 

Bell et al., 

2005 

-$1.9 to -$4.6 Bell et al., 

2005 

-$1.2 to -$2.0 

Meta-analysis Ito et al., 2005 -$2.5 to -$5.2 Ito et al., 2005 -$1.6 to -$2.4 

Levy et al., 

2005 

-$2.6 to -$5.2 

Meta-analysis 

Levy et al., 

2005 

-$1.6 to -$2.4 
73 

Table 5.4-9. Total Monetized Impacts Associated with the Mandated Renewable Fuel 

Volumes – 7% Discount Rate 

Total Ozone and PM Monetized Impacts (billions, 2007$) – 

PM Mortality Derived from the ACS and Six Cities Studies 

2022 RFS2 Compared to RFS1 Reference Case 2022 RFS2 Compared to AEO Reference Case 

Ozone 

Mortality 

Function 

Reference Mean Total 

Benefits 

Ozone 

Mortality 

Function 

Reference Mean Total 

Benefits 

Bell et al., 

2004 

-$1.4 to -$2.6 Bell et al., 

2004 

-$0.60 to -$0.98 

Huang et al., 

2005 

-$1.7 to -$2.9 Huang et al., 

2005 

-$0.81 to -$1.2 Multi-city 

Schwartz, 

2005 

-$1.6 to -$2.8 

Multi-city 

Schwartz, 

2005 

-$0.77 to -$1.1 

Bell et al., 

2005 

-$2.4 to -$3.6 Bell et al., 

2005 

-$1.3 to -$1.7 

Meta-analysis Ito et al., 2005 -$3.0 to -$4.2 Ito et al., 2005 -$1.7 to -$2.1 

Levy et al., 

2005 

-$3.1 to -$4.3 

Meta-analysis 

Levy et al., 

2005 

-$1.7 to -$2.1 

Total Ozone and PM Monetized Impacts (millions, 2007$) – 

PM Mortality Derived from Expert Elicitation (Lowest and Highest Estimate) 

2022 RFS2 Compared to RFS1 Reference Case 2022 RFS2 Compared to AEO Reference Case 

Ozone 

Mortality 

Function 

Reference Mean Total 

Benefits 

Ozone 

Mortality 

Function 

Reference Mean Total 

Benefits 

Bell et al., 

2004 

-$0.83 to -$3.5 Bell et al., 

2004 

-$0.44 to -$1.2 

Huang et al., 

2005 

-$1.1 to -$3.5 Huang et al., 

2005 

-$0.65 to -$1.4 Multi-city 

Schwartz, 

2005 

-$1.1 to -$3.5 

Multi-city 

Schwartz, 

2005 

-$0.61 to -$1.4 

Bell et al., 

2005 

-$1.9 to -$4.3 Bell et al., 

2005 

-$1.1 to -$1.9 

Meta-analysis Ito et al., 2005 -$2.5 to -$4.9 Ito et al., 2005 -$1.6 to -$2.3 

Levy et al., 

2005 

-$2.5 to -$4.9 

Meta-analysis 

Levy et al., 

2005 

-$1.6 to -$2.3 

5.4.4 Methodology 

 Human Health Impact Functions

 Health impact functions measure the change in a health endpoint of interest, such as 

hospital admissions, for a given change in ambient ozone or PM concentration. Health impact 

functions are derived from primary epidemiology studies, meta-analyses of multiple 

epidemiology studies, or expert elicitations. A standard health impact function has four 

components: 1) an effect estimate from a particular study; 2) a baseline incidence rate for the 

health effect (obtained from either the epidemiology study or a source of public health statistics 

such as the Centers for Disease Control); 3) the size of the potentially affected population; and 4) 

the estimated change in the relevant ozone or PM summary measures. 
74 

 A typical health impact function might look like: 

Δ = 0 ⋅( ) −1 ⋅Δx y y e β , 

where y0 is the baseline incidence (the product of the baseline incidence rate times the potentially 

affected population), β is the effect estimate, and Δx is the estimated change in the summary 

pollutant measure. There are other functional forms, but the basic elements remain the same. 

The following subsections describe the sources for each of the first three elements: size of the 

potentially affected populations; PM2.5 and ozone effect estimates; and baseline incidence rates. 

Chapter 5.4.6 describes the ozone and PM air quality inputs to the health impact functions. 

Potentially Affected Populations 

 

 The starting point for estimating the size of potentially affected populations is the 2000 

U.S. Census block level dataset.6

 The Benefits Modeling and Analysis Program (BenMAP) 

incorporates 250 age/gender/race categories to match specific populations potentially affected by 

ozone and other air pollutants. The software constructs specific populations matching the 

populations in each epidemiological study by accessing the appropriate age-specific populations 

from the overall population database. BenMAP projects populations to 2022 using growth 

factors based on economic projections.7

Effect Estimate Sources 

 The most significant quantifiable impacts of exposure to ambient concentrations of ozone 

and PM are attributable to human health risks. EPA’s Ozone and PM Criteria Documents8,9 and 

the World Health Organization’s 2003 and 200410,11 reports outline numerous human health 

effects known or suspected to be linked to exposure to ambient ozone and PM. US EPA recently 

evaluated the ozone and PM literature for use in the benefits analysis for the final 2008 Ozone 

NAAQS and final 2006 PM NAAQS analyses. We use the same literature in this analysis; for 

more information on the studies that underlie the health impacts quantified in this RIA, please 

refer to those documents. 

 It is important to note that we are unable to separately quantify all of the possible PM and 

ozone health effects that have been reported in the literature for three reasons: (1) the possibility 

of double counting (such as hospital admissions for specific respiratory diseases versus hospital 

admissions for all or a sub-set of respiratory diseases); (2) uncertainties in applying effect 

relationships that are based on clinical studies to the potentially affected population; or (3) the 

lack of an established concentration-response (CR) relationship. Table 5.4-10 lists the health 

endpoints included in this analysis. 
75 

Table 5.4-10. Ozone- and PM-Related Health Endpoints 

ENDPOINT POLLUTANT STUDY STUDY POPULATION

Premature Mortality 

Premature mortality – 

daily time series 

O3 Multi-city

Bell et al (2004) (NMMAPS study)12 – Nonaccidental 

Huang et al (2005)13 - Cardiopulmonary 

Schwartz (2005)14 – Non-accidental 

Meta-analyses:

Bell et al (2005)15 – All cause 

Ito et al (2005)16 – Non-accidental 

Levy et al (2005)17 – All cause 

All ages 

Premature mortality 

—cohort study, allcause 

PM2.5 Pope et al. (2002)18

Laden et al. (2006)19 >29 years 

>25 years 

Premature mortality, 

total exposures 

PM2.5 Expert Elicitation (IEc, 2006)20 >24 years 

Premature mortality 

— all-cause 

PM2.5 Woodruff et al. (1997)21 Infant (<1 year) 

Chronic Illness 

Chronic bronchitis PM2.5 Abbey et al. (1995)22 >26 years 

Nonfatal heart attacks PM2.5 Peters et al. (2001)23 Adults (>18 years) 

Hospital Admissions 

Pooled estimate: 

Schwartz (1995) - ICD 460-519 (all resp)24

Schwartz (1994a; 1994b) - ICD 480-486 

(pneumonia)25,26 

Moolgavkar et al. (1997) - ICD 480-487 

(pneumonia)27

Schwartz (1994b) - ICD 491-492, 494-496 

(COPD) 

Moolgavkar et al. (1997) – ICD 490-496 

(COPD) 

>64 years 

O3 

Burnett et al. (2001)28 <2 years 

Respiratory 

PM2.5 Pooled estimate:

Moolgavkar (2003)—ICD 490-496 (COPD)29 >64 years 
76 
77 
78 
79 
80 
81 
82 
83 
84 
85 
86 
87 
88 
89 
90 
91 
92 
93 
94 
95 
96 
97 
98 
99 
100 
101 
102 
103 
104 
105 
106 
107 
108 
109 
110 
111 
112 
113 
114 
115 
116 
117 
118 
119 
120 
121 
122 
123 
124 
125 
126 
127 
128 
129 
130 
131 
132 
133 
134 
135 
136 
137 
138 
139 
140 
141 
142 
143 
144 
145 
146 
147 
148 
149 
150 
151 
152 
153 
154 
155 
156 
157 
158 
159 
160 
161 
162 
163 
164 
165 

Notes: 

a

 The original study populations were 8 to 13 for the Ostro et al. (2001) study and 6 to 13 for the Vedal et al. (1998) 

study. Based on advice from the Science Advisory Board Health Effects Subcommittee (SAB-HES), we extended 

the applied population to 6 to 18, reflecting the common biological basis for the effect in children in the broader age 

group. See: U.S. Science Advisory Board. 2004. Advisory Plans for Health Effects Analysis in the Analytical Plan 

for EPA’s Second Prospective Analysis –Benefits and Costs of the Clean Air Act, 1990—2020. EPA-SABCOUNCIL-ADV-04-004. See also National Research Council (NRC). 2002. Estimating the Public Health Benefits 

of Proposed Air Pollution Regulations. Washington, DC: The National Academies Press. 

b

 Gilliland et al. (2001) studied children aged 9 and 10. Chen et al. (2000) studied children 6 to 11. Based on recent 

advice from the National Research Council and the EPA SAB-HES, we have calculated reductions in school 

absences for all school-aged children based on the biological similarity between children aged 5 to 17.

In selecting epidemiological studies as sources of effect estimates, we applied several criteria to 

develop a set of studies that is likely to provide the best estimates of impacts in the U.S. To 

account for the potential impacts of different health care systems or underlying health status of 

populations, we give preference to U.S. studies over non-U.S. studies. In addition, due to the 

potential for confounding by co-pollutants, we give preference to effect estimates from models 

including both ozone and PM over effect estimates from single-pollutant models.46,47 

Treatment of Potential Thresholds in PM2.5-Related Health Impact Functions 

In recent analyses, OTAQ has estimated PM2.5-related benefits assuming that a threshold 

exists in the PM-related concentration-response functions (at 10 μg/m3) below which there are no 

associations between exposure to PM2.5 and health impacts. For the analysis of the final rule, 

however, we have revised this assumption. As explained in the recently proposed Portland 

Cement MACT RIA, EPA’s preferred benefits estimation approach assumes a no-threshold 

model that calculates incremental benefits down to the lowest modeled PM2.5 air quality levels. 

EPA strives to use the best available science to support our benefits analyses, and we 

recognize that interpretation of the science regarding air pollution and health is dynamic and 

evolving. Based on our review of the body of scientific literature, EPA applied the no-threshold 

model in this analysis. EPA's draft Integrated Science Assessment,48,49 which was recently 

reviewed by EPA’s Clean Air Scientific Advisory Committee,50,51 concluded that the scientific 

literature consistently finds that a no-threshold log-linear model most adequately portrays the 

PM-mortality concentration-response relationship while recognizing potential uncertainty about 

the exact shape of the concentration-response function.AAAA Although this document does not 

represent final agency policy that has undergone the full agency scientific review process, it 

provides a basis for reconsidering the application of thresholds in PM2.5 concentration-response 

functions used in EPA’s RIAs.BBBB It is important to note that while CASAC provides advice 

regarding the science associated with setting the National Ambient Air Quality Standards, 

typically other scientific advisory bodies provide specific advice regarding benefits 

analysis.CCCC,52 

 

AAAA It is important to note that uncertainty regarding the shape of the concentration-response function is 

conceptually distinct from an assumed threshold. An assumed threshold (below which there are no health effects) is 

a discontinuity, which is a specific example of non-linearity. 

BBBB The final PM ISA, which will have undergone the full agency scientific review process, is scheduled to be 

completed in late December 2009. 

CCCC In the Portland Cement RIA, EPA solicited comment on the use of the no-threshold model for benefits analysis 

within the preamble of that proposed rule. The comment period for the Portland Cement proposed NESHAP closed 
166 

As can be seen in Table 5.4-11, we conducted a sensitivity analysis for premature 

mortality, with alternative thresholds at 3 μg/m3 (the “background,” or no-threshold, 

assumption), 7.5 μg/m3

, 10 μg/m3, 12 μg/m3, and 14 μg/m3. By replacing the no-threshold 

assumption in the ACS premature mortality function with a 10 μg/m3 threshold model, the 

number of avoided incidences of premature mortality would change dramatically. 

 on September 4, 2009 (Docket ID No. EPA–HQ–OAR–2002–0051 available at http://www.regulations.gov). EPA 

is currently reviewing those comments. 
167 

Table 5.4-11. PM-Related Mortality Impacts Associated with the Mandated Renewable 

Fuel Volumes: Threshold Sensitivity Analysis Using the ACS Study (Pope et al., 2002)a
168 
169 
170 
171 
172 
173 
174 
175 
176 

Notes: 

a

 Note that this table only presents the effects of a threshold on PMrelated mortality incidence based on the ACS study. Negative values 

indicate a disbenefit, or additional mortality incurred. 

b

 Alternative annual PM NAAQS. 

c Previous threshold assumption 

d

 SAB-HES (2004)86 

e

 NAS (2002)87

5.4.5 Economic Values for Health Outcomes 

 Reductions in ambient concentrations of air pollution generally lower the risk of future 

adverse health effects for a large population, while the reverse is also generally true. Therefore, 

the appropriate economic measure is willingness-to-pay (WTP) for changes in risk of a health 

effect rather than WTP for a health effect that would occur with certainty (Freeman, 1993). 

Epidemiological studies generally provide estimates of the relative risks of a particular health 

effect that is avoided because of a reduction in air pollution. We converted those to units of 

avoided statistical incidence for ease of presentation. We calculated the value of avoided 

statistical incidences by dividing individual WTP for a risk reduction by the related observed 

change in risk. For example, suppose a pollution-reduction regulation is able to reduce the risk 

of premature mortality from 2 in 10,000 to 1 in 10,000 (a reduction of 1 in 10,000). If individual 

WTP for this risk reduction is $100, then the WTP for an avoided statistical premature death is 

$1 million ($100/0.0001 change in risk). 

 WTP estimates generally are not available for some health effects, such as hospital 

admissions. In these cases, we used the cost of treating or mitigating the effect as a primary 

estimate. These cost-of-illness (COI) estimates generally understate the true value of reducing 

the risk of a health effect, because they reflect the direct expenditures related to treatment, but 

not the value of avoided pain and suffering (Harrington and Portney, 1987; Berger, 1987). We 

provide unit values for health endpoints (along with information on the distribution of the unit 

value) in Table 5.4-12. All values are in constant year 2006 dollars, adjusted for growth in real 

income out to 2022 using projections provided by Standard and Poor’s. Economic theory argues 

that WTP for most goods (such as environmental protection) will increase if real income 

increases. Many of the valuation studies used in this analysis were conducted in the late 1980s 

and early 1990s. Because real income has grown since the studies were conducted, people’s 

willingness to pay for reductions in the risk of premature death and disease likely has grown as 

well. We did not adjust cost of illness-based values because they are based on current costs. 

Similarly, we did not adjust the value of school absences, because that value is based on current 

wage rates. For details on valuation estimates for PM-related endpoints, see the 2006 PM 

NAAQS RIA. For details on valuation estimates for ozone-related endpoints, see the 2008 

Ozone NAAQS RIA. 
177 

Table 5.4-12. Unit Values Used for Economic Valuation of Health Endpoints (2000$)a
178 

Table 5.4-12. Unit Values Used for Economic Valuation of Health Endpoints (2000$)a (continued)
179 
180 

Table 5.4-12. Unit Values Used for Economic Valuation of Health Endpoints (2000$)a (continued)
181 
182 

Table 5.4-12. Unit Values Used for Economic Valuation of Health Endpoints (2000$)a (continued) 
183 

a All monetized annual benefit estimates associated with the coordinated strategy have been inflated to reflect values in year 2006 dollars. We use the Consumer 

Price Indexes to adjust both WTP- and COI-based benefits estimates to 2007 dollars from 2000 dollars.64 For WTP-based estimates, we use an inflation factor of 

1.20 based on the CPI-U for “all items.” For COI-based estimates, we use an inflation factor of 1.35 based on the CPI-U for medical care. b Our analysis accounts for expected growth in real income over time. Economic theory argues that WTP for most goods (such as environmental protection) will 

increase if real incomes increase. Benefits are therefore adjusted by multiplying the unadjusted benefits by the appropriate adjustment factor to account for income growth over time. For a complete discussion of how these adjustment factors were derived, we refer the reader to the PM NAAQS regulatory impact 

analysis. Note that similar adjustments do not exist for cost-of-illness-based unit values. For these, we apply the same unit value regardless of the future year of analysis. 
184 

5.4.6 Manipulating Air Quality Modeling Data for Health Impacts Analysis 

In Chapter 3.4, we summarized the methods for and results of estimating air quality 

impacts for the mandated renewable fuel volumes. These air quality results are in turn associated 

with human populations to estimate changes in health effects. For the purposes of this analysis, 

we focus on the health effects that have been linked to ambient changes in ozone and PM2.5

related to emissions associated with the RFS2 mandated fuel volumes. We estimate ambient 

PM2.5 and ozone concentrations using the Community Multiscale Air Quality model (CMAQ). 

This section describes how we converted the CMAQ modeling output into full-season profiles 

suitable for the health impacts analysis. 

General Methodology 

First, we extracted hourly, surface-layer PM and ozone concentrations for each grid cell 

from the standard CMAQ output files. For ozone, these model predictions are used in 

conjunction with the observed concentrations obtained from the Aerometric Information 

Retrieval System (AIRS) to generate ozone concentrations for the entire ozone season.82,83 The 

predicted changes in ozone concentrations from the future-year base case to future-year control 

scenario serve as inputs to the health and welfare impact functions of the benefits analysis (i.e., 

BenMAP). 

To estimate ozone-related health effects for the contiguous United States, full-season 

ozone data are required for every BenMAP grid-cell. Given available ozone monitoring data, we 

generated full-season ozone profiles for each location in two steps: (1) we combined monitored 

observations and modeled ozone predictions to interpolate hourly ozone concentrations to a grid 

of 12-km by 12-km population grid cells for the contiguous 48 states, and (2) we converted these 

full-season hourly ozone profiles to an ozone measure of interest, such as the daily maximum 8-

hour average.84,85 

For PM2.5, we also use the model predictions in conjunction with observed monitor data. 

CMAQ generates predictions of hourly PM species concentrations for every grid. The species 

include a primary coarse fraction (corresponding to PM in the 2.5 to 10 micron size range), a 

primary fine fraction (corresponding to PM less than 2.5 microns in diameter), and several 

secondary particles (e.g., sulfates, nitrates, and organics). PM2.5 is calculated as the sum of the 

primary fine fraction and all of the secondarily formed particles. Future-year estimates of PM2.5

were calculated using relative reduction factors (RRFs) applied to 2005 ambient PM2.5 and PM2.5

species concentrations. A gridded field of PM2.5 concentrations was created by interpolating 

Federal Reference Monitor ambient data and IMPROVE ambient data. Gridded fields of PM2.5 

species concentrations were created by interpolating US EPA speciation network (ESPN) 

 

82 The ozone season for this analysis is defined as the 5-month period from May to September.

83 Based on AIRS, there were 961 ozone monitors with sufficient data (i.e., 50 percent or more days reporting at 

least nine hourly observations per day [8 am to 8 pm] during the ozone season).

84 The 12-km grid squares contain the population data used in the health benefits analysis model, BenMAP. 

85 This approach is a generalization of planar interpolation that is technically referred to as enhanced Voronoi 

Neighbor Averaging (EVNA) spatial interpolation. See the BenMAP manual for technical details, available for 

download at http://www.epa.gov/air/benmap. 
185 

ambient data and IMPROVE data. The ambient data were interpolated to the CMAQ 12 km 

grid. 

The procedures for determining the RRFs are similar to those in US EPA’s draft guidance 

for modeling the PM2.5 standard (EPA, 1999). The guidance recommends that model predictions 

be used in a relative sense to estimate changes expected to occur in each major PM2.5 species. 

The procedure for calculating future-year PM2.5 design values is called the “Speciated Modeled 

Attainment Test (SMAT).” EPA used this procedure to estimate the ambient impacts of the 

coordinated strategy to control ship emissions. 

Table 5.4-13 provides those ozone and PM2.5 metrics for grid cells in the modeled 

domain that enter the health impact functions for health benefits endpoints. The populationweighted average reflects the baseline levels and predicted changes for more populated areas of 

the nation. This measure better reflects the potential benefits through exposure changes to these 

populations. 
186 

Table 5.4-13. 

Summary of CMAQ-Derived Population-Weighted Ozone and PM2.5 Air Quality Metrics 

for Health Impact Endpoints Associated with the Mandated Renewable Fuel Volumes 
187 
188 
189 
190 
191 
192 
193 
194 
195 
196 
197 
198 
199 
200 
201 
202 
203 
204 
205 
206 
207 
208 
209 
210 
211 
212 
213 

Notes: 

a

 Ozone and PM2.5 metrics are calculated at the CMAQ grid-cell level for use in health effects estimates. 

Ozone metrics are calculated over relevant time periods during the daylight hours of the “ozone season” 

(i.e., May through September). 

b

 The change is defined as the reference case value minus the final rule value. A negative change means 

that the population-weighted average has increased from the reference scenario to the final rule scenario. 

c

 Calculated by summing the product of the projected CMAQ grid-cell population and the estimated 

CMAQ grid cell seasonal ozone concentration and then dividing by the total population.

5.4.7 Methods for Describing Uncertainty 

 The National Research Council (NRC)lxv highlighted the need for EPA to conduct 

rigorous quantitative analysis of uncertainty in its benefits estimates and to present these 

estimates to decision makers in ways that foster an appropriate appreciation of their inherent 

uncertainty. In response to these comments, EPA’s Office of Air and Radiation (OAR) is 

developing a comprehensive strategy for characterizing the aggregate impact of uncertainty in 

key modeling elements on both health incidence and benefits estimates. Components of that 

process include emissions modeling, air quality modeling, health effects incidence estimation, 

and valuation. 

 In benefit analyses of air pollution regulations conducted to date, the estimated impact of 

reductions in premature mortality has accounted for 85% to 95% of total benefits. Therefore, it 

is particularly important to characterize the uncertainties associated with reductions in premature 

mortality. The health impact functions used to estimate avoided premature deaths associated 

with reductions in ozone have associated standard errors that represent the statistical errors 

around the effect estimates in the underlying epidemiological studies.86 In our results, we report 

credible intervals based on these standard errors, reflecting the uncertainty in the estimated 

change in incidence of avoided premature deaths. We also provide multiple estimates, to reflect 

model uncertainty between alternative study designs. 

 For premature mortality associated with exposure to PM, we follow the same approach 

that has been used in several recent RIAs.lxvi,lxvii,lxviii First, we use Monte Carlo methods for 

estimating random sampling error associated with the concentration response functions from 

epidemiological studies and economic valuation functions. Monte Carlo simulation uses random 

sampling from distributions of parameters to characterize the effects of uncertainty on output 

variables, such as incidence of premature mortality. Specifically, we used Monte Carlo methods 

to generate confidence intervals around the estimated health impact and dollar benefits. 

Distributions for individual effect estimates are based on the reported standard errors in the 

epidemiological studies. Distributions for unit values are described in Table 6-11. 

 Second, as a sensitivity analysis, we use the results of our expert elicitation of the 

concentration response function describing the relationship between premature mortality and 

ambient PM2.5 concentration.87, lxix Incorporating only the uncertainty from random sampling 

 

86 Health impact functions measure the change in a health endpoint of interest, such as hospital admissions, for a 

given change in ambient ozone or PM concentration. 

87 Expert elicitation is a formal, highly structured and well documented process whereby expert judgments, usually 

of multiple experts, are obtained (Ayyb, 2002). 
214 

error omits important sources of uncertainty (e.g., in the functional form of the model; whether 

or not a threshold may exist). This second approach attempts to incorporate these other sources 

of uncertainty. 

 Use of the expert elicitation and incorporation of the standard errors approaches provide 

insights into the likelihood of different outcomes and about the state of knowledge regarding the 

benefits estimates. Both approaches have different strengths and weaknesses, which are fully 

described in Chapter 5 of the PM NAAQS RIA.lxx

 These multiple characterizations, including confidence intervals, omit the contribution to 

overall uncertainty of uncertainty in air quality changes, baseline incidence rates, populations 

exposed and transferability of the effect estimate to diverse locations. Furthermore, the approach 

presented here does not yet include methods for addressing correlation between input parameters 

and the identification of reasonable upper and lower bounds for input distributions characterizing 

uncertainty in additional model elements. As a result, the reported confidence intervals and range 

of estimates give an incomplete picture about the overall uncertainty in the estimates. This 

information should be interpreted within the context of the larger uncertainty surrounding the 

entire analysis. 
215 

5.5 Economy-Wide Economic Analysis 

It is anticipated that the increase in renewable fuels associated with this RFS2 rulemaking 

will have impacts on the U.S. economy that extend beyond the U.S. transportation sector. By 

requiring 36 billion gallons of renewable fuels in the U.S. transportation sector, it is anticipated 

that the price of motor vehicle fuels will decrease modestly by 2022. This is due to two reasons. 

First, the costs of renewable fuels are estimated to decrease over time, while the price of 

petroleum is projected to increase over time. Therefore, by 2022, the cost of renewable fuels is 

expected to be lower than the cost of gasoline and diesel fuels. Secondly, the decreased demand 

for petroleum products as a result of using more renewable fuels will modestly lower the world 

price of oil. The resulting overall decrease in the price of motor vehicle fuels will affect all 

sectors of the economy that use these fuels as intermediate inputs to production. For example, 

firms that transport products (i.e., shipping companies) will see a decline in their costs of 

production. Also, households will also be beneficially affected as consumers of these goods, and 

directly as consumers of motor vehicle fuels. 

In the U.S. agriculture sector, it is anticipated that the production of renewable fuels will 

increase the demand for farm products, and increase farm incomes. U.S. food prices are 

anticipated to increase modestly as a result of higher agricultural commodity prices. For 

example, corn and soybean prices are anticipated to increase with the RFS2 rule since there is 

greater demand for these commodities for use as feedstocks to produce ethanol and biodiesel 

fuels. Increased agricultural commodity prices will have ripple effects on sectors of the 

economy that supply inputs to the farm sector (e.g., tractors), and sectors that demand outputs 

from the farm sector. The sum of all of these market impacts, both fuel market and agricultural 

sector impacts, will affect the total levels of output (i.e., Gross Domestic Product) and 

consumption of the U.S. economy. 

A further issue that arises in assessing the economy-wide impacts of the RFS2 rule is 

renewable fuel subsidies. For example, for corn ethanol, the current subsidy is $0.45/gallon. 

Alternatively, for biodiesel, the current subsidy is $1.00/gallon. Cellulosic biofuel qualifies for a 

subsidy of $1.01/gallon. By the year 2022, the increase in renewable fuels volumes associated 

with the RFS2 rule will result in an increase in renewable fuel subsidy expenditures of 

approximately $14.2 billion annually. To account for the economy-wide impacts of the 

renewable fuel subsidies associated with the RFS2 renewable fuel volumes, we look at two 

cases. Case one looks at the economy-wide impacts of the increased renewable fuel volumes 

required by the RFS2 rule without accounting for the increase in renewable fuel subsidies. Case 

two examines the impacts of the RFS2 renewable fuel volumes with renewable fuel subsidies 

included. For case two, we assume we assume that the biofuel subsidies will be offset by raising 

taxes to maintain the “revenue neutrality” of the U.S. federal government’s overall budget. 

Since it is not clear what taxes would be raised, we have assumed that the marginal tax rate on 

labor income will be raised to offset the impact of the increase in renewable fuel subsidies on the 

federal government’s budget. Any remaining adjustments required to restore real government 

expenditures to their reference case levels are accomplished through changes in zero bracket 

exemptions (i.e., lump-sum transfers). 
216 

To estimate the impacts of this rule on U.S. Gross Domestic Product (GDP) and personal 

consumption, EPA has used an economy-wide, computable general equilibrium (CGE) model. 

The economy-wide model used in this analysis is the Intertemporal General Equilibrium Model 

(IGEM).88 IGEM is a model of the U.S. economy with an emphasis on the economy’s energy 

and environmental aspects. IGEM is a dynamic model which depicts growth of the economy due 

to capital accumulation, technical change and population change. The model utilizes a unified 

accounting framework consistent with the National Income and Product Accounts and the 

Industry Classification System. 

IGEM is a detailed multi-sector model covering thirty-five industries, consisting of five 

energy and thirty non-energy sectors. The model ensures market balances (i.e., supply equals 

demand) in value and quantity terms, including limits placed on private investment dictated by 

domestic and foreign saving behavior and by the fiscal policies of federal, state and local 

governments. Capital accumulation in the model arises from the saving and investment behavior 

of households and businesses and provides an essential input to production and consumption. 

Households in the model make choices regarding present and future consumption (i.e., saving) 

and make choices regarding the allocation of time between labor and leisure. The model covers 

all aspects of long-run growth including the supply of capital, labor, imported and intermediate 

inputs to production; the rates and directions of exogenous and endogenous technical change for 

each producing sector; and the degrees of substitutability among inputs and commodities in 

production and final demand (i.e., consumption, investment, governments and foreign trade). 

The substitution possibilities for both producers and consumers in IGEM are driven by model 

parameters that are based on observed market behavior revealed over the past forty to fifty years. 

5.5.1 Impacts on U.S. GDP 

In order to estimate the real U.S. GDP impacts of the renewable fuel volumes required by 

the RFS2 rule, the CGE model uses outputs of the transportation and agricultural sector models 

as inputs to determine the economy-wide impacts of this rule. Specifically, IGEM uses 

information from other models on the change in fuel costs and renewable fuel subsidies, the 

change in agricultural sector prices, and the change in the world oil price. The change in fuel 

costs were calculated as described in Chapter 4.4 of this RIA. The value of the renewable fuel 

subsidies through time were calculated by multiplying the per gallon value of the renewable fuel 

subsidies by the volumes of renewable fuels required by the RFS2 rule. The data on changes in 

agricultural sector prices were taken from the FASOM model, as described in Chapter 5.1 of the 

RIA. Finally, the estimates of the changes in world oil price were estimated using the Oil 

Security Metrics Model maintained by Oak Ridge National Laboratory, which is discussed in 

more detail in Chapter 5.2 of the RIA. It is important to note that the economy-wide analysis 

does not include a representation of the benefits associated with reducing greenhouse gas 

emissions, and thus the GDP impacts presented here do not represent the results of a complete 

cost benefit analysis. Figure 5.5-1 and Table 5.5-1 show the Reference Case GDP and GDP with 

the two cases, without and with renewable fuel subsidies. 

 

88 The model is developed and run by Dale W. Jorgenson Associates for EPA. Model Homepage: 

http://www.dja.insightworks.com/ 
217 

Figure 5.5-1. 

Annual GDP Impacts for the RFS2 Control Case vs. the AEO2007 Reference Case 

(Billions of 2007 dollars) 

GDP

$0

$5,000

$10,000

$15,000

$20,000

$25,000

$30,000

2017 2022 2030

Billion 2007$

Reference Case

Control Case with

Subisidies

Control Case

Without

Subsidies

Table 5.5-1. 

Annual GDP Impacts for the RFS2 Control Case vs. the AEO2007 Reference Case 

(Billions of 2007 dollars)
218 
219 
220 
221 
222 
223 
224 
225 
226 
227 
228 
229 
230 
231 
232 
233 
234 
235 
236 
237 
238 

In the reference case, GDP rises from approximately $17.6 trillion in 2017 to $19.9 

trillion in 2022, and $24.3 trillion in 2030. In case one (without renewable fuel subsidies), GDP 

rises from approximately $17.6 trillion in 2017 to $19.9 trillion in 2022 and $24.4 trillion in 

2030. In case two (with renewable fuel subsidies), GDP rises from approximately $17.6 trillion 

in 2017 to $19.9 trillion in 2022 and $24.4 trillion in 2030. 

Figure 5.5.-2 and Table 5.5-2 show the percentage change in GDP with the RFS2 

renewable fuel volumes. The combined impact of higher agricultural commodity prices and 

higher transportation fuel prices decrease GDP in the early years of the program. However, the 

costs of renewable fuels are projected to decrease over time, while the cost of petroleum products 

is projected to increase over time. By 2016, renewable fuels are projected to be cheaper than the 

gasoline and diesel that they replace. By 2030, the impacts of lower transportation fuel costs and 

lower world oil prices outweigh the impacts of higher agricultural commodities, resulting in an 

increase in GDP in the two RFS cases relative to the AEO2007 reference case. Lower prices 

permit additional purchases of investment goods from an unchanged saving-investment balance 

which, in turn, leads to a larger capital stock. These price reductions also allow higher levels of 

real government spending while improving U.S. competitiveness thus promoting increased 

exports relative to the growth driven increase in imports. These combined impacts have a 

favorable impact on GDP over time. 

Figure 5.5-2. 

Percentage Change in Gross Domestic Product 

for the RFS2 Control Case vs. the AEO2007 Reference Case 

Percentage Change in GDP

-0.15%

-0.10%

-0.05%

0.00%

0.05%

0.10%

0.15%

0.20%

0.25%

2017 2022 2030

Control Case

with Subsidies

Control Case

Without

Subsidies
239 

Table 5.5-2. 

Percentage Change in Gross Domestic Product for the 

RFS2 Control Case vs. the AEO2007 Reference Case 
240 
241 
242 
243 
244 
245 
246 
247 
248 
249 
250 
251 
252 
253 
254 
255 

5.5.2 Impacts on U.S. Consumption 

 In addition to showing the impacts of the RFS2 renewable fuel volumes on U.S. GDP, we 

also show their impacts on real personal consumption expenditures. The following Figure 5.5-3 

and Table 5.5-3 show estimates of the Reference Case Consumption and Consumption after the 

imposition of the RFS2 renewable fuel volumes for Case One and Two. 

Figure 5.5-3. 

Annual Consumption Impacts for the Control Case vs. the AEO 2007 Reference Case 

(Billions of 2007 dollars)

Consumption

$0

$2,000

$4,000

$6,000

$8,000

$10,000

$12,000

$14,000

$16,000

2017 2022 2030

Billion 2007$

Reference Case

Control Case

with Subsidies

Control Case

Without

Subsidies
256 

Table 5.5-3. 

Annual Consumption Impacts for the RFS2 Control Case vs. the AEO2007 Reference Case 

(Billions of 2007 dollars)
257 
258 
259 
260 
261 
262 
263 
264 
265 
266 
267 
268 
269 
270 
271 
272 
273 
274 
275 
276 
277 

In the AEO2007 reference case, consumption rises from approximately $10.9 trillion in 

2017 to $12.3 trillion in 2022, and $14.9 trillion in 2030. In case one (without renewable fuel 

subsidies), consumption rises from approximately $10.9 trillion in 2017 to $12.3 trillion in 2022 

and $15.0 trillion in 2030. In case two (the case with renewable fuel subsidies), consumption 

rises from approximately $10.9 trillion in 2017 to $12.3 trillion in 2022 and $14.9 trillion in 

2030. 

Figure 5.5-4 and Table 5.5-4 show the estimated percentage change in consumption with 

the RFS2 rule volumes. The combined impact of higher agricultural commodity prices and 

higher transportation fuel costs decrease consumption in the early years of the RFS2 program. 

However, as the cost of renewable fuels decreases over time, this impact gradually outweighs the 

impact of higher agricultural commodity prices. As a result of the lower renewable fuels costs 

through time, consumption increases relative to the reference case in both RFS2 cases by 2030. 

The increase in labor taxes needed to offset the renewable fuel subsidies partially erodes the 

benefits observed in case one. In case two, the increase in labor taxes reduces the opportunity 

cost of choosing leisure instead of labor. Therefore, households work less when faced with 

higher labor taxes, thereby reducing overall household consumption slightly compared to case 

one. 

Figure 5.5-4. 

Percentage Change in Consumption for the RFS2 Control Case 

vs. the AEO2007 Reference Case 

 

Percentage Change in Consumption

-0.15%

-0.10%

-0.05%

0.00%

0.05%

0.10%

2017 2022 2030

Control Case

with Subsidies

Control Case

Without

Subsidies
278 

Table 5.5-4. 

Percentage Change in Consumption for the RFS2 Control Case 

vs. the AEO2007 Reference Case 
279 
280 
281 
282 
283 
284 
285 
286 
287 
288 
289 
290 
291 
292 
293 
294 

 In addition to showing the impacts of the rule on U.S. GDP and consumption, the impacts 

of the RFS2 renewable fuel volumes on personal consumption expenditures per household are 

estimated. The following Figure 5.5-5 and Table 5.5-5 show estimates of the reference case 

annual consumption per household and consumption per household after the imposition of the 

RFS2 renewable fuel volumes for both case one and case two. 

In the reference case, annual consumption per household rises from $83,041 in 2017 to 

$88,298 in 2022, and $98,139 in 2030. In case one (without renewable fuel subsidies), annual 

consumption per household rises from approximately $82,941 in 2017 to $88,243 in 2022 and 

$98,203 in 2030. In case two (with renewable fuel subsidies), annual consumption per 

household rises from approximately $82,930 in 2017 to $88,203 in 2022 and $98,167 in 2030. 

Thus, consumption per household is roughly $100 lower in case one (without renewable fuel 

subsidies) in 2017 compared to the reference case, and consumption per household is roughly 

$55 lower than the reference case in 2022. By 2030, consumption per household is roughly $64 

higher in case one compared to the reference case. For case two (with renewable fuel subsidies), 

consumption per household is roughly $111 lower than the reference case in 2017 and 

consumption per household is roughly $95 lower than the reference case in 2022. By 2030, 

however, consumption per household is higher than the reference case in case two, by $28. 

Figure 5.5-5. 

Consumption Expenditures Per Household for the 

RFS2 Control Case vs. the AEO2007 Reference Case (2007 dollars) 

Consumption Per Household

$75,000

$80,000

$85,000

$90,000

$95,000

$100,000

2017 2022 2030

2007$

Control Case with

Subsidies

Control Case

Without

Subsidies

Reference Case

Table 5.5-5. 

Consumption Expenditures Per Household for the 
295 

RFS2 Control Case vs. the AEO2007 Reference Case (2007 dollars) 
296 
297 
298 
299 
300 
301 
302 
303 
304 
305 
306 
307 
308 
309 
310 
311 
312 
313 
314 
315 
316 

5.6 Impacts of Increasing Volume Requirements in the RFS2 Program 

 The displacement of gasoline and diesel with renewable fuels has a wide range of 

environmental and economic impacts. As we describe in Sections IV-IX of the preamble, we 

have assessed many of these impacts for the final rule. It is difficult to ascertain how much of 

these impacts might be due to the natural growth in renewable fuel use due to market forces as 

crude oil prices rise versus what might be forced by the RFS2 standards. Regardless, these 

assessments provide important information on the wider public policy considerations related to 

renewable fuel production and use, climate change, and national energy security. Where 

possible, we have tried to provide two perspectives on the impacts of the renewable fuel volumes 

mandated in EISA – both relative to the RFS1 mandated volumes, and relative to a projection 

from EIA (AEO 2007) of renewable fuel volumes that would have been expected without EISA. 

Based on the results of our analyses, when fully phased in by 2022, the increased volume 

of renewable fuel required by this final rule in comparison to the AEO 2007 forecast would 

result in 138 million metric tons fewer CO2-equivalent GHG emissions (annual average over 30 

years), the equivalent of removing 27 million vehicles from the road today. 

At the same time, increases in emissions of hydrocarbons, nitrogen oxides, particulate 

matter, and other pollutants are projected to lead to increases in population-weighted annual 

average ambient PM and ozone concentrations, which in turn are anticipated to lead to up to 245 

cases of adult premature mortality. The air quality impacts, however, are highly variable from 

region to region. Ambient PM2.5 is likely to increase in areas associated with biofuel production 

and transport and decrease in other areas; for ozone, many areas of the country will experience 

increases and a few areas will see decreases. Ethanol concentrations will increase substantially; 

for the other modeled air toxics there are some localized impacts, but relatively little impact on 

national average concentrations. It is important to note that these air quality results represent the 

impact of an incremental increase in ethanol and other renewable fuels and do not estimate the 

total air quality impact of the RFS2 volumes of renewable fuels as compared to near-zero levels. 

EPA will conduct that type of analysis as part of the “anti-backsliding” study required by Clean 

Air Act section 211(v), separate from this final action. The “anti-backsliding” study will use 

improved emissions data and consider different ethanol blend levels. Clean Air Act section 

211(v) requires EPA to issue regulations that mitigate, to the greatest extent achievable, adverse 

impacts on air quality, considering the results of the “anti-backsliding” study. 

In addition to air quality, there are also expected to be adverse impacts on both water 

quality and quantity as the production of biofuels and their feedstocks increase. 

In addition to environmental impacts, the increased volumes of renewable fuels required 

by this final rule are also projected to have a number of other energy and economic impacts. The 

increased renewable fuel use is estimated to reduce dependence on foreign sources of crude oil, 

increase domestic sources of energy, and diversify our energy portfolio to help in moving 

beyond a petroleum-based economy. The increased use of renewable fuels is also expected to 

have the added benefit of providing an expanded market for agricultural products such as corn 

and soybeans and open new markets for the development of cellulosic feedstock industries and 
317 

conversion technologies. Overall, however, we estimate that the renewable fuel standards will 

result in significant net benefits, ranging between $16 and $29 billion in 2022. Table I.B-1 

summarizes the results of our impacts analyses of the RFS2 standards relative to the AEO2007 

reference case and identifies the section where you can find further explanation of it. As we 

work to implement the requirements of EISA, we will continue to assess these impacts. 
318 

Table 5.6-1 

Impact Summary of the RFS2 Standards in 2022 Relative to the AEO2007 Reference Case 

(2007 Dollars) 
319 
320 
321 
322 
323 
324 
325 
326 
327 
328 
329 
330 
331 
332 
333 
334 
335 
336 
337 
338 
339 
340 
341 
342 
343 
344 
345 
346 
347 
348 
349 
350 
351 
352 
353 
354 
355 
356 
357 
358 
359 
360 
361 
362 
363 
364 
365 
366 
367 
368 
369 
370 
371 
372 
373 
374 
375 
376 
377 
378 
379 
380 
381 
382 
383 
384 
385 
386 
387 
388 
389 
390 
391 
392 
393 
394 
395 
396 
397 
398 
399 
400 
401 
402 
403 
404 
405 
406 
407 
408 
409 
410 
411 
412 
413 
414 
415 
416 
417 
418 
419 
420 
421 
422 
423 
424 
425 
426 
427 
428 
429 
430 
431 
432 
433 
434 
435 
436 
437 
438 
439 
440 
441 
442 
443 
444 
445 
446 
447 
448 
449 
450 
451 
452 
453 
454 
455 
456 
457 
458 
459 
460 
461 
462 
463 
464 
465 

a

 The models used to estimate SCC values have not been exercised in a systematic manner that would allow 

researchers to assess the probability of different values. Therefore, the interim SCC values should not be considered 

to form a range or distribution of possible or likely values. See Section VIII.D for a complete summary of the 

interim SCC values. 

b

 Sum of Overall Fuel Costs, Energy Security, Monetized Health Impacts, and GHG Impacts (SCC). 

 

1

 U.S. Environmental Protection Agency. (2006). Final Regulatory Impact Analysis (RIA) for 

the Proposed National Ambient Air Quality Standards for Particulate Matter. Prepared by: 

Office of Air and Radiation. Retrieved March, 26, 2009 at http://www.epa.gov/ttn/ecas/ria.html 

2

 U.S. Environmental Protection Agency. (2008). Final Ozone NAAQS Regulatory Impact 

Analysis. Prepared by: Office of Air and Radiation, Office of Air Quality Planning and 

Standards. Retrieved March, 26, 2009 at http://www.epa.gov/ttn/ecas/ria.html 

3

 U.S. Environmental Protection Agency (U.S. EPA). 2009a. Regulatory Impact Analysis: 

National Emission Standards for Hazardous Air Pollutants from the Portland Cement 

Manufacturing Industry. Office of Air Quality Planning and Standards, Research Triangle Park, 

NC. April. Available on the Internet at 

<http://www.epa.gov/ttn/ecas/regdata/RIAs/portlandcementria_4-20-09.pdf>. 

4

 U.S. Environmental Protection Agency (U.S. EPA). 2009b. Proposed NO2 NAAQS 

Regulatory Impact Analysis (RIA). Office of Air Quality Planning and Standards, Research 

Triangle Park, NC. April. Available on the Internet at 

http://www.epa.gov/ttn/ecas/regdata/RIAs/proposedno2ria.pdf. Note: The revised NO2 

NAAQS may be final by the publication of this action. 

5

 U.S. Environmnetal Protection Agency (U.S. EPA). 2009c. Draft Regulatory Impact Analysis: 

Control of Emissions of Air Pollution from Category 3 Marine Diesel Engines. Office of 

Transportation and Air Quality, June. Available on the Internet at 

http://www.epa.gov/otaq/regs/nonroad/420d09002.htm. Note: The C3 rule may be final by the 

publication of this action. 

6

 GeoLytics Inc. (2002). Geolytics CensusCD® 2000 Short Form Blocks. CD-ROM Release 

1.0. GeoLytics, Inc. East Brunswick, NJ. Available: http://www.geolytics.com/ [accessed 29 

September 2004] 

7

 Woods & Poole Economics Inc. 2008. Population by Single Year of Age CD. CD-ROM. 

Woods & Poole Economics, Inc. Washington, D.C. 

8

 U.S. Environmental Protection Agency. (2006). Air quality criteria for ozone and related 

photochemical oxidants (second external review draft). Research Triangle Park, NC: National 

Center for Environmental Assessment; report no. EPA/600R-05/004aB-cB, 3v. Available: 

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=137307[March 2006] 

9

 U.S. Environmental Protection Agency, 2004. Air Quality Criteria for Particulate Matter 

Volume II of II. National Center for Environmental Assessment, Office of Research and 

Development, U.S. Environmental Protection Agency, Research Triangle Park, NC EPA/600/P99/002bF. 
466 

 10 World Health Organization (WHO). (2003). Health Aspects of Air Pollution with Particulate 

Matter, Ozone and Nitrogen Dioxide: Report on a WHO Working Group. World Health 

Organization. Bonn, Germany. EUR/03/5042688. 

11 Anderson HR, Atkinson RW, Peacock JL, Marston L, Konstantinou K. (2004). Meta-analysis 

of time-series studies and panel studies of Particulate Matter (PM) and Ozone (O3): Report of a 

WHO task group. Copenhagen, Denmark: World Health Organization. 

12 Bell, M.L., et al. (2004). Ozone and short-term mortality in 95 US urban communities, 1987-

2000. JAMA, 2004. 292(19): p. 2372-8. 

13 Huang, Y.; Dominici, F.; Bell, M. L. (2005) Bayesian hierarchical distributed lag models for 

summer ozone exposure and cardio-respiratory mortality. Environmetrics. 16: 547-562. 

14 Schwartz, J. (2005) How sensitive is the association between ozone and daily deaths to control 

for temperature? Am. J. Respir. Crit. Care Med. 171: 627-631. 

15 Bell, M.L., F. Dominici, and J.M. Samet. (2005). A meta-analysis of time-series studies of 

ozone and mortality with comparison to the national morbidity, mortality, and air pollution 

study. Epidemiology. 16(4): p. 436-45. 

16 Ito, K., S.F. De Leon, and M. Lippmann (2005). Associations between ozone and daily 

mortality: analysis and meta-analysis. Epidemiology. 16(4): p. 446-57. 

17 Levy, J.I., S.M. Chemerynski, and J.A. Sarnat. (2005). Ozone exposure and mortality: an 

empiric bayes metaregression analysis. Epidemiology. 16(4): p. 458-68. 

18 Pope, C.A., III, R.T. Burnett, M.J. Thun, E.E. Calle, D. Krewski, K. Ito, and G.D. Thurston. 

(2002). “Lung Cancer, Cardiopulmonary Mortality, and Long-term Exposure to Fine Particulate 

Air Pollution.” Journal of the American Medical Association 287:1132-1141. 

19 Laden, F., J. Schwartz, F.E. Speizer, and D.W. Dockery. (2006). Reduction in Fine 

Particulate Air Pollution and Mortality. American Journal of Respiratory and Critical Care 

Medicine. 173: 667-672. 

20 Industrial Economics, Incorporated (IEc). (2006). Expanded Expert Judgment Assessment of 

the Concentration-Response Relationship Between PM2.5 Exposure and Mortality. Peer Review 

Draft. Prepared for: Office of Air Quality Planning and Standards, U.S. Environmental 

Protection Agency, Research Triangle Park, NC. August. 

21 Woodruff, T.J., J. Grillo, and K.C. Schoendorf. (1997). The Relationship Between Selected 

Causes of Postneonatal Infant Mortality and Particulate Air Pollution in the United States. 

Environmental Health Perspectives. 105(6):608-612. 

22 Abbey, D.E., B.L. Hwang, R.J. Burchette, T. Vancuren, and P.K. Mills. (1995). Estimated 

Long-Term Ambient Concentrations of PM(10) and Development of Respiratory Symptoms in a 

Nonsmoking Population. Archives of Environmental Health. 50(2): 139-152. 

23 Peters, A., D.W. Dockery, J.E. Muller, and M.A. Mittleman. (2001). Increased Particulate Air 

Pollution and the Triggering of Myocardial Infarction. Circulation. 103:2810-2815. 

24 Schwartz J. (1995). Short term fluctuations in air pollution and hospital admissions of the 

elderly for respiratory disease. Thorax. 50(5):531-538. 
467 

 25 Schwartz J. (1994a). PM(10) Ozone, and Hospital Admissions For the Elderly in Minneapolis 

St Paul, Minnesota. Arch Environ Health. 49(5):366-374. 

26 Schwartz J. (1994b). Air Pollution and Hospital Admissions For the Elderly in Detroit, 

Michigan. Am J Respir Crit Care Med. 150(3):648-655. 

27 Moolgavkar SH, Luebeck EG, Anderson EL. (1997). Air pollution and hospital admissions 

for respiratory causes in Minneapolis St. Paul and Birmingham. Epidemiology. 8(4):364-370. 

28 Burnett RT, Smith-Doiron M, Stieb D, Raizenne ME, Brook JR, Dales RE, et al. (2001). 

Association between ozone and hospitalization for acute respiratory diseases in children less than 

2 years of age. Am J Epidemiol. 153(5):444-452. 

29 Moolgavkar, S.H. (2003). “Air Pollution and Daily Deaths and Hospital Admissions in Los 

Angeles and Cook Counties.” In Revised Analyses of Time-Series Studies of Air Pollution and 

Health. Special Report. Boston, MA: Health Effects Institute. 

30 Ito, K. (2003). “Associations of Particulate Matter Components with Daily Mortality and 

Morbidity in Detroit, Michigan.” In Revised Analyses of Time-Series Studies of Air Pollution 

and Health. Special Report. Health Effects Institute, Boston, MA. 

31 Moolgavkar, S.H. (2000). Air Pollution and Hospital Admissions for Diseases of the 

Circulatory System in Three U.S. Metropolitan Areas. Journal of the Air and Waste 

Management Association 50:1199-1206. 

32 Sheppard, L. (2003). Ambient Air Pollution and Nonelderly Asthma Hospital Admissions in 

Seattle, Washington, 1987-1994. In Revised Analyses of Time-Series Studies of Air Pollution 

and Health. Special Report. Boston, MA: Health Effects Institute. 

33 Jaffe DH, Singer ME, Rimm AA. (2003). Air pollution and emergency department visits for 

asthma among Ohio Medicaid recipients, 1991-1996. Environ Res 91(1):21-28. 

34 Peel, J. L., P. E. Tolbert, M. Klein, et al. (2005). Ambient air pollution and respiratory 

emergency department visits. Epidemiology. Vol. 16 (2): 164-74. 

35 Wilson, A. M., C. P. Wake, T. Kelly, et al. (2005). Air pollution, weather, and respiratory 

emergency room visits in two northern New England cities: an ecological time-series study. 

Environ Res. Vol. 97 (3): 312-21. 

36 Norris, G., S.N. YoungPong, J.Q. Koenig, T.V. Larson, L. Sheppard, and J.W. Stout. (1999). 

An Association between Fine Particles and Asthma Emergency Department Visits for Children 

in Seattle. Environmental Health Perspectives 107(6):489-493. 

37 Dockery, D.W., J. Cunningham, A.I. Damokosh, L.M. Neas, J.D. Spengler, P. Koutrakis, J.H. 

Ware, M. Raizenne, and F.E. Speizer. (1996). Health Effects of Acid Aerosols On North 

American Children-Respiratory Symptoms. Environmental Health Perspectives 104(5):500-505. 

38 Pope, C.A., III, D.W. Dockery, J.D. Spengler, and M.E. Raizenne. (1991). Respiratory Health 

and PM10 Pollution: A Daily Time Series Analysis. American Review of Respiratory Diseases

144:668-674. 
468 

 39 Schwartz, J., and L.M. Neas. (2000). Fine Particles are More Strongly Associated than 

Coarse Particles with Acute Respiratory Health Effects in Schoolchildren. Epidemiology 11:6-

10. 

40 Ostro, B., M. Lipsett, J. Mann, H. Braxton-Owens, and M. White. (2001). Air Pollution and 

Exacerbation of Asthma in African-American Children in Los Angeles. Epidemiology

12(2):200-208. 

41 Vedal, S., J. Petkau, R. White, and J. Blair. (1998). Acute Effects of Ambient Inhalable 

Particles in Asthmatic and Nonasthmatic Children. American Journal of Respiratory and 

Critical Care Medicine 157(4):1034-1043. 

42 Ostro, B.D. (1987). Air Pollution and Morbidity Revisited: A Specification Test. Journal of 

Environmental Economics Management 14:87-98. 

43 Gilliland FD, Berhane K, Rappaport EB, Thomas DC, Avol E, Gauderman WJ, et al. (2001). 

The effects of ambient air pollution on school absenteeism due to respiratory illnesses. 

Epidemiology 12(1):43-54. 

44 Chen L, Jennison BL, Yang W, Omaye ST. (2000). Elementary school absenteeism and air 

pollution. Inhal Toxicol 12(11):997-1016. 

45 Ostro, B.D. and S. Rothschild. (1989). Air Pollution and Acute Respiratory Morbidity: An 

Observational Study of Multiple Pollutants. Environmental Research 50:238-247. 

46 U.S. Science Advisory Board. (2004). Advisory Plans for Health Effects Analysis in the 

Analytical Plan for EPA’s Second Prospective Analysis –Benefits and Costs of the Clean Air Act, 

1990—2020. EPA-SAB-COUNCIL-ADV-04-004. 

47 National Research Council (NRC). (2002). Estimating the Public Health Benefits of 

Proposed Air Pollution Regulations. Washington, DC: The National Academies Press. 

48 U.S. Environmental Protection Agency (U.S. EPA). 2008. Integrated Science Assessment for 

Particulate Matter (External Review Draft). National Center for Environmental Assessment, 

Research Triangle Park, NC. EPA/600/R-08/139. December. Available on the Internet at 

<http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=201805>. 

49 U.S. Environmental Protection Agency (U.S. EPA). 2009. Integrated Science Assessment for 

Particulate Matter (Second External Review Draft). National Center for Environmental 

Assessment, Research Triangle Park, NC. EPA/600/R-08/139B. July. Available on the Internet 

at <http://cfint.rtpnc.epa.gov/ncea/prod/recordisplay.cfm?deid=210586>. 

50 U.S. Environmental Protection Agency - Science Advisory Board (U.S. EPA-SAB). 2009. 

Review of EPA’s Integrated Science Assessment for Particulate Matter (First External Review 

Draft, December 2008). EPA-COUNCIL-09-008. May. Available on the Internet at 

<http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86e/73AC

CA834AB44A10852575BD0064346B/$File/EPA-CASAC-09-008-unsigned.pdf>. 

51 U.S. Environmental Protection Agency - Science Advisory Board (U.S. EPA-SAB). 2009. 

Consultation on EPA’s Particulate Matter National Ambient Air Quality Standards: Scope and 

Methods Plan for Health Risk and Exposure Assessment. EPA-COUNCIL-09-009. May. 

Available on the Internet at 

<http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86e/723F

E644C5D758DF852575BD00763A32/$File/EPA-CASAC-09-009-unsigned.pdf>. 
469 

 52 U.S. Environmental Protection Agency. (2009). Regulatory Impact Analysis: National 

Emission Standards for Hazardous Air Pollutants from the Portland Cement Manufacturing 

Industry. Office of Air and Radiation. Retrieved on May 4, 2009, from 

http://www.epa.gov/ttn/ecas/regdata/RIAs/portlandcementria_4-20-09.pdf

53 Viscusi, W.K., W.A. Magat, and J. Huber. (1991). Pricing Environmental Health Risks: 

Survey Assessments of Risk-Risk and Risk-Dollar Trade-Offs for Chronic Bronchitis. Journal 

of Environmental Economics and Management 21:32-51. 

54 Cropper, M.L., and A.J. Krupnick. (1990). The Social Costs of Chronic Heart and Lung 

Disease. Resources for the Future. Washington, DC. Discussion Paper QE 89-16-REV. 

55 Russell, M.W., D.M. Huse, S. Drowns, E.C. Hamel, and S.C. Hartz. (1998). Direct Medical 

Costs of Coronary Artery Disease in the United States. American Journal of Cardiology

81(9):1110-1115. 

56 Wittels, E.H., J.W. Hay, and A.M. Gotto, Jr. (1990). Medical Costs of Coronary Artery 

Disease in the United States. American Journal of Cardiology 65(7):432-440. 

57 Agency for Healthcare Research and Quality (AHRQ). (2000). HCUPnet, Healthcare Cost 

and Utilization Project. Rockville, MD. Accessed April 10, 2009, from http://hcupnet.ahrq.gov/ 

58 Smith, D.H., D.C. Malone, K.A. Lawson, L.J. Okamoto, C. Battista, and W.B. Saunders. 

(1997). A National Estimate of the Economic Costs of Asthma. American Journal of 

Respiratory and Critical Care Medicine 156(3 Pt 1):787-793. 

59 Stanford, R., T. McLaughlin, and L.J. Okamoto. (1999). The Cost of Asthma in the 

Emergency Department and Hospital. American Journal of Respiratory and Critical Care 

Medicine 160(1):211-215. 

60 Industrial Economics, Incorporated (IEc). (1994). Memorandum to Jim DeMocker, Office of 

Air and Radiation, Office of Policy Analysis and Review, U.S. Environmental Protection 

Agency. March 31. 

61 Rowe, R.D., and L.G. Chestnut. (1986). Oxidants and Asthmatics in Los Angeles: A Benefits 

Analysis—Executive Summary. Prepared by Energy and Resource Consultants, Inc. Report to 

the U.S. Environmental Protection Agency, Office of Policy Analysis. EPA-230-09-86-018. 

Washington, DC. 

62 Neumann, J.E., M.T. Dickie, and R.E. Unsworth. (1994). Linkage Between Health Effects 

Estimation and Morbidity Valuation in the Section 812 Analysis—Draft Valuation Document. 

Industrial Economics Incorporated (IEc) Memorandum to Jim DeMocker, U.S. Environmental 

Protection Agency, Office of Air and Radiation, Office of Policy Analysis and Review. March 

31. 

63 Tolley, G.S. et al. January (1986). Valuation of Reductions in Human Health Symptoms and 

Risks. University of Chicago. Final Report for the U.S. Environmental Protection Agency. 

64 Council of Economic Advisors. (2005). The Annual Report of the Council of Economic 

Advisors. In: Economic Report of the President. Table B-60. U.S. Government Printing Office: 

Washington, DC. 
470 

 lxv National Research Council (NRC). (2002). Estimating the Public Health Benefits of Proposed 

Air Pollution Regulations. The National Academies Press: Washington, D.C. 

lxvi U.S. Environmental Protection Agency, (2004a). Final Regulatory Analysis: Control of 

Emissions from Nonroad Diesel Engines. EPA420-R-04-007. Prepared by Office of Air and 

Radiation. Retrieved on April 10, 2009, from http://www.epa.gov/nonroaddiesel/2004fr/420r04007.pdf 

lxvii U.S. Environmental Protection Agency, (2005). Regulatory Impact Analysis for the Clean Air 

Interstate Rule. EPA 452/-03-001. Prepared by Office of Air and Radiation. Retrieved on April 

10, 2009, from http://www.epa.gov/interstateairquality/tsd0175.pdf 

lxviii U.S. Environmental Protection Agency, (2006). Regulatory Impact Analysis for the PM 

NAAQS. EPA Prepared by Office of Air and Radiation. Retrieved on April 10, 2009, from 

http://www.epa.gov/ttn/ecas/regdata/RIAs/Chapter%205--Benefits.pdf 

lxix Industrial Economics, Inc. (2006). Expanded Expert Judgment Assessment of the 

Concentration-Response Relationship Between PM2.5 Exposure and Mortality. Prepared for 

EPA Office of Air Quality Planning and Standards, September. Retrieved on April 10, 2009, 

from http://www.epa.gov/ttn/ecas/regdata/Uncertainty/pm_ee_report.pdf 

lxx U.S. Environmental Protection Agency, (2006). Regulatory Impact Analysis for the PM 

NAAQS. EPA Prepared by Office of Air and Radiation. Retrieved on April 10, 2009, from 

http://www.epa.gov/ttn/ecas/regdata/RIAs/Chapter%205--Benefits.pdf