**Regulatory Impact Analysis:**

**Renewable Fuel Standard Program**

**Assessment and Standards Division**

**Office of Transportation and Air Quality**

**U.S. Environmental Protection Agency**

**April 10, 2007**

***NOTICE***

*This technical report does not necessarily represent final EPA
decisions or positions.*

*It is intended to present technical analyses of issues using data that
are currently available.*

*The purpose in the release of such a report is to facilitate an
exchange of technical information and to inform the public of technical
developments.*

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**Table of Contents**

Statement of
Need\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.....4

Overview\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....5

List of Acronyms and Abbreviations\...\...\...\...\...\...\...\....
\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....7

Chapter 1: Industry
Characterization\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.....11

Chapter 2: Changes to Motor Vehicle Fuel

Under the Renewable Fuel Standard
Program\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....50

Chapter 3: Impacts on Emissions from Vehicles,

Nonroad Equipment, and Fuel Production
Facilities\...\...\...\...\...\...\...\...\...\...\...\...\...116

Chapter 4: National Emission Inventory
Impacts\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...171

Chapter 5: Air Quality
Impacts\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....198

Chapter 6: Lifecycle Impacts on Fossil Energy and Greenhouse
Gases\...\...\...\...\...\.....217

Chapter 7: Estimated Costs of Renewable Fuels, Gasoline and
Diesel\...\...\...\...\...\...\...261

Chapter 8: Agricultural Sector
Impacts\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....322

Chapter 9: Small-Business Flexibility
Analysis\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...334

Endnotes\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.....341

**Statement of Need**

The United States currently consumes about 190 billion gallons of
gasoline and diesel fuel annually to meet its transportation fuel needs.
Of this volume, about 65 percent, or 124 billion gallons, is derived
from foreign sources. The United States' dependence on imported
petroleum to meet its growing demand for transportation fuel exacts a
cost on the nation in terms of energy security. In addition,
petroleum-based fuel exacts a cost on the nation with respect to
environmental quality. The Renewable Fuel Standard (RFS) program
increases national energy security by creating a market for renewable
fuel as a substitute for petroleum-based fuel. By incorporating
incentives for investing in research and development of renewable fuels,
the RFS program also seeks to accelerate the nation's progress toward
energy independence. In addition, the RFS program helps to reduce the
country's greenhouse gas emissions, thereby reducing the nation\'s
contribution to global climate change and its potential effects on the
U.S. economy, security, and public health.

# Overview

EPA is finalizing standards which would implement a renewable fuel
program as required by the Energy Policy Act of 2005 (the Act). The Act
specifies the total volume of renewable fuel that is required to be used
each year, and directs EPA to adjust this amount under certain
circumstances. The resulting standards represent a level of renewable
fuel that each refinery or importer must account for relative to its
annual volume of gasoline produced or imported. In reality, however,
renewable fuel use is forecast to exceed the RFS standards due to market
forces. The analyses of the impacts associated with this increase in
renewable fuel use are discussed in this Regulatory Impact Analysis
(RIA).

[Chapter 1: Industry Characterization]{.underline}

This chapter discusses current gasoline, diesel and renewable fuel
production, importation, marketing and distribution, as well as likely
future changes as a result of increased renewable fuel use.

[Chapter 2: Changes to Motor Vehicle Fuel Under the RFS
Program]{.underline}

This chapter discusses our gasoline and renewable fuel consumption
predictions (compared to a 2004 base year), and the expected impacts of
various ethanol blends on gasoline properties.

[Chapter 3: Impacts on Emissions from Vehicles, Nonroad Equipment, and
Fuel Production Facilities]{.underline}

This chapter evaluates the impacts on vehicle and nonroad equipment
emissions under various oxygenate assumptions, specifically increasing
ethanol and decreasing MTBE, and different modeling techniques. The
effect of biodiesel use on diesel-powered vehicle emissions is also
presented. Finally, emissions from ethanol and biodiesel production
facilities are discussed.

[Chapter 4: National Emissions Inventory Impacts]{.underline}

This chapter discusses the methods used to develop the national
emissions inventories, and quantifies the impact of expanded ethanol and
biodiesel use on those inventories.

[Chapter 5: Air Quality Impacts]{.underline}

This chapter discusses the impacts of expanded renewable fuel use on
ozone and particulate matter formation.

[Chapter 6: Lifecycle Impacts on Fossil Energy and Greenhouse
Gases]{.underline}

This chapter discusses our fuel lifecycle modeling, that is, analysis
which accounts for all energy and emissions of the fuel production
process. A description of the model we used, how we used it, and the
results are presented. Impacts on greenhouse gases, including CO~2,~
fossil fuel use, and petroleum use are presented. The effects on
petroleum imports, import expenditures, and domestic energy security are
also discussed.

[Chapter 7: Estimated Costs of Renewable Fuels, Gasoline and
Diesel]{.underline}

This chapter contains our analysis of the cost of corn and cellulosic
ethanol. We also discuss biodiesel and renewable diesel production
costs. Costs associated with distributing the volumes of ethanol
necessary to meet the requirements of the program, and the costs to
prepare gasoline and diesel blendstocks (for blending with renewable
fuels) are also presented. Finally, we present the overall fuel cost
impacts of expanded renewable fuel use.

[Chapter 8: Agricultural Sector Impacts]{.underline}

This chapter discusses the likely economic impacts on the agricultural
sector that may occur as a result of the large expansion of renewable
fuel production and use expected in the future. On-going work using the
FASOM model is also described.

[Chapter 9: Small Business Flexibility Analysis]{.underline}

This chapter discusses our Small Business Flexibility Analysis (SBFA)
which evaluates the rule to ensure that concerns regarding small
businesses, which would be affected by the rule, are sufficiently
considered.

**List of Acronyms and Abbreviations**

  ----------------- -----------------------------------------------------
  AAM               Alliance of Automobile Manufacturers

  ABT               Averaging, Banking, and Trading

  ACE               American Coalition for Ethanol

  The Act           Energy Policy Act of 2005 (also the Energy Act)

  ADM               Archer Daniels Midland

  AEO               Annual Energy Outlook (an EIA publication)

  ANL               Argonne National Laboratory

  AQIRP             Auto/Oil Air Quality Improvement Research Program

  ARMS              Agricultural Resource Management Survey

  B0, B5, B20, etc  Percent of biodiesel, e.g., B5= 5% biodiesel, 95%
                    diesel

  bbl               Barrel

  BEA               Bureau of Economic Analysis

  Bgal, bgal,       Billions of gallons
  bilgal, billgal,  
  bg                

  BGY               Billions of gallons per year

  BPCD              Barrels Per calendar day

  BPSD              Barrels per stream day

  bpd, bbls/day     Barrels Per Day

  BTU               British Thermal Unit

  BU                Bushel

  Bu/acre           Bushels per acre

  BZ                Benzene

  CA                California

  CAA               Clean Air Act

  CAIR              Clean Air Interstate Rule

  CARB              California Air Resources Board

  CaRFG3            California Phase 3 RFG

  CBG               Cleaner Burning Gasoline

  CBI               Caribbean Basin Initiative

  CD                Census Division

  CFEIS             EPA's Certification and Fuel Economy Information
                    System

  CFR               Code of Federal Regulations

  c/gal             Cents per gallon

  CG                Conventional Gasoline

  CHP               Combined Heat and Power Technology

  CO                Carbon Monoxide

  CO2               Carbon Dioxide

  Co-op             Cooperative

  CRC               Coordinating Research Council

  DDGS              Distillers' Dried Grains with Solubles

  DOE               Department of Energy

  DRIA              Draft Regulatory Impact Analysis

  E&C               Engineering and Construction

  E0                Gasoline Blend which Does Not Contain Ethanol

  E10               Gasoline Blend containing a nominal 10 percent
                    ethanol by volume

  E85               Gasoline Blend containing 85 percent ethanol by
                    volume

  E200              Percent of Fuel Evaporated at 200 Degrees F (ASTM D
                    86)

  E300              Percent of Fuel Evaporated at 300 Degrees F (ASTM D
                    86)

  EIA               Energy Information Administration (part of the U.S.
                    Department of Energy)

  Energy Act        Energy Policy Act of 2005 (also the Act)

  EO                Executive Order

  EPA               Environmental Protection Agency

  EPAct             Energy Policy Act of 2005 (also 'the Energy Act' or
                    'the Act')

  ETBE              Ethyl Tertiary Butyl Ether

  ETOH              Ethanol

  ex CA             Excluding California

  F, °F             Fahrenheit

  FAPRI             Farm and Agricultural Policy Research Institute

  FASOM             Forestry and Agriculture Sector Optimization Model

  FBP               Feed Boiling Point (also Final Boiling Point)

  FCC               Fluidized Catalytic Cracker

  FCCU              Fluidized Catalytic Cracking Unit

  FHWA              Federal Highway Administration

  FOEB              Fuel Oil Equivalent Barrel

  FR                Federal Register

  FRM               Final Rulemaking

  FRTP              Fixed Reduction Trigger Point

  FFV               Flexible Fuel Vehicle

  FTP               Federal test procedure

  GAL               Gallon

  g/Btu             Grams per Btu

  g/day             Grams per day

  GDP               Gross Domestic Product

  GHG               Greenhouse Gases

  GPA               Geographic Phase-in Area

  GREET             Greenhouse Gas, Regulated Emissions, and Energy Use
                    in Transportation model

  GWP               Global warming potentials

  HC                Hydrocarbon(s)

  HCO               Heavy Cycle Oil (a refinery stream)

  HDN               Naphtha Hydrotreater (also Hydro-Denitrogenation
                    Unit)

  HSR               Heavy Straight Run (a refinery stream)

  HVGO              Heavy Vacuum Gas Oil (a refinery stream)

  IBP               Initial Boiling Point

  k                 Thousand

  kbbl              Thousand barrels

  kwh               Kilowatt Hour

  Lb                Pound

  LCO               Light Cycle Oil (a refinery stream)

  LEV               Low emission vehicle

  LLE               Liquid-Liquid Extraction

  LNS               Light Naphtha Splitter

  LP                Linear Programming (a type of refinery model)

  LSR               Light Straight Run (a refinery stream)

  mg/m^3^           Milligrams per cubic meter

  MGY, MMgy         Million Gallons per Year

  MM                Million

  MMBTU             Million British Thermal Units

  MMbbls/cd         Millions of barrels per calendar day

  MMGal/yr          Millions of gallons per year

  MOBILE (5, 6,     EPA's Motor Vehicle Emission Inventory Model
  6.2)              (versions)

  MON               Motor Octane Number

  MOVES2006         EPA's Next Generation Highway Vehicle Emission Model

  MSAT              Mobile Source Air Toxics

  MSAT1             2001 Mobile Source Air Toxics Rule

  MSAT2             2006 Proposed Mobile Source Air Toxics Rule

  MTBE              Methyl Tertiary-Butyl Ether

  NAAQS             National Ambient Air Quality Standards

  NAICS             North American Industrial Classification System

  NASS              National Agricultural Statistics Service

  NBB               National Biodiesel Board

  NCSU              North Carolina State University

  NGL               Natural gas plant liquids

  NMHC              Non-Methane Hydrocarbons

  NMIM              National Mobile Inventory Model (EPA software tool)

  NMOG              Non-methane organic gases

  NONROAD           EPA's Non-road Engine Emission Model

  NONROAD2005       EPA's Non-road Engine Emission Model Released in 2005

  NO~x~             Oxides of nitrogen

  NPRM              Notice of Proposed Rulemaking

  NREL              National Renewable Energy Laboratory

  OMB               Office of Management and Budget

  OMHCE             Organic Material Hydrocarbon Equivalent

  ORNL              Oak Ridge National Laboratory

  OTAQ              Office of Transportation and Air Quality

  Oxy-fuel, oxyfuel Winter oxygenated fuel program

  PADD              Petroleum Administration for Defense District

  PM                Particulate Matter

  PM~10~            Coarse Particle

  PM~2.5~           Fine Particle

  PMA               Petroleum Marketing Annual (an EIA publication)

  POM               Polycyclic Organic Matter

  PONA              Paraffin, Olefin, Naphthene, Aromatic

  ppb               Parts per billion

  ppm               Parts Per million

  PRTP              Percentage Reduction Trigger Point

  PSI               Pounds per Square Inch

  QBtu              Quadrillion btu

  Quadrillion       10^15^

  (R+M)/2           Octane calculation (RON+MON)/2

  RBOB              Reformulated Blendstock for Oxygenate Blending

  RFA               Regulatory Flexibility Act

  RFG               Reformulated Gasoline

  RFS               Renewable Fuels Standard

  RIA               Regulatory Impact Analysis

  RIMS              Regional Input-Output Modeling System

  RIN               Renewable Identification Number

  RON               Research octane number

  RPMG              Renewable Products Marketing Group

  RSM               Response Surface Model

  RVP               Reid Vapor Pressure

  S                 Sulfur

  SBA               Small Business Administration

  SBAR Panel, or    Small Business Advocacy Review Panel
  'the Panel'       

  SBFA              Small Business Flexibility Analysis

  SBREFA            Small Business Regulatory Enforcement Fairness Act
                    (of 1996)

  scf               Standard cubic feet

  SOA               Secondary Organic Aerosol

  SOx               Oxides of Sulfur

  SULEV             Super ultra low emission vehicle

  T50               Temperature at which 50% (by volume) of fuel
                    evaporates (ASTM D 86)

  T90               Temperature at which 90% (by volume) of fuel
                    evaporates (ASTM D 86)

  TAME              Tertiary Amyl Methyl Ether

  ULEV              Ultra low emission vehicle

  U.S.C.            United States Code

  USDA              U.S. Department of Agriculture

  VGO               Vacuum Gas Oil (a refinery stream)

  VMT               Vehicle Miles Traveled

  VOC               Volatile Organic Compound

  vol%              Percent by volume, volume percent

  wt%               Percent by weight, weight percent

  yr, y             Year
  ----------------- -----------------------------------------------------

**Chapter 1: Industry Characterization**

**1.1 Transportation Fuel Providers**

**1.1.1 Petroleum Refiners**

As of the end of 2005, there were 142 crude oil refineries operating in
the United States, representing a total of 16.4 million barrels/day of
refining capacity. (These refineries produce gasoline and other products
and are a separate category than "blender refiners" that do not process
crude oil, but make gasoline from blendstocks.) The greatest number of
refineries per PADD is in PADD 3 (the Gulf Coast region) which has 52
operating refineries as of the end of 2005. This PADD also has the
greatest refining capacity, at 7.9 million barrels per day. Table 1.1-1
presents the refineries and their crude oil production capacity, and
identifies the PADD where the refinery is located.

**Table 1.1-1.**

**Refining Capacity by Individual Refinery**

**(crude oil processing basis)**

  -----------------------------------------------------------------------
  **Company**                       **Capacity          **PADD**
                                    (MMbbls/cd)**       
  --------------------------------- ------------------- -----------------
  **Conoco Phillips**               **2.2**             

  *Wood River, IL*                  *0.31*              *2*

  *Belle Chasse, LA*                *0.25*              *3*

  *Sweeny, TX*                      *0.25*              *3*

  *Westlake LA*                     *0.24*              *3*

  *Linden, NJ*                      *0.24*              *1*

  *Ponca City OK*                   *0.19*              *2*

  *Trainer, PA*                     *0.19*              *1*

  *Borger TX*                       *0.15*              *3*

  *Wilmington CA*                   *0.14*              *5*

  *Ferndale WA*                     *0.10*              *5*

  *Rodeo CA*                        *0.08*              *5*

  *Billings MT*                     *0.06*              *4*

  **Valero Energy Corp.**           **2.0**             

  *Port Arthur TX*                  *0.26*              *3*

  *Memphis TN*                      *0.18*              *2*

  *Lima OH*                         *0.15*              *2*

  *Texas City TX*                   *0.21*              *3*

  *Corpus Christi TX*               *0.14*              *3*

  *Houston TX*                      *0.08*              *3*

  *Sunray TX*                       *0.16*              *3*

  *Three Rivers TX*                 *0.09*              *3*

  *Norco LA*                        *0.19*              *3*

  *Paulsboro NJ*                    *0.16*              *1*

  *Benecia CA*                      *0.14*              *5*

  *Wilmington CA*                   *0.01*              *5*

  *Ardmore OK*                      *0.08*              *2*

  *Wilmington CA*                   *0.08*              *5*

  *Krotz Springs LA*                *0.08*              *3*

  **Exxon Mobil Corp.**             **2.0**             

  *Baytown TX*                      *0.56*              *3*

  *Baton Rouge LA*                  *0.50*              *3*

  *Beaumont TX*                     *0.34*              *3*

  *Joliet IL*                       *0.24*              *2*

  *Torrance CA*                     *0.15*              *5*

  *Billings MT*                     *0.06*              *4*

  *Chalmette, LA*                   *0.19*              *3*

  **BP PLC**                        **1.5**             

  *Texas City TX*                   *0.44*              *3*

  *Whiting IN*                      *0.41*              *2*

  *Toledo OH*                       *0.13*              *2*

  *Los Angeles CA*                  *0.26*              *5*

  *Ferndale WA*                     *0.23*              *5*

  **Chevron Corp.**                 **0.9**             

  *Pascagoula MS*                   *0.33*              *3*

  *El Segundo CA*                   *0.26*              *5*

  *Richmond CA*                     *0.24*              *5*

  *Honolulu HI*                     *0.05*              *5*

  *Salt Lake City UT*               *0.05*              *4*

  **Marathon Oil Corp.**            **1.0**             

  *Garyville LA*                    *0.25*              *3*

  *Cattlettsburg KY*                *0.22*              *2*

  *Robinson IL*                     *0.19*              *2*

  *Detroit MI*                      *0.10*              *2*

  *Canton OH*                       *0.07*              *2*

  *Texas City TX*                   *0.07*              *3*

  *Saint Paul Park MN*              *0.07*              *2*

  **Sunoco, Inc.**                  **0.58**            

  *Marcus Hook PA*                  *0.18*              *2*

  *Toledo OH*                       *0.16*              *2*

  *Westville NJ*                    *0.15*              *1*

  *Tulsa OK*                        *0.09*              *2*

  **PDV America, Inc.**             **0.81**            

  *Citgo; Lake Charles LA*          0.43                *3*

  *Citgo, Lemont IL*                0.17                *2*

  *Citgo; Corpus Christi TX*        0.16                *3*

  **Koch Industries**               **0.57**            

  *Corpus Christi TX*               *0.29*              *3*

  *Saint Paul MN*                   *0.28*              *2*

  **Motiva Enterprises LLC**        **0.76**            

  *Port Arthur TX*                  *0.29*              *3*

  *Convent LA*                      *0.24*              *3*

  *Norco LA*                        *0.23*              *3*

  **Tesoro Corp.**                  **0.51**            

  *Anacortes WA*                    *0.12*              *5*

  *Salt Lake City UT*               *0.06*              *4*

  *Martinez CA*                     *0.17*              *5*

  *Kapolei HI*                      *0.09*              *5*

  *Kenai AK*                        *0.072*             *5*

  **Royal Dutch/Shell Group**       **0.82**            

  *Martinez CA*                     *0.16*              *5*

  *Anacortes WA*                    *0.15*              *5*

  *Wilmington CA*                   *0.10*              *5*

  *Saraland AL*                     *0.08*              *3*

  *Deer Park, TX*                   *0.33*              *3*

  **Lyondell Chem. Co. (Houston)**  **0.27**            *3*

  **Total SA** (Port Arthur, TX)    **0.23**            *3*

  **Sinclair Oil**                  **0.17**            

  *Tulsa OK*                        *0.07*              *2*

  *Sinclair WY*                     *0.07*              *4*

  *Evansville WY*                   *0.03*              *4*

  **Murphy Oil**                    **0.15**            

  *Meraux LA*                       *0.12*              *3*

  *Superior WI*                     *0.03*              *2*

  **Frontier Oil**                  **0.15**            

  *El Dorado KS*                    *0.11*              *2*

  *Cheyenne WY*                     *0.04*              *4*

  **Cenex Harvest States, Inc.**    **0.14**            

  *McPherson KS*                    *0.08*              *2*

  *Laurel MT*                       *0.06*              *4*

  **Coffeyville Acquisitions**      **0.11**            *2*
  (Coffeyville KS)                                      

  **Navajo Refining Corp.**         **0.11**            

  *Artesia NM*                      *0.07*              *3*

  *Woods Cross UT*                  *0.03*              *4*

  *Great Falls MT*                  *0.01*              *4*

  **Pasadena Refining Systems**     **0.10**            *3*
  (Pasadena TX)                                         

  **Giant Industries, Inc.**        **0.10**            

  *Yorktown VA*                     *0.06*              *1*

  *Gallup NM*                       *0.02*              3

  *Bloomfield NM*                   *0.02*              3

  **Big West Oil** (North Salt Lake **0.10**            *4*
  UT)                                                   
  -----------------------------------------------------------------------

> Source: Table 5 in Energy Information Administration, Refinery
> Capacity 2006 found at
> <http://www.eia.doe.gov/pub/oil_gas/petroleum/data_publications/refinery_capacity_data/current/table5.pdf>

Refining capacity has steadily increased in the U.S. due to increased
demand for petroleum products, with gasoline representing approximately
45 percent of product demand. Refining capacity (crude oil input) was
about 14 million bbls/day in 1973 and 17 million bbls/day in 2005. While
refining capacity has increased, however, the number of refineries has
decreased as less economical refineries have been forced to close. (Many
of these came into existence for a very short time due to oil price
supports in the 1970's.) In the 1970's, the number of refineries in the
U.S. was approximately 270 and has decreased by 47 percent. Figure 1.1-1
shows the number of refineries and total capacity in the U.S. from 1973
through 2004.

**Figure 1.1-1.**

![](media/image1.wmf){width="7.75in"
height="3.6243055555555554in"}**Number of Refineries and Total Capacity
in the U.S. from 1973-2004**

Source: EIA; Annual Energy Report, 2005 (Table 5.9)

The increase in capacity combined with the decrease in amount of
refineries and the increased demand for gasoline and diesel fuels, has
resulted in an increase in the average utilization rate of refineries.
In the 1970's, the utilization rate ranged from 84 to 94 percent. In the
last ten years, however, the utilization rate has ranged from 91 to 96
percent. Refineries therefore have to produce more with less overall
capacity. The amount of gasoline and diesel produced by U.S. refiners
has steadily increased. Since 1973 through 2004, gasoline and diesel
production has increased 27 and 36 percent, respectively. Figure 1.1-2
shows the change in gasoline and diesel production from 1973 through
2004

**Figure 1.1-2.**

**Amount of Gasoline and Diesel Fuels Produced in the U.S.**

![](media/image2.wmf){width="6.375in" height="4.360416666666667in"}

Source: EIA Annual Energy Report, 2005; Table 5.8

**1.1.2 Petroleum Imports**

The decrease in U.S. refining capacity discussed in Section 1.1.3, has
resulted in increases in the amount of gasoline and diesel fuels
imported into the U.S. As of 2004, 5.4 and 11.5 percent of the total
respective volumes of gasoline and diesel consumed in the U.S. were
imported.

Number range CHAPTERToday, the United States imports approximately 70
percent of all petroleum products used, with two-thirds of these
products being used for transportation. From 1973 to 2004, the amount of
crude oil imported has increased from 1.2 to 3.7 billion barrels per
year, a tripling of volume, representing an average annual increase of
about 6 percent. Over the same time period, the amount of gasoline
imported has increased from 2 to 7.4 billion gallons per year, more than
three times the amount of volume. The amount of diesel imported in the
same time period decreased slightly from 6 to 5 percent. Figures 1.1-3
and 1.1-4 show the increase in crude oil and gasoline/diesel fuel
imports, respectively, from 1973 to 2004.

**Figure 1.1-3.**

**Increase in Crude Oil Imports from 1973-2004**

![](media/image3.wmf){width="5.872916666666667in"
height="4.101388888888889in"}

(Source: Annual Energy Outlook, 2005; Energy Information Administration)

**Figure 1.1-4.**

**Change in Volumes of Imported Gasoline and Diesel fuels (1973-2004)**

![](media/image4.wmf){width="6.497916666666667in"
height="4.451388888888889in"}

Source: Annual Energy Outlook, 2005; Energy Information Administration

Approximately twenty percent of our trade deficit is from imported
petroleum products, a deficit which reached \$782 billion in 2005.
Figure 1.1-5 shows the trade deficits from 1994 through 2004 (earlier
data on petroleum imports is not available from the U.S. Census web site
at this time). While the overall contribution of petroleum imports to
the total deficit is decreasing as shown in Figure 1.1-5, this is due to
a more rapid growth in the total deficit from other goods and services.
The portion of the deficit due to petroleum imports by itself is
increasing by approximately 4 percent per year. Over the last 25 years,
the cumulative cost of imported crude oil has reached \$2.0 trillion in
2005 dollars.

**Figure 1.1-5.**

**U.S. Trade Deficit and Portions Due to Petroleum Imports**

**1994-2004**

**(Millions of dollars, chain weighted to 2000)**

![](media/image5.wmf){width="6.497916666666667in"
height="4.451388888888889in"}

Source: U.S. Census Bureau, Foreign Trade Statistics, 2006

The amount of import facilities in the U.S. has stayed relatively
constant since the U.S. EPA has been requiring such facilities to
register. In 1995 there were a total of 39 such facilities in the U.S.
The amount has remained relatively constant, in the 50's since that time
and as of 2004 there were 53 such facilities registered with U.S. EPA.
The great majority of such facilities are located in PADD 1; as of 2004,
35 facilities were in PADD 1, and a total of 18 in the other four PADDs.

2.  **Renewable Fuel Production**

While the definition of renewable fuel in the Act does not limit
compliance with the standard to any one particular type of renewable
fuel, ethanol is currently the most prevalent renewable fuel blended
into motor vehicle fuels today. Biodiesel represents another form of
renewable fuel, which while not as widespread as ethanol use (in terms
of volume), has been increasing in production capacity and use over the
last several years. Ethanol and biodiesel are expected to continue to
dominate renewable fuel use in the timeframe when the RFS rule will be
phasing in.

1.  **Current U.S. Ethanol Production**

    1.  **Overview**

There are currently 110 ethanol production facilities in the United
States with a combined production capacity of 5.2 billion gallons per
year[^1]. This baseline, or starting point, for this regulatory impact
analysis is based on U.S. ethanol production facilities operational as
of October 2006.[^2][^3][^4][^5][^6][^7]

Approximately 92 percent of today's ethanol production capacity is
produced exclusively from corn, mainly from a dry-milling process. The
remainder is derived from corn/grain blends, cheese whey, and other
starches. The majority of ethanol plants are located in Midwest where
the bulk of corn is produced. PADD 2 accounts for just over 5 billion
gallons (or 96 percent) of the total U.S. ethanol production. Leading
the Midwest in ethanol production are Iowa, Illinois, Nebraska,
Minnesota, and South Dakota which together represent 76 percent of the
total domestic product. In addition to the concentration of facilities
located in PADD 2, there are also a sprinkling of ethanol plants
situated outside of the Midwest as far west as California and as far
south as Georgia.

**1.2.1.2 Ethanol Feedstocks & Processing Technologies**

All of the ethanol currently produced today comes from grain or
starch-based feedstocks that can easily be broken down into ethanol via
traditional fermentation processes. The primary feedstock is corn,
although grain sorghum (milo), wheat, barley, beverage waste, cheese
whey, and sugars/starches are also fermented to make fuel-grade ethanol.

The majority of ethanol (almost 92 percent by volume) is produced
exclusively from corn. Most of the corn originates from the Midwest and
most of the ethanol is produced in PADD 2 close to where the corn is
grown. However, several corn-ethanol plants are also situated outside
the traditional "corn belt". In California, Colorado, New Mexico, and
Wyoming corn is shipped from the Midwest to supplement locally grown
grains or in some cases, serve as the sole feedstock. As for the
remaining ethanol, almost eight percent is produced from a blend of corn
and/or similarly processed grains (milo, wheat, or barley) and less than
one percent is produced from waste beverages, cheese whey, and
sugars/starches combined. A summary of ethanol production by feedstock
is presented in Table 1.2-1.

**Table 1.2-1.**

**2006 U.S. Ethanol Production by Feedstock**

![](media/image6.wmf){width="3.9965277777777777in"
height="3.301388888888889in"}

There are two primary plant configurations for processing grains into
ethanol: dry mill and wet mill. A summary of the processing technologies
used by today's ethanol plants is found below in Table 1.2-2.

Dry mill plants simply grind the entire kernel and feed the flour into
the fermentation process to produce ethanol. At the end, the
unfermentable parts are recovered as distillers' grains along with a
soluble liquid containing vitamins, minerals, fat and protein. The
distillers' grains are concentrated with the solubles stream to make a
single co-product, referred to as distillers' grains with solubles
(DGS). The co-product is either sold wet (WDGS) or more commonly dried
(DDGS) to the agricultural market as animal feed. If the feed is going
to be used by local markets, it's usually sold wet precluding the need
for process dryers. However, if the feed is going to be shipped (usually
by train) to more distant locations, the product is usually dried to
facilitate storage and transportation.

Wet mill plants typically separate the kernel into four products:
starch, gluten feed, gluten meal, and oil. The starch is used in a
fermentation process the same as in dry mill plants, while the gluten,
oil, and other co-products are sold into food and agricultural markets.
Production of these multiple streams is more capital-intensive than the
dry milling process, and thus wet mill plants are generally more
expensive to build and tend to be larger in size.

**Table 1.2-2.**

**2006 U.S. Ethanol Production by Processing Technology**

![](media/image7.wmf){width="4.275in" height="1.7902777777777779in"}

As shown above in Table 1.2-2, dry milling is the most predominant
production process used by today's ethanol plants. Of the 102 facilities
processing corn and/or other similarly processed grains, 92 utilize dry
milling technologies and the remaining 10 plants rely on wet milling
processes (refer to Table 1.2-3 below). The remaining "other" eight
plants listed above process waste beverages, cheese whey, or
sugars/starches and operate differently than their grain-based
counterparts. These facilities do not require milling and instead
operate a simpler enzymatic fermentation process.

**Table 1.2-3.**

**2006 U.S. Grain Ethanol Production - Wet Mill Plants**

![](media/image8.wmf){width="3.770138888888889in"
height="2.8465277777777778in"}

In addition to grain and starch-to-ethanol production, another method
exists for producing ethanol from a more diverse feedstock base. This
process involves converting cellulosic materials such as bagasse, wood,
straw, switchgrass, and other biomass into ethanol. Cellulose consists
of tightly-linked polymers of starch, and production of ethanol from it
requires additional steps to convert these polymers into fermentable
sugars. Scientists are actively pursuing acid and enzyme hydrolysis as
well as gasification to achieve this goal, but the technologies are
still not fully developed for large-scale commercial production. As of
October 2006, the only known cellulose-to-ethanol plant in North America
was Iogen in Canada, which produces approximately one million gallons of
ethanol per year from wood chips. Several companies have announced plans
to build cellulose-to-ethanol plants in the U.S., but most are still in
the research and development or pre-construction planning phases. The
majority of the plans involve converting bagasse, rice hulls, wood,
switchgrass, corn stalks, and other agricultural waste or biomass into
ethanol. For more a more detailed discussion on future cellulosic
ethanol plants and production technologies, refer to RIA Sections
1.2.3.6 and 7.1.2, respectively.

**1.2.1.3 Ethanol Plant Energy Sources**

Ethanol production is a relatively resource-intensive process that
requires the use of water, electricity, and steam. Steam needed to heat
the process is generally produced onsite or by other dedicated boilers.
Of today's 110 ethanol production facilities, 101 burn natural gas, 7
burn coal, 1 burns coal and biomass, and 1 burns syrup from the process
to produce steam[^8]. Our research suggests that 11 plants currently
utilize co-generation or combined heat and power (CHP) technology,
although others may exist. CHP is a mechanism for improving overall
plant efficiency. Whether owned by the ethanol facility, their local
utility, or a third party; CHP facilities produce their own electricity
and use the waste heat from power production for process steam, reducing
the energy intensity of ethanol production. A summary of the energy
sources and CHP technology utilized by today's ethanol plants is found
below in Table 1.2-4.

**Table 1.2-4.**

**2006 U.S. Ethanol Production by Energy Source**

![](media/image9.wmf){width="4.8in" height="1.8083333333333333in"}

**1.2.1.4 Ethanol Production Locations**

The majority of domestic ethanol is currently produced in the Midwest
within PADD 2 --where most of the corn is grown. Of the 110 U.S. ethanol
production facilities, 100 are located in PADD 2. As a region, PADD 2
accounts for about 96 percent (or over five billion gallons) of domestic
ethanol production, as shown in Table 1.2-5.

**Table 1.2-5.**

**2006 U.S. Ethanol Production by PADD**

![](media/image10.wmf){width="3.48125in" height="1.6069444444444445in"}

Leading the Midwest in ethanol production are Iowa, Illinois, Nebraska,
Minnesota, and South Dakota with capacities of 1.62, 0.71, 0.61, 0.55,
0.49 billion gallons, respectively. Together, these five states' 70
ethanol plants account for 76 percent of the total domestic ethanol
production. However, although the majority of ethanol production comes
from PADD 2, there are a growing number of plants situated outside the
traditional corn belt. In addition to the 15 states comprising PADD 2,
ethanol plants are currently located in California, Colorado, Georgia,
New Mexico, and Wyoming. Some of these facilities ship in feedstocks
(namely corn) from the Midwest, others rely on locally grown/produced
feedstocks, while others rely on a combination of the two. A summary of
ethanol production alphabetically by state is found in Table 1.2-6.

**Table 1.2-6.**

**2006 U.S. Ethanol Production by State**

![](media/image11.wmf){width="3.8430555555555554in" height="4.63125in"}

In addition to the domestic ethanol production described above, the U.S.
also receives a small amount of ethanol from other countries. A
discussion on ethanol imports is found in Section 1.5

**1.2.1.5 Ethanol Producers and Marketers**

The U.S. ethanol industry is currently comprised of a mixture of
corporations and farmer-owned cooperatives (co-ops). More than half (or
60) of the plants are owned by corporations and the remainder (50
plants) are farmer owned co-ops. On average, a U.S. ethanol production
facility has a mean plant capacity of about 47 million gallons per year.
In general, plants owned by corporations ("company-owned") are above
average in size while farmer-owned co-ops are below average. Similarly,
company-owned plants tend to have a much broader range in ethanol
production levels than farmer-owned co-ops. A summary of these results
is presented in Table 1.2-7.

**Table 1.2-7.**

**2006 U.S. Ethanol Production by Plant Ownership**

![](media/image12.wmf){width="4.620138888888889in"
height="1.2034722222222223in"}

Based on the dominating number of company-owned plants and their
above-average production size, company-owned plants account for nearly
64 percent of the total domestic product. Further, more than 50 percent
of today's U.S. ethanol production capacity comes from plants owned by
just 6 different companies. A list of the top six ethanol producing
companies and their respective plant capacities is found in Table 1.2-8.

**Table 1.2-8.**

**2006 Top Six U.S. Ethanol Producers**

![](media/image13.wmf){width="3.23125in" height="1.9902777777777778in"}

Over 80 percent of today's U.S. ethanol production is sold to the
gasoline industry by eight marketing companies[^9]. A list of the top
eight ethanol marketers and their respective marketing capacities based
on plant affiliations is found in Table 1.2-9. The remaining ethanol is
marketed by Kinergy Marketing, The Andersons, Murex International, Noble
Americas, and other small marketing companies.

[]{.mark}

**Table 1.2-9.**

**2006 Top Eight U.S. Ethanol Marketers**

![](media/image14.wmf){width="3.845138888888889in"
height="2.545138888888889in"}

2.  **Forecasted Growth in Ethanol Production**

**1.2.2.1 Overview**

Over the past 25 years, domestic fuel ethanol production has steadily
increased due to environmental regulation, federal and state tax
incentives, and market demand. More recently, ethanol production has
soared due to the phase out of MTBE, an increasing number of state
ethanol mandates, and elevated crude oil prices. As shown in Figure
1.2-1, over the past three years, domestic ethanol production has nearly
doubled from 2.1 billion gallons in 2002 to 4.0 billion gallons in 2005.
For 2006, the Renewable Fuels Association is anticipating about 4.7
billion gallons of domestic ethanol production[^10].

**Figure 1.2-1. U.S. Ethanol Production Over Time**

![](media/image15.wmf){width="6.497916666666667in"
height="4.440972222222222in"}

Source: Renewable Fuels Association, From Niche to Nation: Ethanol
Industry Outlook 2006

EPA forecasts that domestic ethanol production will continue to grow
into the future. In addition to the past impacts of federal and state
tax incentives, as well as the more recent impacts of state ethanol
mandates and the removal of MTBE from all U.S. gasoline, crude oil
prices are expected to continue to drive up demand for ethanol. As a
result, the nation is on track to exceed the renewable fuel requirements
contained in the Act, as explained below.

**1.2.2.2 Expected Increases in Plant Capacity**

Today's ethanol production capacity (5.2 billion gallons) is already
exceeding the 2007 renewable fuel requirement (4.7 billion gallons). In
addition, there is another 3.4 billion gallons of production capacity
currently under construction.[^11][^12][^13][^14] A summary of the new
construction and plant expansion projects currently underway (as of
October 2006) is found in Table 1.2-10.

**Table 1.2-10. Under Construction U.S. Ethanol Production Capacity**

![](media/image16.wmf){width="5.206944444444445in"
height="1.8486111111111112in"}

A select group of builders, technology providers, and construction
contractors are completing the majority of the construction projects
described in Table 1.2-10. As such, the completion dates of these
projects are staggered over approximately 18 months, resulting in the
gradual phase-in of ethanol production shown in Figure 1.2-2[^15].

**Figure 1.2-2. Estimated Phase-In of Under Construction Plant
Capacity**

![](media/image17.wmf){width="4.958333333333333in"
height="4.572916666666667in"}

As shown in Table 1.2-10 and Figure 1.2-2, once all the construction
projects currently underway are complete (estimated by March 2008), the
resulting U.S. ethanol production capacity would be about 8.6 billion
gallons. Without even considering forecasted biodiesel production
(discussed below in 1.2.5), this would be more than enough renewable
fuel to satisfy the 2012 RFS requirements (7.5 billion gallons).
However, ethanol production is expected to continue to grow. There are
more and more ethanol projects being announced each day. These potential
projects are at various stages of planning from conducting feasibility
studies to gaining local approval to applying for permits to
financing/fundraising to obtaining contractor agreements. Together these
potential projects could result in an additional 21 billion gallons of
ethanol production capacity (as shown in Table 1.2-11).

**Table 1.2-11.**

**Other Potential U.S. Ethanol Production Capacity**

![](media/image18.wmf){width="5.206944444444445in"
height="2.2118055555555554in"}

Although there is clearly a great potential for ethanol production
growth, it is highly unlikely that all the announced projects would
actually reach completion in a reasonable amount of time, or at all,
considering the large number of projects moving forward. Since there is
no precise way to know exactly which plants will come to fruition in the
future, we have chosen to focus our subsequent discussion on forecasted
ethanol production on plants which are likely to be online by 2012.[^16]
This includes existing plants as well as projects which are under
construction (refer to Table 1.2-10) or in the final planning stages
(denoted as "planned" in Table 1.2-11). The distinction between
"planned" versus "proposed" is that as of October 2006 planned projects
had completed permitting, fundraising/financing, and had builders
assigned with definitive construction timelines whereas proposed
projects did not.

As shown in Table 1.2-11, once all the under construction and planned
projects are complete, the resulting U.S. ethanol production capacity
would be 14.3 billion gallons. This volume, expected to be online by
2012, exceeds the EIA AEO 2006 demand estimate (9.6 billion gallons by
2012, discussed more in RIA Section 2.1.4.1). The forecasted growth
would nearly triple today's production capacity and greatly exceed the
2012 RFS requirement (7.5 billion gallons). While our forecast
represents ethanol production capacity (actual production could be
lower), we believe it is still a good indicator or what domestic ethanol
production could look like in the future. In addition, we predict that
domestic ethanol production will continue to be supplemented by imports
in the future. A more detailed discussion on future ethanol imports is
found in Section 1.5.

**1.2.2.3 Changes in Feedstocks & Processing Technologies**

Of the 112 forecasted new ethanol plants (47 under construction and 65
planned), 106 would rely on grain-based feedstocks. More specifically,
89 would rely exclusively on corn, 13 would process a blend of corn
and/or similarly processed grains (milo or wheat), 3 would process
molasses, and 1 would process a combination of molasses and sweet
sorghum (milo). Of the remaining six plants (all in the planned stage),
four would process cellulosic biomass feedstocks and two would start off
processing corn and later transition to cellulosic materials. Of the
four dedicated cellulosic plants, one would process bagasse, one would
process a combination of bagasse and wood, and two would process
biomass. Of the two transitional corn/cellulosic plants, one would
ultimately process a combination of bagasse, rice hulls, and wood and
the other would ultimately process wood and other agricultural residues.
In addition to the forecasted new plants described above, an existing
corn ethanol plant plans to expand production and transition to corn
stalks, switchgrass, and biomass in the future.

A summary of the resulting overall feedstock usage (including current,
under construction, and planned projects) is found in Table 1.2-12. A
discussion on how the plants predicted to process cellulosic feedstocks
would help the nation meet the Act's cellulosic biomass ethanol
requirement is found in Section 1.2.2.6

**Table 1.2-12. Forecasted 2012 U.S. Ethanol Production by Feedstock**

![](media/image19.wmf){width="5.4215277777777775in"
height="4.651388888888889in"}

As shown above, the majority of future plants are predicted to process
grains (namely corn). Similarly, the vast majority of plants are
expected to pursue dry milling technology. Our analysis does not foresee
any new wet mill facilities, with the exception of a new 100 MMgy wet
mill plant that is planned for Fort Dodge, IA and a 37 MMgy plant
expansion project that is underway in Loudon, TN. Further, we do not
predict that there will be any new plants processing cheese whey, waste
beverages, or sugars/starches (which do not require milling).

The forecasted cellulosic feedstock plants (described in more detail in
Section 1.2.2.7) will not require milling. However, these facilities
will require complex forms of pretreatment (described in more detail in
Section 7.1.2) to break down the lignocellulosic and hemicellulosic
polymers into fermentable sugars. A summary of the resulting overall
feedstock processing technology utilization is found below in Table
1.2-13.

**Table 1.2-13.**

**Forecasted 2012 U.S. Ethanol Production by Processing Technology**

![](media/image20.wmf){width="4.275in" height="1.8715277777777777in"}

**1.2.2.4 Changes in Plant Energy Sources**

Of the 112 forecasted new plants, 100 would burn some amount of natural
gas - at least initially. More specifically, 91 plants would rely
exclusively on natural gas; two would rely on a combination of natural
gas, bran and biomass; one would burn a combination of natural gas,
distillers' grains and syrup; and six would start off burning natural
gas and later transition to coal. As for the remaining 12 plants, three
would burn manure-derived methane (biogas); seven would rely exclusively
on coal; one would burn a combination of coal and biomass; and one would
burn a combination of coal, tires and biomass. In addition to the new
ethanol plants, three existing plants currently burning natural gas are
predicted to transition to alternate boiler fuels in the future. More
specifically, two plants plan to transition to biomass and one plans to
start burning coal.

Our research suggests that seven of the new plants (mentioned above)
would utilize combined heat and power (CHP) technology, although others
may exist. Three of the new CHP plants would burn natural gas, three
would burn coal, and one would burn a combination of coal, tires, and
biomass. Among the existing CHP plants, two are predicted to transition
from natural gas to coal or biomass at this time. Overall, the net
number of CHP ethanol plants would increase from 11 to 18. A summary of
the resulting overall plant energy source utilization is found below in
Table 1.2-14. A discussion on how the plants predicted to burn waste
materials could help the nation meet the Act's cellulosic biomass
ethanol requirement is found in Section 1.2.2.6.

[]{.mark}

**Table 1.2-14. Forecasted 2012 U.S. Ethanol Production by Energy
Source**

![](media/image21.wmf){width="5.78125in" height="3.3430555555555554in"}

**1.2.2.5 Changes in Ethanol Production Locations**

Once all the forecasted ethanol projects are complete, 87 percent of the
domestic production capacity would originate from PADD 2, followed by
PADDs 1, 3, 5, and 4 (all contributing less than 5 percent). A summary
of the findings is found below in Table 1.2-15.

**Table 1.2-15.**

**Forecasted 2012 U.S. Ethanol Production by PADD**

![](media/image22.wmf){width="3.459722222222222in"
height="1.5784722222222223in"}

While PADD 2 ethanol production is expected to more than double (from
5.0 to 12.4 billion gallons), this represents a decrease in Midwest
marketshare (from 96 to 87 percent). This predicted shift in marketshare
is attributed to the growing number of ethanol plants located outside
the cornbelt. Arizona, Florida, Hawaii, Louisiana, New York, Oregon,
Pennsylvania and Texas are scheduled to join the 19 ethanol producing
states described in Table 1.2-5. A summary of future ethanol production
by state is found below in Table 1.2-16.

**Table 1.2-16.**

**Forecasted 2012 U.S. Ethanol Production by State**

![](media/image23.wmf){width="3.722916666666667in"
height="6.216666666666667in"}

**1.2.2.6 Meeting the Cellulosic Ethanol Requirement in 2013**

The Energy Policy Act of 2005 (the Energy Act or the Act) requires that
250 million gallons of the renewable fuel consumed in 2013 and beyond
meet the definition of cellulosic biomass ethanol. The Act defines
cellulosic biomass ethanol as ethanol derived from any lignocellulosic
or hemicellulosic matter that is available on a renewable or recurring
basis including dedicated energy crops and trees, wood and wood
residues, plants, grasses, agricultural residues, fibers, animal wastes
and other waste materials, and municipal solid waste. The term also
includes any ethanol produced in facilities where animal or other waste
materials are digested or otherwise used to displace 90 percent of more
of the fossil fuel normally used in the production of ethanol.

As discussed above in Section 1.2.2.3, there are seven "planned" ethanol
plants planning on processing cellulosic feedstocks in the future. A
summary of these facilities is found below in Table 1.2.17.

**Table 1.2-17. Potential Cellulosic Feedstock Plants**

![](media/image24.wmf){width="6.4944444444444445in"
height="2.404861111111111in"}

It is unclear whether the above-mentioned cellulosic feedstock plants
would be online and capable of producing 250 million gallons of ethanol
by 2013 to meet the Act's cellulosic biomass ethanol requirement.
However, as described above in Section 1.2.2.4 there are 12 facilities
that burn or plan to burn waste materials to power their ethanol plants
in the future. These facilities, summarized below in Table 1.2.18, could
also potentially meet the definition of cellulosic biomass ethanol under
the Act.

**Table 1.2-18. Potential Waste Energy Plants**

![](media/image25.wmf){width="6.498611111111111in"
height="2.9256944444444444in"}

Depending on how much fossil fuel is displaced by burning these waste
materials (on a plant-by-plant basis), a portion or all of the
above-mentioned 969 MMgy ethanol production capacity could potentially
qualify as "cellulosic biomass ethanol" under the Act. Combined with the
additional 320 MMgy of ethanol production capacity from plants
processing cellulosic feedstocks, the overall cellulosic ethanol
potential could be as high as 1.3 billion gallons. Even if only one
fifth of this ethanol were to end up qualifying as cellulosic biomass
ethanol or come to fruition by 2013, it would be more than enough to
satisfy the 250 million gallon requirement specified in the Act.[^17]

**1.2.3 Current Biodiesel Production**

Biodiesel is defined in several sections of the Act, which we have used
in formulating our definition for the regulations, which call for
meeting ASTM specifications. Biodiesel is registered with the EPA for
commercial sale and is legal for use at any blend level in both highway
and nonroad diesel engines although most engine manufacturers will only
honor the warranty if biodiesel is used in blends of 2, 5 or, in some
limited circumstances, 20 percent.

Biodiesel can be made from almost any vegetable or animal fat, with most
of the world's production coming from plants oils, notably soy bean and
rapeseed (canola) oil. Biodiesel fuel production is rapidly increasing
in many regions of the world. The choice of the feedstock oil used to
make it is dependent upon the vegetable oils and fat supplies that are
economically available. For the U.S. market, there are many potential
plant oil feedstocks that can be used to make biodiesel, including
soybean, peanut, canola, cottonseed and corn oil. Biodiesel can also be
made from animal fats such used restaurant grease (yellow grease) and
tallow. Though, typically for the U.S. market, soybean oil has been the
primary major feed stock supply, followed by use of yellow grease and
animal tallow.

The resulting biodiesel product can be used as a fuel for diesel engines
with minor modifications and is commonly blended with refinery produced
diesel fuel. Raw vegetable and animal oils consist of fatty acids and
glycerine products. Though these oils can directly be used in engines
and give good short term performance, this is highly discouraged as
their use can cause severe engine problems. This is primarily due to the
raw oils forming engines deposits, with coking and plugging in engine
injectors nozzles, piston rings, lubricating oil, etc. This happens due
to polymerization of the triglycerides in the raw oils as the fuel is
combusted. Therefore, it is necessary to convert the raw oils into a
form of esters or biodiesel which prevents these issues. The biodiesel
production process converts the raw vegetable and animal oils into
esters, though the virgin oils themselves are sometimes
(inappropriately) referred to as biodiesel. The production process
called transesterification consists of adding methanol or ethanol to the
virgin vegetable oil and animal oil, in the presence of a catalyst such
as sodium or potassium hydroxide, resulting in esters or biodiesel and a
byproduct glycerol. A subsequent step is usually needed, however, to
remove glycerin, catalysts and other compounds, to allow the biodiesel
to meet the required ASTM specifications.

Biodiesel blends such as B2, B5 and in some cases B20, can be used in
existing engines without modification, and most engines exhibit no
performance problems with the use of biodiesel, though this depends on
the blend and the season. However, engine fuel filters may need to be
changed more often, and there may be cold temperature operations due to
biodiesel\'s higher cloud point. As a result most engine manufacturers
will only recognize their warranties if biodiesel is used in low
concentrations. Biodiesel produced from vegetable oil has practically
zero amounts of sulfur and aromatics and a high cetane value, thus
making it a good for blending into 15 ppm highway and offroad diesel
fuel, though biodiesel made from yellow grease and animal fat may
contain about 24 ppm of sulfur[^18]. Biodiesel also has good lubricity
qualities and can be used in concentration (\~2 vol%) as a
lubricity-enhancing additive for conventional diesel.

**1.2.4 Forecasted Biodiesel Production**

Biodiesel production has been increasing rapidly over the past five
years and is projected to continue at a high rate in part because of the
Renewable Fuel Standard (RFS) program. This expansion has primarily been
driven by better economics, due to the recent large increase in diesel
prices associated with the run up in crude prices, along with the
Biodiesel Blenders Tax Credit programs and the Commodity Credit
Commission Bio-energy Program, both of which subsidize producers and
offset production costs. The Act extended the Biodiesel Blenders Tax
Credit program to year 2008, which provides about one dollar per gallon
in the form of a federal excise tax credit to biodiesel blenders from
virgin vegetable oil feedstocks and 50 cents per gallon to biodiesel
produced from recycled grease and animal fats. This program was started
in 2004 under the American Jobs Act. The existing Commodity Credit
Commission Bio-energy Program also pays biodiesel producers grants when
the economics to produce biodiesel are poor; the program averaged about
one dollar per gallon in 2004. Recent payments through the Commodity
Credit program have been reduced, however, and the program is expiring
in fiscal year 2006. Historically, the cost to make biodiesel was an
inhibiting factor to production. The cost to produce biodiesel was high
compared to the price of petroleum derived diesel fuel, even with
consideration of the benefits of subsidies and credits provided by
federal and state programs. Mandates from states and local
municipalities that require the use of biodiesel in transport fuels are
another factor which is expanding the use of biodiesel.

In 2005 approximately 91 million gallons of biodiesel were produced in
the U.S. based on program payments to biodiesel producers under USDA's
Bio-energy Program. This volume represents approximately 0.15 percent of
all diesel fuel consumed in the domestic market. EIA projects the future
production volume to expand to 414 million gallons per year in 2007 and
then decrease to about 303 MM gallons per year in 2012, assuming that
the biodiesel blender tax credits program expires in 2007 (see Table
1.2-19).

**Table 1.2-19. Estimated Biodiesel Production^a^**

  ------------------------- ---------------------------------------------
  **Year**                  **Million Gallons per Year**

  2001                      5

  2002                      15

  2003                      20

  2004                      25

  2005                      91

  2006                      150

  2007                      414

  2012                      303
  ------------------------- ---------------------------------------------

^a^ Historical data from 2001-2004 obtained from estimates from John
Baize " The Outlook and Impact of Biodiesel on the Oilseeds Sector" USDA
Outlook Conference 06. Year 2005 data from USDA Bioenergy Program. Year
2006 data from verbal quote based on projection by NBB in June of 06.
Production data for years 2007 and higher are from EIA's AEO 2006.

With the increase in biodiesel production, there has also been a
corresponding rapid expansion in biodiesel production capacity.
Presently, there are 85 biodiesel plants in operation with an annual
production capacity of 580 million gallons per year[^19]. The majority
of the current production capacity was built in 2005 and 2006, and was
first available to produce fuel in the later part of 2005 and in 2006.
Though the capacity has grown, historically the biodiesel production
capacity has far exceeded actual production with only 10-30 percent of
this being utilized to make biodiesel, see Table 1.2-20.

**Table 1.2-20. U.S. Production** Capacity History

+-----------+--------+--------+--------+--------+--------+--------+
|           | **     | **     | **     | **     | **     | **     |
|           | 2001** | 2002** | 2003** | 2004** | 2005** | 2006** |
+-----------+--------+--------+--------+--------+--------+--------+
| Plants    | 9      | 11     | 16     | 22     | 45     | 85     |
+-----------+--------+--------+--------+--------+--------+--------+
| Capacity  | 50     | 54     | 85     | 157    | 290    | 580    |
|           |        |        |        |        |        |        |
| (MM       |        |        |        |        |        |        |
| gals/yr)  |        |        |        |        |        |        |
+-----------+--------+--------+--------+--------+--------+--------+
| Pr        | 5      | 15     | 20     | 25     | 91     | 150    |
| oduction, |        |        |        |        |        |        |
| (MM       |        |        |        |        |        |        |
| gals/yr)  |        |        |        |        |        |        |
+-----------+--------+--------+--------+--------+--------+--------+
| Capacity  | 10     | 28     | 24     | 16     | 31     | 26     |
| Ut        |        |        |        |        |        |        |
| ilization |        |        |        |        |        |        |
| for       |        |        |        |        |        |        |
| B         |        |        |        |        |        |        |
| iodiesel, |        |        |        |        |        |        |
| %         |        |        |        |        |        |        |
+-----------+--------+--------+--------+--------+--------+--------+

Note: Capacity Data based on surveys conducted.

Excess production capacity is not easily quantified, though since some
of these plants may not run at full rate all of the time and may be
"idled" for certain days of the week, seasons, time of day, etc. The
capacity can be classified into two types of producers; capacity
dedicated to biodiesel production and capacity available from the
ole-chemical industry. The plants that primarily operate in the
ole-chemical industry produce esters for use in the chemical industry.
These plants are swing producers of biodiesel, which means that when the
economics are favorable they can shift their operations and make
biodiesel esters instead of products for the ole-chemical market.[^20]
The capacity from the ole-chemical industry produces mono-alkyl esters
using a similar transesterification process, with the ester products
being sold for to make plasticizers, soaps, paints, solvents and other
industrial uses. Additionally, the biodiesel production capacity volumes
may be optimistic, as this is not officially tracked. The capacities
listed here are those based on each company's self reported volumes to
the National Biodiesel Board and may have some inaccuracies due to
informal reporting procedures.

We anticipate that future capacity additions will be geared more towards
production of biodiesel for use as transportation fuel, rather than
serving primarily the oleochemicals markets. As of September 2006, there
were 65 plants in the construction phase and 13 existing plants that are
expanding their capacity. All of this new capacity when installed would
provide about 1.4 billion gallons per year of additional throughput
capacity. Table 1.2-21 presents the data for the biodiesel plant
capacities per the categories discussed.

**Table 1.2-21. Biodiesel Plant Capacities**

+-----------------------+-----------------------+-----------------------+
|                       | **Existing Plants**   | **Construction        |
|                       |                       | Phase**               |
+-----------------------+-----------------------+-----------------------+
| Number of Plants      | 85                    | 78                    |
+-----------------------+-----------------------+-----------------------+
| Total Plant Capacity, | 580                   | 1,400                 |
|                       |                       |                       |
| (MM Gallon/year)      |                       |                       |
+-----------------------+-----------------------+-----------------------+

Considering that it takes 12 to 18 months to construct a biodiesel plant
(from the time of project feasibility analysis to startup date), a large
portion of the capacity in the construction phase in late 2006 will be
available to produce fuel in 2007.[^21] Data on biodiesel plant
construction reveals most of the new capacity that is currently being
constructed is expected to be online and producing fuel in 2006 or by
end of 2007. Therefore, the existing capacity plus the capacity in the
construction phase totals an aggregate amount of about two billion
gallons per year. Though there is no volume mandate for biodiesel fuel
under the RFS program, the total capacity available from new and
existing plants exceeds EIA's projected biodiesel volume of 414 MM in
2007 and 303 MM in 2012 by a wide margin.

The plants in the construction phase are larger than existing biodiesel
plants, with average capacity of existing plants at 8.4 MM gallons per
year, while plants in construction phase are averaging 20.9 MM gallons
per year, as presented in Table 1.2-22. The distribution of biodiesel
plants by size and number of companies within each size range are
presented in Table 1.2-23.

**Table 1.2-22. Average Plant Capacity by Feedstock (MM gallons per
year)**

  ------------------------- --------------------- -----------------------
  **Feedstock**             **Existing\***        **Construction\***

  Canola                                          57.5

  Multi Feedstock           6.0                   16.7

  Other Vegetable           2.0                   

  Recycled Cooking Oil      0.5                   1.0

  Soybean Oil               8.8                   19.3

  Tallow                    5.0                   
  ------------------------- --------------------- -----------------------

**Table 1.2-23. Biodiesel Plant Size versus Number of Companies**

+-----------------------+-----------------------+-----------------------+
| **Plant Size**        | **Existing Plants**   | **Construction        |
|                       |                       | Phase**               |
| **(MM gallons per     |                       |                       |
| year)**^a^            |                       |                       |
+-----------------------+-----------------------+-----------------------+
| \<1.00                | 9                     | 5                     |
+-----------------------+-----------------------+-----------------------+
| 1.0- 5.0              | 28                    | 9                     |
+-----------------------+-----------------------+-----------------------+
| 5.0-10.0              | 17                    | 10                    |
+-----------------------+-----------------------+-----------------------+
| 10.0 to 15 .0         | 9                     | 7                     |
+-----------------------+-----------------------+-----------------------+
| 15.0 to 20.0          | 2                     | 3                     |
+-----------------------+-----------------------+-----------------------+
| 20.0+                 | 10                    | 28                    |
+-----------------------+-----------------------+-----------------------+
| Average Plant Size    | 8.4                   | 20.9                  |
+-----------------------+-----------------------+-----------------------+

^a^Total capacity of plants in each category; existing plants are 580 MM
gal/yr while those in the construction phase are 1,400 MM gal/yr.

Because newer plants are likely to be larger than existing plants, have
better technology and may have greater alignment with feedstock and feed
sources, some of the older plants may operate at an economic
disadvantage once the new plants come on line. At the moment, it is not
possible to predict actual biodiesel production based on capacity, since
in the past the capacity was used at rates less than maximum. Thus, how
excess production capacity evolves will be dictated by economics,
profitability, and fuel demand.

The majority of existing biodiesel plant capacity is located in the
middle and Midwestern parts of the country and use soy bean oil as the
feedstock. The other plants are scattered with locations based on the
east and west coasts, with feedstocks based on use of soybean, canola
and other oils as well as yellow grease as the feedstock. The new plants
are being built to process a wider variety of feedstocks, with
multi-feedstock and recycle grease capability. The feedstocks for these
plants are listed in Table 1.2-24.

**Table 1.2-24. Feedstock Selection for Biodiesel Producers**

  --------------------------------- ---------------- ---------------------
  **Feedstock**                     **Existing**     **Construction**

  Camelia                                            

  Canola                                             2

  Cottonseed                        1                

  Multi Feedstock                   29               29

  Palm Oil                                           

  Recycled Cooking Oil              7                3

  Soybean oil                       39               36

  Tallow/Poultry Fat                2                

  Unknown                           7                8
  --------------------------------- ---------------- ---------------------

**1.2.5 Baseline and Projected Biodiesel Volumes for Analysis**

For cost and emission analysis purposes, three biodiesel usage cases
were considered: a 2004 base case, a 2012 reference case, and a 2012
control case. The 2004 base case was formed based on historical
biodiesel usage (25 million gallons as summarized in Table 1.2-16). The
reference case was computed by taking the 2004 base case and growing it
out to 2012 in a manner consistent with the growth of diesel fuel
(described in Section 2.1.3). The resulting 2012 reference case
consisted of approximately 30 million gallons of biodiesel. Finally, for
the 2012 control case, forecasted biodiesel use was assumed to be 300
million gallons based on EIA's AEO 2006 report (rounded value from Table
1.2-16). Unlike forecasted ethanol use (described in 2.1.4), biodiesel
use was assumed to be constant at 300 million gallons under both the
statutory and higher projected renewable fuel consumption scenarios.

2.  **Renewable Fuel Distribution**

**1.3.1 Current Renewable Fuel Distribution System**

Ethanol and biodiesel blended fuels are not currently shipped by
petroleum product pipeline due to operational issues and additional cost
factors.[^22] The ability to ship by pipeline is also limited because
the sources of ethanol and biodiesel are frequently not in the same
locations as the sources of gasoline and petroleum-based diesel fuel.
Hence, a separate distribution system is needed for ethanol and
biodiesel up to the point where they are blended into petroleum-based
fuel as it is loaded into tank trucks for delivery to retail and fleet
operators. Ethanol and biodiesel can either be added by "splash
blending" where the renewable is added separately to the tank truck, or
by in-line injection where the renewable is injected into the petroleum
fuel stream as it is being dispensed into the tank truck. Ethanol and
biodiesel are sometimes added to petroleum-based fuels downstream of the
terminal, but this accounts for little of the total volume of used.

In cases where ethanol and biodiesel are produced within 200 miles of a
terminal, trucking is often the preferred means of distribution.
However, most renewable fuel volumes are produced at greater distances
from potential centers of demand. For longer shipping distances, the
preferred method of bringing renewable fuels to terminals is by rail and
barge. Dedicated pipelines have not been used to distribute renewable
fuels to terminals due to the high cost of installing new pipelines, the
relatively large shipping volumes that would be needed to justify such
expenditures, and the fact that renewable fuel production facilities
tend to be relatively numerous and dispersed.

The relatively low volumes of ethanol used prior to 2002 constrained the
ability of the distribution system to efficiently move ethanol to
distant markets. Ethanol shipments by rail were typically made on an
individual car basis. Under such an approach, small groups of rail cars
travel to market as part of trains that carry other goods. This approach
results in relatively high transportation costs, longer transit times,
and potential delays in delivery. Substantial improvements in the
efficiency of distributing ethanol by rail are being made due the need
to move large volumes of ethanol over long distances as a consequence of
the elimination of MTBE in California, New York, and Connecticut
beginning in 2004. The use of unit trains, sometimes referred to as
"virtual pipelines" reduces delivery costs, shortens delivery times, and
improves reliability. Unit trains are composed entirely of approximately
100 rail cars containing ethanol. Ethanol shipped by unit trains is
delivered to hub terminals for further distribution to other terminals
by barge and tank truck.

Substantial volumes of ethanol can potentially be shipped down the
Mississippi river by barge for temporary storage in New Orleans.[^23]
From New Orleans, ethanol can be loaded onto ocean transport for
delivery to the East and West Coast. There is also potential to move
ethanol via the Missouri and Ohio as well as other river systems and the
Great Lakes. Marine shipments of ethanol require a relatively large
minimum shipment size, determined by the minimum size of the marine tank
compartment.[^24] Similar to the case for "unit trains", there are also
efficiencies in dedicating whole barges, barge tows, or marine tankers
to ethanol distribution. The increased demand for ethanol has made it
possible to better benefit from these efficiencies of scale.

The use of inland barges to transport ethanol from production facilities
is in large part driven by whether there is river access at such
facilities. Historically, corn prices tend to be higher near river
systems that serve as arteries for the export of corn than at inland
locations distant from these river systems. To take advantage of lower
corn prices at inland locations and to avoid competing for corn with
grain elevators that serve the export market, all of the new ethanol
production facilities that have been built since 1999 have been built at
inland locations. [^25] Consequently, the majority of the growth in
ethanol freight volumes since 1999 has been in the rail sector.

**1.3.2 Changes to the Renewable Fuel Distribution System Due to
Increased Demand**

This section addresses that changes that we expect will take place in
the renewable fuel distribution system in response to the anticipated
increase in demand for such fuels through 2012. There may be some
limited opportunity to ship renewable fuels by pipeline in the future as
demand increases. However, because of the constraints discussed
previously (see section 1.3.1), we believe that rail and barge are
likely to remain the predominant means of transportation. The 2002 DOE
Study also reached this conclusion.[^26] While this constraint on the
ability to ship ethanol and biodiesel by pipeline presents logistical
challenges that result in additional transportation costs, the need to
transport these alternative fuels by other means may work to the overall
advantage of the fuel distribution system. Petroleum product pipelines
are nearing capacity. Thus, it seems likely that the pipeline
distribution system will find it increasingly difficult to keep pace
with annual increases in the demand for transportation fuels. Displacing
some of the volume of transportation fuels from the pipeline
distribution system through the use of ethanol and biodiesel will
relieve some of this strain.

Small volume rail shipments made on a by-car basis are likely to remain
an important feature in supplying markets that demand limited volumes.
However, as the demand for ethanol increases we anticipate that the
expansion of the use of unit trains will continue, and that this will be
a significant means of bringing ethanol to distant markets. There has
been some expansion of capacity at existing ethanol plants with river
access and some new plants are projected to be built with river access.
However, we anticipate that most new ethanol capacity will not have
river access. In addition, at least one new ethanol plant slated for
production that does have river access is planning to move its ethanol
to market via rail. Nevertheless, in cases where rail is the means to
transporting ethanol to hub terminals, marine transport can play an
important role in further distribution to satellite terminals.

Substantial improvements to the rail, barge, tank truck, and terminal
distribution systems will be needed to support the transport of the
volumes of renewable fuels necessary to meet the requirements of the RFS
program. These improvements include the addition of a significant number
of additional rail cars, and tank trucks. Additional marine barges will
also be needed. To facilitate the increased use of unit trains, new rail
spurs will be needed at terminals. Terminals will also need to add
facilities to store and blend ethanol. In addition, those terminals and
retail facilities that had not previously handled ethanol blended fuel
will need to make certain one-time upgrades to ensure the compatibility
of their systems with ethanol. These types of changes have been
occurring as demand for ethanol and biodiesel has grown rapidly over the
last several years, and there is no reason to suspect that they would
not continue as demand continues to warrant it. The costs associated
with these changes are discussed in Chapter 7.3 of this RIA.

The most comprehensive study of the infrastructure requirements for an
expanded fuel ethanol industry was conducted for the Department of
Energy (DOE) in 2002 .[^27] The conclusions reached in this study
indicate that the changes needed to handle the increased volume of
ethanol required under the RFS will not represent a major obstacle to
industry.[^28] While some changes have taken place since this report was
issued (as discussed below), we continue to believe that the rail and
marine transportation industries can manage the increased growth in an
orderly fashion. This belief is supported by the demonstrated ability of
the industry to handle the rapid increases and redistribution of ethanol
use across the country over the last several years as MTBE was removed.
Given that future growth in ethanol use is expected to take place in an
orderly fashion in response to economic drivers, we anticipate that the
distribution system will be able to respond appropriately.

The use of unit trains has accelerated beyond that anticipated in the
2002 DOE report, leading to the more efficient distribution of ethanol
by rail. As a result, rail has taken a relatively greater role in the
transportation of new ethanol volumes as compared to shipment by barge
than was projected in the report. Thus, there is likely to be a
relatively greater demand on the rail distribution system and somewhat
less demand on the marine distribution system than was projected in the
DOE study.

The 2002 DOE study estimated that the increase in the volume of ethanol
shipped by rail needed to facilitate the use of 10 billion gallons of
ethanol annually would represent an increase in total tank car loadings
of 0.33 percent. The increase in tank car loadings for Class I railroads
was estimated at 4.75 percent. The DOE report concluded that this
increase is relatively modest by railroad industry standards and could
be accommodated given the available lead time. The DOE study estimated
that the increase in demand on barge movements due to the need to carry
an increased volume of ethanol would equate to a one percent increase in
the total tonnage moved by barge. Given that on the one hand relatively
few new ethanol plants are projected to be cited with river access, and
that on the other hand barge is expected to play an important role in
redistributing ethanol from rail hub terminals, we estimate that the
increase in barge movements will be 30 percent less than that projected
in the 2002 DOE study. This equates to an increase in total tank car
loadings of 0.44 percent rather than the 0.33 percent projected in the
DOE study. We believe that this relatively modest potential increase in
the demand on the rail distribution system can be accommodated without
major difficulty given the available lead time.

Although, the 2002 DOE study generally concluded that the projected one
percent increase in the demand on the river barge industry could be
accommodated without major difficulty, it highlighted two potential
concerns. The report noted that delays are already being experienced at
locks on the Mississippi river. The question was raised regarding how
the projected increase of one percent in river traffic due to increased
ethanol shipments might be accommodated at these locks. The report also
raised concerns regarding the availability of sufficient marine vessels
capable of traveling between two ports in the United States (Jones Act
compliant vessels). Given that it appears that there will be less demand
placed on the river barge industry to transport ethanol than was
projected in the 2002 DOE study, the concerns raised in the study
regarding the capability of the inland waterway system to cope with the
increased traffic associated with shipping the anticipated new volume of
ethanol will be less pronounced.

At the present time, the industry is experiencing a shortage of tractor
trailers and drivers to transport ethanol. The boom in demand for truck
transport is due to a number of factors, including the precipitous
removal of MTBE from gasoline and its replacement by ethanol[^29] which
has taken place when the demand for truck transport was already growing
at a rapid place due to the increased imports. The implementation of
EPA's ultra-low sulfur diesel (ULSD) program this summer may also cause
an increase in the demand for tank trucks if more trucks must be
dedicated to ULSD service. Given the gradual increase expected from year
to year in ethanol production, we anticipate that the industry will be
able to add sufficient additional tank truck service in an orderly
fashion without undue burden.

The necessary facility changes at terminals and at retail stations to
dispense ethanol containing fuels have been occurring at a record pace
due to the removal of MTBE from gasoline. The use of ethanol has also
become more economically attractive due to higher gasoline prices. Now
that MTBE has been removed, a more steady increase in the use of ethanol
is anticipated over time. This will also allow for a smooth transition
for terminals and retail operators.

The volumes of biodiesel that are expected to be used by 2012 to comply
with the RFS will be relatively modest (approximately 300,000,000
gallons). Consequently, we anticipate that biodiesel will continue to be
distributed to terminals by tank truck and by individual rail car
shipments. One hundred percent biodiesel (B100)[^30] forms wax crystals
when the temperature falls to 35 to 45 degrees Fahrenheit.[^31] Thus,
storage tanks for B100 need to be heated to maintain flow-ability during
the cold seasons. Shipping vessels used to transport B100 such as
barges, rail cars, and tank truck containers also typically must either
be insulated (and sometimes heated) during the cold season or
alternatively facilities can be provided at the terminal to reheat the
vessel prior to delivery. Biodiesel that is blended with diesel fuel and
enhanced with cold flow additives (if needed) can have comparable cold
flow performance to petroleum based diesel fuel.[^32]

As temperatures fall during the cold seasons, some terminals currently
avoid the need for heated B100 tanks and facilities to heat shipping
vessels by accepting progressively less concentrated biodiesel blends
(for final blending to produce fuels for use in vehicles). During the
warm seasons, such terminals typically accept B100 or B99.9. As the
weather grows colder, the terminal might switch to accepting B80 and
during the coldest parts of the year might accept B50 (that contains 50
percent number one diesel fuel). The need for insulated tank trucks and
tank cars is also sometimes avoided if transit times are brief by
shipping warmed biodiesel. We believe that as the volume of biodiesel
grows, most terminals will opt to receive B100 (or B99.9) year round for
blending into diesel fuel for the consistency in operations which this
practice offers. A number of terminals are already following this
practice. These terminals have installed heated storage tanks for
biodiesel and insist that biodiesel be delivered in insulated tank
trucks (or rail cars) so that it may be pumped into the terminal storage
tank without concern about the potential need for reheating. The cost of
the necessary heated and/or insulated equipment is not insignificant.
However, the modest additional volumes that will need to be shipped via
rail and tank truck due to the use of biodiesel do not materially affect
the conclusions reached above regarding the ability of the fuel
distribution system to cope with the increased volumes of renewable
fuels.

**1.4 Blenders**

**1.4.1 Ethanol Blending**

Ethanol is miscible with water, and thus can introduce water into the
distribution system causing corrosion and durability problems as well as
fuel quality problems. For this reason, ethanol is blended downstream at
terminals or into tank trucks.

The distribution of ethanol is described in more detail in Section 1.3.
Briefly, ethanol producers provide ethanol either directly to terminals,
to marketers or to terminals that are owned by refiners. In the first
case, ethanol is provided to terminals that are owned entities other
than refining companies. They receive ethanol from the ethanol producer,
and gasoline from any number of refiners. The blenders then add ethanol
to the gasoline at the terminal. For RFG, the terminals receive the
blendstock for RFG, called Reformulated Blendstock for Oxygenate
Blending or RBOB, to which they add the amount of ethanol called for on
the Product Transfer Document that accompanies such shipments. Once the
ethanol is added to the RBOB, the product becomes a finished gasoline
(RFG) and is sent via truck to retailers. For conventional gasoline (CG)
ethanol is also added and shipped to retailers. The tracking mechanism
for CG is not as detailed as it is for RFG, however. The majority of
ethanol that is blended into CG has historically been "splash-blended"
although an increasing volume of ethanol is being blended into special
blends of conventional gasoline (e.g. sub-octane), or "match blended".
Finally, a very small amount is blended as E85.

**1.4.2 Biodiesel Blending**

Biodiesel generally leaves the production facility in its neat form and
is shipped by truck to locations where it can be blended with
conventional diesel fuel. The blending generally occurs at centralized
distribution points such as terminals, although it also sometimes occurs
within tank trucks themselves. Biodiesel is only rarely used in its neat
(unblended) form.

**1.5 Imports/Exports of Renewable Fuel**

Since the early 1980s, the U.S. has maintained a 54 cent per gallon
tariff on imported ethanol, primarily to offset the blending tax subsidy
of the same magnitude that had been put in place to support alternative
energy production and domestic agriculture. Legislation and agreements
implemented since then have waived or significantly reduced the tariff
on imports from Canada, Mexico, and about two dozen Central American and
Caribbean nations covered by the Caribbean Basin Initiative (CBI). Under
the Caribbean Basin Economic Recovery Act, which created the CBI, these
countries can export ethanol duty free to the U.S. at a rate up to 7% of
the U.S. fuel alcohol market; quantities above this limit have
additional stipulations for feedstocks being grown within the supplying
country.

Historically, the CBI nations have had little ethanol production
capacity of their own but have supplemented it by importing Brazilian
ethanol and re-exporting it to the U.S. duty free. More recently, with
the rapid phase-out of MTBE and the high price of ethanol, it has become
economically viable to import significant quantities of ethanol directly
from other nations despite the tariff. Brazil, currently the largest
ethanol producing nation in the world, has become the largest single
country supplier to the U.S. market. As shown in Figure 1.5-1, total
imports have increased more than 30% in 2004-5 over the previous
three-year average.

**Figure 1.5-1. Historic U.S. Ethanol Import Volumes and Origins**^a^

![](media/image26.wmf){width="5.995833333333334in"
height="4.017361111111111in"}

^a^ F.O. Licht, "World Ethanol Markets, The Outlook to 2015" (2006).
Gross imports (does not account for export volumes) including hydrous,
dehydrated, and denatured volumes.

Going forward, as domestic ethanol production capacity increases
rapidly, its price is expected to fall back into the historic range of
30-40 cents per gallon above gasoline (before blending subsidy). This is
expected to once again make direct imports from Brazil and other
full-tariff producers less attractive, and to decrease total imports.
According to a current report by F.O. Licht, U.S. net import demand is
estimated to be around 300 million gallons per year by 2012, being
supplied primarily through the CBI, with some direct imports from Brazil
during times of shortfall or high price.[^33]

Changes in the production and trade climate may influence this however.
The Caribbean countries with duty free status are seeing both internal
and foreign investment to increase ethanol production capacity
significantly over the next several years, making more cheap imports
available. It is unclear at this point what volume of ethanol will be
supplied through these channels.

On the export side, the U.S. has averaged about 100 million gallons per
year since 2000, mostly to Canada, Mexico, and the E.U. Figure 1.5-2
shows historical U.S. exports. There is a trend over the past five years
of exporting larger quantities to fewer countries, with declining
volumes to Asia and increasing volumes to the E.U. and India. The demand
for ethanol in all these areas remains strong, and it appears that Asian
imports from Brazil and China are making up for the decrease in U.S.
ethanol moving into the region.

**Figure 1.5-2. Historic U.S. Ethanol Export Volumes and Origins**^a^

![](media/image27.wmf){width="6.493055555555555in"
height="4.350694444444445in"}

^a^ F.O. Licht, "World Ethanol Markets, The Outlook to 2015" (2006).
Gross exports (does not account for import volumes), includes hydrous,
dehydrated, and denatured volumes.

These numbers are expected to increase modestly as more production comes
online, with more dramatic increases possible during periods of
depressed domestic prices or stock surges. Looking out over the next
decade, the E.U. has a biofuels directive in place that will bolster
demand, and Japan and South Korea are expected to increase their use of
biofuels steadily as well. World ethanol production is projected to grow
from the current 10 billion gallons per year to more than 25 in 2015,
and the international biofuels markets are just beginning to take shape.
During this period we can expect significant changes in who is supplying
and who is demanding as the players determine their places and forge
agreements on subsidies and tariffs. As of 2005, the U.S. became largest
ethanol producing nation, eclipsing Brazil, and ample foreign markets
will be available if conditions are right.

# Chapter 2: Changes to Motor Vehicle Fuel Under the Renewable Fuel Standard Program

In this regulatory impact analysis, we begin by describing the renewable
fuel volume scenarios we used to measure the environmental and economic
impacts of increased renewable fuel blending. From there we narrow our
discussion in on ethanol - the predominant renewable fuel expected to be
used in the future. We describe historical ethanol use, current use and
our projections of future ethanol use. The discussion starts with an
in-depth examination of current ethanol use. More specifically, what
factors drive ethanol use and where ethanol blending currently occurs -
by state, season, and fuel type. The discussion then shifts to where
ethanol is expected to be used in the future. We discuss the ongoing
trend in increased ethanol use, the anticipated phase-out of MTBE, and
ultimately present our LP modeling results which predict where ethanol
will likely be used in 2012 by PADD, season, fuel type. From there, we
describe our methodology for allocating ethanol usage by state and in
some cases, make distinctions on how we think ethanol would fill urban
and rural areas. Once we understand how ethanol use is expected to
change in the future, we measure the anticipated impacts on gasoline
fuel quality (which later feeds into our emissions and air quality
analyses). At the end of this chapter, we also provide a brief estimate
on how increased biodiesel blending will impact diesel fuel properties.

**2.1 Renewable Fuel Volume Scenarios**

The Energy Policy Act of 2005 (the Energy Act or the Act) stipulates
that the nationwide volumes of renewable fuel required under the
Renewable Fuel Standard (RFS) program must be at least 4.0 billion
gallons in 2006 and increase to 7.5 billion gallons by 2012. However, we
expect that actual renewable fuel usage will exceed the RFS requirement
by a significant margin. In Annual Energy Outlook 2006 (AEO 2006), the
Energy Information Administration (EIA) projects that total renewable
fuel demand would be 9.9 billion gallons by 2012. More specifically, EIA
predicts that 9.6 billion gallons of ethanol and 303 million gallons of
biodiesel would be consumed in 2012. The projected renewable fuel
consumption levels were estimated using EIA's LP refinery model which
was based on a crude oil price of \$48/bbl. This figure is lower than
today's crude oil price (tracking around \$55/bbl at the time of our
analysis).[^34][^35][^36] Therefore, current market conditions indicate
that renewable fuel production could be even more favorable and/or
prevalent in the future based on economics.[^37] However, EIA's AEO 2006
analysis also considers the feasibility of building production
facilities to accommodate for the growing renewable fuel demand.
Accordingly, we interpret EIA's ethanol and biodiesel projections to be
reasonable estimates considering both economics and the rate at which
new plants could feasibly come on-line. As a result, in assessing the
impacts of expanded renewable fuel use, we evaluated two renewable fuel
usage scenarios (described in more detail below). The first represents
the statutorily-required minimum and the second reflects the higher
levels projected by EIA in AEO 2006. Although the actual renewable fuel
volumes produced in 2012 may differ from both the required and projected
volumes, we believe that these two volume scenarios represent a
reasonable range for analysis purposes.

The Act also requires that at least 250 million gallons of the total
renewable fuel use in 2013 and beyond meet the definition of cellulosic
biomass ethanol. As described in Chapter 1, there are a number of
companies planning to produce ethanol from cellulosic feedstocks and/or
waste-derived energy sources that could potentially meet the definition
of cellulosic biomass ethanol. Accordingly, we anticipate a ramp-up in
cellulosic biomass ethanol production in the coming years. Furthermore,
for analysis purposes, we have assumed that the 250 million gallon
requirement would be met by 2012.

As discussed in more detail below in Section 2.2.2, we chose 2004 to
represent current baseline conditions. In 2004, 3.5 billion gallons of
ethanol and 25 million gallons of biodiesel were consumed in motor
vehicle fuels. To compare fuel quality impacts on emissions and air
quality, we created a 2012 reference case that maintained current fuel
quality parameters (with the exception of sulfur) but incorporated
forecasted increases in vehicle miles traveled, changes in fleet
demographics, etc. The 2012 fuel reference case was developed by growing
out the 2004 renewable fuel baseline according to EIA's forecasted
energy growth rates. In AEO 2006, EIA predicted that gasoline demand
would grow by 11.2 percent and diesel fuel demand would grow by 20.5
percent from 2004 to 2012. As a result, the 2012 reference case is based
on 3.9 billion gallons of ethanol use and 30 million gallons of
biodiesel use in 2012.

For our analyses, we created two 2012 control cases representing
expanded renewable use -- the "RFS Case" and the "EIA Case". In both
cases, cellulosic biomass ethanol use was assumed to be 250 million
gallons (statutory required minimum) and biodiesel use was assumed to be
303 million gallons (EIA AEO 2006 estimate). The RFS Case was designed
to exactly meet the RFS program requirements considering the effects of
higher equivalence values for cellulosic ethanol and biodiesel. Per §
80.1115, one gallon of cellulosic ethanol counts 2.5 times towards
compliance and one gallon of biodiesel counts 1.5 times towards
compliance. As a result, in the RFS Case we predict that less than 7.5
billion gallons of renewable fuel would actually be consumed in 2012.
The actual volume of renewable fuel analyzed for the RFS Case was
computed to be approximately 7.0 billion gallons. The EIA Case
represents EIA's projections of renewable fuel use in 2012. Based on AEO
2006, the actual volume of renewable fuel analyzed for the EIA Case was
9.9 billion gallons. A summary of the renewable fuel volume scenarios we
evaluated is found below in Table 2.1-1.

**Table 2.1-1 Renewable Fuel Volume Scenarios (MMgal)**

![](media/image28.wmf){width="5.422916666666667in"
height="3.408333333333333in"}

**2.2 Current Gasoline Oxygenate Use**

**2.2.1 Why are oxygenates currently blended into gasoline?**

The blending of oxygenates into gasoline dates back to the 1970's.
However, their use greatly expanded in response to the Clean Air Act
(CAA) amendments of 1990. Areas found to be out of compliance (i.e., in
non-attainment) with the National Ambient Air Quality Standards (NAAQS)
for ozone were required to reformulate their gasoline and use oxygenates
year-round. In addition, several states began to use oxygenated fuel
(oxy-fuel) in the wintertime to address carbon monoxide non-attainment.
In addition, oxygenates (namely ethanol) have historically been used as
a gasoline volume extender and more recently, to meet state mandates.
This section summarizes the current driving forces behind gasoline
oxygenate use in the U.S.

**2.2.1.1 Federal Reformulated Gasoline Program**

As mentioned above, areas found to be in ozone non-attainment were
required to use reformulated gasoline (RFG) year-round. The federal RFG
program contained a minimum oxygenate requirement as well as other fuel
quality standards.[^38] Adding oxygen to gasoline and reformulating
other gasoline properties has helped to reduce the production of
smog-forming pollutants that contribute to unhealthy ground-level ozone.
Besides ozone non-attainment areas, several states/areas also opted into
the RFG program (otherwise known as "opt-in"). In addition, California
and Arizona have state programs that promote the use of oxygenated
gasoline.

A list of the 2004 federal RFG areas and their corresponding
oxygenate(s) is provided in Table 2.2-1. For the purpose of this
analysis, only ethanol (ETOH) and methyl tertiary-butyl ether (MTBE)
have been considered.[^39]

**Table 2.2-1. 2004 Federal RFG Areas by State**[^40]

![](media/image29.wmf){width="5.215277777777778in"
height="7.622916666666667in"}

As shown above in Table 2.2-1, a little more than half of the Federal
RFG areas (on a county-by-county basis) used MTBE as opposed to ethanol
as an oxygenate in 2004. However, on a volumetric basis, more ethanol
was consumed in RFG than MTBE (2.2 billion gallons compared to 1.9
billion gallons as shown in Tables 2.1.5 and 2.1.3, respectively).

**2.2.1.2 State Oxygenated Fuel Programs**

In addition to the RFG program, several states require oxygenated fuel
(oxy-fuel) to be used in the wintertime to address carbon monoxide (CO)
non-attainment. CO is formed from the incomplete combustion of
hydrocarbons (found in all gasoline blends). Production of the poisonous
gas is more prevalent in oxygen-deficient environments and more harmful
to human health in the wintertime due to temperature inversions.[^41]
Together, the winter oxy-fuel program coupled with improving vehicle
emissions control systems has helped to reduce CO emissions. Many areas
have and are continuing to come into attainment with the CO national
ambient air quality standards (NAAQS). However, many former
non-attainment areas continue to use the winter oxy-fuel program as part
of a maintenance plan for remaining in compliance with the CO NAAQS. A
list of the 2004 oxy-fuel areas is provided in Table 2.2-2. All oxy-fuel
areas were assumed to use ethanol in 2004 based on information obtained
from regional EPA offices.

**Table 2.2-2. 2004 State-Implemented Winter Oxy-Fuel Programs**[^42]

![](media/image30.wmf){width="6.366666666666666in"
height="3.658333333333333in"}

**2.2.1.3 Other Motivations for Blending Ethanol**

In addition to the RFG and oxy-fuel programs, gasoline refiners have
several other motivations for blending oxygenate (namely ethanol) into
gasoline. First and foremost, the state they provide gasoline to could
be operating under a state ethanol mandate. In 2004, Hawaii joined
Minnesota in approving a state ethanol requirement.[^43] Second,
blending ethanol into gasoline could help them meet their mobile source
air toxics (MSAT1) performance standards as determined by the Complex
Model.[^44] Third, adding ethanol increases both octane and total fuel
volume, thus helping refiners extend their gasoline production. Finally,
and perhaps most importantly, with record-high crude oil prices and the
growing availability of grain-based ethanol (especially in PADD 2),
ethanol use has become increasingly economical. The 1.1 billion gallons
of ethanol used in PADD 2 conventional gasoline in 2004 (refer to Table
2.2-5 in Section 2.2.2.4) is a good indicator of this trend.

In addition to the increasing availability of ethanol, consumer demand
is also increasing based on the growing number of ethanol-friendly
vehicles on the road. Conventional vehicles consume the majority of fuel
ethanol and are limited to gasoline with 10 volume percent (vol%)
ethanol (E10) or less. However, there are currently around 6 million
flexible fuel vehicles (FFVs) on the road today with more being produced
and sold each day[^45]. FFVs are specifically designed to handle a wide
range of gasoline/ethanol blends up to 85 vol% ethanol (E85).

**2.2.2 Development of the Base Case**

As discussed in 2.1, to evaluate the impacts of increased ethanol
blending and decreased MTBE blending on gasoline properties (and in turn
air quality), we had to create a point of comparison. To do so, we
assembled a 2004 Base Case to represent current baseline conditions,
i.e., current gasoline, ethanol, and MTBE use. The methodology for
assembling the base case, as well as a summary of the results, is
described below.

**2.2.2.1 Strategy for Establishing the 2004 Base Case**

For the purpose of this regulatory impact analysis, the 2004 calendar
year was selected to reflect current baseline conditions. This period
represented the most current year for which gasoline and oxygenate data
were available and also captured the California, New York, and
Connecticut MTBE bans (effective 1/1/04) while avoiding the 2005
calendar year hurricane upsets.

The approach for assembling the 2004 base case consisted of obtaining
gasoline, ethanol, and MTBE usage for all 50 states as well as the
District of Columbia. As mentioned earlier, other low-volume oxygenate
use (e.g., ETBE, TAME, etc.) was assumed to be negligible and thus
ignored for this analysis. All ethanol-blended gasoline was assumed to
contain 10 vol% ethanol, with the exception of California "RFG" (Federal
RFG and California Phase 3 RFG (CaRFG3)).[^46] Current California
gasoline regulations make it very difficult to meet the NOx emissions
performance standard with ethanol content higher than about 6 vol%. For
our analysis, all California RFG was assumed to contain 5.7 vol% ethanol
based on discussions with California Air Resources Board (CARB). This
percentage was also applied to California RFG supplied to the Phoenix
metropolitan area in the summertime under Arizona's clean burning
gasoline (CBG) program.[^47] Finally, all MTBE-blended gasoline was
assumed to contain 11 vol% MTBE.

Total gasoline consumption was obtained from the 2004 Petroleum
Marketing Annual (PMA) report published by the Energy Information
Administration (EIA).[^48] The reported annual average sales volume for
each state was interpreted as total blended gasoline (including
additives, namely oxygenates). 2004 MTBE usage by state was obtained
from EIA.[^49]^,^[^50] The data received was exclusive to states with
RFG programs (including Arizona's CBG program). Thus, for the purpose of
the 2004 base case analysis, MTBE use was assumed to be limited to RFG
areas. 2004 ethanol usage by state was derived from a compilation of
data sources and assumptions. As a starting point, total domestic
ethanol consumption was acquired from EIA's Monthly Energy Review
published in June 2006[^51]. State ethanol contributions originated from
the 2004 Federal Highway Administration (FHWA) gasohol report[^52].
However, there was some ambiguity with the 2004 FHWA data. First, the
total ethanol consumption did not match up with EIA's reported value
(3.7 billion gallons compared to 3.5 billion gallons). Second, the
gasohol (and thus ethanol) volumes were derived from potentially
imprecise motor vehicle fuel tax reports.[^53] And third, not all states
using ethanol reported their gasohol usage so FHWA had to model-estimate
19 states' ethanol usage (accounting for 60% of the total ethanol
volume). To improve upon the FHWA data, we used a series of oxygenate
verification tools including knowledge of state ethanol mandates, state
MTBE bans, Arizona's CBG program, and fuel survey results.[^54][^55] The
state-by-state FHWA data was adjusted accordingly and allocated by fuel
type (RFG, CG, and/or oxy-fuel). The summarized oxygenate results are
presented throughout this section.

**2.2.2.2 2004 Gasoline/Oxygenate Consumption by PADD**

In 2004, 3.5 billion gallons of ethanol and 1.9 billion gallons of MTBE
were blended into gasoline to supply the transportation sector with a
total of 136 billion gallons of gasoline. A breakdown of the 2004
gasoline and oxygenate consumption by PADD is found below in Table
2.2-3.

**Table 2.2-3. 2004 Gasoline & Oxygenate Consumption by PADD**

![](media/image31.wmf){width="5.65in" height="2.3847222222222224in"}

As shown above, in 2004, almost half (or 46 percent) of the ethanol was
consumed in PADD 2, where the majority of ethanol was produced. The next
highest region of use was the State of California which accounted for
nearly a quarter (or 24 percent) of domestic ethanol consumption. This
makes sense since California alone accounts for over 10 percent of the
nation's total gasoline consumption. And in 2004, following their MTBE
ban, all fuel (both Federal RFG and CaRFG3) was presumed to contain 5.7
vol% ethanol. The next highest region of use was PADD 1 (19 percent)
which makes sense considering the high concentration of RFG areas (most
of which used ethanol in 2004 as shown in Table 2.2-1). The remaining 10
percent of ethanol use occurred collectively in PADDs 3, 4, and 5/

In 2004, total ethanol use exceeded MTBE use. Ethanol's lead oxygenate
role is relatively new, however the trend has been a progression over
the past few years. From 2001 to 2004, ethanol consumption more than
doubled (from 1.7 to 3.5 billion gallons), while MTBE use (in RFG) was
virtually cut in half (from 3.7 to 1.9 billion gallons). A plot of
oxygenate use over the past decade is provided below in Figure 2.2-1.

> **Figure 2.2-1. Oxygenate Consumption vs. Time**[^56]**^,^**[^57]

![](media/image32.wmf){width="5.9944444444444445in"
height="4.090972222222222in"}

The nation's transition to ethanol is linked to states' responses to
recent environmental concerns surrounding MTBE groundwater
contamination. Traces of MTBE have been found in both surface and ground
water in and around RFG areas. The MTBE is thought to have made its way
into the water from leaking underground storage tanks, gasoline spills,
and engines. Concerns over drinking water quality prompted several
states to significantly restrict or completely ban MTBE use in gasoline.
At the time of our analysis, 19 states had adopted MTBE bans. Ten states
had bans that impacted the entire 2004 calendar year, four states had
bans that impacted a portion of the year, and five states had bans that
became effective in 2005 and beyond. A list of the states with MTBE bans
(listed in order of phaseout date) is provided below in Table 2.2-4.

**Table 2.2-4. States MTBE Bans by Phaseout Date**[^58]

![](media/image33.wmf){width="3.7381944444444444in"
height="4.958333333333333in"}

As explained above in 2.2.2.1, all MTBE consumption was assumed to occur
in reformulated gasoline in 2004. As shown in Table 2.2-3, 99 percent of
MTBE use (by volume) occurred in PADDs 1 and 3. This reflects the high
concentration of RFG areas in the northeast (PADD 1) and the local
production of MTBE in the gulf coast (PADD 3). PADD 1 receives a large
portion of its gasoline from PADD 3 refineries who either produce the
fossil-fuel based oxygenate or are closely affiliated with
MTBE-producing petrochemical facilities in the area.

**2.2.2.3 2004 Gasoline/Oxygenate Consumption by Season**

In 2004, according to EIA Petroleum Marketing Annual (PMA),
approximately 40 percent of gasoline was consumed in the summertime and
60 percent was consumed in the wintertime.[^59] Similarly, according to
EIA Monthly Energy Review June 2006, 38 percent of the ethanol was
consumed in the summertime and 62 percent was consumed in the
wintertime.[^60]^,^[^61]

Total gasoline use is higher in the wintertime because it's a longer
season. The RFG regulations define summertime fuel as gasoline produced
from May 1^st^ to September 15^th^ (4.5 months total).[^62] The
remaining 7.5 months are considered to be wintertime gasoline. Even
though on an average per day basis summertime consumption is higher,
more gasoline is still sold and consumed in the wintertime based on the
length of the season.

Seasonal ethanol use follows the same general trend as gasoline.
However, besides the associated correlation with seasonal gasoline
consumption, there are additional reasons why 2004 ethanol use may have
been higher in the wintertime. First, the oxy-fuel program requires
oxygenate to be used in certain areas in the wintertime only. These same
areas, which do not require oxygenate in the summer, were all presumed
to use ethanol as their oxygenate (as described in 2.2.1.2). Thus, more
areas use ethanol during the winter months than the summer. Secondly,
there is an economic penalty associated with blending ethanol into
summertime RFG. Refiners supplying summertime gasoline to RFG areas have
to remove butanes and pentanes from their gasoline in order to add
ethanol and still comply with the 7 psi Reid vapor pressure (RVP)
requirement.

**2.2.2.4 2004 Gasoline/Oxygenate Consumption by Fuel Type**

According to fuel survey results, in 2004, approximately 2.2 billion
gallons of ethanol were blended into reformulated gasoline and the
remaining 1.3 billion gallons were used in conventional gasoline
(including wintertime oxy-fuel).[^63]^,^[^64] A breakdown of the 2004
ethanol consumption by fuel type and PADD is found in Table 2.2-5.

**Table 2.2-5.**

**2004 Ethanol Consumption by Fuel Type (MMgal)**

As mentioned above in Section 2.2.2.1, 100 percent of the 1.9 billion
gallons of MTBE blended into gasoline in 2004, was assumed to be
consumed in reformulated gasoline.

**2.2.2.5 2004 Gasoline/Oxygenate Consumption by State**

In 2004, ethanol was blended into gasoline in 34 of the 50 states. No
ethanol use was observed in the remaining 16 states: Maine, New
Hampshire, Vermont, Pennsylvania, Delaware, Georgia, North Carolina,
South Carolina, West Virginia, Tennessee, Oklahoma, Mississippi,
Arkansas, Louisiana, Idaho, and West Virginia, nor was any ethanol used
in Washington DC. A summary of 2004 ethanol usage by state is presented
in Table 2.2-6. Note that a state ethanol percentage less than 10
indicates that only a percentage of the gasoline pool was blended with
ethanol, not that ethanol itself was blended in less than 10 vol% (E10)
proportions, except in the case of California gasoline (E5.7). Figure
2.2-2 shows the percentage of E10 by state.

The states consuming the highest volumes of ethanol in 2004 were
California, Illinois, New York, Minnesota, and Ohio, respectively. With
respect to gasoline use, the highest percentage of ethanol use occurred
in Minnesota, Hawaii, Connecticut, Illinois, and Iowa. Four out of the
five states are not surprising. The first two states have ethanol
mandates and the last two are located in the "corn belt" where ethanol
is produced. Connecticut's high percentage of ethanol use may come as a
surprise at first glance. However, the entire state operates under the
RFG program (refer to Table 2.2-1), and since they also have a state
MTBE ban, ethanol is found in each gallon of gasoline.

**Table 2.2-6. 2004 Gasoline/Ethanol Consumption by State**

![](media/image34.wmf){width="6.266666666666667in"
height="5.856944444444444in"}

**Figure 2.2-2. 2004 Ethanol Consumption, % E10 by State**

![](media/image35.wmf){width="5.977083333333334in"
height="4.499305555555556in"}

**2.2.3 Development of the 2012 Reference Case**

To establish the 2012 reference case, we started with the 2004 Base Case
(presented in Table 2.2-3) and grew out gasoline/oxygenate use according
to the EIA AEO 2006 motor gasoline energy growth rate from 2004 to
2012.[^65] Accordingly, in the resulting 2012 reference case, ethanol
and MTBE use was proportional to 2004 use by both region and fuel type.
A summary of the 2012 ethanol reference case is found in Table 2.2-7.

**Table 2.2-7.**

**2012 Reference Case - Gasoline & Oxygenate Consumption by PADD
(MMgal)**[^66]

![](media/image36.wmf){width="5.7965277777777775in"
height="2.4451388888888888in"}

**2.2.4 Development of the 2012 Control Cases**

In Section 2.2.2 we described our methodology behind building the 2004
Base Case, which was used to produce the 2012 Reference Case (described
above). In this section we will describe how we developed the two 2012
control cases representing increased ethanol fuel use -- the RFS Case
and the EIA Case. Both control cases incorporate our knowledge of future
state ethanol mandates, tax incentives, and anticipated winter oxy-fuel
usage. Our analysis relied on LP modeling (described in more detail
below) to determine how much ethanol would be used in each PADD, season,
and fuel type. From there, we conducted post-processing to determine how
much ethanol would be used on a state-by-state basis and in some cases
and made predictions on how ethanol would likely fill urban and rural
areas.

**2.2.4.1 Forecasting Ethanol Consumption / LP Modeling Results**

As mentioned earlier in Section 2.2.2.2, groundwater contamination
concerns have caused many states to ban the use of MTBE in gasoline. In
response to the Energy Act, all U.S. refiners are expected to eliminate
the use of MTBE in gasoline by the end of 2007, and certainly prior to
2012. Ethanol consumption, on the other hand is expected to continue to
grow in the future. Not only are the Energy Act's RFS requirements
promoting ethanol growth, ethanol is needed to fuel the growing number
of ethanol-friendly vehicles being produced as well as satisfy the
growing number of state ethanol mandates (Washington, Montana,
Louisiana, and Missouri recently joined Minnesota and
Hawaii)[^67]^,^[^68]^,^[^69].

Based on projections from EIA and others, it's abundantly clear that
renewable fuel use (namely ethanol) is growing much faster than the RFS
requirement. However quantifying future ethanol use is a difficult task.
The gasoline refining industry and ethanol industry are currently
undergoing a variety of changes/expansions and there is no precise way
to know exactly how things are going to "fall out" in the future.
Accordingly, as explained in Section 2.1, we have considered two
different 2012 renewable fuel consumption scenarios to represent a
reasonable range of ethanol use. For the RFS Case we modeled 6.7 billion
gallons of ethanol use and for the EIA case we modeled 9.6 billion
gallons (refer to Table 2.1-1). EPA is not concluding that ethanol
consumption could not possibly exceed 9.6 billion gallons by 2012, but
rather that this volume is a reasonable "ceiling" for our analysis.

To estimate how ethanol use would be allocated in the future, we relied
on Jacob's Consultancy LP refinery modeling.[^70] For the Base Case and
Reference Case, the LP refinery model was set up to allocate fixed
volumes of ethanol/MTBE to regions consistent with our analysis of
current gasoline oxygenate use (described above in Sections 2.2.2 and
2.2.3). This essentially fixed the total ethanol and MTBE use in each
PADD. From there, the oxygenates were further allocated by season and
fuel grade to match the oxygen content for RFG, RBOB and CBOB based on
2004 batch report data. Any leftover ethanol was allocated to CG. Based
on the resulting fuel allocation, the LP model generated CG and RFG fuel
properties considering the RVP effects and blending qualities of ethanol
and MTBE (such properties are discussed further and utilized in Section
2.3).

For each of the future control cases, MTBE use was assumed to be zero
and the amount of ethanol added to gasoline was varied. For the RFS
Case, total ethanol use was fixed at 6.7 billion gallons and for the EIA
Case, ethanol use was fixed at 9.6 billion gallons. For each control
case, the LP model used gasoline and ethanol blending economics (e.g.,
ethanol distribution costs, seasonal ethanol and gasoline blendstock
prices, etc.) to determine how much ethanol would be blended into
gasoline by PADD, season, and fuel type. Again, the results were used to
generate CG and RFG fuel properties used in Section 2.3.

Slight adjustments had to be made to the refinery modeling outputs to
ensure that sufficient ethanol was supplied in the wintertime to meet
the oxy-fuel requirements in PADDs 4/5. In addition, small corrections
were required to ensure that ethanol blending in a given region/state
did not exceed the maximum blending criteria assumed for the analysis -
10 volume percent (vol%) ethanol nationwide, and 5.7 vol% ethanol in
California. The adjusted LP refinery modeling results for the RFS and
EIA control cases are summarized below in Tables 2.2-8 and 2.2-9,
respectively.

**Table 2.2-8.**

**Adjusted LP Modeling Results for the RFS Case (MMgal)**

![](media/image37.wmf){width="5.6in" height="2.23125in"}

**Table 2.2-9.**

**Adjusted LP Modeling Results for the EIA Case (MMgal)**

![](media/image38.wmf){width="5.6in" height="2.23125in"}

2.  **Resulting 2012 Ethanol Consumption by PADD**

Starting with the LP refinery modeling results, we segregated the Rocky
Mountain (PADD 4) and West Coast (PADD 5) ethanol use (represented as an
aggregate above in Tables 2.2-8 and 2.2-9) and examined the resulting
ethanol allocation by region. A summary of the 2012 forecasted ethanol
consumption by region (PADDs 1-5 and California) for each control case
is found below in Table 2.2-10.

**Table 2.2-10.**

**2012 Forecasted Ethanol Consumption by PADD**

![](media/image39.wmf){width="6.3381944444444445in"
height="2.2215277777777778in"}

As shown above, in 2012 PADD 2 is expected to continue to dominate
ethanol use. PADD 2 ethanol consumption is expected to double from 1.8
billion gallons (Bgal) in the Reference Case (refer to Table 2.2-7) to
3.1 Bgal in the RFS Case and 3.9 Bgal in the EIA Case. This represents a
slight decrease in Midwest marketshare (from 46% in Reference/RFS Case
to 40% in the EIA Case). The predicted shift in marketshare is
attributed to the growing amount of ethanol use outside of the
traditional cornbelt.

The LP modeling suggests that ethanol usage is expected to greatly
increase in PADDs 1 and 3. In PADD 1, ethanol blending is expected to
more than triple from 735 million gallons in the Reference Case to 2.1
Bgal in the RFS Case and 2.5 Bgal in the EIA Case. In PADD 3, ethanol
use in expected to sharply increase from 88 million gallons in the
Reference Case to 354 million gallons in the RFS Case and 1.8 billion
gallons in the EIA Case. This projected increase in ethanol blending on
the East Coast and Gulf Coast, reflects the phase out of MTBE
(replacement with ethanol) as well as ethanol blending economics.

**2.2.4.3 Resulting 2012 Ethanol Consumption by Season**

Furthermore, we examined the resulting ethanol allocation by season. The
LP refinery modeling assumes equal 182.5-day summer and winter seasons.
A summary of the resulting 2012 forecasted ethanol consumption by season
for each of the control cases is found below in Table 2.2-11.

**Table 2.2-11.**

**2012 Forecasted Ethanol Consumption by Season (MMgal)**

![](media/image40.wmf){width="4.865277777777778in"
height="2.0597222222222222in"}

As shown above, ethanol usage in 2012 is expected to be slightly more
prevalent in the summertime than in the wintertime. This is a shift from
our 2004 Base Case (38% of ethanol use occurred in the summertime and
62% occurred in the wintertime as explained in Section 2.2.2.3), mainly
because we are changing the way we define the seasons. In the Base Case
we defined the seasons based on the RFG regulations (4.5 months of
"summer" and 7.5 months of "winter") whereas in this 2012 forecast we
are defining them based on 6 months of each season. Since gasoline
consumption (gal/day) is higher in the summertime, more ethanol-blended
gasoline could potentially be consumed during the summer months.
However, since there is an economic penalty associated with blending
ethanol into summertime gasoline (refiners have to remove butanes and
pentanes to comply with the RFG RVP requirements), the result is
somewhat of a seasonal balance in both the RFS Case and the EIA Case.

**2.2.4.4 Resulting 2012 Ethanol Consumption by Fuel Type**

In addition to providing a PADD and seasonal breakdown, The LP modeling
determined how much ethanol would be used by fuel type - conventional
gasoline (CG) versus reformulated gasoline (RFG). The first thing we did
was allocate a portion of the CG to the required winter oxy-fuel areas.

**Strategy for Allocating Ethanol to Oxy-Fuel Areas**

In the 2004 Base Case, there were 14 state-implemented winter oxy-fuel
programs in 11 states (summarized previously in Table 2.2-2). Of these
programs, 9 were required in response to non-attainment with the CO
National Ambient Air Quality Standards (NAAQS) and 5 were implemented to
maintain CO attainment status. However, in the future 4 of the 9
required oxy-fuel areas are expected to be reclassified from
non-attainment to attainment and discontinue using oxy-fuel in the
wintertime[^71]. These areas are: Anchorage, AK; Las Vegas, NV;
Provo/Orem, UT; and Spokane, WA. In addition, Colorado is expected to
discontinue using winter oxy-fuel in Denver/Boulder and Longmont to
maintain CO attainment status. The use of oxy-fuel in the
above-mentioned areas is expected to discontinue by 2012 or sooner. With
the removal of these 6 state-implemented programs, that leaves a total
of 8 oxyfuel areas in Tucson and Phoenix, AZ; Los Angeles, CA; Missoula,
MT; Reno, NV; Albuquerque, NM; Portland, OR; and El Paso, TX. We assumed
that these areas would continue to blend 10 vol% ethanol into their
gasoline for their entire winter oxy-fuel period (duration varies by
area, six month maximum) in the 2012 control cases.

Once a portion of the conventional gasoline ethanol was allocated to
meet winter oxy-fuel requirements, this gave use a PADD-by-PADD
breakdown of ethanol use by conventional gasoline, oxy-fuel, and
reformulated gasoline as shown below in Table 2.2-12.

**Table 2.2-12.**

**2012 Forecasted Ethanol Consumption by Fuel Type (MMgal)**

![](media/image41.wmf){width="5.458333333333333in"
height="2.5131944444444443in"}

However, more post-processing was required to determine how much ethanol
would be used on a state-by-state basis to feed into the emissions and
air quality analyses. We begin the latter part of this discussion by
explaining how we allocated the RFG ethanol to specific RFG areas and
how we allocated the CG ethanol to specific states/regions considering
state ethanol mandates and the economic favorability of ethanol blending

**Strategy for Allocating Ethanol Among RFG**

In the 2004 Base Case, there were 18 states/districts with RFG programs
covering a total of 175 counties in 36 areas (summarized previously in
Table 2.2-1). For our analysis of 2012 ethanol use, we assumed that the
number of RFG areas would not change and accordingly, that the RFG fuel
contribution to the gasoline pool would remain the same. However, we
considered the amount of ethanol added to RFG to be a variable, as
discussed below.

In the past, all RFG areas were required to use a minimum amount of
oxygenate in their reformulated gasoline year-round, as discussed
earlier in 2.2.1.1. However, effective May 5, 2006, EPA removed the RFG
oxygenate requirement in response to the Act.[^72] Although the
oxygenate requirement has already been eliminated, many refiners are
still operating under contracts with ethanol blenders. As such, refiners
true response to the removal of the oxygenate requirement is relatively
unknown at this time. While it is difficult to predict exactly how each
refinery supplying an RFG area would behave, the LP modeling has
attempted to do so.

The modeling suggests that some refineries will continue to blend
ethanol into RFG (or even increase blending) in 2012 based on octane,
volume, and/or toxic performance requirements. Some RFG producers may
decidedly replace MTBE with ethanol while others may pare back or
discontinue ethanol use all together. A summary of the 2012 forecasted
RFG ethanol consumption (by season) for each control case is found below
in Table 2.2-13.

**Table 2.2-13.**

**2012 Forecasted RFG Ethanol Consumption (MMgal)**[^73]

![](media/image42.wmf){width="6.496527777777778in"
height="1.9569444444444444in"}

As shown above, the modeling suggests that more ethanol would be
consumed in RFG in the EIA Case in the presence of more ethanol. The
modeling also suggests that the greatest ethanol marketshare would occur
in California RFG (5.2 vol% ethanol on average across both
cases/seasons, or 91% E5.7). The next highest areas of RFG use would be
PADD 1 (6.6 vol% ethanol on average, or 66% E10) followed by PADD 2 (6.1
vol% ethanol on average, or 61% E10). Little ethanol blending was
predicted to occur in Texas RFG (0.8% ethanol or 8% E10).

In both control cases, more ethanol was predicted to be blended into
wintertime RFG. As discussed earlier, this makes sense because in order
to meet the RVP requirements pertaining to summertime RFG (7 psi),
refiners have to remove butanes and pentanes to accommodate for ethanol
blending (which increases overall gasoline volatility). As such, in the
absence of an RVP waiver (which exists exclusively for summertime CG),
refiners are less inclined to blend ethanol into summertime RFG.

To allocate the RFG ethanol (aggregated by PADD and season in Table
2.2-13) by state/RFG area, we assumed that each region would behave
uniformly with the exception of PADD 1 (discussed in more detail below).
For example, consider PADD 2 summertime RFG. In the RFS Case, RFG in
Chicago, Louisville, Milwaukee, etc. would all contain 1.6% ethanol on
average. Or more accurately, 16% of all the gasoline consumed within
PADD 2 RFG areas would contain 10% ethanol.

However, based on our knowledge of the refining industry and
distribution patterns, we did not assume that PADD 1 RFG would be
uniform in ethanol content. The LP modeling assumes that the RFG
produced in PADD 1 contains ethanol but the RFG produced in PADD 3 and
shipped to PADD 1 does not. RFG from PADD 3 comes up the Colonial
Pipeline and passes through Virginia, Washington DC and Maryland on its
way to Pennsylvania and New York. With the exception of a small Yorktown
refinery, the southernmost refineries in PADD 1 are located around the
Philadelphia area. However, there is no cheap way to send fuel south.
Therefore, the RFG coming from PADD 3 is likely to completely fulfill
the RFG demand in Virginia, Washington DC and Maryland. Beyond Maryland,
the fuel from PADD 1 refineries is sold along with any leftover PADD 3
RFG, as distribution costs are roughly the same from Philadelphia north.
As a result, the Virginia, Washington DC and Maryland RFG areas were
assumed to receive less ethanol (in most cases zero E10) than the other
RFG areas located in PADD 1. A summary of the resulting RFG ethanol
distribution by state is found below in Table 2.2-14.

**Table 2.2-14.**

**2012 RFG Ethanol Distribution by State**

![](media/image43.wmf){width="6.496527777777778in"
height="1.5763888888888888in"}

**Strategy for Allocating Ethanol Among CG**

The above-mentioned oxy-fuel requirements combined with state ethanol
mandates created a "floor" for conventional gasoline ethanol use within
each PADD. This essentially forced a specific amount of ethanol to be
used in wintertime CG in PADDs 3 and 5 and a specific amount of ethanol
to be added year-round in Minnesota, Montana, and Missouri (100% E10
mandates); Hawaii (85% E10 mandate); as well as Washington and Louisiana
(20% E10 mandates).

To determine how the remaining ethanol would be allocated to the
leftover conventional gasoline, we devised a systematic way to allocate
ethanol by state/area. Since the primary motivation to blend (or not
blend) ethanol is expected to be economic, we devised a way to rank CG
areas, on a state-by-state and urban/rural basis, as to the economic
favorability of ethanol blending. This was done by calculating an
ethanol margin, which is equal to gasoline price minus ethanol delivered
price. Ethanol delivered price is equal to ethanol plant gate price plus
transportation costs minus any additional state plus other adjustments
(explained below). The greater the ethanol margin, the greater the
economic incentive and the more likely ethanol is to be used in that
area.

At the time the analysis was carried out, ethanol plant gate price was
taken from an older EIA NEMS model. However, since this price was
assumed to be the same for all ethanol, the actual value is not
important when trying to estimate relative allocation preferences
between areas. All ethanol blending was assumed to be done at 10 volume
percent. The gasoline prices for each state were the weighted average
rack price of all conventional grades and all months, taken from EIA
Petroleum Marketing Annual 2004.[^74]

Ethanol distribution costs were taken from figures given in the
documentation for the EIA NEMS model, and are based on a 2002 study by
DAI, Inc.[^75] For the purpose of this consumption analysis, all ethanol
was assumed to be produced in the Midwest in census divisions 3 and 4
(corresponding closely to PADD 2). This is largely consistent with the
production analysis presented in Chapter 1 of the RIA. While the results
of the production analysis do not completely coincide with this
assumption (as shown in Table 1.2-15, about 86 percent of the total
ethanol plant capacity is expected to originate from PADD 2 in 2012 and
the rest would originate from other areas throughout the country), this
simplifying assumption is still very reasonable.

Ethanol consumed within census divisions 3 and 4 was assumed to be
transported by truck, while distribution outside of those areas was via
rail, ship, and/or barge. A single average distribution cost for each
destination census division was generated by weighting together the 2012
freight costs given for each mode in both census divisions 3 and 4
according to their volume share. These cent per gallon figures were
first adjusted upward by 10 percent to reflect higher energy prices, and
then additional adjustments were applied to some individual states based
on their position within the census division. In the cases of Alaska and
Hawaii, differences in ethanol delivery prices from the mainland were
inferred from gasoline prices. Table 2.2-15 shows the gasoline price and
ethanol distribution cost for each state as used in this analysis.

**Table 2.2-15.**

**Gasoline Price & Ethanol Distribution Costs**[^76]
![](media/image44.wmf){width="3.441666666666667in"
height="7.990277777777778in"}

As the final step in the calculation, subsidies and other adjustments
were applied. The federal blending credit of 51 cents per gallon was
given to all areas, and five state retail incentives were included as
follows (all cents per gallon of ethanol): Iowa, 29.5; Illinois, 20.1,
South Dakota, 20; Maine: 7.5; Oklahoma, 1.6.[^77][^78]

In addition to state subsidies, small penalty adjustments were made for
distributing ethanol into rural areas in several states (as presented in
Table 2.2-16). The reasoning behind this is that when large shipments of
ethanol come from the Midwest by barge, ship, or rail, they will be
unloaded initially at large terminals near metropolitan areas. Further
storage and handling will be required to allow smaller quantities to be
distributed via truck into rural areas. Several states have gasoline
pipelines that traverse them with connections at various points, helping
to reduce distribution burdens, but ethanol is not expected to be
shipped via pipeline. Overall, the largest adjustments were applied to
the Rocky Mountain states since they are generally larger in area and
additional expense is required to transport freight through higher
elevations and rugged terrain. Smaller adjustments were applied to
states that are smaller, flatter, or have navigable water access on one
or more sides. The states that do not appear on this list are either
located in the Midwest (where ethanol is produced and readily available
to virtually all areas at similar costs) or are small northeast states
not believed to have significant differences between rural and urban
distribution costs.

**Table 2.2-16.**

**Adjustment for Ethanol Distribution into Rural Areas**

![](media/image45.wmf){width="3.825in" height="1.91875in"}

To determine which in-use areas/counties would receive urban versus
rural ethanol distribution pricing based on the economies of scale
described above, we looked to the U.S. Census Bureau which considers
population density and other factors.

Metropolitan Statistical Areas (MSAs) served as the starting point for
determining the areas within a state that would be considered "urban".
MSAs are geographic entities defined by the U.S. Office of Management
and Budget (OMB) for use by federal statistical agencies in collecting,
tabulating, and publishing federal statistics. An MSA is defined as
having a core urban area of 50,000 or more people. Each MSA consists of
one or more counties including the counties contained the core urban
area, as well as any adjacent counties that have a high degree of social
and economic integration with the urban core. For the purposes of this
analysis, we only considered MSAs with populations greater than 1
million people, or other areas having special qualifications. Such
qualifications include MSAs with less than 1 million people that happen
to be the largest MSA in a less-populated state (i.e. Montana and
Wyoming), or other MSAs deemed likely to receive ethanol by rail based
on proximity to major rail lines.

Once the urban counties for each state were determined, county-level
vehicle miles traveled (VMT) from 2002 were used (as a surrogate for
fuel consumption) to weight the urban counties' approximate fuel demand.
Expressing the urban VMT as a function of statewide VMT gave us the
percentage of ethanol demand that would be considered eligible for an
urban ethanol distribution cost (values presented in Table 2.2-15). The
remaining percentage of ethanol demand was considered to be "rural" and
subject to the ethanol blending penalty adjustments found in Table
2.2-16.

Considering the urban/rural split for each state and the resulting
ethanol margin (ethanol delivered price minus gasoline production cost),
we came up with the resulting ranking system for distributing ethanol
into conventional gasoline. For PADD 1, refer to Table 2.2-17; PADD 2,
refer to Table 2.2-18, PADD 2, Table 2.2-19; and PADDs 4/5, Table
2.2-20. The summer and winter percentages are the same for each
urban/rural area with the exception of states containing winter oxy-fuel
areas. For these states, winter oxy-fuel was deducted from the winter
urban fuel since this volume of gasoline was already accounted for
(refer to Table 2.2-12).

**Table 2.2-17.**

**Precedence for Adding ETOH to PADD 1 CG**

![](media/image46.wmf){width="4.559722222222222in" height="4.23125in"}

**Table 2.2-18.**

**Precedence for Adding ETOH to PADD 2 CG**

![](media/image47.wmf){width="4.559722222222222in"
height="3.504861111111111in"}

**Table 2.2-19.**

**Precedence for Adding ETOH to PADD 3 CG**

![](media/image48.wmf){width="4.559722222222222in"
height="2.5965277777777778in"}

**Table 2.2-20.**

**Precedence for Adding ETOH to PADDs 4/5 CG**

![](media/image49.wmf){width="4.559722222222222in" height="3.86875in"}

5.  **Resulting 2012 Gasoline/Oxygenate Consumption by State**

Applying the CG order of precedence tables to the remaining conventional
gasoline ethanol (less state mandated and winter oxy-fuel volumes) and
factoring in the RFG ethanol distribution (described above in 2.2.4.4),
we came up with an ethanol distribution by state for each control case.
The resulting state-by-state ethanol distribution is summarized below in
Table 2.2-21 and a graphical representation for each control case is
provided in Figures 2.2-3 and 2.2-4 below.

**Table 2.2-21.**

**2012 Forecasted Ethanol Consumption by State**

(continued on next page)

![](media/image50.wmf){width="5.542361111111111in"
height="8.990277777777777in"}

**Figure 2.2-3. 2012 Forecasted Ethanol Consumption**

**6.7 Bgal RFS Case, % E10 by State**

![](media/image51.wmf){width="4.997916666666667in" height="3.75625in"}

**Figure 2.2-4. 2012 Forecasted Ethanol Consumption**

**9.6 Bgal EIA Case, % E10 by State**

![](media/image52.wmf){width="4.997916666666667in" height="3.75625in"}

**2.3 Effects of Ethanol and MTBE on Gasoline Fuel Properties**

For the final rulemaking, we estimate the impact of increased ethanol
use and decreased MTBE use on gasoline quality using refinery modeling
conducted specifically for the RFS rulemaking.[^79] The methods,
analyses, and results of the refinery modeling are discussed in more
detail in Chapter 7. In general, adding ethanol to gasoline reduces the
aromatic content of conventional gasoline (CG) and the mid and high
distillation temperatures (e.g., T50 and T90). RVP increases except in
areas where ethanol blends are not provided a 1.0 RVP waiver of the
applicable RVP standards in the summer. With the exception of RVP,
adding MTBE directionally produces the same impacts. Thus, the effect of
removing MTBE results in essentially the opposite impacts. Neither
oxygenate is expected to affect sulfur levels, as refiners control
sulfur independently in order to meet the Tier 2 sulfur standards.

The impacts of oxygenate use are smaller with respect to RFG. This is
due to the applicability of VOC and toxics emission performance
specifications, which limit the range of feasible fuel quality values.
Thus, RVP and aromatic and benzene contents are not consistently
affected by oxygenate type or level.

Table 2.3-1 shows the fuel quality of a typical summertime,
non-oxygenated conventional gasoline and how these qualities change with
the additional of 10 volume percent ethanol (10 vol%). Similarly, the
table shows the fuel quality of a typical MTBE RFG blend and how fuel
quality might change with either ethanol use or simply MTBE removal.
Note that the table does not reflect county-specific fuel properties.

**Table 2.3**-1. CG and RFG Summer Fuel Quality With and Without
Oxygenates

+---------------+--------+---------+-----------+-------+--------------+
|               | Conven |         | Ref       |       |              |
|               | tional |         | ormulated |       |              |
|               | Ga     |         | Gasoline  |       |              |
|               | soline |         | ^a^       |       |              |
+---------------+--------+---------+-----------+-------+--------------+
| Fuel          | T      | Ethanol | MTBE      | Et    | No           |
| Parameter     | ypical | Blend   |           | hanol | n-Oxygenated |
|               |        |         | Blend     | Blend | Blend        |
|               | 9 RVP  |         |           |       |              |
+---------------+--------+---------+-----------+-------+--------------+
| RVP (psi)     | 8.7    | 9.7     | 7.0       | 7.0   | 7.0          |
+---------------+--------+---------+-----------+-------+--------------+
| T50           | 218    | 205     | 179       | 184   | 175          |
+---------------+--------+---------+-----------+-------+--------------+
| T90           | 332    | 329     | 303       | 335   | 309          |
+---------------+--------+---------+-----------+-------+--------------+
| E200          | 41     | 50      | 60        | 58    | 52           |
+---------------+--------+---------+-----------+-------+--------------+
| E300          | 82     | 82      | 89        | 82    | 88           |
+---------------+--------+---------+-----------+-------+--------------+
| Aromatics     | 32     | 27      | 20        | 20    | 20           |
| (vol%)        |        |         |           |       |              |
+---------------+--------+---------+-----------+-------+--------------+
| Olefins       | 7.7    | 7.7     | 4         | 14    | 15           |
| (vol%)        |        |         |           |       |              |
+---------------+--------+---------+-----------+-------+--------------+
| Oxygen (wt%)  | 0      | 3.5     | 2.1       | 3.5   | 0            |
+---------------+--------+---------+-----------+-------+--------------+
| Benzene       | 1.0    | 1.0     | 0.74      | 0.70  | 0.72         |
| (vol%)        |        |         |           |       |              |
+---------------+--------+---------+-----------+-------+--------------+

^a^ MTBE blend --Reference Case PADD 1 South, Ethanol blend -- RFS Case
PADD 1 North, Non-oxy blend -- RFS Case PADD 1 South

**2.3.1 Effect of Ethanol on Conventional Gasoline Fuel Properties**

To estimate effects of ethanol on conventional gasoline, we used the
refinery model output shown below in Table 2.3-2. These values represent
average properties across the five PADDs and winter and summer seasons.

**Table 2.3**-2. Properties of Conventional Gasoline Per Refinery
Modeling

  ------------- ----------- --------- -------- -------- ------- ------- -------- ---------
  Case          Aromatics   Olefins   E200     E300     T50     T90     MTBE     Ethanol
                (vol%)      (vol%)    (vol%)   (vol%)   (°F)    (°F)    (vol%)   (vol%)

  2012          29.18       12.39     49.96    81.64    190.3   335.3   0.65     1.66
  Reference                                                                      

  2012 RFS      29.02       12.12     51.89    83.68    187.4   326.2   0.00     3.87

  2012 EIA      27.97       12.39     53.04    82.20    185.9   332.8   0.00     5.38
  ------------- ----------- --------- -------- -------- ------- ------- -------- ---------

Using this output, we estimated an average change in each fuel property
per vol% change in ethanol content. To derive these estimations, we
first adjusted the Reference case properties to isolate the effects of
ethanol by mathematically removing the effects of MTBE using factors
derived from the RFG RIA[^80]. Then, we calculated the change in each
fuel property per change in ethanol vol% for each combination of cases.
That is, we compared Reference Case to RFS Case, Reference to EIA, and
EIA to RFS. Finally, we averaged the three results from the case-to-case
comparisons to derive a useful factor for adjusting county-level fuel
properties for a change in county-level ethanol content. These ethanol
effects are shown below in Table 2.3-3.

**Table 2.3**-3. Change in Conventional Gasoline Properties Per Vol%
Increase in Ethanol

  ----------- ----------- ----------- ----------- ----------- -----------
  Aromatics   Olefins     E200 (vol%) E300 (vol%) T50 (°F)    T90 (°F)
  (vol%)      (vol%)                                          

  -0.46       0.02        0.91        0.06        -1.33       -0.28
  ----------- ----------- ----------- ----------- ----------- -----------

**2.3.2 Effects of MTBE on Conventional Gasoline Fuel Properties**

In support of the final rule implementing the RFG program in 1993,
refinery modeling was performed which estimated the impact of MTBE
blending on the various gasoline properties.[^81] While this modeling
was performed in the context of projecting the cost of producing RFG, it
is applicable to the use of MTBE in CG, as well. The refinery modeling
examined a number of incremental steps involved in the production of
RFG. Because RFG was mandated to contain oxygen and MTBE was expected to
be the oxygenate of choice, MTBE was added in the first step of the
analysis, before the fuel met the rest of the RFG requirements. Table
2.3-4 shows the results of adding MTBE based on this refinery modeling.

This modeling of MTBE effects is somewhat dated (circa 1993). However,
since removing MTBE does not involve any predictions of its total usage
level nor the location of its use, economics (such as crude oil price)
are not a factor. It is primarily an issue of chemical properties and
general refinery operation, such as octane management. Also, MTBE is
always match-blended, since gasoline can be shipped with MTBE through
pipelines. Thus, MTBE is always added at the refinery, allowing the
refiner to take full advantage of its properties.

**Table 2.3**-4. Effect of MTBE on Gasoline Properties: RFG Final Rule

  ------------------- -------------------- --------------- ---------------
  Fuel Parameter      Base 9 RVP Gasoline  MTBE Blend      Difference

  RVP (psi)           8.7                  8.7             0

  T50^a^              218                  207             -11

  T90^a^              329                  321             -8

  E200 (vol%)         41                   46.7            5.7

  E300 (vol%)         83                   84.9            1.9

  Aromatics (vol%)    32.0                 25.5            -6.5

  Olefins (vol%)      13.1                 13.1            0

  Oxygen (wt%)        0                    2.1             2.1

  Benzene (vol%)      1.53                 0.95            -0.58
  ------------------- -------------------- --------------- ---------------

^a^ Estimated using correlations developed in support of EPA RFG final
rule, Docket A-92-12, February 1994.

> T50 = 302 -- E200 / 0.49 and T90 = 707 - E300 / 0.22

As with ethanol blending, MTBE blending reduces aromatic content
significantly as refiners take advantage of MTBE's high octane level.
Like ethanol, MTBE also tends to increase E200 and E200 and decrease T50
and T90. Unlike ethanol, MTBE does not increase RVP.

MTBE blending is shown to modestly reduce sulfur and benzene levels, as
well. This refinery modeling was performed prior to the development of
the Tier 2 sulfur standards for gasoline. With these standards, gasoline
must meet a 30 ppm sulfur standard on average with or without MTBE
blending. As refiners can adjust the severity of their hydrotreating
processes to account for various changes in feedstocks and oxygenate
use, we do not expect that the removal of MTBE will result in any
increase in sulfur content. Otherwise, the reversal of the differences
shown in Table 2.3-4 are expected to occur when MTBE is removed from
gasoline (when the MTBE content was 11 vol%).

**2.3.3 Effects of Ethanol and MTBE on Reformulated Gasoline Fuel
Properties**

RFG has historically contained oxygenate due to the applicable 2.0
weight percent oxygen content requirement. RFG has contained 11 vol%
MTBE or ten vol% ethanol, except in California, where 6 vol% ethanol
blends have been common. As discussed in Section 2.2, the use of MTBE in
RFG has ceased. It has been replaced by either 10 vol% or ethanol or
high octane hydrocarbon blending components, such as alkylate or
reformate. In either case, as discussed in Section 2.3.2, RFG will
continue to have to meet stringent VOC, NOx, and toxic emission
performance standards, though compliance with the NOx standard is
essentially assured with compliance with the Tier 2 sulfur standards
applicable to all gasoline[^82].

For the NPRM, we assumed that the properties of RFG other than oxygenate
would not be affected by changes in oxygenate use. For the FRM, we are
utilizing the recent refinery modeling to estimate RFG properties by
PADD and season for the three ethanol use scenarios.

As described above, five refinery models were developed, each
representing one PADD. These models produced fuel for use in its own
PADD, as well as for use in other PADDs in the several cases of
PADD-to-PADD distribution of gasoline. For RFG, refinery modeling
projected that a significant volume of RFG used in PADD 1 would be
produced by PADD 3 refineries. Because this PADD 3 RFG is shipped to
PADD 1 via pipeline, this fuel tends to be used in the southernmost RFG
areas of PADD 1, namely those in Virginia, the District of Columbia and
Maryland. In order to reflect this, we assumed that the RFG produced in
PADD 3 will be used preferentially in the RFG areas of these three
states and the RFG produced in PADD 1 will be used to fulfill the
remaining demand for RFG in PADD 1. For refinery modeling, it was
estimated that a small volume of RFG would be produced in PADD 3 and
shipped to PADD 2. Because of the small volume involved, we did not
assign this volume to a specific RFG area within PADD 2.

As part of their work, the refinery modeling contractors calibrated
their model to match EPA's estimate of fuel quality existing in 2004
(i.e., the base case in this analysis). Therefore, estimates of the
properties of RFG for the base case comprise an accurate estimate of
actual 2004 RFG, at least on a PADD-average basis. We also have
available the results of the RFG fuel survey for each RFG area. This
survey data sometimes reflects significant differences in the properties
of RFG for specific RFG areas within a PADD. Good examples of this would
be RFG areas in New York and Connecticut, which implemented MTBE bans
starting in 2004. We considered using the more precise RFG survey data
to represent RFG fuel quality in the base case, but rejected this
approach for two reasons. One, this would introduce an extraneous
difference in RFG fuel quality between the base case and the RFS/EIA
cases. While refinery model base case projections reasonably match EPA's
estimate of 2004 fuel quality, they do not match exactly. Comparisons
between the base case and the RFS and EIA cases would therefore include
the difference between the RFG survey data and the refinery modeling
contractor's estimate of this data, plus the effect of additional
ethanol use and reduced MTBE use. Two, we primarily present the emission
impacts of the RFS rule on a nationwide basis. On a nationwide basis,
reflecting differences between RFG fuel quality within a PADD would have
little impact. Also, the Ozone RSM can only reflect a single change in
VOC and NOx emissions in non-attainment areas (e.g., RFG areas). Thus,
differences between specific RFG areas would be eliminated by the
limitation that only the average emission effect can be modeled. Thus,
we used refinery modeling projections of RFG fuel quality for all three
fuel scenarios.

Tables 2.3-5, 2.3-6, and 2.3-7 present the fuel properties of summertime
RFG under the base, RFS and EIA fuel scenarios, respectively. Under the
RFS and EIA cases, there is no MTBE or TAME in the fuel, so these rows
are not shown (i.e., total oxygen content is the same as ethanol content
in terms of weight percent).

**Table 2.3**-5. RFG Fuel Properties: Base Case -- Summer

  ------------- ------------ ------------- -------- -------- -------- --------
                PADD 1 South PADD 1 North  PADD 2   PADD 3   PADD 5   All U.S.

  RVP           7.0          7.1           7.0      7.0      6.8      6.9

  Sulfur ppm    6.7          22.6          4.5      6.9      10.0     13.0

  Aromatics     21.0         23.9          21.6     20.0     22.0     22.3

  Benzene       0.74         0.70          0.76     0.70     0.57     0.71

  Olefins       4.3          13.7          8.0      4.4      5.7      8.5

  E200          59.9         55.0          58.4     59.8     54.6     56.3

  E300          88.9         80.3          86.0     88.9     86.2     84.7

  T50           179.2        189.6         182.5    179.8    190.5    186.9

  T90           302.7        341.7         316.0    302.7    315.0    321.9

  Oxygen (wt%)  2.1          2.3           2.3      2.1      1.9      2.1

  MTBE (wt%O)   1.9          0.5           1.3      1.9      0.0      0.7

  TAME (wt%O)   0.2          0.0           0.1      0.2      0.0      0.1

  Ethanol       0.0          1.8           0.9      0.1      1.9      1.3
  (wt%O)                                                              
  ------------- ------------ ------------- -------- -------- -------- --------

**Table 2.3**-6. RFG Fuel Properties: RFS Case -- Summer

  ------------- ------------- ------------ -------- -------- -------- --------
                PADD 1 South  PADD 1 North PADD 2   PADD 3   PADD 5   All U.S.

  RVP           7.0           7.0          7.1      7.0      6.8      6.9

  Sulfur ppm    20.5          7.6          21.3     19.8     9.0      13.6

  Aromatics     20.1          20.0         17.9     20.0     22.5     20.5

  Benzene       0.72          0.70         0.67     0.70     0.57     0.66

  Olefins       14.6          13.6         17.3     14.1     5.7      11.9

  E200          52.0          57.6         54.1     52.0     54.5     54.5

  E300          87.5          81.9         81.8     87.5     86.2     84.9

  T50           174.7         184.2        185.3    195.7    190.5    185.9

  T90           308.8         334.6        334.8    308.8    315.0    321.0

  Ethanol       0.0           3.7          0.6      0.2      1.8      1.6
  (wt%O)                                                              
  ------------- ------------- ------------ -------- -------- -------- --------

**Table 2.3**-7. RFG Fuel Properties: EIA Case -- Summer

  ------------- ------------ ------------- -------- -------- -------- --------
                PADD 1 South PADD 1 North  PADD 2   PADD 3   PADD 5   All U.S.

  RVP           7.0          7.0           7.1      7.0      6.8      6.9

  Sulfur ppm    23.1         10.0          19.3     22.3     8.9      14.9

  Aromatics     20.2         19.7          17.9     20.0     22.6     20.5

  Benzene       0.70         0.74          0.60     0.67     0.57     0.65

  Olefins       18.9         10.3          14.7     18.3     5.7      12.1

  E200          52.0         57.7          55.8     52.0     54.6     54.7

  E300          84.3         81.8          80.9     84.3     86.2     83.8

  T50           179.4        184.0         183.4    195.7    190.5    186.4

  T90           323.6        334.7         339.2    323.6    315.0    325.7

  Ethanol       0.0          3.7           1.8      0.2      1.9      1.8
  (wt%O)                                                              
  ------------- ------------ ------------- -------- -------- -------- --------

As shown in Tables 2.3-6 and 2.3-7, summer RFG produced in PADD 1 under
the two increased ethanol use cases contains 10 vol% ethanol (i.e., 3.7
wt% oxygen). However, RFG produced in the other PADDs contains less than
the maximum 10 vol% ethanol. For other parameters the results generally
support the proposed assumption that they would remain constant,
although there were some small changes. The biggest changes in the RFS
and EIA fuel scenarios include higher levels of olefins, T50 and T90 and
lower aromatic levels.

Tables 2.3-8, 2.3-9, and 2.3-10 present the fuel properties of
wintertime RFG under the base, RFS and EIA fuel scenarios, respectively.

**Table 2.3**-8. RFG Fuel Properties: Base Case -- Winter

  ------------- ------------ ------------- -------- -------- -------- --------
                PADD 1 South PADD 1 North  PADD 2   PADD 3   PADD 5   All U.S.

  RVP           11.2         12.9          12.8     11.2     11.5     12.0

  Sulfur ppm    28.0         25.5          26.2     26.9     9.5      21.1

  Aromatics     21.1         19.0          13.9     20.0     20.8     19.3

  Benzene       0.74         0.70          0.64     0.70     0.47     0.63

  Olefins       12.6         16.0          11.6     12.2     5.7      11.1

  E200          63.6         57.4          66.9     63.1     59.3     61.0

  E300          88.9         80.3          79.5     88.9     86.2     84.5

  T50           179.2        184.7         165.4    173.1    180.8    178.5

  T90           302.7        341.7         345.2    302.7    315.0    322.7

  Oxygen (wt%)  2.1          2.5           3.3      2.1      2.2      2.4

  MTBE (wt%O)   2.1          0.5           0.0      2.0      0.0      0.7

  TAME (wt%O)   0.0          0.0           0.0      0.1      0.0      0.0

  Ethanol       0.0          2.0           3.3      0.1      2.2      1.7
  (wt%O)                                                              
  ------------- ------------ ------------- -------- -------- -------- --------

**Table 2.3**-9. RFG Fuel Properties: RFS Case -- Winter

  ------------- ------------ ------------- -------- -------- -------- --------
                PADD 1 South PADD 1 North  PADD 2   PADD 3   PADD 5   All U.S.

  RVP           11.8         13.1          13.0     11.8     11.5     12.3

  Sulfur ppm    28.0         25.0          25.4     26.7     9.4      20.8

  Aromatics     21.4         19.0          17.8     21.2     23.7     20.9

  Benzene       0.75         0.70          0.89     0.73     0.43     0.65

  Olefins       13.3         16.0          12.3     12.6     5.7      11.4

  E200          53.5         63.1          63.9     53.4     58.2     59.0

  E300          87.5         81.9          79.5     87.5     86.2     84.5

  T50           174.7        173.0         171.4    192.9    183.0    178.4

  T90           308.8        334.6         345.2    308.8    315.0    322.7

  Ethanol       0.0          3.7           3.0      0.0      1.8      2.0
  (wt%O)                                                              
  ------------- ------------ ------------- -------- -------- -------- --------

**Table 2.3**-10. RFG Fuel Properties: EIA Case -- Winter

  ------------- ------------ ------------- -------- -------- -------- --------
                PADD 1 South PADD 1 North  PADD 2   PADD 3   PADD 5   All U.S.

  RVP           11.9         12.8          12.9     11.9     11.5     12.2

  Sulfur ppm    27.5         25.2          23.8     25.6     8.5      19.9

  Aromatics     22.2         19.0          20.7     21.7     23.7     21.2

  Benzene       0.61         0.70          0.95     0.58     0.43     0.63

  Olefins       14.8         16.0          12.3     13.6     5.7      11.9

  E200          52.5         63.6          64.5     52.4     59.4     60.4

  E300          84.3         81.9          79.5     84.3     86.2     83.3

  T50           179.4        172.1         170.1    194.9    180.6    177.5

  T90           323.6        334.3         345.2    323.6    315.0    327.9

  Ethanol       0.0          3.7           3.7      0.0      2.2      2.6
  (wt%O)                                                              
  ------------- ------------ ------------- -------- -------- -------- --------

As shown in Tables 2.3-9 and 2.3-10, winter RFG produced in PADD 1 under
the two ethanol use cases contains 10 vol% ethanol (i..e., 3.7 wt%
oxygen). PADD 2 winter RFG contains 10 vol% ethanol in the EIA case.
However, RFG produced in the other PADDs and cases contains less than
the maximum 10 vol% ethanol. On an annual average basis, RFG produced
for use in California contains about 5.7 vol% ethanol (2.1 wt% oxygen).
This is to be expected given the increase in NOx emissions assigned by
CARB's Phase 3 Predictive Model to blends with more than 2.1 wt% oxygen.
As for the summer cases, changes in other fuel parameters were small and
mixed.

**2.3.4 Estimation of County-Specific Gasoline Properties**

In order to estimate the impact of increased ethanol use and reduced
MTBE use on national emissions and air quality (described in Chapters 4
and 5), we need to estimate gasoline properties on a county-specific
basis throughout the U.S. In support of previous analyses of national
impacts of various rules, EPA has developed a set of gasoline
specifications for each county in the U.S. for various months and
calendar years.[^83] We based our analysis on the fuel quality
specifications for January and July of 2008, since 2008 is the first
year of full implementation of the Tier 2 sulfur standard of 30 ppm.
Some of the EPA county-level gasoline specifications were based on old
data, so we reviewed the estimates and made several modifications before
applying the changes expected due to ethanol addition and MTBE removal.

First, we adjusted RVP values using more recent information on local RVP
programs and to reflect commingling. Second, we revised the oxygenate
content and type in each county to match the levels estimated in Section
2.2 to be sold there under each of the three ethanol use scenarios
evaluated. Third, we adjusted the other properties of gasoline which are
affected by the oxygenate use determined in step three. These
modifications are described in more detail below.

**2.3.4.1 Adjustments to RVP Levels Prior to Oxygenate Use**

Our review of the NMIM database of county-specific RVP levels for July
indicated that the same RVP level was often applied to all the counties
of a specific state. In many cases, this appeared reasonable, since the
same RVP standard applied throughout the entire state. However, in other
cases, for example, Florida, most counties have a 9.0 RVP standard,
while those comprising several large urban areas have a 7.8 RVP
standard. The RVP levels in the NMIM database were consistent with the
7.8 RVP control programs, implying that the 7.8 RVP fuel was sold
throughout the entire state. This was true for much of the south.

As mentioned above, the NMIM fuel quality database was based primarily
on fuel survey data from 1999. Fuel surveys tend to focus on large urban
areas, as opposed to smaller urban or rural areas. Thus, the only
available fuel survey data was likely from the areas with the tighter
local RVP controls. RVP control reduces gasoline supply, since lighter
hydrocarbons must be removed in order to reduce RVP. Some, but not all
of these hydrocarbon components can be moved to higher RVP fuel sold
elsewhere. Obviously gasoline prices are now much higher than they were
in 1999. So the incentive to increase supply is greater now than in
1999. As discussed in Chapter 7, high gasoline prices are projected for
the foreseeable future, at least relative to those existing in 1999.
Thus, we believe that it is reasonable to project that refiners will
market gasoline blends with as high a level of RVP as practical given
the applicable standards. For example, in Florida, two fuels will be
marketed: one to meet the 7.8 RVP standard in several urban areas and
another to meet the 9.0 RVP standard applicable elsewhere. There
certainly could be some spillover of the 7.8 RVP fuel into adjacent 9.0
RVP counties. However, we lack data indicating the degree to which this
is occurring and might occur in the future. Lacking this data, it seems
more reasonable to project only that level of RVP control which is
guaranteed by the applicable standards than to assume that refiners will
over-comply with RVP standards and reduce the volume of gasoline which
they can produce.

Past studies have shown that a typical compliance margin for RVP is
about 0.3 psi. Thus, for those counties where the standard 9.0 RVP
standard applies, we set the July RVP level to 8.7 psi.

EPA maintains a list of counties where its 7.8 RVP standard applies, as
well as any local standards more stringent than 9.0 RVP.[^84] Using this
list, we assigned RVP values in each county equal to 0.3 psi less than
the standard applicable in July. We also reduced the RVP levels of two
sets of counties which had voluntary local RVP control programs (and
therefore not listed the above Guide). These two areas were Seattle and
Tulsa. Based on a review of annual fuel survey data collected by the
Alliance of Automobile Manufacturers (AAM)[^85], the fuel being sold in
these areas was very similar to that for an area with a 7.8 RVP
standard. Thus, we assigned a value of 7.5 psi RVP to Tulsa County,
Oklahoma, and to King, Pierce, and Snohomish Counties, Washington.

We then assigned an RVP value of 6.8 psi to counties subject to the
Federal RFG program, again based on an EPA list of the counties subject
to this program.[^86] The EPA list of RFG counties includes the Baton
Rouge, Louisiana, area. However, litigation has held up implementation
of this program, so these counties were assigned RVP values consistent
with the currently applicable 7.8 RVP standard instead. The RVP value of
6.8 psi was typical for the RFG areas included in the AAM fuel surveys.

For the purposes of our analysis, we also assigned the entire State of
California an RVP of 6.8 psi, since California fuel must meet a similar
VOC performance standard to RFG. Likewise, RVP in Maricopa and Pinal
counties in Arizona were assigned a level of 6.8 psi. These two counties
are subject to Arizona's unique reformulated gasoline program. This
program basically requires that gasoline sold in these two counties meet
either the California RFG or Federal RFG standards. Thus, RVP in these
two counties will be the same as in those other two areas, similar to
national RFG fuel.

These RVP levels for 9.0 RVP and low RVP areas are appropriate when no
ethanol is being blended into gasoline. However, most of these areas
increase the applicable standard by 1.0 psi for ethanol blends, which is
the typical impact of ethanol blending. Therefore, these levels need to
be adjusted for the expected level of ethanol use, which is discussed
below.

**2.3.4.2 County-Specific Oxygenate Type and Content**

The three ethanol use scenarios developed in Section 2.2 assign ethanol
and MTBE use by state and fuel type (i.e., conventional gasoline, RFG,
oxyfuel). In order to develop county level estimates of ethanol and MTBE
use, we simply assume that ethanol and MTBE use within a state and fuel
type is uniform. For example, if the E10 market share in conventional
gasoline Iowa is 34%, then ethanol use in every county receiving
conventional gasoline in Iowa was assigned an E10 market share of 34%.

As described above, we nearly always assume that ethanol use is in the
form of a 10 vol% blend with gasoline. The two exceptions are California
fuel and Arizona RFG. California fuel containing ethanol is assumed to
contain 5.7 vol% ethanol. Arizona RFG is assumed to be a mix of 67%
California fuel and 33% Federal RFG produced in PADD 3. Therefore, its
ethanol content is a 2/1 mix of the ethanol contents of California RFG
and PADD 3 Federal RFG.

Similarly, we assume that MTBE is used at an 11 vol% level in RFG, since
this meets the previously mandated oxygen content of 2.1 wt%. MTBE in
conventional gasoline was assumed to be used at a 3 vol% level. This was
somewhat arbitrary, but does not affect the outcome of the analysis. The
effect of MTBE blending on emissions is very linear. Therefore, whether
the fuel pool in a particular area consists of 10% of a 10 vol% MTBE
blend or 33% of a 3 vol% MTBE blend is immaterial. Though MTBE is
present in the 2012 Reference Case, it is assumed to be completely
phased-out in the RFS and EIA ethanol use cases.

EPA's NMIM model (described in more detail in Chapter 4) will only
accept a single composite fuel for each county. Therefore, we could not
use the mix of fuels often projected to be supplied to counties
developed in Section 2.2. In order to produce a single, composite fuel,
we simply multiplied the ethanol and MTBE contents of each blend by
their market share in that county in order to determine the average
ethanol and MTBE contents of each county's fuel pool, respectively. For
example, if the E10 market share in a specific county was 50%, the
ethanol content for that fuel was set to 5 vol%. We then adjusted the
other fuel properties to account for these oxygenates, which is
discussed below.

**2.3.4.3 Adjustments to Other Gasoline Properties for Oxygenate Use**

We next adjusted other gasoline properties to account for the level of
county-specific oxygenate use projected to occur under the three ethanol
use scenarios. Our review of the NMIM fuel database indicated that
properties, such as aromatics, reflected the level of oxygenate use
existing in 1999. Therefore, we used the oxygenate levels in the NMIM
database, which differ from those developed in Section 2.2 for 2004, as
the basis for our adjustments of the other fuel properties. For example,
if the NMIM database indicated an ethanol content of 3 vol% for fuel
sold in Wayne County, Michigan, and the 2004 projection for this county
was 5 vol%, we adjusted the NMIM fuel properties for this county to
reflect the addition of 2 vol% ethanol.

The bases for these adjustments were those developed in Sections 2.3.1
through 2.3.4 above. As described there, these adjustments apply
primarily to conventional gasoline. These adjustments are summarized in
Table 2.3-11 below.

**Table 2.3-11**

Change in Property per 1 Vol% Increase in Ethanol and MTBE Content

  -------------- ---------- -------- ------------- ---------------- ------------
                 E200 (%)   E300 (%) Aromatics     Olefins (Vol%)   RVP (psi)
                                     (Vol%)                         

  Conventional                                                      
  Gasoline                                                          

  Ethanol        +1.0       +0.24    -0.5          -0.16            +0.1

  MTBE           +0.52      +0.17    -0.59         0                0

  Reformulated                                                      
  Gasoline                                                          

  Ethanol        0          0        0             0                0

  MTBE           0.1        0.1      0             0                0
  -------------- ---------- -------- ------------- ---------------- ------------

To calculate new fuel properties for each county, we applied the ethanol
and MTBE factors to the change in county-level ethanol and MTBE content.
The overall adjustment to the fuel property was the addition of the
ethanol effect and the MTBE effect to the baseline fuel property.

For the impact of ethanol blending on aromatic and olefin contents, we
followed a slightly different approach. We assumed that the ethanol
present in 1999 had been splash-blended, while that being used in the
future will be match-blended. This difference doesn't affect the
adjustment of RVP, E200, or E300, since we assume that these parameters
are affected in the same way regardless of whether the ethanol is
splash- or match-blended. However, the change in aromatics does depend
on which blending approach is used. The situation is similar for
olefins, though to a lesser extent. Thus, we employed what can be
thought of as a two step process in adjusting aromatic and olefin
contents for the change in ethanol content between the NMIM estimate and
those for the three ethanol use scenarios developed in Section 2.2.

The first step is to account for any splash-blended ethanol in the NMIM
database. With splash-blending, aromatic and olefin contents are reduced
simply by dilution, since ethanol contains is neither an aromatic nor an
olefin. The following equation shows how the NMIM level of aromatics was
adjusted:

<table>
<colgroup>
<col style="width: 16%" />
<col style="width: 5%" />
<col style="width: 11%" />
<col style="width: 7%" />
<col style="width: 4%" />
<col style="width: 3%" />
<col style="width: 4%" />
<col style="width: 10%" />
<col style="width: 7%" />
<col style="width: 12%" />
<col style="width: 3%" />
<col style="width: 6%" />
<col style="width: 5%" />
</colgroup>
<tbody>
<tr class="odd">
<td>Intermediate Aromatic Content</td>
<td>=</td>
<td>NMIM Aromatic Content</td>
<td>÷</td>
<td>(</td>
<td>1 --</td>
<td>(</td>
<td><p>NMIM</p>
<p>Ethanol Content</p></td>
<td>)(</td>
<td><p>NMIM Ethanol</p>
<p>Market Share</p></td>
<td>)</td>
<td>÷ 100</td>
<td>)</td>
</tr>
</tbody>
</table>

Then, the effect of any ethanol projected to be sold in that county in
the three ethanol use scenarios developed in Section 2.2 was applied
using the approach described above for RVP, E200 and E300 (and for the
effect of MTBE on aromatics and olefins). In this case, the NMIM ethanol
content and market share is zero, since we already adjusted the NMIM
aromatic and olefin contents to represent those existing for a zero
ethanol content. For example, the equation for the ethanol effect is as
follows:

<table style="width:100%;">
<colgroup>
<col style="width: 14%" />
<col style="width: 5%" />
<col style="width: 12%" />
<col style="width: 4%" />
<col style="width: 5%" />
<col style="width: 9%" />
<col style="width: 7%" />
<col style="width: 8%" />
<col style="width: 4%" />
<col style="width: 3%" />
<col style="width: 4%" />
<col style="width: 15%" />
<col style="width: 4%" />
</colgroup>
<tbody>
<tr class="odd">
<td>New Fuel Property Level</td>
<td>=</td>
<td>Intermediate Fuel Property Level</td>
<td>+</td>
<td>(</td>
<td><p>RFS</p>
<p>Ethanol Content</p></td>
<td>)(</td>
<td>RFS Market Share</td>
<td>)</td>
<td>×</td>
<td>(</td>
<td>Fuel Property Change per 1 vol% Ethanol Increase</td>
<td>)</td>
</tr>
</tbody>
</table>

We make one final adjustment to RVP to add a commingling effect to
account for areas where vehicles may be fueled by a mix of ethanol-blend
gasoline. Commingling of ethanol and non-ethanol blends can increase the
average RVP of gasoline in vehicle fuel tanks by 0.1-0.3 psi. Appendix
2-A presents a detailed analysis of the impact of commingling on the RVP
of gasoline in vehicle fuel tanks. Table 2.3-12 presents our estimate of
the net impact of commingling on in-use RVP as a function of the market
share of ethanol blends.

**Table 2.3**-12. Impact of Ethanol Blends on In-Use RVP (psi)

  ---------------------------- ------------------------------------------
  E10 market share             Commingling Impact

  0%                           0

  2%                           0

  5%                           0.116

  10%                          0.116

  20%                          0.202

  30%                          0.238

  40%                          0.264

  50%                          0.273

  60%                          0.263

  70%                          0.226

  80%                          0.172

  90%                          0.102

  97%                          0.102

  100%                         0.000
  ---------------------------- ------------------------------------------

EPA's MOBILE6.2 model normally accounts for this effect automatically.
However, when NMIM is used to run MOBILE6.2, the commingling effect in
MOBILE6.2 is by-passed. Therefore, any effect of commingling needs to be
accounted for in the average fuel specified to be sold in each county.
To roughly account for this effect, we increased RVP by 0.1-0.27 psi in
all states where the E10 market share was significant (i.e., more than
5%) but less than 95%. The states which fell into this category, for CG
and RFG, are shown in Table 2.3-13. The specific RVP increase depended
on the ethanol market share in that county, as indicated in Table
2.3-12.

**Table 2.3**-13. States Where RVP was Increased Due to Commingling

  ----------- ------------------------------------------------------------
  Fuel Case   **Conventional Gasoline**

  Reference   ILLINOIS SOUTH DAKOTA INDIANA NEBRASKA OHIO IOWA KANSAS
              NORTH DAKOTA WISCONSIN ALABAMA MICHIGAN MISSOURI

  RFS         ALABAMA ARIZONA ARKANSAS COLORADO FLORIDA KENTUCKY LOUISIANA
              MAINE MISSISSIPPI NEVADA NEW MEXICO PENNSYLVANIA TENNESSEE
              WASHINGTON WYOMING

  EIA         COLORADO FLORIDA KENTUCKY LOUISIANA PENNSYLVANIA TENNESSEE
              WASHINGTON WYOMING IDAHO NEW HAMPSHIRE NEW JERSEY NEW YORK
              OKLAHOMA OREGON TEXAS UTAH

              **Reformulated Gasoline**

  Reference   ARIZONA MASSACHUSETTS NEW JERSEY TEXAS

  RFS         KENTUCKY PENNSYLVANIA NEW HAMPSHIRE NEW JERSEY NEW YORK
              TEXAS MAINE CONNECTICUT DELAWARE ILLINOIS INDIANA
              MASSACHUSETTS MISSOURI RHODE ISLAND VERMONT WISCONSIN

  EIA         KENTUCKY PENNSYLVANIA NEW HAMPSHIRE NEW JERSEY NEW YORK
              TEXAS MAINE CONNECTICUT DELAWARE ILLINOIS INDIANA
              MASSACHUSETTS MISSOURI RHODE ISLAND VERMONT WISCONSIN
  ----------- ------------------------------------------------------------

**2.4 Effects of Biodiesel on Diesel Fuel Properties**

Our assessment of the effects of biodiesel on diesel fuel properties is
found in the 2002 EPA report "A Comprehensive Analysis of Biodiesel
Impacts on Exhaust Emissions"[^87]. Table 2.4-1 below displays the
difference in fuel properties between biodiesel (B100) and conventional
diesel. Note that by 2010, all highway and nonroad diesel fuel will meet
a 15 ppm cap on sulfur.

The data in the table below were derived from a wide-range of
biodiesels, primarily plant- and animal-based. The 2002 EPA report did
not provide properties for soy-only based biodiesel.

**Table 2.4-1. Comparison Between Biodiesel and Conventional Diesel
Fuel^a^**

  ----------------------- ----------------------- -----------------------
                          Average Biodiesel       Average Diesel

  Natural cetane number   55                      44

  Sulfur, ppm             54                      333

  Nitrogen, ppm           18                      114

  Aromatics, vol%         0                       34

  T10, deg F              628                     422

  T50, deg F              649                     505

  T90, deg F              666                     603

  Specific gravity        0.88                    0.85

  Viscosity, cSt at 40    6.0                     206
  deg F                                           
  ----------------------- ----------------------- -----------------------

^a^Conventional diesel fuel sold outside of California.

**Chapter 2: Appendix**

**Comprehensive Vehicle Refueling Model**

Vehicle refueling patterns affect non-exhaust emissions in a number of
ways, including the distribution of vehicle fuel tank fill levels
existing at any given time and the quality of fuel in the tank. Given
the interaction between these parameters, we have developed a single
model which represents vehicle refueling patterns. We then use this
model to estimate the distribution of vehicle fuel tank fill levels and
the quality of fuel in the vehicle fuel tanks.

Vehicle fuel tank fill levels are primarily a function of the level at
which people refuel their vehicles and the volume of fuel which they
add. In-use, vehicle fuel tanks will slowly empty until the point of
refueling again. The California Air Resources Board (CARB) recently
conducted a survey of vehicle refueling patterns in three California
cities. We will base our estimates of refueling patterns primarily on
these data.

Most fuel parameters remain unchanged as the fuel is burned. One except
is volatility, particularly RVP, which decreases due to evaporation of
the fuel as the tank heats up either due to rising ambient temperatures
or vehicle operation (e.g., heat transfer from the exhaust system,
engine cooling air flowing under the vehicle, and fuel recirculation
from the engine compartment).

While ethanol content doesn't change significantly while the vehicle is
being operated, the ethanol content of gasoline in vehicle fuel tanks
can be a function of vehicle refueling patterns if some of the specific
gasolines being marketed in an area contain ethanol and some do not. The
effect of ethanol on RVP is not linear. Thus, knowledge of the
distribution of ethanol content in vehicle fuel tanks is important in
estimating the RVP of gasoline in vehicle fuel tanks and non-exhaust
emissions. We use the vehicle refueling model to estimate ethanol
content, fuel RVP, and average fill level.

There are four main aspects of the vehicle refueling model. The first
two aspects affect all types of gasoline, ethanol containing or not. The
first aspect is a description of the refueling patterns of vehicle
operators. How low is the tank when they refuel? How much fuel do they
add? Does the volume of fuel added depend on how low the tank was when
they stopped to refuel? The second aspect is the weathering of the fuel
as the vehicle is operated. In general, the degree of weathering, or RVP
reduction, depends on both the ambient temperature and initial RVP of
the fuel.

The third aspect of the model is the effect of ethanol on RVP. While the
ethanol content of gasoline tends to be either 5.7 or 10 percent by
volume (vol%) at the service station, the ethanol content of gasoline in
a vehicle's fuel tank can vary from zero to 10 vol%. The fourth aspect
of the model is a description of the probability that a vehicle operator
will purchase fuel at the same service station as the last refueling or
at another outlet selling the same brand fuel (i.e., gasoline brand
loyalty). Brand loyalty is relevant, because service stations carrying
the same brand of gasoline almost always sell either gasoline with
ethanol or gasoline without ethanol, but not both. It is the mixing of
gasoline with and without ethanol is vehicles' fuel tanks that can cause
the RVP of fuel in the tank to differ from that dispensed at the service
station. This is referred to as the commingling effect.

Each of these four aspects of the vehicle refueling model is described
below.

**2A.1 Vehicle Refueling Patterns**

During August and September, 2001, the CARB surveyed consumers'
refueling habits at 19 service stations in three local areas (Lake
Tahoe, the Bay Area and Los Angeles).[^88] Basic refueling information
was obtained for 396 vehicle refuelings (i.e., initial fuel tank level
and volume of fuel added). Fuel samples were also obtained from 254
vehicles, though we are most interested in the volumetric data here.
CARB also asked those refueling whether they refueled with the same
brand gasoline the last time the vehicle was refueled.

We obtained and analyzed the raw volumetric fuel data obtained by CARB.
Of the 396 sets of data, 391 included both initial fuel tank level and
volume of fuel added. One of the two pieces of information was missing
for five vehicles, so we discarded these partial data sets from the
analysis. The tank fill level prior to refueling was recorded in terms
of eighths of a fraction of a full tank, as this is usually how the tank
fill level is indicated on the vehicle dash board. Table 2A-1 shows the
probability of a vehicle being refueled at various fuel tank fill
levels.

Table 2A-1. Fill Level Prior to Refueling

  ---------------------------------- ------------------------------------
  Fraction of fill level             Probability

  0.000                              0.414

  0.125                              0.133

  0.250                              0.253

  0.375                              0.054

  0.500                              0.095

  0.625                              0.020

  0.750                              0.020

  0.875                              0.010
  ---------------------------------- ------------------------------------

As can be seen, over 40% of the vehicles surveyed came in with an
"empty" tank.

CARB also recorded whether the vehicle operator "filled up" the tank or
not. We observed that there was a trend towards a greater probability of
a "fill up" as the level of the tank prior to refueling increased. Table
2A-2 shows the probability of a fill-up as a function of tank fill level
prior to refueling.

Table 2A-2. Probability of Fill-Up as a Function of Initial Tank Fill
Level

  --------------------------------------- -------------------------------
  Initial Tank Fill Level                 Likelihood of a Fill-up

  0.000                                   0.117

  0.125                                   0.500

  0.250                                   0.586

  0.375                                   0.619

  0.500                                   0.784

  0.625                                   0.875

  0.750                                   0.750

  0.875                                   0.750
  --------------------------------------- -------------------------------

Overall, when the tank was at least half full, the tank was filled up
79% of the time.

In those cases where the fuel tank was not filled to capacity, the
volume of fuel added was recorded in terms of gallons. Therefore, some
processing was required to estimate the final fill level in terms of
fraction of tank capacity. To do so, we had to estimate the volume of
each vehicle's fuel tank. CARB recorded the basic model type of each
vehicle in the survey. Based on this model type, we placed each vehicle
into one of six possible categories. First, each vehicle was identified
as either a car or light truck. Then, we estimated whether it would have
a relatively small, medium, or large fuel tank for that vehicle class.
The fuel tank sizes assumed for each class are shown in Table 2A-3
below.

Table 2A-3. Estimated Fuel Tank Volumes (gallons)

  ----------------------- ----------------------- -----------------------
  Relative Size           Car                     Light Truck

  Small                   12                      16

  Medium                  16                      20

  Large                   20                      24
  ----------------------- ----------------------- -----------------------

Using these tank volumes, we converted the volume of fuel added during
partial fill ups to an equivalent fraction of tank volume and added this
to the observed initial fill level to estimate the final fill level. In
five cases (out of the 176 partial fills), the estimated final fill
level exceeded 100%. Either the initial gauge reading was off or rounded
up, or more likely, our estimate of the total tank volume was too small.
In these cases, we reduced the final fill level to 95%. (Given that this
was a partial fill-up, the final fuel tank level had to be less than
100%.)

For all of the partial fill-ups, we converted the volume of fuel added
from gallons of fuel to fractional tank volume. Both the mean and
standard deviation of these volumes were determined as a function of
initial fill level. These figures are shown in Table 2A-4.

Table 2A-4. Volume of Fuel Added During Partial Fills (% of Fill Level)

  -------------- ----------------------- --------------------------------
  Initial Fill   Volume of Fuel Added    
  Level                                  

                 Mean                    Standard Deviation

  0.000          0.406                   0.200

  0.125          0.434                   0.157

  0.250          0.382                   0.167

  0.375          0.451                   0.185

  0.500          0.314                   0.120

  0.625          0.325                   \-\--

  0.750          0.225                   0.04

  0.875          0.030                   \-\--
  -------------- ----------------------- --------------------------------

As can be seen from Table 2A-4, the final fill level from partial fills
is very close to a full tank when the initial fuel tank level was 0.625
or higher. The actual number of cases when vehicles initiated refueling
when the tank fill level was 0.625 or higher was quite small (20). The
number of partial fills surveyed was even smaller (4). Given this small
number and the fact that the fraction of fill-ups for half full tanks
exceeded that found for 0.75 and 0.875 full tanks (from Table 2A-3), we
assumed that all tanks which were at least half full when refueling was
initiated were filled-up. When the initial fill level was less than 0.5,
we assumed that the mean volume of fuel added were those shown in Table
2A-4.

As also can be seen from the figures in Table 2A-4, the estimates of the
standard deviation in the volume of fuel added are substantial relative
to the mean volumes of fuel added. We desired to reflect this
variability in the volume of fuel added during partial fills. Thus, we
utilized both the estimates of the mean and standard deviation in the
volume of fuel during refueling. We accomplished this by multiplying the
standard deviation by a randomly generated standard normal deviate and
adding this to the mean volume of fuel added to estimate the volume of
fuel added during each partial refueling.

**2A.2 Weathering**

Fuel weathering is the result of the evaporation of the lighter
components of gasoline when the temperature in the fuel tank rises. This
temperature rise can be the result of diurnal swings in ambient
temperature or from vehicle operation. In the latter case, the heat can
be transferred either convectively from the exhaust system and engine
cooling air flowing under the vehicle or conductively from recirculated
fuel from the engine's fuel system or both. Gasoline is a mixture of
many different chemicals. Some of these chemicals, such as butane,
evaporate more quickly than other chemicals with a higher molecular
weight, such as octane. The loss of lighter chemicals can be sufficient
to reduce the concentration of these lighter chemicals in the liquid
gasoline. This reduces the RVP of the fuel and its tendency to evaporate
as the current tank of fuel is consumed.

We base our estimate of weathering on RVP on the methodology currently
in MOBILE6.2. This estimate was first developed for MOBILE4 and was also
used in MOBILE5. This methodology first calculates an effective in-use
tank temperature (T~evap~) which drives fuel evaporation. This
temperature is a function of the daily minimum temperature (T~min~) and
maximum temperature (T~max~), as indicated in the following equation:

T~evap~ = -1.7474 + 1.029\*T~min~ + 0.99202\*(T~max~ -- T~min~) -
0.0025173 \* T~min~\*(T~max~ -- T~min~)

The loss in RVP is a function of both T~evap~ and dispensed RVP, as
indicated by the following equation:

RVP reduction (psi) due to weathering = -2.4908 + 0.026196 \* T~evap~

> \+ 0.00076898 \* T~evap~ \* Dispensed Fuel RVP

This RVP loss is that occurring when the vehicle fuel tank is 54.57%
full, which is the effective in-use average tank level for estimating
non-exhaust emissions in MOBILE6.2. For a typical high ozone day where
the ambient temperature might range from 72 F to 96 F, and for a
dispensed RVP of 9 psi, the RVP loss due to weathering is 0.54 psi. In
order to estimate weathering at other tank fill levels, we assume that
weathering is linear with tank fill level.

**2A.3 Effect of ethanol on RVP as a Function of Ethanol Content**

In general, the chemicals comprising gasoline blend ideally. That is,
the property of the finished gasoline is the sum of the property of each
component weighted by its molar, volume or mass fraction, whichever is
technically appropriate. Each component of gasoline has its own RVP
level. Adding a component to gasoline at the level of 10 volume percent
(vol%), which is the typical ethanol concentration would increase the
blend's RVP by 10% of the component's RVP and decrease the blend's RVP
by 10% of the original RVP. For example, normal butane with an RVP of 42
psi can be added to gasoline with an RVP of 8 psi. If the butane is
added to a final level of 5 vol%, then the final RVP is 0.05 times 42
plus 0.95 times 8, or 9.7 psi.

Ethanol blending affects the RVP of the finished gasoline quite
differently. Ethanol is a highly polar compound, due to the presence of
the hydroxyl radical. In pure liquid ethanol, these hydroxyl radicals
interact with each other, increasing the degree of attraction between
ethanol molecules and lowering their tendency to evaporate. This
phenomena is commonly known as hydrogen bonding and is most commonly
associated with water. When added to non-polar hydrocarbons at low
concentrations, such as those comprising gasoline, the evaporative
tendency of ethanol increases dramatically. This increase in vapor
pressure is indicated by what is referred to as the activity
coefficient. The activity coefficient is the ratio of a compound's
actual vapor pressure in a mixture to that predicted by ideal blending.
Table 2A-5 shows ethanol's activity coefficient at various levels of
concentration in a typical gasoline.

Table 2A-5. Activity Coefficient of Ethanol in Gasoline Blends [^89]

  ----------------------------------- -----------------------------------
  Ethanol Concentration (vol%) \*     Activity Coefficient

  3%                                  7.5-8.0

  6%                                  3.8-4.1

  10%                                 2.3-2.5

  14%                                 1.9-2.0

  18%                                 1.6-1.8
  ----------------------------------- -----------------------------------

As can be seen, these activity coefficients are substantially greater
than 1.0, indicating a significant increase in the vapor pressure of
ethanol beyond that predicted by ideal blending.

Adding ethanol to gasoline can also increase the vapor pressure of the
hydrocarbon components. In general, instead of the hydrocarbons' vapor
pressures decreasing with the addition of another component (e.g., by
10% with the addition of 10 vol% ethanol), they remain constant or even
increase. This could be due to a tendency of the hydrocarbons in the
vapor phase to "bounce" off of the ethanol molecules at the surface of
the liquid phase.

A number of studies have shown that the full effect of ethanol's impact
on RVP is reached at very low concentrations. For example, a study
performed by the Energy and Environmental Research Center at the
University of North Dakota indicates that 90% of the full impact of
ethanol on RVP is reached when the ethanol concentration is only 2
vol%.[^90] Researchers at the University of Delaware found the same
relationship.[^91] Below 2 vol% ethanol, we have assumed that the effect
is essentially linear.

The full effect of ethanol on gasoline RVP is a function of the RVP of
the base hydrocarbon gasoline. In general, the increase in RVP caused by
ethanol blending increases as the base RVP decreases. The actual RVP of
specific commercial ethanol and non-ethanol gasoline blends are
generally known for a specific area being modeled. Thus, they are not a
primary concern here. However, in order to develop realistic estimates
of weathering and commingling across a range of ethanol blend market
shares, it would be helpful to use realistic RVP levels for commercial
ethanol and non-ethanol gasoline blends.

Ethanol blending generally occurs under two types of RVP standards. One
type of standard requires that both ethanol and non-ethanol blends meet
the same RVP standard. This is the case in reformulated gasoline (RFG)
areas. In most other areas, ethanol blends are allowed to meet an RVP
standard 1.0 psi higher than that applicable to non-ethanol blends.

We will estimate the impact of weathering and commingling for both
situations. For RFG-like situations, we will assume that both ethanol
and non-ethanol blends have the same RVP. In order to estimate the
impact of ethanol blending in areas where ethanol blends are allowed to
have a higher RVP level, we evaluated recent fuel quality data collected
by the Alliance of Automobile Manufacturers. We combined the data
collected from 2001-2003 and found six cities where significant numbers
of both ethanol and non-ethanol blends were sampled and analyzed. These
six cities were: Albuquerque, Cleveland, Denver, Detroit, Minneapolis,
and Seattle. The average RVP levels of the non-ethanol gasoline samples
in each city ranged from 7.46-9.00 psi, while that of the ethanol blends
ranged from 8.50-9.93 psi. We observed a relationship between the RVP of
the non-ethanol gasolines and the difference between the RVP of the
ethanol and non-ethanol blends. In general, as the RVP of the
non-ethanol gasoline increased, the difference between the RVP of the
ethanol and non-ethanol blends decreased. Figure 2A-1 shows this
relationship for the six cities, along with a best-fit line based on
least squares regression. The r-squared value for the best-fit line was
0.25.

Figure 2A-1. Effect of Ethanol Blending on RVP in Six U.S. Cities

![](media/image53.wmf){width="5.9375in" height="3.375in"}

On average, ethanol blending led to a 1.0 psi RVP higher RVP level when
the RVP level of the non-ethanol gasoline was 9.0 psi. Ethanol's effect
increased to 1.2 psi when the RVP level of the non-ethanol gasoline was
7.5 psi. When evaluating the effect of weathering and commingling, we
will evaluate non-ethanol gasoline RVP levels of 6.8, 7.8 and 9.0 psi,
as these are common RVP standards in use today. Using the relationship
indicated in Figure 1, the RVP levels of the ethanol blends associated
with these base RVP levels are 8.1, 9.0, and 10.0 psi, respectively.

**2A.4 Brand Loyalty**

CARB recently conducted a fairly extensive direct survey of vehicle
refueling patterns. This study is both more recent and more extensive
than those made in the past.[^92] During their refueling survey, CARB
asked vehicle operators whether the vehicle was refueled with the same
brand of fuel the previous time the vehicle was refueled. The resulting
responses are summarized in Table 2A-6 below.

Table 2A-6. Brand Loyalty in the CARB Refueling Survey

  ------------------------------- --------------- ----------- ------------
  Response to Question: Did you   Los Angeles     Bay Area    Lake Tahoe
  refuel with the                                             

  "Yes"                           62%             59%         31%

  "No"                            31%             38%         67%

  "Don't Know"                    7%              3%          2%

  Breakdown of Retail Outlets                                 
  Surveyed                                                    

  Major Brands (Shell, Chevron,   4 (100%)        5 (84%)     3 (33%)
  Texaco, Mobil, ARCO)                                        

  Intermediate (Valero)           0 (0%)          1 (16%)     0 (0%)

  Local Brands (USA, Fox, United) 0 (0%)          0 (0%)      6 (67%)
  ------------------------------- --------------- ----------- ------------

CARB thought that the relatively low level of brand loyalty in Lake
Tahoe was due to a high rate of rental car usage in that city. However,
they did not present any data regarding the fraction of total VMT by
rental vehicles to justify their rationale. Rental vehicle VMT would
have to represent roughly half of all VMT in the Tahoe area (assuming
such use was negligible in the other two areas) for this factor to
explain the large difference seen in Table 2A-6. This seems quite
unlikely, despite Lake Tahoe being a resort area.

We believe that there is a more likely explanation for the difference. A
review of the service stations surveyed in the three areas shows
significant differences in the types of brands surveyed. The service
stations surveyed in Los Angeles and the Bay Area were dominated by
major, nationally recognized brands. Those in Lake Tahoe were dominated
by more local brands. We believe that brand loyalty could easily be
stronger for nationally known brands which advertise and which offer
their own credit cards. A few major brands offer a significant discount
when their credit card is used to buy their gasoline (e.g., Shell, BP).

The breakdown of service stations into major and local brands in the
three areas is shown in the lower half of Table 2A-6. We have defined
major brands to include vertically integrated oil companies which have
been in the retail business for several decades, market in several
regions across the U.S. and are known to widely advertise. As shown in
Table 2A-6, all four retail fuel outlets surveyed in Los Angeles fell
into this category, while five out of six stations in the Bay Area
reflected major brands. The sixth outlet in the Bay Area was a Valero
outlet. Valero is a newcomer in the retail market relative to Chevron,
Shell, etc. However, it is currently the largest refiner in the U.S. and
offers its own credit card. Thus, Valero appears to fall into an
intermediate category somewhere between the major brands and the local
brands.

The situation is essentially reversed in Lake Tahoe. Two-thirds of the
retail outlets surveyed were local brands. Only three out of nine
outlets represented major brands.

In order to investigate the potential difference between brand loyalty
between major and non-major brands, we assumed that each type of fuel
brand had its own level of loyalty across the three areas. We then
estimated these two levels of brand loyalty identified to best predict
the overall brand loyalty in each area. Overall, loyalty levels of 62%
for major brands and 15% for non-major brands (including Valero) fit the
survey data reasonably well.

The U.S. Energy Information Administration (EIA) tracks a broad range of
proprietary physical and financial data from large energy producers
through their Financial Reporting System. This information includes the
volume of gasoline sold through retail outlets owned by or leased from
these companies and selling fuel with the company's brand name. Up to
1997, these retail outlets sold about 45% of all gasoline sold in the
U.S. In 1998, EIA expanded the number of companies included in the
Financial Reporting System by 50% (from 22 to 33 companies). The
percentage of gasoline sold by these firms' retail outlets increased to
62% of all gasoline sold in the U.S.

The nature of the firms included in the Financial Reporting System
changed with the eleven companies added in 1998. Prior to 1998, this
system included 22 companies. A few of these firms were not oil
companies, (e.g., Burlington Resources, Enron, Sonat/El Paso Energy,
Union Pacific Resources, USX). Several others were not major gasoline
retailers, at least under their corporate names (e.g., Anadarko
Petroleum, Kerr-McGee, Occidental Petroleum).

In 1998, EIA added an additional 11 companies to the Financial Reporting
System. Most of these were gasoline refiners (e.g., Citgo, Clark,
Equilon, Lyondell-Citgo, Motiva, Sunoco, Tesoro, Ultramar Diamond
Shamrock (UDS), Valero and Williams). At the same time, the volume of
gasoline sold by the original 22 companies decreased to 31% of all
gasoline sold in the U.S. This drop is likely due to the spin-off of
refineries by companies like Shell and Texaco to partnerships like
Motiva and Equilon. The actual retailing of this 14% of gasoline sales
likely didn't change significantly (e.g., the retail brand name
continued to be Shell). The net increase of 17% of U.S. gasoline sales
represented the other refiners, such as Tesoro, Valero, Citgo, USD, etc.
These latter companies have a much more regional footprint and have not
established brand name familiarity coupled with a perception of higher
quality gasoline. With the exception of the single Valero outlet in the
Bay Area, none of the stations surveyed by CARB offered gasoline from
these companies. Thus, we believe that the major brands included in the
CARB survey are more similar to the fuel suppliers included in EIA's
Financial Reporting System prior to 1998 than to those included after
1998.

Given this, we estimate that 45% of U.S. gasoline sales are sold through
stations carrying a major brand. Weighting the loyalty levels of 62% for
major brands by 45% and the loyalty level of 15% for other brands by 55%
yields an overall national average loyalty level of 36%. For non-loyal
consumers, the probability of brand selection is assumed to be random.
Practically, this means that the probability of choosing either a
non-ethanol or ethanol blend depends on each fuel's market share.

The exact question asked by the CARB surveyors was whether the vehicle
had been refueled the last time with the same brand of fuel. CARB
assumed that this meant that the vehicle was always refueled with the
same brand. However, the question was limited to only the refueling
immediately preceding the current one. Our primary estimate of brand
loyalty is that directly addressed by the CARB survey: the likelihood
that the previous refueling was with the same brand of gasoline. We also
estimate the sensitivity of our estimate of commingling to the
assumption made by CARB below.

**2A.5 Procedures for Modeling Vehicle Refueling and Resultant Fuel
Quality**

We developed a model to predict the fuel tank level and fuel quality
existing in a typical onroad vehicle through 500 refuelings. The vehicle
is assumed to begin its life with a full tank of non-ethanol fuel. The
fuel tank level at which the vehicle is refueled is based on the
probabilities shown in Table 2A-1 above. First, a cumulative
distribution of refueling probabilities was generated by adding the
probabilities shown in the second column of Table 2A-1. Then, a random
number valued between 0.0 and 1.0 is generated. If the random number is
less than the cumulative probability of the tank being empty at
refueling (0.414), the tank is assumed to be empty. If the random number
is between this figure and the cumulative probability of the tank being
1/8 full at refueling (0.547), the tank is assumed to be 1/8 full at
refueling, etc.

The RVP level of this fuel is reduced using the weathering equation
shown in Section 2A.2. The level of RVP loss is assumed to be
proportional to the volume of fuel used since the last refueling. For
example, a vehicle might be driven from a full fuel tank down to a tank
which is 20% full. Fuel usage is 80% of a tank. The above RVP weathering
equation represents the RVP drop for a vehicle being driven from a full
tank down to a 60% full tank, or a fuel usage of 40%. Therefore, the RVP
decrease due to weathering in this case would be twice that indicated by
the weathering equation in Section 2A.2. Fuel composition (i.e., ethanol
content) is assumed to be unaffected by driving.

Only the hydrocarbon portion of the fuel is weathered, since the effect
of ethanol on RVP is essentially independent of its concentration. Thus,
the value of RVP used in the weathering equation is that for the
hydrocarbon portion of the fuel, not the total RVP of the blend. This
means that we need to track the RVP of the hydrocarbon portion of the
fuel separately.

The probability of the fuel tank being completely filled during
refueling is determined from the estimates shown in Table 2A-2. Again an
independent random number is generated with a value between 0.0 and 1.0.
If the value is less than the probability shown in Table 2A-2 for that
initial fill level, the tank is assumed to be filled up. When a partial
fill is indicated, another random number between 0.0 and 1.0 is
generated. This random number is used in conjunction with the NORMINV
function in Excel to generate a random level of the standard normal
deviate, or the number of standard deviations to add to the mean
estimate for the volume of fuel added during a partial fill-up. The
values for the mean and standard deviations for volume of fuel added are
shown in Table 2A-4. As discussed in Section 2A.2, whenever the initial
fuel tank level is 0.5 or greater, we assume that the tank is filled up.

Occasionally, the volume of fuel added during a partial fill will exceed
the capacity of the tank. This occurs when the random number generator
produces a large positive number of standard deviations to be added to
the mean fuel volume typically added during a partial fill. In these
cases, we set the final tank level after refueling to 100%.

The type of fuel added, ethanol or non-ethanol blend, is determined both
by the level of brand loyalty and the mix of fuels available in the
local area. As discussed in Section 2A.4, brand loyalty is estimated to
be 36%. Again, an independent random number is generated with a value
between 0.0 and 1.0. If the value is less than 0.36, the type of fuel
added is assumed to be the same as that added during the last refueling.
Otherwise, the probability of refueling with any particular fuel is
assumed to be independent of the previous fuel used. The probabilities
of refueling with a non-ethanol blend and an ethanol blend are the
market shares of the respective fuels. This is selection is made by
choosing a new random number.

We then determine the quality of the fuel in the tank after the
refueling event. The ethanol concentration of the tank fuel is simply
the ethanol concentration of the fuel prior to refueling plus that of
the fuel added during refueling, each weighted by their respective
volumes. We assume that the fuel tank contains some volume of fuel, even
when it indicates empty. Consistent with CARB in their assessment of
commingling, we assume that this tank "heel" is 10% of the tank
capacity. The "ethanol" portion of the ethanol blend is assumed to be
95% pure ethanol (i.e., it contains 5% denaturant). This denaturant is
assumed to have the same RVP as the non-oxygenated gasoline.

Calculating the RVP of the gasoline blend after refueling is more
complicated. We first calculate the RVP of the hydrocarbon portion of
the fuel after refueling in the same way as described above for the
ethanol concentration. The RVP of hydrocarbons blend linearly or
ideally, so we simply weight the RVP of the hydrocarbon portion of the
fuel left in the tank just prior to refueling (adjusted downward for
weathering as indicated above) with that of the new fuel by their
respective contributions to the total volume of fuel in the tank after
refueling. We then increase this RVP based on the concentration of
ethanol in the tank. As described in Section 2A.3, we assume that
ethanol's impact on RVP is constant between 2 and 10 vol% and a function
of the RVP of the hydrocarbon blendstock, as discussed above. Between
zero and 2 vol%, we assume that its RVP effect increases linearly up to
its full effect.

At this point, the vehicle has been refueled and we have determined the
quality of the fuel currently in the tank. The next step is to repeat
the entire process described above, starting with a new level at which
the tank is refueled once again.

Once a vehicle has been refueled 500 times, we determine the RVP and
ethanol concentration of the fuel in the tank over a range of fuel tank
fill levels. We split the full range of possible tank fill levels into
10 discrete segments, each representing a 10% range of tank fill level
(e.g., 20-30% full). We then determine which tank fill levels the
vehicle will be driven through prior to the next refueling. For example,
if a vehicle is refueled to 100% of tank capacity and is then driven
down to 1/8 full before its next refueling, its tank moves from 100%
full to 90% full to 80% full, etc. until it reaches 12.5% full. Thus, in
this example, the tank was never in the range of 0-10% full. We assume
that the vehicle spends the same amount of time and accumulates the same
amount of VMT at each tank fill level between its starting and ending
points. (This is simply equivalent to assuming that vehicles are driven
differently depending on their level of fuel tank fill level; a safe
assumption.) The RVP of the fuel in the tank is adjusted at each fill
level as the vehicle is being driven, including the effect of
weathering. The same is done for ethanol concentration. For each segment
of fuel tank fill level, the RVP and ethanol concentration occurring
between each set of refueling events is averaged.

The entire process is then repeated 50 times. Overall, both RVP and
ethanol concentration versus tank fuel fill level is tracked for 25,000
refuelings (500 refuelings per model pass-through times 50 model
pass-throughs). Overall averages are then determined and retained for
analysis.

One output of the model which is independent of the RVP levels of the
fuels is the distribution of the fuel tank levels of vehicle on the road
at any one time. This distribution is shown in Table 2A-7.

Table 2A-7. Distribution of Fuel Tank Fill Levels for the In-Use Fleet

  -------------------------- ----------------------- ---------------------
  Range of Fuel Tank Fill                            \% of Vehicles
  Level                                              

  Lower Limit                Upper Limit             

  0%                         10%                     7.6%

  10%                        20%                     9.4%

  20%                        30%                     12.9%

  30%                        40%                     12.5%

  40%                        50%                     12.2%

  50%                        60%                     10.6%

  60%                        70%                     9.4%

  70%                        80%                     8.7%

  80%                        90%                     8.4%

  90%                        100%                    8.2%
  -------------------------- ----------------------- ---------------------

As can be seen, the most frequent onroad fuel tank fill levels are
between 20% to 50%. This distribution will be used to weight the effect
of commingling which occurs for each range of fuel tank fill level.

**2A.6 Modeling Results**

We performed the procedure described in Section 2A.5. for a set of base
gasoline RVP levels ranging from roughly 7 RVP to 9 RVP and for the two
types of ethanol blending (matched RVP and increased RVP). An example of
the sequence of calculations is as follows:

1)  Select the RVP of non-oxygenated gasoline (E0), the RVP of the
    ethanol blend (E10), and the market share of E10,

2)  Begin with a tank full of non-oxygenated gasoline,

3)  Choose a random number which is used to probabilistically
    determine: a) the level at which the tank is being refueled, b) the
    level to which the tank is filled, c) the volume of fuel thus being
    added, and d) the type of fuel used to fill the tank (E0 or E10),

4)  Determine which tank fill levels the vehicle passed through between
    the prior fill level and the point at which it was refilled and
    determine the fuel RVP at each 10% increment in tank fill level
    using the weathering equation,

5)  Determine the RVP of the hydrocarbon portion of the fuel at the time
    of refill using the weathering equation,

6)  Determine the concentration of ethanol in the refilled fuel tank
    using the ethanol concentration of the fuel after the prior fill-up,
    the volume of fuel in the tank at the time of refill, the ethanol
    concentration of the fuel used to refill the tank currently and the
    volume of fuel added during this refill,

7)  Determine the RVP of the hydrocarbon portion of the fuel after
    refill by weighting the RVP of the hydrocarbon portion of the fuel
    in the tank at the time of refueling and the RVP of hydrocarbon
    portion of the fuel being added by the volume of the hydrocarbon
    portion of the fuel in the tank at the time of refill and the volume
    of the hydrocarbon portion of the fuel being added during refueling,
    respectively,

8)  Determine the RVP of the total fuel in the tank after refueling from
    the RVP of the hydrocarbon portion of the fuel from step 6 and the
    effect of ethanol on RVP using its concentration from step 5.

9)  Return to step 2) and proceed through 500 refuelings.

Once the model has been applied to 500 refuelings (essentially the life
of the vehicle), the results are compiled. The average RVP level for
each interval of tank fill level is determined. For example, over 500
refuelings, approximately 200 have the tank being refilled when it was
less than one-eighth full, so that the vehicle was driven when the tank
was 10% full. For these 200 occurrances, the tank RVP level is averaged.
This becomes the average RVP at a fuel tank fill level of 10%.
Approximately 260 refueling involve the vehicle being driven when the
fuel tank fill level is 20%. Fuel RVP is again averaged for these 260
situations. The process is repeated for a 30% fuel tank fill level, 40%,
and so on through 100% full (which occurs every time the tank is
completely filled up). The RVP predictions of the model for various
mixes of 9 RVP non-oxygenated gasoline and a 10 RVP ethanol blend are
shown in Tables 2A-8 through 2A-13. The last line of each table shows
the weighted average RVP level using the distribution of in-use fuel
tank fill levels shown above in Table 2A-7.

Table 2A-8. In-Use Fuel Tank RVP Levels -- 9 RVP CG with Ethanol Waiver
(psi)

  ------------ --------- ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------
  Fuel Tank    Ethanol                                                                                              
  Fill Level   Blend                                                                                                
               Market                                                                                               
               Share                                                                                                

               0%        2%     5%     10%    20%    30%    40%    50%    60%    70%    80%    90%    95%    98%    100%

  5%           8.15      8.19   8.26   8.35   8.52   8.67   8.79   8.90   8.98   9.05   9.11   9.15   9.17   9.17   9.17

  15%          8.23      8.27   8.33   8.42   8.58   8.74   8.87   8.97   9.05   9.12   9.18   9.22   9.24   9.25   9.25

  25%          8.29      8.33   8.40   8.49   8.65   8.81   8.93   9.04   9.11   9.19   9.24   9.29   9.30   9.31   9.31

  35%          8.35      8.39   8.45   8.55   8.71   8.87   8.99   9.09   9.17   9.25   9.30   9.35   9.36   9.37   9.37

  45%          8.40      8.44   8.50   8.60   8.77   8.93   9.05   9.15   9.23   9.31   9.35   9.40   9.41   9.41   9.41

  55%          8.44      8.48   8.56   8.65   8.82   8.98   9.11   9.21   9.29   9.36   9.40   9.45   9.46   9.46   9.46

  65%          8.50      8.55   8.62   8.71   8.89   9.05   9.17   9.27   9.35   9.43   9.46   9.51   9.52   9.52   9.52

  75%          8.58      8.62   8.70   8.79   8.97   9.13   9.25   9.35   9.43   9.50   9.54   9.58   9.59   9.60   9.60

  85%          8.66      8.71   8.78   8.87   9.05   9.22   9.34   9.44   9.52   9.59   9.63   9.67   9.68   9.68   9.68

  95%          8.75      8.80   8.87   8.97   9.15   9.31   9.43   9.53   9.61   9.68   9.72   9.76   9.77   9.77   9.77

  Wtd.Avg.     8.42      8.47   8.53   8.63   8.80   8.96   9.08   9.18   9.26   9.34   9.38   9.42   9.44   9.44   9.44
  ------------ --------- ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------

Table 2A-9. In-Use Fuel Tank RVP Levels -- 7.8 RVP CG with Ethanol
Waiver (psi)

  ------------ --------- ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------
  Fuel Tank    Ethanol                                                                                              
  Fill Level   Blend                                                                                                
               Market                                                                                               
               Share                                                                                                

               0%        2%     5%     10%    20%    30%    40%    50%    60%    70%    80%    90%    95%    98%    100%

  5%           7.08      7.13   7.20   7.30   7.50   7.64   7.79   7.91   8.01   8.10   8.15   8.19   8.21   8.22   8.22

  15%          7.14      7.19   7.26   7.36   7.56   7.71   7.85   7.97   8.07   8.15   8.21   8.25   8.27   8.28   8.28

  25%          7.20      7.25   7.31   7.41   7.62   7.76   7.91   8.03   8.12   8.21   8.27   8.30   8.32   8.34   8.34

  35%          7.24      7.30   7.36   7.46   7.67   7.81   7.96   8.08   8.17   8.26   8.31   8.35   8.37   8.38   8.38

  45%          7.29      7.34   7.41   7.51   7.71   7.86   8.01   8.13   8.22   8.30   8.35   8.40   8.41   8.42   8.42

  55%          7.33      7.38   7.45   7.55   7.76   7.92   8.06   8.18   8.27   8.35   8.40   8.44   8.45   8.46   8.46

  65%          7.38      7.43   7.50   7.61   7.82   7.97   8.12   8.23   8.33   8.40   8.45   8.49   8.51   8.51   8.51

  75%          7.44      7.49   7.57   7.67   7.88   8.04   8.19   8.30   8.40   8.47   8.52   8.55   8.57   8.58   8.58

  85%          7.51      7.56   7.64   7.75   7.95   8.12   8.27   8.38   8.47   8.54   8.59   8.62   8.64   8.65   8.65

  95%          7.59      7.64   7.72   7.82   8.03   8.19   8.35   8.45   8.55   8.61   8.67   8.70   8.72   8.73   8.73

  Wtd.Avg.     7.31      7.36   7.43   7.53   7.74   7.89   8.04   8.15   8.25   8.33   8.38   8.42   8.44   8.45   8.45
  ------------ --------- ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------

Table 2A-10. In-Use Fuel Tank RVP Levels -- 6.8 RVP CG with Ethanol
Waiver (psi)

  ------------ --------- ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------
  Fuel Tank    Ethanol                                                                                              
  Fill Level   Blend                                                                                                
               Market                                                                                               
               Share                                                                                                

               0%        2%     5%     10%    20%    30%    40%    50%    60%    70%    80%    90%    95%    98%    100%

  5%           6.19      6.24   6.31   6.43   6.63   6.82   6.96   7.09   7.19   7.29   7.35   7.40   7.41   7.42   7.43

  15%          6.24      6.29   6.37   6.48   6.68   6.86   7.02   7.13   7.24   7.33   7.40   7.45   7.46   7.47   7.48

  25%          6.29      6.34   6.41   6.53   6.73   6.91   7.07   7.18   7.29   7.38   7.45   7.50   7.51   7.52   7.53

  35%          6.33      6.38   6.46   6.57   6.77   6.95   7.11   7.23   7.34   7.42   7.49   7.53   7.55   7.56   7.57

  45%          6.36      6.42   6.49   6.61   6.81   7.00   7.16   7.27   7.38   7.47   7.53   7.57   7.59   7.59   7.60

  55%          6.40      6.45   6.53   6.65   6.86   7.04   7.20   7.31   7.42   7.51   7.57   7.61   7.62   7.63   7.64

  65%          6.44      6.50   6.57   6.69   6.90   7.09   7.25   7.36   7.47   7.56   7.61   7.65   7.67   7.67   7.68

  75%          6.50      6.55   6.63   6.75   6.96   7.15   7.31   7.42   7.53   7.61   7.67   7.71   7.72   7.72   7.73

  85%          6.56      6.61   6.69   6.81   7.02   7.21   7.37   7.49   7.59   7.67   7.73   7.77   7.78   7.79   7.79

  95%          6.63      6.68   6.76   6.88   7.09   7.28   7.44   7.55   7.66   7.74   7.79   7.84   7.85   7.85   7.86

  Wtd.Avg.     6.39      6.44   6.51   6.63   6.84   7.02   7.18   7.30   7.40   7.49   7.55   7.59   7.61   7.61   7.62
  ------------ --------- ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------

Table 2A-11. In-Use Fuel Tank RVP Levels as -- 9 RVP Gasoline with No
Ethanol Waiver

  ------------ --------- ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------
  Fuel Tank    Ethanol                                                                                              
  Fill Level   Blend                                                                                                
               Market                                                                                               
               Share                                                                                                

               0%        2%     5%     10%    20%    30%    40%    50%    60%    70%    80%    90%    95%    98%    100%

  5%           8.15      8.17   8.20   8.26   8.33   8.38   8.40   8.41   8.40   8.36   8.32   8.26   8.23   8.20   8.19

  15%          8.23      8.24   8.28   8.33   8.40   8.45   8.47   8.47   8.46   8.43   8.39   8.33   8.30   8.28   8.27

  25%          8.29      8.31   8.34   8.39   8.47   8.51   8.54   8.54   8.53   8.50   8.45   8.40   8.37   8.35   8.33

  35%          8.35      8.37   8.40   8.45   8.52   8.58   8.60   8.60   8.59   8.56   8.51   8.45   8.42   8.40   8.39

  45%          8.39      8.42   8.45   8.50   8.58   8.63   8.65   8.66   8.65   8.61   8.56   8.50   8.47   8.45   8.43

  55%          8.44      8.47   8.50   8.55   8.63   8.69   8.71   8.72   8.71   8.67   8.62   8.55   8.52   8.50   8.48

  65%          8.50      8.53   8.56   8.62   8.70   8.75   8.78   8.78   8.77   8.73   8.68   8.62   8.58   8.56   8.54

  75%          8.58      8.60   8.64   8.69   8.77   8.83   8.86   8.86   8.85   8.81   8.76   8.69   8.66   8.63   8.62

  85%          8.67      8.69   8.72   8.78   8.86   8.92   8.95   8.95   8.94   8.90   8.85   8.78   8.74   8.72   8.70

  95%          8.76      8.78   8.81   8.87   8.95   9.01   9.04   9.04   9.03   8.99   8.94   8.87   8.84   8.81   8.79

  Wtd.Avg.     8.43      8.45   8.48   8.53   8.61   8.66   8.69   8.69   8.68   8.64   8.60   8.53   8.50   8.48   8.46
  ------------ --------- ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------

Table 2A-12. In-Use Fuel Tank RVP Levels -- 8 RVP Gasoline with No
Ethanol Waiver (psi)

  ------------ --------- ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------
  Fuel Tank    Ethanol                                                                                              
  Fill Level   Blend                                                                                                
               Market                                                                                               
               Share                                                                                                

               0%        2%     5%     10%    20%    30%    40%    50%    60%    70%    80%    90%    95%    98%    100%

  5%           7.26      7.28   7.32   7.36   7.44   7.50   7.53   7.53   7.52   7.48   7.43   7.36   7.33   7.31   7.28

  15%          7.33      7.35   7.38   7.43   7.50   7.57   7.59   7.60   7.57   7.55   7.49   7.42   7.39   7.37   7.35

  25%          7.39      7.41   7.44   7.49   7.56   7.62   7.64   7.66   7.64   7.60   7.56   7.48   7.45   7.43   7.41

  35%          7.43      7.45   7.49   7.54   7.62   7.68   7.70   7.71   7.69   7.66   7.60   7.53   7.50   7.47   7.46

  45%          7.47      7.50   7.53   7.59   7.67   7.73   7.75   7.76   7.74   7.71   7.65   7.57   7.54   7.52   7.50

  55%          7.52      7.54   7.57   7.63   7.72   7.78   7.80   7.81   7.79   7.76   7.70   7.62   7.59   7.56   7.54

  65%          7.57      7.59   7.63   7.69   7.77   7.84   7.86   7.87   7.85   7.81   7.75   7.67   7.64   7.61   7.59

  75%          7.63      7.66   7.69   7.75   7.84   7.91   7.93   7.94   7.92   7.88   7.82   7.74   7.70   7.67   7.66

  85%          7.71      7.73   7.77   7.83   7.92   7.98   8.01   8.02   7.99   7.96   7.90   7.82   7.78   7.75   7.73

  95%          7.79      7.81   7.85   7.91   8.00   8.06   8.09   8.10   8.07   8.04   7.98   7.90   7.86   7.83   7.81

  Wtd.Avg.     7.50      7.52   7.56   7.61   7.69   7.76   7.78   7.79   7.77   7.73   7.68   7.60   7.57   7.54   7.52
  ------------ --------- ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------

Table 2A-13. In-Use Fuel Tank RVP Levels -- 7 RVP Gasoline with No
Ethanol Waiver (psi)

  ------------ --------- ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------
  Fuel Tank    Ethanol                                                                                              
  Fill Level   Blend                                                                                                
               Market                                                                                               
               Share                                                                                                

               0%        2%     5%     10%    20%    30%    40%    50%    60%    70%    80%    90%    95%    98%    100%

  5%           6.37      6.40   6.43   6.49   6.57   6.63   6.66   6.66   6.65   6.61   6.56   6.48   6.44   6.41   6.40

  15%          6.43      6.45   6.48   6.54   6.62   6.68   6.71   6.71   6.70   6.66   6.61   6.54   6.49   6.47   6.45

  25%          6.47      6.50   6.53   6.59   6.68   6.73   6.76   6.76   6.75   6.71   6.66   6.59   6.54   6.51   6.50

  35%          6.51      6.54   6.58   6.64   6.72   6.78   6.81   6.81   6.79   6.76   6.70   6.63   6.58   6.56   6.54

  45%          6.55      6.58   6.62   6.68   6.77   6.82   6.85   6.85   6.84   6.80   6.74   6.67   6.62   6.59   6.58

  55%          6.59      6.61   6.66   6.72   6.81   6.87   6.90   6.90   6.89   6.84   6.78   6.71   6.66   6.63   6.61

  65%          6.63      6.66   6.70   6.77   6.86   6.92   6.96   6.95   6.94   6.89   6.83   6.75   6.70   6.67   6.65

  75%          6.69      6.72   6.76   6.82   6.92   6.98   7.02   7.01   7.00   6.95   6.89   6.81   6.76   6.73   6.71

  85%          6.75      6.78   6.82   6.89   6.98   7.05   7.08   7.08   7.06   7.02   6.95   6.88   6.82   6.79   6.77

  95%          6.82      6.85   6.89   6.96   7.05   7.11   7.15   7.15   7.13   7.08   7.02   6.94   6.89   6.86   6.84

  Wtd.Avg.     6.57      6.60   6.64   6.70   6.79   6.85   6.88   6.88   6.86   6.82   6.76   6.69   6.64   6.61   6.60
  ------------ --------- ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------

The next step is to estimate the impact of commingling on in-use RVP. We
use the in-use tank RVP levels shown in the previous six tables for
ethanol blend market shares of zero and 100% to represent situations
where no commingling occurs. In the absence of commingling at
intermediate levels of ethanol blend market share, the RVP should vary
linearly between those found at zero and 100%. The impact of commingling
is then the difference between the actual level of RVP estimated by the
model and the RVP estimated from the zero and 100% ethanol blend market
share RVP levels. Table 2A-14 shows the impact of commingling for the
case where gasoline RVP is 9 psi and ethanol blends are allowed a 1.0
psi RVP waiver.

Table 2A-14. Commingling as a Function of Fuel Tank Fill Level and
Ethanol Blend Market Share -- 9 RVP CG with Ethanol Waiver

  ---------- --------- ------- ------- ------- ------- ------- ------- ------- ------- ------- ------- ------- -------
  Fuel Tank  Ethanol                                                                                           
  Fill Level Blend                                                                                             
             Market                                                                                            
             Share                                                                                             

             2%        5%      10%     20%     30%     40%     50%     60%     70%     80%     90%     95%     98%

  5%         0.016     0.049   0.101   0.167   0.213   0.235   0.234   0.220   0.183   0.132   0.073   0.036   0.013

  15%        0.017     0.047   0.096   0.164   0.210   0.229   0.228   0.214   0.179   0.129   0.070   0.037   0.015

  25%        0.019     0.049   0.098   0.166   0.210   0.228   0.230   0.217   0.180   0.130   0.072   0.036   0.014

  35%        0.021     0.049   0.097   0.170   0.217   0.234   0.235   0.222   0.184   0.134   0.071   0.037   0.015

  45%        0.023     0.051   0.101   0.175   0.224   0.243   0.243   0.229   0.192   0.138   0.073   0.039   0.013

  55%        0.023     0.052   0.105   0.181   0.232   0.251   0.252   0.240   0.197   0.143   0.076   0.042   0.015

  65%        0.025     0.054   0.110   0.185   0.238   0.259   0.260   0.246   0.201   0.147   0.078   0.043   0.014

  75%        0.025     0.055   0.112   0.187   0.240   0.263   0.263   0.250   0.204   0.150   0.078   0.042   0.013

  85%        0.026     0.055   0.112   0.186   0.242   0.265   0.266   0.251   0.206   0.150   0.078   0.042   0.013

  95%        0.025     0.055   0.112   0.186   0.242   0.265   0.266   0.252   0.206   0.151   0.079   0.043   0.013

  Wtd.Avg.   0.022     0.051   0.104   0.176   0.226   0.246   0.246   0.233   0.192   0.140   0.074   0.039   0.014
  ---------- --------- ------- ------- ------- ------- ------- ------- ------- ------- ------- ------- ------- -------

As can be seen, the impact of commingling increases slightly moving from
low levels of fuel tank fill level to high levels. As found by previous
studies of commingling, the impact of commingling is lowest when either
E0 or E10 fuels predominate the market and peaks when the mix of E0 and
E10 is approximately 50/50. Again, the weighted average of the
commingling impact is determined by applying weighting the commingling
impact at each fuel tank fill level by the distribution of fill levels
in-use shown in Table 2A-7.

Table 2A-15 shows the weighted average commingling impacts for the six
fuel cases.

Table 2A-15. Weighted Average Commingling Impact for Various Sets of E0
and E10 Fuels (psi)

<table>
<colgroup>
<col style="width: 10%" />
<col style="width: 6%" />
<col style="width: 6%" />
<col style="width: 6%" />
<col style="width: 6%" />
<col style="width: 6%" />
<col style="width: 6%" />
<col style="width: 6%" />
<col style="width: 6%" />
<col style="width: 6%" />
<col style="width: 6%" />
<col style="width: 6%" />
<col style="width: 7%" />
<col style="width: 6%" />
</colgroup>
<tbody>
<tr class="odd">
<td rowspan="2"><p>E0/E10</p>
<p>RVP Level</p></td>
<td colspan="13">Ethanol Blend Market Share</td>
</tr>
<tr class="even">
<td>2%</td>
<td>5%</td>
<td>10%</td>
<td>20%</td>
<td>30%</td>
<td>40%</td>
<td>50%</td>
<td>60%</td>
<td>70%</td>
<td>80%</td>
<td>90%</td>
<td>95%</td>
<td>98%</td>
</tr>
<tr class="odd">
<td>9/10</td>
<td>0.022</td>
<td>0.060</td>
<td>0.102</td>
<td>0.171</td>
<td>0.227</td>
<td>0.249</td>
<td>0.249</td>
<td>0.227</td>
<td>0.199</td>
<td>0.143</td>
<td>0.084</td>
<td>0.045</td>
<td>0.020</td>
</tr>
<tr class="even">
<td>7.8/8.9</td>
<td>0.029</td>
<td>0.065</td>
<td>0.110</td>
<td>0.201</td>
<td>0.239</td>
<td>0.273</td>
<td>0.274</td>
<td>0.255</td>
<td>0.218</td>
<td>0.160</td>
<td>0.082</td>
<td>0.043</td>
<td>0.019</td>
</tr>
<tr class="odd">
<td>7/8.2</td>
<td>0.028</td>
<td>0.067</td>
<td>0.122</td>
<td>0.203</td>
<td>0.265</td>
<td>0.299</td>
<td>0.290</td>
<td>0.275</td>
<td>0.236</td>
<td>0.173</td>
<td>0.094</td>
<td>0.046</td>
<td>0.015</td>
</tr>
<tr class="even">
<td>9/9</td>
<td>0.022</td>
<td>0.051</td>
<td>0.104</td>
<td>0.176</td>
<td>0.226</td>
<td>0.246</td>
<td>0.246</td>
<td>0.233</td>
<td>0.192</td>
<td>0.140</td>
<td>0.074</td>
<td>0.039</td>
<td>0.014</td>
</tr>
<tr class="odd">
<td>8/8</td>
<td>0.021</td>
<td>0.055</td>
<td>0.110</td>
<td>0.189</td>
<td>0.249</td>
<td>0.269</td>
<td>0.278</td>
<td>0.253</td>
<td>0.218</td>
<td>0.160</td>
<td>0.080</td>
<td>0.045</td>
<td>0.019</td>
</tr>
<tr class="even">
<td>7/7</td>
<td>0.027</td>
<td>0.065</td>
<td>0.125</td>
<td>0.212</td>
<td>0.267</td>
<td>0.298</td>
<td>0.294</td>
<td>0.277</td>
<td>0.233</td>
<td>0.172</td>
<td>0.096</td>
<td>0.047</td>
<td>0.017</td>
</tr>
</tbody>
</table>

Ethanol use in the three RFS rule fuel cases (Reference, RFS, and EIA)
occurs predominately under three situations: 1) 9 RVP CG with an RVP
waiver for ethanol, 2) 7.8 RVP CG with an RVP waiver for ethanol, and 3)
7 RVP RFG. In order to simplify application of the impact of commingling
to our emission modeling, we averaged the commingling impacts for these
situations from Table 2A-15 and applied that to the entire U.S. as a
function of ethanol blend market share. This average set of commingling
impacts is shown in Table 2A-16.

Table2A-16. Commingling Impact Applied in RFS Rule Emission Modeling

  ----------------------------------------- -----------------------------
  Ethanol Blend Market Share                Commingling Impact (psi)

  0%                                        0

  2%                                        0.026

  5%                                        0.064

  10%                                       0.113

  20%                                       0.194

  30%                                       0.244

  40%                                       0.273

  50%                                       0.272

  60%                                       0.253

  70%                                       0.217

  80%                                       0.159

  90%                                       0.087

  95%                                       0.045

  98%                                       0.019

  100%                                      0.000
  ----------------------------------------- -----------------------------

# Chapter 3: Impacts on Emissions from Vehicles, Nonroad Equipment, and Fuel Production Facilities

As described in Chapter 2, there are a large number of potential fuels
that qualify as renewable. However, only two are expected to be used in
significant volumes by 2012: ethanol and biodiesel. Of these, ethanol
use is expected to predominate. In particular, ethanol is expected to
dominate the "growth" in renewable fuel use between now and 2012. Thus,
our primary focus here will be on the impact of the use of ethanol on
emissions in spark-ignited vehicles and equipment. We will more briefly
touch on the impact of biodiesel fuel use on emissions.

Similarly, we expect that the bulk of the impact of ethanol use on
emissions and air quality will be associated with emissions from
spark-ignited vehicles and equipment using low level ethanol-gasoline
blends. We expect the use of high level ethanol-gasoline blends, like
E85 to be relatively small in comparison. Thus, the discussion here will
focus on emissions from the use of low level ethanol blends. We will
more briefly discuss the per vehicle impacts of use of high level
ethanol-gasoline blends relative to gasoline.

Finally, we present estimates of the emissions related to the production
and distribution of both ethanol and biodiesel. The emissions related to
the production and use of ethanol can be significant relative to the
emission impacts of the use of ethanol blends, due to the significantly
increase in the volume of ethanol expected to be produced in the future.

**3.1 Effect of Fuel Quality on Onroad Spark-Ignited Vehicle Emissions**

Ethanol belongs to a group of gasoline additives commonly referred to as
oxygenates. The two most commonly used oxygenates are ethanol and MTBE,
though TAME has been used in significant volumes, as well. All
oxygenates have relatively high levels of octane (i.e., greater than 100
R+M/2). Both ethanol and MTBE have been used historically to meet the
gasoline oxygen requirements for oxyfuel and RFG. Historically, MTBE was
the predominant oxygenate used in gasoline in the U.S. Over time, MTBE
use has decreased in the U.S, while ethanol use has increased, to the
point where ethanol use now predominates. This trend appears to be
accelerating, to the point where it appears that essentially all MTBE
use will cease in the U.S sometime in 2007.

The impact of oxygenate use on emissions from motor vehicles has been
evaluated since the mid-1980's. Several models of the impact of gasoline
quality on motor vehicle emissions were developed in the early 1990's
and updated periodically since that time. We use the most up-to-date
versions of these models here to estimate the impact of changes in
oxygenate use on emissions. Still, as will be described below,
significant uncertainty exists as to the effect of these gasoline
components on emissions from both motor vehicle and nonroad equipment,
particularly from the latest models equipped with the most advanced
emission controls. Assuming adequate funding, we plan to conduct
significant vehicle and equipment testing over the next several years to
improve our estimates of the impact of these additives and other
gasoline properties on emissions. The results of this testing are not
available for inclusion in this analysis. We hope that the results from
these test programs will be available for reference in the comprehensive
evaluation of the emission and air quality impacts of all the
fuel-related requirements of the Energy Act required by Section 1506. A
draft of this study is required to be completed in 2009. As we discuss
the emission impacts of increased ethanol use below, we identify the
areas where current estimates appear to be the most uncertain and where
we hope to obtain additional data prior to the 2009 study.

**3.1.1 Low Level Ethanol and MTBE-Gasoline Blends**

EPA has developed a number of emission models relating the impact of
gasoline quality on emissions from motor vehicles. In 1993, EPA
published the Complex Model, which predicts the effect of gasoline
quality on VOC, NOx and air toxic emissions from 1990 model year
light-duty motor vehicles (i.e., Tier 0 vehicles). This model is used to
determine refiners' compliance with RFG and anti-dumping standards. The
Complex Model also contains estimates of the impact of gasoline RVP on
non-exhaust VOC emissions. These estimates were taken from the
then-current version of the MOBILE emissions model, MOBILE5.

In 2001, in responding to California's request for a waiver of the RFG
oxygen mandate, EPA performed a new analysis of the impact of gasoline
quality on exhaust VOC and NOx emissions from Tier 0 vehicles. This
analysis included essentially all of the data used to develop the
Complex Model, as well as some additional data developed since 1993. It
also used more advanced statistical tools, such as a mixed statistical
model, which were not available in 1993. These VOC and NOx models are
referred to here as the EPA Predictive Models. Thus, in terms of both
supporting data and modeling tools, the EPA Predictive Models represent
an improvement over the Complex Model, at least for exhaust VOC and NOx
emissions. Because the criteria for granting California a waiver of the
oxygen requirement focused on ozone and PM impacts, EPA did not develop
a similar model for toxics or CO emissions.

In roughly the same timeframe, EPA developed its latest motor vehicle
emission inventory model, MOBILE6. Some of the fuel-emission
relationships from the Complex Model were incorporated into MOBILE6.
These included the effect of selected gasoline properties on exhaust VOC
and NOx emissions and the fraction of VOC emissions represented by
several air toxics (benzene, formaldehyde, acetaldehyde, and
1,3-butadiene). The EPA Predictive Models were not available in time for
their incorporation into MOBILE6. MOBILE6 also contains estimates of the
effect of certain gasoline parameters on CO emissions, namely RVP and
oxygen content. The effect of RVP on non-exhaust VOC emissions contained
in MOBILE6.2 represents an update of the MOBILE5 and Complex Model
estimates.

We desire in this RFS analysis to utilize the most up to date estimates
of the impact of gasoline quality on emissions currently available. No
one model currently contains the most up to date estimates for all the
pollutants of interest. Therefore, we have broken up the remainder of
this sub-section into six parts. The first discusses emissions of VOC
and NOx, as the EPA Predictive Models address these pollutants. The
second discusses CO emissions, as neither the Complex Model nor the EPA
or CARB Predictive Models address this pollutant. The third section
addresses emissions of air toxics, as a combination of models represents
the best estimate of the impact of fuel quality on these emissions. The
fourth section addresses non-exhaust VOC emissions. The fifth section
addresses PM emissions. The sixth section addresses emissions of
aromatic hydrocarbons. The seventh and final section presents the impact
of ethanol and MTBE blending on per mile emissions from gasoline-fueled
motor vehicles.

**3.1.1.1 Exhaust VOC, CO and NOx Emissions**

In this section we evaluate the various models available which predict
the impact of gasoline quality on exhaust VOC, CO and NOx emissions.
Several such models have been developed over the past 15 years. We first
discuss the EPA Complex Model, the EPA Predictive Models and the CARB
Predictive Models due to the wide range of fuel parameters which they
address and their similar form. We next describe the fuel effects
contained in EPA's MOBILE6.2 emission inventory model, as this model
addresses CO emissions, which the other models do not. These models best
predict emissions from Tier 0 vehicles, as most of the emission data
upon which they were based were from these vehicles. A number of fuel
effects test programs which tested later model year vehicles have been
performed since the above emission models were developed. We summarize
these studies below and develop emission projections based on consistent
statistical procedures. Finally, we select which model best predicts the
effect of fuel quality for each pollutant. Due to the uncertainty
involved with predicting the impact of fuel quality on emissions from
Tier 1 and later vehicles, we develop two alternative approaches to
making such predictions for the purpose of this rule.

3.1.1.1.1 EPA Complex Model and CARB and EPA Predictive Models

In 1993, EPA published the Complex model to investigate the effects of
changing gasoline fuel parameters on the exhaust emissions of Tier 0 and
older vehicles. This model predicts the effect of gasoline quality on
exhaust VOC, toxics and NOx emissions and non-exhaust emissions of VOC
and benzene. The Complex Model is used to determine compliance with the
emissions performance requirements for federal RFG by comparing the
predicted emissions of a candidate fuel to that of a baseline fuel for
common baseline vehicle technology. The baseline fuel and the baseline
vehicle technology represent those fuels and vehicles included in the
1990 US light duty vehicle fleet (Tier 0 technology).

In 1999, the state of California petitioned EPA for a waiver of the
oxygen requirement in RFG. The reasoning behind the waiver request
centered on the California Air Resources Board's (CARB) analysis which
showed that reducing the amount of oxygen in RFG would lead to reduced
NOx exhaust emissions. The model that CARB developed to support their
claim was called the Phase 3 predictive model[^93]. This model differed
from the previous version of CARB's predictive model (the Phase 2 model)
in a number of ways. The most significant difference included a
substantially expanded database, mainly for model year 1986 and newer
vehicles, as well as an improved version of the statistical analysis
software package used to develop the model (SAS® PROC MIXED). According
to CARB, the Phase 3 predictive model displays a steeper NOx/Oxygen
response than the Phase 2 Predictive model as a result of eliminating
the RVP by Oxygen term which the previous model had erroneously
included. This caused an increase in the NOx exhaust emissions
predicted, and for many areas this increase would lead to NOx levels
exceeding those set by National Ambient Air Quality Standards (NAAQS).

According to the 1990 Clean Air Act, EPA can waive the RFG oxygen
requirement if it prevents compliance with a NAAQS. In order to properly
perform an environmental impact analysis in response to this waiver
request, EPA considered using both its Complex Model, as well as CARB's
Phase 3 Predictive Model to estimate the impact of gasoline quality on
emissions. The EPA Complex model, while considered statistically robust
due to the large number of vehicles comprising the dataset, was not
considered to be adequate for a number of reasons. First, the Complex
Model was based on an emissions database which did not include several
studies that have since been published. Second, the EPA Complex Model
was developed using a fixed effects statistical modeling approach[^94].
In contrast, both the CARB Phase 2 and 3 models were mixed models,
employing a more sophisticated statistical approach than was available
at the time of development of the Complex model.

EPA also rejected using CARB's Phase 3 Predictive Model in its analysis
of the waiver request. While CARB had developed a very detailed protocol
for developing the Phase 3 model, it rejected the results of this
protocol because the resulting model differed too substantially from the
Phase 2 model. Thus, EPA decided to create its own "predictive models"
for exhaust VOC and NOx emissions which combined the protocols used to
develop the Complex Model with the expanded database and improved
statistical tools which were now available. EPA relied on existing EPA
models for evaporative VOC emission effects. However, these latter
estimates were augmented with recent data indicating that ethanol
increased permeation emissions, as well as the consideration of several
commingling models and associated assumptions about drivers' refueling
behavior.

One main conclusion drawn by EPA in the California Oxygen waiver
analysis was that insufficient data existed at that time to conclusively
determine the response of Tier 1 and newer vehicles to fuel parameters
other than sulfur.[^95] Some data indicated that oxygen increased NOx
emissions from Tier 1 and later vehicles, while other data contradicted
this. Due to this inconsistency, EPA assumed that oxygen did not affect
exhaust VOC, NOx or CO emissions from Tier 1 and later vehicles in its
analysis of CARB's request for a waiver of the RFG oxygen mandate.

None of the three models discussed above (i.e., the Complex Model, the
CARB Predictive Model, and the EPA Predictive Models) address CO
emissions. Historically, this is because the RFG program did not mandate
a specific reduction in CO emissions and the lesser role of CO emissions
in forming ambient ozone. The CARB Predictive Model considers the impact
of fuel oxygen content beyond 2 wt%, but does not address the full range
of oxygen levels on CO emissions, nor the impact of other fuel
parameters. The only EPA model which predicts the impact of fuel quality
on CO emissions is MOBILE6.2, which is discussed in the next section.

3.1.1.1.2 MOBILE6.2

The exhaust emission effects contained in MOBILE6.2 often differ for
normal and high emitting vehicles. They can also vary by model year. As
it is difficult to determine the fraction of emissions coming from each
model year's vehicles in MOBILE6.2, as well as normal and high emitters,
it is not feasible to predict outside of the model how a specific fuel
is going to affect in-use emissions. In addition, the split between
normal and high emitters varies depending on the presence and type of
inspection and maintenance (I/M) program applicable in a particular
local area. Thus, the effect of a specific fuel on emissions can vary to
some degree from one county to another.

In order to quantify the effect of various fuel parameters on exhaust
emissions in MOBLIE6.2 under the conditions existing in the 2012-2020
timeframe, we compared the changes in emissions predicted by the NMIM
modeling described in Chapter 4 of the Draft RIA with the changes in
fuel quality occurring in the ethanol use scenarios. Specifically, we
first determined the percentage change in exhaust VOC, CO and NOx
emissions by county for the base and 7.2 Minimum RFG Use scenarios
evaluated for the NPRM. We then performed a series of linear regressions
of these ratios against the change in fuel RVP, ethanol content and MTBE
content. We did this for the 2012, 2015 and 2020 emission projections
separately. For each combination of county and calendar year, the only
property that changed was fuel quality. All other parameters relevant to
emissions (e.g., the distribution of vehicles by age and class, VMT,
ambient temperature, etc.) were otherwise identical. The results are
summarized in Table 3.1-1.

**Table 3.1-1.** Fuel-Exhaust Emission Effects in MOBILE6.2

  ---------- ------------ ------------------ ----------------- ---------------
             RVP (% /     Ethanol (% / Vol   MTBE (% / Vol%)   Adjusted
             psi)         %)                                   r-Square

  2012                                                         
  (fleet                                                       
  average)                                                     

  VOC        7.1%         -1.1%              -0.7%             0.83

  NOx        0.6%         0.0%               0.0%              0.95

  CO         12.7%        -0.75%             -0.4%             0.36

  2015                                                         
  (fleet                                                       
  average)                                                     

  VOC        7.0%         -1.2%              -0.7%             0.85

  NOx        0.6%         0.0%               0.0%              0.95

  CO         12.7%        -0.73%             -0.4%             0.36

  2020                                                         
  (fleet                                                       
  average)                                                     

  VOC        6.7%         -1.2%              -0.7%             0.87

  NOx        0.6%         0.0%               0.0%              0.95

  CO         12.6%        -0.67%             -0.4%             0.39
  ---------- ------------ ------------------ ----------------- ---------------

For comparative purposes, the effect of RVP, ethanol and MTBE on exhaust
VOC and NOx emissions from the EPA Predictive Models are shown in Table
3.1-2. The base fuel is a typical non-oxygenated, summertime,
conventional gasoline, with 8.7 RVP, 30 ppm sulfur, 32 vol% aromatics,
13 vol% olefins, T50 of 218 F, T90 of 329, and no oxygen.

**Table 3.1-2.**

Fuel-Exhaust Emission Effects per the EPA Predictive Models

  -------- ----------------- ----------------------- ---------------------
           RVP (% / psi)     Ethanol (% / Vol %)     MTBE (% / Vol%)

  VOC      1.1%              -0.16%                  -0.17%

  NOx      1.1%              0.75%                   0.36%
  -------- ----------------- ----------------------- ---------------------

As can be seen, the exhaust emission effects contained in the EPA
Predictive Models differ quite dramatically from those in MOBILE6.2.
Regarding the effect of RVP, both models predict that an increase in RVP
will increase both exhaust VOC and NOx emissions. However, MOBILE6.2
predicts that an increase of 1.0 psi will increase exhaust VOC by
roughly 7%, while the EPA Predictive Models predict only a 1% increase.
Regarding NOx emissions, the EPA Predictive Models predict the larger
effect (1%), while the effect in MOBILE6.2 is smaller (0.6%). While the
ratio of these two effects is significant, the absolute difference
(0.4%) is very small.

Regarding the addition of ethanol, the two models again predict very
different results. MOBILE6.2 predicts roughly 7 times the exhaust VOC
reduction per volume percent of ethanol added, with no increase in NOx.
The EPA Predictive Models project a significant increase in NOx
emissions. The relative differences are similar for the addition of MTBE
to gasoline, though the difference between the two estimates of exhaust
VOC reduction is smaller.

Regarding CO Emissions from Tier 0 vehicles, MOBILE6.2 projects that a
3.5 wt% oxygen fuel (i.e., E10) will reduce CO emissions from normal
emitters by 11% and those from high emitters by 19%.[^96] For Tier 1 and
later vehicles, MOBILE6.2 projects that a 3.5 wt% oxygen fuel (i.e.,
E10) will have no effect on CO emissions from normal emitters and will
reduce CO emissions from high emitters by the same 19% estimated for
Tier 0 vehicles. This latter projection was based on a similar
assumption to those made during EPA's review of California's request for
a RFG oxygen waiver due to the same lack of relevant emission data. We
estimate that the fraction of CO emissions coming from high emitters is
64.8% based on the overall effect of 6.7% in 2020 shown in Table 3.1-1
for 2020, when the fleet is entirely Tier 1 and later vehicles. This
produces a fleet wide CO emission reduction for an E10 blend of 13.8%
for Tier 0 vehicles and 6.7% for Tier 1 and later vehicles. MOBILE6.2
does not project any impact of the other relevant fuel parameters
(aromatics, olefins, T50, and T90) on CO emissions for either Tier 0 or
Tier 1 vehicles.

Since that time, the results of several test programs have been
published. Given the dwindling numbers of Tier 1 vehicles on the road,
these more recent studies have focused on vehicles certified to
California's low emission vehicle (LEV), ultra low emission vehicle
(ULEV), and super ultra low emission vehicle (SULEV) standards, as well
as federal LEV and Tier 2 standards. The results of these more recent
studies, as well as the few available in 2001, are discussed in the
following section.

3.  CRC E-67 Study

3.1.1.1.3.1 Overview

In early 2006, the Coordinating Research Council (CRC) published the
results of their E-67 study investigating the effects of three fuel
parameters, ethanol, T50 and T90, on exhaust emissions from recent model
year vehicles.^2^ The twelve vehicles tested included both cars and
light trucks, certified to California LEV, ULEV and SULEV standards,
with model years ranging from 2001 to 2003. A matrix of twelve (12)
fuels was tested in this program, with varying levels of ethanol, T50,
and T90. Each fuel parameter (ethanol, T50, and T90) was tested at each
of three levels. However, a full factorial matrix of 27 test fuels was
deemed unnecessarily large due to subtle differences between fuels that
may not have yielded statistically significant results, or due to
practical considerations regarding the fuels that could be blended using
existing refinery streams.

The E-67 report presents the results of emission testing for each fuel,
as well as a mixed statistical model created from the emission data. The
model indicates that each of the three fuel parameters always has a
statistically significant effect on both NMHC[^97] and NOx emissions. In
addition, significant interactions between the three fuel parameters are
also often present.

The first step in our analysis of the CRC E-67 model was to compare the
emissions changes predicted by the CRC mixed model to the actual
emissions changes observed for each fuel in the test program. We
calculated average NMHC and NOx emissions over the Federal Test
Procedure for all twelve vehicles on each fuel. The CRC mixed model
predicts the percentage change in emissions for each fuel relative to
another fuel. These changes in predicted exhaust emissions for each fuel
were applied to the measured emissions for fuel "H" in order to create a
set of absolute emission levels for each fuel. We then compared the
emissions predicted by the CRC E-67 model to the measured emission
levels to observe how well the model predicted the effects of each fuel.
The fuel properties of the CRC E-67 test fuels are listed in Table
3.1-3[^98], below, and in greater detail in Table 3A-1 of Appendix 3A.
We selected CRC fuel "H" as the "base" fuel since its properties are the
closest to a national average non-oxygenated conventional gasoline (0%
Ethanol, 215ºF T50, 330 ºF T90). (See Table 2.2-6 in Chapter 2 for data
pertaining to gasoline survey results across the U.S.)

**Table 3.1-3. CRC E-67 Test Program Fuels Properties^a^**

  -------- ------------- --------- ------------- --- ---------- -------- ---------
           **Target                                  **Actual            
           Properties                                Values**            
           for Design                                                    
           Variables**                                                   

  Fuel     T50 (ºF)      T90 (ºF)  Ethanol (%)       T50 (ºF)   T90 (ºF) Ethanol

  A        195           295       0                 195        294      0

  B        195           295       5.7               191        290      5.6

  C        195           330       10                193        329      10.4

  D        195           355       0                 199        355      0

  E        195           355       10                198        352      10.3

  F        215           295       0                 217        295      0

  G        215           295       10                212        291      10.1

  H        215           330       0                 216        327      0.1

  I        215           355       5.7               216        354      5.9

  J        235           330       5.7               237        329      5.9

  K        235           355       0                 236        355      0

  L        235           355       10                233        349      10.5
  -------- ------------- --------- ------------- --- ---------- -------- ---------

^a^See Table 3A-1 in Appendix 3A for detailed properties of all E-67
test fuels

The comparison between predicted and measured NOx emissions is shown in
Figure 3.1-1. The fuels are shown to indicate a trend in ethanol
content, from the lowest levels on the left to the highest levels on the
right. Within a constant level of ethanol content, the fuels are then
shown in order of their level of T50 (lowest again on the left and
highest on the right). The y-axis scale in this figure is set to match
that for NMHC emissions, which will be presented and discussed next.

**Figure 3.1-1. CRC E-67 Predicted vs. Actual Emissions**

Relative NOx Emissions Change from Base (CRC H: 215 T50, 330 T90, 0%
EtOH)

![](media/image54.wmf){width="5.747916666666667in"
height="3.6194444444444445in"}

As shown in Figure 3.1-1, the CRC model for NOx emissions predicts the
general trend in the emission data, which roughly indicates an increase
in NOx emissions with increasing ethanol content. However, the model
clearly does not reflect many of the fuel to fuel differences indicated
by the actual emissions data. One example of this is the change between
fuels G and C -- two 10% ethanol blends with relatively low distillation
temperatures. In changing from fuel G to fuel C, the CRC E-67 model
predicts a 4.3% increase in NOx emissions whereas the actual test data
clearly shows a 9.2% decrease. This likely indicates the existence of
interactions between the fuel parameters which are more complex than
those which could be included in the model. While fuel parameters other
than ethanol, T50, and T90 were held as constant as possible among all
the test fuels, the level of specific compounds (such as toluene or the
various xylenes) could not be held constant. It is possible that some of
these compounds are affecting NOx emissions and confounding the ability
of the model based just on ethanol, T50, and T90 to predict the observed
changes.

Figure 3.1-2 repeats this comparison for NMHC.

**Figure 3.1-2. CRC E-67 Predicted vs. Actual Emissions**

Relative NMHC Emissions Change from Base (CRC H: 215 T50, 330 T90, 0%
EtOH)

![](media/image55.wmf){width="5.747916666666667in"
height="3.6069444444444443in"}

First, it is very apparent that NMHC emissions are much more sensitive
to fuel quality than NOx emissions. The largest increase in NMHC
emissions relative to Fuel H is three times that for NOx emissions.
Except for Fuels A and F (and of course Fuel H), the CRC model generally
under-predicts the measured NMHC data. Directionally, however, the
predicted emissions changes are very consistent with those observed in
the test results. For this dataset at least, the effect of fuel quality
on NMHC emissions are much more predictable than NOx emissions.

The fuels studied in this test program were varied independently at low,
medium, and high levels of T50, T90, and Ethanol. If you include all the
possible linear, quadratic, and interactive terms, there are a total of
possible 10 combinations. The CRC E-67 models included 8 out of the10
possible fixed effects for the NOx, NMHC, and CO models. These terms
were: T50, T90, ethanol (EtOH), T50 squared, T90 squared, EtOH squared,
T50 by EtOH, and T90 by EtOH. The excluded terms were T50 by T90, and
T50 by T90 by EtOH, which CRC excluded from consideration since previous
studies had indicated that these terms had little effect on emissions.

Also, several of the terms that were included in the CRC model had
p-values greater than 0.1, indicating that those terms are less than
marginally significant.[^99] Specifically, the EtOH by EtOH term in the
NMHC model and the T90, T90 by T90 and T90 by EtOH terms in the NOx
model all had p-values above 0.10. In developing both the Complex Model
and the EPA Predictive Models, our procedures would normally exclude the
least significant term. A new regression would then be performed without
this term being included in the model. This process would be repeated
until all the remaining terms were statistically significant.[^100]

We desired to determine how these statistically insignificant terms
might be affecting the predicted emission changes. This, plus the
discrepancies between the CRC E-67 model and the actual emissions data,
especially for the NOx model, prompted us to create our own NOx and NMHC
models using the CRC E-67 dataset. Conducting our own modeling also
provides us with the opportunity to apply a wide range of statistical
tests in order to better understand the role of various fuel parameters
in affecting emissions from these vehicles. The following sections
provide details pertaining to the verification of the CRC model and the
motivation for constructing a new model from this data.

3.1.1.1.3.2. Development of a New Mixed Model: The EPA E-67 Model

Using the E-67 dataset provided by the CRC, EPA first verified the
coefficients and p-values of the CRC E-67 model using the full E-67
dataset (no outliers were removed) with the same 8 fixed fuel effects
that were included by CRC. This was successful and the coefficient and
p-values resulting from this modeling are shown in Table 3.1-4.

**Table 3.1-4. CRC E-67 Model P-Values and C**oefficients

  ---------------- ------------- ----------------- ------------- ----------------- ------------- -----------------
  **CRC E-67**     **NMHC**                        **CO**                          **NOx**       

  **Effect**       **P-Value**   **Coefficient**   **P-Value**   **Coefficient**   **P-Value**   **Coefficient**

  **Intercept**    \<.0001       -3.2942           0.0001        -0.7966           \<.0001       -2.6183

  **T50**          \<.0001       0.0063            0.3099        0.001227          0.8939        -0.00013

  **T50\*T50**     \<.0001       0.000176          0.0428        0.000099          0.2182        -0.00006

  **T90**          0.0541        0.001685          0.0051        -0.0045           0.762         0.00024

  **T90\*T90**     0.0035        0.000058          0.0815        0.000045          0.1163        0.000043

  **EtOH**         0.1124        0.005679          0.0174        -0.01581          0.0504        0.00571

  **EtOH\*EtOH**   0.2816        0.000722          0.0005        0.003118          0.0861        0.001622

  **T50\*EtOH**    0.084         0.000195          0.0182        0.000355          0.0414        -0.00032

  **T90\*EtOH**    0.0004        0.000244          0.0534        0.000174          0.99          -1.19E-06
  ---------------- ------------- ----------------- ------------- ----------------- ------------- -----------------

EPA then created a new model starting with all combinations of T50, T90,
and EtOH along with their squares, cross products, and the interactive
terms T50 by T90 by EtOH for a total of 10 fixed effects. From this
"full model", variables were eliminated in order to improve the fit
statistics between the model and the test data until a "final model" was
created that contained 7 fixed fuel effects for NMHC and CO, and 6 fixed
fuel effects for NOx. Table 3.1-5, on the following page, shows the
p-values and coefficients for the fixed effect terms of each model.

**Table 3.1-5. EPA E-67 Model P-Values and C**oefficients

  -------------------- ------------- ----------------- ------------- ----------------- ------------- -----------------
  **EPA E-67**         **NMHC**                        **CO**                          **NOx**       

  **Effect**           **P-Value**   **Coefficient**   **P-Value**   **Coefficient**   **P-Value**   **Coefficient**

  **Intercept**        \<.0001       -3.2773           0.0002        -0.7684           \<.0001       -2.6418

  **T50**              \<.0001       0.006272          0.5815        0.00066           0.687         -0.00037

  **T50\*T50**         \<.0001       0.000168                                                         

  **T90**              0.0498        0.00172           0.0059        -0.00437          0.7761        0.000224

  **T90\*T90**         0.0039        0.000057                                          0.0735        0.000047

  **EtOH**             0.101         0.005892          0.0111        -0.01726          0.062         0.005393

  **T50\*EtOH**        0.0987        0.000186          \<.0001       0.003843          0.0446        0.001854

  **T90\*EtOH**        0.0002        0.00025           0.0299        0.000178          0.0426        -0.00031

  **T50\*T90**                                         \<.0001       0.000126                         

  **T50\*T90\*EtOH**                                   0.0003        0.000023                         
  -------------------- ------------- ----------------- ------------- ----------------- ------------- -----------------

As shown in Table 3.1-5, the EPA E-67 Model does not include terms with
p-values greater than 0.10 (except for linear terms included in
statistically significant higher order terms). Statistical tests show
that these two models are not significantly different from one another.
The null hypothesis in this case is that EPA E-67 fits the data just as
well as the original CRC E-67 model based on a chi-squared test.
However, based on several fit statistics (AIC, AICC, and BIC) the EPA
E-67 model provides a slightly better fit to the test data than either
the original CRC E-67 model or the full model with all 10 terms
included. The next step is to compare the EPA E-67 model predictions to
both the E-67 data and the predictions of the EPA Predictive Models,
which reflect the emission effects for older vehicles.

Both the EPA E-67 and EPA Predictive models are mixed models that
predict the relative changes in exhaust emissions due to carefully
controlled changes in gasoline quality, including the addition of an
oxygenate such as ethanol. The models are not intended to be accurate at
predicting absolute emission levels, but rather the difference in
emissions when fuel properties are varied. The goal of this analysis is
to determine if the EPA Predictive models, which were developed using
data from Tier 0 and earlier vehicles, predict the same relative changes
in emissions as the Tier 1 vehicles used for the EPA E-67 model.

A key difference between the models is that there are only three fuel
parameters used as inputs for the EPA E-67 model: T50, T90, and ethanol
content. The EPA Predictive Models use these three properties along with
RVP, aromatic content, olefin content, and sulfur content as fuel
parameter inputs to the model.

We ran the EPA E-67 and EPA predictive models with the 12 fuels used in
the CRC test program, inputting the applicable fuel properties used in
each model. Following the same procedure as outlined above, CRC test
fuel H was selected as a "base" fuel in order to compare relative
changes between this fuel and others with varying amounts of ethanol,
T50, and T90. The NOx emissions predicted by the EPA E-67 and EPA
Predictive models, together with the actual E-67 study data, are shown
graphically in Figure 3.1-3, below.

**Figure 3.1-3. EPA E-67 and EPA Predictive vs. Actual Emissions**

Relative NOx Emissions Change from Base (CRC H: 215 T50, 330 T90, 0%
EtOH)

![](media/image56.wmf){width="5.9847222222222225in"
height="3.665277777777778in"}

As shown in Figure 3.1-3, neither model predicts the actual test data
with complete accuracy. The EPA E-67 shows the same general relationship
to the emission data as did the CRC E-67 NOx model. Thus, removing the
statistically insignificant terms had little impact on the relative fit
of the model to the data. The EPA Predictive NOx models, on the other
hand, appear to be primarily sensitive to ethanol, with T50 and T90
playing very limited roles in affecting NOx emissions. In contrast, the
E-67 model shows sensitivities to all three parameters.

Overall, the E-67 study indicates that NOx emissions from recent model
year vehicles (LEVs, ULEVs and SULEVs) are still sensitive to at least
several fuel parameters. As indicated by the inability of either the EPA
E-67 model or the EPA Predictive Models to accurately predict all of the
changes seen in the E-67 data, this study is very valuable in
identifying the continued sensitivity of LEV and cleaner vehicles to
changes in fuel quality.

Figure 3.1-4, below, shows the comparison of NMHC emissions predicted by
the EPA E-67 and Predictive models together with the E-67 study data.

**Figure 3.1-4. EPA E-67 and EPA Predictive vs. Actual Emissions**

Relative NMHC Emissions Change from Base (CRC H: 215 T50, 330 T90, 0%
EtOH)

![](media/image57.wmf){width="5.9847222222222225in"
height="3.729861111111111in"}

From Figure 3.1-4, it is apparent that both models do a better job at
predicting changes in NMHC emissions than was the case for NOx
emissions. The EPA E-67 model is clearly the more accurate of the two
models. However, this is to be expected given it was based on the data
being depicted. The ability of the EPA Predictive Model to predict the
general trend of nearly all the CRC E-67 test fuels is impressive, given
it is based on data from Tier 0 vehicles with 5-10 times the NMHC
emission levels of the vehicles in the E-67 test program. Overall, it
appears that NMHC emissions from LEVs and cleaner vehicles are even more
sensitive to changes in fuel quality than NMHC emissions from Tier 0
vehicles.

The preceding figures illustrate the differences between the models for
all 12 fuels included in the E-67 test program. Some of these fuels are
more practical, or likely to be commercially produced, than others.
Based on the results of AAM fuel surveys presented in Chapter 2,
summertime E10 blends will generally have levels of T50 and T90 that are
about 29 ºF and 7 ºF lower than non-ethanol blends. Thus, it could be
useful to focus on sets of fuels in the CRC E-67 study which reflect
these differences.

The fuel pair which most closely reflects these differences are CRC fuel
"C", a 10 vol% ethanol blend, and CRC fuel "H", a non-oxygenated fuel.
Both fuels have a mid-range level of T90. A second, more complex set of
fuels involve those with higher levels of T90. The CRC "E" fuel contains
10 vol% ethanol and has the high level of T90. However, there is not a
good match to this fuel which is non-oxygenated. Yet two non-oxygenated
fuels ("D" and "K"), when considered together, represent a reasonable
match to fuel "E. Fuel D reflects no change in T50 relative to fuel E,
while fuel K reflects slightly more than a typical drop in T50. Thus, by
averaging the emissions for fuels K and D and then comparing this to the
emissions with fuel E, we are able to generate a second direct
indication of the impact of ethanol blending on emissions from these low
emitting vehicles. The general properties of these five fuels and the
emissions changes predicted by the two models are shown in Table 3.1-6
below.

**Table 3.1-6.**

**Predicted NOx and NMHC Emissions Changes**

**for EPA E-67and Predictive Models**[^101]

  ------------------------------- ------------ -------------- -------------
  **Fuel Changes**                **H to C**   **K to E**     **D to E**

  T50 (ºF)                        Mid - Low    High -- Low    Low - Low

  T90 (ºF)                        Mid - Mid    High -- High   High - High

  Oxygen (vol%)                   0 - 10       0 -- 10        0 - 10

                                                               

  **Change in Emissions**                                      

  EPA Predictive Model NOx        9.5%         8.4%           9.4%

  EPA E-67 NOx                    11.0%        5.8%           10.4%

  Actual E-67 Data                3.3%         1.6%           -1.8%

                                                               

  EPA Predictive Model NMHC       -3.7%        -11.1%         7.3%

  EPA E-67 NMHC                   -3.8%        -17.3%         8.8%

  Actual E-67 Data                -6.3%        -21.0%         9.2%
  ------------------------------- ------------ -------------- -------------

As shown in Table 3.1-6, the two models agree quite closely on the
effect of fuel C relative to fuel H on both NMHC and NOx emissions.
However, that said, both models tend to overestimate the impact of fuels
C and E on NOx emissions and underestimate the impact of these fuels on
NMHC emissions.

Regarding the comparison of fuel E to fuels K and D, the two models tend
to agree on the effect of fuel E to fuel D, but differ more with respect
to the effect of fuel E to fuel K, particularly for NMHC emissions. One
reason for the difference in the latter comparison is that the EPA E-67
NMHC model is more sensitive to very high levels of T50 than the EPA
Predictive Model for NMHC.

Overall, the results of the E-67 study suggest that our assumption that
Tier 1 and later vehicles would not be sensitive to fuel parameters such
as ethanol, T50 ad T90 (made in our consideration of California's
request for a waiver of the RFG oxygen requirement) may not be valid.
The observation that NMHC emissions from LEVs, *et. al*. could actually
be more sensitive than Tier 0 vehicles (on a percentage basis),
particularly challenges our assumption. While the effect of fuel quality
on NOx emissions from low emitting vehicles is still not clear from the
recent test data, these emissions do appear to be sensitive to fuel
quality.

3.1.1.1.3.3. Effect of Oxygen Content Alone on Emissions: CRC E-67 Study

In order to provide an estimate of fuel-emission effects comparable to
those of the other studies of the effect of fuel quality on emissions
from LEV and later vehicles, we selected three pairs of fuels tested in
the CRC E67 study where the only change in fuel properties was oxygen
content. One pair of fuels compared non-oxygenated fuel versus E6 (fuels
A and B). Two pairs of fuel compared non-oxygenated fuel versus E10
(fuels D and E and fuels F and G). We applied mixed univariate
statistical models to the logarithm of the emission data from fuels A
and B and to fuels D, E, F and G, with vehicle as a random variable and
fuel type as a fixed variable. Table 3.1-7 presents the results in terms
of the percentage change in emissions between the non-oxygenated fuel
and the ethanol blend in each pair.

**Table 3.1-7.**

**Matched Fuel Pair Results from the CRC E-67 Study: Effect of Oxygen
Alone**

+-------------------------------+-------------+------------+----------+
|                               | NMHC        | CO         | NOx      |
+-------------------------------+-------------+------------+----------+
|                               | \% Change   | \% Change  | \%       |
|                               |             |            | Change   |
+-------------------------------+-------------+------------+----------+
| 6 vol% ethanol vs. no         | -4.5%\*     | -7.5%      | 2.6%     |
| oxygenate                     |             |            |          |
|                               |             |            |          |
| (Fuels A and B)               |             |            |          |
+-------------------------------+-------------+------------+----------+
| 10 vol% ethanol vs. no        | **4.7%**    | **-18.1%** | **9.5%** |
| oxygenate                     |             |            |          |
|                               |             |            |          |
| (Fuels D, E, F and G)         |             |            |          |
+-------------------------------+-------------+------------+----------+

\* Bold type indicates the difference was significant at a 90%
confidence level.

As can be seen, the addition of 6 vol% ethanol increased NOx emissions,
while decreasing NMHC and CO emissions. None of the effects were
statistically significant at a 90% confidence level. The addition of 10
vol% ethanol decreased CO emissions, while increasing NMHC and NOx
emissions. All three pollutant effects were statistically significant at
a 90% confidence level.

4.  AAM-AIAM Sulfur and Oxygenate Study

AAM, together with AIAM and Honda, performed a test program in 2001 to
evaluate the effect of fuel sulfur and oxygen on emissions of CARB Tech
5 vehicles (i.e., LEV and later vehicles)[^102]. This program was
performed at the request of the CARB in conjunction with the MTBE ban
and new Phase 3 Cleaner Burning Gasoline regulations. The first of the
program's two distinct goals was to evaluate the emissions of very low
sulfur fuels at 1, 30, and 100 ppm sulfur, with other fuel parameters
held constant. Part two was to compare the emissions effects of MTBE and
ethanol blended fuels to non-oxygenated fuel, again with other
parameters held as constant as possible. The fuel specifications for the
oxygenated fuel matrix are shown in Table 3.1-8. (The sulfur-related
testing is not relevant here, so the fuels which only reflect a change
in sulfur are not shown.)

**Table 3.1-8.**

**AAM Fuel Properties for Oxygenated Test F**uels

  --------------------- -------------- --------------- -------------------
  **Test Fuel**         **Non-Oxy**    **MTBE**        **E10**

  **RVP (psi)**         7.1            6.9             7.1

  **Arom (vol.%)**      24.6           22              21.6

  **Olefins (vol.%)**   4.6            3.4             4.1

  **T50 (ºF)**          210            202             205

  **T90 (ºF)**          297            294             291

  **MTBE/EtOH (vol.%)** 0              11.3            11.5

  **Sulfur (ppm)**      35             31              28
  --------------------- -------------- --------------- -------------------

The sulfur effects portion of this study tested 13 TLEV, LEV, and ULEV
vehicles while the oxygenate phase of the study used 8 and 2 of phase
1's LEV and ULEV vehicles, respectively. These vehicles were a mixture
of light and medium duty passenger cars and trucks of undisclosed make
and model year. The FTP 75 was selected as the drive cycle, with
regulated data collected both at the tailpipe and engine out for a
subset of vehicles to evaluate catalyst efficiency.

The sulfur related testing found a clear relationship between the level
of sulfur in the fuel and the natural log of emissions (CO, NOx and
NMHC). Bag weighted emissions of NOx, CO, and HC emissions were reduced
by 16%, 12%, and 11% (respectively) when fuel sulfur levels were reduced
from 30 to 1 ppm. The effects found in the oxygenate portion of the
study were less clear, since some results were not statistically
significant. Average bag weighted emissions of HC and CO tended to
decrease with increasing oxygen content. Both oxygenated fuels showed a
decrease in NOx emissions, with MTBE having slightly lower emissions
than ethanol. The details of the statistical procedures applied to the
data were not described in the documentation.

In order to provide a consistent basis for comparing the results of this
study to the other studies of the effect of oxygenate on LEV and later
vehicle emissions, we applied a mixed univariate statistical model to
the logarithm of the emission data from the AAM-AIAM oxygenate study,
with vehicle as a random variable and fuel as a fixed variable. Table
3.1-9 presents the results in terms of the percentage change in
emissions between the 11 vol% MTBE blend, the 11 vol% ethanol blend and
the non-oxygenated fuel.

**Table 3.1-9.**

**Fuel Effects from the AAM-AIAM Oxygenate Study: EPA Mixed Model**

  --------------------------------- --------------- ------------ ------------
                                    NMHC            CO           NOx

                                    \% Change       \% Change    \% Change

  11 vol% MTBE vs. no oxygenate^a^  **-15.3%**^b^   **-23.7%**   **-12.7%**

  11 vol% ethanol vs. no oxygenate  **-12.6%**      **-24.6%**   **-6.6%**

  11 vol% ethanol vs. 11 vol% MTBE  -1.4%           -5.7%        **25.2%**
  --------------------------------- --------------- ------------ ------------

^a^ Fewer vehicles were tested on the MTBE blend.

^b^ Bold type indicates the difference was significant at a 90%
confidence level.

As can be seen in Table 3.1-9, the addition of 11 vol% MTBE was found to
significantly reduce the emissions of all three pollutants by 10% or
more relative to the non-oxygenated California RFG. The addition of 11
vol% ethanol was also found to significantly reduce the emissions of all
three pollutants relative to the non-oxygenated California RFG. However,
the reductions were smaller than those of the MTBE blend for all three
pollutants. Also, the reduction in NOx emissions was not statistically
significant at a 90% confidence level. When we compared the emissions
with the ethanol blend to those with the MTBE blend, we found that the
increases were all statistically significant at a 90% confidence level.

Based solely on this single study of seven vehicles and three fuels, it
appears that adding ethanol to a severely reformulated gasoline while
holding other properties constant tends to reduce NMHC, CO and NOx
emissions. However, replacing MTBE with the same volume of ethanol tends
to increase these emissions. Focusing on just this one study, the effect
of increased ethanol use could differ in RFG areas, where MTBE has
traditionally been used, and conventional fuel areas, where no oxygenate
has traditionally been used. However, this one study is not a sufficient
basis to draw a firm conclusion regarding the effect of ethanol blending
on exhaust emissions.

5.  ExxonMobil Study of Oxygenate Type and Content

In the fall of 1999, ExxonMobil (Mobil Oil at that time) conducted a
test program to investigate the emissions effects of MTBE and ethanol on
LEV and ULEV vehicles. The information which follows was made publicly
available on the CARB website[^103] and is taken directly from a
presentation posted on that webpage. The vehicles tested are listed in
Table 3.1-10.

**Table 3.1-10.** Vehicles Tested in 1999 ExxonMobil Study

  ------------------------------ ------------------------------- -------------------------- --------------------------
                                 **Emissions**                                              **Fuel**

  **[Make/Model]{.underline}**   **[Calibration]{.underline}**   **[Engine]{.underline}**   **[System]{.underline}**

  1999 Dodge Stratus             LEV                             2.4L                       MFI

  1999 Chevrolet Malibu          LEV                             3.1L                       PFI

  1999 Mazda Protégé             ULEV                            1.6L                       MPF

  1999 Ford Crown Victoria       LEV                             4.6L                       SFI

  1998 Honda Accord              ULEV                            2.3L                       MFI
  ------------------------------ ------------------------------- -------------------------- --------------------------

Four test fuels were developed with varying MTBE and ethanol content,
with other parameters controlled as tightly as possible. The fuel
specifications are listed below in Table 3.1-11.

**Table 3.1-11.** ExxonMobil Test Fuel Specifications

  ---------------------- ---------------------- ---------------------- ---------------------- ----------------------
  **Test Fuel**          **[M1]{.underline}**   **[M2]{.underline}**   **[M3]{.underline}**   **[M4]{.underline}**

  **RVP, psi**           6.5                    6.4                    7.3                    7.2

  **T50 (ºF)**           192                    191                    198                    203

  **T90 (ºF)**           279                    272                    274                    282

  **MTBE (vol%)**        0                      10                     0                      0

  **Ethanol (vol%)**     0                      0                      7                      10

  **Aromatics (vol%)**   21                     21                     21                     21

  **Olefins (vol%)**     \<1                    \<1                    \<1                    \<1

  **Oxygen (wt%)**       0                      1.82                   2.436                  3.48
  ---------------------- ---------------------- ---------------------- ---------------------- ----------------------

Vehicles were tested in duplicate over the FTP 75 drive cycle, and
composite weighted emissions for regulated pollutants reported. The
average results for all vehicles, based on least squares means from the
analysis of variance, is shown in Table 3.1-12., below. The oxygenate
effect is the percent change in emissions between a given fuel and the
base fuel M1.

**Table 3.1-12. Average Exhaust Emissions: ExxonMobil Study** (all
vehicles)

  ------------------------ ----------------------------- ---------------------- ---------------------- ----------------------- ---------------------- ---------------------- -----------------------
                                                         **[Exhaust Emissions,                                                 ** **                  **[Oxygenate Effect    ** **
                                                         g/mi]{.underline}**                                                                          ^(a)^]{.underline}**   

  **[Fuel]{.underline}**   **[Oxygenate]{.underline}**   **[HC]{.underline}**   **[CO]{.underline}**   **[NOx]{.underline}**   **[HC]{.underline}**   **[CO]{.underline}**   **[NOx]{.underline}**

  M1                       None                          0.058                  0.70                   0.187                                                                  

  M2                       10% MTBE                      0.058                  0.72                   0.198                   0%                     3%                     6%

  M3                       7% EtOH                       0.059                  0.66                   0.213                   1%                     -6%                    **14%**

  M4                       10% EtOH                      0.061                  0.66                   0.239                   5%                     -6%                    **28%**
  ------------------------ ----------------------------- ---------------------- ---------------------- ----------------------- ---------------------- ---------------------- -----------------------

\(a\) Bold font represents statistically significant (α=0.1)

The bold numbers in the above table represent statistically significant
differences in emissions at the 90% confidence level. Only the effect of
ethanol on NOx emissions was found to be statistically significant while
the effects on other criteria pollutants were not. As was the case for
the AAM-AIAM study, the presentation to CARB did not describe the
statistical analysis applied to the detail in sufficient detail to
replicate the results. Thus, we applied a mixed univariate statistical
model to the logarithm of the emission data from the ExxonMobil
oxygenate study, with vehicle as a random variable and fuel type as a
fixed variable. Because of the greater number of fuels in this study, we
applied two different types of mixed models. One set of models compared
emissions between various pairs of fuels (e.g., M1 and M2). We applied
five models of this type. A sixth model used oxygen content as the fuel
variable and considered the significance of the square of oxygen
content, as well. The upper half of Table 3.1-13 presents the results in
terms of the percentage change in emissions between the various fuel
pairs. The bottom half of Table 3.1-13 presents the estimated emission
effects for 2.1 wt% and 3.5 wt% oxygen fuels using the results of the
fuel oxygen content based model.

**Table 3.1-13**. Fuel Effects from the ExxonMobil Oxygenate Study

  ------------------------------- -------------- ------------- -----------
                                  THC            CO            NOx

                                  \% Change      \% Change     \% Change

  Two Fuel Comparisons                                         

  10 vol% MTBE vs. no oxygenate   -2.5%^a^       -5.1%         -0.4%

  7 vol% ethanol vs. no oxygenate -0.9%          **-11.3%**    9.3%

  10 vol% ethanol vs. no          3.6%           **-11.6%**    **20.3%**
  oxygenate                                                    

  10 vol% ethanol vs. 10 vol%     **6.3%**       -7.6%         **21.1%**
  MTBE                                                         

  Oxygen Content model                                         

  2.1 vol% Oxygen                 **-2.0%**      **-9.1%**     **2.9%**

  3.5 vol% Oxygen                 **3.5%**       **-15.2%**    **17.3%**
  ------------------------------- -------------- ------------- -----------

^a^ Bold type indicates the difference was significant at a 90%
confidence level.

As can be seen, the results of our statistical analysis differ from
those performed by ExxonMobil. This may have been due to our focus on
the logarithm of emissions or the use of a mixed model. As shown in
Table 3.1-13, the addition of 10 vol% MTBE was found to reduce the
emissions of all three pollutants relative to the non-oxygenated
California RFG. While the differences tended to be substantial in
magnitude on average, they were not statistically significant at a 90%
confidence level. The addition of 7 vol% ethanol to the non-oxygenated
California RFG (with an increase of roughly 1.0 psi RVP) was also found
to reduce the emissions of all three pollutants. Again, the reductions
in NMHC and NOx emissions were not statistically significant. While the
reduction in CO emissions was slightly smaller on average than that for
MTBE, the effect was more consistent across vehicles and statistically
significant at a 90% confidence level. The addition of 10 vol% ethanol
to the non-oxygenated California RFG (again with an increase of roughly
1.0 psi RVP) was also found to reduce emissions of NMHC and CO, but
increased NOx emissions slightly. Like that for the 7 vol% ethanol
blend, only the CO emission effect was statistically significant at a
90% confidence level. Finally, the substitution of 10 vol% ethanol for
10 vol% MTBE was found to increase emissions of THC and NOx
substantially, while reducing CO emissions slightly. None of the effects
were significant at a 90% confidence level, though the NOx increase was
nearly so (e.g., 89% confidence).

With respect to the model using oxygen content to describe the four
fuels in the study, the effect of oxygen on emissions was found to be
statistically significant for all three pollutants. In addition, the
square of the oxygen content was statistically significant at the 90%
confidence level for THC and NOx emissions. The predictions shown in
Table 3.1-13 utilize the square of oxygen content for these two
pollutants, but not for CO emissions. As can be seen, a 2.1 wt% oxygen
fuel (e.g., an 11 vol% MTBE or 6% ethanol blend) is predicted to
decrease THC and CO emissions, but increase NOx emissions. A 3.5 wt%
oxygen fuel (e.g., a 10% ethanol blend) is predicted to decrease CO
emissions, but increase THC and NOx emissions.

The two modeling approaches produce markedly different predictions for a
10 vol% ethanol blend, especially for THC and NOx. The direct comparison
of the clear fuel and the E10 blend shows the E10 blend to reduce THC
and NOx slightly. In contrast, the oxygen content approach predicts that
the E10 blend will increase both THC and NOx emissions. This underlines
the need for more data to accurately predict the emission effect of
various types and levels of oxygenate on modern vehicles.

6.  Toyota Study of MTBE and Ethanol Blends

Toyota Motor Company of Japan performed a small study, presented in
2000, evaluating the effect of MTBE and ethanol blended gasoline on
exhaust emissions of nine LEV, TLEV, ULEV vehicles.[^104] The model
years for these vehicles were not presented.. Three test fuels were
evaluated in this study: Phase 2 California RFG containing MTBE, a
matched RVP E10 and a higher RVP E10. However, exhaust emission testing
was only performed on the MTBE fuel and the higher RVP ethanol fuel. The
fuel properties for these test fuels are listed in Table 3.1-14, below:

**Table 3.1-14. Fuels Tested in the Toyota Oxygenate Test** Program

+-------------+-----------+---------------------+---------------------+
| **Fuel**    | **MTBE    | **Matched RVP       | **Higher RVP        |
|             | Blend**   | Ethanol Blend**     | Ethanol Blend**     |
| **          |           |                     |                     |
| Parameter** |           |                     |                     |
+-------------+-----------+---------------------+---------------------+
| **RVP       | 6.8       | 7                   | 7.6                 |
| (psi)**     |           |                     |                     |
+-------------+-----------+---------------------+---------------------+
| **Arom      | 24        | 23                  | 24.1                |
| (vol.%)**   |           |                     |                     |
+-------------+-----------+---------------------+---------------------+
| **Olefins   | 5         | 5                   | 3.4                 |
| (vol.%)**   |           |                     |                     |
+-------------+-----------+---------------------+---------------------+
| **T50       | 156       | 208                 | 212                 |
| (ºF)**      |           |                     |                     |
+-------------+-----------+---------------------+---------------------+
| **T90       | 290       | 294                 | 297                 |
| (ºF)**      |           |                     |                     |
+-------------+-----------+---------------------+---------------------+
| **MTBE      | 11.1      | 0                   | 0                   |
| (vol.%)**   |           |                     |                     |
+-------------+-----------+---------------------+---------------------+
| **EtOH      | 0         | 11.2                | 8.9                 |
| (vol.%)**   |           |                     |                     |
+-------------+-----------+---------------------+---------------------+
| **Sulfur    | 30        | 29                  | 30                  |
| (ppm)**     |           |                     |                     |
+-------------+-----------+---------------------+---------------------+

As can be seen in the above table, aromatics, olefins, and sulfur were
held relatively constant while other parameters varied. The emission
test cycle used was not stated. We assume it was the FTP 75 test. Only
regulated emissions results are provided.

The study found that, on average across all vehicles and tests, NOx
emissions increased by 5% for E10-B relative to MTBE. Correspondingly,
CO emissions were reduced by 6% and NMHC emissions were decreased by
0.6% for E10-B relative to MTBE.

Again for comparison purposes, we applied a mixed univariate statistical
model to the logarithm of the emission data from the Toyota oxygenate
study, with vehicle as a random variable and fuel type as a fixed
variable. Table 3.1-15 presents the results in terms of the percentage
change in emissions between the 11 vol% MTBE and the 9 vol% ethanol
blends.

> **Table 3.1-15**. Fuel Effects from the Toyota Oxygenate Study

  ------------------------------- -------------- ------------- -----------
                                  NMHC           CO            NOx

                                  \% Change      \% Change     \% Change

  9 vol% ethanol vs. 11 vol% MTBE -0.8% ^a^      -6.1%         3.8%
  ------------------------------- -------------- ------------- -----------

^a^ Bold type indicates the difference was significant at a 90%
confidence level.

As can be seen, the substitution of 9 vol% ethanol for 11 vol% MTBE in a
RFG-type blend was found to reduce the emissions of NMHC and CO, but
increase NOx emissions. None of the emission effects was found to be
statistically significant at a 90% confidence level.

Based solely on this single study of nine vehicles and three fuels, it
appears that replacing MTBE with roughly the same volume of ethanol in
reformulated gasoline while holding other properties constant (except
RVP) tends to reduce NMHC and CO, and increases NOx emissions. It is not
possible to separate the effect of ethanol from RVP in this study.

7.  Mexican Petroleum Institute Fuel-Emission Effects Study

In 2006, the Instituto Mexicano del Patroleo (hereafter referred to as
Mexican Petroleum Institute) published a paper on a recently conducted
research program investigating the effects of gasoline properties on
exhaust and evaporative emissions on 30 light duty cars and trucks,
ranging in model year from 1993 to 2002.[^105] The fuel quality
parameters investigated include RVP, oxygen, Sulfur, olefins, aromatics,
and distillation parameters. The results of this study were used to
develop a statistical model for predicting emissions based on fuel
quality for use in guiding national air quality improvement program
policy. In their analysis, a comparison was made between the "predictive
model," developed based in this test data, and EPA's Complex model. The
end result is a general qualitative agreement with the EPA Complex
model, with some quantitative differences pertaining to the vehicles and
fuels used in each model's development. Selected properties of the test
fuels are shown below in Table 3.1-16.

**Table 3.1-16**. Fuel Tested in the Mexican Petroleum Institute Fuel
Effects Study

  ---------- ------------- ----------- ------------ ----------- --------- -------- -------- ----------
  **Fuel**   **Aromatics   **Olefins   **Oxygen**   **Benzene   **RVP     **T50    **T90    **Sulfur
             (vol%)**      (vol%)**    ^a^          (vol%)**    (psi)**   (ºF)**   (ºF)**   (ppm)**
                                       **(wt%)**                                            

  **1**      19.1          6.6         0            0.86        6.8       224      325      411

  **2**      19.3          6.6         0.98         1.09        6.8       222      324      412

  **3**      18.8          6.5         2.03         0.9         6.9       216      324      406

  **4**      20.6          6.7         2.1          1.13        7         227      326      386

  **5**      19            6.6         1            1.15        8.6       213      323      423

  **6**      18.7          6.9         1.03         0.98        10.7      199      325      387

  **7**      40.2          6.9         0.98         2.26        6.6       233      324      402

  **8**      20.7          15          1.07         1.31        10.9      197      322      415

  **9**      35.8          15.5        1.06         1.25        10.8      209      326      403

  **10**     19.8          5           1.03         0.6         6.6       220      335      89

  **11**     20            7.4         0.98         0.8         6.7       223      321      209

  **12**     19.8          6.1         1.05         0.75        6.6       222      321      817

  **L-S**    40.3          4.8         1.14         1.06        8.1       232      324      34

  **Ref**    28            13.5        0.34         1.14        8.9       207      332      724

  **ZM**     24.1          9           1.21         1           7.7       213      326      403
  ---------- ------------- ----------- ------------ ----------- --------- -------- -------- ----------

^a^ Oxygenate is MTBE, except fuel 4 (Ethanol)

The base fuel from which other fuel parameters were varied to form
additional fuels is indicated as fuel 2 in the above table, and is a
low-level MTBE fuel blended to represent a composite average of all
brands of commercially available Mexican gasoline. The reference fuel
for comparative fuel effects purposes, fuel "Ref", is a fuel blended to
have refinery average levels of sulfur, benzene, RVP, aromatics,
olefins, and distillation properties for the year 2000.

The technology groups investigated were referred to as either "Tier 0"
and "Tier 1" vehicles, which is a bit of a misnomer as these vehicles
were not equipped with on-board diagnostic equipment (OBD), nor were
they subject to emissions durability standards[^106]. Rather, each
technology class acts as a surrogate for emission control technologies
with the certification standards shown in Table 3.1-17.

**Table 3.1-17**. Certification Standards of Test Vehicles by Technology
Class

  -------------- ------------ ----------- ----------- ------------ ----------
  **Technology   **\# of      **Model     **CO        **NMHC       **NOx
  class**        Vehicles**   Years**     (g/km)**    (g/km)**     (g/km)**

  \"Tier 0\"     12           1993-1998   2.1         0.25 (THC)   0.62

  \"Tier 1\"     14           1999-2002   2.1         0.156        0.25
  -------------- ------------ ----------- ----------- ------------ ----------

Vehicles were tested on a chassis dynamometer over the FTP-75 test.
Regulated emissions, as well as speciated hydrocarbons and carbonyls,
were collected for each test. The procedures and statistical methods
employed to develop the predictive model for this test program were
similar to those used to construct the complex models for exhaust VOC
and NOx emissions.

The emissions test results were separated into "Tier 0" and "Tier 1"
categories, and reported as mean emissions rates for all vehicles of
that type on each test fuel. The natural logarithm of emissions was then
regressed to develop a predictive model, using a statistical approach
that is "similar to the techniques used to construct the complex model
for exhaust VOC and NOx emissions" (Schifter et al, 1275). The report
then goes into details on the experimental vs. model predicted results,
as well as a validation of their model which will not be discussed here.
The model predicted percent changes in emissions (for both vehicle
fleets) for the Mexican Petroleum Institute predictive model and EPA
Complex model are shown in Figures 3.1-5 and 3.1-6, below.

**Figure 3.1-5. Emissions Effects Predicted by EPA Complex Model**

![](media/image58.wmf){width="6.252777777777778in"
height="2.5395833333333333in"}

**Figure 3.1-6. Emissions Effects Predicted by Mexican Petroleum
Institute** **"Predictive" Model**

![](media/image59.wmf){width="6.439583333333333in" height="2.475in"}

The values above represent average emissions across both vehicle
technology fleets considered together, and with the industry average
fuel ("Ref") as a baseline for predicted changes in emissions. As you
can see, their model predicts an even greater reduction in exhaust THC
and NOx emissions with ethanol (and MTBE) than the complex model, along
with directionally inconsistent results for fuels with high aromatics,
RVP, and olefins. This is partly due to differences in the fuels and
vehicles used to develop each model (along with the properties of the
base fuel selected) and speaks to the fact that there is a high degree
of uncertainty and sensitivity to consider when extrapolating the
results of a fuel effects model to the larger vehicle and fuel
population.

As with the other studies, we applied a mixed univariate statistical
model to the logarithm of the emission data from the Mexico fuel study,
with vehicle as a random variable and fuel type as a fixed variable. We
restricted the vehicles to those which had NOx emissions of 0.20 g/mi or
less on the low sulfur fuel (30 ppm) included in the test matrix. This
resulted in the inclusion of seven vehicles (numbers 9, 15, 25, 27, 28,
29, and 31). Table 3.1-18 presents the results in terms of the
percentage change in emissions between the non-oxygenated base fuel, the
5.5 vol% and 11 vol% MTBE blends and the 6 vol% ethanol blend.

**Table 3.1-18**. Fuel Effects from the Mexico Fuels Study

  ------------------------------- -------------- ------------- -----------
                                  NMHC           CO            NOx

                                  \% Change      \% Change     \% Change

  5.5 vol% MTBE vs. no oxygenate  11.1% ^a^      -5.5%         -3.1%

  11 vol% MTBE vs. no oxygenate   **-11.1%**     -9.7%         10.0%

  6 vol% ethanol vs. no oxygenate 3.9% ^a^       -6.3%         **27.2%**

  6 vol% ethanol vs. 6 vol% MTBE  -5.8%          -0.9%         **32.8%**

  6 vol% ethanol vs. 11 vol% MTBE **19.5%**      4.0%          14.2%
  ------------------------------- -------------- ------------- -----------

^a^ Bold type indicates the difference was significant at a 90%
confidence level.

As can be seen, the addition of 5.5 vol% MTBE to a low-RVP type fuel at
constant RVP was found to increase the emissions of NMHC and CO, and
decrease NOx emissions. The addition of 11 vol% MTBE to a low-RVP type
fuel at constant RVP was found to increase the emissions of NOx and CO,
and decrease NMHC emissions. Only the NMHC reduction was found to be
statistically significant at a 90% confidence level. The addition of 6
vol% ethanol to a low-RVP type fuel at constant RVP was found to reduce
the CO emissions, and increase NMHC and NOx emissions. Only the NOx
emission increase was statistically significant at a 90% confidence
level.

The substitution of 6 vol% ethanol for 11 vol% MTBE at constant RVP and
oxygen content was found to decrease NMHC emissions and increase NOx
emissions. CO emissions were essentially unchanged. Only the NOx
emission increase was statistically significant at a 90% confidence
level. The substitution of 10 vol% ethanol for 11 vol% MTBE was found to
increase emissions of all three pollutants. Only the NMHC emission
increase was statistically significant at a 90% confidence level.

Based solely on this single study of six vehicles and three fuels, it
appears that either MTBE or ethanol blends with roughly 2 wt% oxygen
increase NOx emissions, while the effect on NMHC and CO emissions are
inconsistent. Substituting ethanol for MTBE at the same oxygen content
appears to increase NMHC, CO and NOx emissions.

8.  Overview of LEV and Later Vehicle Studies

The differences in the details of the various studies prevent a simple
quantitative comparison of their results. However, we have performed a
qualitative comparison by simply determining whether the study found an
increase or a decrease in emissions of 2% or more and whether the effect
was statistically significant at 90% confidence or not. The results of
this determination are shown in Table 3.1-19 below for a number of fuel
pairs.

**Table 3.1-19**. Summary of Oxygenate Emission Effects: LEV and Later
Vehicles

+-------+-----------+-----------+-----------+-----+-------+-----------+
|       | THC/NHMC  |           | CO        |     | NOx   |           |
+-------+-----------+-----------+-----------+-----+-------+-----------+
|       | Lower     | Higher    | Lower     | Hig | Lower | Higher    |
|       |           |           |           | her |       |           |
+-------+-----------+-----------+-----------+-----+-------+-----------+
| 10/11 | **A       |           | **A       |     | **    |           |
| vol%  | AM-AIAM** |           | AM-AIAM** |     | AAM-A |           |
| MTBE  |           |           |           |     | IAM** |           |
|       | *         |           | Mexico    |     |       |           |
|       | *Mexico** |           |           |     | M     |           |
|       |           |           | E         |     | exico |           |
|       | Exx       |           | xxonMobil |     |       |           |
|       | onMobil\* |           |           |     |       |           |
+-------+-----------+-----------+-----------+-----+-------+-----------+
| E6/E7 | Mexico    |           | Mexico    |     |       | *         |
|       |           |           |           |     |       | *Mexico** |
|       | CRC E67   |           | **Exx     |     |       |           |
|       |           |           | onMobil** |     |       | E         |
|       |           |           |           |     |       | xxonMobil |
|       |           |           | CRC E67   |     |       |           |
|       |           |           |           |     |       | CRC E67   |
+-------+-----------+-----------+-----------+-----+-------+-----------+
| E1    | **A       | **CRC     | **A       |     | **    | **Exx     |
| 0/E11 | AM-AIAM** | E67**     | AM-AIAM** |     | AAM-A | onMobil** |
|       |           |           |           |     | IAM** |           |
|       |           | E         | **Exx     |     |       | **CRC     |
|       |           | xxonMobil | onMobil** |     |       | E67**     |
|       |           |           |           |     |       |           |
|       |           |           | **CRC     |     |       |           |
|       |           |           | E67**     |     |       |           |
+-------+-----------+-----------+-----------+-----+-------+-----------+
| E     | Toyota    | **Exx     | E         |     | T     | **A       |
| 10/11 |           | onMobil** | xxonMobil |     | oyota | AM-AIAM** |
| vs.   | AAM-AIAM  |           |           |     |       |           |
| MTBE  |           |           | AAM-AIAM  |     |       | **Exx     |
|       |           |           |           |     |       | onMobil** |
+-------+-----------+-----------+-----------+-----+-------+-----------+

Starting with MTBE, none of the three studies which tested both
non-oxygenated and MTBE fuels found MTBE to increase the emissions of
any of the pollutants. One to two studies found statistically
significant reductions in THC/NMHC, CO and NOx emissions with the use of
MTBE.

Three studies tested a non-oxygenated fuel and a 6-7 vol% ethanol blend.
Ethanol blending at this level likely reduces CO emissions and increases
NOx emissions. Two of the three studies showed an increase in NMHC
emissions, but neither result was statistically significant at a 90%
confidence level. Thus, the effect of E6 on NMHC emissions is
particularly unclear.

Three studies tested a non-oxygenated fuel and a 10-11 vol% ethanol
blend. Ethanol blending at this level likely reduces CO emissions, as
all three studies showed a statistically significant reduction. Two of
three studies found an increase in NOx emissions, while one found a
decrease, all statistically significant at a 90% confidence level.
Again, the effect of ethanol blending on exhaust NMHC emissions is not
clear. Two of three studies found an increase in THC/NMHC emissions,
while one found an increase. The results of one of the two studies
finding an increase and those of the study finding a decrease were
statistically significant at a 90% confidence level.

Finally, three studies tested both MTBE and 10-11 vol% ethanol blends.
Ethanol blending at this level appears to reduce CO emissions relative
to MTBE. The effect on the other two pollutants is less clear. Two of
three studies found a decrease in THC/NMHC emissions, though the one
study finding an increase was the only one where the result was
statistically significant at a 90% confidence level. Two of three
studies found an increase in NOx emissions and both of these were
statistically significant at a 90% confidence level.

3.1.1.1.9 Selection of Models for Exhaust NMHC, CO and NOx Emissions

3.1.1.1.9.1 Exhaust NMHC and NOx Emissions

For Tier 0 vehicles, the EPA Predictive Models are based on more data
and the most advanced statistical tools. Therefore, we will use these
models here to project the effect of increased ethanol use and decreased
MTBE use on exhaust VOC and NOx emissions.

For Tier 1 and later vehicles, the choice is much less clear. In our
analysis of California's request for an RFG oxygen waiver, we assumed
that there was too little data upon which to project the effect of fuel
quality on the emissions of these vehicles. Substantially more data
exist on the effect of oxygenates on LEV and later vehicle exhaust
emissions than did in 2001. However, as indicated by the summary of
these results shown in Table 3.1-19, there does not appear to be
sufficiently consistency to confidently predict the impact of oxygenate
type and content on exhaust NMHC and NOx emissions.

For the NPRM, we developed two separate sets of predictions: a primary
analysis assuming no effect of oxygen on NMHC and NOx emissions from
Tier 1 and later vehicles, and a sensitivity analysis which applied the
Predictive Model effects to Tier 1 and later vehicles. The qualitative
summary shown in Table 3.1-19 supports a continuation of this approach.
The effect of ethanol blending on NMHC emissions is unclear in Table
3.1-19. This is reasonable bracketed by the primary and analysis, which
assumes no effect and the sensitivity analysis which assumes a
reduction. Table 3.1-19 also indicates that five out of six studies
found that 6-10 vol% ethanol blends increased NOx emissions from LEV and
later vehicles. This is also reasonable bracketed by the primary and
analysis, which assumes no effect and the sensitivity analysis which
assumes an increase. Given the uncertainty in the fuel-emissions effects
for Tier 1 and later vehicles, there is also some value in maintaining
consistency with our analysis conducted in response to California's
request for an RFG oxygen waiver. The primary analysis does this.

The varied results across these studies indicate the need for additional
test data. It may also be possible in the future to combine the emission
data from all such studies (as was done for the Complex and Predictive
Models) in order to develop a more robust estimate of the impacts of
oxygenate blending on emission from late model year vehicles.

3.1.1.1.9.1 Exhaust CO Emissions

For Tier 0 vehicles, MOBILE6.2 is the standard modeling tool for
estimating the effect of fuel quality on CO emissions. Therefore, we
will use that model here to project the effect of increased ethanol use
and decreased MTBE use on CO emissions.

Regarding later vehicles, the five studies of LEV and later vehicles all
found that increasing oxygen content in terms of MTBE or ethanol reduces
CO emissions. This is consistent with both the primary and sensitivity
analyses, as both approaches include a reduction in CO emissions.

The five studies of LEV and later vehicles all tested normally emitting
vehicles. MOBILE6.2 estimates that an E10 blend will reduce CO emissions
from Tier 0 vehicles by 11%. The quantitative results of the five
studies generally support this degree of reduction (Toyota, ExxonMobil,
Mexican Petroleum Institute) or perhaps a larger degree of reduction
(CRC E67, AAM-AIAM). Due to the absence of an EPA Predictive Model for
CO emissions, for the NPRM, we did not develop a sensitivity case for CO
emissions. However, given the possibility that the CO emission reduction
is larger than that currently estimated by MOBILE6.2 for these vehicles,
it appears reasonable to include a sensitivity analysis for CO
emissions, as well as NMHC and NOx. An approach analogous to that taken
for NMHC and NOx emissions appears reasonable. That is, for the primary
analysis, we will continue to use MOBILE6.2 to project the effect of
fuel properties on CO emissions. This means essentially a 6.7% reduction
in CO emissions from Tier 1 and later vehicles for an E10 blend. For the
sensitivity analysis, we will apply the MOBILE6.2 CO emission reduction
of 13.8% for Tier 0 vehicles to Tier 1 and later vehicles.

As discussed above, the five studies of LEV and later vehicles are not
sufficient for use in quantitatively projecting the impact of fuel
quality on emissions from these vehicles. Additional data must still be
collected over a broader set of vehicles, fuel changes, and conditions.

**3.1.1.2 Exhaust Toxic Emissions**

Two EPA models project the impact of fuel quality on exhaust toxic
emissions: the Complex Model and MOBILE6.2. The Complex Model projects
the impact of fuel quality on toxic emissions directly. That is, any
impact of fuel quality on total exhaust VOC emissions (which includes
the air toxics) is implicitly included in the model's predictions.
MOBILE6.2 separates the process into two steps. Total exhaust VOC
emissions are projected first, in part based on fuel quality. Then, the
fraction of VOC represented by each air toxic is estimated, in part
based on fuel quality.

The effect of fuel quality on exhaust VOC emissions in MOBILE6.2 was
already discussed above. The effect of fuel quality on the fraction of
exhaust VOC emissions represented by each air toxic in MOBILE6.2 is
based on the projections contained in the Complex Model. These Complex
Model's effects of fuel quality on exhaust toxic emissions were used
with the effect of fuel quality on exhaust VOC emissions backed out.
Thus, with respect to the effect of fuel quality on the fraction of
exhaust VOC emissions represented by each air toxic, the Complex Model
is the basis of both the Complex Model and MOBILE6.2 predictions.

With respect to exhaust VOC emissions, we already decided above that the
EPA Predictive Models represent the best estimate for Tier 0 vehicles.
For Tier 1 and later vehicles, we assume in our primary analysis that
these vehicles' exhaust VOC emissions are unaffected by fuel quality. As
a sensitivity analysis, we decided to extend the impacts indicated by
the EPA Predictive Models to all vehicles.

We follow the two-step process taken in MOBILE6.2 here in modeling the
impact of fuel quality on exhaust toxic emissions. We will use the EPA
Predictive Models to project the impact of fuel quality on exhaust VOC
emissions, as discussed above. We will use the effects of fuel quality
on the toxic fractions of exhaust VOC emissions contained in MOBILE6.2.

**3.1.1.3 Non-Exhaust Emissions**

Both the Complex Model and MOBILE6.2 evaluate the effect of gasoline
quality on non-exhaust VOC emissions. However, the effects in the
Complex Model were taken from an older version of MOBILE, as was
mentioned above. Therefore, MOBILE6.2 represents the better of the two
estimates of the effect of gasoline quality on non-exhaust VOC
emissions. The EPA Predictive Models do not address non-exhaust
emissions, so they are not applicable here.

In EPA's second analysis of California's request for a waiver of the RFG
oxygen requirement, we enhanced the estimate of non-exhaust emissions in
MOBILE6.2 by adding additional permeation emissions related to the use
of ethanol.[^107] Recent testing at that time indicated that ethanol
increases the rate of permeation of hydrocarbons through plastic fuel
tanks and elastomers used in fuel line connections, as well as
permeating ethanol itself. Subsequent testing has confirmed this effect.
Therefore, we have added the effect of ethanol on permeation emissions
to MOBILE6.2's estimate of non-exhaust VOC emissions in assessing the
impact of gasoline quality on emissions here.

Air Improvement Resource, Inc. for the American Petroleum Institute,
recently summarized the available test data on the effect of ethanol on
permeation emissions and developed a methodology for estimating in-use
permeation emissions in several U.S. cities.[^108] This study provides a
useful starting point for incorporating these emissions into this RFS
analysis.

Before examining this study, it is useful to point out that the
non-exhaust emission estimates in MOBILE6.2 include permeation emissions
for non-oxygenated gasoline. Typical extended diurnal emission tests
(e.g., those lasting 2-3 days) automatically include any emissions
permeating through plastic and elastomeric fuel system components.
However, since the emission tests used as the basis of the MOBILE6.2
estimates of non-exhaust emissions primarily were performed with
non-oxygenated gasoline. Those tests that did include ethanol blends
only exposed the vehicle to this fuel for a few days. The CRC study of
ethanol-related permeation indicates that it takes at least a week or
two for the effect of ethanol to fully develop. Therefore, it is very
unlikely that the tests performed by EPA and others to assess the impact
of ethanol and other fuel components on non-exhaust emissions included
the effect of ethanol on permeation emissions. In those cases where a
vehicle may have been exposed to an ethanol blend for some time prior to
testing, the increased permeation emissions likely were still present
when the vehicle was tested on a non-oxygenated gasoline, still masking
the effect. Therefore, our task here is to develop an estimate of the
incremental impact of ethanol use on permeation emissions, and not an
estimate of total permeation emissions with and without ethanol.

The primary source of ethanol permeation emission data is the CRC E-65
study.[^109] This study tested 10 vehicles, 6 cars and 4 light trucks,
ranging in model year from 1989 to 2001. Permeation emissions were
measured using two fuels, a non-oxygenated gasoline and a 6 vol% ethanol
blend like that commonly sold in California. AIR placed these vehicles
into three groups, based on a combination of model year and applicable
evaporative emission standards. The vehicles in the test program were
certified to two distinct evaporative emission requirements. The older
vehicles were certified to EPA's or California's 2 gram hot soak plus
diurnal emission standard based on an accelerated one-hour diurnal test.
The three newest vehicles were certified to the enhanced evaporative
emission requirements first implemented in the 1996 model year, which
included an extended two or three day diurnal test. In addition, the
data indicated that the three pre-1990 model year vehicles had much
larger incremental ethanol permeation emissions than the later
pre-enhanced evaporative emission vehicles. Therefore, AIR split the
pre-enhanced evaporative emission category into two groups, pre-1990
model year vehicles and 1990 and later model year vehicles. We believe
that this is appropriate and apply this split here, as well.

Since the earliest calendar year during which emissions are assessed in
the RFS analysis is 2012 and MOBILE6.2 only considers vehicles which are
24 years old or newer, at most only two model years of pre-1990 vehicles
are present in our analysis. Due to accumulated scrappage, these
vehicles comprise a very small percentage of the on-road fleet in 2012
and disappear from our analysis by 2015. Therefore, we decided to ignore
the pre-1990 model year data here.

AIR estimated the average incremental ethanol permeation emission rates
for the 1990 and later model year pre-enhanced evaporative emission
vehicles to be 0.86 gram per day (g/day), while that enhanced
evaporative emission vehicles was 0.80 g/day. Given the small number of
vehicles tested and the variability in the rates measured for individual
vehicles, for the purposes of this analysis, we consider these two
levels to be generally equivalent. Therefore, we use an average
incremental ethanol permeation emission rate of 0.8 g/day for all
vehicles. A follow-on study performed by CRC indicates that the
permeation emissions associated with a 10 vol% ethanol blend could not
be distinguished statistically from those of the 6 vol% blend.[^110]
Therefore, we use the 0.8 g/day incremental permeation emission rate for
both 6 vol% and 10 vol% ethanol blends here.

Beginning with the 2004 model year, EPA and California implemented
further enhancements to their evaporative emission standards. The EPA
"Tier 2" requirements include accumulating mileage on durability data
vehicles with an ethanol blend. However, actual emission testing is
still performed using non-oxygenated gasoline. We believed that this
combination of requirements would incorporate any effects of ethanol on
emissions, including potential permeation effects. Because of these and
other aspects of the 2004 and later standards, AIR estimated that the
permeation emissions due to ethanol would be reduced to 0.43 g/day for
these vehicles.

We believe that it is likely that permeation emissions for
non-oxygenated blends will be lower for these vehicles, due to the fact
that the diurnal emission standard was reduced from 2 g/day to 0.95
g/day and lower in some cases. However, as mentioned above, the effect
of ethanol on permeation emissions takes about 2 weeks to fully develop
and to fully disappear. Therefore, it is possible to accumulate mileage
on a certification vehicle using an ethanol blend, change the fuel to
the emission test fuel, wait two weeks and then test the vehicle. In
this case, the effect of ethanol on permeation will have disappeared
during the certification testing. Thus, until EPA requires certification
emission testing with ethanol blends, we have no assurance that
manufacturers will modify their vehicle designs to address the effect of
ethanol on permeation. Thus, for the purposes of this analysis, we
maintain the estimate of 0.8 g/day for Tier 2, and earlier vehicles.

Permeation emissions vary significantly with ambient temperature, with
emissions increasing with increases in temperature. The 0.8 g/day
emission estimate applies at an average temperature of 95 F. The
literature indicates that permeation varies exponentially with
temperature. The CRC testing indicates that permeation emissions double
with every increase in temperature of 18 F. Vice versa, permeation
emissions drop 50% with every decrease in temperature of 18 F. We apply
this relationship in Chapter 4 in developing incremental ethanol
permeation emissions for each hour of the day in each county in the U.S.

We plan to update our projections of the effect of gasoline quality on
non-exhaust VOC emissions from Tier 1 and later model year vehicles
based on additional testing which is expected to begin in 2007.
Additional testing of permeation emissions is already underway with the
CRC E-77 test program. These updated projections will be used in the
comprehensive assessment of the impact of the fuel-related provisions of
the Energy Act which due in 2009.

Non-exhaust emissions are a function of ambient temperature and
temperatures vary across the nation. Therefore, it is not as simple to
determine the effect of RVP and other fuel qualities on non-exhaust
emissions on a per vehicle basis as it is for exhaust emissions.
Therefore, we performed a regression of the non-exhaust VOC and benzene
emissions developed in Chapter 4 as a function of fuel properties in
order to estimate these effects on a per vehicle basis. Specifically, we
regressed the ratio of non-exhaust VOC and benzene emissions in each
county in July between two fuel scenarios (the RFS case and the base
case) against the change in RVP, ethanol content and MTBE content. The
results are summarized in Table 3.1-20.

**Table 3.1-20**. Fuel-Non-Exhaust Emission Effects in MOBILE6.2: 2012

  --------- ----------- ----------------- --------------- ----------------
            RVP (%/psi) Ethanol (%/Vol %) MTBE (%/Vol%)   Adjusted
                                                          r-Square

  VOC       11.8%       0.3%              0.0%            0.53

  Benzene   2.0%        1.0%              -0.5%           0.50
  --------- ----------- ----------------- --------------- ----------------

Non-exhaust emissions of benzene are estimated in MOBILE6.2. MOBILE6.2
adjusts these emissions for RVP, benzene fuel content, MTBE fuel
content, temperature and the total non-exhaust VOC emissions produced by
the vehicle fleet in question. We will use MOBILE6.2 here to project the
impact of decreased MTBE use and increased ethanol use on non-exhaust
benzene emissions.

Benzene is also emitted via permeation. Just as MOBILE6.2 does not
include the effect of ethanol on VOC emissions via permeation, it does
not include the effect of ethanol on benzene emissions via permeation.
Thus, we will add this effect outside of the MOBILE6.2 model in the same
way as was just described for VOC emissions via permeation.

Both the CRC E-65 study and the E-65 Phase 3 follow-on study referenced
above measured the benzene content of permeation emissions during some
of the tests performed. Table 3.1-21 presents the available data by fuel
type for both studies.

**Table 3.1-21. Benzene Permeation Emissions: CRC E-65 Studies**

  -------------- ---------- ---------- ------------ ------------ ------- ---------
  E-65 Phase     No. of     E0         E6 (Average  E6 (High     E10     MTBE
                 Vehicles              Aromatics)   Aromatics)           Blend

  Fuel Benzene                                                           
  Content (vol%)                                                         

  Phase 1        \-\--      0.73       0.72         \-\--        \-\--   0.53

  Phase 3        \-\--      0.41       0.55         0.43         0.51    \-\--

  Benzene                                                                
  Emissions (%                                                           
  of total                                                               
  permeation                                                             
  emissions)                                                             

  Phase 1        10         2.5-2.6%   2.2%         \-\--        \-\--   2.2%

  Phase 3        4-5        1.4%       1.4%         1.4%         1.7%    \-\--

  Benzene                                                                
  Emissions                                                              
  Adjusted to                                                            
  0.88 vol% Fuel                                                         
  Benzene (% of                                                          
  total                                                                  
  permeation                                                             
  emissions)                                                             

  Phase 1        10         3.0-3.1%   2.7%         \-\--        \-\--   3.6%

  Phase 3        4-5        3.0%       2.2%         2.8%         2.9%    \-\--
  -------------- ---------- ---------- ------------ ------------ ------- ---------

As can be seen from Table 3.1-21, the benzene content of permeation
emissions is slightly higher for the Phase 1 study than Phase 3. This is
consistent with the higher benzene contents of the fuels tested in
Phase 1. The Phase 1 fuels have particularly low benzene contents
compared to levels typical across the U.S. Therefore, assuming a linear
relationship between benzene fuel content and benzene permeation
emissions, we adjusted the benzene permeation emissions to those for a
fuel benzene content of 0.87 vol%, which is the average of the benzene
fuel content of summertime gasoline produced nationwide in the base, RFS
and EIA cases from the recent refinery modeling described in Chapter 2.
We focused on summer benzene content since permeation emissions are a
strong function of temperature. These figures are shown in the bottom
third of Table 3.1-21. As can be seen, the benzene content of permeation
emissions is much more consistent across the two Phases of E-65 after
being adjusted to consistent fuel benzene content than before this
adjustment.

There appears to be no definite trend in the benzene content of
permeation emissions with increasing ethanol fuel content. Both CRC
studies found that the addition of ethanol does not simply increase
permeation emissions via increased emissions of ethanol, but also
through increased emissions of other fuel components. Thus, we will
assume for this analysis that the benzene fraction of permeation
emissions is independent of ethanol fuel content (i.e., benzene
emissions increase at the same rate as total permeation emissions). This
is also consistent with our conclusion in Chapter 2 that ethanol
blending does not affect benzene fuel content. Given this, we determined
the average benzene fraction of permeation emissions by averaging the
benzene fraction across all the fuels and vehicles tested, excluding the
MTBE blend. This average was 3%. This figure will be used here to
estimate the benzene portion of the increase in permeation emissions
resulting from ethanol blending.

Given this, we determined the average benzene fraction of permeation
emissions by averaging the benzene fraction across vehicles. Thus, the
average benzene fraction of permeation emissions is the average of the
figures in the rightmost column of Table 3.1-21, or 2%.

**3.1.1.4 PM Emissions**

The amount of data evaluating the impact of ethanol and MTBE blending on
direct emissions of PM from gasoline-fueled vehicles is extremely
limited. Three fairly limited studies have evaluated the impact of
ethanol blending on PM emissions from gasoline vehicles. These studies
are summarized below.

The Colorado Department of Public Health and Environment tested 24
vehicles on two winter grade commercial fuels at 35ºF in Denver (i.e.,
at high altitude).[^111] Both fuels were obtained from a local refinery.
One fuel was non-oxygenated and represented fuel sold outside of the
Denver area. The other contained 10 vol% ethanol and represented fuel
sold in the Denver area, which has an oxygenated fuel mandate. As would
be expected, the fuels differed in other qualities besides ethanol
content. The ethanol blend had a 2 vol% lower aromatic content, which is
somewhat less than expected. However, it also had a 53 F lower T50
level, which is a much greater difference than is typical. The two fuels
used during this testing appear to have been used in random order (i.e.,
sometimes the non-oxygenated fuel was tested first, other times the E10
fuel was tested first).

Half of the 24 vehicles were certified to Tier 0 emission standards,
while the other half were certified to Tier 1 standards. Each group of
12 vehicles included 8 cars and 4 light trucks.

The study found that PM emissions for the 24 vehicles over the FTP
decreased from about 9 mg/mi to about 6 mg/mi with the ethanol blend,
for a reduction of 36%. In addition, the vehicles with the highest base
PM emission rates showed by far the largest reductions, both in absolute
terms and in terms of percentage. PM emissions from Tier 1 vehicles
decreased from roughly 5.5 mg/mi to 4 mg/mi with the ethanol blend, for
a reduction of 27%. Essentially all of the emission reduction occurred
during Bag 1 of the test (i.e., related to the cold start).

PM emissions were also measured over a warmed up California Unified
Cycle (i.e., no cold start). PM emissions for the 24 vehicles over this
cycle for the two fuels were not statistically different. The ethanol
blend increased PM emissions from the Tier 0 vehicles slightly and
decreased those from the Tier 1 vehicles slightly.

Finally, PM emissions were also measured over an EPA REP05 Cycle, again
with no cold start. PM emissions over this cycle were 4-5 times those
over the California Unified Cycle, indicating the impact of high speed,
aggressive driving on PM emissions. However, despite this general
increase in PM emissions, for the 24 vehicles PM emissions over the
REP05 cycle were again very similar for the two fuels. This time,
however, the ethanol blend decreased PM emissions from the Tier 0
vehicles slightly and increased those from the Tier 1 vehicles slightly.

Overall, this testing indicates that the effect of ethanol (together
with lower aromatics and T50 levels) may reduce PM emissions due to cold
starting at 35 F under high altitude conditions. However, PM emissions
during warmed up driving are very low and an effect of fuel quality was
indiscernible.

The State of Alaska, in conjunction with General Motors Corp. and EPA,
measured PM emissions from ten vehicles ranging in model year from 1977
to 1994 using two fuels.[^112] The non-oxygenated fuel was a commercial
wintertime fuel from the Fairbanks areas. The ethanol blend in this
study was created from the non-oxygenated fuel via splash blending.
Testing was performed in Alaska using a portable dynamometer. Three of
the vehicles were also tested at EPA's laboratory in Research Triangle
Park, N.C, ranging in model year from 1987 to 1994. The testing in
Alaska was performed at -20ºF, 0ºF, and 20ºF. The EPA testing was
performed at these same temperatures plus 75 F. Both sets of testing
began with the non-oxygenated fuel, followed by testing with the E10
fuel. This could introduce a bias into the results, but the degree of
this is unknown.

The cold conditions led to difficulties in measuring PM emissions in
Alaska. Therefore, few acceptable measurements of PM were made and the
results were not presented in the paper. The fact that the EPA testing
was conducted in a laboratory made vehicle conditioning and operation
and particulate collection more feasible. The PM emissions from the
three vehicles tested by EPA on the two fuels are presented in the
paper.

Only one measurement of PM emissions was made for each combination of
vehicle, fuel and temperature. Thus, no direct measurement of test to
test variability is available. We calculated the percentage difference
in PM emissions between the E10 and E0 fuel for each of the eleven
combinations tested. PM emissions with the ethanol blend ranged from 81%
lower to 84% higher than those with the E0 fuel. Thus, there appears to
be considerable variability in the test results. Taken together, the
average of the percentage changes for each condition showed the ethanol
blend reducing PM emissions by 21%. However, this decrease was not
statistically significant at the 90% confidence level. The ethanol blend
more consistently decreased PM emissions at -20 F and 0F, but not at 20
F or 75 F. The paper states that PM emissions at the higher two
temperatures were very low and the differences tended to be within
measurement accuracy. It is important to note, however, that the lower
end of this range is 20 F. Only a small percentage of driving in the
U.S. occurs below this temperature.

The third and final study was performed by EPA's laboratory in Research
Triangle Park, N.C.[^113] This study was conducted in three phases; the
last two of which are relevant here. In Phase II, PM emissions from two
1993-1995 model year vehicles were tested at -20 F, 0 F, and 20 F. In
Phase III, PM emissions from an additional five 1987-2001 model year
vehicles were tested at -20 F, 0 F, 20 F and 40 F. Both phases utilized
two fuels, one a wintertime non-oxygenated fuel and the other a 10 vol%
ethanol fuel created from the non-oxygenated fuel via splash blending.
Both phases measured PM emissions over the FTP and over a series of four
back-to-back IM240 tests.[^114] It is not clear whether the fuels were
always tested in the same order or tested randomly. Some testing was
performed with various malfunctions induced on the vehicles, like
disconnecting the oxygen sensor. We focus on the emissions from the
properly operating vehicles here.

Of the 26 combinations of vehicles and temperatures tested, valid PM
measurements over the FTP were successfully obtained for both fuels in
21 of them. The average percent change in PM emissions due to ethanol
blending was +1%, in other words a very slightly increase. In contrast
to the results of the two test programs discussed previously, the
ethanol blend did not show a benefit at -20 F, and showed only a very
slight 1% reduction in PM emissions at 0 F. The data show some tendency
for the ethanol blend to produce a greater PM emission reduction for the
highest emitting vehicles. However, this trend is not as clear as in the
Colorado study. Thus, this study indicates no clear effect of ethanol on
PM emissions.

The IM240 testing showed much lower PM emission levels due to the warmed
up nature of the test. There was also no clear trend in the effect of
ethanol on PM emissions in this testing.

The available data indicate that ethanol blending might reduce exhaust
PM emissions under very cold weather conditions (i.e., 0 F or less),
particularly at high altitude. There is no indication of PM emission
reductions at higher temperatures and under warmed up conditions. The
data are certainly too limited to support a quantitative estimate of the
effect of ethanol on PM emissions.

Fine particles can also be formed through a series of chemical reactions
in the atmosphere from gasses such as sulfate (SO~4~^2-^), nitrate
(NO~3~^-^), ammonia (NH~3~), and volatile organic compounds (VOC)
emitted from motor vehicles. This aerosol formed secondarily in the
atmosphere through these gas to particle conversions will be discussed
in greater detail in Chapter 5 of this document. Emerging science is
indicating that gaseous aromatic compounds are likely among the most
important VOCs which are precursors of carbonaceous PM which is formed
in the atmosphere. Therefore, we discuss the effect of fuel quality on
aromatic hydrocarbon emissions in the next section.

**3.1.1.5 Aromatic Emissions**

The Auto/Oil Air Quality Improvement Research Program tested over 100
vehicles from model years 1983 - 1989 on a fuel matrix of over 80 fuel
blends to determine the exhaust emission effects of varying fuel
parameters - including ethanol and aromatics.[^115] Phase 1 of this
study tested two fleets of vehicles: twenty (20) 1989 model year
vehicles, and fourteen (14) 1983-1985 model year vehicles. A matrix of
16 fuels (Matrix A) was developed in the first portion of the study with
half the fuels containing 20% aromatics by volume and half with 45%
aromatics by volume. This data was used to investigate the impact of
changing aromatic levels in the fuel on the aromatics emitted in the
exhaust as a function of total hydrocarbon emissions. Linear regression
of the test data indicates that there is a linear relationship between
the level of aromatics by volume in the fuel and the mass of aromatics
emitted in the exhaust. Based on the results of this regression,
aromatics have a tendency to be emitted less than proportionally to
their percent volume in the fuel, as shown in the following equation:

$$\text{Aromatics}_{\text{Exhaust}}(\text{wt}\text{\%}) = 0\text{.}\text{64} \times \text{Aromatics}_{\text{Fuel}}(\text{vol}\text{\%})$$

The coefficient in the above equation was statistically significant at
the 90% confidence level (0.64 ± 0.02). However, when we considered the
presence of an intercept, it was not statistically significant (-0.35 ±
2.26). Therefore, we forced the regression line through zero and
repeated the regression.

The Auto/Oil program also produced data which allows the effect of
ethanol on aromatic hydrocarbon emissions to be assessed. As discussed
in Section 2.2, ethanol blending tends to reduce the aromatic content of
gasoline. Of interest here is whether ethanol has any other effect on
aromatic hydrocarbon emissions beyond that associated with reducing the
aromatic content of gasoline.

The Auto/Oil data contained a subset of fuels designed specifically for
this analysis. A total of 4 ethanol blends were produced by splash
blending ethanol into four non-oxygenated fuels. Two of the
non-oxygenated fuels came from the original group of sixteen tested
during Phase 1 of the research study. Base fuel A (industry average
fuel) was a 9 RVP fuel with 32% aromatics. Base fuel F was also a 9 RVP
gasoline with 20% aromatics. Two new non-oxygenated fuels were created
from base fuels A and F. In both cases, and butane was removed to lower
the RVP level by 1 psi resulting in fuels V and S, respectively. To each
of these 4 non-oxygenated fuels, 10% ethanol was splash blended
resulting in the final 8 fuel test matrix. A summary of the differences
between the expected and actual aromatic content in the fuel and in the
exhaust as a function of THC is shown in Table 3.1-22.[^116]

**Table 3.1-22.**

**Expected vs. Predicted Non-Oxy and E10 Fuel Properties and**

**Exhaust Aromatics Reductions for Auto/Oil AQIRP "Fuel Matrix B"**

  ----------- ------------- -------- --------------- ------------- --------------- -------------
  **Non-Oxy   **Fuel        **E10    **Fuel          **Measured    **% Reduction   **% Reduction
  Fuel**      Aromatics**   Fuel**   Aromatics       Fuel          in Exhaust      in Exhaust
                                     Expected**^a^   Aromatics**   Aromatics       Aromatics
                                                                   Expected**      Actual**

  A           32            X        28.8            27.2          2.05            2.39

  V           33.5          W        30.15           29.0          2.14            1.54

  F           20            U        18              19.1          1.28            0.93

  S           21.2          T        19.08           18.1          1.36            0.63
  ----------- ------------- -------- --------------- ------------- --------------- -------------

^a^Based on dilution as a result of splash blending 10% ethanol

The aromatic contents of the ethanol blends, as listed in Table 3.1-22,
do not reflect the 10% reduction from the aromatic contents of their
non-oxygenated base fuels which would be expected from splash blending
with 10 vol% ethanol. The discrepancies between the measured and
estimated fuel aromatic contents are small, on the order of 1-2 vol%
aromatics. However, when the total difference is on the order of 3 vol%
ethanol, these discrepancies are significant. The discrepancies are
likely the result of measurement uncertainty of both base and ethanol
fuels.

Using the relationship between fuel aromatic content and aromatic
hydrocarbon emissions developed above, we can predict the reduction in
exhaust aromatic emissions associated with the differences in the
aromatic contents of the non-oxygenated fuels and their ethanol
containing counterparts. We believe that it is most accurate to use the
expected aromatic contents for the ethanol blends rather than the
measured levels, since it is likely that the volume of ethanol added was
very close to 10 vol%.

The expected reduction in the percentage of VOC emissions represented by
aromatic hydrocarbons based on the expected reduction in fuel aromatic
content is shown in the second to the last column in Table 3.1-22. The
measured reduction in the percentage of VOC emissions represented by
aromatic hydrocarbons is shown in the last column. In three out of four
cases (all but fuels A and X), the actual reduction in aromatic
emissions is less than the predicted reduction based on dilution. Had we
used the measured aromatic contents for the ethanol blends, the outcome
would have been the same: In three out of four cases (in this case all
but fuels F and U), the actual reduction in aromatic emissions is less
than the predicted reduction based on dilution.

Qualitatively, this indicates that there does not appear to be any
additional benefit in reducing aromatic hydrocarbon emissions associated
with the use of ethanol beyond that expected from the reduction in the
aromatic content of gasoline portion associated with ethanol blending.

Based on our analysis in Section 2.2, increased ethanol blending will
significantly reduce gasoline aromatic content. This could cause a
corresponding reduction in the aromatic fraction of exhaust VOC
emissions relative to non-oxygenated conventional gasoline. In addition,
ethanol also reduces total exhaust VOC emissions from older vehicles and
may do so from newer vehicles, based on the CRC E-67 study. This would
further reduce emissions of aromatic hydrocarbons. As will be discussed
further in Chapter 5, this reduction in aromatic hydrocarbon emissions
could reduce ambient levels of secondary organic PM.

**3.1.1.6 Emission Effects Associated with Specific Fuel Blends**

3.1.1.6.1 Conventional Gasoline Analysis

In Section 2.2 of Chapter 2, we estimated the effect of blending ethanol
and MTBE on the quality of conventional gasoline (see Tables 2.2-3 and
2.2-4). Here, we present the effect of these changes in fuel quality on
emissions from motor vehicles in percentage terms, relative to those of
a typical non-oxygenated U.S. gasoline blend. Because of the Tier 2
sulfur standards, sulfur is held constant at 30 ppm. Also, due to the
MSAT standards, we assume that benzene levels are not affected, as well.
Table 3.1-23 presents the gasoline qualities of a typical 9 RVP CG, as
well as MTBE and ethanol blends which reflect the effect of adding these
two oxygenates to gasoline.

**Table 3.1-23. CG Fuel Quality With and Without Oxygenates^a^**

  ----------------- ------------------- ---------------- -----------------
  Fuel Parameter    Typical 9 RVP CG    MTBE CG Blend    Ethanol CG Blend

  RVP (psi)         8.7                 8.7              9.7

  T50               218                 207              205

  T90               332                 321              329

  Aromatics (vol%)  32                  25.5             27.4

  Olefins (vol%)    7.7                 7.7              7.5

  Oxygen (wt%)      0                   2                3.5

  Sulfur (ppm)      30                  30               30

  Benzene (vol%)    1.0                 1.0              1.0
  ----------------- ------------------- ---------------- -----------------

^a^Assumes summer (July) conditions

Table 3.1-24 presents the differences in emissions of the MTBE and
ethanol blends relative to that of non-oxygenated conventional gasoline.

**Table 3.1-24. Effect of Oxygenates on Conventional Gasoline
Emissions^a^**

  ---------------- -------------- -------------- -------------- ----------
  Pollutant        Source         11 Vol% MTBE   10 Vol%        9.7 RVP CG
                                                 Ethanol        

  Exhaust VOC      EPA Predictive -9.2%          -7.4%          +1.1%
                   Models                                       

  NOx                             2.6%           7.7%           +1.1%

  CO^b^            MOBILE6.2      -6% / -11%     -11% / -19%    +12.7%

  Exhaust Benzene  EPA Predictive -22.8%         -24.9%         -2.6%
                   and Complex                                  
                   Models                                       

  Formaldehyde                    +21.3%         +6.7%          -3.7%

  Acetaldehyde                    +0.8%          +156.8%        -2.0%

  1,3-Butadiene                   -3.7%          -13.2%         -2.6%

  Non-Exhaust VOC  MOBILE6.2      Zero           +30%           +30%

  Non-Exhaust      MOBILE6.2 &    -9.5%          +15.8%         +15.8%
  Benzene          Complex Models                               
  ---------------- -------------- -------------- -------------- ----------

^a^Assumes summer (July) conditions

^b^The first figure shown applies to normal emitters; the second applies
to high emitters.

The two oxygenated blends both reduce exhaust VOC and CO emissions, but
increase NOx emissions. The MTBE blend does not increase non-exhaust VOC
emissions due to the fact that non-oxygenated and MTBE blends have to
meet the RVP standard. Ethanol blending increases non-exhaust VOC
emissions in two ways. First, ethanol blends are allowed 1.0 psi higher
RVP levels in most areas with CG. Second, ethanol increases permeation
emissions. The most notable effect on toxic emissions in percentage
terms is the increase in acetaldehyde with the use of ethanol.
Acetaldehyde emissions more than double. However, as will be seen below,
base acetaldehyde emissions are low relative to the other toxics. Thus,
the absolute increase in emissions is relatively low.

3.1.1.6.2 Reformulated Gasoline (RFG) Analysis

The previous section discussed the relative emission changes to expect
when adding ethanol to the conventional non-oxygenated gasoline pool. A
second scenario to consider is the case where RFG areas change from
MTBE, a commonly used oxygenate in RFG areas, to either ethanol RFG or a
non-oxygenated RFG.

Table 3.1-25 presents the gasoline qualities of three types of RFG:
non-oxygenated, a typical MTBE RFG as has been marketed in the Gulf
Coast and a typical ethanol RFG which has been marketed in the Midwest.
The fuel specifications shown are based on specific RFGs predicted by
the refinery modeling discussed in Chapter 2 for the cases and PADDs
shown. These specific fuels were selected as they represented PADD-wide
RFGs which contained primarily one oxygenate at the desired volumetric
concentration.

**Table 3.1-25. Summer RFG Fuel Quality With and Without Oxygenates^a^**

  ------------------- ----------------- -------------------- -------------
  Fuel Parameter      Non-Oxygenated    MTBE RFG             Ethanol RFG
                      RFG                                    

  Case                PADD 3 Reference  PADD 1 Reference     PADD 2 RFS

  OXYGEN (wt%)        0.0               2.1                  3.7

  SULFUR (ppm)        30.0              30.0                 30.0

  RVP (psi)           7.0               7.0                  7.0

  E200 (%)            52.0              59.9                 57.6

  E300 (%)            87.5              88.9                 81.9

  T50 (F)             184               190                  185

  T90 (F)             335               342                  335

  AROMATICS (vol%)    20.1              21.0                 20.0

  OLEFINS (vol%)      14.6              4.3                  13.6

  BENZENE (vol%)      0.7               0.7                  0.7
  ------------------- ----------------- -------------------- -------------

^a^Assumes summer (July) conditions

Table 3.1-26 presents the emission impacts of these three types of RFG
relative to the 9 RVP CG described in Table 3.1-23.

**Table 3.1-26.**

**Effect of RFG on Per Mile Emissions from Tier 0 Vehicles**

**Relative to a Typical Conventional Gasoline^a^**

  --------------- -------- ----------- -------------- ----------- -------------- --------------
  Pollutant       Source                              Non-Oxy RFG 11 Volume      10 Volume
                                                                  Percent MTBE   Percent
                                                                                 Ethanol

  Exhaust                                                                        
  Emissions                                                                      

  VOC                                  EPA Predictive -13.4%      -15.3%         -9.7%
                                       Models                                    

  NOx                                                 -2.4%       -1.7%          7.3%

  CO                                   MOBILE6.2      -22%        -31%           -36%

  Exhaust Benzene                      EPA Predictive -21.2%      -29.7%         -38.9%
                                       and Complex                               
                                       Models                                    

  Formaldehyde                                        -5.9%       19.4%          2.3%

  Acetaldehyde                                        -0.2%       -9.5%          173.7%

  1,3-Butadiene                                       20.9%       -29.2%         6.1%

  Non-Exhaust                                                                    
  Emissions                                                                      

  VOC                      MOBILE6.2 &                -30%        -30%           -18%
                           CRC E-65                                              

  Benzene                  MOBILE6.2 &                -40%        -43%           -32%
                           Complex                                               
                           Models                                                
  --------------- -------- ----------- -------------- ----------- -------------- --------------

^a^Assumes summer (July) conditions

As can be seen, the oxygenated RFG blends are predicted to produce a
greater reduction in CO emissions, but increase NOx emissions. Exhaust
VOC emission effects are mixed. Non-exhaust VOC emissions with the
exception of permeation are roughly the same due to the fact that the
RVP level of the three blends is the same. However, the increased
permeation emissions associated with ethanol reduces the overall
effectiveness of ethanol RFG. The most notable effect on toxic emissions
in percentage terms is the increase in acetaldehyde with the use of
ethanol. Acetaldehyde emissions more than double. However, as will be
seen below, base acetaldehyde emissions are low relative to the other
toxics. Thus, the absolute increase in emissions is relatively low. The
ethanol RFG also produces a greater reduction in exhaust benzene
emissions and somewhat lower reduction in non-exhaust benzene emissions.

The exhaust emission effects shown for VOC and NOx emissions only apply
to Tier 0 vehicles in our primary analysis. In the sensitivity analysis,
these effects are extended to Tier 1 and later vehicles. The effect of
RVP on non-exhaust VOC emissions is temperature dependent. The figures
shown above represent the distribution of temperatures occurring across
the U.S. under summer conditions (average July fuel specifications).

**3.1.2 High-Level Ethanol Blends**

The vast majority of ethanol blended into gasoline as a result of the
RFS is expected to be used in a 10 vol% ethanol blend (E10) rather than
an 85 vol% ethanol blend (E85), as discussed in Chapter 1. At the same
time, some ethanol is likely to be used as E85, and its use is growing.
Current estimates indicate that roughly 6 million FFVs are on the road
today, with US automakers projecting an additional 3 to 5 million FFVs
produced annually over the next several years. The analysis to follow
relies upon the limited amount of data available on both older and
current technology (Tier 2) FFVs, the later of which will dominate the
FFV fleet in future years. Based on this data of Tier 2 vehicles, we
believe that with the increased use of E85 (which is inherently a
low-sulfur fuel) emissions should be neutral or better than operation on
E0 or E10 fuel blends for CO and NOx. NMOG emissions may be higher
primarily due to emissions of unburned ethanol at cold starts, while
running NMOG emissions are lower with E85 based on certification data.
The sections to follow examine these issues in greater detail.

**3.1.2.1 Exhaust emissions**

3.1.2.1.1 Regulated Gaseous Pollutants

Relatively little data is available for investigating the effects of
high level ethanol blends on exhaust emissions. Part of the 1993
Auto/Oil Air Quality Improvement Research Program (AQIRP) investigated
the emissions associated with the use of E85 blends. Emissions over the
Federal Test Procedure (FTP) were measured from three Tier 0 and Tier 1
certified flexible-fuel vehicles with three test fuels. A source of
emission data for Tier 2 FFVs is EPA's Certification and Fuel Economy
Information System (CFEIS) database, which contains certification data
for five model year 2006 FFVs certified to Tier 2 standards (bins
5-8).[^117] However, certification data, composed of regulated emissions
while operating on E85, represents very limited operating conditions. It
does not include aggressive driving or cooler ambient temperature starts
or operation.

The Auto/Oil Study found that E85 reduced FTP composite NOx emissions by
49% compared to conventional gasoline with 1988 industry average fuel
properties. This is likely the result of improved catalyst efficiency
due to the low sulfur concentration in E85 (only 5 ppm vs. 339 ppm in
the industry average fuel). The 2006 CFEIS data from Tier 2 FFVs, on the
other hand, shows only a 3% decrease in NOx emissions with E85 from a
cold start test but a significant 45% decrease in hot running NOx
emissions. CO emissions are reduced at least 33% on a cold start
according to CFEIS, while the Auto/Oil study did not find statistically
significant changes in CO emissions. Emissions of Non-Methane Organic
Gases (NMOG) increased 10% for the Tier 2 CFEIS vehicles. The Auto/Oil
data showed NMOG increased by 26%, but this change was not statistically
significant (p-value of 0.28). However, CFEIS data indicated a 50%
reduction in CO and HC emissions during hot operation. The measurement
used to determine NMOG in both CFEIS and Auto/Oil data include the mass
of oxygen in all measured organic species except methane.

While the emissions of NMOG appear to increase with E85 compared to E0
for Tier 2 certified vehicles, the majority (\~55%) of E85 NMOG
emissions are direct emissions of ethanol, which has a relatively low
reactivity compared to other NMOG species. Thus there may still be a
slight NMOG benefit based on ozone reactivity despite a potential net
increase in total NMOG emissions. An important point worth noting is
that the cold start emissions with E85 represent a greater % of bag
weighted emissions than with E0. This manifests itself primarily in the
form of unburned ethanol emissions during cold start, before the
combustion chamber has reached a high enough temperature to promote
complete ethanol vaporization. Thus NMOG emissions with E85 at colder
temperatures could be much greater (2 to 3 times higher) than with E0
due to prolonged periods at low temperature. Because of this unique
start behavior and the lower emissions observed during hot operation,
emissions from E85 may be better quantified if separated between starts
and hot driving operation.

3.1.2.1.2 Air Toxics

With increasing use of E85, some air toxics may increase while others
decrease relative to E0. Emissions of Benzene and 1,3-Butadiene decrease
while acetaldehyde, formaldehyde, and emissions of ethanol increase. The
net result is an increase in total air toxics, but this is largely
driven by increase ethanol and acetaldehyde emissions. Table 3.1-27,
below, shows the percent change in FTP composite g/mile emissions of
several air toxics for the three FFVs tested on three fuels as part of
the 1993 Auto/Oil study. The fuels tested were AQIRP gasoline with1988
Industry average qualities (CG), a 1996 California phase 2 reformulated
gasoline (RFG blended with MTBE), and an E85 blend with identical
gasoline specs as RFG.[^118]

**Table 3.1-27.**

Percent Difference in Toxic Emissions Between E0 and E85

  ---------------------- ---------------- --------------- ----------------
                         \% Difference                    
                         Between Fuels                    

                         RFG vs CG        E85 vs CG       E85 vs RFG

                                                           

  Formaldehyde           -2               93              97

  Benzene                -55              -87             -72

  1,3-Butadiene          -31              -85             -79

  Acetaldehyde           -18              2620            3220

  Total Toxics           -42              108             255
  ---------------------- ---------------- --------------- ----------------

The increase in acetaldehyde emissions is substantial, on the order of
20 to 30 times that of E0. This is substantially higher than the15 to 20
fold increase shown with Tier 2 FFVs in the CFEIS data. Emissions of
benzene, 1,3-butadiene, ethylbenzene, hexane, styrene, toluene,
m-xylene, p-xylene, o-xylene, and naphthalene are all expected to
decrease significantly (50-80%) with the use of E85 vs. E0 according to
CFEIS, which is consistent with the Auto/Oil results presented in Table
3.1-27. Regardless of vehicle technology, the increased emissions of
acetaldehyde could be a potential concern due to its strong odor, as
well as its respiratory system irritating and potentially carcinogenic
properties.

3.1.2.1.3 Particulate Matter

Even less data exists to draw firm conclusions on direct particulate
matter emissions due to increased E85 use. Theoretically, E85 use has
the potential to increase direct emissions of PM under modes of rich
engine operation. This is especially important at cold start, before the
catalyst has reached its operating temperature and when an E85 fueled
vehicle runs substantially richer than if it were fueled with E0. In
this situation, the low temperatures in the combustion chamber,
compounded by the evaporative cooling effect of ethanol, makes fuel
vaporization difficult and may increases exhaust emissions of raw fuel
and PM at cold start. Sustained periods of high load may also have
increased emissions of PM with E85 than with E0 due to richer operation
with E85. Results from a 2003 SAE paper showed a negligible increase in
direct PM emissions from E85 vs. E0 fueled vehicles over the European
Test Cycle (Directive 70/220/EEC and its amendments).[^119] Tests
conducted at 23ºC and 16ºC showed an increase in PM emissions with
decreasing temperature for both E0 and E85, with slightly higher PM
emissions at cold temperature with E85. This study only used one E85
blend and one model year 2002 FFV, however, so these results cannot be
considered entirely representative of the on-road FFV vehicle fleet.
Again, since the projected use of ethanol as E85 is very small compared
to its use as E10, the emissions impacts associated with E85 will be
also be quite small on an absolute scale.

As discussed above, emerging science is beginning to identify gaseous
aromatics as an important precursor to secondary organic aerosol.
Exhaust aromatic emissions should be reduced with E85 since the fuel
aromatics content of E85 is much lower than that for E0 or E10 blends.
This reduction in exhaust aromatics should reduce the formation of
secondary organic aerosol. However, as mentioned earlier, no
specifications currently exist for the 15% gasoline portion of E85.
Thus, the degree to which the aromatic content of E85 will be lower than
gasoline is not known with any confidence. Lack of data regarding the
speciation of VOC emissions also prevents any quantitative estimate of
any benefit in this area.

**3.1.2.2 Non-Exhaust Emissions**

We currently have very little data regarding non-exhaust emissions from
E85 vehicles. Theoretically, evaporative emissions of E85 fueled
vehicles have the potential to be lower than with E0 or E10. This is
because ethanol blended with a given gasoline at the 85% level is likely
to be less volatile than E0 or E10 (with the same gasoline fuel
quality). This is not entirely certain, however, since there is no fuel
specification for the hydrocarbon composition of the 15% of E85 that is
gasoline. Thus, the RVP of the final E85 blend could be closer to that
of E0 or E10 fuels than commonly thought to be the case. Moreover, since
the volatility of ethanol blends peaks between 6 and 30 vol% ethanol,
the fuel in the tank of drivers of flex-fuel vehicles who alternate
between E85 and gasoline will experience a wide range of ethanol
concentrations in the fuel at any given time, and therefore a wide
variation in the corresponding evaporative emissions.

Similarly, we have very little data with which quantitative predictions
of the impact of E85 use on non-exhaust emissions of air toxics (e.g.,
benzene) can be drawn. The Auto/Oil study mentioned in the previous
section tested the same three Tier 0 and Tier 1 vehicles for hot-soak
evaporative emissions. They found no statistically significant change in
NMOG or OMHCE[^120] evaporative emissions, yet found a statistically
significant 60% reduction in benzene emissions. Directionally, you would
expect both hydrocarbon and air toxic evaporative emissions to decrease
due to the dilution of the hydrocarbon portion of the fuel with ethanol.
However, again, it is highly dependent on the volatility of the gasoline
component of the specific E85 used and its benzene content, neither of
which is regulated.

**3.2 Effect of Fuel Quality on Spark-Ignited Nonroad Equipment
Emissions**

We use EPA's NONROAD emission model to estimate the effect of gasoline
quality on emissions from nonroad equipment. We use the 2005 version of
this model, NONROAD2005, which includes the effect of ethanol on
permeation emissions from several types of equipment: all small
spark-ignition equipment (including handheld and non-handheld equipment
less than or equal to 25 hp), all spark-ignition recreational marine
watercraft (includes all outboard, stern-drive inboard, and personal
watercraft). Note that these categories do not include recreational
vehicles (motorcycles, ATVs, and snowmobiles) or large spark-ignition
equipment.

Only a limited number of fuel parameters affect emissions in NONROAD.
Exhaust VOC, CO and NOx emissions are a function of sulfur and oxygen.
Here, only the latter fuel parameter is of interest. Emissions of all
three pollutants are assumed to change proportionally with fuel oxygen
content. Table 3.2-1 shows the effect of moving to a 10 volume percent
ethanol blend (3.5 wt% oxygen) on these emissions, either from a
non-oxygenated fuel or from an 11 volume percent MTBE blend (2.0 wt%
oxygen).[^121]

**Table 3.2-1.**

Effect of a 10 Volume Percent Ethanol Fuel on Nonroad Exhaust Emissions

  -------------- ---------------- -------------- ---------------- --------------
                 4-Stroke Engines                2-Stroke Engines 

  Base Fuel      Non-Oxygenated   11 Volume      Non-Oxygenated   11 Volume
                                  Percent MTBE                    Percent MTBE

  VOC            -16%             -7%            -2%              -1%

  CO             -22%             -9%            -23%             -10%

  NOx            +40%             +17%           +65%             +28%
  -------------- ---------------- -------------- ---------------- --------------

As can be seen, the higher oxygen content of ethanol blends reduces
exhaust VOC and CO emissions. However, it also increases NOx emissions
quite substantially, especially from 4-stroke engines. However, it
should be noted that NOx emissions from these engines tend to be fairly
low to start with, given the fact that these engines run richer than
stoichiometric. Thus, a large percentage increase of a relative low base
value can be a relatively small increase in absolute terms. This will be
seen below in Chapter 4, when we evaluate the impact of increased
ethanol use on the local and national emission inventories.

Non-exhaust VOC emissions (other than permeation) are a function of
gasoline RVP and ethanol content in NONROAD2005. Ethanol content only
affects permeation emissions. Both of these emissions are temperature
dependent, so the effect of ethanol and RVP is also temperature
dependent. Based on the results of modeling national emissions in July,
a 10 volume percent ethanol blend increases non-exhaust VOC emissions by
15 percent. This assumes a 1.0 psi increase in RVP.

Hose permeation emissions in the public version of NONROAD2005 are
independent of fuel quality. In support of the development of new
emission standards for small nonroad engines, EPA has been testing small
nonroad engines for hose permeation emissions using fuels with and
without ethanol. Based on this testing, as well as discussions with
nonroad equipment manufacturers, we developed new hose permeation
emission rates for NONROAD2005 for both gasoline and E10 fuels. Roughly,
these revised permeation rates indicate that emissions with E10 are 2-3
times higher than those for gasoline. This increase is similar to that
found for the three oldest onroad vehicles in the CRC E-65 study,
discussed in section 3.1.1.1.4 above.[^122] The NONROAD2005 hose
permeation factors[^123] were adjusted as follows:

> 1\) Permeation emissions in the public version of NONROAD2005 were 450
> grams per meter-squared per day (g/m^2^/day) for both small
> spark-ignition engines and for the supply hoses on portable fuel tanks
> in recreational marine watercraft, applicable to all fuels. For both
> types of equipment, the permeation emission rates were changed to 122
> g/m^2^/day for gasoline and 222 g/m^2^/day for E10.
>
> 2\) Permeation emissions in the public version of NONROAD2005 were 100
> g/m^2^/day for supply hoses on outboard recreational marine watercraft
> (\> 25 hp), 300 g/m^2^/day for supply hoses on personal watercraft
> (PWC), and 110 g/m^2^/day for fill neck hoses on both outboards and
> PWC. These three permeation emission rates were changed to 42
> g/m^2^/day for gasoline and 125 g/m^2^/day for E10.
>
> 3\) Permeation emissions from sterndrive/inboard recreational marine
> watercraft in the public version of NONROAD2005 were 100 g/m^2^/day
> for supply hoses and 110 g/m^2^/day for fill neck hoses. Both of these
> permeation emission rates were changed to 22 g/m^2^/day for gasoline
> and 40 g/m^2^/day for E10.
>
> 4\) Permeation emissions in the public version of NONROAD2005 were 0
> g/m^2^/day for vent hoses on all recreation marine watercraft. This
> permeation emission rate was changed to 2.5 g/m^2^/day for gasoline
> and 4.9 g/m^2^/day for E10.
>
> 5\) One final adjustment was to double the vent hose length for all
> gasoline-fueled outboards, personal watercraft, and sterndrive/inboard
> watercraft.

The NONROAD emissions model does not estimate emissions of toxic air
pollutants from nonroad equipment. However, the National Mobile
Inventory Model (NMIM) does make such estimates. NMIM utilizes the
MOBILE and NONROAD models to develop national emission estimates for
motor vehicles and nonroad equipment. For the most part, NMIM provides
the relevant inputs to MOBILE6.2 and NONROAD and processes the results.
However, with respect to nonroad toxic emissions, NMIM takes exhaust and
non-exhaust VOC emission estimates from NONROAD and applies a set of
toxic fractions of VOC emissions based on fuel quality.[^124] NMIM
contains estimates of the toxic fractions of VOC emissions for three
fuels: a non-oxygenated gasoline, an MTBE blend and an ethanol blend.
NMIM applies the fraction of VOC emissions represented by each of the
air toxics to either the exhaust or non-exhaust VOC emissions estimated
by NONROAD. The toxic fractions of VOC were derived from motor vehicle
testing. Thus, they are considered approximate.

We hope to update our projections of the effect of gasoline quality on
exhaust and non-exhaust emissions from nonroad equipment based on
additional testing to be conducted over the next several years if
funding allows. These updated projections could be used in the
comprehensive assessment of the impact of the fuel-related provisions of
the Energy Act which is due in 2009.

**3.3 Effect of Fuel Quality on Compression-Ignited Vehicle and
Equipment Emissions -- Biodiesel**

Biodiesel is expected to be one of two renewable fuels to be used in
significant volumes through 2020. While ethanol will dominate the
market, biodiesel use is likely to grow considerably reaching 300
million gallons by 2012, according to EIA estimates. It is produced
domestically from vegetable oils, animal fats and recycled cooking oils,
with the majority of this product coming from soybean oil. It is
typically used in 2%, 5% and 20% blends with diesel fuel which have been
assigned B2, B5 and B20 designations, respectively.

In 2002, EPA issued a report entitled "A Comprehensive Analysis of
Biodiesel Impacts on Exhaust Emissions" based on existing data from
various test programs. This report included a technical analysis of
biodiesel effects on regulated and unregulated pollutants from diesel
powered vehicles and concluded that biodiesel fuels improved PM, HC and
CO emissions of diesel engines while slightly increasing their NOx
emissions.

While the conclusions reached in the 2002 EPA report relative to
biodiesel effects on VOC, CO and PM emissions have been generally
accepted, the magnitude of the B20 effect on NOx remains controversial
due to conflicting results from different studies. Significant new
testing is being planned with broad stakeholder participation and
support in order to better estimate the impact of biodiesel on NOx and
other exhaust emissions from the in-use fleet of diesel engines. We hope
to incorporate the data from such additional testing into the analyses
for other studies required by the Energy Act in 2008 and 2009, and into
a subsequent rule to set the RFS program standard for 2013 and later.

**3.4 Emissions from Fuel Production Facilities**

**3.4.1 Ethanol**

The primary impact of renewable fuel production and distribution regards
ethanol, since it is expected to be the predominant renewable fuel used
in the foreseeable future. For the NPRM, we estimated the impact of
increased ethanol production, including corn farming, on emissions based
on DOE's GREET model, version 1.6. This estimate also included emissions
related to distributing the ethanol and take credit for reduced
emissions related to distributing displaced gasoline. Since the time of
the NPRM analysis, DOE has published the next version of GREET, version
1.7. The emission estimates related to ethanol production and
distribution in GREET1.7 differ significantly from those in GREET1.6. In
addition, through EPA's regional offices, we contacted a number of
States to obtain the latest emission estimates for ethanol plants
currently in production. These plant-specific estimates provide a useful
comparison to the inherently generic emission factors used by a
nationwide-average model, such as GREET.

In Section 3.4.1.1, we describe and compare the emission estimates from
the GREET model, both versions 1.6 and 1.7. In Section 3.4.1.2, we
describe the data obtained from the States and consolidate it into two
sets of emission factors; one for wet mills and one for dry mills.
Finally, in Section 3.4.1.3, we describe how we will use both the GREET
and State estimates in estimating national emissions from new ethanol
plants in Chapter 4.

**3.4.1.1 GREET Emission Estimates**

The emissions related to producing and distributing ethanol for use in
gasoline blends from both GREET1.6 and 1.7 are summarized in Table
3.4-1. GREET presents emission factors in a variety of units, such as
per bushel of corn harvested, gallons of ethanol produced, etc. All the
emission factors shown in this table have been converted to a per gallon
of ethanol produced or distributed basis using the default conversion
factors contained in GREET. One of GREET's default assumptions is that
80% of ethanol plants are associated with dry corn milling, while the
other 20% are associated with wet milling. Nearly all future ethanol
plants are planned to be dry mill facilities. Therefore, we only show
the emission factors for dry mill ethanol plants below.

**Table 3.4-1.**

**Well-to-Pump Emissions for Producing and Distributing Ethanol from
Corn**

**Dry Mill Facility** (grams per gallon ethanol)

  ----------- ---------------- ------------ ------------ ----------- ---------------- -----------
  Pollutant   Corn Farming and Ethanol      Co-Product   Ethanol     Gasoline         Total
              Transportation   Production   Credits      Trans-      Transportation   Emissions
                                                         portation   Credit           

  Dry Mills -                                                                         
  GREET1.6                                                                            

  VOC         0.8              6.8          -4.1         0.5         -0.9             3.1

  CO          4.3              2.9          -3.3         0.2         -0.1             4.1

  NOx         11.3             4.6          -6.9         1.5         -0.4             10.1

  PM10        8.1              0.4          -2.5         0.0         0.0              6.1

  SOx         1.2              6.1          -0.9         0.2         -0.1             6.5

  Dry Mills -                                                                         
  GREET1.7                                                                            

  VOC         1.6              2.6          -2.5         1.6         -1.5             1.8

  CO          4.0              1.7          -1.7         0.2         -0.1             4.1

  NOx         10.7             3.9          -4.1         1.3         -0.4             11.4

  PM10        1.1              4.2          -0.5         0.0         0.0              4.9

  SOx         5.0              3.7          -2.4         0.2         -0.1             6.4

  Wet Mills -                                                                         
  GREET1.6                                                                            

  VOC         0.8              6.8          -1.8         0.5         -0.9             5.5

  CO          4.3              3.3          -2.1         0.2         -0.1             5.7

  NOx         11.3             6.2          -4.8         1.5         -0.4             13.8

  PM10        8.1              0.5          -2.3         0.0         0.0              6.4

  SOx         1.2              9.2          -0.6         0.2         -0.1             9.9

  Wet Mills -                                                                         
  GREET1.7                                                                            

  VOC         1.6              2.6          -3.2         1.6         -1.5             1.1

  CO          4.0              2.3          -2.1         0.2         -0.1             4.3

  NOx         10.7             4.9          -4.6         1.3         -0.4             11.9

  PM10        1.1              5.9          -0.6         0.0         0.0              6.5

  SOx         5.0              4.1          -2.7         0.2         -0.1             6.5
  ----------- ---------------- ------------ ------------ ----------- ---------------- -----------

As can be seen, the emission estimates from the two versions of GREET
differ significantly. In particular, VOC emissions in GREET1.7 are about
60% as large as those estimated in GREET1.6 for dry milss and even lower
for wet mills. The other differences are in the [+]{.underline}20%
range, and differ in direction depending on pollutant.

The default mix of dry and wet mill ethanol plants is 80/20 in both
versions of GREET. This is a reasonable estimate for current production.
However, the vast majority of new plants are expected to be of the dry
mill variety. Therefore, we will use the above emission factors for a
80/20 mix of dry and wet mill plants to estimate the emissions from
current ethanol plants and the dry mill emission factors to estimate the
emissions from future plants.

While emissions related to ethanol production and distribution will
increase,,areas with refineries might experience reduced emissions, not
necessarily relative to current emission levels, but relative to those
which would have occurred in the future had ethanol use not risen.
However, to the degree that increased ethanol use reduces imports of
gasoline, as opposed to the domestic production of gasoline, these
reduced refinery emissions will occur overseas and not in the U.S.
Therefore, we will not take any credit for reduced refinery emissions
here.

Similarly, areas with MTBE production facilities might experience
reduced emissions from these plants as they cease producing MTBE.
However, some of these plants are likely to be converted to produce
other gasoline blendstocks, such as iso-octane or alkylate. In this
case, their emissions are not likely to change substantially.

The emission factors shown in Table 3.4-1 do include a credit for
reduced emissions related to a reduction in the volume of non-oxygenated
gasoline being distributed. These are taken directly from the GREET
emission estimates for conventional gasoline production and
distribution. We assumed that ethanol use will reduce gasoline use one
for one on an energy basis.

2.  **Ethanol Production Emissions Received from States**

The emissions from most of the steps involved in ethanol production and
distributions are very diffuse (e.g., tractors plowing corn fields).
However, the emissions from ethanol production plants are point sources
whose emissions are often measured and tracked by local or state
governments. We contacted over a dozen States in an attempt to improve
our estimate of emissions from ethanol production. The results of this
process are summarized below.[^125]

We received emission estimates from 13 States for current ethanol plants
with a combined capacity of 3 billion gallons per year. The emission
data involved annual emission estimates for one or more years between
2001-2005. Overall, these plants represent roughly three-fourths of
current ethanol capacity. The emission data cover a very wide range of
plant capacities, ranging from 0.4 to 274 million gallons of ethanol per
year.

The capacity-weighted average emissions of wet and dry mill ethanol
plants are summarized in Table 3.4-2.

**Table 3.4-2.** Emissions From Ethanol Plants: State Data (g/gal
ethanol)

  -------------- ------------------------ -------------------------------
                 Wet Mills                Dry Mills

  VOC            17.5                     4.0

  CO             15.0                     1.9

  NOx            18.3                     5.5

  PM10           8.8                      2.2

  SOx            24.6                     7.0
  -------------- ------------------------ -------------------------------

As can be seen, the emissions from wet mills are much higher than those
from dry mills. In general, the wet mill plants are older than the dry
mills. They also involved a different set of processes and produce a
different set of by-products.

The ethanol plant emissions from GREET1.6 and GREET1.7 are shown in
Table 3.4-3 below.

**Table 3.4-3**. Emissions From Ethanol Plants: GREET (g/gal ethanol)

  ------------- ------------- --------------- ------------- ---------------
                Wet Mills                     Dry Mills     

                GREET1.6      GREET1.7        GREET1.6      GREET1.7

  VOC           6.8           2.6             6.8           2.6

  CO            3.3           2.3             2.9           1.7

  NOx           6.2           4.9             4.6           3.9

  PM10          0.5           5.9             0.4           4.2

  Sox           9.2           4.1             6.1           3.7
  ------------- ------------- --------------- ------------- ---------------

As can be seen from comparing the emission estimates in Tables 3.4-2 and
3.4-3, the State data indicate that the emission from wet mills are much
higher than those estimated in either version of GREET. In contrast, the
emission data obtained from the States for dry mills is generally
consistent with the estimates in GREET1.6 and higher than those in
GREET1.7. An exception to the latter are emissions of PM10, which the
state data indicate are lower than the estimate in GREET1.7 and higher
than that in GREET1.6.

The reasons for the differences in the State data and the estimates in
GREET1.6 and GREET1.7 are not known. It is possible, particularly for
wet mills, that the State estimates include the emissions from an entire
geographically-defined facility which may include more operations than
just corn milling. It is also possible that the estimates in GREET
represent emissions from plants which would be designed today or in the
future and are less representative of plants which were built over 20
years ago. This deserves further investigation. At this time, we will
use the average of the State emission data as a second estimate of
ethanol plant emissions, along with GREET1.7, in order to better
indicate the range of possible emissions from these plants.

3.  **Selection of Ethanol Production and Distribution Emission
    Estimates**

We have available three estimates of the emissions from ethanol plants
and two estimates of the emissions from the other steps in the process
of growing corn through ethanol distribution. The estimates contained in
GREET1.7 represent an update of those in GREET1.6. Therefore, we will
use the emission factors from GREET1.7 in lieu of those in GREET 1.6 in
Chapter 4 where we estimate national emissions from ethanol production
and distribution.

In addition, we develop a second estimate of these emissions by
substituting the average emission factors based on the State data for
the dry mill ethanol plant emissions contained in GREET1.7. While the
State data represents emissions from current plants and our primary
focus is future plants, it is not certain that the emissions of the two
sets of ethanol plants will differ. Therefore, the use of the State data
will provide a useful indication of the potential uncertainty in the
GREET1.7 estimates. We chose not to use the state data for wet mills, as
these emission factors are often a factor of 10 higher than those from
GREET1.6 or GREET1.7. More understanding of the processes producing
these emissions is needed before they can be all assigned to ethanol
production. Thus, we use the GREET1.7 emission factors for wet mill
ethanol production, while using the state data for dry mill ethanol
production.

The two sets of estimates are shown in Table 3.4-4. In both sets of
estimates, the emission factors for current ethanol plants assume an
80/20 mix of dry and wet mills, while those for future ethanol plants
assume 100% dry mills.

**Table 3.4-4.**

**Selected Emission Factors for Ethanol Production and Distribution**

(g/gal ethanol)

  ------------- ------------- --------------- ------------- ---------------
                GREET1.7                      GREET1.7 +    
                                              State Data    

                Current       Future Plants   Current       Future Plants
                Plants                        Plants        

  VOC           1.8           1.8             3.6           3.2

  CO            4.0           4.1             4.4           4.3

  NOx           11.4          11.4            10.8          13.0

  PM10          4.9           4.9             6.1           2.8

  Sox           6.4           6.4             7.2           9.7
  ------------- ------------- --------------- ------------- ---------------

**3.4.2 Biodiesel**

Like ethanol, we base our emission factors for biodiesel production
distribution on the estimates contained in the GREET model, version 1.7.
Table 3.4-6 shows the emission factors associated with soybean farming,
soy oil production and esterification, and biodiesel distribution. We
also include emissions related to distributing the biodiesel and take
credit for reduced emissions related to distributing displaced diesel
fuel.

**Table 3.4-5.**

**Well-to-Pump Emissions for Producing and Distributing Biodiesel from
Soybeans**

(grams per gallon biodiesel)

  ----------- ---------------- ------------ ---------------- ---------------- -----------
  Pollutant   Total: Soybean   Biodiesel    Biodiesel        Diesel Fuel      Total
              Farming and      Production   Transportation   Transportation   Emissions
              Transportation                                 Credit           

  VOC         2.7              34.8         0.2              -0.2             37.6

  CO          10.6             2.1          0.2              -0.1             12.7

  NOx         19.3             5.5          0.9              -0.6             25.1

  PM10        2.6              2.2          0.0              0.0              4.8

  SOx         17.6             4.1          0.1              -0.1             21.8
  ----------- ---------------- ------------ ---------------- ---------------- -----------

At the same time, areas with refineries might experience reduced
emissions, not necessarily relative to current emission levels, but
relative to those which would have occurred in the future had biodiesel
use not risen. However, to the degree that increased biodiesel use
reduces imports of diesel fuel, as opposed to the domestic production of
diesel fuel, these reduced refinery emissions will occur overseas and
not in the U.S.

**Chapter 3: Appendix**

**Fuel Property Tables and Summary of Predicted Emissions Changes**

**Table 3A-1. CRC E-67 Study Test Fuel Properties**
![](media/image60.png){width="8.75in" height="5.771527777777778in"}

**Table 3A-2. Summary of EPA E-67 vs. EPA Predictive Model Effects of
E10 and MTBE Use Relative to CG and RFG**

![](media/image61.wmf)

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**Chapter 4: National Emission Inventory Impacts**

This chapter describes the methods used to develop national emissions
inventories under the Renewable Fuel Standard (RFS) program. These
inventories account for impacts from ethanol use, the removal of MTBE,
and the resulting changes to gasoline. These inventories also account
for the impacts of ethanol and biodiesel production and distribution.
This chapter also presents and discusses these inventories.

**4.1 Impact of Ethanol Use**

This section describes the methods used to develop national emissions
inventories with respect to ethanol consumption. This section also
presents and discusses these inventories. These inventories reflect only
emissions from vehicles and equipment operating on ethanol-blend
gasoline, from both onroad and off-road sources. The off-road sources do
not include nonroad diesel, locomotive, or marine applications.

**4.1.1 Overview of Cases**

As described in Section 2.1, we consider three cases for the future use
of ethanol-blend gasoline: a Reference Case, an RFS Case, and an EIA
Case. The main difference between the cases is our assumption about how
much ethanol will be used and where it will go. The Reference case
represents our estimate of fuel quality by county which existed in 2004
when approximately 3.5 billion gallons of ethanol were consumed
nationwide. In terms of 2012 fuel consumption, about 4.0 billion gallons
of ethanol is consumed nationwide in the Reference case. The RFS case
assumes 6.7 billion gallons of ethanol consumption in 2012, in
accordance with the requirements of the RFS mandate. The EIA case
assumes 9.6 billion gallons of ethanol is used nationwide in 2012, based
on projections made in the Energy Information Agency's 2006 Annual
Energy Outlook. We evaluate each case by predicting fuel quality in each
county of the U.S. in 2012. This 2012 fuel matrix is then used for all
inventory and air quality assessments.

While Chapter 2 discusses our methods for determining how much ethanol
will go to each state in each case and how fuel properties will be
affected, this section of the RIA uses those distributions to derive
estimates of the impact on national emissions inventories.

**4.1.2 National Emissions Inventory Estimation Procedure**

Having approximated the effects of adding ethanol and removing MTBE on
fuel properties (see Chapter 2), the next step was to use the EPA's
National Mobile Inventory Model (NMIM)[^126] to calculate emissions
inventories for gasoline fueled motor vehicles and nonroad equipment in
years 2012, 2015, and 2020. For all three years, we ran NMIM for January
and July, assuming that each was representative of winter and summer
conditions, respectively. We estimate annual emission inventories by
summing the two monthly inventories and multiplying by six. This was
done in order to reduce the amount of time needed to actually run the
model.

One additional simplification was made to shorten the time required to
run NMIM for the three years, three fuel cases, two months and roughly
3100 counties in the U.S. Counties within a state with identical fuels
and inspection-maintenance programs and similar temperatures were
grouped together and run through NMIM as a single geographical area. The
temperatures used for this area were those of the county with the
highest VMT in the group. As the specific counties within a state with
identical fuels sometimes changed across the three fuel cases, the
groupings of counties sometimes changed across the NMIM runs of the
three fuel cases. This occasionally introduced a change in the
temperatures estimated for a county between fuel cases. This in turn
produced a change in emissions independent of changes in fuel quality.

We evaluated the potential for this simplification to bias the projected
emission impacts of the various fuel cases. Counties where RFG is sold
were always modeled consistently across all three fuel cases and so are
unaffected by this simplification. Counties with low RVP and 9 RVP fuel
were sometimes affected. On average, the changes in emissions occurring
due to a change in temperature appear to be unbiased (i.e., emissions
increase as often as they decrease). Also, many of the emission impacts
of changing fuel quality (e.g., exhaust VOC and NOx impact) were applied
outside of the NMIM model and so are unaffected by this simplification.
Since we do not present or use the emission impacts for individual
counties, we believe that this simplification does not significantly
impact the emission impacts presented below.

We chose 2012 as the first projection year, because it is the year of
full RFS program implementation. We also chose 2015 and 2020 to
illustrate how the emissions will change over time as the fleet changes.
We increased ethanol consumption beyond 2012 only by volumes required to
maintain the same proportion to gasoline that existed in 2012, and not
by growth predicted in EIA estimates. By restricting ethanol growth in
this way, the same fuel quality that existed in 2012 would apply to 2015
and 2020, which would better highlight the effects of fleet turnover.

NMIM's estimates of both onroad and nonroad emissions were
"post-processed" to reflect factors not yet included in the model. For
onroad emissions, the effect of fuel quality on exhaust VOC and NOx
emissions contained in the model (i.e., those in MOBILE6.2) were
replaced with those from the EPA Predictive Model. We further adjusted
the NMIM estimates of exhaust VOC, CO and NOx emissions from onroad
vehicles in a "sensitivity" analysis in order to reflect the significant
degree of uncertainty which currently exists with respect to these
effects. Air toxic emissions were adjusted in order to reflect changes
in total exhaust VOC emissions,. Finally, the effect of ethanol on
permeation VOC and benzene emissions also were added to the onroad
emission estimates. This series of post-processing steps are further
described in the sections below.

For nonroad emissions, the only adjustment to the NMIM estimates was to
adjust air toxic emissions in the two control cases to reflect the
change in the toxic fraction of VOC emissions to that estimated for
onroad vehicles, as opposed to that estimated for nonroad equipment.
These steps for calculating emissions inventories are described in the
following sections. A summary of the models used and fundamental
post-processing steps are shown in Table 4.1-1 below.

**Table 4.1-1. Estimation of National Emissions Inventories:**

Models Used and Fundamental Post-Processing Steps

+-------+------------------------------+------------------------------+
|       | Exhaust Emissions            | Non-Exhaust Emissions        |
+-------+------------------------------+------------------------------+
| O     | Model: NMIM which runs       | Model: NMIM which runs       |
| nroad | MOBILE6.2.                   | MOBILE6.2.                   |
|       |                              |                              |
|       | Post-processing:             | Post-processing:             |
|       |                              |                              |
|       | 1\. Replace VOC and NOx fuel | 1\. Add effect of ethanol on |
|       | effects for Tier 0 vehicles  | permeation emissions of VOC  |
|       | from MOBILE6.2 with fuel     | and benzene.                 |
|       | effects from EPA Predictive  |                              |
|       | Model;                       |                              |
|       |                              |                              |
|       | 2\. Conduct sensitivity      |                              |
|       | analysis by applying fuel    |                              |
|       | effects for Tier 0 vehicles  |                              |
|       | to all vehicles.             |                              |
|       |                              |                              |
|       | 3\. Adjust exhaust air       |                              |
|       | toxics emissions to reflect  |                              |
|       | adjustment to exhaust VOC    |                              |
|       | emissions.                   |                              |
+-------+------------------------------+------------------------------+
| No    | Model: NMIM which runs       | Model: NMIM which runs       |
| nroad | NONROAD2005 (modified to     | NONROAD2005 (modified to     |
|       | account for hose             | account for hose             |
|       | permeation).                 | permeation).                 |
|       |                              |                              |
|       | Post-processing:             | Post-processing:             |
|       |                              |                              |
|       | 1\. Changes in toxic         | 1\. Changes in toxic         |
|       | fraction of VOC emissions in | fraction of VOC emissions in |
|       | two fuel control cases based | two fuel control cases based |
|       | on onroad estimates instead  | on onroad estimates instead  |
|       | of nonroad estimates.        | of nonroad estimates.        |
+-------+------------------------------+------------------------------+

**4.1.2.1 Onroad Emission Estimation Procedures**

We ran NMIM to estimate county-specific emissions from gasoline motor
vehicles for January and July in years 2012, 2015, and 2020. For each
month and year combination, we ran the three onroad cases (Reference,
RFS, and EIA). The NMIM model utilizes the MOBILE6.2[^127] model to
estimate motor vehicle emissions, as well as the effect of fuel quality
on emissions. As discussed in Chapter 3, the EPA Predictive Model
contains more recent estimates of the impact of fuel quality on exhaust
VOC and NOx emissions. Therefore, we removed the impact of fuel quality
on exhaust VOC and NOx emissions as estimated by MOBILE6.2 and replaced
these impacts with those of the EPA Predictive Model. As also discussed
in Chapter 3, MOBILE6.2 does not include the impact of ethanol on
permeation emissions. Therefore, we added these emissions to those
estimated by NMIM. Finally, we arrived at annual emissions estimates by
summing the January and July results, then multiplying by six. The
procedures for making these changes are discussed below.

4.1.2.1.1 Onroad Exhaust Emissions

MOBILE6.2 performs most of its emission estimation procedures for a
non-oxygenated 8.7 RVP gasoline. The effect of differing fuel quality is
represented by a set of adjustment factors, which can vary by vehicle
type, model year, and whether the vehicle is properly operating or not
(i.e., is a low or high emitter). Because the mix of vehicle types,
model years, and low and high emitters varies by county and calendar
year, it is infeasible to estimate the net impact of each fuel parameter
on emissions outside of the model. In Section 3.1.1.1.2 of Chapter 3, we
describe a process whereby we performed linear regressions on the
exhaust emissions estimated by NMIM in order to determine the average
effect of RVP, ethanol content and MTBE content on exhaust VOC and NOx
emissions. Also in Section 3.1.1.1.2, we describe these same impacts
using the EPA Predictive Model. We combined these fuel-emission effects
with the fuel quality expected to exist in each county under each
ethanol use case to estimate the adjustment which NMIM had applied to
exhaust VOC and NOx emissions. This NMIM adjustment for fuel quality was
removed and replaced by one based on the EPA Predictive Models. In our
primary analysis, the fuel-emission effects from the EPA Predictive
Models were only applied to the fraction of exhaust VOC and NOx
emissions which are emitted by Tier 0 vehicles. In our sensitivity
analysis, the fuel-emission effects from the EPA Predictive Models were
applied to all exhaust VOC and NOx emissions.

Table 4.1-2 shows the values for "Tier 0 Fraction"; i.e., the fraction
of VOC and NOx emissions from vehicles with Tier 0 emissions
characteristics. Note that the fraction drops as time progress,
reflecting the attrition of such vehicles in the national fleet. In the
sensitivity analysis, the Tier 0 vehicle emission fraction is 1.0 for
all years and pollutants.

**Table 4.1-2. Fraction of In-Use Exhaust Emissions Attributable to**

Vehicles with Tier 0 Emissions Characteristics

  ----------------------- ----------------------- -----------------------
  Calendar Year           VOC                     NOx

  2012                    0.339                   0.162

  2015                    0.183                   0.065

  2020                    0                       0
  ----------------------- ----------------------- -----------------------

After adjusting exhaust VOC and NOx according to the methods described
above, we adjusted the four exhaust toxic emissions: benzene,
1,3-butadiene, formaldehyde, and acetaldehyde. MOBILE6.2 estimates
exhaust toxic emissions by first estimating the fraction of exhaust VOC
emissions represented by each toxic based on fuel quality. The model
then applies this fraction to exhaust VOC emissions to estimate absolute
emissions of air toxics. Since we adjusted exhaust VOC emissions, it was
necessary to adjust exhaust toxic emissions, as well, by the ratio of
the change in exhaust VOC emissions.

As described in Section 3.1.1.1.2 of Chapter 3, carbon monoxide
emissions were also adjusted. The following equation illustrates the CO
adjustment:

  ------ --- ------ --- ----------------------------------------------------------
  Adj.   =   NMIM   ×   (1+(Etoh Vol% x Etoh Mkt Shr + MTBE Vol% x MTBE Mkt Shr x
  CO         CO         0.5454) × CO Adj. Factor )

  ------ --- ------ --- ----------------------------------------------------------

4.1.2.1.2 Onroad Non-Exhaust Emissions

The only adjustment to the non-exhaust emission estimates from NMIM was
to add county-specific estimates of the increase in permeation emissions
due to ethanol use. In Section 3.1.1.3 of Chapter 3, we determined that
a 10 vol% ethanol blend increased permeation emissions by 0.8 grams per
day at 95 F. We also concluded there that permeation emissions double
with every increase in temperature of 18 °F. Because of this temperature
relationship, permeation effects were only accounted for in the July
emission estimate since emissions during the winter months could be at
least four times lower, and thus negligible.

Permeation emissions occur whether a vehicle is being used or is parked.
Therefore, the average hourly emission factor in each county in July is
determined by adjusting the 0.8 gram per day emission rate for the
average fuel tank temperature occurring in that hour of the day in each
county in July and multiplying by the market share of E10 fuel in that
county. Total monthly emissions in each county were determined by
summing across hours of the day, multiplying by 31 days and multiplying
by the number of vehicles estimated to reside in that county.

The average fuel tank temperature is a function of the average ambient
temperature at that hour of the day, adjusted to account for the
increase in fuel tank temperature for those vehicles which are operating
or which are still cooling down from operating. We obtain estimates of
these latter two factors from EPA's Draft MOVES2006 model.[^128] These
are shown in Table 4.1-3. The fuel tank temperature of vehicles which
have been parked some time tend to lag the ambient temperature both when
the latter is rising and falling. We assume here that the fuel tank
temperature of these parked vehicles is equal to the ambient
temperature, which is true on average for the day.

**Table 4.1-3.** Increase in Fuel Tank Temperature Relative to Ambient

  -------------- --------------------------- -----------------------------
  Hour of the    Vehicles Operating or in    Average Tank Temperature Rise
  Day            Hot Soak                    (F)

  Midnight       2.6%                        10.0

  1:00 AM        2.8%                        6.9

  2:00 AM        1.2%                        6.1

  3:00 AM        0.9%                        4.9

  4:00 AM        0.8%                        3.1

  5:00 AM        2.6%                        3.0

  6:00 AM        6.6%                        3.7

  7:00 AM        12.3%                       4.6

  8:00 AM        14.0%                       3.5

  9:00 AM        10.0%                       3.8

  10:00 AM       11.1%                       3.8

  11:00 AM       12.5%                       4.9

  Noon           15.6%                       4.8

  1:00 PM        16.0%                       5.5

  2:00 PM        17.2%                       6.6

  3:00 PM        21.0%                       7.7

  4:00 PM        23.7%                       8.6

  5:00 PM        28.5%                       8.3

  6:00 PM        30.0%                       8.8

  7:00 PM        25.7%                       9.2

  8:00 PM        18.7%                       8.3

  9:00 PM        13.5%                       7.6

  10:00 PM       10.6%                       8.0

  11:00 PM       7.8%                        8.4
  -------------- --------------------------- -----------------------------

The total number of gasoline vehicles in the U.S. in 2004 is estimated
to be 228 million.[^129] We increased this figure by 1.9% per year[^130]
to derive estimates of the gasoline vehicle fleet in 2012, 2015 and
2020. This produced estimates for the fleet of gasoline vehicles in the
U.S. of 265, 281 and 308 million vehicles in 2012, 2015 and 2020,
respectively. These vehicles were allocated to each county based on the
county-specific distribution of national VMT by gasoline vehicles
contained in NMIM.

As described in Section 3.1.1.3 of Chapter 3, we estimate that benzene
represents 3% of the increased VOC permeation emissions due to ethanol
use. Thus, we added this 3% to the non-exhaust emissions of benzene
estimated by NMIM.

**4.1.2.2 Nonroad Emissions**

NMIM is capable of utilizing any one of a series of EPA's NONROAD
emission models. We chose to use the NONROAD2005[^131] model to estimate
emissions from nonroad equipment here, as it reflects EPA's latest
estimates of emission factors for nonroad equipment. EPA has also
recently developed a set of emission factor inputs for the NONROAD model
which include the effect of ethanol on permeation emissions from a
number of types of nonroad equipment (see Chapter 3).

For the proposed rule inventories, the NONROAD model was not able to
select ethanol related emission factors based on the fuel quality inputs
to the model. It was therefore necessary to run NMIM for two extreme
ethanol use cases (no ethanol use and 100% ethanol use) and use those
results to estimate emissions for the five ethanol use cases which were
the focus of the proposed rule.

For the final rule, NONROAD model capabilities were updated to account
for oxygenate effects. Therefore, we were able to run NMIM (which runs
NONROAD) using the same fuel property inputs that were used for onroad
emissions inventories. This eliminated the need to interpolate between
the "No Oxygen" and "All Oxygen" NONROAD runs that were needed for the
proposal.

For nonroad toxic exhaust emissions, the toxic emissions factors for
nonroad equipment are based on very limited data. In EPA's recent final
rule which implemented new Mobile Source Air Toxic (MSAT) standards, we
adjusted the fraction of nonroad VOC emissions represented by the
various air toxics contained in NMIM for a reduction in fuel benzene
content with those estimated for the same fuel change by MOBILE6.2 for
onroad motor vehicles. This was done because of the very limited amount
of nonroad emission test data which both varied fuel quality and
measured toxics emissions. We take the same approach here. We begin with
the estimate of nonroad toxic emissions from NMIM for the Reference
Case. Then, any change in the toxics fraction of nonroad VOC emissions
due to a change in fuel quality predicted by NMIM is replaced by the
change in the toxics fraction of onroad VOC emissions due to the same
change in fuel quality predicted by MOBILE6.2. This adjustment is
illustrated in the following equation:

+------+---+-------+---+-----------+---+---------------------------------+
| Adju | = | NMIM  | × | NMIM VOC  | × | [MOBILE6.2 Toxic Emissions (RFS |
| sted |   | Toxic |   |           |   | or EIA Case)]{.underline}       |
| Non  |   |       |   | Emissions |   |                                 |
| road |   | Emis  |   | (RFS      |   | MOBILE6.2 VOC Emissions (RFS or |
|      |   | sions |   |           |   | EIA Case                        |
| T    |   |       |   | or EIA    |   |                                 |
| oxic |   |       |   | case)     |   |                                 |
| E    |   |       |   |           |   |                                 |
| miss |   |       |   |           |   |                                 |
| ions |   |       |   |           |   |                                 |
+------+---+-------+---+-----------+---+---------------------------------+
|      |   |       |   | NMIM VOC  |   | [MOBILE6.2 Toxic Emissions      |
|      |   |       |   |           |   | (Reference Case)]{.underline}   |
|      |   |       |   | Emissions |   |                                 |
|      |   |       |   |           |   | MOBILE6.2 VOC Emissions         |
|      |   |       |   | (         |   | (Reference Case)                |
|      |   |       |   | Reference |   |                                 |
|      |   |       |   | case)     |   |                                 |
+------+---+-------+---+-----------+---+---------------------------------+

**4.1.3 National Emissions Inventory Projections**

**4.1.3.1 Emission Inventories: Primary Analysis**

This section provides the national emissions inventories for the primary
case analyses. Criteria pollutant inventories are included, along with a
brief discussion of the trends. A short discussion of air toxics
inventories is also included. See Tables 4A-1 through 4A-7 in the
Chapter 4 Appendix for complete primary-case inventories on air toxics
and criteria pollutants, as well as the percent changes in inventories
from the Reference case.

Table 4.1-4 shows ethanol impacts on VOC inventories for each of the
three cases of renewable fuel use in years 2012, 2015, and 2020. In any
given year, the data suggest that total VOC emissions will increase as
ethanol use increases. The largest increase is seen in the EIA case,
where the increase is about 1% of the Reference case inventory.

Our analysis indicates that this increase is a result of VOC non-exhaust
emissions, such as those from evaporation or permeation. While VOC
exhaust emissions decrease, they do not decrease enough to counteract
the increase from non-exhaust emissions.

**Table 4.1-4.**

**National VOC Emissions from Gasoline Vehicles and Equipment:**

**Reference Case Inventory and Change in Inventory for Control** Cases
(Tons/Year)

  --------------------------- -------------- -------------- --------------
  **Primary Case**            Tons/Year                     

  **Total**                   **2012**       **2015**       **2020**

  Reference                   5,882,000      5,569,000      5,356,000

  RFS Case (Change)           18,000         25,000         34,000

  EIA Case (Change)           43,000         49,000         58,000

  **On-Road**                 **2012**       **2015**       **2020**

  Reference                   3,417,000      3,269,000      3,244,000

  RFS Case (Change)           10,000         16,000         23,000

  EIA Case (Change)           32,000         36,000         42,000

  **Non-Road**                **2012**       **2015**       **2020**

  Reference                   2,465,000      2,300,000      2,112,000

  RFS Case (Change)           8,000          9,000          11,000

  EIA Case (Change)           11,000         13,000         16,000
  --------------------------- -------------- -------------- --------------

Table 4.1-5 shows ethanol impacts on CO inventories for each of the
three cases of renewable fuel use in years 2012, 2015, and 2020. In any
given year, data suggest that total CO emissions will decrease as
ethanol use increases. The largest reduction is seen in the EIA case;
this decrease is still less than 3% of the Reference inventory.

**Table 4.1-5.**

**National CO Emissions from Gasoline Vehicles and Equipment:**

**Reference Case Inventory and Change in Inventory for Control** Cases
(Tons/Year)

  --------------------------- -------------- -------------- --------------
  **Primary Case**            Tons/Year                     

  **Total**                   **2012**       **2015**       **2020**

  Reference                   55,022,000     53,702,000     53,949,000

  RFS Case (Change)           -483,000       -473,000       -460,000

  EIA Case (Change)           -1,366,000     -1,329,000     -1,286,000

  **On-Road**                 **2012**       **2015**       **2020**

  Reference                   37,656,000     36,171,000     35,723,000

  RFS Case (Change)           -45,000        -39,000        -19,000

  EIA Case (Change)           -359,000       -321,000       -252,000

  **Non-Road**                **2012**       **2015**       **2020**

  Reference                   17,366,000     17,531,000     18,226,000

  RFS Case (Change)           -438,000       -434,000       -441,000

  EIA Case (Change)           -1,007,000     -1,008,000     -1,034,000
  --------------------------- -------------- -------------- --------------

Table 4.1-6 shows ethanol impacts on NOx inventories for each of the
three cases of renewable fuel use in years 2012, 2015, and 2020. In any
given year, the data suggest that total NOx emissions will increase as
ethanol use increases. The largest increase is seen in the EIA case,
which is around 2% of the Reference inventory.

Our analysis also indicates that nonroad NOx emissions increase much
greater than onroad emissions. While onroad inventories increase less
than one percent in control cases, nonroad inventories increase up to
11% in the EIA case.

**Table 4.1-6.**

**National NOx Emissions from Gasoline Vehicles and Equipment:**

**Reference Case Inventory and Change in Inventory for Control** Cases
(Tons/Year)

  --------------------------- -------------- -------------- --------------
  **Primary Case**            Tons/Year                     

  **Total**                   **2012**       **2015**       **2020**

  Reference                   2,487,000      2,059,000      1,695,000

  RFS Case (Change)           23,000         18,000         17,000

  EIA Case (Change)           40,000         33,000         32,000

  **On-Road**                 **2012**       **2015**       **2020**

  Reference                   2,240,000      1,797,000      1,407,000

  RFS Case (Change)           9,000          3,000          0

  EIA Case (Change)           13,000         4,000          0

  **Non-Road**                **2012**       **2015**       **2020**

  Reference                   247,000        262,000        288,000

  RFS Case (Change)           14,000         15,000         17,000

  EIA Case (Change)           27,000         29,000         32,000
  --------------------------- -------------- -------------- --------------

Table 4.1-7 shows ethanol impacts on air toxic emissions for each of the
three cases of renewable fuel use in 2012.

For all air toxics shown, the most extreme changes occur in the EIA
case. The data suggest that, in 2012, total benzene emissions will
decrease by about 4% due to decreases in both onroad and nonroad
emissions. Total 1,3-butadiene emissions decrease by less than 2% due to
decreases in both onroad and nonroad emissions. Total formaldehyde
emissions decrease by up to 1.5%. Total acetaldehyde emissions increase
by as much as 36% due to increases in both onroad and nonroad emissions.

Generally, the trends in 2015 and 2020 parallel those of 2012 and are
shown in the appendix to this chapter. Benzene maintains a drop of up to
about 6% with increased ethanol use. Formaldehyde remains fairly flat,
ranging from a 0.5% increase to a 1.2% decrease. Acetaldehyde maintains
an increase of as much as 36.5%. Finally, 1,3-butadiene remains fairly
flat, ranging from no change to a 0.5% increase.

Again, we emphasize that the toxics inventories are based on very
limited data, especially when it comes to emissions from nonroad
equipment.

**Table 4.1-7.**

**National Toxic Emissions from Gasoline Vehicles and Equipment in
2012:**

**Reference Case Inventory and Change in Inventory for Control**
(Tons/Year)

  ----------------- ------------- ------------------- ------------------ ------------------
  **Primary Case**  **Benzene**   **1,3-Butadiene**   **Formaldehyde**   **Acetaldehyde**

  **Total**                                                              

  Reference         178,000       18,900              40,400             19,900

  RFS Case (Change) -3,200        -200                -600               3,400

  EIA Case (Change) -7,200        -300                -200               7,100

  **Onroad**                                                             

  Reference         124,100       12,000              29,900             15,500

  RFS Case (Change) -2,300        -200                -600               2,400

  EIA Case (Change) -5,400        -200                -300               5,400

  **Nonroad**                                                            

  Reference         53,900        6,900               10,500             4,400

  RFS Case (Change) -900          0                   0                  1,000

  EIA Case (Change) -1,800        -100                100                1,700
  ----------------- ------------- ------------------- ------------------ ------------------

**4.1.3.2 Emission Inventories: Sensitivity Analyses**

This section provides the national emissions inventories for the
sensitivity case analyses. Criteria pollutant inventories are included,
along with a brief discussion of the trends. See Tables 4A-1 through
4A-7 in the Chapter 4 Appendix for complete sensitivity-case inventories
on air toxics and criteria pollutants, as well as the percent changes in
inventories from the reference case.

Table 4.1-8 shows ethanol impacts on VOC inventories for each of the
three cases of renewable fuel use in years 2012, 2015, and 2020. Where
the primary analysis showed total VOC emissions increasing with ethanol
use in all cases, the sensitivity analysis shows that total VOC
emissions decrease. Onroad emissions decrease in all cases, while
nonroad emissions increase to the same extent as under the primary
analysis.

**Table 4.1-8.**

**National VOC Emissions from Gasoline Vehicles and Equipment:**

**Reference Case Inventory and Change in Inventory for Control** Cases
(Tons/Year)

  --------------------------- -------------- -------------- --------------
  **Sensitivity Case**        Tons/Year                     

  **Total**                   **2012**       **2015**       **2020**

  Reference                   5,834,000      5,510,000      5,281,000

  RFS Case (Change)           -20,000        -23,000        -27,000

  EIA Case (Change)           -4,000         -10,000        -17,000

  **On-Road**                 **2012**       **2015**       **2020**

  Reference                   3,369,000      3,210,000      3,169,000

  RFS Case (Change)           -28,000        -32,000        -38,000

  EIA Case (Change)           -15,000        -23,000        -33,000

  **Non-Road**                **2012**       **2015**       **2020**

  Reference                   2,465,000      2,300,000      2,112,000

  RFS Case (Change)           8,000          9,000          11,000

  EIA Case (Change)           11,000         13,000         16,000
  --------------------------- -------------- -------------- --------------

Table 4.1-9 shows ethanol impacts on CO inventories for each of the
three cases of renewable fuel use in years 2012, 2015, and 2020. In any
given year, the data suggest that total CO emissions will decrease as
ethanol use increases. The onroad vehicle CO emission reductions
increase by roughly a factor of three compared to the primary analysis.
This increases the overall CO emissions reduction from about 3% in the
primary case to 4% in the sensitivity case.

**Table 4.1-9.**

**National CO Emissions from Gasoline Vehicles and Equipment:**

**Reference Case Inventory and Change in Inventory for Control** Cases
(Tons/Year)

  --------------------------- -------------- -------------- --------------
  **Sensitivity Case**        Tons/Year                     

  **Total**                   **2012**       **2015**       **2020**

  Reference                   54,315,000     52,998,000     53,183,000

  RFS Case (Change)           -692,000       -676,000       -676,000

  EIA Case (Change)           -1,975,000     -1,929,000     -1,937,000

  **On-Road**                 **2012**       **2015**       **2020**

  Reference                   36,949,000     35,467,000     34,957,000

  RFS Case (Change)           -254,000       -242,000       -235,000

  EIA Case (Change)           -968,000       -921,000       -903,000

  **Non-Road**                **2012**       **2015**       **2020**

  Reference                   17,366,000     17,531,000     18,226,000

  RFS Case (Change)           -438,000       -434,000       -441,000

  EIA Case (Change)           -1,007,000     -1,008,000     -1,034,000
  --------------------------- -------------- -------------- --------------

Table 4.1-10 shows ethanol impacts on NOx inventories for each of the
three cases of renewable fuel use in years 2012, 2015, and 2020. In any
given year, the data suggest that total NOx emissions will increase as
ethanol use increases. The largest increase is seen in the EIA case,
where the increase in total emissions is as high as 4.6% of the
reference inventory. As in the primary analysis, nonroad NOx emissions
increase much greater than onroad emissions. While onroad inventories
increase up to 3.5%, nonroad inventories increase upwards of 11.1% in
the EIA case.

**Table 4.1-10.**

**National NOx Emissions from Gasoline Vehicles and Equipment:**

**Reference Case Inventory and Change in Inventory for Control** Cases
(Tons/Year)

  --------------------------- -------------- -------------- --------------
  **Sensitivity Case**        Tons/Year                     

  **Total**                   **2012**       **2015**       **2020**

  Reference                   2,519,000      2,087,000      1,717,000

  RFS Case (Change)           68,000         57,000         48,000

  EIA Case (Change)           106,000        91,000         79,000

  **On-Road**                 **2012**       **2015**       **2020**

  Reference                   2,272,000      1,825,000      1,429,000

  RFS Case (Change)           54,000         42,000         31,000

  EIA Case (Change)           79,000         62,000         47,000

  **Non-Road**                **2012**       **2015**       **2020**

  Reference                   247,000        262,000        288,000

  RFS Case (Change)           14,000         15,000         17,000

  EIA Case (Change)           27,000         29,000         32,000
  --------------------------- -------------- -------------- --------------

Table 4.1-11 shows ethanol impacts on air toxic emissions for each of
the five cases of renewable fuel use in 2012. The impacts in 2015 and
2020 are shown in the Appendix to this chapter.

**Table 4.1-11.**

**National Toxic Emissions from Gasoline Vehicles and Equipment in
2012:**

**Reference Case Inventory and Change in Inventory for Control**
(Tons/Year)

  --------------- ------------- ------------------- ------------------ ------------------
  **Sensitivity   **Benzene**   **1,3-Butadiene**   **Formaldehyde**   **Acetaldehyde**
  Case**                                                               

  **Total**                                                            

  Reference Case  175,700       18,600              39,600             19,500

  RFS Case        -5,000        -400                -1,100             3,000
  (Change)                                                             

  EIA Case        -9,400        -600                -700               6,600
  (Change)                                                             

  **Onroad**                                                           

  Reference Case  121,800       11,700              29,100             15,100

  RFS Case        -4,100        -400                -1,100             2,000
  (Change)                                                             

  EIA Case        -7,600        -500                -800               4,900
  (Change)                                                             

  **Nonroad**                                                          

  Reference Case  53,900        6,900               10,500             4,400

  RFS Case        -900          0                   0                  1,000
  (Change)                                                             

  EIA Case        -1,800        -100                100                1,700
  (Change)                                                             
  --------------- ------------- ------------------- ------------------ ------------------

As in the primary analysis, the most extreme changes in the sensitivity
analysis tend to occur in the EIA case.

The data suggest that, in 2012, total benzene emissions will decrease by
as much as 5.4% due to decreases in both onroad and nonroad emissions.
Total formaldehyde emissions decrease by up to 2.8%. Nonroad
formaldehyde emissions tend to remain relatively flat, while onroad
emissions decrease. Total acetaldehyde emissions increase by as much as
34% due to increases in both onroad and nonroad emissions. Total
1,3-butadiene emissions decrease by about 3%.

**4.1.3.3 Local and Regional VOC and NOx Emissions (Summer 2015)**

We also estimate the percentage change in VOC, NOx, and CO emissions
from gasoline fueled motor vehicles and equipment in those areas which
actually experienced a significant change in ethanol use. Specifically,
we focused on areas where the market share of ethanol blends was
projected to change by 50 percent or more. We also focused on summertime
emissions, as these are most relevant to ozone formation as discussed in
Chapter 5. We modeled 2015 because the ozone Response Surface Model
(RSM) used for air quality modeling (also discussed in Chapter 5) is
based upon a 2015 emissions inventory, though we would expect similar
results in 2012. Finally, we developed separately estimates for: 1) RFG
areas, including the state of California and the portions of Arizona
where their CBG fuel programs apply, 2) low RVP areas (i.e., RVP
standards less than 9.0 RVP, and 3) areas with a 9.0 RVP standard. This
set of groupings helps to highlight the emissions impact of increased
ethanol use in those areas where emission control is most important.

Table 4.1-12 presents our primary analysis estimates of the percentage
change in VOC, NOx, and CO emission inventories for these three types of
areas when compared to the 2015 reference case. Note that the analyses
here is very similar to that described in Section 5.1, with the
exception that Table 4.1-12 below reflects 50-state emissions (instead
of 37 eastern states) and excludes diesel emissions.

**Table 4.1-12.**

Change in July 2015 Emissions from Gasoline Vehicles and Equipment in
Counties Where Ethanol Use Changed Significantly -- Primary Analysis

  ----------------------- ----------------------- -----------------------
  Ethanol Use             RFS Case                EIA Case

  RFG Areas                                       

  Ethanol Use             Down                    Up

  VOC                     0.8%                    2.3%

  NOx                     -3.4%                   1.6%

  CO                      6.1%                    -2.6%

  Low RVP Areas                                   

  Ethanol Use             Up                      Up

  VOC                     4.2%                    4.6%

  NOx                     6.2%                    5.7%

  CO                      -12.5%                  -13.7%

  Other Areas (9.0 RVP)                           

  Ethanol Use             Up                      Up

  VOC                     3.6%                    4.6%

  NOx                     7.3%                    7.0%

  CO                      -6.4%                   -6.0%
  ----------------------- ----------------------- -----------------------

As expected, increased ethanol use tends to increase NOx emissions. The
increase in low RVP and other areas is greater than in RFG areas, since
the RFG in the RFG areas included in this analysis all contained MTBE.
Also, increased ethanol use tends to increase VOC emissions, indicating
that the increase in non-exhaust VOC emissions exceeds the reduction in
exhaust VOC emissions. This effect is muted with RFG due to the absence
of an RVP waiver for ethanol blends. See Chapter 2 for a discussion of
how ethanol levels will change at the state-level.

Table 4.1-13 presents the percentage change in VOC, NOx, and CO emission
inventories under our sensitivity analysis (i.e., when we apply the
emission effects of the EPA Predictive Models to all motor vehicles).

**Table 4.1-13.**

Change in July 2015 Emissions from Gasoline Vehicles and Equipment in
Counties Where Ethanol Use Changed Significantly -- Sensitivity Analysis

  ----------------------- ----------------------- -----------------------
  Ethanol Use             RFS Case                EIA Case

  RFG Areas                                       

  Ethanol Use             Down                    Up

  VOC                     -1.0%                   1.0%

  NOx                     -0.9%                   5.6%

  CO                      7.3%                    -3.0%

  Low RVP Areas                                   

  Ethanol Use             Up                      Up

  VOC                     3.4%                    3.7%

  NOx                     10.4%                   10.8%

  CO                      -15.0%                  -16.4%

  Other Areas (9.0 RVP)                           

  Ethanol Use             Up                      Up

  VOC                     3.0%                    3.9%

  NOx                     10.8%                   11.0%

  CO                      -9.0%                   -8.9%
  ----------------------- ----------------------- -----------------------

Directionally, the changes in VOC and NOx emissions in the various areas
are consistent with those from our primary analysis. The main difference
is that the increases in VOC emissions are smaller, due to more vehicles
experiencing a reduction in exhaust VOC emissions, and the increases in
NOx emissions are larger.

**4.2 Impact of Biodiesel Use**

As discussed in Chapter 1, biodiesel use totaled 25 million gallons in
2004 and is projected to increase to 300 million gallons in 2012. Total
diesel fuel use in onroad diesels in 2004 was roughly 39.4 billion
gallons and is expected to grow to 47.5 billion gallons per year by
2012.[^132] The volumes of biodiesel produced thus represent 0.06% and
0.6% of onroad diesel fuel consumption in 2004 and 2012, respectively.
Given the very small contribution of biodiesel to the pool of diesel
fuel, the nationwide emission impacts of biodiesel are expected to be
similarly small for the foreseeable future. As a result, we have not
included biodiesel emission impacts in our emission inventory estimates
for this rule.

We do intend to investigate these impacts in the future, however. As
stated in Chapter 3, the 2002 EPA report entitled "A Comprehensive
Analysis of Biodiesel Impacts on Exhaust Emissions" concluded that
biodiesel fuels improved PM, CO and HC emissions of diesel engines while
slightly increasing their NOx emissions. Nevertheless, these conclusions
remain controversial due to conflicting results from different studies.
As a result, preparations are being made to launch a test program with
stakeholder participation to further investigate the emission impacts of
biodiesel.

**4.3 Impact of Renewable Fuel Production and Distribution**

**4.3.1 Ethanol**

In Chapter 2, we estimated that 3.5 billion gallons of ethanol was
produced for use in motor fuel in 2004, which comprises our estimate of
fuel quality for the base case . Maintaining fuel quality, but
increasing fuel volume to that expected in 2012,[^133] ethanol use would
increase to 3.9 billion gallons. The increases in emissions associated
with ethanol production and distribution under the RFS and EIA cases
are, thus, determined relative to the emissions associated with
producing and distributing 3.9 billion gallons of ethanol.

We describe the emissions associated with producing and distributing
ethanol on a per gallon basis in Chapter 3.4.1. There, we compare
emissions factors from DOE's GREET model, versions 1.6 and 1.7, as well
as estimates of ethanol plant emissions obtained from the States. We
decided there to use two emission estimates here, one from GREET1.7, and
the other from GREET1.7 augmented by the State estimates for ethanol
plant emissions. Here, we simply multiply those emission factors by the
volume of ethanol being used in each scenario. Table 4.3-1 shows
estimates of annual emissions expected to occur nationwide due to
increased production of ethanol. It should be noted that emissions in
the base case assume a 80/20 mix of dry mill and wet mill facilities.
New plants (and thus, the emission increases) assume 100% dry mil
facilities.

**Table 4.3-1.**

**Annual Emissions Nationwide from Ethanol Production and
Transportation: 2012**

(tons per year)

  ------ ----------- ----------- ---------- ------------ ----------- ----------
         GREET1.7                           GREET1.7 +               
                                            State Data               

         Base Case   RFS Case    EIA Case   Base Case    RFS Case    EIA Case

         Emissions   Increase in            Emissions    Increase in 
                     Emissions                           Emissions   

  VOC    8,000       5,000       11,000     14,000       10,000      20,000

  NOx    17,000      13,000      26,000     18,000       14,000      27,000

  CO     49,000      35,000      72,000     56,000       40,000      81,000

  PM10   21,000      15,000      30,000     12,000       9,000       18,000

  SOx    27,000      20,000      41,000     42,000       30,000      61,000
  ------ ----------- ----------- ---------- ------------ ----------- ----------

As can be seen, the potential increases in VOC and NOx emissions from
ethanol production and transportation are of the same order of magnitude
as those from ethanol use. Generally, ethanol plants are not located in
ozone non-attainment areas, so the ozone impact of the increased VOC and
NOx emissions should be minimal.

According to our estimates, almost 120 counties throughout the nation
are constructing new ethanol plants, expanding existing plants, or
planning construction for future plants. The increases in ethanol
production across these counties range from as low as 2 million gallons
per year for modest expansions, to over 270 million gallons per year due
to the construction of entirely new facilities. To estimate the
potential increase in VOC and NOx emissions associated with these
plants, whether construction is planned or underway, we apply the
ethanol production emission factors (EFs) derived from state data as
well as those found in GREET 1.7. See Chapter 3.4 for a discussion of
the emission factors related to ethanol production and plant emissions.

The ethanol production emission factors are applied to the increase in
the volume of ethanol production expected in each of the counties.
Figures 4.3-1 and 4.3-2 illustrate potential increases in future monthly
VOC and NOx emissions, respectively, in counties that can expect a
growth in ethanol production. The emissions reflect plants operating for
one month at 90% capacity. In each figure, the distribution of counties
is presented in order from the lowest-to-highest increase in ethanol
production volume. The figures show results based upon both state-based
emission factors and GREET 1.7 emission factors.

+----------------------------------+-----------------------------------+
| **Figure 4.3-1.**                | **Figure 4.3-2.**                 |
|                                  |                                   |
| **Monthly VOC Emissions in       | **Monthly NOx Emissions in        |
| Counties Expecting an Increase   | Counties Expecting an Increase in |
| in Ethanol Production**          | Ethanol Production**              |
+----------------------------------+-----------------------------------+
| ![](media/image62.w              | ![](media/image63.                |
| mf){width="3.1416666666666666in" | wmf){width="3.1256944444444446in" |
| height="1.417361111111111in"}    | height="1.417361111111111in"}     |
+----------------------------------+-----------------------------------+
| Distribution of Counties         | Distribution of Counties          |
|                                  |                                   |
| (in order of increasing ethanol  | (in order of increasing ethanol   |
| production)                      | production)                       |
+----------------------------------+-----------------------------------+

As the figures indicate, most counties will see an increase of less than
40 tons/month VOC and less than 60 tons/month NOx, according to the
distribution based upon the state data emission factors. The average
emissions are about 26 tons/month VOC and 35 tons/month NOx using state
data, and about 17 tons/month VOC and 25 tons/month NOx using GREET 1.7
emission factors. However, average VOC and NOx emissions increase to
about 61 tons/month and 83 tons/month, respectively, in the 10% of
counties expecting largest increases in ethanol production. The average
emissions for the remaining 90% of counties is about 21 tons/month VOC
and 29 tons/month NOx. For both VOC and NOx, emissions estimates are
about 35% less when using the GREET 1.7 emission factors.

**4.3.2 Biodiesel**

In Chapter 1, we estimated that 25 million gallons of biodiesel were
produced for use in motor fuel in 2004. Based on growth in overall
diesel fuel demand between 2004 and 2012,[^134] this would represent the
equivalent of 30 million gallons of biodiesel in 2012 for our reference
case. Here, we estimate the increase in emissions which will occur with
an increase in biodiesel production and distribution from 30 million
gallons to 300 million gallons per year.

We describe the emissions associated with producing and distributing
biodiesel on a per gallon basis in Chapter 3. Here, we simply multiply
those emission factors by the volume of biodiesel being used in each
scenario. Table 4.3-2 shows estimates of annual emissions expected to
occur nationwide due to increased production of biodiesel.

**Table 4.3-2.**

**Annual Emissions Nationwide from Biodiesel Production and
Transportation: 2012**

**(**tons per year)

+-------+--------------------------+-----------------------------------+
|       | Reference Inventory:     | Increase in Emissions:            |
|       |                          |                                   |
|       | 28 mill gal biodiesel    | 300 mill gal biodiesel per year   |
|       | per year                 |                                   |
+-------+--------------------------+-----------------------------------+
| VOC   | 1,400                    | 14,000                            |
+-------+--------------------------+-----------------------------------+
| NOx   | 1,500                    | 15,000                            |
+-------+--------------------------+-----------------------------------+
| CO    | 800                      | 8,000                             |
+-------+--------------------------+-----------------------------------+
| PM10  | 50                       | 500                               |
+-------+--------------------------+-----------------------------------+
| SOx   | 250                      | 2,500                             |
+-------+--------------------------+-----------------------------------+

As can be seen, the potential increases in emissions from biodiesel
production and transportation are of the same order of magnitude as
those from biodiesel use, with the exception of CO emissions. Generally,
biodiesel plants are not located in ozone non-attainment areas, so the
ozone impact of the increased VOC and NOx emissions should be minimal.

4.  **Total Emission Impacts of Renewable Fuel Production and Use**

Tables 4.4-1 and 4.4-2 combine the VOC, CO and NOx emission impacts for
ethanol use from Section 4.1 and renewable fuel production and
distribution from Section 4.3. Table 4.4-1 includes the emission impacts
from gasoline vehicles and equipment under our primary analysis and
renewable fuel production and distribution from GREET1.7. Table 4.4-2
includes the emission impacts from gasoline vehicles and equipment under
our sensitivity analysis and renewable fuel production and distribution
from GREET1.7 augmented with the State data for ethanol production
plants. Emissions from renewable fuel production and distribution in
2012 were increased by 1.9% per year to account for growth in gasoline
and diesel fuel demand.

**Table 4.4-1.**

**National Emissions from Gasoline Vehicles and Equipment and Renewable
Fuel Production and Distribution: Primary Case and GREET1.7
(**Tons/Year)

  --------------------------- -------------- -------------- --------------
                              Tons/Year                     

  **VOC Emissions**           **2012**       **2015**       **2020**

  Reference                   5,891,000      5,578,513      5,366,368

  RFS Case (Change)           33,000         41,969         51,584

  EIA Case (Change)           63,000         71,311         83,496

  **CO Emissions**            **2012**       **2015**       **2020**

  Reference                   3,467,000      3,321,850      3,301,600

  RFS Case (Change)           50,000         58,337         69,232

  EIA Case (Change)           108,000        116,446        130,856

  **NOx Emissions**           **2012**       **2015**       **2020**

  Reference                   2,483,000      2,319,026      2,132,736

  RFS Case (Change)           33,000         36,482         39,952

  EIA Case (Change)           38,000         42,596         48,256
  --------------------------- -------------- -------------- --------------

**Table 4.4-2.**

**National Emissions from Gasoline Vehicles and Equipment and Renewable
Fuel Production** and Distribution: Sensitivity Case and GREET1.7/State
Data (Tons/Year)

  --------------------------- -------------- -------------- --------------
                              Tons/Year                     

  **VOC Emissions**           **2012**       **2015**       **2020**

  Reference                   5,849,000      5,525,855      5,298,280

  RFS Case (Change)           -1,000         -746           -3,656

  EIA Case (Change)           25,000         22,824         18,864

  **CO Emissions**            **2012**       **2015**       **2020**

  Reference                   3,426,000      3,270,249      3,234,664

  RFS Case (Change)           16,000         15,622         13,992

  EIA Case (Change)           70,000         67,959         66,224

  **NOx Emissions**           **2012**       **2015**       **2020**

  Reference                   2,484,000      2,320,083      2,133,888

  RFS Case (Change)           34,000         37,539         41,104

  EIA Case (Change)           50,000         55,280         62,080
  --------------------------- -------------- -------------- --------------

# Chapter 4: Appendix 

# Table 4A-1. VOC Emission Inventories under Various Ethanol Use Cases

  ----------------- ----------- ----------- ----------- ----------- ---------- ---------- ----------- ---------- ----------
  **Primary**       Tons/Year                           Change from                       \% Change              
                                                        Reference                         from                   
                                                        (tons)                            Reference              

  **Total**         **2012**    **2015**    **2020**    **2012**    **2015**   **2020**   **2012**    **2015**   **2020**

  Reference         5,882,000   5,569,000   5,356,000   \--         \--        \--        \--         \--        \--

  RFS Case          5,900,000   5,594,000   5,390,000   18,000      25,000     34,000     0.3%        0.4%       0.6%

  EIA Case          5,925,000   5,618,000   5,414,000   43,000      49,000     58,000     0.7%        0.9%       1.1%

  **On-Road**       **2012**    **2015**    **2020**    **2012**    **2015**   **2020**   **2012**    **2015**   **2020**

  Reference         3,417,000   3,269,000   3,244,000   \--         \--        \--        \--         \--        \--

  RFS Case          3,427,000   3,285,000   3,267,000   10,000      16,000     23,000     0.3%        0.5%       0.7%

  EIA Case          3,449,000   3,305,000   3,286,000   32,000      36,000     42,000     0.9%        1.1%       1.3%

  **Non-Road**      **2012**    **2015**    **2020**    **2012**    **2015**   **2020**   **2012**    **2015**   **2020**

  Reference         2,465,000   2,300,000   2,112,000   \--         \--        \--        \--         \--        \--

  RFS Case          2,473,000   2,309,000   2,123,000   8,000       9,000      11,000     0.3%        0.4%       0.5%

  EIA Case          2,476,000   2,313,000   2,128,000   11,000      13,000     16,000     0.4%        0.6%       0.8%

                                                                                                                 

  **Sensitivity**   Tons/Year                                                                                     

  **Total**         **2012**    **2015**    **2020**    **2012**    **2015**   **2020**   **2012**    **2015**   **2020**

  Reference         5,834,000   5,510,000   5,281,000   \--         \--        \--        \--         \--        \--

  RFS Case          5,814,000   5,487,000   5,254,000   -20,000     -23,000    -27,000    -0.3%       -0.4%      -0.5%

  EIA Case          5,830,000   5,500,000   5,264,000   -4,000      -10,000    -17,000    -0.1%       -0.2%      -0.3%

  **On-Road**       **2012**    **2015**    **2020**    **2012**    **2015**   **2020**   **2012**    **2015**   **2020**

  Reference         3,369,000   3,210,000   3,169,000   \--         \--        \--        \--         \--        \--

  RFS Case          3,341,000   3,178,000   3,131,000   -28,000     -32,000    -38,000    -0.8%       -1.0%      -1.2%

  EIA Case          3,354,000   3,187,000   3,136,000   -15,000     -23,000    -33,000    -0.4%       -0.7%      -1.0%

  **Non-Road**      **2012**    **2015**    **2020**    **2012**    **2015**   **2020**   **2012**    **2015**   **2020**

  Reference         2,465,000   2,300,000   2,112,000   \--         \--        \--        \--         \--        \--

  RFS Case          2,473,000   2,309,000   2,123,000   8,000       9,000      11,000     0.3%        0.4%       0.5%

  EIA Case          2,476,000   2,313,000   2,128,000   11,000      13,000     16,000     0.4%        0.6%       0.8%
  ----------------- ----------- ----------- ----------- ----------- ---------- ---------- ----------- ---------- ----------

**Table 4A-2. CO Emission Inventories under Various Ethanol Use Cases**

  ----------------- ------------ ------------ ------------ ------------ ------------ ------------ ----------- ---------- ----------
  **Primary**       Tons/Year                              Change from                            \% Change              
                                                           Reference                              from                   
                                                           (tons)                                 Reference              

  **Total**         **2012**     **2015**     **2020**     **2012**     **2015**     **2020**     **2012**    **2015**   **2020**

  Reference         55,022,000   53,702,000   53,949,000   \--          \--          \--          \--         \--        \--

  RFS Case          54,539,000   53,229,000   53,489,000   -483,000     -473,000     -460,000     -0.9%       -0.9%      -0.9%

  EIA Case          53,656,000   52,373,000   52,663,000   -1,366,000   -1,329,000   -1,286,000   -2.5%       -2.5%      -2.4%

  **On-Road**       **2012**     **2015**     **2020**     **2012**     **2015**     **2020**     **2012**    **2015**   **2020**

  Reference         37,656,000   36,171,000   35,723,000   \--          \--          \--          \--         \--        \--

  RFS Case          37,611,000   36,132,000   35,704,000   -45,000      -39,000      -19,000      -0.1%       -0.1%      -0.1%

  EIA Case          37,297,000   35,850,000   35,471,000   -359,000     -321,000     -252,000     -1.0%       -0.9%      -0.7%

  **Non-Road**      **2012**     **2015**     **2020**     **2012**     **2015**     **2020**     **2012**    **2015**   **2020**

  Reference         17,366,000   17,531,000   18,226,000   \--          \--          \--          \--         \--        \--

  RFS Case          16,928,000   17,097,000   17,785,000   -438,000     -434,000     -441,000     -2.5%       -2.5%      -2.4%

  EIA Case          16,359,000   16,523,000   17,192,000   -1,007,000   -1,008,000   -1,034,000   -5.8%       -5.7%      -5.7%

                                                                                                                         

  **Sensitivity**   Tons/Year                                                                                             

  **Total**         **2012**     **2015**     **2020**     **2012**     **2015**     **2020**     **2012**    **2015**   **2020**

  Reference         54,315,000   52,998,000   53,183,000   \--          \--          \--          \--         \--        \--

  RFS Case          53,623,000   52,322,000   52,507,000   -692,000     -676,000     -676,000     -1.3%       -1.3%      -1.3%

  EIA Case          52,340,000   51,069,000   51,246,000   -1,975,000   -1,929,000   -1,937,000   -3.6%       -3.6%      -3.6%

  **On-Road**       **2012**     **2015**     **2020**     **2012**     **2015**     **2020**     **2012**    **2015**   **2020**

  Reference         36,949,000   35,467,000   34,957,000   \--          \--          \--          \--         \--        \--

  RFS Case          36,695,000   35,225,000   34,722,000   -254,000     -242,000     -235,000     -0.7%       -0.7%      -0.7%

  EIA Case          35,981,000   34,546,000   34,054,000   -968,000     -921,000     -903,000     -2.6%       -2.6%      -2.6%

  **Non-Road**      **2012**     **2015**     **2020**     **2012**     **2015**     **2020**     **2012**    **2015**   **2020**

  Reference         17,366,000   17,531,000   18,226,000   \--          \--          \--          \--         \--        \--

  RFS Case          16,928,000   17,097,000   17,785,000   -438,000     -434,000     -441,000     -2.5%       -2.5%      -2.4%

  EIA Case          16,359,000   16,523,000   17,192,000   -1,007,000   -1,008,000   -1,034,000   -5.8%       -5.7%      -5.7%
  ----------------- ------------ ------------ ------------ ------------ ------------ ------------ ----------- ---------- ----------

**Table 4A-3. NOx Emission Inventories u**nder Various Ethanol Use Cases

  ----------------- ----------- ----------- ----------- ----------- ---------- ---------- ----------- ---------- ----------
  **Primary**       Tons/Year                           Change from                       \% Change              
                                                        Reference                         from                   
                                                        (tons)                            Reference              

  **Total**         **2012**    **2015**    **2020**    **2012**    **2015**   **2020**   **2012**    **2015**   **2020**

  Reference         2,487,000   2,059,000   1,695,000   \--         \--        \--        \--         \--        \--

  RFS Case          2,510,000   2,077,000   1,712,000   23,000      18,000     17,000     0.9%        0.9%       1.0%

  EIA Case          2,527,000   2,092,000   1,727,000   40,000      33,000     32,000     1.6%        1.6%       1.9%

  **On-Road**       **2012**    **2015**    **2020**    **2012**    **2015**   **2020**   **2012**    **2015**   **2020**

  Reference         2,240,000   1,797,000   1,407,000   \--         \--        \--        \--         \--        \--

  RFS Case          2,249,000   1,800,000   1,407,000   9,000       3,000      0          0.4%        0.2%       0.0%

  EIA Case          2,253,000   1,801,000   1,407,000   13,000      4,000      0          0.6%        0.2%       0.0%

  **Non-Road**      **2012**    **2015**    **2020**    **2012**    **2015**   **2020**   **2012**    **2015**   **2020**

  Reference         247,000     262,000     288,000     \--         \--        \--        \--         \--        \--

  RFS Case          261,000     277,000     305,000     14,000      15,000     17,000     5.7%        5.7%       5.9%

  EIA Case          274,000     291,000     320,000     27,000      29,000     32,000     10.9%       11.1%      11.1%

                                                                                                                 

  **Sensitivity**   Tons/Year                           Change from                       \% Change              
                                                        Reference                         from                   
                                                        (tons)                            Reference              

  **Total**         **2012**    **2015**    **2020**    **2012**    **2015**   **2020**   **2012**    **2015**   **2020**

  Reference         2,519,000   2,087,000   1,717,000   \--         \--        \--        \--         \--        \--

  RFS Case          2,587,000   2,144,000   1,765,000   68,000      57,000     48,000     2.7%        2.7%       2.8%

  EIA Case          2,625,000   2,178,000   1,796,000   106,000     91,000     79,000     4.2%        4.4%       4.6%

  **On-Road**       **2012**    **2015**    **2020**    **2012**    **2015**   **2020**   **2012**    **2015**   **2020**

  Reference         2,272,000   1,825,000   1,429,000   \--         \--        \--        \--         \--        \--

  RFS Case          2,326,000   1,867,000   1,460,000   54,000      42,000     31,000     2.4%        2.3%       2.2%

  EIA Case          2,351,000   1,887,000   1,476,000   79,000      62,000     47,000     3.5%        3.4%       3.3%

  **Non-Road**      **2012**    **2015**    **2020**    **2012**    **2015**   **2020**   **2012**    **2015**   **2020**

  Reference         247,000     262,000     288,000     \--         \--        \--        \--         \--        \--

  RFS Case          261,000     277,000     305,000     14,000      15,000     17,000     5.7%        5.7%       5.9%

  EIA Case          274,000     291,000     320,000     27,000      29,000     32,000     10.9%       11.1%      11.1%
  ----------------- ----------- ----------- ----------- ----------- ---------- ---------- ----------- ---------- ----------

**Table 4A-4. Benzene Emission Inventories u**nder Various Ethanol Use
Cases

  ----------------- ----------- ---------- ---------- ----------- ---------- ---------- ----------- ---------- ----------
  **Primary**       Tons/Year                         Change from                       \% Change              
                                                      Reference                         from                   
                                                      (tons)                            Reference              

  **Total**         **2012**    **2015**   **2020**   **2012**    **2015**   **2020**   **2012**    **2015**   **2020**

  Reference         178,000     175,400    179,900    \--         \--        \--        \--         \--        \--

  RFS Case          174,800     164,800    178,200    -3,200      -10,600    -1,700     -1.8%       -6.0%      -0.9%

  EIA Case          170,800     169,100    174,200    -7,200      -6,300     -5,700     -4.0%       -3.6%      -3.2%

  **On-Road**       **2012**    **2015**   **2020**   **2012**    **2015**   **2020**   **2012**    **2015**   **2020**

  Reference         124,100     124,200    130,600    \--         \--        \--        \--         \--        \--

  RFS Case          121,800     122,600    129,400    -2,300      -1,600     -1,200     -1.9%       -1.3%      -0.9%

  EIA Case          118,700     119,400    126,100    -5,400      -4,800     -4,500     -4.4%       -3.9%      -3.4%

  **Non-Road**      **2012**    **2015**   **2020**   **2012**    **2015**   **2020**   **2012**    **2015**   **2020**

  Reference         53,900      51,200     49,300     \--         \--        \--        \--         \--        \--

  RFS Case          53,000      42,200     48,800     -900        -9,000     -500       -1.7%       -17.6%     -1.0%

  EIA Case          52,100      49,700     48,100     -1,800      -1,500     -1,200     -3.3%       -2.9%      -2.4%

                                                                                                               

  **Sensitivity**   Tons/Year                                                                                   

  **Total**         **2012**    **2015**   **2020**   **2012**    **2015**   **2020**   **2012**    **2015**   **2020**

  Reference         175,700     172,700    176,500    \--         \--        \--        \--         \--        \--

  RFS Case          170,700     168,000    171,900    -5,000      -4,700     -4,600     -2.8%       -2.7%      -2.6%

  EIA Case          166,300     163,600    167,200    -9,400      -9,100     -9,300     -5.4%       -5.3%      -5.3%

  **On-Road**       **2012**    **2015**   **2020**   **2012**    **2015**   **2020**   **2012**    **2015**   **2020**

  Reference         121,800     121,500    127,200    \--         \--        \--        \--         \--        \--

  RFS Case          117,700     117,500    123,100    -4,100      -4,000     -4,100     -3.4%       -3.3%      -3.2%

  EIA Case          114,200     113,900    119,100    -7,600      -7,600     -8,100     -6.2%       -6.3%      -6.4%

  **Non-Road**      **2012**    **2015**   **2020**   **2012**    **2015**   **2020**   **2012**    **2015**   **2020**

  Reference         53,900      51,200     49,300     \--         \--        \--        \--         \--        \--

  RFS Case          53,000      50,500     48,800     -900        -700       -500       -1.7%       -1.4%      -1.0%

  EIA Case          52,100      49,700     48,100     -1,800      -1,500     -1,200     -3.3%       -2.9%      -2.4%
  ----------------- ----------- ---------- ---------- ----------- ---------- ---------- ----------- ---------- ----------

**Table 4A-5. Acetaldehyde Emission Inventories u**nder Various Ethanol
Use Cases

  ----------------- ----------- ---------- ---------- ----------- ---------- ---------- ----------- ---------- ----------
  **Primary**       Tons/Year                         Change from                       \% Change              
                                                      Reference                         from                   
                                                      (tons)                            Reference              

  **Total**         **2012**    **2015**   **2020**   **2012**    **2015**   **2020**   **2012**    **2015**   **2020**

  Reference         19,900      20,000     21,100     \--         \--        \--        \--         \--        \--

  RFS Case          23,300      23,400     24,700     3,400       3,400      3,600      17.1%       17.0%      17.1%

  EIA Case          27,000      27,300     28,800     7,100       7,300      7,700      35.7%       36.5%      36.5%

  **On-Road**       **2012**    **2015**   **2020**   **2012**    **2015**   **2020**   **2012**    **2015**   **2020**

  Reference         15,500      15,800     17,000     \--         \--        \--        \--         \--        \--

  RFS Case          17,900      18,300     19,800     2,400       2,500      2,800      15.5%       15.8%      16.5%

  EIA Case          20,900      21,500     23,300     5,400       5,700      6,300      34.8%       36.1%      37.1%

  **Non-Road**      **2012**    **2015**   **2020**   **2012**    **2015**   **2020**   **2012**    **2015**   **2020**

  Reference         4,400       4,200      4,100      \--         \--        \--        \--         \--        \--

  RFS Case          5,400       5,100      4,900      1,000       900        800        22.7%       21.4%      19.5%

  EIA Case          6,100       5,800      5,500      1,700       1,600      1,400      38.6%       38.1%      34.1%

                                                                                                               

  **Sensitivity**   Tons/Year                                                                                   

  **Total**         **2012**    **2015**   **2020**   **2012**    **2015**   **2020**   **2012**    **2015**   **2020**

  Reference         19,500      19,500     20,400     \--         \--        \--        \--         \--        \--

  RFS Case          22,500      22,400     23,400     3,000       2,900      3,000      15.4%       14.9%      14.7%

  EIA Case          26,100      26,100     27,200     6,600       6,600      6,800      33.8%       33.8%      33.3%

  **On-Road**       **2012**    **2015**   **2020**   **2012**    **2015**   **2020**   **2012**    **2015**   **2020**

  Reference         15,100      15,300     16,300     \--         \--        \--        \--         \--        \--

  RFS Case          17,100      17,300     18,500     2,000       2,000      2,200      13.2%       13.1%      13.5%

  EIA Case          20,000      20,300     21,700     4,900       5,000      5,400      32.5%       32.7%      33.1%

  **Non-Road**      **2012**    **2015**   **2020**   **2012**    **2015**   **2020**   **2012**    **2015**   **2020**

  Reference         4,400       4,200      4,100      \--         \--        \--        \--         \--        \--

  RFS Case          5,400       5,100      4,900      1,000       900        800        22.7%       21.4%      19.5%

  EIA Case          6,100       5,800      5,500      1,700       1,600      1,400      38.6%       38.1%      34.1%
  ----------------- ----------- ---------- ---------- ----------- ---------- ---------- ----------- ---------- ----------

**Table 4A-6. Formaldehyde Emission Inventories u**nder Various Ethanol
Use Cases

  ----------------- ----------- ---------- ---------- ----------- ---------- ---------- ----------- ---------- ----------
  **Primary**       Tons/Year                         Change from                       \% Change              
                                                      Reference                         from                   
                                                      (tons)                            Reference              

  **Total**         **2012**    **2015**   **2020**   **2012**    **2015**   **2020**   **2012**    **2015**   **2020**

  Reference         40,400      40,100     41,400     \--         \--        \--        \--         \--        \--

  RFS Case          39,800      39,600     41,200     -600        -500       -200       -1.5%       -1.2%      -0.5%

  EIA Case          40,200      40,100     41,600     -200        0          200        -0.5%       0.0%       0.5%

  **On-Road**       **2012**    **2015**   **2020**   **2012**    **2015**   **2020**   **2012**    **2015**   **2020**

  Reference         29,900      30,100     32,000     \--         \--        \--        \--         \--        \--

  RFS Case          29,300      29,700     31,800     -600        -400       -200       -2.0%       -1.3%      -0.6%

  EIA Case          29,600      30,100     32,200     -300        0          200        -1.0%       0.0%       0.6%

  **Non-Road**      **2012**    **2015**   **2020**   **2012**    **2015**   **2020**   **2012**    **2015**   **2020**

  Reference         10,500      10,000     9,400      \--         \--        \--        \--         \--        \--

  RFS Case          10,500      9,900      9,400      0           -100       0          0.0%        -1.0%      0.0%

  EIA Case          10,600      10,000     9,400      100         0          0          1.0%        0.0%       0.0%

                                                                                                               

  **Sensitivity**   Tons/Year                                                                                   

  **Total**         **2012**    **2015**   **2020**   **2012**    **2015**   **2020**   **2012**    **2015**   **2020**

  Reference         39,600      39,200     40,300     \--         \--        \--        \--         \--        \--

  RFS Case          38,500      38,100     39,200     -1,100      -1,100     -1,100     -2.8%       -2.8%      -2.7%

  EIA Case          38,900      38,400     39,500     -700        -800       -800       -1.8%       -2.0%      -2.0%

  **On-Road**       **2012**    **2015**   **2020**   **2012**    **2015**   **2020**   **2012**    **2015**   **2020**

  Reference         29,100      29,200     30,900     \--         \--        \--        \--         \--        \--

  RFS Case          28,000      28,200     29,800     -1,100      -1,000     -1,100     -3.8%       -3.4%      -3.6%

  EIA Case          28,300      28,400     30,100     -800        -800       -800       -2.7%       -2.7%      -2.6%

  **Non-Road**      **2012**    **2015**   **2020**   **2012**    **2015**   **2020**   **2012**    **2015**   **2020**

  Reference         10,500      10,000     9,400      \--         \--        \--        \--         \--        \--

  RFS Case          10,500      9,900      9,400      0           -100       0          0.0%        -1.0%      0.0%

  EIA Case          10,600      10,000     9,400      100         0          0          1.0%        0.0%       0.0%
  ----------------- ----------- ---------- ---------- ----------- ---------- ---------- ----------- ---------- ----------

**Table 4A-7. 1,3-Butadiene Emission Inventories u**nder Various Ethanol
Use Cases

  ----------------- ----------- ---------- ---------- ----------- ---------- ---------- ----------- ---------- ----------
  **Primary**       Tons/Year                         Change from                       \% Change              
                                                      Reference                         from                   
                                                      (tons)                            Reference              

  **Total**         **2012**    **2015**   **2020**   **2012**    **2015**   **2020**   **2012**    **2015**   **2020**

  Reference         18,900      18,500     19,100     \--         \--        \--        \--         \--        \--

  RFS Case          18,700      18,600     19,100     -200        100        0          -1.1%       0.5%       0.0%

  EIA Case          18,600      18,500     19,100     -300        0          0          -1.6%       0.0%       0.0%

  **On-Road**       **2012**    **2015**   **2020**   **2012**    **2015**   **2020**   **2012**    **2015**   **2020**

  Reference         12,000      12,000     12,800     \--         \--        \--        \--         \--        \--

  RFS Case          11,800      12,000     12,800     -200        0          0          -1.7%       0.0%       0.0%

  EIA Case          11,800      12,000     12,800     -200        0          0          -1.7%       0.0%       0.0%

  **Non-Road**      **2012**    **2015**   **2020**   **2012**    **2015**   **2020**   **2012**    **2015**   **2020**

  Reference         6,900       6,500      6,300      \--         \--        \--        \--         \--        \--

  RFS Case          6,900       6,600      6,300      0           100        0          0.0%        1.5%       0.0%

  EIA Case          6,800       6,500      6,300      -100        0          0          -1.4%       0.0%       0.0%

                                                                                                               

  **Sensitivity**   Tons/Year                                                                                   

  **Total**         **2012**    **2015**   **2020**   **2012**    **2015**   **2020**   **2012**    **2015**   **2020**

  Reference         18,600      18,200     18,700     \--         \--        \--        \--         \--        \--

  RFS Case          18,200      18,000     18,300     -400        -200       -400       -2.2%       -1.1%      -2.1%

  EIA Case          18,000      17,800     18,200     -600        -400       -500       -3.2%       -2.2%      -2.7%

  **On-Road**       **2012**    **2015**   **2020**   **2012**    **2015**   **2020**   **2012**    **2015**   **2020**

  Reference         11,700      11,700     12,400     \--         \--        \--        \--         \--        \--

  RFS Case          11,300      11,400     12,000     -400        -300       -400       -3.4%       -2.6%      -3.2%

  EIA Case          11,200      11,300     11,900     -500        -400       -500       -4.3%       -3.4%      -4.0%

  **Non-Road**      **2012**    **2015**   **2020**   **2012**    **2015**   **2020**   **2012**    **2015**   **2020**

  Reference         6,900       6,500      6,300      \--         \--        \--        \--         \--        \--

  RFS Case          6,900       6,600      6,300      0           100        0          0.0%        1.5%       0.0%

  EIA Case          6,800       6,500      6,300      -100        0          0          -1.4%       0.0%       0.0%
  ----------------- ----------- ---------- ---------- ----------- ---------- ---------- ----------- ---------- ----------

**Chapter 5: Air Quality Impacts**

**5.1 Ozone**

We performed ozone air quality modeling simulations for the eastern
United States using the ozone Response Surface Model (RSM) to estimate
the effects of the projected changes in emissions from gasoline vehicles
and equipment. The ozone RSM is a screening-level air quality modeling
tool that allows users to quickly assess the estimated air quality
changes over the modeling domain. The ozone RSM is a model of a
full-scale air quality model and is based on statistical relationships
between model inputs and outputs obtained from the full-scale air
quality model. In other words, the ozone RSM uses statistical techniques
to relate a response variable to a set of factors that are of interest,
e.g., emissions of precursor pollutants from particular sources and
locations. The following section describes the modeling methodology,
including the development of the multi-dimensional experimental design
for control strategies and implementation and verification of the RSM
technique. Additional detail is available in the Air Quality Modeling
Technical Support Document (AQMTSD) that was drafted for the Mobile
Source Air Toxics Rule Proposal (published March 29, 2006).[^135]

The foundation for the ozone response surface metamodeling analyses was
the CAMx modeling done in support of the final Clean Air Interstate Rule
(CAIR). The CAIR modeling is fully described in the CAIR Air Quality
Modeling Technical Support Document, but a brief description is provided
below.[^136] The modeling procedures used in the CAIR analysis (e.g.,
domain, episodes, meteorology) have been used for several EPA rulemaking
analyses over the past five years and are well-established at this
point.

The ozone RSM uses the 2015 controlled CAIR emissions inventory as its
baseline, assuming future fuel quality remains unchanged from pre-Act
levels, which serves as the baseline for the analysis of the final RFS
standards.[^137] We then compare these baseline emissions to the
emissions which would have occurred in the future if fuel quality had
remained unchanged from pre-Act levels to those which will occur with
fuel quality reflecting the increased renewable fuel use projected in
the future. This approach differs from that traditionally taken in EPA
regulatory impact analyses. Traditionally, we would have compared future
emissions with and without the requirement of the Act. However, as
described in Chapter 1, we expect that total renewable fuel use in the
U.S. in 2012 to exceed 7.5 billion gallons even in the absence of the
Renewable Fuel Standard (RFS). Thus, a traditional regulatory impact
analysis would have shown no impact on emissions or air quality.

The modeling simulations that comprised the metamodeling were conducted
using CAMx version 3.10. It should be noted that because the ozone RSM
is built from CAMx air quality model runs, it therefore has the same
strengths and limitations of the underlying model and its inputs. CAMx
is a non-proprietary computer model that simulates the formation and
fate of photochemical oxidants including ozone for given input sets of
meteorological conditions and emissions. The gridded meteorological data
for three historical episodes were developed using the Regional
Atmospheric Modeling System (RAMS), version 3b.[^138] In all, 30 episode
days were modeled using frequently-occurring, ozone-conducive,
meteorological conditions from the summer of 1995. Emissions estimates
were developed for the evaluation year (1995) as well as a future year
(2015).

The CAMx model applications were performed for a domain covering all, or
portions of, 37 States (and the District of Columbia) in the Eastern
U.S., as shown in Figure 5.1-1. The domain has nested horizontal grids
of 36 km and 12 km. However, the output data from the metamodeling is
provided at a 12 km resolution (i.e., cells from the outer 36 km cells
populate the nine finer scale cells, as appropriate). Although the
domain of the ozone RSM is the 37 Eastern states, the expanded use of
ethanol in fuel is expected to occur nationwide. Chapter 4 describes the
nationwide inventory impacts associated with the F standards.

**Figure 5.1-1.** **Map of the CAMx Domain Used for RFS Ozone
Metamodeling**

![](media/image64.png){width="5.263888888888889in"
height="4.134722222222222in"}

The ozone RSM used for assessing the air quality impacts of expanded
ethanol use in fuel was developed broadly to look at various control
strategies with respect to attaining the 8-hour ozone NAAQS. The
experimental design for the ozone RSM covered three key areas: type of
precursor emission (NOx or VOC), emission source type (i.e., onroad
vehicles, nonroad vehicles, area sources, electrical generating utility
(EGU) sources, and non-utility point sources), and location in or out of
a 2015 model-projected residual ozone nonattainment area. This resulted
in a set of 14 emissions factors.

The 14 emission factors were randomly varied and used as inputs to CAMx.
The experimental design for these 14 factors was developed using a
Maximin Latin Hypercube method. Based on a rule of thumb of 10 runs per
factor, we developed an overall design with 140 runs (a base case plus
139 control runs). The range of emissions reductions considered within
the metamodel ranged from 0 to 120 percent of the 2015 CAIR emissions.
This experimental design resulted in a set of CAMx simulations that
serve as the inputs to the ozone response surface metamodel.

To develop a response surface approximation to CAMx, we used a
multidimensional kriging approach, implemented through the MIXED
procedure in SAS. We modeled the predicted changes in ozone in each CAMx
grid cell as a function of the weighted average of the modeled responses
in the experimental design. A response-surface was then fit for several
ozone metrics, namely the ozone design value, the 1-hour maximum value,
the 24-hour average value and the average ozone level between 9 am and 5
pm. The effect of changes in VOC and NOx emissions on ozone was
estimated in each grid cell covered by the model for each ozone metric
except the ozone design value. The ozone design value is the
mathematically determined pollutant concentration at a particular
monitoring site that must be reduced to, or maintained at or below the
National Ambient Air Quality Standard to assume attainment. The 8-hour
ozone design value is the 3-year average of the fourth-highest daily
maximum 8-hour average ozone concentrations measured at each monitor
within an area over each year, which must not exceed 0.08 ppm (85 ppb,
considering round-off). Thus, ozone design values only exist for grid
cells which contain ozone monitoring stations and where ozone attainment
has been an issue. Ozone design values have been developed for 525 of
the 2696 counties in the 37 state region. of the 31 these The specific
ozone design values used in this analysis are those for 2001, which
represent the average of the ozone design values determined for three,
three-year periods (1999-2001, 2000-2002, and 2001-2003). Validation was
performed and is summarized in the Mobile Source Air Toxics rule Air
Quality Modeling Technical Support Document. The validation exercises
indicated that the ozone RSM replicates CAMx response to emissions
changes very well for most emissions combinations and in most locations.

The ozone RSM limits the number of geographically distinct changes in
VOC and NOx emissions which can be simulated. Emissions from motor
vehicles and nonroad equipment can be varied separately. Distinct
percentage changes in either the motor vehicle or nonroad inventories
can also be applied in ozone nonattainment and attainment areas.
However, distinct emission impacts cannot be simulated in various ozone
nonattainment areas (e.g., Chicago and Houston or New York and Kansas
City). This limits our ability to simulate the impact of increased
ethanol use in a couple of ways. First, ethanol use is not
geographically uniform across the U.S., either currently or in the
future. Thus, the emission impacts resulting from changes in ethanol use
also varies geographically. Second, the emission impacts of ethanol use
are not uniform. Ethanol use in RFG and other areas which do not grant
ethanol blends a 1.0 psi RVP waiver will not experience as much of an
increase in VOC emissions with increased ethanol use as areas which
grant ethanol blends an RVP waiver. Third, the impacts of new ethanol
plants will be even more geographically focused. The Ozone RSM cannot
generally be applied to model the emission impacts from such local
sources for a couple of reasons. One, the location of new ethanol plants
is difficult to predict in many cases. Two, the impact of these plants
on local emissions can be very large in percentage terms given the
absence of a lot of other industrial activity. The Ozone RSM was
designed to represent the ozone impact of the same change in VOC or NOx
emissions across a broad region (e.g., all attainment areas). Therefore,
it cannot be used to model the impact of a large change in one county's
emissions without also assuming the same change in the upwind county's
emissions. As not every county will contain a new ethanol plant, the
assumptions inherent in the Ozone RSM do not match the situation of a
new individual point source, such as an ethanol plant.

We developed a methodology which would best approximate the impact of
changes in local emissions on the ozone level in each local area, while
maintaining as much of the impact of ozone transport from other areas as
possible given the above mentioned limitations. We do this by running
the ozone RSM twice for each scenario and drawing the resultant ozone
impact from the run which best matched the emission impact expected in a
particular local area, considering both the change in emissions modeled
for that particular local area, as well as that occurring in upwind
areas.

First, as mentioned above, ethanol use is expected to change
dramatically in some areas, but not at all in others. Averaging the
emission impacts across these two types of areas and estimating the
associated ozone impact would be very misleading. No area would be
likely to experience the ozone impact predicted. Some areas would
experience a much greater impact, while others would experience no
impact. Therefore, the first step in using the Ozone RSM to predict the
ozone impacts related to the RFS is to estimate the change in VOC and
NOx emissions in those areas ethanol blend market share changed
significantly. As was done in the analysis of local emission impacts
presented in Section 4.1.3.3 above, we defined a significant change in
ethanol blend market share as a change of 50% or more. This focuses the
change in emissions in those areas where the change is likely to occur.

As discussed in Chapters 3 and 4, the effect of ethanol use on emissions
differs depending on the baseline fuel quality and the applicable RVP
standards. In particular, ethanol use has significantly different
impacts on emissions in RFG, low RVP and 9 RVP areas. Therefore, in
order to better predict the ozone impact likely to occur in specific
areas, we estimate the change in VOC and NOx emissions separately for
RFG, low RVP and 9 RVP areas (per above, only for those areas in each
case where ethanol blend market share changed by 50% or more).

The Ozone RSM only covers the 37 easternmost states in the U.S.
Therefore, we limited the calculation of VOC and NOx emission impacts to
only those states. The Ozone RSM was developed with the year 2015 as the
default year. Since we develop most of our impacts of the RFS for the
year 2012 and 2015, we chose to run the Ozone RSM for, 2015. The Ozone
RSM is designed to accept emission changes in terms of total onroad and
total nonroad sources, respectively, and both emission categories
include diesels. Therefore, we included estimates of VOC and NOx
emissions from diesel vehicles and equipment in 2015 in our calculation
of the emission impacts. These diesel emissions do not change between
the various RFS scenarios. However, they do reduce the effective
percentage change in VOC and NOx emissions which is projected to occur.
Overall, these analyses are very similar to those described in Section
4.1.3.3 above, with the exceptions of the limitation to 37 states and
the inclusion of diesel emissions. The results of these calculations are
shown in Table 5.1-1.

**Table 5.1-1.**

**Emission Inputs to Ozone Modeling: Change in Total Mobile Sources
Emissions in 37 Eastern States where Ethanol Use Changes Significantly,
July 2015 (percent change)**

  --------------- ---------- ----------- ----------------- ----------- ----------------- ----------- ----------------- ----------- -----------------
                             **VOC**                                                     **NOx**                                   

                             **On Road**                   **Non                         **On Road**                   **Non       
                                                           Road**                                                      Road**      

                             **Attain.   **Non-Attain.**   **Attain.   **Non-Attain.**   **Attain.   **Non-Attain.**   **Attain.   **Non-Attain.**
                             (9 RVP)**                     (9 RVP)**                     (9 RVP)**                     (9 RVP)**   

  **Primary                                                                                                                        
  Analysis**                                                                                                                       

  **RFS**         **RFG**    7.5%        -1.2%             1.5%        1.8%              0.2%        0.1%              3.0%        -1.9%

                  **LRVP**   7.5%        8.9%              1.5%        1.7%              0.2%        0.2%              3.0%        3.3%

  **EIA**         **RFG**    8.2%        1.7%              2.3%        2.4%              0.2%        0.1%              3.2%        0.7%

                  **LRVP**   8.2%        9.3%              2.3%        2.0%              0.2%        0.2%              3.2%        3.3%

  **Sensitivity                                                                                                                    
  Analysis**                                                                                                                       

  **RFS**         **RFG**    6.0%        -5.3%             1.5%        1.8%              2.9%        1.7%              3.0%        -1.9%

                  **LRVP**   6.0%        7.0%              1.5%        1.7%              2.9%        3.5%              3.0%        3.3%

  **EIA**         **RFG**    6.6%        -1.2%             2.3%        2.4%              3.0%        2.2%              3.2%        0.7%

                  **LRVP**   6.6%        7.4%              2.3%        2.0%              3.0%        3.6%              3.2%        3.3%
  --------------- ---------- ----------- ----------------- ----------- ----------------- ----------- ----------------- ----------- -----------------

Our category of 9 RVP areas is very similar to the set of attainment
areas in the Ozone RSM. Therefore, the application of the emission
impacts expected in 9 RVP areas in the Ozone RSM was straightforward.
However, both RFG and low RVP areas together generally comprise the set
of nonattainment areas in the Ozone RSM. As seen in Table 5.1-1, the
expected emission impacts of the various RFS scenarios differ
significantly depending on whether the area has RFG or low RVP fuel.
Both sets of emission impacts could not be run in the Ozone RSM at the
same time. Therefore, we ran the Ozone RSM twice. The first run applied
the emission impacts estimated for RFG areas to the ozone nonattainment
areas in the Ozone RSM and applied the emission impacts for 9.0 RVP
areas to the ozone attainment areas in the Ozone RSM. This run should
produce satisfactory projections of ozone impacts for all areas except
those areas with low RVP, as well as those areas where ethanol use is
not expected to change.

The second run applied the emission impacts estimated for Low RVP areas
to the ozone nonattainment areas in the Ozone RSM and applied the
emission impacts for 9.0 RVP areas to the ozone attainment areas in the
Ozone RSM. This run should produce satisfactory projections of ozone
impacts for all areas except those areas with RFG, as well as those
areas where ethanol use is not expected to change.

For both runs of the Ozone RSM, we set the predicted change in ozone to
zero in those counties not expected to experience a significant change
in ethanol use. This ignores any impact from ozone transport from other
areas where ethanol use did change. However, we believe that the ozone
impacts due to transport are much smaller than those associated with
changes in local emissions. This is particularly true in this case,
where the percentage change in emissions would be the same in both the
local and upwind areas.

We merged the results of the two runs by attributing ozone impacts from
each county according to their nonattainment designation and fuel type.
For non-attainment areas, this choice was obvious. Non-attainment
counties with RFG programs were assigned the ozone impacts from the
first run (i.e., the run where the changes in VOC and NOx emissions were
the average of those observed for RFG areas). Non-attainment counties
with Low RVP programs were assigned the ozone impacts from the second
run (i.e., the run where the changes in VOC and NOx emissions were the
average of those observed for Low RVP areas). For attainment areas
(i.e., 9 RVP areas), the results of either run could be used, as both
runs of the Ozone RSM applied the same emission changes to attainment
areas. Thus, the local emission impacts would be identical in the two
Ozone RSM runs. Ozone transport is also likely identical for the vast
majority of these counties, given that they are likely downwind from
other attainment area counties. The only difference occurs if an
attainment area is downwind of a RFG or Low RVP area. For a nationwide
analysis such as this one, we were not able to determine for each
attainment area whether a potential upwind area was more likely to be an
RFG or Low RVP area. Therefore, we chose to use the ozone impacts
results from the first Ozone RSM run of the model (i.e., where the
emission impacts for RFG areas were applied to ozone nonattainment
areas) for all attainment areas. We chose this run because RFG areas
tend to have the higher ozone levels than Low RVP areas and thus, would
be more likely to affect areas downwind. We present the ozone impacts of
increased ethanol use resulting from this methodology in the following
section.

**5.1.1 Ozone Response Surface Metamodel Results**

[]{.mark}

This section summarizes the results of our modeling of ozone air quality
impacts in the future with and without the expanded use of ethanol in
fuel. The impact of increased ethanol use on the 8-hour ozone design
values in 2015 are presented in Table 5.1-2. The changes presented in
Table 5.1-2 are for those counties with 2001 modeled design
values.[^139] The Chapter 5 Appendix presents the impacts of increased
ethanol use on a number of alternative measures of ambient ozone
concentration.

**Table 5.1-2.**

**Impact of Increased Ethanol Use on 8-hour Ozone Design Values** in
2015 (ppb)

  --------------------------------------------- ------------ ------------
                                                RFS Case     EIA Case

  Primary Analysis                                           

  Minimum Change                                -0.015       0.000

  Maximum Change                                0.329        0.337

  Average Change Across 37 States               0.057        0.079

  Population-Weighted Change Across 37 States   0.052        0.056

  Average Change Where Ethanol Use Changed      0.153        0.181
  Significantly States                                       

  Population-Weighted Change Where Ethanol Use  0.154        0.183
  Changed Significantly States                               

  Sensitivity Analysis                                       

  Minimum Change                                -0.115       0.000

  Maximum Change                                0.624        0.549

  Average Change Across 37 States               0.111        0.142

  Population-Weighted Change Across 37 States   0.092        0.096

  Average Change Where Ethanol Use Changed      0.300        0.325
  Significantly States                                       

  Population-Weighted Change Where Ethanol Use  0.272        0.315
  Changed Significantly States                               
  --------------------------------------------- ------------ ------------

As can be seen, ozone levels generally increase with increased ethanol
use. This is likely due to the projected increases in both VOC and NOx
emissions. Some areas do see a small decrease in ozone levels. In our
primary analysis, where exhaust emissions from Tier 1 and later onroad
vehicles are assumed to be unaffected by ethanol use, the
population-weighted increase in ambient ozone levels is 0.052-0.056 ppb.
Since the 8-hour ambient ozone standard is 0.08 ppm (85 ppb), this
increase represents about 0.06 percent of the standard, a very small
percentage[^140]. While small, this figure includes essentially zero
changes in ozone in areas where ethanol use did not change. When we
focus just on those areas where the market share of ethanol blends
changed by 50 percent or more, the population-weighted increase in
ambient ozone levels rises to 0.154-0.183 ppb. This increase represents
about 0.2 percent of the standard.

In our sensitivity analysis, where exhaust emissions from Tier 1 and
later onroad vehicles are assumed to respond to ethanol like Tier 0
vehicles, the population-weighted increase in ambient ozone levels
across the entire 37 state area is slightly less than twice as high, or
0.092-0.096 ppb. This increase represents about 0.11 percent of the
standard. When we focus just on those areas where the market share of
ethanol blends changed by 50 percent or more, the population-weighted
increase in ambient ozone levels rises to 0.272-0.315 ppb. This increase
represents about 0.35 percent of the standard.

For the primary analysis, we also present the counties with the largest
increases in the ozone design value. Table 5.1-3 presents the county
level ozone design value impacts of the RFS case, while Table 5.1-4
presents the same information for the EIA case. It is important to note
that the results of this ozone response surface metamodeling exercise is
meant for screening-level purposes only and does not represent the
results that would be obtained from full-scale photochemical ozone
modeling. It is also important to note that the ozone RSM results
indicate that the counties which are projected to experience the
greatest increase in ozone design values are generally counties that are
projected to have ambient concentrations well below the 0.08 ppm ozone
standard in the 2015 baseline.

**Table 5.1-3. RFS Case, Primary Analysis:**

**2015 Ozone Response Surface Metamodeling Results^a^ for Counties
with**

Largest Increases in Ozone 8hr Design Value (ppb) Due to Increased Use
of Ethanol

  ------------------------ ------------ ------------------ ------------- ------------- --------------
  **State Name**           **County     **2015 Baseline    **2015 RFS    **Effect of   **2015
                           Name**       (Post-CAIR)**^b^   Case**        Expanded      Population**
                                                                         Ethanol Use   
                                                                         (ppb)**       

  Arkansas                 Crittenden   78                 78.3289       0.3           53,852
                           Co                                                          

  Ohio                     Geauga Co    82.5               82.7972       0.3           108,600

  Ohio                     Mahoning Co  74.7               74.9943       0.3           248,545

  Indiana                  Lake Co      80.7               80.9929       0.3           490,796

  Ohio                     Medina Co    72                 72.2909       0.3           187,686

  Ohio                     Summit Co    77.4               77.6901       0.3           557,892

  Maine                    York Co      77.6               77.8825       0.3           210,006

  Ohio                     Stark Co     71.7               71.9707       0.3           384,672

  Ohio                     Clinton Co   75.7               75.9705       0.3           50,635

  Louisiana                West Baton   77.4               77.6685       0.3           23,202
                           Rouge Parish                                                

  Louisiana                Livingston   76.6               76.8656       0.3           141,807
                           Parish                                                      

  Illinois                 Cook Co      81.1               81.3605       0.3           5,362,932

  Indiana                  Shelby Co    76.2               76.4587       0.3           47,904

  Ohio                     Knox Co      71.4               71.6541       0.3           62,138

  Alabama                  Mobile Co    68                 68.2514       0.3           430,341

  Louisiana                Jefferson    77.1               77.351        0.3           512,963
                           Parish                                                      

  ^a^ The Ozone RSM is                                                                 
  meant for                                                                            
  screening-level purposes                                                             
  only and does not                                                                    
  represent results that                                                               
  would be obtained from                                                               
  full-scale photochemical                                                             
  ozone modeling. In                                                                   
  particular, the model                                                                
  does not account for                                                                 
  changes in CO emissions                                                              
  or VOC reactivity, both                                                              
  of which should decrease                                                             
  with increased ethanol                                                               
  use and directionally                                                                
  reduce ozone. in areas                                                               
  where ozone formation is                                                             
  VOC-limited.                                                                         

  ^b^ The Clean Air                                                                    
  Interstate Rule (CAIR)                                                               
  modeling is fully                                                                    
  described in the CAIR                                                                
  Air Quality Modeling                                                                 
  Technical Support                                                                    
  Document (Docket                                                                     
  EPA-HQ-OAR-2005-0036).                                                               
  ------------------------ ------------ ------------------ ------------- ------------- --------------

**Table 5.1-4. EIA Case, Primary Analysis:**

**2015 Ozone Response Surface Metamodeling Results for Counties with**

**Largest Increases in Ozone 8hr Design Value (ppb) Due to Increased Use
of** Ethanol

  --------------- ------------- ------------------ ----------- ------------- --------------
  **State Name**  **County      **2015 Baseline    **2015 EIA  **Effect of   **2015
                  Name**        (Post-CAIR)^c^**   Case**      Expanded      Population**
                                                               Ethanol Use   
                                                               (ppb)**       

  Ohio            Geauga Co     82.5               82.8369     0.3           108,600

  Ohio            Clinton Co    75.7               76.0218     0.3           50,635

  Ohio            Mahoning Co   74.7               75.0213     0.3           248,545

  Arkansas        Crittenden Co 78                 78.3204     0.3           53,852

  Ohio            Summit Co     77.4               77.7175     0.3           557,892

  Mississippi     Adams Co      67.2               67.5164     0.3           33,495

  Ohio            Stark Co      71.7               72.0153     0.3           384,672

  Indiana         Shelby Co     76.2               76.5115     0.3           47,904

  Maine           York Co       77.6               77.902      0.3           210,006

  New York        Wayne Co      71.6               71.8926     0.3           103,846

  Texas           Travis Co     69.4               69.6912     0.3           1,022,772

  Ohio            Medina Co     72                 72.2909     0.3           187,686

  Maine           Hancock Co    76.8               77.0904     0.3           55,606

  Maine           Kennebec Co   64.9               65.1903     0.3           122,363

  Louisiana       Livingston    76.6               76.8883     0.3           141,807
                  Parish                                                     

  Louisiana       West Baton    77.4               77.6869     0.3           23,202
                  Rouge Parish                                               

  Louisiana       Lafourche     72.7               72.984      0.3           95,881
                  Parish                                                     

  Mississippi     Warren Co     56.2               56.4827     0.3           52,075

  Michigan        Huron Co      71.9               72.1766     0.3           37,530

  Ohio            Franklin Co   77                 77.2716     0.3           1,181,578

  Ohio            Trumbull Co   80                 80.2713     0.3           227,546

  Louisiana       Jefferson     77.1               77.3707     0.3           512,963
                  Parish                                                     

  Florida         Pinellas Co   62.3               62.5639     0.3           998,864

  Ohio            Delaware Co   72.1               72.3606     0.3           149,341

  Ohio            Knox Co       71.4               71.6579     0.3           62,138

  Florida         Duval Co      50.6               50.8568     0.3           895,678

  Indiana         Marion Co     74.6               74.8565     0.3           889,645

  Indiana         Madison Co    72.9               73.1564     0.3           140,575

  Massachusetts   Middlesex Co  75.8               76.0564     0.3           1,498,849

  Michigan        Oakland Co    79.2               79.4542     0.3           1,355,671

  Pennsylvania    Beaver Co     70.5               70.7528     0.3           184,649

  New York        Monroe Co     74.3               74.5521     0.3           754,405

  Mississippi     Harrison Co   69.3               69.5517     0.3           216,926
  --------------- ------------- ------------------ ----------- ------------- --------------

There are a number of important caveats concerning our estimated ozone
impacts using the Ozone RSM. The Ozone RSM does not account for changes
in CO emissions. As shown in Chapter 4, ethanol use should reduce CO
emissions significantly, directionally reducing ambient ozone levels in
areas where ozone formation is VOC-limited. Accounting for the reduction
in CO emissions in NOx-limited areas, however, may have little impact on
the ozone impact of ethanol use.

The Ozone RSM also does not account for changes in VOC reactivity. With
additional ethanol use, the ethanol content of VOC should increase.
Ethanol is less reactive than the average VOC. Therefore, this change
should also reduce ambient ozone levels in a way not addressed by the
Ozone RSM. Again, like the impact of reduced CO emissions, this effect
applies to those areas where ozone formation is VOC-limited. Another
limitation is the RSM's inability to simulate the spatial distribution
of emission impacts associated with the standard. Instead, we are forced
to make simplifying assumptions about the geographic uniformity of RFS
emissions impacts, explained above. The caveats and limitations
associated with the RSM highlight the fact that it should only be used
as a screening-level tool to characterize broad trends associated with
changes in different source categories of ozone precursors.

Finally, our application of the Ozone RSM here does not include the
impact of emissions from new ethanol plants. Directionally, this will
increase ozone levels in the vicinity of the new plant. As discussed in
Chapter 4, the overall VOC and NOx emission impacts of new ethanol
plants are only slightly lower than the emission impacts resulting from
increased use of ethanol in vehicles and equipment. Given the
concentrated nature of these impacts, the ozone impacts of these new
plants should be a focus of further study in the future.

[]{.mark}

Keeping these limitations in mind, the expanded use of ethanol will
impact the national emissions inventory of precursors to ozone, such as
VOCs and NOx, as described in Chapter 4. Exposure to ozone has been
linked to a variety of respiratory effects including premature
mortality, hospital admissions and illnesses resulting in school
absences. Ozone can also adversely affect the agricultural and forestry
sectors by decreasing yields of crops and forests.

The Wisconsin Department of Natural Resources (DNR) recently performed a
similar study of the impact of increased ethanol use on ozone.[^141]
They estimated that the conversion of gasoline outside of RFG areas in
Wisconsin to E10 blends would increase ozone in these areas on the order
of 1 ppb to as much as 2 ppb. (RFG areas in Wisconsin already contain 10
vol% ethanol.) This ozone increase was due to the predicted increase in
NOx emissions associated with ethanol use, since the non-RFG areas in
Wisconsin are generally NOx limited for ozone formation.

The Wisconsin DNR estimated the ozone impact for calendar year 2003 and
assumed that all vehicles experience the increase in NOx emissions.
Thus, their results are more comparable to our sensitivity analysis,
than our primary analysis. For the two increased ethanol use scenarios,
our sensitivity analysis projects increased ozone levels for several
Wisconsin counties of 0.35-40 ppb. Because the Wisconsin DNR analyzed
calendar year 2003 emissions and air quality, their base emission levels
are much higher than those estimated here for the year 2015. Emission
standards applicable to new vehicles and equipment are continually
reducing emissions over time. Per the emission models used here and by
the State of Wisconsin (NONROAD and MOBILE6), the effect of fuel quality
is generally estimated in terms of a percentage change in the base
emission level. As emissions from vehicles and equipment decrease over
time, the absolute impact of fuel quality changes decreases at the same
rate. Thus, the absolute emission changes predicted here for 2015 could
easily be a factor of two lower than those predicted by Wisconsin for
2003. This is likely the primary cause of the difference in the two sets
of projected ozone impacts.

We received a comment from the Missouri Department of Natural Resources
on the ozone impact analysis performed for the NPRM which expressed a
concern that our approach of zeroing out the ozone impact in areas which
did not experience a significant change in ethanol use had the effect of
ignoring the impact of ozone transport due to increased ethanol use in
upwind areas. This comment is correct. In a national analysis such as
this one, it is not practical to go through over 3100 counties to
determine which counties might have not experienced a change in ethanol
use in a particular ethanol use case, but is downwind of an area which
did. Still, the issue is a potentially relevant one and of reasonable
interest particularly to those tasked with air quality management.

In an attempt to approximate the impact of ozone transport from areas
which did experience a change in ethanol use on ozone in areas which did
not, we performed one additional run of the Ozone RSM. This additional
run applied the changes in VOC and NOx emissions estimated above for
attainment areas from our sensitivity analysis for the EIA case to
emissions in attainment areas, and applied no change in emissions in
non-attainment areas. We then compared the resulting ozone levels to
those from the base case, focusing on the difference in ozone levels in
non-attainment areas. Emissions in non-attainment areas were the same in
both cases (no change from the base case). Thus, the difference in ozone
levels in non-attainment areas should only be due to changes in
emissions and ozone levels in upwind attainment areas.

The results of this comparison indicated that, in terms of the 8-hour
ozone design value, ozone levels in non-attainment areas (i.e., RFG or
Low RVP areas) decreased by 0.03 ppb. Thus, the average impact due to
ozone transport is a reduction in ozone in downwind areas. However, the
standard deviation in the ozone impact was 0.05 ppb, indicating that a
significant number of areas experienced an increase, though most
experienced a decrease. This is not surprising given that ozone in some
attainments areas is VOC limited and may be decreasing in this fuel
case, while others are NOx limited and may be increasing. The maximum
ozone reduction was 0.17 ppb, while the maximum increase was 0.12. More
precise local atmospheric dispersion modeling will be needed in order to
estimate this type of impact for specific non-attainment areas.

In summary, we estimate that the measurable changes in VOC and NOx which
are a result of increased ethanol use will, on average, result in small
increases in ambient ozone formation. As we discussed above, the ozone
modeling results in a net increase in the average population weighted
ozone design value metric measured within the modeled domain (37 Eastern
states and the District of Columbia). In Appendix A, we also present the
impacts of increased ethanol use on a number of alternative measures of
ambient ozone concentration. We acknowledge, however, that to the extent
it occurs, increased future levels of ambient concentrations of ozone
related to the increased use of ethanol may result in detrimental health
and welfare effects due to ozone.

**5.2 Particulate Matter**

**5.2.1 Impact of Changes in Direct PM Emissions**

The amount of data evaluating the impact of ethanol and MTBE blending on
direct emissions of PM from gasoline-fueled vehicles is extremely
limited, as discussed in Chapter 3. Most studies do not test PM
emissions from vehicles fueled with unleaded gasoline, because the level
of PM emissions from properly operating vehicles is usually very low,
less than 0.1 g/mi.

Two studies indicate that the addition of ethanol might reduce direct PM
emissions from gasoline vehicles[^142] ^,^[^143]. However, both studies
were performed under wintertime conditions and one at high altitude. One
of the studies only consisted of three vehicles. The available data
indicate that ethanol blending might reduce exhaust PM emissions under
very cold weather conditions (i.e., 0 F or less), particularly at high
altitude. There is no indication of PM emission reductions at higher
temperatures or under warmed up conditions. Thus, the data are certainly
too limited to support a quantitative estimate of the effect of ethanol
on PM emissions.

**5.2.2 Potential Impact of Changes in Secondary PM Formation**

In addition to being emitted directly from a combustion source, fine
particles can be formed through a series of chemical reactions in the
atmosphere when SO~2~, NOx, and VOC oxidize or otherwise react to form a
wide variety of secondary PM. For example, SO~2~ oxidizes to SO~3~ and
sulfuric acid and NOx oxidizes to NO~3~ and nitric acid which, in turn,
react with ammonia in the atmosphere to form ammonium sulfate and
ammonium nitrate. Particles generated through this gas to particle
conversion are referred to as secondary aerosols (SA) and represent a
significant portion of ambient fine particulate matter. Studies have
shown that as much as 70% of the total organic carbon in urban
particulate matter can be attributed to secondary organic aerosol (SOA)
formation although the amount can also be less.[^144] Secondary PM tends
to form more in the summer with higher temperatures and more intense
sunlight.

Source-receptor modeling studies conducted in the Los Angeles area is
1993 by Schauer et al[^145] indicate that as much as 67% of the fine
particulate matter collected could not be attributed to primary sources.
The authors concluded that much of this unidentifiable organic matter is
secondary organic aerosol formed in the atmosphere. This is consistent
with previous studies conducted by Turpin and Huntzicker in 1991 who
concluded that 70% of the total organic carbon in urban PM measurements
made in southern California can be attributed to SOA.

Gas phase VOCs are oxidized by OH, NO~2~, peroxyacetylnitrate (PAN), and
ozone in the atmosphere, but their propensity to condense in the
particle phase is a function of two factors: volatility and reactivity.
To accumulate as an aerosol, a reaction product must first be formed in
the gas phase at a concentration equal to its saturation concentration.
This requirement will not be met if the relevant gas-phase reactions of
the VOC are too slow or if the vapor pressure of the reaction product is
higher than the initial concentration of its VOC precursor.[^146]
Limited data for reaction rate constants determined both experimentally
and estimated by structural relationships are available in the published
literature. However, the atmospheric chemistry behind SOA reaction rates
and the estimated aerosol yield is highly complex and carries with it a
great deal of uncertainty. Research in this area is ongoing and thus the
capacity to quantitatively model SOA formation is not yet a
straightforward process.

In general, all reactive VOC are oxidized by OH or other compounds.
Additionally, alkenes, cycloalkenes, and other olefinic compounds can
react with ozone and NO~2~ to form secondary aerosols. In fact, ozone is
responsible for nearly all the SOA formation from olefins, while OH
plays little or no role at all (Grosjean and Seinfeld, 1989; Izumi and
Fukuyama, 1990). Many VOC, however, will never form secondary organic
aerosol under atmospheric conditions regardless of their reactivity.
This is because the products of reactions of these compounds have vapor
pressures that are too high to form aerosols at atmospheric temperatures
and pressures. These include all alkanes and alkenes with up to 6 carbon
atoms, benzene and many low-molecular weight carbonyls, chlorinated
compounds and oxygenated solvents (Grosjean, 1992).

The VOC that have the greatest propensity to form SOA include aromatic
hydrocarbons (such as toluene but even including benzene), higher
molecular weight olefins and cyclic olefins, and higher molecular weight
paraffins. Kleindienst et al suggest that a high fraction of SOA is due
to aromatic hydrocarbon precursors. Furthermore, "aromatic products
having a single alkyl group on the aromatic ring were found to represent
a 'high-yield' family (e.g., toluene, ethylbenzene); compounds having
multiple methyl groups (e.g., m-xylene, 1,2,4-trimethylbenzene) were
found to represent a 'low-yield' family" (Kleindienst, 269). All of the
above mentioned VOC precursors are important either because there are
large amounts of these particular VOC emitted per day, or because a
large fraction of the VOC reacts, or a combination of the two. Based on
VOC emissions inventory data collected in the Los Angeles area, the most
important aerosol precursors (in the LA area using 1982 VOC emissions
inventories) are listed in Table 5.2-1 below:

**Table 5.2-1.**

**Predicted In Situ SOA Formation**

**During a Smog Episode in Los Angeles**

  -------------------------- ------------------- ---------------- ---------
  **VOC Functional Group**   **kg emitted        **Secondary PM   **%
                             daily\***           Produced         yield**
                                                 (kg)\***         

  [Aromatics]{.underline}    223985              3061             1.37

  [Olefins]{.underline}                                            

  Alkenes                    31163               608              1.95

  Cyclic Olefins             3220                144              4.47

  Terpenes                   6000                626              10.43

  [Paraffins]{.underline}                                          

  Alkanes                    140493              368              0.26

  Cycloalkanes               37996               96               0.25

  \*Source: Grosjean et al,                                        
  1992                                                            
  -------------------------- ------------------- ---------------- ---------

These predictions are a function of input data collected in the Los
Angeles area, and assume ambient levels of \[ozone\] = 100 ppb,
\[OH\]=1.0x10^6^ molecules/cm^3^, and \[NO3\]=0 with 6 hours of reaction
time. Aromatics are the largest functional group in terms of the
absolute quantities of VOC emitted daily, and thus they eventually form
the most SOA. Likewise, many high molecular weight paraffins (alkanes)
form SOA on a significant scale simply because their emissions are high.
However, the relative fraction of paraffins that react is less than that
of aromatics in smog chamber experiments simulating SOA formation in the
atmosphere. For olefins, the alkenes exhibit a combination of both
relatively high emissions, and a high fraction of VOC reacted to form
SOA. Cyclic olefins, in contrast, are emitted in relatively low levels,
but a high fraction of these VOC react and the end result is a
proportionally higher SOA yield than with the alkenes. Lastly, there are
several "miscellaneous" compounds and terpenes that are emitted on a
relatively small scale (in southern California), but that produce a
substantial amount of secondary organic aerosol.

Researchers at EPA recently completed a field study in the
Raleigh/Durham area of North Carolina that investigated the contribution
of various sources to ambient PM 2.5 concentrations.[^147] In the study
they identified toluene as an SOA precursor. They estimate that mobile
sources contribute nearly 90% of the total toluene emissions in that
region based on a chemical mass balance approach. At the same time,
however, SOA attributable to non-fuel-related VOC (i.e., biogenic
emissions) was found to be an even larger contributor to SOA (i.e.,
toluene was not likely the dominant source of SOA in this area). This
study is currently undergoing peer review and will be published shortly.
Qualitatively, however, this information is still quite useful since the
study identifies a contributing source of SOA that is attributable
almost entirely due to mobile sources.

VOC reaction rates increase with increasing ambient temperature and
sunlight intensity, so the level of SOA formed is much higher in summer
than in winter. Even in the more temperate coastal climates of southern
CA, studies have found the summertime concentration of SOA calculated
through Chemical Mass Balance models show SOA formation to be anywhere
from 2 -- 5 times higher in summer than winter. In a study conducted at
both urban and rural locations in the southeastern United Sates, the
concentration of SOA in the summer and early fall was roughly 2-3 times
that of colder months[^148].

As mentioned in Chapters 2 and 3, the addition of ethanol should reduce
aromatics in gasoline, which will in turn reduce the aromatics emitted
in the exhaust. However, quantifying the emission reduction is not
possible at this time due to a lack of speciated exhaust data for newer
vehicles running on ethanol blends. In addition, increased NOx emissions
resulting from the increased use of ethanol could increase the formation
of nitrate PM.

Based on the following, we believe that it is likely that the decrease
in secondary PM from organic aromatic hydrocarbons is likely to exceed
the increase in secondary nitrate PM. In 1999, NOx emissions from
gasoline-fueled vehicles and equipment comprised about 20% of national
NOx emissions from all sources[^149]. In areas where ethanol use
increases dramatically, NOx emissions from gasoline-fueled vehicles and
equipment increases roughly 5-10%. This is roughly equivalent to a 1-2%
increase in NOx emissions nationwide.

In contrast, gasoline-fueled vehicles and equipment comprised over 60%
of all national gaseous aromatic VOC emissions[^150]. In areas where
ethanol use increases dramatically, fuel aromatic content decreases by
about 4 vol% in the summertime, averaged across conventional gasoline
and RFG. This represents about a 15% reduction from a base level of
around 27 vol%. Assuming a proportional relationship between fuel
aromatics and aromatic emissions, this represents about a 24% reduction
in aromatic emissions nationwide.

In most urban areas, ambient levels of excess summer carbonaceous PM (a
reasonable estimate of secondary organic PM) tend to exceed those of
secondary nitrate PM. Thus, directionally, it appears likely that a net
reduction in ambient PM levels will result from increased ethanol use.
However, this should be considered a rough comparison at this time. A
more precise comparison will have to await the incorporation of
secondary organic aerosol formation into models, such as CMAx.

The research to facilitate this incorporation is currently underway. EPA
ORD scientists are currently carrying out a wide variety of laboratory
studies to refine the SOA chemistry mechanisms for use in the next
version of the CMAQ model, which is expected to be completed in 2007 and
submitted for peer review. This information should be available in time
for the comprehensive study of the Act's fuel requirements which is due
in 2009.[^151]

**Chapter 5: Appendix**

**Table 5A-1. 2015 Ozone Response Surface Metamodeling Summary
Statistics for the RFS Rule^a,^; Primary Scenario**

  ----------------------- ----------------------- -----------------------
  ***8hour Design Value                           
  (ppb)***                                        

  **Statistic**           **RFS Scenario**        **EIA Scenario**

  Minimum Change          -0.015                  0.000

  Maximum Change          0.329                   0.337

  Average Change          0.057                   0.079

  Standard Deviation      0.086                   0.100

  Population-Weighted     0.052                   0.056
  Change                                          

  ***24hr Average                                 
  (ppb)***                                        

  **Statistic**           **RFS Scenario**        **EIA Scenario**

  Minimum Change          -0.168                  -0.162

  Maximum Change          0.197                   0.074

  Average Change          0.008                   0.013

  Standard Deviation      0.018                   0.021

  Population-Weighted     0.014                   0.013
  Change                                          

  ***1hr Maximum (ppb)***                         

  **Statistic**           **RFS Scenario**        **EIA Scenario**

  Minimum Change          -0.104                  -0.094

  Maximum Change          0.213                   0.180

  Average Change          0.015                   0.024

  Standard Deviation      0.033                   0.040

  Population-Weighted     0.029                   0.033
  Change                                          

  ***Average 9-to-5                               
  (ppb)***                                        

  **Statistic**           **RFS Scenario**        **EIA Scenario**

  Minimum Change          -0.107                  -0.097

  Maximum Change          0.203                   0.141

  Average Change          0.012                   0.019

  Standard Deviation      0.027                   0.031

  Population-Weighted     0.022                   0.024
  Change                                          

  ***Average 10-to-3                              
  (ppb)***                                        

  **Statistic**           **RFS Scenario**        **EIA Scenario**

  Minimum Change          -0.108                  -0.107

  Maximum Change          0.207                   0.149

  Average Change          0.012                   0.019

  Standard Deviation      0.027                   0.032

  Population-Weighted     0.023                   0.024
  Change                                          

  ^a^ Note that the                               
  statistics presented                            
  here represent ethanol                          
  use changes across the                          
  entire 37-state ozone                           
  RSM domain.                                     
  ----------------------- ----------------------- -----------------------

**Table 5A-2. 2015 Ozone Response Surface Metamodeling Summary
Statistics for the RFS Rule^a^; Sensitivity Scenario**

  ----------------------- ----------------------- -----------------------
  ***8hour Design Value                           
  (ppb)***                                        

  **Statistic**           **RFS Scenario**        **EIA Scenario**

  Minimum Change          -0.115                  0.000

  Maximum Change          0.624                   0.549

  Average Change          0.111                   0.142

  Standard Deviation      0.158                   0.170

  Population-Weighted     0.092                   0.096
  Change                                          

  ***24hr Average                                 
  (ppb)***                                        

  **Statistic**           **RFS Scenario**        **EIA Scenario**

  Minimum Change          -0.181                  -0.173

  Maximum Change          0.184                   0.142

  Average Change          0.015                   0.024

  Standard Deviation      0.034                   0.041

  Population-Weighted     0.025                   0.028
  Change                                          

  ***1hr Maximum (ppb)***                         

  **Statistic**           **RFS Scenario**        **EIA Scenario**

  Minimum Change          -0.150                  -0.133

  Maximum Change          0.498                   0.346

  Average Change          0.027                   0.043

  Standard Deviation      0.060                   0.072

  Population-Weighted     0.052                   0.062
  Change                                          

  ***Average 9-to-5                               
  (ppb)***                                        

  **Statistic**           **RFS Scenario**        **EIA Scenario**

  Minimum Change          -0.177                  -0.163

  Maximum Change          0.400                   0.260

  Average Change          0.022                   0.034

  Standard Deviation      0.049                   0.057

  Population-Weighted     0.040                   0.046
  Change                                          

  ***Average 10-to-3                              
  (ppb)***                                        

  **Statistic**           **RFS Scenario**        **EIA Scenario**

  Minimum Change          -0.182                  -0.167

  Maximum Change          0.431                   0.273

  Average Change          0.022                   0.035

  Standard Deviation      0.050                   0.058

  Population-Weighted     0.041                   0.047
  Change                                          

  ^a^ Note that the                               
  statistics presented                            
  here reflect the impact                         
  of ethanol use changes                          
  across the entire                               
  eastern U.S. 37-state                           
  ozone RSM domain.                               
  ----------------------- ----------------------- -----------------------

# Chapter 6: Lifecycle Impacts on Fossil Energy and Greenhouse Gases

**6.1 Lifecycle Modeling**

Lifecycle modeling accounts for the energy and emissions from a
production process. It incorporates the material aspects, input and
output, of each step in a product system. This method helps to identify
key processes and emission sources and facilitates comparisons between
processes, consumption of natural resources, pollutant generation and
environmental burden. It is important to note that lifecycle modeling
typically provides only general comparisons, based on industry-wide
estimates and assumptions; it does not reflect general equilibrium
impacts, such as effects on input markets. The results of this type of
analysis are highly dependent upon the input data used, the variables
considered, and the assumptions made. Nevertheless, within these
limitations, it can be an extremely useful tool for evaluating some of
the environmental impacts of products and processes.

For transportation fuels, lifecycle modeling considers all steps in the
production of the fuel. This includes production of the fuel feedstock,
transportation of the fuel feedstock to a processing facility, fuel
processing, and distribution of the fuel to the retail outlet. If the
analysis considers only the finished product, it is sometimes called a
'well-to-pump' analysis; if the fuel combustion emissions are included,
it can be called a 'well-to-wheel' analysis. While both approaches have
advantages, in this work we have considered 'well-to-wheel' impacts.
However, we are not addressing the issues of vehicle technology and
energy efficiency, since we are making the assumption that the vehicle
issues will not be affected by the presence of renewable fuels (i.e.,
efficiency of combusting one Btu of renewable fuel is equal to the
efficiency of combusting one Btu of conventional fuel).

To put this type of analysis into perspective, consider the example of
gasoline. The fuel feedstock is crude oil. The lifecycle analysis
accounts for the energy used to extract the oil from the ground and any
associated emissions, such as the natural gas that is flared at the well
head. Next you evaluate transportation of the crude oil to the refinery.
If it is domestic crude oil, it may be delivered by pipeline and/or
barge. The analysis takes into account national trends for domestic oil
transportation, and apportions energy used and emissions generated to
each type of transportation. For foreign crude oil, the energy and
emissions from ocean tankers is included, with an estimate of the
average distance traveled by these tankers. Next is an estimation of the
energy use and emissions from the refinery. Because gasoline is not the
only product produced at the refinery, only a portion of the energy and
emissions is allocated to gasoline production. There are different
methods for making this allocation, based on the value of the
co-products or an engineering assessment of the energy use and emissions
from the various units in the refinery. You then evaluate the energy use
and emissions from transporting the gasoline to market, via pipeline and
truck, based on national average distances. Finally, vehicle energy use
and emissions are estimated. Figure 6.1-1 illustrates this process.

**Figure 6.1-1: Lifecycle Production Process, 'Well-to-Wheel', for
Gasoline**

![](media/image65.wmf){width="1.0708661417322836in"
height="1.8618110236220473in"}![](media/image66.wmf){width="1.2181102362204725in"
height="1.1173228346456694in"}![](media/image67.wmf){width="2.19251968503937in"
height="1.29251968503937in"}![](media/image68.wmf){width="0.9263779527559055in"
height="1.0in"}![](media/image69.wmf){width="1.1799212598425197in"
height="0.6090551181102363in"}

Lifecycle modeling has been a useful tool in evaluating the
environmental benefits of various alternative transportation fuels. It
allows the replacement fuel to be fairly compared against the
conventional transportation fuels -- gasoline and diesel fuel. There
have been several significant lifecycle analyses of transportation fuels
done in the last decade. The lifecycle analysis done for this Renewable
Fuel Standard (RFS) program uses a model developed by the Department of
Energy (DOE) Argonne National Laboratory (ANL) called the Greenhouse
Gases, Regulated Emissions, and Energy Use in Transportation (GREET)
model. EPA has reviewed and modified GREET somewhat to reflect the data
and assumptions appropriate for the RFS. These modifications are
discussed further in section 6.1.2.

**6.1.1 Scope of the Lifecycle Analysis**

An important step in conducting a lifecycle analysis is to define the
scope of the study. Varying results can be obtained depending on the
scope identified. The scope of the analysis includes (1) the goal (2)
the system boundaries (3) what flows are considered (4) temporal
considerations and (5) modeling tools used. Each of these components is
examined in the following sections.

**6.1.1.1 Goal**

The goal of this analysis is to determine the GHG emission and fossil
fuel impact of the increased use of renewable fuels. This analysis is
based on comparing future scenarios representing an increased percentage
of the overall transportation sector fuel pool coming from renewable
fuels compared to a reference case with the percentage of renewable
fuels use at current levels. This implies that our future scenarios
assume renewable fuels are displacing their petroleum based counterparts
and causing less to be used. This RIA reflects increases in ethanol
production of 85% and 150% respectively from the baseline. As this
analysis is compared to a reference case we are only interested in the
savings of the new or marginal renewable fuels used.

We have evaluated the absolute savings (e.g., tons of GHG emissions) as
well as determining what percentage these absolute savings are in terms
of overall transportation sector and economy wide emissions and energy
use.

**6.1.1.2 System Boundaries**

The lifecycle analysis for the relevant activities identified in the
GREET model is conducted without any regard to the geographic attributes
of where emissions or energy use occurs. While the primary emphasis of a
rulemaking analysis is typically to examine the domestic implications of
a rulemaking, the lifecycle analysis of this final rule represent global
reductions in GHG emissions and energy use, not just those occurring in
the U.S. For example, under a full lifecycle assessment approach, the
savings associated with reducing overseas crude oil extraction and
refining are included here, as are the international emissions
associated with producing imported ethanol. This assumes that for every
gallon of gasoline that's not imported into the US, the corresponding
quantity of crude oil is not extracted or processed to make this
gasoline regardless where the extraction or production takes place. This
type of modeling does not allow for behavioral changes that may be
occur, called "rebounding effect," discussed later.

There are two important caveats to this analysis, both dealing with
secondary impacts that may result internationally due to the expanded
use of renewable fuels within the United States. The first caveat is the
emissions associated with international land use change. Due to
decreasing corn exports some changes to international land use may
occur, for example, as more crops are planted in other regions to
compensate for the decrease in crop exports from the U.S. While the
emissions associated with domestic land use change are well understood
and are included in our lifecycle analysis, we did not include the
potential impact on international land use and any emissions that might
directly result. Our currently modeling capability does not allow us to
assess what international land use changes would occur or how these
changes would affect greenhouse gas emissions. For example, we would
need to know how international cropping patterns would change as well as
farming inputs and practices that might affect emissions assessment. The
second caveat results from the assumption of reduced petroleum imports.
It is commonly presumed in economic analyses that demand for a normal
good (i.e., oil) will increase as price decreases. A world wide
reduction of oil price that could result from reduced U.S. imports can
reduce the cost of producing transportation fuel which in turn would
tend to reduce the price consumers would have to pay for this fuel. To
the extent fuel prices are decreased, demand and consumption would tend
to increase; this impact of reduced cost of driving is sometimes
referred to as a "rebound effect." Such a greater consumption would
presumably result in an increase in greenhouse gas emissions as
consumers would drive more. These increased emissions would in part
offset the emission benefits otherwise accounted for this rule[^152]. It
is important to note that GREET does not model behavioral changes that
may affect prices of relevant commodities and goods which through
various feedback loops ultimately energy use. The model does not include
a general equilibrium approach that examines how a shock (whether
economic, technical or legal) affects not only the sector of interest
but also other sectors and the economy as a whole.[^153] While such
impacts of U.S. actions are important to understand, we have not have
fully considered and quantified the rebound effects of this renewable
fuel standard. Nevertheless, such impacts remain an important
consideration for future analysis.

The system boundaries for this study encompass both the renewable fuels
lifecycle stages as well as their petroleum based counterparts. Table
6.1-1 shows the lifecycle stages considered for each fuel.

**Table 6.1-1.** Lifecycle Stages Included in Analysis

  ---------------------- ---------------------- ------------------------- ----------------------- -------------------
  [Corn                  [Cellulosic            [Biodiesel]{.underline}   [Petroleum-Based        [Petroleum-Based
  Ethanol]{.underline}   Ethanol]{.underline}                             Gasoline]{.underline}   Diesel
                                                                                                  Fuel]{.underline}

  Corn Farming           Biomass Farming        Soybean Farming           Crude Oil Extraction    Crude Oil
                                                                                                  Extraction

  Corn Transport         Biomass Transport      Soybean Transport         Crude Oil Transport     Crude Oil Transport

                                                Soybean Crushing                                  

  Ethanol Production     Ethanol Production     Biodiesel Production      Refining                Refining

  Ethanol T&D            Ethanol T&D            Biodiesel T&D             Gasoline T&D            Diesel Fuel T&D

  Ethanol Tailpipe       Ethanol Tailpipe       Biodiesel Tailpipe        Gasoline Tailpipe       Diesel Fuel
  Emissions              Emissions              Emissions                 Emissions               Tailpipe Emissions
  ---------------------- ---------------------- ------------------------- ----------------------- -------------------

The boundaries around each lifecycle stage include the emissions and
energy use associated with that operation as well as upstream components
that feed into it. For example, the corn farming stage includes
emissions from fuel used in tractors as well as from producing and
transporting the fertilizer used in the field. Electricity production
emissions are included in almost all of the stages shown. These
components typically have the biggest impact on the results. We did not
include for example, energy and emissions associated with producing the
steel and concrete used to construct the ethanol plants or petroleum
refineries.

As other lifecycle studies of renewable fuels have included an expanded
set of system boundaries, a sensitivity analysis was performed that
includes the energy use and the emissions associated with producing farm
equipment, and is described in section 6.1.2.7.

A potentially important system boundary affect, however, could be
changes in land use. This is particularly the case for GHGs if new land
(e.g., rainforest land) must first be cleared in order to grow the
biofuel feedstocks. This lifecycle analysis is conducted without any
regard to the geographic attributes of where emissions or energy use
occurs. The benefits of this final rule represent global reductions in
GHG emissions and energy use, not just those occurring in the U.S. For
example, the savings associated with reducing overseas crude oil
extraction and refining are included here, as are the international
emissions associated with producing imported ethanol. One exception to
this is the emissions associated with international land use change. Due
to decreasing corn exports and modest decreases in soybean exports,
there may be some additional corn and soybean acres planted
internationally to meet world demand. The emissions associated with
domestic land use change are included in our lifecycle analysis but
international land use change was not as it was outside the scope of our
agriculture sector analysis. However, if emissions from international
land use change were included it would lower the overall benefits of
this rule. This is an area we will continue to examine for future
analysis.

**6.1.1.3 Environmental Flows Considered**

One issue that has come to the forefront in the assessment of the
environmental impacts of transportation fuels relates to the effect that
the use of such fuels could have on emissions of greenhouse gases
(GHGs). The combustion of fossil fuels has been identified as a major
contributor to the increase in concentrations of atmospheric carbon
dioxide (CO~2~) since the beginning of the industrialized era, as well
as the build-up of trace GHGs such as methane (CH~4~) and nitrous oxide
(N~2~O). This lifecycle analysis evaluates the impacts of increased
renewable fuel use on greenhouse gas emissions.

The relative global warming contribution of emissions of various
greenhouse gases is dependant on their radiative forcing, atmospheric
lifetime, and other considerations. For example, on a mass basis, the
radiative forcing of CH~4~ is much higher than that of CO~2~, but its
effective atmospheric residence time is much lower. The relative warming
impacts of various greenhouse gases, taking into account factors such as
atmospheric lifetime and direct warming effects, are reported on a
'CO~2~-equivalent' basis as global warming potentials (GWPs). The GWPs
used in this analysis were developed by the UN Intergovernmental Panel
on Climate Change (IPCC) as listed in their Third Assessment
Report[^154], and are shown in Table 6.1-2.

**Table 6.1-2.**

Global Warming Potentials for Greenhouse Gases

  --------------------------------------- -------------------------------
  Greenhouse Gas                          GWP

  CO~2~                                   1

  CH~4~                                   23

  N~2~O                                   296
  --------------------------------------- -------------------------------

Greenhouse gases are measured in terms of CO~2~-equivalent emissions,
which result from multiplying the GWP for each of the three pollutants
shown in the above table by the mass of emissions for each pollutant.
The sum of impacts for CH~4~, N~2~O, and CO~2~, yields the total
effective GHG impact.

The impact increased volumes of renewable fuels use has on GHG emissions
(in terms of CO~2~-eq.) as well as for only CO~2~ emissions which
represent a subset of the overall GHG emissions, is considered in this
analysis. The impact increased volumes of renewable fuels use has on
fossil energy (in terms of Btus) is also considered. Fossil energy use
includes energy associated with coal, natural gas, and petroleum
products. Fossil energy use is strongly linked with CO~2~ and GHG
emissions and is an important consideration when looking at overall
sustainability.

Petroleum energy use is a subset of fossil energy use and is the major
contributor to overall transportation sector energy use. Petroleum
energy use is also linked to CO~2~ and GHG emissions but also has
impacts on national energy concerns such as dependence on foreign
sources of petroleum. Therefore, petroleum energy was also considered
separately in this analysis and examined in terms of overall energy use,
as well as in terms of petroleum imports avoided through the increased
use of renewable fuels.

**6.1.1.4 Time Frame and Volumes Considered**

The results presented in this analysis represent a snapshot in time.
They represent annual GHG and fossil fuel savings in the year
considered, in this case 2012.

Consistent with the renewable fuel volume scenarios described in Chapter
2, our analysis of the GHG and fossil fuel consumption impacts of
renewable fuel use was conducted using three volume scenarios. The first
scenario was a reference case representing 2004 renewable fuel
production levels, projected to 2012. This scenario provided the point
of comparison for the other two scenarios. The other two renewable fuel
scenarios for 2012 represented the RFS program requirements and the
volume projected by EIA.

In both the RFS and EIA scenarios, we assumed that the biodiesel
production volume would be 0.303 billion gallons based on an EIA
projection. Furthermore, the Energy Act requires that 250 million
gallons of cellulosic ethanol be produced starting in the year 2013, for
both scenarios we assume that 250 million gallons of ethanol that
qualify for cellulosic credit will be produced in 2012. The remaining
renewable fuel volumes in each scenario would be ethanol made from corn
and imports. The import volume is based on EIA's projections for the
percent of total ethanol volume supplied by imports in 2012. The total
volumes for all three scenarios are shown in Table 6.1-3.

**Table 6.1-3. Volume S**cenarios in 2012 (billion gallons)

+----------------+------------+---------------------+-----------------+
|                | Reference  | RFS Case            | EIA Case        |
|                |            |                     |                 |
|                | Case       |                     |                 |
+----------------+------------+---------------------+-----------------+
| Corn-ethanol   | 3.947      | 5.985               | 8.758           |
+----------------+------------+---------------------+-----------------+
| Cellulosic     | 0.0        | 0.25                | 0.25            |
| ethanol        |            |                     |                 |
+----------------+------------+---------------------+-----------------+
| Biodiesel      | 0.030      | 0.303               | 0.303           |
+----------------+------------+---------------------+-----------------+
| Ethanol        | 0.0        | 0.436               | 0.630           |
| imports        |            |                     |                 |
+----------------+------------+---------------------+-----------------+
| Total volume   | 3.977      | 6.974               | 9.941           |
+----------------+------------+---------------------+-----------------+

As we are comparing against a reference case, we are only interested in
the emissions and energy savings associated with new or marginal
renewable fuels production that comes on-line after 2004 (the baseline
assumed for the reference case).

**6.1.1.5 Model Used**

The lifecycle model used in the evaluation of the impacts of the RFS
program is the fuel-cycle model developed by DOE's Argonne National
Laboratory. For this work, EPA used the most recent version of this
model, GREET 1.7 (November 10, 2006 release). GREET, a multi-dimensional
spreadsheet model, is one of the most widely used model of this type for
transportation fuels. It has been reviewed, used, and referenced by a
wide variety of analysts, including General Motors, National Corn
Growers Association, several fuel industry organizations, and a wide
variety of academic institutions. It is the most comprehensive and
user-friendly model of its type. It has been under development for over
10 years, with input from EPA, USDA, DOE laboratories, and industry
representatives. The model addresses the full lifecycle for an
exhaustive number of alternative transportation fuels and automotive
technologies. For these reasons, EPA felt it was the best tool for
evaluating the energy and emission impacts of the RFS program.

The GREET model has been developed to calculate per-mile energy use and
emission rates of various combinations of vehicle technologies and fuels
for both fuel cycles and total energy cycles. The model actually
consists of three components: GREET 1.x, which calculates fuel cycle
energy use and emissions, GREET 2.x, which calculates light-duty vehicle
cycle energy use and emissions, and GREET 3.x, which calculates
heavy-duty vehicle cycle energy use and emissions. All discussion here
refers to GREET 1.7, the most recent version of the fuel component of
GREET.

To estimate fuel cycle energy use and emissions, GREET first estimates
energy use and emissions for a given upstream stage. The model then
combines the energy use and emissions from all upstream stages for a
fuel cycle, to estimate total upstream fuel cycle energy use and
emissions. Inputs are national-average energy usage rates, efficiencies
and emission factors for each stage. The model calculates total energy
use, fossil energy use, and emission rates for the regulated pollutants
and greenhouse gases, reported as grams per mile or grams per million
Btu. These results allow comparison of transportation fuels, based on
energy use and/or emissions.

One of the main comments we received on our lifecycle approach was that
our sole reliance on the GREET model should be avoided, given other
models are available. There are several other models that have been
developed for conducting renewable fuels lifecycle analysis. For
example, researchers at the Energy and Resources Group (ERG) of the
University of California Berkeley have developed the ERG Biofuel
Analysis Meta-Model (EBAMM) and Mark Delucchi at the Institute of
Transportation Studies of the University of California Davis has
developed the Lifecycle Emissions Model (LEM). There are also other
non-fuel specific lifecycle modeling tools that can be used to perform
renewable fuel lifecycle analysis. The main differences in these models
are with input assumptions used as described below.

Several studies have been released recently making use of these other
models and showing different results than we find in the analysis done
for this rule. For example, whereas GREET estimates a net GHG reduction
of about 22% for corn ethanol compared to gasoline, the previously cited
works by Farrell et al. utilizing the EBAMM show around a 13% reduction.
While there may be small differences in the models in terms of emissions
and energy uses associated with ancillaries (e.g., emissions to produce
fertilizer, electricity, etc.) the main difference in results is not due
to model used but assumptions on scope and input data used.

For example, most studies focus on average or current ethanol production
which uses a current mix of wet and dry mill ethanol production and use
of coal and natural gas as process energy. In contrast, we consider new
or marginal ethanol production which implies a higher portion of more
efficient dry mill production and mix of process fuels. Other studies
also typically base ethanol and farm energy use on historic data while
we are assuming a state of the art dry milling plant and most current
farming energy use data. Assumptions concerning land use change CO~2~
emissions and agriculture related GHG emissions could also have an
impact on overall results. Other studies also differ in the
environmental flows considered. For example, Delucchi[^155] uses
different types of greenhouse gases and GWPs compared to those used in
this analysis as shown in Table 6.1-2 to determine GHG emissions.

Other researchers have performed lifecycle analysis of renewable fuels
not specifically focused on GHG emissions. One result that has been
debated recently is the net energy balance of corn-based ethanol fuel.
Some analysts have suggested that there is actually a negative energy
balance for corn ethanol, meaning that it takes more fossil energy to
produce the ethanol than is contained in the resulting fuel, making it
an unattractive transportation fuel. While we do not believe this is an
appropriate metric to use when examining renewable fuels, as discussed
in Section 6.2.3, it is still useful in examining the range of lifecycle
results. Two studies Pimental (2003)[^156] and Patzek (2005), [^157]
concluded that the energy balance is negative. . Many other researchers,
however, have criticized that work as being based on out-dated farming
and ethanol production data, including data not normally considered in
lifecycle analysis for fuels, and not following the standard methodology
for lifecycle analysis in terms of valuing co-products. Furthermore,
several recent surveys have concluded that the energy balance is
positive, although they differ in their numerical
estimates.[^158]^,^[^159]^,^[^160] Authors of the GREET model have also
concluded that the lifecycle amount of fossil energy used to produce
ethanol is less than the amount of energy in the ethanol itself. Based
on our review of all the available information, and the results of our
own analysis, we also believe that the energy balance is positive.

The differences found by different studies and models used emphasize the
importance of the input data and methodology when using lifecycle
analysis. It also shows how dependent this type of analysis is on the
assumptions made throughout the model. Based on differences in scopes
and input data considered between these other studies and what we
defined in this analysis, we believe the differences in results that are
seen are reasonable and the values we are obtaining from our use of the
GREET model are acceptable for this analysis.

**6.1.2 Modifications to GREET**

EPA chose to use GREET 1.7 to evaluate the lifecycle impacts of the RFS
program. GREET 1.7 is the most recently released version of the GREET
model. However, this version of the model does not reflect the potential
impacts on transportation fuel industries as a result of the RFS
program. In addition, for this regulation our intent was to evaluate the
impact of incremental renewable fuel production resulting from the RFS
program and not a current industry average. Therefore, EPA has modified
some of the input variables and assumptions made in the GREET model. The
renewable fuels considered in this analysis were modeled as being
produced from the following feedstocks and processes:

-   Corn Ethanol:

    -   Wet Milling

        -   Mix of coal and natural gas as process fuel

    -   Dry Milling

        -   Natural gas as process fuel

        -   Coal as process fuel

        -   Biomass as process fuel

-   Cellulosic Ethanol:

    -   Hybrid Poplar Feedstock

        -   Fermentation route

    -   Switchgrass Feedstock

        -   Fermentation route

    -   Corn Stover Feedstock

        -   Fermentation route

    -   Forest Waste Feedstock

        -   Gasification route

-   Biodiesel:

    -   Soybean Oil Feedstock

        -   Transesterification route

    -   Yellow Grease Feedstock

        -   Transesterification route

These feedstocks and processes were primarily based on what was
available in the GREET model with some minor modifications as described
below. However, there are other pathways for producing renewable fuels
not covered here, for example different feedstocks for cellulosic
ethanol production (e.g., MSW) as well as different process for the
feedstocks considered, like gasification of switchgrass and production
of soybean oil diesel fuel through hydrotreating.

Furthermore, the lifecycle analysis used for this rulemaking is based on
averages of the different renewable fuels modeled. For example, the GHG
emission and fossil energy savings associated with increased use of corn
ethanol are calculated based on a mix of process fuels, assuming a
certain projected mix of each process fuel as outlined below. While this
method may not exactly represent the reductions associated with a given
gallon of renewable fuel, it is reasonable for the purpose of this
analysis which is to determine the impact of the total increased volume
of renewable fuels used.

We recognize that different feedstocks and processes will each have
unique characteristics when it comes to lifecycle GHG emissions and
energy use. However, we understand that other feedstocks and processes
as well as differences in other parts of the renewable fuel lifecycle
will impact the savings associated with their use and this is the focus
of ongoing work at the agency.

GREET is subject to periodic updates by ANL, each of which results in
some changes to the inputs and assumptions that form the basis for the
lifecycle estimates of emissions generated and energy consumed. These
updates generally focus on those input values for those fuels or vehicle
technologies that are the focus of ANL at the time. As a result there
are a variety of other inputs related to ethanol and biodiesel that may
not have been updated in some time. In the context of the analysis of
the RFS and EIA scenarios, we determined that some of the GREET input
values that were either based on outdated information or did not
appropriately reflect market conditions under a renewable fuels mandate
should be examined more closely, and updated if necessary.

Since the analysis done for the NPRM, several changes have been made to
the GREET model, some as part of periodic updates ANL had planned and
some as part of an interagency agreement between ANL and EPA to
investigate a variety of GREET input values. A summary of the changes is
as follows:

-   Included CO~2~ emissions from corn farming lime use

-   Updated the corn farming fertilizer use inputs

-   Added cellulosic ethanol production from corn stover and forest
    waste

-   Modeled biomass as a process fuel source in corn ethanol dry milling

In addition to the changes above we also examined and updated other
GREET input assumptions for corn ethanol and biodiesel production. A
summary of the GREET input values we investigated and modified is given
below. We also examined several other GREET input values, but determined
that the default GREET values should not be changed for a variety of
reasons as discussed in the following sections. These included corn and
ethanol transport distances and modes and byproduct allocation methods.
Our investigation of these other GREET input values are discussed more
fully below. The current GREET default factors for these other inputs
were included in the analysis for this final rule.

We did not investigate the input values associated with the production
of petroleum-based gasoline or diesel fuel in the GREET model for this
final rule. However, the refinery modeling discussed in Chapter 7 will
provide some additional information on the process energy requirements
associated with the production of gasoline and diesel under a renewable
fuels mandate. We will use information from this refinery modeling in
future analysis to determine if any GREET input values should be
changed.

A summary of the GREET corn ethanol input values we investigated for
this final rule is given below.

**6.1.2.1 Wet Mill versus Dry Mill Ethanol Plants**

As described in Chapter 1, the two basic methods for producing ethanol
from corn are dry milling and wet milling. In the dry milling process,
the entire corn kernel is ground and fermented to produce ethanol. The
remaining components of the corn are then dried for animal feed (dried
distillers grains with solubles, or DDGS). In the wet milling process,
the corn is soaked to separate the starch, used to make ethanol, from
the other components of the corn kernel. Wet milling is more complicated
and expensive than dry milling, but it produces more valuable products
(ethanol plus corn syrup, corn oil, and corn gluten meal and feeds). The
majority of ethanol plants in the United States are dry mill plants,
which produce ethanol more simply and efficiently.

While other lifecycle models often base the mix of wet and dry milling
on existing plants, for this analysis, we are only interested in
marginal ethanol production. We expect most new ethanol plants will be
dry mill operations. That has been the trend in the last few years as
the demand for ethanol has grown, and our analysis of ethanol plants
under construction and planned for the near future has verified this.
Our analysis of production plans, as outlined in Chapter 1, indicates
that essentially all new ethanol production will be from dry mill plants
(99%).

**6.1.2.2 Coal versus Natural Gas in Ethanol Plants**

The type of fuel used within the ethanol plant for process energy to
power the various components that are used in ethanol production
(dryers, grinders, heating, etc.) can vary among ethanol plants. The
type of fuel used has an impact on the energy usage, efficiency, and
emissions of the plant, and is primarily determined by economics. Most
new dry mill plants built in the last few years have used natural gas.
However, some new plants are using coal. For these cases, EPA is
promoting the use of combined heat and power, or cogeneration, in
ethanol plants to improve plant energy-efficiency and to reduce air
emissions. This technology, in the face of increasing natural gas
prices, may make coal a more attractive energy source for new ethanol
plants.

GREET default factors represent the average percentage of fuel use for
the entire industry, and may not reflect the recent growth in the
industry. Therefore, we based our fuel mix assumptions on the review of
plants under construction and those planned for the near future outlined
in Chapter 1. Our analysis indicates that coal will be used as process
fuel for approximately 14% of the new dry mill under construction and
planned ethanol production volume capacity. This is the value we used in
GREET for our analysis of dry milling ethanol production fuel mix.

As opposed to typical dry mill plants, corn wet mill ethanol plants can
use a mix of process fuel sources at the same plant. For the 1% of
additional ethanol production from wet mills, the GREET model defaults
of 40% coal and 60% natural gas process fuel was used in this analysis.

As described below, the ethanol production stage of the lifecycle
typically represents the stage where the largest amount of fossil fuel
energy is consumed and where the impact on lifecycle emissions is the
greatest. Therefore, the type of process fuel used in ethanol production
will have a significant impact on the fuel's lifecycle GHG results. For
example, our analysis indicates that ethanol produced in a coal fired
dry mill plant would not have any GHG benefits as compared to petroleum
gasoline. Given that the relative prices of natural gas and coal could
change over time, and thus change the percentage of each used in ethanol
production, our analysis of fuels used in plants under construction and
those planned for the future would need to be reevaluated for future
work.

**6.1.2.3 Ethanol Plant Process Efficiency**

For the corn-to-ethanol fuel cycle, the largest amount of fossil fuel
energy consumed occurs at the ethanol production plant. The energy use
at a dry mill plant using natural gas was based on the model developed
by USDA which was documented in a peer-reviewed journal paper on cost
modeling of the dry-grind corn ethanol process.[^161] This model was
modified by EPA for use in the cost analysis of this rulemaking
described in Chapter 7. GREET inputs are total energy use per gallon of
ethanol produced. The USDA model predicts the annual thermal (natural
gas) and electricity demand shown in Table 6.1-4.

**Table 6.1-4.**

**Annual Energy Use at Dry Mill Ethanol P**lant

  ----------------------------------------------- -----------------------
  **Energy Input**                                **Value**

  Purchased Electricity (MWh/yr.)                 41,308

  Natural Gas (mmBtu/yr.)                         1,617,094

  **Output**                                      

  Ethanol (mmgal/yr.)                             50
  ----------------------------------------------- -----------------------

Electricity energy use was converted from MWh to Btu based on a
conversion of 3,410 btu/kWh. The primary energy used to produce
electricity is accounted for in the GREET model. Table 6.1-5 shows the
GREET input used for natural gas process fuel dry milling plants in this
analysis.

**Table 6.1-5.**

**GREET Inputs for Corn Ethanol**

**Natural Gas** Dry Mill Energy Use

  ------------------------------------------------- ---------------------
  Total Energy Use (mmBtu/gal.)                     35,159

  \% electricity                                    8.0%
  ------------------------------------------------- ---------------------

Energy requirements for a coal fired ethanol plant are different from a
natural gas fired plant. Typically coal boilers are slightly less
efficient than natural gas boilers. Furthermore additional electricity
is required for coal storage and handling as compared to natural gas.
Additionally a large portion of the energy at an ethanol plant is due to
drying the DDGS. A natural gas plant utilizes natural gas driers for
this process while a coal fired plant would use steam dryers, the
efficiency loss of converting coal to steam represents additional
thermal energy required at a coal fired plant vs. a natural gas one.

Most other lifecycle models assume the same energy efficiency for both
coal and natural gas ethanol plants, however, for this analysis, it was
assumed that a coal plant would require 15%[^162] more electricity
demand due to coal handling and have a 13% increase in thermal demand
for steam dryers as compared to the natural gas fueled plant. The
increase in thermal demand was based on breaking out the drying energy
in the USDA process model and assuming the same amount of energy would
be produced by 78% efficient coal boilers. Table 6.1-6 shows the GREET
input used for coal process fuel dry milling plants in this analysis.

**Table 6.1-6.**

**GREET Inputs for Corn Ethanol**

**Coal** Dry Mill Energy Use

  ------------------------------------------------- ---------------------
  Total Energy Use (mmBtu/gal.)                     40,079

  \% electricity                                    8.1%
  ------------------------------------------------- ---------------------

The Energy Act also allows ethanol made from non-cellulosic feedstocks
to receive cellulosic ethanol production volume credit if 90 percent of
the process energy used to operate the facility is derived from a
renewable source. In the context of our cost analysis, we have assumed
that 250 million gallons of corn ethanol will be produced using 90
percent or more biomass energy and receive the cellulosic ethanol volume
credit. Further discussion of this issue can be found in Chapter 1.

For the lifecycle analysis we considered the case where a corn ethanol
dry mill plant utilized biomass as a fuel source. For this case the same
amount of fuel and purchased electricity energy per gallon as a coal
powered plant was assumed. This assumption is based on the biomass plant
having more fuel handling than a natural gas plant and producing steam
for DDGS drying.

As discussed in section 6.2.3, CO~2~ emissions from combustion of
biomass are not assumed to increase net atmospheric CO~2~ levels.
Therefore, CO~2~ emissions from biomass combustion as a process fuel
source are not included in the lifecycle GHG inventory of the ethanol
plant. The fossil energy use and GHG emissions from producing the
electricity used at the plant are included.

For the 1% of corn ethanol produced from wet milling, the GREET process
energy use default of 49,950 Btu/gallon of ethanol produced by the wet
milling process was used in the analysis.

**6.1.2.4 Corn Transport Distances**

Corn transport distances selected for use in this analysis are 100 miles
round trip. Corn used in the ethanol production process is assumed to
travel from corn fields to ethanol production facilities in a two-step
process; first, corn is transported from outlaying farms to
centrally-located collection facilities, such as county elevators.
Second, this corn is transported from the collection facilities to the
ethanol production facilities. The first leg of the corn transport
process is assumed to be a 20-mile round trip and the second leg is
assumed to be an 80-mile round trip. These assumptions coincide with
those used in GREET[^163] Version 1.7 and GREET Version 1.5.

Corn transport data is limited, however; Graboski[^164] found that the
average one-way hauling distance for corn from fields to county
elevators was 7.5 miles and from county elevators to ethanol processing
facilities was 49.7 miles for an effective average round-trip corn
transport distance of 74.6 miles. Similarly, Gervais and Baumel[^165]
found that average one-way corn transport distances for the 1994-1995
Iowa growing season was 37.2 miles for semi-trucks (35.8%), 4.9 miles
for wagons (33.3%), and 9.1 miles for single and tandem axel vehicles
(30.9%). Several Minnesota corn mills indicated that the maximum radius
of supply for their mills was 65 to 80 miles (values apparently cited in
the same study).

The available data on corn transport distances does not provide a clear
indication that the default values in GREET are unreasonable. Therefore,
we retained the GREET default values for our analysis. This assumes that
the land use pattern (where corn is planted) and the plant location
decisions by ethanol plants will not change significantly. We believe
this is reasonable for the fuel volumes considered. This is an area we
will continue to examine for future analysis.

**6.1.2.5 Ethanol Transportation Distances and Modes**

The default values in GREET for ethanol transportation and modes are
shown in Table 6.1-7. These values correspond to numbers in a USDA study
on the energy balance of corn ethanol.[^166]

**Table 6.1-7.** GREET Ethanol Transportation Input Data

  -------------- ------------ ----------------- ------------ ----------------
  **Mode**       **Plant to                     **Terminal   
                 Terminal**                     to Station** 

                 \%           Distance (miles)  \%           Distance (miles)

  Rail           40%          800               0%           

  Barge          40%          520               0%           

  Truck          20%          80                100%         30
  -------------- ------------ ----------------- ------------ ----------------

The GREET default values are consistent with the analysis we performed
on ethanol distribution infrastructure. Chapter 1 of this document
discusses current ethanol transportation and distribution and indicates
that if ethanol facilities are located within 100-200 miles of a
terminal, trucking is preferred. Rail and barge are used for longer
distances. Pipelines are not currently used to transport ethanol and are
not projected to play a role in ethanol transport in the future time
frame considered.

We also discuss in Chapter 1 future ethanol transportation and
distribution needs based on the increased amounts of renewable fuels
used as a result of this rule. We concluded that most new ethanol
capacity will not have river access. In addition, at least one new
ethanol plant slated for production that does have river access is
planning to move its ethanol to market via rail so most new ethanol
freight volumes will be handled by rail and that ethanol transport by
inland waterway will remain constant.

A recent USDA Cost of Ethanol Production report also provides
information on ethanol distribution distances and modes.[^167] The
report includes 2002 data from a survey of 21 dry mill ethanol plants.
The survey collected data on modes and distances traveled for ethanol
transport from the facilities. The report concluded that 46 percent of
the ethanol produced at the surveyed plants in 2002 was shipped by truck
an average one way distance of 93 miles, with a range of 30 to 250
miles. The remaining 54 percent of ethanol produced was shipped by rail
an average one way distance of 1,163 miles, with a range of 800 to 2,500
miles. However, this data is for a subset of existing plants where, for
example, there is no barge transportation listed, and also does not take
into account the increased demand for ethanol projected by this rule.

Comparing the GREET default values to these other sources indicates that
the GREET defaults values for percent of ethanol transported by rail may
be low. However, due to lack of precise data on future ethanol
transportation by mode, we concluded that the current GREET default
values for percent of ethanol transported by mode are appropriate for
the RFS analysis.

The GREET default values for miles shipped by mode fall within the range
of values listed in the USDA survey data of existing plants. The USDA
survey data indicate higher than average transportation distances;
however the data is not comprehensive enough, only representing a small
fraction of total and projected ethanol production capacity, thus not
warranting a change to the default GREET values. Therefore, the default
values shown in Table 6.1-7 were used in this analysis. This is an area
we will continue to examine for future analysis.

**6.1.2.6 Biodiesel Transportation Distances and Modes**

The default values in GREET for biodiesel transportation and modes are
shown in Table 6.1-8.

**Table 6.1-8. GREET Biodiesel** Transportation Input Data

  -------------- ------------ ----------------- ------------ ----------------
  **Mode**       **Plant to                     **Terminal   
                 Terminal**                     to Station** 

                 \%           Distance (miles)  \%           Distance (miles)

  Barge          8%           520               0%           

  Pipeline       63%          400               0%           

  Rail           29%          800               0%           

  Truck          0%                             100%         30
  -------------- ------------ ----------------- ------------ ----------------

The GREET default assumptions for mode of biodiesel transportation are
not consistent with the analysis we performed on biodiesel distribution
infrastructure. The distribution infrastructure discussion in Chapter 1
of this document indicates pipelines are not currently used to transport
biodiesel and are not projected to play a role in biodiesel transport in
the future time frame considered.

Therefore, GREET default factors for biodiesel transportation from plant
to terminal were modified to remove pipeline transport. The percent of
biodiesel shipped by barge and rail were increased in the same
proportion as the current percentage split. The result was 22% of
biodiesel shipped by barge and 78% shipped by rail. The GREET default
distances for biodiesel rail and barge transport as well as terminal to
station assumptions are consistent with ethanol transportation and
distribution assumptions and were used in this analysis.

**6.1.2.7 Corn Yield and Related Inputs**

GREET includes a collection of energy use and material inputs to corn
farming per bushel (bu) of corn produced. Several corn farming input
data parameters and default values were updated from the version of
GREET used for the NPRM to the version used in the FRM analysis. The
current GREET corn farming input data default values are shown in Table
6.1-9.

**Table 6.1-9.** GREET Corn Farming Input Data

  ----------------------------------------------- -----------------------
  **Input Parameter**                             **Default Value**

  Energy Use for Corn Farming                     22,500 Btu/bu

  \- Energy use from diesel fuel                  38.3%

  \- Energy use from gasoline                     12.3%

  \- Energy use from natural gas                  21.5%

  \- Energy use from LPG                          18.8%

  \- Energy use from purchased electricity        9.0%

                                                  

  Nitrogen Fertilizer (as N)                      420 g/bu

  Phosphate Fertilizer (as P~2~O~5~)              149 g/bu

  Potash Fertilizer (as K~2~O)                    174 g/bu

  Lime (as CaCO~3~)                               1,202 g/bu

                                                  

  Herbicide Use:                                  8.1 g/bu

  Insecticide Use:                                0.68 g/bu
  ----------------------------------------------- -----------------------

The default GREET input values for corn farming shown in Table 6.1-9 are
based in part on farm energy use and material inputs per acre divided by
an assumed corn yield in bu/acre. Therefore, while corn yield is not a
direct input in GREET, it is a critical part of the calculation of corn
energy and material input requirements. Although corn yields have been
generally rising over time, see Figure 6.1-2, the annual variation is
volatile.

**Figure 6.1-2. U.S. Average Corn Yield**[^168]

![](media/image71.wmf){width="4.429166666666666in"
height="3.2194444444444446in"}

We examined data on farm energy use, material input, and yield data to
determine if the GREET default values needed to be updated. The
lifecycle modeling conducted for the RFS program is based on future
predictions. Unfortunately, no good projections of future energy use
associated with corn farming are available. USDA does list projections
for corn yield. The 2012 projected U.S. average corn yield is 158.5
bu/acre.[^169] Historic data on corn farming energy use is available
from the following USDA information sources.

-   The USDA Agricultural Resource Management Survey (ARMS) provides
    data from selected States on fuel, electricity, natural gas, and
    seed corn used per acre on the farm and activities of moving farm
    products to initial storage facilities.

-   The USDA National Agricultural Statistics Service (NASS) produces
    annual data on crop production including yields per acre and total
    production of corn by state.

USDA NASS data on corn yields and production values are provided
annually. However, the three most recent years of the ARMS data and
specifically the costs-of-production portion of the survey dedicated to
corn are 1991, 1996, and 2001[^170]. Table 6.1-10 lists corn farming
energy input data for the three years of the ARMS study.

**Table 6.1-10.** Farm Energy Use Data per Acre

  --------------- -------------- ----------- ----------- ----------- -----------
  **Input**       **Units**      **9-State                           
                                 Weighted                            
                                 Average                             
                                 Values**                            

                                 1991        1996^a^     2001        3 Yr. Avg.

  Seed            bu/acre        1.51        1.50        1.69        1.57

  Energy:                                                            

  \- Diesel       Gallons/acre   7.81        9.80        6.40        8.01

  \- Gasoline     Gallons/acre   3.42        3.07        1.65        2.71

  \- LPG          Gallons/acre   3.86        7.25        5.10        5.41

  \- Electricity  kWh/acre       32.72       79.38       38.22       50.11

  \- Natural Gas  Cubic ft/acre  284.73      208.12      207.09      233.31

  Total Energy    mmBtu/acre     2.12        2.71        1.78        2.20
  Use                                                                

  ^a^ High energy                                                    
  use in the 1996                                                    
  survey is due                                                      
  to increased                                                       
  corn drying                                                        
  requirements.                                                      
  See the                                                            
  discussion                                                         
  below.                                                             
  --------------- -------------- ----------- ----------- ----------- -----------

Although USDA corn data is available for every state that produces corn,
the data documented in Table 6.1-10 is for nine major corn producing
States: Illinois, Indiana, Iowa, Minnesota, Nebraska, Ohio, Michigan,
South Dakota, and Wisconsin. In 2005, these nine States accounted for 80
percent of U.S. corn production. In 2001 these nine States represented
92 percent of U.S. ethanol production, and based on our analysis
outlined in Chapter 1 are projected to represent 82 percent of ethanol
production in 2012. The data in Table 6.1-10 are weighted based on corn
production data for each of the nine States from the NASS. The total
energy use values listed in Table 6.1-10 were calculated by converting
fuel use to Btu based on the lower heating values of the fuels as listed
in the GREET model. These estimates may be biased downward if the corn
production attributable to the incremental increase in ethanol
production will occur on less productive land than was used in the
1991-2001 period, when corn prices were lower than they are projected to
be in this analysis. Also, as corn production expands due to expanded
ethanol production, it may increasingly take place in dryer climates
that may increase irrigation demand and result in different yields. This
is an area we will continue to examine for future analysis.

The ARMS surveys include information on energy use and also on dollars
spent by farmers on custom work. This custom work includes farmers
contracting outside services for corn drying, planting, fertilizing and
harvesting. The cost of custom work includes machine overhead, fuel
charges, and labor costs. Therefore, there is some energy use associated
with the dollars spent on fuel used in custom work. It was assumed that
10% of custom work cost was spent on fuel[^171]. This fuel cost was
assumed to be split between LPG and diesel fuel in the same percentage
as reported energy use for each state. Cost was converted to gallons
based on price paid by farmers for LPG and diesel fuel in each of the
survey years[^172]. Custom work energy use is included in Table 6.1-10.

It can be seen from Table 6.1-10 that there is substantial variation in
the three years of energy use survey data. Several factors can influence
corn farming energy use. For example, it was reported that 1991 was a
dry year, lowering the moisture content of the corn crop and thus
requiring less energy to dry the corn, whereas the 1996 crop was
reported to have a higher moisture content and thus require more energy
to dry resulting in the high energy use values for 1996. Farm diesel use
is also dependent on tillage type and soil conditions, wetter soil
requiring more diesel use, and decreased tillage requirements (e.g., no
till) reducing diesel use.[^173]

To project corn farming fuel use in 2012, the average energy use from
the three years of survey data were taken, in terms of energy per acre.
As energy use is somewhat weather related and it is impossible to
confidently predict future conditions, it was felt that the three years
of historic data represented a good mix of high and low energy use
years. The average energy use in terms of Btu/acre was divided by the
projected corn yield in 2012 of 156.9 bu/acre. This is the USDA
projected corn yield adjusted to account for seed corn energy use as
shown in Table 6.1-10. The seed use shown in Table 6.1-10 accounts for
seed corn energy use. We assumed that growing seed corn requires 4.7
times the energy and material inputs to grow than corn[^174]^,^[^175].
The result was 14,036 Btu of energy needed to produce a bushel of corn,
which was used in GREET for this analysis.

The GREET default values for corn farming material inputs were updated
from the values in the NPRM version. GREET defaults were based on
historic data provided from the following USDA sources.

-   The USDA National Agricultural Statistics Service (NASS) produces
    annual reports listing quantities of fertilizers and chemicals used
    per acre of corn.

-   The USDA Economic Research Service (ERS) produces an Agricultural
    Resources and Environmental Indicators report that has data on lime
    used per acre of corn.

The USDA sources provide average material use data per harvested acre of
corn. The GREET defaults are based on the assumption that material input
use per acre will be flat from 2005 into the future. The 2005 values are
based on a three year average of 2003 through 2005 data. Data on inputs
per acre are divided by projected corn yields to get GREET defaults in
terms of g/bushel of corn. While these values are felt to be reasonable
to be used in this analysis, the agency cautions that these estimates
are based on the historical record while the incremental corn production
attributable to expanded ethanol production may occur on less productive
land than was used historically. As a result, these estimates may be
biased downward, resulting in over-estimates of ethanol displacement
indices.

Another potential input to corn farming is the energy and emissions
associated with producing farm equipment. As described in Section
6.1.1.2, this input is considered outside the system boundaries of our
lifecycle analysis. However, the latest version of GREET has an option
to include energy use and emissions associated with producing farm
equipment in the corn ethanol lifecycle results. We performed a
sensitivity analysis on expanding the corn production system to include
farm equipment production to determine the impact it has on the overall
results of our analysis.

It was found that including farm equipment production energy use and
emissions increases ethanol lifecycle energy use and GHG emissions and
decreases the corn ethanol displacement index by approximately 1
percent. Furthermore, to be consistent in the modeling if system
boundaries are expanded to include production of farming equipment they
should also be expanded to include producing other material inputs to
both the ethanol and petroleum lifecycles. For example, this expansion
of system boundaries would include the energy use and emissions
associated with producing concrete and steel used in the petroleum
refinery[^176]. The net effect of this would be a slight increase in
both the ethanol and petroleum fuel lifecycle results and a smaller or
negligible effect on the comparison of the two.

The corn farming material and energy use used in the lifecycle analysis
is based on producing and average bushel of corn. There are differences
associated with variations in corn yield, inputs required for existing
land vs. land converted to crops, etc. Furthermore, there are ripple
effects associated with increased corn used for ethanol that could have
GHG emission implications, ranging from changes in manure management to
the acres of rice grown. One such effect is CO~2~ associated with land
use change which is examined in the following section. Other effects and
variations in corn farming will be examined as part of future analysis.

**6.1.2.8 CO~2~ from Land Use Change**

Farming practices could potentially release carbon stored in soil as
CO~2~ emissions. If non-cropland (e.g., pastureland, Conservation
Reserve Program (CRP) land) is converted to crop production, carbon
sequestered in the soil and existing cover could be released. The
agricultural sector modeling work done for this rulemaking examined the
issue of land use change due to increases in renewable fuel production
and use. The agricultural sector modeling results indicate that,
compared to the 2012 Reference Case, approximately two and a half
million acres will come out of CRP land as a result of increased
renewable fuel production. Not all of these two million acres will go
directly into corn production used to produce ethanol. However, the
entire amount of CO~2~ emissions from the CRP land use change is
attributable to the increased amount of ethanol produced, as without the
increased demand for corn there would be no change in CRP land. The
agricultural modeling results also indicated a reduction in U.S. corn
exports and a modest decline in U.S. soybean exports which could impact
crop production in other countries. However, we did not consider impacts
on non-U.S. land use that might result from decrease in U.S. exports of
corn and soybeans.

The GREET model has a default factor for CO~2~ from land use change that
was included in the NPRM analysis. This factor was updated based on the
results of the agricultural sector modeling mentioned above and included
in the final rulemaking lifecycle analysis. The CO~2~ emissions from
land use change used in the final rulemaking represent approximately 1%
of total corn ethanol lifecycle GHG emissions. However, this value could
be more significant if increased amounts of renewable fuels are used.

The issue of CO~2~ emissions from land use change associated with
converting forest or CRP land into crop production for use in producing
renewable fuels is an important factor to consider when determining the
overall sustainability of renewable fuel use. While the analysis
described above is indicating that this rulemaking will not cause a
significant change in land use, this is an area we will continue to
research for any future analysis.

**6.1.2.9 Ethanol Production Yield**

Modern ethanol plants are now able to produce more than 2.7 gallons of
ethanol per bushel of corn compared with less than 2.4 gallons of
ethanol per bushel of corn in 1980. The development of new enzymes
continues to increase the potential ethanol yield. We used a value of
2.71[^177] gal/bu in our analysis, which may underestimate actual future
yields. However, this value is consistent with the ethanol model
developed by USDA described in Section 6.1.2.2 and was used in the cost
modeling of corn ethanol discussed in Chapter 7.

**6.1.2.10 Byproduct Allocation**

There are a number of by-products made during the production of ethanol.
In lifecycle analyses, the energy consumed and emissions generated by an
ethanol plant must be allocated not only to ethanol, but also to each of
the by-products. There are a number of methods that can be used to
estimate by-product allocations. These include methods based on the
economic value of each by-product, or on energy usage, based on
engineering analysis of the actual processes related to each product.
The method preferred by EPA is called the displacement method. This
method most accurately accounts for these by-products by calculating the
lifecycle emissions of the products that will be displaced by them. In
this method the lifecycle emissions of the displaced product are
calculated and subtracted from the ethanol lifecycle. The ethanol
receives a credit for the lifecycle emissions of whatever product is
displaced, since a quantity of that product is no longer needed and is
displaced by the ethanol by-products.

For example, the DDGS produced by an ethanol dry mill plant is a
replacement for corn and soybean animal feed. We based the amount of
DDGS produced by an ethanol dry mill plant on the USDA model used in the
cost analysis work of this rulemaking. That model predicted 6.21 dry lb.
of DDGS per gallon of ethanol produced. As per the agricultural sector
modeling done for this rulemaking, we assumed that this DDGS displaces
50% corn and 50% soybean meal on a mass basis. So the lifecycle
emissions of producing 3.1 lb. of corn and 3.1 lb. of soybean meal were
calculated and subtracted from the lifecycle emissions associated with
producing a gallon of ethanol.

By-products from the ethanol wet milling process include corn gluten
meal and corn gluten feed that are assumed to displace corn production,
as well as corn oil that is assumed to displace soybean oil. Ethanol
produced from cellulosic feedstock through the fermentation route is
assumed to produce excess electricity as a by-product, from onsite
combustion of lignin. This excess electricity is assumed to displace
electricity from the grid. The fermentation process used to produce
ethanol in corn wet and dry milling and cellulosic ethanol production
also produces CO~2~ as a by-product. This CO~2~ could be sold to an
organization that specializes in cleaning and pressurizing it for use in
the food industry for example to carbonate beverages, to manufacture dry
ice, and to flash freeze meat. While CO~2~ could potentially displace
other sources of CO~2~ production, this was not considered in our
analysis and no value was associated with this CO2 co-product.

The displacement method for by-product allocation is the default for the
GREET model. EPA supports that approach and continues to use that method
in this analysis. However, other researchers have used different
allocation methods in their ethanol fuel cycle studies. We evaluated one
of these other methods used by USDA in a recent ethanol energy balance
report[^178] to determine the impact this assumption has on the overall
results of the analysis. The method used by USDA was to split the energy
use and emissions of corn agriculture and ethanol production between the
ethanol and co-products. The lifecycle analysis results were then based
on only the ethanol portion. A process simulation was used to allocate
the energy used in the ethanol plant to ethanol and by-products. Using
this approach they determined that on average 59 and 64 percent of the
energy used in dry and wet mills respectively is used to produce
ethanol. The remaining energy is used for the production of by-products.
Therefore, for dry mill ethanol production only 59 percent of the plant
energy use and associated emissions were allocated to the ethanol
lifecycle. Corn production energy use and emissions were allocated based
on the starch content of the corn, assumed to be 66 percent of corn
kernel weight. So, only 66 percent of the energy and emissions used to
produce corn were allocated to the ethanol lifecycle.

Use of the process energy based allocation method reduces ethanol
lifecycle energy use and GHG emissions by approximately 30 percent
compared to the displacement allocation approach. This indicates that
ethanol lifecycle analysis results are extremely sensitive to the choice
of allocation method used. However, as mentioned above, EPA feels that
the displacement allocation method is the most reasonable and is the
preferred method to use. This decision is supported by international
lifecycle assessment standards which indicate that whenever possible the
product system should be expanded to include the additional functions
related to the co-products[^179].

**6.1.2.11 Biodiesel Production**

Two scenarios for biodiesel production were considered, one utilizing
soybean oil as a feedstock and one using yellow grease.

For the soybean oil scenario, the energy use and inputs for the
biodiesel production process were based on a model developed by NREL and
used by EPA in the cost modeling of soybean oil biodiesel, as discussed
in Chapter 7.

The GREET model does not have a specific case of biodiesel production
from yellow grease. Therefore, as a surrogate we used the soybean oil
based model with several adjustments. For the yellow grease case, no
soybean agriculture emissions or energy use was included. Soybean
crushing was still included as a surrogate for yellow grease processing
(purification, water removal, etc.). Also, due to additional processing
requirements, the energy use associated with producing biodiesel from
yellow grease is higher than for soybean oil biodiesel production. As
per the cost modeling of yellow grease biodiesel discussed in Chapter 7,
the energy use for yellow grease biodiesel production was assumed to be
1.72 times the energy used for soybean oil biodiesel.

The biodiesel lifecycle results were based on a 50% / 50% split between
soybean oil and yellow grease biodiesel production based on EIA's AEO
2006 projections for biodiesel produced from the different feedstocks.

**6.2 Methodology**

As outlined in the scoping discussion, the goals of this analysis are to
both examine the total GHG and fossil fuel reductions of increased
renewable fuel use in absolute tons and gallons and to compare these
reductions to the U.S. transportation sector and nationwide GHG
emissions and fossil fuel use. The output of the GREET model can be used
directly to calculate tons of GHG and gallons of petroleum reduced.
However, these results are not entirely consistent with transportation
sector and nationwide emissions inventories which are based on slightly
different assumptions concerning fuel heating values and carbon content.
As a result we could not use GREET directly to estimate the nationwide
impacts of replacing some gasoline and diesel with renewable fuels.

To be consistent between our modeling of savings and overall sector
inventories, we used GREET instead to generate comparisons between
renewable fuels and the petroleum-based fuels that they displace. These
comparisons allowed us to develop displacement indexes which represent
the percent of lifecycle GHGs or fossil fuel reduced when a Btu of
renewable fuel replaces a Btu of gasoline or diesel. In this way GREET
was used to generate percent reductions and not absolute values. These
percent reductions or displacement values were then applied to the same
gasoline and diesel fuel inventories used to generate transportation
sector and nationwide inventories. This ensured that savings and sector
wide inventories in terms of absolute values were calculated in a
consistent manner.

In order to estimate the impacts of increased use of renewable fuels on
fossil energy and greenhouse gases, we first determined how much
gasoline and diesel would be replaced as a result of this rule. We then
combined lifecycle percent reductions from GREET with lifecycle
inventories and petroleum consumption values for gasoline and diesel
fuel use to get the amounts of fossil energy and greenhouse gases
reduced. For example, to estimate the impact of corn-ethanol use on
GHGs, these factors were combined in the following way:

S~GHG,corn\ ethanol~ = R~corn\ ethanol~ x LC~gasoline~ x
DI~GHG,corn\ ethanol~

where:

S~GHG,corn\ ethanol~ = Lifecycle GHG emission reduction over the
reference case associated with use of corn ethanol (million metric tons
of GHG)

R~corn\ ethanol~ = Amount of gasoline replaced by corn ethanol on an
energy basis (Btu)

LC~gasoline~ = Lifecycle emissions associated with gasoline use (million
metric tons of GHG per Btu of gasoline)

DI~GHG,corn\ ethanol~ = Displacement Index for GHGs and corn ethanol,
representing the percent reduction in gasoline lifecycle GHG emissions
which occurs when a Btu of gasoline is replaced by a Btu of corn ethanol

Variations of the above equation were also generated for impacts on all
four endpoints of interest (fossil fuel consumption, petroleum
consumption emissions of CO~2~, and emissions of GHGs) as well as all
three renewable fuels examined (corn-ethanol, cellulosic ethanol, and
biodiesel). These values are then compared to the total U.S.
transportation sector and nationwide inventories of fossil energy and
greenhouse gases to get the overall impacts of the rule.

In this regard, the impact on overall transportation sector GHG
emissions due to the increased use of renewable fuels can be described
mathematically as follows:

TSector~%,GHG~ = S~GHG,corn\ ethanol~ + S~GHG,cell\ ethanol~ +
S~GHG,biodiesel~

\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

TSector~GHG~

*where:*

TSector~%,GHG~ = Percent reduction in overall transportation sector GHG
emissions resulting from the use of renewable fuels (%)

S~GHG,corn\ ethanol~ = Lifecycle GHG emission reduction over the
reference case associated with use of corn ethanol (million metric tons
of GHG)

S~GHG,cell\ ethanol~ = Lifecycle GHG emission reduction over the
reference case associated with use of cellulosic ethanol (million metric
tons of GHG)

S~GHG,biodiesel~ = Lifecycle GHG emission reduction over the reference
case associated with use of biodiesel (million metric tons of GHG)

TSector~GHG~ = Overall transportation sector GHG emissions in 2012
(million metric tons of GHG)

We used the same approach to estimate fossil energy, petroleum energy,
and CO~2~ reductions in the transportation sector. We also used the same
approach to estimate nationwide reductions.

Section 6.2.1 describes how we estimated the amount of gasoline and
diesel fuel replaced as modeled for this rule. Section 6.2.2 describes
the lifecycle emissions and energy associated with gasoline and diesel
fuel use. In Section 6.2.3 below, we outline how we generated
displacement indexes using GREET. Section 6.2.4 outlines how we
developed the overall transportation sector and nationwide fossil energy
and greenhouse gas emissions.

**6.2.1 Modeling Scenarios**

In general, the volume fraction (R) in the equation above represents the
amount of conventional fuel no longer consumed -- that is, displaced --
as a result of the use of the replacement renewable fuel. Thus R
represents the incremental amount of renewable fuel used under each of
our renewable fuel volume scenarios, in units of Btu. We make the
assumption that vehicle energy efficiency will not be affected by the
presence of renewable fuels (i.e., efficiency of combusting one Btu of
ethanol is equal to the efficiency of combusting one Btu of gasoline).

As described in Section 6.1.1.4, our analysis of the GHG and fossil fuel
consumption impacts of renewable fuel use was conducted using three
volume scenarios. The total volumes for all three scenarios are shown in
Table 6.1-3. For the purposes of calculating the R values, we assumed
the ethanol volumes shown in Table 6.1-3 are 5% denatured, and the
ethanol volumes were adjusted down to represent pure (100%) ethanol. The
adjusted volumes were then converted to total Btu using the appropriate
volumetric energy content values (76,000 Btu/gal for ethanol, and
118,000 Btu/gal for biodiesel).

Since the impacts of increased renewable fuel use were measured relative
to the 2012 reference case, the value of R actually represented the
incremental amount of renewable fuel between the reference case and each
of the two other scenarios. The results are shown in Table 6.2-1. The
results shown in Table 6.2-1 are direct reductions in fuel use and do
not represent lifecycle savings.

**Table 6.2-1.**

**Direct** Conventional Fuel Replaced in 2012 (quadrillion Btu)

  ------------------------------------------- ------------- -------------
                                              **RFS Case**  **EIA Case**

  Gasoline Replaced by Corn Ethanol           0.147         0.347

  Gasoline Replaced by Cellulosic Ethanol     0.018         0.018

  Diesel Fuel Replaced by Biodiesel           0.032         0.032

  Gasoline Replaced by Ethanol Imports        0.031         0.045

  Total Energy                                0.229         0.443
  ------------------------------------------- ------------- -------------

**6.2.2 Lifecycle Impacts of Conventional Fuel Use**

In order to determine the lifecycle impact that increased renewable fuel
volumes may have on any particular endpoint (fossil fuel consumption or
emissions of GHGs), we also needed to know the conventional fuel
inventory on a lifecycle basis. Since available sources of GHG emissions
are provided on a direct rather than a lifecycle basis, we converted
these direct emission and energy estimates into their lifecycle
counterparts.

To do this, we used GREET to develop multiplicative factors for
converting direct (vehicle-based) emissions of GHGs, or direct
(vehicle-based) consumption of petroleum, into full lifecycle factors.
GREET output was used to generate the conversion factors shown in Table
6.2-2.

**Table 6.2-2.**

**Direct (wheel only) Conversion Factors to Well-to-Wheel (lifecycle)**

**Emissions or Energy Use**

  ------------------------- ---------------------- ----------------------
                            **Gasoline**           **Diesel**

  Petroleum                 1.11                   1.10

  Fossil fuel               1.22                   1.21

  GHG                       1.26                   1.25

  CO~2~                     1.23                   1.21
  ------------------------- ---------------------- ----------------------

The factors in Table 6.2-2 were applied to gasoline and diesel fuel
inventories of emissions or energy consumption at the consumer level
(i.e. direct emissions or energy) to convert them into alternative
inventories representing full lifecycle contributions.

The direct petroleum energy for gasoline and diesel fuel is just the
energy content of the fuels used. Consistent with U.S. EPA National
Inventory calculations[^180], we converted energy use values for
gasoline and diesel fuel to direct CO~2~ emissions by multiplying by a
carbon content coefficient, a carbon oxidation factor, and converting
the resulting carbon emissions into CO~2~. The CO~2~ emissions were then
scaled up by assuming a fraction increase to the CO~2~ emissions to
account for non-CO~2~ GHGs (CH~4~ and N~2~O). The fraction increase was
based on the U.S. EPA National Inventory 2004 values for both CO~2~ and
total GHG emissions. Table 6.2-3 shows the total lifecycle petroleum and
GHG emissions associated with direct use of a Btu value of gasoline or
diesel fuel. These values represent factor LC in the equation described
above.

**Table 6.2-3.**

Lifecycle Emissions and Energy (LC Values)

  ---------------------------------- ------------------ ------------------
                                     **Gasoline**       **Diesel**

  Petroleum (Btu/Btu)                1.11               1.10

  Fossil fuel (Btu/Btu)              1.22               1.21

  GHG (Tg-CO~2~-eq/QBtu)             99.4               94.5

  CO~2~ (Tg-CO~2~/QBtu)              94.2               91.9
  ---------------------------------- ------------------ ------------------

**6.2.3 Displacement Indexes**

In order to permit a quantitative evaluation of the degree to which a
renewable fuel reduces lifecycle fossil fuel consumption or GHG
emissions, several metrics have been developed. Three of the most
prominent metrics are shown in Table 6.2-4.

**Table 6.2-4. Metrics Used to Measure Lifecycle Impacts of Renewable
F**uels

+-------------------+--------------------------------------------------+
| **Metric**        | **Calculation**                                  |
+-------------------+--------------------------------------------------+
| Net energy        | Renewable energy out - fossil energy in          |
| balance           |                                                  |
+-------------------+--------------------------------------------------+
| Energy efficiency | Fossil energy in ÷ renewable energy out          |
|                   |                                                  |
|                   | (or alternatively renewable energy out ÷ fossil  |
|                   | energy in)                                       |
+-------------------+--------------------------------------------------+
| Displacement      | \% reduction in emissions or energy compared to  |
| index             | the fuel that it replaces                        |
+-------------------+--------------------------------------------------+

Of these metrics, we believe the displacement index is the most
appropriate to use as it compares the renewable fuel to the petroleum
fuel it is displacing. The net energy balance and energy efficiency
approaches only consider the renewable fuel itself and do not account
for the fact that the use of renewable fuels result in decreased use of
petroleum fuels and thus provide misleading results.

As an example, if 81,000 Btu of fossil fuels were required to make,
transport, and store one gallon of ethanol, then the energy efficiency
would be calculated as follows:

Energy efficiency = 81,000 Btu/gal ÷ 76,000 Btu/gal = 1.07

This result would imply that ethanol cannot be labeled \"renewable,\"
since one gallon of ethanol contains less energy than was required to
make that one gallon. However, the use of ethanol may still reduce
overall lifecycle fossil fuel use even in this case. If, for example,
18,000 Btu of fossil fuels were required to make one ethanol-equivalent
gallon of gasoline (i.e. 76,000 Btu of gasoline), then a total of 94,000
Btu of fossil fuel energy would be consumed whenever 76,000 Btu of
gasoline energy was combusted in a conventional vehicle. Since 81,000
Btu is less than 94,000 Btu, the use of ethanol would result in less
fossil fuel consumption than the use of gasoline, even though the energy
efficiency is greater than 1.0. The 81,000 Btu of fossil energy required
to produce the ethanol includes lifecycle energy. The energy content of
the ethanol (76,000 Btu) itself is not considered fossil energy and
therefore not included in the comparison with gasoline calculation
above. Thus, even in cases where the net energy balance of a renewable
fuel is negative or has energy efficiency less than 1.0[^181], there may
still be an overall reduction in lifecycle fossil fuel use (and
associated GHG emissions) due to decreased petroleum fuel use.

Therefore, studies that rely on the energy balance metric and conclude
for example that the net energy balance of corn ethanol is negative, or
the energy efficiency is less than 1.0, making it an unattractive
transportation fuel, are not capturing the full implications of the use
of the fuel and are providing misleading results.

Because of this potential for the net energy balance and energy
efficiency metrics to provide misleading information, for our analysis
of this rule we have chosen to use the displacement index. The
displacement index provides the most direct measure of the impacts of
replacing conventional gasoline or diesel with a renewable fuel, and is
also better suited to describing impacts of renewable fuel use on fossil
fuel consumption and GHGs.

The displacement index (DI) represents the percent reduction in GHG
emissions or fossil fuel energy brought about by the use of a renewable
fuel in comparison to the conventional gasoline or diesel that the
renewable fuel replaces. The formula for calculating the displacement
index depends on which fuel is being displaced (i.e. gasoline or
diesel), and which endpoint is of interest (e.g. petroleum energy, GHG).
For instance, when investigating the CO~2~ impacts of ethanol used in
gasoline, the displacement index is calculated as follows:

DI~CO2~ = 1 - [lifecycle CO~2~ emitted for ethanol in g/Btu]{.underline}

lifecycle CO~2~ emitted for gasoline in g/Btu

The units of g/Btu ensure that the comparison between the renewable fuel
and the conventional fuel is made on a common basis, and that
differences in the volumetric energy content of the fuels is taken into
account. The denominator includes the CO~2~ emitted through combustion
of the gasoline itself in addition to all the CO~2~ emitted during its
manufacturer and distribution. The numerator, in contrast, includes only
the CO~2~ emitted during the manufacturer and distribution of ethanol,
not the CO~2~ emitted during combustion of the ethanol.

The combustion of biomass-based fuels, such as ethanol from corn and
woody crops, generates CO~2~. However, in the long run the CO~2~ emitted
from biomass-based fuels combustion does not increase atmospheric CO~2~
concentrations, assuming the biogenic carbon emitted is offset by the
uptake of CO~2~ resulting from the growth of new biomass[^182]. As a
result, CO~2~ emissions from biomass-based fuels combustion are not
included in their lifecycle emissions results and are not used in the
CO~2~ displacement index calculations shown above. Net carbon fluxes
from changes in biogenic carbon reservoirs in wooded or crop lands are
accounted for separately in the GREET model.

When calculating the GHG displacement index, however, the CH~4~ and
N~2~O emitted during biomass-based fuels combustion are included in the
numerator. Unlike CO~2~ emissions, the combustion of biomass-based fuels
does result in net additions of CH~4~ and N~2~O to the atmosphere. We
assume that combustion CH~4~ and N~2~O emissions are not offset by
carbon uptake of renewable biomass production. As shown in Table 6.1-2,
CH~4~ and N~2~O emissions contribute to the total GHG impact. Therefore,
combustion CH~4~ and N~2~O emissions are included in the lifecycle GHG
emissions results for biomass-based fuels and are used in the GHG
displacement index calculations.

Using GREET, we calculated the lifecycle values for energy consumed and
GHGs produced for corn-ethanol, cellulosic ethanol, and soybean-based
biodiesel, as well as the gasoline and diesel fuel that would be
displaced. For both renewable and conventional fuels, we summed the
lifecycle results for both the feedstock and the fuel. The results are
shown in Table 6.2-5.

**Table 6.2-5. Output from GREET Used to Develop Displacement I**ndexes

+---------+--------+-------+------+-----------+-------+------+------+
|         | > **U  | >     | > ** | > **Corn  | > **  | >    | > *  |
|         | nits** | **Gas | Corn | > ethanol | Cellu |  **L | *Bio |
|         |        | oline | > e  | >         | losic | > S  | dies |
|         |        | ^a^** | than |  (biomass | >     | >    | el** |
|         |        |       | ol** | > fuel)** |  etha | Dies |      |
|         |        |       |      |           | nol** | el** |      |
+---------+--------+-------+------+-----------+-------+------+------+
| Well-   |        |       |      |           |       |      |      |
| to-Pump |        |       |      |           |       |      |      |
+---------+--------+-------+------+-----------+-------+------+------+
| >       | Btu    | 22    | 742  | 290,324   | 8     | 207  | 464  |
|  Fossil | /mmBtu | 4,133 | ,411 |           | 8,973 | ,008 | ,594 |
| >       |        |       |      |           |       |      |      |
| >       |        |       |      |           |       |      |      |
|  energy |        |       |      |           |       |      |      |
+---------+--------+-------+------+-----------+-------+------+------+
| > Pe    | Btu    | 10    | 90   | 88,896    | 9     | 98   | 96   |
| troleum | /mmBtu | 7,298 | ,771 |           | 1,977 | ,656 | ,539 |
| >       |        |       |      |           |       |      |      |
|  energy |        |       |      |           |       |      |      |
+---------+--------+-------+------+-----------+-------+------+------+
| CO~2~   | g      | 1     | 56   | 26,089    | -71   | 16   | 28   |
|         | /mmBtu | 7,893 | ,275 |           |       | ,629 | ,468 |
+---------+--------+-------+------+-----------+-------+------+------+
| C       | g      | 2     | 75   | 43,043    | 6,427 | 19   | 31   |
| O~2~-eq | /mmBtu | 0,435 | ,219 |           |       | ,134 | ,193 |
+---------+--------+-------+------+-----------+-------+------+------+
| End     |        |       |      |           |       |      |      |
| point   |        |       |      |           |       |      |      |
| com     |        |       |      |           |       |      |      |
| bustion |        |       |      |           |       |      |      |
+---------+--------+-------+------+-----------+-------+------+------+
| Fossil  | Btu    | 1,00  | 0    | 0         | 0     | 1    | 0    |
| energy  | /mmBtu | 0,000 |      |           |       | ,000 |      |
|         |        |       |      |           |       | ,000 |      |
+---------+--------+-------+------+-----------+-------+------+------+
| > Pe    | Btu    | 1,00  | 0    | 0         | 0     | 1    | 0    |
| troleum | /mmBtu | 0,000 |      |           |       | ,000 |      |
| >       |        |       |      |           |       | ,000 |      |
|  energy |        |       |      |           |       |      |      |
+---------+--------+-------+------+-----------+-------+------+------+
| > CO~2~ | g      | 7     | 74   | 74,755    | 7     | 77   | 79   |
| >       | /mmBtu | 6,419 | ,755 |           | 4,755 | ,570 | ,388 |
|  combus |        |       |      |           |       |      |      |
| tion^b^ |        |       |      |           |       |      |      |
+---------+--------+-------+------+-----------+-------+------+------+
| >       |        | 7     | 0    | 0         | 0     | 77   | 0    |
|  Fossil |        | 6,419 |      |           |       | ,570 |      |
| > CO~2~ |        |       |      |           |       |      |      |
| > com   |        |       |      |           |       |      |      |
| bustion |        |       |      |           |       |      |      |
+---------+--------+-------+------+-----------+-------+------+------+
| > C     | g      | 7     | 2    | 2,596     | 2,596 | 77   | 99   |
| O~2~-eq | /mmBtu | 9,015 | ,596 |           |       | ,669 |      |
| >       |        |       |      |           |       |      |      |
|  combus |        |       |      |           |       |      |      |
| tion^c^ |        |       |      |           |       |      |      |
+---------+--------+-------+------+-----------+-------+------+------+
| > ^a^   |        |       |      |           |       |      |      |
| > V     |        |       |      |           |       |      |      |
| olume-w |        |       |      |           |       |      |      |
| eighted |        |       |      |           |       |      |      |
| >       |        |       |      |           |       |      |      |
| average |        |       |      |           |       |      |      |
| > of    |        |       |      |           |       |      |      |
| > conve |        |       |      |           |       |      |      |
| ntional |        |       |      |           |       |      |      |
| > g     |        |       |      |           |       |      |      |
| asoline |        |       |      |           |       |      |      |
| >       |        |       |      |           |       |      |      |
|  (65%), |        |       |      |           |       |      |      |
| > RFG   |        |       |      |           |       |      |      |
| > ble   |        |       |      |           |       |      |      |
| ndstock |        |       |      |           |       |      |      |
| >       |        |       |      |           |       |      |      |
|  (25%), |        |       |      |           |       |      |      |
| > and   |        |       |      |           |       |      |      |
| > CaRFG |        |       |      |           |       |      |      |
| > ble   |        |       |      |           |       |      |      |
| ndstock |        |       |      |           |       |      |      |
| >       |        |       |      |           |       |      |      |
|  (10%). |        |       |      |           |       |      |      |
| >       |        |       |      |           |       |      |      |
| > ^b^   |        |       |      |           |       |      |      |
| > Based |        |       |      |           |       |      |      |
| > on    |        |       |      |           |       |      |      |
| >       |        |       |      |           |       |      |      |
|  carbon |        |       |      |           |       |      |      |
| >       |        |       |      |           |       |      |      |
| content |        |       |      |           |       |      |      |
| > of    |        |       |      |           |       |      |      |
| > the   |        |       |      |           |       |      |      |
| > fuel. |        |       |      |           |       |      |      |
|         |        |       |      |           |       |      |      |
| ^c^     |        |       |      |           |       |      |      |
| I       |        |       |      |           |       |      |      |
| ncludes |        |       |      |           |       |      |      |
| Fossil  |        |       |      |           |       |      |      |
| CO~2~,  |        |       |      |           |       |      |      |
| CH~4~,  |        |       |      |           |       |      |      |
| and     |        |       |      |           |       |      |      |
| N~2~O   |        |       |      |           |       |      |      |
| t       |        |       |      |           |       |      |      |
| ailpipe |        |       |      |           |       |      |      |
| emi     |        |       |      |           |       |      |      |
| ssions. |        |       |      |           |       |      |      |
| CH~4~   |        |       |      |           |       |      |      |
| and     |        |       |      |           |       |      |      |
| N~2~O   |        |       |      |           |       |      |      |
| em      |        |       |      |           |       |      |      |
| issions |        |       |      |           |       |      |      |
| based   |        |       |      |           |       |      |      |
| on      |        |       |      |           |       |      |      |
| a       |        |       |      |           |       |      |      |
| ssuming |        |       |      |           |       |      |      |
| an      |        |       |      |           |       |      |      |
| i       |        |       |      |           |       |      |      |
| ncrease |        |       |      |           |       |      |      |
| over    |        |       |      |           |       |      |      |
| CO~2~   |        |       |      |           |       |      |      |
| emi     |        |       |      |           |       |      |      |
| ssions, |        |       |      |           |       |      |      |
| the     |        |       |      |           |       |      |      |
| percent |        |       |      |           |       |      |      |
| i       |        |       |      |           |       |      |      |
| ncrease |        |       |      |           |       |      |      |
| is from |        |       |      |           |       |      |      |
| the     |        |       |      |           |       |      |      |
| U.S.    |        |       |      |           |       |      |      |
| EPA     |        |       |      |           |       |      |      |
| N       |        |       |      |           |       |      |      |
| ational |        |       |      |           |       |      |      |
| In      |        |       |      |           |       |      |      |
| ventory |        |       |      |           |       |      |      |
| for     |        |       |      |           |       |      |      |
| CO~2~   |        |       |      |           |       |      |      |
| and GHG |        |       |      |           |       |      |      |
| em      |        |       |      |           |       |      |      |
| issions |        |       |      |           |       |      |      |
| from    |        |       |      |           |       |      |      |
| on-road |        |       |      |           |       |      |      |
| s       |        |       |      |           |       |      |      |
| ources. |        |       |      |           |       |      |      |
+---------+--------+-------+------+-----------+-------+------+------+

We used the values from the table above to calculate the displacement
indexes. The results are shown in Table 6.2-6.

**Table 6.2-6. Displacement Indexes D**erived from GREET

  ------------------ ----------- ------------- ------------- ----------- -----------
                     Corn        Corn ethanol  Cellulosic    Imported    Biodiesel
                     ethanol     (biomass      ethanol       ethanol     
                                 fuel)                                   

  DI~Fossil\ Fuel~   39.3%       76.3%         92.7%         69.0%       61.5%

  DI~Petroleum~      91.8%       92.0%         91.7%         92.0%       91.2%

  DI~GHG~            21.8%       54.1%         90.9%         56.0%       67.7%

  DI~CO2~            40.3%       72.3%         100.1%        71.0%       69.8%
  ------------------ ----------- ------------- ------------- ----------- -----------

The displacement indexes in this table represent the impact of replacing
a Btu of gasoline or diesel with a Btu of renewable fuel. Thus, for
instance, for every Btu of gasoline which is replaced by corn ethanol,
the total lifecycle GHG emissions that would have been produced from
that Btu of gasoline would be reduced by 21.8 percent. For every Btu of
diesel which is replaced by biodiesel, the total lifecycle petroleum
energy that would have been consumed as a result of burning that Btu of
diesel fuel would be reduced by 91.2 percent.

Consistent with the cost modeling done for this rule, for the 2012 cases
we assume the "cellulosic" ethanol volume is actually produced from corn
utilizing a biomass fuel source at the ethanol production plant. The
displacement index for that fuel as shown in Table 6.2-6, is used in the
calculation of reductions.

The displacement index for imported ethanol in all cases is based on an
average of corn and cellulosic ethanol. While not exclusively, we
anticipate much imported ethanol to be primarily sugarcane based
ethanol. There currently is no sugarcane ethanol lifecycle values
included in GREET. The GHG emissions when producing sugarcane ethanol
differs from corn ethanol in that the GHG emissions from growing
sugarcane is likely different than for growing a equivalent amount of
corn to make a gallon of ethanol, the process of turning sugar into
ethanol is easier and therefore less energy intensive (which typically
translates into lower GHG) and, importantly, we understand that at least
some of the ethanol produced in Brazil uses the bagasse from the
sugarcane itself as a process fuel source. We know from our analysis
that using a biomass source for process energy greatly improves the GHG
benefit of the renewable fuel. These factors would result in sugarcane
ethanol having a greater GHG benefit per gallon than corn ethanol,
certainly where natural gas or coal is the typical process fuel source
used. Conversely, sugarcane ethanol production does not result in a
co-product such as distillers grain as in the case of corn ethanol. In
our analyses, accounting for co-products significantly improved the GHG
displacement index for corn ethanol. Furthermore, there would be
additional transportation emissions associated with transporting the
imported ethanol to the U.S. as compared to domestically produced
ethanol. Developing a technically rigorous lifecycle estimate for energy
needs and GHG impacts for sugarcane ethanol is not a simple task and was
not available in the timeframe of this rulemaking. Considering all of
the differences between imported and domestic ethanol, for this
rulemaking, we assumed imported ethanol would be predominately from
sugarcane and have estimated DI's approximately mid-way between the DI's
for corn ethanol and DI's for cellulosic ethanol. We are continuing to
develop a better understanding of the lifecycle energy and GHG impacts
of producing ethanol from sugarcane and other likely feedstocks of
imported ethanol for any future analysis.

**6.2.4 Transportation Sector and Nationwide Inventories**

For our analysis described above, we need estimates of transportation
sector and nationwide fossil energy and GHG emissions to determine the
percent reduction impacts of the program (e.g., TSector~GHG~ factor in
the equation above). These inventories are direct not lifecycle and are
needed for 2012 to compare to the projected renewable fuel savings in
2012.

**6.2.4.1 Fossil Fuel Inventory**

The transportation sector and nationwide fossil fuel inventory is just
the energy content of the fuels used. Fossil fuel use in the
transportation sector includes gasoline and diesel as well as other
petroleum fuels, such as residual oil and LPG. It also includes other
fossil energy use in the form of natural gas and the fossil portion of
electricity used. Inherent with the assumptions on the amounts of
renewable fuels use projected to 2012, there are also assumed values for
gasoline and diesel fuel use. Values for energy use of the different
transportation fuels other than gasoline and diesel (e.g., jet fuel,
natural gas, etc.) were taken directly from the 2006 Annual Energy
Outlook.

The nationwide fossil fuel inventory includes petroleum, natural gas,
and coal energy use. The direct fossil fuel inventory values are shown
in Table 6.2-7.

**Table 6.2-7. Direct Fossil Fuel Inventories (QBtu)**

  ---------------------------------------- ------------------------------
                                           **2012**

  Nationwide                               94.53

  Transportation Sector                    31.41
  ---------------------------------------- ------------------------------

**6.2.4.2 Petroleum Inventory**

As with fossil energy, the transportation sector and nationwide
petroleum inventory is just the energy content of the fuels used. The
transportation sector petroleum inventory includes gasoline and diesel
as well as other petroleum fuels, such as residual oil and LPG.

The nationwide petroleum inventory includes petroleum use in the
transportation sector as well as other sectors. The direct petroleum
inventory values are shown in Table 6.2-8.

**Table 6.2-8.** Direct Petroleum Inventories (QBtu)

  --------------------------------------- -------------------------------
                                          **2012**

  Nationwide                              43.87

  Transportation Sector                   30.47
  --------------------------------------- -------------------------------

**6.2.4.3 CO~2~ Inventories**

We calculated direct CO~2~ emissions for the transportation sector in
2012 by applying carbon emissions factors to the projected amount of
fuels used in those years.

Direct CO~2~ emissions from the transportation sector as a whole are
calculated in the same way as direct gasoline and diesel emissions are
calculated as described in Section 6.2.2. We converted energy use values
for transportation sector fuels to direct CO~2~ emissions by multiplying
by a carbon content coefficient, a carbon oxidation factor, and
converting the resulting carbon emissions into CO~2~. Emissions from
electricity use in the transportation sector (rail) are calculated based
on the U.S. average mix of fossil fuels used to generate electricity.

Consistent with the EPA inventory report we made an adjustment to diesel
fuel, jet fuel and residual oil use to subtract out the emissions
associated with bunker fuel. The AEO values include the energy use of
bunker fuels, but the emissions of these fuels are not considered part
of the U.S. transportation sector emissions. This adjustment was done by
decreasing emissions of diesel fuel, jet fuel, and residual oil by the
portion of emissions associated with bunker fuels as determined in the
EPA inventory report.

Direct nationwide CO~2~ emissions are also calculated in the same way
applying factors for all fossil fuels used as reported by the 2006
Annual Energy Outlook. This type of analysis results in a small
understatement of total Nationwide CO~2~ emissions as it does not
capture other industrial sources of CO~2~ emissions for example CO~2~
emissions from calcinations of limestone in the cement industry.
However, there are no projections of these other emissions sources for
2012, and they are a relatively small part of total Nationwide CO~2~
emissions, representing only 6% of total CO~2~ emissions in 2004
according to the EPA National Inventory values. Therefore, while impacts
of increased renewable fuel use as a percent of nationwide CO~2~
emissions may be slightly overestimated the impacts on results are not
thought to be significant. The results of direct CO~2~ emission
calculations are shown in Table 6.2-9.

**Table 6.2-9. CO~2~ Direct Inventories (Tg CO~2~**)

  -------------------------------------------- --------------------------
                                               **2012**

  Nationwide                                   6,406

  Transportation Sector                        2,108
  -------------------------------------------- --------------------------

**6.2.4.4 GHG Inventories**

Projections for direct GHG emissions can not be calculated directly from
the energy projections as was done for CO~2~. The approach to estimating
CO~2~ emissions from mobile combustion sources varies significantly from
the approach to estimating non-CO~2~ GHG emissions (CH~4~ and N~2~O
emissions). While CO~2~ can be reasonably estimated by applying an
appropriate carbon content and fraction of carbon oxidized factor to the
fuel quantity consumed, CH~4~ and N~2~O emissions depend largely on the
emissions control equipment used (e.g., type of catalytic converter) and
vehicle miles traveled. Emissions of these gases also vary with the
efficiency and vintage of the combustion technology, as well as
maintenance and operational practices. Due to this complexity, a much
higher level of uncertainty exists in the estimation of CH~4~ and N~2~O
emissions from mobile combustion sources, compared to the estimation of
CO~2~ emissions.

Projections for direct transportation sector and nationwide GHG emission
are done by assuming a fraction increase to the CO~2~ emissions to
account for non-CO~2~ GHGs. The fraction increase was based on the U.S.
EPA National Inventory 2004[^183] values for both CO~2~ and total GHG
emissions. This same increase is applied to 2012 CO~2~ values. Table
6.2-10 shows the fraction increase values for GHGs over CO~2~ emissions
calculated from the U.S. EPA National Inventory report.

**Table 6.2-10. U.S. National Inventory 2004 CO~2~** and GHG Inventories

+-----------------+-----------------+-----------------+-----------------+
|                 | **CO~2~         | **GHG           | **Fraction      |
|                 | (Tg-CO~2~)**    | (T              | Increase**      |
|                 |                 | g-CO~2~--eq.)** |                 |
+-----------------+-----------------+-----------------+-----------------+
| Nationwide      | 5,988           | 7,074           | 1.1807          |
+-----------------+-----------------+-----------------+-----------------+
| >               | 1,860           | 1,960           | 1.0538          |
|  Transportation |                 |                 |                 |
| > Sector        |                 |                 |                 |
+-----------------+-----------------+-----------------+-----------------+

The results of direct GHG emission calculations are shown in Table
6.2-11.

**Table 6.2-11. GHG Direct Inventories (Tg CO~2~**-eq.)

  --------------------------------------- -------------------------------
                                          **2012**

  Nationwide                              7,564

  Transportation Sector                   2,222
  --------------------------------------- -------------------------------

**6.3 Impacts of Increased Renewable Fuel Use**

We used the methodology described above to estimate impacts of increased
use of renewable fuels on consumption of petroleum and fossil fuels and
also emissions of CO~2~ and GHGs. This section describes our results.

**6.3.1 Fossil Fuels and Petroleum**

We used the S equation in Section 6.2 to estimate the reduction
associated with the increased use of renewable fuels on lifecycle fossil
fuel and petroleum consumption. These values are then compared to the
total U.S. transportation sector and nationwide inventories to get a
percent reduction. The estimates are presented in Tables 6.3-1 and
6.3-2.

**Table 6.3-1.**

**Estimated Fossil Fuel Impacts of Increased Use of Renewable Fuels in
2012,**

**In Comparison to the Reference Case**

  -------------------------------- ------------------ -------------------
                                   **RFS Case**       **EIA Case**

  Reduction (quadrillion Btu)      0.15               0.27

  Percent reduction in             0.48 %             0.85 %
  Transportation Sector Energy Use                    

  Percent reduction in Nationwide  0.16 %             0.28 %
  Energy Use                                          
  -------------------------------- ------------------ -------------------

**Table 6.3-2.**

**Estimated Petroleum Impacts of Increased Use of Renewable Fuels in
2012,**

**In Comparison to the Reference Case**

  -------------------------------- ------------------ -------------------
                                   **RFS Case**       **EIA Case**

  Reduction (billion gal)          2.0                3.9

  Percent reduction in             0.82 %             1.60 %
  Transportation Sector Energy Use                    

  Percent reduction in Nationwide  0.57 %             1.11 %
  Energy Use                                          
  -------------------------------- ------------------ -------------------

**6.3.2 Greenhouse Gases and Carbon Dioxide**

We used the S equation in Section 6.2 to estimate the reduction
associated with the increased use of renewable fuels on lifecycle
emissions of CO~2~. These values are then compared to the total U.S.
transportation sector and nationwide emissions to get a percent
reduction. The estimates are presented in Table 6.3-3.

**Table 6.3-3.**

**Estimated CO~2~ Emission Impacts of Increased Use of Renewable Fuels
in 2012,**

**In Comparison to the Reference Case**

  ---------------------------------- ------------------ ------------------
                                     **RFS Case**       **EIA Case**

  Reduction (million metric tons     11.0               19.5
  CO~2~)                                                

  Percent reduction in               0.52 %             0.93 %
  Transportation Sector Emissions                       

  Percent reduction in Nationwide    0.17 %             0.30 %
  Emissions                                             
  ---------------------------------- ------------------ ------------------

Carbon dioxide is a subset of GHGs, along with CH~4~ and N~2~O as
discussed above. It can be seen from Table 6.2-6 that the displacement
index of CO~2~ is greater than for GHGs for each renewable fuel. This
indicates that lifecycle emissions of CH~4~ and N~2~O are higher for
renewable fuels that for the conventional fuels replaced. Therefore,
reductions associated with the increased use of renewable fuels on
lifecycle emissions of GHGs are lower than the values for CO~2~. The
estimates for GHGs are presented in Table 6.3-4.

**Table 6.3-4.**

**Estimated GHG Emission Impacts of Increased Use of Renewable Fuels in
2012,**

**In Comparison to the Reference Case**

+----------------------------------+-----------------+-----------------+
|                                  | **RFS Case**    | **EIA Case**    |
+----------------------------------+-----------------+-----------------+
| > Reduction                      | 8.0             | 13.1            |
| >                                |                 |                 |
| > (million metric tons           |                 |                 |
| > CO~2~-eq.)                     |                 |                 |
+----------------------------------+-----------------+-----------------+
| Percent reduction in             | 0.36 %          | 0.59 %          |
| Transportation Sector Emissions  |                 |                 |
+----------------------------------+-----------------+-----------------+
| Percent reduction in Nationwide  | 0.11 %          | 0.17 %          |
| Emissions                        |                 |                 |
+----------------------------------+-----------------+-----------------+

**6.4 Implications of Reduced Imports of Petroleum Products**

**6.4.1 Impacts on Imports of Petroleum Products**

To assess the impact of the RFS program on petroleum imports, the
fraction of domestic consumption derived from foreign sources was
estimated using results from the AEO 2006. We describe in this section
how fuel producers might change their levels and mix of imports in
response to a decrease in fuel demand.

We compared the levels and mix of imports in the AEO reference case with
the AEO low macroeconomic growth case and AEO high oil price case. The
latter two cases reflect different assumptions by EIA regarding economic
growth and world oil prices, respectively. The net effect for both cases
is a reduction in domestic petroleum consumption compared to the AEO
reference case. The changes in the level and mix of imports were
examined, given a reduction in petroleum consumption similar to the
amount estimated in the RFS for 2012 (0.25 to 0.49 Quads). Note that the
EIA has conducted three separate analyses of Congressional bills which
included earlier forms of the renewable fuel standard. These separate
analyses however were based on earlier AEO versions and, in some
instances, considered numerous provisions in addition to an RFS which
collectively affected world oil prices and domestic oil consumption.
Thus, we did not directly use these earlier analyses, rather opting to
use only the results in the AEO 2006 cases, as discussed above, to
assess the RFS impacts on imports.

Comparison of the AEO 2006 reference case against the low macroeconomic
growth case allowed us to evaluate how a decrease in domestic petroleum
demand might affect the mix of imported finished products, imported
crude oil, and domestic production. Note that the world price of crude
oil remains the same between the AEO low macroeconomic growth and
reference cases. Comparison of the two cases show that with an initial
decrease in petroleum consumption (approximately 300,000 barrels per day
or 0.61 Quads, higher than 2012 values), net imports will account for
approximately 95% of the reductions on an energy basis.[^184] These net
imports include imports of crude oil or petroleum products minus exports
of crude oil or petroleum products.[^185] Both reduced domestic crude
production and natural gas plant liquids account for most of the
remainder. Note that for all levels of reduced petroleum demand,
domestic crude production appears to account for less than 5% of the
change. In addition, the reductions shown here do not reflect any
rebound effect that may occur. Out of the initial reductions in net
petroleum imports, imported finished products account for almost all the
reductions. As domestic petroleum demand is reduced even further (over
860,000 barrels per day), approximately 50% of the reductions come from
imported finished products, 44% from imported crude oil, and the
remainder from reduced domestic, natural gas plant liquid (NGL)
production, and exports.

Under the low macroeconomic growth case assumptions, imported finished
products are initially reduced presumably because they represent the
higher marginal cost source for refineries versus imported crude oil.
Refineries may prefer to refine crude oil as opposed to importing
finished products because of the higher margins involved with the former
and the potentially more optimum use of refining capacity. Crude oil, as
an international commodity, will be purchased at the market price by
refineries. Thus, while crude oil from abroad may be produced more
cheaply than domestic production sources, refineries that purchase from
either source will pay the international market price for that specific
grade of crude oil based on specific gravity and sulfur content plus the
cost of transport to the U.S.

Note that there is uncertainty in quantifying how refineries will change
their mix of sources with a decrease in petroleum demand, particularly
at the levels estimated for the RFS. Changes in world oil price from the
reference case could also significantly alter the mix of sources from
which refineries choose. For example, a comparison between the AEO high
price case and the reference case (under a decrease in petroleum
consumption of 0.64 Quads) shows that 80% of the reductions (on an
energy basis) come from reductions in net petroleum imports, while the
remaining 20% comes from reductions in domestic production. As petroleum
consumption is reduced even further, reductions in net petroleum imports
make up an even greater percentage. For the reductions in net petroleum
imports, imports of finished products are observed to actually increase
while imports of crude oil decrease even more.

We believe that the actual refinery response might range between these
two AEO cases, so that net import reductions could compose 80-95% of the
reductions in petroleum demand for 2012. The split between the changes
in imports of finished products versus crude oil are more uncertain.
Discussions with EIA suggest the split could be close to 50-50. Thus, we
believe the range could be between these two estimates (nearly all to
50% finished product). For the purposes of this RIA, we show values for
the case where net import reductions come entirely from imports of
petroleum products, with an example shown below.

By using the petroleum reduction levels as discussed in 6.3.2 of the RIA
and comparing these to the AEO 2006 results, we estimate that 95% of the
lifecycle petroleum reductions will be met through reductions in net
petroleum imports. Table 6.4-1 shows the reductions in net petroleum
imports estimated for the RFS program. We expect that these import
reductions will be met almost exclusively from finished petroleum
products rather than from crude oil, for the reasons given above and
consistent with the results of the AEO 2006 low macroeconomic growth
case. As an example calculation, we apportioned 95% of the total
reductions in gasoline and diesel to displaced finished product imports.
By 2012, imports of finished products are estimated to be reduced by
123,000 and 240,000 barrels per day, respectively, for the RFS and EIA
cases. We compare these reductions in imports against the AEO projected
levels of net petroleum imports. The range of reductions in net
petroleum imports are estimated to be between 0.9 to 1.7%.

**Table 6.4-1.** Net reductions in Imports in 2012

  -------------------------------- -------------------- ------------------
                                   RFS Case             EIA Case

  Reduction in finished            123,000              240,000
  products^a^ (barrels per day)                         

  Percent reduction^b^             0.89%                1.73%
  -------------------------------- -------------------- ------------------

> ^a^ Net reductions relative to 2012 reference case

^b^ Compared to AEO2006 projections for 2012 reference case

**6.4.2 Impacts on Import Expenditures**

The reductions in petroleum imports were discussed in Section IX.D of
the preamble. As noted in the preamble, we calculate the change in
expenditures on petroleum imports and ethanol imports assuming this
would not result in any other changes in consumer behavior that would be
reflected in fuel use. 95% of all reductions in petroleum imports were
calculated to be from finished petroleum products rather than crude oil,
as discussed in the prior section. The economic savings in petroleum
product imports was calculated by multiplying the reductions in gasoline
and diesel imports by their corresponding price. According to the EIA,
the price of imported finished products is the market price minus
domestic local transportation from refineries and minus taxes.[^186] An
estimate was made by using the AEO 2006 wholesale gasoline, distillate,
and ethanol price forecasts for the specific analysis years. The current
ethanol import tariff of \$0.54/gallon placed on countries outside the
Caribbean and Central America is not included in the import
expenditures, since the tariff revenue collected would remain in the
U.S.

As an example calculation, the RFS case is expected to yield a reduction
of 2.0 billion gallons of gasoline in the year 2012. 95% of these
reductions, or 1.9 billion gallons, are expected to come from imports of
finished gasoline. Thus, the domestic refining sector would avoid
purchases of 1.9 billion gallons of gasoline and diesel at the wholesale
price. According to the AEO 2006, the end-user prices of gasoline and
diesel are forecasted to be \$2.01 per gallon and \$1.98 per gallon
respectively. Minus federal taxes, state taxes, and distribution costs,
the wholesale prices of gasoline and diesel forecasted in the AEO 2006
are \$1.376 and \$1.382 per gallon, respectively (2004\$). Note that the
AEO wholesale prices were used for this calculation, as opposed to the
gasoline and diesel production costs in Chapter 7 of the RIA, to stay
consistent with the other AEO results used herein. The avoided petroleum
payments abroad thus total \$2.6 billion in 2012 as shown in Table
6.4-2. The additional ethanol import expenditures, using the same
approach, is estimated to be \$0.7 billion in 2012. The net avoided
expenditures in imports is thus the difference, or \$1.9 billion in 2012
as shown in Table 6.4-2.

We compare these avoided petroleum import expenditures against the
projected value of total U.S. net exports of all goods and services
economy-wide. Net exports is a measure of the difference between the
value of exports of goods and services by the U.S. and the value of U.S.
imports of goods and services from the rest of the world. For example,
according to the AEO 2006, the value of total import expenditures of
goods and services exceeds the value of U.S. exports of goods and
services to the rest of the world by \$695 billion for 2006 (for a net
export level of minus \$695 billion) and by \$383 billion for 2012 (for
a net export level of minus \$383 billion).[^187] In Table 6.4-2, we
compare the avoided expenditures in imports versus the total value of
U.S. net exports of goods and services for the whole economy for 2012.
Note that changes to corn exports, discussed in Chapter 8 of the RIA,
are also included in the calculation of net exports. Relative to the
2012 projection, the avoided import expenditures due to the RFS would
represent 0.4 to 0.7% of economy-wide net exports.

**Table 6.4-2.**

Avoided Import Expenditures (\$2004 billion)

+--------+---------+---------+---------+---------+--------+-------+
| Cases  | AEO     | Expen   | Expen   | De      | Net    | Pe    |
|        | Total   | ditures | ditures | creased | Expend | rcent |
|        |         | on      | on      | Corn    | itures | of    |
|        | Net     | Pe      | Ethanol | Exports | on     | Total |
|        | Exports | troleum | Imports |         | I      | Net   |
|        |         | Imports |         |         | mports | Ex    |
|        |         |         |         |         |        | ports |
+--------+---------+---------+---------+---------+--------+-------+
| RFS    | \-      | \-      | \+      | \+      | \-     | 0.4%  |
| Case   | \$383   | \$2.6   | \$0.7   | \$0.6   | \$1.4  |       |
|        |         |         |         |         |        |       |
|        | (year   |         |         |         |        |       |
|        | 2012)   |         |         |         |        |       |
+--------+---------+---------+---------+---------+--------+-------+
| EIA    |         | \-      | \+      | \+      | \-     | 0.7%  |
| Case   |         | \$5.1   | \$1.0   | \$1.3   | \$2.8  |       |
+--------+---------+---------+---------+---------+--------+-------+

**6.5 Energy Security Implications of RFS**

**6.5.1** **Background**

One of the effects of increased use of renewable fuels in the U.S. from
the RFS is that it diversifies the energy sources in making
transportation fuel. A potential disruption in supply reflected in the
price volatility of a particular energy source carries with it both
financial as well as strategic risks. These risks can be reduced to the
extent that diverse sources of fuel energy reduce the dependence on any
one source. This reduction in risks is a measure of improved energy
security.

At the time of the proposal, EPA stated that an analysis would be
completed and estimates provided in support of this rule. In order to
understand the energy security implications of the RFS, 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 new study produced for the RFS, entitled
\"*TNumber range CHAPTERhe Energy Security Benefits of Reduced Oil Use,
2006-2015*,\" ORNL has updated and applied the method used in the 1997
report \"*Oil Imports: An Assessment of Benefits and Costs*\", by Leiby,
Jones, Curlee and Lee.[^188],[^189] While the 1997 report including a
description of methodology and results at that time has been used or
cited on a number of occasions, this updated analysis and results have
not been available for full public consideration. Since energy security
will be a key consideration in future actions aimed at reducing our
dependence on oil, it is important to assure estimates of energy
security impacts have been thoroughly examined in a full and open public
forum. Since the updated analysis was only recently available, such a
thorough analysis has not been possible. Therefore, EPA has decided not
to rely on the results of this report for the purposes of this
rulemaking. Rather, we are including it as part of the record of this
rulemaking and are inviting further public analysis and consideration of
both this particular report and other perspectives on how to best
quantify energy security benefits. To facilitate that additional
consideration, we highlight below some of the key aspects of this
particular analysis.

The approach developed by ORNL estimates the incremental benefits to
society, in dollars per barrel, of reducing U.S. oil imports, called
"oil premium." Since the 1997 publication of this report, changes in oil
market conditions, both current and projected, suggest that the
magnitude of the oil premium has changed. Significant driving factors
that have been revised include: oil prices, current and anticipated
levels of OPEC production, U.S. 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 from
the EIA\'s AEO 2006 were used. Using the \"oil premium\" approach,
estimates of benefits of improved energy security from reduced U.S. oil
imports from increased use of renewable fuels are calculated.

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 so called \"demand\" or \"monoposony\" 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).

1\. [Effect of Oil Use on Long-Run Oil Price, U.S. Import Costs, and
Economic Output]{.underline}

The first component of the full economic costs of importing petroleum
into 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 reduced U.S. petroleum demand can reduce the world price of crude
oil. Thus, one consequence of decreasing U.S. oil purchases due to
increased use of renewable fuel is the potential decrease in the crude
oil price paid for all crude oil purchased.

2\. [Short-Run Disruption Premium From Expected Costs of Sudden Supply
Disruptions]{.underline}

The second component of the external economic costs resulting from U.S.
oil imports arises from the vulnerability of the U.S. economy to oil
shocks. The cost of shocks depends on their likelihood, size, and
length, the capabilities of the market and U.S. Strategic Petroleum
Reserve (SPR), the largest stockpile of government-owned emergency crude
oil in the world, to respond, and the sensitivity of the U.S. economy to
sudden price increases. While the total vulnerability of the U.S.
economy to oil price shocks depends on the levels of both U.S. petroleum
consumption and imports, variation in import levels or demand
flexibility can affect the magnitude of potential increases in oil price
due to supply disruptions. Disruptions are uncertain events, so the
costs of alternative possible disruptions are weighted by disruption
probabilities. The probabilities used by the ORNL study are based on a
2005 Energy Modeling Forum[^190] synthesis of expert judgment and are
used to determine an expected value of disruption costs, and the change
in those expected costs given reduced U.S. oil imports.

3\. [Costs of Existing U.S. Energy Security Policies]{.underline}

The last often-identified component of the full economic costs of U.S.
oil imports is the costs to the U.S. taxpayers of existing U.S. energy
security policies. The two primary examples are maintaining a military
presence to help secure stable oil supply from potentially vulnerable
regions of the world and maintaining the SPR to provide buffer supplies
and help protect the U.S. economy from the consequences of global oil
supply disruptions.

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 U.S.
oil imports. Similarly, while the costs for building and maintaining the
SPR are more clearly related to U.S. oil use and imports, historically
these costs have not varied in response to changes in U.S. oil import
levels. Thus, while SPR is factored into the ORNL analysis, the cost of
maintaining the SPR is excluded.

As stated earlier, we have placed the report in the docket of this
rulemaking for the purposes of inviting further consideration. However,
the results of that report have not been used in quantifying the impacts
of this rule.

# Chapter 7: Estimated Costs of Renewable Fuels, Gasoline and Diesel

This section describes our methodology for estimating the cost impacts
of increased production, distribution, and blending of renewable fuels,
including corn and cellulosic ethanol and biodiesel. Detailed
information is given on expected changes to the nation's fuel
distribution system, as well as changes in refining processes that will
likely occur as larger volumes of ethanol are blended into gasoline. The
impact of subsidies is also addressed.

**7.1 Ethanol**

This subsection provides a description of the analysis we conducted for
estimating the cost of corn and cellulosic ethanol. Our analysis
indicates that corn ethanol will cost \$1.32 per gallon to produce (2004
dollars) in 2012. We also estimated that using cellulosic feedstock, the
production costs for ethanol would be approximately \$1.65 per gallon
(2004 dollars). By 2012 this cellulosic cost may decline with
breakthroughs and advances in technology. Based on reports from a
variety of sources and discussions we held with members of academia as
well as those directly involved in the industry, we believe several
hurdles still have to be overcome in the production of large volumes of
cellulosic derived ethanol. However, it appears that good progress
continues to be made and we remain optimistic that cellulosic ethanol
will become increasingly important in the future.

**7.1.1 Corn Ethanol**

Of the new ethanol production capacity expected to be built, according
to Section 1.2.2 of this RIA, less than three percent combined is
expected to be produced from cellulosic feedstocks or in plants that
differ significantly from dry mill corn ethanol plants. Several plants
will be able to utilize other starchy feedstocks besides corn, such as
milo, barley, wheat, and sorghum. However, corn is the primary
feedstock, and therefore, the following analyses will focus on dry mill
starch ethanol production.

**7.1.1.1 Engineering and Construction Requirements for Corn Ethanol
Plants**

To meet the RFS Case goal of 6.7 billion gallons per year (Bgal/y) of
ethanol in 2012 from the October 2006 capacity of 5.2 Bgal/yr, 1.5
Bgal/yr of additional capacity will have to be constructed.[^191] If we
consider that it is likely that at least 9.6 Bgal/yr of actual ethanol
capacity will come on-line by 2012 (EIA Case), the annual capacity
increase is 4.4 Bgal/yr. Our industry characterization work considering
plants that are either under construction or are planned to be
constructed in the next 2-3 years suggests the average new plant size
will be 81 million gallons per year (MMgal/yr) (including a small number
of expansions).

Based on conversations with representatives from design-build firms
working in this field, as well as material from public sources, each new
plant requires design engineering work lasting about six months followed
by construction lasting 12-14 months before plant startup is possible,
resulting in a total project timeline of 18-20 months. The design phase
for a basic 50 MMgal/yr plant is expected to require the attention of
about 12 engineers full time, and the construction phase will employ an
average of about 125-150 workers each day. To correlate these figures
with requirements for an 81 MMgal/yr plant, the number of construction
personnel (150) were scaled proportionally, while the number of
engineering personnel were assumed to be constant.

These figures provide a basis for estimating the personnel requirements
of the total volume needed to meet the expected volumes. Over the
six-year build-up period, a maximum of 970 construction workers and 30
engineers would be required on a monthly basis for the RFS case, while
for the EIA case, these numbers increase to 2,328 and 75, respectively.

These figures simply estimate the number of workers required at the
final assembly stage of the plant, and do not capture many more
personnel hours that will go into designing and constructing vessels,
pipe fittings, control systems, and other pieces of equipment that will
be installed and brought online by the plant construction crews. A
report produced by one consultant suggested that expansion of the
ethanol industry was responsible for more than 65,000 construction jobs
in 2005.[^192]

**7.1.1.2 Corn Ethanol Production Costs**

Corn ethanol costs for our work were estimated using a model developed
by USDA that was documented in a peer-reviewed journal paper on cost
modeling of the dry-grind corn ethanol process.[^193] It produces
results that compare well with cost information found in surveys of
existing plants.[^194]

The USDA model is for a 40 MMgal/yr corn plant producing ethanol with a
primary co-product of distillers dried grains with solubles (DDGS). The
ethanol yield used in the model is 2.87 gallons per bushel with 5.0%
gasoline denaturant. The model is based on work done in chemical process
simulation software to generate equipment sizes, stream flowrates, and
material and energy balances. These results were then put together with
feedstock, energy, and equipment cost information in a spreadsheet
format to arrive at a final per-gallon cost estimate. Although the model
is current in terms of technology, yields, and capital estimates, we
made some modifications to allow estimation of costs for ethanol plants
of different sizes and operating under different energy and feedstock
prices. We believe that these updates, in combination with the industry
and supplier surveys done by USDA in developing the model, result a
reasonable estimate for projected ethanol production costs.

We estimate an average corn ethanol production cost of \$1.26 per gallon
in 2012 (2004 dollars) for the RFS case and \$1.32 per gallon for the
EIA case. The cost of ethanol production is most sensitive to the prices
of corn and the primary co-product, DDGS. Utilities, capital, and labor
expenses also have an impact, although to a lesser extent. Corn
feedstock minus DDGS sale credit represents about 48% of the final
per-gallon cost, while utilities, capital and labor comprise about 19%,
9%, and 6%, respectively. For this work, we used corn and DDGS price
projections generated by the Forestry and Agricultural Sector
Optimization Model, which is described in Chapter 8.1.1 of this RIA.
Corn and DDGS prices are given there in Table 8.1-1. Figure 7.1-1 shows
the cost breakdown for production of a gallon of ethanol. Note that this
production model does not account for the cost to pelletize or ship the
DDGS. Those costs are external and are expected to increase the price of
DDGS an end user located far from the plant. More details are given in
Section 8.1.1 where the FASOM model is discussed.

**Figure 7.1-1.**

**Cost Breakdown of Corn Ethanol Production (2004\$).**

![](media/image72.wmf){width="6.5in" height="4.229166666666667in"}

The ability to address plant scaling in the model was accomplished by
applying an engineering scaling factor to all plant equipment. In past
rulemakings involving modifications to refineries we have used a
material scaling factor of 0.65. This factor is applied as an exponent
to the ratio of the new size to the original size, the result of which
is then multiplied by the original capital cost. However, there is
information suggesting that a general factor may be considerably higher
for ethanol plants. Based on a recent journal publication, a factor of
0.84 was used in this work.[^195] With this factor, the model indicates
that the change in per-gallon production cost due to economies of scale
is very small over the range of typical plant sizes, on the order of
\$0.02 between 40 and 100 MMgal/yr. In this analysis we used an average
new plant size of 81 MMgal/yr, derived from our industry
characterization work in Chapter 1.

We also added functions to estimate the per-gallon cost impact of coal
combustion as a process energy source rather than natural gas. Our
industry characterization work suggests that about 14% of ethanol
production from new plants being constructed will use coal for the
process energy source, so the effect on average costs is relatively
small. Capital cost used for an 81 MMgal/yr gas-fired plant was \$99
million (2004\$). For the coal system versus natural gas, additional
requirements were estimated at \$45 million in capital for the same size
plant, as well as one additional operator per shift and 10% additional
electric utility use. These figures should be considered conservative
estimates, and were based on information from press releases as well as
a conversation with staff of a company that designs and builds ethanol
plants. Additionally, we adjusted the thermal efficiency of coal
combustion processes downward by 13% relative to natural gas, and
electricity consumption upward by 10% to reflect operational differences
in the processes.[^196] Using this information in the model, the cost
savings is about \$0.04 per gallon of ethanol for a coal-fired plant
compared to natural gas firing. The results presented here are a
weighted average of coal and gas production costs (using 14% coal).

Under the Energy Act, starch ethanol can be counted as cellulosic if at
least 90% of the process energy is derived from animal wastes or other
waste materials.[^197] It is expected that the vast majority of the 250
million gallons per year of cellulosic ethanol production required by
2013 will be made using this provision. While we have been unable to
develop a detailed production cost estimate for ethanol from corn which
meets cellulosic criteria, we assume that the costs will not be
significantly different from conventionally produced corn ethanol. We
believe this is reasonable because to the extent that these processes
are utilized, we expect them to be in locations where the very low or
zero cost of the feedstock or biogas itself will likely offset the costs
of hauling the material and the additional capital for handling and
combusting it. In addition, because the quantity of ethanol produced
using these processes is still expected to be a relatively small
fraction of the total ethanol demand, the sensitivity of the overall
analysis to this assumption is also very small.

In general, energy prices used in the model were taken from historical
EIA data for 2004 and scaled according the ratios of 2004-2012 price
forecasts published in the Annual Energy Outlook 2006. [^198]^,^[^199]
The prices used in the modeling are shown in Table 7.1-2. Several
sensitivity cases were run using the model, and the results are shown in
Table 7.1-3. Input values in this table were chosen to give a
significant margin around current and anticipated future prices.

**Table 7.1-2.**

**Energy Prices Used for Ethanol Cost Modeling for 2012** (2004\$)

  ----------------- ----------------- -------------------- -----------------
  **Natural         **Coal**^a^       **Electricity**^a^   **Natural
  Gas**^a^                                                 Gasoline**^b^

  **\$/MMBtu**      **\$/MMBtu**      **\$/kWh**           **\$/gal**

  6.16              1.94              0.044                1.36
  ----------------- ----------------- -------------------- -----------------

^a^ Historical data based on averages for Iowa, Illinois, Minnesota, and
Nebraska

^b^ Natural gasoline (or natural gas liquids) is the typical denaturant
used in ethanol production, since it is cheaper than finished gasoline.
The price used was based on its value being 20 cents per gallon below
wholesale gasoline.

**Table 7.1-3.**

**Energy and Feedstock Price Sensitivities (2004\$)**

![](media/image73.wmf){width="5.36875in" height="4.413194444444445in"}

**7.1.2 Cellulosic Ethanol**

**7.1.2.1 How Ethanol Is Made from Cellulosic Feedstocks**

It is not clear when the first processes to produce ethanol from
cellulosic biomass were discovered. While ethanol produced from starch
can be traced historically to ancient times, cellulosic derived ethanol
appears to have been investigated in the 1800's. Until recently, the
demand for fuel ethanol has been somewhat limited, and not sufficient to
support a cost-competitive, commercial process to convert cellulose into
ethanol.

With the increasing demand for fuel ethanol during the past few years,
good progress has been made toward producing ethanol from cellulosic
feedstocks. Interest in ethanol has continued to grow, initially
fostered in part from EPA's reformulated gasoline (RFG) regulations that
required such gasoline to contain a minimum of 2 percent oxygen by
weight in the fuel. This minimum oxygen requirement has recently been
revoked by EPA in response to the Energy Act, which revised the Clean
Air Act requirement for oxygen in RFG. The Renewable Fuel Standard (RFS)
continues to create a demand for ethanol. Likewise, there is an
additional incentive to produce cellulosic ethanol because the Energy
Act mandates that, starting in 2013, renewable fuels used in gasoline
must include.

There is a wide variety of government and renewable fuels industry
research and development programs dedicated to improving our ability to
produce renewable fuels from cellulosic feedstocks. There are at least
three completely different approaches to producing ethanol from
cellulosic biomass, sometimes referred to as "platforms". The first is
based on what NREL refers to as the "sugar platform,"[^200] which refers
to pretreating the biomass, then hydrolyzing the cellulosic and
hemicellulosic components into sugars, and then fermenting the sugars
into ethanol. Corn grain is a nearly ideal feedstock for producing
ethanol by fermentation, especially when compared with cellulosic
biomass feedstocks. Corn grain is easily ground into small particles,
following which the exposed starch which has α-linked saccharide
polymers is easily hydrolyzed into simple, single component sugar which
can then be easily fermented into ethanol. By comparison, the biomass
lignin structure must be either mechanically or chemically broken down
to permit hydrolyzing chemicals and enzymes access to the saccharide
polymers. The central problem is that the cellulose/hemicellulose
saccharide polymers are β-linked which makes hydrolysis much more
difficult. Simple microbial fermentation used in corn sugar fermentation
is also not possible, since the cellulose and hemicellulose (6 & 5
carbon molecules, respectively) have not been able to be fermented by
the same microbe. We discuss various pretreatment, hydrolysis and
fermentation technologies, below. The second and third approaches have
nothing to do with pretreatment, acids, enzymes, or fermentation. The
second is sometimes referred to as the "syngas" or "gas-to-liquid"
approach; we will call it the "Syngas Platform." Briefly, the cellulosic
biomass feedstock is steam-reformed to produce syngas which is then
converted to ethanol over a Fischer-Tropsch catalyst. The third approach
uses plasma technology.

Technologies that are currently being developed may solve some of the
problems associated with producing cellulosic ethanol. Specifically, one
problem, mentioned previously, with cellulosic feedstocks is that the
hydrolysis reactions produce both glucose, a six-carbon sugar, and
xylose, a five-carbon sugar (pentose sugar, C~5~H~10~O~5~; sometimes
called "wood sugar"). Early conversion technology required different
microbes to ferment each sugar. Recent research has developed better
cellulose hydrolysis enzymes and ethanol-fermenting organisms.[^201]
Now, glucose and xylose can be co-fermented---hence, the terminology,
weak-acid enzymatic hydrolysis and co-fermentation. In addition, at
least one group is researching the use of recently developed genome
modifying technology to produce a variety of new or modified enzymes and
microbes that show promise for use in a process known as weak-acid,
enzymatic-prehydrolysis [^202]

**7.1.2.2 Difficulties in Estimating Capital and Operating Costs for New
or Pioneer Process Plants**

Many years ago the petroleum and chemical process industries learned
that it can be financially problematic to scale-up a bench or laboratory
scale process to a full commercial sized operation. There are simply too
many process variables that act one way at a batch rate of one- or
two-gallons per day, or even 100-gallons per day, but then act a
completely different way in a continuous, 70,000 gallon per day
operation. Under these, admittedly somewhat extreme expectations, there
is also absolutely no reasonable way to optimize a process. We expect
that at least pilot or demonstration size projects will be necessary
before a fully commercial sized, reasonably optimized plant can be
constructed.

The petroleum and chemical process industries have also learned that if
a different feedstock, with similar, but at the same time sufficiently
different characteristics, becomes available, it is nearly always
necessary to make several pilot plant runs before the feedstock in
introduced into the process. There are a wide variety of potential
cellulosic feedstocks, such as switch grass, forest thinnings, municipal
waste, wood chips, and corn stover (corn stalks). The physical
characteristics of these materials, such as size, composition, and
density vary widely. As a result, there could be significant differences
in the process configurations, as well as differences in the enzyme
"cocktails" required to hydrolyze and convert each of them into ethanol.
Compositional and density variations may require different reactor
residence times for each feedstock, which will impact throughput. Many
of the process streams will actually be slurries of the feedstock. It is
also quite likely that each slurry stream will have its own flow and
compositional characteristics. The flow characteristics of any slurry,
under real operating conditions, must be well understood in order to
properly design an optimum system. Additionally, valve and pump types,
sizes, and materials of construction, as well as line sizes and
configurations, may vary. Apart from the various process issues,
questions also remain regarding which of the feedstocks is actually the
best in terms of ethanol yield per dollar.

Consequently, we believe a good deal more process data is necessary
before a reasonably accurate cost to design, engineer, and build a
commercial scale cellulosic based ethanol plant can be expected. At the
present time, there is only one cellulosic ethanol plant in North
America (Iogen[^203] a privately held company, based in Ottawa, Ontario,
Canada). On February 28, 2007, however, the Department of Energy (DOE)
announced that it will provide grants of up to \$385 billion for six
biorefinery projects over the next four years. These facilities are
expected to produce more than 130 million gallons of celluloic ethanol
per year. As additional information on these future facilities are made
available, EPA will have more information on process design from which
we will better be able to project production costs for cellulosic
ethanol.

Although the industry seems to be moving down several different
pathways, one of the more mature process being tested and improved uses
dilute acid enzymatic prehydrolysis with simultaneous saccharification
(enzymatic) and co-fermentation. Because there is more publicly
available information about this process, the model we used incorporates
this type of process to estimate the cost of producing ethanol from corn
stover. We chose corn stover because it is ubiquitous and because of the
likelihood it will eventually be used as a feedstock.

In 1999, the National Renewable Energy Laboratory (NREL) published a
report outlining its work with the USDA to design a computer model of a
plant to produce ethanol from hard-wood chips.[^204] Although the
cellulosic model was originally prepared for hardwood chips, it was
meant to serve as a modifiable-platform for ongoing research using
cellulosic biomass as feedstock to produce ethanol. Their long-term plan
was that various indices, costs, technologies, and other factors would
be regularly updated.

NREL modified the model in order to compare the cost of using corn-grain
with the cost of using corn stover to produce ethanol. We used the corn
stover model from the second NREL/USDA study for this analysis. Because
there are no operating plants that could potentially provide real world
process design, construction, and operating data for processing
cellulosic ethanol, NREL had originally considered modeling the plant
based on assumptions associated with a pioneer plant. Such assumptions
would likely result in costs significantly higher than corn ethanol
plants due to the higher level of uncertainty in both the design and
engineering as well as the final construction and operating costs. The
literature indicates that such models often underestimate actual costs
since the high performance assumed for pioneer process plants is
generally unrealistic.

The NREL analysis assumed that the corn stover plant was an N^th^
generation plant, built after the industry had been well enough
established to provide verified costs. The corn stover plant was
normalized to the corn kernel plant, e.g., placed on a similar basis.
Additional costs for risk financing, longer start-ups, and other costs
associated with first-of-a-kind or pioneer plants were not included in
the study.[^205] It is also reasonable to expect the cost of cellulosic
ethanol will be higher than corn ethanol because of the complexity of
the cellulose conversion process. During the recent past, process
improvements and other advancements in corn production have considerably
reduced the cost of producing corn ethanol. We also believe it is
realistic to assume that cellulose-derived ethanol process improvements
will be made and that one can likewise reasonably expect that as the
industry matures, the cost of producing ethanol from cellulose will also
decrease.

**7.1.2.3 Methods, Data Sources, and Assumptions**

For our analysis, we used the spreadsheet model that NREL developed for
its comparison of the costs of producing ethanol from corn grain and
corn stover.[^206] We believe NREL's approach was reasonable and well
thought out. They also had an outside engineering firm validate their
work, to the extent possible. The Delta-T Corporation (Delta-T) assisted
in preparing, reviewing, and estimating costs for the process design.
Delta-T worked with NREL process engineers to review all the process
design and equipment costs (with the exclusion of wastewater treatment
and the burner-boiler system, which were reviewed by Merrick Engineering
and Reaction Engineering, Inc., respectively). For the plant areas that
are actively being investigated under DOE programs (e.g., prehydrolysis,
cellulase enzyme production, and simultaneous saccharification and
co-fermentation), Delta-T used the results of the DOE sponsored research
to identify process design criteria and equipment requirements. These
were used as a basis for sizing and costing major equipment components
in the facility. The results of Merrick Engineering's work on wastewater
treatment and REI's work on the burner/boiler were also included.[^207]
The NREL model used the Aspen Plus™ process simulator to calculate the
flows and the heat and material balances for the process. We decided to
use the NREL spreadsheet corn stover model, as is, since we did not have
access to the Aspen Plus™ model nor to all the input. Rather, we left
the feedrate, yields, and streams flows as they were, but adjusted
equipment capital and installation costs, and utility, chemical, and
labor costs to 2004 dollars. We used the same indices used by NREL to
update their corn stover study; however, we used actual costs and
indices for 2004 where possible. For example, in their 2000
calculations, NREL had extrapolated the Chemical Engineering Plant Cost
Index and the Chemical Cost Index through 2012. However, we used actual
2004 data rather than the extrapolated data.

We did not change the corn stover cost. Several issues remain to be
settled regarding the amount of stover that should be left in place and
how it should be gathered, baled, and shipped. We found cost ranges of
from \$25 per dry ton to \$45 per dry ton. For purposes of this analysis
we used the \$35 per dry ton that NREL assumed in its analysis.

For the analysis, we calculated the annual production cost in dollars
per gallon of fuel ethanol. The annual production cost includes
equipment straight-line depreciation for the life of the plant (10
years), and variable costs, labor, supplies and overhead, minus any
by-product credits. Gasoline for denaturant and diesel for bulldozers to
move the stover were projected into 2012 prices using IEA's AEO
2006[^208] report. The market selling price minus the annual production
cost is the before-tax profit. We calculated variable operating costs
using NREL's best estimate of quantities of chemicals and additives
based on their laboratory work. NREL calculated fixed costs using
industry standards for percentages of direct labor (indirect labor was
40% of direct labor and overhead was 60% of total labor); other
operating supplies, insurance, etc. totaled 3.25% of total installed
cost. According to the analysis three major cost categories made up the
majority of the total production cost: feeds stock -- 31.2%; fixed costs
-- 23.8%; and depreciation (reflects installed capital cost of
equipment) -- 33.8%.

As previously stated, several feedstock issues remain to be settled, not
least being which of the many available feedstocks will be the best or
most efficient. We chose an average cost of \$35 per dry ton; we don't
believe the cost will rise and as a result of the research that is
currently under way, there is reason to expect it to come down a little.
On the other hand, several researchers have indicated switch grass may
be better than corn stover; others point to forest wastes, etc. In the
end, the best feedstock will likely be the one that is readily available
and close to the plant; gathering, baling, and hauling continue to be
important issues that will definitely impact the viability of a
feedstock. Equipment cost reductions may have a significant impact on
future costs. For example, there appear to be reasons to expect
significant savings from purchasing enzymes rather than growing them
onsite. Another issue that remains to be investigated is whether a
particular kind of feedstock can be processed using one type of
technology while a different kind may require the use of a completely
different technology.

**7.1.2.4 Results and Discussion**

Given the limitations we've already discussed, and perhaps others, we
determined that it would have cost approximately \$1.65 in 2012 (2004
dollars) to produce a gallon of ethanol using corn stover as a
cellulosic feedstock.

The provisions offering grants and shared financing included in Title XV
of the Energy Act[^209] will likely encourage process development work
to generate the necessary construction and operating cost estimates. We
assume the results produced by the above referenced NREL study are
accurate and reasonable given the state of our current knowledge.

**7.2 Biodiesel and Renewable Diesel Production Costs**

**7.2.1 Overview of Analysis**

We based our estimate for the cost to produce biodiesel on the use of
USDA's, NREL's and EIA's biodiesel computer models, along with estimates
from engineering vendors that design biodiesel plants. Biodiesel fuel
can be made from a wide variety of virgin vegetable oils such as canola,
corn oil, cottonseed, etc. though, the operating costs (minus the costs
of the feedstock oils) for these virgin vegetable oils are similar to
the costs based on using soy oil as a feedstock, according to an
analysis by NREL[^210]. Biodiesel costs are therefore determined based
on the use of soy oil, since this is the most commonly used virgin
vegetable feedstock oil, and the use of recycled cooking oil (yellow
grease) as a feedstock. Production costs are based on the process of
continuous transesterification, which converts these feedstock oils to
esters, along with the ester finishing processes and glycerol recovery.
The models and vendors data are used to estimate the capital, fixed and
operating costs associated with the production of biodiesel fuel,
considering utility, labor, land and any other process and operating
requirements, along with the prices for feedstock oils, methanol,
chemicals and the byproduct glycerol.

The USDA, NREL and EIA models are based on a medium sized biodiesel
plant that was designed to process raw degummed virgin soy oil as the
feedstock. Additionally, the EIA model also contains a representation to
estimate the biodiesel production cost for a plant that uses yellow
grease as a feedstock. In the USDA model, the equipment needs and
operating requirements for their biodiesel plant were estimated through
the use of process simulation software. This software determines the
biodiesel process requirements based on the use of established
engineering relationships, process operating conditions and reagent
needs. To substantiate the validity and accuracy of their model, USDA
solicited feedback from major biodiesel producers. Based on responses,
they then made adjustments to their model and updated their input prices
to year 2005. The NREL model is also based on process simulation
software, though the results are adjusted to reflect NREL's modeling
methods, using prices based on year 2002. The origin of the EIA model is
not known, though it is based on 2004 prices. The output for all of
these models was provided in spreadsheet format. We also use engineering
vendor estimates as another source to generate soy oil and yellow grease
biodiesel production costs. These firms are primarily engaged in the
business of designing biodiesel plants.

The production costs are based on a 10 million gallon per year biodiesel
plant located in the Midwest using feedstock oils and methanol, which
are catalyzed into esters and glycerol by use of sodium hydroxide.
Because local feedstock costs, distribution costs, and biodiesel plant
type introduce some variability into cost estimates, we believe that
using an average plant to estimate production costs provides a
reasonable approach. Therefore, we simplified our analysis and used
costs based on an average plant and average feedstock prices since the
total biodiesel volumes forecasted are not large and represent a small
fraction of the total projected renewable volumes.

The models and vendor estimates are further modified to use input prices
for feedstocks, byproducts and energy that reflect the effects of the
fuels provisions in the Energy Act. In order to capture a range of
production costs, we generated cost projections from all of the models
and vendors. We present the details on these estimates later in this
section.

For soy oil biodiesel production, based on the USDA model, we estimate a
production cost of \$2.06 per gallon in 2004 and \$1.89 per gal in 2012
(in 2004 dollars). With the NREL model, we estimate soy oil biodiesel
production costs of \$2.28 and \$2.11 per gallon in 2004 and 2012,
respectively, which is slightly higher than the USDA results. The EIA
model generated soy oil based costs of \$2.33 and \$ 2.15/gal, while the
engineering vendor's costs averaged \$2.27 and \$2.09/gal, in years 2004
and 2012, respectively.

For yellow grease derived biodiesel, we used the EIA and vendor
estimates and generated a range of costs, as discussed later. The total
production costs ranged from \$1.24 to \$1.60/gal in 2004, and from
\$1.11 to \$1.56 for year 2012.

**Table 7.2-1.**

**Summary of Production Costs for Biodiesel made from Soy Oil, per
Gallon**

**(2004 cents**)

  -------------- ------------ -------------- ---------- ------------- ------------- ----------- ----------------------
                 **Total      **Subsidized   **Feed**   **Capital**   **Reagent and **Labor**   **Energy/Utilities**
                 Production   Production                              Chemicals**               
                 Cost**       Cost**                                                            

  USDA           189          89             156        11.3          12.7          5.0         4.8

  NREL           211          111            165        17.0          17.0          6.0         7.4

  EIA            215          115            161        14.4          NA            NA          16.0

  PSI-Lurgi      220          120            174        18.8          12.6          8.2         5.5

  Superior       224          124            175        11.7          16.5          7.6         5.0
  Process                                                                                       
  Technologies                                                                                  
  -------------- ------------ -------------- ---------- ------------- ------------- ----------- ----------------------

**Table 7.2-2.**

**Summary of Production Costs for Biodiesel made from Yellow Grease, per
Gallon**

**(2004 cents**)

  -------------- ------------ -------------- ---------- ------------- ------------- ----------- ----------------------
                 **Total      **Subsidized   **Feed**   **Capital**   **Reagent and **Labor**   **Energy/Utilities**
                 Production   Production                              Chemicals**               
                 Cost**       Cost**                                                            

  EIA            138          88             80         14.4          NA            NA          16.9

  Superior       167          117            114        14.7          18.3          8.7         9.0
  Process                                                                                       
  Technologies                                                                                  
  -------------- ------------ -------------- ---------- ------------- ------------- ----------- ----------------------

With the current Biodiesel Blender Tax Credit Program, producers using
virgin vegetable oil stocks receive a one dollar per gallon tax subsidy
while yellow grease producers receive 50 cents per gallon, reducing the
net production cost to a range of 89 to 115 c/gal for soy oil and 61 to
106 c/gal for yellow greased derived biodiesel fuel in 2012. This
compares favorably to the projected wholesale diesel fuel prices of 138
cents per gallon in 2012, signifying that the economics for biodiesel
are positive under the effects of the blender credit program, though the
tax credit program will expire in 2008 if it is not extended. Congress
may later elect to extend the blender credit program, though, following
the precedence used for extending the ethanol blending subsidies.
Additionally, the Small Biodiesel Blenders Tax credit program and state
tax and credit programs offer some additional subsidies and credits,
though the benefits are modest in comparison to the Blender's Tax
credit.

**7.2.2 Inputs to and Results of USDA's Model**

We used USDA's biodiesel model as a source to generate an estimate for
the cost to produce biodiesel fuel. The model is in spreadsheet format
with inputs in 2005 dollars, and contains all of the capital and
operating costs for a plant to produce 10 million gallons per year of
biodiesel fuel.

**7.2.2.1 Feedstock Costs**

Feedstock prices are the largest component in generating production
costs for biodiesel fuel. For soy oil prices, we used prices based on
USDA=s 2006 Outlook, which has forecasted soy oil prices considering
production of biodiesel under EPAct 2005. USDA=s Outlook is a national
forecasting analysis that models the effects of demand for farm products
and farm product prices for soy beans, soy bean oil, corn and other farm
commodities. The 2006 Outlook estimated soy oil prices considering the
demand of soy oil derived biodiesel fuel at approximately 160 MM gallons
per year in 2006 and 312 MM gallons a year in 2007[^211]. This is in
close proximity to EIA=s soy oil derived biodiesel volume projection of
135 MM gals and 265 MM gals in 2006 and 2007, respectively. We therefore
used the soy oil prices from USDA=s Outlook to determine biodiesel
production costs. The USDA does not forecast yellow grease prices, so we
assumed that yellow grease feedstocks costs would maintain the same
relative historical pricing differential to virgin soy oil. In the past,
some analysis has shown that yellow grease has sold for about half the
price of soy oil[^212]. The resulting feedstock costs to make a gallon
of biodiesel under projected volumes for RFS are in Table 7.2-3.

**Table7.2-3.**

Projected Prices of Feedstock (2004 Dollars per Gallon)

  ----------------------- ----------------------- -----------------------
  **Marketing Year**      **Soy Oil**^a^          **Yellow Grease**

  2004                    1.71                    0.86

  2012                    1.56                    0.78
  ----------------------- ----------------------- -----------------------

^a^Production of Biodiesel assumed to consume 7.42 lbs of soy oil per
gallon. USDA prices in 2012 are adjusted to 2004 dollars to account for
inflation, using GDP index of 109.7 in year 2004 and 130.8 in year 2012.

**7.2.2.2 Capital Costs**

For capital costs we used USDA=s total installed capital cost of \$10.66
MM for a 10 MM gallon per year plant. This estimate was determined by
the USDA, using a detailed analysis to generate costs for equipment
needs, installation, land, engineering and construction work, buildings,
utility needs, contingencies, startup costs etc. The USDA model is based
on 2005 dollars, so we adjusted the numbers to 2004 values using the GDP
index. Per the USDA method, the total installed capital costs on a per
gallon basis was amortized on a 10 year straight line depreciation rate
using a facility dependent cost of 10 percent times the capital costs.
Maintenance charges, insurances and facility supply costs were also
calculated as percentages of the capital. The total of all of these are
equal to 16 cents per gallon.

**7.2.2.3 Operating Costs**

The total operating expenses were 20 and 18 c/gal for a soy based
biodiesel plant in 2004 and 2012, respectively. The operating cost
included a 4 cent per gallon offset from sale of the glycerol product at
a price of 5 cents/lb. The operating costs include values for utilities,
feed reagents, manpower and were based on the USDA=s model. The
components of the operating costs are discussed below.

**7.2.2.4 Utility and Labor Costs**

We estimated utility costs using energy requirements from USDA=s model
and adjusted the inputs to match the energy and electricity prices for
the Midwest, using prices from EIA=s AEO. The cost for steam was
estimated using the price of natural gas. Each pound of steam was
produced from heating water, which required 810 British Thermal Units
(BTUs) per pound of steam. Additionally, the steam costs are estimated
assuming that the BTUs to make steam are increased by a factor of two,
to account for steam distribution efficiency losses, treatment of boiler
water to prevent fouling, maintenance and other miscellaneous costs. The
utility requirements per gallon of biodiesel and energy prices are
presented in Tables 7.2-4 and 7.2-5

**Table 7.2-4.**

**Utility Requirements per Gallon Biodiesel^a^**

  ----------------------------------- -----------------------------------
  Medium Pressure Steam, lbs          4.0

  Electricity, kWh                    0.10

  Cooling Tower Water, lbs            96.1
  ----------------------------------- -----------------------------------

^a^Utilities per USDA model from the production of biodiesel from soy
oil.

**Table 7.2-5.**

**Midwest Energy Prices per Year (in 2004** \$)

  ----------------------------------- ----------------- -----------------
  **Year**                            **2004**          **2012**

  Electricity, \$/kWh                 0.046             0.044

  Natural gas, \$/MM BTU              7.16              6.16
  ----------------------------------- ----------------- -----------------

Labor costs include the salaries and benefits for personnel to operate a
biodiesel plant. This was estimated in the USDA model, though the labor
costs were in 2005 dollars, which we adjusted to 2004 dollars using the
GDP price index. The resulting labor costs are 5 cents per gallon of
produced biodiesel fuel.

**7.2.2.5 Chemical Reagents**

Another operating expense, the production of biodiesel also requires the
use of chemicals and chemical reagents, as these act as a catalyst in
the transesterification process. Additionally, methanol is required as
it is the feedstock that is chemically combined with soy oil and yellow
grease during the transesterification process, yielding the biodiesel
product. The amount of chemicals and methanol required to make a gallon
of biodiesel are listed in Table 7.2-6.

**Table 7.2-6.**

Reagent Requirements

  ----------------------------------- -----------------------------------
  **Reagent**                         **Annual Requirement, lbs per
                                      gallon**

  Water                               0.0323

  Hydrochloric acid                   0.0185

  Methanol                            0.8006

  Sodium Methoxide                    0.0231

  Sodium Hydroxide                    0.0031
  ----------------------------------- -----------------------------------

For the prices of chemical reagents, we used prices that were supplied
in USDA=s 2005 model and adjusted them to 2004 dollars. Additionally,
since we have no forecasting mechanism we assumed that the chemical
reagent prices remained unchanged in 2012. However, we estimated
methanol prices, as the cost for this feedstock is a significant
component of the total operating costs. For our analysis, we generated
values by use of a correlation that calculates methanol's price as a
function of the price of natural gas[^213]. In 2004 and 2012, using
Midwest natural gas prices, we estimated methanol prices of 13.1 and
11.6 cents per pound, respectively. All other chemical prices, we
assumed were constant over time and are in Table 7.2-7.

**Table 7.2-7.**

**Reagent Prices (in 2004** \$)

  ----------------------------------- -----------------------------------
  **Reagent**                         **Prices, \$/lb**

  Hydrochloric acid                   0.167

  Sodium Methoxide                    1.358

  Sodium Hydroxide                    0.273
  ----------------------------------- -----------------------------------

**7.2.2.6 Glycerol Byproduct**

The feedstock cost credit for the glycerin by product in our modeling
work was 5 cents per pound, based on recent pricing trends, assuming
that additional glycerol generated from expansion of biodiesel
production will continue to keep prices low. The model, like many
biodiesel plants, produces a crude 80% glycerin stream, which is usually
sold to glycerin refiners for purification. In the past, crude glycerin
has sold for around \$0.15 / pound. Because of the increase in biodiesel
production around the world, however, the crude glycerin market has
become saturated and the price is now around \$0.05 / pound. As more
biodiesel capacity comes on line, this price may very well drop further,
though other markets for the use of glycerol are likely to develop
because glycerol is a platform chemical used throughout industry. We
assumed that the current glycerin pricing environment will continue in
the future. For our cost estimation, the byproduct glycerin was sold at
5 cents per pound, reflecting current saturated market and low pricing
conditions. The income from sale of the byproduct glycerin lowered
biodiesel production costs by 2 percent and 4 percent for soy oil and
yellow grease derived biodiesel fuel, respectively.

The total biodiesel production costs derived using the USDA's model are
summarized in Table 7.2-8

**Table7.2-8.**

**Projected Production Costs for Biodiesel by Feedstock per Gallon**

(2004 Dollars)

  ----------------------------------- -----------------------------------
  **Marketing Year**                  **Soy Oil**

  2004                                2.06

  2012                                1.89
  ----------------------------------- -----------------------------------

**7.2.3 Inputs to and Results of NREL's Biodiesel Model**

We used NREL's biodiesel model as another source to generate an estimate
for the cost to produce biodiesel fuel. Similar to the USDA's model, the
NREL biodiesel model also represents a continuous transesterification
process that uses sodium hydroxide and methanol to convert soy oil to
biodiesel and which has the finishing processes for biodiesel and
glycerol. The model is in spreadsheet format, and contains all of the
capital and operating costs for a plant to produce 10 million gallons
per year of biodiesel fuel. We again simplified our analysis, and used
the NREL model to estimate production costs for an average biodiesel
plant that makes 10 MM gallon per year. To make the results directly
comparable to USDA's model, we used energy costs in the Midwest, and
based the analysis on production of soy oil derived biodiesel.

Based on the results of the NREL model, we estimate that the total
production costs to make soy oil derived biodiesel fuel are \$2.28 and
\$2.11 per gallon for years 2004 and 2012, respectively. This is 22
cents more per gallon than the estimate derived from USDA's model. The
components that make up our NREL estimate are discussed in the sections
that follow.

**7.2.3.1 Feedstock Costs**

The feedstock costs increase because the NREL model assumes 7.87 pounds
of soy oil are required to make a gallon of biodiesel fuel. This is
slightly higher than the pounds required by the USDA model, though the
difference may be due to each model being based on soy oils with
differing chemical structures, i.e. more esters, differing densities.
The higher amount of soy oil required by the NREL model raises the
production costs for biodiesel by about 10 cents per gallon for
feedstock costs alone, versus the USDA model. The feedstock costs are
summarized in Table7.2-9.

**Table7.2-9.**

Projected Prices of Feedstock (2004 Dollars per Gallon)

  ----------------------------------- -----------------------------------
  **Marketing Year**                  **Soy Oil**

  2004                                181.0

  2012                                165.2
  ----------------------------------- -----------------------------------

**7.2.3.2 Capital Costs**

The total capital cost in the NREL model account for all of the costs
for building a plant, including but not limited to the expenses for
equipment, tanks, installation costs, engineering, tanks, construction,
land and site development, start up and permitting charges. These costs
do not account for expenses incurred from maintenance, insurance and
taxes, however. The total capital coats for a plant are \$14.8 million
in 2002 dollars, which we adjusted to 2004 dollars using the GDP price
index. The capital costs were amortized assuming a seven percent return
on investment, resulting in a cost of 17 cents per gallon. All of the
economic factors used for amortizing the capital costs are summarized in
Table 7.2-10.

**Table 7.2-10.**

Economic Factors Used in Deriving the Capital Cost Amortization Factor

  --------------------------------------------------------------------------------------
  **Amortization   **Depreciation   **Economic   **Federal   **Return on  **Resulting
  Scheme**         Life**           and Project  and State   Investment   Capital
                                    Life**       Tax Rate**  (ROI)**      Amortization
                                                                          Factor**
  ---------------- ---------------- ------------ ----------- ------------ --------------
  Societal Cost    10 Years         15 Years     0%          7%           0.11

  --------------------------------------------------------------------------------------

**7.2.3.3 Operating Costs**

The total operating costs are 31 and 30 cents per gallon for years 2004
and 2012, respectively. These costs are not directly comparable to those
from the USDA model, as fixed operating cost are included in the
operating costs for the NREL model, while the USDA model accounts for
fixed costs in the capital estimate. The operating cost for the NREL
analysis includes items for utilities, reagents, manpower, insurance,
taxes, general administration and maintenance costs, though do not
account for capital costs. Additionally, the sale of the glycerol
byproduct (80% strength) generated income of 4 cents per gallon of
produced biodiesel, using glycerol price of 5 cents per pound. The cost
associated with insurance, taxes, general administration and supplies
incur a cost of 2.4 cents per gallon of biodiesel. The remaining
components of operating costs for the NREL modeling analysis are
discussed below.

**7.2.3.4 Utility and Labor**

The utility costs were estimated using the energy requirements in the
NREL model along with the same prices for energy, steam and electricity,
as those used in our USDA analysis. The utility requirements per gallon
of biodiesel fuel are listed in Table 7.2-11

**Table 7.2-11.**

Utility Requirements per Gallon of Biodiesel

  ----------------------------------- -----------------------------------
  Natural Gas, SCF                    2.0

  Medium Pressure Steam, lbs          3.2

  Electricity, kWh                    0.1

  Cooling Tower Water, lbs            8.3
  ----------------------------------- -----------------------------------

The NREL model accounts for the salaries of 4 employees per shift to run
and maintain the plant. In addition to salaries for these personnel, the
labor expenses also accounted for employee fringe benefits and the cost
for a plant supervisor. The resulting labor costs are 6 cents for each
gallon of biodiesel.

**7.2.3.5 Chemical Reagents**

The NREL model also requires the use of the same chemicals and chemical
reagents that are used in the USDA model. The amount of chemical
reagents in the NREL model, however, reflect the use of diluted
hydrochloric acid (HCl) and sodium methoxide for the biodiesel
production process. Hydrochloric acid is listed as being at 33 percent
strength, which we assumed also applied to the strength of sodium
methoxide, since the amount of HCl in the model is reflective of about
one third the value of the USDA's model. For the chemical and reagents
prices, we used the same pricing values as those in our USDA modeling
analysis. The resulting total chemical and reagent costs on a per gallon
basis are about 17 cents for each gallon of biodiesel fuel produced. All
of the required chemicals and reagents for the production of biodiesel
are presented on an undiluted basis in Table 7.2-12.

**Table 7.2-12.**

Reagent Requirements

  ----------------------------------- -----------------------------------
  **Reagent**                         **Annual Requirement, lbs per
                                      gallon**

  Water                               3.4646

  HCl^a^                              0.0098

  Methanol                            0.6037

  NAOCH~3~^a^                         0.0338

  Sodium Hydroxide                    0.1901
  ----------------------------------- -----------------------------------

^a^HCl is Hydrochloric acid, NAOCH~3~ is sodium methoxide.

The total biodiesel production costs derived from the NREL model are
summarized in Table 7.2-13.

**Table 7.2-13.**

**Projected Production Costs for Biodiesel by Feedstock per Gallon**

(2004 dollars)

  ----------------------------------- -----------------------------------
  **Marketing Year**                  **Soy Oil^a^**

  2004                                2.28

  2012                                2.11
  ----------------------------------- -----------------------------------

> ^a^Production consumes 7.87 lbs of soy oil per gallon of biodiesel.
> USDA prices in 2012 are adjusted to 2004 dollars to account for
> inflation, using GDP index of 109.7 in year 2004 and 130.8 in year
> 2012.

**7.2.4 EIA NEMS Model for Biodiesel**

We also estimated production costs using the biodiesel plant
representation in EIA's NEMS model. This biodiesel model is in
spreadsheet format and has the aggregated cost components for an average
soy oil and yellow grease based biodiesel plant. We could not locate
written documentation that describes the basis for these models, though
we will assume for our analysis that it represents an average biodiesel
plant.

EIA's model requires 7.65 lbs/gal of soy oil and yellow grease feedstock
to produce a gallon of biodiesel fuel. Using the oil feedstock costs as
discussed in section 7.2.2.1, feed stock costs for soy oil are
\$1.76/gal and \$1.61/gal for 2004 and year 2012, respectively, while
yellow grease feedstock costs are \$0.88/gal and \$0.80/gal for years
2004 and 2012, respectively.

The EIA model does not provided specific individual cost components for
biodiesel production, though it does have an estimate for total energy,
operating and capital costs for both plant types. Capital costs are
estimated at 14.4 cents/gal in 2004 for both plant types, which we
assumed contains all of costs associated with building a plant, along
with the depreciation and capital payoff costs. The energy costs are
provided in the model on an aggregated basis and do not contain the
individual amounts of natural gas, electricity and steam used by a
plant. The model, though, has the total energy needs in year 2004, which
are 13.7 c/gal for soy oil and 14.5 cents/gal for yellow grease. For
2012, we determined the energy costs by adjusting the 2004 aggregate
energy cost by the EIA projected price change of natural gas in the
Midwest from 2004 to 2012, resulting in an energy cost of 16.0 and 16.9
cents/gal for soy oil and yellow grease, respectively. All of the other
operating costs are represented by an aggregate number, which in year
2004 is 32.6 and 34.5 cent/gal for soy oil and yellow grease,
respectively. This cost represents all of the operating costs not
associated with energy and capital requirements. For our analysis, we
assume that this cost does not change in 2012. We used a glycerin price
of 5 c/lb, which generates income and offsets operating cost by 4 c/gal.

The net production cost for yellow greases (minus feedstock costs) is
about 3 c/gal more than the net production cost for soy based biodiesel,
indicating the extra cost incurred for the yellow grease process. The
resulting total production cost are presented in Table 7.2-14

**Table7.2-14.**

**Projected Production Costs for Biodiesel by Feedstock per Gallon**

(2004 Dollars)

  ----------------------- ----------------------- -----------------------
  **Marketing Year**      **Soy Oil^a^**          **Yellow Grease**

  2004                    2.33                    1.47

  2012                    2.15                    1.38
  ----------------------- ----------------------- -----------------------

^a^Production consumes 7.65 lbs of soy oil per gallon of biodiesel. USDA
prices in 2012 are adjusted to 2004 dollars to account for inflation,
using GDP index of 109.7 in year 2004 and 130.8 in year 2012.

**7.2.5 Vendor Production Estimates for Biodiesel**

We used engineering vendor estimates as another source to generate the
cost to produce biodiesel fuel. For this, we used engineering details
from two firms, Superior Technologies and PSI-Lurgi Engineering Inc.
These engineering vendors are engaged in the business of designing,
constructing and building biodiesel plants. The biodiesel production
processes provided by these firms are also based on the continuous
transesterification process, using sodium hydroxide and methanol to
convert soy oil and yellow grease to biodiesel, along with the finishing
processes for biodiesel and glycerol, similar to the other models. The
vendors generated estimates of the total cost to build and operate a
biodiesel plant, providing the requirements for the equipment, energy,
capital and operating. We adjusted these estimates to a 2004 year costs
basis for comparative purposes, and used energy costs in the Midwest.

The vendor estimates we used for PSI-Lurgi are those listed in the
report "Economic Feasibility of Producing Biodiesel in Tennessee"
[^214]. The biodiesel plant in this analysis was sized for soy oil
feedstock, based on a 13 MM gallon per year plant, which we assumed is
directly comparable to the 10 MM gallon plant used in the USDA, NREL and
EIA models. In making this comparison, we relied on a report[^215] from
Superior Process Technologies to generate the production costs to make
biodiesel from soy oil and yellow grease. This report has the various
cost components for a 10 MM gallon per year plant.

The total soy oil based biodiesel production cost estimate for year 2004
is \$2.20/gal and \$2.34/gal for PSI-Lurgi and Superior Technology,
respectively with an average cost of \$2.27/gal. For 2012, we project
the soy oil production cost from both vendors would average
approximately \$2.09 gal. The Superior Technology's yellow grease
biodiesel production cost is \$1.66/gal in 2004.

**7.2.5.1 PSI-Lurgi estimate of Biodiesel Costs**

The feedstock costs assume that 7.569 pounds of soy oil are required to
make a gallon of biodiesel fuel. The resulting feedstock cost is 174.1
cents/gal for year 2004 . We adjusted all other cost items in the PSI
estimate from a 2002 year to 2004 basis, scaling by the relative change
of GDP, though the costs for reagent needs and utilities are adjusted
using the methods as discussed in the following sections.

**7.2.5.2 Capital Costs**

The total capital cost provided in the estimate account for all of the
costs for building a plant, though excluding maintenance costs, similar
to the capital requirements in the NREL model. The total capital coats
for a plant are \$19.7 million in 2004 dollars, which we adjusted from
2002 dollars. The capital costs were amortized assuming a seven percent
return on investment, resulting in an annualized cost of 16.7 cents per
gallon. The economic factors used for amortizing the capital costs are
the same as those listed in Table 7.2-8.

**7.2.5.3 Operating Costs**

The total operating costs are 29 cents per gallon for year 2004,
excluding capital charges. The cost associated with insurance, taxes and
general administrations is 7.3 cents per gallon, while the cost for
maintenance is 2 cents/gal. The sale of the glycerol byproduct at 80%
strength generates incomes of 4 cents per gallon of produced biodiesel,
assuming a glycerol price of 5 cents per pound. The remaining components
of operating costs are discussed below.

**7.2.5.4 Utility and Labor**

The utility costs were estimated using the energy requirements presented
in Table 3.1 of the report, along with the same prices for energy, steam
and electricity, as those used in our 2004 model analysis. The total
utility requirements are 5.5 cents per gallon. The utility requirements
per gallon of biodiesel fuel are listed in Table 7.2-15.

**Table 7.2-15.**

Utility Requirements per Gallon of Biodiesel

  ----------------------------------- -----------------------------------
  Natural Gas, SCF                    0

  Medium Pressure Steam, lbs          3.92

  Electricity, kWh                    0.093

  Cooling Tower Water, lbs            200.7
  ----------------------------------- -----------------------------------

The PSI-Lurgi estimate accounts for the salaries of 4 employees per
shift to run and maintain the plant. In addition to salaries for these
personnel, the labor expenses also accounted for employee fringe
benefits. The resulting labor costs are 6.3 cents for each gallon of
biodiesel. In addition to these costs, the SG&A expenses are estimated
at 6.3 c/gal.

**7.2.5.5 Chemical Reagents**

The PSI-Lurgi estimate also requires the same chemicals and chemical
reagents in the USDA/NREL models. We assumed that the hydrochloric acid
and the sodium methoxide used in the PSI estimate is 33% strength, as
the prices listed in the study are reflective of being on a diluted
basis. We also assumed that the price for the amount of caustic soda
required is on an undiluted basis. We adjusted the prices for the
chemical and reagents using the 2004 year pricing values used in our
USDA/NREL modeling analysis, though the price of phosphoric acid was
adjusted using the GDP index. The resulting total chemical and reagent
costs on a per gallon basis are about 12.6 cents for each gallon of
biodiesel fuel produced. All of the required chemicals and reagents for
the production of biodiesel are presented on an undiluted basis in Table
7.2-16.

**Table 7.2-16.**

Reagent Requirements

  ----------------------------------- -----------------------------------
  **Reagent**                         **Annual Requirement, lbs per
                                      gallon**

  Water                               1.666

  HCl^a^                              0.026

  Methanol                            0.700

  NAOCH~3~^a^                         0.038

  Caustic Soda / Sodium Hydroxide     0.04

  Phosphoric Acid                     0.013
  ----------------------------------- -----------------------------------

^a^HCl is Hydrochloric acid, NAOCH~3~ is sodium methoxide.

The total soy oil based biodiesel production costs derived from the
PSI-Lurgi are \$2.20 dollars in year 2004.

**7.2.6 Superior Process Technologies Estimate**

The Superior feedstock costs assume that 7.60 pounds of soy oil and
yellow grease are required to make a gallon of biodiesel fuel, which
results in a feedstock cost of 175 cents/gal for year 2004 for soy oil
and 87.5 c/gal for yellow grease, using the feedstock costs in section
7.2.2.1. We adjusted all other cost items in the Superior estimate from
a 2006 year to 2004 basis, adjusting the cost by the relative change of
GDP, though the costs for reagent needs and utilities are adjusted using
the methods as discussed below.

**7.2.6.1 Capital Costs**

The total capital coats for a soy oil based plant are \$10.7 million,
while the costs for a yellow grease plant are \$13.4 million, in 2004
dollars. These costs are inclusive of the amount needed for a new plant,
which is similar to the other biodiesel estimates. The capital costs
were amortized assuming a seven percent return on investment, resulting
in a cost of 11.7 cents per gallon and 14.7 cents per gallon for a soy
oil and yellow grease based plant, respectively.

**7.2.6.2 Operating Costs**

The total operating costs, excluding capital charges are 47 and 64 cents
per gallon for soy oil and yellow grease plants in year 2004,
respectively. Insurance, taxes, rent and local taxes incur a cost of
about 6 and 7 cents per gallon, for soy oil and yellow grease,
respectively. The costs for maintenance, plant overhead costs and
supplies account for about 16.0 and 21.5 cents/gal, for soy oil and
yellow grease. The sale of the glycerol byproduct at 80% strength
generates incomes of 4 cents per gallon of produced biodiesel, assuming
glycerol price of 5 cents per pound. The remaining components of
operating costs are discussed below.

**7.2.6.3 Utility and Labor**

The overall utility costs were provided, though the specific amounts of
natural gas, electricity were not provided for the 10 MM gallon plant.
We adjusted the Superior utility cost estimate from a 2006 year to a
2004 year basis, using the relative price change of natural gas and
electricity, assuming that natural gas supplies 90 percent of the
energy, and electricity supplies the remaining 10 percent. The resulting
energy requirement is 5.0 and 8.6 cents per gallon, for soy oil and
yellow grease, respectively.

The estimate accounts for personnel cost to run and maintain the plant,
including laboratory, plant supervisory and administration costs. The
overall labor costs on a 2004 year basis are 7.6 and 12.8 cents for each
gallon of biodiesel, for soy oil and yellow grease, respectively.

**7.2.6.4 Chemical Reagents**

The Superior vendor estimate also requires the same chemicals and
chemical reagents as used in the USDA/NREL models and Tennessee study.
The total chemical reagent cost from Superior on a 2006 year reagent
pricing basis is 18.1 and 20.1 c/gal, respectively for soy and yellow
grease plants. Superior provided the prices for each of the chemicals,
though the specific amounts of each chemical were not provided for the
soy oil based estimate. We therefore, adjusted the total chemical
reagent cost to a 2004 year basis, assuming the demands for reagent as
documented in the PSI-Lurgi estimate. The resulting reagent costs on a
2004 year basis are 16.5 and 18.3 cents/gal, for soy oil and yellow
grease. The Superior prices for the required chemicals and reagents are
presented in Table 7.2-17.

**Table 7.2-17.**

Superiors Reagent Prices

  ----------------------------------- -----------------------------------
  **Reagent**                         **Dollar / lb**

  NAOCH~3~ ^a^ (25% solution)         0.50

  HCl ^a^ (32%)                       0.091

  Methanol                            0.146

  Phosphoric Acid (75% )              0.42

  Caustic Soda / Sodium Hydroxide     0.14
  (50%)                               
  ----------------------------------- -----------------------------------

^a^HCl is Hydrochloric acid, NAOCH~3~ is sodium methoxide.

The total resulting biodiesel production costs derived from Superior
Process description and engineering estimate is \$2.34 and \$ 1.66 per
gallon for soy oil and yellow grease derived biodiesel respectively, in
year 2004. Yellow greases net production cost (minus feedstock costs)
are about 20 c/gal more than the net production cost for soy based
biodiesel.

**7.2.7 Yellow Grease Production Costs**

Yellow grease's production cost is higher than soy oil produced
biodiesel fuel, due to the extra capital and operating costs required to
remove contaminants in the grease feedstock. In the prior sections, the
EIA and Superior analysis indicated that the yellow grease production
cost for biodiesel is higher than the production cost based on use of
soy oil, excluding the feedstock costs. The EIA analysis showed that
yellow grease's production cost is 3 c/gal higher, while the Superior
results showed that the yellow grease's production cost is about 20
c/gal higher than soy oil production costs. Both of these provide a
measurement of the extra production costs (excluding feedstock)
associated with making biodiesel from yellow grease versus soy oil.

In this section, we use the EIA and Superior results to generate yellow
grease costs as inputs to the models, adjusting the soy oil production
costs to reflect the extra cost for producing yellow grease. We assume
the same feedstock costs for yellow grease as those listed in section
7.2.2.1, and that it takes 7.6 lbs of yellow grease to produce a gallon
biodiesel fuel. Table 7.2-18, contains the resulting yellow grease
production costs based on the EIA and Superior analyses.

**Table 7.2-18.**

**Yellow Grease Costs Based on** EIA and Superior Results

  ---------------- ----------- --------------- ------------- --------------
                   **EIA       **Superior      **EIA 2012**  **Superior
                   2004**      2004**                        2012**

  USDA, c/gal      124         141             111           128

  NREL, c/gal      138         155             131           148

  EIA, c/gal       143         160             132           149

  Vendor avg,      142         159             139           156
  c/gal                                                      

  Average          136.8       153.8           128.3         145.2
  ---------------- ----------- --------------- ------------- --------------

We averaged all of the yellow grease results, and generated an average
production cost of \$1.45/gal in 2004 and \$1.37/gal in 2012 for yellow
grease derived biodiesel.

**7.2.8 Biodiesel Blending Credit Programs**

There are numerous credit and incentive programs that encourage the
blending of biodiesel. These programs reimburse blenders and producers
for adding biodiesel to transport diesel fuel, which acts to lower the
production costs and makes the production of biodiesel more economically
competitive with petroleum derived diesel fuel. There are several
federal/nationwide biodiesel credit programs that offer subsidies for
blending or use of biodiesel as a transport diesel fuel which are
discussed below.

The Commodity Credit Commission Bio-energy Program is an existing
program that expires at the end of fiscal year 2006, though due to a
funding shortfall the program will terminate on July 31, 2006. This
program was administered by the USDA and pays biodiesel producers grants
when the economics to produce biodiesel are poor. The stipend is
determined based on available funding and the volume of renewable fuel
that can receive the credit. For historical purposes, the payments in
2004 and 2005 averaged about 107 and 50 c/gal of fuel produced,
respectively. For the first half of 2006, the credit on a per gallon
basis is reduced further, as the payment is diluted by increased
production volume of fuels available to receive the credit.

The Energy Act extended the Biodiesel Blenders Tax Credit program to the
end of year 2008. This program was created under the American Jobs
Creation Act of 2004 which created an excise tax credit that can be
claimed by anyone who blends biodiesel into transport diesel fuel. Under
this program, blenders may claim a credit against the applicable federal
motor fuels excise tax for blends containing biodiesel. According to IRS
guidelines, the credit may be claimed by anyone who adds biodiesel into
diesel fuel at a level greater than 0.1 percent in the final blend. The
full credit for biodiesel made from virgin vegetable oils and animal
fats is \$1.0 per gallon, while biodiesel derived from recycled grease
receives 50 cents/gallon. A blender with more excise tax credits than
taxes owed can receive a refund from the IRS. Additionally, under the
current program, imported biodiesel and fuel made from imported
feedstocks can also receive the credit.

The Income Tax Credit Alternative is a program that is also available.
This program does not require any blending of biodiesel, though it does
offers allow a similar excise tax credit as in the blenders tax credit
program. The excise tax can only be taken against actual income,
however, which makes the program less economically attractive than the
blenders' credit program.

The Energy Act also created the Small Biodiesel Blenders Tax credit
program. Under this program, a credit of 10 c/gal is available to small
producers who make biodiesel fuel from virgin vegetable oils. This
stipend is limited to companies with annual production volumes less than
60 MM gallons per year, using the aggregated capacity from all
production sites for an individual company. The maximum payment per
company is capped at \$15 MM per year and the program is set to expire
at the end of year 2008.

In addition to the federal programs, there are state and local programs
that offer state fuel tax exemptions, tax credits, and incentives that
are more modest.

**7.3 Distribution Costs**

**7.3.1. Ethanol Distribution Costs**

There are two components to the costs associated with distributing the
volumes of ethanol necessary to meet the requirements of the Renewable
Fuels Standard (RFS): 1) the capital cost of making the necessary
upgrades to the fuel distribution infrastructure system, and 2) the
ongoing additional freight costs associated with shipping ethanol to
terminals. The most comprehensive study of the infrastructure
requirements for an expanded fuel ethanol industry was conducted for the
Department of Energy (DOE) in 2002 .[^216] This study provides the
foundation for our estimates of the capital costs associated with
upgrading the distribution infrastructure system as well as the freight
costs to handle the increased volume of ethanol needed in 2012.
Distribution costs are evaluated here for the RFS and EIA cases.

**7.3.1.1 Capital Costs to Upgrade the Ethanol Distribution System**

The 2002 DOE study examined two cases regarding the use of renewable
fuels. The first case assumed that 5.1 Bgal/yr of ethanol would be used
in 2010, and the second case assumed that 10 Bgal/yr of ethanol would be
used in 2015. We interpolated between these two cases to provide the
foundation for our estimate of the capital costs to support the use of
6.67 Bgal/yr of ethanol in 2012 (the RFS case). The 10 Bgal/yr case from
the DOE study was used as the foundation to estimate the capital costs
under the EIA case. For both the 6.67 Bgal/yr and 9.64 Bgal/yr cases, we
adjusted the results from the DOE study to reflect a 3.9 Bgal/yr 2012
ethanol use baseline. Table 7.3-1 contains our estimates of the
infrastructure changes and associated capital costs for the two ethanol
use scenarios examined in today's rule.[^217]

**Table 7.3-1.**

**Ethanol Distribution Infrastructure Capital Costs**

**Relative to a 3.9 Billion Gallon per Year Reference Case**

+---------------------------------------+---------------+--------------+
|                                       | RFS Case      | EIA Case     |
|                                       |               |              |
|                                       | (6.67         | (9.64        |
|                                       | Bgal/yr)      | Bgal/yr)     |
+---------------------------------------+---------------+--------------+
| New Terminal Blending Systems for     |               |              |
| Ethanol                               |               |              |
+---------------------------------------+---------------+--------------+
| Number of terminals                   | 243           | 515          |
+---------------------------------------+---------------+--------------+
| Capital cost                          | \$73,044,000  | \            |
|                                       |               | $154,530,000 |
+---------------------------------------+---------------+--------------+
| New Ethanol Storage Tanks at          |               |              |
| Terminals                             |               |              |
+---------------------------------------+---------------+--------------+
| Number of tanks                       | 168           | 370          |
+---------------------------------------+---------------+--------------+
| Capacity                              | 1,526,000     | 3,415,000    |
|                                       | barrels       | barrels      |
+---------------------------------------+---------------+--------------+
| Capital cost                          | \$21,939,000  | \$48,803,000 |
+---------------------------------------+---------------+--------------+
| Terminal Storage Tanks Converted to   |               |              |
| Ethanol                               |               |              |
+---------------------------------------+---------------+--------------+
| Number of tanks                       | 44            | 83           |
+---------------------------------------+---------------+--------------+
| Capacity                              | 319,000       | 592,000      |
|                                       | barrels       | barrels      |
+---------------------------------------+---------------+--------------+
| Capital cost                          | \$931,000     | \$1,739,000  |
+---------------------------------------+---------------+--------------+
| Terminals Using Ethanol for the First |               |              |
| Time^a^                               |               |              |
+---------------------------------------+---------------+--------------+
| Number of terminals                   | 212           | 453          |
+---------------------------------------+---------------+--------------+
| Capital cost                          | \$4,238,000   | \$9,065,000  |
+---------------------------------------+---------------+--------------+
| New Rail Delivery Facilities at       |               |              |
| Terminals                             |               |              |
+---------------------------------------+---------------+--------------+
| Number of terminals                   | 42            | 76           |
+---------------------------------------+---------------+--------------+
| Capital cost                          | \$14,869,000  | \$27,127,000 |
+---------------------------------------+---------------+--------------+
| Retail Facilities Using Ethanol for   |               |              |
| First Time^a^                         |               |              |
+---------------------------------------+---------------+--------------+
| Number of retail facilities           | 33,600        | 74,820       |
+---------------------------------------+---------------+--------------+
| Capital cost                          | \$19,824,000  | \$44,146,000 |
+---------------------------------------+---------------+--------------+
| New Tractor Trailer Transport Trucks  |               |              |
+---------------------------------------+---------------+--------------+
| Number of Trucks                      | 209           | 435          |
+---------------------------------------+---------------+--------------+
| Capital Costs                         | \$24,027,000  | \$50,075,000 |
+---------------------------------------+---------------+--------------+
| New Barges                            |               |              |
+---------------------------------------+---------------+--------------+
| Number of new barges                  | 11            | 23           |
+---------------------------------------+---------------+--------------+
| Capital cost                          | \$21,475,000  | \$43,204,000 |
+---------------------------------------+---------------+--------------+
| New Rail Cars                         |               |              |
+---------------------------------------+---------------+--------------+
| Number of new rail cares              | 2,024         | 3,491        |
+---------------------------------------+---------------+--------------+
| Capital cost                          | \$172,012,000 | \            |
|                                       |               | $296,729,000 |
+---------------------------------------+---------------+--------------+
| Total Capital Costs                   | \$352,361,000 | \            |
|                                       |               | $675,418,000 |
+---------------------------------------+---------------+--------------+
| Capital Costs Attributed to Terminal  | \$134,847,000 | \            |
| and Retail (i.e. fixed) Facilities    |               | $285,410,000 |
+---------------------------------------+---------------+--------------+
| Capital Costs Attributed to Mobile    | \$217,514,000 | \            |
| Facilities (tank trucks, rail cars, & |               | $390,008,000 |
| barges)                               |               |              |
+---------------------------------------+---------------+--------------+

^a^ Terminal and retail facilities using ethanol for the first time will
need to make various modifications to ensure the compatibility of their
systems with ethanol.

Our estimated capital costs in this final rule differ from those in the
proposal for several reasons. First, the volume for the RFS case was
updated to reflect the fuel rule provisions. Second, we adjusted our
estimate of capital costs from those in the proposal to reflect an
increase in the cost of rail tank cars and barges since the DOE study
was conducted. Third, we are assuming a 30 percent increase in the
reliance on rail versus marine transport over that projected in the DOE
study. The 2002 DOE study estimated that 53 percent of the increase in
ethanol volume shipped between PADDs would be carried by barge and 47
percent by rail. For the purposes of this analysis, we assumed that 30
percent of the increased volume in ethanol shipments that were projected
to be carried by barge in the DOE study would instead be carried by
rail. This equates to 37 percent of the increase in ethanol shipments
being carried by barge and 63 percent by rail. To provide a
conservatively high estimate of the potential economic impact, we
assumed that this shift translates into a 30 percent increase in rail
infrastructure costs. This incorporates the increased cost to prepare
additional terminals to receive ethanol by rail, and to provide a
sufficient number of additional rail tank cars for ethanol transport.
The actual increase in rail infrastructure costs may be somewhat lower
given improvements in the efficiency of ethanol transport by rail.

Amortized over 15 years at a 7 percent cost of capital, the total
capital costs (of \$352,361,000 under the RFS case and 675,418,000 under
the EIA case) equates to an annual cost of approximately \$38,687,000
under the RFS case and \$74,157,000 under the EIA case. This translates
to approximately 1.4 cents per gallon of new ethanol volume under the
RFS case and 1.2 cents per gallon under the EIA case. Under both cases,
approximately 0.5 cents per gallon is attributed to mobile facilities
and the remainder to fixed facilities.

**7.3.1.2 Ethanol Freight Costs**

The 2002 DOE study contains estimated ethanol freight costs for each of
the 5 PADDs. These estimated costs are summarized in the following Table
7.3-2.[^218] A map of the PADDs is contained in Figure 7.3-1.

**Table 7.3-2.**

**Estimated Ethanol Freight Costs from the 2002 DOE Study**

+--------------+--------------------------+----------------------------+
| PADD         | 5.1 billion gallons per  | 10.0 billion gallons per   |
|              | year                     | year                       |
|              |                          |                            |
|              | (cents per gallon)       | (cents per gallon)         |
+--------------+--------------------------+----------------------------+
| 1            | 11.1                     | 7.2                        |
+--------------+--------------------------+----------------------------+
| 2            | 4.3                      | 2.4                        |
+--------------+--------------------------+----------------------------+
| 3            | 6.6                      | 5.8                        |
+--------------+--------------------------+----------------------------+
| 4            | 4.7                      | 7.4                        |
+--------------+--------------------------+----------------------------+
| 5            | 12.7                     | 10.7                       |
+--------------+--------------------------+----------------------------+
| National     | 7.7                      | 5.7                        |
| Average      |                          |                            |
+--------------+--------------------------+----------------------------+

**Figure 7.3-1.**

**PAD District Definitions.**

![](media/image74.png){width="6.125in" height="4.040972222222222in"}

The Energy Information Administration (EIA) translated the cost
estimates from the 2002 DOE study to a census division basis.[^219] A
summary of the resulting (EIA) ethanol distribution cost estimates are
contained in the following Table 7.3-3. A map of the census divisions is
contained in Figure 7.3-2.

**Table 7.3-3.**

**EIA Estimated Ethanol Freight Costs**

**(derived from the 2002 DOE Study)**

+----------------------+-----------------------+----------------------+
| Census Division      |                       | Freight Cost         |
|                      |                       |                      |
|                      |                       | (cents per gallon)   |
+----------------------+-----------------------+----------------------+
| From                 | To                    |                      |
+----------------------+-----------------------+----------------------+
| East North Central   | New England           | 9.8                  |
+----------------------+-----------------------+----------------------+
| East North Central   | Middle Atlantic       | 9.8                  |
+----------------------+-----------------------+----------------------+
| East North Central   | East North Central    | 4                    |
+----------------------+-----------------------+----------------------+
| East North Central   | South Atlantic        | 9.8                  |
+----------------------+-----------------------+----------------------+
| East North Central   | East South Central    | 4.7                  |
+----------------------+-----------------------+----------------------+
| East North Central   | Pacific               | 14.0                 |
+----------------------+-----------------------+----------------------+
| West North Central   | New England           | 11.4                 |
+----------------------+-----------------------+----------------------+
| West North Central   | Middle Atlantic       | 11.4                 |
+----------------------+-----------------------+----------------------+
| West North Central   | East North Central    | 4                    |
+----------------------+-----------------------+----------------------+
| West North Central   | West North Central    | 4                    |
+----------------------+-----------------------+----------------------+
| West North Central   | South Atlantic        | 11.4                 |
+----------------------+-----------------------+----------------------+
| West North Central   | East South Central    | 4.7                  |
+----------------------+-----------------------+----------------------+
| West North Central   | West South Central    | 4.7                  |
+----------------------+-----------------------+----------------------+
| West North Central   | Mountain              | 4.5                  |
+----------------------+-----------------------+----------------------+
| West North Central   | Pacific               | 13.0                 |
+----------------------+-----------------------+----------------------+

**Figure 7.3-2.**

**Census Divisions**

![](media/image75.png){width="6.239583333333333in"
height="5.375694444444444in"}

We took the EIA projections and translated them into State-by-State
ethanol freight costs. For the purposes of this analysis, all ethanol
was assumed to be produced in the East and West North Central Census
Divisions (corresponding closely to PADD 2). We believe that this is a
reasonable approach because the cost of shipping corn feedstock from
PADD 2 to ethanol plants located outside of PADD 2 will typically negate
any potential reduction in freight cost from reduced shipping distances
for ethanol or dried distiller grains. The vast majority of ethanol
plants planned for outside of PADD 2 are projected to begin operation
using corn supplied from PADD 2.[^220] Many have stated plans to
transition to local feedstocks. However, we believe that such a
transition will typically not be accomplished within the timeframe
considered by this analysis (i.e. 2012). Other local considerations may
provide a unique cost advantage to locating a plant outside of PADD 2.
One such consideration might be that if by locating a plant outside of
PADD 2 the ethanol producer could avoid the need to dry the distiller
grains it produces and sell the wet distiller grains to a local market.
Although this might result in a significant cost savings, it is unclear
the extent to which this will be possible given the short shelf life of
wet distiller grain (\~3 days). Also, any potential cost savings might
be offset by the relatively lower price that can be negotiated for wet
versus dry distiller grains. In any event, there is insufficient data at
this time to evaluate the extent to which such local conditions may
result in an advantage in lower freight costs for ethanol plants outside
of PADD 2. Further, our projection of where new ethanol production
plants might be located indicates that only 10 percent of production
capacity could be located outside of PADD 2. Thus, any potential freight
cost advantage that might be enjoyed by such plants would not likely
have a significant impact on our national analysis. Furthermore, to the
extent that the location of ethanol plants outside of PADD 2 imparts a
savings in ethanol distribution costs, this would suggest that our
estimates of ethanol freight costs in this rule are conservatively high.

Ethanol consumed within census divisions belonging to PADD 2 was assumed
to be transported by truck, while distribution outside of these areas
was assumed to be by rail, ship, and/or barge. A single average
distribution cost for each destination census division was generated by
weighting together the 2012 freight costs given for each mode in both
source census divisions according to their volume share. These cents per
gallon figures were first adjusted upward by 10 percent to reflect the
increased cost of transportation fuels used to ship ethanol since the
2002 DOE study, and then additional adjustments were applied to some
individual states based on their position within the census division. In
the case of Alaska and Hawaii, differences in ethanol delivery prices
from the mainland were inferred from gasoline prices.

For some states, different freight costs for ethanol supplied to large
hub terminals versus small satellite terminals was estimated. The
reasoning behind this is that large shipments of ethanol shipped from
the Midwest by barge, ship, and/or unit train will often be initially
unloaded at hub terminals for further distribution to satellite
terminals. In cases where redistribution from a hub to a satellite
terminal doesn't take place, the volume of ethanol shipped directly from
the producer to a lesser volume ("satellite") terminal will also incur a
higher freight rate than ethanol shipped to a larger-volume "hub"
terminal. The largest adjustment was applied to the Rocky Mountain
States since they are generally large in area and additional expense is
required to transport freight through higher elevations and rugged
terrain. Smaller adjustments were applied to states that are smaller,
flatter, or have access by navigable waterways. The states to which an
adjustment was not applied were generally in the Midwest or were so
small as to not warrant different distribution costs. Given the large
number of ethanol plants in the Midwest, we do not believe that there
are substantial differences in the cost of distributing ethanol with
that area.

We made several adjustments in our estimates of ethanol freight costs
from those in the proposal. First, the differential cost of shipping
ethanol to satellite terminals versus hub terminals was increased to
better reflect the additional costs incurred in either redistributing
the ethanol from a hub to a satellite terminal, or of shipping ethanol
directly from the producer to the satellite terminal in a lesser volume.
The estimated additional freight cost of shipping ethanol to satellite
terminals versus hub terminals is contained in the following Table
7.3-4.

**Table 7.3-4.**

**Additional Freight Cost to Deliver Ethanol to a Satellite Terminal**

**Compared to a Hub Terminal**

  ----------------------------------- -----------------------------------
  States                              cents per gallon

  OH                                  2

  AL, AR, FL, GA, KY, LA, MD, ME, MS, 4
  NC, NH, NY, OK, OR, PA, SC, TN, VA, 
  VT, WA, WV                          

  AK, AZ, CO, ID, NM, NV, TX, UT, WY  5
  ----------------------------------- -----------------------------------

Another change that we made from the proposal was with respect the
volume of ethanol we estimated would be delivered to hub versus
satellite terminals. The proposal assumed a 50/50 spit. For this final
rule, we project that all of the ethanol volume blended into
reformulated gasoline would be used in urban areas served by hub
terminals. The percentage of ethanol blended into conventional gasoline
that is used in an urban area (and hence delivered to a hub terminal)
versus that used in a rural area (and hence delivered to a satellite
terminal) was based on our analysis of the percentage of vehicle miles
traveled in urban versus rural areas.[^221]

The final change from the proposal pertains to our consideration of the
cost of shipping ethanol from the production plant to the rail head /
marine terminal either for large volume shipment by unit train or marine
shipment to hub terminals, or for shipment at single car rates via
multiple-product trains directly to satellite terminals. Our review of
current ethanol freight rates conducted in response to a comment on the
proposed rule indicates that we did not adequately account for this
added cost in the proposal. Chicago is a primary ethanol gathering point
from producers for further distribution. A 4 cent per gallon conveyance
fee is charged to account for delivery of ethanol from the production
plant gate to the Chicago Board of Trade delivery point for taking
ethanol. This includes train shipments, loading costs, and other
miscellaneous fees. Based on this information, we have added 4 cents per
gallon to our ethanol freight estimates.

Our estimates of the State-by-State ethanol freight costs under the RFS
and EIA cases are contained in Tables 7.3-5 and 7.3-6. National and PADD
average freight costs under both the RFS and EIA cases are contained in
Table 7.3-7. We are assuming that these freight costs do not include the
costs associated with the recovery of capital for the distribution
facility changes that are necessary to accommodate the increased volume
of ethanol. This may tend to overstate distribution costs to some extent
because some capital recovery may be incorporated into the 4 cent per
gallon conveyance fee. The inclusion of rail tank car lease fees also
suggests that these estimated freight costs may be conservatively high
given that rail car lease fees incorporate a capital recovery and profit
margin.

**Table 7.3-5.**

**State-by-State Ethanol Freight Costs**

  ------------------- ------- ----------- ----------- ------------ ------------
  State               PADD    Ethanol                              
                              Freight                              
                              Cost^\*^:                            
                              (cents per                           
                              gallon)                              

                              Hub         Satellite   Average      
                              Terminal    Terminal    Freight      
                                                      Cost^\*\*^   

                                                      RFS Case     EIA Case

  Connecticut         1       15.4        15.4        15.4         15.4

  Maine               1       17.4        21.4        20.0         20.0

  Massachusetts       1       15.4        15.4        15.4         15.4

  New Hampshire       1       16.4        16.4        16.4         16.4

  Rhode Island        1       15.4        15.4        15.4         15.4

  Vermont             1       16.4        16.4        NA^\*\*\*^   16.4

  New Jersey          1       15.4        15.4        15.4         15.4

  New York            1       15.4        19.4        15.4         15.7

  Pennsylvania        1       12.4        16.4        13.8         13.7

  Delaware            1       15.4        15.4        15.4         15.4

  District of         1       15.4        15.4        NA^\*\*\*^   15.4
  Columbia                                                         

  Florida             1       12.4        16.4        14.0         14.0

  Georgia             1       15.4        19.4        NA^\*\*\*^   17.4

  Maryland            1       15.4        15.4        NA^\*\*\*^   15.4

  North Carolina      1       15.4        19.4        NA^\*\*\*^   NA^\*\*\*^

  South Carolina      1       15.4        19.4        NA^\*\*\*^   NA^\*\*\*^

  Virginia            1       15.4        19.4        NA^\*\*\*^   15.4

  West Virginia       1       15.4        19.4        NA^\*\*\*^   NA^\*\*\*^

  Illinois            2       4.4         4.4         4.4          4.4

  Indiana             2       5.4         5.4         5.4          5.4

  Michigan            2       6.4         6.4         6.4          6.4

  Ohio                2       5.4         7.4         6.6          6.6

  Wisconsin           2       4.4         4.4         4.4          4.4

  Iowa                2       3.4         3.4         3.4          3.4

  Kansas              2       4.4         4.4         4.4          4.4

  Minnesota           2       4.4         4.4         4.4          4.4

  Missouri            2       4.4         4.4         4.4          4.4

  Nebraska            2       4.4         4.4         4.4          4.4

  North Dakota        2       5.4         5.4         5.4          5.4

  South Dakota        2       4.4         4.4         4.4          4.4

  Kentucky            2       6.2         10.2        7.1          7.3

  Tennessee           2       6.2         10.2        8.7          8.7

  Oklahoma            2       8.3         12.3        NA^\*\*\*^   11.1
  ------------------- ------- ----------- ----------- ------------ ------------

\* Freight rates from PADD 2 production facilities

\*\* Hub and satellite freight rates were volume weighted to arrive at
an average freight rate.

> \*\*\* No significant ethanol use. See Chapter 2 of this RIA regarding
> our estimates of where ethanol will be used.

**Table 7.3-5.**

**State-by-State Ethanol Freight Costs (continued)**

  ------------------- ------- ----------- ----------- ------------ ------------
  State               PADD    Ethanol                              
                              Freight                              
                              Cost^\*^:                            
                              (cents per                           
                              gallon)                              

                              Hub         Satellite   Average      
                              Terminal    Terminal    Freight      
                                                      Cost^\*\*^   

                                                      RFS Case     EIA Case

  Alabama             3       11.2        15.2        14.0         14.0

  Mississippi         3       10.2        14.2        13.9         13.9

  Arkansas            3       11.3        16.3        15.0         15.0

  Louisiana           3       11.3        15.3        13.8         13.8

  Texas               3       14.3        19.3        14.3         16.0

  New Mexico          3       16.4        21.4        19.0         19.6

  Colorado            4       14.4        19.4        NA^\*\*\*^   17.0

  Idaho               4       19.4        24.4        NA^\*\*\*^   22.8

  Montana             4       17.4        22.4        21.8         21.8

  Utah                4       17.4        22.4        NA^\*\*\*^   20.5

  Wyoming             4       16.4        21.4        NA^\*\*\*^   20.8

  Arizona             5       19.4        24.4        20.3         20.5

  Nevada              5       20.4        25.4        21.1         22.3

  Alaska              5       45.5        50.5        NA^\*\*\*^   NA^\*\*\*^

  Hawaii              5       40.5        40.5        NA^\*\*\*^   40.5

  Oregon              5       20.5        24.5        20.5         20.5

  Washington          5       20.5        24.5        21.9         21.9

  California          5       20.5        20.5        20.5         20.5
  ------------------- ------- ----------- ----------- ------------ ------------

\* Freight rates from PADD 2 production facilities.

\*\* Hub and satellite freight rates were volume weighted to arrive at
the average freight rate.

\*\*\* No significant ethanol use. See Chapter 2 of this RIA regarding
our estimates of where ethanol will be used.

**Table 7.3-6.**

**National and PADD Average Ethanol Freight Costs**

  -------------------------- ----------------------- ---------------------
                             Ethanol Freight         
                             Cost^\*^ (cents per     
                             gallon)                 

                             RFS Case                EIA Case

  National Average           11.3                    11.9

  PADD 1                     14.9                    15.1

  PADD 2                     5.1                     5.3

  PADD 3                     14.6                    15.2

  PADD 4                     21.8                    19.8

  PADD 5 excluding AK & HI   20.6                    20.7

  PADD 5 including AK & HI   20.6                    22.1
  -------------------------- ----------------------- ---------------------

\* Freight rates from PADD 2 production facilities.

The national average ethanol freight cost of 11.3 cents per gallon under
the RFS case and 11.9 cents per gallon under the EIA case translates to
an annual freight cost for the additional volume of ethanol used in 2012
of \$313,123,000 and \$678,300,000 respectively. Adding in the
annualized capital costs, results in a total annual ethanol distribution
cost of 351,810,000 or 12.7 cents per gallon under the RFS case and
\$752,457,000 or 13.1 cents per gallon under the EIA case.[^222]

**7.3.2 Biodiesel Distribution Costs**

The volume of biodiesel used by 2012 under the RFS is estimated at 300
million gallons per year. The 2012 reference case against which we are
estimating the cost of distributing the additional volume of biodiesel
needed to meet the requirements of the RFS is 30 million gallons.[^223]

The capital costs associated with distribution of biodiesel are higher
per gallon than those associated with the distribution of ethanol due to
the need for storage tanks, blending systems, barges, tanker trucks and
rail cars to be insulated and in many cases heated during the winter
months.[^224] In the proposal, we estimated that that these capital
costs would be approximately \$50,000,000. We adjusted our estimate of
these capital costs for this final rule based on additional information
regarding the cost to install the necessary storage and blending
equipment at terminals and the need for additional rail tank cars for
biodiesel.[^225] We now estimate that handling the increased biodiesel
volume will require a total capital cost investment of \$145,500,000
which equates to about 6 cents per gallon of new biodiesel volume.[^226]

In the proposal, we estimated that the freight costs for ethanol
adequately reflect those for biodiesel as well. In response to comments,
we sought additional information regarding the freight costs for
biodiesel. This information indicates that freight costs for biodiesel
are typically 30 percent higher than those for ethanol which translates
into an estimate of 15.5 cents per gallon for biodiesel freight
costs.[^227]

Including the cost of capital recovery for the necessary distribution
facility changes, we estimate the cost of distributing biodiesel to be
21.5 cents per gallon.

**7.4 Gasoline and Diesel Blendstock Costs**

The previous sections of this chapter have presented estimates of the
cost of producing and distributing ethanol and biodiesel. In this
section, we summarize the results of refinery modeling conducted by
Jacobs Consultancy under contract to EPA. Jacobs's used the Haverly
Linear Programming (LP) model to conduct the analysis. This model is
widely used by the refining industry, consultants, engineering firms and
government agencies to analyze refinery economics, refinery operations,
fuel quality changes, refinery capital investments, environmental
changes and demand changes. The Haverly model uses Jacobs's Refining
Process Technology Database to represent refining operations

The modeling was conducted to analyze the effect of the increased
renewable fuel use on the production costs and composition of the
nation's gasoline and diesel fuel. The refinery modeling output
described in this section includes the changes in volumes and capital
investments as well as the resulting capital and fixed operating costs,
the variable costs, and the total of all these costs. The costs are
expressed in 2006 dollars and capital costs are amortized at 7% before
tax return on investment (ROI). The costs for the RFS and EIA cases are
expressed incremental to the reference case. We first report the results
of the RFS case, followed by the results of the EIA case.

**7.4.1 Description of Refinery Modeling Cases Modeled**

The modeling cases were set up to analyze the RFS and EIA cases
described in Chapter 2. The primary renewable fuel modeled was ethanol
in gasoline, while considering a fixed production amount of biodiesel as
projected by EIA in 2012. Along with the increased use of renewable
fuels, the analyses for the RFS and EIA cases both include the
elimination of the RFG oxygen content standard and the resulting removal
of MTBE from the U.S. gasoline market. These scenarios both assume the
current Mobile Source Air Toxics standard (MSAT1) is in place. The
effects of the MSAT2 standard are modeled in that rulemaking which has
just recently been made final.

Jacobs conducted a Linear Programming (LP) modeling analysis of the
refining industry for the various RFS scenarios using a model developed
by Haverly's LP technology. The modeling was set up to analyze the
extent to which ethanol will be used in CG versus RFG by region and the
resulting effects on gasoline composition. The refining industry was
modeled based on five aggregate complex refining regions, representing
PADD's 1, 2, 3, 4 & 5 together minus California and California
separately. All of the PADDS were modeled simultaneously together in the
LP model, in order to balance and meet the national gasoline and fuel
demands.

**7.4.1.1 RVP**

The analysis modeled summer and winter seasons, with all gasoline types
including California RFG, Federal RFG, 7.0, 7.8 RVP controlled areas and
9.0 CG. The control cases consisted of the minimum renewable fuel volume
as specified by EPAct and discussed in Chapter 2 and the 2006 AEO
projection of 9.6 billion gallons of ethanol per year in 2012. Winter
gasoline RVP levels were adjusted higher than EPA data, to account for
refiner RVP reporting inaccuracies from use of the complex model in the
winter season. (Some refiners reported lower RVP levels than actually
produced, as the complex model has a fixed upper reporting limit of 8.8
in the winter season.)

**7.4.1.2 Base case (2004)**

The base case was established by modeling fuel volumes for 2004.
Information was based on process capacities from Oil and Gas Journal,
EIA data and gasoline emissions and property data from EPA. Fuel
property data for this base case was built off of 2004 refinery batch
reports provided to EPA; however, the base case assumed sulfur standards
based on gasoline data in 2004, not with fully phased in of Tier 2
gasoline standards at the 30 ppm level. In addition we assumed the
phase-in of 15 ppm sulfur standards for highway, nonroad, locomotive and
marine diesel fuel. The supply/demand balance for the U.S. was based on
gasoline volumes from EIA and the California Air Resources Board (CARB).
Our decision to use 2004 rather than 2005 as the baseline year was
because of the refinery upset conditions associated with the Gulf Coast
hurricanes in 2005.

**7.4.1.3 MSAT1 Provisions for Refinery Cases in 2012**

For CG and RFG, gasoline qualities were modeled to assure Complex model
Phase 2 calculations seasonal and annual compliance, taking into account
the elimination of the oxygen requirement for RFG; (by PADD and
California), and under MSAT1 gasoline standards. Incremental gasoline
volumes above the 2004 base case volumes for each PADD were allowed to
conform to less stringent toxics performance standards as allowed by the
MSAT1 provisions. For this, the MSAT 1 PADD constraints were calculated
using gasoline data from 1998-2000 EPA batch reports, considering that
new incremental volumes of gasoline above the 1998-2000 annual average
would comply with MSAT1 provisions, as predicated by EPact 2005. The
following tables show the resulting conventional and RFG gasoline MSAT1
baseline constraints, which was applied to gasoline produced for the
cases modeled in year 2012.

**Table 7.4-1.**

Conventional Gasoline MSAT 1 2012 Baseline Data

+-----------------------+-----------------------+-----------------------+
|                       | Exhaust Toxics        | NOx mg/mi\*           |
|                       | mg/mi\*               |                       |
+-----------------------+-----------------------+-----------------------+
| PADD 1                | 88.33                 | 1,440.84              |
+-----------------------+-----------------------+-----------------------+
| PADD 2                | 92.79                 | 1,432.57              |
+-----------------------+-----------------------+-----------------------+
| PADD 3                | 88.79                 | 1,438.76              |
+-----------------------+-----------------------+-----------------------+
| PADD 4 & 5, excluding | 99.85                 | 1,414.00              |
|                       |                       |                       |
| California            |                       |                       |
+-----------------------+-----------------------+-----------------------+

\*mg/mi is milligram per mile.

**Table 7.4-2.**

RFG Gasoline MSAT 1 2012 Baseline Data

+-----------------------+-----------------------+-----------------------+
|                       | Total Toxics mg/mi\*  | Toxics Percent        |
|                       |                       | Reduction             |
+-----------------------+-----------------------+-----------------------+
| PADD 1                | 75.11                 | 27.39                 |
+-----------------------+-----------------------+-----------------------+
| PADD 2                | 80.11                 | 22.56                 |
+-----------------------+-----------------------+-----------------------+
| PADD 3                | 74.74                 | 27.75                 |
+-----------------------+-----------------------+-----------------------+
| PADD 4 & 5, excluding | NA                    | NA                    |
|                       |                       |                       |
| California            |                       |                       |
+-----------------------+-----------------------+-----------------------+

\*mg/mi is milligram per mile.

**7.4.1.4 Reference case (2012)**

The reference case was based on modeling the base case, using 2012 fuel
prices, and scaling the 2004 fuel volumes to 2012 based on growth in
fuel demand. In addition, we scaled MTBE and ethanol upward, in
proportion to gasoline growth, and assumed the RFS program would not be
in effect. For example, if the PADD 1 gasoline pool MTBE oxygen was 0.5
wt% in 2004, the reference case assumed it should remain at 0.5 wt%.
Finally, we assumed the MSAT 1 standards would remain in place as would
the RFG oxygen mandate. We assumed the crude slate quality in 2012 is
the same as the baseline case.

**7.4.1.5 Control cases (2012)**

Two control cases were run for 2012. The assumptions for the control
cases are summarized below:

-   Control Case 1 (RFS case): 6.7 billion gallons/yr (BGY) of ethanol
    in gasoline; it reflects the renewable fuel mandate. In addition, it
    is assumed that no MTBE is in gasoline, MSAT1 is in place, the 1 psi
    waiver for CG containing 10 volume percent ethanol remains in effect
    for all states where it currently applies, the RFS is in effect, and
    there is no RFG oxygenate mandate.

-   Control Case 2 (EIA case): Same as Control Case 1, except that the
    ethanol volume in gasoline is 9.6 BGY.

**7.4.2 Assumptions made for Refinery Modeling**

**7.4.2.2 Fuels Production and Demand**

The production of and demand for gasoline and other refinery fuels in
the reference and control cases were based on EIA's AEO 2006 projections
for year 2012 . The modeling also was set up to meet demand based on
terminals' sales in each refining area, using EIA fuel sales data. The
LP modeling accounted for inter-PADD transfers of finished products and
gasoline blendstocks from refiners, to meet demand at terminals, based
on historical transfer data from EIA, including CBOB and RBOB. Both the
RFS and EIA control cases did not model any production of biodiesel fuel
in fulfilling transportation diesel fuel demand.

**7.4.2.3 Ethanol**

The control cases were based on fixed national ethanol volumes as
specified in Chapter 2. For the control cases, however, the LP modeling
analysis used ethanol blending economics and ethanol distribution costs
to allocate ethanol to each PADD, and to allocate ethanol use in CG and
RFG grades of gasoline. Additionally, the modeling assumed that all
ethanol added to gasoline is match-blended for octane by refiners in the
reference and control cases, while splash blending of ethanol was
assumed as appropriate for the base case using EPA gasoline data.

The price of ethanol was based on the 2004 yearly average price spread
between regular conventional gasoline sold on spot market in Houston and
ethanol sold on spot market on Chicago Board of Trade (CBOT). This was
used to determine a Midwest ethanol production price. To derive ethanol
prices for all other PADDs outside the Midwest, the Midwest ethanol
production price was then adjusted for transportation costs to deliver
ethanol from the Midwest to end use terminals (see section 7.3 for
additional details). The price of ethanol was also adjusted to account
for the 51 cent/gal rebate from the Federal subsidies, but did not
account for the impact of state subsides.

The reference and control cases where modeled assuming that ethanol CG
blends are entitled to the 1.0 psi RVP waiver during the summer (i.e.,
for all 9.0 RVP and low RVP control programs) so as to assess the impact
on summertime butane removal.

**7.4.2.4 Processes and Capital**

All changes in refining capital was assessed at a 15% Return on
Investment (ROI) after taxes, which was adjusted to 7 % ROI before
taxes. Crude and other input prices were based on Jacobs' projection of
refinery margins and crude prices in 2012 cases, which was also based on
the historical price spreads of fuels between PADDS, using information
from EIA's 2004 price information tables, Platts, and AEO 2006, see the
Jacob's report for the petroleum fuel prices used in the modeling
analysis.

**7.4.3 Results of Refinery Modeling**

**7.4.3.1 Summary of Changes in Refinery Inputs and Outputs to the RFS
Case**

There are a number of changes in individual and overall volume for
specific gasoline blendstocks between the RFS case and reference case
based on the refinery model results. The changes include the increased
blending of ethanol, the removal of MTBE, and the increased volumes of
isooctane, isooctene and alkylate from the reuse of isobutylene formerly
used to produce MTBE. The isooctane and isooctene are produced by
merchant MTBE plants that formerly produced MTBE from mixed butanes,
ethylene crackers, and propylene oxide plants as determined by a survey
of how those plants are being converted to produce other gasoline
blendstocks. The alkylate is produced from the isobutylene previously
used to produce MTBE at captive (refinery-based) MTBE plants. The total
volume of these gasoline blend stocks is summarized in Table 7.4-3 for
both the reference case and RFS case adjusting the volume of ethanol and
MTBE to reflect their gasoline energy-equivalent volumes.

**Table 7.4-3.**

**Comparison of Ethanol, MTBE, Isooctane, Isooctene, and Alkylate
Volumes**

**by PADD for the RFS Case and Reference Case (barrels per day)**

![](media/image76.wmf){width="5.995138888888889in"
height="1.9895833333333333in"}

As the bottom row in Table 7.4-3 shows, the gasoline-equivalent volumes
for the aggregated volume of these gasoline blendstocks are expected to
increase as we compare the RFS case to the reference case. It is this
net increase in gasoline blendstock volume that is expected to result in
a net reduction in petroleum consumption.

The addition of ethanol to wintertime gasoline, and to summertime RFG,
will cause an increase of approximately 1 psi in RVP that needs to be
offset to maintain constant RVP levels. An obvious means that refiners
could choose to offset the increase in RVP is to reduce the butane
levels in their gasoline. To some extent, the modeling results showed
some occurrences of that, but it also did not report an overall increase
in butane sales as a result of the increased use of ethanol.

To convert the captive MTBE over to alkylate, after the rejection of
methanol, refiners will need to combine one molecule of refinery
produced isobutane with the isobutylene that was the feedstock for MTBE.
The use of the isobutane will reduce the RVP of the gasoline pool from
which it comes, helping to offset the RVP impacts of ethanol. Also, the
increased production of alkylate provides a low RVP gasoline blendstock
that offsets a portion of the cracked stocks produced by the fluidized
catalytic cracker unit. Other means that the refinery model used to
offset the high blending RVP of ethanol includes purchasing gasoline
components with lower RVP, producing more poly gasoline which has low
RVP and selling more high-RVP naphtha to petrochemical sales.

In Table 7.4-4, we summarize the inputs into and gasoline outputs from
the refinery model separate from the ethanol and converted MTBE
blendstocks summarized above. The summary shows that crude oil and
vacuum gas oil and residual fuel purchases are expected to decrease
about 1 percent averaged over all the PADDs. The refinery model also
estimates that the volume of purchased gasoline components will increase
in most PADDs. These gasoline components include renewable blendstocks
for ethanol blending (RBOB), which is a very low RVP gasoline
blendstock. A likely reason for the increased gasoline blendstock
volumes over purchased crude oil purchases is that the refinery model is
seeking to purchase low RVP blendstocks to offset the volatility impacts
of ethanol, as opposed to having to crack crude oil which produces more
volatile four carbon compounds. Table 7.4-4 also shows the volume of
gasoline projected to be produced for both the RFS case and reference
case. We adjusted the gasoline volume for the RFS case to reflect the
same energy density of the gasoline reported for the reference case.
While the national energy-adjusted gasoline production levels for RFS
case are about the same as the reference case, the energy adjusted
gasoline production levels vary significantly by PADD. The refineries in
PADDs 1 and 2 are projected to produce more gasoline in the RFS case
compared to the reference case, while the refineries in PADD 3 are
projected to produce less gasoline in the RFS case.

**Table 7.4-4.**

**Summary of Refinery Model Input and Output Volumes by**

**PADD for the RFS Case and Reference Case (barrels per day)**

![](media/image77.wmf){width="5.810416666666667in"
height="2.551388888888889in"}

The addition of ethanol, the phase out of MTBE and the reuse of former
MTBE feedstocks to make other gasoline blendstocks is expected to change
the capital investments that would otherwise occur if these changes were
not made. Table 7.4-5 summarizes the change in refinery unit throughputs
by PADD comparing the RFS case to the reference case.

**Table 7.4-5.**

**Change in the Refinery Unit Capacities by PADD between the RFS Case
and Reference Case (thousand barrels per day)**

![](media/image78.wmf){width="6.375in" height="6.114583333333333in"}

Most of the capacity throughput changes are negative, reflecting the
decreased processing of crude oil and vacuum gas oil and decreased
downstream refining units as projected by the refinery model. Of the
negative throughput changes, the large reduced volume of the fluidized
catalytic cracker is important. As discussed above, the refinery model
likely chose to decrease the fluidized catalytic cracker throughput to
crack less heavy hydrocarbons to light hydrocarbons, producing less
four-carbon compounds to offset the volatility impacts of ethanol. There
are several units which show throughput capacity increases, primarily in
PADD 3. PADD 3 refineries will have a substantial loss in octane because
of the removal of a substantial volume of MTBE, but the refinery model
did not choose to blend much ethanol in PADD 3. Instead, the refinery
model chose to make additional alkylate from the captive MTBE plants
formerly operating in PADD 3, and blend in isooctene from the conversion
of merchant MTBE plants. The refinery model also added some reformer
capacity to make up the balance of octane loss. The refinery model added
depentanizer capacity mostly in PADD 3 to enable the blending of ethanol
into RFG.

Refiners can also control the gasoline production and quality by
adjustments they can make to several of their refinery conversion units.
Refiners can adjust the conversion of their FCC and hydrocracker units,
and change the severity of their reformers. Table 7.4-6 contains the
percent conversions and severities of these units.

**Table 7.4-6.**

**Comparison of Key Refinery Unit Operations by PADD between**

**the RFS Case and Reference Case (percent)**

![](media/image79.wmf){width="5.995138888888889in"
height="2.076388888888889in"}

The refinery model maintains the same FCC unit conversion percentage for
the RFS case compared to the reference case, except for PADD 1 which
showed a small decrease in FCC unit conversion. For all PADDs,
hydrocracker conversion percentage remains the same. Continuous reformer
severity is projected to increase slightly in PADD 1 likely because of
the octane loss caused by the removal of MTBE from the RFG pool which is
not completely made up by the increased ethanol volume there. In PADD 2
where a lot of ethanol is being blended, reformer severity decreases
significantly from 99 RON to 96 RON. Reformer severity remains the same
in PADD 3. Reformer severity is projected to increase slightly in
California due to an anticipated small decrease in ethanol. Finally,
reformer severity is projected to decrease slightly in PADDs 4 and 5
despite the small decrease in ethanol there.

These changes in refinery unit throughputs are associated with changes
in capital investments. Table 7.4-7 summarizes the projected change in
capital investments between the reference case and the RFS control case.
Table 7.4-7 shows that incremental to the reference case, refiners are
expected to reduce their capital investments by \$5.8 billion compared
to business as usual. Most of the reduction occurs in PADDs 1 and 2
where large volumes of ethanol, and other gasoline blendstocks, are
expected to enter the gasoline pool. Of course, this capital cost
decrease is countered by the \$2.3 billion in capital costs being
incurred to build new ethanol plants and put into place the distribution
system required to distribute the new ethanol.

**Table 7.4-7.**

**Comparison of Capital Expenditures by PADD**

**between the RFS Case and Reference Case (million dollars)**

![](media/image80.wmf){width="5.999305555555556in"
height="7.704166666666667in"}

**7.4.3.2 Summary of Changes in Refinery Inputs and Outputs to the EIA
Case**

The EIA case has some similarities to the RFS case. The MTBE is still
estimated to no longer be blended into gasoline, and the former MTBE
feedstocks are converted over to other low-RVP gasoline blendstocks. The
annual volume of ethanol blended into gasoline, however, is almost 3
billion gallons higher. This increased volume of ethanol is expected to
be spread over all the PADDs, although PADD 3 is projected to absorb the
most. The much increased volume of very high octane ethanol is expected
to slightly reduce the consumption of the gasoline blendstocks produced
from former MTBE feedstocks. The net gasoline-equivalent volume increase
by ethanol and other gasoline blendstock changes is expected to be over
100 thousand barrels per day. Table 7.4-8 contains the volumes of these
gasoline blendstocks by PADD.

**Table 7.4-8.**

**Comparison of Ethanol, MTBE, Isooctane, Isooctene, and Alkylate
Volumes**

**by PADD for the EIA Case and Reference Case (barrels per day)**

![](media/image81.wmf){width="5.997222222222222in"
height="2.0145833333333334in"}

Table 7.4-9 summarizes the inputs into and gasoline outputs from the
refinery model separate from the ethanol and converted MTBE blendstocks
summarized in Table 7.4-12 above. Crude oil and vacuum gas oil and
residual fuel purchases are expected to decrease about 1.7 percent
averaged over all the PADDs. The refinery model also estimates that the
volume of purchased gasoline components will increase incrementally over
the RFS case. It seems that a likely reason for the increased gasoline
blendstock volumes over purchased crude oil purchases is that the
refinery model is seeking to purchase low RVP blendstocks to offset the
volatility impacts of ethanol, as opposed to having to crack crude oil
which produces more volatile four carbon compounds. Table 7.4-15 also
shows the energy-adjusted volume of gasoline projected to be produced
for both the RFS case and reference case. The national energy-adjusted
gasoline production levels for EIA case is somewhat lower than the
reference case which suggests that the crude oil savings described above
are somewhat overstated. The refineries in PADDs 1 and 2 are projected
to produce much more gasoline in the EIA case compared to the reference
case, while the refineries in PADD 3 are projected to produce much less
gasoline in the EIA case.

**Table 7.4-9.**

**Summary of Refinery Model Input and Output Volumes by PADD**

**for the EIA Case and Reference Case (barrels per day)**

![](media/image82.wmf){width="5.683333333333334in" height="2.925in"}

The addition of ethanol, the phase out of MTBE and the reuse of former
MTBE feedstocks to make other gasoline blendstocks is expected to change
the capital investments that would otherwise occur if these changes were
not made.

Table 7.4-10 summarizes the change in refinery unit throughputs by PADD
comparing the EIA case to the reference case.

**Table 7.4-10.**

**Change in the Refinery Unit Capacities by PADD between the EIA Case
and Reference Case (thousand barrels per day)**

![](media/image83.wmf){width="5.99375in" height="7.002777777777778in"}

Most of the capacity throughput changes are negative, reflecting the
decreased processing of crude oil and vacuum gas oil and decreased
downstream refining units as projected by the refinery model. Of the
negative throughput changes, the reduced volume of the fluidized
catalytic cracker is important. As discussed above, the refinery model
likely chose to decrease the fluidized catalytic cracker throughput to
crack less heavy hydrocarbons to light hydrocarbons, producing less
four-carbon compounds to offset the volatility impacts of ethanol. The
reduction in FCC unit throughput is relatively less for the EIA case
than it was for the RFS case. There are several units which show
throughput capacity increases, primarily in PADD 3. PADD 3 refineries
will have a substantial loss in octane because of the removal of a
substantial volume of MTBE. Unlike the RFS case, however, much of the
ethanol is projected to be blended into PADD 3's gasoline pool making up
for the octane loss. This can be seen in Table 7.4-15 as reformer and
alkylation throughputs volumes are projected to be lower for the EIA
case. The refinery model added depentanizer capacity mostly in PADD 3 to
enable the blending of ethanol into RFG.

Refiners can also control the gasoline production and quality by
adjustments they can make to several of their refinery conversion units.
Refiners can adjust the conversion of their FCC and hydrocracker units,
and change the severity of their reformers. Table 7.4-11 contains the
percent conversions and severities of these units.

**Table 7.4-11.**

**Comparison of Key Refinery Unit Operations by PADD between**

**The EIA Case and Reference Case (percent)**

![](media/image84.wmf){width="5.995138888888889in"
height="2.076388888888889in"}

The refinery model maintains the same FCC unit conversion percentage for
the RFS case compared to the reference case, except for PADD 1 which
showed a small decrease in FCC unit conversion. For all PADDs,
hydrocracker conversion percentage remains the same. Continuous reformer
severity is projected to increase slightly in PADD 1 despite the ethanol
blended into that PADD's gasoline. In PADD 2 where a lot of ethanol is
being blended, reformer severity decreases significantly from 99 RON to
94 RON. Reformer severity is projected to decrease slightly in PADD 3
due to the large volume of ethanol being blended into the gasoline in
that PADD. Reformer severity is projected to decrease slightly in
California due to an anticipated small increase in ethanol. Finally,
reformer severity is projected to decrease slightly in PADDs 4 and 5 due
to the increase in ethanol there.

These changes in refinery unit throughputs are associated with changes
in capital investments. Table 7.4-12 summarizes the projected change in
capital investments between the reference case and the EIA control case.
Table 7.4-12 shows that incremental to the reference case, refiners are
expected to reduce their capital investments by \$7.3 billion compared
to business as usual. Most of the reduction occurs in PADDs 1 and 2
where large volumes of ethanol, and other gasoline blendstocks are
expected to enter the gasoline pool. Of course, this capital cost
decrease is countered by the estimated \$6.5 billion in capital costs
incurred to build new ethanol plants and put into place the distribution
system that the new ethanol requires.

**Table 7.4-12.**

**Comparison of Capital Expenditures by PADD between**

**the EIA Case and Reference Case (million dollars)**

![](media/image85.wmf){width="5.999305555555556in"
height="7.990277777777778in"}

**7.4.3.3 Adjustments to the LP Refinery Model's Cost Estimate**

We made several adjustments to the costs directly estimated by the LP
refinery cost model for the RFS and EIA cases which are included in the
costs reported below. One adjustment made was to adjust the costs based
on the ethanol prices used in the LP cost model to reflect the ethanol
production costs estimated and reported above in section 7.1.1. This
adjustment resulted in much lower ethanol costs to refiners because
Jacobs largely based its ethanol prices on ethanol's octane costs
instead of its historical price relationship to gasoline, which is much
lower. We also adjusted the ethanol distribution costs from those used
in the LP refinery cost study, which roughly corresponded to those used
for the proposed rule cost analysis, to those estimated for the final
rule as discussed above in section 7.3.1. In Table 7.4-13 we summarize
the ethanol production and distribution costs used in the LP refinery
cost model and those we estimated for the final rule.

**Table 7.4-13**

**Ethanol Price and Distribution Costs used in the LP Refinery Model
versus**

**T**hose used for the Final Rule Cost Analysis (cents per gallon)

  ---------- -------------- ------- --------- --------- --------- --------- -------
                            Case    PADD 1    PADD 2    PADD 3    PADD 4/5  CA
                                                                  ex CA     

  Prices     Ethanol Price  RFS and 158       158       158       158       158
  used in LP in Midwest     EIA                                             
  Refinery                  Case                                            
  Cost Model                                                                

             Ethanol        RFS and 12        0         10        17        18
             Distribution   EIA                                             
             Cost           Case                                            

             Ethanol Price  RFS and 170       158       168       175       176
             in PADD        EIA                                             
                            Case                                            

  Costs used Ethanol        RFS     126       126       126       126       126
  in Final   Production     Case                                            
  Cost       Cost                                                           
  Analysis                                                                  

                            EIA     131       131       131       131       131
                            Case                                            

             Ethanol        RFS and 16        6.5       16        23        22
             Distribution   EIA                                             
             Cost           Case                                            

             Ethanol Cost   RFS     142       132.5     142       149       148
             in PADD        Case                                            

                            EIA     147       137.5     147       154       153
                            Case                                            
  ---------- -------------- ------- --------- --------- --------- --------- -------

Another adjustment we made to the costs directly estimated by the LP
refinery cost model was to add a cost for distributing gasoline. The
refinery cost model did not include distribution costs for gasoline for
moving the gasoline from the refinery to the terminal. We assigned
gasoline distribution costs to be 4 cents per gallon applied as a cost
savings to the gasoline-equivalent volume of ethanol blended into each
PADD's gasoline, since this roughly corresponded to the volume of
gasoline displaced by the ethanol.

**7.4.3.4 Estimated Costs**

**7.4.3.4.1 Estimated Costs for the RFS Case**

Table 7.4-14 summarizes the costs for the RFS case excluding federal and
state ethanol consumption subsidies. The costs are reported by different
cost component as well as aggregated total and the per-gallon
costs.[^228] This estimate of costs reflects the changes in gasoline
that are occurring with the expanded use of ethanol, including the
corresponding removal of MTBE and reuse of MTBE feedstocks. The
operating costs include the labor, utility and other operating costs and
are a direct output from the refinery model. These costs are adjusted to
reflect ethanol's production cost plus distribution costs instead of the
ethanol prices used in the refinery cost model. The fixed costs are 3
percent of the capital costs. The costs associated with lower energy
density gasoline are accounted for using the fractional change in energy
density shown in Table 7.4-4, multiplied times the wholesale price of
gasoline. By excluding the federal and state ethanol consumption
subsidies in the table, we avoid the transfer payments caused by these
subsidies that would hide a portion of the program's costs.

**Table 7.4-14.**

**Summary of RFS Case Costs without Ethanol Consumption Subsidies**

**(million dollars per year and c/gal, except as noted; 2004 dollars, 7%
ROI before taxes** )

+-----------------------------------+----------------------------------+
|                                   | RFS Case                         |
|                                   |                                  |
|                                   | 6.7 Billion Gals                 |
|                                   |                                  |
|                                   | Incremental to Reference Case    |
+-----------------------------------+----------------------------------+
| Capital Costs (\$MM)              | **-5,878**                       |
+-----------------------------------+----------------------------------+
| Amortized Capital Costs (\$MM/yr) | -647                             |
+-----------------------------------+----------------------------------+
| Fixed Operating Cost (\$MM/yr)    | -178                             |
+-----------------------------------+----------------------------------+
| Variable Operating Cost (\$MM/yr) | -201                             |
+-----------------------------------+----------------------------------+
| Lower Energy Density Gasoline     | 1,848                            |
| (\$MM/yr)                         |                                  |
+-----------------------------------+----------------------------------+
| Total Cost (\$MM/yr)              | **823**                          |
+-----------------------------------+----------------------------------+
| Capital Costs (c/gal)             | -0.40                            |
+-----------------------------------+----------------------------------+
| Fixed Operating Cost (c/gal)      | -0.11                            |
+-----------------------------------+----------------------------------+
| Variable Operating Cost (c/gal)   | -0.12                            |
+-----------------------------------+----------------------------------+
| Lower Energy Density Gasoline     | 1.13                             |
| (c/gal)                           |                                  |
+-----------------------------------+----------------------------------+
| Total Cost Excluding Subsidies    | **0.50**                         |
| (c/gal)                           |                                  |
+-----------------------------------+----------------------------------+

Our analysis shows that when considering all the costs associated with
these fuel changes resulting from the expanded use of ethanol that these
various possible gasoline use scenarios will cost the U.S. \$820 million
in the year 2012. Expressed as per-gallon costs, these fuel changes
would cost the U.S. 0.50 cent per gallon of gasoline.

Table 7.4-15 expresses the total and per-gallon gasoline costs for the
RFS case with the federal and state ethanol subsidies included. The
federal tax subsidy is 51 cents per gallon for each gallon of new
ethanol blended into gasoline. The state tax subsidies apply in 5 states
and range from 1.6 to 29 cents per gallon. The cost reduction to the
fuel industry and consumers are estimated by multiplying the subsidy
times the volume of new ethanol estimated to be used in the state.

**Table 7.4-15.**

**Estimated RFS Case Cost including Ethanol Consumption Subsidies**

**(million dollars per year and cents per gallon; 2004 dollars, 7% ROI
before taxes**)

+------------------------------------+---------------------------------+
|                                    | RFS Case                        |
|                                    |                                 |
|                                    | 6.7 Billion Gals                |
|                                    |                                 |
|                                    | Incremental to Reference Case   |
+------------------------------------+---------------------------------+
| Total Cost (\$MM/yr)               | 823                             |
+------------------------------------+---------------------------------+
| Federal Subsidy (\$MM/yr)          | -1376                           |
+------------------------------------+---------------------------------+
| State Subsidies (\$MM/yr)          | -5                              |
+------------------------------------+---------------------------------+
| Revised Total Cost (\$MM/yr)       | **-558**                        |
+------------------------------------+---------------------------------+
| Per-Gallon Cost Excluding          | 0.50                            |
| Subsidies (c/gal)                  |                                 |
+------------------------------------+---------------------------------+
| Federal Subsidy (c/gal)            | -0.84                           |
+------------------------------------+---------------------------------+
| State Subsidies (c/gal)            | -0.003                          |
+------------------------------------+---------------------------------+
| Total Cost Including Subsidies     | **-0.34**                       |
| (c/gal)                            |                                 |
+------------------------------------+---------------------------------+

The cost including subsidies better represents gasoline's production
cost as might be reflected to the fuel industry as a whole and to
consumers "at the pump" because the federal and state subsidies tends to
hide a portion of the actual costs. Our analysis estimates that the fuel
industry and consumers will see a 0.34 cent per gallon decrease in the
apparent cost of producing gasoline for the RFS case.

**7.4.3.4.2 Estimated Costs for the EIA Case**

Table 7.4-16 summarizes the costs for the EIA case. The costs in this
table exclude federal and state ethanol consumption subsidies. The costs
are reported by different cost components as well as the aggregated
total and the per-gallon costs. This estimate of costs reflects the
changes in gasoline that are occurring with the much expanded use of
ethanol, including the removal of MTBE and reuse of MTBE feedstocks. The
operating costs include the labor, utility and other operating costs and
are a direct output from the refinery model, adjusted for ethanol's
production cost at this higher volume including ethanol distribution
costs. The fixed costs are 3 percent of the capital costs. The costs
associated with lower energy density gasoline, as shown in Table 7.4-9,
are estimated by the fractional change in energy content times the
wholesale price of gasoline. The increment of the EIA case to the RFS
case indicates the economic impact of the additional volume of ethanol
between the two cases.

**Table 7.4-16.**

**Summary of EIA Case Costs without Ethanol Consumption Subsidies**

**(million dollars per year and c/gal, except as noted; 2004 dollars, 7%
ROI before taxes**)

+--------------------------+----------------------+--------------------+
|                          | EIA Case             | EIA Case           |
|                          |                      |                    |
|                          | 9.6 Billion Gals     | 9.6 Billion Gals   |
|                          |                      |                    |
|                          | Incremental to       | Incremental to RFS |
|                          | Reference Case       | Case               |
+--------------------------+----------------------+--------------------+
| Capital Costs (\$MM)     | **-7,311**           | **-1,433**         |
+--------------------------+----------------------+--------------------+
| Amortized Capital Costs  | -804                 | -158               |
| (\$MM/yr)                |                      |                    |
+--------------------------+----------------------+--------------------+
| Fixed Operating Cost     | -222                 | -43                |
| (\$MM/yr)                |                      |                    |
+--------------------------+----------------------+--------------------+
| Variable Operating Cost  | -491                 | -290               |
| (\$MM/yr)                |                      |                    |
+--------------------------+----------------------+--------------------+
| Lower Energy Density     | 3,255                | 1407               |
| Gasoline (\$MM/yr)       |                      |                    |
+--------------------------+----------------------+--------------------+
| Total Cost (\$MM/yr)     | **1739**             | **915**            |
+--------------------------+----------------------+--------------------+
| Capital Costs (c/gal)    | -0.49                | -0.10              |
+--------------------------+----------------------+--------------------+
| Fixed Operating Cost     | -0.14                | -0.03              |
| (c/gal)                  |                      |                    |
+--------------------------+----------------------+--------------------+
| Variable Operating Cost  | -0.30                | -0.18              |
| (c/gal)                  |                      |                    |
+--------------------------+----------------------+--------------------+
| Lower Energy Density     | 1.98                 | 0.86               |
| Gasoline (c/gal)         |                      |                    |
+--------------------------+----------------------+--------------------+
| Total Cost Excluding     | **1.06**             | **0.56**           |
| Subsidies (c/gal)        |                      |                    |
+--------------------------+----------------------+--------------------+

Our analysis shows that when considering all the costs associated with
these fuel changes resulting from the expanded use of subsidized ethanol
that these various possible gasoline use scenarios will cost the U.S.
\$1,740 million in the year 2012 for the EIA case. Expressed as
per-gallon costs, these fuel changes would cost the U.S. about 1.1 cents
per gallon of gasoline. The incremental volume of ethanol added between
the RFS and EIA cases is expected to cost \$915 million in the year
2012, resulting in a 0.56 cent per gallon cost.

Table 7.4-17 expresses the total and per-gallon gasoline costs for the
EIA case with the federal and state ethanol subsidies included. The
federal tax subsidy is 51 cents per gallon for each gallon of new
ethanol blended into gasoline. The state tax subsidies apply in 5 states
and range from 1.6 to 29 cents per gallon. The cost reduction to the
fuel industry and consumers are estimated by multiplying the subsidy
times the volume of new ethanol estimated to be used in the state.

**Table 7.4-17.**

**Estimated EIA Case Cost including Ethanol Consumption Subsidies**

**(million dollars per year and cents per gallon; 2004 dollars, 7% ROI
before taxes**)

+-----------------------+-----------------------+---------------------+
|                       | EIA Case              | EIA Case            |
|                       |                       |                     |
|                       | 9.6 Billion Gals      | 9.6 Billion Gals    |
|                       |                       |                     |
|                       | Incremental to        | Incremental to RFS  |
|                       | Reference Case        | Case                |
+-----------------------+-----------------------+---------------------+
| Total Cost (\$MM/yr)  | 1739                  | 915                 |
+-----------------------+-----------------------+---------------------+
| Federal Subsidy       | -2865                 | -1489               |
| (\$MM/yr)             |                       |                     |
+-----------------------+-----------------------+---------------------+
| State Subsidies       | -31                   | -26                 |
| (\$MM/yr)             |                       |                     |
+-----------------------+-----------------------+---------------------+
| Revised Total Cost    | **-1158**             | **-600**            |
| (\$MM/yr)             |                       |                     |
+-----------------------+-----------------------+---------------------+
| Per-Gallon Cost       | 1.06                  | 0.56                |
| Excluding Subsidies   |                       |                     |
| (c/gal)               |                       |                     |
+-----------------------+-----------------------+---------------------+
| Federal Subsidy       | -1.74                 | -0.90               |
| (c/gal)               |                       |                     |
+-----------------------+-----------------------+---------------------+
| State Subsidies       | -0.02                 | -0.02               |
| (c/gal)               |                       |                     |
+-----------------------+-----------------------+---------------------+
| Total Cost Including  | **-0.71**             | **-0.37**           |
| Subsidies             |                       |                     |
|                       |                       |                     |
| (c/gal)               |                       |                     |
+-----------------------+-----------------------+---------------------+

The cost including subsidies better represents gasoline's production
cost as might be reflected to the fuel industry as a whole and to
consumers "at the pump" because the federal and state subsidies tends to
hide a portion of the actual costs. Our analysis estimates that the fuel
industry and consumers will see a 0.71 cent per gallon decrease in the
apparent cost of producing gasoline for the EIA case. Incremental to the
RFS case, the consumer would be expected to see a 0.37 cent per gallon
price decrease "at the pump."

7.4.3.4.3 Sensitivity Cost Analyses for the RFS and EIA Cases

In Table 7.1-5 above, we presented various corn-ethanol production cost
estimates based on varying corn and dried distillers grain prices. We
entered a range of low and high production ethanol cost estimates from
that table into our cost spreadsheet created from the output from the LP
refinery cost modeling. The range of ethanol production costs that we
chose represents a reasonable bound around the possible range of future
ethanol production costs. This allowed us to estimate the cost of using
ethanol at these other possible ethanol production costs at the ethanol
volumes analyzed for the RFS and EIA cases. We present these costs in
Table 7.4-18. We did not conduct sensitivity analyses around higher or
lower crude oil prices.[^229]

**Table 7.4-18.**

**Summary of the Sensitivity Cost Analysis at Higher and Lower Ethanol
Production Costs**

(Costs in 2012, 2004 dollars, 7% ROI before taxes)

+--------------+------------+------------+-------------+-------------+
| Ethanol      |            | Units      | Costs       | Costs       |
| Production   |            |            |             |             |
| Cost         |            |            | RFS Case    | EIA Case    |
+--------------+------------+------------+-------------+-------------+
| 0.86         | Cost       | \$MM/yr    | -260        | -846        |
|              | without    |            |             |             |
|              | Subsidies  |            |             |             |
+--------------+------------+------------+-------------+-------------+
|              |            | c/gal      | -0.16       | -0.52       |
+--------------+------------+------------+-------------+-------------+
|              | Cost with  | \$MM/yr    | -1640       | -3740       |
|              | Subsidies  |            |             |             |
+--------------+------------+------------+-------------+-------------+
|              |            | c/gal      | -1.00       | -2.28       |
+--------------+------------+------------+-------------+-------------+
| 2.04         | Cost       | \$MM/yr    | 2930        | 5784        |
|              | without    |            |             |             |
|              | Subsidies  |            |             |             |
+--------------+------------+------------+-------------+-------------+
|              |            | c/gal      | 1.79        | 3.53        |
+--------------+------------+------------+-------------+-------------+
|              | Cost with  | \$MM/yr    | 1546        | 2890        |
|              | Subsidies  |            |             |             |
+--------------+------------+------------+-------------+-------------+
|              |            | c/gal      | 0.95        | 1.76        |
+--------------+------------+------------+-------------+-------------+

**7.4.4 Impact on Diesel Prices**

Biodiesel fuel is added to highway and nonroad diesel fuel, which
increases the volume and therefore the supply of diesel fuel and thereby
reduces the demand for refinery-produced diesel fuel. In this section,
we estimate the overall cost impact, considering how much refinery based
diesel fuel is displaced by the forecasted production volume of
biodiesel fuel. The cost impacts are evaluated considering the
production cost of biodiesel with and without the subsidy from the
Biodiesel Blenders Tax credit program. Additionally, the diesel cost
impacts are quantified with refinery diesel prices as forecasted by
Jacobs's which are based on EIA's AEO 2006.

We estimate the net effect that biodiesel production has on overall cost
for diesel fuel in year 2012 using total production costs for biodiesel
and diesel fuel. The costs are evaluated based on how much refinery
based diesel fuel is displaced by the biodiesel volumes as forecasted by
EIA, accounting for energy density differences between the fuels. The
cost impact is estimated from a 2012 year basis, by multiplying the
production costs of each fuel by the respective changes in volumes for
biodiesel and estimated displaced diesel fuel. We further assume that
all of the forecasted bio-diesel fuel volume is used as transport fuel,
neglecting minor uses in the heating oil market.

For this analysis, the production costs for biodiesel fuel are based on
the costs generated using the USDA, NREL, EIA and the design vendors
estimates in the preceding sections. We average these results to
developed costs for soy oil and yellow grease feedstocks. Additionally,
the production costs are based on EIA's projection in 2012 that half of
the total biodiesel volume will be made from soy oil feedstock with the
remaining volume being produced from yellow grease. To these estimates,
we add distribution costs of 21.5 c/gal to the biodiesel production
costs, reflecting the distribution estimates derived in section 7.3.2.
For the refinery diesel production costs in 2012, we used the projected
wholesale national average diesel price of 160 c/gal projected by the
Jacobs' pricing forecast. Distribution costs of 4 c/gal were added to
the Jacobs's wholesale diesel price projection, to account for the
additional costs to move diesel fuel from the wholesale market to end
use terminals.

Our estimate for the reduction in refinery produced diesel fuel is based
on EIA's forecast for approximately 300 MM gallons of biodiesel in 2012,
along with the 2012 Reference Case year biodiesel production volume of
28 MM gallons. With this and accounting for differences in energy
density between biodiesel and diesel fuel, we estimate in 2012 that the
additional biodiesel production reduces the need for 250 MM gallons of
refinery produced diesel fuel. Table 7.4-19 contains the energy
densities used in this analysis.

**Table 7.4-19.**

Energy Content of Fuels per Gallon

  ----------------------------------- -----------------------------------
  **Fuel**                            **Lower Heating Value
                                      (BTU/gallon)**

  Biodiesel                           117,093

  Refinery Produced Diesel            128,700
  ----------------------------------- -----------------------------------

For all RFS case scenarios, the net effect of biodiesel production on
diesel fuel costs, including the biodiesel blenders' subsidy, is a
reduction in the cost of transport diesel fuel costs by \$114 MM per
year, which equates to a fuel cost reduction of about 0.20 c/gal[^230].
Without the subsidy, the transport diesel fuel costs are increased by
\$91 MM per year, or an increase of 0.16 c/gal.

**7.5 Other Potential Economic Impacts**

Ideally, we would prefer to assess all economic and environmental
impacts of increased ethanol use and decreased fossil fuel use in a
holistic manner. Such an analysis is beyond the scope of this RIA.
However, we can approximate some of the impacts of increased ethanol
production and use, and we can discuss other impacts qualitatively. The
preceding discussion quantifies the impact of expanded use of renewable
fuels on the cost of gasoline and diesel fuel. It does so by quantifying
the direct costs of ethanol production, as well as the direct costs of
state and federal tax subsidies for the renewable fuels, which are
financed through tax payments. There are many other economic impacts
associated with the use of renewable fuels and the fossil fuels they
replace which go well beyond the scope of the analysis conducted for the
RIA. We have not attempted to quantify all of them here. For example,
increased renewable fuel production and use may have adverse impacts on
surface and ground water quality and soil erosion, while decreased
fossil fuel, distribution and use may have positive impacts. To quantify
the economic impact associated with this would require extensive
analysis of the likely responses of farmers to the increased demand for
renewable fuels, the cost of actions taken to remedy the impacts, and
the cost of any resulting health and welfare impacts.

Furthermore, the renewable fuel production costs assumed in our analysis
do not reflect the entire cost to society associated with the production
of the corn and soybean feedstocks used in their production due other
state and federal agricultural policies. Direct payments,
counter-cyclical payments, marketing loans, and subsidized crop
insurance are all examples of policies outside of this rulemaking that
impact the price of corn and soybeans that are not reflected in the
production cost for ethanol and biodiesel, but do impact costs borne by
consumers indirectly through taxes. Quantifying the incremental impacts
of this rulemaking on the effects of these pre-existing programs would
represent a significant challenge. However, the challenge is complicated
even more by the direct and indirect economic support provided for the
production, supply, and distribution of the fossil fuels which would be
replaced by these renewable fuels. Again, any assessment of the overall
costs to society for increase renewable use would have to look at the
economic support provided across the entire fuel supply. Such an
analysis is well beyond the scope of this RIA.

Despite our inability to fully capture all the potential impacts on the
cost to society of increased renewable fuel use, two potential impacts
were touched on briefly in our analysis, and these are discussed in this
subsection.

*Economic Impacts of Emission Changes*

As discussed in Chapters 4.1 and 5.1, we estimate that there may be an
increase in emissions and a corresponding small increase in ozone
resulting from the expanded use of renewable fuels. Our vehicle and
equipment emission estimates are highly uncertain, however, given the
lack of data in particular on vehicles and engines complying with the
latest standards. However, to the extent that there are emission and
ozone increases resulting from the expanded use of renewable fuels,
there can be a cost associated with them. In some cases, areas that see
an increase in emissions resulting from renewable fuel use may be forced
to take other actions to offset these emission increases. In other
cases, particularly in attainment areas, the impact, while not affecting
attainment, may adversely impact air quality and human health. It is
difficult to provide any quantitative estimate of what the mitigation
costs might be to offset emission increases, or to quantify the health
impacts resulting from the air quality impacts. Not only are the
emission and air quality impacts highly uncertain, but they are also
very location dependent. While we have made projections on where the
ethanol use may rise or fall for the purposes of estimating nationwide
fuel cost impacts and potential emissions impacts, these projections are
much less reliable when trying to predict specific local air quality
impacts.

Despite all of the above caveats, we have attempted to provide a rough
estimate of the potential national-level cost impacts; As a surrogate
for additional emission control costs in nonattainment areas and
potential health impacts in attainment areas, we looked at the potential
health costs associated with the secondary nitrate PM resulting from the
decreases in NOx emissions estimated in previous EPA rules. We note
again that we actually expect an overall decrease in ambient PM~2.5~
formation due to the increased use of ethanol in fuel (See Chapter
5.2).[^231] Thus, we expect most areas to have lower health impact costs
and certainly lower abatement costs related to PM control.

In recent rulemakings we monetized PM emission impacts, including those
resulting from changes in secondarily formed PM~2.5~ due to NOx emission
changes. Using this information as a guide, we provide a screening-level
estimate of the monetized PM-related health impacts associated with an
increase in NOx emissions associated with the final rule. For this
analysis, we derived a dollar-per-ton value based on recent benefits
modeling conducted for the Clean Air Nonroad Diesel Rule (CAND).[^232]
This value (\$8,000 in PM-related monetized health impacts per ton of
NOx reduced) is based on air quality modeling conducted in 2004 for the
CAND rule. This benefits transfer method is consistent with approaches
used in other recent mobile and stationary source rules.[^233] We refer
the reader to the final CAND RIA for more details on this benefits
transfer approach. The dollar-per-ton value represents monetized health
impacts in 2015 (in year 2000 dollars).

We combined the dollar-per-ton estimate of monetized health effects with
the projected 2015 emission changes presented in Table 4.4-1, which
includes emissions from gasoline vehicles and equipment and renewable
fuel production and distribution. We estimate that the potential
PM~2.5~-related monetized impact associated with NOx emissions from
increased use of ethanol to be up to \$290 million for the RFS control
scenario, and up to \$340 million for the EIA control scenario. Note
that this impact is based on monetized changes in health effects,
including changes in mortality risk, chronic bronchitis, nonfatal heart
attacks, respiratory hospital admissions, asthma attacks, and other
minor health endpoints. It is also important to point out that this
value does not represent the cumulative monetized health impacts
associated with the potential PM changes associated with the future use
of ethanol described above.

This estimate is subject to a number of additional caveats. The
dollar-per-ton values reflect specific geographic patterns of emissions
reductions and specific air quality and benefits modeling assumptions
which are derived from previous analyses and will not match those
associated with increased ethanol use in fuel for two reasons. One, the
geographical distribution of the emission sources affected by increased
ethanol use differs from that addressed in the CAND rulemaking. Two, the
CAND rule was national in scope and the emission reductions were spread
out across the entire nation. Increased ethanol use will be very
geographically focused. Many major population centers will not
experience an increase in ethanol use as their fuel already contains
ethanol. Care should be taken when applying these estimates to emission
reductions that occur in any specific location, since the
dollars-per-ton for emission reductions in specific locations may be
very different than the national average. Given these caveats and the
potential decrease in ambient PM~2.5~ due to the decrease in aromatic
fuel content, we can not say for certain in which direction the total
monetized PM-related health impact will be. In reality there may be an
overall reduction in PM-related health costs, despite the increase due
to increased NOx emissions.

**Chapter 8: Agricultural Sector Impacts**

As described in the Preamble, we used the Forest and Agricultural Sector
Optimization Model (FASOM) to estimate the U.S. agricultural impacts of
increasing renewable fuel volumes to 7.5 billion gallons per year (BGY)
by 2012, as required by the Renewable Fuels Standard (RFS) and to 9.9
BGY, the volume of renewable fuel the Energy Information Administration
(EIA) predicts for the year 2012 in the Annual Energy Outlook
2006.[^234] Although these renewable fuel volumes are lower than current
market predictions, these assumptions were established during the NPRM
and are used throughout this FRM.

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 will 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 long term trends.[^235]

FASOM uses supply and demand curves for the 11 major U.S. domestic
regions,[^236] which are calibrated to historic price and production
data. FASOM also includes detailed supply and demand data for corn,
wheat, soybeans, rice and sorghum across 37 foreign regions.[^237] FASOM
maintains 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.[^238]

**8.1 Commodity Prices**

**8.1.1 Corn and DDGS Prices**

FASOM predicts that as renewable fuel volumes increase, agricultural
prices over a range of products (not just corn and soybean renewable
fuel feedstocks) will increase as well. Since the principal feedstock
for ethanol is corn, corn prices are anticipated to rise. For
consistency, all of the dollar estimates are presented in 2004 dollars.
In the RFS Case, corn prices increase to \$2.50/bushel by 2012 (compared
to a Reference Case price of \$2.32/bushel in 2012). With the higher
renewable fuels volumes in the EIA Case, corn prices rise to
\$2.71/bushel (2004\$) by 2012. (See Table 8.1-1) To place this
difference in perspective, in 2012, corn prices are about 8 percent
higher in the RFS Case and 17 percent higher in the EIA Case relative to
the Reference Case.[^239]

**Table 8.1-1. Corn and DDGS Prices in 2012**

  ---------------------- ----------------- --------------- ----------------
                         **Reference       **RFS Case**    **EIA Case**
                         Case**                            

  Corn Price             \$2.32/bushel     \$2.50/bushel   \$2.71/bushel

  Distillers Dried       \$85.55/ton       \$83.35/ton     \$86.15/ton
  Grains with Solubles                                     
  (DDGS) Price                                             
  ---------------------- ----------------- --------------- ----------------

The cost of producing ethanol is dependent upon, among other factors,
the price of corn and the price of related byproducts. As part of the
analytical approach described in the NPRM, we used FASOM to estimate the
future prices of the major ethanol production byproduct: distillers
dried grains with solubles (DDGS). FASOM estimates that the price of
DDGS will remain relatively constant with the renewable volume scenarios
that we are examining in this rulemaking. An increase in DDGS supply is
anticipated to be offset by an increase in DDGS demand as technology
improves to pelletize and distribute DDGS to a wider market. DDGS prices
in the U.S. in 2012 are predicted to be \$83.35/ton in the RFS Case and
\$86.15/ton in the EIA Case. (See Table 8.1-1) Hence, the overall price
of DDGS remains within 3 percent of the DDGS Reference Case price.

Note that the DDGS price given here is the price an ethanol producer
would expect to receive at the plant gate. FASOM predicts a higher value
for the DDGS at the place of end use, based on its nutritional ability
to be substituted as half soy meal and half corn in animal feed. The
difference between its feed value and the ethanol plant gate price is
made up of the cost of handling and shipping, which may include
pelletizing or other measures required to support a national commodity
market for DDGS.

**8.1.2 Soybean and Soybean Byproducts**

FASOM predicts relatively modest changes in soybean prices as a result
of the increases in the renewable fuel volumes examined in this
rulemaking. In the RFS Case, soybean prices rise to \$5.44/bushel
(2004\$) by 2012 (compared to a Reference Case price of \$5.26/bushel in
2012). In the EIA Case, soybean prices rise to \$5.47/bushel (2004\$) by
2012. (See Table 8.1-2) Soybeans prices are expected to increase by
about 3 percent (RFS Case) and 4 percent (EIA Case) relative to the
Reference Case by 2012. The slightly higher prices of soybeans reflect
the consequences of the higher demand for soybeans for renewable fuels
as well as the slightly higher input costs (e.g., land prices). It is
also expected that in medium-term the acres planted to soybeans will
fall, due to increased corn plantings, which will also increase soybean
prices.

**Table 8.1-2.** Soybean and Soybean Meal Prices in 2012

  ------------------------------------------------------------------------
                     **Reference       **RFS Case**      **EIA Case**
                     Case**                              
  ------------------ ----------------- ----------------- -----------------
  Soybean Price      \$5.26/bushel     \$5.44/bushel     \$5.47/bushel

  Soybean Meal Price \$176.70/ton      \$171.73/ton      \$170.05/ton
  ------------------------------------------------------------------------

Soybean meal is produced when crushing soybeans and extracting soybean
oil, the primary feedstock of biodiesel in the U.S. Under the RFS
scenario, FASOM estimates the price of soybean meal will decrease by
about 3 percent in 2012, relative to the Reference Case.[^240] (See
Table 8.1-2) This decrease is slightly larger under the EIA scenario,
with the price of soybean meal dropping by about 4 percent. Several
factors influence the small change in soybean meal prices. First, more
acres of soybeans are being planted to rotate with increased planting of
corn, and this leads to increased soybean supplies. Second, increased
DDGS supplies can substitute for soybean meal as a feed ingredient by
reducing the soybean meal needed in feed rations using higher levels of
DDGS. Third, the size of the livestock herd is smaller due to higher
meat prices, reducing the demand for animal feeds overall.

**8.2 Impact on U.S. Farm Income**

The increase in renewable fuel production provides a significant
increase in farm income to the U.S. agricultural sector. FASOM predicts
that in 2012, U.S. farm income from the sale of agricultural commodities
will increase by \$2.65 billion dollars in the RFS Case and \$5.41
billion in the EIA Case. (See Figure 8.2-1) The RFS and EIA farm income
changes represent roughly a 5 and 10 percent increase, respectively, in
U.S. farm income from the sale of farm commodities over the Reference
Case of roughly \$53 billion[^241]. Most of the increase in net income
is likely to be concentrated in rural areas, and may contribute to rural
wealth creation.

**Figure 8.2-1. Change in Net Farm Income Relative to Reference Case in
2012**

![](media/image86.wmf){width="6.458333333333333in"
height="4.458333333333333in"}

**8.3 Impact on Employment**

Agricultural employment was not directly modeled but is likely to be
very small since modern farm practices are not labor intensive and
increases in production as modeled here will have negligible impact on
direct farm employment. Some additional employment will result from
ethanol plant construction and operation. 30 to 50 people per ethanol
production facility seems typical.

**8.4 Commodity Use Change**

**8.4.1 Corn and Ethanol Byproducts**

For this analysis, U.S. corn uses are broken down into four categories:
domestic (i.e., household) consumption, ethanol production, livestock
feed, and U.S. corn exports. (See Figure 8. 4-1) As the demand for corn
increases to produce more renewable fuel, U.S. corn utilization patterns
are expected to be altered. In 2005, approximately 13 percent of all
corn produced in the U.S. was used for ethanol production. With the two
renewable fuel volumes that we are examining, the percentage of corn
feedstock used for renewable fuels increases significantly. By 2012, in
the RFS Case, 20 percent of all corn produced in the U.S. is used to
produce ethanol. In 2012, in the EIA Case, 26 percent of all corn is
used to produce fuel ethanol. These estimates are similar to the
percentages included in the NPRM.

The increasing use of corn for ethanol raises the price of corn which
has a direct impact on the other uses of corn. FASOM predicts higher
U.S. corn prices leads to lower U.S. exports of corn. U.S. corn exports
drop from about 2 billion bushels in the Reference Case to 1.6 billion
bushels in the RFS Case and 1.3 billion bushels in the EIA Case by 2012.
In value terms, U.S. exports of corn fall by \$573 million in the RFS
Case and by \$1.29 billion in the EIA Case in 2012.

**Figure 8.4-1. Corn Uses in 2012**

![](media/image87.wmf){width="6.498611111111111in"
height="3.763888888888889in"}

Higher U.S. prices for corn due to increased demand for ethanol
production results in decreased use of corn for U.S. livestock feed. The
amount of corn used for livestock feeding decreases by about 320 million
bushels in the RFS Case and by about 690 million bushels in the EIA Case
relative to the Reference Case. Substitutes are available for corn as a
feedstock, and this market is highly price sensitive. One alternate
feedstock is DDGS because feed ration using increased levels of DDGS
would need less corn. The relatively flat prices for DDGS predicted
across all ethanol volume scenarios results from the significant
increase in the demand for DDGS as a feed ingredient parallels the
increase in supply of DDGS. FASOM estimates that DDGS use for livestock
feeding for the RFS Case will almost double by 2012, increasing from 8.5
million tons to 15.2 million tons. Under the EIA Case, FASOM predicts
that DDGS will increase to 22.2 million tons by 2012. (See Figure 8.4-2)
Domestic (i.e., household) consumption of corn for food use declines
slightly with the different renewable fuel volumes analyzed in this
rulemaking.

**Figure 8.4-2. Livestock Feed Sources in 2012**

![](media/image88.wmf){width="6.5in" height="4.895833333333333in"}

**8.4.2 Soybean and Soybean Byproducts**

As described previously, only a modest increase in the demand for
soybeans are expected to be used to produce biodiesel in the renewable
fuel scenarios analyzed in this rulemaking. Although changes in soybean
uses in this analysis are limited, U.S. exports are expected to drop by
41.8 million bushels (RFS Case) and 35.6 million bushels (EIA Case). In
terms of export earnings, U.S. exports of soybeans fall by \$220 million
in the RFS Case and by \$194 million in the EIA Case in 2012.

**8.5 U.S. Land Use Patterns and Land Prices**

**8.5.1 Corn Acreage**

FASOM predicts that total production of corn in the U.S. in 2012 will be
11.9 billion bushels under the RFS Scenario and 12.1 billion bushels
under the EIA Scenario (compared to 11.7 billion bushels in the
Reference Case)[^242]. (See Table 8.5-1) With higher renewable fuel
volumes, more corn will be produced in the U.S. Increased U.S. corn
production can result from two sources: greater productivity on existing
acres of land devoted to corn or from "new" acres that are brought into
the corn production. Much of the high quality, suitable land in the U.S.
is already being used to produce corn. Improvement in the productivity
of growing corn on existing U.S. land is projected to grow by roughly 1
percent annually through 2012. As a result, most of the increased demand
for corn from increased use of renewable fuels will be met from
increased productivity on existing acres of corn relative to the 2005
baseline year. However, corn production from new acres plays an
important role in corn supply. FASOM estimates an increase in land
devoted to corn production of 1.6 million acres (RFS Case) and 2.6
million acres (EIA Case) in 2012 compared to the Reference Case.

**Table 8.5-1.**

**U.S. Corn Acres Harvested, Corn Production, and Agricultural Land
Prices in 2012**

  --------------------- ----------------- -------------- -----------------
                        **Reference       **RFS Case**   **EIA Case**
                        Case**                           

  Corn Acres Harvested  78.5              80.1           81.1
  (million acres)                                        

  Total Corn Production 11.7              11.9           12.1
  (billion bushels)                                      

  Land Prices (percent  N/A               8.4%           16.8%
  increase relative to                                   
  Reference Case)                                        
  --------------------- ----------------- -------------- -----------------

Higher renewable fuel volumes will have a direct impact on the value of
U.S. agricultural land. As demand for corn and other farm products
increases, the price of U.S. farm land will also increase. The FASOM
analysis shows that in 2012, higher renewable fuel volumes increase
average agricultural land prices in the U.S. by about 8 percent (RFS
Case) and 17 percent (EIA Case).

**8.5.2 Soybean Acreage**

Increasing use of biodiesel fuel in the renewable fuel scenarios does
not cause a significant change in U.S. soybean production. Soybean
production stays relatively flat at 3.3 billion bushels in all three
scenarios analyzed. (See Table 8.5-2) Soybean acreage increases modestly
as well in the renewable fuel scenarios examined. In the RFS Case, total
soybean acres are 74.6 million. For the EIA Case, total soybean acres
are 74.4 million acres, compared to 73.4 million acres of soybeans in
the Reference Case.

Table 8.5-2. U.S. Soybean Acres Harvested and Soybean Production in 2012

  ----------------------- ----------------- --------------- ---------------
                          **Reference       **RFS Case**    **EIA Case**
                          Case**                            

  Soybean Acres Harvested 73.4              74.6            74.4
  (million acres)                                           

  Total Soybean           3.3               3.3             3.3
  Production (billion                                       
  bushels)                                                  
  ----------------------- ----------------- --------------- ---------------

**8.5.3 CRP Acreage**

Current lands in the Conservation Reserve Program (CRP) total
approximately 40 million acres. To qualify for inclusion in the CRP, the
acres must have been at one time in active agricultural use. Farmers are
paid to take these lands out of production and place them in CRP to
provide environmental benefits, including limiting erosion and providing
wildlife habitat. Farmers put land into the CRP voluntarily, considering
among other factors the value of the land if it were to remain in
agricultural production versus the amount paid under the CRP contract.
The amount of government payments can change over time.

For this analysis, we have assumed current per-acre payment levels to
landowners are maintained through 2012. However higher commodity prices
and higher land rents associated with higher renewable fuel volumes
would likely require higher CRP payments to maintain the same level of
CRP enrollments. The RFS and EIA renewable fuel volumes are estimated to
result in CRP withdrawals of 2.3 million and 2.5 million acres,
respectively, relative to the Reference Case. Most of the CRP lands are
not likely to go into corn or soybean production since much of the CRP
lands tend to be marginal lands due to their location and productivity.
For example, only a relatively small portion of CRP lands are in the
Corn Belt. Instead, additional corn or soybeans acres will probably be
planted on lands that were previously used for other crops or pasture,
for example, wheat, grain, sorghum or planted forage crops. It is
expected that some of the land removed from CRP will be used for these
other agricultural purposes. Table 8.5-3 depicts the estimate of CRP
impacts.

Table 8.5-3. CRP Acreage Changes Relative to Reference Case

  ------------------------------------- ----------------- ----------------
                                        **RFS Case**      **EIA Case**

  Reduction in CRP Acreage (million     2.3               2.5
  acres)                                                  
  ------------------------------------- ----------------- ----------------

**8.6 Fertilizer Use**

Under the RFS scenario, the total amount of nitrogen applied on all
farms increases by 1.2 percent, or 480,000 pounds, relative to the
Reference Case in 2012. Under the EIA scenario, the total amount of
nitrogen applied on all farms increases by 2 percent, or 790,000 pounds,
relative to the Reference Case in 2012. (See Table 8.6-1) We note that
this percent increase in fertilizer is largely accounted for by the 2
percent increase in land used for corn production and 1 percent increase
in land for soybean production. The fact that the amount of nitrogen
used increases at a smaller percent than the amount of land increase for
corn production suggest that much of the corn production land is already
in agricultural use (with fertilizer applied) and is not likely to be
land newly released from CRP.

Table 8.6-1. Nitrogen and Phosphorous Use in 2012

+----------------------+----------------+--------------+--------------+
|                      | **Reference    | **RFS Case** | **EIA Case** |
|                      | Case**         |              |              |
+----------------------+----------------+--------------+--------------+
| Total Nitrogen       | 40.28          | 40.76        | 41.07        |
| Applied              |                |              |              |
|                      |                |              |              |
| (million pounds)     |                |              |              |
+----------------------+----------------+--------------+--------------+
| Total Phosphorous    | 4.24           | 4.27         | 4.29         |
| Applied              |                |              |              |
|                      |                |              |              |
| (million pounds)     |                |              |              |
+----------------------+----------------+--------------+--------------+

Under the RFS scenario, the total amount of phosphorous applied on all
farms increases by 0.7 percent, or 30,000 pounds, relative to the
Reference Case in 2012. Under the EIA scenario, the total amount of
phosphorous applied on all farms increases by 1.2 percent, or 50,000
pounds, relative to the Reference Case in 2012. See Table 8.6-1.

**8.7 Environmental Analysis**

Although this analysis does not include a comprehensive and integrated
environmental assessment of the impacts in the agricultural sector of
higher renewable fuel volumes from this rulemaking, we looked at two
factors directly impacted by the production of agricultural crops that
may relate to environmental impacts. FASOM does estimate the amount of
fertilizer used and changes in CRP land, two indicators that could be
associated with water pollution.[^243]

Marathon commented that it believes that EPA's assessment of
environmental impacts does not consider all environmental impacts and is
therefore incomplete, especially with respect to water quality impacts.
As described above, our analysis predicts a modest increase in
fertilizer use and modest withdrawals of CRP lands due to the higher
renewable fuel volumes. While increased agricultural development would
likely increase pressure on environmentally sensitive areas such as
wetlands and prairie lands and rural ecosystems in general, FASOM does
not represent this level of land detail in the national model and
therefore cannot quantify any potential impacts on these subsets of land
types. To the extent that CRP withdrawals are managed in an
environmentally sustainable way, however, water pollution impacts would
be minimized.

Increasing worldwide demand for biofuels and decreasing U.S. exports of
feedstocks used in producing renewable fuels will likely lead to
increased prices, production, and different trade patterns for renewable
fuel feedstocks (i.e., corn and soybeans) in parts of the world outside
of the U.S. FASOM includes the export effect as it contains supply
curves for rest of world production of key agricultural products, but it
does not contain a mechanism for appraising world environmental
implications since FASOM is a domestic model of the U.S. agricultural
sector. Therefore, this analysis focuses only on impacts of the higher
renewable fuels volumes in the U.S.

**8.8 U.S. Food Prices**

Despite the wider use of U.S. agricultural feedstocks, principally corn,
for renewable fuels, FASOM estimates only a modest increase in U.S.
household food costs. Annual wholesale U.S. food costs are estimated to
increase by approximately \$7 per person with the RFS renewable volumes
and by about \$12 per person annually with the EIA renewable volumes by
2012. (See Figure 8.8-1) Agricultural costs are only a portion of
ultimate household food costs so significant increases in corn prices
and, to a lesser degree, soybean prices results in a much smaller
relative increase in household food costs.

**Figure 8.8-1. Increase in Annual Food Costs Per Person\
Relative to Reference** **Case in 2012**

![](media/image89.wmf){width="6.21875in" height="4.125694444444444in"}

FASOM estimates a relatively modest increase in U.S. prices for meat and
agricultural products associated with the higher renewable fuel volumes.
When evaluating changes in overall U.S. food prices, FASOM uses the All
Farm Products Price Index, which is a weighted average of prices
received by farmers at the \"farm gate\" for crop and livestock products
relative to the Reference Case.[^244] FASOM estimates a 4 percent
increase in the RFS Scenario and a 7 percent increase in the EIA
Scenario in the weighted price of all farm products. (See Table 8.8-1)

To evaluate changes in U.S. meat prices, FASOM uses the All Meat
Products Price Index which is a weighted average of the prices that
farmers receive for meat products at the farm gate. This index is based
upon changes in the weighted average of beef, pork, chicken, and turkey
prices. U.S. meat prices that farmers receive in 2012 are estimated to
increase by 0.3% in the RFS Case and by 1.3% in the EIA Case compared to
the Reference Case.

**Table 8.8-1. Increase in the All Farm Products Price Index and the All
Meat Products Price Index** Relative to the Reference Case in 2012

  -------------------------------- ------------------- -------------------
                                   **RFS Case**        **EIA Case**

  All Farm Products Price          3.8% increase       6.9% increase

  All Meat Products Price          0.3% increase       1.3% increase
  -------------------------------- ------------------- -------------------

Because corn is a major component of the All Crop Price Index, a
significant change in corn prices will result in a pronounced change in
this index. The impact of corn price changes on the Meat Price Index
will be less pronounced for two reasons. First, as corn prices rise,
meat producers will modify feed rations and production systems to reduce
their corn usage. Second, there will also be substitution among meats
leading to higher consumption of meat from animals using less of the
higher priced corn (e.g., increased production of poultry products
relative to beef products).

**Chapter 9: Small-Business Flexibility Analysis**

This chapter presents our Small Business Flexibility Analysis (SBFA)
which evaluates the potential impacts of the new standards on small
entities. The Regulatory Flexibility Act, as amended by the Small
Business Regulatory Enforcement Fairness Act of 1996 (SBREFA), generally
requires an agency to prepare a regulatory flexibility analysis of any
rule subject to notice and comment rulemaking requirements under the
Administrative Procedure Act or any other statute unless the agency
certifies that the rule will not have a significant economic impact on a
substantial number of small entities. Prior to issuing a proposal for
this rulemaking, we analyzed the potential impacts of these regulations
on those entities that we believe are small entities (see section 9.3,
below). As a part of this analysis, we conducted outreach with those
entities to gather information and recommendations from these entities
on how to reduce the impact of the rule on small businesses.

**9.1 Requirements of the Regulatory Flexibility Act**

We are generally required under the Regulatory Flexibility Act (RFA) to
conduct a regulatory flexibility analysis unless we certify that the
requirements of a regulation will not cause a significant impact on a
substantial number of small entities. The key elements of the RFA
include:

• a description of and, where feasible, an estimate of the number of
small entities to which the proposed rule will apply;

• the projected reporting, record keeping, and other compliance
requirements of the proposed rule, including an estimate of the classes
of small entities which will be subject to the requirements and the type
of professional skills necessary for preparation of the report or
record;

• an identification to the extent practicable, of all other relevant
Federal rules which may duplicate, overlap, or conflict with the
proposed rule; and,

• any significant alternatives to the proposed rule which accomplish the
stated objectives of applicable statutes and which minimize any
significant economic impact of the proposed rule on small entities.

The RFA was amended by SBREFA to ensure that concerns regarding small
entities are adequately considered during the development of new
regulations that affect them. Although we are not required by the Clean
Air Act to provide special treatment to small businesses, the RFA
requires us to carefully consider the economic impacts that our proposed
rules will have on small entities. Specifically, the RFA requires us to
determine, to the extent feasible, our rule's economic impact on small
entities, explore regulatory options for reducing any significant
economic impact on a substantial number of such entities, and explain
our ultimate choice of regulatory approach.

We have concluded that the final RFS rule will not have a significant
impact on a substantial number of small entities. We based this
conclusion on several criteria. First, the industry is expected to be
overcomplying by a wide margin independent of the standard, thus causing
compliance costs to be minimal. Second, the Energy Policy Act of 2005
(Energy Policy Act) already provides relief from the renewable fuels
standards until 2011 for the majority of the small entities; and lastly,
we are extending this relief to the remaining small entities. This is
discussed further below.

**9.2 Need for the Rulemaking and Rulemaking Objectives**

A detailed discussion on the need for and objectives of this rule are in
the preamble to the final rule. As previously stated, EPA is required to
promulgate regulations implementing a renewable fuel program under
Section 1501 of the Energy Policy Act, which amended the Clean Air Act
by adding Section 211(o). The Energy Policy Act requires EPA to
establish a program to ensure that U.S. gasoline contains specific
volumes of renewable fuel for each calendar year beginning in 2006, to
increase the amount of renewable fuel used in vehicles and engines in
the U.S.

**9.3 Description of Affected Entities**

**9.3.1 Definition of Small Entities**

Small entities include small businesses, small organizations, and small
governmental jurisdictions. For the purposes of assessing the impacts of
the rule on small entities, a small entity is defined as: (1) a small
business that meets the definition for business based on the Small
Business Administration's (SBA) size standards (see Table 9-1); (2) a
small governmental jurisdiction that is a government of a city, county,
town, school district or special district with a population of less than
50,000; and (3) a small organization that is any not-for-profit
enterprise which is independently owned and operated and is not dominant
in its field. Table 9.3.1-1 provides an overview of the primary SBA
small business categories potentially affected by this regulation.

**Table 9.3.1-1. Small Business Definitions**

  ----------------------- ------------------------------ ----------------
  **Industry**            **Defined as small entity by   **NAICS Codes
                          SBA if:**                      ^a^**

  Gasoline refiners       ≤1,500 employees[^245]         324110

  ^a^ North American                                     
  Industrial                                             
  Classification System                                  
  ----------------------- ------------------------------ ----------------

**9.3.2 Summary of Small Entities to Which the Rulemaking Will Apply**

The refiners that are potentially affected by this proposed rule are
those that produce gasoline. For our recent rulemaking "Control of
Hazardous Air Pollutants from Mobile Sources" (72 FR 8428, February 26,
2007), we performed an industry characterization to determine the
universe of potentially affected gasoline refiners. Information about
the characteristics of refiners comes from sources including the Energy
Information Administration within the U.S. Department of Energy, and
from Hoover's (a division of Dun and Bradstreet). The refining industry
is located primarily in NAICS code 324110.

The industry characterization was then used to determine which refiners
met the SBA definition of a small refiner. From the industry
characterization, and further analysis following the Notice of Proposed
Rulemaking (71 FR 55552, September 22, 2006), we determined that there
were 15 gasoline refiners (owning 16 refineries) that met the definition
of a small refiner. It should be noted that because of the dynamics in
the refining industry (e.g., mergers and acquisitions), the actual
number of refiners that ultimately qualify for small refiner status
could be different from this estimate.

Title XV the Energy Policy Act provides, at Section 1501(a)(2) \[42
U.S.C. 7545(o)(9)(A)-(D)\], special provisions for "small refineries",
which includes a temporary exemption from the standards until calendar
year 2011. Further, the Energy Policy Act states that EPA must use the
definition of "small refinery" and apply the special provisions provided
for small refineries in the RFS program. The Energy Policy Act defines
the term "small refinery" as "...a refinery for which the average
aggregate daily crude oil throughput for a calendar year...does not
exceed 75,000 barrels."

A small refinery (as defined by the Energy Policy Act) is very different
from a small [refiner]{.underline} (as defined in SBA's regulations at
13 CFR 121.201). Per 13 CFR 121.201, and as stated above in Table 9-1, a
small refiner is a small business that employs less than or equal to
1,500 employees. A small [refinery]{.underline}, per the Energy Policy
Act, is a small-capacity refinery and could be owned by a larger refiner
that exceeds the criterion specified in SBA's small entity definition;
whereas small refiners generally only own a few (and more often than
not, only one) refineries.

In our analysis of the potentially affected small refiners, we found
that 42 refineries met the Energy Policy Act's definition of a small
refinery. Of these, we determined that 13 of these refineries were owned
by small refiners. Therefore, 12 of the 15 small refiners owned
refineries that also met the Energy Policy Act's definition of a small
refinery. As a result, we believe that all but three small refiners
would automatically be granted relief by implementing the provisions
specified in the Energy Policy Act.

**9.4 Issues Raised By Public Comments**

During the public comment period we received numerous comments regarding
various aspects of the proposed rule, including our proposed small
refiner provisions. The following section provides a summary of the
comments that we received on our proposed provisions. More information
on these comments can be found in the Final Summary and Analysis of
Comments, which is a part of the rulemaking record.

**9.4.1 Extension of the Small Refinery Exemption to Small Refiners**

Commenters that supported the provision extending the small refinery
exemption to small refiners generally stated that they believe that a
small refiner exemption is necessary as those entities that would
qualify as small refiners are generally at an economic disadvantage due
to their company size -- whereas the Energy Policy Act only recognizes
facilities, based on the size of each location. These commenters also
stated that they have concerns with the cost and the availability of
credits under the RFS program, and believe that provisions for small
refiners are necessary to help mitigate any significant adverse economic
impact on these entities.

Commenters that opposed the provision commented that they believe that
EPA exceeded its discretionary authority, that there appears to be no
basis on which the Agency can legitimately expand this statutory
exemption to add small refiners, and that Congress "clearly did not
intend that the exemption be broadened to also include small refiners."
One commenter also stated that it does not believe that small refiner
provisions are necessary because the RFS program does not require costly
capital investments like previous fuel regulations.

**9.4.2 Application Deadline**

We proposed that refiners would need to apply for the small refinery
exemption, and that the exemption would be effective 60 days after
receipt of the application by EPA (unless EPA notifies the applicant
that the application was not approved or that additional documentation
is required). We received comments on this provision in which commenters
stated that requiring small refinery applications was inconsistent with
the language set out in the Energy Policy Act. The commenters stated
that the Energy Policy Act intended that small refineries would
automatically receive the small refinery exemption upon the effective
date of the standard, and that these parties should not be considered
obligated parties in 2007 even if they do not submit a small refinery
application.

**9.4.3 Provisions for Foreign Small Refineries and Refiners**

For consistency with prior gasoline-related fuel programs, we also
proposed to extend the RFS small refinery (and small refiner) exemption
to foreign refiners, and we requested comment on this provision. We
received some comments in which commenters stated that they believe that
there is no reason to extend the small refinery exemption to these
refiners. One commenter even stated that it believed that such an
allowance would be unlawful.

**9.4.4 Other**

We received some comments which stated that EPA needed to clarify
whether or not exempt small refineries (and small refiners) could
separate a RIN simply by owning a batch of fuel. We also received a
comment which stated that it was not clear in the proposed rule whether
or not small refineries (and small refiners) blending ethanol at a
terminal or any location without formally opting into the program could
separate RINs.

**9.5 Related Federal Rules**

Other current and proposed Federal rules that are related to this rule
are: the Mobile Source Air Toxics (MSAT2) rule (72 FR 8428, February 26,
2007), the Tier 2 Vehicle/Gasoline Sulfur rulemaking (65 FR 6698,
February 10, 2000), and the fuel sulfur rules for highway diesel (66 FR
5002, January 18, 2001) and nonroad diesel (69 FR 38958, June 29, 2004).

**9.6 Projected Reporting, Recordkeeping, and Other Compliance
Requirements**

For any fuel control program, EPA must have the assurance that refiners
meet the applicable standards. Thus, requirements are imposed to ensure
that compliance obligations are met.

The recordkeeping, reporting and compliance provisions of this program
are fairly consistent with those currently in place for our other 40 CFR
part 80 fuel programs, including the highway and nonroad diesel and MSAT
regulations. These provisions include:

· Registration (the registration numbers will also be used in the RINs)

· Submission of annual reports summarizing a refiner's annual gasoline
production and a demonstration of its compliance with the renewable
fuels standard and submission of annual reports detailing and tracking a
refiner's RINs; EPA's Central Data Exchange will be used for report
submissions

· Recordkeeping will consist of the retention of all compliance
documents (such as Product Transfer Documents and all reports submitted
to EPA) for at least five years

For a more detailed discussion of these provisions, please see section
IV of the preamble to this final rule.

**9.7 Steps to Minimize Significant Economic Impact on Small Entities**

As stated above, we conducted outreach to a number of stakeholders that
met the definition of a small entity to gain feedback and advice on the
needs of small businesses and potential challenges that these entities
may face. The feedback that we received from these entities as a result
of these meetings was used during the development of the proposed rule
for developing regulatory alternatives to mitigate the impacts of the
rulemaking on small businesses. General concerns raised by these
entities were the potential difficulty and costs of compliance with the
upcoming standards given the other fuel compliance requirements that the
fuel refining industry is subject to. Below we discuss the regulatory
flexibility alternatives and provisions which are being finalized in
this action.

While we do not believe that the RFS program with just the
statutorily-prescribed temporary relief for small refineries would have
a significant economic impact on a substantial number of small entities,
we continue to believe that some refiners, due to their size, generally
face greater challenges compared to larger refiners. These refiners
generally have greater difficulty in raising and securing capital for
investing in capital improvements and in competing for engineering
resources and projects. This rulemaking does not require that refiners
make capital improvements, however there are still costs associated with
meeting the standard. Thus, we find it appropriate to extend the small
refinery temporary exemption, as set out in the Energy Policy Act, to
small refiners. Under this exemption, any gasoline produced at a
refinery owned by a small refiner will not be counted in determining the
renewable fuel obligation of a refiner until January 1, 2011; further,
the small refiner may exclude gasoline produced at its refineries from
its compliance calculations. Beginning in 2011, refineries owned by
small refiners will be required to meet the same renewable fuel
obligation as all other refineries.

Past fuels rulemakings have included a provision that, for the purposes
of the regulatory flexibility provisions for small entities, a refiner
must also have an average crude capacity of no more than 155,000 barrels
of crude per day (bpcd). To be consistent with these previous rules, we
are finalizing in this rule that refiners that meet this criterion (in
addition to having no more than 1,500 total corporate employees) will be
considered small refiners for the purposes of the regulatory flexibility
provisions for RFS program. Further, the refiner must have produced
gasoline at its refineries by processing crude oil through refinery
processing units. We are also finalizing that eligibility will be based
on 2004 data.

We agree with statements from commenters that the Energy Policy Act did
in fact intend to provide the small refinery exemption without the need
for the submission of small refinery applications, and that these
parties should receive the exemption upon the effective date of the
rule. We also believe that this should be the case for small refiners as
well. Therefore, we are finalizing that small refiners will also receive
the exemption immediately upon the effective date of the rule. However,
to ensure that only those refiners who meet the criteria above receive
this exemption, we believe that it is necessary for refiners to verify
that they do in fact meet the criteria. Therefore, these refiners will
also be required to submit a verification letter showing that they meet
the criteria for qualification as a small refiner for the regulatory
flexibility provisions. This letter will be similar to the small refiner
status applications required under other EPA fuel programs (and must
contain all the required elements specified at §80.1142 of the
regulations), except the letter will not be due prior to the program.
Small refiner status verification letters for this rule that are later
found to contain false or inaccurate information will be void as of the
effective date of this rule. Small refiners who subsequently do not meet
all of the RFS program's regulatory flexibility qualification criteria
(i.e., cease producing gasoline by processing crude oil, employ more
than 1,500 people, or exceed the 155,000 bpcd crude oil capacity limit)
as a result of a merger with or acquisition of or by another entity, are
disqualified as small refiners, except in the case of a merger between
two previously approved small refiners. As in other EPA programs, where
such disqualification occurs, the refiner must notify EPA in writing no
later than 20 days following the disqualifying event.

We are finalizing the proposed provision allowing foreign refiners to
apply for a small refinery or small refiner exemption under the RFS
program. The Energy Policy Act definition of "small refinery" is not
limited to domestic facilities, and we believe that we have the
discretion to apply the definition of small refinery, and the similar
relief that we are providing to small refiners, to foreign producers. We
believe that this provision is necessary for consistency with prior fuel
programs (anti-dumping, MSAT, and the fuel sulfur rules) which allowed
foreign refiners to receive such exemptions. Under this provision,
gasoline produced at approved foreign small refineries, and by approved
foreign small refiners, will be exempt from the RFS standard such that
obligated parties (importers or blenders) would not count these volumes
towards their renewable volume obligations.

We are also finalizing the proposed provision that the automatic five
year exemption, and any small refinery extended exemptions (extensions
of the small refiners exemption will only be available to small
refineries), may be waived upon notification to EPA. Gasoline produced
by a small refiner who waives its exemption will be included in the RFS
program and will be included in the gasoline used to determine the
refiner's renewable fuel obligation. If a refiner waives the exemption,
the refiner will be able to separate and transfer RINs like any other
obligated party. However, exempt small refiners cannot separate a RIN
simply by owning a batch, a RIN can only be separated by these parties
once the volume of renewable fuel is blended with gasoline or diesel to
produce a motor vehicle fuel (as stated in the regulations at §80.1129).
If a small refiner does not waive its small refiner exemption, it can
still separate and transfer RINs, but only for the renewable fuel that
the refiner itself blends into gasoline (i.e., the refinery operates as
an oxygenate blender facility). Lastly, exempt small refiners who blend
ethanol can separate RINs from batches without formally opting in to the
program.

**9.8 Conclusions**

After considering the economic impacts of today's proposed rule on small
entities, we do not believe that this action will have a significant
economic impact on a substantial number of small entities. While the
Energy Policy Act provided for a temporary exemption for small
refineries from the requirements of today's proposed rule, these parties
will have to comply with the requirements following the exemption
period. Therefore, we had to take into account the economic effects of
the program on small entities when they would need to comply with the
standard. As described in section VI of the preamble to this final rule,
the annual projections of ethanol production are greater than the annual
renewable fuel volumes required by the Energy Policy Act. For example,
in 2011, when the Energy Policy Act's small refinery exemption ends,
over one billion gallons in excess RINs are projected to be available.
Further, excess RINs are anticipated for each year of the program. Due
to this projected excess supply in comparison to the standard, the cost
of RINs should be very low---near the level of the transaction costs.

Due to the low cost to affected small entities, and the projected RIN
availability, as well as the temporary relief provided to small
refineries and small refiners, we do not believe that this program will
impose a significant economic impact on a substantial number of small
entities.

[^1]: This analysis does not consider ethanol plants that may be located
    in (or planned for) the Virgin Islands or U.S. territories.

[^2]: The October 2006 ethanol production capacity baseline was
    generated based on the June 2006 NPRM plant list and updated on
    October 18, 2006 based on a variety of data sources including:
    Renewable Fuels Association (RFA), Ethanol Producer Magazine (EPM),
    ICF International, BioFuels Journal, and ethanol producer websites.
    The baseline includes small-scale ethanol production facilities as
    well as former food-grade ethanol plants that have since
    transitioned into the fuel-grade ethanol market. Where applicable,
    current ethanol plant production levels have been used to represent
    plant capacity, as nameplate capacities are often underestimated.

[^3]: **Endnotes**

    ^?^ Renewable Fuels Association (RFA), Ethanol Biorefinery Locations
    (Updated October 16, 2006).

[^4]: Ethanol Producer Magazine (EPM), Plant List (Downloaded October
    18, 2006) monthly magazine publications (June 2006 through October
    2006).

[^5]: Ethanol Producer Magazine (EPM), monthly magazine publications
    (June 2006, July 2006, August 2006, September 2006 and October
    2006).

[^6]: ^?^ ICF International, Ethanol Industry Profile (September 30,
    2006).

[^7]: ^?^ BioFuels Journal, News & Information for the Ethanol and
    BioFuels Industries (breaking news posted June 16, 2006 through
    October 18, 2006).

[^8]: Facilities were assumed to burn natural gas if the plant fuel type
    was not mentioned or unavailable.

[^9]: Based on information obtained from ethanol marketer websites,
    ethanol producer websites, and conversations with ethanol
    marketers/producers.

[^10]: Based on RFA comments received in response to the proposed
    rulemaking, 71 FR 55552 (September 22, 2006).

[^11]: Under construction plant locations, capacities, feedstocks, and
    energy sources as well as planned/proposed plant locations and
    capacities were derived from a variety of data sources including
    Renewable Fuels Association (RFA), Ethanol Producer Magazine (EPM),
    ICF International, BioFuels Journal, and ethanol producer websites.

[^12]: ^?^ Renewable Fuels Association (RFA), Ethanol Biorefinery
    Locations (Updated June 19, 2006).

[^13]: ^?^ Ethanol Producer Magazine (EPM), U.S. & Canada Fuel Ethanol
    Plant Map (Spring 2006).

[^14]: ^?^ International Fuel Quality Center (IFQC), Special Biofuels
    Report #75 (April 11, 2006).

[^15]: Construction timelines based on information obtained from press
    releases and ethanol producer websites.

[^16]: A more detailed summary of the plants we considered is found in a
    March 5, 2007 note to the docket titled: RFS Industry
    Characterization -- Ethanol Production.

[^17]: We anticipate a ramp-up in cellulosic ethanol production in the
    years to come so that capacity exists to satisfy the Act's 2013
    requirement (250 million gallons of cellulosic biomass ethanol).
    Therefore, for subsequent analysis purposes, we have assumed that
    250 million gallons of ethanol would come from cellulosic biomass
    sources by 2012.

[^18]: ^?^ "Biodiesel Performance, Costs, and Use", Anthony Radich, EIA
    page 6.

[^19]: ^?^ NBB Survey September 13, 2006 "U.S. Biodiesel Production
    Capacity".

[^20]: Oleochemicals are derived from biological fats and oils using
    hydrolysis or alcoholysis with products of fatty acid esters and
    glycerol.

[^21]: ^?^ From Independent Biodiesel Feasibility Group Presentation.

[^22]: ^?^ "Pipeline Considerations for Ethanol", Department of
    Agricultural Economics, Kansas State University, August 2002.
    <http://www.agmrc.org/NR/rdonlyres/4EE0E81C-C607-4C3F-BBCF-B75B7395C881/0/ksupipelineethl.pdf>
    , and "Shipping Ethanol Through Pipelines", American Petroleum
    Institute, April 6, 2006,
    http://api-ep.api.org/printerformat.cfm?ContentID=54FD1A11-95FA-4B7C-ACE1D7C6F121FB1C.

[^23]: ^?^ Infrastructure Requirements for an Expanded Fuel Ethanol
    Industry, Downstream Alternatives Inc., January 15, 2002.

[^24]: River barges typically have a capacity of 10,000 barrels. Ocean
    barges typically have a capacity of 20,000 barrels. Barges are
    sometimes subdivided into 2 or 3 compartments.

[^25]: ^?^ "Ethanol Industry's Interest in River Sites Grows", River
    Transport News, Vol. 15, No. 10, May 22, 2006.

[^26]: ^?^ Infrastructure Requirements for an Expanded Fuel Ethanol
    Industry, Downstream Alternatives Inc., January 15, 2002.

[^27]: ^?^ Infrastructure Requirements for an Expanded Fuel Ethanol
    Industry, Downstream Alternatives Inc., January 15, 2002.

[^28]: See section 7.3 of this RIA regarding the projected costs of the
    necessary infrastructure improvements.

[^29]: MTBE is typically blended with gasoline at the refinery. MTBE
    production plants are often located nearby to refineries allowing
    transport to the refinery by dedicated pipeline. In cases where, the
    sources of MTBE are more distant from the refinery, barge and rail
    are the preferred means of transport and relatively little MTBE is
    transported by truck.

[^30]: ^?^ The concentration of biodiesel in a biodiesel blend is
    indicated by the number following the "B" designation. For example,
    B99.9 indicates a biodiesel blend containing 99.9 percent biodiesel,
    and B80 indicates a blend containing 80 percent biodiesel.
    Manufactures of biodiesel sometimes blend in one tenth of one
    percent diesel fuel into biodiesel to create B99.9 prior to shipping
    the fuel to terminals to create more dilute biodiesel blends so that
    the producer can claim the biodiesel tax credit (pursuant to
    Internal Revenue Service requirements).

[^31]: ^?^ The point at which wax crystals form is referred to as the
    cloud point. The cloud point of B100 varies depending on the feed
    stock used in its production.

[^32]: ^?^ The relatively low concentration biodiesel blends that are
    typically used in vehicles (up to 20% biodiesel) can be formulated
    to have comparable cold flow performance to petroleum based diesel
    fuel. Thus, there is no need to heat such biodiesel blends in
    vehicle fuel tanks.

[^33]: ^?^ F.O. Licht, "World Ethanol Markets, The Outlook to 2015"
    (2006).

[^34]: West Texas Intermediate (WTI) crude oil pricing was \$59.08/bbl
    in November, 2006; \$61.96/bbl in December, 2006; and \$54.51/bbl in
    January 2007 according to EIA spot pricing.

[^35]: ^?^ EIA AEO 2006 Table 12: Petroleum Product Prices

[^36]: ^?^ EIA Crude Oil Spot Pricing
    (http://tonto.eia.doe.gov/dnav/pet/pet_pri_spt_s1_d.htm)

[^37]: In AEO 2007, EIA forecasted an even higher ethanol consumption of
    11.2 billion gallons by 2012. The draft report was issued on
    December 5, 2006, and we were unable to incorporated it into the
    refinery modeling used to conduct our analyses.

[^38]: RFG oxygenate requirement found at 40 CFR 80.41(f). This
    requirement was effective for 2004 but has since been eliminated by
    the Energy Act Section 1504, promulgated on May 8, 2006 at 71 FR
    26691.

[^39]: Other low-usage oxygenates (e.g. ETBE, TAME, etc.) were assumed
    to be negligible for the purpose of this analysis.

[^40]: ^?^ Sources: U.S. EPA Office of Transportation & Air Quality,
    List of Federal Reformulated Gasoline Areas (Updated February 23,
    2004) and U.S. EPA Office of Transportation & Air Quality, 2004 RFG
    Fuel Survey Results
    (<http://www.epa.gov/otaq/regs/fuels/rfg/properf/rfgperf.htm>).

[^41]: Temperature inversions in the lower atmosphere are relatively
    common, especially during winter months in cold climates. A
    temperature inversion occurs when cold air close to the ground is
    trapped by a layer of warmer air, creating stagnation and trapping
    pollution close to the ground.

[^42]: ^?^ Source: U.S. EPA Office of Transportation and Air Quality,
    State Winter Oxygenated Fuel Program Requirements for Attainment or
    Maintenance of CO NAAQS (November 2005).

[^43]: For analysis purposes, both states were assumed to have ethanol
    mandates which required 100% of the gasoline to contain 10% ethanol.
    However, in reality, Hawaii's ethanol mandate only requires that 85%
    of the gasoline contain 10% ethanol.

[^44]: This RFS proposal is based on MSAT1 conditions. Impacts of the
    recent MSAT2 rule (72 FR 8428) which removes individual refinery
    toxic performance standards (baselines) in exchange for a nationwide
    benzene standard are reflected in the analysis for that rulemaking.

[^45]: ^?^ National Ethanol Vehicle Coalition
    (<http://www.e85fuel.com/e85101/faqs/number_ffvs.php>).

[^46]: The small volumes of E85 (85 percent ethanol) gasoline have been
    ignored for this analysis.

[^47]: For the Base Case analysis, all Arizona CBG was classified as
    "RFG". In 2004, wintertime Arizona RFG was assumed to contain 10%
    ethanol (governed by the Phoenix oxy-fuel program). Summertime RFG
    was assumed to be comprised of 2/3 California RFG (containing 5.7
    percent ethanol) and 1/3 PADD 3 RFG (containing either 10 percent
    ethanol or 11 percent MTBE in 2004).

[^48]: ^?^ EIA 2004 Petroleum Marketing Annually (Table 48: Prime
    Supplier Sales Volumes of Motor Gasoline by Grade, Formulation, PAD
    District, and State).

[^49]: ^?^ EIA reported 2004 total MTBE usage (in RFG) as 2.0 billion
    gallons. The reported MTBE usage was reduced from 2.0 to 1.9 billion
    gallons under the assumption that CA, NY, and CT implemented their
    state MTBE bans on time (by 1/1/04). (EIA showed small amounts of
    MTBE use in these states in 2004). EIA's allocation of MTBE by state
    was also adjusted based on fuel survey results. Most noteworthy, EIA
    reported MTBE usage in Arizona "RFG" as zero. However, the 2004
    Phoenix fuel survey results suggest otherwise. As such, an
    appropriate amount of MTBE was allocated to Arizona based on the
    assumption that 1/3 of all summertime Arizona "RFG" resembles PADD 3
    RFG (which contained some level of MTBE in 2004).

[^50]: ^?^ EIA Historical RFG MTBE Usage (file received from EIA
    representative on March 9, 2006).

[^51]: ^?^ EIA Monthly Energy Review, June 2006 (Table 10.1: Renewable
    Energy Consumption by Source, Appendix A: Thermal Conversion
    Factors).

[^52]: ^?^ FHWA Highway Statistics 2004: Estimated Use of Gasohol (April
    2006).

[^53]: ^?^ The U.S. Department of Treasury requires a distinction
    between gasohol and gasoline on motor vehicle fuel tax reports for
    states with gasohol sales tax exemptions. These financial records
    are the source of FHWA's gasohol/ethanol data. However, since state
    gasohol tax exemptions have become virtually nonexistent over the
    past several years, gasohol reporting (namely the distinction
    between gasoline and gasohol) has suffered.

[^54]: ^?^ EPA Reformulated Gasoline (RFG) Survey
    (<http://www.epa.gov/otaq/regs/fuels/rfg/properf/rfgperf.htm>).

[^55]: ^?^ Alliance of Automobile Manufacturers (AAM) North American
    Fuel Survey 2004. The report and data can be purchased through the
    AAM website, <http://autoalliance.org/fuel/fuel_surveys.php>, or by
    contacting the Alliance at 1401 Eye Street, N.W., Suite 900,
    Washington, DC 20005, Tel: (202) 326-5500.

[^56]: ^?^ EIA Monthly Energy Review, June 2006 (Table 10.1: Renewable
    Energy Consumption by Source, Appendix A: Thermal Conversion
    Factors).

[^57]: ^?^ EIA Historical RFG MTBE Usage (file received from EIA
    representative on March 9, 2006).

[^58]: ^?^ U.S. EPA, State Actions Banning MTBE (Statewide), June 2004.

[^59]: ^?^ EIA 2004 Petroleum Marketing Annually (Table 48: Prime
    Supplier Sales Volumes of Motor Gasoline by Grade, Formulation,
    PADD, and State).

[^60]: ^?^ Aforementioned seasonal split for gasoline and ethanol based
    on RFG production seasons (Summer: May 1 through September 15^th^;
    Winter: January 1^st^ through April 30^th^ and September 16^th^
    through December 31^st^).

[^61]: ^?^ EIA Monthly Energy Review June 2006 (Table 10.1: Renewable
    Energy Consumption by Source, Appendix A: Thermal Conversion
    Factors).

[^62]: ^?^ We acknowledge that the aforementioned seasonal split does
    not exactly match the new summer/winter seasons defined in the
    Energy Act (Summer: April 1^st^ through September 30^th^; Winter:
    January 1^st^ through March 31^st^ and November 1^st^ through
    December 31^st^).

[^63]: ^?^ EPA Reformulated Gasoline (RFG) Survey
    (<http://www.epa.gov/otaq/regs/fuels/rfg/properf/rfgperf.htm>)

[^64]: ^?^ AAM North American Fuel Survey 2004
    (<http://autoalliance.org/fuel/fuel_surveys.php>).

[^65]: ^?^ EIA Annual Energy Outlook 2006 (Table 2: Energy Consumption
    by Sector and Source).

[^66]: The total ethanol volume reported in table 2.2-7 (3.895 Bgal) is
    slightly lower than the reference case value reported in Table 2.1-1
    (3.947 Bgal). The reason for the slight discrepancy is because the
    numbers presented here were based off the estimated 2004 base case
    (3.5 Bgal) whereas the numbers presented in Table 2.1-1 were based
    off a more precise 2004 ethanol use (3.548 Bgal) reported by EIA in
    July 2006 Monthly Energy Review.

[^67]: The Montana state mandate requires all gasoline to contain 10
    vol% ethanol once plant production ramps up to 40 MMgal/yr. The
    Washington state mandate requires 20% of all gasoline to contain 10
    vol% ethanol by 12/1/08. Similarly, the Louisiana state mandate
    requires 20% of all gasoline to contain 10 vol% ethanol once plant
    production ramps up to 50 MMgal/yr. Finally, the Missouri state
    mandate requires all gasoline to contain 10 vol% ethanol by 1/1/08.
    At the time of our analysis, these were the only four new state
    ethanol mandates. However, EPA recognizes that as of 7/13/06,
    several others have new/additional biofuel standards pending
    (California, Colorado, Idaho, Illinois, Indiana, Kansas, Minnesota,
    New Mexico, Pennsylvania, Virginia, and Wisconsin).

[^68]: ^?^ International Fuel Quality Center (IFQC), Special Report:
    United States -- State Renewable Content Standards (June 6, 2006).

[^69]: ^?^ Renewable Fuel News and World Refining & News Today (January
    2006).

[^70]: ^?^ Modeling Impacts of Energy Bill RFS, Jacobs Consultancy
    December 2006.

[^71]: ^?^ Based on conversations with state officials and regional EPA
    officials.

[^72]: ^?^ The Energy Act Section 1504, promulgated on May 8, 2006 at 71
    FR 26691.

[^73]: ^?^ Gasoline consumed in the greater Phoenix metropolitan area
    under the Arizona Clean Burning Gasoline (CBG) Program, has not been
    considered "RFG" by the LP refinery modeling and thus discussed in
    the conventional gasoline section.

[^74]: ^?^EIA 2004 Petroleum Marketing Annually (Table 32: Conventional
    Motor Gasoline Prices by Grade, Sales Type, PAD District, and
    State).

[^75]: ^?^ EIA NEMS Petroleum Market Model Documentation, Appendix I,
    Table I4.

[^76]: ^?^ The following states have intentionally been excluded from
    this CG gasoline/ethanol cost table because they do not consume any
    CG (100% RFG): CA, CT, DC, DE, MA, NJ, RI.

[^77]: ^?^ EPA acknowledges that other states are considering (or may
    have even approved) retail pump incentives for gasohol. However, at
    the time this consumption analysis was completed, these were the
    only five states offering retail pump incentives that were likely to
    be applicable in 2012.

[^78]: ^?^ American Coalition for Ethanol, *STAT***US**: State by State
    Ethanol Handbook 2006 (supplemented by information obtained from
    state websites and conversations with state government officials).

[^79]: ^?^ Refinery modeling performed in support of the original RFG
    rulemaking is also used to help separate the effects of the two
    oxygenates.

[^80]: ^?^ *Final Regulatory Impact Analysis for Reformulated Gasoline*,
    Table VI-A6, December 13, 1993.

[^81]: ^?^ U.S. EPA, *Final Regulatory Impact Analysis for Reformulated
    Gasoline*, Table VI-A6, EPA420-R-93-017, December 13, 1993.
    Available from RFG Final Rule, Docket No. A-92-12, February 1994.

[^82]: ^?^ Though the MSAT2 final rulemaking (72 FR 8428, February 26,
    2007) eliminates these air toxics and NOx requirements beginning in
    2011.

[^83]: ^?^ U.S. EPA, *EPA's National Mobile Inventory Model (NMIM), A
    Consolidated Emissions Modeling System for MOBILE6 and NONROAD*,
    EPA420-R-05-024, December 2005, version NMIM20060310 with county
    database NCD20060201 modified according to discussion in Chapter
    2.2.

[^84]: ^?^ U.S. EPA, *Guide on Federal and State Summer RVP Standards
    for Conventional Gasoline Only*, EPA420-B-05-012, November 2005.

[^85]: ^?^ *Alliance of Automobile Manufacturers North American Fuel
    Survey 2005*. The report and data can be purchased through the AAM
    website, http://autoalliance.org/fuel/fuel_surveys.php, or by
    contacting the Alliance at 1401 Eye Street, N.W., Suite 900,
    Washington, DC 20005, Tel: (202) 326-5500. Information about the
    data and methods is contained in the report Survey Sampling and
    Testing Details (May 2005), which may be downloaded from the AAM
    website at no charge.

[^86]: ^?^ U.S. EPA website www.epa.gov/otaq/rfg/whereyoulive.htm, as of
    April 11, 2006.

[^87]: ^?^ U.S. EPA, *A Comprehensive Analysis of Biodiesel Impacts on
    Exhaust Emissions, Draft Technical Report*, EPA420-P-02-001, October
    2002.

[^88]: "Draft Assessment of the Real-World Impacts of Commingling
    California Phase 3 Reformulated Gasoline, California Environmental
    Protection Agency, Air Resources Board, August 2003.

[^89]: Harley, Robert A. and Shannon C. Coulter-Burke, "Relating Liquid
    Fuel and Headspace Vapor Composition for California Reformulated
    Gasoline Samples Containing Ethanol," Environmental Science and
    Technology, Volume 34, Number 19, 2000. pp 4088+4094, Figure 3. It
    should be noted that the ethanol concentrations shown in the
    reference are in terms of mole fraction, which are essentially a
    factor of 2 higher than volume fraction.

[^90]: Aulich, Ted and John Richter, "Addition of Nonethanol Gasoline to
    E10 -- Effect on Volatility," Energy and Environmental Research
    Center at the University of North Dakota, July 15, 1999.

[^91]: Bennett, Alison, Stephan Lamm, Hasan Orbey and Stanley I.
    Sandler, "Vapor-Liquid Equilibria of Hydrocarbons and Fuel
    Oxygenates. 2," Jounal of Chemical Engineering Data, Volume 38,
    1993, pp. 263-269, Figure 7. This reference shows the ethanol
    concentration in terms of mass fraction, which is nearly identical
    to volume fraction.

[^92]: Caffrey, Peter J., and Paul A. Machiele, U.S. EPA, "In-Use
    Volatility Impact of Commingling Ethanol and Non-Ethanol Fuels," SAE
    940765, 1994.

[^93]: ^?^ "Analysis of California's Request for Waiver of the
    Reformulated Gasoline Oxygen Content Requirement for California
    Covered Areas." U.S. EPA, EPA420-R-01-016, June 2001.

[^94]: ^?^ A \"fixed effects\" model of this kind makes no attempt to
    estimate the error introduced by sampling from some larger
    population of vehicles or fuels. The model just describes
    quantitatively the relationships among variables that are present in
    the dataset that was analyzed. A \"mixed\" model, as was used by
    CARB in both the Phase 2 and Phase 3 predictive models'
    construction, attempts to go beyond description of the available
    data to make statistical inference to some larger population from
    which the available data were sampled. In this case CARB treated the
    vehicle effects as random (assuming that the test vehicles were
    sampled from some larger fleet) while fuel effects were treated as
    \"fixed\" (assuming that all fuels of interest were represented in
    the data). Such a modeling approach makes it possible to estimate
    the probable error in modeled effects in a way that is not possible
    with a fixed effects model. The approach, moreover, improves the
    accuracy of the significance measures used to decide which terms to
    include in the model.

[^95]: At the time of that 1999 analysis, sufficient data existed on the
    emissions effects of Sulfur on Tier 1 vehicles to be modeled.
    However, sulfur levels were not expected to change as a result of
    the removal of oxygen from RFG in California, so the effect of
    sulfur was moot in this situation.

[^96]: ^?^ A normal emitter is generally a vehicle whose emissions are
    no more than twice its certification emission standards. A high
    emitter is a vehicle whose emissions exceed this level.

[^97]: NMHC is essentially equivalent to VOC for our purposes in this
    study.

[^98]: ^?^Durbin, T., Miller, J., Younglove, T., Huai, T., and Cocker,
    K. "Effects of Ethanol and Volatility Parameters on Exhaust
    Emissions". CRC Project No. E-67. Coordinating Research Council,
    Inc. 3650 Mansell Road, Suite 140. Alpharetta, GA 30022. January,
    2006.

[^99]: ^?^ Using the widely accepted criteria of a 95% confidence
    interval, p ≤ 0.05 is considered to be statistically significant,
    0.05 \< p ≤ 0.10 is marginally significant, and p \> 0.10 is not
    considered statistically significant.

[^100]: ^?^ One exception to this process is that the linear form of a
    variable, such as ethanol, would always be retained in the model if
    a second order term included ethanol (e.g., the ethanol by T90
    term).

[^101]: ^?^ For an additional comparison between the models with an
    expanded set of fuels to be used later in this analysis, refer to
    Table 3A-2 in Appendix 3A.

[^102]: ^?^ "AAM and AIAM Study: Sulfur Oxygen Vehicle Emissions Test
    Program". Available on the California Air Resources Board Website:
    http://www.arb.ca.gov/fuels/gasoline/carfg3/carfg3.htm.

[^103]: ^?^ "ExxonMobil: LEV/ULEV Gasoline Oxygenate Study". Available
    on the California Air Resources Board's Website:
    http://www.arb.ca.gov/fuels/gasoline/carfg3/carfg3.htm.

[^104]: ^?^ "Toyota Study: Effects of Ethanol on Emissions of Gasoline
    LDVs". Available on the California Air Resources Board Website:
    http://www.arb.ca.gov/fuels/gasoline/carfg3/carfg3.htm.

[^105]: ^?^ Schifter, I., Diaz, L., and Lopez-Salinas, E. "A Predictive
    Model to Correlate Fuel Specifications with On-Road Vehicles
    Emissions in Mexico". Environmental Science and Technology. 2006,
    40, pp. 1270-1279.

[^106]: ^?^ The Mexican Environmental Agency currently mandates
    emissions durability certificates to be issued on new vehicles

[^107]: ^?^ "California Waiver Decision Document," US EPA,
    EPA420-S-05-005, June 2005.

[^108]: ^?^ "Final Report, Effects of Gasoline Ethanol Blends on
    Permeation Emissions Contribution to VOC Inventory from On-Road and
    Off-Road Sources," Air Improvement Resource, Inc., for the American
    Petroleum Institute, March 3, 2005.

[^109]: ^?^ Haskew, Harold M., Thomas F. Liberty and Dennis McClement,
    "Fuel Permeation from Automotive Systems," Final Report, for the
    Coordinating Research Council and the California Air Resources
    Board, CRC Project E-65, September 2004.

[^110]: ^?^ Haskew, Harold M., Thomas F. Liberty and Dennis McClement,
    "Fuel Permeation from Automotive Systems: E0, E6, E10 and E85,"
    Prepared for the Coordinating Research Council, Interim Report CRC
    Project No. E-65-3, August 2006.

[^111]: ^?^ Ragazzi, Ron and Ken Nelson, "The Impact of a 10% Ethanol
    Blended Fuel on the Exhaust Emissions of Tier 0 and Tier 1
    Light-Duty Gasoline Vehicles at 35 F," Colorado Department of Public
    Health and Environment, March 26, 1999. For additional information
    or copies, contact Gerald L. Gallagher or Margie M. Perkins.

[^112]: ^?^ Mulawa, Patricia.A. et. al. \"Effect of Ambient Termperature
    and E-10 Fuel on Primary Exhaust Particulate Mater Emissions from
    Light-Duty Vehicles,\" Environ. Sci. Technol. 1997, 31, 1302-1307.

[^113]: ^?^ Stump, F., et. al., Characterization of Emission from
    Malfunctioning Vehicles Fueled with Oxygenated Gasoline - E10 Fuel,
    Part II and Part III, EPA RTP.

[^114]: ^?^ The IM240 test is a warmed up test consisting of a portion
    of the FTP driving cycle. It was designed as a short transient test
    cycle for use in vehicle inspection and maintenance programs.

[^115]: ^?^ "Initial Mass Exhaust Emissions from Reformulated
    Gasolines". Technical Bulletin No.1. Auto/Oil Air Quality
    Improvement Research Program. Coordinating Research Council Inc.
    Atlanta, Georgia. December 1990.

[^116]: ^?^ Hochhauser, A.M., et al. "Effects of Oxygenated Fuels and
    RVP on Automotive Emissions -- Auto/Oil Air Quality Improvement
    Research Program." Society of Automotive Engineers publication No.
    920326.

[^117]: ^?^ EPA Certification and Fuel Economy Information System.
    http://www.epa.gov/otaq/cfeis.htm.

[^118]: ^?^ Benson, J., Knepper, J., Reuter, R., Koehl,W., Leppard, W.,
    Rippon, B., Burns, V., Painter, L., Rutherford, J., Hochhauser, A.,
    and Rapp, L. "Emissions with E85 and Gasolines in Flexible/Variable
    Fuel Vehicles--The Auto/Oil Air Quality Improvement Research
    Program." SAE paper no. 952508. Warrendale, PA: SAE. October 1995.

[^119]: ^?^ Aakko, P. and Nylund, N.-O. Particle Emissions At Moderate
    and Cold Temperatures Using Different Fuels. SAE Technical Paper
    2003-01-3285.

[^120]: ^?^ Organic Material Hydrocarbon Equivalent

[^121]: ^?^ "Exhaust Emission Effects of Fuel Sulfur and Oxygen on
    Gasoline Nonroad Engines," U.S. EPA, December 2005, EPA420-R-05-016,
    NR-003c.

[^122]: ^?^ Permeation emissions from nonroad equipment are not
    regulated. Thus, the elastomers used in the fuel systems of nonroad
    equipment are likely to be more similar to those of older onroad
    vehicles than those of later onroad vehicles which were subject to
    stringent non-exhaust VOC emission standards.

[^123]: ^?^ "Nonroad Evaporative Emission Rates," Table 2, U.S.
    Environmental Protection Agency, EPA420-R-05-020, NR-012c, December
    2005. This document is available in docket
    EPA-HQ-OAR-2004-0008-0362.

[^124]: ^?^ "EPA's National Mobile Inventory Model (NMIM), A
    Consolidated Emissions Modeling System for MOBILE6 and NONROAD,"
    U.S. EPA, EPA420-R-05-024, December 2005.

[^125]: ^?^ Jackson, Cleophas, "Ethanol Plant Emissions as Reported to
    the States," EPA Memorandum to the Docket, February 16, 2007.

[^126]: U.S. EPA, *EPA's National Mobile Inventory Model (NMIM), A
    Consolidated Emissions Modeling System for MOBILE6 and NONROAD*,
    EPA420-R-05-024, December 2005, version NMIM20061128 with county
    database NCD20061227RfsFinal modified according to discussion in
    Chapter 2.2.

[^127]: U.S. EPA, *User's Guide to MOBILE6.1 and MOBILE6.2 Mobile Source
    Emission Factor Model*, EPA420-R-03-010, August 2003, model used
    version M6ChcOxFix.

[^128]: Gururaja ,Prashanth, U.S. EPA, \"Fuel Tank Temperature
    generation for evaporative emissions modeling in MOVES," EPA
    Memorandum, August 31, 2006.

[^129]: Federal Highway Administration, *Highway Statistics 2004*,
    Section V, Table VM-1 "Annual Vehicle Distance Traveled in Miles and
    Related Data", October 2005.

[^130]: ^?^ Annual growth rate in gasoline consumption on an energy
    basis per EIA Annual Energy Outlook, 2006 (therefore it applies
    regardless of future ethanol use scenario). Assumes constant annual
    mileage per vehicle over this timeframe.

[^131]: U.S. EPA, *User's Guide for the Final NONROAD2005 Model*,
    EPA420-R-05-013, December 2005, model used version NR05c-Bond Base.

[^132]: ^?^ Based on linear interpolation between estimate for 2001 from
    Table 7.1.2-1 and that for 2014 from Table 7.1.3-4, both from the
    2010 Nonroad FRM Final RIA, EPA420-R-04-007, May 2004, available in
    EPA Docket OAR-2003-0012.

[^133]: ^?^ EIA projects gasoline demand of 16.93 and 18.84 quadrillion
    Btu in 2004 and 2012, respectively. This represents overall growth
    between these two years of 11.3%.

[^134]: ^?^ EIA projects gasoline demand of 16.93 and 18.84 quadrillion
    Btu in 2004 and 2012, respectively. This represents overall growth
    between these two years of 11.3%. Source: Annual Energy Outlook
    2006, DOE/EIA-0383(2006), Reference Case Table 2, available in
    docket EPA-HQ-OAR-2005-0161.

[^135]: U.S. EPA. 2006. Technical Support Document for the Proposed
    Mobile Source Air Toxics Rule -- Air Quality Modeling. This document
    is available in Docket EPA-HQ-OAR-2005-0036.

[^136]: ^?^ U.S. EPA. 2005. Technical Support Document for the Final
    Clean Air Interstate Rule - Air Quality Modeling. Available from:
    http://www.epa.gov/cair/technical.html. This document is available
    in Docket EPA-HQ-OAR-2003-0053.

[^137]: ^?^ U.S. EPA. 2005. Clean Air Interstate Rule Emissions
    Inventory Technical Support Document. Available from:
    http://www.epa.gov/cair/technical.html. This document is available
    in Docket EPA-HQ-OAR-2003-0053.

[^138]: ^?^ Pielke, R.A., W.R. Cotton, R.L. Walko, et al. 1992. "A
    Comprehensive Meteorological Modeling System -- RAMS." *Meteor.
    Atmos. Phys.,* Vol. 49, pp. 69-91.

[^139]: ^?^ 2001 design values were calculated as an average of the
    1999-2001, 2000-2002 and 2001-2003 design values at each monitoring
    site. Please see the Air Quality Modeling Technical Support Document
    for the final Clean Air Interstate Rule for additional information.

[^140]: ^?^ Appendix I of 40 CFR Part 50.

[^141]: ^?^ Ozone Air Quality Effect of a 10% Ethanol Blended Gasoline
    in Wisconsin, Wisconsin Department of Natural Resources, September
    6, 2005.

[^142]: ^?^ Ragazzi, R., Nelson, K. "The Impact of a 10% Ethanol Blended
    Fuel on the Exhaust Emissions of Tier 0 and Tier 1 Light Duty
    Gasoline Vehicles at 35F." Colorado Department of Public Health and
    Environment. March 26, 1999. For additional information or copies,
    contact Gerald L. Gallagher or Margie M. Perkins.

[^143]: ^?^ Mulawa, P.A. "Effect of Ambient Temperature and E-10 on
    Primary Exhaust Particulate Matter Emissions from Light-Duty
    Vehicles." Environmental Science and Technology 31, pp 1302-1307.
    1997.

[^144]: ^?^ Grosjean, D. "In Situ Organic Aerosol Formation During a
    Smog Episode: Estimated Production and Chemical Functionality."
    Atmospheric Environment. Vol. 26A. No. 6. pp. 953-963: 1992.

[^145]: ^?^ Schauer, J.J., Fraser, M.P., Cass, G.R., Simoneit, B.R.T.,
    "Source Reconciliation of Atmospheric Gas-Phase and Particle-Phase
    Pollutants during a Severe Photochemical Smog Episode."
    Environmental Science and Technology 36, pp 3806-3814. 2002.

[^146]: ^?^ Pun, Betty K. and Christian Seigneur, "Investigative
    Modeling of New Pathways for Secondary Organic Aerosol Formation,"
    Final Report, CRC Project A-59, Coordinating Research Council, March
    2006.

[^147]: ^?^ Edney. RTP PM 2.5 field study. Currently undergoing peer
    review and will be published shortly.

[^148]: ^?^ Zheng, M., Cass, G.R., Schauer, J.J., Edgerton, E.S. "Source
    apportionment of PM2.5 in the Southeast United States using
    solvent-extractable organic compounds as tracers." Environmental
    Science and Technology 36, pp. 2361--2371. 2002.

[^149]: ^?^ U.S. EPA, "National Emissions Inventory (NEI) Air Pollutant
    Emissions Trends Data, 1970 - 2002 Average annual emissions." Posted
    August 2005 on http://www.epa.gov/ttn/chief/trends/, filename
    "NOX07182005.xls." (Accessed August 14, 2006). July 2005.

[^150]: ^?^ Based on internal analyses of emissions inventories.

[^151]: ^?^ Subject to funding.

[^152]: The extent to which this offset would occur would depend on
    sensitivity of demand to price.

[^153]: ^?^ Since GREET is not a behavioral model, it cannot assess any
    economic efficiency implications associated with increased ethanol
    production. Analyzing these implications would be important for
    future ethanol rulemakings.

[^154]: ^?^ IPCC "Climate Change 2001: The Scientific Basis", Chapter 6;
    Intergovernmental Panel on Climate Change; J.T. Houghton, Y. Ding,
    D.J. Griggs, M. Noguer, P.J. van der Linden, X. Dai, C.A. Johnson,
    and K. Maskell, eds.; Cambridge University Press. Cambridge, U.K.
    2001. http://www.grida.no/climate/ipcc_tar/wg1/index.htm

[^155]: ^?^ Delucchi, Mark A. (2003) A Lifecycle Emissions Model (LEM):
    APPENDIX D: CO2 Equivalency Factors. Institute of Transportation
    Studies, University of California, Davis, Research Report
    UCD-ITS-RR-03-17D.

[^156]: ^?^ Pimentel, David "Ethanol Fuel: Energy Balance, Economics,
    and Environmental Impacts are Negative", Vol. 12, No 2, 2003
    International Association for Mathematical Geology, Natural
    Resources Research

[^157]: Pimentel, D.; Patzek, T. \"Ethanol production using corn,
    switchgrass, and wood; biodiesel production using soybean and
    sunflower.\" Nat. Resour. Res. 2005, 14 (1), 65-76.

[^158]: Hammerschlag, R. \"Ethanol\'s Energy Return on Investment: A
    Survey of the Literature 1990 - Present.\" Environ. Sci. Technol.
    2006, 40, 1744 - 1750.

[^159]: Farrell, A., Pelvin, R., Turner, B., Joenes, A., O'Hare, M.,
    Kammen, D., "Ethanol Can Contribute to Energy and Environmental
    Goals", Science, 1/27/2006, Vol 311, 506-508.

[^160]: Hill, J., Nelson, E., Tilman, D., Polasky, S., Tiffany, D.,
    "Environmental, economic, and energetic costs and benefits of
    biodiesel and ethanol biofuels", Proceedings of the National Academy
    of Sciences, 7/25/2006, Vol. 103, No. 30, 11206-11210.

[^161]: ^?^ Kwaitkowski, J.R., McAloon, A., Taylor, F., Johnston, D.B.,
    *Industrial Crops and Products* 23 (2006) 288-296. A copy of the
    current USDA model can be obtained by contacting the corresponding
    author.

[^162]: ^?^ Baseline Energy Consumption Estimates for Natural Gas and
    Coal-based Ethanol Plants - The Potential Impact of Combined Heat
    and Power (CHP), Prepared for: U.S. Environmental Protection Agency
    Combined Heat & Power Partnership, Prepared by: Energy and
    Environmental Analysis, Inc., July 2006.

[^163]: ^?^ Wang, Saricks, and Wu, 1997, *Fuel-Cycle Fossil Energy Use
    and Greenhouse Gas Emissions of Fuel Ethanol Produced from U.S.
    Midwest Corn*, pp. 13; cited in ANL/ESD-38, pp. 65.

[^164]: ^?^ The authors assume that the corn payload weight is equal to
    the transport vehicle weight, that the vehicle returns empty, and
    the effective average round-trip vehicle distance can be estimated
    as being one and a half times the one-way travel distance (1.5 times
    49.7 miles = 74.6 miles); Graboski, 2002, *F*o*ssil Energy Use in
    the Manufacture of Corn Ethanol*, Colorado School of Mines,
    (Prepared for the National Corn Growers Association).

[^165]: ^?^ Gervais and Baumel, 19XX, *The Iowa Grain Flow Survey: Where
    and How Iowa Grain Producers Ship Corn and Soybeans*, CTRE, Iowa
    State University.

[^166]: ^?^ The Energy Balance of Corn Ethanol: An Update, Shapouri, H,
    J.A. Duffield, and M. Wang, 2002 AER-813, Washington DC: USDA Office
    of the Chief Economist.

[^167]: ^?^ USDA's 2002 Ethanol Cost-of-Production Survey, Shapouri, H,
    P Gallagher, Agricultural Economic Report Number 841, July 2005.

[^168]: ^?^ The USDA National Agricultural Statistics Service (NASS)
    website, national statistics on field corn:
    http://www.nass.usda.gov:8080/QuickStats/Create_Federal_All.jsp.

[^169]: ^?^ USDA Agricultural Baseline Projections to 2015, USDA Office
    of the Chief Economist, World Agricultural Outlook Board, Baseline
    Report OCE-2006-1, Feb 2006.

[^170]: ^?^ Use of historic farming energy use may not be representative
    of current practice. Higher energy prices relative to the years
    considered here could lead to farmers adopting practices that lower
    overall energy use.

[^171]: ^?^ Fossil Energy Use in the Manufacture of Corn Ethanol,
    Graboski, M., Report Prepared for the National Corn Growers
    Association, August 2002.

[^172]: ^?^ LPG and diesel fuel prices from USDA Agricultural Statistics
    report, Prices Paid by Farmers, multiple years.

[^173]: ^?^ *Ibid.*

[^174]: ^?^ Shapouri, H., Duffield, J., Mcaloon, A.J. the 2001 Net
    Energy Balance of Corn-Ethanol. 2004. Proceedings of the Conference
    on Agriculture As a Producer and Consumer of Energy, Arlington, VA.,
    June 24-25.

[^175]: ^?^ Fossil Energy Use in the Manufacture of Corn Ethanol,
    Graboski, M., Report Prepared for the National Corn Growers
    Association, August 2002.

[^176]: ^?^ The expansion of system boundaries would apply to existing
    refineries as ethanol is assumed to replace gasoline from existing
    production.

[^177]: ^?^ All yield values presented represent pure ethanol production
    (i.e. no denaturant).

[^178]: ^?^ Shapouri, H., Duffield, J., Mcaloon, A.J. the 2001 Net
    Energy Balance of Corn-Ethanol. 2004. Proceedings of the Conference
    on Agriculture As a Producer and Consumer of Energy, Arlington, VA.,
    June 24-25.

[^179]: ^?^ ISO 14044:2006(E), "Environmental Management -- Life Cycle
    Assessment -- Requirements and Guidelines", International
    Organization for Standardization (ISO), First edition, 2006-07-01,
    Switzerland.

[^180]: ^?^ Inventory of U.S. Greenhouse Gas Emissions and Sinks:
    1990-2004, EPA 430-R-06-002, April 2006.

[^181]: ^?^ A net energy balance of zero, or an energy efficiency of
    1.0, would indicate that the full lifecycle fossil fuels used in the
    production and transportation of ethanol are exactly equal to the
    energy in the ethanol itself.

[^182]: ^?^ *Inventory of U.S. Greenhouse Gas Emissions and Sinks:
    1990-2004,* EPA 430-R-06-002, April 2006.

[^183]: ^?^ *Inventory of U.S. Greenhouse Gas Emissions and Sinks:
    1990-2004,* EPA 430-R-06-002, April 2006*.*

[^184]: ^?^ Net imports of petroleum include imports of crude oil,
    petroleum products, unfinished oils, alcohols, ethers, and blending
    components minus exports of the same.

[^185]: ^?^ Petroleum products, according to the Annual Energy Outlook
    2006, includes imports of finished petroleum products, unfinished
    oils, alcohols, ethers, and blending components.

[^186]: ^?^ EIA (September 1997), "Petroleum 1996: Issues and Trends",
    Office of Oil and Gas, DOE/EIA-0615, p. 71.
    (http://tonto.eia.doe.gov/FTPROOT/petroleum/061596.pdf).

[^187]: For reference, the U.S. Bureau of Economic Analysis (BEA)
    reports that the 2005 import expenditures on energy-related
    petroleum products totaled \$235.5 billion (2004\$) while petroleum
    exports totaled \$13.6 billion -- for a net of \$221.9 billion in
    expenditures. Net petroleum expenditures made up a significant
    fraction of the \$591.3 billion current account deficit in goods and
    services for 2005 (2004\$). (http://www.bea.gov/)

[^188]: ^?^ 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.

[^189]: ^?^ 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.

[^190]: ^?^ Stanford Energy Modeling Forum, Phillip C. Beccue and
    Hillard G. Huntington, "An Assessment of Oil Market Disruption
    Risks," Final Report, EMF SR 8, October, 2005.

[^191]: ^?^ For details on current and expected ethanol capacity, refer
    to Section 1.2 of this RIA. Note that volumes considered cellulosic
    are also included here, since we believe that virtually all
    near-term cellulosic ethanol production will be from starch-based
    feedstocks that meet the alternative definition in the Act
    (discussed further in Section 7.1.1.2).

[^192]: Report entitled "Contribution of the ethanol industry to the
    economy of the United States," prepared by LECG, LLC February 21,
    2006. The impact figures in the report were generated using the U.S.
    Bureau of Economic Analysis Regional Input-Output Modeling System.

[^193]: ^?^ Kwaitkowski, J.R., McAloon, A., Taylor, F., Johnston, D.B.,
    *Industrial Crops and Products* 23 (2006) 288-296. A copy of the
    current USDA model can be obtained by contacting the corresponding
    author.

[^194]: ^?^ Shapouri, H., Gallagher, P., USDA's 2002 Ethanol
    Cost-of-Production Survey (published July 2005).

[^195]: ^?^ Gallagher, P.W., Brubaker, H., and Shapouri, H. "Plant Size:
    Capital Cost Relationships in the Dry Mill Ethanol Industry,"
    *Biomass and Bioenergy* Vol. 28. 2005. Pp. 565-7.

[^196]: ^?^ Baseline Energy Consumption Estimates for Natural Gas and
    Coal-based Ethanol Plants -- The Potential Impact of Combined Heat
    and Power (CHP), Prepared by Energy and Environmental Analysis,
    Inc., July 2006.

[^197]: ^?^ Energy Policy Act of 2005, Section 1501(a)(2).

[^198]: ^?^ Historical data at
    http://tonto.eia.doe.gov/dnav/pet/pet_pri_allmg_d_nus_PTA_cpgal_m.htm
    (gasoline),
    http://tonto.eia.doe.gov/dnav/ng/ng_pri_sum_dcu_nus_m.htm (natural
    gas),
    http://www.eia.doe.gov/cneaf/electricity/page/sales_revenue.xls
    (electricity),
    http://www.eia.doe.gov/cneaf/coal/page/acr/table28.html (coal).

[^199]: ^?^ EIA Annual Energy Outlook 2006, Tables 8, 12, 13, 15.

[^200]: ^?^ *Enzyme Sugar Platform (ESP), Project Next Steps National
    Renewable Energy,* Dan Schell, FY03 Review Meeting; Laboratory
    Operated for the U.S. Department of Energy by Midwest Research
    Institute • B NREL, Golden, Colorado, May 1-2, 2003; U.S. Department
    of Energy by Midwest Research Institute • Battelle • Bechtel

[^201]: "Purdue yeast makes ethanol from agricultural waste more
    effectively." Purdue News, June 28, 2004; Writer: Emil Venere, (765)
    494-4709, venere@purdue.edu; source: Dr. Nancy Ho, (765) 494-7046,
    <nwyho@ecn.purdue.edu>.

[^202]: *DOE Genomics: GTL Roadmap, Systems Biology for Energy and
    Environment*, U.S. Department of Energy, Office of Science, Office
    of Biological and Environmental Research, Office of Advanced
    Scientific Computing Research, Germantown, MD 20874-1290, August
    2005; [DOEGenomesToLife.org/roadmap]{.underline}; downloadable as
    whole or in sections.

[^203]: ^?^ http://www.iogen.ca/company/about/index.html.

[^204]: ^?^ *Lignocellulosic Biomass to Ethanol Process Design and
    Economics Utilizing Co-Current Dilute Acid Prehydrolysis and
    Enzymatic Hydrolysis Current and Futuristic Scenarios*, Robert
    Wooley, Mark Ruth, John Sheehan, and Kelly Ibsen, Biotechnology
    Center for Fuels and Chemicals Henry Majdeski and Adrian Galvez,
    Delta-T Corporation; National Renewable Energy Laboratory, Golden,
    CO, July 1999, NREL/TP-580-26157.

[^205]: ^?^ *Determining the Cost of Producing Ethanol from Corn Starch
    and Lignocellulosic Feedstocks;* A Joint Study Sponsored by: USDA
    and USDOE, October 2000 • NREL/TP-580-28893 • Andrew McAloon, Frank
    Taylor, Winnie Yee, USDA, Eastern Regional Research Center
    Agricultural Research Service; Kelly Ibsen, Robert Wooley, National
    Renewable Energy Laboratory, Biotechnology Center for Fuels and
    Chemicals, 1617 Cole Boulevard, Golden, CO, 80401-3393; NREL is a
    USDOE Operated by Midwest Research Institute · Battelle · Bechtel;
    Contract No. DE-AC36-99-GO10337.

[^206]: ^?^ The first, woodchip-plant study was designed to produce 52.2
    million gallons of ethanol per year from about 2,200 tons per day
    (350 operating days per year; 15 days for downtime, including
    turn-around) of woodchips. The second study normalized the original
    woodchip plant into the corn stover plant to produce 25 million
    gallons of ethanol per year (about 1,235 wet tons per day), in 1999
    dollars. The adjustments included feedrate and feedstock volume and
    cost adjustments; equipment sizes with adjustments to capital and
    installation costs, and the cost of capital, labor, and process
    chemicals, including denaturant.

[^207]: ^?^ *Lignocellulosic Biomass to Ethanol Process Design and
    Economics Utilizing Co-Current Dilute Acid Prehydrolysis and
    Enzymatic Hydrolysis Current and Futuristic Scenarios*, Robert
    Wooley, Mark Ruth, John Sheehan, and Kelly Ibsen, Biotechnology
    Center for Fuels and Chemicals Henry Majdeski and Adrian Galvez,
    Delta-T Corporation; National Renewable Energy Laboratory, Golden,
    CO, July 1999, NREL/TP-580-26157.

[^208]: ^?^ Energy Information Administration, U.S. Department of
    Energy. See publications, Annual Energy Outlook, 2006.

[^209]: ^?^ Energy Policy Act of 2005: TITLE XV---ETHANOL AND MOTOR
    FUELS, Subtitle A---General Provisions, SEC. 1511, 1512, 1514.

[^210]: ^?^ NREL Presentation "U.S. Biodiesel Feedstock Supply" June
    2004.

[^211]: ^?^ Per USDA phone discussion 6/22/06

[^212]: ^?^Energy Information Administration NEMS Petroleum Marketing
    Model Documentation page J-2

[^213]: ^?^ Per EIA paper "MTBE Production Economics" Tancred C. M.
    Lidderdale, methanol price cents per gallon = 15.79 + 0.099
    \*natural gas price (\$ per million BTU)

[^214]: ^?^ "Economic Feasibility of Producing Bio-diesel in Tennessee"
    AIM-AG Agri-Industry Modeling & Analysis Group, 2002

[^215]: ^?^ Superior Process Technologies, " Biodiesel Plant Economics
    and Process Description", 8/18/06

[^216]: ^?^ Infrastructure Requirements for an Expanded Fuel Ethanol
    Industry, Downstream Alternatives Inc., January 15, 2002.

[^217]: ^?^ These capital costs will be incurred incrementally during
    the period of 2007-2012 as ethanol volumes increase. For the purpose
    of this analysis, we assumed that all capital costs will be incurred
    in 2007.

[^218]: ^?^ Tables ES-9 and ES 10, Infrastructure Requirements for an
    Expanded Fuel Ethanol Industry, Downstream Alternatives Inc, January
    15, 2002.

[^219]: ^?^ Petroleum Market Model of the National Energy Modeling
    System, Part 2, March 2006, DOE/EIA-059 (2006),
    http://tonto.eia.doe.gov/FTPROOT/modeldoc/m059(2006)-2.pdf .

[^220]: ^?^ Hawaii is a special case because plants potentially located
    there will use local feedstocks from their initial start up date.

[^221]: See Chapter 2 of this RIA for additional discussion of our
    estimate of the percentage of ethanol that will be used in urban
    versus a rural areas

[^222]: ^?^ All capital costs were assumed to be incurred in 2007 and
    were amortized over 15 years at a 7 percent cost of capital.

[^223]: ^?^ See Chapter 1 of this RIA regarding the 2012 reference case.

[^224]: ^?^ See Chapter 1.3 of the Regulatory Impact Analysis associated
    with today's rule for a discussion of the special handling
    requirements for biodiesel under cold conditions.

[^225]: ^?^ Information on biodiesel facility costs was obtained from a
    number of biodiesel blenders on the condition that the specific
    source of such information would not be identified. Biodiesel rail
    tank cars typically have a capacity of 25,500 gallons as opposed to
    30,000 gallons for an ethanol tank car. Thus, additional tank cars
    are need to transport a given volume of biodiesel relative to the
    same volume of ethanol.

[^226]: ^?^ Capital costs will be incurred incrementally over the period
    of 2007-2012 as biodiesel volumes increase. For the purpose of this
    analysis, all capital costs were assumed to be incurred in 2007 and
    were amortized over 15 years at a 7 percent cost of capital.

[^227]: ^?^ This is based on our review of publicly available biodiesel
    and ethanol freight rates from CSX and BNSF rail at
    [www.csx.com](http://www.csx.com/) and
    [www.bnsf.com](http://www.bnsf.com/) , on information regarding the
    lease rates for biodiesel versus ethanol freight cars considering
    the smaller size of biodiesel tank cars, and on discussions with
    biodiesel distributors. The estimated ethanol freight costs were
    increased by 30 percent to arrive at the estimate of biodiesel
    freight costs.

[^228]: ^?^ EPA typically assesses social benefits and costs of a
    rulemaking. However, this analysis is more limited in its scope by
    examining the average cost of production of ethanol and gasoline
    without accounting for the effects of farm subsidies that tend to
    distort the market price of agricultural commodities.

[^229]: ^?^ This sensitivity analysis conducted at lower and higher
    ethanol production costs can also be used as a surrogate for a
    sensitivity analysis of higher and lower crude oil prices. Analyzing
    a lower ethanol cost is similar to analyzing a higher crude oil
    price with ethanol production costs at the levels we analyzed them
    at which was 126 and 131 cents per gallon, and vice versa for our
    sensitivity analysis at the higher ethanol production cost.

[^230]: ^?^ Based on EIA's AEO 2006, the total volume of highway and
    off-road diesel fuel consumed in 2012 was estimated at 58.9 billion
    gallons.

[^231]: Overall, we expect that the decrease in secondary organic PM is
    likely to exceed the increase in secondary nitrate PM. In 2006,
    NO~x~ emissions from gasoline-fueled vehicles and equipment comprise
    about 37% of national NO~x~ emissions from mobile sources. In
    contrast, gasoline-fueled vehicles and equipment comprise almost 90%
    of national gaseous aromatic VOC mobile source emissions. The
    percentage increase in national NO~x~ emissions due to increased
    ethanol use should be smaller than the percentage decrease in
    national emissions of gaseous aromatics. Finally, in most urban
    areas, ambient levels of secondary organic PM exceed those of
    secondary nitrate PM. Thus, directionally, we expect a net reduction
    in ambient PM levels due to increased ethanol use. However, we are
    unable to quantify this reduction at this time.

[^232]: ^?^ U.S. Environmental Protection Agency. May 2004. Final
    Regulatory Analysis: Control of Emissions from Nonroad Diesel
    Engines. Prepared by: Office of Air and Radiation. EPA420-R-04-007.
    Available at
    <http://www.epa.gov/nonroad-diesel/2004fr.htm#documents> . Accessed
    August 1, 2006.

[^233]: See: Clean Air Nonroad Diesel final rule (69 FR 38958, June 29,
    2004); Nonroad Large Spark-Ignition Engines and Recreational Engines
    standards (67 FR 68241, November 8, 2002); Final Industrial Boilers
    and Process Heaters NESHAP (69 FR 55217, September 13, 2004); Final
    Reciprocating Internal Combustion Engines NESHAP (69 FR 33473, June
    15, 2004); Final Clean Air Visibility Rule (EPA-452/R-05-004, June
    15, 2005); Ozone Implementation Rule (70 FR 71611, November 29,
    2005).

[^234]: ^?^ We analyzed the U.S. agricultural impacts of producing
    renewable fuels domestically after adjusting for equivalence values
    of cellulosic ethanol and biodiesel and projected U.S. imports. For
    the RFS Case, we assumed 440 million gallons of corn based ethanol
    will be imported, while we assumed 630 million gallons of corn based
    ethanol will be imported for the EIA Case. For both cases, we assume
    250 million gallons of cellulosic ethanol will be produced (with a
    2.5 equivalence value), and 300 million gallons of biodiesel will be
    produced (with a 1.5 equivalence value).

[^235]: ^?^ FASOM calculates output in five year increments. For this
    analysis, 2010 and 2015 data were interpolated to estimate 2012
    values.

[^236]: ^?^ 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.

[^237]: ^?^ 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.

[^238]: ^?^ For additional details on the FASOM model, see the report by
    Professor Bruce McCarl, Texas A&M University, "The Impacts of the
    Renewable Fuel Standard Program on the U.S. Agricultural Sector,"
    February 2007, included in the docket.

[^239]: ^?^ The current price of corn in the U.S. is approximately
    \$3.50 per bushel (2004\$), which is considerably higher than the
    FASOM prediction and is a likely a result of the fact that recent
    demand for corn for ethanol is higher than the currently available
    stocks. The model results for 2012 reflects medium-term spatial
    equilibrium prices, where rising demand for corn is met by rising
    supply \-- due to increased acres planted to corn and to increased
    corn yields per acre. Note that while the model assumes that markets
    for corn and related agricultural commodities will settle at a price
    of \$2.50 per bushel (in the RFS case) by 2012, that this may be a
    conservative estimate to the extent that the agricultural sector is
    able to adjust to the increased use of corn in ethanol production by
    2012.

[^240]: ^?^ The current price of soybeans in the U.S. is considerably
    higher than the FASOM prediction and is a likely a result of the
    fact that the market expects acres planted to soybeans in the short
    term are likely to decline due to increased corn plantings. As with
    the corn results,.the model reflects medium-term spatial equilibrium
    prices, where rising demand for corn is met by rising supply \-- due
    to increased acres planted to corn and to increased corn yields per
    acre by 2012. Similarly, over time, farmers will begin to plant more
    soybeans in response to relatively higher short-term prices. The
    model expects soybean prices to reach an equilibrium price of \$5.44
    per bushel (in the RFS case) by 2012.

[^241]: ^?^While U.S. government farm payments are currently part of the
    U.S. farm income, what programs will be in place in 2012 and their
    impact on farm income is unclear. For our modeling, we assumed the
    support programs were in place in 2010 but none were in place in
    2015; interpolation between 2010 and 2015 provided the assumed
    impact in 2012.

[^242]: ^?^ FASOM includes corn equivalent feeding of by products.in the
    estimate for total corn production.

[^243]: ^?^ The FASOM model can describe the proportion of fertilizer
    that potentially will affect groundwater quality and surface water
    quality. FASOM also details the extent to which shifts in
    agricultural production may affect soil erosion and carbon
    sequestration. In the short timeframe available, we were not able to
    devote significant efforts to this type of analysis, but this area
    of inquiry could be investigated more extensively in the future. We
    do note that we capture the sequestration impacts in the GREET
    analysis.

[^244]: ^?^ The All Farm Products Price Index includes: cotton, corn,
    soybeans, wheat, sorghum, rice, oats, barley, silage, hay,
    sugarcane, sugar beet, potatoes, tomatoes, oranges, grapefruit,
    switch grass, hybrid poplar, willow, beef, cows, milk, pigs, lamb,
    wool, horses and mules, eggs, chicken, and turkey.

[^245]: ^?^ In the Draft RIA, we also referred to a 125,000 barrels of
    crude per day (bpcd) crude capacity limit. This criterion was
    inadvertently used and is not applicable for this program (as it
    only applies in cases of government procurement). We note that the
    number of small entities remains the same whether this criterion is
    used or not.