# Regulatory Impact Analysis: Renewable Fuel Standard Program

# Regulatory Impact Analysis: Renewable Fuel Standard Program

Assessment and Standards Division Office of Transportation and Air Quality U.S. Environmental Protection Agency

NO TICE

T

his tec hnical report does not necessarily represent final EP A decisions or positions. It is intended to present tec hnical analy sis of issues using dat a that are cur rently a v ailable. T he purpose in the release of suc h reports is to f acilit ate an e x

c

hange of tec hnical inf ormation and to inf orm the public of tec hnical de v elopments.

EP A420-R-07-004 April 2007

## Table of Contents

Statem ent of Need

......................................................................................................... 4
Overview

....................................................................................................................... 5 List of Acronym s and Abbreviations

......................... ................................................. 7 Chapter 1 : Industr y Characterization

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

................................................. 50 Chapter 3 : Impacts on Em issions from Vehicles, Nonroad Equipm ent, and Fuel Production Facilities

...................................... 118 Chapter 4 : National Em i ssion Inventory Impacts

...................................................... 173 Chapter 5 : Air Quality Impacts

.................................................................................. 200 Chapter 6 : Lifecycle Impacts on Fo ssil Energy and Greenhouse Gases

.................... 218 Chapter 7 : Estim ated Costs of Rene wable Fuels, Gasoline and Diesel

...................... 262 Chapter 8 : Agricultur al Sector Im pacts

....................................................................... 323 Chapter 9 : Sm all-Busine ss Flexibility Analysis

.......................................................... 335
Endnotes

....................................................................................................................... 342

4

## Statem ent of Need

The United States currently consum es about 190 billion gallons of gasoline and diesel fuel annually to m eet its transportation fuel n eeds. Of this volum e, about 65 percent, or 124 billion gallons, is derived from foreign s ources. The United States ’ dependence on imported petroleum to m eet its growing dem a nd for transportation fuel exacts a cost on the nation in term s of energy security. In addition, petroleum

- based fuel exacts a cost on the nation with respect to environm ental quality. T h e Renewable Fuel Stan dard (RFS) program incr eases n a tio n al energy security by creating a m arket for renewable fuel as a substitute for petroleum

- based fuel. By incorpor atin g incentives f o r investing in rese archand developm ent of rene wable fuels, the RFS program also seeks to accelerate the nation’s pr ogress toward energy independence. In addition, the RFS program helps to reduce the country’s greenhouse gas em issions, thereby reducing the nation' s contribution to global clim ate change and its potential effects on the U.S. economy, security, and public health.

5

## Overview

EPA is finalizing standards which would imple m ent a renewable fuel program as required by the Energy Policy Act of 2005 (the A c t). The Act specifies the total volum e of renewable f u el that is required to be used each year, and directs EPA to adjust th is am ount under certain circu m stances. The resulting standard s represent a lev el of re newable fuel that each refinery or importer mu st account for relative to its annual volum e of gasoline produced or imported. In reality, however, renewable fuel use is forecast to exceed the RFS standards due to
m arket forces. The analy ses of the im pacts as soci ated with this increase in renewable f uel use are dis cusse d in this Reg u lato ry Im pact Analys is (RIA). Chapter 1: Industry Characterization This chapter discusses current gasoline, di esel and renewable fu el production, importation,
m arketing and distribution, as well as likely future changes as a result of increased renewable fuel use. Chapter 2: Changes to Motor Ve hicle Fuel Un der the RFS Program This chapter discusses our gasoline and renewabl e fuel consumption predictions (compared to a 2004 base year), and the expected impacts of various ethanol blends on gasoline properties. Chapter 3: Impacts on Em issions from Vehicl es, Nonroad Equipm ent, and Fuel Production Facilities This chapter evaluates the impacts on vehicl e and nonroad equipm ent e m issions under various oxygenate assumptions, specifically increasing ethanol and decreasing MT BE, and different modeling techniques. T he effect of biodiesel us e on diesel-powered vehicle em issions is also presented. Finally, em i ssions from ethanol and biodiesel pr oduction facilities are discussed. Chapter 4: National Emi ssions Inventory Impacts This chapter discusses the m e thods used to de velop the national emissions inventories, and quantif ies th e impact of e xpanded ethanol and biodiesel use on those inventories. Chapter 5: Air Quality Im pacts This chapter discusses the impacts of expande d renewable fuel use on ozone and particulate
m atter for m ation. Chapter 6: Lifecycle Impacts on Fossil Energy and Greenh ouse Gases This chapter discusses o ur fuel lifecycle m odeli ng, that is, analysis which accounts f o r all energy and em issions of the fuel production process. A description of the m odel we used, how we used it, and the results are presented. Im pacts on greenhouse gases, including CO 2, fossil fuel use, and petro l eum use are p r esented. The effects on petroleum i mports, import expenditures, and dom e stic energy security are also discussed.

6Chapter 7: Estim ated Costs of Renewable Fuels, Gasoline an d Diesel This chapter contains our analysis of the cost of corn and cellulosic ethanol. W e also discuss biodiesel and renewable diesel pr odu ction co sts. Costs associatedwith distributing the volum es of ethanol n ecessary to m eet the req u irem ents of the program, and the costs to prepare gasoline and diesel blendstocks (for blending with rene wable fuels) are also presented. Finally, we present the o v erall fuel cost im pact s of expanded renewable f u el use. Chapter 8: Agricultural Sector Impacts This chapter discusses th e likely econ o m i c impacts on the agricultu ral s ector that m ay occur as a result of the large expansion of renewable fuel production and us e expected in the future. Ongoing work using the F A SOM m odel is also described. Chapter 9: Sm all Busin ess Flexib ility Analysis This chapter discusses o ur Sm all Business Flexibili ty Analy sis (SBFA) which evalu ates the ru le to ensure that concerns regarding sm all busine sses, which would be affected by the rule, are sufficiently considered.

7

## List of Acronyms and Abbreviations

AAM Allian ce of Auto m o b ile Man u factu r ers AB T Ave ra g i n g, B anki ng , a n dTra d i n g ACE Am erican Coal ition for Ethanol Th e Act En erg y Po licy Act of 20 05 (also t h e En erg y Act) ADM Arc h er Daniels M i dlan d AEO Annu al En erg y Ou tlo ok (an EIA pu b lication ) ANL Ar go n n e Natio nal La bo rato ry AQIRP Au t o /Oil Ai r Qu ality Im p r o v e men t Research Pro g ram ARMS Agricultural R esource Ma na ge m ent Survey B0, B 5 , B 2 0, e t c Perce n t of biodiesel , e.g., B 5 = 5% bi odiesel, 95% diesel bbl B arrel B E A B u reau of Eco nomi c Anal y s i s Bg al, b g al, b i l g al, b illg al, bg Billio n s of g a llo n s
BGY Billio n s of g allo n s per year BPCD Barrels Pe r cal endar day BPSD Barrels per stre am day b p d , bb ls/d ay Barrels Per Day BTU British Th erm a l Un it BU Bu sh el Bu/acre Bushels pe r ac re BZ Benzene CA Califo rn i a CAA Clean Air Act CAIR Clean Air In terstate Ru le CARB Califo rn i a Ai r Reso urces B o ard CaRFG3 Califo rn i a Ph ase 3 RFG CBG Clean er B u rn ing Gaso li n e CBI Carib b e an Basi n In itiativ e C D C e nsus Di vi si o n CFEIS EPA’s Certifica t i on a n dF u el Eco nomy Inf o rm at i on Sy st em C F R C ode o f Fe de r a l R e gul at i o ns c/ gal C e nt s pe r gal l o n CG Co nv en tio n a l Gaso li n e CHP Co m b in ed Heat an d Po w e r Tech no log y CO Carb o n Mono xid e C O2 C arb on Di oxi d e Co-op Cooperative CRC Co ord i n a tin g Research Cou n cil DDGS Distillers’ Dried Grai n s with So lub l es DOE Depa rt m e nt of Ener gy DRIA Draft Regu latory Im p act An al ysis E&C En gi neeri n g a n d C onst ruct i o n E0 Gasol i ne B l en d whi c h Doe s Not C o nt ai n Et han o l E1 0 Gasol i ne B l en d c ont ai ni n g a nomi n al 10 p e r cent etha n o l by v o l u m e E8 5 Gasol i ne B l en d c ont ai ni n g 8 5 perce n t etha n o l by v o l u m e E2 00 Perce n t o f F u el Evap orat edat 20 0 De grees F (AS T M D 8 6 ) E3 00 Perce n t o f F u el Evap orat edat 30 0 De grees F (AS T M D 8 6 ) EIA Ener gy In fo rm at i on Admi ni strat i o n ( p art of t h e U.S . Depa rtm e nt of Ene r g y )

8En erg y Act En erg y Po licy Act of 20 05 (also t h e Act) EO Exec utive Order EPA Environm ental Protection Agency EPAct Ener gy P o l i c y Act o f 2 0 0 5 (al s o ‘the Ene r gy Act ’ or ‘t he Ac t ’) ETBE Ethyl Tertiary Butyl Ether ETOH Ethanol ex C A Excl u d i n g C a l i f orni a F, ° F Fahrenheit FAPR I Farm an d Ag ri cu ltu ral Po licy Research In stitu te FASOM Fo restry an d Ag ricu ltu re Sect o r Op timizatio n Mod e l FBP Feed B o ilin g Po in t (also Fin a l Bo ilin g Po in t) FCC Fluidized Catalytic Cracker FCCU Flu i d i zed Catalytic Crack ing Un it FH WA Fede ral Hi gh w a y Admi ni strat i on FOEB Fu el Oil Equ i valen t Barrel FR Fede ral Regist er FR M Fi nal R u l e m aki ng FRTP Fix e d Redu ction Trigg e r Po in t FFV Flexible Fuel Vehicle FTP Fede ral test procedure GAL Gallon g/B tu Gram s per B tu g/ day Gram s per day GDP Gross Dom e stic Product GH G Gree n h o u se G a ses GPA Geographic Phase-in Area GREET Gree nhouse Gas, Regulated Em issions, a nd Ene r gy Use in Tra n s p ortation m odel G WP Gl o b al wa rmi ng pot e n t i a l s HC Hy dr oca rb o n (s ) HCO Heavy Cy cle Oil (a refi nery stream ) HDN Naph th a Hy d r o treater (also Hyd r o-Den itrog en ation Un it) HSR Heavy Straig ht Ru n (a re fine ry stream ) HV G O Heavy Vac u u m Gas Oil (a r e finery stream ) IBP In itial Bo ilin g Po in t k Th ousan d kb bl Th ousan d bar r e l s k w h Kilo watt Hour Lb Po un d LCO Light Cy cle Oil (a re fine ry str e am ) LEV Lo w emi ssi on vehi cl e LLE Liqui d-Li qui d Extraction LNS Lig h t Nap h t h a Sp litter LP Li near Pr o g ra m m i ng (a ty pe of re fi nery m odel ) LSR Light Straig ht Run (a refi nery stream ) mg /m 3 Millig ra m s p er cub i c m eter MGY, MMg y Millio n Gallons p e r Year MM Millio n MMBTU Millio n British Th erm a l Un its MMb b l s/cd Millio n s of b arrels p e r calen dar d a y MMGal/yr Millio n s of g allo n s p e r year M O B I LE( 5 , 6 , 6 .2 ) EPA ’ s M o t o r Vehi cl e Emi ssion I n vent ory M odel (ve r si o n s ) M ON M o tor Octane Num b er M OVE S2 0 06 EPA ’ s Next G e nerat i o n Hi g h w ay Ve hi cl e E m i ssi on M o del

9M S AT M obi l e S o urce Ai r To xi cs M S AT 1 20 0 1 M o bi l e S o u r ce Ai r To xi cs R u l e M S AT 2 20 0 6 P r o p o se dM obi l e So urc e Ai r To xi cs Ru l e MTBE Meth yl Tertiary-Bu tyl Eth er NAAQS Natio n al Am b i en t Ai r Qu ality Stand ard s NA ICS No rth Am erican In d u st rial Classification Syste m NASS Natio n a l Ag ricu ltu ral Statistic s Serv ice NB B Nat i onal B i odi esel B o ar d NCSU North Caro lin a State Un iv ersity NGL Natu ral gas pl an t l i qui ds NM HC No n-M e t h ane Hy dr oca rb o n s NM IM Nat i onal M o bi l e I nve nt o ry M odel (EP A so ft ware t o ol ) NM O G No n-m e th ane or ga ni c gase s NO NR O A D EPA ’ s No n
- r o ad E n gi ne Emi ssi on M o del N O N R OAD 200 5 EPA’ s N on- road Eng i n e Emissio n Mod e l R e leased in 2005 NO x Oxi d es of nitrogen NPR M Not i ce of Pr o p o se d R u l e m aki ng NR EL Nat i onal R e ne wabl e Ene r gy Lab o rat o ry O M B O f f i ce of Manag e m e n t an d Bu dg et OM HC E Or ga ni c M a t e ri al Hy dr ocarb o n E q ui val e nt ORNL Oak Rid g e National La b o rat o ry OTAQ Office of Transp ortatio n and Air Quality Oxy-fuel, oxy fuel W i nter oxyge n ated fuel progra m PA DD Petr ol e u m Ad m i ni strat i on f o r D e fe nse Di stri ct PM Particu l ate Matter PM 10 Co arse Particle PM 2.5 Fin e Particle PMA Petro l eu m Mark etin g Ann u a l (an EIA pu b licatio n ) POM Po lycyclic Organ i c Matter PO NA Paraf f in , Olefin, Napht hene , Ar om atic p p b Parts p e r b illion ppm Part s Pe r mi l lion PR TP Perce n t a ge R e duct i o n Tri gge r P o i n t PSI Po un ds pe r S q uare I n ch QBtu Qu adrillio n b tu Qu adrillio n 1 0 15 (R +M )/ 2 Oct a ne cal c u l at i on (R O N +M ON )/ 2 RBOB Reformu lated Blen d s to ck fo r Ox yg en ate Blen d i n g RFA Reg u l atory Flex ib ility Act RFG Reformu l ated Gaso li n e RFS Renewa ble Fuels Standard RIA Regulatory Im pact Analysis R I M S R e gi o n al I n p u t

- O u tp ut M o del i ng Sy st em R I N R e newa bl e I d e n t i f i cat i on N u m b er RON Research octane num b er R P M G R e newa bl e P ro duct s M ark et i n g Gr ou p RSM Response Surface Model RVP Reid Vapo r Pressu re S Sulfur SB A Sm al l B u si ness A d mi ni strat i on SBAR Panel, or ‘t he Panel’ Sm a ll Business Advocacy Re view Panel SBFA Sm a ll Bu sin e ss Flex ib ility Analysis SBREFA Sm a ll Bu sin ess Regu lato ry Enforcem en t Fairn ess Act (of 1 9 9 6 )

10scf St andar d c ubi c feet SO A Seco ndary Or g a nic Ae r o sol SO x Oxi d es of S u l f ur SULE V Su per ul tr a l o w emi ssi on ve hi cl e T5 0 Tem p erature at w h i c h 5 0 % (b y vol um e) of fuel evap or ates (ASTM D 86) T9 0 Tem p erature at w h i c h 9 0 % (b y vol um e) of fuel evap or ates (ASTM D 86) TAME Tertiary Amyl Methyl Ether ULE V Ultra low emis sion ve hicle U.S

. C. United States Code US DA U.S

. Depa rtm e nt o f A g ricultu re VG O Vacu um Gas Oil (a refi nery stream ) VMT Vehicle Miles Travele d VOC Vo latile Org a nic Co m p o u n d vol % Perce n t by v o l u m e , vol um e p e rcent wt % Perce n t by wei ght , wei ght pe r cent yr , y Y ear

11

## Chapter 1: Industr y Characterization

1.1 Transportation Fuel Providers

1.1.1 Petroleu m Refiners As of the end of 2005, there were 142 crude oi l refineries operating in the United States, repres enting a total of 16.4 m illion barrels/day of refining cap acity. (Thes e refineries produce gasoline and other products and are a separate category than “blender re finers” that do not process crude oil, but m a ke gasoline from blends tocks.) The greatest nu mber of refineries p er PADD is in PADD 3 (the Gulf Coast region) whic h has 52 operating refineri es as of the end of 2005. This PADD also has the greatest refining cap acity, at 7.9 m illion barrels per day. Table 1.1-1 presents the refineries and their crude oil production cap acity, and identifies the PADD where the re f i nery is loc ated.
Table 1.1-1. Refining Capacity by Individual Refinery (crude oil processing basis) Company Capacity (MMbbls/cd) PADD Conoco Phillips 2.2 Wood River, IL 0.31 2 Belle Chasse, L A 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

12Company Capacity (MMbbls/cd) PADD
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 C A 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 U T 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 C i t go; La ke C harl e s LA 0. 43 3 C i t go, Le mo nt IL 0. 17 2 C i t go; Co r p us C h ri st i TX 0. 16 3 Koch Industries 0.57 Corpus Christi TX 0.29 3 Saint Paul MN 0.28 2 Motiva Enterprises LL C 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 U T 0.06 4

13Company Capacity (MMbbls/cd) PADD

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 Sincla ir WY 0.07 4 Evansv ille 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 Acquisition s (Cof f

eyville KS) 0.11 2 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 (Pasadena T X ) 0.10 3

Giant Indu stries, Inc. 0.10

Yorktown VA 0.06 1 Gallup NM 0.02 3 Bloomfield NM 0.02 3 Big West Oil (North Salt Lake UT) 0.10 4

So urce: Ta bl e 5 i n E n er gy I n f o rm at i on A d mi ni strat i o n , R e fi nery C apaci ty 20 0 6 fo u n dat h ttp ://www.eia.do e .g ov /pub /oil_ g a s/p e tro leum/d a ta_p u b licatio n s /refin ery_ cap acity_d ata/cu rren t/tab le5.p df

Ref i ning capacity h as steadily incr e ased in the U.S. due to increased dem a nd for petroleum products, with gasoline representing approxim a tely 45 percent of product dem a nd. Refining capacity (crud e oil input) was about 1 4 m illion bb ls/day in 19 73 and 17 million bbls/day in 2005. W hile refining cap acity has increased, however, th e num ber of refineries has

14Capacity(Billion gallons/yr) N u m b e r o f r e f i n e r i e s C a p a c i t y of r e f i n e r i e sCapacity(Million barrels/day)Capacity(Billion gallons/yr) N u m b e r o f r e f i n e r i e s C a p a c i t y of r e f i n e r i e sCapacity(Billion gallons/yr)

1 97

3 197

4 197

5 19

7 6 197

7 197

8 1979 1 980

1 98

1 198

2 19

83

19

84

1 985

198

6 19

87

19

88

19

89

199

0 199

1 1 99

2 19

93

199

4 199

5 19

9 6 19

9 7 1 998

199

9 200

0 2 00

1 20

0 2 2 003

2004 decreased as less econom i cal refineries have been forced to close. (Man y of these ca m e into existence for a very short tim e due to oil price supports in the 1970’s.) In the 1970’s, the num ber of refineries in the U.S. was approxim a tely 270 and has decreased by 47 percent. Figure 1.1-1 shows the num ber of refineries and tota l capacity in the U.S. from 1973 through 2004.

Figure 1.1-1. Number of Refineries and Total Capacity in th e U.S. from 1973-2004

3 5 0 2 0

. 0

N um b e r of r e

fi

n er i es C a p a ci t y of r efi

ne r i e s 1 8

. 0 3 0 0
1 6

. 0

1 4

. 0

1 2

. 0

1 0

. 0

8 . 0

6 . 0

4 . 0

2 . 0

0 . 0 2 5 0

2 0 0

1 5 0

1 0 0

5 0

0Capacity(Million barrels/day)Number ofrefineriesNumber ofrefineriesmber ofrefineries

Sou r ce: EIA ; A nnu al En erg y Repo r t, 20 05 (Tab le 5

. 9 )

The increase in capacity com b ined with the decrease in am ount of refineries and the increased dem a nd for gasoline and diesel fuels, h a s resu lted in an increas e in the average utilization rate of refineries. In the 1970’s, the utili zation rate ranged from 84 to 94 percent. In the la st ten y ears, howev er, the u t iliz ation rate has ranged from 91 to 96 percent. Refineries therefore have to produce m ore with less overall capacity. T he am ount of gasoline and diesel produced by U.S. refiners has steadily incr eased. Since 1973 through 200 4, gasoline and diesel production has increased 27 and 36 percent, resp ectively. Figure 1.1-2 shows the change in gasoline and diesel production from 1973 through 2004

151 97

3 1 97

5 1 97

7 1 97

9 1 98

1 19 8 3 19 8 5 1 98

7 19 8 9 19 9 1 1 99

3 19 9 5 19 9 7 1 99

9 20 0 1 20 0 3 Figure 1.1-2. Amount of Gasoline and Diesel Fuels Produced in the U.S.Billiongallons/yr 140. 0

120. 0

100. 0

80. 0
D i es el f ue l G a s o lin e 60. 0

40. 0

20. 0

0. 0

So urce: E I A A n n u al E n er gy R e po rt , 20 0 5 ; Tabl e 5. 8

1.1.2 Petroleu m Imports The decrease in U.S. refining capacity di scussed in Section 1.1.3, has resulted in increases in the am ount of gasoline and diesel fu els imported into the U.S. As of 2004, 5.4 and 11.5 percent of the total respective volum e s of gasoline and diesel consum ed in the U.S. were imported. Today, the United States imports approxim a tely 70 percent of all petroleum products used, with two-thirds of these products being used fo r transportation. Fr om 1973 to 2004, the amount of crude oil imported has increased from 1.2 to 3.7 b illion barrels per year, a tripling of volum e , representing an average annual increase of about 6 percent. Over the sam e ti m e period, the
am ount of gasoline imported has increased from 2 to 7.4 billion gallons per year, m or e than three tim es the amount of volum e. The a m ount of diesel imported in the sam e tim e period decreased slightly from 6 to 5 percent. Figures 1.1- 3 and 1.1-4 show the incr ease in crude oil and gasoline/diesel fuel imports, respectively, from 1973 to 2004.

1619 73

19

74

1 9 7 5

1 9 7 6

1 9 77

19

78

19

7 9

1 9 8 0

1 9 8 1

1 9 82

19

83

19

8 4

19

8 5

1 9 8 6

19

87

19

88

19

89

19

9 0

19

91

19

92

19

93

19

94

19

95

19

96

19

97

19

98

1 9 99

20

00

20

01

20

02

2 0 0 3 Figure 1.1-3. Increase in Crude Oil I m ports fro m 1973-200 4

4. 00

3. 50

3. 00

2. 50

2. 00

1. 50

1. 00

0. 50

0. 00Imported crude(bilbbls)

Years (So u rce: A n nu al Ener gy Out l oo k, 2 0 05; Ene rgy In f o rm at i o n Admi ni strat i o n )

1719 73 19 7 5 19 77 1 9 79 19 8 1 19 83 1 9 8 5 19 87 19 89 1 9 9 1 19 93 19 95 1 9 9 7 19 99 2 0 0 1 20 0 3 Figure 1.1-4. Change in Volumes of Imported Gasoline and Diesel fuels (1973-2004)

0. 00 2. 00 4. 00 6. 00 8. 00 10. 00 12. 00 14. 00Imports(bil gal) Ga s o l i n e Di es el To ta l

Y ears So urce: A n n u a l Ener gy Out l o ok , 20 0 5 ; E n er gy I n f o rm ati o n A d mi ni strat i on

Approxim a tely twenty percent of our trade de ficit is from i mported petroleum products, a deficit which reached $7 82 billion in2005. Fi gure 1.1-5 sho w s the trad e deficits fro m 1994 through 2004 (earlier data on petroleum i mports is not available from the U.S. Census web site at this tim e ). While the overall contribution of pe troleum imports to the to tal def i c it is decrea sing as shown in Figure 1.1-5, this is due to a m ore rapid growth in the total def i cit from other goods and services. The portion of th e deficit due to petroleum i m po rts by itself is increasing by approxim a tely 4 percent per year. Over the last 25 years, the cumu lative cost of imported crude oil has reach ed $2.0 trillion in 2005 dollars.

18Figure 1.1-5. U.S. Trade Deficit and Portions Due to Petro l eum Imports 1994-2004 (Millions o f dollars, ch ain w e ighted to 2000)

0 100, 000 200, 000 300, 000 400, 000 500, 000 600, 000 700, 000Millionsofdollars

T r ade def i c i t Def i c i t due t o pet r ol eum

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
So urce: U. S. C e ns us B u reau , For e i g n T r ade St at i s t i c s, 20 06

The am ount of i mport facilities in the U.S. has stayed relati vely 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 am ount has rem a ined relatively constant, in the 50’s since that tim e and as of 2004 there were 53 such facilities regi stered with U.S. EPA. The great m a jority 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

.



### 1.2 Renew a ble Fuel Production

While the definition of renewable fuel in th e Act does not limit com p lianc e with the standard to any one particular type of renewable f uel, ethanol is currently the m o st prevalent renewable f u el blended into m o tor vehicle fuel s today. Biodiesel represents another form of renewable f u el, which while not as widespread as ethano l u se (in term s of volum e), has been increasing in production capacity and use over the la st several years. Ethanol and biodiesel are

19expected to continue to dom i nate renewable fuel use in the tim e fram e when the RFS rule will be phasing in.
1.2.1 Current U.S. Ethanol Production
1.2.1.1 Overview There are currently 110 ethanol pr oduction facilities in the Unit ed States with a com bined 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 A B C D E
Approxim a tely 92 percent of today’s ethanol production capacity is produ ced exclu siv ely f r om corn, m ainly f r om a dry-millin g process. T h e rem ainder is de rived f r om corn/grain blend s, cheese whey , and other s t arch es. Th e m ajority o f ethanol plants are located in Midwest where the bulk of corn is produced. PADD 2 accounts f or ju st over 5 billion gall ons (or 96 percent) of the total U.S . ethanol production. Leading the Midwest in ethanol produc tion are Iowa, Illinois, Nebraska, Minnesota, and South Dakota which together represent 76 percent of the total dom estic product. In ad dition to the concentratio n of facilities located inPADD 2, there are also a sprinkling of ethanol plants situ ated 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 pr oduced today com e s from grain or starch-based feedstocks that can easily be broken down into ethanol via tr aditional ferm entation processes. The prim ary feedstock is corn, although grai n sorghum ( milo), wheat, barley, beverage waste, cheese whey, and sugars/starches are also ferm ente d to m ake fuel-grade ethanol.
The m ajority of ethanol (alm ost 92 percent by volum e ) is produced exclusively from
corn. Most of the corn o r igin ates fro m the Midwest and m ost of the ethanol is produced in PADD 2 close to where the corn is grown. Ho wever, seve ral corn-e th anol plants are a l so situated outside the traditiona l “corn belt”. In California, Colorado, New Mexico, and W yom ing corn is shipped from the Midwest to supplem ent lo cally grow n grains or in som e cases, serve as the sole feedstock. As for the rem a ining ethanol , alm o st eight percent is produced from a blend of corn and/or sim ilarly processed grains (m ilo, wheat, or barley) and less than one percent is

1 Thi s a n al y s i s doe s not c o nsi d er etha nol pl a n t s th at m a y be l o cat ed i n (o r pl an nedf o r ) t h e Vi r g i n Isl a nd s o r U.S

. territo ries.

2 The Oct obe r 2 0 0 6 et han o l pr od uct i o n capac i ty basel i n e wa s ge nerat e d bas e d on th e Ju ne 20 0 6 NPR M pl ant l i s t an d up dat e d o n Oct obe r 1 8 , 2 0 0 6 base d on a vari ety of dat a s o u r ces i n cl udi n g : R e ne wabl e Fu el s Ass o ci at i o n (R F A ), Ethan o l P r o d u cer M a gazi ne ( E PM ), IC F I n t e rnat i o nal , B i o F uel s J o urnal , andetha nol p r od uce r websi t e s. T h e b a selin e i n cludes sm al l-scale eth a no l pro d u c tio n facilities as well as fo rm e rfoo d -g rad e et h a no l p l an ts t h at h a v e sin ce tran sitio ned in t o th e fu el -grad e eth a n o l mark et. Where app licab le, cu rren t et h a no l p l an t p r od u c tion lev e ls h a v e b een used to rep r esen t p l an t cap acity, as n a mep l ate cap acities are often un d e restim ated

.

20produced from waste beverages, cheese whey, and sugars/starches com b ined. A summ ary of ethanol production by feedstock is presented in T able 1.2-1.
Table 1.2-1. 2006 U.S. Ethanol Production by Feedstock

Plan t F eed st o ck Cap acit y MMg y % o f Ca pacity No. of Plan t s % o f Plan t s Chees e W hey Corn a Cor n, Bar l ey C o r n , Milo b Cor n, W heat Milo, W heat Sugar s , Star c hes W a s t e Bever ages c 8 4,780 40 244

90 40

2 16 0.1% 91.6%

0.8% 4.7% 1.7% 0.8% 0.0% 0.3% 2 90

1 8 2 1 1 5 1.8% 81.8%

0.9% 7.3% 1.8% 0.9% 0.9% 4.5% T o t al 5,218 100.0% 110 100.0%

a Includes tw o facilities processing seed corn and another facility processing corn w hich intends to trans ition to corn stalks, sw itchgrass, and biomass in the future. b Includes one facilityprocessing small a m ounts of molasses in addition to corn and milo.

c Includes tw o facilities processing brew eryw aste.

There are tw o prim ary plant configurations for processing grains into ethanol: dry mill and wet m ill. A summary of the processing technolo gie s used by today’s ethanol plan ts is found below in Table 1.2-2. Dry mill pla n ts simp ly grind th e entire kern el an d f eed the f l our into the f erm entation process to produce ethanol. At the end, the unf erm e ntable parts are recovered as distillers’ grains along with a soluble liqui d containing vitami ns, mi nerals, fa t and protein. The distillers’ grains are co ncentrated with th e so lubles stream to m ake a single co-p rod u ct, referredto as distillers’ grains with solubles (DGS). The co-product is either soldwet (WDGS) or more commonly dried (DDGS) to the agricultural m arket as anim al feed. If the feed is going to be used by local m arkets, it’s usually sold wet prec luding the need for process dryers. However, if the feed is going to be shipped (usually by train) to m ore distant locations, the product is usually dried to facilitate stor age and transportation.

Wet m ill plants typically separate th e kernel in to four products: starch, g l u t en feed, glu t en m eal, and oil. The star c h is used in a f erm entati on process the sam e as in dry m ill plants, while the gluten, oil, and other co-products are sold into food and agricultural m arkets. Production of these multiple str e am s is m o re capital- i nten sive th an th e dry milling process, andthus wet mill plants ar e generally more expensive to build an d tend to be larger in size.

21Table 1.2-2. 2006 U.S. Ethanol Production by Processing T echnology Pro cessin g T e c hnology Cap acit y MMg y % o f Cap acit y No. of Plan t s % o f Plan t s D r y Milling W et Milling O t her a 4,057 1,137 25 77.7% 21.8%

0.5% 92 10

8 83.6% 9.1% 7.3% T o t al 5,218 100.0% 1 10 100.0%

a Plants that do not process traditional grain-based crops a nd thus do not require milling. T his categoryincludes plants processing ch eese w hey , sugars & starches, o rw aste bev erages.

As shown above in Table 1.2-2, dry milling is th e m o st predominant pro duction p r ocess used by today’s ethanol plants. Of the 102 facilities processing corn and/or other similarly processed g r ains, 92 utilize dry m illin g technolog ies and the rem a ining 10 plants rely o n wet
m illing processes (refer to Table 1.2-3 below). Th e rem a ining “other” eight plan ts lis t ed above process waste beverages, cheese whey, or sugars /starches and operate differently than their grain-b a sed counterp arts

. These facilities do no t require m illing and instead operate a simp ler enzym atic ferm entation process.
Table 1.2-3. 2006 U.S. Grain Ethanol Production - Wet Mill P l ants

E t ha nol P l a n t L oc a t ion Cap acit y MMg y Ar c herDan i els Midland a Ar c herDan i els Midland a Ar c herDan i els Midland Ar c herDan i els Midland a Ar c herDan i els Midland Aventine Renew able Ener gy C a r g ill, Inc

. C a r g ill, Inc

. G r ain Pr oc e s s i ng Cor p T ate & Ly le CedarRapids , IA Clinton, IA Colum bus , NE Dec atur , IL M a r s hall, MN P ek i n, IL Eddy ville, IA Blair , N E M us c atine, IA L oudon, T N 300 150

90 250

40 100

35 85 20 67 T o t al 1,137

a Estimated AD M plant capacities

In addition to grain and star ch-to-ethanol production, anothe r m e thod exists for producing ethanol from a m ore diverse feedstock base. T his process involves converting cellulosic
m a terials such as bagasse, wood, straw, switchgra ss, and other biom ass into ethanol. C e llulose consists of tightly-linked polym ers of star ch, and production of ethanol from it requires additional steps to convert these polym ers into ferm entable sugars. Scientists are actively pursuing acid and enzyme hydrolysis as well as gasification to achieve this goal, but the

22technologies 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 ap proxim ately one million gallons of ethanol per y e ar f r om wood chips. Sev eral
companies have announced plans to build cellulose -to-ethanol plants in the U.S., but most are still in the research and developm ent or pre-c onstruction planning phases. The m a jority of the plans involve converting bagasse, rice hulls, wood, switchgrass, corn stalks, and other agricultural waste or biom ass into ethanol. Fo r more a m ore detailed discussion on future cellulosic ethanol plants a nd production technologies, refer to RIA Sections 1.2.3.6 and 7.1.2, respec tive l y.
1.2.1.3 Ethanol Plant Energy Sources Ethanol production is a relatively resource-intensive proce ss 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 ethano l production facilities, 10 1 burn natural gas, 7 burn coal, 1 burns coal and biom ass, and 1 burns syrup from t he process to produce steam 3

. Our research suggests that 11 plants currently ut ilize co-generationor com b ined heat and p o wer (CHP) technology, although others m a y exist. CHP is a m e chanism for improving overall plant efficiency. Whether owned by the ethanol facil ity, their loca l utility, or ath i r d par t y; CHP facilities produce their own electricity and use the waste heat from powerproduction for process steam , reducing the energy intens ity of ethanol production. Asummary 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 Plan t En erg y S our c e Cap acit y MMg y % o f Cap acit y No. of Plan t s % o f Plan t s CHP T ech

. Coal Coal, Biom as s Natural Gas a Sy r up 1,042 50 4,077 49 20.0% 1.0% 78.1%

0.9% 7 1 101 1 6.4% 0.9% 91.8%

0.9% 2 0 9 0 T o t al 5,218 100.0% 110 1 00.0% 11

a Includes three facilities burning natural gas w hich intend to transitio n to coal or biomass in the future.

1.2.1.4 Ethanol Production Locations
The m a jority of do m e stic ethanol is curren tly produced in the Midwest within PADD 2 – where m ost of the corn is grown. Of the 110 U. S. ethanol production f acilities, 100 are located in PADD 2. As a region, PADD 2 accounts for about 96 percent (or over fi ve billion gallons) of dom e stic ethanol production, as show n in Table 1.2-5.

3 Facilities were assu m e d to burn n a t u ral g a s i f th e p l an t fu el typ e was no t men tio n e d or u n av ailab le.

23Table 1.2-5. 2006 U.S. Ethanol Production by PADD

PA D D Cap acit y MMg y % o f Capacity No. of Plant s % o f Plan t s PADD 1 PADD 2 PADD 3 PADD 4 PADD 5 0 .4 5,012 3 0 105

7 1 0.0% 96.0%

0 .6% 2.0% 1 .4% 1 100 1 4 4 0 .9% 90.9%

0.9% 3.6% 3.6% T o t al 5,218 100.0% 110 100.0%

Leading the Midwest in ethanol production are Iowa, Illinoi s, Nebraska, Minnesota, and South Dakota with capacities of 1.62, 0.71, 0.61, 0.55, 0.49 billion g a llon s , respectively. Together, these five states’ 70 ethanol plants account for 76 percen t of the total dom e stic ethanol production. However, although the m a jority of ethanol production com e s from PADD 2, there are a growing num ber of plants s itu ated outsid e th e trad itional co rn belt. In addition to the 15 states com prising PADD 2, ethanol pl ants are currently located in California, Colorado, Georgia, New Mexico, and W y om ing. Some of these facilitie s ship in feedstocks (nam ely corn) from the Midwest, others rely on locally grow n/produced f eedstocks, w hile others rely on a combination of the two. A summ ary of et hanol production alphabetically by state is found in Table 1.2-6.

24Table 1.2-6. 2006 U.S. Ethanol Production by State

St at e Cap acit y MMg y % o f Ca p acit y No. of Plan t s % o f Plan t s Calif or nia Color ado G eor gia Iow a Illinois Indiana Kans as Kentuc k y Mic h igan Minnes ota Mis s our i North Dak o ta Nebr as k a N e wM e xi co Oh i o O k lahom a South Dak ota T ennes s ee W i s c ons in Wy o m i n g 7 1 93 0.4 1,618

706 122 219

38 155 546 155

51 606

30

3 2 493

67 233

12 1.4% 1.8% 0.0% 31.0% 13.5%

2.3% 4.2% 0.7% 3.0% 10.5%

3.0% 1.0% 11.6%

0.6% 0.1% 0.0% 9.4% 1.3% 4.5% 0.2% 4 3 1 25

6 2 8 2 3 16

4 2 12

1 1 1 11

1 6 1 3.6% 2.7% 0.9% 22.7%

5.5% 1.8% 7.3% 1.8% 2.7% 14.5%

3.6% 1.8% 10.9%

0.9% 0.9% 0.9% 10.0%

0.9% 5.5% 0.9% T o t al 5,218 10 0.0% 110 100.0%

In addition to the dom estic ethanol production de scribed above, the U.S. also receives a
sm all a m ount 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 comprise d of a m i xture of corporations and farm erowned cooperatives (co-ops). More than half (o r 60) of the plants are ow ned by corporations and the remainder (50 plants) are farm er owned co-ops. On average, a U.S. ethanol production facility has a m ean plant capacity of about 47 m illion gallons per ye ar. In general, plants owned by corporations (“company-owned” ) are above average in size wh ile farm e r-owned co-ops are below average. Sim ilarly, company- owned plants tend to have a much broader range in ethanol production levels than farm er-owned co-ops. A su mm ary of t hese results is presented in Table 1.2-7.

25Table 1.2-7. 2006 U.S. Ethanol Production by Plant Ow nership Plan t Ow ne r s hip No. of Plan t s P r oduc tion Ca pa c i ty , M M gy To t a l A v g M i n M a x Com pany - O w ned a F ar m er - O w ne d 60 50 3,31 5 1,90 3 55 38 0.4 3 300 60 T o t al 110 5,21 8 47 0.4 300

a Includes etha nol producers w ith public of ferings.

Based on the dom inating num b er of comp a ny-owned plants and their above-average production size, company-owned plants accoun t fo r nearly 64 percen t of the total dom e stic product. Further, m ore than 50 percent of t oday’s U.S. ethanol produc tion capacity com es from plants owned by just 6 different companies. A list of the top six ethanol producing companies and their respective plant capac ities is found in Table 1.2-8.
Table 1.2-8. 2006 Top Six U.S. Ethanol Producers
Company a Cap acit y MMg y No. of Plan t s Ar c herDaniels Midland Br oin Ver aSun Energy Haw k ey e Rene w ables , LLC G l obal / MG P Ingr edients Aventine Rene w able Ener gy 1,07 0 8 3 8 23 0 20 0 19 0 15 0 7 18

2 2 3 2 To t a l 2 , 6 7 8 3 4

a Includes majo rityand minorityplant ow nership.

Over 80 percent of today’s U.S. ethanol pr oduction is sold to the gasoline industry by eight m arketing com p anies 4

. A list of the top eight ethano l m a rketers and their respective
m arketing capacities b as ed on plant affiliation s is found in Table 1.2-9. The rem a ining ethanol is
m arketed by Kinergy Marketing, The Andersons, Murex International, Noble Am ericas, and other sm all m arketing comp anies.

4 B a sed o n i n f o rm at i on o b t a i n e d fr om ethanol m arket er web s i t e s, etha nol p r o duce r we bsi t e s, an d c o nve rsati ons wi th ethanol m arketers/producers

.

26Table 1.2-9. 2006 Top Eight U.S. Ethanol Marketers

M a rket in g Co mp an y Cap acit y MMg y a No. of Plan t s Ar c herDan i els Midland Ethanol Pr o duc ts Renew able Pr oduc ts Mar k eting Gr oup Aventine Re new able Ener gy Ec o- Ener gy Pr ovis ta ( f or m e r l y U BE) C a r g ill, Inc

. Abengoa B i oener gy 1,172 991 612 666 325 217 120 110 9 22 15 14

5 5 2 3 T o t al 4,212 75

a Volumes ba sed on marketing agre ements and respectiv e ethanol plant capacit ies

1.2.2 Forecasted Grow th in Ethanol Production
1.2.2.1 Overview Over the past 25 years, dom e stic fuel ethanol production has stead ily in cr eased due to environm ental regu lation , federal andstate tax incentives, and m arket de mand. More recently, ethanol production has soared due to the phase out of MTBE, an increasing num ber of state ethanol m a ndates, and elevated crude oil prices

. As shown in Figure 1.2-1, over the past three years, dom estic ethanol production has nearly do ubled 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 dom e stic ethanol production 5

.

5 Based on RFA comm ents received i n response to t h e pr oposed rulem aking, 71 FR 55552 (Septem b er 22, 2006).

271 9 8 0 1 9 8 1 1 982 1 983 1 9 8 4 1 9 8 5 1 986 1 9 8 7 19 8 8 1 989 1 990 1 9 9 1 19 9 2 1 993 1 994 1 9 9 5 1 996 1 997 1 9 9 8 1 9 9 9 2 000 2 0 0 1 2 0 0 2 2 003 2 004 2 0 0 5 Figure 1.2-1. U.S. Ethanol Production Over Time

1, 630 1,770 2, 810 3, 410

2,130 4,000

0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500Ethanol Production,MMgal

Ye a r
So urce: R e new abl e F u el s Ass o ci at i on, Fr om Ni che t o Nat i o n: Etha n o l I n d u stry Out l o o k 20 0 6

EPA forecasts that dom e stic etha no l production will con tinu e to grow in to the future. In addition to the past impacts of fe deral and state tax incentives, as well as the m o re recent impacts of state ethanol m a ndates and the removal of MTBEfrom al l U.S. gasoline, crude oil prices are expected to continue to drive up dem a nd for ethanol. As a result, the nation is on track to exceed the ren ewable fuel requ irem ents containe d in the Act, as explained below.
1.2.2.2 Expected Increases in Plant Capacity Today’s ethanol production capacity (5.2 billi on gallons) is already exceeding the 2007 renewable f uel requirem ent (4.7 billion gallons). In addition, there is another 3.4 billion gallons of production capacity curre ntly under construction. 6 F G H A summ ary of the new construction and plant expansion projects cu rrently underway (as of October 2006) is found in Table 1.2-10.

6 Un d e r con s tructio n p l an t l o catio n s , cap acities, feed st o c k s , an d en erg y sources as well as p l ann e d/ p r op osed p l an t lo catio n s and cap acities were d e ri v e d fro m a v ariety of d a ta so urces in clud i n g Ren e wab l e Fu els Asso ciatio n (RFA), Ethan o l P r o d u cer M a gazi ne ( E PM ), IC F I n t e rnat i o nal , B i o F uel s J o urnal , andetha nol p r od uce r websi t e s.

28Table 1.2-10. Under Construction U.S. Ethanol Production Capacity

PA D D O ct

. 2006 Baselin e U n d er Co n st

. Bas e + Un d er Co n st

. MMg y P l a n t s MMg y a Plan t s MM g y a Plan t s PADD 1 PADD 2 PADD 3 PADD 4 PADD 5 0. 4 5,01 2 30 105

71 1 100 1 4 4 115 2,764

230

50 198 1 39

3 1 3 115 7,776

260 155 269 2 139 4 5 7 T o t al 5,21 8 110 3,357 47 8,575 157

a Includes plant ex pansion s

A select group of builders, technology pr oviders, and construction contractors are
completing the m a jority of the construction proj ects described in Table 1.2-10. As such, the
completion dates of these projects are staggere d over approxim ately 18 m onths, resulting in the gradual phase-in of ethanol production shown in Figure 1.2-2 7

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

0 1, 000 2, 000 3, 000 4, 000 5, 000 6, 000 7, 000 8, 000 9, 000 10, 000

O c t

- 06 D ec - 06 M ar - 07 J un- 07 S ep- 07 D ec - 07 M a r - 08 Ti m eEthanolCapacity(MMgy)

A ddi t i onal C ap ac i t y T ot al E t hanol P l ant C apac i t y

7 C o nstruct i o n t i m e li nes ba sedo n i n f o rm at i on obt ai ne d fr o m press rel ease s an d et han o l p r o d u cer we bsi t e s.

29As shown in Table 1.2-10 and Figure 1.2-2, once all the construction projects currently underway are comp lete (estim ated by March 2008), the resulting U.S. ethanol production capacity would be about 8.6 billion gallons. W ithout even considering forecasted biodiesel production (discussed below in 1.2.5), this would be m ore than enough renewable fuel to satisfy the 2012 RFS requirem e nts (7.5 billion gallons). However, ethanol production is expected to continue to grow. There are m o re and m o re eth a nol pro j ects being anno unced each day. These potential p r o j ects are at v arious s t ages of planning from conducti ng f eas ibility stud ie s to gain ing local approval to applying for perm its to fi nancing/fundraising to obtaining contractor agreem ents. Together th ese poten tial projects co uld result in an additiona l 21 billion gallons of ethanol production capacity (a s shown in Table 1.2-11).
Table 1.2-11. Other Potential U.S. Ethanol Production Capacity

PA D D Base + Un d e r Co n s t

. P l a nne d P r opos e d MMg y a Plan t s MMg y a Plan t s MM g y a Plan t s PADD 1 PADD 2 PADD 3 PADD 4 PADD 5 115 7,77 6

260 155 269 2 139 4 5 7 548.0 4,633

250 100 232 8 44

4 1 8 934 11,722 876 783 775 21 136

14 14 23 Su b t o t al 8,57 5 157 5,763 65 15,090 208 To t a l b 14,339 222 29,428 430

a Includes plant ex pansion s b T otal including ex isting pl us under construction plants.

Although there is clearly a great potential fo r ethanol production growth, it is highly unlikely that all the announced projects would ac tually reach completion in a reasonable am ount of tim e, or at all, consideri ng the large num ber of projects m o ving forward. Since there is no precis e way to know exactly which p l ants will co m e to fruition in the futu re, we have chosen to focus our subsequent d i s c ussion on forecas ted ethanol produ ction on p l ants which are likely to be online by 2012. 8 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” ve rsus “proposed” is that as of October 2006 planned pro j ects had co mpleted perm itting, fundr ais i ng/financing, and h a d builders assigned with definitive construction tim elines whereas proposed projects did not.
As shown in Table 1.2-11, once all the unde r construction and pl anned projects are
complete, the resulting U.S. ethanol production capacity would be 14.3 billion gallons. This volum e, expected to be o n line by 201 2, exceeds the EIA AEO 2006 de mand estim a te (9.6 billion gallons by 2 012, discussed m ore in RIA Sectio n2.1.4.1). The forecasted growth would nearly trip le tod a y’s productioncapacity an d greatly ex ceed the 201 2 RFS requirem e nt (7.5 billion

8 A m o re d e tailed su mm ary o f th e p l an ts we co nsid ered is fo und in a March 5 , 20 07 no te to th e do ck et titled : RFS In d u stry C h ar a c t e ri zat i on – Et han o l P r od uct i o n
.

30gallons). While our forecast represents ethanol production capacity (act ual production could be lower), we b eliev e it is s t ill a good in dicato ror w h at dom estic ethanol p r o duction cou l d look lik e in the future. In addition, we predict that dom estic ethanol production will continue to be supplem ented by imports in the future. A m o re de tailed 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 und er construction and 65 planned), 106 would rely on grain-based feedstocks. More sp ecifically, 89 would rely exclusively on corn, 13 would process a blend of corn and/or sim ilarly pr ocessed grains (m ilo or wheat), 3 would process molasses, and 1 would process a com bination of molasses an d sweet sorg hum ( milo). Of the
rem aining six plants (all in th e planned stage), four would pro cess cellu losic biom ass feedstocks and two would start off processing corn and later tr ans i tion to cellu losic materi als. Of the f our dedicated cellulosic plants, one would process bagasse, one w ould process a com bination of bagasse and wood, and two would process biom ass. Of the two transi tional corn/cellulosic plants, on e would ultim ately p r oces s a com b ina tion of bagasse, rice hulls , and wood and the other would ultim ately p r ocess woodand other a g ricu ltur al r esidues. In addition to the forecasted n e w plants described abov e, an exis tin g corn eth an o l plan t plan s to expand production and transition to corn stalks, switchgrass, and biom ass in the future.

A summ ary of the resulting overall feed stock usage (including current, under construction, and planned projects) is found inTable 1.2-12. A discussion on how the plants predicted to process cellulosic feedstocks w ould help the n ation m eet the Act’s ce llu losic biom ass ethanol requirem en t is found in Section 1.2.2.6

31Table 1.2-12. Forecasted 2012 U.S. Ethanol Production by Feedstock

Plan t F eed st o ck Ca p acit y MMg y % o f Cap acit y No. of Plan t s % o f Plan t s Bagas s e Bagas s e, W ood Bagas s e, W ood, Ric e Hulls a Biom as s Chees e W hey Corn b Co rn , B a rl e y C o r n , Milo c Cor n, W heat Cor n Stalk s , Sw itc hgr as s , Biom as s a Milo, W heat M o l a sse s d Sugar s , Star c hes W a s t e Bever ages e W ood Agr i c ultur al Res i dues a 7 2 108

55

8 12,495 4 0 1,132

235

40 40 52

2 16 108 0.1% 0.0% 0.8% 0.4% 0.1% 87.1%

0.3% 7.9% 1.6% 0.3% 0.3% 0.4% 0.0% 0.1% 0.8% 1 1 1 2 2 178 1 20

3 1 1 4 1 5 1 0.5% 0.5% 0.5% 0.9% 0.9% 80.2%

0.5% 9.0% 1.4% 0.5% 0.5% 1.8% 0.5% 2.3% 0.5% T o t al 14,339 100.0% 222 100.0%

a Facilities plan to start off processing corn. b Includes tw o facilities processing seed corn.

c Includes one facilityprocessing small amounts of mo lasses in addition to corn and milo. d Includes one facilityplanning to process sw eet sorgh um (milo) in addition to molasses.

e Includes tw o facilities processing brew eryw aste.

As shown above, the majority of future plan ts are predicted to process grains (nam ely corn). Simi larly, the vas tm ajority of plants are expected to p u rsue dry m illing techno logy. Our analys is does not foresee any new wet m ill facili ties, with th e exception of a new 100 MMgy wet
mill plant th at is p l annedfor Fort Dodge, IA and a 37 MMgy plant exp a ns ion project that is underway in Loudon, TN. Further, we do not pr edict that there will be any new plants processing cheese whey, waste beverages, or su gars/starches (which do not require m illing). The forecasted cellulos i c feedstock plants (describ ed in m o re detail in Section 1.2.2.7) will not require m illing. However, these facilities will re q u ire com p lex for m s of pretreatm ent (described in m o re detail in Section 7.1.2) to break down the lignocellulosic and he m i cellulosic polym e rs into ferm entable sugars. A summ ary of th e resulting overall f e edstock processing technology utilization is found be low in Table 1.2-13.

32Table 1.2-13. Forecasted 2012 U.S. Ethanol Production by Processing T echnology Pro cessin g T e c hnology Ca p acit y MMg y % o f Cap acit y No. of Plant s % o f Plan t s D r y Milling W e t Milling O t her a 12,668 1 ,274

397 88.3% 8.9% 2.8% 192 11 1 9 86.5% 5.0% 8.6% T o t al 14,339 100.0% 222 100.0%

a Plants that do not process traditional grain-ba sed crops and thus do not req uire milling. T his categoryincludes plants processing c heese w hey , sugars & starches, or w aste bev er ages as w e ll as plants that plan to pr oces s molasses or cellulosic feedst ocks.

1.2.2.4 Changes in Plant Energy Sources Of the 112 forecasted new plants, 10 0 would bur n som e a m ount of natural gas - at least initially. M o re spec if ica lly, 91 plants would rely exclusively on natural gas; two would rely on a
com b ination of natural gas, bran and biom ass; one would burn a com bination of natural gas, distillers’ grains and syrup; a nd six would start off burning natu ra l gas a nd late r tran sition to coal. As for the rem a ining 12 plants, three woul d burn m a nure-derived m e thane (biogas); seven would rely exclusively on coa l; one would burn a com b ination of coal and biom ass; and one would burn a com bination of coal, tires and biom ass. In addition to the ne w ethanol plants, three existing plants currently burning na tural gas are predic ted to trans i tion to alte rnate boiler f u els in the f u ture. More spec if ically, two plants plan to transition to biom ass and one plans to start burning coal. Our research suggests that seven of the new plants (m entioned above) would utilize
com bined heat and pow er (CHP) technology, although others m a y exist. Three of the new CHP plants would burn natural gas, three would burn coal, and one would burn a com bination of coal, tires, and biom ass. Among the existing CHP plants , two are predicted to transition from natural gas to coal or biom ass at this tim e
. Overal l, the net num ber of CHP ethanol plants would increase from 11 to 18. A summ ar y of the resulting overall plan t energy source utilization is found below in Table 1.2-14. A discussion on how the plants predicted to burn waste m a terials could help the nation m eet the Act’s cellulosic biom ass ethanol requirem ent is found in Section 1.2.2.6.

33Table 1.2-14. Forecasted 2012 U.S. Ethanol Production by Energy Source

Plan t En erg ySo u r ce Cap acit y MMg y % o f Cap acit y No. of Plan t s % o f Plan t s CHP T ech

. Biom as s a Coal b Coal, Biom as s Coal, Biom as s , T i r es Manur e Biogas c Natur al G as Natur al G as , Br an, Biom a s s N a tur a l G a s , D i s t iller s ' G rain, Sy r u p Sy r up 112 2,095 75 275 144 11,275 264

5 0 49 0.8% 14.6%

0.5% 1.9% 1.0% 78.6%

1.8% 0 .3% 0.3% 2 21

2 1 3 189 2 1 1 0.9% 9.5% 0.9% 0.5% 1.4% 85.1%

0.9% 0.5% 0.5% 1 6 0 1 0 10

0 0 0 T o t al 14,339 100.0% 222 100.0% 18

a R epresents tw o ex isting nat ural gas-fired plan ts that plan to transition to biomass. b Includes tw o plants planning on burning lignite coal orcoal fines. Includes one e x isting plant currently burning natural gas that plan s to transition to coal. Includes six new plants that w ill start off burning natural gas and later transiti on to coal.

c Includes one facilityplannin g on burning cotton gin in addition to manure biogas.

1.2.2.5 Changes in Ethanol Production Locations Once all the forecasted ethanol pro j ects ar e comp lete, 87 percent of the do m e stic production capacity would originate from PA DD 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

PA D D Cap acit y MMg y % o f Cap acit y No. of Plant s % o f Plan t s PADD 1 PADD 2 PADD 3 PADD 4 PADD 5 663 12,409 510 255 501 4.6% 86.5%

3.6% 1.8% 3.5% 1 0 183 8 6 1 5 4.5% 82.4%

3.6% 2.7% 6.8% T o t al 14,339 100.0% 222 100.0%

While PADD 2 ethanol production is expect ed to m ore than double (from 5.0 to 12.4 billion gallo n s), this rep r esents a d ecrease in M i d w est m arketshare (from 96 to 87 percent). Th is predic ted sh if t in m arketshare is attributed to the growing number of ethanol plants located outside the cornbelt. Arizona, Florida, Hawaii, L ouisiana, Ne w York, Oregon, Pennsylvania and Texas are scheduled to join the 19 ethanol producing states descri bed in Table 1.2-5. A summary of future ethanol production by state is found below in Table 1.2-16.

34Table 1.2-16. Forecasted 2012 U.S. Ethanol Production by State

St at e Ca p acit y MMg y % o f Cap acit y No. of Plant s % o f Plan t s Ar iz ona Calif or nia Color ado F l or ida G eor gia Haw aii Iow a Illinois Indiana Kans as Kentuc k y Louis i ana Mic h igan Minnes ota Mis s our i NewY o rk North Dak o ta Nebr as k a N e wM e xi co Oh i o O k lahom a O r egon Penns y l vania South Dak ota T ennes s ee Te x a s W i s c ons in Wy o m i n g 55 244 243 80.0 150.4

59.2 3,016 1 ,606

855 569

38 110 212 882 382 325 251 2,543 30 420 112 143 108 953 109 370 463

12 0.4% 1.7% 1.7% 0.6% 1.0% 0.4% 21.0% 11.2%

6.0% 4.0% 0.3% 0.8% 1.5% 6.2% 2.7% 2.3% 1.7% 17.7%

0.2% 2.9% 0.8% 1.0% 0.8% 6.6% 0.8% 2.6% 3.2% 0.1% 1 7 5 2 3 5 38 16 11 13

2 2 4 20

6 4 5 31

1 7 3 2 1 16

2 5 9 1 0.5% 3.2% 2.3% 0.9% 1.4% 2.3% 17.1%

7.2% 5.0% 5.9% 0.9% 0.9% 1.8% 9.0% 2.7% 1.8% 2.3% 14.0%

0.5% 3.2% 1.4% 0.9% 0.5% 7.2% 0.9% 2.3% 4.1% 0.5% T o t al 14,339 100.0% 222 100.0%

1.2.2.6 Meeting the Cellulosic Ethanol Requirement in 2013 The Energy Policy Act o f 2005 (the Energy Ac t or the Act) requires that 250 m illion gallons of the renewable fuel consumed in 2013 and beyond m eet the definition of cellulosic biom ass ethanol. The Act defines cellulosic bi om ass ethanol as etha nol derived from any lignocellulosic or hemi ce llulosic m a tter that is available on a renewable or recurring basis including dedicated energy crops and trees, wood and wood residues, plants, grasses, agricultural residues, fibers, anim al wastes and other waste materials, and municipal solid waste. The term also includes any ethanol producedin facilities where anim al or o ther w aste m aterials are

35digested or otherwise used to di splace 90 percent of m ore of t he f o ssil f u el norm ally used in th e production of ethanol.
As discussed above in S ection 1.2.2.3, there ar e seven “planned” ethanol plants planning on processing cellulosic feedstocks in the future. A summ ary of these f acilitie s is f o u nd below in Table 1.2.17.
Table 1.2-17. Potential Cellu losic Feedstock Plants

E thano l P l an t Loca tion P la nt Fe ed stoc k Ca pa city MM g y St a t u s W o r l d w id e Ener g y G r ou p a C e lu no l C o r p

. b G S Agr i f u els C or p or a t ion c X et h ano l C oa s t a l LLC Biono l Xe tha n ol Co rpo r ati o n BioEner gy I n te r na t ion al Ka um ak an i , HI J e nning s , LA Me m p h i s, T N Aug us t a, G A L a k e Pr ovid en c e , L A B l ai r s t o w n, IA C lea r f i e l d C o un ty , PA Ba ga s s e Bag as s e, W o od Bi om ass Bio m a s s C o r n t h e n Ba ga s s e , R i c e H u lls , W o o d Corn th en Co rn S t a l k s , Sw i t c h Gras s, B i o m as s C o r n t he n W ood , Agr i c u lt ur a l R es i d ue s 7 2 5 50 10 8 4 0 10 8 P l a n n e d P la nn ed P l an ne d P la nn ed P la nn ed P l an ne d d P la nn ed T o tal Cel l ulo s ic E thano l P o tenti a l Bas e d on P l a n t Fe eds toc ks 3 2 0

a C o m p any als o /f or m e r l y k n own as C l ea r f u e ls T e c hno lo gy b C o m p any als o /f or m e r l y k n own as BC I n t e r n at io nal

c Pr oj e c t als o /f or m er l y k n own as Mea n G r e en Bio f ue ls d In c l ud es 5 Mm gy ex is tin g p la nt c a p ac it y p lu s 35 MM gy p la nn ed e x p an s i on

.

It is unclear whether the above-m entioned cel lulosic feedstock plants would be online and capable of producing 250 m illio n gallons of ethanol by 2 013 to m eet the Act’s cellulosic biom ass ethanol requirem ent. However, as described above in S ection 1.2.2.4 there are 12 facilities that burn or plan to bur n waste m a terials to power their ethanol plants in the f uture. These facilities, summ arized below in Table 1.2.18, could also potentially m e et the definition of cellulosic biom ass etha nol under the Act.
Table 1.2-18. Potential Waste Energy Plants Capa city Eth a n o l Pla n t L o cat ion P lan t En ergySource MM g y St a t us C o r n L P G old f i eld, IA C oa l , Biom as s 50 E x i s t in g E C a r u s o E t han ol G o o d lan d , KS C o a l , Biom as s 2 5 U n der C ons tr uc tion Ar c h erD a niels M i dla nd C olu m b us , N E C oa l , T i r e s , Biom as s 275 Pla nne d E3 Biof uels , LLC M ead, N E M anu r e Bio gas 24 U n der C ons tr uc tion H a r r i s on Etha nol, LLC C ad i z , O H M anu r e Bio gas 20 Pla nne d Pa nda Ethan ol H e r e f o r d , T X M anu r e Bio gas , C o tton G i n 100 U n der C ons tr uc tion C e ntr a l Min nes o t a Eth ano l C o- o p Lit tle F alls , M N N at ur al G as the n Bio m a s s 22 E x i s t in g C h ippe w a Va lley Eth ano l C o. B en so n, M N N at ur al G as the n Bio m a s s 90 Ex is tin g a Eth ane x at SEM O Por t C a p e G i r a r d e au, MO N a t u r a l G a s , Br a n , Bio m a s s 132 Pla nne d Eth ane x South er n Illino i s b B e nt o n , I L Na tu ra l Ga s, B r an , B i om ass 1 3 2 P l an n ed G r een Plains R e newable Ener gyc Sup e r i o r , IA N a t u r a l G a s , D i s t ille r s G r a i n, Sy r u p 5 0 U n der C ons tr uc tion C o r n Plu s , LLP W i n neb ago, MN Sy r u p 49 E x i s t in g T o tal Cellu los ic Eth a no l Pot e nt ial Ba sed on Plan t Ene r g y S o urce s 969

a I nc l udes 45 M M gy ex isting plan t c ap ac i typlus 45 M M gyp l ann ed ex pansio n. b Joint v e n t ure w i t h Star Ethan ol

c P r oject also/for m er lyk n ow n as Sup e rior Ethano l

36Depending on how mu ch fossil fuel is disp l aced by burning these waste m aterials (o n a plant-by-plant basis), a porti on or all of the above-m enti oned 969 MMgy ethanol production capacity cou l d poten tially qualify as “cellulosic b i om ass 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 ce llulosic biom ass ethanol or com e to fruition b y 2013, it would be m ore than enoug h to satisfy the 250 m illion gallon requirem e nt specified in the Act. 9

1.2.3 Current Biodiesel Pro duction
Biodiesel is defined in several sections of th e Act, which we have used in f o rmulating our definition for the regulations, which call for m eeting ASTM specifications. Biodiesel is regis t ered with th e EPA for comm ercial sale an d is legal for use at any blend level in both highway and nonroad diesel eng i nes although m o st engin e m a nufacturers will only ho nor the warranty if biodiesel is used in blends of 2, 5 or, in som e limited circum stances, 20 percent.
Biodiesel can be m a de fr om alm ost a ny vegetabl e or anim al fat, with m ost of the world’s production com i ng 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 m ake it is dependent upon the vegetabl e oils and fat supplies that are economically available. For the U.S. m arket, there are m a ny pot ential plant oil feedstocks that can be used to
m ake biodiesel, including soybean, peanut, canola, cottonseed and corn oil. Biodiesel can also be m a de from anim al fat s such used restau rant grease (yellow grease) and tallow. Though, typically for the U.S. m arket, soybean oil ha s been the prim ary m a jor feed stock supply, followed by use of yellow grease and anim al tallow. The resulting biodiesel product can be used as a f uel for diesel engines with m i nor modifications and is com m only blended with refi nery produced diesel fuel. Raw vegetable and anim al oils consist of fatty aci ds and glycerine products. Though these oils can directly be used in engines and give good short term perform a nce, this is highly discouraged as their use can cause severe engine problem s. This is prim arily due to the raw oils form i ng engines deposits, with coking and plugging in engine injectors nozzles, pi ston rings, lubricating oil, etc. This happens due to polym erization of the trig lyce ride s in the raw oils as the f u el is com busted. Therefore, it is necessary to convert the raw oils in to a form of esters or b i odiesel which prevents these issues. The biodiesel production process c onverts the raw vegetable and anim al oils into esters, though the virgin oils th em selves are som etim es (inappropr iately) referred to as biodiesel. The production process called transe sterification consists of addi ng m e thanol or ethanol to the virgin vegetable oil and anim al oil, in the pres en ce of a catalyst such as sodium or potassium hydroxide, resulting in esters or biodiesel and a b yproduct glycerol. A subse quent step is usually

9 W e an ticip ate a ram p -up in cellu lo sic eth ano l pro d u c tion i n th e years t o co m e so th at cap acity ex ists to satisfy th e A c t’ s 20 13 r e qu ir em en t ( 250 mil lio n gallo n s of cellu lo sic bio m ass eth a n o l )

. Th er efor e, fo r sub s eq uen t an alysis p u rp o s es, we hav e assu m e d that 2 5 0 millio n g a llo ns o f et

h a n o l wo u l d co me fro m cellu lo sic b i o m ass sources b y 2 012

.

37needed, however, to remove glycerin, catalysts and other compounds, to allow the biodiesel to
m eet the req u ired ASTM specificatio ns. Biodiesel blends such as B2, B5 and in som e cases B20, can be used in existing engines without m odification, and m ost engines exhib it no perform ance probl em s with the use of biodiesel, though this depends on the blend and the season

. However, engine fuel filters m a y need to be changed m or e often, and there m a y be cold tempe r ature operations due to biodiesel' s higher cloud point. As a result m ost engine m a nufacturers will onl y recogn ize th eir warranties if biodiesel is used in low concen trations. Biodiesel produced from vegetable oil has practically
zero am ounts of sulfur and arom atics and a high cetane value, thus m aking it a good f o r blending into 15 ppm highway and offroad diesel fuel , though biodiesel m a de from yellow grease and anim al fat may contain about 24 ppm of sulfur I

. Biodiesel also has good lubricity qualities and can be used in concentration (~2 vol%) as a lubricity-enha ncing additive for conventional diesel.
1.2.4 Forecasted Biodiesel P r oduction Biodiesel production has been in creasing rapidly over the past five years and is projected to continue at a high rate in part because of the Renewable Fu el Standard (RFS) program . This expansion has prim arily been driven by better ec onom i cs, due to the recent large increase in diesel prices associated with the run up in crude prices, along with the Biodiesel Blenders Tax Credit p r ogr am s 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 vi rgin vegetable oil feedstocks and 50 cents per gallon to biodiesel produced from recycled grease and anim al fats. This program was s t arted in 2004 under the Am erican Jobs Act. The exis ting Commodity Credit Comm ission Bio-energy Program also pays biodiesel producers grants wh en the econom i cs to produce biodiesel are poor; the program averaged ab out one dollar per g a llonin 2004. Recent paym ents through the Commodity Credit program have been reduced, howe ver, and the program is expiring in fiscal year 2006. Historically, the cost to m ake biodiesel was an inhibi ting factor to production. The cost to produce biodiesel was high compared to th e price of petroleum derived diesel f u el, even with consideration of the benefits of subsid ies and credits provided by federal and state program s. Mandates from states and local municipa lities th atrequ ire th e use of biodiesel in transpo r t fuels are another factor whic h is expanding the use of biodiesel. In 2005 approxim a tely 91 m illion gallons of bi odiesel were produced in th e U.S. based on program paym ents to biodiesel producers und er USDA’s Bio-energy Program

. This volum e represents approxim a tely 0.15 percen t of all diesel fuel consum ed in the dom e stic m arket. EIA projects th e future produ ction vo lume to expand to 414 m illion gallons p e r year in 20 07 and then decrease to about 303 MM gallons per year in 2012, assum i ng that the biodiesel blender tax credits program expires in 2007 (see Table 1.2-19).

38Table 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

aHi st o r i cal dat afr om 20 01

- 2 00 4 o b t a i n edf r o m est i m a tes from John Baiz e “ T h e O u tlook an d I m p act of Biodiesel on t h e Oilseeds Sect or” USDA O u tlo o k C o nf er ence 06

. Y e ar 200 5 d a ta f r o m U S DA Bio e n e rgy Pr ogr am
. Year 2 0 0 6 dat a fr om ver b al q uot e base d o n pr o j ect i o n by NB B i n Ju ne o f 06 . P r o d u ct i o n dat af o r

y ear s 2 0 0 7 an d h i gh er ar e f r om EI A ’ s A E O 2 006

.

W ith the increase in biod iesel production, there has also been a corresponding rapid expansion in biodiesel production capacity. P r esently, there are 85 biodiesel plants in operation with an ann ual production capacity of 580 m illion gallons p e r year J

. The m ajority of the cur r ent 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 capac ity has grown, historically the biodiesel production capacity has far exceeded actual produ ction with only 10-30 percent of this being utilized to make biodi esel, 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 (MM gals/yr) 50 54 85 157 290 580

Production, (MM gals/yr) 5 15 20 25 91 150
Capacity Utilization f o r Biodiesel, % 10 28 24 16 31 26

N o t e : C apaci ty Dat aba sedo n s u r v ey s c o nd uct e d.

Excess production capacity is not easily qua ntified, though since som e of these plants
m a y not run at full rate all of the tim e and m a y be “idled” for certain days of the week, seasons,

39tim e of day, etc. The cap acity can be class i fiedinto two types of produ cers ; capacity dedicated to biodiesel production and capacity available from the ole-chem i cal industry. The plants that prim arily operate in the ole-chem ical industry produce esters for use in the chem ical industry. These plants are swing producer s of biodiesel, w hich m eans that when the econom ics are favorable they can shift their ope rations and m ake biodiesel esters instead of products for the olechem ical m a rket. 1 0 The capacity from the ole-chem ical industry produces mono-alkyl esters using a sim i lar transes t erification pro cess, withthe ester products be ing sold for to make plasticizers, soaps, pain ts , solvents an d othe r industrial uses. A dditiona lly , the biod ies el production capacity volum es m a y be optim istic, as th is is not officially tracked. The capacities listed here are those b a s e d on each comp any’s se lf reported v o lum e s to the National Biodies el Board and m ay have som e inaccuracies due to infor m al rep orting p r ocedures. We anticipate that future capacity additions will be geared m ore towards production of biodiesel for use as transportation fuel, rather than serving p r im arily the oleochem icals m arkets. As of September 2006, there were 65 plants in th e construction phase and 13 existing plants that are expanding their capacity. Al l of this new capacity when installed would provide about 1.4 billion gallons per year of a dditional throughput capacity. T able 1.2-21 presents the data for the biodiesel plant capacities pe r the categories d i scu ssed.
Table 1.2-21. Biodiesel Plant Capacities Existing Plants Constructio n Phase

Num b er of Plants 85 78

Total Plan t Capacity, (MM Gallon/year) 580 1,400

Considering that it takes 12 to 18 m o nths to construct a biodiesel plant (from the tim e of projec t f easibility ana l ys is to startup date), a larg e portion of the capacity in the construction phase in late 2006 will b e avai lable to produce fuel in 2007. K Data on biodiesel plant construction reveals m os tof the new capacity that is currently being constructed is exp ected 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 ag gregate am ount of about two billion gallons per year. Though there is no volum e m a ndate for bi odiesel fuel under the RF S program , the total capacity availab l e from new and existing plants exceeds EIA ’ s projected biodies el vo lum e of 414 MM in 2007 and 303 MM in 2012 by a wide m argin. The plants in the construction phase are larger th an exis ting b i odies el plan ts, with av erage capacity of existing plants at 8.4 MM gallons pe r year, while plants in construction phase are

10 Ol eoc h emi cals are de ri ve d f r o m bi ol ogi cal f a t s an d oi l s usi n g hy dr ol y s i s or al co hol y s i s wi th pr o duct s of fatty aci d ester s an d g lycer o l.

40averaging 20.9 MM gallons per year , as presented in Table 1.2-22. The distribution of biodiesel plants by size and num be r of comp anies within each size range are pr esented in Table 1.2-23.
Table 1.2-22. Average Plant Capacity by Feedstock (MM gallons p er year) Feedstock Existing* Construction* Canola 57.5 Multi Feedstock 6.0 16.7 Other Vegetable 2.0 Recycled C ooking Oil 0.5 1.0 Soybean Oil 8.8 19.3 Tallow 5.0

Table 1.2-23. Biodiesel Plant Siz e versus Number of Companies Plant Siz e (MM gallon s per year) a Existing Plants Constructio n Phase

<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 ea ch category; existing pl an ts are 58 0 MM g a l/ yr wh ile tho s e in th e con s tru c tio n p h a se ar e 1,400 MM g a l/yr

.
Because newer plants are likely to b e larg er th anexisting plants, have better technolo gy and m a y have greater alignm ent with feedstock and feed sources, som e of the older plants m a y operate at an econom ic disadvantage once the new pl ants com e on line. At the m o ment, it is not possible to predict actual biodi esel production based on capacity, si nce in the past the capacity was used at rates less tha n m aximu m. Thus, how excess prod uction c apac ity evo l ves will be dictated by econom ics, profitability, and fuel dem a nd.
The m a jority of existing biodies el plant capac ity is loc ated in the mi ddle and Midwestern parts of the country and use soy bean oil as the feedstock. The ot her plants are scattered with locations based on the east and west coasts, withfeedstocks based on use of soybean, canola and other o ils as well as yello w grease as the feedst ock. The new plants are be ing built to process a

41wider variety of feedstocks, with multi-feedstoc k and recycle greas e cap ability. The feedstocks for these plants are li sted in Table 1.2-24.
Table 1.2-24. Feedstock Selection for Biodiesel Producers Feedstock Existing Construction Cam e lia Canola 2 Cottonseed 1 Multi Fe edstock 29 29 Palm Oil Recycled C ooking Oil 7 3 Soybean oil 39 36 Tallow/Poultry Fat 2 Unknown 7 8

1.2.5 Baseline an d Projected Biodiesel V o lumes for Analysis
For cost and em ission analysis purposes, thr ee biodiesel usage cases were considered: a 2004 base case, a 2012 reference case, and a 2012 control case. The 2004 base case was form ed based on historica l biodiese l usag e (25 millio n gallons as summarizedin Table 1.2 -16). The r e f e rence cas e was comput ed by taking the 2004 base case and grow ing it out to 2012 in a m a nner consistent with the growth o f diesel fuel (described in Section 2. 1.3). The resulting 20 12 refe rence case consis ted of approxim ately 30 millio n gallons of biodies el. F i nally, for th e 2012 control case, forecasted biodies el us e was assumed to be 300 million gallons based o n EIA’s AEO 2006 report (rounded value from Table 1.2-16 ). Unlike forecasted eth a nol use (d escribed in 2. 1.4), biodiesel use was
assum ed to be constan tat 300 millio n gallons un der both the statu t ory an d higher pro j ected renewable f uel consumption scenarios.



### 1.3 Renew a ble Fuel Distribution

1.3.1 Current Renew a ble Fuel Distribution System
Ethanol and biodiesel blended fuels ar e not currently shipped by petroleum product pipeline due to operational issu es and additional cost factors. LThe ability to ship by p i peline is also lim ited because the sources of ethanol and bi odiesel are f r equently no t in th e sam e locations as the sources of gasoline and petroleum

- based dies el fuel. Hence, a separate distribution system is needed for ethanol and biodies el u p to the po in t where th ey are blended into petroleum

- based fuel as it is loaded into tank trucks f or delive ry to retail and f l eet op erators. Ethanol and

42biodiesel can either be added by “splash blending ” where the renewable is added separately to the tank truck, or by in-line injec tion where the renewable is inj ected into the petroleu m fuel stream as it is being dispensed in to the tank truck. Ethanol and biodiesel are som e tim e s added to petroleum

- based fuels downstream of the term inal , but this accounts for lit tle of the total volum e of used.
In cases where eth a nol and biodies el are produced within 200 miles of a terminal, trucking is often the pref erred m eans of distribu tion. However, m ost renewable fuel volum e s are produced at greater distances from potential cent ers of dem a nd. For longer shipping distances, the preferred m e thod of bringing renewable fuels to term inals is by rail and barge. Dedicated pipelines have not been used to distribute renew able fuels to terminals d u e to the h i g h cost of installing new pipelines, the relati vely large shipping volum es that would be needed to justify such expenditures, and the fact that renewabl e fuel production facilitie s tend to be relatively
num e rous and dispersed. The relatively low volumes of ethanol used prior to 2002 constrained the ability of the distribution system to efficientl y m o ve ethanol to distant m a rkets. E thanol shipm e nts by rail were typically m a de on an individual car basis. Under such an approach, sm all groups of rail cars travel to m arket as part of trains that carry other goods. Th is approach results in relatively high transportation costs, longer transit tim e s, and potential de lays in delivery. Substantial improve m e nts in the efficiency of distributing ethanol by rail are being made due the need to move large volum es of ethanol ov er long distances as a consequen ce of the elim ination of MTBE in California, New York, and Connecticut beginn ing in 2004. The use of unit trains, som e ti m e s referred to as “virtual pipelines” reduces delive ry costs, shortens delivery tim es, and improves reliability. Unit trains are composed entirely of approxim a tely 100 rail ca rs containing ethanol. Ethanol shipped by unit trains is delivered to hub term inals for further distribution to other term inals by barge and tank truck.
Substantial volum es of ethanol can potential ly be shipped down the Mississippi river by barge for temporary sto r age in New Orleans. M From New Or leans, ethanol can be loaded onto ocean transp ort for deliv e ry to the East and W e st Coast. There is also potentia l to m ove ethano l via the Missouri and Ohio as well as other river system s and the Great Lakes. Marine shipm e nts of ethanol require a relatively large minimu m shipm ent size, determined by the mi ni mum size of the m arine tank comp artm ent. 1 1 Sim ilar to the cas e f o r “unit tr ains ”, there are also efficiencies in dedicating whole barges, barge tow s, or m arine tankers to ethanol dist ribution. The increased dem a nd for ethanol has m a de it possi ble to better benefit from thes e efficiencies of s cale. The use of inland barges to transport ethanol from production facilities is in large part driven by w h ether there is river a ccess at such facilities. Historic ally, corn prices tend to be higher near river system s that serve as arteries for the export of corn than at inland locations distant from these river system s. To take advant age of lower corn prices at inland locations and to avoid competing for corn with grain elevator s that serve the export m arket, all of the new ethanol production facilities that have been bui lt since 1999 have been bu ilt at inland locations. N

11 River ba rges typically have a capacity of 10,000 barrels

. Ocean barges ty pically have a capacity of 20,000 barrels. Barge s are som etim e s subd iv id ed i n to 2 or 3 co m p artm en ts.

43Consequently, the m a jority of the growth in ethanol freight volum e s since 1999 has been in the rail sector.
1.3.2 Changes to the Renew a ble Fuel Distri bution S ystem Due to Increased Demand
This section addresses th at changes that we expe ct will take place in the renewable f u el distribution system in response to the anticipated increase in dem a nd for such fuels through 2012. There m a y be some lim ited opportunity to sh ip renewable fuels by pipeline in the future as dem a nd increases. However, because of the constraints discussed previously (see section 1.3.1), we believe that rail and barge are likely to rem a in the predom inant m eans of transportation. The 2002 DOE Study also reached this conclusion. O W h ile this constraint on the ability to ship ethanol and biodies el by pipeline presents logisti cal challeng es that resu lt in additional tr ansportationcosts, th e n eed to tran sp ort th ese alternative fuels by other m eans m ay work to the overall advantage of the fuel dist ribution system

. Petroleum product pipelines are nearing capa c ity. Thus, it seem s likely that the pipeline d i stribution sys t e mwill f i nd it increasing ly difficult to keep pace w ith annual in creases in th e dem a nd for tran sportation fuels. Displacing som e of the volum e of transportation f uels from the pipeline distribution system through the use of ethanol and biodiese l will relieve som e of this strain.

Sm all volum e rail shipm ents m ade on a by-car ba sis ar e lik ely to rem ain an important feature in supplying m arkets thatdem a nd li m ited volum es. However, as the dem and for ethanol increases we anticipate that the expansion of the use of unit trains wi ll continue, and that this will be a significant m eans of bringing ethanol to dist ant m arkets. There has been som e expansion of capacity at existing ethanol plants with river access and some new plants are projected to be built with riv er ac cess. However, we anticipate tha tmost new ethanol capa city will no t h a ve riv e r access. In addition, at least one new ethanol p l an t slated for p r oduction th at does hav e river access is planning to m o ve its ethano l to m arket v i a rail. Nev e rtheless, in cases where rail is the
m eans to transporting ethanol to hub term inals, m arine transport can play an important role in f u rther dis tri bution to satellite term inals. Substantial improvem e nt s to the rail, barge, tank truck, and term inal distribution systems will be need ed to support the tran spo r t of the volum es of renewabl e fuels necessary to m eet the requirem ents of the RFS program

. These im prove m e nts include the addition of a significant
num b er of addition al r ail cars, and ta nk trucks. Additiona l m arine barges will also be needed. To f acilitate the inc r eas e duse of unit tra i ns, ne w rail spurs will be ne eded at te rm inals. Term inals will also need to add f acilities to s t ore and blend eth a nol. In add ition, those terminals and retail f a cilities thathad not prev iously ha nd led ethano l b l ended f u el will need to m ake certain one-tim e upgrades to ensure the comp atibili ty of their system s with ethanol. T h ese types of changes have been occurring as dem a nd for ethanol and biodiesel has grown rapidly over the last several years, and there is no reason to su spect that they would not continue as dem a nd continues to warrant it. The co sts associated with these change s are discussed in Chapter 7.3 of this RIA.
The m ost comprehensive study of the infrastr ucture requirem ents for an expanded fuel ethanol industry was conducted for th e Departm e nt of Energy (DOE) in 2002

. P The conclusions reached in this study ind i cate that the changes ne eded to handle the in creased volum e of ethanol

44required und er the RFS will not represent a m ajor obstacle to industry. 1 2 While som e changes have taken p l ace s i nce th is repo rt was issued (as d i scussed b e low), we continue to believe that the ra il and m arine trans porta ti on industries can m a nage the incr eased growth in an orderly fashion. This belief is supported by the dem onstrat ed ability of the industr y to handle the rapid increases and redistribution of ethanol use acro ss the country over the last several years as MTBE was rem oved. Given that future growth in ethanol use is expected to take place in an orderly f ash ion in respon se to econo mi c drivers, we antic ipate that the dis tribu tion sy s t em will be able to respond appropriately.
The use of unit train s has accelerated beyond that anticipated in the 2002 DOE report, leading to th e m o re efficient dis tribution of ethanol by rail. As a result, rail has taken a relatively greater ro le in the transp ortati on of new ethanol volum e s as compared to shipm e nt by barge than was projected in the report. T hus, there is likely to be a rela tively greater dem a nd on t he rail distribution system and som e what less dem and on the m arine distribution system than was projected in the DOE study. The 2002 DOE study estim a ted that the increase in the volum e of ethanol shipped by rail needed to facilitate th e u se of 10 billion gallons of ethanol annua lly would represent an increase in total tank car loadings of 0.33 percent. The in crease in tank car loadi ngs for Class I railroads was estim ated at 4.75 percent. The DOE report conc luded that this increase is relatively m odest by railroad industry stan dards and co uld be acco mmodated given the available lead tim e
. The DOE study estim ated that the incr ease in dem a nd on barge move m e nt s due to the need to carry an increased volum e of e thanol would equate to a one percent incre ase in the tota l tonn age moved by barge. Given that on the one hand rela tively few new ethanol pl ants are p r ojected to be cited with river acces s, and that o n the other h a nd barge is expected to play an im portan trole in redistributing ethanol from rail hub term in als, we estim ate that the increase in b arg e move m ents will be 30 p ercen t les s than that p r oj ected in the 2002 DOE study. This equates to an increase in total tank car loadi ngs of 0.44 percent rather than the 0.33 percent projected in the DOE study. W e believe that this relatively m ode st potential incr ease in the dem a nd on the rail distribution system can be accomm odated witho u t m a jor difficulty given the ava ilab l e lead tim e
. Although, the 2002 DOE study generally conclu ded that the projected one percent increase in the dem a nd on the river barge industry could be accommodated without major difficulty, it highlighted two poten tial concerns. The report noted that delays are already being experienced at lock s on the Miss issip p i riv er. The question was raised regarding how the projected increase of one percen t in river tr af f i c due to in creased ethanol shipm e nts m i ght be
accomm odat e d at th ese locks. The report also ra ised concern s regard ing the availability of sufficient marine v e ssels capable of traveling be tween two ports in the Un ited States (J ones Act
comp liant vessels ). Giv en that it ap pears th at th ere will be less dem a nd placed on th e river barg e industry to transport ethanol than was projecte d in the 2002 DOE study, the concerns raised in the study regarding the capability of the in land waterway sy stem to cope with the increased traffic associated with sh ipping th e anticipatedn e w volum e of ethanol will b e les s pronounced.

12 See section 7.3 of this RIA regard ing t h e projecte d c o sts of the neces sary infrastructure im provem ents.

45At the present tim e, the industry is experiencing a shortage of tractor traile rs and drivers to transport ethanol. The boom indem a nd for truck transport is due to a num b er of factors, including the precipitous rem oval of MTBE from gasoline and its replacem e nt by ethanol 1 3 which has taken place w h en the dem a nd for truck tr anspo r t was already growing at a rapid p l ace due to the in creas ed imports. The implem enta tio n of EPA’s ultr a-low sulfur diesel (ULSD) program this summer m a y also caus e an increase in the dem a nd for tank trucks if m o re trucks must be dedicated to ULSD servi ce. Given the gradual increase e xpected from year to year in ethanol p r od uction, we anticipate that the industry will be ab le to add sufficien t additional tank truck service in an orderly fashion without undue burden. The necessary facility ch anges at term inals a nd at retail stations to dispense ethanol contain i ng fuels have b e en occurring at a r ecord pace due to the rem oval of MTBE from gasoline. The use of ethanol has also becom e more econom ically attractive due to higher gasoline prices. Now that MTBE has been rem ove d, a m ore steady increase in the use of ethanol is anticip ate d over tim e. This will also allo w f o r a sm ooth transition f o r te rminals and reta il operators.
The volum e s of biodiesel that are expectedto be used by 2012 to com ply with the RF S will be relatively m odest (approxim a tely 300,000, 000 gallons ). Consequently, we anticipate that biodiesel will continue to be distributed to term inals by tank truck and by individual rail car shipm e nts. One hundred percent biodiesel (B100) 1 4 for m s wax crystals w h en the temperatu r e falls to 35 to 45 degrees Fahrenheit. 1 5 Thus, storage tanks for B100 need to be heated to
m a intain flow-ability during the cold seasons. Shipping vessels us ed to transport B100 such as barges, rail cars, and tank truck con t ainers also typically mu st ei ther b e insulated (an dsom etim es heated) during the cold season or a lte rnatively f ac ilitie s can be provi ded at the term inal to reheat the vesse l prior to d eliv e r y. Biodies e l thatis b l ended with diesel fuel and enhanced with cold f l ow additiv es (if needed ) can have c o mp arable c o ld f l ow perf orm ance to petro l eum based dies el fuel

. 1 6

13 M T B E i s ty pi cal l y bl ende d wi th gas o l i n e at th e re fi ne ry
. M T B E p r o d u ct i on pl ant s are o f t e n l o cat ed ne arby t o refine ries allo win g tra n s p o r t to the refi nery by de dicated pi peline. In cas e s where , the s o urces of MTB E are m o re d i stan t fro m th e refi n e ry, b arge and rail are t h e p r eferred m ean s o f tran sport and relativ ely litt le MTBE is transpo r ted by truck

.

14 The co nce n tr at i on of bi odi es el i n a bi o d i e se l bl en d i s i ndi c a t e d by t h e n u m ber fol l o wi n g th e “B ” desi g n at i o n
. Fo r exam pl e, B 9 9

. 9 i n di cat es a bi odi esel bl en d c ont ai ni ng 9 9

. 9 perce n t bi o d i e s e l , an d B 8 0 i n d i cat es a bl en d c ont ai ni ng 80 pe rce n t bi o d i e sel

. M a n u f actures o f bi o d i esel som e t i m e s bl e n di n o n e t e nth o f one pe r cent di esel f u el i n t o b i od iesel to create B9 9.9 prior to sh ipp i ng t h e fu el to termin als to create mo re d ilu te b i od i e sel b l end s so th at th e pr o duce rca n c l aim th e bi o d i e sel t a x cre d i t ( p u r s u ant t o Int e rnal R e ven u e Ser v i ce re qui re m e nt s).

15 The p o i n t at w h i c h wa x c r y s t a l s fo rm i s refe rre d t o as t h e c l ou d poi nt

. Th e cl ou d p o i n t o f B 1 0 0 vari es d e pen d i n g o n th e feed stock u s ed in its produ ctio n.

16 Th e relativ ely lo w con cen t r atio n b i od iesel blen d s th at are ty pi cal l y used i n vehi cl es (u p t o 20 % bi o d i e sel ) can be formulated to have com p ara b le cold flow pe rf orm a nce t o p e tr ol eum base ddi esel fuel. T hus , t h ere is no nee d to heat
suc h bi o d i e sel bl en ds i n vehi c l e fuel t a nks

.

46As temperatures fall during the cold seasons, some term inals currently avoid the need for heated B100 tanks and facilities to heat shipp i ng vessels by accepting pro g ressiv e ly less concentrated biodiesel blends (f or final blending to produce fuels for use in vehicles). During the warm seasons, such term inals typically accept B100 or B99.9. As the weather grows colder, the term inal m i ght switch to accep tin g B80 and during th e coldest parts of the year m i ght accep t B50 (that contains 50 percent num be r one diesel fuel). The need for insulated tank trucks and tank cars is also som e tim e s avoi ded if transit tim es are brief by shipping warm ed bi odiesel. W e believ e that as the vo lume of biodiesel grows, m o st terminals will opt to receive B100 (or B99.9) year round f or blending into diesel fuel for th e consistency in operations which this practice offers. A number of term inals are already follow i ng this practice. These term inals have installed heated storage tanks for biodiesel and insist that biodies el be delivered in insulated tank trucks (or rail cars) so that it m a y be pumpe d into the term inal storag e tank without concern about the potential need for reheating. The cost of the ne cessary heated and/or in sulated equipm ent is not insignif i cant. However, the m odest addition al volum es that will need to be shipped via rail and tank truck due to the use of biodiesel do not m a terially affect the c onclusions reached above regarding the ability of the fuel distribution system to cope with the increased volum es of renewable f u els.



### 1.4 Blenders
1.4.1 Ethanol Blending
Ethanol is miscib le with wate r, and thus can introduce water into the distribution system causing co rrosion and du rability p r ob lem s as well as fuel quality prob lem s. For this reason, ethanol is blended downstream at terminals or into tank trucks.
The distribution of ethanol is described in m ore detail in Section 1.3. Briefly, ethanol producers provide ethanol either directly to termi nal s, to m arketers or to term inals that are owned by refiners. In the first case, et hanol is provided to term inals that are owned entities other than refining companies. They receive ethanol fromthe ethanol producer, and gasoline from any num ber of refiners. The blenders the n add ethano l to th e gaso line atthe te rminal. For RFG, the term inals receive the blendstock for RFG, ca lled Refor mulated Blendstock for Oxygenate Blending or RBOB, to which they add the am oun t of ethanol called for on the Product Transfer Docum e nt that accomp anies such sh ipm e nts. Once the ethan o l is add e d to the RBOB, the product becom e s a finished gasoline (RFG) and is sent via truck to retailers. For conventional gasoline (C G) ethanol is also a dded and shipped to retailers. The trackin g m echanis m for CG is not as detailed as it is for RFG, however. The ma jority of ethanol that is blended into CG has historically been “splash-blended” although an increasing volum e of etha nol is being blended into special blends of conventi onal gasoline (e.g. sub-octane), or “m atch blended”. Finally, a very sm all amount is blended as E85.
1.4.2 Biodiesel Blending Biodiesel generally leaves the produ ction f acility in its n eatf orm and is shipped by truck to locations where it can be bl ended with conventional diesel fuel. The blending generally

47occurs at centralized distribution points such as term inals, although it also som e ti m e s occurs within tank trucks them selves. Biodiesel is onl y rarely used in its neat (unblended) form.



### 1.5 Imports/Exports of Rene wa ble Fuel Since the early 1980s, the U.S. has maintain ed a 54 cent per gallon tariff on imported ethanol, prim arily to offset the blending tax subsi dy of the sam e magnitude that had been put in place to sup port alternative energy p r oduction an d dom e stic agricu lture. Legislation and agreem ents implem ented since then h ave waivedor significantly reduced the tariff on imports from Canada, Mexico, and about tw o dozen Central Am erican and Caribbean nations covered by the Caribbean Basin Initiative (CBI). Under the Caribbean B a sin Econom i c 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 m arket; quantities abov e this lim it have additional stipulations for feedstocks being grown w ithin the supplying country. Historically, the CBI nations have had little ethanol production capacity of their own but have supplem ented 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 pr ice of ethanol, it has becom e econom ically viable to import signi f i cant quantities of ethanol dir ectly from other nations despite the tariff. Brazil, currently the larges t ethano l producing nation in the world, has becom e the largest single country supplier to the U.S. m arket. As shown in Figure 1.5-1, total imports have increased more than 30% in 2004-5 over the previous three-year average.

48Figure 1.5-1. Historic U.S. Ethano l Import V o lumes and Origins a

0 50 10 0 15 0 20 0 25 0 30 0

200 1 200 2 2 003 200 4 200 5MillionGallon Ot h

e r EU Ca n a d a Br a z i l CB I

aF.O. Licht, “World Ethanol Mark ets, The Outlook to 2015 ” (2006). Gross im ports (does not account for exp o rt v o l u m e s) i n cl udi ng hy dr o u s, de hy drat ed, an dde nat u redv o l u m e s.

Going forward, as dom estic 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 ag ain m ake direct im ports from Brazil and other full-tariff producers less attractive, and to decrease total imports. According to a cu rrent report by F.O. Licht, U.S. net import dem a nd is estim a ted to be around 300 m illion gallons per year by 2012, being supplied prim arily through the CBI, w ith som e direct im ports from Brazil during tim es of shortfall or high price. Q
Changes in the production and trade clim ate m a y influence this however. The Caribbean countries with duty free status ar e seeing both internal and foreign investm e nt to increase ethanol production capacity significantly over the next several years, m aking m or e cheap imports available. It is unclear at this poin tw hatvolum e of ethanol will b e supplied through these channels. On the export sid e , the U.S. has averagedabou t 100 million gallons p er year sin ce 2 000, 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 qua ntities to fewer count ries, with declining volum es to Asia and increasing volum e s to the E. U. and India. The demand for ethanol in all these areas rem ains stron g, and it app ears th at Asian imports from Brazil and China are m aking up for the decrease in U.S. ethanol m oving into the region.

49Figure 1.5-2. Historic U.S. Ethano l Export Volumes and Origins a

0 20 40 60 80 10 0 12 0 14 0

2 001 20 02 20 03 2 004 20 05MillionGallon Ca n a d a Me x i c o Jap a n EU
In

d i

a Ot h

e r s

aF.O

. Lich t, “W or ld Eth ano l Mark ets, Th e O u tlook to 2015 ” ( 200 6)

. Gro ss exp o r ts ( does no t acco u n t f o r im port vol um es), i n cl u d es hy dr o u s, de hy drat ed, an dde nat u redv o l u m e s.

These num bers are expected to increase m odestly as m o re production com e s online, with more dram atic increas es possible during periods of depr essed dom e stic prices or stock surges. Looking out over the n e xt decade, th e E.U. has a biofuels directive in place that will b olster dem a nd, and Japan and S outh Korea are expected to increase their use of biofuels steadily as well. W orld ethanol production is projected to gr ow from the current 10 billion gallons per year to m ore than 25 in 2015, and the international biof ue ls m arkets are just beginning to take shape. During this period we can expect significant ch anges in who is supplying and who is dem a nding as the players determine their pl aces and forge agreem ents on s ubsidies and tariffs. As of 2005, the U.S. becam e largest ethanol p r od ucing nati on , eclip sing B r azil, and ample foreig n m arkets will be av ailable if conditions are rig h t.

50

## Chapter 2: Chan ges t o M o t o r V e hicle Fuel Under t h e Renewable Fuel S t andard Pr ogram

In this regu latory im pact analys is, we begin by describing th e renewable fuel volum e scenarios we used to m easure the environm ental and econom ic i mpacts of increased renewable fuel blen ding. From there we narrow ourdiscussion in on ethanol - the predominant ren ewable fuel exp ected to be used in the future. W e describe historical ethanol use, current use and ourprojections of future ethanol use. The discussion starts with an in -d epth exam ination of current ethanol use. More speci fically, what factors drive ethanol use and where ethanol blending currently occurs - by state, season, and fuel type. The discussion then shifts to w here etha nol is expected to be used in the future. We discuss the ongoing trend in increased et hanol use, the anticipated phase-out of MTBE, and ultim ately p r esent ou r L P m odeling results whic h predic t where e thanol will likely be used in 2012 by PADD, season, fuel type. From there, we describe our
m e thodology for allocating ethanol usage by stat e and in som e cases, m ake distinctions on how we think ethano l would fill u rban and ru ral areas. On ce we understand how ethanol use is expected to change in the future, w e m easure the anticipatedim pacts on gasolin e f u el quality (wh i ch later f ee d s into our em issions and air qua lity analyses ). At the end of th is chap ter, w e also prov ide a brief estim ate on how increas ed biodies el blending will impact diesel fuel properties.



### 2.1 Rene wable Fu el Vol u me Scen arios The Energy Policy Act of 2005 (the Energy Act or the Act) stipulates that the nationwide volum es of r e newable fuel requ ired under the Renewable Fuel Standard (RFS) program must be at le ast 4.0 billion gallons in 2006 and increase to 7.5 billion gallons by 2012. Howe ver, we expect that ac tual renewable f uel usage will exceed the RFS requirem e nt by a significant m argin. In Annual Energy Outlook 2006 (AEO 2006), the Energy Infor m ation Adm i nistration (EIA) projects that total renewable fuel dem a nd would be 9.9 billion gallons by 2012. More sp ecifically, EIA predic ts that 9.6 billion gallons of ethanol and 3 03 m illion g a llons of bio diesel wouldbe consum e d in 2012. The projected renewable fuel consumption levels were estim a ted 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 tim e of our analysis). 1 7 R S Therefore, current m arket conditions indi cate that renewable fuel pr oduction could be even m ore favorable and/or prevalent in the future based on econom ics. 1 8 However, EIA’s AEO

17 W e st Te xas I n t e rm edi a t e (WTI) crude oi l pri c i ng was $ 5 9

. 08/ bbl i n No ve m b er, 2 0 0 6 ; $6 1. 96/ bbl i n Decem ber, 2006; and $54.51/ bbl i n January 2007 acc ording to EIA spot pricing.

18 In AEO 200 7, EIA fo recasted an ev en h igh er eth a no l co nsum p t io n o f 11 .2 b illio n g a llon s b y 2 012

. Th e dra f t re p o rt wa s issue d o n D e cem b er 5, 2 0 0 6 , an d we were u n able t o i n co rp orate d it int o the refine ry m odel i ng u s edt o co n duct o u r anal y s es.

512006 analysis also considers the feasibili ty of building production facilities to
accomm odat e for the gro w ing renewable fuel dem a nd. Accordingly, we interpret EIA ’ s ethanol and biodiesel projecti ons to be reasonable estim a tes considering both econom ics and the rate at which new plants could feasib ly c o m e on-line. As a result, in asses sing the im pacts of expanded renewable f u el use, we evaluated tw o renewable fuel usage
scenarios (d escrib ed in more detail below). The f i rst rep r es e n ts the s t atutorily -requ ire d
m i ni mu m and the second reflects the highe r levels projected by EIA in AEO 2006. Although the actual renewable fuel volum e s produced in 2012 m a y differ from both the required and projected volum e s, we believe that these two volum e scenarios represent a reasonable range for analysis purposes.
The Act also requir es th atat leas t 250 million ga llons of the to tal renewable f u el use in 2013 and beyond m eet the definition of cel lulosic biom ass ethanol. As described in Chapter 1 , there are a num b er of co mpanies planning to produce ethanol from cellulosic feedstocks and/or wa ste

- d erived energ y sources th at could potentially m eet the definition of cellulosic biom ass ethanol. Accordingly, we anticipate a ramp-up in cellulosic biom ass ethanol produc tion in the com i ng years. F u rtherm ore, for analysis purposes, w e have assu m ed that the 250 milliongallon requirem ent would be m et by 2012.
As discussed in m ore detail below inSection 2.2.2, we chose 2004 to represent
curren tbaseline cond itio ns. In 2004, 3.5 bill ion g a llons of eth a nol and 25 m illion gallons of biodiesel were consum ed in m o tor vehicle fuels. To comp are fuel quality impacts on em issions and air quality, we created a 2012 re ference case that m a intained current fuel quality para m eters (withthe excep tio n of sulf u r) but incorporated for ecasted increases in vehicle m iles traveled, changes in fleet dem ographics, etc. T he 2012 fuel reference case was developed by growing out the 2004 rene wable fuel baseline according to EIA’s forecasted energy growth rates. In AE O 2006, EIA predicted that g a soline dem a nd would grow by 11.2 percent and diesel fuel dem a nd would grow by 20.5 percent from 2004 to 2012. As a result, the 2012 reference ca se is based on 3.9 billion gallons of ethanol u se and 30 million gallons o f biodiesel u se in 2012. For our analyses, we created two 20 12 control cases representing expanded renewable use – the “RF S Case” and the “EIA Case”. In both cases, cellulosic biomass ethanol u se was assum e d to be 250 m illion ga llons (statuto ry required minimu m ) and biodies el us e was assumed to be 303 milli on gallons (EIA AEO 2006 estim ate). The RFS Case was designed to exactly m eet the RFS program requirem ents considering the effects of higher equivalence values for cellulosic ethanol a nd biodiesel. Per § 80.1115, one gallon of cellulosic ethanol counts 2.5 tim es towards comp liance and one gallon of biodiesel counts 1.5 tim es towards comp liance. As a result, in the RFS Case we predict that less than 7.5 billion gallons of renewabl e fuel would actually be consum ed in 2012. The actual v olum e of renewable fuel analyzed for the RFS Case was comp uted to be approxim a tely 7.0 billion gallons. T he EIA Case represents EIA’s projections of renewable f uel use in 2012. Based on AEO 2006 , the actual volum e of renewable fuel

52analyzed for the EIA Case was 9.9 billion ga llons. A summ a r y of the renewable fuel volum e scenarios we evaluated is found below in Table 2.1-1.
Table 2.1-1 Rene w able Fuel Volume Scenarios (MMgal)

Re ne w a ble Fue l 2004 Base Case a 2012 Ref Case b RFS Ca se E I A Case c Cor n Ethanol d Cellulos i c Ethanol e Biodies el 3,548 0 25 3,947 0 30 6,421 250 303 9,388 250 303 T o tal Renew able Volum e 3,573 3,977 6,974 9,941 T o tal C o m p lianc e Volum e f n/a n /a 7,500 n/a

a H i storical ethanol usag e deriv ed from EIA’s June 2006 M onthlyEnergyR ev i ew

. Biodiesel usage deriv ed from “T h e O utlook and Impact of Biodiesel on the O ilseed s Sector” presented byJohn Baiz e at the 20 06 U S D A O utlook C onference. b T he reference case w a s calculated byapply ing the 2004-2012 gasoline/ diesel energygrow th rates reported in AEO 2 006 to the 2004 Base C a se.
c EIA C ase based on eth anol and biodiesel ener gycontributions reported in AEO 2006. d Includes ethanol import s

. e Ethanol meeting the de finition of cellulosic biomass ethanol in T he Act. f Based on apply ing a 2. 5 equiv alence v alue to cellulosic biomass ethano l and a 1.5 equiv alence v alue to bi odiesel.



### 2.2 Current Gasoline Oxygenate Use

2.2.1 Why are oxygenates cu rrently b l en ded into gasoline? The blending of oxygenates into gasoline dates back to the 1970’s. However, their use greatly expanded in response to th e Clean Air Act (C AA) a m endm ents of 1990. Areas found to be out of compliance (i.e., in non-attainm e nt) with the National Am bient Air Quality Standards (NAAQS ) for ozone were required to reformulate their gasoline and use oxy genates y ear-round. In addition, s e ve ral s t ates began to use o xygenated f u el (oxy-fuel) in the wintertim e to address carbon m o noxide non-attainm e nt. In addition, oxygenates (nam ely ethanol) have historically been used as a ga soline volum e extender and m o re recently, to meet s t ate m andates. This section summ arizes the curren tdriving forces behind gasoline oxygenate use in the U.S.
2.2.1.1 Federal Reformulated Gasoline P r ogram
As m e ntioned above, areas found to be in ozone non-attainm e nt were required to use reformulated gasoline (RFG) year-round. The federal RF G program c ontained a
mi ni mu m oxygenate requirem e nt as we ll as other fuel quality standards. 1 9 Adding

19 RFG ox yg en at e r e q u ir e m e n t fo und at 4 0 CFR 80 .4 1( f)

. This r e qu ir em en t w a s ef f ectiv e fo r 200 4 bu t h a s sin ce b een elimin ated b y t h e En er g y A c t Sectio n 1 504 , pr omu lg a ted on M a y 8 , 20 06 at 71 FR 266 91

.

53oxygen to gasoline and refor mulating other ga soline properties has helped to reduce the production of s m og-for m ing pollutants that c ontribute to unhealthy ground-level ozone. Besides ozone non-attainm e nt areas, several stat es/areas also opted into the RFG program (otherwise known as “opt-in” ). In addition, California a nd Arizona have state program s that prom ote 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 (E TOH) and
m ethyl tertiary-buty l eth er (M TBE) have been considered. 2 0

20 Othe r l o w- usa g e oxy genat e s (e. g
. E T B E , T A M E , et c. ) we re ass u m e d t o be negl i g i b l e f o r th e p u r p o s e of th is an alysis.

54Table 2.2-1. 2004 Federal RFG Areas by State T

State C ity No. of Counties a Ty pe of RFG A r e a Primary O x ygenate b C alifornia Lo s Angeles 5 R eq' d E T O H S acramento 6 R eq' d E T O H S an D i ego 1 R eq' d E T O H S an Joaquin Valley 8 R eq' d E T O H C onnecticut c H a rtford 6 R eq' d E T O H Lo ng Island Area 1 R eq' d E T O H W i ndham C ounty 1 O p t In ET O H De l a w a re c S u ssex C ounty 1 O p t In M T BE W ilmington 2 R eq' d M T B E D istr ict of C olumbia c W ashington D C Area 1 O p t In d MT B E Illinois C hicago Area 8 R eq' d E T O H Indiana C hicago Area 2 R eq' d E T O H Kentucky C o v i ngton 3 O pt In ET O H Lo uisv ille 3 O pt In ET O H M ary land B altimore 6 R eq' d M T B E P hiladelphia Area 1 R eq' d M T B E Q ueen Anne/Kent C ounties 2 O p t In M T BE W ashington D C Area 5 O p t In d MT B E M a ssachusetts c B o ston Area 10 O p t In M T BE S p ringfield 4 O pt In M T BE M i ssouri S t. Louis 5 O p t In ET O H N e w H a mpshire B oston Area 4 O pt In M T BE Ne w J e rs e y c A t lantic City 2 O pt In M T BE Lo ng Island Area 12 R eq' d B oth T r enton 6 R eq' d M T B E W a rren C ounty 1 O p t In M T BE N e w Y o rk P oughkeepsie 2 O pt In ET O H Lo ng Island Area 11 R eq' d E T O H Pennsy l v ania P hiladelphia Area 5 R eq' d M T B E R hode Island c P r ov idence Area 5 O pt In M T BE T e x a s D allas/Fort W o rth 4 O p t In M T BE H ouston/G alv eston 8 R eq' d M T B E Virginia N o rfolk/Virginia Beach 1 1 O pt In M T BE R i chmond 7 O pt In M T BE W ashington D C Area 10 O p t In d MT B E W isconsin M ilw aukee-R a cine 6 R eq' d E T O H

a I nc l udes par t i al c ount i es

. b Ox ygenat e det er mi nat i o n bas ed on 2004 FHW Agas ohol dat a and EPA f u el s u r v ey r e s u l t s

. c Ent i r e s t at e/ di s t r i c t operat es undert he Feder al RFG pr ogr am. d Wa s " o p t

- i n " i n 2004 , no w a r e q ui r ed RFG ar ea.

As shown above in Table 2.2-1, a little m ore than half of the Federal RF G areas (on a county-by-county basis) used MTBE as opposed to et hanol as an oxygenate in 2004. However, on a volum e tric basis, m ore ethanol was consum ed in RFG than MTBE

55(2.2 billion gallons compared to 1.9 billi on gallons as shown in Tables 2.1.5 and 2.1.3, respec tive l y )

. 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 wintertim e to address carbon m o noxide (CO) non-attainm e nt. CO is for m ed from the incomplete com bustion of hydrocarbons (found in al l gasoline blends). Production of the poisonous gas is more prev alent in oxygen-deficient environm ents and more harm ful to hum an health in the wintertim e due to tem p eratu r e inv er sions. 2 1 Together, the winter oxy-fuel program coupled with improving vehicle em issions control system s has helped to reduce CO e m issions

. Many areas have a nd are continuing to
com e into attainm e nt with the CO nationa l am bient air quality standards (NAAQS). However, many for m er non-attainm e nt areas con tinue to use the wint er oxy-fuel program as part of a m aintenance plan for rem aini ng in comp liance with the CO NAAQS. A list of the 2004 oxy-fuel areas is provided in Tabl e 2.2-2. All oxy-fuel ar eas were assumed to use ethanol in 2004 based on information obtained from regional EPA offices.
Table 2.2-2. 2004 State-Implemen ted Winter Oxy-Fuel P r ograms U

O x y - Fuel A r ea Location O x y - Fuel Period Car bon Monoxide Status W inter O x y - Fuel Pr ogr am State C ity Designation Pur s uing RD a Re quir e d Part of M P b Alaska Anchorage 11/1-2/29 N on-attainment c X X Ariz ona T ucson 10/1-3/31 Attainment X Phoenix 11/2-3/15 N on-attainment X X C alifornia Los Angeles 10/1-2/29 N on-attainment X X C o lorado D env er/Boulder 11/1-1/31 Attainment X Longmont 11/1-1/31 Attainment X M ontana M i ssoula 11/1-2/29 N on-attainment X X N ev ada Las Vegas 10/1-3/31 N on-attainment X R eno 10/1-1/31 N on-attainment X X N ew M ex i co Albuquerque 11/1-2/29 Attainment X O r egon Portland 11/1-2/29 Attainment X T ex as E l Paso 10/1-3/31 N on-attainment X U t ah Prov o/O r em 11/1-2/29 N on-attainment X X W ashington Spokane 9/1-2/29 N on-attainment d X X

a Cur r ent l ypur s u i ng r edes i gna t i on t o CO at t a i n ment

. b Ar ea i s i n c u r r ent l yi n CO at t a i n ment but oxy- f uel pr ogr am r e mai n s as par t of mai n t enanc e pl an.
c Ar ea was r edes i gnat ed t o at tai nment ef f ec t i v e 7/ 23/ 04. d Ar ea was r edes i gnat ed t o at tai nment ef f ec t i v e 8/ 29/ 05.

2.2.1.3 Other Motivations for Blending Ethanol

21 Tem p erature i nve rsions in t h e lowe r atm o sphere are relatively comm on, especially du ri n g wi nt er m onth s in col d clim ate s. A tem p erature inversion oc curs whe n co ld airclo s e to t h e gr oun d is tr app e d b y a layer o f
warm er ai r, c re a t i ng st a gnat i o n an dtra ppi ng pol l u t i o n cl ose t o th e g r ou n d

.

56In addition to the RFG and oxy-fuel progr am s, gasoline refiners have several other m otivations for blendi ng oxygenate (nam ely ethanol) in to gasoline. First and fore m ost, the state they provide gasoline to could be operating under a state ethanol
m a ndate. In 2004, Hawaii joined Minnesota in approving a state ethanol requirem e nt. 2 2 Second, blending ethanol into gasoline could help them m e e ttheir m obile source air toxics (MS A T1) perform a nce standards as determ ined by the Comp lex Model. 2 3 Third, adding ethanol increases both octane and total fuel volum e, thus helping refiners extend their gasoline production. Finally, and perhap s most importantly, with record-high crude oil prices and the grow ing availability of grain-based ethanol (especially in PADD 2), ethanol use has becom e increasingly econom ical

. The 1.1 billion gallons of ethanol used in PADD 2 conventional gasoline in 2004 (refe r 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, consum er dem a nd is also increasing based on the growing number of ethanol-friendly vehicles on the road. Conventional vehicles consum e th e majority of fuel ethanol and are lim ited to gasoline with 10 volum e percent (vol%) ethanol (E10) or less. However, there are currently around 6 m illion flexib le fuel vehicles (FFVs) on the road tod a y with m o re being produced an d sold each day V

. FFVs are specifically designe d 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 imp acts of increased ethanol blending and decreased MTBE blending on gasoline propertie s (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, an d MTBE use. The
m e thodology for assem b ling 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 cu rren tbaseline co nditions

. This period represented the most current year for which gasoline and oxygenate datawere available and also captured the California, New York, and Conn ecticut MTBE bans (effective 1/1/04) while avoiding the 2005 calendar year hurricane upsets.

22 Fo r a n al y s i s p u r p oses, b o ths t at es were a ssu m e d t o have et han o l m a ndat e s w h i c hre qui r e d 10 0% o f th e g a so lin e to contain 10 % et h a no l. Ho wev e r, i n reality , Hawaii’s eth a no l m a n d a te on ly requ ires t h at 85 % o f th e ga sol i n e co nt ai n 10 % etha nol

.

23 Thi s R FS pr o p o sal i s base d o n M S AT 1 c o n d i t i ons
. Im pact s of th e rece nt M S AT 2 rul e ( 7 2 FR 8 4 2 8 ) whi c h rem oves i ndi vi d u al re fi nery t oxi c per f o rm ance st an d ards ( b asel i n es ) i n exc h a nge f o r

a nat i o n w i d e benze n e standard are reflecte d in the analysis for t h at rulemaking.

57The approach for assem b ling the 2004 base case consisted of obtaining gasoline, ethanol, and MTBE usage for all 50 states as well as the District of Columbia. As
m e ntioned earlier, other low-volum e oxyge nate use (e.g., ET BE, TAME, etc.) was
assum e d to be negligible and thus ignored fo r this analys is. All ethanol-blended gasoline
was assum e d to contain 10 vol% ethanol, with th e exception of California “RFG” (Federal RFG and California Phase 3 RFG (CaRFG3)). 2 4 Current California gasoline regulations m ake it very difficult to m eet th e NOx em issions perform a nce standard with ethanol content higher than about 6 vol%. For our analysis, al l California RFG was
assum e d to contain 5.7 vol% ethanol based on discussions with California Air Resources Board (CARB). This percentage was also app lied to Calif ornia RFG supplied to the Phoenix m e tropolitan area in the summ erti m e under Arizona’s clean burning gasoline (CBG) program

. 2 5 Finally, all MTBE -blended gasoline was assum e d to contain 11 vol% MTBE. Total gasoline consumption was obtai ned from the 2004 Petroleum Marketing Annual (PMA) report published by the En ergy Infor m ation Adm i nistration (EIA). W The reported ann u al average sales vo lume for eachstate was in terpreted as to tal blend ed gasoline (including additives, nam e ly oxygenates). 2004 MTBE usage by state was obtained from EIA. 2 6, X The data receiv ed was exclus ive to states with RFG program s (including A r izona’s CBG program ). T hus, for the purpose of the 2004 base case analysis, MTBE use wa s assum e d to be lim ited to RFG areas. 2004 ethanol usage by state was derived from a compilation of datasources and assumptions. As a starting point, total dom e stic ethanol consumpti on was acquired from EIA’s Monthly Energy Review published in June 2006 Y

. State ethanol contributi ons originated from the 2004 Federal Highway Adm i nistra tion (F HWA) gasohol report Z

. However, there was some
am biguity with the 2004 FHWA data. First, the total ethanol consumption did not m a tch up with EIA’s reported value (3.7 billion ga llons compared to 3.5 billion gallons). Second, the gasohol (and thus ethanol) volum es were derived from potentially im precise motor vehicle fuel tax reports. 2 7 And third, not all states using ethanol reported their

24 The sm all vol um es of E 8 5 ( 8 5 perce n t etha n o l ) gas o l i n e ha ve been i g no re d fo r th i s anal y s i s
.

25 For t h e Base Case analysis, all Arizona CB G was cl assifie d as “RFG”

. In 2 004 , w in ter ti m e A r izon a R F G was as su m e d t o c ont ai n 1 0 % et han o l ( g o v e r ne d by th e Ph oe ni x o x y
- fuel pr o g ram )

. Sum m ert i m e R F G was as su m e d t o be c o mp ri se d of 2/ 3 C a l i f orni a R F G (co n t a i n i n g 5. 7 pe rcent et

han o l ) an d 1/ 3 P A D D 3 RFG (con tain i n g eith er 10 p e rcen t et h a no l or 1 1 p e rcen t M T BE in 20 04 ).

26 EIA repo rted 2 004 to tal MTBE u s ag e (in R F G) as 2

. 0 b illio n g a llon s
. The rep o rted MTBE u s ag e was redu ced fro m 2

. 0 t o 1

. 9 b illio n g a llon s un d e r th e assu m p tio n th at CA, NY, an d CT im p l emen ted th eir st ate M T B E ba ns o n t i m e (by 1/ 1/ 0 4 )

. (EI A s h ow ed sm al l am ount s of M T B Eu s e i n t h ese st at es i n 2 0 0 4 )

. EIA’s allocati o n of MTBE by state was als o adjuste d based on f u el s u r v ey resu lts. Mo st no tewo rt h y , EIA rep o rt e d M T B E usa g e i n Ari z ona “R F G ” as zer o. H o weve r, t h e 20 0 4 P h o e ni x f u el su r v e y resul t s s u gge st othe rwi s e. As suc h , a n ap pr o p ri at e am ount of M T B E was al l o cat ed t o A ri z ona base d o n th e ass u m p t i on th at 1/ 3 o f al l s u m m e rt im e Ari z ona “R FG” r e sem b l e s PAD D 3 R F G (w hi ch c ont ai n e ds o m e l e vel of M T B E in 200 4)

.

27 The U .S. De p art m e nt of T re a sury re qui re s a di st i n ct i o n be t w een gas o hol andga sol i n e o n m o t o r ve hi cl e fu el tax repo rts fo r states with g a soh o l sales tax ex em ptions. T h ese financial records are t h e s o urce of FH WA ’s gas o hol / e th a nol dat a
. Ho we ver , si nce st at e gaso h o l t a x e x em pt i ons h a ve bec o m e vi rtual l y

58gasohol usage so FHWA had to m o del-estim ate 19 states’ ethanol usage (accoun ting for 60% of the total ethanol volum e). To improve upon the FHWA data, we used a series of oxygenate verification tools including knowledge of state ethanol m a ndates, state MTBE bans, Arizona’s CBG program , and fuel survey res u lts. A A B B 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 pr esented throughout this section.

2.2.2.2 2004 Gasoline/Oxygenate Consumption by PADD In 2004, 3.5 billion gallons of ethanol a nd 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 gaso line and oxygenate consumption by PADD is found below in Table 2.2-3.
Table 2.2-3. 2004 Gasoline & Oxyge nate Consumption by PADD

PA D D G aso lin e MMg a l Et h a n o l M T B E a
MMg a l % o f G aso lin e % o f T o t E T O H MMg a l % o f Gaso lin e % o f To t M TB E PADD 1 PADD 2 PADD 3 PADD 4 P A DD 5 b Calif or nia 49,193 38,789 20,615

4,542 7,918 14,836 660 1,616 79 83 209 853 1.3% 4.2% 0.4% 1.8% 2.6% 5.8% 18.9% 46.2%

2.3% 2.4% 6.0% 24.4% 1,360 1 498 0 19

0 2.8% 0.0% 2.4% 0.0% 0.2% 0.0% 72.4% 0.1% 26.5%

0.0% 1.0% 0.0% T o t al 135,893 3,500 2.6% 100.0% 1,878 1.4% 100.0%

a M T BE blended into R FG b PAD D 5 ex cluding C aliforni a

As shown above, in 2004, alm ost half (or 46 percent) of the ethanol was
consum ed in PADD 2, where the m a jority of ethanol was produced. The next highest region of use was the State of California wh ich accounted for near ly a quarter (or 24 percent) of dom e stic ethanol consumption. This m akes 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 Fe deral RFG and CaRFG3) was presum ed to contain 5.7 vol% ethanol. Th e next highest region of us e was PADD 1 ( 19 percent) which m ake s sense considering the high concen tration of RFG areas (m ost of which used ethanol in 2004 as shown in Table 2.2-1). The rem a ining 10 percent of ethanol use occurred collectively in PADDs 3, 4, and 5/

no ne xi st ent ov er th e past seve ral y ears, gas o hol re po rt i n g ( n am ely th e di sti n ct i on bet w ee n gas o l i n e a n d g a so ho l) h a s su ff e r ed

.

59In 2004, total ethano l use exceeded MTBE us e. Ethanol’s lead oxygenate role is relatively new, however the trend has been a progression over the pastfew 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 inhalf (from 3.7 to 1.9 billion gallons). A plot of oxygenate use ov er the p a st d ecade is pro v ided below in Figure 2. 2-1. Figure 2.2-1. Oxygenate Consumption vs. Time C C , D D

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5Oxygenate Use (Bgal)

1995 1996 1997 1 998 199 9 200 0 2001 2002 2003 20 04 Ye a r

E t ha no l C o n s umpti o n, T o ta l M T BE C ons um p t ion , R F G

The nation ’ s trans i tion to ethanol is linked to stat es’ responses to recent environm ental concerns surrounding MTBE groundwater con t am ination. Traces of MTBE have been found in both surface and ground water in and around RFG areas. The MTBE is thought to have m a de its way into the water from leaking underground storage tanks, gasoline spills, and e ngines. Concerns over dr inking water quality prompted
severa l state s to sign if ica n tly r estric tor co mpletely ban MTBE use in gasoline. At the tim e of our analysis, 19 states had adopted MTBE bans. Ten states had bans that im pacted the entire 200 4 calendar year, four states had bans that impacted a portion of the year, and five states had bans that becam e effective in 2005 and beyond. A list of the states with MTBE bans (lis te d in order of phaseout date) is provided below in Table 2.2­ 4.

60Table 2.2-4. States MT BE Bans by Phaseout Date E E
State a Phaseout Date Ty pe of Ban b Iow a M i nnesota N ebraska South D a kota C olorado M i chigan C alifornia C onnecticut N e w Y o rk W ashington Kansas Illinois Indiana W i sconsin O h io M i ssouri Kentucky M a ine N ew H ampshire 07/01/00 07/02/00; 07/02 /05 07/13/00 07/01/01 04/30/02 06/01/03 12/31/03 01/01/04 01/01/04 01/01/04 07/01/04 07/24/04 07/24/04 08/01/04 07/01/05 07/31/05 01/01/06 01/01/07 01/01/07 Partial P artial; C omplete Partial Partial C omplete C omplete C omplete C omplete C o mplete Partial Partial Partial Partial Partial Partial Partial Partial Partial Partial

a Ar i zona i s not i nc l uded bec aus e t h ey do not have an of f i c i al s t at e MTBE ban. Theyadopt ed legis l ation on 4/ 28/ 00 c a lling f o ra c o mpl e t e phas eout of MTBE as s o on as f eas i b l e but no l a t e rt han s i x mont hs af t erCal i f or ni a' s phas e out

. The l egi s l at i on ex pi r ed on J une 30, 2001, s o it ' s not of f i c i al p o lic y

. Alt hough t he s t at e s t ill i nf or m al l yenc our ages t he phas eo ut of MTBE. b A par t i al ban r e f e r s t o no mor e t h a n 0. 5 vol % MTBE ex c ept i n t he ca se o f M N (1 /3 % ), N E (1 % ), a n d W A(0 .6 % )

As explained above in 2.2.2.1, all MTBE consumption was assum e d to occur in reformulated gasoline in 2004. As shown in Table 2.2-3, 99 percent of MTBE use (by volum e) occurred in PA DDs 1 and 3. This re flects 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 larg e portion of its gaso lin e from PADD 3 refineries w ho either produce th e fossil-fuel b ased oxygen ate or ar e closely affiliated with MT BE-producing petroch e m i cal facilities in the area.
2.2.2.3 2004 Gasoline/Oxygenate Consumption by Season In 2004, according to E I A Petroleum Mark eting Annual (PMA), approxim a tely 40 percent of gasoline was consum e d in the sum m e rti m e and 60 percent was consumed in the wintertim e. F FSim ilarly, acco rdin g to EIA Monthly Energ y Review June 2006, 38 percent of the ethanol was c onsum ed in the summertim e and 62 percent was consum ed in the wintertim e. 2 8, G G

61Total gaso line use is h i g h er in the wint ertim e because it’s a lo nger season. The RFG regulations define summ ertim e fuel as gasoline produced from May 1 st to Septem ber 15 th(4.5 m o nths total). 2 9 The rem a ining 7.5 m onths are considered to be wintertim e gasoline. Even though on an av erage per day basis summertim e consumption is higher, more gasoline is still sold and c onsum e d in the wintertim e 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 m a y have been higher in the wintertim e
. First, the oxy-fuel program requires oxygenate to be used in certain areas in the wintertim e only. These sam e areas, which do not requ ire oxy genate in the summer, were a ll presum ed to use ethanol as their oxygenate (as described in 2.2.1.2). Thus, m ore areas use ethanol during the winter m onths than the summer. Secondly, there is an economic penalty associated with bl ending ethanol into summertim e RFG. Refiners supplying summertim e gasolin e to RFG areas have to rem o ve butanes and pentanes from their gasolin e in o r der to add e thanol and still co m p ly with th e 7 psi Reid vapor pressur e (RVP) requirem e nt.
2.2.2.4 2004 Gasoline/Oxygenate Consumption by Fuel Type According to fuel survey results, in 2004, approxim ately 2.2 billion gallons of ethanol were blended in to reformulated gaso line and the remaining 1.3 b i llion g allon s were used in conventional gaso line (including wintertim e oxy-fuel). H H, I IA br eakdown of the 2004 ethanol consumption by fuel type and P ADD is fou nd in Table 2.2-5.

28 Aforem en tio n e d season al sp lit fo r g a so lin e an d eth an o l b a sed on RFG p r o d u c tion season s (Su mmer: May 1 th r ou g h Se pt em ber 15 th ; W i nt er: Janua ry 1 st th ro ugh April 3 0 tha n d Se pt em ber 16 tht h rough Decem ber 31 st ).

29 W e ackn owled g e th at the afo r em en tio n e d season al sp lit do es no t ex actly m a tch th e n e w su mm er/win ter seaso n s defi ne d i n th e Ene r gy Act (S um m e r: A p ri l 1 st th r o u g hSe pt em ber 30 th ; Wi nt er: J a nuary 1 st th ro ug h Mar c h 31 st and No v e m b er 1 st through Dece m b er 31 st ).

62Table 2.2-5. 2004 Ethanol Consumption by Fuel Type (MMgal) PADD CG OX Y a RFG b Total PADD 1 PADD 2 PADD 3 PADD 4 PADD 5 c California 0 1,072 31

0 45

0 0 0 21 83 89

0 660 544

26

0 75 853 660 1,616 79 83 209 853 Total 1,149 193 2,158 3,500

a W i nter oxy-fu el programs b F ederal RF G plus CA Ph ase 3 RF G and Arizona CBG
c PADD 5 e x cl u d in g Cal i forni a

As m e ntioned above in S ection 2.2.2.1, 100 percent of the 1.9 billion gallons of MTBE blended into gasoline in 2004, was a ssum e d to be consum ed 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 stat es: Maine, New Ha mps hire, Verm ont, Pennsylvania, Delaware, Georgia, North Ca rolina, South Carolina, W e st Virginia, Tennessee, Oklahom a, Mississippi, Arkansas, L ouisiana, Idaho, and W e st Virginia, nor was any ethanol used in Wash ington DC. A summ ary of 200 4 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 ble nded with ethanol, notthat ethanol itself was blended in less than 10 vol% (E10) pr oportions, except in th e case of California gasoline (E5.7). Figure 2.2-2 shows the percentage of E10 by state.
The states consum ing the highest volum es of ethanol in 2004 were California, Illinois, New York, Minnesota, and Ohio, respec tive l y. W ith respec t to gasolin e use , the highest p e rcentage of ethanol use occurred in Minnesota, Hawaii, Connecticu t , Illino i s, and Iowa. Four out of the five states are not surprising. The first two states have ethanol
m andates and the la st tw o are lo catedin th e “cor n bel t ” wher e et hanol is produced. Connecticut’s high percentage of ethanol use m a y com e 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, etha nol is found in each ga llon of gasoline.

63Table 2.2-6. 2004 Gasoline/Ethanol Consumption by State

State G aso line MM gal Ethanol

S tate G aso line MM gal Ethanol

MMgal % o f G asoline MMgal % o f G asoline Alabama Alaska Ariz ona Arkansas C alifornia C olorado C onnecticut D e law a re D i strict of C olumbia Florida b G eorgia Ha w a i i a Idaho Illinois Indiana Iow a Kansas Kentucky Louisiana M aine M a ry land b M assachusetts M i chigan M innesota M ississippi M i ssouri 2,392 302 2,187 1,406 14 , 836

1,999 1,522

449 119 8,605 4,729

452 632 5,177 3,059 1,635 1,396 2,177 2,287

757 2,480 2,934 4,861 2,684 1,617 3,159 31 3 88

0 853

80 152

0 0 0 0 45

0 422 148 117

41 50

0 0 0 18 77 268 0 122 1.3% 1.1% 4.0% 0.0% 5.8% 4.0% 10.0%

0 .0% 0.0% 0.0% 0.0% 10.0%

0.0% 8.1% 4.8% 7.1% 2.9% 2.3% 0.0% 0.0% 0.0% 0.6% 1.6% 10.0%

0.0% 3.9% M ontana N ebraska

N ev ada N ew H ampshire N ew Jersey N ew M ex i co N ew Y ork N orth C a rolina

N orth D akota O hio O klahoma O r egon P ennsy l v ania R hode Island b South C arolina

South D akota

T ennessee T ex as

U tah V ermont Virginia b W ashington W est Virginia W isconsin W y oming 503 819 857 705 4, 235

966 5, 626 4 , 302

350 5, 156 2, 158 1, 500 4, 786

490 2, 422

434 3, 251 11 , 948

1, 097

338 3, 920 2, 621

772 2, 471

311 1 37 23

0 188

8 301 0 11 192

0 31

0 0 0 24

0 39

2 0 0 18

0 109

0 0.2% 4.5% 2.7% 0.0% 4.4% 0.8% 5.4% 0 .0% 3.0% 3.7% 0.0% 2.1% 0.0% 0.1% 0.0% 5.5% 0.0% 0.3% 0.2% 0.0% 0.0% 0.7% 0.0% 4.4% 0.0% Total 135,8 9 3 3 ,500 2.6%

a Haw ai i w as as s um ed t o have a 100% E 10 m andat e i n t he 2004 B as e Cas e bas ed on RF A ' s H om egrow n f or t he Hom el and: E t hanol I ndus t ry O ut l ook2005. b T r ac e am ount s of et hanol us e (<1 M M G al ) i n F L, M D , RI and V A

.

64Figure 2.2-2. 2004 Ethanol Consumption, % E10 by State

20 04 % E 1 0 b y S ta te
0% E1 0
<50 % E 1 0
50

- 99 % E 10
10

0% E1 0 Not Pi c t u r e d AK : 13% E10 H I : 10 0% E 1 0 DC : 0 % E 1 0 10

0% E5.7

2.2.3 Development of the 20 12 Reference Case
To establish the 2012 ref e rence case, we started with the 2004 Base Case (presen t ed in Table 2.2-3) and grew out gasoline/oxygenate use acco rdin g to the EIA AEO 2006 motor gasoline energy growth rate from 2004 to 2012. J J Accordingly, in the resulting 2012 reference case, ethanol and MTBE use was proportional to 2004 use by both region and fuel type. A summ a r y of the 2012 ethanol reference case is found in Table 2.2-7.

65Table 2.2-7. 2012 Reference Case - Gasoline & O xygenate Consumption by PADD (MMgal) 3 0

G aso lin e E t ha nol M T BE a % o f % o f % o f % o f PA D D MMg a l MMg a l G aso lin e T o t E T O H MMg a l G aso lin e To t M TB E PADD 1 54,743 735 1.3% 18.9% 1,513 2.8% 72.4% PADD 2 43,166 1,798 4.2% 46.2% 2 0.0% 0.1% PADD 3 22,941 88 0.4% 2.3% 554 2.4% 26.5% PADD 4 5,055 93 1.8% 2.4% 0 0.0% 0.0% P A DD 5 b 8,812 232 2.6% 6.0% 21 0.2% 1.0% Calif or nia 16,509 949 5.8% 24.4% 0 0.0% 0.0% T o t al 151,225 3,895 2.6% 100.0% 2,090 1.4% 100.0%

a M T BE blended into R FG b PAD D 5 ex cluding C alifornia

2.2.4 Development of the 2012 Control Cases
In Section 2.2.2 we described our m e thodology behind building the 2004 Base Case, which was used to produce the 2012 Refere nce Case (described above). In this section we will des c rib e how we devel oped th e two 2012 control cases represen ting increased ethanol fuel use – the RFS Case and the EIA Case. Both control cases incorporate our knowledge of future stat e ethanol m a ndates, tax incentives, and anticipated winter oxy-f uel usage. Our anal ysis relied on L P m odeling (described in more detail below) to determ ine how much ethanol would be used in each PADD, season, and fuel type. From there, we conduc ted post-processing to determ ine how much ethanol would be used on a state-by-state ba sis and in som e cases and m a de predictions on how ethanol would likely f ill u rb a n and rur a l areas.
2.2.4.1 Forecasting Ethanol Consumption / LP Modeling Results

As m e ntioned earlier in Section 2.2. 2.2, groundwater contam ination concerns have caused m a ny states to ban the use of MTBE in gasoline. In response to the Energy Act, all U.S. refiners are expected to elimin ate th e 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 requirem e nts prom oting ethanol growth, etha nol is needed to fu el the growing number of ethanolfriendly vehicles being producedas well as satisfy the grow ing num b er of state ethanol

30 Th e to tal eth a n o l vo lu m e repo rted in tab l e 2.2-7 (3 .8 95 B g al) is sligh tly lo wer t h an th e referen ce case val u e re po rt ed i n Ta bl e 2 .1

- 1 (3 .9 4 7 B g al ). The reas on f o r th e sl i g ht di sc r e pancy i s because t h e num ber s prese n t e dhe re were base d o ff th e est i m at ed 2 0 0 4 ba se case (3.5 B g al) whe r eas the num b ers prese n ted i n Tab le 2

. 1

- 1 w e r e b a sed of f a m o r e p r ecise 20 04 et h a no l u s e (3 .5 48 B g al) r e po r ted b y EIA in Ju ly 20 06 M ont hl y Ene rg y R e vi ew.

66m a ndates (Washington, Montana, L ouisian a, an d Missouri recently join ed Minneso ta and Hawaii) 3 1, K K, L L

. Based on projections from EI A and others, it’s abundan tly clear that renewable fuel use (nam ely ethanol) is growing much faster than the RF S requirem e nt. However quantifying future ethanol use is a difficu lt task. The gasoline refining industry and ethanol industry are currently undergoing a variety of change s/expansions and there is no precise way to know exactly how things ar e going to “fall out” in the future. Accordingly, as explained in Section 2.1, we have considered two different 2012 renewable f uel consumption scenarios to repres ent a reasonable range of ethanol use. For the RFS Case we m odeled 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 conc luding that ethanol
consumption could not possibly exceed 9.6 bill ion gallons by 2012, but rather that this volum e is a reasonab l e “ceili ng” for our analysis.
To estim ate how ethanol use would be a llo cated in the f u ture, we relied on Jacob’s Consultancy LP refinery m odeling. M M For the Base Case and Reference Case, the LP ref i n ery m odel was set up to alloc ate fixed volum es of ethanol/MTBE to regions consistent w ith our analysis of current ga soline oxygenate use (described above in Sections 2.2.2 and 2.2.3). This essentially fixe d the total ethanol and MT BE use in each PADD. From there, the oxygenates were further allocated by season and fuel grade to
m a tch the oxygen content for RFG, RBOB and CBOB based on 2004 batch report data. Any leftover ethanol was alloca ted to CG. Based on the resu lting fuel allocation, the L P model generated CG and RFG fuel properties con sidering the RVP effects and blendin g qualities of ethanol and MTBE (such propertie s are discussed furthe r an d utilized in Section 2.3).
For each of the future co ntrol cases, MTBEuse was assum e d to be zero and the
am ount of et hanol added to gasoline was vari ed. 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 contro l case, the L P m odel used gasolin e and ethanol b l ending
econom ics (e.g., ethanol distribution costs, se asonal ethanol and gasoline blendstock prices, etc.) to determine how mu ch ethanol would be blen ded into gasoline by PADD, season, and fuel type. Again, the results we re used to generate CG and RFG fuel properties used in Section 2.3. Slight adjustm e nts had to be m a de to the refinery modeling outputs to ensure that sufficient ethanol was supplied in the wintertim e to m eet the oxy-fuel requirem ents in PADDs 4/5. In addition, sm all corrections we re required to ensure that ethanol blending in a given region/state d i d not exceedthe m a xi mu m blending criteria assu m e d for the

31 The M o nt ana st at e m a ndat e r e qui res al l g a s o l i n e t o co nt ai n 10 v o l % et ha nol o n ce pl ant pr o duct i o n ramp s u p to 40 MMgal/yr. Th e Wash ing t on state man d a te r e qu ires 20 % of all gaso lin e t o co n t ain 10 vo l% ethan o l by 1 2 / 1 / 0 8. Si mi l arl y , th e L o ui si an a st at e m a ndat e re qui re s 20% o f al l gas o l i n e t o c ont ai n 10 vol % ethan o l o n ce pl ant p r od uct i o n ram p s up t o 5 0 M M g al /y r. Fin ally, th e Misso u ri state m an d ate requ ires al l gas o l i n e t o co n t ai n 1 0 vol % ethan o l by 1/ 1/ 0 8

. At t

h e tim e of our analysis, thes e were th e on ly fo ur n e w state eth a no l man d a tes. Ho wev e r, EPA reco gn izes th at as o f 7 /13 /06 , sev e ral o th e rs h a v e n e w/ad d ition a l bi o fuel st an d ar d s pe ndi ng (C a l i forni a, C o l o r a do , Idah o, Il l i noi s , I n di ana, Kans as, M i nne sot a , Ne w M e x i co, Penn sylv an ia, Virg in ia, and W i scon sin).

67analysis - 10 volum e percent (vol%) ethanol nationwide, and 5.7 vol% ethanol in California. The adjusted LP refinery m ode ling results for the RFS and EIA control cases
are summ arized below in Tables 2.2-8 and 2.2-9, respectively.
Table 2.2-8. Adjusted LP Modeling Results for the RFS Case (MMga l )

PA D D S u m me r E t ha nol Us e W inte r E t ha nol Us e To t a l E t ha nol CG a RFG b To t a l C G a RFG b To t a l PADD 1 PADD 2 PADD 3 P A DDs 4/5 c Calif or nia 39 9 1,66 7

16 1 13 5 0 679 59 47

0 414 1,078 1,726

208 135 414 350 1,082

146 138 0 706 288 0 0 398 1,057 1,370

146 138 398 2,134 3,096

354 274 813 T o t al 2,36 2 1,200 3,562 1,717 1,392 3,109 6,671

a Includes Ariz ona C B G and w i nter oxy - fuel b Federal R F G and C alifornia Phase 3 R F G

c PAD D s 4 and 5 ex cluding C alifornia

Table 2.2-9. Adjusted LP Modeling Results for the EIA Case (MMga l )

PA D D S u m me r E t ha nol Us e W inte r E t ha nol Us e To t a l E t ha nol CG a RFG b To t a l C G a RFG b To t a l PADD 1 PADD 2 PADD 3 P A DDs 4/5 c Calif or nia 61 0 1,73 5

90 1 33 9 0 630 185

47

0 435 1,240 1,919

949 339 435 267 1,631

856 154 0 973 366 0 0 470 1,240 1,998

856 154 470 2,481 3,917 1,805

492 905 T o t al 3,58 4 1,298 4,882 2,908 1,809 4,718 9,600

a Includes Ariz ona C B G and w i nter oxy - fuel b Federal R F G and C alifornia Phase 3 R F G

c PAD D s 4 and 5 ex cluding C alifornia

2.2.4.2 Resulting 2012 Ethanol Consumption by PADD Starting with the LP refinery m odeli ng results, we segregated the Rocky Mountain (PADD 4) and W e st Co ast (PADD 5) ethanol use (repr esented as an aggregate above in Tables 2.2-8 and 2.2-9) and exam inedthe resulting ethanol allocation by region. A summ ary of the 2012 forecasted ethanol consu mption by region (PADDs 1-5 and California) for each con trol case is fo und below in Table 2.2-10.

68Table 2.2-10. 2012 Forecasted Ethanol Consumption by PADD

PA D D 6.7 Bg al RF S Case 9.6 Bg al EIA Case G aso lin e MMg a l ET O H MM g a l % o f G aso lin e % o f To t E TO H G aso lin e MMg a l ET O H MMg a l % o f G aso lin e % o f To t E TO H PADD 1 PADD 2 PADD 3 PADD 4 P A DD 5 a Calif or nia 60,468 48,451 24,845

4,869 8,537 16,494 2,134 3,096

354

54 220 813 3.5% 6.4% 1.4% 1.1% 2.6% 4.9% 32.0% 46.4%

5.3% 0.8% 3.3% 12.2% 60,468 48,451 25,112

4,928 8,626 16,494 2,481 3,917 1,805

15 1 34 2 90 5 4.1% 8.1% 7.2% 3.1% 4.0% 5.5% 25.8% 40.8% 18.8%

1.6% 3.6% 9.4% T o t al 163,664 6,671 4.1% 100.0% 164,078 9,600 5.9% 100.0%

a PAD D 5 ex cluding C alifornia

As shown above, in 2012 PADD 2 is exp ected to continue to domi nate ethanol
use. PADD 2 ethanol consumption is expect ed 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 d ecrease in Midwest m ark etshare (from 46% in Ref e rence/R FS Case to 40% in th e EI A Case). The predicted s h if t in m arketshar e is attributed to the growing am ountof et hanol use outside of the traditional cornbelt.
The LP m odeling suggests that ethanol us age is expected to g r eatly inc r ea se in PADDs 1 and 3. In PADD 1, ethanol blending is expected to more th an triple from 735
m illion gallons in the R e ference Case to 2.1 Bgal in the RFS Case and 2.5 Bgal in the EIA Case. In PADD 3, ethanol u s e in e xpected to sharply in creas e f r om 88 million gallons in th e Reference Case to 354 milli on gallons in the R FS Case and 1.8 billion gallons in the EIA Case. This projected in crease in ethanol blending on the East Coast and Gulf Coast, reflects the phase out of MTBE (replacem ent with ethano l) as well as ethanol blending econom i cs.
2.2.4.3 Resulting 2012 Ethanol Consumption by Season
Furtherm ore, we exam ined the resulti ng ethanol allocation by season. T he LP refinery m odeling assum e s equal 182.5-day summe r and winter seasons. A summ a r y of the resulting 2012 forecasted ethanol consum p tion by season for each of the control cases is found below in Table 2.2-11.

69Table 2.2-11. 2012 Forecasted Ethanol Consumption by Season (MMgal)

PA D D 6 .7 Bg al RF S Case 9.6 Bg al EIA Case S u mmer W inter T otal S u mmer W inter T otal PADD 1 PADD 2 PADD 3 PADD 4 P A DD 5 a Calif or nia 1,07 8 1,72 6

2 08

2 9 1 06 4 14 1,057 1,370

146

25 113 398 2,134 3,096

354

54 220 813 1,240 1,919

949 125 213 435 1,240 1,998

856

25 128 470 2,481 3,917 1,805

151 342 905 T o t al 3,56 2 3,109 6,671 4,882 4,718 9,600

a PAD D 5 ex cluding C alif ornia

As shown above, ethanol usage in 2012 is e xpected to be slightly m ore prevalent in the summertim e than in the wintertim e
. This is a shift from our 2004 Base Case (38% of ethanol use occurred in the summ e r tim e and 62% occurred in the wintertim e as explain e d inSection 2.2. 2.3), m a inly because we are chang i n g the way we define the
seasons. In the Base Case we defined th e seasons based on the RFG regulations (4.5 months of “summ e r” and 7.5 m onths of “win ter”) whereas in this 2012 forecast we are defining them based on 6 m onths of each seas on. Since gaso line consum ption (g al/d ay ) is higher in the summ ertim e, more ethanol -blended gasoline c ould potentially be
consum ed during the summer m ont hs. Howeve r, since there is an econom ic penalty associated w ith blending ethanol into summe rtim e gasoline (refiners have to rem ove butanes and pentanes to comply with the RF G RVP requirem e nts), the result is som e what of a seasonal balance in both the RF S Case and the EIA Case.
2.2.4.4 Resulting 2012 Ethanol Consumption by Fuel Type In addition to providing a PADD an d seasonal breakdown, The LP m odeling determ ined how mu ch e thanol would be used by fuel type - conve ntional gasoline (C G) versus reformulated gasoline (RFG). The firs t thing we did was allocate a portion of the CG to the required winter oxy-fuel areas. Strategy fo r Allocating Ethanol to Oxy-Fuel A reas In the 2004 Base Case, there were 14 st ate-implem ented winter oxy-fuel program s in 11 states (summarized previ ously in Table 2.2-2). Of these program s, 9 were required in response to non-attainm e nt with the CO National Am bient Air Quality Standards (NAAQS) and 5 were implem e nted to m a intain CO attainm e nt status. However, in the future 4 of the 9 required oxy-fuel areas ar e expected to be reclassified fromnonattainm e nt to attainm e nt and disconti nue using oxy-fuel in the wintertim e 3 2

. These areas are: Anchorage, AK; Las Vegas, NV; Provo/ Orem, UT; and Spokane, WA. In addition,

32 Based on co nver s atio ns w ith state o f ficials and re gional EPA officials.

70Colorado is expected to discontinue usi ng winter oxy-fuel in Denver/Boulder and Longm ont to m aintain CO attainm ent status

. T he use of oxy-fuel in the above-m entioned areas is expected to discontinue by 2012 or sooner. W ith the rem oval of these 6 stateimple m ented program s, 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 assum e d that these areas w ould continue to ble nd 10 vol% ethanol into their gasoline for their entire winter oxy-fuel period (duration varies by area, six m onth
m a xi mu m) in the 2012 control cases. Once a portion of the conventional gasoline ethanol was allocated to m e e twinter oxy-fuel requirem ents, this gave use a PADD-by -PADD breakdown of ethanol use by conventional gasoline, oxy-fuel, and reformul ated gasoline as shown below in Table 2.2­ 12.
Table 2.2-12. 2012 Forecasted Ethanol Consumption by Fuel Type (MMgal)

PA DD 6. 7 B gal R FS C ase 9.6 B ga l EIAC ase CG a OX Y b RFG c Total CG a OXY b RFG c Total PAD D 1 PAD D 2 PAD D 3 PAD D 4 PADD 5 d C alifornia 750 2 ,749

283

5 4 106

0 0 0 24

0 113

0 1 ,385 347

47

0 0 813 2,134 3,096

354

5 4 220 813 877 3,366 1,733

151 228 0 0 0 24

0 11 3

0 1 ,603 551

47

0 0 905 2,481 3,917 1,805

151 342 905 Total 3,942 137 2,592 6,671 6,356 13 7 3,107 9,600

a Convent i onal gas ol i ne i nc l ud i ng Ar i z ona CBG b W i nt eroxy- f uel pr ogr am s
c Feder al RFG pl us CA Phas e 3 RFG dPADD 5 ex c l udi n g C a lifo r n ia

However, more post-processing was re quired to determ ine how mu ch ethanol would be used on a state-by-state basis to feed into the em issions and air quality analyses. We begin the latter part of this discussion by explaining how we allocated the RFG ethanol to sp ecific RFG areas and ho w we allocated the CG ethanol to sp e c if ic states/region s considerin g state ethan o l m a ndates and the econ o m i c favorability of ethanol blending Strategy for Allocating Ethanol Among RFG
In the 2004 Base Case, there were 18 stat es/districts with RFG program s covering a total of 175 counties in 36 areas (summarized previously in Table 2.2-1). For our analysis of 2012 ethanol use, we assum e d that the num b er of RFG areas would not change and accord ingly, that the RFG fuel c ontribution to the gasolin e po ol would rem a in the sam e
. However, we considered the am ount of ethanol added to RFG to be a variable, as discussed below.

71In the past, all RFG areas were requiredto use a m i ni mu m a m ount of oxygenate in their reformulated gasoline year-round, as discussed earlier in 2.2.1.1. However, effective May 5, 2006, EPA re m oved the RFG oxyge nate requirem ent in response to the Act. N NAlthough the oxygenate requirem e nt has al ready been elim inated, many refiners are still operating under contracts with ethanol blenders. As such, refiners true response to the rem o val of the oxygenate requirem e nt is relatively unkn own at this tim e
. While it is difficult to predict exactly how each refinery supplying an RFG area would behave, the LP m odeling has attemp ted to do so.
The m odeling suggests that som e refineries will continue to blend ethanol into RFG (or even increase blending) in 2012 based on octane, volum e , and/or toxic perform a nce requirem e nts. Som e RFG pr oducers m a y decidedly replace MTBE with ethanol while others m a y pare back or disc ontinue ethanol use all together. A summa ry of the 2012 forecasted R F G ethanol consumpti on (by season ) for each co ntrol case is found below in Table 2.2-13.
Table 2.2-13. 2012 Forecasted RFG Ethanol Consumption (MMgal) 3 3

PA D D/St at e Seaso n a l RFG U s e MM g a l a 6.7 Bg al RFS Case 9.6 Bg al EIA Cas e Av e r a g e % E T O H in RFG Summer W inter S u mmer W inter ETOH MM gal % o f G asoli ne ET O H MM gal % o f G aso l i ne ETO H MM g a l % o f G asol i ne ETOH MM g a l % o f G asol i ne PADD 1 PADD 2 PADD 3 / T X P A DD 5 / CA b 11, 380 3, 661 2, 939 8, 247 679 59 47 414 6.0% 1.6% 1.6% 5.0% 706 288

0 398 6.2% 7.9% 0 .0% 4.8% 630 185

4 7 435 5.5% 5.0% 1 .6% 5.3% 97 3 36 6

0 47 0 8.5 % 10. 0%

0.0 % 5.7 % 6.6% 6.1% 0.8% 5.2% T o t al 26, 227 1 , 200 4

. 6% 1,392 5.3% 1,298 4.9% 1,80 9 6.9% 5.8%

a E qual amounts of ref orm ulat ed gasoline assumed t o be used i n the sum m er and w i nt er seasons. b I ncl udes Federal R F G and C AP hase 3 R F G

As shown above, the modeling suggests that m ore ethanol would be consum ed in RFG in the EIA Case in the presence of m o re ethanol. The modeling also suggests that the greatest ethanol m arketshare would o ccur in California R F G (5.2 vol% ethanol on average across both cases/season s, or 91% E5.7). The nexthighest areas of RFG use would be PADD 1 (6.6 vol% ethanol on averag e, 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% ethano l or 8% E10). In both control cases, more ethanol was pr edicted to be blended into wintertim e RFG. As di scussed earlier, this m akes sense because in order to m eet the RVP requirem e nts pertaining to summ ertim e RFG (7 psi), refiners have to remove butanes and

33 Gaso lin e con s u m ed in th e greater Ph o e n i x metro p o litan area un d e r t h e Arizon a Clean Bu rn ing Gaso line (C B G ) Pr o g ra m , has n o t bee n c o n s i d ere d “ R FG” by th e L P re fi ne ry m odel i ng a n dth us di scuss e di n t h e conve n tional gasoline secti o n.

72pentanes to accomm odat e for ethanol bl ending (which increas es overall gasoline volatility). As such, in the absence o f an RVP waiver (whichexists exclu sively for summertim e CG), ref i ners are less in clined to blend ethanol into summ ertim e RFG. To allocate the RFG ethanol (aggregated by PADD and season in Table 2.2-13) by state/RFG area, we assum e d that each reg i on would behave uniform l y with the exception of PADD 1 (discussed in m ore detail below). For example, consider PADD 2 summertim e RFG. In the RFS Case, RFG in Chi cago, Louisville, Milwaukee, etc. w ould all con t ain 1 .6% ethano l on average. Orm ore accurately, 16% of all the ga soline
consum ed within PADD 2 RFG areas would contain 10% ethanol. However, based on our knowledge of th e refining industry and distribution patterns, we did not assum e th at PADD 1 RFG would be uniform in ethanol content. The LP m odeling assum e s 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 com e s up the Colonial Pipeline and pa sses through Virginia, W a shingt on DC and Maryland on its way to Pennsylvania and New York. W ith th e exception of a sm all 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 com i ng from PADD 3 is likely to complete ly f u lf ill th e RFG dem a nd in Vi rginia, W a shington DC and Maryland. Beyond Maryland, the fuel from PA DD 1 refineries is sold along with any leftover PADD 3 RFG, as distribution cost s are roughly the sam e fro m Philadelphia north. As a result, the V i rginia, W a shingt on DC and Maryland RFG areas were assumed to receive less ethano l (in m o st cases zero E10) than the oth e r 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 b y State

PA D D/St at e 6.7 B g al RF S C a s e 9.6 Bgal EIA Cas e S u mm e r W i nte r S u mme r W inter

P A DD 1 78 % E1 0 i n a l l s t ate s ex c e pt DC, MD, VA (0% E1 0) 81% E1 0 in all s t a t e s ar ea s ex c e pt DC , MD , VA ( 0 % E1 0) 73 % E1 0 i n a l l state s exce pt DC, M D , V A (0 % E 1 0 ) 10 0% E1 0 in all s t a t es e xcep t DC , M D , V A (3 9 % E 1 0 ) PAD D 2 16 % E1 0 i n a l l s t ate s 78% E1 0 in all s t a t e s 5 1% E10 in all s t a t es 10 0% E1 0 in all s t a t es PAD D 3 / T X 16 % E1 0 i n T X 0% E10 in T X 1 6% E10 in T X 0% E1 0 in T X PADD 5/CA 88% E5.7 in CA 85% E5.7 i n CA 93% E5. 7 in CA 100% E5.7 in CA

Strategy for Allocating Ethanol Among CG
The above-m entioned oxy-fuel requirem en ts combined withstate ethanol
m a ndates created a “floor” for conventional gasoline ethanol use within each PADD. This essentially forced a specific am ount of ethanol to be used in wintertim e CG in PADDs 3 and 5 and a specific am ount of etha nol to be added year-round in Minnesota,

73Montana, and Missouri (100% E10 m a ndates); Hawaii (85% E10 m a ndate); as well as Washington and Louisiana (20% E10 m a ndates).
To determine how the rem aining ethanol would be allocated to the leftover conventional gasoline, w e devised a system atic way to allocate ethanol by state/area. Since the prim ary m otivation to blend (or not blend) ethanol is exp ected to be econom ic, we devised a way to rank CG areas, on a state- by-state and urban/rural basis, as to the
econom ic fa vorability of ethanol b l ending. This was done by calcu lating an ethano l
m argin, which is equal to gasoline price m i nus ethanol delivered price. Ethanol delivered price is equal to ethanol plan t gate price plus transportation costs m i nus any additional state plus other adjustments (explained be low). The greater the eth anol m argin, the greater the econom ic incentive and the m o re likel y ethanol is to be used in that area.
At the tim e the analys is was carried out, ethanol plant gate price was taken from an older E I A NEMS model. However, since this price was assum e d to be the sam e for all ethanol, the actual value is not important when trying to estim ate relative allocatio n preferences between areas. All ethanol blending was assum e d to be done at 10 volum e percen t. The gasoline prices for eachstate were the weighted average rack price of all conventional grades and all m onths, take n from EIA Petroleum Marketing Annual 2004. O O

Ethanol distribution costs were taken from figures given in the docum entation for the EIA NEMS m odel, and are based on a 2002 study by DAI, Inc. P P For the purpose of this consumption analysis, all ethanol was a ssumed 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 Chap ter 1 of the RIA. W hile the results of the production analysis do not completely coincide with this assumpti on (as shown in Table 1.2-15, about 86 percent of the to tal ethanol plant capacity is expected to originate from PADD 2 in 2012 and the rest would originat e from other areas throughout the country), this s i mp lif ying assump tion is still very reasonable.
Ethanol consum ed within census divisions 3 and 4 was assumed to be transported by truck, while distribution outside of those ar eas was via rail, ship, and/or barge. A single average distribu tion cost for each destination census division was generated by weighting to gether the 2 012 freight costs gi ven fo r each m ode in both cen sus divisions 3 and 4 accord ing to th eir volum e share. These cent per gallonfigures were first ad justed upward by 10 percent to reflect higher energy prices, and then additional adjustm e nts were applied to som e individual states base d on their position withinthe census division. In the cases of Alaska and Hawaii, differen ces in ethanol delivery prices f r om the
m a inland were inferred from gasoline prices

. Table 2.2-15 shows th e gasoline price and ethanol d i stribution cost for each st ate as used in this an alys is

.

74Table 2.2-15. Gasoline P r ice & Ethan o l Distr i but ion Costs 3 4

State G asoline R ack Pr ice (c/gal) ETO H Distr ibution C o st (c/gal) Alabama Alaska Ariz ona Arkansas C olorado Florida G eorgia H aw aii Idaho Illinois Indiana Iow a Kansas Kentucky Louisiana M aine M a ry land M i chigan M i nnesota M ississippi M i ssouri M ontana N ebraska N ev ada N ew H ampshire N ew M ex ico N e w Y o rk N o rth C a rolina N o rth D a kota O h io O k lahoma O r egon Pennsy l v ania South C a rolina South D a kota T ennessee T ex as U t ah Vermont Virginia W ashington W e st Virginia W isconsin W y oming 123.2 157.0 138.0 123.3 129.5 124.9 125.8 151.7 134.2 125.7 125.6 127.5 124.3 125.9 123.1 125.5 124.8 126.5 127.4 123.0 126.0 130.5 126.0 141.6 125.3 128.4 126.0 124.4 127.7 126.2 123.4 133.8 126.1 124.9 127.8 124.5 122.5 132.3 127.3 123.4 132.1 125.8 125.2 130.4 7.2 41.5 15.4 7 .3 10.4 8 .4 11.4 36.5 15.4 4.4 5 .4 3 .4 4 .4 6.2 7.3 13.4 11.4 6.4 4.4 6.2 4.4 13.4 4.4 16.4 12.4 12.4 11.4 11.4 5 .4 5 .4 8.3 16.5 8.4 11.4 4 .4 6.2 10.3 13.4 12.4 11.4 16.5 11.4 4.4 12.4

75As the final step in the calculation, subs idies and other adjustm e nts were applied. The federal blending credit of 51 cents per ga llon was given to all areas, and five state retail incentives were included as follows (all cents pe r gallon of ethanol): Iowa, 29.5; Illinois, 20.1, South Dakota, 20; Maine: 7.5; Oklahom a, 1.6. 3 5 Q Q
In addition to state subsidies, sm all penalty adjustm e nts were m a de for distributing ethanol into rural areas in several states (as presented in Table 2.2-16). T he reasoning behind this is that when large ship m ents of ethanol com e from the Midwest by barge, ship, or rail, th ey will be un lo aded initially at large terminals near m etropolitan
areas. Furth er sto r age an d handling will be required to a llow sm aller qua ntities to be distributed via truck into rural areas. Several states have ga soline 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, th e larg est ad justm ents 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 terr ain. Smalle r adju stm ents were applied to states that are sm aller, flatter, or hav e navigable water acces s o n one or m o re sides. Th e states th at do not appear on this list are eithe rloc ate d in the M i d w est (where etha nol is produced and readily available to virtu ally all areas at s i m i lar cos t s) o rare sm all northeast states not believed to have significan t d i fferences b etween ru ra l and urban distribution costs.
Table 2.2-16. Adjustment for Ethanol Distribution into Rural Areas

St at es Rura l A r ea A d just ment ( c /gal)
OH 2 AL, AR, FL, G A , KY , LA, ME, MS, NC, NY , O K , O R , PA, SC, T N , VA, W A , W V 4

AK, AZ, CO , ID, NM, NV, UT , WY , T X 5

To determine which in-u se are as/cou n ties 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 f actors.

34 The f o l l o wi ng st at es ha ve i n t e nt i onal l y been excl u d e d fr om th i s C Gga sol i ne/ e th an ol c o st t abl e be cause they do not c o nsum e any CG( 1 00% R FG ) : CA, CT , DC, DE, MA, NJ , RI.

35 EPA ac kn o w l e dge s th at othe r st at es are co n s i d eri n g ( o r m a y have eve n ap pr o v ed ) ret a i l pum p i n ce nt i v es fo r gas o h o l

. H o we ve r, at t

h e t i m e th i s cons u m pti on a n al y s i s was com p l e t e d, t h ese were th e onl y fi ve st at es o f

feri n g retail p u m p in cen tives th at were li kely to b e app licab le in 20 12

.

76Metropo lita n Statis tical Areas (MSA s) serv ed as the starting point for determ ining the areas within a state th at would be consid ered “urban”. MS As are geographic en tities defined by the U.S. Offi ce of Managem e nt and Budget (OMB) for use by federal statistical agencies in collect ing, tabulating, and publishing fede ral statistics. An MSA is defined as having a core urba n area of 50,000 or m ore people. Each MSA consists of one
or m ore counties including the counties cont ained the core urban area, as well as any adjacen t cou n ties that h a ve a high degree of social and econo m i c integration with the urban core. For the purposes of this analys is, we only considered MSAs with populations greater th an 1 million pe ople, or o the r are a s havin g specia l qu alif ications. Such qualif ication s includ e M S As with les s than 1 m illion people that happen to be the largest MSA in a less-populated state (i.e. Montan a and Wyoming), or other MSAs deem ed likely to receive ethanol by rail base d on proximity to m ajor rail lines.
Once the urban counties for each state were determ ined, county-level vehicle
m iles traveled (VMT) from 2002 we re used (as a surrogate for fuel consumption) to weight the urban counties’ approximate fu el dem a nd. Expressing the urban VMT as a function of statewide VMT gave us the per centage of ethanol dem a nd that would be considered eligible for an urban ethanol dist ribution cost (values pr esented in Table 2.2­ 15). The rem a ining percentage of ethanol dem a nd was considered to be “rural” and subject to the ethanol blending pena lty adjustm e nts found in Table 2.2-16. Considering the urban/ ru ral sp lit for each state an d the resu lting ethanol m argin (ethanol delivered price m i nus gasoline producti on cost), we cam e up with the resulting ranking system for distributing ethanol into conventional gasoline. For PADD 1, refe r to Table 2.2-17; PADD 2, r e fer to Table 2.2- 18, PADD 2, Table 2.2-19; and PADDs 4/5, Table 2.2-20

. The summer and winter percenta ges are th e sam e for each urban/ru ral area with the exception of states containing wint er oxy -fuel areas. For these s t ates, winter oxy-fuel was deducted f r om the winter urban fuel since this volum e of ga soline was already acco unted for (refer to Table 2.2-12).

77Table 2.2-17. Precedence for Adding ETOH to PADD 1 CG

State Rural / Urba n Ethanol Margin (c/gal) PA DD 1 Pr ecedence % of CG Volume Sum m er Winter M E PA FL M E VT N Y G A W V PA SC M D N C N H FL VA N Y W V G A SC N C VA u u u r

- u u u r u

- u

- r u r r r r r r 50.6 48.7 47.5 46.6 45.9 45.6 45.4 45.4 44.7 44.5 44.4 44.0 43.9 43.5 43.0 41.6 41.4 41.4 40.5 40.0 39.0 1 2 3 4 5 6 7 8 9 1 0 11 12 13 1 4 1 5 16 17 18 1 9 20 2 1 35.7 % 44.7 % 59.9 % 64.3 % 100. 0% 67.8 % 51.2 % 21.1 % 55.3 % 20.2 % 100. 0% 14.7 % 100. 0% 40.1 % 63.9 % 32.3 % 78.9 % 48.8 % 79.8 % 85.3 % 36.1 % 35.7% 44.7% 59.9% 65.3% 100.0% 67.8% 51.2% 21.1% 55.3% 20.2% 100.0% 14.7% 100.0% 40.1% 63.9% 32.3% 78.9% 48.8% 79.8% 85.3% 36.1%

781 2 3 4 5 6 7 8 9 10 11 12 Table 2.2-18. Precedence for Adding ETOH to PADD 2 CG

State Rural / Urba n Ethanol M a rgin (c/gal) PA DD 2 Pr ecedence % of CG Volume Sum m er Winter IA SD IL N D N E O H W I IN M I KS KY O H T N O K KY T N O K -

-

-

-

- u

-

-

-

- u r u u r r r 84.6 1 74.4 2 72.4 3 53.3 4 52.6 5 51.8 6 51.8 7 51.2 8 51.1 9 50.9 1 0 50.7 1 1 49.8 12 49.3 13 47.7 14 46.7 1 5 45.3 16 43.7 17 100. 0% 100. 0% 100. 0% 100. 0% 100. 0% 37.7 % 100. 0% 100. 0% 100. 0% 100. 0% 10.2 % 62.3 % 38.4 % 29.9 % 89.8 % 61.6 % 70.1 % 100.0% 100.0% 100.0% 100.0% 100.0% 37.7% 100.0% 100.0% 100.0% 100.0% 10.2% 62.3% 38.4% 29.9% 89.8% 61.6% 70.1%

Table 2.2-19. Precedence for Adding ETOH to PADD 3 CG

State Rural / Urba n Ethanol M a rgin (c/gal) PA DD 3 Pr ecedence % of CG Volume Sum m er Winter M S N M AR AL LA M S T X AR AL LA N M T X u u u u u r u r r r r r 47.8 47.0 47.0 47.0 46.8 43.8 43.2 43.0 43.0 42.8 42.0 38.2 6.8% 31.3 % 26.4 % 31.2 % 16.7 % 93.2 % 61.8 % 73.7 % 68.8 % 63.3 % 68.7 % 38.2 % 6.8% 16.0% 26.4% 31.2% 16.7% 93.2% 58.2% 73.7% 68.8% 63.3% 68.7% 38.2%

79Table 2.2-20. Precedence for Adding ETOH to PADDs 4/5 CG

State Rural / Urba n Ethanol M a rgin (c/gal) PA DDs 4/ 5 Pr ecedence % of CG Volume Sum m er Winter N V AZ N V C O U T ID W Y AZ O R W A AK H I C O U T ID O R W Y W A AK u 56.2
u 53.6
r 51.2
u 50.1
u 49.9
u 49.8
u 49.0
r 48.6
u 48.3
u 46.6
u 46.5

- 46.2
r 45.1
r 44.9
r 44.8
r 44.3
r 44.0
r 42.6
r 41.5 1 2 3 4 5 6 7 8 9 10 1 1 12 13 14 1 5 16 17 18 1 9 57.8 % 55.2 % 42.2 % 48.6 % 38.7 % 32.3 % 11.6 % 44.8 % 38.2 % 43.6 % 36.4 % 15.0 % 51.4 % 61.3 % 67.7 % 61.8 % 88.5 % 36.4 % 63.6 % 49.2% 5.4% 42.2% 48.6% 38.7% 32.3% 11.6% 44.8% 1.4% 43.6% 36.4% 15.0% 51.4% 61.3% 67.7% 61.8% 88.5% 36.4% 63.6%

2.2.4.5 Resulting 2012 Gasoline/Oxygenate Consumption by State Applying th e CG order of preceden ce tab l es to the rem aining conventio nal gasoline ethanol (less state m a ndated and wint er oxy-fuel volum es) and factoring in the RFG ethanol distribution (des cribed above in 2.2.4.4), we cam e up with an ethanol distribution by state for each control case. The resulting state-by-s t ate ethanol distribution is summ arized be low in Table 2.2-21 and a grap hical representation for each control case is provided in Fi gures 2.2-3 and 2.2-4 below.
Table 2.2-21. 2012 Forecasted Ethanol Consumption by State (continued on next page)

806.7 Bg al R F S C ase 9.6 Bg a l EI A Cas e

St a t e A b b rv G asoline MM ga l ET O H MMg a l % o f G asoline G asoline MM ga l ETOH MMg a l % o f G asoline A l abam a A l aska A ri z ona A rkan s as Cali forni a Colorado Connecticut Delaw are Distr i ct of Colu mbi a Fl orida G eorgia Haw aii Idaho Illi noi s Indian a Iow a K a nsas K e ntu cky Loui sian a M ai ne M ary l and M a ssach usetts M i chigan M i nnesota M i ssissip pi M i ssouri M ontana Nebraska Nev ada New Ham pshire New Jersey New M e x i co New Y ork North C a r o lin a North D a kota O hio O k lah oma O regon P e nnsy l v ani a Rhode Isl and S o uth Caroli na S o uth Dakota T ennessee T ex as Utah V e r m ont V i rgini a W a sh ington W est V i rgi nia W i sconsin W y o m i ng A L AK A Z A R C A C O C T D E DC F L G A HI ID I L I N IA KS KY LA M E M D M A M I M N M S M O M T N E N V N H N J NM N Y N C ND O H O K O R P A RI S C S D T N T X UT V T VA W A W V W I W Y 2 ,869 324 2 , 377 1 , 686 16 ,494 2 , 143 1 ,827

538 143 10 , 734 5 ,899

484 677 6 ,737 3 ,820 2 , 017 1 , 722 2 , 742 2 ,743

944 2 , 990 3 , 522 5 , 995 3 ,310 1 ,940 3 ,986

539 1 , 010

920 855 5 , 083 1 , 152 6 , 876 5 ,366

432 6 , 358 2 , 661 1 , 623 5 ,909

588 3 , 022

535 4 ,010 14 , 454

1 , 176

422 4 , 786 2 , 810

964 3 , 117

333 90 0 123 44 813

0 146 43

0 521

0 0 0 454 352 202

86 42 99 34

0 281 346 331

25 343

54 101

6 50 406

35 289

0 43 438

0 34 318

47

0 54 37 62

0 0 0 56

0 268

0 3.1% 0 .0% 5.2% 0.0% 4.9% 0.0% 8.0% 0.0% 0.0% 4.8% 0.0% 0.0% 0.0% 6.7% 9.2% 10.0% 5.0% 1.5% 0.0% 0.0% 0.0% 8.0% 5.8% 10.0% 0.0% 8.6% 10.0% 10.0%

0.7% 0.0% 8.0% 3.0% 4.2% 0 .0% 10.0% 6.9% 0.0% 2.1% 0.0% 8.0% 0 .0% 10.0%

0.0% 0.4% 0 .0% 0.0% 0.0% 2.0% 0.0% 8.6% 0.0% 2 , 910 327 2 , 397 1 , 710 16 ,494 2 ,169 1 , 827

538 143 10 ,734 5 ,899

490 686 6 , 737 3 , 820 2 , 017 1 , 722 2 , 742 2 , 782

944 2 ,990 3 , 522 5 , 995 3 , 310 1 , 968 3 , 986

546 1 , 010

931 855 5 , 083 1 , 167 6 , 876 5 ,366

432 6 , 358 2 ,661 1 , 638 5 , 909

588 3 ,022

535 4 , 010 14 ,574

1 ,191

422 4 ,786 2 , 843

964 3 , 117

337 291 0 149 171 905

57 158 46

3 474

12 42 12 570 368 202 172

72 278

64 52 304 599 331 197 372

55 101 52 54 439 109 423

0 43 636

28 34 328

51

0 54 78 759

25 21 52 64

0 291

2 10 .0% 0 .0% 6

. 2% 10 .0%

5

. 5% 2

. 6% 8

. 6% 8

. 6% 2 .0% 4

. 4% 0

. 2% 8

. 5% 1

. 7% 8

. 5% 9

. 6% 10 .0% 10 .0% 2

. 6% 10 .0% 6

. 8% 1

. 7% 8

. 6% 10 .0% 10 .0% 10 .0%

9

. 3% 10 .0% 10 .0% 5

. 6% 6

. 3% 8

. 6% 9

. 3% 6

. 1% 0 .0% 10 .0% 10 .0%

1

. 0% 2

. 1% 5

. 6% 8

. 6% 0 .0% 10 .0% 2

. 0% 5

. 2% 2

. 1% 5

. 0% 1

. 1% 2

. 3% 0 .0% 9

. 3% 0 .6% Tot a l 163 ,664 6,671 4.1% 164 ,078 9,600 5.9%

81Figure 2.2-3. 2012 Forecasted Eth anol Consumption 6.7 Bgal RFS Case, % E10 by State

2012 % E1 0 by S t at e 0% E1 0

< 50% E10
50- 99 % E10
100% E10 Not P i ct u r e d AK : 0% E10 HI : 0 % E1 0 DC : 0 % E 1 0 86% E5.7

Figure 2.2-4. 2012 Forecasted Eth anol Consumption 9.6 Bgal EIA Case, % E10 by State

2012 % E 10 b y S tate
0% E1 0

< 50% E10
50- 99 % E10
100% E10 Not P i ct u r e d AK : 0% E10 H I: 85% E10 D C : 20% E10 96% E5.7

82

### 2.3 Effects of Ethanol and MTBE on Gasoline Fu el Properties

For the final rulem aking, we estim ate the impact of increased ethanol use and decreased M T BE use on gasolin e quality us ing refinery m odeling conducted specifically for the RFS rulem aking. 3 6 The m e thods, analyses, and re sults of the refinery m odeling are discussed in m ore detail in Chapter 7. In general, adding etha nol to gasoline reduces the arom atic content of c onventional gasoline (C G) and the m i d and high distillation temperatures (e.g., T50 and T90). RVP increas es except in areas where ethanol blends are not provided a 1.0 RVP waiver of the appli cable RVP standards in the summ er. W ith the exception of RVP, adding MTBE directiona lly produces the sam e impacts. Thus, the effect of removing MTBE results in essent ially the opposite impacts. Neither oxygenate is expected to affect sulfur levels, as refi ners control sulfur inde pendently in order to
m eet the Tier 2 sulfur standards.
The impacts of oxygenate use are smaller withrespect to RFG. This is due to the applicability of VOC and toxics emi ssion perform ance specifications, w h ich limit the range of feasible fuel quality values. Thus , RVP and arom atic and benzene contents are not consistently affected by oxygenate type or level. Table 2.3-1 shows the fuel quality of a typical summertim e, non-oxygenated conventional gasoline and how these qualities change withthe additional of 10 volum e percent ethanol (10 vol%). Sim ilarly, the tabl e shows the fuel quality of a typical MTBE RFG blend and how fuel quality m i ght change with either ethanol use or simply MTBE
rem oval. Note that the table does not refl ect county-specific fuel properties.
Table 2.3-1. CG and RFG Summer Fuel Quality With and Without Oxygenates C o n v e n t i onal Gasol i ne R e fo rmul at ed Gasol i ne a Fuel Param e ter Typical 9 R V P Ethanol B l end MTBE B l end Ethanol B l end Non-Oxyge n at ed B l end R VP ( psi ) 8. 7 9. 7 7. 0 7. 0 7. 0 T5 0 21 8 20 5 17 9 18 4 17 5 T9 0 33 2 32 9 30 3 33 5 30 9 E2 00 41 50 60 58 52 E3 00 82 82 89 82 88 Ar om at i c s (vol %) 32 27 20 20 20 Ol efi ns ( v ol %) 7. 7 7. 7 4 14 15 Oxy gen ( wt % ) 0 3. 5 2. 1 3. 5 0 B e nzene ( vol % ) 1. 0 1. 0 0. 74 0. 70 0. 72

aM T B E bl en d–R efe re n ce C a se PA D D 1 S out h, Ethanol b l end – R FS C a se PA D D 1 No rth, N o n- oxy bl en d – R F S C a se PA D D 1 S out h

R e fi ne ry m o d e l i ng per f o r m e d i n s u p p o rt of th e o r i g i n al R F G ru l e m aki ng i s al so use d t o h e l p se parat e t h e effects of the t w o oxyge n ates

.

83 362.3.1 Effect of Ethanol on Conventional Gasoline Fuel Properties
To estim ate effects of ethanol on conven tional gasoline, we used the refinery model output shown below in Table 2.3-2. These values represent average properties across the five PADDs a nd winter and summer seasons.
Table 2.3-2. Properties of Conventional Gasoline Per Refinery Modeling
Case Aro m atic s ( vol % ) Olefins (v ol%) E200 (v ol%) E300 (v ol%) T50 (°F ) T90 (°F ) MTBE (v ol%) Ethanol (v ol%) 20 1 2 R e fe re nc e 29 .1 8 12 .3 9 49 .9 6 81 .6 4 19 0. 3 33 5. 3 0. 65 1. 66 20 1 2 R FS 29 .0 2 12 .1 2 51 .8 9 83 .6 8 18 7. 4 32 6. 2 0. 00 3. 87 20 1 2 E IA 27 .9 7 12 .3 9 53 .0 4 82 .2 0 18 5. 9 33 2. 8 0. 00 5. 38

Using this o u tput, we es tim a ted an average ch ang e in each fuel property pe r vol% change in ethanol content. To derive thes e estim ations, we first ad justed the Reference case prop erties to isolate the effects of etha nol by m a them atically rem oving the effects of MTBE using factors derived from the RFG RIA R R

. Then, we calculated the change in each fuel property per change in eth a nol vol% for each com bination of cases. That is, we
compared Reference Case to RFS Case, Refere nce to EIA, and EIA to RFS. Finally, we averaged the three resu lts from the case-to -cas e comparisons to derive a u seful factor for adjusting county-level f uel prope rties for a change in county-le vel ethanol content. These ethanol effects are shown below in T able 2.3-3.
Table 2.3-3. Change in Conventional Ga soline Properties Per Vol % I n crease in Ethanol Aro m atics

(v ol%) Ol efi n s ( v ol %) E2 00 ( vol % ) E3 00 ( vol % ) T5 0 ( °F ) T9 0 ( °F )
-0 .4 6 0. 02 0. 91 0. 06 -1 .3 3 -0 .2 8

2.3.2 Effects of MTBE on Conventi onal Gasoline Fuel Properties In support of the final rule implem enting the RFG program i n 1993, refinery modeling was perform e d which estim ated the impact of MTBE blending on the various gasolin e pro p erties. S S W h ile this m odeling was perfor m e d in the context of projecting the cost of producing RFG, it is applicable to the use of MTBE in CG, as well. The refinery m odeling examined a num ber of incr em ental steps involved in the production of RFG. Beca use RFG wa s m a ndated to contain oxygen and MTBE was expected to be the oxygenate of choice, MTBE was added in the fi rst step of the analysis, before the fuel
m e t the rest of the RFG r e quirem e nts. Table 2.3-4 shows the results of adding MTBE based on this refinery m odeling.
This m odeling of MTBE effects is som e wh at dated (circa 1993). However, since
rem oving MTBE does not involve any predictions of its total usage level nor the location of its use, econom i cs (such as cr ude oil price) are not a factor. It is p r im arily an issue of chem ical properties and general refinery ope ration, such as octane m a nagem e nt. Also,

84MTBE is always m a tch-blended, since gaso line can be shipped with MT BE through pipelines. T hus, MTBE is always added at th e refinery, allowing the refin e r to take full advantage of its properties.
Table 2.3-4. Effect of MTBE on Ga soline Properties: RFG Final Rule Fuel Param e ter Base 9 RVP Gasoline MTBE Blend Difference RVP (psi) 8.7 8.7 0 T50 a218 207 -11 T90 a329 321 -8 E200 (vol%) 41 46.7 5.7 E300 (vol%) 83 84.9 1.9 Arom atics (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

aEst i m a t e d usi n g co rrel a t i o ns de vel o pedi n s u p p o rt of EP A R F G fi nal rul e , D o cket A

- 9 2

- 1 2 , Fe b ruary 1 994

. T50 = 30 2 – E2 00 / 0

. 49 an d T90 = 70 7 - E30 0 / 0.22

As with ethanol blending, MTBE blending reduces arom atic content significantly as refiners take advantage of MTBE’ s high octa ne 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 show n to m odestly reduce sulfur and benzene levels, as well. This refinery m odeling was performed prior to the developm ent of the Tier 2 sulfur standards for gasoline. W ith these standards, gasoline mu st m eet a 30 ppm sulfur standard on average with or without MTBE ble nding. As refiners can adjust the severity of their hydrotreating proce sses to account for various changes in feedstocks and oxygenate u se, we do not expect that the rem oval of MTBE will resu lt in any increase in sulfur content. Otherwise, the reversal of the dif f

erences shown in Table 2.3-4 are expected to occur when MTBE is removed from gasoline (when the MT BE content was 11 vol%).
2.3.3 Effects of Ethan ol and MTBE on Refo rmulated Gasoline Fuel Properties RFG has historically contained oxygenate due to the applicable 2.0 weight percent oxygen content requirem ent. RFG has cont ained 11 vol% MT BE or ten vol% ethanol, except in California, where 6 vol% ethanol bl ends have been comm on. As discussed in Section 2.2, the use of MTBE in RFG has cease d. It has been repl aced by either 10 vol% or ethanol or high octane hydrocarbon ble nding components, such as alkylate or reform ate. In either cas e, as discus sed in Section 2.3.2, RFG will con tinu e to have to
m eet stringent VOC, NOx, and toxic em issi on perfor m ance standards, though compliance

85with the NOx standard is essentially assure d withcomp liance with the Tie r 2 sulf ur standard s ap plicab le to a ll gaso line 3 7

. For the NPRM, we assum e d 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 m odeling to estim a te RFG properties by PADD and season for the three ethanol u se scenarios. As described above, five refinery m ode ls were developed, each representing one PADD. These m odels produced fuel for use in its own PADD, as well as f or use in other PADDs in the several cases of PADD-to-P ADD distribution of gasoline. For RFG, refinery m odeling projected that a significan t volu m e of R F G used in PADD 1 would be produced by PADD 3 refineries. Because th is PADD 3 RF G is shipped to PADD 1 via pipeline, this fuel tends to be used in the southernmost RFG a r eas of PADD 1, na m e ly those in Virginia, the District of Colum bia and Maryland. In order to reflect this, we
assum e d that the RFG produced in P ADD 3 will be used pref erentially in the RFG areas of these three states and the RFG producedin PADD 1 will be used to fulfill the
rem a ining dem a nd for RFG in PADD 1. For refinery m odeling, it was estim ated that a
sm all volume of RFG would be produced inPADD 3 and shipped to PADD 2. Beca use of the sm all volum e involved, we did not a ssign this volum e to a specific RFG area within PADD 2. As part of their work, the refinery m ode ling con tr actors calibr ated th eir m odel to
m a tch EPA’ s estim ate of fuel quality existing in 2004 (i.e., the base case in this analysis). Therefore, estim ates of the properties of RFG forthe base cas e comp rise an accurate estim ate of actual 2004 R F G, at least on a PADD- average basis. W e also have available the resu lts o f the RFG fuel survey for each RFG area. This su rvey data so m e ti m e s reflects sign ificant d i fferences in the properties of RFG for specific RFG areas with in a PADD. Good examples of this would be RFG areas in New York and Connecticut, which implem ented MTBE bans starting in 2004. W e considered using the m o re precise RFG survey data to represent RFG fuel qual ity in the base cas e, but re jec t ed this approach for two reasons. One, this would intro duce an extraneous difference in RFG fuel quality between the base cas e an d the RFS/EIA cases. While refinery m odel base case projections reasonably m a tch EPA’s estim ate of 2004 fuel quality, they do not m a tch exactly. Comparisons b e tween the b a se cas e andthe RFS and EIA cases would theref ore include the difference b e tween the RFG su rvey data and the refinery m odeling contractor’s estim ate of this data, plus the effect of additional et hanol use and reduced MTBE use. Two, we prim arily present the em ission impacts of the RFS rule on a nationwide basis. On a nationwide basis, re flecting differences betw een RFG fuel quality within a PADD would h a ve little impact. Al so, the Ozone RSM can only reflect a single change in V O C and NOx em issions in non-a ttainm e nt areas (e.g., RFG areas). Thus, differences between specific RFG areas would b e eliminatedby the limitation that on ly

37 Thou gh t h e M S A T 2 f in a l ru l e m ak in g (7 2 FR 84 28 , Feb ruar y 26 , 200 7) eli m in ates th ese airtox ics and NO x req u i rem ent s begi nni ng i n 2 0 1 1

.

86the average em ission effect can b e mode led. Thus, we used refinery m odeling projections of RFG fuel quality for all th ree fuel scenarios. Tables 2.3-5, 2.3-6, and 2.3-7 present th e fuel properties of summ ertim e 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 sam e as ethanol cont ent in term s of weight percent).
Table 2.3-5. RFG Fuel Properties: Base Case – Summer

PADD 1 South P ADD 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 Arom atics 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 (wt%O) 0.0 1.8 0.9 0.1 1.9 1.3

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

PADD 1 South PADD 1 North P ADD 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 Arom atics 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 (wt%O) 0.0 3.7 0.6 0.2 1.8 1.6

87Table 2.3-7. RFG Fuel Properties: E I A Case – Summer PADD 1 South P ADD 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 Arom atics 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 (wt%O) 0.0 3.7 1.8 0.2 1.9 1.8

As shown in Tables 2.3-6 and 2.3-7, su mmer RFG produced in PADD 1 under the two increased ethanol use cases contai ns 10 vol% ethanol (i.e., 3.7 wt% oxygen). However, RFG produced in the other PADDs co ntains less than the m a ximu m 10 vol% ethanol. For other parameters the results generally support the proposed assumption that they would rem a in constant, although there were som e s m all changes. The biggest changes in the RFS and EIA fuel scenarios in clude higher levels of olefins, T50 and T90 and lower arom atic levels. Tables 2.3-8, 2.3-9, and 2.3-10 present th e fuel properties of wintertim e RFG under the base, RFS and EIA fuel scenarios, respectively.
Table 2.3-8. RFG Fuel Properties: B ase Case – Winter PADD 1 South P ADD 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 Arom atics 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 (wt%O) 0.0 2.0 3.3 0.1 2.2 1.7

88Table 2.3-9. RFG Fuel Properties: RFS Case – Winter PADD 1 South P ADD 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 Arom atics 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 (wt%O) 0.0 3.7 3.0 0.0 1.8 2.0

Table 2.3-10. RFG Fuel Properties: E I A Case – Winter PADD 1 South P ADD 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 Arom atics 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 (wt%O) 0.0 3.7 3.7 0.0 2.2 2.6

As shown in Tables 2.3-9 and 2.3-10, wi nter RFG produced in PADD 1 under the two ethanol use cases contai ns 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 m a xi mu m 10 vol% ethanol. On an annual average basis, RFG produced for use in Ca lifornia contains about 5.7 vol% ethanol (2.1 wt% oxygen). This is to be expected given the increase in N O x e m issions assigned by CARB’s Phase 3 Predictive Model to blends with m ore than 2.1 wt% oxygen. As for the summer cases, changes in other fuel param eters were sm all and mi xed.
2.3.4 Estimation of C ounty-Speci fic Gasoline Properties
In order to estim ate the impact of incr eased ethan ol use and reduced MTBE use on national emissions and air qual ity (described in Chapters 4 and 5), we need to estim a te gasoline properties on a county-specific basis throughout the U.S. In support of previous analyses of national impacts of various ru les, EP A has developed a set of gasoline

89specification s for each co unty in th e U.S. for various m onths and calend ar years. T TW e based our analysis on the fuel quality speci fications for January and July of 2008, since 2008 is the f i rst year of full implem e n tation of the Tier 2 sulfur st andard of 30 ppm

. Som e of the EPA county-level gasoline speci fications were based on old data, so we reviewed the estim a tes and m a de several m odifications before applying the changes expected due to ethanol addi tion and MTBE removal.
First, we ad justed RVP values us i ng more recen t inform ation on local RV P program s and to reflect comm ingling. S econd, we revised the oxygenate content and type in each county to m a tch the levels esti m a ted in Section 2.2 to be sold there under each of the three ethanol use scenarios evaluate d

. Third, we adjusted the other prop erties of gasoline which are af fected by the oxygenate use determ ined in step three. These modifications are described in m ore 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 th at the sam e RVP leve l was of ten applied to a ll the countie s of a specif ic state. In m a ny cases, this appeared reasonable, sin ce the sam e RVP standard applied throughout the entire state. However, in other cases, for example, Florida, m ost 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 N M IM database we re consistent with the 7.8 RVP control program s, implying that the 7.8 RVP fuel was so ld throughout the entire state. This was true for much of the south.

As m e ntioned above, the NMIM fuel quality database was based prim arily on fuel survey data from 1999. Fuel surveys tend to focus on large urban areas, as opposed to
sm aller urban or rural areas. Thus, the only av ailable fuel survey data was likely from the areas with the tighter local RVP controls. RVP control reduces gasoline supply, since lighter hydrocarbons must be rem oved in order to reduce RVP. Som e , but not all of these hydrocarbon components can be m o ved to highe r RVP fuel sold elsewhere. Obviously gasoline prices are now much higher than they w e re in 1999. So the incentive to increase supply is greater now than in 1999. As disc ussed in Chapter 7, high gasoline prices are projected for the foreseeable future, at least relative to those exis ting in 1999. Thus, we believ e tha tit is rea sonable to p r ojec t tha tre f i ne r s will m arket gasoline blends with as high a level of RVP as practical given the applic able standards. For example, in Florida, two fuels will be m arketed: one to m eet th e 7.8 RVP standardin several u rban areas and another to meet the 9.0 RVP st andard applicable elsewhere. T here certainly could be
som e spillover of the 7.8 RVP fuel into adj acen t 9. 0 RVP counties. Howev e r, we lack data ind i ca ting the degr e e to which this is occurring and m i ght occur in the future. Lacking this data, it seem s more reasonable to project only that level of RVP control which is guaranteed by the applicable standard s than to assum e that refiners will overcomply with RVP standards and reduce the vo lum e of gasoline which they can produce.

90Past studies have shown th at a typical compliance m argin 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 m a intains a list of counties where it s 7.8 RVP standard applies, as well as any local standards m o re stringent than 9.0 RVP. U U Using this list, we assigned RVP values in each county equal to 0.3 psi less than the st andard applicable in July. W e also reduced the RVP levels of two sets of c ounties which had voluntary local RVP control program s (and therefore not liste d the above Guide). These two areas were Seattle and Tulsa. Based on a review of annual fuel survey data collected by the Alliance of Autom obile Manufacturers (AAM) V V , 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 Snohom i sh Countie s, W a shington. 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. W W The EPA list of RFG counties includes the Baton Rouge, Louisiana, area. However, litigation has held up implem entation of this program , so these counties were assigned RVP values consistent w ith the currently applicable 7.8 RVP standard instead. The R V P value of 6.8 psi was typical for the R F G areas incl uded in the AAM fuel surveys. For the purposes of our analysis, we also as signed the entire State of California an RVP of 6.8 psi, since Californi a fuel must m eet a sim ilar VOC perfor m a nce standard to RFG. Like wise, 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 refor mulated gasoline program
. This program basically requires th at g a soline so ld in these two countie s m e et either the California RF G or Federal RFG st andards. Thus, RVP in these two counties will be the s am e as in those other two areas, s i m ilar to nation al RFG f u el.
These RVP levels for 9.0 RVP and low RVP areas are app r op riate when n o ethanol is being blended into gaso lin e. However, m o st of these areas increase th e applicable standard by 1.0 psi for ethanol blen ds, which is the typica l impact of ethanol blending. T herefore, these levels need to be adjusted for the exp ected level of ethanol use, which is discussed below.
2.3.4.2 County-Specific Oxygenate Type and Content
The three ethanol use scenarios deve loped in Section 2.2 assign ethanol and MTBE use by state and fuel type (i.e., conven tional gasoline, RFG, oxy fuel). In order to develop county level estim a tes of ethanol and MTBE use, we simp ly assum e that ethanol and MTBE use within a state and fuel type is unif o rm
. For exam ple, if the E10 m arket share in con v entiona l ga soline Iowa is 34%, th en ethanol use in every county receiving conventional gasoline in Iowa was assi gned an E 10 m arket share of 34%. As described above, we nearly always assum e that ethanol use is in the form of a 10 vol% blend with gasoline. The two exceptions are Californ ia fuel and Arizona RFG.

91California f uel containing ethanol is assum e d to contain 5.7 vol% et hanol. Arizona RFG is assum e d to be a m i x o f 67% California fuel and 33% Federal RFG produced in PADD 3. Therefore, its ethanol cont ent is a 2/1 mi x of t h e ethano l contents of California RFG and PADD 3 Federal RFG.
Sim ilarly, we assum e that MTBE is used at an 11 vol% level in RFG, since this
m eets the previously m a ndated oxygen cont ent of 2.1 wt%. MTBE in conventional gasoline was assum e d to be used at a 3 vol% level. This was som e what arbitrary, but does not affect the outcom e of the analysis. Th e effect of MTBE blending on em issions is very linear. Therefore, whether the fuel pool in a particular area consists of 10% of a 10 vol% MT BE blend or 33% of a 3 vol% MTBE blend is imm a terial. T hough MTBE is present in the 2012 Reference Case, it is assum e d to be completely phased-out in the RFS and EIA ethanol use cases.

EPA’s NMIM m odel (describ ed in more detail in Chapter 4) will only ac cept a single comp osite fuel for each county

. Therefor e, we could not use th e mix of fuels often projected to be supplied to counties devel oped in Section 2.2. In order to produce a single, comp osite fuel, w e simp ly mu ltip lie d the e thanol and MTBE contents of each blend by their m arket share in that county in order to determ ine the average ethanol and MTBE contents of each county’s fuel pool, resp ectively. For example, if the E10 m arket share in a sp ecific coun ty was 50%, the eth anol content for that fuel was set to 5 vol%. We then adjusted the other fuel propertie s to account for these oxygenates, which is discussed below.
2.3.4.3 Adjustments to Other Gasolin e P r operties for Oxygenate Use We next adjusted other gasoline properti es to account for the level of countyspecific oxygenate use projected to occur unde r the three ethanol use scenarios. Our review of the NMIM fuel database indicated th at properties, such as arom atics, reflected the level of oxygenate use existing in 1999. Th erefore, we used the oxygenate levels in the NMIM database, which differ from thos e developed in Section 2.2 for 2004, as the basis for our adjustm e nts of the other fuel properties. For example, if the NMIM database indicated an ethanol content of 3 vol% for fuel sold in W a yne County, Michigan, and the 2004 projecti on f or this county was 5 vol%, we adjusted the NMIM fuel properties for this county to re flect the addition of 2 vol% ethanol. The bases for these adjustm e nts were those developed in Sections 2.3.1 through 2.3.4 above. As described there, these adju stm e nts apply prim arily to conventional gasoline. These adjustm e nts are summarized in Table 2.3-11 below.

92Table 2.3-11 Change in Property per 1 Vol % I n crease in Ethanol and MTBE Content
E2 00 (% ) E3 00 (% ) Aro m atics (V ol%) Olefins (Vol%) RVP (psi )
Co nv en tio n a l Gaso li n e Ethan o l +1. 0 +0. 24 -0 .5 -0 .1 6 +0. 1 M T B E +0. 52 +0. 17 -0 .5 9 0 0 Refo rmulated Gasoli ne Ethan o l 0 0 0 0 0 M T B E 0. 1 0. 1 0 0 0

To calculate new fuel properties for each county, we applied the eth a nol and MTBE factors to the change in county-leve l ethanol and MT BE content. The overall adjustm e nt to the fuel property was the a ddition of the ethanol effect and the MTBE effect to the baseline fuel property.
For the impact of ethanol blending on arom atic and olefin contents, we followed a slightly different approach. W e assum e d 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 assum e that these parameters are affected in the sam e way regardless of whether the ethanol is splashor m a tch-blended. However, the change in arom atics does depend on which blending approach is used. The situation is sim ilar fo r olefins, though to a lesser extent. Thus, we
employed what can be thought of as a two step process in adjusting aromatic and olef in contents for the change in ethanol content between the NMIM estim ate an d those for the three ethanol use scenarios developed in Section 2.2.
The f i rst s t e p is to a ccou n t f o r any sp la sh-blended ethanol in the NMIM database. W ith splash-blending, arom atic and olefin c ontents are reduced simply by dilution, since ethanol contains is neither an arom atic nor an olefin. The following equation shows how the NMIM level of aromatics was adjusted:

NMI M Interm ediat e NMI M 

# ( 1 -

- 

# ( NMI M E thanol

Aro m atic = Aro m atic ÷ E thanol 

# )( ) ÷ 100 

# ) Mark et Content Content Content Share Then, the effect of any ethanol projected to b e so ld in th at co unty in th e three ethanol use scenarios developed in S ection 2.2 w a s applied using the approach described above for RVP, E200 and E300 (and for the effect of MTBE on arom atics and olefins). In this case, the NMIM ethanol con t ent a nd m arket share is zero, since we already adjusted the NMIM aro m atic and olefin cont ents to rep r esen t those exis ting for a zero ethanol content. For example, the equati on for the ethanol effect is as follows:

93New Fuel Interm ediat e RFS RFS Fuel Propert y Pr opert y = Fuel Propert y + 

# ( E thanol 

# )( Mark et 

# ) × 

# ( Change per 1 

# ) vol% E thanol Lev el Lev e l Content Share Increase

We m ake one final adjustm e nt to RVP to add a comm ingling effect to account for areas where vehicles m a y be fueled by a m i x of ethanol-blend gasoline. Comm ingling of ethanol and non-ethanol blends can increase the average RV P of gasoline in vehicle fuel tanks by 0.1-0.3 psi. Appendix 2-A presents a detailed analysis of the impact of
comm ingling on the RVP of gasoline in vehicl e fuel tanks. Table 2.3-12 presents our estim ate of the net im pact of commingling on in-use RVP as a function of the m arket share of ethanol blends.
Table 2.3-12. Impact of E t hanol Blends on In-Use RVP (psi) E10 m arket share Comm ingling 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 m o del norm a lly accounts fo r this effect autom a tically. However, when NMIM is used to ru n MOBILE6.2, the commingling effect in MOBILE6.2 is by-passed. Therefore, any eff ect of comm ingling needs to be accounted for in the average fuel specified to be soldin each county. To roughly account for this effect, we increased RVP by 0.1-0.27 psi in a ll states where the E10 m arket share was significant (i.e., m ore than 5%) but less than 95%. The states which f ell in to this category, for CG and RFG, are shown in Table 2.3-13. The specific RVP increase depended on the ethanol m arket share in th at county, as indicated in Table 2.3-12.

94Table 2.3-13. States W h ere RVP w as Increased Due to Commingling Fuel Case Conventional Gasoline

Reference ILLINOIS SOUTH DAKOTA INDIANA NEBR AS KA OHIO IOWA KANSAS NORTH DAKOTA W I SCONS I N AL ABAM A MICHIGAN MISSOURI

RFS ALAB AMA ARIZONA ARKANS AS COLORADO FLORIDA KENTUCKY LOUISIANA MAINE MISSISSIPPI NEVADA NEWMEXICO PENNSYLVANI A TENNESSEE WASHINGTON WYOMING

EIA COLORADO FLORIDA KENTUCKY LOUISIANA P ENNS YLVANIA TENNESSEE WAS H INGTON WYOMING IDAHO NE W HAMPSHIRE NEW J ERSEY NEWYORK OKLAHOM A OREGON TEXAS UTAH Reformulat ed Gasolin e Reference ARIZONA MASSACHUSETTS NEWJERSEY TEXAS

RFS KENT UCKY PENNS YL VANIA NE WHAM PSHIRE NEWJERSEY NE WYORK TEXAS MAINE CONNECTICUT DELAWARE ILLINOIS INDIANA MASSAC HUS E TTS MISSOURI RHODE ISLAND VERMONT W I SCONSIN

EIA KENT UCKY PENNS YL VANIA NE WHAM PSHIRE NEWJERSEY NE WYORK TEXAS MAINE CONNECTICUT DELAWARE ILLINOIS INDIANA MASS ACHUSET TS MISSOURI RHODE ISL AND VE RMONT W I SCONSI N



### 2.4 Effects of Biodiesel on Diesel Fuel Prop erties

Our assessm ent of the effects of biodiesel on diesel fuel properties is found in the 2002 EPA report “A Comprehensive Analys is of Biodiesel Impacts on Exhaust Emissions” X X

. Table 2.4-1 below displays the di fference in fuel properties between biodiesel (B 100) and conventional diesel. Note that by 2010, all highway and nonroad diese l f u el will m eet a 15 ppm cap on sulf ur.
The data in the table below were deri ved from a wide-range of biodiesels, prim arily plant- and anim al-based. The 2002 EPA report did not provide properties for soy-only based biodiesel.

95Table 2.4-1. Comparison Betw een Biod iesel and Convent i onal Diesel Fuel a Average Bio d iesel Average Diesel Natural cetane num ber 55 44 Sulfur, ppm 54 333 Nitrogen, ppm 18 114 Arom atics, 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 deg F 6.0 206

a Co nv en ti o n a l d i esel fu el so ld ou tsid e o f Californ i a.

96

## Chapter 2: Appendix Comprehensive Vehicl e Refu eling M o del

Vehicle refueling patterns affect non-exhaust em issions in a num ber of wa ys, including the distributio n of vehicle fuel tank fill levels existing at any given tim e and the quality of fuel in the tank. Given the interaction between these param e ters, we have developed a single m odel which represents vehi cle refueling patterns. W e then use th is model to estim ate the dis tribu tion of vehicle f u el tank f ill leve ls and th e qu ality of f

u el in the vehicle f u el tank s.
Vehicle f u el tank f ill lev els ar e primaril y a f unction of the lev e l at which people refuel their vehicles and the volum e of fuel which they add. In-use, vehicle fuel tanks will slowly em pty until the point of r ef u eling aga i n. The Calif ornia Air Resources Bo ard (CARB) recently condu cted a su rvey of vehi cle refueling patterns in th ree California cities. W e will bas e ou r estim ates of ref u eling p atte rns primarily on thes e data. Most fuel param e ters rem a in unchanged as the f uel is burned. One except is volatility, particularly R V P, which decreases due to evaporation of the fuel as the tank heats up either due to rising am bient temp eratur es or vehicle operation (e.g., heat transf er from the exhaust system , engine cooling air flowing under the vehicle, and fuel recirculationfrom the engine comp artm ent).
While ethan o l conten t d o esn’t ch ang e si gnificantly 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 m arketed in an area contain ethanol and som e do not

. The effect of et hanol on RVP is not linear. Thus, knowledge of the distribution of ethanol content in vehi cle f u el tank s is important in estim ating the RVP of gasoline in vehicle fuel tanks and non-exhaust em issions. W e use the vehicle refueling m odel to estim a te ethanol content, fuel RVP, and average fill level.
There are four m a in aspects of the vehicl e refueling m odel. The first two aspects affect all typ es of gasolin e, ethanol containing or not. The fi rst aspect is a description of the refueling patterns of vehicle operators. How low is the ta nk when they ref uel? H ow much fuel do they add? Does the volum e of fuel added depend on how low the tank was when they stopped to ref uel? The second aspe ct is the weathering of the fuel as the vehicle is operated. In general, the degr ee of weathering, or R V P reduction, depends on both the am bient temp erature and initia l RVP of the f u el.
The third as pect of the model is the ef f

ect of ethanol on RVP. W h ile th e ethano l content of gasoline tends to be either 5.7 or 10 percent by volum e ( vol%) at the service station, the ethanol content of gasoline in a ve hicle’s fuel tank can vary from zero to 10 vol%. The fourth aspect of the m odel is a description of the probability that a vehicle operato r will purchas e fuel at the sam e service s t ation as th e last refuelin g or at ano th er outlet selling the sam e brand fuel (i.e., gasoline brand loyalty). Bra nd loyalty is relevant, because s erv ice s t ations carry ing the sam e br and of gasoline alm o st always sell either gasoline with ethanol or gasoline without ethanol, but not both. It is the mi xing of

97gasoline with and without ethanol is vehicles’ fuel tanks that can cause th e RVP of fuel in the tank to differ from that dispensed at the se rvice station. T his is referred to as the
comm ingling effect.
Each of thes e four aspect s of the vehicle refueling m odel is described below.
2A.1 Vehicle Refueling Patterns During August and Septem ber, 2001, the CARB surveyed consum ers’ refueling habits at 19 service stations in three local areas (Lake Tahoe, the Bay Area and Los Angeles). 3 8 Basic refueling inform ation was obt ained for 396 vehicle refuelings (i.e., initial f u el ta nk level andvolum e of fuel added). Fuel samp les were also obtained from 254 vehicles, though we are m ost interested inthe volum etric data here. CARB also asked those refueling whether they refueled w ith the sam e brand gasolin e the las ttim e the vehicle was refueled. We obtained and analyzed the raw volum e tr ic fuel data obtained by CARB. Of the 396 sets of data, 391 include d both initial fuel tank level and volum e of fuel added. One of the two pieces of inform ation was m i ssin g for five vehicles, so we discarded these partial data sets from the analysis. The tank f i ll le vel prior to r ef u eling was record ed in term s of eighths of a fraction of a full tank, as th is is usu ally how the tan k fill lev el is indicated on the vehicle dash board. Table 2A-1 shows the pr obability of a vehicle being ref u eled a tv arious f u el tank f ill levels.
Table 2A-1. Fill Leve l Prior to Ref u eling Fraction of fill leve l 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 cam e in with an “emp ty” tank.
CARB also record ed whether the vehicle op erator “filled up” the tank or not. W e observed th atthe r e was a trend towar d s a greater probability of a “f ill up” as the level of the tank p r io r to ref u e lin g incre ased. Table 2A-2 shows the pr obability of a f ill-up as a function of tank fill level prior to refu eling.

“ D ra ft A sses s m e nt of th e R eal

- W orl dIm pact s of C o m mingl i n g C a l i f orn i a Phase 3 R e f o rmul at ed G a so li n e , Califo rn i a En v i r onmen tal Pr o t ectio n Ag en cy, A i rReso ur ces B o ar d, A ugu st 200 3.

98 38Table 2A-2. Probability of Fill-Up as a Functi o n of Initia l Tank Fill L evel 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, wh en the tank was at least half full, the tank was filled up 79% of the tim e.
In those cases where the fuel tank was not filled to capacity, the volum e of fuel added was recorded in term s of gallons. Therefore, som e processing was required to estim ate the f i nal f ill lev el in term s of f

r ac tion of tank capa c ity. To do so, we had to estim ate the volum e of e ach vehicle’s fuel ta nk. CARB recorded the basic m odel type of each vehicle in the su rvey. Based on this m ode l type, we placed each veh i cle in to one of six possib l e catego r ies. First, each v e hicle wa s identified as either a car or light truck. Then, we estim a ted whether it would have a relatively sm all, m e dium , or large fuel tank for that veh i cle class. T h e fuel tank sizes assumed for each class are sho w n in Table 2A3 below.
Table 2A-3. Estimated Fuel Tank Volumes (g allons) Relative Size Car Light Truck Sm all 12 16 Medium 16 20 Large 20 24

Using these tank volum es, we converted th e volum e of fuel added during partial f ill ups to anequivalent f r action of ta nk volum e and added this to the obs e r ved in itial f ill level to es tim ate the final f ill level. In five cases (outof the 176 partial f ills ), th e estim ated final fill leve l exceeded 10 0%. E ithe rthe initia l gauge read ing was off or rounded up, or m o re likely, our estim ate of the to tal tank volum e was t oo sm all. In these cases, we reduced the final fill level to 95%. (Given that th is 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 volum e of fue l added from gallons of fuel to fractional tank volum e. Both the m ean and standard devi ation of these volumes were dete rmined as a f u n ction of initial f ill leve l. These f i gure s are shown in Table 2A- 4.

99Table 2A-4. Volume of Fuel Added During Partia l Fills ( % of Fill Level)

Initial F ill L evel Volum e of Fuel Added 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 se en f r om Table 2A-4, the f i nal f ill lev el f r om partial f ills is ve ry close to a full tank when the initial fuel tank le vel was 0.625 or higher. The actual num ber of cases when vehicles in itiated refueling wh en the tank fill lev el was 0.625 or higher w as quite sm all (20). The number of partial fills su rv eyed was ev en sm aller (4). Given th is
sm all num b er and th e fact that the fraction of fill-ups for half full tanks exceeded th at found for 0.75 and 0.875 full tanks (from Table 2A -3), we assum e d that all tanks which were at leas t half full when refueling was in itiatedwere filled

- up. W h en the initial fill level was less than 0.5, we assum e d that the m ean volum e of fuel added w e re those shown in Table 2A-4. As also can be seen from the figures in Table 2 A -4, the estim ates of the standard deviation in the volum e of fuel added are substa ntia l re lative to the m ean volum es of fuel added. W e desired to r e f l ect th is vari ability in th e volum e of f

u el added d u ring part ial f ills. Thus, we utilized both the e stim a tes of the m ean and standard dev i ation in the volum e of fuel during refueling. W e acco m p lished this by mu ltip lying th e standard deviation by a random ly generated standard norm a l deviate and adding this to the m ean volum e of fuel added to estim ate the volum e of fuel added during each partial refueling.
2A.2 Weathering Fuel weathering is the resu lt of the evaporation of the lighter components of gasolin e when the tem p erature in the fuel tank ris e s. This temperatu r e rise can be the result of diurnal swings in am bient temperatur e or from vehicle operation. In the latter case, the heat can be transfe rred either convectively from the exhaust system and engine cooling air f l owing under the vehicle or conduc tively from recirculated fuel from the engine’s fuel system or both. Gasoline is a m i xture of m a ny different chem icals. Som e of these chem icals, such as butane, evaporat e m o re quickly th an other chemicals with a higher m olecular weight, such as octane. The loss of lighter chem icals can be sufficient to reduce the concentration of these lighter chemicals in th e liquid gasoline. This reduces the RVP of t he fuel and its tend ency to evaporat e as the current tank of fuel is consum ed.
We base our estim ate of weathering on RVP on t he m e thodology currently in MOBILE6.2. This estimate was first deve loped f or MOBILE4 and was also used in

100MOBILE5. This m e thodology first calculates an effective in-use tank temperature (T evap ) which drives fuel evaporation. This temp erature is a function of the daily m i ni mu m temp erature (T mi n ) and maximu m temperatu r e (T ma x ), as ind i cated in the f o llowing equation:

T evap = -1.7474 + 1.029*T mi n + 0.99202*(T ma x – T mi n ) - 0.0025173 * T mi n *(T ma x – T mi n )

The loss in RVP is a function of both T evap and dispensed RVP, as indicated by the f o llowin g equation : RVP reduction (psi) due to w eathering = -2.4908 + 0.026196 * T evap + 0.00076898 * T evap * Dispensed Fuel RVP

This RVP loss is that occurring when the vehicle f uel tank is 54.57% full, which is the effective in-use av erage tank leve l for estimating non-exhaust em issions in MOBILE6.2. For a typical high ozone day wh ere the am bient temp erature m i ght 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 estim ate weathering atother tank fill lev e ls, we assu m e that weatheri ng is line ar withtank f ill lev el.
2A.3 Effect of ethanol on RVP as a Function of Ethanol Content In general, the chemicals com p rising gaso line blend ideally. Th at is, the property of the finish ed gasoline is the sum of th e property of each component weig hted by its molar, volum e or m a ss fraction, whichever is technically appropriate. Each compone nt of gasoline has its own RVP level. Adding a component to gasolin e at the level of 10 volum e 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 e xample, norm a l butan e 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 leve l of 5 vol%, then the final RVP is 0.05 tim es 42 plus 0.95 tim es 8, or 9.7 psi. Ethanol blending affects th e RVP of t he finished gasoline quite differently. Ethanol is a highly polar compound, due to the presence of the hydroxyl radical. In pure liquid ethan o l, these hydroxyl rad i cals intera ct with each o ther, in creasing the degree of attraction between ethan o l m o lecules and lowe ring their tendency to evaporate. This phenom ena is comm onl y known as hydrogen bondi ng and is m ost commonly associated with water. When added to non-polar hydrocarbons at low concentrations, such as those
comprising gasoline, the evaporative tendency of ethanol increases dram a tically. This increase in vapor pressure is indicated by what is ref erred to a s the ac tiv ity coef f i cient. The activity coefficient is the ratio of a com pound’s actual vapor pressure in a m i xture to that predicted by ideal blending. Table 2A-5 shows ethanol’s activ ity coefficient at various levels of concentrat ion in a typical gasoline.

101Table 2A-5. Activity C o efficien t of Ethanol in Gasoline B l ends 3 9 Ethanol Concentration (vol %) * Activity Coef f i cient 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 coefficien ts 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 increas e the vapor pressure of the hydrocarbon components. In general, in stead of the hydrocarbons’ vapor pressures decreasing with the addition of another component (e.g., by 10% with the addition of 10 vol% ethanol), they rem a in constant or even in crease. This could be due to a tendency of the hydrocarbons in the vapor phase to “boun ce” off of the ethanol m olecules at the surface of th e liqu i d phas e
. A num ber of studies have shown that the full effect of ethanol’s impact on RVP is reached at v e ry low con centration s. For example, a study perform e d by t he Energy and Environm ental ResearchCenter at th e Univer sity of North Dakota indicates that 90% of the full impact of ethanol on RVP is reached when the eth a no l concen tration is only 2 vol%. 4 0 Researchers at the Un iversity of Delaware f ound the sam e relationship. 4 1 Below 2 vol% ethanol, we have assum e d that th e effect is essen t ially 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 decreas es. The actual RVP of specific comm er cial ethano l and non-ethanol gasoline blends are generally known for a specific area being modeled. Thus, they are not a prim ary concern here. However, in order to develop realistic estim ates of weathering and comm ingling acro ss a range of ethanol blend m arket shares,

39 Harl ey , R obe rt A . a n dS h an no n C . C o ul t e r
- B u rke , “Relating Liquid Fu el and Headspac e Vapor C o m posi t i on f o r

C a l i f orni a R e fo rmul at ed G a sol i n e Sam p l e s C o nt ai ni n g E than o l ,” E n vi r onm ent a l Sci e nce an d Tech no logy, Vo lu m e 3 4 , N u m b er19 , 200 0. pp 4 088 +40 9 4 , Figur e 3

. I t shou ld b e noted th at th e ethanol conce n trations shown in the refe re nc e are i n te rm s o f m o le fraction , wh ich are essen tially a facto r of

2 hi ghe r th an vol um e fract i o n
.

40 Au lich , Ted an d John Rich ter, “Ad d ition o f No n e t h ano l Gaso lin e t o E10 – Effect on Volatili ty,” En ergy andE nvi r o nm ent a l R e searc h C e nt er at t

h e U n i v e rsi ty of N o rth Dakot a, Jul y 15 , 19 9 9

.

41 Ben n e tt, Aliso n , Step h an Lamm, Hasan Orb ey and Stan ley I. Sand ler, “Vapor-Li q u i d Equ ilib ria o f H y d r o c arbon s an d Fu el Ox ygen a tes. 2 , ” Joun al of C h emica l Eng i n eer i n g D a ta, V o l u m e 3 8 , 1 993 , pp . 26 3­ 26 9, Fi g u r e 7. Thi s refe rence sho w s th e et ha nol c o ncent r at i o n i n t e rm s of m a ss fract i o n, whi c h i s nearl y id en tical to vo lu m e fractio n.

102it would be helpful to use re alistic R V P levels for comm er cial ethanol and non-ethanol gasoline blends. Ethanol blending generally occurs under tw o types of RVP sta ndards. One type of standard requires that both ethanol and non-ethanol bl ends m eet the sam e RVP standard. T h is is the case in reformulated gasoline (RFG) areas. In m ost other areas, ethanol blends are allow e d to m eet an RVP st andard 1.0 psi higher than that applicable to non-ethanol blends.

W e will estim ate the impact of weatheri ng and c o mm ingling f or both situations. For RFG-like situ ation s, we will assu m e that both ethanol andnon-ethano l blends have the sam e RVP. In order to es tim ate the imp act of ethanol blending in areas where ethanol blends are allowed to have a higher RVP level, we evalua ted recen t fuel quality data collected by the Alliance of Au tom o bile M anuf acturers. W e com b ined the data co llec t ed from 2001-2003 and found six cities where signi f i cant numbers of both ethanol and nonethanol blends were sampled and analyze d. These six cities were: Albuquerque, Cleveland, Denver, Detroit, Minneapolis, and S eattle. The average RV P 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. W e observed a relationship between the R V P of the non-ethanol gasolines and the differe nce between the RVP of the ethanol and nonethanol blends. In general, as the RVP of the non-ethanol ga soline increased, the difference between the RVP of the ethanol a nd non-ethanol blends de creased. Figure 2A1 shows this relationship for the six cities, al ong with a best-fit line based on least squares regression. The r-squared value fo r the best-fit line was 0.25. Figure 2A-1. Effect of Ethanol Ble nding on RVP in Six U.S. Cities



### y = - 0

. 1538x + 2. 3606 R 2 

### = 0. 2508 0. 6 0. 8 1. 0 1. 2 1. 4

7

. 0 7

. 5 8

. 0 8

. 5 9

. 0 9

. 5 N on- E t hanol B l end R V PEthanolBlend RVPDifference

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 ps i. Ethanol’s effect increased to 1.2 psi

103when the RVP level of the non-ethanol gasoline was 7.5 psi. W hen evaluating the effect of weatherin g and commingling, we will eval uate non-eth a n ol gasoline RVP levels of 6.8, 7.8 and 9.0 psi, as these are common RVP standards in use today. U sing the relationship indicated in Figure 1, the RVP levels of the etha n o l blends ass o ciated with these base RVP levels are 8.1, 9.0, and 10.0 psi, respectively.
2A.4 Brand Loyalty CARB recently conducted a fairly extensiv e direct surv ey o f vehicle refueling patterns. Th is study is b o th m o re recent a nd m o re extensive than those m a de in th e past. Y Y Durin g their refue ling su rvey, CARB asked vehicle op erato r s whether the v ehicle was refueled with the sam e brand of fuel th e prev ious tim e th e vehicle was refueled. The resulting responses are summ ari zed in Table 2A-6 below.
Table 2A-6. Brand Loyalty in the CARB Refueling Survey Response to Question: D i d you refuel with the Los Angeles Bay Area Lake Tahoe

“Yes” 62% 59% 31% “No” 31% 38% 67% “Don’t Know” 7% 3% 2% Breakdown of Retail Outlets Surveyed Major Brands (Shell, C h evron, Texaco, Mobil, ARCO) 4 (100%) 5 (84%) 3 (33%)

Interm ediate (Valero ) 0 (0%) 1 (16%) 0 (0%) Local Brands (USA, Fox, United) 0 (0%) 0 (0%) 6 (67%)

CARB thought that the relatively low leve l 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 ve hicle s to ju stif y thei r rationale. Rental vehicle VMT would have to represent r oughly half of all VMT in the Tahoe area (assum i ng such use was negligible in the other tw o areas) for this factor to explain the large d i fference seen in Table 2A-6. This seem s quite unlikely, despite Lake Tahoe being a resort area.
We believe that there is a m ore likely expl anation for the difference. A review of the serv ice s t ations surv eyed in the th ree ar eas shows significant differences in the types of brands surveyed. The service stations su rveyed in Los Angeles and the Bay Area were dom i nated by m a jor, nationally recognized br ands. Those in L ake Tahoe were dom inated by m ore local brands. We believe that brand loyalty could easily be stronger for nationally known brands which advertise and w hich offer their own credit cards. A few
m a jor brands offer a significant discount whentheir credit card is used to buy their gasoline (e.g., Shell, BP).
The breakdown of service stations into m a jo r and local b r ands in the three areas is shown in the lower half of Table 2A-6. W e have defined m a j or brands to include

104vertically in tegrated oil c o mp anies which ha ve been in the reta il bus iness f o r severa l decades, m arket in several reg i ons across the U. S. and are kno wn to widely advertise. As shown in Table 2A-6, all four retail fuel outle ts s u rveyed in L o s Angeles f ell into th is category, while five out of six stations in the Bay Area reflected m a jor brands. The sixth outle t in th e Bay Area was a Vale ro outle t. Vale ro is a newc om er in the reta il m arket relative to Chevron, Shell, etc. However, it is currently the largest refiner in the U.S. and offers its own credit card. Thus, Valero appear s to f all into aninte rm ediate catego ry som e where between the m ajor brands and the local brands.
The situa tio n is essen t ially reve rsed in Lake Tahoe. Two-thirds of the retail outlets surveyed were local brands. O nly three out of nine outlets represented m a jor brands. In order to investigate th e potential d i fference betw een brand loyalty betw een m ajor and non-m ajor brands, we assum ed that ea ch type of fuel brand had its own level of loyalty across the three area s. W e then estim ated these two levels of brand loyalty identified to best pred ict the overall b r and loyalty in each area. Overall, lo yalty levels of 62% for m a j o r brands and 15% for non-m a jor brands (including Vale ro) fit the survey data reasonably well. The U.S. Energy Information Adm i nistration (EIA) tracks a broad range of proprietary physical and fina ncial data from large ener gy producers through their Financial Reporting System

. This inform ation includes the volum e of gasoline sold through retail outlets owned by or leased from these comp anies and selling fuel with the
comp any’s brand nam e
. Up to 1997, these retail outlets sold about 45% of all gasoline sold in the U.S. In 1998, EIA expanded th e number of companies included in the Financial Reporting System by 50% (from 22 to 33 compani e s). The percentage of gasolin e sold by these f i rm s’ retail o u tle ts inc r ea sed to 62% of all gasoline sold in th e U.S.
The nature of the firm s i ncluded in the Financial Reporting S ystem changed with the eleven companies added in 1998. Prior to 1998, this system included 22 compani e s. A few of these firm s wer e not oil compan ies, (e.g., Burlington Resources, Enron, Sonat/El Paso Energy, Union Pacific Resources , USX). Several others w e re not m a jor gasoline retailers, at least under their corporate nam e s (e.g., Anadarko Petroleum , KerrMcGee, Occidental Petroleum )

. In 1998, EIA added an additional 11 companies to the Financial Reporting System

. Most of these w ere gasoline re finers (e.g., Citgo, Clark, Equilon, LyondellCitgo, Motiva, Sunoco, Tesoro, Ultram ar Dia m ond Sha mrock (UDS), Valero and W illiam s). At the sam e tim e, the volum e of gasoline so ld by the origina l 22 comp anies decreased to 31% of all gasolin e sold in the U.S. This dr op is likely due to the spin-off of refineries by comp anies like Shell an d 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 nam e continued to be Shell)

. The net increase of 17% of U.S. gasoline sales represented the other refiners, such as Te soro, Valero, Citgo, USD, etc. These latter

105comp anies have a mu ch more regional footprint and have not es tablished brand nam e fa m iliarity coupled with a percep tionof highe r quality gasoline. W ith th e exception of the single V a lero outlet in the Bay Area, none of the stations surveyed by CARB offered gasoline from these companies. Thus, we be lieve that the major brands included in the CARB survey are m o re sim ilar to the fu el suppliers included in EIA’s Financial Reporting S ystem prior to 1998 than to those included after 1998.
Given this, we estim ate that 45% of U.S. gasoline sales are sold through stations carrying a major brand. Weighting the loyalt y levels of 62% for m a jor 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 consum er s, the probability of brand selection is
assum e d to be random

. Practically, this m ean s that the probability of choosing either a non-ethano l or ethano l blend depends on each fuel’s m arket share. The exact q uestion asked by the CARB su rveyors was whether the vehicle had been refueled the last tim e with the sam e br and of fuel. CARB assum e d that this m eant that the vehicle was always refueled with the same brand. However, the question was limited to on ly the ref u eling imm ediately preceding the current one. Our prim ary estim ate of brand loyalty is that directly addr ess e d by the C A RB survey: the like liho od that the prev ious ref u elin g was with the sam e brand of gasoline. W e also estim ate the sensitivity of our estim ate of comm ingling to the assump tion m a de by CARB below.
2A.5 Procedures for Modelin g Vehicle Re fueling and Resultant Fuel Quality We developed a m odel to predict the fuel tank level and fuel quality existing in a typical onroad vehicle through 500 refuelings. The vehicle is assum e d to begin its life with a full tank of non-ethanol fuel. The fuel tank level at w hich the vehicle is refueled is based on the probabilities shown in Table 2A-1 above. First, a cumu lative distribution of refueling probabilities was generated by a dding the probabilities shown in the second column of Table 2A-1. Then, a random num b er valued between 0.0 and 1.0 is generated. If

the rando m num b er is less th an th e cumu la tive probability of th e tank being em pty at refueling (0.414), the tank is assum e d to be em pty. If the random num b er is between this figure and the cumu lative probabi lity of the tank being 1/8 fu ll at refueling (0.547), the tank is assum e d to be 1/8 full at refueling, etc.
The RVP level of this f u el is reducedusing the w eathering equation show n in Section 2A.2. The level of RVP loss is assu m e d to be proportional to the volum e of fuel used since the last refueling. For example, a vehicle m i ght 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 th at indicated by the weathering equation in Section 2A.2. Fuel composition (i.e., ethanol content) is assum e d to be unaffected by driving.

106Only the hydrocarbon portion of the fuel is weathered, since th e effect of ethanol on RVP is essentially independent of its con centration. Thus, the value of RVP used in the weathering equation is thatfor the hydrocarbon portion of the fuel, not the total RVP of the blend. This m eans that we need to track the RVP of the hydrocarbon portion of the fuel separately. The probability of the f u el tank b ein g com p letely f illed during ref u eling is determ ined from the estim a tes shown in Table 2A-2. Again an independent random
num ber is generated with a value between 0.0 and 1.0. If the value is less than the probability s hown in Table 2A-2 f o r that in itial f i ll leve l, the tank is assum e d to be f i lle d up. W hen a partial fill is indicated, another random num ber between 0.0 and 1.0 is generated. This random num ber is used in conjunction with the NORMINV function in Excel to generate a random level of the st andard norm a l deviate, or the num ber of standard deviations to add to the m ean es tim ate for the volume of fuel added during a partial fill-up. The values for the mean and standard deviations fo r volum e of fuel added are shown in Table 2A-4. As discussed in Section 2A.2, whe never th e initial fuel tank level is 0.5 or greater, we assu m e that the tank is filled up.
Occasiona lly, the volum e of fuel adde d during a partial fill will exceed th e capacity of the tank. This occurs when th e random num ber generator produces a large positiv e nu mber of standard deviations to be ad ded to the m ean f u el volum e typically added during a partial fill. Inthese cases, we se t the f i na l ta nk level af te r ref u eling to 100%. The type of fuel added, ethanol or n on-ethanol blend, is determ ined both by the level of brand loyalty and the m i x of fuels availab l e in the local area. As discussed in Section 2A.4, brand loyalty is estimated to be 36%. Again, an independent random
num ber 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 assum e d to be the sa m e as that added during the last refueling. Otherwise, the probability of refueling withany particular fuel is assum e d 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 respec tive f u els. T h is is s ele ction is m a de by choosing a new random num ber.

W e then determine the quality of the f u el in the ta nk af ter the ref u eling ev ent. The ethanol concentration of the tank f

u el is sim p ly the e tha nol concentration of the fuel prior to refu eling p l us th at of the fuel added durin g refueling, each weighted by their respective v o lum es. W e assum e that the fuel tank contains som e volum e of fuel, even when it indicates empty. Consistent with CARB in their assessm ent of commingling, we
assum e that this tank “heel” is 10% of the tank capacity. The “etha nol” portion of the ethanol blend is assum e d to be 95% pure ethanol (i.e., it contains 5% denaturant). This denaturant is assum e d to have the sam e RVP as t he non-oxygenated gasoline. Calculating the RVP of t he gasoline ble nd after refueling is more complicated. We first calculate the RVP of the hydrocarbon po rtion of the f u el after refueling in the
sam e way as described above for the ethanol concentration. T he RVP of hydrocarbons blend linearly or ideally, so we simpl y we ight the RVP of the hydrocarbon portion of the

107f u el lef tin the tank ju st prior to ref u eli ng (adjusted downward for weathering as indicated above) with that of the new fuel by their resp ective contributions to the total volum e of fuel in the tank after refueli ng. W e then increase this RVP based on the concentration of ethanol in the tank. As descri bed 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 assum e t hat its RVP effect incre ases lin early up to its f u ll ef f

ect. At this point, the vehicle has been refuel ed and w e have determ ined the quality of the fuel currently in the tank. The next step is to repeat the en tire process described above, starting with a new level at whic h the tank is refueled once again.
Once a vehicle has b eenrefueled 50 0 tim es, we determ ine the RVP and ethanol concentratio n of the f u el in the tank o v er a r a nge of f

u el tank f ill lev els. W e split th e f u ll range of possible tank fill levels in to 10 di screte s e gm ents, each representing a 10% range of tank fill level (e.g., 2 0 -30% full). W e then determ ine which tank fill levels the vehicle will be dr iven through prior to th e next refueling. For example, if a vehicle is refueledto 100% of tank capacity and is then driven down to 1/8 full before its next refueling, its tank m oves from 100% full to 90 % full to 80 % full, etc. until it reaches 1 2.5% full. Thus, in this example, the tank was never in the range of 0-10% full. W e assum e that the vehicle spen ds the sam e am ount of ti m e and accumulates th e sam e a m ount of VMT at each tank fill level between its s t artin g and endi n g points. (T his is s i mp ly equivalent to
assumi ng that vehicles are driv en dif f

eren tly 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, in cluding the effect of weathering. The sam e is done for ethanol concentration. F o r each s eg m ent of fuel tank fill lev e l, the RVP and ethano l concentratio n occurring between each set of refueling ev ents is averaged. The entire p r ocess is then repeated 5 0 tim es. Overall, both R V P and ethanol concentratio n versus tan k fuel fill level is tracked for 25,000 refuelings (500 refuelin gs per m odel pass-through tim es 50 m o del pass-throughs). Overall averages are then determ ined and retained for analysis. One output of the m odel which is independento f the RVP levels of the f u els is the distribution of the fuel tank levels of ve hicle on the road at any one tim e. This distribution is show n in Table 2A-7.

108Table 2A-7. Distribution of Fuel Tank Fill Le vels for the In-Use Flee t Range of Fuel Tank Fill Level % of Vehicles 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 se en, the m o st f r equent onr oad f u el tan k f

ill lev els are be tween 20% to 50%. This d i str i bution will b e used to weight the ef f

ect of commingling which occur s f or each rang e o f fuel tank fill level.
2A.6 Modeling Results We perfor med the procedure described in Section 2A.5. for a se t 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 RV P). An example of the sequence of calcu lations is as f o llows : 1) Select the R V P of non-oxygenated gaso line (E0), the RVP of the ethanol blend (E10), and the m arket share of E10, 2) Begin with a tank full of non-oxygenated gasoline, 3) Choose a random num b er which is used to probabilistically determ ine: a) the level at whic h the tank is being ref u el ed, b) the le vel to whichthe tank is f illed, c ) the volum e of fuel thus b ein g a dded, andd) the type of f

u el used to f ill the tank (E0 or E10), 4) Determ ine which tank fill levels the ve hicle pass ed through b etween the p r ior f ill lev el andthe poin tat which it was re f illed and determine th e f u el RVP at each 10% in crem ent in tank fill level using the w eathering eq uation, 5) Determ ine the RVP of t he hydrocarbon portion of the fuel at the tim e of refill using the weathering equation, 6) Determine the concentra tion of ethan o l in th e ref i lled f u el tan k using the ethanol concentration of th e fuel after the prior fill- up, the volum e of fuel in the tank at the tim e of ref ill, the etha nol concen tr ation of the f u el used to ref ill the tank curr ently and th e volum e of f

u el added d u ring th is r e f ill, 7) Determ ine the RVP of t he hydrocarbon portion of the fuel after refill by weighting the RVP of the hydrocarbon por tion of the fuel in the tank at the tim e of refueling and the RVP of hydro carbon portion of the fuel being added by the volum e of the hydrocarbon portion of the fuel in the tank at the tim e of refill and the volum e of t he hydrocar bon portion of the fuel being added during refueling, respectively,

1098) Determine the RVP of the tota l f u el inthe tank a f ter ref u elin g f r om 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 pro ceed th roug h 500 refuelings.
Once the m o del has been app lied to 5 00 ref u eling s (essen tia lly the lif e of the vehicle), th e results ar e c o m p iled. The averag e RVP level f o r each inte r v al of tank f ill level is determ ined. For example, over 500 refuelings, approxim ately 200 have the tank being ref i lle d when it was less than one-eigh th full, so th at the vehicle was driven wh en the tank was 10% full. For these 200 occurran ces , the tank R V P level is a v eraged. Th is becom e s the average RV P at a fuel tank fill lev el of 10%. Approxim ately 260 refuelin g involve the vehicle bein g driven wh en the fuel tank fill level is 20%. Fu el RVP is again averaged for these 260 s i tuations. Th e process is repeated for a 30% fuel tank fill level, 40%, and so on through 100% full (which occurs every tim e the tank is com p letely filled up). The RVP predictions of the m o de l for various m i xes 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 R V P level using the dis tribution of in-use fuel tank fill levels shown above in T able 2A-7.

110This page intentionally left blank.

111Table 2A-8.In-Use Fuel Tank RVP Levels– 9 RVP CG with Ethanol Waiver (psi)Fuel Tank Fill LevelEthanol Blend Market Share0%2%5%10%20%30%40%50%60%70%80%90%95%98%100%5%8.158.198.268.358.528.678.798.908.989.059.119.159.179.179.1715%8.238.278.338.428.588.748.878.979.059.129.189.229.249.259.2525%8.298.338.408.498.658.818.939.049.119.199.249.299.309.319.3135%8.358.398.458.558.718.878.999.099.179.259.309.359.369.379.3745%8.408.448.508.608.778.939.059.159.239.319.359.409.419.419.4155%8.448.488.568.658.828.989.119.219.299.369.409.459.469.469.4665%8.508.558.628.718.899.059.179.279.359.439.469.519.529.529.5275%8.588.628.708.798.979.139.259.359.439.509.549.589.599.609.6085%8.668.718.788.879.059.229.349.449.529.599.639.679.689.689.6895%8.758.808.878.979.159.319.439.539.619.689.729.769.779.779.77Wtd.Avg.8.428.478.538.638.808.969.089.189.269.349.389.429.449.449.44Table 2A-9.In-Use Fuel Tank RVP Levels– 7.8 RVP CG with Ethanol Waiver (psi)Fuel Tank Fill LevelEthanol Blend Market Share0%2%5%10%20%30%40%50%60%70%80%90%95%98%100%5%7.087.137.207.307.507.647.797.918.018.108.158.198.218.228.2215%7.147.197.267.367.567.717.857.978.078.158.218.258.278.288.2825%7.207.257.317.417.627.767.918.038.128.218.278.308.328.348.3435%7.247.307.367.467.677.817.968.088.178.268.318.358.378.388.3845%7.297.347.417.517.717.868.018.138.228.308.358.408.418.428.4255%7.337.387.457.557.767.928.068.188.278.358.408.448.458.468.4665%7.387.437.507.617.827.978.128.238.338.408.458.498.518.518.5175%7.447.497.577.677.888.048.198.308.408.478.528.558.578.588.5885%7.517.567.647.757.958.128.278.388.478.548.598.628.648.658.6595%7.597.647.727.828.038.198.358.458.558.618.678.708.728.738.73Wtd.Avg.7.317.367.437.537.747.898.048.158.258.338.388.428.448.458.45112Table 2A-10.In-Use Fuel Tank RVP Levels– 6.8 RVP CG with Ethanol Waiver (psi)Fuel Tank Fill LevelEthanol Blend Market Share0%2%5%10%20%30%40%50%60%70%80%90%95%98%100%5%6.196.246.316.436.636.826.967.097.197.297.357.407.417.427.4315%6.246.296.376.486.686.867.027.137.247.337.407.457.467.477.4825%6.296.346.416.536.736.917.077.187.297.387.457.507.517.527.5335%6.336.386.466.576.776.957.117.237.347.427.497.537.557.567.5745%6.366.426.496.616.817.007.167.277.387.477.537.577.597.597.6055%6.406.456.536.656.867.047.207.317.427.517.577.617.627.637.6465%6.446.506.576.696.907.097.257.367.477.567.617.657.677.677.6875%6.506.556.636.756.967.157.317.427.537.617.677.717.727.727.7385%6.566.616.696.817.027.217.377.497.597.677.737.777.787.797.7995%6.636.686.766.887.097.287.447.557.667.747.797.847.857.857.86Wtd.Avg.6.396.446.516.636.847.027.187.307.407.497.557.597.617.617.62Table 2A-11.In-Use Fuel Tank RVP Levelsas – 9 RVP Gasoline with No Ethanol WaiverFuel Tank Fill LevelEthanol Blend Market Share0%2%5%10%20%30%40%50%60%70%80%90%95%98%100%5%8.158.178.208.268.338.388.408.418.408.368.328.268.238.208.1915%8.238.248.288.338.408.458.478.478.468.438.398.338.308.288.2725%8.298.318.348.398.478.518.548.548.538.508.458.408.378.358.3335%8.358.378.408.458.528.588.608.608.598.568.518.458.428.408.3945%8.398.428.458.508.588.638.658.668.658.618.568.508.478.458.4355%8.448.478.508.558.638.698.718.728.718.678.628.558.528.508.4865%8.508.538.568.628.708.758.788.788.778.738.688.628.588.568.5475%8.588.608.648.698.778.838.868.868.858.818.768.698.668.638.6285%8.678.698.728.788.868.928.958.958.948.908.858.788.748.728.7095%8.768.788.818.878.959.019.049.049.038.998.948.878.848.818.79Wtd.Avg.8.438.458.488.538.618.668.698.698.688.648.608.538.508.488.46113Table 2A-12.In-Use Fuel Tank RVP Levels –8 RVP Gasoline with No Ethanol Waiver (psi)Ethanol Blend Market ShareFuel Tank Fill Level0%2%5%10%20%30%40%50%60%70%80%90%95%98%100%5%7.267.287.327.367.447.507.537.537.527.487.437.367.337.317.2815%7.337.357.387.437.507.577.597.607.577.557.497.427.397.377.3525%7.397.417.447.497.567.627.647.667.647.607.567.487.457.437.4135%7.437.457.497.547.627.687.707.717.697.667.607.537.507.477.4645%7.477.507.537.597.677.737.757.767.747.717.657.577.547.527.5055%7.527.547.577.637.727.787.807.817.797.767.707.627.597.567.5465%7.577.597.637.697.777.847.867.877.857.817.757.677.647.617.5975%7.637.667.697.757.847.917.937.947.927.887.827.747.707.677.6685%7.717.737.777.837.927.988.018.027.997.967.907.827.787.757.7395%7.797.817.857.918.008.068.098.108.078.047.987.907.867.837.81Wtd.Avg.7.507.527.567.617.697.767.787.797.777.737.687.607.577.547.52Table 2A-13.In-Use Fuel Tank RVP Levels –7 RVP Gasoline with No Ethanol Waiver (psi)Ethanol Blend Market ShareFuel Tank Fill Level0%2%5%10%20%30%40%50%60%70%80%90%95%98%100%5%6.376.406.436.496.576.636.666.666.656.616.566.486.446.416.4015%6.436.456.486.546.626.686.716.716.706.666.616.546.496.476.4525%6.476.506.536.596.686.736.766.766.756.716.666.596.546.516.5035%6.516.546.586.646.726.786.816.816.796.766.706.636.586.566.5445%6.556.586.626.686.776.826.856.856.846.806.746.676.626.596.5855%6.596.616.666.726.816.876.906.906.896.846.786.716.666.636.6165%6.636.666.706.776.866.926.966.956.946.896.836.756.706.676.6575%6.696.726.766.826.926.987.027.017.006.956.896.816.766.736.7185%6.756.786.826.896.987.057.087.087.067.026.956.886.826.796.7795%6.826.856.896.967.057.117.157.157.137.087.026.946.896.866.84Wtd.Avg.6.576.606.646.706.796.856.886.886.866.826.766.696.646.616.60114The next step is to estimate the impactof commingling on in-use RVP.We use the in-use tank RVP levels shown in the previoussix tablesfor ethanol blend market shares of zero and 100% to represent situationswhere no commingling occurs.In the absence of commingling atintermediate levels of ethanol blend market share, the RVP should vary linearly between those found at zero and 100%.The impact of comminglingis then the difference between the actuallevel of RVP estimated by the model and theRVP estimated fromthe zero and 100% ethanol blend marketshare RVP levels.Table 2A-14 shows the impact of comminglingfor the case where gasoline RVP is9 psi and ethanol blends areallowed a 1.0 psiRVP waiver.Table 2A-14.Commingling as a Function of Fuel Tank Fill Level and EthanolBlend Market Share – 9 RVPCG with Ethanol WaiverFuel TankFill LevelEthanol Blend Market Share2%5%10%20%30%40%50%60%70%80%90%95%98%5%0.0160.0490.1010.1670.2130.2350.2340.2200.1830.1320.0730.0360.01315%0.0170.0470.0960.1640.2100.2290.2280.2140.1790.1290.0700.0370.01525%0.0190.0490.0980.1660.2100.2280.2300.2170.1800.1300.0720.0360.01435%0.0210.0490.0970.1700.2170.2340.2350.2220.1840.1340.0710.0370.01545%0.0230.0510.1010.1750.2240.2430.2430.2290.1920.1380.0730.0390.01355%0.0230.0520.1050.1810.2320.2510.2520.2400.1970.1430.0760.0420.01565%0.0250.0540.1100.1850.2380.2590.2600.2460.2010.1470.0780.0430.01475%0.0250.0550.1120.1870.2400.2630.2630.2500.2040.1500.0780.0420.01385%0.0260.0550.1120.1860.2420.2650.2660.2510.2060.1500.0780.0420.01395%0.0250.0550.1120.1860.2420.2650.2660.2520.2060.1510.0790.0430.013Wtd.Avg.0.0220.0510.1040.1760.2260.2460.2460.2330.1920.1400.0740.0390.014As can be seen, the impact ofcommingling increases slightlymoving from low levelsoffuel tank fill level to high levels.Asfound byprevious studies of commingling, the impactof commingling is lowest wheneither E0 or E10 fuels predominate the market and peaks when the mixof E0 and E10 is approximately 50/50.Again, the weighted average of the commingling impact is determined by applying weighting thecommingling impact at each fuel tankfill level bythe distribution of fill levels in-use shown in Table 2A-7.115Table 2A-15shows the weighted average commingling impacts for the six fuel cases.Table 2A-15.Weighted Average Commingling Impactfor Various Sets of E0 and E10 Fuels (psi)E0/E10Ethanol Blend Market ShareRVP Level2%5%10%20%30%40%50%60%70%80%90%95%98%9/100.0220.0600.1020.1710.2270.2490.2490.2270.1990.1430.0840.0450.0207.8/8.90.0290.0650.1100.2010.2390.2730.2740.2550.2180.1600.0820.0430.0197/8.20.0280.0670.1220.2030.2650.2990.2900.2750.2360.1730.0940.0460.0159/90.0220.0510.1040.1760.2260.2460.2460.2330.1920.1400.0740.0390.0148/80.0210.0550.1100.1890.2490.2690.2780.2530.2180.1600.0800.0450.0197/70.0270.0650.1250.2120.2670.2980.2940.2770.2330.1720.0960.0470.017116This page intentionally left blank.117Ethanol use in the three RFS rule fuel cases (Reference, RFS, and EIA) occurs predominately under three situatio ns: 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 em ission m odeling, we averaged the comm i ngling impacts for these situations from Ta ble 2A-15 and applied th at to the en tire U.S. as a function of ethanol blend m arket share. Th is averag e set of comm ingling impacts is shown in Table 2A-16.
Table2A-16. Commingling Impact Applied in RFS Rule Emiss i on Mo deling Ethanol Blend Market S h are Commingling Im pact (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

118

## Chapter 3: Impacts on E m i ssions fr om V e hicles, Nonr oad Equipment, and Fuel Pr oduction Facilities

As described in Chapter 2, there are a larg e number of potential fuels that qualify as renewable. However, only two are expected to be used in significant volum e s by 2012: ethanol and biodiesel. Of these, ethanol use is expectedto predom inate. In particular, ethanol is expected to dom i nate the “gro wth” in renewable fuel use between now and 2012. Thus, our prim ary focus here will be on the impact of th e use of ethanol on em issions in spark-ignited vehicles andequipm ent. W e will m o re brief l y to uch on the impact of biodiese l f u el u se on emissions.
Sim ilarly, we expect that the bulk of the im pact of ethanol use on emissions and air quality will be associate dwith em issions f r om spark-ign i ted vehicles andequipm ent using low level ethanol-gasoline blends. W e e xpect the use of high level ethanol-gasoline blends, like E85 to be relatively sm all in comp arison. Thus, the discussion here will focus on em issions from the use of low level e thanol blends. W e will m o re brief l y discu ss the per v ehicle im pacts of use of high level ethanol-gasoline blends relative to gasoline. Finally, we present estim ates of the em issi ons related to the pr oduction and distribution of both ethanol and biodiesel. The em issions re lated to the production and use of ethanol can be signif i can t r elative to th e emissi on impacts of the use of ethanol blends, due to the significantly increase in the volum e of ethanol exp ected to be produced in the future.



### 3.1 Effect of Fuel Quality on Onr oad Spark-Ignited Vehicl e Emissions Ethanol belongs to a group of gasoline a dditives commonly referre d to as oxygenates. The two m o st comm onl y used oxygenates are ethanol and MTBE, though TAME has been used in significant volum es, 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 hist orically to m e et the gasoline oxygen requirem ents for oxyfuel and RFG. Historically, MT BE was the predom inant oxygenate used in gasoline in the U

. S. Over tim e , MTBE use has decreased in the U.S, while ethanol use has increased, to the point where ethanol use no w predom inates. This trend appears to be accelerating, to the po int where it app ears th at e ss entia lly a ll MTBE use will ce ase inthe U.S
som e ti m e in 2007.
The impact of oxygenate use on em issions from motor vehicles has been evaluated since the m i d-1980’s. Several m odels of the impact of gasoline quality on m ot or vehicle em issions were developed in the early 1990’ s and updated periodically since that tim e
. We use the m o st up-to-date versions of these m odels here to es tim ate the impact of changes in oxygenate use on
em issions. Still, as will be desc ri bedbelow, sign if icant unc ertain ty exis ts as to th e ef f

ect of these gasoline components on em issions from both motor vehicle and nonroad equipm ent, particularly from the latest m odels equipped with the m o st advanced em ission controls. Assum i ng adequate funding, we plan to conduct significant vehicle an d equipm ent testing over the next several years

119to improve our estim ates of the impact of these ad ditiv es and o ther gaso lin e propertie s on emissions. The results of this te sting are not available for inclusion in this analysis. We hope that the re su lts f r om these tes tprogr a m s will be a v ailab l e f o r ref erence in the comp rehensive evaluation of the em ission and air quality impact s of all the f uel-related requirem e nts of the Energy Act required by Section 1506. A draft of this study is requi red to be completed in 2009. As we discuss the em ission impacts of increased ethanol use below, we identify the areas where current estim a tes appear to be the most uncerta in and where we hope to obtain additional data prior to the 2009 study.
3.1.1 Lo wLevel Ethanol and MTBE-Gasoline Blends EPA has developed a nu mber of emission m odels relating th e im pact of gasolin e qua lity
on em issions from m o tor vehicles. In 1993, EPA published the Comple x Model, which predicts the ef f

ect of gasolin e qua lity on VOC, NOx and ai r toxic emissions f r om 1990 m odel year lightduty m otor vehicles (i.e., Tier 0 vehicles). This model is used to determ ine refiners’ comp liance with RFG and anti-dumping standards. The Co mplex Model also contains estim ates of the impact of ga soline RVP on non-exhaust VOC e m issions. These estim a tes were taken f r om the then-current version of the MOBILE em issions model, MOBILE5. In 2001, in responding to California’s request for a waiver of the RFG oxygen m a nda te, EPA perf orm e d a new analysis of th e impact of gasolin e qua lity on exhaust VOC and NOx
em issions from Tier 0 vehicles. This analysis in cluded ess en tia lly a ll of th e data used to develop the Comp le x Model, as well as som e additional da ta developed since 1993. It also used m ore advanced statistical tools, such as a m i xed sta tistical m odel, which were not available in 1993. These VOC and NOx models are re f e rred to here as the EPA Pred ictive Models. Thus, in term s of both supporting data and m odeling tools, the E P A Predictive Models represent an improvem ent over the Comp lex Model, at leas t for exhaust V O C and NOx em issions. Because the criteria f o r granting Californi a a waiver of the oxygen requirem e nt focused on ozone and PM impacts, EPA did not develop a sim i lar m odel for toxics or C O e m issions. In roughly the sam e timefram e , EPA devel oped its latest m o tor vehicle em ission inventory model, MOBILE6. Som e of the fuel -em i ssion relationships from the Comp lex Model were inco rp orated into MOBILE6. These inclu d edthe effect of selected gasoline properties on exhaust VOC and NOx em issions and the fraction of VOC em issions repres ented by several air toxics (benzene, form aldehyde, acetaldehyde, and 1,3-butadiene). The EPA Predictive Models were not ava ilab l e in tim e f o r their in corpor ation into MOBILE6. MOBILE6 also contains estim ates of the effect of certain gasoline param e ters on CO em issions, nam e ly RVP a nd oxygen content. The effect of RVP on non-exhaust VOC em issions contained in MOBILE6.2 represents an update of the MOBILE5 and Complex Model estim ates.
W e desire inthis RFS analysis to utilize th e m o st up to date estim ates of the impact of gasolin e qua lity on em issions cur r ently available. No one m o del cur r ently contain s the most up to date estimates f o r a ll the pollu tants of interes t

. Therefore, we have broken up the rem a inder of this sub-section into six parts. The first discusses emissions of VOC and NOx, as the EPA Predictive Models address these pollutants. Th e second discusses CO em issions, as neither the Complex Model nor the EPA or CARB Predictive Models address this pollutant. The third

120section addresses em issions of air to xics, as a co mbination o f m odels represents the b est es tim ate of the impac tof fuel quality on these em issions

. The fourth section addresses non-ex haust VOC
em issions. The fifth section addresses PM em issi ons. The sixth section addresses emissions of
arom atic hydrocarbons. The seventh and final sec tion presents the impact of ethanol and MTBE blending on per mile emi ssions from gasoline-fueled m o tor vehicles.
3.1.1.1 Exhaust VOC, CO and NOx Emissions
In this sec tio n we evaluate the various m odels availabl e which predic t the impact of gasolin e qua lity on exhaust VOC, CO and NOx emissions. Several su chm odels hav e been developed over the past 15 years. W e f i rst d i scus s the EPA Com p lex Model, the EPAPredic tive Models and the CARB Predic tive M odels due to th e wide range of fuel param e ters which they address and their similar for m
. W e next de scribe the fuel effects con t ained in EPA’s MOBILE6.2 em ission inventory m ode l, as this m odel addresses CO e m issions, which the other models do not. These models best predict em i ssions from Tier 0 vehicles, as m o st of the
em ission data upon which they were based were fr om these vehicles. A number of fuel effects test program s which tested later m o del year ve hicles have been perform e d since the above
em ission m o dels were developed. We summ ar ize these studies below and develop em ission projections based on consistent statistical procedures. Finall y, we select which m ode l best predicts th e effect of fue l qua lity for each pollutant. Due to the uncertain ty involv e d with predicting the impact of fuel qual ity on em issions from Tier 1 and later vehicles, we develop two alternative approaches to m aking 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 Comp le x m odel to investigate the e ffects of changing gasoline fuel param e ters on the exhaust em ission s of Tier 0 and older vehicles. This model predicts the effect of gaso line quality on exhaust VOC, toxics and NOx emissions and nonexhaust em issions of VOC and benzene. The Comp lex Model is used to determ ine comp liance with the emissions perform ance requirem ents for federal RFG by comparing the predicted
em issions of a candidate fuel to that of a ba seline fuel for common baseline vehicle technology. The baseline fuel and the baseli ne vehicle technology represent those fuels and vehicles included in the 1990 US light duty vehicl e fleet (Tier 0 technology).
In 1999, the state of California petitioned EPA for a waiver of the oxygen requirem e nt in RFG. The r easoning behind the waiver request ce ntered on the California Air Resources Board’s (CARB) analysis which showed that reducing the am ount of oxygen in RFG would lead to reduced NOx exhaust em issions. The m odel th at CARB de veloped to support their claim was called the P h ase 3 pred ictive m odel Z Z

. This m odel differed from the previous version of CARB’s predictive m odel (the Phase 2 m odel) in a num ber of ways. The most significant difference included a substantially expanded da tabase, m a inly for m odel year 1986 and newer vehicles, as well as an improved version of the statistical analys is software package used to develop the model (SAS® PROC MI XED). Acc ord ing to C A RB, the Phase 3 predictive m odel displays a steeper NOx/Oxygen response than th e Phase 2 Predictive m o del as a result of elim inating the RVP by Oxygen term which the pr evious m odel had erroneously included. This caused an in creas e in th e NOx exhaust em issions predicted, and for m a ny areas this in creas e

121would lead to NOx levels exceed ing those se t by National A mbient Air Quality Stan dards (NAAQS). According to the 1990 Clean Air Act, EPA can waive the R F G oxygen requirem e nt if it prevents compliance with a NAAQS. In order to properly perform an en vironm ental im pact analysis in response to this wa iver request, EPA considered us ing both its Comp lex Model, as well as CARB’s Phase 3 Predic tive Model to esti m a te the impact of gasoline quality on
em issions. The EPA Complex m ode l, while cons idered statistically r obust due to the large
num b er of vehicles comprising th e d atase t, was n o t consid ere dto be adequate for a number of
reasons. First, the Comp lex Model was based on an em issions database which did not include several studies that have sin ce been published. S econd, the E P A Complex Model was developed using a fixed effects statistical m odeling approach 4 2

. In contrast, both the CARB Phase 2 and 3 models were mi xed m o dels, em ploying a m o re s ophisticated statistica l approach than was available at the tim e of developm ent of the Com p lex m odel. EPA also rejected us ing CARB’s Phase 3 Predic tive Model in its anal ys is of the waive r request. W hile CARB had developed a very detaile d protocol for developing the Phase 3 m odel, it rejected th e results of this proto col because the resulting m odel differed too substan t ially from the Phase 2 model. Thus, EPA decided to creat e its own “predictive m o dels” for exhaust VOC and NOx e m issions which com bined the protocols used to develop the Co mplex Mode l with th e expanded database and improved st a tistica l too l s which were now availab l e. EPA relied on existing EPA m odels for evaporative VOC e m is si on effects. However, these latter estim ates were augm ented with re cent data ind i cating thatethanol increased perm e a tion em issions, as well as the consideration of several commingling models and associat ed assump tions about drivers’ refueling behavior.

One m a in conclusion drawn by EPA in the Ca lifornia Oxygen waiver analysis was that insuf f i cient data ex isted at thattim e to conclus i ve ly dete rmine the respon se of Tier 1 and newer vehicles to f uel param e ters other than sulfur. 4 3 Som e data indicated that oxygen increased NOx
em issions from Tier 1 and later vehicles, while other data contradicted this. Due to this inconsistency, EPA assum e d that oxygen did not affect exhaust VOC, NOx or CO e m issions from Tier 1 and later vehicles in its analysis of CARB’s request for a waiver of the RFG oxyge n m a ndate.

42 A "fix ed effects" m o d e l o f t

h is k i nd m ak e s n o atte m p t to esti m a te th e erro r in tro d u c ed by sam p lin g from so m e larger populati o n of ve hicles or fuel s. T h e m odel just descri bes quantitativel y the relationships am ong variables that
are p r esen t in th e d a taset th at was an alyzed. A "mixed" m odel, as was use d by C A R B i n bot h th e P h ase 2 a n dP h ase 3 p r ed ictiv e mo d e ls’ co nstru c tio n , attem p ts to g o b e y o nd d e scrip tion o f th e av ailab l e d a ta to m ak e statisti cal in feren ce t o some larg er po pulatio n fro m which th e av a ilab l e d a ta were sam p led

. In th is case CARB treated th e vehi cl e e ffect s as rand om (ass umi ng th at th e t e st vehi cl es w e r e s a mp l e d f r o m s o me l ar g er fleet) while fuel effects
were treated as "fixe d " (ass uming that all fuels o f in terest were rep r esen t e d in th e d a ta). Su ch a m o d e lin g appro a ch mak e s it po ssi b l e to esti m a te th e pro b a b l e error in m o d e led e ffects i n a way that is not possible with a fixed effects
m odel . The ap pr oac h , m o reo v er , i m prove s th e acc uracy o f th e si g n i f i c an c e m easures use d t o deci de whi c h t e rm s t o i n cl ude i n t h e m odel

.

43 At th e tim e o f t

h at 199 9 an aly s is, sufficien t data ex isted on t h e emissions e ffects of Sulfur on Tier 1 ve hi cles to be
m odel e d. Ho w e ver , s u l f ur l e v e l s were n o t e x pect ed t o c h a n ge as a re sul t o f th e rem oval o f oxy gen f r om R F G i n Califo rn i a, so t h e effect o f su l f ur was m o o t i n th is situ ation

.

122None of the three m odels discussed above (i.e., the Complex Model, the CARB Predic tive Model, and the EPA Predictive Mod e ls) address CO e m issions. Historic ally, this is because the RFG program did not mandate a specif ic redu ction in CO em issions and the lesser role of CO em issions in form ing a m bient ozon e. The CARB Predictiv e Model cons iders the impact of fuel oxygen content beyond 2 wt%, but does not address th e full range of oxygen levels on CO e m issions, nor the impact of othe r fuel param e ters. The only EPA m ode l which predicts the impact of fuel qua lity on CO e missions is MOBI LE6.2, which is discussed in the next section.

3.1.1.1.2 MOBILE6.2
The exhaust em ission effects contained inMOBILE6.2 often differ for norm a l and high
emitting veh i cle s. They can also v ary by m odel year. As it is dif f i cult to determine the f r action of emissions comi ng from each m o del year’s veh i cles in MO BILE6.2, as well as norm a l and high emitter s, it is not f e asible to pr e d ict ou tside of the m odel how a specif i c f u el is go ing to affect in-use em issions. In addition, the spl it between norm a l and high em itters varies depending on the presence and type of inspection and m a intena nce (I/M) program appli cab le in a particular local area. Thus, the effect of a specific fuel on em issions can vary to som e degree from one county to another. In order to quantify the effect of various fuel param eters on exhaust emissions in MOBLIE6.2 under the conditions ex isting in th e 2012-2020 tim efram e, we comp ared the changes in em issions predicted by the NMIM m o de ling described in Chapter 4 of the Draft RIA with the ch a nges in f u el quality occu rring in th e ethanol u se scenarios. S p ecifically, we first determ ined the percentage change in exhaus t VOC, CO and NOx e missions by county for the base and 7.2 Minimu m RFG Use sc enarios evaluate d for the NPRM. We then perform e d a series of

linear reg r ession s of these ratios a g ainst the ch ange in fuel RVP, ethanol content and MTBE content. W e did this for the 2012, 20 15 and 2020 em ission projections separately. Fo r each
com b ination of county and calendar year, th e onl y property that changed was fuel quality. All other parameters relevant to em issions (e.g., the di stribution of vehicles by age and class, VMT,
am bient temperature, etc.) were otherwise identic al. The results are summarized in Table 3.1-1.

123Table 3.1-1. Fuel-Exhaust Em ission Effects in MOBILE6.2 RVP (% / psi) Ethanol (% / Vol %) MTBE (% / Vol%) Adjusted 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 pu rposes, the effect of RVP, ethanol and MTBE on exhaust VOC and NOx e m issions from the EPA Predictive Models ar e shown in Table 3.1-2. The base fuel is a typical non-oxygenated, summertim e , conventional gasoline, with 8.7 RVP, 30 ppm s ulfur, 32 vol% arom atics, 13 vol% olefins, T 50 of 218 F, T90 of 329, and no oxygen.
Table 3.1-2. Fuel-Exhaust Emission Effects per the EPA Predictive Mo dels 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 em ission effect s contained in the EPA Predictive Models differ quite dram atically from those in MOBILE6 .2. Regarding the effect of RVP, both m odels predic t thatan incr ease in RVP will increas e bothexhaust VOC and NOx em issions. However, MOBILE6.2 predicts th at an increas e of 1.0 ps i will increase exhaust VOC by roughly 7%, while the EPA Predictive Mod els pred ic t o n ly a 1% increase. Regarding NOx em issions, the EPA Predictive Models predic t the larger effect (1 %), while the effect in MOBILE6.2 is sm aller (0.6%). W h ile the ratio of these two ef f

ects is signif i cant, th e absolu te difference (0.4 %) is v e ry sm all. Regarding the addition of ethanol, the two m o de ls again predict very dif f

erent results. MOBILE6.2 predicts roughly 7 tim es the exhaus t VOC reduction per volum e percent of ethanol added, with no increase in NOx. The EPA Predic tive Mod els projec t a s i g n if icant incr ease in NOx em issions. The relative d i fferences are si milar f o r the a ddition of M T BE to gasoline, though the difference between the tw o estim ates of exhaust VOC reduction is sm aller. Regarding C O Emissions from Tier 0 ve hicles, MOBILE6.2 projects that a 3.5 wt% oxygen fuel (i.e., E10) w ill reduce C O e missions from nor m a l emitters by 11% and th ose from

124high emitter s by 19%. 4 4 For Tier 1 and later vehicles , MOBILE6.2 projects that a 3.5 wt% oxygen fuel (i.e., E10) w ill h a ve no effect on CO emissions from nor m al emitters and will reduce CO emissions f r om high emitters by the sam e 19% estim ated f o r Tier 0 v ehicles. This latter projection w as based on a similar assum p tion to those m a de during EPA’s review of California’s request for a RFG oxyge n waiver due to the same lack of relevant em ission data. W e estim ate that the fraction of CO e m issions com i ng from high emitters is 64.8% bas ed on the ov erall effect of 6.7% in 2020 shown in Table 3.1-1 for 2020, whenthe fleet is entirely Tier 1 and later vehicles. T his produces a fleet wide CO e missi on 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 proj ect any impact of the other relevant fuel param e ters (arom a tics, olefin s, T50, and T90) on CO em issions for either Tier 0 or Tier 1 vehicles.
Since that tim e, the results of several testprogram s have been published. Given the dwindling n u m b ers of Tier 1 veh i cles on the road, these m o re recen t stud ies have focused on vehicles ce rtif ied to Calif o rnia’ s low em i ssion vehicle (LEV), ultra low em ission vehicle (ULEV), and super ultra low em ission vehicle (S ULEV) standards, as well as federal LEV and Tier 2 stand ards. The results of these m ore recent studies, as well as the few availab l e in 2001, are discussed in the following section.

3.1.1.1.3. CRC E-67 Study

3.1.1.1.3.1 Overview In early 2006, the Coordinating Research Counc il (CRC) published the results of their E67 study investigating the effect s of three fuel param e ters, 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 Califor nia L E V, ULEV and SULEV standards, with m odel years ranging from 2001 to 2003. A m a trix of twelve (12) fuels wa s tested in this program , with varying levels of ethanol, T50, and T90. Each fuel param e ter (e thanol, T50, and T90) was tested at each of three leve ls. However, a f u ll f actorial m a trix of 27 test fuels was deem ed unnecessarily large due to subtle differences between fuels that m a y not have yielded statistically significant results, or due to practical considerations regarding the fuel s that could be blended u sing exis ting ref i nery stream s. The E-67 report presents the results of e m issi on testing for each fuel, as well as a m i xed statistical model created from the emission data. The m odel i ndicates that each of the three fuel param e ters always has a statisti cally significant effect on both NMHC 4 5 and NOx e missions. In addition, significant interactions between the three fuel param e ters are also often present. The first step in our analysis of the CRC E-67 m o del was to co mpare the em issions changes predicted by the CRC mi xe d m odel to th e actual emissions chan ges observed for each

44 A norm al e mitter is ge ne rally a vehicle whose emissions are no m o re th an twice its certificatio n emissio n stan d ards. A hig h emitter is a v e h i cle wh ose emissio n s ex ceed th is lev e l.

NM HC is essen tially eq u i v a len t to VOC for o u r p u rp o s es in th is st u d y
.

125 45fuel in the test program

. We calculated av erage NMHC and NOx e m issions over the Federal Test Procedure for all twelve vehicles on each f uel. The CRC m i xed m o del predicts the percentage change in em issions for each fuel relative to another fuel. These changes in predicted exhaust em issions for each fuel were applied to the m easured em issions for fuel “H” in order to create a set of absolute em ission levels for each f u el. W e then com p ared the emission s predicted by the CRC E-67 m odel to the m easured em issi on levels to observe how well the m ode l predicted the effects of each fuel. T he fuel pr operties of the CRC E-67 te st fuels are listed in Table 3.1-3 A AA , below, and in greater detail in Tabl e 3A-1 of App e ndix 3A. We selected CRC fuel “H” as the “base” fu el sin ce its p r operties are the clo sest to a nation al average non

oxygenated conventional gasoline (0% Ethanol, 215oF T50, 330 oF T90). (See Table 2.2-6 in Chapter 2 for data pertaining to gaso line su rvey results acros s the U.S.)
Table 3.1-3. CRC E-67 Test Program Fuels Properties a Targe t Prop erties for De sign Variables Actual Values 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 Tabl e 3 A

- 1 i n A p pen d i x 3A f o r

det a iled prop erties o f all E-67 test fu els

The comp arison between predic ted and m easured NOx e m issions is show n in Figure 3.1­ 1. The fuels are shown to indicate a trend in ethanol content, from the lowest leve ls on the lef tto the highest levels on the right. W ithin a constantlevel of ethanol conten t, the f u els ar e then shown in order of their level of T50 (lowest ag ain on the left and higheston the right). The yaxis sca l e inthis f i gur e is set to m atc h that f o r NMHC emissions, which will be p r ese n ted and discussed next.

126Figure 3.1-1. CRC E-67 Predicte d vs. Actual Emissions Relative NOx Em issions Change from Ba se (CRC H: 215 T50, 330 T90, 0% EtOH)

0 % Et han ol 5

. 7 % E t ha nol 1 0% Et han ol

-10

. 0%

0. 0 % 10 .0% 20 .0% 30 .0% 40 .0% 50 .0%

A F H D K B J I G C E L CRC Fue l s ( or de r of i nc re a s i n g O 2 , T5 0 , T 9 0 )% ChangeNOx CRC E

- 6 7 NOx P r e d i cte d Ac tual C R C Em i s s i ons

As shown in Figure 3.1-1, the CRC model for NO x e missions predicts the general trend in the em ission data, which roughly indicates anincrease in N O x e m issions with increasing ethanol content. However, the m odel clearly does not reflect m a ny of the fu el 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 dis tillation temperatures. In changing from fuel G to fuel C, the CRC E-67 m odel predicts a 4.3% increase in N O x e m issions whereas the actual test data clearly shows a 9.2% decrease. This likely indicates the exis tence of interactions between the fuel param e ters which are m o re comp lex than those which could be included in the model. W hile fuel param e ters other than ethanol, T50, and T90 were held as constant as possible
am ong all the test fuels, the level of specific co mpounds (such as toluene or the various xylenes) could not be held constant. It is possible that som e of these compounds are affecting NOx
em issions and confounding the ability of the m o del based just on ethanol, T50, and T90 to predict the observed changes. Figure 3.1-2 repeats this comparison for NMHC.

127Figure 3.1-2. CRC E-67 Predicte d vs. Actual Emissions Relative NMHC E miss i ons Change fro m Base (CRC H: 215 T50, 330 T90, 0% EtOH)

- 10.0%

0.0% 10.0% 20.0% 30.0% 40.0% 50.0%% ChangeNMHC CR C E-6 7 NM HC Pre d i cte d A c tual C R C Em is s i ons 0 % Et ha nol 5. 7% E t h ano l 10% E t han ol

A F H D K B J I G C E L CRC Fue ls (order of inc re a s i ng O2 , T50 , T90 )
First, it is very apparent that NMHC em issi ons are much more sensitive to fuel quality than NOx emissions. The larg est in creas e in N MHC em issions rela tiv e to Fuel H is three tim es that for NOx em issions. Except for Fuels A and F (and of course Fuel H), the CRC m o del generally under-predicts the m easured NMHC da ta. Directionally, however, the predicted
em issions changes are very consistent with those obs erved in the te st r es u lts. For this datase t at least, the effect of fuel quality on NMHC e missions are mu ch m o re predictable than NOx
emissions. The fuels studied in this test program were varied independently at low, m e dium , and high levels of T50, T90, and Ethanol. If you in clude all the possible linear, quadratic, and interactive term s, there are a to tal of possible 10 com binations. The CRC E-67 m odel s included 8 out of the10 possible fixed effects for the NOx, NMHC, and CO m odels

. These terms were: T50, T90, ethanol (EtO H), T50 squared, T90 squared, EtOH squared, T50 by EtOH, and T90 by EtOH. The excluded term s were T50 by T 90, and T50 by T90 by EtOH, which CRC excluded from consideration since previous studies had indicated that th ese te rm s had little effect on
emissions.
Also, several of the terms that were include d in the CRC m o del had p-values greater than 0.1, indicating that those term s are less than m arginally significant. 4 6 Specifically, the E t OH by EtOH term i n the NMHC m odel and the T90, T90 by T90 and T90 by EtOH term s in the NOx model all had p-values above 0.10. In deve loping both the C o mplex Model and the EPA

46 Using t h e widely accepted c riteria of a 95% confi d ence int e rval, p ≤ 0

. 05 is con s id ered to b e statisticall y si gni fi ca nt , 0. 0 5 < p ≤ 0

. 10 is marg in ally sign ifican t, and p > 0

. 10 is no t co n s i d ered statistically sig n i fican t.

128Predictive Models, our procedures would normally ex clude the le ast s i g n if icant term
. A new regression w ould then be perform e d without this term being include d in the m odel. This process would be re peated un til all the rem aini ng te rm s were statis tically signif i cant. 4 7

We desired to determ ine how these statistica lly in signif i can t term s m i ght be affecting the predicted em ission changes. This, plus the di screpancies between the C RC E-67 m o del and the actual em issions data, especially for the NOx m o del, prompted us to create our own NOx and NMHC mod e ls using the CRC E-67 dataset. Conducting our own m odeling also provides us with the opportunity to apply a wide range of statistical tests in order to better understand the role of various fuel param e ters in affecti ng emissions from these vehicles. The following sections provide details pertai ning to the verification of the CRC m odel and the m otivation for constructing a new m odel from this data.

3.1.1.1.3.2. Developm ent of a New Mixe d Model: The EPA E-67 Model Using the E-67 dataset provided by the CRC, EP A first verified the coefficients and pvalues of the CRC E-67 model using the full E-67 dataset (no outliers were rem oved) with the
sam e 8 fixed fuel effects that were in cluded by C RC. This was succes sful and the coefficient and p-values resulting from this m o deling are shown in Table 3.1-4.
Table 3.1-4. CRC E-67 Model P-Values and Coefficients CRC E-67 NMHC CO NOx Effect P-Value Coe fficien t P-Value Coe fficien t P-Value Coe fficien t Interc ept <.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 withall com binations of T50, T90, and EtOH along with their squares, cro ss products, and the interactiv e term s T50 by T90 by EtOH for a total of 10 fixed effects. From this “full m odel” , variab les we re eliminat ed in order to improve the f it s t atistics be tween the m odel and the te st d ata until a “f inal m odel” 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 follo wing page, s hows the p-values and coe fficients for the fixed effect term s of each model.

On e ex cep tion to th is p r o cess is th at th e linear form o f a variable, s u c h as ethanol, woul d always be ret a ined i n th e m odel i f a seco ndo r de r t e rm i n cl udedethan o l (e. g ., th e ethan o l by T 9 0 t e rm ).

129 47Table 3.1-5. EPA E-67 Model P-Values and Coefficien ts EPA E-67 NMHC CO NOx Effect P-Value Coe fficien t P-Value Coe fficien t P-Value Coe fficien t Interc ept <.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*Et OH 0.0003 0.000023

As shown in Table 3.1-5, the EPA E-67 Mode l does not include te rm s with p-values greater than 0.10 (except fo r linear term s included in statistically significant higher order term s). Statis tical te sts show thatthes e two models are not significantly di fferent from one another. The null hypothesis in this case is th at EP A E-67 fits the data just as well as the original C RC E-67 model based on a chi-squared test. However, based on several fit statistics (AIC, AICC, and BIC) the EPA E-67 m odel provides a slightly be tter f it to the test data thaneithe r the o rigina l CRC E-67 model or the full m odel with all 10 term s included. The next step is to co mpare the EPA E-67 model predictions to both the E-67 data and the predictions of the EPA Predictive Models, which reflect the em ission effects for older vehicles.
Both the EPA E-67 and EPA Predictive m odels are m i xed m o dels that pr e d ict the re la tive changes in exhaust em issions due to carefully co ntrolled changes in ga soline quality, including the addition of an oxygenate such as ethanol. Th e m odels are not intended to be accu rate at predic ting ab solute emission lev els, but rather th e difference in em issions when fuel properties are varied. The goal of this an alys is is to de termine if the EP A Predictive m odels, which were developed using data from Tier 0 an d earlier v e h i cles, pred ict the sam e relative changes in
em issions as the Tier 1 vehicles used for the EPA E-67 m odel. A key difference between the m odels is that th ere are only three fuel param e ters used as inputs for the EPA E-67 model: T50, T90, and ethanol content. The EPA Predictive Models use these three p r operties alo ng with RVP, arom atic c ontent, olefin content, and sulfur content as fuel param e ter inputs to the m odel. We ran the EPA E-67 and EPA predictive m od e ls with the 12 fuels used in the CRC test program , inputting the applicable fuel properties used in each m odel. Following the sam e procedure as outlin ed ab ove, CRC test fuel H was se lected as a “base” fu el in o r der to compare relative changes between this fu el and others with varying am ounts of ethanol, T50, and T90. The NOx e m issions predicted by the EPA E-67 and EPA Predictive m o dels, tog e the r with th e actual E-67 study data, are shown graphically in Figure 3.1-3, below.

130Figure 3.1-3. EPA E-67 and EPA P redictive vs. Actual Emissions% ChangeNOx Relative NOx Em issions Change from Ba se (CRC H: 215 T50, 330 T90, 0% EtOH)
50.0%

40.0%

30.0%

20.0%

10.0%

A F H D K B J I G C E L E PA E

- 6 7 E PA P r e d i c t i v e ac tual

0 % Et ha nol 5. 7% E t h ano l 10% Et ha nol

0.0%

- 10.0%

- 20.0% CR C T e s t Fu els (in o r der of increasin g O 2 , T 50, T 9 0)
As shown in Figure 3.1-3, neither model predic ts the actual test data with complete accuracy. The EPA E-67 shows the sam e general relationship to the em ission data as did the CRC E-67 NOx m odel. Thus, rem o ving the statisti cally insignificant term s had little impact on the relative fit of the m o del to the data. The EPA Predictive NOx m odel s , on the other hand, appear to be prim arily se nsitiv e to e thanol, w ith T50 and T90 playing ve ry limited ro les in affecting NOx e m issions. In contrast, the E-67 model shows sensitivitie s to all th ree param e ters. Overall, the E-67 study indicates that NOx e missions from recent m odel year veh i cles (LEVs, ULEVs and SULEVs) are stil l sensitiv e to at le ast s eve ral fuel param e ters. As indicated by the inab ility of eith er the EPA E-67 m odel or the EPA Predictive Mod e ls to accur a t ely p r edic t all of the ch anges seen in the E-67 d ata, th is s tudy is very valuable in identifying the continued sensitivity of LEV and c l eaner vehicl es to chang e s in fuel qu ality. Figure 3.1-4, below, shows the comp arison of NMHC e missions predicted by the EPA E67 and Predictive m odels together with the E-67 study data.

131Figure 3.1-4. EPA E-67 and EPA P redictive vs. Actual Emissions Relative NMHC E miss i ons Change fro m Base (CRC H: 215 T50, 330 T90, 0% EtOH)

E PA E

- 6 7 E PA P r e d i c t i v e Ac tual 0% Et han ol 5. 7% Et han ol 1 0% E t han ol

A F H D K B J I G C E L

CR C T est Fuels (in o r der of in creasin g O 2 , T 50, T 90) - 20.0% - 10.0%

0.0% 10.0% 20.0% 30.0% 40.0% 50.0%% ChangeNMHC

From Figure 3.1-4, it is apparent that both m odels do a better job at predicting changes in NMHC e m i ssions than was the case for NOx e m issions. The EPA E-67 model is clearly the more accurate of the two m odels. However, this is to be expected giv e n it was based o n the data being depicted. The ability of the EP A Predictive Model to p r edict the gen eral 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 tim es the NMHC e m ission levels of the vehicles in the E-67 test program

. Overall, it appears that NMHC emissions f r om LEVs and cleane r ve hicle s are ev en m o re sensitiv e to ch a nges in fuel quality than NMHC em issions from Tier 0 vehicles. The preced ing figures illustrate th e d i ffere nces b e tween the models for all 12 fuels included in the E-67 test program

. Som e of these fuels are m o re practical, or likely to be commercially produced, than others. Based on th e results of AAM fuel surveys presented in Chapter 2, s u mm ertim e E10 blends will generally ha ve 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 m os tclosely reflects these differences are CRC fuel “C”, a 10 vol% ethanol blend, and CRC fuel “H”, a non-oxygenated fuel. Both fuels have a m i d-range level of T90. A second, m ore complex set of fuels invol ve those with higher levels of T90. T he CRC “E” fuel contains 10 vol% ethanol and has the high level of T90. Howe ver, there is not a good
m a tch to this fuel which is non-oxygenated. Yet two non-oxygenated fuels (“D” and “K”), when considered together, represent a r easonable m a tch to fuel “E. Fuel D reflects no change in T50 relative to fuel E, while f uel K reflects slig htly m ore than a typical drop in T50. Thus, by averag ing th e em issions for fuels K and D and then com p aring this to the emissions with f u el E,

132we are able to generate a second direct indicati on of the impac tof etha nol blending on em issions f r om these low emitting vehicles. T h e genera l p r operties of these f i ve f u els and th e e missions changes predicted by the two m odel s are shown in Table 3.1-6 below.
Table 3.1-6. Predicted N O x and NMHC Emissions Changes for EPA E-67and Predictive Models 4 8

Fuel Chan ge s H to C K to E D to E T50 (oF ) Mid - Lo w H igh – L ow Low - L ow 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 m o dels ag ree quite closely on the effect of fuel C relative to fuel H on both NMHC and NOx e missions

. However, that said, both m odels tend to overestim ate the impact of fuels C a nd E on NOx em issions and underestim ate the impact of these fuels on NMHC e m issions. Regarding the comparison of fuel E to fuels K and D, the two m odels tend to agree on the effect of fue l E to fuel D, but differ more withrespect to the effect of fue l E to fuel K, particularly for NMHC e m issions. One reason for the d i f f

erence in th e la tte r comp arison is tha t the EPA E-67 NMHC model is m o re sensitive to very high levels of T50 than the EPA Predic tive Model f o r NMHC.
Overall, the results of the E- 67 study suggest that our assumption that Tier 1 and later vehicles would not be s e nsitiv e to f u el par a m e ters such as e th a nol, T50 adT90 (m ade in our consideration of Califor nia’s request for a waiver of the RFG oxygen requirem ent) m a y not be valid. The observation that NMHC em issions from LEVs, et. al . could actually be more sensitive than Tier 0 vehicles (on a percentage basis), particul arly challenges our assumption. W h ile the ef f

ect of f

u el quality on NOx emissions f r om low emitting vehicles is still n o t cle ar f r om the rec ent te st data, these emissions do appe ar to be s ens itiv e to f u el quality.

48 Fo r a n a d di t i onal com p ari s on bet w ee n t h e m o d e ls with an ex p a nd ed set o f fu els t o b e u s ed later i n th is an alysis, refe r to Table 3A -2 in A p pen d ix 3 A

.

1333.1.1.1.3.3. Effect of Ox ygen Content Alone on Emissions: CRC E-67 Study In order to provide an estim a te of fuel -em i ssion effects com p arable to those of the other studies of the effect of fuel quality on em issions from LEV and later vehi cles, we selected th ree pairs of fuels tested in the CRC E67 study wher e the only change in fu el properties was oxygen content. One pair of fuels compared non-oxygenate d fuel versus E6 (fuels A and B). Two pairs of fuel comp ared non-oxygenated fuel versus E10 (fuels D and E and fuels F and G). W e applied m i xed univariate statistical models to the logarithm of the em ission 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 term s of the percentage change in em issions between the non-oxy genated 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 oxygenate (Fuels A and B) -4.5%* -7.5% 2.6% 10 vol% ethanol vs. no oxygenate (Fuels D, E, F and G) 4.7% -18.1% 9.5%

* B o l d ty pe i n di cat es th e di f f e rence was significant at a 90% confi d ence l evel.

As can be seen, the addition of 6 vol% ethanol increased NOx em issions, while decreasing NMHC and CO e m issions. None of the effects were statis tica lly significant at a 90% confidence level. The addition of 10 vol% ethanol decreased CO e missions, while increasing NMHC and NOx e missions. All three pollutant e ffects were statistically significant at a 90% confidence level.

3.1.1.1.4. AAM-AIAM Sulfur and Oxygenate Study AAM, together with AIAM and Honda, perform e d a test program in 2001 to evaluate the effect of fue l sulfur and oxygen on em issions of CARB Tech 5 vehicles (i.e., LEV and later vehicles) B BB

. This program was performed at the re quest of the C A RB in conjunction with the MTBE ban and new Phase 3 Cleaner Burning Gaso line regulations. The first of the program ’s two distinct goals was to evalua te the em issions of very low sulfur fuels at 1, 30, and 100 ppm sulfur, with other fuel param e ters held constant

. Part two was to comp are the em issio n s effects of MTBE and ethanol blended fuels to non-oxygenate d fuel, again with other param e ters held as constant as possible. T he fuel specifications for the oxygenated fuel m a trix are shown in Table 3.1-8. (The sulfur-related testing is n o t relevant h ere, so th e fuels which o n ly reflect a change in sulfur are not shown.)

134Table 3.1-8. AAM Fuel Properties for Oxygenated Test Fuels Test Fu el Non
- Oxy MTBE E10 RVP (psi) 7.1 6.9 7.1 Arom (v ol. % ) 24.6 22 21.6 Olefins (v ol.% ) 4.6 3.4 4.1 T50 (oF ) 210 202 205 T90 (oF ) 297 294 291 MTBE/EtO H (v ol. % ) 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 L E V and ULEV vehicles, respectively. These vehicles were a m i xture of light and m e dium duty passenger cars and trucks of undisclosed m ake and m odel year. The FTP 75 wa s selected as the drive cycle, with regulated data collected both at the tailpipe and engine out f or 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 em issions (CO, NOx and NMHC). Bag weighted em issions of NOx, CO, and HC e m issions were reduced by 16%, 12%, and 11% (respectively) w hen fuel sulfur levels were reduced from 30 to 1 ppm

. The effects f ound in the oxygenate port ion of the study were less c l ear, since som e results wer e n o t statis tic a lly significan t. Average bag weighted em issions of HC and CO tended to decrease with incr easing oxygen content. Both oxygenated f uels showed a decrease in N O x e m issions, with MTBE ha ving slightly lower em i ssions than ethanol. The details of the statistical p r ocedu r es appl ied to the data were not described in the docum entation. In order to provide a consistentbasis for comp aring the result s of this study to the other studies of the effect of oxyge nate on LEV and later vehicle em issions, we applied a m i xed univar i ate statis tic al m o del to the lo garith m of the em ission 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 term s of the percentage change in em issions between the 11 vol% MTBE blend, the 11 vol% ethanol blend and the non-oxygenated fuel.
Table 3.1-9. Fuel Effects from the A A M-AIAM Oxygenate Study: EPA Mixed Model NMHC CO NOx % Change % Change % Change 11 vol% MT BE 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%

aFewe r vehicl es we re tested on the MTBE blend. b Bo ld ty p e indicates th e d i fferen c e was si gnificant at a 90% confi d ence l e vel.

135As can be seen in Table 3.1-9, the ad dition of 11 vol% MTBE was f ound to significantly reduce the emissions of all th ree pollutants by 10% or m ore relative to the non-oxygenated California R F G. The addition of 11 vol% ethano l was also found to significantly reduce the
emissions of all three pollutan ts relative to the non-oxygenated California RFG. Howe ver, the reductions were sm aller than those of the MTBE blend for all three po llutants. Also, the reduction in NOx e missions was not statistically significan t at a 90% conf idence level. W h en we comp ared the em issions with the ethanol blend to those with the MTBE blend, we found that the incre ases we re all statistically signif i cant ata 90 % conf idenc e leve l. Based solely on this single study of seven vehi cles and three fuels, it appears that adding ethanol to a severely ref o rmulated gasoline wh ile holding other properties constant tends to reduce NMHC, CO and NOx e missions. However, replacing MTBE with the sam e volum e of ethanol tends to increase these em issions. Focusing on just this one study, th e effect of increased ethanol use could differ in RFG areas, wher e MT BE has traditionally been used, and convention a l fuel areas, where no oxygenate has trad ition ally been used. However, this one study is not a sufficient basis to draw a fir m conclusion regarding the effect of ethanol blending on exhaust em issions.
3.1.1.1.5. ExxonMobil Study of Oxygenate Type and Content In the fall of 1999, ExxonMobil (Mobil Oil at that tim e) conducted a test program to investigate the em issions effects of MTBE and ethanol on L E V and ULEV vehicles. The inf o rm ation which f o llo ws was m a de public ly a v ailab l e on the CARB website C CC 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 Stud y Emissions Fuel Make/Mod e l Calibration Engine Sy stem 1999 Dod ge Stratus LEV 2.4L MFI 1999 Chevrol e t Malibu LEV 3.1L PFI 1999 Ma zd a Protégé ULEV 1.6L MPF 1999 Fo rd Crown Victo r ia LEV 4.6L SFI 1998 Hon da Acco rd ULEV 2.3L MFI

Four test fuels were developed with vary ing MTB E and ethanol content, with other param e ters controlled as tightly as possible. Th e fuel specific ations are lis ted below inTable 3.1-11.

136Table 3.1-11. ExxonMobil Test Fu el Specifications Test Fu el M1 M2 M3 M4 RVP, psi 6.5 6.4 7.3 7.2 T50 (oF ) 192 191 198 203 T90 (oF ) 279 272 274 282 MTBE (v ol % ) 0 10 0 0 Ethanol (v ol% ) 0 0 7 10 Aroma t ics (v ol% ) 21 21 21 21 Olefins (v ol% ) <1 <1 <1 <1 O x y g e n (w t% ) 0 1.82 2.436 3.48

Vehicles were tes t ed in duplicate ov er the FTP 75 drive cycle, and com p osite weighted
em issions for regulated pollutants rep o rted. The average results for all vehicles, based on least squares m eans from the analysis of variance, is shown in Table 3.1-12., below. The oxygenate effect is the percen t chan ge in emissions be tween a given fuel and the base fuel M1.
Table 3.1-12. Average Exhaust Emission s: E xxonMobil Study (all vehicles)

Exhaus t Emissions, g/mi Ox y g enate Effec t (a) Fuel Ox y g enate HC CO NOx HC CO NOx M1 Non e 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) Bo ld f o n t rep r e s en ts statistically significant ( α =0.1)

The bold numbers in the above table represen t statistically significant differences in
em issions at the 90% confidence level. Only the effect of ethanol on NOx emissions was found to be statistically signif i cant wh ile the effects on other criteria pollutants were not. As was the case for the AAM-AIAM study, the presentation to CARB did not descri be the statistical analys is app lied to the d etail in suf f i cient d eta il to replicate the results. Thus, we applied a
m i xed univariate statistical m odel to the loga rithm of the em ission data from the ExxonMobil oxygenate study, with vehicle as a random variab le a nd fuel type as a fixed variab le. Because of the greater num ber of fuels in this study, we applied two differen t types of mi xed m o dels. One set of m odel s compared em issions between various pairs of fuels (e.g., M1 and M2). W e applied five m odels of this type. A sixth m o del used oxy gen conten t 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 term s of the percentage change in em issions between the various fuel pairs. The bottom half of Table 3.1-13 presents the estim a ted em ission effects for 2.1 wt% and 3.5 wt% oxygen fuels using the results of the fuel oxygen content based m odel.

137Table 3.1-13. Fuel Effects from the E xxonMobil Oxygenate Study THC CO NOx % Change % Change % Change Two Fuel Com p arisons 10 vol% MT BE 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 oxygenate 3.6% -11.6% 20.3% 10 vol% ethanol vs. 10 vol% MTBE 6.3% -7.6% 21.1% Oxygen Content m odel 2.1 vol% Oxygen -2.0% -9.1% 2.9% 3.5 vol% Oxygen 3.5% -15.2% 17.3%

aBo ld typ e indicates th e d i fferen c e was si gnificant at a 90% confi d ence l e vel.

As can be seen, the results of our statisti cal analysis differ from those perform e d by ExxonMobil. This m a y have been due to our focu s on the logarithm of em issions or the use of a
m i xed m ode l. As shown in Table 3.1-13, th e addition of 10 vol% MTBE was found to reduce the em issions of all three pollutants relative to the non-oxygenated California RFG. While the differences tended to be substantial in m a gnitude on average, they were n o t statis tic ally significant at a 90% conf idence level. The a ddition of 7 vol% ethanol to the non-oxygenated California R F G (with an increase of roughl y 1.0 psi RVP) was also found to reduce the
em issions of all three pollutants. Again, the reductions in NMHC and NOx em issions were not statistica lly signif i can t. W h ile the r e duction in CO e m issions was slightly sm aller on average than that for MTBE, the effect was more consiste nt across vehicles and st atis tic ally signif i cant a t 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 N O x e m issions slightly. L i ke that for the 7 vol% ethanol blend, only the CO em ission effect was statistically significan t at a 90% conf idence level. Finally, the substitution of 10 vol% ethanol for 10 vol% MT BEwas found to increase em issions of THC and NOx substantially, while reducing CO e m issions slight ly. N one of the effects were significant at a 90% confidence level, though the NOx increase was nearly so (e.g., 89% confidence).

W ith respect to the m odel using oxygen content to describe the four fuels in the study, the effect of oxygen on em issions was found to be statis tically significant for a ll three pollutants. In addition, the square of the oxygen content was st atistically significant atthe 90% confidence level for TH C and NOx em issions. The predictions shown in Table 3.1-13 u tilize the square of oxygen content for these two pollutants, but not for CO e m issions. As can be seen, a 2.1 wt% oxygen fuel (e.g., an 11 vol% MTBE or 6% ethanol bl end) is predicted to decrease T H C and CO
em issions, but increase NOx e m issions. A 3.5 wt % oxygen fuel (e.g., a 10% ethanol blend) is predicted to decrease CO em issions, but increase THC and NOx e m issions.
The two m o deling approaches produce m arkedly different predictions for a 10 vol% ethanol blend, especially for THC and NOx. The di rect comp arison of the clear fuel and the E10 blend shows the E10 blend to reduce THC and NO x slightly. In contrast, the oxygen content approach p r edicts thatth e E10 blend will in crea se both THC and NOx emissions. This

138underlines the need for more data to accurately p r edict the em ission effe ct of various types and levels of oxygenate on modern vehicles.

3.1.1.1.6. Toyota Study of MTBE and Ethanol Blends Toyota Motor Company of Japan perform e d a s m all study, presented in 2000, evaluating the effect of MTBE and ethanol blended gaso line on exhaust em issions of nine LEV, TLEV, ULEV vehicles. D DD The m o del years for these vehicles were not presented.. Three test fuels were evaluated in this study: Phase 2 Calif ornia R F G containing MTBE, a m a tched RVP E10 and a higher RVP E10. However, exhaust em i ssion testing was only perf orm e d on the MTBE fuel and the higher RVP ethanol fuel

. The fuel properties for these tes tfuels are listed in Table 3.1-14, below:
Table 3.1-14. Fuels Tested in the T oyota Oxygenate Test Program Fuel Parameter MTBE Blen d Match e d RV P Ethanol Blend Higher RVP Ethanol Blen d RVP (psi) 6.8 7 7.6 Arom (v ol. % ) 24 23 24.1 Olefins (v ol.% ) 5 5 3.4 T50 (oF ) 156 208 212 T90 (oF ) 290 294 297 MTBE (v ol. % ) 11.1 0 0 EtOH (v ol

. % ) 0 11.2 8.9 Sulfur (ppm) 30 29 30

As can be seen in the above table, ar om atics, ole f ins, a nd sulfur were held relatively constant while other param e ters varied. The em is sion test cycle used was not stated. We assum e it was th e FTP 75 test. Only regula t ed em issions results are provided. The study found that, on average across all vehi cles and tests, NOx e missions increased by 5% for E10-B relative to MTBE. Correspondi ngly, CO em issions were reduced by 6% and NMHC e mi ssions were decreased by 0.6% for E10-B relative to MTBE. Again for comparison purposes, we applied a mi xed univariate statistical model to the logarithm of the em ission data from the Toyot a oxygenate study, with vehicle as a random variab le andfuel type as a fi xed variable. Table 3.1-15 presen ts the resu lts in te rm s of the percentage change in em issions betw een the 11 vol% MTBE and the 9 vol% ethanol blends.
Table 3.1-15. Fuel Effects from the T oyota 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 i n dicates the di ffe rence was si gnificant at a 90% confi d ence l e vel.

139As can be seen, the substitution of 9 vol % ethanol for 11 vol% MTBE in a RFG-type blend was found to reduce the em issions of NMHC and CO, but increase NOx e m issions. None of the em ission effects was found to be statistic ally significant at a 90 % confidence level. Based solely on this single study of nine vehicl es and three fuels, it appears that replacing MTBE with roughly the sam e volum e of ethanol in reformulatedgasoline while holding other properties constant (except RVP) tends to reduce NMHC and CO, and increases NOx em issions. It is not possible to separa te the effect of ethanol from RVP in this study.

3.1.1.1.7. Mexican Petroleum Institute Fuel-E m i ssion Effects Study In 2006, the Instituto Mexicano del Patroleo (her eafter referred to as Mexican Petroleum Institute) published a paper on a recently conducted research progr am investigating the effects of gasoline properties on exhaust and evaporativ e em issions on 30 light duty cars and trucks, ranging in model year from 1993 to 2002. E EEThe fuel quality param e ters investigated include RVP, oxyge n, Sulfur, olefins, arom atics, and dist illation param e ters. The results of this study were used to develop a statistical m o del for pred icting em issions based on fuel quality for use in guiding national air quality improvem e nt program policy. Intheir analy s is, a com p arison was
m a de betwe e n the “predictive m odel,” developed based in this test d a ta, and EPA’s Comp lex model. The end result is a ge nera l qu alitative ag r eem ent with the EPA Complex m odel, with
som e quantitative differences pe rta i ning to th e v e hicles and fuels used in each m odel’s developm ent. 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 S t udy

Fuel Aromatics (vol% ) Ole fins (vol% ) Oxygen a (w t% ) Benz ene (vol% ) RVP (psi) T50 (oF) T90 (oF) Sulfur (ppm) 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 Oxyge n ate is MTBE, e x cept fuel 4 (Et h anol)

140The base fuel from which other fuel param e te rs were varied to form addi tional 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 comm ercially available Mexican gasoline. The ref e rence fuel for comp arative fuel effects purpos es , fuel “Ref”, is a fuel blended to have refinery average levels of sulfur, benzene, RVP, aromatics, ol efins, and distillation pr operties for the year 2000. The technology groups investigated were referr ed to as either “Tier 0” and “Tier 1” vehicles, which is a bit of a m i snom e r as th ese vehicles were not equipped with on-board diagnostic equipm ent (OBD), nor were they subject to emissions durability standards 4 9

. Rather, each technology class acts as a surrog ate for em i ssion control technologies with the certification standards shown in Table 3.1-17.
Table 3.1-17. Certification Standards of Test Vehicles by Technology Class Technolo g y class # of Vehicles Model Years CO (g/km ) NMHC (g/ k m) NOx (g/km) "Tier 0" 12 1993 -19 98 2.1 0.25 (T HC) 0.62 "Tier 1" 14 1999 -20 02 2.1 0.156 0.25

Vehicles were tested on a chassis dyna m o meter over the FT P-75 test. R e gulated
em issions, as well as sp eciated hydro carbons and carbonyls, w e re co llectedfor each test. The procedures and statistical m e thods employed to develop the predictive m o del for this test program we re sim ilar to those used to constr uct the complex models for exhaust VOC and NOx
emissions.
The emissions test resu lts were separated in to “Tier 0” and “T ier 1” categories, and reported as m ean emissions rates for all veh i cles of that type on each te st fuel. The natural logarithm of em issions was then regressed to de velop a predictive m odel, using a statistical approach th at is “similar to the techn i ques usedto construct the complex model for exhaust VOC and NOx e m issions” (Schifter et al, 1275). The re port then goes into details on the experim ental
vs. m odel predicted resu lts, as well as a valid ationof their m o del which will no t be dis cussed here. The model predicted percent changes in emissions (for both vehicle fleets ) for the Mex i can Petroleum Institu te p r ed ictive m odel and EPA Com p lex m o del ar e shown in Figures 3 .1-5 and 3.1-6, below

.

49 Th e Mex i can Env i ron m en tal Ag en cy curren tly m a n d a tes e missio n s d u rab ility certificat es to b e issu ed o n n e w vehi cl es

141Figure 3.1-5. Emissions Effects P redicted by EPA Complex Model

Figure 3.1-6. Emissions Effects P redic ted by Mexican Petroleum Institute “Predictive” Model

The values above represent average em i ssions across both vehicle technology fleets considered together, andwith the ind ustry aver ag e fuel (“Ref”) as a b a seline for pred icted changes in em issions. As you can see, their m odel predicts an even greate r reduction in exhaust THC and NOx e m issions with ethanol (and MT BE) than the complex model, along with direc tiona lly inconsis ten t results f o r f u els with h i gh arom atics, RVP, and olefins. This is partly due to differences in the fuels a nd vehicles used to develop each m odel (along with the properties of the base fuel selected) and sp eaks to the fact th at there is a high degree of uncertainty and sensitivity to consider w hen extrapolating the resu lts of a fuel effects m o de l to the larg er vehicle and fuel population. As with the other studies, we applied a m i xed univariate statistical m odel to the logarithm of the em ission data from the Me xico fuel study, with vehicle as a random variable and fuel type as a fixed variable. W e restrict ed the vehicles to those which ha d NOx e m issions of 0.20 g/mi or

142less on the low sulfur fuel (30 ppm ) included in the tes tm atrix. This resu lted in the in clusion of seven vehicles (num bers 9, 15, 25, 27, 28, 29, and 31). Table 3.1-18 presents the results in term s of the percentage change in em issions betw een the non-oxygenated base fuel, the 5.5 vol% and 11 vol% MT BE blends and the 6 vol% ethanol blend.
Table 3.1-18. Fuel Effects fr om 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% MT BE 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 Bo ld typ e ind i cates th e d i fferen c e was sign ificant at a 90% confi d ence l e vel.

As can be seen, the addition of 5.5 vol% MTBE to a low-RVP type fuel at constant R V P was found to increase the em issions of NMHC and CO, and decrease NOx e m issions. The addition of 11 vol% MT BE to a low-RVP type fu el at constant RVP was found to increase the
em issions of NOx and CO, and decrease NMHC em issions. Only the NMHC reduction was found to be statistically significan t at a 90% conf idence level. Th e addition of 6 vol% ethanol to a low-RVP type fuel at constant RVP was f ound to reduce the CO e m issions, and increase NMHC and NOx e missions. Only the NOx e mi ssion inc r ea se was statis tica lly s i gnif i cant 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 NMH C e m issions and increase NOx e missions. CO e m issions were essentially unchanged. Only the NOx e mi ssi on inc r ea se was statis tica lly s i gnif i cant at a 90% confidence level. T he substitution of 10 vol% ethanol for 11 vol% MTBE was found to incre ase emissions of all three po llu ta nts. Only th e NMHC emission incr ease was statistica lly significant at a 90% conf idence level. Based solely on this single study of six vehicl es and three fuels, it appears that either MTBE or ethanol blends with roughly 2 wt% oxygen increase NOx e missions, while the effect on NMHC and CO emissions ar e inc onsisten t

. S ubstitu ting e thanol f o r M T BE at the s am e oxygen content appears to increase N M HC, CO a nd NOx e mi ssions.

3.1.1.1.8. Overview of LEV and Later Vehicle Studies
The differences in the details of the vari ous studies prevent a simple quantitative
comparison of their results. However, we have perform e d a qualitativ e comparison by simply determ ining whether the study found an increase or a decrease in em issions of 2% or more and whether th e effect was statis tica lly significant at 90% confidence or not. The results of this determ ination are shown in Table 3.1- 19 below for a num ber of fuel pairs.

143Table 3.1-19. Summary of Oxygenate Emissi on Effects: LEV and Late r Vehicles THC/NHM C CO NOx Lower Higher Lower Higher Lower Higher 10/11 vol% MTBE AAM-AIAM Mexico ExxonMobil* AAM-AIAM Mexico ExxonMobil AAMAIAM Mexico

E6/E7 Mexico CRC E67 Mexico ExxonMobil CRC E67 Mexico ExxonMobil CRC E67 E10/E11 AAM-AIAM CRC E67 ExxonMobil AAM-AIAM ExxonMobil CRC E67 AAMAIAM ExxonMobil CRC E67

E10/11
vs. MTBE Toyota AAM-AIAM ExxonMobil ExxonMobil AAM-AIAM Toyota AAM-AIAM ExxonMobil

Starting with MTBE, none of the three studies which tested both non-oxygenated and MTBE fuels found MTBE to increase the em issions of any of the pollutants. One to two studies found statistically signif icant reductions in THC/NMHC, CO and NOx em issions 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 e mi ssions and increases NOx e missions. Two of the three s tudies showed an increas e in N MHC emissions, but neither re su lt w as sta tis tic ally significant at a 90% conf idence level. Thus, the e ffect of E6 on NMHC e m issions 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 e m issi ons, as all three studies sho w ed a statistically significant reduction. T w o of three studies found an increase in NOx em issions, while one found a decrease, all statistically significan t at a 90% confidence level. Again, the effect of ethanol blending on exhaust NMHC e m issions is not clear. Two of thr ee studies found an increase in THC/NMHC e missions, 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 statis tically significant at a 90% confidence level. Finally, three studies test ed both MT BE and 10-11 vol% ethanol blends. Ethanol blending at this level appears to reduce CO e m i ssions relative to MTBE. The effect on the other two pollutants is less clear. Two of three st udies found a decrease in THC/NMHC e mi ssions, though the one study finding an increase was the only one where the resu lt was statistically significant at a 90% conf idence leve l. Two of three studies found an increase in NOx em issions and both of these were statistically si gnificant at a 90% confidence level.
3.1.1.1.9 Selection of Models for Exhaust NMHC, CO and NOx Em iss i ons

1443.1.1.1.9.1 Exhaust NMHC and NOx Em issions
For Tier 0 v e hicles, the EPA Predictive M odels are based on m ore data and the m ost advanced s t atis tic al too l s. Theref ore , we will use these m odels here to p r o j ect the ef f

ect of increased ethanol use and decreased MTBE use on exhaust V O C and NOx em issions.
For Tier 1 and later vehicles, the cho i ce is mu chless clear. In our an alysis of California’s request for an RFG oxygen waiver , we assum e d that there w a s too little data upon which to project the effect of fuel quality on the em issions of these vehi cles. Substantially m ore data exist on the effect of oxygenates on LEV and later ve hicle exhaust em issions than did in 2001. However, as indicated by the summ a r y of thes e results shown in Table 3.1-19, there does not appear to be sufficiently consistency to confid ently predict the impact of oxygenate type and content on exhaust NMHC and NOx em issions. For the NPRM, we developed two separate sets of predictions: a prim ary analysis
assum i ng no effect of oxygen on NMHC and NOx em issions from Tier 1 and later vehicles, and a sensitiv ity analys is which applied the Predic tiv e Model effects to T i er 1 and later vehicles. The qualitative summ ary shown in Tabl e 3.1-19 supports a continuation of this approach. The effect of ethanol blending on NMHC e m issions is unc lear in Table 3.1-19. This is reasonable bracketed by the prim ary and analysis, which as sum e s no effect and the sensitivity analysis which assum e s a reduction. Table 3 .1-19 also indicates that five outof six studies found that 6­ 10 vol% ethanol blends increased NOx e missions fr om LEV and later vehicles. This is also reasonab l e b r acke t ed by the prim ary and analys is , which assu m es no ef f

ect and the sen sitiv ity analys is which assum e s an increase. Given the u n certainty inthe fuel-em i ssions effects for Tier 1 and later vehicles, there is also som e value in m a intaining consistency with our analysis conducted in response to Californi a’s request for an RFG oxygen waiver. The prim ary analysis does this.
The varied r esults acros s these s tudie s indic ate th e need f o r a ddition al tes tdata. It m ay also be possible in the future to com bine the emission data from all such studies (as was done for the Comple x and Predictive Models) in order to de velop a m o re robust estim ate of the im pacts of oxygenate blending on em ission from late m ode l year vehicles.
3.1.1.1.9.1 Exhaust CO Em issions
For Tier 0 vehicles, MOBILE6.2 is the standardmodeling tool for esti m a ting the effect of f u el quality on CO emissions. Ther ef ore, we will use that m odel her e to projec t the e f f

ect of increased ethanol use and decrease d MTBE use on CO e missions. Regarding later vehicles, the five studies of LEV and later vehicles all found that increasing oxygen content in term s of MTBE or ethanol reduces CO e missions. This is consistent w ith both the prim ary and sensitivity analyses, as both appro aches include a reduction in CO e m issions.

145The f i ve stu d ies of LEV and later ve hicle s all tes t ed norm ally emitting ve hicle s. MOBILE6.2 estim ates th at an E10 blend will red u ce CO emissions f r om Tier 0 veh i cles by 11%. The quantitative results of the five studies ge nerally support this degree of reduction (T oyota, ExxonMobil, Mexican P etro l eum Institu te) o r pe rhaps a larger degree of reduction (CRC E67, AAM-AIAM). Due to the absen ce of an EPA Predic tive M odel f o r CO emissions, for the NPRM, we did not develop a sensitivity case for CO e m issions. However, given the possibility that the CO emission red u ction is lar g er than that curren tly es tim ated by MOBILE6.2 for these vehicles, it appears reaso nable to include a se nsitivity analysis for CO e mi ssions, as w e ll as NMHC and NOx. An approach analogous to thattaken for N MHC and NOx e m issions appears reasonab l e. That is, for the prim ary analysis, we will con tinu e to use MO BILE6.2 to project the effect of fue l properties on CO e mis sions. This m eans essentia lly a 6.7% reduction in CO
em issions from Tier 1 and later vehicles for an E 10 blend. For the sen sitivity analysis , we will apply the MOBILE6.2 CO e m ission reduction of 13.8% for Tier 0 ve hicles 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 em issions from these vehicles. Additional data mu st 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 fu el quality on exhaust toxic em issions: the Comp lex Model and MOBILE6.2. The Comp lex Mode l projects the impact of fuel quality on toxic em issions directly. That is, any impact of fuel quality on total exhaust VOC em issions (which inclu d es the air toxics ) is im plicitly inclu d ed in the m odel’s p r edictions. MO BILE6.2 separates th e process into two steps. Total exhaust VOC e mi ssions are projected first, in part based on fuel quality. Then, the fraction of VOC re presen ted b y each air to xic is estim a ted, in part based on fuel quality. The effect of fuel quality on exhaust VOC e miss i ons in MO BILE6.2 wa s already discussed above. The effect of fuel quali ty on the fraction of exhaust VOC e missions represented by each air toxic in MO BILE6.2 is based on the projections contain e d inthe Com p lex Model. These Com p lex Model’s ef f

ect s of f

u el quality on exhaust toxic emissions were used with th e ef f

ect of f

u el quality on exhau st VOC emissions back e dout. Thus, with respect to th e effect of fuel qua lity on the fraction of exhaust VOC e miss i ons represented by each air toxic, the Comp lex Model is the b asi s of both the Comple x Model and MOBILE6.2 predictions.

W ith respect to exhaust VOC e m issions, we already decided above that the EPA Predic tive Models r epr esent the bes testim ate f o r Tier 0 vehicles. For T i er 1 and later vehicles,
we assum e i n our prim ary analysis that these vehicles’ exhaust VOC e m is sions are un affected by fuel quality. As a sensitivity analysis, we d ecided to extend the impacts indicated by the EPA Predictive Models to all vehicles. We follow the two-step process taken in MOBI LE6.2 here in modeling the impact of fuel quality on e xhaust tox i c emissions. W e will use th e EPA Predictive Mod e ls to p r oje c tthe impact

146of f

u el quality on exhaus t VOC emissions, as d i s cussed abov e. W e will use the ef f

ec t s of f

u el quality on the toxic fractions of exhaus t VOC e mi ssions contained in MO BILE6.2.
3.1.1.3 Non-Exhaust Emission s Both the Complex Model and MOBILE6.2 evalua te the effect of gasoline quality on nonexhaust VOC e m issions. However, the effects inthe Comp le x Model were taken from an older version of MOBILE, as was m e n tioned above. Therefore, MOBILE 6.2 represents the better of the two estim a tes of the eff ect of gasoline quality on non-exhaust VOC em issions. The EPA Predictive Models do not addres s non-exhaust em issions, so th ey are not applicable here. In EPA’s second analysis of California’ s request for a waiv er of the RFG oxygen requirem e nt, we enhanced the estim ate of non-exhaust em issions in MOBILE6.2 by adding additional perm eation em issions re lated to the use of ethanol. F FFRecent tes ting at that tim e indicated that ethanol increases the rate of perm eation of hydro carbons through plastic fuel tanks and elas tomers used in fuel line con n ection s, as well as permeating ethano l itself. Subsequent testing has confirm e d this effect. Therefore, we have added the effect of ethanol on perm eation
em issions to MOBILE6.2’s estim a te of non-exhaust VOC e m issions in assessing the impact of gasolin e qua lity on emissions here.

Air Im provem ent Resource, Inc. for the Am erican Petroleum Institute, recently summarizedthe availab l e tes tdata o n the e ffect of ethanol on perm eation em issions and developed a m e thodology for estim ating in-use pe rm eation em issions in several U.S. cities. G GG This study provides a useful starting point for incorporating th ese em issions into this RFS analysis.
Before exam ining this study, it is useful to point out that the non-exhaust em ission estim ates in MOBILE6.2 include perm eation e m issions for non-oxygenated gasoline. Typical extended diurnal em ission tests (e.g., those lastin g 2-3 days) autom a tically include any em issions perm eating through plastic and elastom e ric fuel system comp onents. However, since the
em ission tests used as the basi s of the MOBILE6.2 estim ates of non-exhaust emissions prim arily were perform e d with non-oxygenated gasoline. Those tests that di d include ethanol blends only exposed the vehicle to this fuel for a few da ys. T he CRC study of ethanol-related permeation indicates that it takes at least a w eek or two for the effect of etha nol to fully develop. Therefore, it is v ery un likely that th e tes t s perf o r m ed by EPA and others to assess the impact of ethanol and other fuel components on non-exhaust em issions included the effect of ethanol on perm eation
em issions. In those cases where a vehicle m a y ha ve been exposed to an ethanol blend for som e tim e prior to testing, the increased perm eation emissions likely were st ill present when the vehicle was tested on a non-oxygena ted gasoline, still m a sking the effect. Therefore, our task here is to develop an estim a te of the increm ental impact of ethanol use on perm eation emissions, and not an estim ate of total perm eation em issions with and without ethanol. The prim ary source of ethanol perm eation em ission data is the CRC E-65 study. H HH This study tested 10 vehicles, 6 cars and 4 light trucks, ranging in model year from 1989 to 2001. Perm eation emissions were m easured using tw o fuels, a non-oxygenated gasoline and a 6 vol% ethanol b l en d like that comm only sold in Califo rnia. AIR placed these v e hicles in to three

147groups, based on a com b ination of model year and applicable evaporative em ission standards. The vehicles in the test p r ogram were certi fied to two distin ct evaporative emission requirem e nts. The older vehicles were certified to EPA’s or California’s 2 gram hot soak plus diurnal em ission stand ard based on an acceleratedone-hour d i urnal test. The three n e west vehicles were certified to the enhanced evapor ative em ission requirem e nts first implem ented in the 1996 m o del year, which included an extended two or three day diurnal te st. In addition, the data indicated that the three pre-1990 model year vehicles had much larger increm ental ethanol perm eation em issions than the later pre-enhanced evaporative em ission vehicles. Therefore, AIR split the p r e-enhanced ev aporative em ission category into two groups, pre-1990 m o del year vehicles and 1990 and later m odel year vehicles. We believe that this is appropriate and apply this sp lit her e, as well. Since the earliest calendar year during whic h emi ssions are assessed in the RFS analysis is 2012 and MOBILE6.2 only consider s vehicles which are 24 years old or newer, at most only two m odel years of pre-1 990 vehicles are pr esent in our analy sis. Due to accumu lated scrappage, these vehicles comprise a very sm a ll 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 estim ated the average increm ental ethanol p e rm eation em ission rates for the 199 0 and later m o del year pre-enhanced evaporativ e em ission vehicles to be 0.86 gram per day (g/day), while that enhanced evaporative em i ssion vehicles was 0.80 g/day. Given the sm all
num b er of vehicles tested and th e variab ility in the rates m easuredfor individual vehicles, for the purposes of this analysis, we cons ider these two levels to be gene rally equivalent. Therefore, we use an average increm ental ethanol perm eation emission rate of 0.8 g/day for all vehicles. A follow-on study performed by CRC indicates that th e perm eation em issions associated with a 10 vol% ethanol blend could not be distinguished statistically from those of the 6 vol% blend. I II Therefore, we use the 0.8 g/day increm ental perm eation e m ission rate for both 6 vol% and 10 vol% ethanol blends here. Beginning with the 2004 m odel year, EP A and California implem e nted further enhancem ents to th eir ev aporative emission stan dards. The EPA “Tier 2” requirem e nts include
accumu latin g mileage o n durability data veh i cles with an eth a nol blend. However, actual
emission tes ting is still p erform ed using non-oxyge nated gasoline. W e believed that this
com b ination of requirem e nts would incorporate any effects of ethanol on em issions, including potential perm eation effe cts. Because of these an dother asp ects of the 2004 and later standards, AIR estim ated that the p erm eation emissions due to ethanol would be reduced to 0.43 g/day for these veh i cles. We believe that it is likely that perm e a ti on em issions for non-oxygena ted blends will be lower for th ese vehicles, due to the f act th at the diurnal em ission standard was reduced from 2 g/day to 0.95 g/day and lower in som e cases. However, as m e ntioned above, the effect of ethanol on perm eation em issions takes about 2 w eek s to fully develop and to fully disappear. Therefore, it is possib l e to accumu late m ileage on a certification vehicle u s ing an ethanol blend, change the f u el to the e mission test f u el, wait tw o weeks and then test the vehicle. In this case, the ef f

ect of ethanol on p erm eation will h ave dis appeared du ring the ce rtif ication tes tin g. Thus,

148until EPA re quires c ertif ication emission tes ting w ith ethano l blends, we have no assu rance that
m a nufacturers will m odify their veh i cle des i gns to address th e effect of ethanol on perm eation. Thus, for the purposes of this analysis, we m a in tain the estim ate of 0.8 g/day for Tier 2, and earlier v e hicles.

Perm eation em issions vary significantly w ith ambient temp erature, with em issions increasing w ith in creases in temp erature. The 0.8 g/day em ission estim a te applies at an average tem p erature of 95 F. The lite rature indicates that perm ea tion varies exponentially with temperature. The CRC t e sting i ndicates that perm eation e missions double with every increase in temperature of 18 F. Vic e versa, perm eati on e m issions drop 50% with every decrease in temp erature of 18 F. W e apply this relationship in Chapter 4 in developing increm ental ethanol perm eation emissions for each hou r of the day ineach county in the U.S. We plan to update our projections of the effect of gasoline qu ality on non-exhaust VOC
emissions from Tier 1 and later m o del year ve hicles based on additi onal testing which is expected to begin in 200 7. Addition al te sting of perm eation emissions is alre ady und erway with the CRC E-77 test program

. These u pdated proj ections will be used in the comprehensive
assessm ent of the impac tof the fuel-related pr ovisions of the Energy Act which due in 2009.
Non-exhaust em issions are a function of a mbient tem p erature and tem p eratures vary across the nation. Therefore, it is not as simple to determ ine the effect of RVP and other fuel qualities on non-exhaust em issions on a per vehicle basis as it is for exhaust em issions. Therefore, we perform e d a regression of the non-exhaust VO C and benzene em issions developed in Chapter 4 as a function of fuel properties in o r der to es tim ate th ese effects on a p er vehicle basis. Specifically, we regr essed the ratio of non-exhaust VO C and benzene em issions in each county in July between two fuel scenarios (the R FS case and the base case) against the change in RVP, ethanol content and MTBE content. Th e results are summarizedin Table 3.1-20.
Table 3.1-20. Fuel-Non-Exhaust Em ission E ffects 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 em issions of benzene are estim a ted in MOBILE6.2. MOBILE6.2 adjusts these em issions for RVP, benzene fuel content, MTBE fuel content, temperatu r e andthe to tal non-exhaust VOC e m issions produced by the vehicl e fleet inquestion. We will use MOBILE6.2 here to project the impact of decreased MT BE use and increased ethanol use on non-exhaust benzene emissions.
Benzene is also emitted via perm eation. Just as MOBILE6.2 does notin clude the effect of ethanol on VOC e mis sions via perm eation, it does not include the effect of ethanol on benzene
em issions via perm eatio n. Thus, we will add this effect outside of the MOBILE6.2 model in the
sam e way as was just described for V O C e m issions via perm eation.

149Both the CRC E-65 study and the E-65 Phase 3 follow-on study referenced above
m easured the benzene content of perm eation e missions during som e of the tests perform e d. Table 3.1-21 presents the available data by fuel type for both studies.
Table 3.1-21. Benz ene Pe rmeation Emissions: C RC E-65 St udies E-65 Phase No. of Vehicles E0 E6 (Average Arom atics) E6 (High Arom atics) E10 MTBE Blend Fuel Benzen e Content (v ol%) Phase 1 --- 0.73 0.72 --- --- 0.53 Phase 3 --- 0.41 0.55 0.43 0.51 --- Benzene Emissions (% o f to tal perm e ation 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 B e nzene (% of total perm e a tion em issions) 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 ben zene content of perm eation e m issions is slightly higher for the Phase 1 study than Phase 3. This is consistent with the higher benzene contents of the fuels tes t ed in Phase 1. The Phas e 1 fuels have particularly low benzene contents
compared to levels typical across the U.S. Therefore, assum i ng a linear relationship between benzene fuel content andbenzene perm eation emissions, we adjusted the benzene permeation
em issions to those for a fuel benzene content of 0.87 vol%, which is the av erage of the benzene fuel content of summ ertim e gasoline produced nationwide in the base, RFS and EIA cases from the recent refinery m ode ling described in Chap ter 2. W e focused on summ er benzene content since perm e a tion em issions are a strong function of temp erature. These f i gures are shown in the bottom third of Table 3.1-21. As can be seen, th e benzene content of perm eation emissions is much m ore consistent across the tw o Phases of E-65 after being adjusted to consistent fuel benzene content than before this adjustm e nt. There appears to be no definite trend in the benzene content of per m eation emissions with increasing ethanol fuel content. Both CRC studi es found that the addition of ethanol does not sim p ly increase perm eation emissions via incr eased em issions of ethanol, but also through increased em issions of other fuel co mponents. T hus, we will assum e for this analy sis that the benzene fraction of permeation emissions is independent of etha nol fuel content (i.e., benzene
em issions in creas e at the sam e rate a s tota l perm ea tion em issions). This is also consistent with our conclusion in Chapter 2 that ethanol blending does not affect benzene fuel content. Given this, we determ ined the average ben z ene fraction of per m eation em i ssions by averaging the benzene fraction across all the fu els and vehicles tested, excluding the MT BE blend. This average was 3%. This figure will be used here to estim ate the be nzene portion of the increase in perm eation em issions resulting from ethanol blending.
Given this, we determ ined the average benzene f r action of perm eation emissions by averag ing th e benzene fraction acros s vehicles

. Thus, the av erage ben z ene fraction o f

150perm eation em issions is the average of the figur es in the rightmost column of Table 3.1-21, or 2%.
3.1.1.4 PM Emissions
The am ount of data evaluating the impact of ethanol and MTBE blending on direct
em issions of PM from ga soline-fueled vehicles is extrem ely limited. Three f airly limited studies have evaluated the impact of ethanol blending on PM em issions from gasoline vehicles. These studies are s u mm arized below. The Colorado Departm e nt of Public Health and Environm ent tested 24 vehicles on two winter grade commercial fuels at 35 oFin Denver (i.e., at high altitude). J JJ Both fuels were obtained from a local refinery. One fuel was non-oxygenated and represente d fuel sold outside of the Denver area. The other contained 10 vol% ethanol and represented fu el sold in the Denver area, which has an oxygenated fuel m a ndate. As would be expected, the fuels differed in other qualities besides ethanol content. The ethanol blend had a 2 vol% lower ar om atic content, which is som ewhat less th an ex pected. Ho wever, it al so had a 53 F lower T50 level, which is a much greater difference than is typical. Th e two fuel s used during this testing appear to have been used in random order (i.e., som e ti m e s the non-oxyge nated fuel was tested first, other tim e s the E10 fuel was tested first).
Half of the 24 vehicles were certified to Tier 0 emission st andards, 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 em issions for the 24 vehicles over the FTP decreased from about 9 m g /m i to about 6 m g / m i with the eth a no l blend, for a reduction of 36%. In ad dition, the vehicles with the highest base PM em ission ra tes showed by far the largest reductions, both in absolute term s and in term s of percentage. PM emissions from Tier 1 ve hicles decreased from roughly 5.5 mg/ m i to 4 mg/ m i with the ethanol ble nd, for a reduction of 27%. Essentially all of the em ission reduction occurred duri ng Bag 1 of the test (i.e., related to th e cold s t art).

PM emissions were also m easured o v er a warm ed up California Unified Cycle (i.e., no cold start). PM em issions for the 24 vehicles ov er th is cycle for the two fuels were no t statistically different. The ethanol blend increased PM em is sions from t he Tier 0 vehicles slightly and decreased those from the Tier 1 vehicles slightly. Finally, PM em issions were also m easured over an EPA REP05 Cycle, again with no cold start. P M em issions over this cycle were 4-5 tim es those over the California Unified Cycle, indicating the im pact of high speed, aggressive driving on P M em issions. However, despite this general increase in PM em issions, for the 24 vehi cles PM emissions over the REP05 cycle were again very sim ilar for the two fuels. This tim e, however, the ethanol b l en d decreased PM
emissions from the Tier 0 vehicles slightly and in creas ed thos e from th e Tier 1 vehicles slightly.
Overall, this testing ind i cates that th e ef f

ect of ethanol (together with lower arom atics and T50 levels ) m a y reduce PM em issions due to co ldstarting at 35 F under hi gh altitud e conditions.

151However, PM em issions during warm ed up driving ar e very low and an effect of fuel quality was indiscernible. The State of Alaska, in conjunction with Ge neral Motors Corp. and EPA, m easured PM
emissions from ten vehicles ranging in m odel year from 1977 to 1994 using two fuels. K KK The non-oxygenated fuel was a comm erc i al wintertim e fuel from the Fairbanks areas. T he ethanol blend in this study was created from the non-oxyge nated fuel via splash blending. Testing was perform e d in Alaska using a portable dynam o meter. 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 perfor m e d at -20oF, 0oF , a nd 20oF. The EPA testing was perfor m ed at these sam e temp eratures plus 75 F. Both sets of testing began with the non-oxygenated fuel, followed by testing with the E10 fuel. This could introdu ce a bias in to the results, but the degree of this is unknown.
The cold co nditions led to dif f i cultie s in m eas uring PM em is sions in Alaska. Theref ore, few acceptable m easurem ents of PM were m ade and the res u lts were no t presen ted in the paper. The fact that the EPA testing was conducted in a laboratory made ve hicle conditioning and operation and particulate collecti on more feasible. The PM em issions from the three vehicles tested by EPA on the two fuels are presented in the paper. Only one measurem ent of PM emissions was m a de for each com b ination of vehicle, fuel and temp erature. Thus, no direct m e asurem ent of test to test variab ility is available. W e calcu lated th e percen tage difference in PM emi ssions betweenthe E10 andE0 fuel for each of the eleven com binations tested. PM em issions w ith the ethanol blend ranged from 81% lower to 84% higher than thos e with th e E0 f u el. Thus, th er e appe ars to be conside r able v ariability in the test results. Taken together, th e average of the per centage changes for ea ch condition showed the ethanol blend reducing PM em is sions by 21%. However, this d ecr e a se was not statistica lly significan t at the 90 % co nfidence lev el. The ethanol blend m ore consistently decreased PM
em issions at -20 F and 0F, but not at 20 F or 75 F. The paper states that P M emissions at the higher two temp eratures were ve ry low and the differences tended to be within m easurem ent accuracy. It is important to note, ho wever, that th e lower en d of this ran g e is 20 F. Only a sm all percentage of driving in the U.S. occurs below this temperature. The third and final study was perform e d by EP A’s labo rato ry in Re search Triangle Park, N.C. L LLThis study was conducted in three phases; th e last two of which are relevant here. In Phase II, P M em issions from two 1993-1995 model year vehicles were tested at -20 F, 0 F, and 20 F. In Phase III, PM em issions from an additional five 1987-2001 m o del year vehicles were tested at -20 F, 0 F, 20 F and 40 F. Both phases utilized two fu els, one a wintertim e nonoxygenated fuel and the other a 10 vol% ethanol fuel created from the non-oxygenated fuel via splash blending. Both phases m easured PM emissions over the FTP and over a series of four back-to-back IM240 tests. 5 0 It is not clear whether the fuel s we re always te sted in the s am e order or tested random ly. Som e testing was perform e d with various m a lfunctions induced on the

Th e I M 24 0 test is a w a r m ed u p test con s istin g of a po r tion of th e FTP dr i v ing cycle. I t w a s d e sign ed as a sh or t transient test c y cle for use in vehicl e i n specti on a n d m a i n t e nance p r og ram s
.

152 50vehicles, like disconnecting the oxygen sensor. We focus on the emissions from the properly operating vehicles here.
Of the 26 combinations of vehicles and temp eratures tested, valid PM m easurem ents over the FTP were successfully obtained f or both fuels in 21 of them. The average percent change in PM em issions due to ethanol blending was +1%, in other words a very slightly increase. In contrast to the results of the two test program s discussed previ ously, the ethanol blend did not show a benefit at -20 F, and showed only a very slight 1% reduction in PM em issions at 0 F. The data show som e tendency for the ethanol blend to produce a greater P M em ission reduction for the highest em itting v e hicles. However, this tr end is no t as clear as in the Colorad o study. Thus, this study indicates no clear eff ect of ethanol on PM em issions. The IM240 testing show ed mu ch lower PM em i ssion levels due to the w arm ed up nature of the test. There was also no clear trend in the e ffect of ethanol on PM emi ssions in this testing.
The available data indicate that eth a n o l blending mi ght reduce exhaust P M emissions under very cold weather conditions (i.e., 0 F or less), particu l arly at high altitud e
. There is no indication of PM em ission reductions at higher temperatures and under warm ed up conditions. The data are certainly too lim ited to support a quan titative estim a te of the effect of ethanol on PM em issions. Fine particles can also be for m ed through a seri es of che mical reacti ons in the atm osphere from gasses such as sulfate (SO 4 2- ), n i trat e (NO 3 - ), ammonia (NH 3 ), and volatile organic
com pounds (VOC) e mitted from m o t o r vehicles. This aerosol for m ed secondarily in the atm o sphere through th es e gas to particle convers ions will be discussed in greater detail in Chapter 5 of this docum ent. Em erging science is indicating that gase ous arom atic compounds are likely among the most importan tVOCs whi c h are p r ecu rsors of carb onaceous P M which is for m ed in the atm osphere. Therefore, we di scuss the effect of fuel quality on arom atic hydrocarbon em issions in the next section.
3.1.1.5 Aromatic E m issions The Auto/Oil Air Quality Improve m e nt Resear ch Program tested over 100 vehicles from model years 1983 - 1989 on a fuel m a trix of over 80 fuel blends to determine the exhaust
em ission effects of varying fuel param e ters - including ethanol and arom atics. M MM Phase 1 of this study tested two fleets of vehicles: twenty (20) 1989 m odel year vehicles, and fourteen (14) 1983-1985 model year vehicles. A m a trix of 16 fuels (Matrix A) was developed in the first portion of the study with half th e fuels containing 20% arom atic s by volum e and half with 45%
arom atics by volum e. This data was used to in v e stigate th e impact of ch anging arom atic levels in the fuel o n the arom atics emitted in the exha us t as a function of total h ydrocarbon em issions. Linear regression of the test dataindicates that th ere is a linear relationship between the level of
arom atics b y volum e in the f u el and the m ass of arom atics e mitted in the exhaust. B ased on the results of this regression, arom atics h a ve a te nd en cy to be emitted less than proportion ally to their percent volum e in the fuel, as shown in the following equation:


### Aromatics Exhaust 

### ( wt %) = 0

. 64 × Aromatics Fuel 

### ( vol %)

153The coefficient in the ab ove equatio n was statistically sign if icant atthe 9 0 % conf iden ce level (0.64 ± 0.02). However, when we consider ed 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 whic h allows the effect of ethanol on arom atic hydrocarbon em issions to be assessed. As disc ussed in Section 2.2, ethanol blending tends to reduce the arom atic content of gasoline. Of intere st here is w hether ethanol has any other effect
on arom atic hydrocarbon em issions beyond that associ ated with reducing the arom atic content of gasoline.
The Auto/Oil data contained a subset of fuel s designed specifically for this analysis. A total of 4 ethanol blends were produced by sp lash blending ethanol in to four non-oxygenated fuels. Two of the non-oxygenated f u els cam e from the original group of sixteen tested during Phase 1 of the research study. Base fuel A (i ndustry average fuel) was a 9 RVP fuel with 32%
arom atics. Base fuel F was also a 9 RVP gasoline with 20% arom atics. Two new nonoxygenated fuels were created from base fuels A and F. In both cases, and butane was rem oved to lower the RVP level by 1 psi re sulting in fuels V and S, respec tively. T o each of these 4 nonoxygenated fuels, 10% ethanol was splash blended re sulting in the final 8 fuel test m a trix. A summary of the differences between the expected and actual arom atic conten t in the f u el and in the exhaust as a function of THC is shown in Table 3.1-22. N NN

Table 3.1-22. Expected vs. Predicted Non-Oxy and E10 Fue l Properties and Exhaust Aromatics Reductions for Auto/Oil AQIRP “Fuel Matr ix B”
% Reduction in % Reduction Non
- Fuel Measur ed Exhaus t in Exhaust Ox y Fuel E10 Aroma t ics Fuel Aroma t ics Aroma t ics Fuel Aroma t ics Fuel Expected a Aroma t ics Expected Act u al

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 B a sed on di l u ti on a s a res u l t of spl a s h bl en d i ng 1 0 % et han o l

The arom atic contents of the ethanol blends , as listed in Table 3.1-22, do not reflect the 10% reduction from the arom atic contents of their non-oxygenated base fuels which would be expected from splash blending with 10 vol% ethanol. The discrepancies between the m easured and estim ated fuel arom atic con t ents are sm all, on the order of 1-2 vol% arom atics. However, when the total difference is on the order of 3 vol % ethanol, these discre pancies are significant. The discr ep ancies are likely the resu lt of m eas urem ent uncertainty of both base and ethanol fuels. Using the relationship between fuel ar om atic content and arom atic hydrocarbon
em issions developed above, we can predict th e reduction in exhaust arom atic e m issions

154associated w ith the differences in the arom a tic contents of the non-oxygenated fuels and their ethanol containing counterparts. W e believ e thatit is m o st ac curate to use the expec t e darom atic contents for the ethanol blends rathe r than the measured leve ls, si nce it is likely that the volum e of ethanol added was very close to 10 vol%. The expected reduction in the percentage of VOC em issions represented by arom atic hydrocarbons based on the expected reduction in fuel arom atic content is shown in the second to the last column in Table 3.1-22. The m easured reduction in the percentage of VOC e m issions represented by arom atic hydrocarbons is shown in the last colum n. In three out of four cases (all but fuels A and X), the actual reduc tion in aromatic emissions is le ss tha n the pred icted reduc tion based on dilution. Had we used the m easured ar om atic contents for the ethanol blends, the outcom e would have been the sam e : In three out of four cases (in this case all but fuels F and U), the actual reduction in arom atic em issions is less than the predic ted reduction based on dilution. Qualitative l y, this ind i c ates that ther e does not appear to b e any addition al benef it in reducing arom atic hydrocarbon em issions associ ated with the use of ethanol beyond that expected from the reduction in the arom atic cont ent of gasoline portion associated with ethanol blending. Based on our analys is in Section 2.2, increase d ethanol blend i ng will significantly redu ce gasoline arom atic content. This could cause a corresponding reduction in the arom atic fraction of exhaust VOC e m issions relative to non-oxyge nated conventional gaso line. In addition, ethanol also reduces total exhaus t VOC e missions from older vehicles and m ay do so from newer vehicles, based on the C RC E-67 study. This w ould further reduce em issions of aromatic hydrocarbon s. As will b e discuss e d further in Ch apter 5, this reduction in arom atic hydrocarbon
emissions could reduce am bient leve ls of secondary organic PM.
3.1.1.6 Emission E ffects Associated w i th Specific Fuel Blends

3.1.1.6.1 Conventional Gasoline Analysis In Section 2.2 of Chapter 2, we estim ated th e 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 em issions from mo tor vehicles in percentage term s, 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

. Al so, due to the MSAT standards, we assum e that benzene levels are not affected, as well. Table 3.1-23 pr esents the gasoline qualiti es of a typical 9 RVP CG, as well as MTBE and ethanol blends which re flect the effect of adding these two oxygenates to gasoline.

155Table 3.1-23. CG Fuel Quality With and Without Oxygenates a Fuel Param e ter Typical 9 R V P CG MTBE CG Blend Ethanol CG Blend RVP (psi) 8.7 8.7 9.7 T50 218 207 205 T90 332 321 329 Arom atics (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 Assum e s summ er (J uly) conditions

Table 3.1-24 presents the differences in em issions of the MTBE and ethanol blends relative to that of non-oxygenate d conventional gasoline.
Table 3.1-24. Effect of Oxygenates on Conventional Gasoline Emissions a Pollutant Source 11 Vol% MTBE 10 Vol% Ethanol 9.7 RVP CG Exhaust VOC EPA Predictive Models -9.2% -7.4% +1.1% NOx 2.6% 7.7% +1.1% CO b MOBILE6.2 -6% / -11% -11% / -19% +12.7% Exhaust Benzene EPA Predictive and Com p lex Models -22.8% -24.9% -2.6% Form aldehyde +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 Benzene MOBILE6.2 & Comp lex Models -9.5% +15.8% +15.8%

a Assum e s summ er (J uly) conditions b Th e first figu re sho w n app lies to no rm al e m itters; th e secon dapp lies to h i g hemi tters.

The two oxygenated blends both reduce exhaust VOC and CO e mi ssions, but increase NOx e missions. The MTBE blend does not increa se non-exhaust VOC em issions due to the fact that non-oxygenated and MTBE blends have to m eet the RVP standard. E thanol blending increases non-exhaust V O C e m issions in two ways

. First, ethanol blends are allowed 1.0 psi higher RVP levels in m o st areas with CG. Seco nd, ethanol increases pe rm eation e m issions. The most notable effect on toxic em issions in percenta ge te rm s is the inc r eas e in ace t aldeh yde with the use of ethanol. Acetaldehyde em issions m o re than double. However, as will b e seen below, base acetaldehyde emissions are lo w relative to the other toxics. Thus, the absolu te increase in
emissions is relative l y lo w.

1563.1.1.6.2 Refor mulated Gasoline (RFG) Analysis The previous section discussed the relative em ission changes to expect when adding ethanol to the conventional non-oxy genated gasoline pool. A second scenario to consider is the case where RFG areas change from MTBE, a comm only used oxygenate in RFG areas, to either ethanol RF G or a non-oxygenated R F G. Table 3.1-25 presents the gasoline qualitie s of three types of RFG: non-oxygenated, a typical MT BE RFG as has been m arketed in the Gulf Coast and a typical ethanol RF G which has been m arketed in the Midwest. The fuel specifications show n are based on specific RFGs predicted by the refine ry m odeling discussed in C h apter 2 for the cas es an d PADDs shown. These specific fuels were selected as they represented PADD-wide RFGs which contained prim arily one oxygenate at the de sired volum etric concentration.
Table 3.1-25. Summer RFG Fuel Qua lity With and Without Oxygen a tes a Fuel Param e ter Non-Oxygenated RFG MTBE RFG Ethanol RFG

Case PADD 3 Re ference PADD 1 Re ference PADD 2 RFS OXYGEN ( w t%) 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 AROM ATICS (vol%) 20.1 21.0 20.0 OLEFINS ( vol%) 14.6 4.3 13.6 BENZENE (vol%) 0.7 0.7 0.7

a Assum e s summ er (J uly) conditions

Table 3.1-26 presents the em ission impacts of these three types of RFG relative to the 9 RVP CG de scribed in T able 3.1-23.

157Table 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 Volum e Percent MT BE 10 Volum e Percent E thanol Exhaust Emissions VOC EPA Predictive Models -13.4% -15.3% -9.7% NOx -2.4% -1.7% 7.3% CO MOBILE6.2 -22% -31% -36% Exhaust Benzene EPA Predictive and Com p lex Models -21.2% -29.7% -38.9% Form aldehyde -5.9% 19.4% 2.3% Acetaldehyde -0.2% -9.5% 173.7% 1,3-Butadiene 20.9% -29.2% 6.1% Non-Exhaust Em issions VOC MOBILE6.2 & CRC E-65 -30% -30% -18%

Benzene MOBILE6.2 & Comp lex Models -40% -43% -32%

a Assum e s sum m e r (J uly) conditions

As can be seen, the oxygenated RFG blends are predicted to produ ce a greater reduction in CO e missions, but increase NOx em issions. Exhaust VOC em ission effects are m i xed. Nonexhaust VOC e missions with the exception of pe rm eation are roughly the sam e due to the fact that the RVP level of th e thre e blen ds is the sa m e. However, the increased perm eation emissions associated w ith ethanol reduces th e o v erall eff ectiveness of ethanol RFG. The m ost notable effect on toxic em issions in percentage term s is the increase in acetaldehy de with the use of ethanol. Acetaldehyde em issions m o re than double

. However, as will be seen below, base acetaldehyd e em issions are low relative to the other tox i cs. Thus, the ab solute increase in
em issions is relatively low. The ethanol RFG also produces a greater reduction in exhaust benzene emissions and som ewhat lower re duction in non-exhaust benzene em issions.
The exhaust em ission effects shown for VOC and NOx e m issions only apply to Tier 0 vehicles in our prim ary analysis. In the sensitiv ity analys is, these effects are ex tendedto Tier 1 and later vehicles. The effect of RVP on non-e xhaust VOC em issions is temp erature dependent. The figures shown above represen t the distribution of temper atur es occurring across the U.S. under summer conditions (average July fuel specifications).
3.1.2 High-Level Ethanol Blends
The vast m a jority of ethanol blended into gasoli ne as a result of the RFS is expected to be used in a 10 vol% ethanol blend (E10) rather than an 85 vol% et hanol blend (E85), as discussed in Chapter 1. At the sam e tim e, som e ethanol is likely to be used as E85, and its use is growing. Current es tim a tes indicate that rough ly 6 m illion FFVs are on the road tod a y, with US autom akers projecting an additional 3 to 5 millio n FFVs produced annually over the n e xt several years. The analysis to follow relies upon the li m ited am ount of data available on both older and

158current technology (Tier 2) FFVs, the later of which will dominate the FF V fleet in future years. Based on this data of Tier 2 vehi cles, we believe that with the increased use of E85 (which is inherently a low-sulfur fuel) em issions should be neutral or better than operation on E 0 or E10 fuel blends for CO and NOx. NM OG e missions m ay be higher prim arily due to emissions of unburned ethanol at cold starts, while runni ng NMOG e m issions are lower with E85 based on certification data. The sections to follow examine these issues in greater d e tail.
3.1.2.1 Exhaust emissions

3.1.2.1.1 Regulated G a seous Pollutants
Relativ ely little data is a v ailab l e f o r investig ating the effects of hi gh level ethanol blends on exhaust em issions. Part of the 1993 Auto/O il Air Quality Im prove m ent Research Program (AQIRP) investig ated th e emissions associated w ith the use of E85 blends. Em issions over the Federal Test Procedure (FTP) were m easured from three Tier 0 and Tier 1 certified flexible -fuel vehicles with three test fuels. A source of emission data for Tier 2 FFV s is EPA’s Certif ication and Fuel Economy Inform ation System (CFEIS) da tabase, which contains certification data for five m odel year 2006 FFVs certified to Tier 2 standards (bins 5-8). O OO However, certification data, composed of regulated em issions while ope rating on E85, represents very lim ited operating conditions. It does not include ag gressive driving or cooler ambient tem p erature starts or operation. The Auto/Oil Study found that E85 reduced FTP composite NOx e m issions by 49%
comp ared to conventional gasoline with 1988 industry average fuel pr operties. This is likely the result of improved catalyst efficiency due to th e low sulfur concentrati on 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 N O x e m issions with E85 from a cold start test but a significant 45% decrease in hot running NOx e mi ssi ons. CO em issions are reduced at least 33% on a cold s t ar t ac cording to CFEIS, while the Au to/Oil study did not find statistically significan t changes in CO e m issions. Em issions of Non- Methane Organic Gases (NMOG) increas ed 10% for the Tier 2 CFEIS vehicles. The Auto/Oil data showed NMOG increased by 26%, but this change was not statistically sign ificant (p-value of 0.28). Howeve r, CFEIS data indicated a 50% reduction in CO and HC em issions during hot op eration. The m easure m ent used to determine NMOG in both CFEIS and Auto/Oil data include the m a ss of oxygen in all m easured organic species except m e thane.
While the em issions of NMOG appear to increa se with E85 compared to E0 for Tier 2 certified vehicles, the m a jority (~55%) of E85 NMOG em issions are d i rect em issions of ethanol, which has a relative l y lo w reactivity com p ared to other NMOG species. Thus there m ay still be a slight NMOG benefit based on ozone reac tiv ity despite a p o tential ne t increas e in to tal NMOG
em issions. An i mportant point worth noting is that the cold start emissio n s with E85 repres ent a greater % of bag weighted em issions than with E0. This m a nifests itself prim arily in the for m of unburned ethanol em issions during cold start, be f ore the combustion chamber has reached a high enough temperature to prom ote co mplete ethanol vaporization. Thus NMOG e missions with E85 at colder temp eratures coul d be much greater (2 to 3 tim es higher) than with E0 due to prolonged p e riods at lo w temp erature. Because of this uniqu e start behav i or and the lower

159em issions observed during hot operation, em ission s from E85 m a y be better quantified if separated between starts a nd hot driving operation.

3.1.2.1.2 Air Toxics

W ith increasing use of E85, som e air toxics m a y increase while others decrease relative to E0. Emis sions of Benzene and 1,3-Butadiene decrease while acetaldehyde, for m aldehyde, and
em issions of ethanol increase. The net result is an incre ase intota l air tox i cs, but this is large l y driven by increase ethanol and acetaldehyde em i ssions. Table 3.1-27, below, shows the percent change in F T P composite g/m ile em i ssions of seve ral air toxic s f o r the thr e e FFVs teste don three fuels as part of the 1993 Auto/Oil study. Th e fuels tested were AQIRP ga soline with1988 Industry average qualities (CG), a 1996 California phase 2 reformulated gasoline (RFG blended with MTBE), and an E8 5 blend withidentical g a soline sp ecs as RFG. P PP

Table 3.1-27. Percent Difference in Toxic Emis s i ons Betw een E0 and E85 % Differen c e Between F uel s RFG v s CG E85 v s CG E85 v s RFG

Formal dehyd e -2 93 97 Benze ne - 55 -87 -72 1,3-Butadi en e - 31 -85 -79 Acetalde hyde -18 2620 3220

Total Toxics -42 108 255

The increase in acetaldehyde em issions is subs tantial, on the order of 20 to 30 tim es that of E0. This is substantially hi gher than the15 to 20 fold increa se 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 naph thalen e are all expected to decrease significantly (50-80%) with the us e of E85 vs. E0 accord ing to CFEIS, which is consistent with th e Auto/Oil results presented in Table 3.1-27. Regardless of vehicle technology, the increased em issions of acetaldehyd e could be a potential co ncern due to its strong o dor, as well as its resp iratory system irritating and potentially carcinogenic properties.

3.1.2.1.3 Particulate Matter
Even less data exists to dr aw f i rm conclusi ons on direct particul ate m atter emissions due to increased E85 use. Theoretically, E85 use has the poten tia l to inc r ease di rect em issions of PM under m ode s of rich engine operation. This is esp ecially important at cold start, before the catalyst has reached its o p erating tem p eratu r e and when an E85 fueled veh i cle runs sub stantially richer than if it were fueled with E0. In this situation, the low temper atures in the combustion cham ber, compounded by the evaporative cooling effect of ethanol, m akes fuel vaporization difficult and m a y increases exhaust em issions of raw fuel andPM at cold start. Sustained periods of high load m a y also ha ve increased emissions of PM w ith E85 than with E0 due to richer operation with E85. Results from a 2003 SA E paper showed a negligible increase in

160direct PM em issions from E85 vs. E0 fueled ve h i cles over th e European Test Cycle (Directive 70/220/EEC and its am e n dm ents). Q QQ Tests conducted at 23oC a nd 16oC showed an increase in PM em issions with decreasing temp erature for both E0 and E85, with slightly higher PM
em issions at cold temp erature with E 85. This study only used one E85 blend and one model year 2002 FFV, however, so these results cannot be consid ered entirely representative of the on-road FFV vehicle fleet. Again, since the projected use of ethanol as E85 is very sm all compared to its use as E1 0, the emissions im pacts associated w ith E85 will be also be quite sm all on an absolute scale. As discussed above, emerging science is begi nning to identify gaseous arom atics as an important precursor to secondary organic aero sol. Exhaust arom atic em issions should be reduced with E85 since the fuel aro m atics content of E85 is much lower than that for E0 or E10 blends. This reduction in exhaust arom atics sh ould reduce the for m ation of secondary organic aerosol. However, as mentioned earlier, no spec if ications cu rrently ex ist f o r the 15% gasolin e portion of E 85. Thus, the degree to which the arom atic content of E 85 will be lower than gasoline is not known with any confidence. L ack of data regarding the speciation of VOC
em issions also prevents any quantitative estim ate of any benefit in this area.
3.1.2.2 Non-Exhaust Emission s We currently have very little data regardi ng non-exhaust emissions from E85 vehicles. Theoretically, evaporative em issions of E85 fue l ed vehicles have the potential to be lower than with E0 or E 10. This is because ethanol blended with a givengasolin e at the 85% lev e l is likely to be less volatile than E 0 or E10 (with the sam e gasolin e f u el quality). T h is is no t entire ly certain, how ever, sin ce there is no fu el specif ication for the hydrocarbon composition of the 15% of E85 that is gasoline. Thus, the RVP of the fi nal E85 blend could be clos er to that of E0 or E10 fuels than commonly thought to be the case. Moreover, since th e volatility of ethanol blends peaks between 6 and 30 vol% ethanol, the fuel in the tank of drivers of flex-fuel vehicles who alte rnate between E85 and gasolin e will expe rie n ce a wide r ange of etha nol concen tr ations in the fuel at any given tim e , a nd therefore a wide variation in the co rresponding evaporative
emissions.
Sim ilarly, we have very little d a ta with whic h quantitative predictions of the impact of E85 use on non-exhaust em issions of air toxics (e.g., benzene) can be drawn. The Auto/Oil study m e ntioned in the previous se ction tested the sam e three Tier0 and Tier 1 vehicles for hotsoak evaporative em issions. They found no st atistically significan t change in NMOG or OMHCE 5 1 evaporative em issions, yet found a statisti cally significant 60% reduction in benzene
em issions. Directionally, you would expect both hydrocarbon and ai r toxic evaporative
em issions to decrease due to th e dilution of the hydrocarbon porti on of the fuel with ethanol. However, again, it is h i g hly dependent on the vo la tility of the gasolin e co mponent of the specific E85 used and its benzene content, neither of which is regulated.

51 Or ga ni c M a t e ri al Hy dr ocar b o n E q ui val e nt

161

### 3.2 Effect of Fuel Quality on Spar k

- Ignited Nonroad Equipment Emissions

We use EPA’s NONR OAD e mission m odel to es tim ate the effect of gasoline quality on
em issions fro m nonroad equipm ent. We us e the 2005 version of this m o del, NONROAD2005, which includes the effect of ethanol on perm ea tion emissions from several types of equipm ent: all sm all spark-ignition equipm ent (including handheld and non-handheld equipm ent less than or equal to 25 hp), all spark-igniti on recreational marine watercraft (includes all outboard, sterndrive inboard, and personal watercraft). Note that these categories do not include recreational vehicles (m otorcycles, ATVs, and snowm ob iles) or large spark-ignition equipm ent. Only a lim ited num ber of fuel parameters affect em issions in NONROAD. Exhaust VOC, CO a nd NOx e mi ssions are a function of sulfur and oxygen. Here, only the latter fuel param eter is of interes t

. Em issions o f all thr ee pollutants are assum e d to change proportionally with fuel oxygen content. Table 3.2-1 shows the effect of moving to a 10 volum e pe rcent ethanol blend (3.5 wt% oxygen) on these em issions, either from a non-oxygenated fuel or from an 11 volume percent MTBE blend (2.0 wt% oxygen). R RR

Table 3.2-1. Effect o f a 10 Volume Percent Eth a nol Fuel o n Nonroad Exhaust Emiss i ons 4-Stroke Engines 2-Stroke Engines Base Fuel NonOxygenated 11 Volum e Percent MT BE NonOxygenated 11 Volum e Percent MT BE 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 redu ces exhaust VOC and CO e m issions. However, it also increases NOx em issions quite substantially, especially from 4­ stroke engines. However, it should be noted th at NOx e m issions from these engines tend to be f

airly low to start with, g i ven the f ac t that thes e engines run richer than stoichiom etric. Thus, a large p ercen tage in creas e of a relativ e low ba se v alue can b e a relativ ely s m all increas e in absolute te rm s. This will be s een be low in Chap ter 4, when we evaluate the im pact of increased ethanol use on the local and na tional em ission inventories.
Non-exhaust VOC e miss i ons (other than pe rm eation) are a function of gasoline RVP and ethanol content in NONROAD2005. Ethanol content only affects perm eation em issions. Both of these em issions are temperature dependent, so the effect of ethanol andRVP is also temp erature dependent. Based on the results of modeling national emissions in July, a 10 volum e percent ethanol blend increases non-ex haust VOC em issions by 15 percent. This
assum e s a 1.0 psi increase in RVP. Hose perm e a tion em issions in the public version of NONROAD2005 a r e independent of fuel quality. In support of the developm ent of new e m ission standards for sm all nonroad engines, EP A has been testing sm all nonroad en gines for hose perm eation em issions using fuels

162with and without ethanol. Based on this testi ng, as well as discussions with nonroad equipm ent
m a nu facturers, we developed new hose perm eation em ission rates for NONROAD200 5 for both gasoline and E10 fuels. Roughly, these revised pe rm eation rates indicate that em issions with E10 are 2-3 tim es higher than those f o r gasoline. This increase is sim ilar to that found for the three oldest onroad vehicles in the C RC E-65 study, discussed in section 3.1.1.1.4 above. 5 2 The NONROAD2005 hose perm eation factors S SS were adjusted a s f o llows: 1) Perm eation emissions in the public version of NONROAD2005 were 450 gram s per m e ter-squared per day (g/m 2 /day) for both sm all spark-ignition engines and for the supply hoses on portable fuel tanks in recreational m arine waterc raf t , applicab le to all f u els. For both types of equipm ent, the perm eation em ission rates were changed to 122 g/m2 /day f o r gasoline and 222 g/m 2 /day for E10. 2) Perm eation emissions in the pub lic version of NONROAD2005 were 100 g/m 2 /day for supply hoses on outboard recreational m arine watercraft (> 25 hp), 300 g/m 2 /day for supply hoses on personal watercraft (PW C ), and 110 g/m 2 /day f o r f ill nec k hoses on b o th outboards and PW C. These three perm eation em ission rates were changed to 42 g/m 2 /day for gasoline and 125 g/m 2 /day for E10. 3) Perm eation emissions from sterndrive/inboard recreational m arine watercraft in the public version of N ONROAD2005 were 100 g/m 2 /day for s upply hoses and 110 g/m 2 /day f

o r f ill neck hoses. Bothof these perm eation emission rates were change d to 22 g/m 2 /day for gasoline and 40 g/m 2 /day for E10. 4) Perm eation emissions in the pub lic version of NONROAD2005 were 0 g/m 2 /day f o r vent hoses on all recreation m arine waterc raft. T h is perm eation 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 adjustm e nt was to double the vent hose length f or all gasoline-fueled outboards, personal watercraft, and sterndrive/inboard watercraft.
The NONR OAD e m issions m odel d oes not esti m a te e m issions of toxic air pollutants from nonroad equipm ent. However, the Nationa l Mobile Inventory Mo del (NMIM) does m ake such estim a tes. NMIM utilizes the MOBI LE and NONROAD m odels to develop national
em ission estim a tes for motor vehicles and nonr oad equipm ent. For the most part, NMIM provides the relevant inputs to MOBILE6.2 and NONROAD and processes the results. However, with respect to nonroad toxic em issi ons, NMIM takes exhaust and non-exhaust VOC
emission estim ates from NONROAD and applies a set of toxic fractions of VOC e m is sions based on fuel quality. T TTNMIM contains estim ates of the toxic fractions of VOC e m issions for three fuels: a non-oxygenated gaso line, an MTBE blend and an ethanol blend. NMIM applies the fraction of VOC e mi ssions repr esented by each of the air toxics to either the exhaust or non52 Perm eat i on e m i ssi ons f r o m no n r oa d e q ui p m ent are not re gul at ed

. Th us, th e el ast o m e rs use d i n th e f u e l sy st em s of
no n r oa d e q ui p m ent are l i k el y t o be m o re si m i l ar t o th o s e of ol der o n r o a d vehi cl es t h an th o s e of l a t e r on r o adve hi cl es w h ich w e r e sub j ect t o str i ng en t non -e xhaust VOC emission sta ndards.

163exhaust VOC e m issions estim ated by NONROAD. The toxic fractions of VOC were derived from m o tor vehicle testing. Thus, they are considered approxim ate. We hope to update our projections of the e ffect of gasoline qual ity on exhaust and nonexhaust emissions from nonroad equipm ent based on additional testing to be conducted over the next several years if funding allows. Thes e updated projections could be used in the
com p rehensive assessm ent of the impact of the fuel-related provisions of the Energy Act which is due in 2009.



### 3.3 Effect of Fuel Quality on Comp ression-Ignited Vehicle and Equipment Emissions – Biodiesel Biodiesel is expected to be one of two renewable fuels to be u sed in sign if icant vo lumes through 2020. W h ile ethanol will dom i nate the m arket, biodiesel use is likely to grow considerably reach ing 30 0 m illion gallons by 201 2, accord ing to EIA estim ates. It is produced dom e stically from vegetable oils, anim al fats and recyc l ed co oking oils , with the m ajority of th is product com i ng from soybean oil. It is typically us ed in 2%, 5% and 20% bl ends with diesel fuel which have been assigned B2, B5 and B20 designations, respectively. In 2002, EPA issued a report entitled “A Comp r e hensive Analysis of Biodiesel Impacts on Exhaust Em issions” based on existing data from various test programs. This report included a technical analysis of biodiesel effects on regul ated and unregulated pollutants from diesel powered vehicles and concluded that biodiesel fuels improved PM, HC and CO e missions of diese l engin es while s lig htly in cr easing their NOx em issions.
While the conclusions reached in the 2002 EP A report relative to biodiesel effects on VOC, CO and PM em issions have b een generally accepted, the m a gnitude of the B20 effect on NOx re m a ins controversial due to conflicting results from differ ent studies. Significant new testing is being planned with broad stakeholder participation and support in order to better estim ate the impact of biodiesel on NOx and othe r exhaust emissions from the in -use f l eet of diese l engin es. W e hope to inco rpor ate th e data f r om such addition al tes ting in to the analyses f o r other studies required by the Ener gy 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 prim ary im pact of renewable fuel pr oduction and distribution regards ethanol, since it is expected to be the p r edominant renewable f u el used in the foreseeab le future. F o r the NPRM, we estim ated the im pact of increased ethanol production, including corn farm ing, on
em issions based on DOE’s GREET model, version 1.6. This estim ate also included em issions rela ted to d i stribu ting th e ethanol and take credit for reducedem issions related to distributing displaced gasoline. Since the tim e of the NPRM analysis, DOE has publishe d the next version of

164GREET, ver sion 1.7. The em ission estim ates 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 n u m ber of States to obtai n the latest em ission estim a tes for ethanol plants currently in production. These plant-specific estim a tes provide a useful comparison to the inherently generic em ission factors used by a nationwide-average m odel, such as GREET.
In Section 3.4.1.1, we describe and co m par e the em ission estim a tes fro m 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 e mi ssion factors; one for wet m i lls and one for dry
m ills. Finally, in Section3.4.1.3, we describ e ho w we will use both the G R EET and State estim ates in estim ating n ationa l em issions f r om new ethanol plants in Chapter 4.
3.4.1.1 GREET Emission Estimates
The em issions related to producing and distri buting ethanol f or use in gasoline blends fro m both GREET1.6 an d 1.7 are summarized in Tabl e 3.4-1. GREET pr esents em ission factors in a variety of units, such as per bushel of cornharvested, gallons of ethanol produced, etc. All the em ission factors shown in th is table have been converted to a per gallon of ethanol produced or distributed basis us ing the default conversion factors co ntained in GR EET. One o f GREET’s def a ult assu mptions is that 80% of e thanol p l ants are as sociat ed with dr y corn milling , while the other 20% are associated with wet m illing. Nearly all future ethano l plants are p l ann ed to be dry
mill f acilitie s. Theref ore , we only show the emission f actors f o r dry mill ethanol p l an ts below.

165Table 3.4-1. Well-to-Pu m p Emissio n s for Producing a nd Distributing Ethanol from Corn Dry Mill Fa cility (g ram s per gallonethanol)

Pollutan t Corn Farm i ng and Transportation Ethanol Production CoProduct Credits EthanolTransporta tion Gasoline Transportation Credit Total Emissions 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 em ission estim a te s from the two versions of GREET d i ffer significantly. In particular, VOC e m issions in GREET1.7 are about 60% as large as those estim ated in GREET1.6 for dry milss and even lo wer for wet mills. The other d i fferences are in the + 20% range, and differ in dire ction depending on pollutant.
The def ault mi x of dry and wet mill ethanol p l an ts is 80 /20 in both vers ions of GREET. This is a reasonable estim a te fo r current production. However, th e vast m a jority of new plants are expe ctedto be of the dry mill variety. Ther ef ore, we will use the abov e emission f actors f o r a 80/20 mi x of dry and wet mill plan ts to estim ate the em issio n s f r om current eth anol plants and the dry mill emission f actors to estimate th e emissions f r om future p l ants.
While em issions related to ethano l production an d distributio n will in crease,,areas with refineries m i ght experien ce reduced em issions, not necess arily relative to current emission

166levels, but relative to those which would have oc curred in the future had ethanol use not risen. However, to the degree that increased ethanol use reduces imports of gasoline, as opposed to the dom e stic production of gasoline, these reduced refi nery em issions will occur overseas and not in the U.S. Theref ore, we will not take any cred it f o r reduc ed r ef i nery emissions her e.
Sim ilarly, areas with MT BE production fac ilities mi ght experien ce reduced em issions from these plants as they cease p r odu cing MTBE. Ho wever, som e of these plants are likely to be converted to produce other gasoline blendstocks, su ch as iso-octane or alkylate. In this case, their emissions are not likely to change substantially.
The em ission factors shown in Table 3.4-1 do include a credit for reduced em issions related to a reduction in the volum e of non-oxygena ted gasoline being distributed. These are taken directly fro m the GREET e mi ssion estim ates for conventional gasoline production and distribution. W e assum ed that ethanol use will reduce gasoline use one for one on an energy basis.
3.4.1.2 Ethanol Production Emiss i ons Received from States
The em issions from m o s tof the steps involve d in ethanol production and distributions are very diffuse (e.g., tractors plowing corn fields). H o wever, the em issions from ethanol production plants are po int sources whose emissions are often m easured and trac ked by local or state governm e nts. W e contacted over a dozen States in an attempt to improve our estim a te of
emissions from ethanol produc tion. The results of this process are summarized below. U UU

We receivedemission estim ates from 13 Stat es fo r current eth a nol plan ts with a
com bined capacity of 3 billion gallons per year

. The em ission data involved annual em ission estim ates for one or m o re years betw een 2001-20 05. Overall, these pl ants represent roughly three-fourths of current ethanol capacity. The em ission data cover a very wide range of plant capacities, ranging from 0.4 to 274 m illion gallons of ethanol per year. The capacity-weighted average emissions of wet and dry mill e thanol p l ants ar e summarized in Table 3.4-2.
Table 3.4-2. Emissions From Ethanol Pl ants: State Data (g/gal ethanol) W et 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 se en, the emissions f r om wet mills ar e mu ch higher than those f r om dry m ills. In gener al, the wet mill plants are o l der than the dry mills. They also inv o lved a dif f

erent se t of processes and produce a diffe rent set of by-products.

167The ethanol plant em issions fro m GREET 1.6 an d GREET1. 7 are shown in Table 3.4-3 below.
Table 3.4-3. Emissions From Ethano l Plants: GREET (g/gal ethanol) W et 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 em i ssion estim a tes in Tables 3.4-2 and 3.4-3, the State data in dicate that the emission from wet mi lls are mu ch higher than those es tim ated in eithe r ver sio n of GREET. In contr ast, the emissio n data obtained f r om the States f o r dr y mills is generally consistent with the estim ates in GREET1.6 and higher than those in GREET1 .7. An exception to the latter are em issi ons of PM10, which the state data indicate are lower than the estim ate in GREET1.7 and higher than that in GREET1.6.
The reasons for the differences in the St ate data and the estim ates in GREET1.6 and GREET1.7 are not kno wn. It is po ssible, p articul arly for wet m ills, that the State es tim a tes include the em issions from an entire geographica lly-d ef ined f acility whic h m ay inclu d e m o re operations than just corn m illing. It is also po ssible that the estim ates in GREET represent
emissions from plants which would be designed toda y or in the future and are less representative of plants which were built over 20 years ago. This deserves further investigation. At this tim e, we will use the averag e o f the State em ission data as a second estim ate of ethanol p l ant em issions, along with GREET1.7, in order to better indicate the range of possible emissions from these p l ants.
3.4.1.3 Selection of Ethanol Production a nd Distribution Emission Estimates
We have availab l e th ree estim ates of the emi ssions from ethanol plants and two estim ates of the emissions from the other steps in the proc ess of growing corn thr ough ethanol distribution. The estim ates contained in GREET1 .7 represent an update of those in GREET1.6. Th erefore, we will use the em ission factors from GREET1.7 in lieu of those in GREET 1.6 in Chapter 4 where we estim ate national emissions from ethanol production a nd distribution. In addition, we develop a second estim a te of these em issions by substituting the average
emission f actors bas ed o n the Stat e d ata f o r the dr y mill ethan o l plan t emissions con t a i ned in GREET1.7. W hile the S t ate data represents em issi ons from c urrent plants and our prim ary focus is f u ture p l a n ts, it is no t certain th at the emissions of the two sets of ethanol plants will differ. Theref ore, the use of the State data will prov ide a usef ul indication of the potential un certainty in the GREET1.7 estim ates. W e chose not to use the state data for wet m ills, as these em ission factors are often a factor of 10 higher th an those from GREET1.6 or GREET1.7. More understanding of the processes pr oducing these em issions is need ed before they can be all

168assigned to ethanol p r od uction. Thu s, we use the GREET1.7 em ission factors for wet m ill ethanol p r od uction, while using th e s t ate d a ta for dry m ill ethanol production.
The two sets of estim ates are shown in Table 3.4-4. In both sets of estim a tes, the
emission f actors f o r curr ent eth anol plants assu m e an 80/20 mi x of dry and wet mills, while thos e f

o r f u ture ethanol plan ts assum e 100% dry mills.
Table 3.4-4. Selected Emiss i on Factors for Et hanol Production and Distribution (g/gal ethanol) GREET1.7 GREET1.7 + State Data Current Plants Future Plants Current Plants Future 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 em ission factor s for biodiesel production distribution on the estim ates contained in the GR EET model, version 1.7. Table 3.4- 6 shows the em ission factors associated w ith soybean far m ing, soy oil producti on and esterification, and biodiesel distribution. We also include em issions related to distribut ing the biodiesel and take credit for reduced
em issions related to dis tributing d i splaced diesel f u el.
Table 3.4-5. Well-to-Pump Emissions for Producing and Distributing Biodiesel from Soybeans (grams pergallon biod iese l)

Pollutan t Total: Soybean Farm ing and Transportation Biodiesel Production Biodiesel Transportation Diesel Fuel Transportation Credit Total Em issions 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 sam e tim e, areas with refineries m i ght experience reduced em issions, not necessarily relative to current em ission levels, but relative to thos e 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 dom e stic production of diesel fuel, these reduced ref i nery emissio n s will oc cu r overse as a nd not in th e U.S.

169Chapter 3:AppendixFuel Property Tables and Summaryof Predicted EmissionsChanges170Table 3A-1.CRC E-67 StudyTest Fuel Properties171Table 3A-2.Summary of EPA E-67 vs.EPA Predictive Model Effects of E10and MTBE Use Relativeto CG and RFGBase Fuel:AAM Summeravg. non-oxyfuelE0:AAM CGRVP -1psideltaE0:AAM CGRVP-2psideltaE10: AAMSummer AvgE10: AAMSummeravg,T50 limitedto195oFE10: AAMSummeravg,T50 T90 O2onlyforEPAmodel(T50=195oF)11% MTBE:Fuel propsaredeltasfromAAMCGPhase 2RFG: Nonoxy,from1993 region2classC Data,low RVPRFGClass CMTBE: 1993region 2data,low RVPRFG E10:1993region 2data(L),T50& T90deltafromAAMdata, lowRVPFuel ParametersRVP (psi)8.77.86.89.79.78.78.76.76.76.7T50 (oF)218218218186195195206214212194T90 (oF)332332332325325325324325321322Aromatics(vol %)32323227273225.525.4825.4825.48Olefins(vol %)7.77.77.76.16.17.77.713.113.113.1Oxygen (wt%)0003.53.53.52.102.13.5Sulfur(ppm)30303030303030303030Predicted Emissions ChangesEPA PredictiveModels(%change)NOx0.0-1.0-2.17.77.37.72.6-1.72.46.3NMHC0.0-1.0-2.0-7.4-7.0-7.5-9.2-7.7-11.1-12.9EPA E-67Model (%change)NOx0.00.00.09.26.76.7-1.9-0.7-2.28.8NMHC0.00.00.0-5.7-6.5-6.5-4.7-3.2-1.9-7.2172This page intentionally left blank.173

## Chapter 4: National Emis sion Inventory I m pacts

This chapter describes the m e thods used to develop national em issi ons inventories under the Renewable Fuel Standard (RFS) program

. These inventories account for impacts from ethanol use, the rem oval of MTBE, a nd the resulti ng changes to gasoline. These inven t ories 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 m e thods used to develop national em issi ons inventories with respect to ethanol consumption. This section al so presents and discusses these inventories. These inventories reflect only em issions from vehicles and equipm ent operating on ethanolblend gasoline, from both onroad and off-road sources. The off-road sources do not include nonroad diesel, locom ot i ve, or m arine applications.
4.1.1 Overviewof Cases
As described in Section 2.1, we consider thr ee cases for the future use of ethanol-blend gasoline: a Reference Case, an RFS Case, and an EIA Case. The m a in difference between the cases is our assumption about how much etha n o l will b e us ed and where it will go. The Reference case rep r esen ts our es tim ate of fuel quality by county which existed in 2004 when approxim a tely 3.5 billion gallons of ethanol were consum ed nati onwide. In term s of 2012 fuel
consumption, about 4.0 billion gallons of ethanol is consum ed nationwide in the Reference case. The RFS case assum e s 6.7 billion gallons of ethanol consumption in 2012, in acco rdance with the requirem ents of the R FS m a ndate. The EIA case assum e s 9.6 billion gallons of ethanol is used nationwide in 2012, base d on projections m a de in the Energy Inform ation Agency’s 2006 Annual Energy Outlook. W e evaluate each case by predictin g fuel quality in each co unty of the U.S. in 2012. This 2012 fuel m a trix is then used f or all inventory and air quality assessm e nts. While Chapter 2 discu sses our m e thods for determ ining how much ethan o l will go to each state ineach cas e a nd how fuel properties w ill b e affecte d, this se ctio n of the RIA uses those distributions to derive es tim ates of the impact on natio nal em issions inventories.
4.1.2 National Emissions In vent ory Estimation Procedure Having approxim a ted the effects of addi ng ethanol and rem oving MTBE on fuel properties (see Chapter 2), the next step was to use the EPA’s National Mobile Inventory Model (NMIM) V VV to calcu late emissions inve ntories for gasoline fueled m otor vehicles and nonroad equipm ent in years 2012, 2015, and 2020. For all three years, we ran NMIM for January and July, assum i ng that each was representative of winter and summer conditi ons, respectively. W e estim ate ann u al emissioninvento r ies by summi ng the two m o nthly inv entories and mu ltip lying by six. This was done in order to reduce the am ountof tim e needed to actually run the m odel.

174One additio nal sim p lif ic ation was m ade to shorte n the tim e required to ru n NMIM f o r the three years, three fuel cases, two m o nths and roughly 3100 counties in the U.S. Counties within a stat e with identic al f u els and inspec tion-m ainte nance program s and si m ilar temp eratures were grouped together and run through NMIM as a singl e 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 w ith id entical fuels som e tim es changed across the three fuel cases, the groupings of counties som e ti m e s changed across the NMIM runs of the three fuel cases. This occasion ally introdu ced a change in the tem p eratur es es tim ated for a cou n ty between fuel cases. This in turn produced a change in em issions independent of changes in fuel quality.

W e evaluate d the poten tial f o r this simplif icationto bias the p r ojec ted emission im pacts of the various fuel cases. Counties where RFG is sold were always m ode led consis ten tly ac ross all three fuel cases and s o are unaffected by th is simp lif icatio n. Counties with low RVP and 9 RVP fuel were som e times affected. On average, the changes in emissi ons occurring due to a change in temperature appear to be unbiased (i.e., em issions increase as often as they decrease). Also, m a ny of the em ission impacts of cha nging fuel quality (e.g., exhaust VOC and NOx im pact) were applied outside of the NMIM m o de l and so are unaffected by this simp lification. Since we do not present or use th e em ission impacts for individual c ounties, we believe that this simplification does not significantly impact th e em ission impacts presented below. We chose 2012 as the first projection year, be cause it is the year of full RFS program imple m entation. W e also chose 201 5 and 2020 to illus trate h o w the em issions will ch ange over tim e as the fleet changes. W e increase d ethanol consumption beyond 2012 only by volum es required to m a intain the sam e proportion to gasoline that ex isted in 2012, and not by growth predic ted in EIA estim ates. By re stricting ethanol growth in this way, the sam e fuel quality that existed in 2012 would apply to 2015 and 2020, which would better highlight the effects of fleet turnover.
NMIM’s estim a tes of both onroad and nonroa d em issions were “post-processed” to reflect factors not yet included inthe m odel. For onroad em issions , the effect of fuel quality on exhaust VOC and NOx em issions contained in the m odel (i.e., those in MOBILE6.2) were replaced with those from the EPA Predictive Mo del. W e furt her adjus t ed the NMIM estim ates of exhaust VOC, CO and NOx e m issions from onro ad vehicles in a “sen sitivity ” analy s is in order to reflect the signif icant degr e e of uncerta inty which c u rrently ex is ts with r espe ct to these effects. Air toxic em issions were adjusted in o r d er to r ef l ect changes in total exh aust VOC
em issions,. Finally, the effect of ethanol on perm eation VOC and benzene em issions also were added to the onroad em ission estim ates. This series of post-processing steps are further described in the sections below.
For nonroad em issions, the only adjustm e nt to the NMIM estim a tes was to adjust air toxic em issions in the two control cases to re flect the chang e in the toxic fraction of VOC
em issions to that estim ated for onroad vehicl es, as opposed to that estim ated for nonroad equipm ent. These steps for calculating em issions inventories are described in the following sections. A summary of the m odels used and fundam e ntal post-processing steps are shown in Table 4.1-1 below.

175Table 4.1-1. Estimation of Nation al Emissions Inventories: Models Used and Fundamental Post-Processing Steps
Ex haust Emi s si ons No n- Ex haust E m i ssi ons

On ro ad M odel : NM I M whi c h ru ns M OB ILE 6. 2. Po st-p ro cessing : 1. Replace VOC a n d NOx fuel effects for Tier 0 vehi cl es fr om M O B I LE 6. 2 w i th f u el ef fect s fr om EPA Pred ictive Mod e l; 2

. Cond u c t sen s itiv ity an alysis b y ap p l ying fu el effects fo r Tie r 0 ve hicl es to a ll vehicles. 3. A d just ex haust ai r t oxi cs e m i ssi ons t o re f l ect adj u st m e nt t o e xhaust VOC e m i ssi ons. M odel : NM I M whi c h ru ns M OB ILE 6. 2. Po st-p ro cessing : 1. A d d e ffect of etha nol o n p e rm eati on emi s si ons of

V OC an dbe nzene
.

No nr oa d M odel: NM I M whic h ru ns NO NRO A D 2 0 0 5 (m odified t o ac cou n t fo r ho se perm eation). Po st-p ro cessing : 1. C h a n ges i n t oxi c fract i o n o f V O C emi ssi ons i n two fuel control cases ba sed on o n ro ad esti mates instead of nonroad estim a t es. M odel: NM I M whic h ru ns NO NRO A D 2 0 0 5 (m odified t o ac cou n t fo r ho se perm eation). Po st-p ro cessing : 1. C h a n ges i n t oxi c fract i o n o f V O C emi ssi ons i n two fuel control cases ba sed on o n ro ad esti mates instead of nonroad estim a t es.

4.1.2.1 Onroad Emission Estimation Procedures
We ran NMIM to estim a te county-specific em issions from gasoline m o tor vehicles for January and July in years 2012, 2015, and 2020. For each m o nth and year com bination, we ran the th ree onr oad cases (Ref erence, RFS, and EIA). The NMIM m odel utilize s the MOBILE6.2 W WWm odel to estim ate m o tor vehicle emissions, as well as the effect of fuel quality
on em issions. As discussed in Chapter 3, the EP A Predictive Model contains m ore recent estim ates of the impact of fuel quality on exhaust VOC and NOx e m issions. Therefore, we
rem oved the impact of fuel quality on exhaust V O C and NOx em issions as estim a ted by MOBILE6.2 and replaced these impa cts with those of the EPA Pr edictive Model. As also discussed in Chapter 3, MOBILE 6.2 does not include the im p act of ethanol on perm eation
emissions. Therefore, we added th ese emissi ons to those estim ated by NMIM. Finally, we arriv ed at an nual emissio n s estim ates by summing the January and July re s u lts, th en multip lying by six. The procedures for m aking thes e changes are discussed below.

4.1.2.1.1 Onroad Exhaust Em issions MOBILE6.2 perform s most of its emission estim ation procedures for a non-oxygenated 8.7 RVP gasoline. The effect of differing fuel quality is repres ented by a s e t of adjustm e nt factors, which can vary by vehicle type, m ode l year, and w h ether the v ehicle is p r o p erly operating or not (i. e., is a low or high emitter). Be cause th e mi x of vehicle types, m odel years, and low and high em itters varies by county and cale ndar y ear, it is infeasib le to estim ate the net im pact of each fuel param eter on e m issions ou ts ide of the m o del. In Section 3.1.1.1.2 of Chapter 3, we describe a process whereby we perform e d linear regressions on the exhaust em issions

176estim ated by NMIM in o r der to d ete rmine the av erage effect o f RVP, ethanol content and MTBE content on exhaust VOC and NOx em issions. Al so in Section 3.1.1.1.2, we describe these sam e im pacts usin g the EPA Predic tive M odel. W e comb ined thes e fuel-em i ssion effects with the fuel quality expected to exis t in each coun ty under each ethanol u s e case to estim ate the adjustm e nt which NMIM had applied to exhaust VOC and NOx e m issions. This NMIM adjustm e nt for fuel quality was rem oved and replaced by one based on the EPA Predictive Models. In our prim ary analys is, the fuel-em i ssion effects from the EP A Predictive Models were only applied to the fraction of exhaust VOC and NOx em issions whic h are em itted by Tier 0 vehicles. In our sensitivity analysis, the f u el-em i ssion effects from the EPA Predictive Mo dels were ap plied to all exhaust VOC and NOx em issions.
Table 4.1-2 shows the values for “Tier 0 Fr action”; i.e., the fraction of VOC and NOx
emissions from vehicles with Tier 0 emissions ch arac teri stic s. Note th at the fraction drops as tim e progres s, ref l ec ting the attriti on of such vehicles in the nati ona l f l ee t. In the sen sitivity analys is, the Tier 0 veh i cle em ission fract ion is 1.0 for all years and pollutants.
Table 4.1-2. Fraction of In-Use Exhaust Emissions Attributable to Vehicles w i th Tier 0 Emissions Characteristics C a l e ndar Year VOC NO x 20 1 2 0. 33 9 0. 16 2 20 1 5 0. 18 3 0. 06 5 20 2 0 0 0

After adjusting exhaust VOC and NOx accordin g to the m e thods describ e d above, we adjusted the four exhaust toxic em issions : benzene, 1,3-butadiene, form aldehyde, and acetaldehyd e. MOBILE6.2 estim ates exhaust to xic em ission s by first estim a ting the fraction of exhaust VOC emissions repres ented by each tox i c ba sed on fuel quality. The m odel then applies this fraction to exhaust VOC e m issions to estim a te absolute em issions of air toxics. S i nce we adjusted exh a ust VOC em issions, it was necessary to adjust exhaust toxic em issions, as well, by the ratio of the change in exhaust VOC e missions. As described in Section 3.1.1.1.2 of Chapter 3, carbon m onoxide em issions were also adjusted. The following equation i llustra tes the CO adjustment:

Ad j. = N MI M × ( 1 +( Etoh Vo l% x Etoh M kt Sh r + MTBE V o l% x MTBE Mkt Sh r x 0.54 54) × C O A d j. Factor ) CO CO

4.1.2.1.2 Onroad Non-Exhaust E m issions The only adjustm e nt to the non-exhaust em ission estim ates from NMIM was to add county-specific estim ates of the increase in perm e a tion em issions due to ethanol use. In Section 3.1.1.3 of Chapter 3, we determ ined that a 10 vol% ethanol blend increased perm eation
em issions by 0.8 gram s per day at 95 F. W e al so concluded there that perm eation emissions double with every in crease in temp erature of 18 ° F

. Because of this temperatu r e relationship,

177perm eation effects were only accounted for in the July emission estim ate since emissions during the winter months could be at least four tim es lower, and thus negligible.

Perm eation emissions occur whether a vehicle is being used or is parked. Therefore, the average hourly em ission factor in each county in July is determined by adjusting the 0.8 gram per day emission rate for the average fuel tank te mperature occurring in that hour of the day in each county in July and multiply ing by the m arketshare of E10 fuel in th at county. T o tal monthly em i ssions in each county were dete rmined by summ i ng across hours of the day, multiply ing by 31 days a nd mu ltiply ing by the nu mber of vehicles es tim ated to res i de in that county. The average fuel tank temperature is a func tion o f the averag e am bient te mperature at that hour of the day, adjusted to account for the increase in f u el tank temperature for those vehicles which are operating or which are still cooling down from operating. W e obtain estim ates of these latter two fact ors from EPA’s Draft MOVES2006 m ode l. X XX These are shown in Table 4.1-3. T h e fuel tank temp erature of vehicles which have been parked som e ti m e tend to lag the am bient temp erature both when the latter is rising and falling. W e ass u m e here that the fuel tank temp erature of thes e parked veh i cles is equ a l to th e am bient tem p erature, which is true on average for the day.
Table 4.1-3. Increase in Fuel Tank Tempera ture Relativ e to Ambient
Hour of t h e Day Vehicles Operating or in Hot Soak Ave r a g e Ta nk Tem p erature R i se (F) M i dni g ht 2. 6% 10 .0 1: 0 0 AM 2. 8% 6. 9 2: 0 0 AM 1. 2% 6. 1 3: 0 0 AM 0. 9% 4. 9 4: 0 0 AM 0. 8% 3. 1 5: 0 0 AM 2. 6% 3. 0 6: 0 0 AM 6. 6% 3. 7 7: 0 0 AM 12 .3 % 4. 6 8: 0 0 AM 14 .0 % 3. 5 9: 0 0 AM 10 .0 % 3. 8 10: 00 AM 11 .1 % 3. 8 11: 00 AM 12 .5 % 4. 9 No o n 15 .6 % 4. 8 1: 0 0 PM 16 .0 % 5. 5 2: 0 0 PM 17 .2 % 6. 6 3: 0 0 PM 21 .0 % 7. 7 4: 0 0 PM 23 .7 % 8. 6 5: 0 0 PM 28 .5 % 8. 3 6: 0 0 PM 30 .0 % 8. 8 7: 0 0 PM 25 .7 % 9. 2 8: 0 0 PM 18 .7 % 8. 3 9: 0 0 PM 13 .5 % 7. 6 10: 00 PM 10 .6 % 8. 0 11: 00 PM 7. 8% 8. 4

178The total number of gasoline vehicles inthe U.S. in 2004 is estim ated to be 228
m illion. Y YY W e increased this figure by 1.9% per year 5 3 to derive estim ates of the gasoline vehicle fleet in 2012, 2015 and 2020. This produced es tim a tes for the fleet of gasoline vehicles in the U.S. of 265, 281 and 308 m illion vehicles in 2012, 2015 and 2020, respectively. These vehicles were allo cated 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 estim ate that benzene represents 3% of the increased VOC per m eation e missions due to etha nol use. Thus, we added this 3% to the nonexhaust em issions 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 e m ission m odels. We chose to use the NONROAD200 5 Z ZZm odel to estim ate emissions from nonroad equipm ent here, as it reflects EPA’s latestestim ates of em ission factors for nonroad equipm ent. EPA has also recently developed a set of em ission fact or inputs for the NONROAD m odel which include the effect of ethanol on perm eation em issions fr om a num b er of types of nonroad equipm ent (see Chapter 3). For the proposed rule invent ories, the NONR OAD m odel wa s notable to select ethanol related em ission factors based on the fuel quality i nputs to the m odel. It w a s therefore necessary to run NMIM for two extrem e ethanol use cases (no ethanol use and 100% ethanol use) and use those results to estim a te em issions for the five ethanol use cases which were the focus of the proposed rule.
For the final rule, NONROAD m od e l capabili ties were updated to account for oxygenate effects. Therefore, we were ab le to run NM IM (which runs NONROAD ) using the sam e fuel property inputs that were used for onroad em issions inventories. This elim inated the need to interpolate between the “No Oxygen” and “All Oxygen” NONROAD ru ns that were needed for the proposal.
For nonroad toxic exhaust emissions, the toxi c em issions factors for nonroad equipment are bas ed onvery limited data. In EP A’s recen t final ru le which im plem ented new M obile Source Air Toxic (MSAT) standards, we adju sted the fraction of nonroad VOC e miss i ons represented by the various air toxi cs contained in NMIM for a re duction in fuel benzene content with those estim ated for the sam e fuel change by MOBILE6.2 for onroad motor vehicles. This was done because of the very lim ited am ount of nonroad em ission test data which both varied fuel quality and m easured toxics em i ssions. W e take the same approach here. W e begin with the estim ate of nonroad toxic emissions from NM IM for the R e ference Case. Then, any change in the toxics fraction of nonroa d VOC e missions due to a change in fuel quality predicted by NMIM is replaced by th e change in the toxics fr action of onroad VOC em issions due to the same

A n nual g r o w t h rat e i n gas o l i n e c o n s um pt i on on an ene r gy basi s pe r E I A An n u al E n er gy O u t l o o k , 2 0 0 6 (the ref o re i t

appl i e s r e gar d l e ss of f

u tu re ethan o l u s e sce n ari o ). Ass u m e s co nst a nt an n u al mi l eage pe r vehi cl e ove r t h i s t i me f r a me
.

179 53change in f u el quality p r edicted by MOBILE6.2. This adjus t m ent is illu strated in th e f o llowing equation:

NM IM VOC M O B I LE 6. 2 T oxi c Emi ssi ons (R FS o rE I A C a se) Ad justed Em issions (RF S M O B I LE 6. 2 V O C Emi ssi ons (R FS or E I A C a se No nr oa d NM IM or E I A case)
= Toxic × × Toxic Em issions NM IM VOC M O B I LE 6. 2 T oxi c Emi ssi ons (R efe r ence C a se) Em issions Em issions M O B I LE 6. 2 V O C Emi ssi ons (R efere n ce C a s e ) (Refere n ce cas e)

4.1.3 National Emissions In ventory Projections
4.1.3.1 Emission In ventories: P r imary Analysis This section provides the nati onal emissions inventories for the prim ary 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 comp lete prim ary-case invent ories on air toxics a nd criteria pollutants, as well as th e percen t ch anges in inv entories fro m the Reference cas e.
Table 4.1-4 shows ethanol impacts on VOC inve ntories for each of the three cases of renewable f uel use in years 2012, 2015, and 2020. In any given year, the datasuggest that total VOC emissions will incr ease as etha nol use in cre ase s. The largest incr eas e is seen in the EIA case, where the increase is about 1% of the Reference case inventory. Our analysis indicates th at th is inc r ea se is a result of VOC non-e xhaust em issions, such as those from evaporation or perm eation. W h ile VOC exha ust em issions decrease, they do not decrease enough to counteract the incr ease from non-exhaust em issions.
Table 4.1-4. National VOC Emissions from Ga soline Vehicles and Equipment: Reference Case Inventory and Change in Inventory for Control Cases (Tons/Y e ar) Primary Case Tons/Year To ta l 2 012 2 015 2 020 R e fere nce 5, 88 2, 0 00 5, 56 9, 0 00 5, 35 6, 0 00 RFS Case ( C han g e ) 1 8 ,000 2 5 ,000 3 4 ,000 EIA C a se (C ha nge ) 43 ,0 0 0 49 ,0 0 0 58 ,0 0 0 On-R oa d 2 012 2 015 2 020 R e fere nce 3, 41 7, 0 00 3, 26 9, 0 00 3, 24 4, 0 00 RFS Case ( C han g e ) 1 0 ,000 1 6 ,000 2 3 ,000 EIA C a se (C ha nge ) 32 ,0 0 0 36 ,0 0 0 42 ,0 0 0 N on-R oa d 2 012 2 015 2 020 R e fere nce 2, 46 5, 0 00 2, 30 0, 0 00 2, 11 2, 0 00 R FS C a se (C h a nge ) 8, 00 0 9, 00 0 11 ,0 0 0 EIA C a se (C ha nge ) 11 ,0 0 0 13 ,0 0 0 16 ,0 0 0

180Table 4.1-5 shows ethanol impacts on CO inve ntories for each of the three cases of renewable f uel use in years 2012, 2015, and 2020. In any given year, data suggest that total CO
emissions will d ecre ase as ethano l u se incr ease s. The large st reduction is seen in the EIA case; this de crea s e is still le ss than 3% of the Reference inventory.
Table 4.1-5. National CO Emissions from Ga soline Vehicles and Equipment: Reference Case Inventory and Change in Inventory for Control Cases (Tons/Y e ar) Primary Case Tons/Year To ta l 2 012 2 015 2 020 Ref e r e n ce 5 5 ,022 ,0 00 5 3 ,702 ,0 00 5 3 ,949 ,0 00 RFS Case ( Chan g e )

- 483 ,0 00

- 473 ,0 00

- 460 ,0 00 EI A Case ( Ch a n g e )

- 1 ,366 ,0 00

- 1 ,329 ,0 00

- 1 ,286 ,0 00 On-R oa d 2 012 2 015 2 020 Ref e r e n ce 3 7 ,656 ,0 00 3 6 ,171 ,0 00 3 5 ,723 ,0 00 R FS C a se (C h a nge ) -4 5, 0 00 -3 9, 0 00 -1 9, 0 00 EI A Case ( C h an g e )

- 359 ,0 00

- 321 ,0 00

- 252 ,0 00 N on-R oa d 2 012 2 015 2 020 Ref e r e n ce 1 7 ,366 ,0 00 1 7 ,531 ,0 00 1 8 ,226 ,0 00 RFS Case ( Chan g e )

- 438 ,0 00

- 434 ,0 00

- 441 ,0 00 EI A Case ( Ch a n g e )

- 1 ,007 ,0 00

- 1 ,008 ,0 00

- 1 ,034 ,0 00

Table 4.1-6 shows ethanol impacts on NOx inve ntories for each of the three cases of renewable f uel use in years 2012, 2015, and 2020. In any given year, the datasuggest that total NOx emissions will incr ease as etha nol use in cre ase s. The largest incr eas e is seen in the EIA case, which is around 2% of the Reference inventory. Our analysis also indicates that nonroad NOx e m issions increase much greater than onroad em issions. W h ile onroad inventories incr ease less th an one percen t in con trol cases, nonroad inventories increase up to 11% in the EIA case.

181Table 4.1-6. National NOx Emissions from Ga soline Vehicles and Equipment: Reference Case Inventory and Change in Inventory for Control Cases (Tons/Y e ar) Primary Case Tons/Year To ta l 2 012 2 015 2 020 R e fere nce 2, 48 7, 0 00 2, 05 9, 0 00 1, 69 5, 0 00 RFS Case ( C han g e ) 2 3 ,000 1 8 ,000 1 7 ,000 EIA C a se (C ha nge ) 40 ,0 0 0 33 ,0 0 0 32 ,0 0 0 On-R oa d 2 012 2 015 2 020 R e fere nce 2, 24 0, 0 00 1, 79 7, 0 00 1, 40 7, 0 00 R FS C a se (C h a nge ) 9, 00 0 3, 00 0 0 EIA C a se (C ha nge ) 13 ,0 0 0 4, 00 0 0 N on-R oa d 2 012 2 015 2 020 R e fere nce 24 7, 0 00 26 2, 0 00 28 8, 0 00 RFS Case ( C han g e ) 1 4 ,000 1 5 ,000 1 7 ,000 EIA C a se (C ha nge ) 27 ,0 0 0 29 ,0 0 0 32 ,0 0 0

Table 4.1-7 shows ethan o l im pacts on air toxic em issions for each of the three cases o f renewable f u el use in 2012.
For all air to xics shown, the m o st extrem e changes occur in th e EIA case. The data suggest th at, in 2012, total benzen e em issions will d ecrease b y about 4% due to decreases in bo th onroad and nonroad em issions. Total 1,3-butadiene em issions decrease by less than 2% due to decreases in both onroad and nonr oad em issions. Total form aldehyde em issions decrease by up to 1.5%. Total acetaldehyde em issions increase by as much as 36% due to increases in both onroad and nonroad em issions. Generally, the trends in 2015 and 2020 parallel those of 2012 and are shown in the appendix to this chapter. Ben zene maintains a drop of up to a bout 6% with increased ethanol
use. Form aldehyde rem a ins fairly flat, rangi ng from a 0.5% increase to a 1.2% decrease. Acetaldehyde m a intains an increas e of as mu ch as 36.5%. Finall y, 1,3-butadiene rem a ins fairly flat, ranging from no change to a 0.5% increase. Again, we emphasize th at the toxi cs inventories are ba sed on very lim ited data, especially when it comes to em issions from nonroad equipm ent.

182Table 4.1-7. National Toxic Emissions from Gaso line Vehicles and Equipment in 2012: Reference Case Inventory and Change in Inventory for Control (Tons/Year) Pri m ary C a se B e nz ene 1, 3-B u ta di ene Form aldehyde Acetaldehyde To ta l R e fere nce 17 8, 0 00 18 ,9 0 0 40 ,4 0 0 19 ,9 0 0 RFS Case ( C han g e )

- 3 ,200

- 200

- 600 3 , 4 0 0 EIA C a se (C ha nge ) -7 ,2 0 0 -3 0 0 -2 0 0 7, 10 0 Onroad R e fere nce 12 4, 1 00 12 ,0 0 0 29 ,9 0 0 15 ,5 0 0 RFS Case ( C han g e )

- 2 ,300

- 200

- 600 2 , 4 0 0 EIA C a se (C ha nge ) -5 ,4 0 0 -2 0 0 -3 0 0 5, 40 0 Nonroad R e fere nce 53 ,9 0 0 6, 90 0 10 ,5 0 0 4, 40 0 R FS C a se (C h a nge ) -9 0 0 0 0 1, 00 0 EIA C a se (C ha nge ) -1 ,8 0 0 -1 0 0 10 0 1, 70 0

4.1.3.2 Emission In ventories: S e nsitivity A n alyses This section provides the nationa l emissions inventories for the sensitivity case analyses. Criteria pollutant inventories ar e included, along with a brief di scussion of the trends. See Tables 4A-1 through 4A-7 in the Chapter 4 Appe ndix for complete sensitivity-case inventories on air toxics and criteria polluta nts, a s well as the percen t ch anges in inventories from the reference case.
Table 4.1-8 shows ethanol impacts on VOC inve ntories for each of the three cases of renewable f uel use in years 2012, 2015, and 2020. W h ere the prim ary analysis showed total VOC e m issions increasing with ethanol use in all cases, the s ensitivity an alysis shows that tota l VOC e m issions decrease. Onroad em issions decrease in all cases, while nonroad em issions increase to the sam e extent as under the prim ary analysis.

183Table 4.1-8. National VOC Emissions from Ga soline Vehicles and Equipment: Reference Case Inventory and Change in Inventory for Control Cases (Tons/Y e ar) Sensitivi t y Case Tons/Year To ta l 2 012 2 015 2 020 R e fere nce 5, 83 4, 0 00 5, 51 0, 0 00 5, 28 1, 0 00 R FS C a se (C h a nge ) -2 0, 0 00 -2 3, 0 00 -2 7, 0 00 EIA C a se (C ha nge ) -4 ,0 0 0 -1 0, 0 00 -1 7, 0 00 On-R oa d 2 012 2 015 2 020 R e fere nce 3, 36 9, 0 00 3, 21 0, 0 00 3, 16 9, 0 00 R FS C a se (C h a nge ) -2 8, 0 00 -3 2, 0 00 -3 8, 0 00 EIA C a se (C ha nge ) -1 5, 0 00 -2 3, 0 00 -3 3, 0 00 N on-R oa d 2 012 2 015 2 020 R e fere nce 2, 46 5, 0 00 2, 30 0, 0 00 2, 11 2, 0 00 R FS C a se (C h a nge ) 8, 00 0 9, 00 0 11 ,0 0 0 EIA C a se (C ha nge ) 11 ,0 0 0 13 ,0 0 0 16 ,0 0 0

Table 4.1-9 shows ethanol impacts on CO inve ntories for each of the three cases of renewable f uel use in years 2012, 2015, and 2020. In any given year, the datasuggest that total CO emissions will de cre ase as e than o l use in crea ses. The onr oad vehic l e CO emission reductions increase by roughly a factor of three comp ared to the prim ary analysis. T his increases the overall CO e m issions reduction from about 3% in the prim ary case to 4% in the sensitivity
case.
Table 4.1-9. National CO Emissions from Ga soline Vehicles and Equipment: Reference Case Inventory and Change in Inventory for Control Cases (Tons/Y e ar) Sensitivi t y Case Tons/Year To ta l 2 012 2 015 2 020 Ref e r e n ce 5 4 ,315 ,0 00 5 2 ,998 ,0 00 5 3 ,183 ,0 00 RFS Case ( Chan g e )

- 692 ,0 00

- 676 ,0 00

- 676 ,0 00 EI A Case ( Ch a n g e )

- 1 ,975 ,0 00

- 1 ,929 ,0 00

- 1 ,937 ,0 00 On-R oa d 2 012 2 015 2 020 Ref e r e n ce 3 6 ,949 ,0 00 3 5 ,467 ,0 00 3 4 ,957 ,0 00 RFS Case ( Chan g e )

- 254 ,0 00

- 242 ,0 00

- 235 ,0 00 EI A Case ( C h an g e )

- 968 ,0 00

- 921 ,0 00

- 903 ,0 00 N on-R oa d 2 012 2 015 2 020 Ref e r e n ce 1 7 ,366 ,0 00 1 7 ,531 ,0 00 1 8 ,226 ,0 00 RFS Case ( Chan g e )

- 438 ,0 00

- 434 ,0 00

- 441 ,0 00 EI A Case ( Ch a n g e )

- 1 ,007 ,0 00

- 1 ,008 ,0 00

- 1 ,034 ,0 00

Table 4.1-10 shows ethanol impacts on NOx i nventories for each of the three cases o f renewable f uel use in years 2012, 2015, and 2020. In any given year, the data suggest that total NOx emissions will incr ease as etha nol use in cre ase s. The largest incr eas e is seen in the EIA case, where the increase in total em issions is as high as 4.6% of the reference inventory. As in

184the prim ary analysis, nonroad NOx em issions in creas e mu ch greater th anonroad emissions. While onroad inventories increase up to 3.5%, n onroad inventories increase upwards of 11.1% in the EIA case.
Table 4.1-10. National NOx Emissions from Ga soline Vehicles and Equipment: Reference Case Inventory and Change in Inventory for Control Cases (Tons/Y e ar) Sensitivi t y Case Tons/Year To ta l 2 012 2 015 2 020 R e fere nce 2, 51 9, 0 00 2, 08 7, 0 00 1, 71 7, 0 00 RFS Case ( C han g e ) 6 8 ,000 5 7 ,000 4 8 ,000 EIA C a se (C ha nge ) 10 6, 0 00 91 ,0 0 0 79 ,0 0 0 On-R oa d 2 012 2 015 2 020 R e fere nce 2, 27 2, 0 00 1, 82 5, 0 00 1, 42 9, 0 00 RFS Case ( C han g e ) 5 4 ,000 4 2 ,000 3 1 ,000 EIA C a se (C ha nge ) 79 ,0 0 0 62 ,0 0 0 47 ,0 0 0 N on-R oa d 2 012 2 015 2 020 R e fere nce 24 7, 0 00 26 2, 0 00 28 8, 0 00 RFS Case ( C han g e ) 1 4 ,000 1 5 ,000 1 7 ,000 EIA C a se (C ha nge ) 27 ,0 0 0 29 ,0 0 0 32 ,0 0 0

Table 4.1-11 shows ethanol impacts on air toxi c em issions for each of the five cases o f renewable f u el use in 2012. The im pacts in 2015 and 2020 are shown in the Appendix to this chapter.
Table 4.1-11. National Toxic Emissions from Gaso line Vehicles and Equipment in 2012: Reference Case Inventory and Change in Inventory for Control (Tons/Year) Sensitivi t y Case Benz ene 1, 3-Bu tadiene Form aldeh y d e Acetaldeh y de To ta l R e fere nce C a s e 17 5, 7 00 18 ,6 0 0 39 ,6 0 0 19 ,5 0 0 RFS Case ( C han g e )

- 5 ,000

- 400

- 1 ,100 3 , 0 0 0 EI A Case ( Ch an g e )

- 9 ,400

- 600

- 700 6 , 6 0 0 Onroad R e fere nce C a s e 12 1, 8 00 11 ,7 0 0 29 ,1 0 0 15 ,1 0 0 RFS Case ( C han g e )

- 4 ,100

- 400

- 1 ,100 2 , 0 0 0 EI A Case ( Ch an g e )

- 7 ,600

- 500

- 800 4 , 9 0 0 Nonroad R e fere nce C a s e 53 ,9 0 0 6, 90 0 10 ,5 0 0 4, 40 0 R FS C a se (C h a nge ) -9 0 0 0 0 1, 00 0 EIA C a se (C ha nge ) -1 ,8 0 0 -1 0 0 10 0 1, 70 0

As in the prim ary analysis, the m o st extrem e changes in th e s ensitiv ity analysis tend to occur in the EIA case.

185The data suggest that, in 2012, total benzene em issions will decrease by as much as 5.4% due to decreases in both onroad and nonroad emi ssions. Total form aldehyde em issions decrease by up to 2.8%. Nonroad for m aldehyde em issions tend to rem a in relatively flat, while onroad
em issions decrease. Total acetaldehyde em issions increase by as much as 34% due to increases in both onroad and nonroad em issions. Total 1, 3-butadiene em issions decrease by about 3%.
4.1.3.3 Local and Regional VOC a nd NOx Emissions (Summer 2015)
We also estim ate the percentage change in VOC, NOx, and CO e m issions from gasoline fueled m o tor vehicles and equipm ent in those areas which actually exp e rienced a s i gn ificant change in ethanol use. S pecifically, we focu sed on areas where the m arket share of ethanol blends was projected to cha nge by 50 percent or more. W e also focused on summertim e em issions, as these are most relevant to ozone f o rm ation as discussed in Chapter 5. We m odeled 2015 becaus e the ozone Response Surface Model (RSM) used for air quality m odelin g (also discussed in Chapter 5) is based upon a 2015 emissions inventory, t hough we would expect sim ilar results in 2012. Finally, we developed separately estim a te s for: 1) RFG areas, including the state of California and the portions of Arizona where their CBG fuel program s 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 em issions impact of increased ethanol use in those areas where em ission control is m o st i mportant. Table 4.1-12 presents our prim ary analysis es tim ates of the percen tage change in VOC, NOx, and CO e m ission inventories for these thre e types of areas when compared to the 2015 reference case. Note that the analyses here is very similar to that desc ribed in Section 5.1, with the exception that Table 4.1-12 belo w reflects 50-state em issions (instead of 37 eastern states) and excludes diesel em issions.
Table 4.1-12. Change in July 2015 Emissions from Gasolin e Vehicles and Equipm ent in Counties Where Ethanol Use Changed Signific an tly – Primary Analysis Eth ano l Use RFS Case EIA Case RFG Areas Ethan o l Use Do w n Up VOC 0. 8% 2. 3% NO x -3 .4% 1. 6% C O 6. 1% -2 .6% Lo w RV P Are a s Ethan o l Use Up Up VOC 4. 2% 4. 6% NO x 6. 2% 5. 7% C O -1 2. 5% -1 3. 7% Othe r Areas ( 9 .0 R V P ) Ethan o l Use Up Up VOC 3. 6% 4. 6% NO x 7. 3% 7. 0% CO -6 .4% -6 .0%

186As expected, increased ethanol use tends to in crease NOx e missions. The incre a se in low RVP and other areas is greater th an in RFG ar eas, since the RFG in the RFG areas included in this analysis all contained MTBE. Also, increas ed ethanol use tends to increase VOC em issions, indicating th at the increase in non-ex haust VOC emissions exceeds th e reduction in exhaust VOC e missions. This effect is muted with RFG due to the absence of an RVP waive r for ethanol blends. See Chapter 2 f o r a discu ssio n of how et hanol lev els will chang e at the stat e- level. Table 4.1-13 presents the percentage change in V O C, NOx, and CO e m is sion inventories under our sensitiv ity analysis (i.e., w h en we appl y the em ission effects of the EPA Predictive Models to all m o tor vehicles).
Table 4.1-13. Change in July 2015 Emissions from Gasolin e Vehicles and Equipm ent in Counties Where Ethanol Use Changed Significan tly – Sensitivity Analysis Eth ano l Use RFS Case EIA Case RFG Areas Ethan o l Use Do w n Up VOC -1 .0% 1. 0% NO x -0 .9% 5. 6% C O 7. 3% -3 .0% Lo w RV P Are a s Ethan o l Use Up Up VOC 3. 4% 3. 7% NO x 10 .4 % 10 .8 % C O -1 5. 0% -1 6. 4% Othe r Areas ( 9 .0 R V P ) Ethan o l Use Up Up VOC 3. 0% 3. 9% NO x 10 .8 % 11 .0 % CO -9 .0% -8 .9%

Directionally, the changes in VOC and NOx e mi ssions in the various areas are consistent with those from our primary analysis. The m a in dif f

erence is that the incr e ases in VOC
emissions are sm aller, d u e to m o re vehicles experiencing a reduction in exhaust VOC emissions, and the in creases in NO x em issions are larger.



### 4.2 Impact of Biodiesel Use As discussed in Chapter 1, biodies el use totaled 2 5 m illion gallons in 2004 and is projected to increase to 3 00 million g allons in 20 12. Total diesel fuel us e in onroad d i esels in 2004 was roughly 39.4 billion gallons and is expectedto grow to 47.5 billi on gallons per year by 2012. 5 4 The volum es of bi odiesel produced thus represent 0.06% and 0.6% of onroad diesel fuel

54 B a se d on l i n ear i n t e r p ol at i o n bet w ee n est i m a t e for 2 0 0 1 fr om Tabl e 7. 1 .2

- 1 an d t h at f

o r 20 1 4 f r o m Tabl e 7 .1 .3

- 4, bot h fr om th e 20 1 0 No nr oa d FR M Fi nal R I A, EPA 4 20 -R

- 0 4

- 00 7, M a y 2 0 0 4 , avai l abl e i n E P A D o c k et OAR -2 0 0 3

­ 0 012

.

187consumption in 2004 and 2012, respectively. Given th e very sm all contribution of biodiesel to the pool of diesel fuel, the nationwide em ission imp acts of biodiesel are exp ected to be sim ilarly
sm all for the foreseeable future. As a result, we have not in cluded biodies el em ission impacts in
our em ission inventory estim ates for this rule.

W e do inten dto inves tig ate th ese 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 biodie sel fuels im proved PM, CO and HC e m issions of diesel engines while slightly increasing their NOx em issions

. Nevertheless, these conclusions rem a in controversial due to conf licting results from differ e nt stud ies. As a result , preparations are being
m a de to launch a test program with stakeholder participa tionto f u rther in vestig ate the em ission im pacts of biodiesel.



### 4.3 Impact of Renew a ble Fuel Production and Distribution

4.3.1 Ethanol In Chapter 2, we estim ated that 3.5 billi on gallons of ethanol was produced for use in motor fuel in 2004, which comprises our estim a te of fuel quality for the b ase cas e . Maintaining fuel quality, but increasing fuel volum e to that expected in 2012, 5 5 ethanol use would increase to 3.9 billion gallons. The increases in em issi ons associated with ethanol production and distribution under the R FS and EIA cases are, thus, determ ined relative to the em issions associated w ith producing and distribu ting 3.9 billion gallons of ethanol.
We describe the em issio ns associated w ith producing and distributing ethanol on a per gallon basis in Chapter 3.4.1. There, we compar e em issions factors from DOE’s GREET m odel, versions 1.6 and 1.7, as well as estim a tes of etha nol plant em issions obtained from the States. We decided there to use two emission estim ates here, one from GREET1. 7, and the other from GREET1.7 augm ented by the State estim ates for ethanol plant em issions. Here, we simply multiply tho se emission factors by th e volum e of ethanol b e in g used in each scenario. Table 4.3­ 1 shows estim a tes of annual em issions expectedto occur nationwide due to increased production of ethanol. It should b e noted that em issions in the base case assum e a 80/20 m i x of dry m ill and wet mill facilities. New plants (and thus, th e emission increas es) as s u m e 100% dry mil f

acilities.

EIA proj ects g a so lin e d e m a n d of 16 .9 3 and 1 8 .84 qu adrilli o n Btu in 20 04 an d 201 2, resp ectiv ely. Th i s rep r esen ts o v e rall growth b e tween th es e t w o y e ar s of 1 1

. 3 %.

188 55Table 4.3-1. Annual Em issions Nationw i de from Ethano l 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 Em issions Increase in Em issions Em issions Increase in Em issions 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 e m i ssions from ethanol production and transportation are of the sam e orde r of m a gnitude as those from ethanol use. Generally, ethanol plants are not located in ozone non-attainm e nt areas, so the ozone impact of the increased VOC and NOx e missions should be mi ni m a l. According to our estim a tes, alm ost 120 c ounties throughout the nation are constructing new ethanol plants, expanding exis ting plants, or planning construc tion for future plants. The incre ases in ethanol p r od uction a cros s these c oun ties range f r o m as low as 2 million gallons p er year for m o dest expans ions, to over 270 m illion gallons p e r year due to the constru c tion of entirely new f acilities. T o estim ate th e potenti al increas e in V O C and NOx em issions associated with these plants, whether construction is pl anned or underway, we apply the ethanol production
em ission factors (EFs) derived from state da ta as well as those found in GREET 1.7. See Chapter 3.4 for a discussion of the em ission f actors related to ethanol production and plant
emissions. The ethanol production em ission factors are applied to the increase in the volum e of ethanol p r od uction exp e cted in each o f the counties. Figures 4 .3-1 and 4.3-2 illus trate p o tential increases in future m ont hly VOC and NOx e m is si ons, respectively, in coun ties that can expect a growth in ethanol production. The em issions re flect plants operating for one m onth a t90% capacity. Ineach figure, the distribution of counties is presented in order fromthe lowest-tohighest increase in ethanol pr oduction volum e. The figures s how results based upon both statebased em ission factors and GREET 1.7 em ission factors.

189VOC (tons/moNOx(tons/mo Fi gure 4. 3- 1. Fi gure 4. 3- 2. Monthl y VO C Emissions in Counties Exp ecting an Monthl y NO x Emissions in Counties Exp ecting an Increase in Ethanol Pr oduction Increase in Ethanol Pr oduction

140 14 0

120 12 0 N Ox ( S tated ata E F s ) V O C

(S tate dat a E F s) 100 10 0)) V O C ( G REE T 1. 7 EF s ) NOx ( G REE T 1. 7 E F s )
80

60

40 80

60

40

20 20

0 0 20 40 60 80 100 Distr i bution of Co unties ( i n or der of incr eas ing ethanol pr oduc tion) 12 0 0 0 2 0 4 0 6 0 8 0 1 0 0 Distr i bution of Co unties ( i n or der of incr eas ing ethanol pr oduc tion) 1 2 0

As the figures indicate, most counties will s ee an increase of less th an 40 tons/m onth VOC and less than 60 to ns/m onth NOx, according to the d i stribu tion based upon the state data
emission factors. The av erage emissions ar e about 26 tons/m onth VOC and 35 tons/m onth NOx using state data, and about 17 tons/month VOC a nd 25 tons/month NOx using GREET 1.7
emission factors. However, average VOC and NOx e m issions increase to about 61 tons/m onth and 83 tons/month, respectively, in the 10% of counties expecting largest increases in ethanol production. The average em issions for the rem a ining 90% of counties is about 21 tons/m onth VOC and 29 tons/m onth NOx. For both VOC an d NOx, e m is sions estim ates are about 35% less when using the GREET 1.7 em ission factors.

4.3.2 Biodiesel In Chapter 1, we estim ated that 25 m illion ga llons of biodiesel were produced for use in motor fuel in 2004. Based on growth in ove rall diesel fuel dem a nd between 2004 and 2012, 5 6 this would r epresen t the equivalent o f 30 million gallons of biodiese l in 20 12 f o r our re f e rence case. Here, we estim ate the inc r eas e in emissions which will occur with a n incre ase inbiodies el production and distribution from 30 m illion ga llons to 300 m illion gallons per year.
We describe the em issio n s associated withproducing and distributing biodiesel on a per gallon ba sis in Chapter 3

. Here, we sim p ly mu ltiply those emission f ac t or s by the volum e of biodiesel being used in each s cenario. Table 4.3-2 shows estim a te s of annual em issions expected to occur nationwide due to increas ed production of biodiesel.

EIA proj ects g a so lin e d e m a n d of 16 .9 3 and 1 8 .84 qu adrilli o n Btu in 20 04 an d 201 2, resp ectiv ely. Th i s rep r esen ts o v e r a ll gr ow th b e tw een th ese tw o year s o f 11 .3 %. Sou r ce: An nu al En ergy O u tlook 2 006 , DO E/EIA

- 03 83( 200 6) , Refere nce Cas e Table 2, avai lable in d o ck et EPA- HQ -OA R

- 200 5- 016 1.

190 56Table 4.3-2. Annual Em issions Nationw i de from Biodie sel Production and Trans p ortation: 2012 (tons per year) Reference Inventory: 28 mill gal b i odies el pe r year Increase in Em issions: 300 mill gal biodies el 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 inem issions from biodiesel production and transpo r tatio n are of the sam e or der of m agnitude as those from bi odiesel use, with the exception of CO e miss i ons. Generally, biodiesel plants ar e not located in ozone non-attainm e nt areas, so the ozone impact of the increased V O C and NOx em issions should be minim al.



### 4.4 Total Emission Impacts of Rene w a ble Fuel Production and Use

Tables 4.4-1 and 4.4-2 com bine the VOC, CO a nd NOx e m i ssion impacts for ethanol use from Section 4.1 and renewable fuel production and distribution from Section 4.3. T able 4.4-1 includes the em ission impacts from gasoline vehi cles and equipm ent under our prim ary analysis and renewable fuel production and distributi on from GREET1. 7. Table 4.4-2 includes the
em ission impacts from gasoline vehicles and equipm ent under our sensitivity analysis and renewable f uel production and distribution from GREET1.7 augm ented with the State data for ethanol production plants. Em issions from renewable fuel production and distribution in 2012 were increased by 1.9% per year to account for growth in gasoline and diesel fuel dem a nd.

191Table 4.4-1. National Emissions from Gasolin e Vehi cles and Equipm ent and Renew a ble Fuel Production and Distribution: Prim ary Case and GREET1. 7 (Tons/Year) Tons/Year VOC Emissions 2 012 2 015 2 020 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 Gasolin e Vehi cles and Equipm ent and Renew a ble Fuel Production and Distrib u tion: 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

192

## Chapter 4: Appendix

193Table 4A-1. VOC Emission Inventor ies under Various Ethanol Use Cases
Pri m a r y T ons/Year Change fr o m Refer e nce ( t ons) % Change fr o m Refer e nce Tot al 2012 2015 2020 2012 2015 2020 2012 2015 2020 Refer e nce 5, 882, 00 0 5, 569, 00 0 5, 356, 00 0 - ­ - ­ - ­ - ­ - ­ - ­ RFS Case 5, 900, 00 0 5, 594, 00 0 5, 390, 00 0 18, 000 25, 000 34, 000 0. 3% 0. 4% 0. 6% E I A C a se 5, 925, 00 0 5, 618, 00 0 5, 414, 00 0 43, 000 49, 000 58, 000 0. 7% 0. 9% 1. 1% O n -Road 2012 2015 2020 2012 2015 2020 2012 2015 2020 Refer e nce 3, 417, 00 0 3, 269, 00 0 3, 244, 00 0 - ­ - ­ - ­ - ­ - ­ - ­ RFS Case 3, 427, 00 0 3, 285, 00 0 3, 267, 00 0 10, 000 16, 000 23, 000 0. 3% 0. 5% 0. 7% E I A C a se 3, 449, 00 0 3, 305, 00 0 3, 286, 00 0 32, 000 36, 000 42, 000 0. 9% 1. 1% 1. 3% Non- Road 2012 2015 2020 2012 2015 2020 2012 2015 2020 Refer e nce 2, 465, 00 0 2, 300, 00 0 2, 112, 00 0 - ­ - ­ - ­ - ­ - ­ - ­ RFS Case 2, 473, 00 0 2, 309, 00 0 2, 123, 00 0 8, 000 9, 000 11, 000 0. 3% 0. 4% 0. 5% E I A C a se 2, 476, 00 0 2, 313, 00 0 2, 128, 00 0 11, 000 13, 000 16, 000 0. 4% 0. 6% 0. 8%

Sensitivity To n s /Year Tot al 2012 2015 2020 2012 2015 2020 2012 2015 2020 Refer e nce 5, 834, 00 0 5, 510, 00 0 5, 281, 00 0 - ­ - ­ - ­ - ­ - ­ - ­ RFS Case 5, 814, 00 0 5, 487, 00 0 5, 254, 00 0

- 20, 000

- 23, 000

- 27, 000

- 0

. 3 %

- 0

. 4 %

- 0

. 5 % E I A C a se 5, 830, 00 0 5, 500, 00 0 5, 264, 00 0

- 4, 000

- 10, 000

- 17, 000

- 0. 1%

- 0. 2%

- 0. 3% O n -Road 2012 2015 2020 2012 2015 2020 2012 2015 2020 Refer e nce 3, 369, 00 0 3, 210, 00 0 3, 169, 00 0 - ­ - ­ - ­ - ­ - ­ - ­ RFS Case 3, 341, 00 0 3, 178, 00 0 3, 131, 00 0

- 28, 000

- 32, 000

- 38, 000

- 0

. 8 %

- 1

. 0 %

- 1

. 2 % E I A C a se 3, 354, 00 0 3, 187, 00 0 3, 136, 00 0

- 15, 000

- 23, 000

- 33, 000

- 0. 4%

- 0. 7%

- 1. 0% Non- Road 2012 2015 2020 2012 2015 2020 2012 2015 2020 Refer e nce 2, 465, 00 0 2, 300, 00 0 2, 112, 00 0 - ­ - ­ - ­ - ­ - ­ - ­ RFS Case 2, 473, 00 0 2, 309, 00 0 2, 123, 00 0 8, 000 9, 000 11, 000 0. 3% 0. 4% 0. 5% E I A C a se 2, 476, 00 0 2, 313, 00 0 2, 128, 00 0 11, 000 13, 000 16, 000 0. 4% 0. 6% 0. 8%

194Table 4A-2. CO Emission Inventories under Various Ethanol Use Cases
Pri m a r y T ons/Year Change fr o m Refer e nce ( t ons) % Change fr o m Refer e nce Tot al 2012 2015 2020 2012 2015 2020 2012 2015 2020 Refer e nce 55, 022, 0 00 53, 702, 0 00 53, 949, 0 00 - ­ - ­ - ­ - ­ - ­ - ­ RFS Case 54, 539, 0 00 53, 229, 0 00 53, 489, 0 00

- 483, 000

- 473, 000

- 460, 000

- 0. 9%

- 0. 9%

- 0. 9% E I A C a se 53, 656, 0 00 52, 373, 0 00 52, 663, 0 00

- 1, 366, 000

- 1, 329, 000

- 1, 286, 000

- 2. 5%

- 2. 5%

- 2. 4% O n -Road 2012 2015 2020 2012 2015 2020 2012 2015 2020 Refer e nce 37, 656, 0 00 36, 171, 0 00 35, 723, 0 00 - ­ - ­ - ­ - ­ - ­ - ­ RFS Case 37, 611, 0 00 36, 132, 0 00 35, 704, 0 00

- 45, 000

- 39, 000

- 19, 000

- 0

. 1 %

- 0

. 1 %

- 0

. 1 % E I A C a se 37, 297, 0 00 35, 850, 0 00 35, 471, 0 00

- 359, 000

- 321, 000

- 252, 000

- 1. 0%

- 0. 9%

- 0. 7% Non- Road 2012 2015 2020 2012 2015 2020 2012 2015 2020 Refer e nce 17, 366, 0 00 17, 531, 0 00 18, 226, 0 00 - ­ - ­ - ­ - ­ - ­ - ­ RFS Case 16, 928, 0 00 17, 097, 0 00 17, 785, 0 00

- 438, 000

- 434, 000

- 441, 000

- 2. 5%

- 2. 5%

- 2. 4% E I A C a se 16, 359, 0 00 16, 523, 0 00 17, 192, 0 00

- 1, 007, 000

- 1, 008, 000

- 1, 034, 000

- 5. 8%

- 5. 7%

- 5. 7%

Sensitivity To n s /Year Tot al 2012 2015 2020 2012 2015 2020 2012 2015 2020 Refer e nce 54, 315, 0 00 52, 998, 0 00 53, 183, 0 00 - ­ - ­ - ­ - ­ - ­ - ­ RFS Case 53, 623, 0 00 52, 322, 0 00 52, 507, 0 00

- 692, 000

- 676, 000

- 676, 000

- 1. 3%

- 1. 3%

- 1. 3% E I A C a se 52, 340, 0 00 51, 069, 0 00 51, 246, 0 00

- 1, 975, 000

- 1, 929, 000

- 1, 937, 000

- 3. 6%

- 3. 6%

- 3. 6% O n -Road 2012 2015 2020 2012 2015 2020 2012 2015 2020 Refer e nce 36, 949, 0 00 35, 467, 0 00 34, 957, 0 00 - ­ - ­ - ­ - ­ - ­ - ­ RFS Case 36, 695, 0 00 35, 225, 0 00 34, 722, 0 00

- 254, 000

- 242, 000

- 235, 000

- 0. 7%

- 0. 7%

- 0. 7% E I A C a se 35, 981, 0 00 34, 546, 0 00 34, 054, 0 00

- 968, 000

- 921, 000

- 903, 000

- 2. 6%

- 2. 6%

- 2. 6% Non- Road 2012 2015 2020 2012 2015 2020 2012 2015 2020 Refer e nce 17, 366, 0 00 17, 531, 0 00 18, 226, 0 00 - ­ - ­ - ­ - ­ - ­ - ­ RFS Case 16, 928, 0 00 17, 097, 0 00 17, 785, 0 00

- 438, 000

- 434, 000

- 441, 000

- 2. 5%

- 2. 5%

- 2. 4% E I A C a se 16, 359, 0 00 16, 523, 0 00 17, 192, 0 00

- 1, 007, 000

- 1, 008, 000

- 1, 034, 000

- 5. 8%

- 5. 7%

- 5. 7%

195Table 4A-3. NOx Emission Inventor ies under Various Ethanol Use Cases
Pri m a r y T ons/Year Change fr o m Refer e nce ( t ons) % Change fr o m Refer e nce Tot al 2012 2015 2020 2012 2015 2020 2012 2015 2020 Refer e nce 2, 487, 00 0 2, 059, 00 0 1, 695, 00 0 - ­ - ­ - ­ - ­ - ­ - ­ RFS Case 2, 510, 00 0 2, 077, 00 0 1, 712, 00 0 23, 000 18, 000 17, 000 0. 9% 0. 9% 1. 0% E I A C a se 2, 527, 00 0 2, 092, 00 0 1, 727, 00 0 40, 000 33, 000 32, 000 1. 6% 1. 6% 1. 9% O n -Road 2012 2015 2020 2012 2015 2020 2012 2015 2020 Refer e nce 2, 240, 00 0 1, 797, 00 0 1, 407, 00 0 - ­ - ­ - ­ - ­ - ­ - ­ RFS Case 2, 249, 00 0 1, 800, 00 0 1, 407, 00 0 9, 000 3, 000 0 0. 4% 0. 2% 0. 0% E I A C a se 2, 253, 00 0 1, 801, 00 0 1, 407, 00 0 13, 000 4, 000 0 0. 6% 0. 2% 0. 0% Non- Road 2012 2015 2020 2012 2015 2020 2012 2015 2020 Refer e nce 247, 00 0 262, 00 0 288, 00 0 - ­ - ­ - ­ - ­ - ­ - ­ RFS Case 261, 00 0 277, 00 0 305, 00 0 14, 000 15, 000 17, 000 5. 7% 5. 7% 5. 9% E I A C a se 274, 00 0 291, 00 0 320, 00 0 27, 000 29, 000 32, 000 10. 9% 11. 1% 11. 1%

Sensitivity T ons/Year Change fr o m Refer e nce ( t ons) % Change fr o m Refer e nce Tot al 2012 2015 2020 2012 2015 2020 2012 2015 2020 Refer e nce 2, 519, 00 0 2, 087, 00 0 1, 717, 00 0 - ­ - ­ - ­ - ­ - ­ - ­ RFS Case 2, 587, 00 0 2, 144, 00 0 1, 765, 00 0 68, 000 57, 000 48, 000 2. 7% 2. 7% 2. 8% E I A C a se 2, 625, 00 0 2, 178, 00 0 1, 796, 00 0 106, 00 0 91, 000 79, 000 4. 2% 4. 4% 4. 6% O n -Road 2012 2015 2020 2012 2015 2020 2012 2015 2020 Refer e nce 2, 272, 00 0 1, 825, 00 0 1, 429, 00 0 - ­ - ­ - ­ - ­ - ­ - ­ RFS Case 2, 326, 00 0 1, 867, 00 0 1, 460, 00 0 54, 000 42, 000 31, 000 2. 4% 2. 3% 2. 2% E I A C a se 2, 351, 00 0 1, 887, 00 0 1, 476, 00 0 79, 000 62, 000 47, 000 3. 5% 3. 4% 3. 3% Non- Road 2012 2015 2020 2012 2015 2020 2012 2015 2020 Refer e nce 247, 00 0 262, 00 0 288, 00 0 - ­ - ­ - ­ - ­ - ­ - ­ RFS Case 261, 00 0 277, 00 0 305, 00 0 14, 000 15, 000 17, 000 5. 7% 5. 7% 5. 9% E I A C a se 274, 00 0 291, 00 0 320, 00 0 27, 000 29, 000 32, 000 10. 9% 11. 1% 11. 1%

196Table 4A-4. Benz ene Emission Inventories un der Various Ethanol Use Cases
Pri m a r y T ons/Year Change fr o m Refer e nce ( t ons) % Change fr o m Refer e nce Tot al 2012 2015 2020 2012 2015 2020 2012 2015 2020 Refer e nce 178, 00 0 175, 40 0 179, 90 0 - ­ - ­ - ­ - ­ - ­ - ­ RFS Case 174, 80 0 164, 80 0 178, 20 0

- 3 , 200

- 10, 600

- 1 , 700

- 1

. 8 %

- 6

. 0 %

- 0

. 9 % E I A C a se 170, 80 0 169, 10 0 174, 20 0

- 7 , 200

- 6 , 300

- 5 , 700

- 4

. 0 %

- 3

. 6 %

- 3

. 2 % O n -Road 2012 2015 2020 2012 2015 2020 2012 2015 2020 Refer e nce 124, 10 0 124, 20 0 130, 60 0 - ­ - ­ - ­ - ­ - ­ - ­ RFS Case 121, 80 0 122, 60 0 129, 40 0

- 2 , 300

- 1 , 600

- 1 , 200

- 1

. 9 %

- 1

. 3 %

- 0

. 9 % E I A C a se 118, 70 0 119, 40 0 126, 10 0

- 5 , 400

- 4 , 800

- 4 , 500

- 4

. 4 %

- 3

. 9 %

- 3

. 4 % Non- Road 2012 2015 2020 2012 2015 2020 2012 2015 2020 Refer e nce 53, 900 51, 200 49, 300 - ­ - ­ - ­ - ­ - ­ - ­ RFS Case 53, 000 42, 200 48, 800

- 900

- 9 , 000

- 500

- 1

. 7 %

- 17. 6%

- 1

. 0 % E I A C a se 52, 100 49, 700 48, 100

- 1 , 800

- 1 , 500

- 1 , 200

- 3

. 3 %

- 2

. 9 %

- 2

. 4 %

Sensitivity To n s /Year Tot al 2012 2015 2020 2012 2015 2020 2012 2015 2020 Refer e nce 175, 70 0 172, 70 0 176, 50 0 - ­ - ­ - ­ - ­ - ­ - ­ RFS Case 170, 70 0 168, 00 0 171, 90 0

- 5 , 000

- 4 , 700

- 4 , 600

- 2

. 8 %

- 2

. 7 %

- 2

. 6 % E I A C a se 166, 30 0 163, 60 0 167, 20 0

- 9 , 400

- 9 , 100

- 9 , 300

- 5

. 4 %

- 5

. 3 %

- 5

. 3 % O n -Road 2012 2015 2020 2012 2015 2020 2012 2015 2020 Refer e nce 121, 80 0 121, 50 0 127, 20 0 - ­ - ­ - ­ - ­ - ­ - ­ RFS Case 117, 70 0 117, 50 0 123, 10 0

- 4 , 100

- 4 , 000

- 4 , 100

- 3

. 4 %

- 3

. 3 %

- 3

. 2 % E I A C a se 114, 20 0 113, 90 0 119, 10 0

- 7 , 600

- 7 , 600

- 8 , 100

- 6

. 2 %

- 6

. 3 %

- 6

. 4 % Non- Road 2012 2015 2020 2012 2015 2020 2012 2015 2020 Refer e nce 53, 900 51, 200 49, 300 - ­ - ­ - ­ - ­ - ­ - ­ RFS Case 53, 000 50, 500 48, 800

- 900

- 700

- 500

- 1

. 7 %

- 1

. 4 %

- 1

. 0 % E I A C a se 52, 100 49, 700 48, 100

- 1 , 800

- 1 , 500

- 1 , 200

- 3

. 3 %

- 2

. 9 %

- 2

. 4 %

197Table 4A-5. Acetaldeh yde Emission In ventories under Various Ethanol Use Cases
Pri m a r y T ons/Year Change fr o m Refer e nce ( t ons) % Change fr o m Refer e nce Tot al 2012 2015 2020 2012 2015 2020 2012 2015 2020 Refer e nce 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% E I A C a se 27, 000 27, 300 28, 800 7, 100 7, 300 7, 700 35. 7% 36. 5% 36. 5% O n -Road 2012 2015 2020 2012 2015 2020 2012 2015 2020 Refer e nce 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% E I A C a se 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 Refer e nce 4, 400 4, 200 4, 100 - ­ - ­ - ­ - ­ - ­ - ­ RFS Case 5, 400 5, 100 4, 900 1, 000 900 800 22. 7% 21. 4% 19. 5% E I A C a se 6, 100 5, 800 5, 500 1, 700 1, 600 1, 400 38. 6% 38. 1% 34. 1%

Sensitivity To n s /Year Tot al 2012 2015 2020 2012 2015 2020 2012 2015 2020 Refer e nce 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% E I A C a se 26, 100 26, 100 27, 200 6, 600 6, 600 6, 800 33. 8% 33. 8% 33. 3% O n -Road 2012 2015 2020 2012 2015 2020 2012 2015 2020 Refer e nce 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% E I A C a se 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 Refer e nce 4, 400 4, 200 4, 100 - ­ - ­ - ­ - ­ - ­ - ­ RFS Case 5, 400 5, 100 4, 900 1, 000 900 800 22. 7% 21. 4% 19. 5% E I A C a se 6, 100 5, 800 5, 500 1, 700 1, 600 1, 400 38. 6% 38. 1% 34. 1%

198Table 4A-6. Formaldehyde Emission Invento r ies under Various Ethanol Use Cases
Pri m a r y T ons/Year Change fr o m Refer e nce ( t ons) % Change fr o m Refer e nce Tot al 2012 2015 2020 2012 2015 2020 2012 2015 2020 Refer e nce 40, 400 40, 100 41, 400 - ­ - ­ - ­ - ­ - ­ - ­ RFS Case 39, 800 39, 600 41, 200

- 600

- 500

- 200

- 1

. 5 %

- 1

. 2 %

- 0

. 5 % E I A C a se 40, 200 40, 100 41, 600

- 200 0 200

- 0. 5% 0. 0% 0. 5% O n -Road 2012 2015 2020 2012 2015 2020 2012 2015 2020 Refer e nce 29, 900 30, 100 32, 000 - ­ - ­ - ­ - ­ - ­ - ­ RFS Case 29, 300 29, 700 31, 800

- 600

- 400

- 200

- 2

. 0 %

- 1

. 3 %

- 0

. 6 % E I A C a se 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 Refer e nce 10, 500 10, 000 9, 400 - ­ - ­ - ­ - ­ - ­ - ­ RFS Case 10, 500 9, 900 9, 400 0

- 100 0 0. 0%

- 1

. 0 % 0. 0% E I A C a se 10, 600 10, 000 9, 400 100 0 0 1. 0% 0. 0% 0. 0%

Sensitivity To n s /Year Tot al 2012 2015 2020 2012 2015 2020 2012 2015 2020 Refer e nce 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 % E I A C a se 38, 900 38, 400 39, 500

- 700

- 800

- 800

- 1

. 8 %

- 2

. 0 %

- 2

. 0 % O n -Road 2012 2015 2020 2012 2015 2020 2012 2015 2020 Refer e nce 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 % E I A C a se 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 Refer e nce 10, 500 10, 000 9, 400 - ­ - ­ - ­ - ­ - ­ - ­ RFS Case 10, 500 9, 900 9, 400 0

- 100 0 0. 0%

- 1

. 0 % 0. 0% E I A C a se 10, 600 10, 000 9, 400 100 0 0 1. 0% 0. 0% 0. 0%

199Table 4A-7. 1,3-Butadiene Emissio n Inventories under Various Ethanol Use Cases
Pri m a r y T ons/Year Change fr o m Refer e nce ( t ons) % Change fr o m Refer e nce Tot al 2012 2015 2020 2012 2015 2020 2012 2015 2020 Refer e nce 18, 900 18, 500 19, 100 - ­ - ­ - ­ - ­ - ­ - ­ RFS Case 18, 700 18, 600 19, 100

- 200 100 0

- 1

. 1 % 0. 5% 0. 0% E I A C a se 18, 600 18, 500 19, 100

- 300 0 0

- 1. 6% 0. 0% 0. 0% O n -Road 2012 2015 2020 2012 2015 2020 2012 2015 2020 Refer e nce 12, 000 12, 000 12, 800 - ­ - ­ - ­ - ­ - ­ - ­ RFS Case 11, 800 12, 000 12, 800

- 200 0 0

- 1

. 7 % 0. 0% 0. 0% E I A C a se 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 Refer e nce 6, 900 6, 500 6, 300 - ­ - ­ - ­ - ­ - ­ - ­ RFS Case 6, 900 6, 600 6, 300 0 100 0 0. 0% 1. 5% 0. 0% E I A C a se 6, 800 6, 500 6, 300

- 100 0 0

- 1

. 4 % 0. 0% 0. 0%

Sensitivity To n s /Year Tot al 2012 2015 2020 2012 2015 2020 2012 2015 2020 Refer e nce 18, 600 18, 200 18, 700 - ­ - ­ - ­ - ­ - ­ - ­ RFS Case 18, 200 18, 000 18, 300

- 400

- 200

- 400

- 2

. 2 %

- 1

. 1 %

- 2

. 1 % E I A C a se 18, 000 17, 800 18, 200

- 600

- 400

- 500

- 3

. 2 %

- 2

. 2 %

- 2

. 7 % O n -Road 2012 2015 2020 2012 2015 2020 2012 2015 2020 Refer e nce 11, 700 11, 700 12, 400 - ­ - ­ - ­ - ­ - ­ - ­ RFS Case 11, 300 11, 400 12, 000

- 400

- 300

- 400

- 3

. 4 %

- 2

. 6 %

- 3

. 2 % E I A C a se 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 Refer e nce 6, 900 6, 500 6, 300 - ­ - ­ - ­ - ­ - ­ - ­ RFS Case 6, 900 6, 600 6, 300 0 100 0 0. 0% 1. 5% 0. 0% E I A C a se 6, 800 6, 500 6, 300

- 100 0 0

- 1

. 4 % 0. 0% 0. 0%

200

## Chapter 5: Ai r Quality I m pacts

5.1 Oz one We perfor med ozone air quality m odeling simula tions f o r th e easte rn United States using the ozone R e sponse Surface Model (RSM) to estim at e the effects of the p r ojected changes in
em issions from gasoline vehicles and equipm en t. The ozone RSM is a sc reening

- lev e l air quality m o deling tool that allows users to quickly a ssess the estim ated air quality changes over the m odeling dom ain. The ozone RSM is a m odel of a full-scale air quality m odel and is based on statistical relationships betw een model inputs and outputs obta ined fro m the full-sc ale a i r quality m odel. In other words, the ozone RSM uses statistical technique s to relate a response variab le to a set of f

actor s that are of inte rest, e.g., em issions of precursor pollutants from particular sources and locations. The follo wing section describes the m o deling m e thodology, including the developm ent of th e mu lti-dim ensional experimental design for control strategies and imple m entation and verification of the RSM tec hnique. Additional detail is available in the Air Quality Modeling T echnical Support Docum e nt (AQM TSD) that was drafted for the Mobile Source Air Toxics Rule Propos al (published March 29, 2006). A AAA

The foundation for the o z one respon se surface m e tam odeling analyses was the CAMx modeling done in support of the final Clean Air In terstate Rule (CAIR). The CAIR m o deling is fully described in the CA IR Air Quality Mode ling Technical Support Docum e nt, but a brief description is provided below. B BBB The modeling procedures used in the CA IR analysis (e.g., dom a in, episodes, m e teorology) have been used for several EP A rulem aking analyses over the past five years and are well-e stablish ed at th is po int.
The ozone RSM uses the 2015 controlled CAIRem issions inventor y as its baseline,
assum i ng f u ture f u el qua lity rem a ins unchanged f r om pre-Act levels, whic h serves a s the baseline for the analysis of the final RFS standards. C CCC W e then com p are these baseline
em issions to the em issions which would have occurre d in the f u ture if f

u el quality had rem ained unchanged f r om pre-Act levels to th ose which will o ccur with f uel quality ref l ecting the increased renewable fuel use projected in the future. This approach differs from that trad ition a lly taken in EP A regulato r y impact analyses. Traditionally, we would have comp ared future em issions with and without the requirem e nt of the Act. However, as described in Chapter 1, we expect that total renewabl e fuel use in the U.S. in 2012 to exceed 7. 5 billion gallons even in the ab sen ce of the Renewable Fuel Standard(RFS). Thus, a traditional regulatory impact analysis would have shown no i mpact on em issions or air quality.
The m odeling simu lations that comp r i sed the m e tam odeling were conducted using CAMx version 3.10. It s hould be noted that b ecause the ozon e RSM is built from CAMx air quality m o del runs, it therefore has the sam e strengths and lim itations of the underlying m odel and its inputs. CAMx is a non-proprietary comput er m odel that simu lates the form ation and fate of photochem i cal oxidants includin g ozone for give n inpu t sets of m e teorological conditions an d
em issions. The gridded m e teorological data for three historical episode s were developed using the Regional At m ospheric Modeli ng System (RAMS), version 3b. D DDD In all, 30 episode days
were m odeled using frequently-occurring, ozone- conducive, m e teorological conditions from the

201summer of 1995. Em issions estim ates were devel oped for the evaluation y ear (1995) as well as a future year (2015). The CAMx model applications were perform e d for a dom ain covering all, or portions of, 37 States (and the District of Columbia) in the E a stern U.S., as shown in Figure 5.1-1. The dom a in has nested horizontal grids of 36 km a nd 12 km

. However, the output data from the
m e ta m odeling 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 dom a i n of the ozone RSM is the 37 Eastern states, the expanded use of ethanol in fu el is expected to occur nationwide. Chapter 4 describes the nationwide inventory impacts a sso ciated with the F standards. Figure 5.1-1. Map of the CAMx Domain Used for RFS Ozone Metamodeling

The ozone RSM used for assessing the air qual ity impacts of expanded ethanol use in fuel was developed broadly to look at various control strate gies with respect to attaining the 8-hour ozone NAAQS. The experim e ntal design for the ozone RSM covered three key areas: type of precursor em ission (NOx or VOC) , em ission sour ce type (i.e., onroad vehicles, nonroad vehicles,
area sou r ces , electrical g enerating utility (EGU) sources, and non-utility p o int sources ), and location in or out of a 2015 m odel-projected residual ozone nonattainm e nt area. This resulted in a set of 14 em issions factors.
The 14 em is sion factors were random l y vari ed and used as inputs to CAMx. The experim e ntal design for these 14 factors was developed using a Maximi n Latin Hypercube
m e thod. Ba sed on a rule of thum b of 10 runs pe r factor, we developed an overall design with

202140 runs (a base case plus 139 control runs). The range of em issions reductions considered within the metam odel ranged from 0 to 120 pe rcent of the 2015 CAIRe m issions. This experim e ntal design resulted in a set of CAMx si mulations that se rve as the inputs to the ozone response su rface m e ta model. To develop a response s u rface appro x im ation to CAMx, we used a mu ltidim ensional kriging approach, im plem ented through the MI XED procedure in SAS. W e m odeled the predicted ch anges in ozo n e in each C A Mx grid ce ll as a function of the weighted av erage of the modeled responses in th e experim e ntal design. A response-surface was then fit for several ozone
m e trics, nam e ly the ozone design value, the 1- hour m a xi mu m 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 estim ated ineach grid cell covered b y the m odel for each ozo n e m etric except the ozone design value. The ozone desi g n value is th e m athem atically d ete rmined pollutant concentration at a particul ar m onitoring site that mu st be reduced to, or m a intained at or below the National Am bient Air Quality S t andardto assum e attainm e nt. The 8-hour ozone design value is the 3-year average of the fourth-highest daily m a xi mu m 8-hour average ozone concentratio ns m easured at each m onitor withinan area over each year, which must not exceed 0.08 ppm (85 ppb, considering round-off). Thus, oz one design values only exist for grid cells which contain ozone m o nitoring stations and where ozone attainm e nt has been an issue. Ozone design values have been developed f or 525 of the 2696 counties in the 37 state region. of the 31 these The specific ozone design values used inthis analysis are thos e for 2001, which represent the average of the ozone design values determ ined for three, three-year periods (1999-2001, 2000-2002, and 2001-2003). Validation was perform e d and is summarized in the Mobile Source Air Toxics rule Air Quality Modeling Technica l Support Docum e nt. The validation exercises indicated that the ozone RSM replicates CAMx response to em issions changes very well for most em issions com binations and in most locations.
The ozone RSM lim its the num ber of geogra phically distinct changes in V O C and NOx
emissions which can be simu lated. Emissions from m o tor vehicles and nonroad equipm ent can be varied separately. Distinct percentage ch ang e s in either th e m o tor vehicle or non ro ad inventories can also be applied in ozone nonattainm ent and attainm e nt areas. However, distinct
em ission impacts cannot be simulated in vari ous ozone nonattainm ent areas (e.g., Chicago and Houston or New York and Kansas City). This limits our ability to s i mu late the im pact of increased ethanol use in a coupl e of ways. First, ethanol us e is not geographically uniform across the U.S., either cu rrently or in the future. Thus, the emission im pacts resultin g from changes in ethanol use also varies geographically. Second, the em ission 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 exp e rien ce as mu ch of an increas e in V O C emissions with inc r eased ethanol use as areas which gran t eth a nol blend s an RVP wai ver
. Third, the impacts of new ethanol p l an ts will b e ev en m o re geographically f ocused. The Ozone RSM cannot generally be applied to model the emi ssion im pacts from such local sources for a couple of reasons. One, the location of new ethanol plants is difficult to predict in m a ny cases. Two, the impact of these plants on local em issions can be very large in percentage term s given the absence of a lot of other industrial activity. The Ozone RSM was de signed to represent the ozone impact of the
sam e change in VOC or NOx e m issions across a broad region (e.g., all attainm e nt areas). Therefore, it cannot be used to m odel the impactof a large change in one county’s em issions

203without also assum i ng the sam e change in the upwind county’s em issions. As not every county will con t ain a new ethan o l plan t, the assum p tions inheren tin the Ozone RSM do not m a tch the situation of a new individual point s ource, such as an eth ano l plan t. We developed a m e thodology which would best approxim a te the impact of changes in local emissions on the o zone level ineach local area, while maintaining as much of the impact of ozone transport from other areas as possible give n the above m e ntioned lim itations. We do this by running the ozone RSM twice for each scen ario and drawing the resu ltant ozone impact from the run which best m atched the emission im pact e xpected in a particular lo cal area, con sidering both the change in em issions m odeled for that particular local area, as we ll as that occurring in upwind areas.
First, as m e ntioned above, ethanol use is expe cted to change dram atically in som e areas, but not at all in others. Averaging the em i ssion im pacts acro ss these two types of areas and estim ating the associated ozone im pact would be very m i sleading. No area would be likely to experience the ozone impact predicted. Som e areas would e xperien ce a much greater im pact, while others would experience no impact. Theref ore, the f i rst step in us ing the Ozone RSM to predic t the o zone im pacts related to the RFS is to estim ate the change in V O C and NOx
em issions in those areas ethanol blend m arket share changed si gnificantly. As was done in the analysis of local em ission impacts presented in S ection 4.1.3.3 above, we defined a significant change in ethanol blend m arket share as a change of 50% or more. This focuses the change in
emissions inthose areas where th e change is likely to occur. As discussed in Chapters 3 and 4, the eff ect of ethanol use on em issions differs depending on the baseline fuel quality and the app licable RVP standards. In particular, ethanol use has sign ificantly d i fferent im pacts on emissions in RFG, low RVP and 9 RVP areas. Therefore, in order to better pr ed ict the ozone impact likely to occur in s p ecific areas , we estim ate the change in V O C and NOx emissions separately f or RFG, low RVP and 9 RVP areas (per abov e, only for thos e areas in each case where ethanol blend m arket share changed by 50%
or m o re). The Ozone RSM only covers the 37 easternm ost states in the U.S. Theref ore, we limited the calculation of VOC and NOx e m ission impact s to only those states. The Ozone RSM was developed w ith the year 2015 as the default year. Since we develop m o st of our impac t s 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 term s of t o tal on road and total no nroad sources, respectively, and both em ission cate gories inclu d e diesels. Theref ore, we included estim ates of VOC and NOx e m issions from diesel vehicles and equipm ent in 2015 in our calculation of the
em ission impacts. These diesel em issions do no t change between the various RFS scenarios. However, they do reduce the effective percentage change in V O C and NOx em issions which is projected to occur. Overall, these analyses ar e very similar to those described in Section 4.1.3.3 above, with the exceptions of the limitation to 37 states and the in clusion of diesel em issions. The results of these calculati ons are shown in Table 5.1-1.

204Table 5.1-1. Emission In puts to Oz one Modelin g: Change in Total Mobile Source s Emissions in 37 Eastern Sta t es w h ere Ethanol Use Changes Significantly, July 2015 (percent cha n ge) VOC NOx On Roa d Non R oad On Roa d Non R oad Attain. (9 RVP) Non
Attain. Attain. (9 RVP) Non
Attain. Attain. (9 RVP) Non
Attain. Attain. (9 RVP) Non
Attain. Primar yAnaly s is
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% Sensitiv it yAnal y s is
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 catego ry of 9 RVP areas is ve ry similar to th e set of attainm ent areas in the Ozone RSM. Therefore, the ap plication of the em issi on impacts expected in 9 RVP areas in the Ozone RSM was straightforward. However, both RFG a nd low RVP areas together generally comprise the set of nonattainm e nt areas in the Ozone RSM. As seen in Table 5.1-1, the expected em ission impacts of the various R FS scenarios differ si gnificantly depending on whether the area has RFG or low RVP fuel. Both sets of em ission impacts could not be run in the Ozone RSM at the sam e tim e. Therefore, we ran the Ozone RSM twice. The f i rst runapplied the emission impacts estim ated for RFG areas to the ozone nonattainme nt areas in the Ozone RSM and applied the
em ission impacts for 9.0 RVP areas to the ozone a ttainm e nt areas in the O z one RSM. This run should produce satisfactory projections of ozone impacts for all areas except those areas with low RVP, as well as thos e areas where etha nol use is not expect ed to change. The second run applied the em ission impacts estim a ted for Low RVP are a s to the ozone nonattainm e nt areas in the Ozone RSM and appl ied the em ission impacts for 9.0 RVP areas to the ozone attainm e nt areas in the Ozone RSM. This run should produce satisfactory projections of ozone impacts for all areas ex cept those areas with RFG, as well as tho se areas where eth a nol use is not expected to change.
For both runs of the Ozone RSM, we set the predicted change in oz one to zero in those counties not expected to experien ce 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 transpo r t are mu ch sm aller than tho se associated with chan ges in lo cal
em issions. This is particularly true in this ca se, where the percentage change in em issions would be the sam e in both the local and upwind areas.

205We m erged the resu lts o f the two runs by attribu ting ozone impacts from each county accord ing to their nonattainm e nt designation and fuel type. F o r non-attainm ent areas, this cho i ce was obvious. Non-attainm e nt counties with RF G program s were assigned the ozone impacts from the first run (i.e., th e run where the cha nges in VOC and NOx e missions were the average of those observed for RFG areas). N on-atta inm e nt 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 e missions were the average of those observe d 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 sam e e m ission changes to attainm e nt areas. Thus, the local em ission impacts would be identical in the two Ozone RSM runs. Ozone tr anspo r t is also likely identical for the vast
m a jority of these counties, gi ven that they are lik ely downwind from other attainm ent area counties. The only difference occurs if an attainm e nt area is downwi nd of a RFG or Low RVP
area. For a nationwide analys is such as this on e, we were not able to determ ine for each attainm ent area whether a potential upwind area was m ore likely to be an RFG or Low RVP are a
. Therefore, we chose to use the ozon e im pacts re sults from the first Ozone RSM run of the m odel (i.e., where the em ission impacts for RFG areas we re applied to ozone nonattainm e nt areas) for all attainm e nt areas. We chose th is run becaus e RFG areas tend to have the higher o z one levels than Low RVP areas and thus, would be m ore lik ely to affect areas downwind. W e present the ozone impacts of increased ethanol use resulti ng from this m e thodology in the following section.
5.1.1 Oz one Response Surface Metamodel Results
This sec tion summarizes the resu lts o f our m odeling of ozone air quality impacts in the future with and without the expanded use of ethano l in fuel. T h e impact of increased ethanol use on the 8-hour ozone design values in 2015 are pres ented in Table 5.1-2. T h e changes presented in Table 5.1-2 are for those counties with 2001 modeled design values. 5 7 The Chapter 5 Appendix presents the impacts of increas ed ethanol use on a num b er of alternative m easures of
am bient ozone concentration.

57 20 01 d e si g n valu es w e r e calcu lated as an aver ag e of th e 199 9-2 001 , 20 00- 200 2 and 20 01 -20 0 3 d e sign v a lu es at each m onitori ng site. Please see the Air Quality Modelin g Technical Support Doc u m ent for the final Clea n Air In terstate Ru le for ad d ition a l i n fo rm atio n
.

206Table 5.1-2. Impact of Increased Ethanol Use on 8-ho ur Ozone Design Values in 2015 (ppb)
RFS Case EIA Case Primary Ana l y s i s M

i ni mum Cha n ge

- 0 .015 0.000 M

axi m u m Chang e 0.329 0.337 Average Change Across 37 St at

es 0.057 0.079 Po pul a t i on- W ei ght e d Change Acro s s 37 St a t

es 0.052 0.056 Average Change Where Et ha nol Us e Ch anged Si gnif i c

a n tl y St ates 0.153 0.181 Po pul a t i o n

- W ei ght e d Change Where Et hano l Us e Changed Si gnif icant l y States 0.154 0.183

Sens it iv it y Anal ys is M

i ni mum Cha n ge

- 0 .115 0.000 M

axi m u m Chang e 0.624 0.549 Average Change Across 37 St at

es 0.111 0.142 Po pul a t i on- W ei ght e d Change Acro s s 37 St a t

es 0.092 0.096 Average Change Where Et ha nol Us e Ch anged Si gnif i c

a n tl y St ates 0.300 0.325 Po pul a t i o n

- W ei ght e d Change Where Et hano l Us e Changed Si gnif icant l y States 0.272 0.315

As can be seen, ozone levels generally increase w ith increased ethanol use. This is likely due to the projected increases in both VOC and NOx e m issions. Som e areas do see a sm all decrease in ozone levels. In our prim ary analys is, where exhaust em issions from Tier 1 and later onroad vehicles are assum e d to be unaffected by ethanol use, the population- weighted increase in
am bient ozone levels is 0.052-0.056 ppb. Since th e 8-hour ambient ozone standard is 0.08 ppm (85 ppb), this increase represents about 0.06 perc ent of the standard, a very sm all percentage 5 8

. W h ile sm all, this f i gur e includes es se ntia lly ze ro changes in ozone in areas where ethanol use did not change. When we focus ju st on those areas where th e m arket share of ethanol b l en ds changed by 50 percent or m o re, the population-w e i ghted increase in am bi ent ozone levels rises to 0.154-0.183 ppb. This increase represen ts about 0.2 percent of the standard. In our sensitivity analysis, where exhaust em i ssions from Tier 1 and later onroad vehicles
are assum e d to respond to ethanol like Tier 0 ve hicles, the population-we ighted increase in
am bient ozone levels across the entire 37 state area is slightly less th an tw ice 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 m arket sh are o f ethanol blends changed by 50 percent or m ore, the population-weighted increase in am bient ozone levels rises to 0.272-0.315 ppb. This increase represents about 0.35 per cent of the standard. For the primary analysis, we also present th e cou n ties with th e larg est inc r eases in th e ozone design value. Table 5.1-3 presents the county level ozone design value impacts of the RFS case, while Tab l e 5. 1-4 presen ts the sam e info rm ation f o r the EIA cas e. It is importan tto note that the results of this ozone response surface m e ta m ode ling exercise is m eant for screening-level purposes only and do es not rep r es ent the resu lts that would be obtained from

58 A ppe n d i x I of 40 C F R Part 5 0

.

207full-scale photochem i cal ozone m ode ling. It is al so important to note that the ozone R S M results indicate that the counties which are projected to experience the gr eatest increase in ozone design values are g e nerally cou n ties that are projected to have am bient concentrations well below the 0.08 ppm ozone standard in the 2015 baseline.
Table 5.1-3. RFS Case, Primary A n alysis: 2015 Oz one Response Surface Metamodeling Results afor Counties with Largest Increases in Ozone 8hr De sign Va lue (ppb) Due to Increased Use of Ethanol
State Name County Name 2 015 B a seline (Po s t
- C A I R ) b 2 015 R F S Ca se Effect of E x p a nded Ethanol Use (ppb) 2 015 Popul ati o n Arka nsas Crittend en Co 78 78.328 9 0.3 53,852 Ohio Geau ga Co 82.5 82.797 2 0.3 108,60 0 Ohio Mahoni ng Co 74.7 74.994 3 0.3 248,54 5 Indiana Lake Co 80.7 80.992 9 0.3 490,79 6 Ohio Medina Co 72 72.290 9 0.3 187,68 6 Ohio Summit Co 77.4 77.690 1 0.3 557,89 2 Maine YorkCo 77.6 77.882 5 0.3 210,00 6 Ohio StarkCo 71.7 71.970 7 0.3 384,67 2 Ohio Clinton Co 75.7 75.970 5 0.3 50,635
Loui siana We st Baton Rou ge Pari sh 77.4 77.668 5 0.3 23,202
Loui siana Livingsto n Parish 76.6 76.865 6 0.3 141,80 7 Illinois Coo kC o 81.1 81.360 5 0.3 5,362,93 2 Indiana Shelby Co 76.2 76.458 7 0.3 47,904 Ohio Knox Co 71.4 71.654 1 0.3 62,138 Alabama Mobile Co 68 68.251 4 0.3 430,34 1 Loui siana J e ffers on Paris h 77.1 77.351 0.3 512,96 3

aThe Oz one R S M is m eant for sc reeni n g-level purpose s o n l y an d do es no t r e pr esen t r e su lts th at w ou ld b e obt ai ne d fr om ful l

- scal e p hot ochemi cal ozo n e m odel i n g. I n part i c ul ar , t h e m odel doe s n o t acco u n t f

o r chan ges i n C O emi ssi ons or V O C re act i v i ty , b o th o f w h i c h sh o u l d de crease wi th i n c r eased ethanol use an d di rect i onal l y reduce oz one
. i n ar eas whe r e ozone form ation is VOC -limited. b Th e Clean Air In terstate Ru le (CAIR) m o d elin g is fu llydescrib e d in th e CAIR Air Qu ality Mo d e lin g Techn i cal Su pp ort D o c u m e nt (D oc ket EPA -H Q- O AR -2 0 0 5 -0 0 3 6 ).

208Table 5.1-4. EIA Case, Primary A n alysis: 2015 Oz one Response Surface Metamodeling Results for Counties w i th Largest Increases in Ozone 8hr De sign Va lue (ppb) Due to Increased Use of Ethanol
State Name County Name 2 015 B a seline (Po s t
- C A I R ) c 20 1 5 EI A Cas e Effect of E x p a nded Ethanol Use (ppb) 2 015 Popul ati o n Ohio Geau ga Co 82.5 82.836 9 0.3 108,60 0 Ohio Clinton Co 75.7 76.021 8 0.3 50,635 Ohio Mahoni ng Co 74.7 75.021 3 0.3 248,54 5 Arka nsas Crittend en Co 78 78.320 4 0.3 53,852 Ohio Summit Co 77.4 77.717 5 0.3 557,89 2 Missi s s ippi Adams Co 67.2 67.516 4 0.3 33,495 Ohio StarkCo 71.7 72.015 3 0.3 384,67 2 Indiana Shelby Co 76.2 76.511 5 0.3 47,904 Maine YorkCo 77.6 77.902 0.3 210,00 6 Ne w Yor k Wayne Co 71.6 71.892 6 0.3 103,84 6 Texas Travis Co 69.4 69.691 2 0.3 1,022,77 2 Ohio Medina Co 72 72.290 9 0.3 187,68 6 Maine H a nc ockC o 76.8 77.090 4 0.3 55,606 Maine Kenneb ec Co 64.9 65.190 3 0.3 122,36 3 Loui siana Livingsto n Parish 76.6 76.888 3 0.3 141,80 7
Loui siana We st Baton Rou ge Pari sh 77.4 77.686 9 0.3 23,202 Loui siana Lafourch e Pa rish 72.7 72.984 0.3 95,881 Missi s s ippi Wa rre n Co 56.2 56.482 7 0.3 52,075 Michigan Huron Co 71.9 72.176 6 0.3 37,530 Ohio Franklin Co 77 77.271 6 0.3 1,181,57 8 Ohio Trumbull Co 80 80.271 3 0.3 227,54 6 Loui siana J e ffers on Paris h 77.1 77.370 7 0.3 512,96 3 Florid a Pinellas Co 62.3 62.563 9 0.3 998,86 4 Ohio Dela wa re C o 72.1 72.360 6 0.3 149,34 1 Ohio Knox Co 71.4 71.657 9 0.3 62,138 Florid a Duv al C o 50.6 50.856 8 0.3 895,67 8 Indiana Marion Co 74.6 74.856 5 0.3 889,64 5 Indiana Madison Co 72.9 73.156 4 0.3 140,57 5 Massachuset t s Middle s ex Co 75.8 76.056 4 0.3 1,498,84 9 Michigan Oakl and Co 79.2 79.454 2 0.3 1,355,67 1 Pennsylvani a Beaver Co 70.5 70.752 8 0.3 184,64 9 Ne w Yor k Monroe Co 74.3 74.552 1 0.3 754,40 5 Missi s s ippi Har r i s on Co 69.3 69.551 7 0.3 216,92 6

There are a num b er of important caveats concerning our es tim a ted ozone impacts using the Ozone RSM. The Ozone RSM does not account for changes in CO em issions. As shown in Chapter 4, ethanol use should reduce CO e m issions significantly, directionally reducing am bient ozone levels in areas where ozone form ation is VOC-limited. Accounting f o r the redu ction in CO e m issions in NOx-lim ited areas, however, may have little impact on the ozone impact of ethanol use.

209The Ozone RSM also does not ac co unt f o r chan ges in VOC reac tivity. W ith additio nal ethanol use, the ethanol conten t of VOC should increase. Etha nol is less reactive than the average VOC. Therefore, this change should al so reduce am bient ozone levels in a w a y not addressed by the Ozone RSM. Again, like the impact of reduced CO e m issions, this effect applies to those areas where ozone form ation is VOC-lim ited. Another limitation is the RSM’s inability to s i mu late the s p atia l dis tri b u tion of emission im pacts associated with the s t an dard. Instead, we are forced to m ake sim p l i fying a ssumptions about the geographic uniform ity of RFS
em issions impacts, explained above. The caveat s and lim itations associated with the RSM highlight the fact that it should only be used as a screening-level tool to characterize broad trends associated w ith chang e s in different source categories of ozone precursors. Finally, our application of th e Ozone RSM here does not include the impact of em issions from new ethanol plants. Directionally, this will increas e ozo n e levels in the vicin ity o f the new plant. As discussed in C hapter 4, the overall VOC and NOx em ission impacts of new ethanol plants are on ly sligh tly lo wer than th e emission impacts resulting from increased use of ethanol in vehicles and equipm e nt. Given the concentratednature of these impacts, the ozone impacts of these new plants should be a focus of further study in the future. Keeping these lim itation s in m i nd, the expande d use of ethan o l will impa ct the n ation al
em issions inventory of precursors to ozone, such as VOCs and NOx, as described in Chapter 4. Exposure to ozone has been linked to a variet y of respiratory effects including prem ature mortality, h o spital adm i ssions and illnesses resu lting in school absences. Ozone can also adversely affect the ag ricultu ral and forestry sect ors by decreasing yields of crops and forests.
The W i sconsin Departm e nt of Natural Res ources (DNR) recently performed a sim ilar study of the impact of incr eased ethanol use on ozone. E EEEThey estim ated that the conversion of gasoline outside of RFG areas in W i sconsin to E 10 blends would increase ozone in these areas on the order of 1 ppb to as much as 2 ppb. (RFGareas in W i sconsin already contain 10 vol% ethanol.) T his ozone increase was due to the pr edicted increase in NOx em issions associated with ethanol use, since the non-RFG areas in W i sconsin are generally N O x limited for ozone for m ation.
The W i sconsin DNR estim ated the ozone imp act for calendar year 2003 and assum e d that all veh i cles experience the increas e in NOx emissions. Thus, their resu lts are m o re comparable to our sensitivity analysis, than our prim ary analy sis. For th e two increased ethanol use scenarios, o u r sensitiv ity analys is projects increased ozone levels for several W i sconsin counties of 0.35-40 ppb. Because the W i sconsin DNR analyzed calen dar year 200 3 em issions and air quality, their base em ission levels are much higher than those estim ated here for the year 2015. Em ission standards applicable to new vehicles an d equipm ent are con tin ually reducing em issions over tim e. P er the emission m odels used here and by the State of W i sconsin (NONR OAD and MOBILE6), the effect of fuel quality is generall y estim ated in term s of a percen tage ch ange in the base emi ssion lev el. As e mission s from vehicles and equ i pm ent decrease over tim e, the absolute impact of fuel quality chan ges decreases at the same rate. Thus, the absolute em ission changes predicted here f or 2015 could easily be a factor of two lower th an those predicted by

210W i sconsin for 2003. This is likely the prim ary cause of the difference in the two sets of projected ozone impacts.
We receiveda comm ent from the Missouri Departm e nt of Nat ural Resources on the ozone impact analysis perform e d for the NPRM wh ich expressed a concern that our approach of zeroing out the ozone im pact in areas which did no t experience a significant change in ethanol use had the effect of ignoring th e impact of ozone transport du e to increased ethanol use in upwind areas. This comm ent is correct. In a nati onal an alys is such as th is one, it is n o t prac tic al to go through over 3100 counties to determ ine wh ich counties m i ght have not experienced a change in ethanol use in a part icular ethanol use case, but is downwind of an area which did. Still, the iss u e is a po ten tia lly r elev a nt one and o f reasonable interest partic ular ly to th ose tasked with air quality m a nagem e nt.
In an attemp t to app r oxim a te the impact of ozone transport f r om areas which did experience a change in ethanol use on ozone in areas which did not, we perform e d one additional run of the Ozone RSM. This additional run applied the changes in VOC and NOx e mi ssions estim ated ab ove f o r atta inm ent areas f r om our se nsitiv ity analysis f o r the EIA case to emissions in attainm e nt areas, and applied no change in em issions in non-attainm e nt areas. W e then
compared the resulting ozone levels to those fr om the base case, focusing on the difference in ozone levels in non-attainm e nt areas. Em issions in non-attainm e nt areas were the sam e in both cases (no change from the base case). Thus, the difference in ozone levels in non-attainm e nt areas should only be due to changes in em issions and ozone levels in upwind attainm e nt areas.
The results of this comp arison indicated that , in term s of the 8-hour ozone design value, ozone levels in non-attainm e nt areas (i.e., RFG or Low RVP areas) decreased by 0.03 ppb. Thus, the average impac tdue to ozone transport is a redu ctio n in oz one in downwind areas. However, the standard deviation in the ozone impact was 0.05 ppb, indica ting that a significant
num ber of areas experienced an increase, thoughm ost experienced a decrease. This is not surprising given that ozone in som e attainm e nt s areas is VOC limited and m ay be decreasing in this fuel case, while others are NOx lim ite d and m a y be increasing. The m a xi mu m ozone reduction was 0.17 ppb, while the m a ximu m increase was 0.12. More precise local atmospheric dispersion modeling will be neededin order to estim ate this t ype of impa ct for specific nonattainm ent areas.
In summ ary, we estim ate that the m e asur able changes in VOC and NOx which are a result of increased ethan ol use will, on average, result in sm all increases in am bient ozone for m ation. As we discussed above, the ozone m odeling results in a net increase in the average population weighted ozone design value m e tric m easured within the m o deled dom ain (37 Eastern s t ates and the District of Colum bia). In Appendix A, we also present th e im pacts of increased ethanol use on a num ber of alternative m easures of am bient ozone concentration. W e acknowledge, however, that to the extent it occurs, increasedfuture levels of a m bient concentrations of ozone related to th e incr eased use of ethan o l m ay result in de trim ental he alth and welfare effects due to ozone.

211

### 5.2 Particulate Matter

5.2.1 Impact of C h anges in Direct PM Emissions
The am ount of data evaluating the impact of ethanol and MTBE blending on direct
em issions of PM from ga soline-fueled vehicles is extrem ely lim ited, as discussed in Chapter 3. Most stud ies do not tes tPM emissions from vehi cles fueled with unlead ed gasoline, because the level of PM emissions from prope rly operating vehicles is usually very low, less than 0.1 g/mi

. Two studies indicate that the addition of ethanol mi ght redu ce direct PM emissions from gasoline vehicles F FFF , G GGG

. However, both studies were pe rform e d under wintertim e conditions and one at h i gh altitud e
. One of the studies on ly c onsisted of three veh i cle s. The available da ta indicate that ethanol blending m i ght reduce ex haust PM em issions under very cold w eather conditions (i.e., 0 F or less), particu l arly at hi gh altitud e
. There is no ind i cation of PM em ission reductions at higher temp eratures or under warmed up conditions. Thus, the data are certainly too limited to support a quantitative estim ate of the effect of ethanol on P M em issions.
5.2.2 Potential Impact of C h anges in Secondary PM Formation In addition to being em itted directly f r om a com b ustion sou r c e , f i ne partic les can b e for m ed through a series of che mical reactions in the atm osphere 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 atm o sphere to form ammonium sulfate and ammonium nitr ate. Particles generated through this gas to particle conversion are referred to as secondary aerosols (SA) and repres ent a s i gnifican t po rtion of am bient fine particulate m att er. Studies have shown that as much as 70% of the total organic carbon in ur ban particulate m a tter ca n be attributed to secondary organic aerosol (SOA) form ati on although the am ount can also be less. H HHH Secondary P M tends to f o rm more in the summ erwith higher temp eratures and m o re intense sunlight. Source-receptor m odeling studies conducted in the Los Angeles area is 1993 by Schauer et al I IIIindicate that as mu ch as 67% of the fi ne particulate m a tter collected could not be attributed to prim ary sources. The authors conclu ded that much of this unidentifiable organic
m atter is secondary organic aerosol f o rm ed in the atm osphere. 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 m easurem ents m ade in southern California can be attributed to SOA. Gas phase VOCs are oxidized by OH, NO 2 , peroxyacety l nitra t e (PAN), and ozone in the atm osphere, but their propensity to condense in th e particle ph ase is a f unction of two factors: volatility an d reactivity. To accumu l a te as an aerosol, a reaction pr oduct must first be for m ed in the gas phas e at a con centration equ al to its s a tu ration concentration. Th is requ irem ent will no t be m e t if the relevant gas-phase reactions of the VOC are too slow or if the vapor pressure of the reaction product is higher th an the in itia l concen tr ation of its VOC precursor. J JJJ Limited data for reaction rate constan t s determined both experim e ntally an d estim ated by structural

212relationsh i ps are availab l e in the pub lished l iterature. However, the atm o spheric chem istry behind SOA reaction rates and the estim ated aerosol yield is highly complex and carries with it a great deal of uncertainty. Research in this area is ongoing and thus the cap acity to quantitatively model SOA for m ation is not ye t a straightforward process. In general, all reactive V O C are oxidizedby OH or other compounds. Addition a lly, alkenes, cycloalkenes, and other olefin ic compounds can react with ozone and NO 2 to for m secondary aerosols. In fact, ozone is responsible for nearly all the SOA form ation from olefins, while OH plays little or no role at all (Grosj ean and Seinfeld, 1989; Izum i and Fukuyam a , 1990). Many VOC, however, will n e ver form secondary organic aerosol under atm o spheric conditions regard less o f their reactivity. This is because the products of reactions of these compounds have vapor pressures that are too high to form aerosol s at atm o spheric temp eratures and p r essures. These include all alkanes and alkenes withup to 6 carbon atom s, benzene and m a ny l owmolecular w e ight carbonyls, chlorinated com pounds and oxygenated solvents (Grosjean, 1992). The VOC that have the greatest propensity to form SOA include arom atic hydrocarbons (such as tolu ene but eveninclud ing b enzene), h i g h er m o lecular weight o l efins and cy clic o l efins, and higher molecular w e ight paraffi ns. Kleindienst et al suggest that a high fraction of SOA is due to arom atic hydrocarbon precu rsors. Furthermore, “aromatic products having a single alkyl group on the arom atic ring were found to repr esent a ‘high-yield’ fam ily (e.g., toluene, ethylben zen e); com pounds having multip le m e thyl groups (e. g., m
- xylene, 1,2,4­ trim ethylbenzene) were found to represent a ‘low -yield’ fam ily” (Kleindienst, 269). A ll of the above m e ntioned VOC precurso r s are important e ither becau se there are large am ounts of these particular VOC emitted per day, or b ecause a large fraction o f the VOC r eacts, o r a c o m b ination of the two. Based on VOC e missions inventory dataco llected in the Lo s Angeles area, the m o st important aerosol precursors (in the LA area usi ng 1982 VOC e missions invent ories) 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 Func tio nal Group kg emitte d daily * Seconda r y PM % Produced (k g)* yi e l d Aromatics 2239 85 3061 1.37 Olefins Alkene s 3116 3 608 1.95 Cycli c Olefin s 3220 144 4.47 Terp ene s 6000 626 10.43 Paraffins Alkane s 1404 93 368 0.26 Cyclo a lkane s 3799 6 96 0.25 *Source: Grosjean et al, 1992

These predictions are a functi on of input data collected in the Los Angeles area, and
assum e a m bient levels of [ozone] = 100 ppb, [OH]=1.0x10 6 molecules / cm 3 , and [NO3]=0 with 6 hours of reaction tim e. Arom atics are the larg est functional group in te rm s of the absolute

213quantities of VOC emitted daily, and thus they ev entually f o rmthe m o st SOA. Likewise, m a ny high m o lecular weigh tp araffins (alk anes) form SOA on a significant s cale sim p ly because the i r em issions are high. However, the relative fraction of paraffins that react is less than that of
arom atics in sm og cha mber experiments simu la ting SOA form ation in the atm osphere. For olefins, the alkenes exhibit a com bination of both relatively hi gh em issions, and a high fraction of VOC reacted to form SOA. Cyclic olefins, incontras t , ar e em itted in relatively lo w levels, bu t a high fraction of these VOC react and the end re sult is a p r o portion ally higher SOA yield than with the alkenes. Lastly, th ere are several “miscellaneous” compounds and terp enes that are
em itted on a relatively s m all scale (in southern C a lifornia), b u t that produ ce a substantial am ount of secondary organic aerosol. Researchers at EPA recently completed a field study in the R a leigh/Durham area of North Carolina that investigated the contribu tion of various sources to ambient PM 2.5 concentratio ns. K KKK In the study they identified toluene as an SOA precursor. They estim ate that m obile sources con tribute near ly 90% of the tota l tolu ene em i ssions in that region based on a chem ical m a ss balance approach. At the sam e time, however, SOA attribu t able to non- f u elrelated VOC (i.e., biogenic em issions) was found to be an even larger contributor to S OA (i.e., toluene was not lik ely th e dominan t source of SOA in this area). This study is currently undergoing peer review and will be published sh ortl y. Qualitatively, ho wever, this infor m ation is still quite useful since the study identifies a contributing source of SOA that is attributable alm o st entirely due to m obile sources.
VOC reactio n rates inc r e ase with incr easing am bient temp erature a nd sunlight intensity, so the level of SOA for m ed is mu ch higher insummer than in winter. E ven in the m ore temperate coastal clim ates of southern CA, studies have found the summ er tim e concentration of SOA calculated through Che mical Mass Balance models show SOA for m ation to be anywhere from 2 – 5 tim es higher in summer than winter. In a study conducted at both urban and rural locations in the south eas tern United Sates, th e co ncentration of SOA in the summ er a nd early fall was roughly 2-3 tim es that of colder months L LLL

. As m entioned in Chapters 2 and 3, the addition of ethanol should reduce arom atics in gasolin e, which will in tu rn reduc e th e arom atics emitted in th e exhaust. However, quantif ying the em ission reduction is not possible at this tim e due to a lack of speci ated exhaust data for newer vehicles runn ing on ethanol b l ends. In ad dition, increased NOx em issions res u lting from the increased use of ethanol could in creas e the fo rm ation of nitrate PM. Based on the following, we believe that it is likely that the decr ease in secondary PM from organic arom atic h ydrocarbons is lik ely to exceed th e increase in secondary nitrate PM. In 1999, NOx em issions from gasoline-fueled vehicl es and equipm ent comprised about 20% of national NOx emissions from all sources M MMM

. In areas where eth a nol use increases dram atically, NOx e m issions from ga soline-fuel ed vehicles and equipm e nt increases roughly 5­ 10%. This is roughly equivalent to a 1-2% increase in NOx em issions nationwide. In contrast, gasoline-fueled vehicles and equipment compri sed over 60% of all national gaseous arom atic VOC em issions 5 9

. In areas where ethano l us e increases dr am atically, fuel

214arom atic content decreases by about 4 vol% in the summ ertim e, averaged across conventional gasoline and RFG. This represents about a 15% reduction from a base le vel of around 27 vol%. Assum i ng a proportional relationship between fuel arom atics and arom atic emissions, this represents about a 24% reduction in arom atic emissions nationwide.

In m o st urban areas, am bient levels of excess summ er carbonaceous PM (a reasonab l e estim ate of secondary organic PM) tend to exc eed those of secondary nitrate PM. Thus, direc tiona lly , it appe ars likely that a net redu ctio n in am bient PM levels will r esult f r om increased ethanol use. However, this should be considered a rough com p arison at this tim e. A more precis e com p arison will have to await th e incorporationof secondary organic aerosol for m ation into m odels, such as CMAx.
The research to facilitate th is incorporation is currently underway. EPA ORD scientists
are cu rren tly carry ing out a wide variety of laboratory studies to ref i ne the SOA chem istry
m echanism s for use in th e next version of the CMAQ m odel, which is exp ected to be c o m p leted in 2007 and subm itted for peer review. This in form ation should be available in tim e for the
comprehensive study of the Act’s fuel requirem ents which is due in 2009. 6 0

59 Based on inte rnal analyses of emissio n s i n ven t or ies.

60 Subj ect to fu nd ing

.

215

## Chapter 5: Appendix

216Table 5A-1. 2015 Oz one Response Surface Meta modeling Summary Statistics for the RFS Rule a, ; Primary Scenario 8hour Design Value (pp b

) Statistic RFS Scenario EIA Scenari o Minimum Change -0.015 0.000 Maximum Ch ange 0.329 0.337 Average Change 0.057 0.079 Standard Dev i ation 0.086 0.100 Populati on-Weighted Chan ge 0.052 0.056 24hr Average (pp b

) Statistic RFS Scenario EIA Scenari o Minimum Change -0.168 -0.162 Maximum Ch ange 0.197 0.074 Average Change 0.008 0.013 Standard Dev i ation 0.018 0.021 Populati on-Weighted Chan ge 0.014 0.013 1hr Maximum (ppb) Statistic RFS Scenario EIA Scenari o Minimum Change -0.104 -0.094 Maximum Ch ange 0.213 0.180 Average Change 0.015 0.024 Standard Dev i ation 0.033 0.040 Populati on-Weighted Chan ge 0.029 0.033 Average 9-t o-5 (pp b

) Statistic RFS Scenario EIA Scenari o Minimum Change -0.107 -0.097 Maximum Ch ange 0.203 0.141 Average Change 0.012 0.019 Standard Dev i ation 0.027 0.031 Populati on-Weighted Chan ge 0.022 0.024 Average 10- to-3 (ppb) Statistic RFS Scenario EIA Scenari o Minimum Change -0.108 -0.107 Maximum Ch ange 0.207 0.149 Average Change 0.012 0.019 Standard Dev i ation 0.027 0.032 Populati on-Weighted Chan ge 0.023 0.024

a Note th at th e st atistics pr esent ed here represen t eth a nol u se chan ges acr o ss th e entire 37-st ate ozone RS M domain

.

217Table 5A-2. 2015 Oz one Response Surface Meta modeling Summary Statistics for the RFS Rule a ; Sens itiv ity Scen ario 8hour Design Value (pp b

) Statistic RFS Scenario EIA Scenari o Minimum Change -0.115 0.000 Maximum Ch ange 0.624 0.549 Average Change 0.111 0.142 Standard Dev i ation 0.158 0.170 Populati on-Weighted Chan ge 0.092 0.096 24hr Average (pp b

) Statistic RFS Scenario EIA Scenari o Minimum Change -0.181 -0.173 Maximum Ch ange 0.184 0.142 Average Change 0.015 0.024 Standard Dev i ation 0.034 0.041 Populati on-Weighted Chan ge 0.025 0.028 1hr Maximum (ppb) Statistic RFS Scenario EIA Scenari o Minimum Change -0.150 -0.133 Maximum Ch ange 0.498 0.346 Average Change 0.027 0.043 Standard Dev i ation 0.060 0.072 Populati on-Weighted Chan ge 0.052 0.062 Average 9-t o-5 (pp b

) Statistic RFS Scenario EIA Scenari o Minimum Change -0.177 -0.163 Maximum Ch ange 0.400 0.260 Average Change 0.022 0.034 Standard Dev i ation 0.049 0.057 Populati on-Weighted Chan ge 0.040 0.046 Average 10- to-3 (ppb) Statistic RFS Scenario EIA Scenari o Minimum Change -0.182 -0.167 Maximum Ch ange 0.431 0.273 Average Change 0.022 0.035 Standard Dev i ation 0.050 0.058 Populati on-Weighted Chan ge 0.041 0.047

a Note th at th e st atistics pr esent ed here reflect the im pact of e t han o l use ch ange s acr o ss the entire east ern U.S

. 37-st ate ozone RSM d o main

.

218

## Chapter 6: Lifecycle Impacts on Fossil Energy and Gr eenhouse Gases

6.1 Lifecycl e Modeling Lifecycle modeling accounts fo r the energy and em issions froma production process. It incorporates the m a terial aspects, inp ut and outpu t, of each ste p in a produ ct system

. This
m e thod helps to identify key processes and em issi on sources and facilitate s comparisons between processes, consumption of natura l resources, pollutant generation and environm ental burden. It is im portant to note that lifecycle m odeling t ypically provides only general comparisons, based on industry

- wide estim a tes and assu mptions; it d o es not reflect general eq uilib rium i mpacts, such as effects on input m arkets. The results of this type of analysis are highly dependent upon the input data used, the variables considered, and th e assum p tions m ade. Neve rtheless, within these lim itations, it can be an extrem ely useful tool for evaluating som e of the environm ental im pacts of products and processes. For transpo rtation fuels, lifecycle m o deling co nsiders all steps in the production of the fuel. This includes production of the fuel feed stock, transportation of the fuel feedstock to a processing facility, fuel processing, and distribution of the fuel to th e retail outlet. If the analysis considers only the finished product, it is som e times called a ‘well- to-pump’ analysis; if the fuel
com bustion em issions are included, it can be ca lled a ‘well-to -wheel’ analysis. W h ile both approaches have advantages, in this work we have considered ‘well-to-w heel’ im pacts. However, we are not addressing th e issues of vehicle technology and energy efficiency, since we
are m aking the assum p tion that the v ehicle issu es will not b e af f

ected by the presen ce of renewable f uels (i.e., efficiency of com busting one Btu of renewable fuel is equal to the efficiency of com busting one Btu of conventional fuel). To put this type of analysis in to pers pectiv e, con sider the example of gasoline. The f uel feedstock is crude oil. T h e lif ecycle analysis accounts for the ener gy used to extract the oil from the ground and any associated em issions, such as the natural gas that is flared at the w e ll head. Next you evaluate transportation of the crude oil to the refinery. If it is dom e stic crude oil, it
m ay be delivered by pip eline and/or barge. The analys is tak es into acco unt national trends for dom e stic oil transportation, and apportions energy used and em issions generated to each type of transpo r tatio n. For foreign crude oil, the ener gy and emissions from ocean tankers is included, with an es tim a te of the average d i stance traveled by these tankers. Next is an estim ation of the energy use and em issions from the refinery. Be cause gasoline is not the only product produced at the refinery, only a portion of the energy and em issions is allocated to gasoline production. There are different m e thods for m aking this allo cation, based on the value of the co-products or an engineering assessm ent of the energy use and em i ssions from the various units in the refinery. You then evaluate the en ergy use andem issions from transporting the gasoline to m arket, via pipeline and truck, based on na tional average d i stances. Fi nally, vehicle energy use and
emissions are estim ated. Figure 6.1-1 illus trates this pro cess.

219Figure 6.1-1: Lifecycle Production Pr ocess, ‘Well-to-Wh eel’, for Gasoline

C

r u d e R e fue l i n g

F ue l C

r u d e C r u d e R e f

i n

i n g F ue l P

r o d uc t i on T

r a n s p o r t T r a n s p o r t

v

i a pi p e l i ne vi a pi p e l i ne U s e

Lifecycle modeling has been a useful tool in evaluating the environm ental benefits of various alternative transportation fu els. It allows the rep l acem ent fuel to be fairly co mpared against the conventional transportation fuels – gasoline and diesel fuel. T here 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 m o del developed by the Departm e nt of Energy (DOE) Argonne Nati onal Laboratory (ANL) called the Greenhouse Gases, Regulated Em issions, and Energy Use in Transportation (GREET) m odel. EPA has reviewed and m odified GREET so m e what to reflect the data and assumptions appropriate for the RFS. These m odifications are disc ussed further in section 6.1.2.
6.1.1 Scope of th e Lifecycle Analysis

An i m portan tstep in con ducting a lif ecycle anal ysis 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 syst em boundaries (3) what flows are considered (4) tem p oral considerations and (5) m odeling tools used. Ea ch of these components is exam ined in the following sections.
6.1.1.1 Goal The goal of this analysis is to determ ine the GHG em ission and fossil fuel impact of the increased use of renewable fuels. T his anal ysis is based on comp aring future scenarios representing an increased percen tage of the overall transportation sector fuel pool com i ng from renewable f u els comp ared to a reference cas e with the percentage of ren e wable fuels use at curren tlev els. This im plies th at our f u ture sc enarios assum e renewable fuels are disp lacing th eir petroleum based counterparts and causing less to be used. This RIA reflects increases in ethanol production of 85% and 150% respectively from the ba seline. As this analysis is comp ared to a reference case we are on ly interes t edin the s avin g s of the new or m arginal renewable fuels used.

220We have evaluated the absolute savings (e.g., tons of GHG emissions) as well as determ ining what percentage these absolute savi ngs are in term s of overall transportation sector and economy wide em issions and energy use.
6.1.1.2 System Boundaries The lifecycle analysis for the relevant activities identified inthe GREET model is conducted w ithout any regard to the geographic at tributes of where em issions or energy use
occurs. W h ile the prim ary em phasis of a rule m aking analy sis is typically to examine the dom estic im plications of a rulem aking, the lifecycl e analysis of this fina l rule represent global reductions in GHG e mis sions and energy use, not just those occurring in the U.S. For example, under a full lifecycle assessm ent a pproach, the savings associatedwith reducing overseas crude oil ex trac tio n and ref i nin g are in clud ed here, as are the intern a tional em issions associated with producing imported ethanol. This assum e s that fo r every gallon of gasoline that’s not imported into the US, the corresponding quantity of crude o il is no t ex tr acted o rpro cessed to m ake this gasolin e reg ardles s where the ex traction or produ ction takes p l ace. This type of m odeling does not allow for behavioral changes that m a y be o ccur, called “rebounding eff ect,” discussed later.
There are tw o im portant caveats to th is analys is, both dealing with secondary impacts that
m a y result internationally due to the expanded use of renewable fu els within the United States. The first cav eat is the emissions asso ciated with internationa l land use change. Due to decreasing corn exports som e changes to interna tional land use m ay occur, for exam ple, as m o re crops are planted in other regions to compensate for the decrease in crop exports from the U.S. While the em issions associated with dom e stic 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 th at mi ght dire ctly resu lt. Our curren t ly m odeling capability d o es not allow us to a ssess what internationa l land use changes would occur or how these changes would affect greenhouse gas em issions. For example, we would need to know how international cropping patterns would change as w e ll as farming inputs and practices that m i ght affe ct em issions assessm ent. The second caveat results from the assumption of reduced petroleum imports. It is commonly presum ed in econom ic analyses that dem a nd fora norm a l good (i.e., oil) will increase as p r ice decreases. A world wi de reduction of oil price that could result from reduced U.S. imports can reduce the cost of prod ucing transportation fuel which in turn would tend to reduce the price consum ers would have to pay for this fuel. To the extent fuel prices are decreased, dem a nd and c onsumption would tend to increase; this impact of reduced cost of driving is som eti m es referred to as a “rebound effect.” Such a greater consumption would presum ably result in an increase in greenhouse ga s em issions as consum ers would drive m o re. These increased em issions would in part offset the em ission benefits otherwise accounted for this rule 6 1

. It is important to note that GREET does notm odel behavioral changes that m a y affect prices of relevant commodities and goods wh ich through various feedback loops ultim a tely energy use. The m odel does not include a gene ral equilibrium approach that exam ines how a shock (whether econom ic, technical or legal) affects not only the sect or of interest but also other sectors and the economy as a whole. 6 2 While such impacts of U.S. actions are important to

61 Th e ex ten t to wh ich th is offset wou ld o c cur wo u l d d e p e nd o n sen s itiv ity o f d e m a n d to p r i ce.

221understand, we have not have fully considered and quantified the re bound effects of this renewable f uel standard. Nevertheless, such impacts rem a in an important consideration for future analy sis. The system boundaries f o r this study encomp a ss both the renewable fuels lifecycle stages as well as their petroleum based counterparts. T able 6.1-1 shows the lif ecycle stages considered for each fuel.
Table 6.1-1. Lifecycle Stage s Included in Analysis Corn Ethanol Cellulos i c E thanol Biodiesel Petroleum

- Based Gasoline Petroleum

- Based Diesel Fuel Corn Farm i ng Biom ass Farm ing Soybean Farm ing Crude Oil Extrac tion Crude Oil Extrac tion Corn Transport Biom ass Transport Soybean Tran sport Crude Oil T r ansport Crude Oil T r ansport Soybean Crushing Ethanol Production Ethanol Production Biodiesel Production Refining Refining

Ethanol T&D Ethanol T&D Biodiesel T&D Gasoline T&D Di esel Fuel T&D Ethanol Tailpipe Emissions Ethanol Tailpipe Emissions Biodiese l Ta ilpip e Emissions Gasoline Ta ilpip e Emissions Diesel Fuel Tailpip e Emissions

The boundaries around each lifecycle stage include the em issions and energy use associated w ith that operation as well as upstream components that feed into it. For example, the corn farming stage inclu d es emissions from fuel used in tractors as well as from producing and transporting the fertilizer used in the field. Electricity produc tion em issions are included in alm ost all of the stages shown. These compone nts typically have the biggest impact on the results. W e did not include for example, en ergy and em issions associated with producing the steel and concrete used to construct the et hanol plants or petroleum refineries. As other lifecycle studies of renewable fuel s have included an expanded set of system boundaries, a sensitivity analysis was perform e d that includes th e energy use and the em issions associated w ith producing far m equipm en t, 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 la nd (e.g., rainforest land) mu st first be cleared in order to grow the biofuel feedstocks. This lifecy cle analysis is conducted without any regard to the geographic attributes of where em issions or ener gy use occurs. The benef its of this f i nal rule represent global reductions in GHG emissions and energy use, not just those occurring in the U.S. For example, the savings asso ciated with reducing overseas crud e o il ex tr action and refining are included here, as are the internati onal em issions associated with producing imported ethanol. One exception to this is the em issions associated with international land use change. Due to decreasing corn exports and modest decr eases in soybean exports, there m a y be som e

62 Si nce GR EET i s n o t a be havi oral m odel , i t c a nn ot asse ss any economi c efficiency im p lica tio n s asso ciated with i n crease d et ha nol p r o d u ct i o n
. A n al y z i n g th e s e i m pl i c at i ons w oul d be i m port a nt f

o r

f

u tu r e ethan o l rul e m aki ngs
.
222additional corn and soybean acres planted intern ationally to m eet worl d dem and. The emissions associated w ith dom estic land use ch ange are incl uded in our lifecycle an al ysis but international land use change was not as it was outside the scope of our agricultu re sector analysis. However, if emissions from international land use change w ere in cludedit would lo wer the ove r all ben ef its of

this rule. This is an ar ea we will c ontinue to e x amine f o r future an alys is.
6.1.1.3 Environmental Flow s Considered
One issue th at has com e to the forefront inth e as sessm ent of the environ m ental impacts of transportation fuels relates to the ef fect that the use of such fuels could have on em issions of greenhouse gases (GHGs). The combustion of fo ssil fuels has been identified as a m a jor 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 m e thane (CH 4 ) and nitrous oxide (N 2 O). This lifecy cle analysis evalua te s the impacts of increased renewable fuel use on greenhouse gas em issions.
The relative global warm ing c ontribution of em issions of various greenhouse gases is dependant on their radiative forcing, atm osphe ric lifetim e, and other considerations. For
example, on a m a ss basis, the radiative forcing of CH 4 is mu ch higher than that of CO 2 , but its effective atmospheric residence tim e is mu ch lower. The relative warm i ng impacts of various greenhouse gases, taking into account factors su ch as atm o spheric lifetim e and direct warm ing effects, are reported on a ‘CO 2 -equivalent’ basis as global warm ing potentials (GW P s )

. The GW Ps used in this analysis were develope d by the UN Intergovernm e ntal Panel on Clim ate Change (IP CC) as listed in their Third Assessm ent Report 6 3 , and are shown in Table 6.1-2.
Table 6.1-2. Global War ming Poten t ials for Gr eenhouse Gases Greenhouse Gas GW P CO 2 1 CH 4 23 N 2 O 296

Greenhouse gases are m e asured in term s of CO 2 -equivalent em issions, which result from multiply ing the GW P foreach of the three po llu tants shown inthe above table by the m a ss of
em issions for each po llutant. The su m of i mpacts for CH 4 , N 2 O, and CO 2 , yields the total effective GHG i mpact.
The impact increased volu m es of renewable fuels use has on GHG e missions (in term s of CO 2 -eq.) as well as for only CO 2 em i ssions which represent a subset of the overall G HG
em issions, is considered in this analysis. The impact increased volum es of r e newable fuels use has on fossil energy (in term s of Btus) is also con sidered. Fos sil en ergy us e includ es energy

63 I P CC “Clim at e Ch an g e 200 1: Th e Scien tif i c Basis”, Ch apte r6 ; I n ter gover n m e n t al Panel o n Cli m ate Ch ang e ; J.T. Ho u ght on , Y. Di n g , D.J

. Gri ggs , M

. N o gue r, P

. J. van de r Li nde n, X

. Dai , C

. A

. J o hns o n , an d K. M a ske l l , eds.; Ca m b rid g e Univ ersity Press. Ca m b rid g e , U.K. 20 01

. h ttp ://www.g rida.n o/cli m a te/ip cc_ tar/wg 1 /ind e x
. ht m
223associated w ith coal, natural gas, and petrol eum products. Fossil energy use is strongly linked with CO 2 and GHG e m is sions and is an important consideration when looking at overall sustainability.
Petroleum e nergy use is a subset of fossil energy use and is the m a jor contributor to overall transportation sector en ergy use. Petro l eum energy use is also linked to CO 2 and GHG
em issions but also has impacts on national ener gy concerns such as dependence on foreign sources of petroleum

. Therefore, petroleum en ergy was also consider ed s eparately in this analys is andexamined in term s of overall energ y use, as well as in term s of petroleum i mports avoided through the increased use of renewable f uels.
6.1.1.4 Time Frame and Volumes Consid ered The results presented in this an alys is repres en t a snapshot in tim e
. They represent annual GHG and fo ssil fuel savings in the ye ar considered, in this case 2012.
Consisten twith the ren ewable fuel volum e scenarios described in Chapter 2, our analysis of the GHG and fossil fuel consumption impact s of renewable fuel use was conducted using three volume scenarios. The first scenario was a reference case re presenting 2004 renewable fuel production levels, projected to 2012. This sc enario provided the point of comparison for the other two scenarios. The other two renewabl e fuel scenarios for 2012 represented the RFS program requirem e nts and the volum e projected by EIA.
In both the RFS and EIA scenarios, we assu m e d that the bio d iese l production volume would be 0.303 billion gallons based on an EI A projection. F u rtherm ore, the Energy Act requires th at 250 milliongallons of cellu losic eth a nol be prod uced startin g in the year 2013, for both scenarios we assume that 250 m illion gallon s of ethanol that qualif y for cellulosic credit will be p r od uced in 201 2. The rem a ining r enewable f u el volum es in eac h scenario would be ethanol m a de from corn and imports. The import volum e is based on EIA’s projections for the percent of total ethanol volum e supplied by im ports in 2012. The total volum es for all three scenarios are shown in Table 6.1-3.
Table 6.1-3. Volume Scenario s in 2012 (billion gallons) Reference Case RFS Case EIA Case

Corn-ethanol 3.947 5.985 8.758 Cellulosic ethanol 0.0 0.25 0.25 Biodiesel 0.030 0.303 0.303 Ethanol imports 0.0 0.436 0.630 Total volum e 3.977 6.974 9.941

As we are com p aring ag ainst a reference cas e, we are only interested in the em issions and energy savings associated with new or m arginal renewable f uels producti on that comes on-line after 2004 (the baseline assum e d for the reference case).

2246.1.1.5 Model Used The lifecycle m odel used in the ev aluation of the impacts of the RFS programis the fuelcycle m odel developed by DOE’s Argonne Nationa l Laboratory. For this work, EPA used the most recen t version of th is m odel, GREET 1.7 (Nove m ber 10, 2006 release). GREET, a multidim e nsional spreadsheet m odel, is one of the m o st widely used m odel of this type for transportation fuels. It has been reviewed, use d, and referenced by a wide variety of analysts, including General Motors, Na tional Corn Growers Associat ion, several fuel industry organiz ation s, and a wid e variety of academic institu tion s. It is the m o st com p rehensive and user-friendly m odel of its type. It has been under development for ove r 10 years, with input from EPA, USDA, DOE laboratories, and industry representatives. The m odel addresses the full lifecycle for an exhaustive num ber of alternative tr ansportationf u els and au tom o tive technologies. For these reasons, EPA felt it was the best tool for eval uating the energy and
em ission impacts of the RFS program.
The GREET m odel has been developed to calculate per-m ile energy use and em ission rates of various com binations of vehicle technologies and fuels for both fuel cycles and total energy cycles. The m od e l actually consists of three components: GREET 1.x, which calculates fuel cycle energy use and em issions, GREET 2.x, which calculates light-duty vehicle cycle energy use and em issions, and GREET 3.x, which calculates heavy-duty vehicle cycle energy use and em issions. All discussion h e re refers to GREET 1.7, the m ost recent version of the fuel
component of GREET.
To estim ate fuel cycle energy use and emissions, GREET first estim ates energy use and
em issions for a given upstream stage. The m odel then com bines the energy use and em issions from all upstream stages for a fuel cycle, to es timate to tal up stream fuel cycle energy u se and
em issions. Inputs are national-average energy usag e rates, efficiencies and em ission factors for each stage. The m odel calculates total energy us e, fossil energy use, and em ission rates for the regulated pollutants and greenhouse ga ses, reported as gram s per mile or gram s per million Btu. These resu lts allow com p arison of tr ansportatio n fuels, based on energy use and/or emissions.
One of the m a in comments we received on our lifecycle app r oach was th at our so le reliance on the GREET model should be avoided, gi ven other m odels are available. T here are several other m odels that have been developed f or conducting renewable fuels lifecycle analysis. For example, researchers at the Energy and Resources Group (ERG) of the University of California B e rkeley have developed the ERG Biofuel Analysis Meta-Model (EBAMM) and Mark Deluc chi at the In stitu te of Tr ansportationStudies of the University of Calif ornia Davis ha s developed the Lifecycle Em issions Model (LE M ). There are also other non-fuel specific lifecycle m o deling tools that can be used to perf orm renewable fuel lifecycle analysis. The m a in differences in these m odels are with input assumptions used as described below.
Several studies have been released recen tly m aking use of these other m o dels and showing different results than we find in the analysis done for th is rule. For example, whereas GREET estim ates a net GHG reduction of about 22% fo r corn ethanol compared to gasoline, the previously cited works by Farrell et al. ut ilizing the EBAMM show around a 13% reduction. While there m a y be s m al l differences in the m odels in term s of e m issions and energy uses

225associated w ith ancillaries (e.g., em issions to produce fertilizer, elect ricity, etc.) the m ain difference in results is not due to m odel used but assumptions on scope an d input data used.

For exam ple, m o st studies focus on average or current ethanol pr oduction which uses a
curren tmi x of wet and dry mill ethan o l productio n and use of coal and natural gas as p r ocess energy. In contrast, we consid er new or m arginal ethanol pr oduction which implies a higher portion of more ef f i cient dry mill pro duction and mi x of process f u els. Other stud ies a l so typically base ethanol and far m energy use on hist oric data while we are assum i ng a state of the art dry m illing plant and m ost current farm i ng energy use data. Assumpti ons concerning land use change CO 2 e missions and agriculture related GHG e mis sions could also have an impact on overall results. Other studies also dif f

er in the environm ental flows c onsidered. For example, Delucchi N NNNuses different types of greenhouse gases a nd GW Ps comp ared to those used in this analysis as shown in Table 6.1-2 to determ ine GHG e m issions.
Other res earchers hav e p erform ed lifecycle anal ysis of renew able fuels not specifically focused on GHG e missions. One result that has b een debated recently is the net energy balance of corn-based ethanol fuel. Som e analysts have suggested that there is actually a negative energy balance for corn ethanol, m eaning that it takes mo re fossil energy to produ ce the ethanol than is contained in the resulting fuel, m aki ng it an unattrac tive tran sporta tion fuel. W hile we do not believe this is an appropriate m e tric to use wh en exam ining renewable fuels, as discussed in Section 6.2. 3, it is still u seful in exam ining the range of lifecycle results. Two studies Pim e ntal (2003) 6 4 and Patzek (2005), 6 5 concluded that the energy balan ce is negative.

. Many other researchers, however, have critic ized that work as being based on out-dated farm ing a nd ethanol production data, including data not norm ally consid ered in lifecycle analys is for fuels, and not following the standard methodology f or lifecycle anal ysis in term s of valuing co-products. Furtherm ore, several recent surveys have conclu ded that the energy balan ce is positive, although they differ in their num erical es tim ates. 6 6, 6 7, 6 8 Authors of the GREET m odel have also concluded that the lifecycle am ount of fossil en ergy used to produce ethanol is less than the
am ount of e n ergy in the ethanol its elf. Based on our review of a ll the available information, and the resu lts o f our own analysis, we a l so beli eve th at the energ y balance is positiv e.

64 Pim entel, David “Etha n ol Fuel: Energy Balance, Econo mi cs, a n d Environm ental I m pacts are Negative ”, Vol. 12, No 2, 2 003 In t e rn ation a l Asso ciatio n fo r M a th em atical Geology, Natu ral Resources Res earch

65 Pi m e nt el , D.; Pat zek, T. "Et h anol p r o d u ct i o n usi n g c o rn , s w i t c hg rass , a n d wo o d ; bi odi e s el pr o duct i o n usi n g so yb ean and sun f l o w e r
. " N a t. Reso ur . Res. 20 05 , 14 (1 ) , 65- 76

.

66 Ham m erschl ag, R

. "Et h an ol ' s Ene r gy R e tu rn on I n vest m e nt : A Su r v ey o f th e Li t e ratu re 1 9 9 0 - P r ese n t ." E nvi r o n. Sci. Tech no l. 20 06 , 40 , 174 4 - 17 50

.

67 Far r ell, A ., Pe lvin, R ., T u rne r , B. , J o e n es, A ., O’ Hare , M

. , Kam m e n, D ., “ E th an ol C an C ont ri b u t e t o En ergy an d Env i r o n m en tal Go als”, Science, 1 / 2 7 / 2 006 , V o l 31 1, 5 06- 50 8.

68 Hill, J., Nelson , E., Til m an , D., Po lask y, S., Tiffan y, D., “Env iron m e n t al, econo mi c, an d en erg e tic costs and bene fits of biodiesel and et hanol biofuels”, Proceedi ngs of the National Academy of Sc i e nces, 7/ 25/ 2006, Vol. 103, N o . 3 0 , 1 120 6-1 121 0.

226The differences found by different studies a nd m odels used emphasize the importance of the input data and m e thodology when using lifecycle analysis. It also shows how dependent this type of analysis is on the assumptions m a de 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 th at are seen are reasonab l e and the valu es we are obtain i ng from our use of the GREET m odel are accep table for this analysis.
6.1.2 Modifications to GREET
EPA chose to use GREET 1.7 to evaluate th e lifecycle impacts of the RFS program

. GREET 1.7 is the m ost recently releas ed version of the GREET m odel. However, this version of the m odel does not reflect the potential im pacts on tr ansportation fuel industri es as a re sult of the RFS program. In addition, for this regulation our intent was to evaluate the impact of increm ental renewable f u el production resulti ng from the RFS program a nd not a current industry average. Therefore, EPA has m odifiedsom e of the input variables and assumptions
m a de in the GREET mo del. The renewable fuels c onsidered in this analysis were m od e led as being produced from the followi ng feedstocks and processes:

− Corn Ethanol:
o W et Milling

 Mix of coal and natural gas as process fuel
o Dry Milling

 Natural g as as proces s fuel

 Coal as process fuel

 Biom ass as process fuel

− Cellulos i c E thanol:
o Hybrid Poplar Feedstock

 Ferm entatio n route
o Switchgrass Feedstock

 Ferm entatio n route
o Corn Stover Feedstock

 Ferm entatio n route
o Forest W aste Feedstock

 Gasificationroute

− Biodiesel:
o Soybean Oil Feedstock

 Transesterification route
o Yellow Grease Feedsto c k

 Transesterification route These feedstocks and processes were prim arily based on what was available in the GREET mo del with som e mi nor modi fications as described below. However, there are other pathways for producing renewable f u els not covere d here, for example different feedstocks for cellulosic ethanol produ ction (e.g., MSW ) as well as differe nt process for the feedstocks

227considered, like gasification of switchgrass a nd production of soybean oil diesel fuel through hydrotreating.
Furtherm ore, the lifecycle analys is us ed for this rulem aking is based on averages of the different renewable fuels m odeled. For exam ple, the GHG e m ission and fossil energy savings associated w ith in creaseduse of corn ethanol are calculated based on a m i x of process fuels,
assum i ng a certain pro j ected m i x of each process fu el as outlined below. While this m e thod m a y not exactly represent the reductions associated with a given gallon of renewable fuel, it is reasonab l e f o r the purpo se of this an alysis whichis to de termine the impact of the to ta l inc r eased volum e of re newable fuels used. We recognize that d i fferent feedstoc k s and proces ses will eachhave unique characteristics when it co m e s 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 f u el lif ecycle will impact the sav i ngs asso ciat ed w ith th eir u se and this is th e f o cus of ongoing work at the agency.
GREET is s ubject to periodic updates by ANL, e ach of which results in so m e changes to the inputs and assumptions that form the basis fo r the lifecycle estim a tes of e m issions generated and energy consum ed. These updates generally fo cus on those input values for those fuels or vehicle tech nologies that are the focus of ANL a tthe tim e. As a result there are a variety of other inputs related to ethanol and biodiesel that m a y not have been updated in som e tim e. In the context of the analysis of the RFS and EIA scen arios, we determined that som e o f the GREET input values that were either based on outdated inform ation or did not appropriately reflect
m arket conditions under a renewable fuels m a nda te should be exam ined more closely, and updated if n ecessary. Since the analysis done for the NPRM, seve ral changes have been m a de to the GREET model, som e as part of periodic updates ANL ha d planned and som e as part of an interagency agreem ent between ANL and EPA to investigate a variety of GREET inp ut values. A summary of the chang e s is as follo w s:

− Included C O 2 emissions from corn far ming lime use

− Updated the corn farm ing fertilizer use inputs

− Added cellulosic ethanol production fr om corn stover and forest waste

− Modeled b i o m ass as a process f u el so urce in corn ethanol dr y milling In addition to the changes above we also exam ined and updated other GREET input
assumptions for corn ethanol and biodiesel pr oduction. A summary of th e GREET in put values we investigated and m od i fied is given below. We also exam i ned several other GREET input values, but determ ined that the default GREET va lues should not be changed for a variety of reasons as discussed in the following sections

. These included corn and ethanol transport distances and m odes and byproduct allocation m e thods. Our inves tigation of these other GREET input values are discussed m ore fully below. The current GREET default factors for these other inputs were included in the anal ysis for this final rule.

228We did not investigate the input values associated withthe production of petroleum

based gasoline or diesel fuel in the G R EET m ode l for this final rule. However, the refinery modeling discussed in C hapter 7 will provide so m e additional inform ation on the process energy requirem e nts associated with the production of gasoline and diesel under a renewable fuels
m andate. W e will us e in f o rm ation f r om this ref i n ery m odeling in f u ture a n alysis to de term ine if any GREET input values should be changed.

A summ ary of the GREET corn et h anol inpu t v alues we inv estig ated f o r

this f i na l ru le is given below

. 6.1.2.1 Wet Mill versus Dry Mill Ethanol Plants
As described in Chapter 1, the two basic m e thods for producing ethanol from corn are dry
milling and wet milling. In the dry milling proc ess, the en tire corn kernel is ground and fer m ented to produce ethanol. The rem a ining components of the corn are then dried for anim al feed (dried distillers grains with solubles, or DDGS). In the wet m illing process, the corn is soaked to separate the starch, used to m ake ethanol, from the other components of the corn kernel. W et milling is more com p licated and exp ensive than dry milling, but it p r oduces m o re valuable products (ethanol plus corn syrup, corn oil, and corn gluten m eal and feeds). The
m a jority of ethanol p l an ts in th e United Stat es are dry m ill plants, which produce eth a nol m o re simp ly and efficiently.
W h ile other lif ecycle m o dels of ten ba se the mi x of wet and dry milling onexisting pla n ts, for this analysis, we are only interested in m arginal ethanol production. We expect most new ethanol p l an ts will b e dry mill oper ations. That h as been th e trend in the last f ew year s as the dem a nd for ethanol has grown, and our analysis of ethanol plants under construction and planned for the near future has v erified this. Our anal ysis of production plans, as outlin ed in Chapter 1, indicates that essen tially 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 am ong ethanol plants. The type of fuel used has an impact on the energy usage, efficiency, an d
emissions of the plan t, and is prim arily determined by economics. Most new dry mill plants bu ilt in the last few years have used natural gas. Ho wever, som e new plants are using coal. For these cases, EPA is prom oting the use of combined heat and power, or cogeneration, in ethanol plants to improve plant en ergy-efficiency and to reduce air em ission s. This tech nology, in th e face of increasing natural gas prices, m a y make coal a more attractiv e energy sou r ce for new ethanol plants.
GREET de fault factors represent the average percentage of fuel use for the entire industry, and m a y not reflect the re cent growth in the industry. Th erefore, we based our fuel m i x assumptions on the review of plants under construction and those planned for the near future outlin ed in Chapter 1. Our analysis indicates th at coa l will be used as p r ocess f u el f o r approxim ately 14% of th e new dry mill und er con struc tion an d planned e thanol produ ction

229volum e capacity. This is the valu e we used in G R EET for our analysis o f dry m illing ethanol production fuel m i x. As opposed to typical dry mill plan ts , corn wet mill ethano l plants can us e a m i x of process fuel sources at th e sam e plant. For the 1 % of additio nal eth anol production from wet
m ills, the GREET m ode l defaults of 40% coal an d 60% natural gas pro c ess fuel was used in th is analysis. As described below, the ethanol production stag e of the lif ec ycle typ i ca lly repres ents the stage where the largest amount of fos s il fuel energy is cons um ed and where the impact on lifecycle emissions is the greatest. T herefore, the type of pr ocess fuel used in ethanol production will have a significant impact on the fuel’s life cycle GHG results. For example, our analysis indicates that ethanol produced in a coal fired dry m ill plant w ould not have any GHG benefits as
comp ared to petro l eum gasolin e. Giv en that the relative prices of natural gas and coal could change over tim e, and thus change th e percen tage of each used in ethano l 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 larg est am ount of fossil fuel energy consum ed
occurs atthe ethanol p r o duction p l an t. The energ y use at a dry mill plan t using natu ra l gas was based on the m odel developed by USDA which wa s docum ented in a peer-reviewed journal paper on cost m odeling of the dry-grind corn ethanol process. O OOO This m odel was m odified by EPA for use in the cost analysis of this rule m aking described in Chapter 7. GREET in puts are total energy use per gallo n of ethanol produced. T he USDA m odel predicts the annual therm al (natural gas) and el ectricity dem a nd shown in Table 6.1-4.
Table 6.1-4. Annual Ene r gy Use at Dry Mill Ethanol Plant Energy Input Value Purchased E l ectricity (MWh/yr.) 41,308 Natural Gas (mmBtu/yr.) 1,617,094 Output Ethanol (mmgal/yr.) 50

Electricity energy use w a s converted from M W h to Btu based on a conversion of 3,410 btu/kW h. The prim ary energy used to produce electricity is accounted for in the GREET m odel. Table 6.1-5 shows the GREET input used for natural gas p r o cess fuel dry m illing p l an ts in th is analysis.

230Table 6.1-5. GREET In puts for Corn Ethanol Natural Ga s Dry Mill Energy Use Total Energy Use (mmBtu/gal.) 35,159 % electricity 8.0%

Energy requirem ents for a coal fired ethanol plant are different from a na tural gas fired plant. Typically coal boilers ar e s ligh tly less ef f i c i ent than natural gas bo ilers. Furth erm ore additional electricity is required for coal storage and handling as compared to natural gas. Additionally a large porti on of the energy at an ethanol plan t is due to drying the DDGS. A natura l gas p l ant u tilizes natura l gas dr ier s f o r this process while a coal fired plant would use steam dryers , the ef f i cien cy loss of co nverting coa l to steam represents add ition al th erm al energy required at a coal fired plan t vs. a natural gas one. Most other lifecycle m odels assum e the sam e energy efficiency for both coal and natural gas ethanol plants, however, for th is analysis, it was assum e d th at a coal plant would require 15% 6 9 m o re electricity dem a nd due to coal handling and have a 13% inc r e ase in therm al dem and for steam dryers as com p ared to the natural gas f ueled plant. The increase in therm a l dem a nd was based on breaking out the drying energy in the USDA process m odel and assum i ng the sam e am ount of e nergy would be produced by 78% effi cient coal boilers. Table 6.1-6 shows the GREET input used for coal process fuel dry milling plants in this an alysis.
Table 6.1-6. GREET In puts for Corn Ethanol Coal Dry Mill Energy Use Total Energy Use (mmBtu/gal.) 40,079 % electricity 8.1%

The Energy Act also allows ethanol m a de fr om non-cellulosic feedstocks to receive cellulosic ethanol producti on volume credit if 90 percent of the process energy used to operate the facility is derived from a re newable source. In the context of our cost analysis, we have
assum e d that 250 m illio n gallons of corn eth a nol will be p r od uced using 9 0 percen t or more biom ass energy and receive the cellu losic ethanol volum e credit. Fu rther discussion of this issue can be found in Chapter 1.
For the lif ec y cle ana l ysis we conside r ed the case where a corn ethanol dry mill plan t utilized biom ass as a fuel source. For this case the sam e a m ount of fuel and purchased electricity energy per g a llon as a co al powered plant was assum e d. Thi s assumption is based on the biom ass plant having m o re fuel handling than a natural gas plant and producing steam for DDGS drying.

69 Baselin e En erg y Con s u m p tio n Estim ates fo r Natural Gas an d Co al-b ased Eth a no l Plan ts - Th e Po ten tial Im p act o f Com b ined Heat andP o we r (C HP) , P r e p are d fo r: U .S. E nvi r onm ent a l Pr ot ect i on Age n cy C o m b i n ed Heat & P o we r Part ne rs hi p, Pr epare d by : En e rgy an d E nvi ro nm ent a l Anal y s i s , I n c. , J u l y 2 0 0 6

.
231As discussed in section 6.2.3, CO 2 emissions from com bustion of biom ass are not assum e d to increas e net atm o spheric CO 2 levels. Therefore, CO 2 e missions from biom ass com bustion as a process fuel source are not in cluded in the lifecycle GHG inventory of the ethanol plant. The fossil energy use and GHG e mi ssions from producing the electricity used at the plant are included. For the 1% of corn ethanol produced fr om wet milling, the GREET process energy use default of 49,950 Btu/gallon of ethan ol produced by the wet milling process was usedin the analysis.
6.1.2.4 Corn Trans p ort Distan ces Corn transp ort dis t anc es selec t ed f o r

use in th is analysis are 100 m iles round trip. Corn used in the ethanol production process is assu m e d to travel from corn fields to ethanol production facilities in a two-step process; first, corn is tran sported from outlaying farm s to centrally-located colle ction facilities, such as county elevat ors. Second, this corn is transported from the collection facilities to the ethanol production facilities. The first leg of the corn transport process is assum e d to be a 20-m ile r ound trip and the second leg is assum e d to be an 80-m ile round trip. These assumptions coincide with those used in GREET P PPPVersion 1.7 and GREET Version 1.5. Corn transp ort data is lim ited, howe ver; Graboski 7 0 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 wa s 49.7 miles for an effective aver age round-trip corn transport distance of 74.6 m iles. S i milarly, Gervais and Baum el Q QQQ found that average one-way corn transport distances for the 1994-1995 Iowa gr owing season was 37.2 m iles for sem i -trucks (35.8%), 4.9 m iles for wagons (33.3%), and 9.1 miles for single and tandem axel vehicles (30.9%). Several M i nnesota corn m ills ind i cate d that the m aximu m radius of supply for their
m ills was 65 to 80 m iles (values apparently cited in the sam e study).
The available data on co rn tran sport distan ces does not provide a cl e ar in dication that the default values in GREET are unreasonable. Ther efore, we retained the G R EET defau lt values for our analysis. This assum e s that the land use patte rn (whe re corn is planted) and the plant location dec i sions by e th anol plan ts will not cha nge signif i c antly. W e believ e this is reasonab l e f

o r the f u el volum es considered. Th is is an ar ea we will continue to ex a mine f o r f u ture ana l ysis.

70 Th e au t h ors assu m e th at th e co rn p a ylo a d weig h t is equ a l to th e tran sport v e h i cle weigh t , th at th e v e h i cle returns
e m pty, and the effective a v erag e round-trip vehicle distanc e can be e stim a ted as being one and a half times the oneway trav el d i st an ce (1.5 ti m e s 49 .7 miles = 7 4

. 6 miles); Grab o s k i , 200 2, F o ssil En ergyUse in t h e Ma nufa c tu re o f Co rn Et h a nol , Co lo rado Schoo l of Min e s, (Prep ared fo r th e Natio n a l C o rn Gro w ers Asso ciatio n )

.
2326.1.2.5 Ethanol Transportation Distances and Modes
The default values in GREET for ethanol tr ansportation and modes are shown in Table 6.1-7. These values correspond to numbers in a USDA study on the ener gy balance of corn ethanol. R RRR

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 wi th the analysis we perform e d on ethanol distribution infrastructure. Chap ter 1 of this docum ent discusse s current ethanol transportation and distribution and indicates thatif ethanol facilities are lo cated within 100-200 m iles of a term inal, trucking is preferred. Rail and barge ar e used for longer distances. Pipelines are not currently used to transport ethanol and are not projected to play a role in ethanol tran sport in the future tim e fram e considered. We also discuss in Chapter 1 future ethanol transportation and di stribution needs based on the increased am ounts of renewable fuels used as a result of this rule. We concluded that most new ethanol capacity will no t h a ve riv e r acce ss. In add i tion, at leas t one new eth anol plan t slated for production that does have river access is planning to m ove its ethanol to m a rket v i a rail
so m o st new ethanol f r e i ght volum es will be ha n d led by ra il and that etha nol tr ansport by inland waterway will rem ain constant.
A recent USDA Cost of Ethanol Pro duction repo rt also prov ides inform ation on ethan o l distribution distances and m odes. S SSS The report includes 2002 d a ta from a survey of 21 dry m ill ethanol plants. The survey collected data on m o des and distances traveled for ethanol transport f r om the f acilitie s. The r eport con clu ded that 46 percent of the ethanol pr oduced at th e surveyed plants in 2002 was shipped by truc k an averag e one way distance of 93 m iles, with a range of 30 to 250 m iles. The rem a ining 54 percent of etha nol produced was shipped by rail an average one way distance of 1,163 miles, with a range of 800 to 2,500 m iles. However, this data is for a subset of existing plants where, for example, th ere is no barge transportation listed, and also does not take into account the increased dem a nd for ethanol projected by this rule.

233Co mparing the GREET default values to thes e other sources indicates that the GREET defaults values for percent of ethanol transported by rail m a y be low. However, due to lack of precise data on future ethanol transportation by mode, we concluded that the current G R EET default values for percent of ethanol transported by m ode are appropriate for the RFS analysis.
The GREET default values for m iles shipped by mode f all within th e range of values listed in the USDA survey data of e x isting p l an ts

. The USDA survey data indicate higher than average transportation distan ces; however the data is not comprehensive enough, only representing a sm all fraction of total and projected ethanol production capacity, thus not warranting a change to the default G R EET values. Therefore, the default values show n in Table 6.1-7 were u sed in th is analysis. Th is is an area we will continue to ex am ine for future analysis.
6.1.2.6 Biodiesel T r ansportation Distances and Modes
The default values in GREET for bi odiesel tr ansportation and m odes 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 transportati on are not consistent with the analysis we perform e d on biodiesel distribution infrastructu re. T he distribution infrastructure discussion in Chapter 1 of this doc u m ent indicates pipelines are not currently used to transport biodiesel and are not p r o j ected to play a role in biodiese l tr ans port in the f u ture tim e fra m e considered.
Therefore, GREET de fault factors for biodiesel transportation fro m plant to term inal were modified to rem ove pipeline transport. The per cent of biodiesel shipped by barge and rail were increased in the sam e proportion as the current percentage split. The result was 22% of biodiesel shipped by barge and 78% shipped by rail. Th e GREET de fault distances for biodiesel rail and

234barge transp ort as well as terminal to station assumptions are consistent w ith ethanol transportation and distribution assumptions and w e re used in this analysis.
6.1.2.7 Corn Yield and Related Inputs GREET includes a collection of energy use and m a terial inputs to corn farm ing per bushel (bu) of corn produced. Several corn farming input data param e ters and default values were updated fro m the version of GREET used for the NPRM to the version used in the FRM analysis. The current G R EET corn farm ing input da ta default values are shown in Table 6.1-9.
Table 6.1-9. GREET Corn Farmin g Input Data Input Parameter Default Value Energy Use for Corn Farm ing 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 F e rtilizer (as P 2 O 5 ) 149 g/bu Potash Fertilizer (as K 2 O ) 174 g/bu Lim e (as CaCO 3 ) 1,202 g/bu

Herbicide Use: 8.1 g/bu Insecticide Use: 0.68 g/bu

The default GREET inp ut values for corn farming shown in Table 6.1-9 are based in part on far m energy use and m a terial inputs per acre divided by an assum e d c orn yield in bu/acre. Therefore, while corn yield is nota direct input in GREET, it is a critical part of the calculation of corn energy and m a terial input requirem e nts. Although corn yields have been generally rising over tim e , s ee Figure 6.1-2, the annu al variation is volatile.

235Figure 6.1-2. U.S. Ave r age Corn Yield T TTT

60 80 100 120 140 160 180

1 990 19 95 200 0 200 5 2010bu/acre

We exa m ined data on farm energy use, m a terial input, and yield data to determ ine if the GREET de fault 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 w ith corn farm ing are available. USDA does list projections for corn yield. The 2012 projected U.S. average corn yield is 158.5 bu/acre. U UUU Historic data on corn far m ing energy use is available from the following USDA infor m ation sources.

• The USDA Agricultural Resource Managem e nt Survey (ARMS) provides data from selected States on fuel, electricity, natural ga s, andseed corn u sed per acre on the farm and activities of m oving far m products to initial storage facilities.

• The USDA National Ag ricu ltur a l Statis tic s Serv ice (NASS) produces annual data on crop production including yields pe r acre and total producti on of corn by state. USDA NASS data on corn yields and pr oduction values are provided annually. However, the three m ost recen t years of the AR M S data and specifically the costs-of-production portion of the survey dedicat ed to corn are 1991, 1996, and 2001 7 1

. Table 6.1-10 lists corn far ming energy input data for the three years of the ARMS study.

71 Use o f hi st ori c farmi ng e n er g y use m a y not be rep r ese n t a t i v e of curre nt practice. Highe ren erg y prices relativ e to the years c o nsi d ere d here could lead t o farm ers a d opting practices that lower overall e n ergy use.
236Table 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 kW h/acre 32.72 79.38 38.22 50.11 - Natural Gas Cubic ft/acre 284.73 208.12 207.09 233.31 Total Energy Use mmBtu/acre 2.12 2.71 1.78 2.20

a H igh en erg y u s e i n th e 199 6 sur v ey is du e t o in cr eased co rn d r ying r e qu ire m en ts. See the d iscu ssion b e lo w
.
Although USDA corn data is available for every state that produces corn, the data docum ented in Table 6.1 -10 is for nine m a jor co rn producing States : Illino i s, Indiana, Iowa, Minnesota, Nebraska, Ohio, Michig an, South Dakota, and Wisconsi n. In 2005, these nine States accounted f or 80 percent of U.S. corn production

. In 2001 these nine S t ates represented 92 percen t of U.S. ethanol p r oduction, and based on our analys is outlin ed 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 th e GREET m odel. These estim a tes m a y be biased downward if the corn production attributable to the increm enta l increase in ethanol production will occu r o n less produ ctive land th an was used in the 1991 -2001 period, when corn prices were lower than they are projected to be in this an alysis. Also, as corn production expands due to expanded ethanol produ ction, it m a y increasing ly take place in dryer clim ates that may increase irri gation de m a nd and result in d i f f

e rent yie l ds. Th is is an ar ea we will c ontinue to e x amine f o r future analysis. The ARMS surveys include inform a tion on energy use and also on dollars spent by far m ers on custom work. This custom work in cludes farm ers contracting outside services for corn drying, planting, fertilizi ng and harvesting. The cost of custom wor k includes machine overhead, fuel charges, and labor costs. Therefor e, there is so m e energy use associated with the dollars spent on fuel used in custom work. It was assum e d that 10% of custom work c ost was spent on fuel V VVV

. This f u el cost was ass u m e d to be s p lit b e tweenLPG and diesel f u el in the
sam e percentage as reported energy u se for each st ate. Cost was convertedto gallons b a sed on price p a id b y far m ers for LPG and diesel fuel in each of the s urvey years W WWW

. Custom work energy use is includedin 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 infl u e nce corn farm ing energy use. For example, it was reported that 1991 was a dry ye ar, lowering the m oisture 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

2371996. Farm diesel use is also depend ent on till ag e type and s o il cond ition s, wetter soil requiring more diesel use, and decreas ed tillage requirem e nts (e.g., no till) reducing diesel use. X XXX
To project corn farm ing fuel use in 2012, th e average energy use from the three years of survey data were taken, in term s of e nergy per ac re. As energ y use is som e what weath e r related and it is im possible to confidently predict future conditions, it wa s felt that the three years of historic data represen ted a good m i x of high and low energy us e years. The average energy use in term s of Btu/acre was divided by the projected corn yield in 2012 of 156.9 bu/acre. This is the USDA projected corn yield adju sted to account for seed corn energy use as shown in Table 6.1­ 10. The seed use shown in Table 6.1 -10 accoun ts for seed corn energy use. W e assumed that growing seed corn requires 4.7 tim e s the en ergy and m a terial inputs to grow than corn Y YYY, Z ZZZ

. 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 farm i ng m a terial inputs were updated from the values in the NPRM version. GREET defaults we re based on historic data provided f r om the following USDA sources.

• The USDA National Ag ricu ltur al Statis tic s Serv ice (NASS) produces an nual repo rts listing quantities of fertilizers and chem icals used per acre of corn.

• The USDA Econom ic Research Service (ERS) produces an Agricultural R e sources and Environm ental Indicators report that has data on lim e used per acre of corn. The USDA sources p r ov ide average m a terial us e data per harvested acre of corn. The GREET de faults are based on the assumption that m a terial input use per acre will be flat from 2005 into the future. The 2005 values are ba sed on a three year av erage of 2003 through 2005 data. Data on inputs per acre are divided by pr ojected corn yields to get G R EET defau lts in term s of g/bushel of corn. W h ile th ese valu es are felt to be reasonabl e to be used in this analysis, the agency cautions that these es tim ates are base don the his t o r ica l re cord while the in crem ental corn production attributable to expanded ethanol production m a y occu r on less productive land than was used historically. As a result, thes e estim a tes m a y be biased downward, resulting in over-estim ates of ethanol displacem e n t indices. Another potential input to corn farm ing is the energy and em issions as sociated with producing farm equipm e n t. As described in Se ction 6.1.1.2, this input is considered outside the system boun daries of our lifecycle analysis. How e ver, the latest version of GREET ha s an option to include energy use and em issions asso ciated with producing farm equipm ent in the corn eth anol lif ecycle res u lts. W e perf orm e d a sensitiv ity ana l ysis on exp anding the c o rn production system to include farm equipm ent pr oduction to determ ine the impact it has on the overall results of our analysis. It was found that including far m equipm ent production energy use and em issions increases ethanol lifecycle energy u s e and GHG emissions and decrea ses the corn eth anol displacem ent index by approxim a tely 1 percent. Furtherm ore, to be consistent in the modeling if system boundaries are expanded to include production of farm ing equipm ent they should also be expanded to include producing othe r m aterial inp u ts to both the ethanol and petroleum lifecycles.

238For example, this expansion of system boundari es would include the energy use and emissions associated w ith producing concrete a nd steel used in the petroleum refinery 7 2

. The net effect of this would be a slight increase in both the eth a nol and petro l eu m fuel lifecycle results and a
sm aller or n eglig ible ef f

ect on the comparison of the two.
The corn farm ing m a terial and energy use used in the lifecycle analysis is based on producing and average bushel of corn. There are di fferences associated w ith variations in corn yield, inputs required for existing la nd vs. land converted to crops, etc. Furtherm ore, there are ripple effects associated with increased corn used for ethanol that could have GHG e mission implications, ranging from changes in m a nure manage m e nt to the acres of rice grown. One such effect is CO 2 associated with land use change which is exam ined in the following section. Other ef

f

ects and variations in corn f arming will be ex amined as part of f

u ture analys is.
6.1.2.8 CO 2 from L and Use Change

Farm ing practices could potentially releas e carbo n stored in s o il as CO 2 emissions. If non-cropland (e.g., pastureland, Conservation R e serv e Program (CRP) land) is converted to crop production, carbon sequestered in the soil and existing cover could be released. The agricultural sector m odeling work done for this rulem aking exam ined the issue of land use change due to increases in renewable f u el produc tion and use. The agricultural sector modeling re sults indicate that, comp ared to the 20 12 Ref e rence Case, appr oxim a tely two and a half m illion acr es will
com e out of CRP land as a result of increased renewable fuel production. Not all of these two
million acres will go d i rectly into co rn productio n used to produce ethan o l. However, the en tire
am ount of CO 2 emissions from the CRP land use change is attributable to the increased am ount of ethanol produced, as without the increased dem a nd for corn th ere would be no change in CRP land. The agricultural modeling re sults also indicated a reducti on in U.S. corn exports and a modest decline in U.S. soybean exports which could impact crop producti on in other countries. However, we did not consider impa cts on non-U.S. land use that m i ght result from decrease in U.S. exports of corn and soybeans.
The GREET m odel has a default factor for CO 2 from land use change that was included in the NPRM analysis. This f actor was updated based on the resu lts of the agricultural sector modeling mentioned above and included in the f i nal rulem aking lifecycle analysis. T he CO 2 emissions from land use change used in the fina l rulem aking represent appr oxim ately 1% of total corn ethanol lifecycle GHG e missions. However, this value could be m o re significant if increased amounts of renewable fuels are used.
The issue of CO 2 e missions from land use change asso ciated with converting forest or CRP land into crop production for use in producing renewable fuels is an important factor to consider when determ ining the overall sustainability of renew able fuel use. W h ile the analysis describ ed ab ove is ind i cating th at th is rulem aking will not caus e a sign if ica n t change inland use, this is an ar e a we will co ntinue to res earch f o r an y f u ture analysis.

72 The expa nsion of system boundaries wo uld apply to existi ng re fine ries as ethanol is as sumed to replace gas o line fro m ex istin g produ ctio n.
2396.1.2.9 Ethanol Production Yield Modern ethanol plants are now able to produce more than 2.7 gallons of ethanol per bushel of corn comp ared with less than 2.4 gallo ns of ethanol per bushel of corn in 1980. The developm ent of new enzym e s continues to increase the potential ethanol yield. W e used a value of 2.71 7 3 gal/bu in our analysis, which m a y underestim at e 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 m odeling of corn ethanol discussed in Chapter 7.
6.1.2.10 Byproduct Allocation
There are a num b er of by-products m a de dur ing the production of ethanol. In lifecycle analyses, the energy consum ed and em issions gene rated by an ethanol plant must be allocated not only to ethanol, but also to each of the by-pr oducts. There are a num ber of m e thods that can be used to estim ate by-product allocations. Th ese include m e thods base d on the econom i c value of each by-p r oduct, or o n energy usage, based onengineering analysis of the actual processes related to each product. The m e thod preferred by EPA is called the displacem ent m e thod. This
m e thod m os taccu rately accounts for these by-p r oduc ts by calculating the lifecycle emissions of the products that will be displa ced by them

. In this m e thod the lifecycle em issions of the displaced product are calculated and subtracted fro m the ethanol lifecycle. The ethanol receives a cred it for the lifecycle emissions of whatever p r oduct is d i sp laced, since a quantity of that product is n o longer needed and is d i splaced by the ethano l b y -products.

For example, the DDGS produced by an ethano l dry mill plant is a replacem ent for corn and soybeananim al feed. W e based the am ount of DDGS pr oduced by an ethanol dry m ill plan t on the USDA m odel used in the cost analysis wo rk of this rulem aking. That m odel predicted 6.21 dry lb. of DDGS pe r gallon of ethanol produced. As per the agricultural sector modeling done for this rulem aking, we assum e d that this D DGS displaces 50% cornand 50% so ybean
m eal on a mass basis. So the lifecycle em issi ons of producing 3.1 lb. of corn and 3.1 lb. of soybean m e al were calculated and subtracted from the lifecycle em issio n s associated with producing a gallon of ethanol. By-products from the ethanol wet milling pr ocess include corn gluten m e al and corn gluten feed that are assu m e d to displace corn p r o duction, as well as co rn oil th at is assum e d to displace soybean oil. E thanol produced from ce llulosic feedstock throughthe ferm entation route is assum e d to produce excess electricity as a by- product, from onsite combustion of lignin. This excess electricity is assu m e d to displace electricity from the grid. The ferm entation process used to produce ethanol in corn wet and dry m illing an d cellu losic ethanol p r od uction also p r oduces CO 2 as a by-product. This CO 2 could be sold to an organizati on that specializes in cleaning and pressurizing it for use in the food industry for example to carbonate be verages, to m a nufacture dry ice, and to flash freeze m eat. While CO 2 co uld poten tially displace o ther sou r ces of CO 2 production, this was not considered in our analys is and no value was associated with this CO2 co-product.

73 Al l y i el d v a l u es p r ese n t e d re prese n t pu re et han o l pr o duct i on (i .e
. no de n a tu ra nt ).

240The displacem e nt m e tho d fo r by-product allocation is the default for the GREET mo del. EPA supports that approach and continues to use that m e thod in this analysis. However, other researchers have used different allocation m e thods in their ethanol fuel cycle studies. W e evaluated one of these other m e thods used by USDA in a recent ethanol energy balance report A AAAAto determ ine the impact this a ssump tion ha s on the ov erall result s of the analysis. The m e thod used by USDA was to split the energy use and em issions of corn agriculture and ethanol production between the ethanol and co-products. The lifecycl e analysis results were then based on only the ethanol portion. A process simulati on was used to allocate the energy used in the ethanol plant to ethanol a nd by-products. Using this approach they determ ined that on average 59 and 64 percent of the energy used in dry and wet mills respectively is usedto produce ethanol. Th e rem aining energy is u s ed for the production of by-products. Therefore, for dry m ill ethanol production only 59 per cen t o f the plant energy use an d associated emissions were allocated to the ethanol lifecycle. Corn pr oduction energy use and em issions were allocated based on the starch content of the corn, assum e d to be 66 percent of corn kernel weight. So, only 66 percent of the energy and em issions used to produce corn were allo cated to the eth anol lifecycle. Use of the process energy based allocati on m e thod reduces ethanol lifecycle energy use and GHG e m issions by approxim a tely 30 percent comp ared to the displacem e nt allocation approach. T his indicates that ethanol lifecycle an alysis resu lts are ex trem ely sens itive to the choice of allocation m e thod used. However, as m e ntioned above, EPA feels that the displacem ent allocation m ethod is the m o st reasonabl e and is the preferred m e thod to use. This decision is supported by international lifecycle assessm ent sta ndards which indicate that whenever possible the product system should be expanded to include th e additional functions related to th e co-produ cts B BBBB

. 6.1.2.11 Biodiesel P r oduction Two scenarios for biodiesel production were co nsidered, one utilizing soybean oil as a feedstock and one using yellow grease. For the soybean oil scenari o, the energy use and inputs for the biodiesel production process were based on a m odel developed by NR EL and used by EPA in the cost m odeling of soybean oil biodiesel, as di scussed in Chapter 7.
The GREET m odel does not have a specific case of biodiesel production from yellow grease. Therefore, as a s urrogate we used the so ybean oil based m odel wi th several adjustm e nts. For the yellow grease case, no s oybean agriculture em issions or energy use was included. Soybean cru shing was still includ ed as a surroga te for yellow grease p r ocessing (pu r ification, water r em o val, etc.). Als o, due to ad dition al pro c essing r equirem ents, the energy use associat ed with producing biodiesel from ye llow grease is higher than for s oybean oil biodiesel production. As per the cost m odeling of yellow grease biodies el discussed in Chapter 7, the energy use for yellow grease biodiesel production was assum e d to be 1.72 tim e s the energy used for soybean oil biodiesel.

241The biodies el lifecycle results were based on a 5 0 % / 50% sp lit b etween s oybean oil and yellow grease biodiesel producti on based on EIA’s AEO 2006 projec tions for biodiesel produced from the different feedstocks.



### 6.2 Methodol ogy
As outlined in the s copin g discuss i on , the goals o f this analy sis are to bo thexamine the total GHG a nd fossil fuel reductions of increased re newable f uel use in absolute tons and gallons and to com p are th ese red u ctions to th e U.S. tr ansportation sector and nationwide GHG e missions and fossil fuel use. The output of the GREET mo de l can be used directly to calculate tons of GHG and ga llons of petroleum reduced. However, these results are not en tirely consistent with transportation sector and nationwide em issions in ventories which are based on slightly different
assump tions concerning fuel heating values and carbon content. As a result we could not use GREET directly to estimate the nationwide impa ct s of replacing som e ga soline and diesel with renewable f u els.
To be consistent between our m odeling of savi ngs and overall sector i nventories, we used GREET instead to generate comparisons between renewable f u els and the petroleum

- based fuels that they dis p lace. Thes e comp arisons allo wed u s to develop displacem ent indexes which represent the percent of lifecycle GHGs or fossil fuel reduced when a Btu of renewable fuel replaces a B tu of gasoline or di esel. In this way GREET was used to generate percent reductions and not absolute values. These percent reductions or displacem e nt values were then ap plied to the sam e gasoline and d i esel fuel inv entories used to generate transportation sector and nationwide inventories. This en sured that savings and sector wide inventories in terms of absolute values were calculated in a consistent m a nner.
In order to estim ate the impacts of increas eduse of renewable fuels on fossil energy and greenhouse gases, we first determ ined how mu chgasoline and diesel would be replaced as a result of this rule. W e then com b ined lif ecycle percent reductions from GREET with lifecycle inventories and petroleum consumption values for gasoline and diesel fuel use to get the am ounts of fossil energy and greenhouse gases reduced. F o r example, to estim a te the impact of cornethanol use on GHGs, th ese factors were com bined in the following way:

242____ ___ ___ ___ __

____ ___ ___ ___ __

____ ___ ___ ___ __

____ ___ ___ S GHG, corn ethanol = R cor n ethanol x LC gasoline x DI GHG,corn ethanol
where:

S GHG, corn ethanol = Lifecycle G HG e m ission reduction over the reference case associated with use of corn ethanol (million m etric tons of GHG)

R cor n ethanol = Am ount of gasoline replaced by co rn ethanol on an energy basis (Btu)

LC gasoline = Lif ecycle e missions associated withgasolin e us e (million metric tons of GHG per Bt u of gasoline)

DI GHG,corn ethanol = Displacem ent Index for GHGs and c o rnethanol, representing the percent reduction in gasoline lifecycle GHG em issions 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 f our endpoints of interest (fossil fuel consumption, pe troleum consumption em i ssions of CO 2 , and em i ssions of GHGs) as well as all three renewable fuels examined (corn-ethanol, ce llulosic ethanol, and biodies el). These values are th en co mpared to th e tota l U.S. transportati on sector and nationwide inventories of fossil energy and greenhouse gases to get the overall im p acts of the rule. In this regar d, the impact on overall transportation sector GH G e m issions due to the increased use of renewable fuels can be de scribed m a them atically as follows:

TSector %,GHG = S GHG, corn ethanol + S GHG,cell ethanol + S GHG,biodiesel T S e c t o r GHG
where:

TSector %,GHG = Percent reduction in overall transp ortation sector GHG e m issions resulting from the use of renewable fuels (%)

S GHG, corn ethanol = Lifecycle G HG e m ission reduction over the reference case associated with use of corn ethanol (million m etric tons of GHG)
S GHG, cell ethano l = Lifecycle G HG e m ission reduction over the reference case associated with use of cellulosic ethanol (million m e tric tons of GHG)

S GHG,biodiesel = Lifecycle G HG e m ission reduction over the reference case associated with use of biodiesel (m illion m e tric tons of GHG)

TSector GHG = Overall transportation sector GHG emissions in 2012 (m illion m e tric tons of GHG)

243We used the sam e approach to estim ate fossil energy, petroleum energy, and CO 2 reductions in the transportation sector. W e also used the sam e approach to estim a te nationwide reductions.
Section 6.2.1 describes how we estim a ted the amount of gasoline and diesel fuel replaced
as m odeled for this rule. Section 6.2.2 descri bes the lifecycle em issions and energy associated with gasolin e and dies el fuel use. In S ection 6.2. 3 below, we outlin e how we generated displacem ent indexes using GR EET. Section 6.2. 4 outlin es h ow we developed the overall transportation sector and nationwide f oss il energy and greenhouse gas em issions.
6.2.1 Modeling S cenarios In general, the volum e fr action (R) in th e equation above repres ents the amount of conventional fuel no longer consum ed – that is , displaced – as a result of the use of the replacem ent renewable f uel. Thus R represents the increm ental am ount of renewable fuel used under each o f our renewable fuel volu m e scenarios, in units of Btu. W e make the assu mption that veh i cle energy ef f i ciency will no t be af f ectedby the pres e n ce of rene wable f u els (i.e., efficiency of com busting one Btu of ethanol is eq ual to the efficiency of com busting one Btu of gasoline). As described in Section 6.1.1.4, our analysis of the GHG and fossil fuel consum ption impacts of renewable fuel use was conducted usi ng three volum e scenarios. The total volum es for all three scenarios are shown in Table 6.1-3. For the purposes of calculating the R values, we
assum e d the ethanol volum e s shown in Table 6.1 -3 are 5% denatured, an d the ethanol volum es were adjusted down to represent pure (100%) ethanol. The adjusted volum e s were then converted to total Btu using the appropriate volum etric energy c ontent values (76,000 Btu/gal for ethanol, and 118,000 Btu/gal for biodiesel). Since the impacts of increased renewable fu el use were m easured relative to the 2012 reference case, the value of R actually repres ented the incremen tal am ount of renewable fuel between the reference case and eachof the two other scen ario s. The results are shown in Table 6.2-1. The results shown in Table 6.2-1 are dir ect reductions in fuel use and do not represent lifecycle sav i ngs.

244Table 6.2-1. Direct Conventional F u el Repl aced in 2012 (quadrillion Btu)
RFS Case EIA Case

Gasoline Replaced by C orn Ethanol 0.147 0.347

Gasoline Replaced by C e llu losic E th a nol 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 C onventional Fuel Use
In order to d etermine the lif ecycle impact th at in creased renewable fuel v o lum es m ay have on any particular endpoint (fossil fuel cons umption or emissions of GHGs), we also needed to know the conventional fuel inventory on a lif ecycle basis. Since av ailable sources of GHG
em issions are provided on a direct rather than a lif ecycle basis, we converted these direct
em ission and energy estim a tes into their lifecycle counterparts. To do this, we used GREET to develop mu ltiplicativ e factors for converting direct (vehicle-b as ed) em ission s of GHGs , or direct (vehicle-based) c onsumption of petroleum, into full lifecycle factors. GREET output was used to generate the conversion f actors shown in Table 6.2-2.
Table 6.2-2. Direct (w heel only) Co nversion Fact ors to W e ll-to-Wheel (lifecycle) Emissions or Energy Use Gasoline Diesel Petroleum 1.11 1.10
Fossil f u el 1.22 1.21
GHG 1.26 1.25
CO 2 1.23 1.21

245The factors in Table 6.2-2 were applied to gasoline and diesel fu el inventories of
emissions or energy consum ption at the consum er level (i.e. direct em issions or energy) to convert them into alternative inventories representing full lifecycle contributions. The direct p etro l eum energy for gas o line and d i es el fuel is just the energ y content of the fuels used. Consistent with U.S. EPA National Inve ntory calculations C CCCC , we converted energy use v a lues for gas o line and d i esel fuel to direct CO 2 emissions by multiply ing by a carbon content coefficient, a carbon oxida tion factor, and converting the resulting carbon em issions into CO 2 . The CO 2 em issions were then scaled up by assum i ng a fraction increase to the CO 2 em issions to account for non-CO 2 GHGs (CH 4 a nd N 2 O). The fraction increase was based on the U.S. EPA National Inventory 2004 values for both CO 2 and total GHG em issions. Table 6.2-3 shows the total lifecycle petroleum a nd GHG e missions associated with direct use of a Btu value of gasoline or diesel fuel. These values repr esent factor LC in the equation described above.
Table 6.2-3. Lifecycle Emiss i ons and Energy (LC Values) Gasoline Diesel Petroleum (Btu/Btu) 1.11 1.10
Fossil f u el ( B tu/Btu) 1.22 1.21 GHG (Tg-C O 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 p erm it a quantitativ e eva l uati on of the degree to which a renewable fuel reduces lifecycle fossil fuel consumption orGHG em issions, several m e trics have been developed. Three of the m ost prom i nen t m e trics are shown in Table 6.2-4.
Table 6.2-4. Metrics U s ed to Measure Li fecycle Impacts of Renew abl e Fuels Metric Calculation Net energy balance Renewable en ergy out - fossil energy in Energy efficiency Fossil energy in ÷ renewable energy out (or alternatively ren e wable energy o u t ÷ fossil energy in) Displacem ent index % reduction in em issions or en ergy comp ared to the fuel that it r epla c es

Of these m e trics, we believe the d i splacem ent index is th e m o st approp riate to use as it
compares the renewable fuel to the petroleum fu e l it is displacing. The net energy balance and energy efficiency approaches only co nsider the renewable fuel itself and do not account for the

246fact that the use of renewable fuels result in decreased use of petroleum fuels and thus provide
m i sleading results. As an example, if 81,000 Btu of fos s il fuels were required to m ake, transport, and store one gallon of ethanol, then the energy e fficiency would be calculated as follows: Energy efficiency = 81,000 Btu/gal ÷ 76,000 Btu/gal = 1.07

This result would im ply that ethanol cannot be labeled "renewable," since one gallon of ethanol contains less energy than was required to m ake that one gallon. However, the use of ethanol m a y still reduce overall lifecycle fossil fueluse even in this case. If, for example, 18,000 Btu of fossil fuels were required to make one ethanol-equivalent gall on of gasoline (i.e. 76,000 Btu of gasoline), then a total of 94,000 Btu of fos sil fuel energy would be consum ed whenever 76,000 Btu of gasoline energy was com busted in a conventional vehicle. Since 81,000 Btu is less than 94,000 Btu, the use of ethanol would resu lt 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 h a s energy efficiency less than 1.0 7 4 , th ere m ay still be an overall reduction in lifecycle fossil fuel use (and associated G HG e m issions) due to decreased petroleum fuel use.
Therefore, studies th at rely on the energy balance m e tric a nd conclude for example that the net energy balance of corn ethanol is negativ e, or the energy efficiency is less than 1.0,
m aking it an unattractive transportation fuel, are not captu rin g the f u ll implications of the use of the fuel and are prov idin g m i sleading results. Because of this poten tial for the net energy balan ce and energ y efficiency m e trics to provide m i sleading inform ation, for our analysis of this rule we have chosen to use the displacem ent index. The displacem e nt index provides th e m o st direct m easure of the impacts of replacing conventional gasoline or diesel with a renewable fuel, and is also better suited to describing impacts of renewable fuel us e on fossil fuel consumption and GHGs. The displacem e nt index (DI) re presents the percent reducti on in GHG e mis sions or fossil fuel energy brought about by the use of a renewable fuel in comp arison to the conventional gasoline or diesel that the renewable fuel replaces

. The for mula for calculating the displacem e nt index depends on which fuel is being displaced (i.e . gasoline or diesel), and which endpoint is of interest (e.g. petroleum e nergy, GHG). For instance, when investigating the CO 2 impac t s of ethanol used in gasoline, the displace m e nt index is calculated as follows:

A net e n e r gy balance of ze ro, or a n e n ergy efficiency of 1

. 0, w oul d i n di c a t e th at th e full lifecycle fossil fuels used in th e produ ctio n and t r an sportatio n o f eth ano l are ex actly eq u a l t o th e en erg y i n th e eth an o l itself.

247 74DI CO2 = 1

- lifecycle CO 2 emitted f o r ethanol in g /Btu lifecycle CO 2 emitted f o r gasolin e in g/Btu

The units of g/Btu ensure that the comp arison between the renewable fuel and the conventional fuel is m a de on a common basis, and that differences in the volum etric energy content of th e f u els is tak en into a ccount. The denom i nator includes the CO 2 e mittedthrough
com bustion of the gasoline its elf in a ddition to all the CO 2 emitted during its m a nufacturer and distribution. The num era t or, in contrast, includes only the C O 2 emitted during th e m a nuf acturer and distribution of ethanol, not the CO 2 emitted during com bustion of the ethanol.
The com bus tion of biomass-based fuels, su ch as ethanol from corn and woody crops, generates C O 2 . However, in the long run the C O 2 emitted f r om biom ass-based f u els com bustion does not increase atm o spheric CO 2 concentratio ns, assum i ng the biogen i c carbon em itted is offset by the uptake of CO 2 resulting from the growth of new biom ass D DDDD

. As a result, C O 2 emissions from biom ass-based fuels com bustion ar e not inclu d ed in their lifecycle emissions results and are not used in the CO 2 displacem ent index calcu lati ons shown above. Net carbon fluxes from changes in biogenic carbon reservoi rs in wooded or crop lands are accounted for separately inthe GREET m odel. When calculating the GHG displacem e nt index, however, the CH 4 and N 2 O em itted during biom ass-based fuels com bustion are in clu d ed in the n u m erator. U n like CO 2 emissions, the com bustion of biom a ss-based fuels does result in net additions of CH 4 and N 2 O to the atm o sphere. W e assum e that com bustion CH 4 and N 2 O e missions are not offset by carbon uptake of renewable biom ass produc tion. As shown in Table 6.1-2, CH 4 and N 2 O e m issions contr i bute to the to tal G HG imp act. Therefore, com bustion CH 4 and N 2 O e missions are included in the lifecycle GHG e m issions re sults for biom ass-based fuels and are used in the GHG displacem ent index calculations. Using GRE ET, we calculated the lifecy cle values for energy consum ed a nd GHGs produced for corn-ethanol, cellulosic ethanol, an d soybean-based biodiesel, as well as the gasoline and diesel fuel that would be displace d. For both renewable and conventional fuels, we summed the lifecycle res ults for boththe feedstock and the fuel. The results are show n in Table 6.2-5.

248Table 6.2-5. Output from GREET Used to Develop Displacement In dexes
Units Ga so line aCor n ethanol C o rn et h a n o l (biom a ss fuel ) Cellulo sic ethanol L S Diesel Bio d iesel

Well-to -Pu m p

Fo ssil

energy Btu /mmBtu 22 4, 1 33 74 2, 4 11 29 0, 3 24 88 ,9 7 3 20 7, 0 08 46 4, 5 94

Petrole u m energy B tu/m m B tu 10 7, 2 98 90 ,7 7 1 88 ,8 9 6 91 ,9 7 7 98 ,6 5 6 96 ,5 3 9

CO 2 g /mmBtu 1 7 ,893 5 6 ,275 2 6 ,089

- 7 1 1 6 ,629 2 8 ,468

CO 2 -e q g/m m B tu 20 ,4 3 5 75 ,2 1 9 43 ,0 4 3 6, 42 7 19 ,1 3 4 31 ,1 9 3

En d poi nt com bust i on

Fossi l e ner gy B tu/m m B tu 1, 00 0, 0 00 0 0 0 1, 00 0, 0 00 0

Petrole u m energy Btu /mmBtu 1, 00 0, 0 00 0 0 0 1, 00 0, 0 00 0

CO 2 com bust i o n b g /mmBtu 7 6 ,419 7 4 ,755 7 4 ,755 7 4 ,755 7 7 ,570 7 9 ,388

Fo ssil CO 2 com bust i on 76 ,4 1 9 0 0 0 77 ,5 7 0 0

CO 2 -e q com bust i o n c g /mmBtu 79 ,0 1 5 2, 59 6 2, 59 6 2, 59 6 77 ,6 6 9 99

a Vol u m e -wei g h t e d a v e rage o f c o n v e n t i onal gas o l i n e ( 6 5%) , R F Gbl e n dst o ck (2 5% ), an d C a R F G bl en ds t o ck ( 10% ). b Based on carb on con t ent of the fuel.

c Incl udes Foss il CO 2 , CH 4 , an d N 2 O tailp ipe emissio n s
. C H 4 an d N 2 O e m issions based on ass u mi ng an inc r ease over CO 2 emi ssi ons, t h e perce n t i n c r eas e i s f r om th e U .S. EPA Nat i o n al I n vent ory f o r

C O 2 a n d G H Gemi ssi ons fr om on- roa d s o u r ces.

We used the values from the table above to calculate th e disp lacem ent indexes. The results are shown in Table 6.2-6.

249Table 6.2-6. Displacement I ndexes Derived from GREET Corn ethanol Corn ethanol (biom ass fuel) Cellulosic ethanol Imported ethanol Biodiesel

DI Fossil Fuel 39.3% 76.3% 92.7% 69.0% 61.5%
DI Petroleu m 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 displacem e nt indexes in this table repres ent the impact of replacing a Btu of gasoline or diesel with a Btu of renewable fuel. Thus, for instan ce, for every Btu o f gasoline which is replaced by corn ethanol, the to tal lifecycle GHG em issions that w ould have been produced from that Btu of gasoline would be re duced by 21.8 percent. For every Btu of diesel which is replaced by biodies el, the to tal lif e cycle pe tro l eum energy th at would have been consum ed as aresult of burning that Btu of diesel fuel would be reduced by 91.2 percent.
Consistent with the cost m odeling done for this rule, fo r the 2012 cases we assum e t he “cellulos i c” ethanol vo lu m e is actually produced fr om corn utilizing a bio m ass fuel source at the ethanol production plant. The displacem ent index fo r that fuel as shown in Table 6.2-6, is used in the calculation of reductions. The displacem e nt index for imported ethanol in all cases is based on an av erage of corn and cellulos i c ethano l. W h ile not exclusively, we anticipate mu ch i mported ethanol to be prim arily su garcane b as ed ethano l. There curr ently is no sugarcane ethanol lifecycle values included in GREET. Th e GHG e mis sions when pr oducing sugarcane ethanol differs fro m corn ethanol in that the GHG em issions fro m growing sugarcane is likel y different than for growing a equivalent amount of corn to m ake a gallon of et hanol, the process of turning sugar into ethanol is easier and therefore less ener gy intensive (which typically tr anslates into lower GHG) and, importantly, we understand that at least som e of the ethanol produced in Brazil uses the bagasse from the sugarcane itself as a process fuel sour ce. W e know from our analysis that using a biom ass source for process energy greatly impr oves the GHG benefit of the renewable fuel. These factors would result in sugarcane ethano l having a greater GHG benefit per gallon than corn ethanol, certainly w h ere natural gas or co al is the typ i cal process fuel source used. Conversely, sugarcane ethanol produ ction does not result in a co-pr oduct such as distillers grain as in the case of corn ethanol. In our an alyses, accounting for co -products significantly improved the GHG displacem ent index for corn etha nol. Furtherm ore, there would be additional transpo r tatio n emissions associat ed w ith transp orting the imported etha nol to the U.S. as
comp ared to dom e stically produced ethanol. Developing a technica lly rigorous lifecycle estim ate for energy needs and GHG impacts for sugarcane ethanol is not a simp le task and was not available in the tim efra m e of this rulem aking. Considering all of the differences between imported and dom e stic ethanol, for this rule m aking, we assum e d i mported ethanol w ould be predom inately from suga rcane and have estim a ted DI’s approxim ately m i d-way between the DI’s for corn ethanol and DI’s for cellulosic ethanol. W e are continuing to develop a better

250understanding of the lifecycle energy and GHG im pacts of p r oducing ethanol from su garcane and other likely feedstocks of i mported ethanol for any future analysis.
6.2.4 Transportation Sector and Nationw i de Invent ories For our analysis described above, we needestimates of trans portation sector and nationwide f ossil energy and GHG e m issions to de termine the percent re duction im p acts of the program (e.g., TSector GH Gfactor in the equation above). These inventories are direct not lifecycle and are need ed for 2012 to compare to the projected renewable f uel savings in 2012.
6.2.4.1 Fossil Fuel Inventory The transpo rtation sec t or and nation w ide f o ssil fuel inventory is just the energy content of the fuels used. Fossil fuel use in the transp ortation sector includes ga s o line and d i esel 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 am ounts of renewable fuels use projecte d to 2012, there are also assum e d values for gasolin e anddiesel fuel use. Values for ener gy use of the different tr ansportation fuels other than gasoline and diesel (e.g., je t fuel, natural gas, etc.) were taken directly from the 2006 Annual Energy Outlook.
The nationw ide fossil fuel inventory includes petr oleum , natural gas, and coal energy use. The direct fossil fuel inventory values are show n in Table 6.2-7.
Table 6.2-7. Direct Fossil Fuel Inv e ntories (QBtu)

2012

Nationwide 94.53

Transportation Sector 31.41

6.2.4.2 Petroleu m Inventory As with fossil energy, the transportation sect or and nationwide petroleum inventory is just the energy content of the fuels used. The transportation sector petr oleum inventory includes gasolin e anddiesel as well as o ther p etro l eu m fue l s, such as residual oil and LPG.
The nationw ide petroleum inventory includes pe troleum use in the transportation sector as well as other sectors. Th e direct petroleum inventory va lues are shown in Table 6.2-8.

251Table 6.2-8. Direct Petrol eum Inv e ntories (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 em issions factors to the projected am ount of fuels used in those years.
Direct CO 2 em issions from the transportation sector as a whole are calcu lated in the s am e way as direct gasoline and dies el em issions are calculated as described in Section 6.2.2. W e converted energy use values for trans portation sector fu els to direct CO 2 emissions by multiply ing by a carbon content coefficient, a carbon oxidatio n factor, andconverting the resulting carbon em issions into CO 2 . Emissions from electricity us e in the transpo r tation s ecto r (rail) are calculated based on the U.S. average m i x of fossil fuels used to generate electricity.
Consisten twith the EP A invent ory report we m a de an adjustm e nt to diesel fuel, jet fuel and residual oil use to subtractout the em issions associated w ith bunker fuel. The AEO values include the energy use of bunker fuels, but the emissi ons of these fuels are not considered part of the U.S. transportation sector em issions. This adjustm e nt was done by decreasing em issions of diesel fuel, jet fuel, and residual oil by the por tion of e m issions associated with bunker fuels as determ ined in the EPA inventory report.
Direct n a tio nwide CO 2 em issions are also calculated in the sam e way applying factors for all fossil fuels used as repor ted by the 2006 Annual Energy Outlook. This type of analysis results in a sm all understatem e nt of total Nationwide CO 2 em issions as it does not capture other industrial sources of CO 2 em issions for example CO 2 e m issions from calcinations of lim e stone in the cem ent industry. However, there are no proj ections of these other em issions sources for 2012, and they are a relatively sm a ll part of total Nationwide CO 2 e m issions, representing only 6% of total CO 2 e m issions in 2004 according to the EPA National Inventory values. T h erefore, while impacts of increased renewable fu el use as a percen t of nationwide CO 2 e m issions m a y be slightly overestim ated the impacts on results are not thought to be significant. The results of direct CO 2 em ission calculations are shown in Table 6.2-9.

252Table 6.2-9. CO 2 Direct Inventories (Tg CO 2 )

2012

Nationwide 6,406

Transportation Sector 2,108

6.2.4.4 GHG Inv entories Projections f or direct GHG e m issions can not be calculated directly from the energy projections as was done for CO 2 . The approach to estim ating CO 2 e m issions from m o bile
com bustion sources varies significantly fr om the approach to estim ating non-CO 2 GHG
emissions (CH 4 and N 2 O e m issions). W h ile CO 2 can be reasonably estim a ted by applying an appropriate carbon content and frac tion of carbon oxidized f actor to the f uel quantity consum ed, CH 4 and N 2 O e missions depend largely on the emissions control equipm ent used (e.g., type of catalytic con v erter) and v e hicle m iles traveled. E m issions of these gases also vary with the efficiency and vintage of the com bustion tec hnology, as well as m a intenance and operational practices. D ue to this complexity, a much higher level of unc erta inty ex is ts in th e es tim ation of CH 4 and N 2 O e missions from m obile com bustion sour ces, compared to the estim a tion of CO 2 emissions. Projections f o r direct transportation sect or and nationwide G HG e mission are done by
assum i ng a fraction increase to the CO 2 em issions to account for non-CO 2 GHGs. The fraction increase was based on the U. S. EPA National Inventory 2004 E EEEEvalues for both CO 2 and tota l GHG e missions. This sam e increase is applied to 2012 CO 2 values. Table 6.2-10 shows the fraction increase values for GHGs ov er CO 2 em is sions 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 (Tg-CO 2 ) GHG (Tg- CO 2 –eq.) F raction In crease

Nationwide 5,988 7,074 1.1807 Transportation Sector 1,860 1,960 1.0538

The results of direct GHG e m ission cal culations are show n in Table 6.2-11.

253Table 6.2-11. GHG Direct Inventories (Tg C O 2 -eq. )

2012

Nationwide 7,564

Transportation Sector 2,222



### 6.3 Impacts of Increased Rene wa ble Fuel Use

We used the m e thodology described above to estim a te i mpacts of increased use of renewable f u els on consumption of petroleum a nd fossil fuels and also em issions 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 estim ate the redu ctio n associat ed with the increased use of renewable fuels on lifecycle fo ssil fuel and petroleum c onsumption. These values are th en comp ared to the total U.S. transp ortation sector and nation wide inventories to get a percent reduction. The estim a tes are presented in Tables 6.3-1 and 6.3-2.
Table 6.3-1. Estimated Fossil Fuel Impacts o f In creased Use of Renew a ble Fuels in 2012, In Comparison to the Reference Case
RFS Case EIA Case

Reduction (quadrillion Btu) 0.15 0.27

Percent redu ction in Transportation Sector E nergy Use 0.48 % 0.85 %

Percent redu ction in Nationwide Energy Use 0.16 % 0.28 %

254Table 6.3-2. Estimated Petroleu m Impacts of Increa sed Use of Renewable Fuels in 2012, In Comparison to the Reference Case
RFS Case EIA Case

Reduction (billion gal) 2.0 3.9

Percent redu ction in Transportation Sector E nergy Use 0.82 % 1.60 %

Percent redu ction in Nationwide Energy Use 0.57 % 1.11 %

6.3.2 Greenhouse Gases and Carbon Dioxide We used the S equation in Section 6.2 to estim ate the redu ctio n associat ed with the increased use of renewable fu els on lifecycle emissions of CO 2 . These values are th e n com p ared to the total U.S. transportation sector and nationwide em issions to get a percent reduction. The estim ates are presented in Table 6.3-3.
Table 6.3-3. Estimated CO 2 Emission Impacts of Increased Use of Renew a ble Fuels in 2012, In Comparison to the Reference Case
RFS Case EIA Case

Reduction ( million m etric tons CO 2 ) 11.0 19.5

Percent reduction in Transportation Sector Emissions 0.52 % 0.93 %

Percent redu ction in Nationwide Emissions 0.17 % 0.30 %

Carbon dioxide is a subs et of GHGs, along with CH 4 and N 2 O as discussed above. It can be seen fro mTable 6.2-6 that the d i splacem ent index of CO 2 is greater th an for GHGs for each

255renewable f uel. This indicates that lifecycle em issions of CH 4 and N 2 O are high er for renewab l e fuels that for the conven t ional fuels replaced. Th erefore, redu ctions associated with th e increased use of renewable fuel s on lifecycle emissions of GHGs ar e lower than the values for CO 2 . The estim ates for GHGs are presented in Table 6.3-4.
Table 6.3-4. Estimated GHG Emis sion Impacts of Increas e d Use of Renew able Fuels in 2012 , In Comparison to the Reference Case
RFS Case EIA Case

Reduction (million m etric tons CO 2 -eq.) 8.0 13.1

Percent reduction in Transportation Sector Emissions 0.36 % 0.59 %

Percent redu ction in Nationwide Emissions 0.11 % 0.17 %



### 6.4 Implicati o ns of Reduced Im ports of Petroleum Products

6.4.1 Impacts on Imports of Petroleu m Products
To assess the impact of the RFS program on petroleum i mports, the fraction of dom e stic
consumption derived from foreign sources was estim ated using results from the AEO 2006. W e describe in this section how fuel producers m i ghtchange their levels and m i x of i mports in response to a decrease in fuel dem a nd.

We compared the lev e ls and m i x of i mports in th e AEO reference cas e with the AEO low
m acroeconom ic growth case and AE O high oil price case. The latter two cases reflect different
assump tions by EIA regarding econom ic growthand world oil prices, respectively. T he net effect for both cases is a reduc tion in dom e stic petroleum cons um ption co mpared to th e AEO reference case. The chan ges in th e level and m i x of i mports were exam ined, given a reduction in petroleum c onsum ption similar to the am ount estim ated in the RFS for 2012 (0.25 to 0.49 Quads). Note that the EIA has conducted three se parate analyses of Congressional bills which included earlier form s of the ren ewable fuel stan dard. These separate analyses however were based on earlier AEO versions and, in som e instances, considered num erous provisions in addition to an RFS whic h collectively affected world oil prices and dom e stic oil consumption.

256Thus, we did not directly use these earlier analys es, rath er op ting to us e o n ly the results in th e AEO 2006 c a ses, as discussed above, to assess the RFS i mpacts on imports.
Comparison of the AEO 2006 reference case agai nst the low m acroeconomic growth case allowed us to evaluate how a decrease in dom estic petroleum dem a nd m i ght affect the m i x of imported finished products, imported crude oil, and dom estic production. Note that the world price of crude oil rem ains the sam e between the AEO low macroeconom ic growth and reference cases. Com p arison of the two cases show that with an initia l d ecrea se in p etro l eum consumption (approxim a tely 300,000 barrels per d a y or 0.61 Quads, higher than 2012 v a lues), n e t imports will account for approxim a tely 95% of th e reductions on an energy basis. F FFFFThese net im por ts include imports of crude oil or petroleum produc ts m i nus exports of crude oil or petroleum products. G GGGGBoth reduced dom e stic crude production a nd natural gas plant liquids account for most of the rem a inder. Note that for all leve ls of reduced petroleum de m a nd, dom e stic crude production appears to account for less than 5% of the change. In additi on, the reductions shown here do not reflect any rebound eff ect that m ay occur. Out of th e initial reductions in net petroleum imports, imported finished products acc ount for almost all the reductions. As dom e stic petroleum dem and is reduced even further (ove r 860,000 barrels per day), approxim a tely 50% of the reductions com e from i mported finished pr oducts, 44% f r om i mported crude oil, and the
rem ainder from reduced dom estic, natural ga s plant liquid (N GL) production, and exports.
Under the low m acroeconom i c growth case a ssumptions, imported finished products are initially red u ced presum ably becau s e they repres en t the h i gh er m arginal cost source for refineries versus imported crude oil. Refineries m a y prefer to refine crude oil as opposed to importing finished products because of the higher m arg ins involved with the form er and the potentially more optimum use of ref i ning capac ity. Crude o i l, as an inte rn ationa l co mmodity, will be purchased at the m arket price by refineries. Thus , while crude oil from abroad m a y be produced more cheaply than dom e s tic production sources, refi neries that purchase from either source will pay the in tern ationa l m arket price f o r

that sp eci fic grade of crude oil based on specific gravity and sulfur content plus the co st of transport to the U.S.
Note that th ere is un certainty in qua ntif yi ng ho w ref i nerie s will chang e their m i x of sources with a decrease in petroleum dem a nd, particularly at the levels estim a ted for the RFS. Changes in world oil price from the reference case could also signifi can tly alter the mi x of sources from which refineries choose. For ex ample, a comparison between the AEO high price case and the reference case (under a decrease in petroleum c onsumption of 0.64 Qua ds) shows that 80% of the reductions (on an energy basis) com e from reduc tions in net petroleum i mports, while the rem aining 20% com es from reductio ns in dom estic production. As petroleum
consumption is reduced even further, reductions in net petroleum i mports m ake up an even greater percentage. For th e reductions in net petroleum i mports, imports of finished products are observed to actually in crease while imports of crude oil decrease even m ore.
W e believe that the actual refinery response mi ght range between these two AEO cas es, so that net import reductions could compose 80- 95% of the reductions in petroleum dem a nd for 2012. The split b e tweenthe changes in imports of finished products versu s crude oil are m o re uncertain. D i scussions with EIA suggest the spl it could be close to 50- 50. Thus, we believe the range could be between these two estim a tes (n early all to 50% fini shed product). For the

257purposes of this RIA, we show values for th e cas e where net import reductions com e entirely from i mports 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 r e sults, we es tim ate that 9 5 % of the lif ecycle p etro l eum reductions will be m e t through reductions in net petroleum i mpo rts. Table 6.4-1 shows the reductions in net petroleum imports estim ated for the RFS program

. W e expect th at th es e import red u ctions will be m e t al m o st exclusively from finished petrol eum products rather than from crude oil, for the reasons given above and consistent w ith the re sults of the AEO 2006 low m acroeconomic growth case. As an example calculation, we apportioned 95% of the total reductions in gasoline and diesel to displaced finished product imports. By 2012, impor ts of finished products are estim a ted to be reduced by 123,000 and 240,000 barrels per day, respectively, for the RFS and EIA cases. We comp are these reductions in imports agains t the AEO projected levels of net petroleum imports. The range of reductions in net petrol eum imports are estim ated to be between 0.9 to 1.7%.
Table 6.4-1. Net reductions in Imp orts in 2012 RFS Case EIA Case Reduction in finished products a (barrels per day) 123,000 240,000

Percent redu ction b 0.89% 1.73%

a Net re ductio n s relative t o 20 12 re fere nce ca se b Co m p ar ed t o A E O 200 6 p r oj ectio n s f o r 20 12 re fere nce ca se

6.4.2 Impacts on Import Expenditures The reductions in petroleum i mports were disc ussed in Section IX.D of the pream ble. As noted in the pream ble, we calcu late the change in expenditures on petrol eum i mports and ethanol imports assum i ng this would not result in any othe r changes in consum er be havior that would be reflected in fuel use. 95% of all reductions in petroleum i m ports were calculated to be from finished petroleum products rather than crude oil, as discussed in the pr ior section. The econom ic savings in p etro l eum product im ports was calculated by mu ltiplying the reduction s in gasolin e and diesel imports by their corre sponding price. According to th e EIA, the price of imported finished products is the m arketprice mi nus domestic local tran sportation from refineries and
mi nus taxes. H HHHH An estim ate was m a de by using the AE O 2006 wholesale gasoline, distillate, and ethano l price forecasts for the sp ecific analys is years. The current ethanol im port tariff of $0.54/gallonplaced on countries outside th e Caribb ean and Central Am erica is no t included in the import e xpenditu res, since th e tariff revenue collected would rem a in in the U.S. As an example calculation, the RFS case is expected to y i elda reduction of 2.0 billio n gallons of gasoline in the year 2012. 95% of these reductions, or 1.9 billion gallons, are expected to com e from imports of finished ga soline. Thus, the dom estic refining sector would avoid purchases of 1.9 billion gallons of gasoline a nd diesel at the wholesale price. According to the AEO 2006, the end-user prices o f gasoline an d diesel are forecasted to be $2.01 per gallon and $1.98 per gallon respectively. Minus federal taxe s, state taxes, and di stribution costs, the

258wholesale p r ices of gasoline and diesel forecastedin the AEO 2006 are $1. 376 and $1.382 per gallon, respectively (2004$). Note that the AEO whol esale 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 a voided petroleum paym ents abroad thus total $2.6 billion in 2012 as shown in Table 6.4-2. The a dditional ethanol import expenditures, using the
sam e approach, is estim ated to be $0.7 billi on in 2012. The net avoided expenditures in imports is thus the difference, or $1.9 bill ion in 2012 as shown in Table 6.4-2.

We com p are these avo i d ed petro l eu m i m port expenditu res against the projected valu e of total U.S. net exports of all goods and services economy-wide. Ne t exports is a m easure of the difference between the value of exports of goods a nd services by the U.S. and the value of U.S. imports of goods and services from the rest of th e world. For example, according to the AEO 2006, the value of total import expen ditures of goods and serv ices ex ceeds the valu e of U.S. exports of goods and services to the rest of the w orld by $695 billion fo r 2006 (for a net export level of m i nus $695 billion) and by $383 billion for 2012 (for a net export level of m i nus $383 billion). 7 5 In Table 6.4-2, we com p are th e avoided expenditures in impor ts versus the total value of U.S. net exports of goods and services for th e whole economy for 2012. Note that changes to corn exports, discussed in Chapter 8 of the RI A, are also included inthe calculation of net exports. Relative to the 2012 projection, the avoi ded import expenditures due to the R F S would represent 0.4 to 0.7% of ec onomy-wide net exports.
Table 6.4-2. Avoided Import Expenditures ($2004 billion)

Cases AEO Total Net Exports Expenditures on Petroleum Imports Expenditures on Ethanol

Imports Decreased Corn Exports Net Expenditure s on Imports Percent of Total Net

Exports RFS Case - $383 - $2.6 + $0.7 + $0.6 - $1.4 0.4% EIA Case (year 2012) - $5.1 + $1.0 + $1.3 - $2.8 0.7%



### 6.5 Energy Security Implications of RFS

6.5.1 Background
One of the effects of increased use of renewabl e f u els in the U

. S. f r om the RFS is that it diversifies the energy sources in m aking trans portation fuel. A potentia l disruption in supply ref l ected in the pric e volatility of a p articular en e r gy source c arries with it both f i nancial as well as strateg i c risks. These risks can be reduced to th e exten ttha t divers e sources of fuel energy reduce the dependence on any one source. This reduction in risks is a m easure of impr oved energy security.

75 Fo r refe rence , the U

. S. Bu reau of Eco n o mi c Analy s is (B EA) r e po r ts t h at the 20 05 im p o r t ex p e nd itu r e s on en erg y
related petroleum products totaled $235.5 bi llion (2004$) while petroleum exports t o taled $13.6 billio n – for a net of $ 221 .9 b illio n i n ex p e nd itu res. Net p e tro leum ex p e n d itu res m a d e u p a sign ifican t fraction o f th e $5 91 .3 b illio n cu rren t account d e ficit in go od s and serv ices fo r 20 05 (2 004 $). (h ttp ://w ww .b ea.g o v /)

259At the tim e of the proposal, EPA stated that an analysis would be completed and estim ates provided in support of this rule. In or der to understand the energy security implications of the RFS, EPA has worked with O ak Ri dge National Laboratory (ORNL), which has developed approaches f or evaluating the social cost s and en ergy secu rity imp lications of oil use. In a new study produced f o r the RFS, entitled " The Energy S ecurity Bene fits o f

Reduc ed Oil Use, 2006-2015 ," ORNL has updated and applied th e m ethod used in the 1997 report " Oil I m

ports: An Assessment of Benefits and Costs ", by Leiby, Jones, Curlee and Lee. 7 6
, 7 7 W h ile the 1997 report including a description of m e t hodology and results at that tim e has been used or cited on a
num b er of occasions, this updated an alysis and results hav e n o t been available for full public consider atio n. Since ene r gy security will be a k e y consider ation in f u ture action s aim ed at reducing our dependence on oil, it is important to assure e stim at es of energ y security impacts have been thoroughly exam ined in a full and open public forum

. Since the updated analysis was only recently available, such a thorough analysis has not been possible. Therefore, E P A has decided not to rely on th e results of this report for the purpo ses of this rulem aking. Rather, we are in cludin g it as p art o f the record of this rulemaking and are inviting further public analysis and consideration of both this particular report and other perspe ctives on how to best quantify energy security benefits. To fac ilitate that add i tio nal cons ideration, we highlight b e lo w som e of the key aspects of this particular analysis. The approach developed by ORNL es tim ates the increm ental benef its to s o ciety, in dollars per barrel, of reducing U.S. oil imports , called “oil prem ium .” Si nce the 1997 publication of this report, changes in oil m arket conditions, both current a nd projected, suggest that the
m a gnitude of the oil prem ium has changed. Signif icant driving factors that have been revised include: oil prices, current and anticipated leve ls of OPEC production, U. S. im port levels, the estim ated responsiveness of regiona l oil supplies and dem a nds to pr ice, and the likelihood of oil supply disruptions. For this analysis, oil prices from the EIA's AEO 2006 were used. Using the "oil prem ium " approach, estim ates of benefits of improved energy security from reduced U.S. oil imports from increased use of re newable fuels are calcu lated. In conducting this analysis, ORNL cons idered the full econom ic cost of importing petro l eum into the U.S. The full eco nomi c cost of i mporting petroleum into the U.S. is defined for this analysis to inclu de two components in a ddition to th e purchas e p r ice of petro l eum itself. These are: (1) the h i gher costs for oil imports re sulting from the effect of U.S. i mport dem a nd on the world oil price and OPEC m arket power (i.e., the so called "dem and" or "m onoposony" costs); and (2) the risk of reductions in U.S. econom i c output and disruption of the U.S. economy caused by sudden disruptions in the supply of importe d oil to the U.S. (i.e., m a croeconom i c disruption/adjustm e nt costs).

76 Lei b y , Paul N. , D o nal d W. J o nes, T. Ra ndall Curlee, and Ru ssell Lee, Oil Imp o rts: An Assessmen t o f

Ben e fits an d Co sts , ORNL

- 6 8 5 1 , Oak Rid g e National La bo rato ry , No ve m b er, 1 9 9 7

.

77 Th e 199 7 OR NL p ap e r was cited and its resu lts u s ed in DOT/ N HTSA’s ru les estab lish i ng C A FE stan d ard s for 20 0 8 th r o u g h 2 0 1 1 m odel y e arl i g ht truc ks

. See DO T/N H T SA, Fi nal R e gul at o r y Im pac t s An al y s i s : C o r p orat e A v er ag e Fu el Econ o my an d CA FE Ref or m MY 200 8-2 011 , Mar c h 200 6.

2601. Effect of Oil Use on Long-Run Oil Pr ice, U.S. Import Costs, and Econom ic Output The first component of the full econom i c co sts of i mporting petroleum into the U.S. follows from the effect of U.S. i mport dem a nd on the world oil price over the long-run. Because the U.S. is a sufficiently large pur chaser of foreign oil supplies, its purchases can affect the world oil price. T h is m onopsony power means that in creases in U.S. petroleum dem a nd can cause the world price of crude oil to ri se, and conversely, that reduced U.S. petroleum de m a nd can reduce the world price of crude oil. Thus, one conse quence of decreasing U.S. oil purchases due to increased use of renewable fuel is th e potential d ecrease in th e crude o il p r ice p aid for all crude oil purchased.

2. Short-Run Disruption P r emium From Expected Costs of Sudden Supply Disruption s
The second component of the external economic costs resulting from U.S. oil im ports arises from t he vulnerability of the U. S. economy to oil sho c ks

. The cost o f shocks depends on their likelihood, size, and length, the capabilities of the m ark e tand U.S. Strategic Petroleum Reserve (SP R ), the largest stoc kpile of governm e nt-owned em erge ncy crude oil in the world, to respond, and the sensitivity of the U.S. economy t o sudden price increases. W hile the total vulnerability of the U.S. economy to oil price shocks depends on the levels of both U.S. petroleum c onsumption and imports, variation in im port levels or dem a nd flexibility can affect the m a gnitude of potential increases in oil pric e due to supply disruptions. Disruptions are uncertain ev ents, so th e 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 7 8 synthesis of expert judgm ent and are used to determ ine an expected value of disruption costs, and the change in those expectedcosts given reduced U.S. oil imports.
3. Costs of Existing U.S. Energy Security Policies
The last often-identif ied com ponent of the full economi c co sts of U.S. oil imports is the costs to the U.S. taxpayers of existing U.S. ener gy security polic ies. Th e two prim ary exam ples
are m a intain ing a m ilitary presence to help secu re stab le oil s upply from potentially v u lnerab le regions of the world and m a inta ining 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 ar e e x cluded fro m the analys is perform e d by ORNL because their attribution to particular m i ssions or activ ities is dif f i cult. M o st military f o rces serve a broad range of sec u rity and f o reign policy objectives. A ttemp ts to attribute so m e share of U.S. military costs to o il imports are f u rther challenged by the need to estim a te how those costs mi ght vary with inc r emental v ariations in U.S. oil imports. Similarly, while the cos t s f o r buildin g and
m aintaining the SPR are more clearly related to U

. S. oil use and imports, hi storically these costs

78 Stanford Energy Modeling Forum , Phillip C. Beccue an d Hillard G. Huntington, “ A n Assessm ent of Oil Market D isru p tion Risk s,” Fin a l Repo r t, EMF SR 8, O c tob e r , 20 05.

261have not varied in response to changes in U.S. oil import leve ls. Thus, while SPR is f a ctored into the ORNL a nalysis, the cost of m aintain ing the S P R is exclud ed.
As stated earlier, we hav e placed the repor t in th e docket of th is rulem aking for the purposes of inviting further consideration. However, the results of that report have not been used in quantifying the imp acts of this ru le.

262

## Chapter 7: Estim a t e d Cos t s of Ren e wable F u els, Gas o line an d Dies el

This section describes our m e thodology for es tim ating the cost im pacts of increased production, distribution, and blending of renewable fuels, incl uding corn and cellulosic ethanol and biodiesel. Detailed infor m a tion is given on expected changes to the nation’s fuel distribution system , as well as chang es in refin i n g processe s that will lik ely occur as larger volum es of ethanol are blended into ga soline. The impact of subsidies is also addressed.


### 7.1 Ethanol This subsection provides a description of th e analysis we conducted for estim ating the cost of corn and cellulos i c ethano l. Our analys is indicates that corn eth ano l will cost $ 1.32 per gallon to produce (2004 dollars) in 2012. W e also estim ated that using cellulosic feedstock, the production costs for ethanol would be approxim a tely $1.65 per gallon (2004 dollars). By 2012 this cellulosic cost m a y dec line with breakthroug hs and advances in technology. Based on reports from a variety of sources and discussions we held with m e m b ers of academia as well as those directly involved in the i ndustry, we believe several hurdles still have to be overcom e in the production of large volum e s of ce llulosic de rived ethanol. However, it appears that good progress continues to be m a de and we rem a in optimistic thatcellu losic ethanol will b ecom e increasingly important in the future.
7.1.1 Corn Ethanol Of the new ethanol production capacity expect ed to be built, accord ing to Section 1.2.2 of this RIA, les s than th ree percen t combined is ex p ected to be p r oduced fr om cellulosic feedstocks or in plants that dif f

er sig n if icantly f r om dry mill corn eth anol plants. Sev eral p l ants will b e able to utilize other starchy feedstocks besides corn, such as milo, barley, wheat, and sorghum. However, corn is the prim ary feedstock, and th erefore, the f ollowing analyses will f ocus on dry
mill starch ethanol p r odu ction.
7.1.1.1 Engineerin g and Const r uction Requirements for Corn Ethanol Plants

To m eet the RFS Case goal of 6.7 billion gallons per year (Bga l/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. 7 9 If we consider that it is likely that at least 9.6 Bgal/yr of actual ethanol capacity will com e on-lin e by 20 12 (EIA Case), the annu al capacity increas e is 4. 4 Bgal/yr. Our industry characterization work considering plants that are either under cons truction or are planned to be construc ted in the nex t2

- 3 years sug g ests th e ave r age new pla n t siz e will be 81 million gallons per year (MMgal/yr) (including a sm all num ber of expansions).

79 For details on current a n d e x pected et hanol capacity, re fer to Section 1

. 2 o f th is RIA. No te th at v o l u m es considere d cell u losic ar e als o include d he re, since we b e liev e th at v i rt u a lly all n ear-term cellu lo sic eth a n o l p r od u c tion will b e fro m starch-b ased feed stock s t h at m eet t h e altern ativ e d efi n itio n i n the Act (d iscu ssed furth e r in Sect i on 7. 1. 1. 2 ).

263Based on conversations with representative s from design-build firm s working in this field, as well as m a terial from public sources, each new plant requires design engineering work lasting about six m onths followed by construction lasting 12-14 m onths before plant startup is possible, res u lting in a to tal p r ojec t tim eline of 18-20 m onths. The de sign phase for a basic 50 MMgal/yr plant is expected to require the attention of about 12 engineers full tim e, and the construction phase will employ an average of about 125-150 workers each day. To correlate these figures with requirem ents for an 81 MMgal /yr plant, the num b er of construction personnel (150) were scaled proportionally, while the num ber of engineering personnel were assum e d to be constant. These figures provide a basis for estim a ti ng the personnel requirem ents of the total volum e needed to m eet the expected volum es. Over the six-year build-up period, a m a ximu m of 970 construction workers and 30 engineers would be required on a m ont hly basis for the RFS case, while for the EIA case, these numbers increase to 2,328 and 75, respectively. These figures simply estim a te the number of wor kers requ ired at the final assem bly stage of the plant, and do not capture m a ny m ore pe rsonnel hours that will go into designing and construc ting vessels, p i p e fitting s, co ntrol sy stem s, and other pieces of eq uipm ent that will be installed and brought online by the plant cons truction crews. A report produced by one consultant suggested that expans ion of the ethanol industry was responsible for m ore than 65,000 construction jobs in 2005. I IIII

7.1.1.2 Corn Ethanol Production Costs
Corn ethano l costs for o u r work were es tim a ted using a m odel developed by USDA t hat was docum ented in a p e e r-rev i ewed journal pap e r on cost m o deling of the dry-grind c o rn ethano l process. J JJJJ It produces results that compare well w ith cost inform ation found in surveys of existing plants. K KKKK
The USDA model is for a 40 MMgal/yr corn plant producing ethanol with a prim ary coproduct of distillers dried grains with solubles (DDGS). The etha nol yield used in the m odel is 2.87 gallons per bushel with 5.0% gasoline denaturant. The model is based on work done in chem ical process simulation software to generate equipm ent sizes, stream flowrates, and m a terial and energy balances. These results were th en put together with feedstock, energy, and equipm ent cost inform ation in a sp readsheet form at to arrive at a final p e r-gallon cos t estim ate. Although the m odel is current in term s of tec hnology, yields, and capital estim ates, we m ade
som e modifications to allow estim ation of cost s for ethanol plants of different sizes and operating under different energy and feedstock prices. W e believe that these updates, in
com b ination with the industry and supplier surveys done by USDA in developing the m odel, result a reasonable estim ate for pr ojected ethanol production costs.
We estim ate an average corn ethanol pr oduction cost of $1.26 per gallon in 2012 (2004 dollars) for the RFS case and $1.32 per gallon for th e EIA case. The cost of ethanol production is m ost sensitive to the prices of corn and th e primary co-product, DDGS. Utilities, capital, and labor expenses also have an impact, although to a lesser extent. Corn feedstock mi nus DDGS

264sale credit represents about 48% of the final pe r
- g allon co st, w h ile u tilities, 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 Agri c u ltur al Sec t o r 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 producti on of a gallon of ethanol. Note that this production model does not account fo r the cost to pelletize or sh ip the DDGS. Thos e costs are extern al andare 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 Breakdow n of Corn Ethanol Production (2004$).

-$ 0

. 4 0 -$ 0

. 2 0 $0. 0 0 $0. 2 0 $0. 4 0 $0. 6 0 $0. 8 0 $1. 0 0 $1. 2 0 $1. 4 0$ pergallon D D G S S a l e s C r edit
D enat ur ant O t her R awM at e r i als
C o r n F eeds t o c k H auli n g Labor , S uppl ies , O ver heads
D epr ec iat i on U t il it i e s S hell ed C o r n
A s s um es 81 M M gal /y r pl ant s i z e , 5% denatur ant, $2.50/bu c o r n , $83.35/ton D D G S

.Costofcornasfeedstock

The ability to address p l ant scaling inthe m odel was accom p lished by ap plying an engineering scaling factor to all plant equipm ent. In past rule m akings involving m odifications to refineries w e have used a m a terial scaling factor of 0.65. This fact or is applied as an exponent to the ratio of the new size to the o r igin al size, th e r esult of which is th en multip lied by the orig inal capital cost. However, there is inform ation sugge sting that a general fact or m a y be considerably higher for ethanol plan ts. Based on a recen t journ a l publicatio n, a factor of 0.84 was used in th is work. L LLLLW ith this factor, the m odel indicates th at the change in per-gallon production cost due to econom i es of scale is ve ry small over the range of typical plant sizes, on the order of $0.02 between 40 and 100 MMgal/yr. In this analys is we used an averag e new plant size of 81 MMgal/yr, derived from our industry characterization work in Chapter 1.

265We also added functions to estim a te 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 fr om new plants being constructed will use coal for the process energy source, so the effect on average costs is re latively small. Capital cost used for an 81 MMgal/yr gas-fired plant was $99 m illion (2004$). For the coal system versus natural gas, additional requirem ents were estim ated at $45 million in cap ital for the s a m e size plant, as well as one additional operator per shif t and 10% additional electric util ity use. These figures should be considered conservative estim a tes, and were based on inform ation from press releases as well
as a convers ation with staff of a company that d e signs and bu ilds ethanol plants. Add ition a lly, we adjusted the therm a l efficiency of coal com bustion processes downward by 13% relative to natural gas, and electricity consumption upward by 10% to reflect operational differences in the processes. M MMMM Using this infor m ation in the m odel, the cost savings is about $0.04 per gallon of ethanol for a coal-fired plant compared to n a tu ral gas firing. The results presented here are a weighted average of coal and ga s production costs (using 14% coal).
Under the E nergy Act, starch ethanol can be cou n ted as c ellu losic if at le ast 90% of the process energy is derived from ani m al wastes or other waste m a terials. N NNNN It is expected that the vas tm ajority of the 250 million gallons p er year of cellu losic ethano l production required by 2013 will be m ade using this prov is io n. W h ile we have been u nable to d e v e lop a detailed production cost estim a te for ethanol f r om corn wh ich m eets cellulosic criteria, we assum e that the costs will not be signifi cantly different from conventiona lly produced corn ethanol. W e believ e this is reasonab l e because to the exten tth at thes e processes are utilized, we exp ect th em to be in locations where the very low or zero cost of the feedst ock or biogas itself will likely of

f

set the co sts of haulin g the m aterial and th e ad dition al cap ital f o r hand ling and combusting it. In addition, because the quantity of ethanol p r odu ced using th ese processes is still exp ected to be a relativ ely s m all fractio n of the total ethanol d e m a nd, the sensitiv ity of the overall analysis to this as sum p tion is a l so v ery sm all. In general, energy prices used in the mode l were taken from historical EIA data for 2004 and scaled according the ratios of 2004-2012 pr ice forecasts published inthe Annual Energy Outlook 2006. O OOOO, P PPPPThe prices used in the m odeling are shown in Table 7.1-2. Several sensitivity cases were run using the model, a nd the results are shown in Table 7.1-3. Input values in this table were chosen to give a significant m argin around current and anticipated future prices.
Table 7.1-2. Energy Prices Used for Ethanol Cost Modeling for 2012 (2004$) Na tur a l G a s aCo al aElectricity aNatura l Ga so line b

$/MMBtu $/MMBtu $/kWh $/gal 6. 16 1. 94 0. 04 4 1. 36

a Hi st ori cal dat abase d o n a v e r ages f o r

I o wa, Il l i noi s, M i n n e sot a , a n d Neb r aska b Natu ral g a so lin e (or n a tural gas liq u i d s ) is the typ i cal d e n a tu ran t u s ed in et h a no l p r od u c ti o n , si n ce it is ch eap er t h an fi ni she d gas o l i n e. T h e pri ce use d w a s base d on i t s val u e b e i ng 2 0 ce nt s p e r gal l o n bel o w wh ol esal e g a sol i n e.

266Table 7.1-3. Energy and Feedstock Pri ce Sensitivities (2004$) Natu ral G a s = $6.00/ M M B tu N a tu ral G a s = $ 12.00/ M M B tu Co rn DDG S E tha n o l Co rn DDG S E tha n o l $/b u $/ton $/ gal $/b u $/ton $/ gal $2.00 $50.0 0 $1.18 $2.00 $50.0 0 $1.35 $100.00 $1.02 $100 .00 $1.19 $150.00 $0.86 $150 .00 $1.03 $2.50 $50.0 0 $1.36 $2.50 $50.0 0 $1.52 $100.00 $1.20 $100 .00 $1.36 $150.00 $1.04 $150 .00 $1.20 $3.00 $50.0 0 $1.53 $3.00 $50.0 0 $1.70 $100.00 $1.37 $100 .00 $1.54 $150.00 $1.21 $150 .00 $1.38 $3.50 $50.0 0 $1.71 $3.50 $50.0 0 $1.87 $100.00 $1.55 $100 .00 $1.71 $150.00 $1.39 $150 .00 $1.55 $4.00 $50.0 0 $1.88 $4.00 $50.0 0 $2.04 $100.00 $1.72 $100 .00 $1.88 $150.00 $1.56 $150 .00 $1.72 $5.00 $50.0 0 $2.23 $5.00 $50.0 0 $2.39 $100.00 $2.07 $100 .00 $2.23 $150.00 $1.91 $150 .00 $2.07 $6.00 $50.0 0 $2.58 $6.00 $50.0 0 $2.74 $100.00 $2.42 $100 .00 $2.58 $150.00 $2.26 $150 .00 $2.42

7.1.2 Cellulosic Ethanol
7.1.2.1 HowEthanol Is Made from Cellu losic Feedstocks
It is no t c l ear when the f i rst pro cesse s to produce ethanol from cellulosic biom ass were discovered. While ethanol producedfrom starch can be traced hi storica l ly to ancient tim es, cellu losic derived ethano l appears to have beenin vestig ated inthe 1800’s. Until recently, the dem a nd for fuel ethanol has been som e what lim ited, and not sufficient to support a costcomp etitive, commercial process to c onvert cellu lose into ethanol.

W ith the increasing demand for fuel ethanol during the past few years, good progress has been m a de toward producing ethanol from cellu losic feedstocks. Interest in ethanol has continued to grow, initially fostered in part fr om EPA’s reformulated ga soline (RFG) regulations that required such gasoline to contain a mi ni mu m of 2 percent oxygen by weight in the fuel. This m i ni mum oxygen requirem e nt has recently been revoked by EPA in response to the Energy Act, which revised the Clean Air Act requirem e nt for oxygen in RFG. The Renewable Fuel Standard (R FS) continues to create a dem a nd for ethanol. L i kewise, there is an additional incentive to produce cellulosic ethan o l because the Energy Act m a ndates that, starting in 2013, renewable f uels used in gasoline mu st include.

267There is a wide variety of governm e nt and renewable fu els industry research and developm ent program s dedicated to improving our ability to produce ren ewable fuels from cellu losic feedstocks. T h ere are at least three co mpletely d i ffe rent approaches to producing ethanol from cellulosic biom ass, som eti m es referre d to as “platform s ”. The first is based on what NREL refers to as the “sugar p l atform ,” 8 0 which refers to pretr eating the biom ass, then hydrolyzing the cellulosic and hem i cellulosic components into sugars, an d then ferm e n ting the sugars into ethanol. Corn grain is a n early ideal feedstock for producing ethanol by fer m entation , especially when comp ared with cellul osic biomass feedstocks. Corn grain is easily ground into sm all particles, following which the exposed starch which has α -linked saccharide polym ers is easily hydrolyzed into simple, singl e component sugar which can then be easily fer m ented into ethanol. By co mp arison, the biom ass lignin structure mu st be either m echanically or chem ically broken down to perm it hydrol yzing chem icals and enzym e s access to the saccharide p o lym ers. The central p r o b lem is that the cellulo se/hemicellulo se sacch arid e polym ers are β -linked w hich m akes hydrolysis much m ore difficult. Simple m i crobial fer m entation used in corn sugar fermentation is also not possible, si n ce th e cellulose and hem i cellulose (6 & 5 carbon m olecules, respective ly) have not been able to be ferm ent e d by the
sam e mi crobe. W e discuss various pretreatm e nt, hydrolysis and ferm ent a tion technologies, below. The second and third approaches have nothing to do with pretreatm e nt, acids, enzym e s, or ferm entation. The second is som e tim es refe rred to as the “syngas” or “gas-to-liquid” approach ; we will call it the “Syngas Platform .” Briefly, the cellu losic biom ass feedstock is steam -reform ed to produce syngas which is then converted to ethanol over a Fischer-Tropsch catalyst. The third approach uses plasm atechnology. Technologies that are current ly being developed m a y solve som e of the problem s associated w ith producing cellulosic ethanol. Specifically, one problem , m e ntioned previously, with cellulo sic feedsto c ks is that th e hydrolys is reactions pro duce both glucose, a six

- carbon sugar, and xylose, a five-carbon sugar (pentose sugar, C 5 H 10 O 5 ; som e times called “w ood sugar”). Early conversion technology requir ed different m i crobes to fer m en t each sugar. Recent research has developed better cellulose hydrolysis enzymes and ethanol-ferm enting organism s. 8 1 Now, glucose and xylose can be co-ferm e nted—hen ce, the term inology, weak-acid enzym a tic hydrolysis and co-ferm e ntation. In addition, at least one g r o up is researching the use of recently developed genom e modifying techno logy to produce a variety of new or modified enzym e s and
m i crobes that show prom ise for use in a proc ess known as weak-acid, enzym a tic-prehydrolysis 8 2

80 Enzy me S u g a r Pl at f

o r

m ( E S

P ) , Proj ect N

ext Steps N a t i o nal Renew a bl e E n ergy, Dan Sche l l , FY 03 R e vi e w Meetin g ; Lab o rato ry Op erated for th e U.S. Dep artm e n to f En erg y b y Midwest Research In stitu te • B NREL, Go l d en, C o lo rad o , May 1

- 2 , 2 003 ; U.S. Dep artm en t o f En erg y b y Midwest Research In stitu te • Battel l e • Bech tel “Purdue yeast m akes ethanol from ag ricultural waste m o re effectively.” P u r d ue News , J une 2 8 , 2 0 04; Wri t e r: Emil V e n e r e , (7 65) 4 9 4

- 47 09, v e n e r e @p urdu e.ed u ; sou r ce: Dr

. N an c y H o , (76 5 ) 4 94- 704 6, nwy h o @ec n. p u r d ue.e du

.

82 DO E G e nom i c s: G T L Ro adm ap , S y stem s Bio logy fo r Energy an d En viro nm en t , U. S. Depa rt m e nt of Ener gy , Office of Science, Office of B i ological a n d E nvi ro nm ent a l R e search , Of fi ce o f Ad va nce d Sci e nt i f i c C o m put i ng Resear ch, G e rman to wn , M D 2 087 4-1 290 , Au gu st 20 05 ; DO EGeno m esTo Lif e .or g /ro a dmap ; d own lo ad ab le as who le
o r

in section s
.

268 817.1.2.2 Difficu lties in Estima ting Capital and Operating Costs fo r Newor Pi oneer Process Plants Many years ago the petroleum and chem ical pr ocess industries learned that it can be financially problem atic to scale- up a bench or laboratory scale pr ocess to a full commercial s i zed operation. There are simply too m a ny process variab les that act one way ata batch rate of oneor two-gallons per day, or even 100-gallons per day, but then act a completely different way in a continuous, 70,000 gallo n per day op eration. Under thes e, ad m ittedly so m e what extrem e expectations , there is als o absolu tely no reasonable way to optim ize a process. W e expect that at least pilot or dem onstration size projects will be necessary before a fully comm ercial sized, reasonably optim ized plant can be constructed. The petroleum and chemical process industrie s have also learned that if a different f

eedstock, with similar, b u t at the sam e tim e sufficiently different characteristics, b eco m e s available, it is nearly always necess ary to m ake several p ilo t plant runs bef o re the feeds t ock in introdu ced into the p r ocess. There are a wide va rie t y of pote n tia l cellulo si c feedsto c ks, such as switch grass, forest thinnings, municipal waste, w ood chips, and corn stover (corn stalks). The physical characteristics o f these m a terials, suchas size, composition, and density v ary widely. As a result, there could be signi ficant differences in the pro cess configurations, as well as differences in the enzym e “cocktails” required to hydrolyze and convert each of them into ethanol. Compositional and densit y variations m a y require differe nt reactor residence tim es for each feedsto ck, which will im pact th roughput. Ma ny of the process s tre am s will actually be slurries of th e feedstock. It is also qu ite like l y tha teach s l urry stream w ill have its own flow and
compositional characteristics. Th e flow charact eristics of any slurr y, under real operating conditions, must be well unde rstood in order to properly design an optimum system

. Additionally, valve and pump types, sizes, and m a te rials of construction, as well as line sizes and configurations, m a y var y. Apart from the various process issues, questions also rem a in regarding which of the feedstocks is actua lly th e best in term s of ethanol yield per dollar. Consequently, we believe a good deal m ore pr ocess data is necessary before a reasonably accurate cost to design, engineer, andbuild a co mm ercial scale cellulosic based eth an o l plan t can be expected. At the pres ent tim e, there is on ly on e cellulos i c ethanol p l an t in North A m erica (Iogen Q QQQQ a privately held comp any, based in Otta wa, Ontario, C a nada). On February 28, 2007, however, the Departm e nt of Energy (DOE) announ ced that it will provide grants of up to $385 billion for six biorefinery projec ts over the next four years. Th ese f acilities are expected to produce m o re than 130 m illion gallons of celluloic eth a nol per year. As additional inform ation on these f u ture f acilities are m ade availab l e, EPA will have m o re inf o rm ation on proce ss design f r om which we will better be ab le to projec t prod uction co sts f o r cellu losic ethano l. Although the industry seem s to be moving down several differe nt pathways, one of the
more m a ture process being tested and improved us es dilu te a cid enzym atic prehydrolysis with simu ltaneous saccharification (enzy m atic) and co-f erm e ntation. Because th ere is m o re publicly available inform ation about this process, the m odel we used incorporates this type of process to estim ate the cost of producing ethanol from corn stover. W e chose corn stover because it is ubiquitou s and because of the likelih ood it will eventually be used as a feedstock.

269In 1999, the National Renewable Energy Labor atory (NREL) published a report outlining its work with the USDA to design a computer m odel of a plant to produce ethanol from hardwood chips. R RRRRAlthough the cellulosic m odel was origin ally prepared for hardwood chips, it
was m eant to serve as a modifiable-platform for ongoing research using cellulosic biom ass as feedstock to produce ethanol. Thei r long-term plan was that vari ous indices, costs, technologies, and other factors would be regularly updated.
NREL modified the m o del in o r der to comp are th e cost of using corn-g ra in with th e c o st of using corn stover to produce ethanol. W e use d the corn stover m odel from the second NREL/USDA study for this analysis. Because th ere are no o perating plants that could potentially provide real world process design, construction, and operating data for processing cellulosic ethanol, NREL had originally consid ered m odeling the plant based on assumptions associated w ith a p i oneer plant. Suchassump tions would like l y resu lt in c o sts sign if ic antly higher than corn ethanol plants due to the highe r level of unc erta inty in b o th the de sig n and engineering as well as the final construction and operating costs. The lite ratur e ind i cates th at such m odels often underestim ate actual costs since the high perform a nce assum e d forpioneer process plants is generally unrealistic. The NREL analysis assu m e d that the corn stover plant was an N th generation plant, built af

ter the ind u stry had be en well enough established to provide ve rified co sts. The corn stover plant was no rm alized to the corn ke rn el plan t, e.g. , placed on a similar basis. Addition al cos t s for risk financing, longer start-ups, and other co sts associated with firs t-of-a-kind or pioneer plants were not included in the study. S SSSS It is also reasonable to expect the c o st of cellu losic ethanol will be higher th an corn ethanol becaus e of the com p l exity of the cellu lose co nversion process. During the recent past, process im provem e nts and other advancem ents in corn production have considerably reduced the cost of producing corn ethanol. We also believe it is realistic to a ssum e that cellu lose-d erived etha no l process improvem ents will be m ade and that one can likewise reasonably expect that as th e industry m a tures, the cost of producing ethanol f r om cellulo se will also decrea se.
7.1.2.3 Methods, Data Sources, and Assumptions For our analysis, we used the spreadsheet m odel that NREL developed for its comparison of the costs of producing ethanol from corn grain and corn stover. 8 3 W e believe NREL’s approach was reasonable and we ll thought out. They also had an outside engineering firm validate their work, to the extent possible. The Delta-T Cor poration (De lta -T) ass i sted in preparing, reviewing, and estim ating costs for the process design. Delta-T worked with NREL process eng i neers to rev i ew all th e process des i gn and equipment costs (with the exclusion of wastewater treatm e nt and the burner-boiler system, which were reviewed by Merrick Engineering and Reaction Engineering, Inc., respec tively). For the plant areas that are actively

83 Th e f ir s t, wood ch i p -p lan t st ud y w a s design ed to pro d u ce 52 .2 mil lio n g a llo n s of et

h a no l p e r year f r o m ab ou t 2 , 2 0 0 t o ns p e r d a y (35 0 op er atin g d a ys p e ry ear ; 15 d a ys f o r d o w n tim e, in clu d ing t u rn -aro und ) of w oodch ip s
. Th e seco ndstu d y n o rm al i zed th e ori g i n al wo o d c h i p pl ant i n t o th e c o rn st ove r pl ant t o p r od uc e 2 5 mi ll i on ga l l ons of ethan o l pe r y e ar(a b out 1, 2 3 5 wet t o ns per day ) , i n 1 9 9 9 d o l l ars. The ad j u s t m e nt s i n cl ude d feed rat e a n d feedst ock
v o l u m e an d cost adj u stm e n t s; equ i p m en t sizes with adju stmen t s to cap ital an d in stallatio n costs, an d th e co st o f cap ital, labo r, an d pro cess che micals, in clu d in g d e n a turan t

.

270being investigated under DOE program s (e.g., pr ehydrolysis, cellulase enzym e production, and simu ltaneous saccharification and co -ferm e nta tio n), Delta-T used the res u lts of the D O E sponsored research to identify process design cr iteria and equipm ent requirem e nts. These were used as a basis for sizing and costing m a jor equi pm ent com p onents in the f acility. The results of Merrick Engineering’s w ork on wastewater treatment and REI’s work on the burner/boiler were also included. T TTTTThe NREL m odel used the Aspen Plus TM proc ess simu lato r to ca lculate the flows and the heat and m a terial b a lan ces fo r the process. W e decided to use the NREL spreadsheet corn stover model, as is, since we did not have access to the Aspen PlusTM m odel nor to all the inp u t. Rather, we lef tthe f eedrate, yi elds, and stream s flows as they were, but adjusted equipm ent capital and installation costs, and utility, chem ical, a nd labor costs to 2004 dollars. We used the sam e indices used by NREL to upda te their corn stover st udy; however, we used actual costs and indices for 2004 where possible. For example, in their 2000 calculations, NREL had extrapolated th e Chem ical Engineering P l ant 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. Seve ra l issues rem a in to be s e ttled regarding the
am ount of st over that sh ould be left in place a nd how it should be gathered, baled, andshipped. We found c ost 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 assum e d in its analysis. For the analysis, we calculated the annual pr oduction cost in dollars per gallon of fuel ethanol. The annual production cost includes equipm ent straight-lin e depreciation for the life of the plant (10 years), and vari able costs, labor, supplies a nd overhead, m i nus any by-product credits. Gasoline for denaturant and diesel for bu lldozers to move the stov er were projected into 2012 prices using IEA’s AEO 2006 U UUUU report. The m arket selling price m i nus the annual production cost is the bef ore-tax profit. W e calculated variable operating costs using NREL’s best estim ate of quantities of chem ic als and ad ditives based on their laboratory work. NREL calculated fixed costs using industry standards for percen tages of direct labor(indirect labor was 40% of direct labor and overheadwas 60% of total labor); othe r operating supplies, insurance, etc. totaled 3.25% of total instal led cost. According to the anal ysis three m a jor cost categories
m a de up the m a jority of the to tal production cost: feeds stoc k – 31.2%; fixed costs – 23.8%; and depreciation (reflects installed capital cost of equipm ent) – 33.8%. As previously stated, several feedstock issues remain to be settle d, not least being which of the m a ny available feedstocks will be the bes to r m o st efficient. W e chose an averag e 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 com e down a little. On the other hand, several researchers have indicated switch grass m a y be be tte r than c o rn stove r; o thers point to forest wastes, etc. In the end, the best feedstock will likely be the one th at is readily available and close to the plant; gathering, baling, a nd hauling continue to be importa nt issues that will definitely im pact the viability of a f eedstock. Equipm ent cost reduc tio ns m ay have a signif i c an tim pact on future costs. For example, there appear to be reasons to expect significant savings from purchasing enzym e s rather than growing them onsite. Another issue that rem a ins to be investigated is whether a particular kind of feedstock can be processed using one type of technology while a different kind m a y require th e use of a completely different technology.

2717.1.2.4 Results and Discussion
Given the lim itations we’ve already discusse d, and perhaps others, we determ ined that it would have cost approxim ately $1.65 in 2012 (2004 dollars) to produ ce a gallon of ethanol using corn stover as a cellulo s i c feedstock. The provisions offering grants and shared fi nancing included in Title XV of the Energy Act V VVVV will lik e l y encour ag e process d e velopm ent work to gen erate the ne c essary construction and operating cost estim a tes. W e assum e the results produced by the above referenced NREL study are accurate and reason able given the state of our current knowledge.



### 7.2 Biodiesel and Renew a ble Di esel Production Costs

7.2.1 Overviewof Anal ysis
We based our estim a te for the cost to pr oduce biodiesel on the use of USDA’s, NREL’s and EIA’s biodiesel computer m odels, along wi th estim ates from engineering vendors that design biodiesel plants. Biodiesel fuel can be m a de from a wide variety of virgin vegetable oils such as canola, corn oil, cottonseed, etc. though, the operating costs (m inus the costs of the feedstock oils) for these virgin vegetable oils ar e sim ilar to th e costs ba sed on using soy oil as a feedstock, according to an analys is by NREL 8 4

. Biodiesel costs are therefore determ ined based on the use of soy oil, since this is the m ost co mmonly 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 transester ification, which converts these f eedstock oils to esters, along with the ester finishing processes and glycerol recovery. The models a nd vendors data are used to estim a te the capital, fixed and operating costs a ssociated w ith the produc tion of biodiesel fuel, considering utility, labor, land a nd any other process and operati ng requirem e nts, along with the prices for feedstock oils, m e thanol, chem icals and the byproduct glycerol.
The USDA, NREL and EIA m ode ls are based on a m e dium s i zed biodiesel plant that was designed to process raw degumm e d virgin s oy oil as the feedstock. Additiona lly , the EIA model also contains a representation to estim at e the biodiesel production cost for a plant that uses yellow grease as a feedstock. In the USDA model, the equipm ent needs and operating requirem e nts for their biodiesel plant were es tim a ted through the use of process simulation software. This software determ ines the biodi esel process requ irem en ts based on the use of established engineering relationships, process operating conditions and reagent need s. To substantiate the validity and accuracy of their model, USDA solicited feedback from m a jor biodiesel producers. Based on responses, they th en m ade adjustm ents to their m odel and updated their input prices to year 2005. The NREL mode l 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 m odel is not know n, though it is based on 2004 prices. T he output for all of these m odels was provided in spread sheet form at. We also use engineering vendor

84 NRELPre s ent a tion “ U .S . Bi odiesel Feed st o c k Su pp ly” Jun e 2 004

.

272estim ates as another source to generate soy o il and yellow grease biodi esel production costs. These firm s are prim arily engaged in the bus iness of designing biodiesel plants. The production costs are based on a 10 m illion g a llon per year biod iese l plant located in the Midwest using feedstock oils and m e thanol, which are catalyzedinto esters and glycerol by use of sodium hydroxide. Because lo cal feedsto c k costs, distribution cost s, and biodiesel plan t type in trodu ce som e variability in to c o st estim ate s, we believ e that u sing an averag e p l ant to estim ate production costs provides a reasonab l e approach. Th erefor e, we simplified our analysis and used cos t s based on an averag e p l ant and aver age feedsto ck prices sin ce the tota l biodies el volum es for ecasted are not large an d repres ent a sm all fraction of the total pro j ected renewable volum es.
The m odels and vendor estim ates are further m odified to use input prices for feedstocks, byproducts and energy that reflect th e effects of the fuels provisions in the Energy Act. In order to capture a range of production co sts, we generated cost projections from all of the m odels and vendors. W e present the details on these estim a tes later in this section. For soy oil biodiesel production, based on the USDA model, we estim ate a production cost of $2.06 per gallon in 2004 and $1.89 per ga l in 2012 (in 2004 dollars). W ith the NREL model, we estim ate soy oil biodiesel produc tion costs of $2.28 and $2.11 per gallon in 2004 and 2012, respectively, which is slight ly higher than the USDA re sults. The E I A m odel 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 estim a tes and generated a range of costs, as discussed later. The total production costs range d from $1.24 to $1.60/gal in 2004, and from $1.11 to $1.56 for year 2012.

Table 7.2-1. Summary o f Productio n Costs for Biodiesel m a de from S oy Oil, pe r Gallon (2004 cents) To ta l Prod uction Cos t Subsidiz ed Prod uction Cos t Feed Capital Reagent and Chemic a ls La bor Energy / Utiliti es

US DA 18 9 89 15 6 11 .3 12 .7 5. 0 4. 8 NR EL 21 1 11 1 16 5 17 .0 17 .0 6. 0 7. 4

EIA 21 5 11 5 16 1 14 .4 NA NA 16 .0 PSI -Lu r gi 22 0 12 0 17 4 18 .8 12 .6 8. 2 5. 5 Su peri or Process Tech nol ogi es 22 4 12 4 17 5 11 .7 16 .5 7. 6 5. 0

273Table 7.2-2. Summary o f Productio n Costs for Biodiese l m a de from Y ellowGreas e, per Gallo n (2004 cents) To ta l Prod uction Cos t Subsidiz ed Prod uction Cos t Feed Capital Reagent and Chemic a ls La bor Energy / Utiliti es

EIA 13 8 88 80 14 .4 NA NA 16 .9 Su peri or Process Tech nol ogi es 16 7 11 7 11 4 14 .7 18 .3 8. 7 9. 0

W ith the current Biodiesel Blender Tax Cred it P r ogram , producers using virgin vegetable oil stocks receive a one dolla r 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 deri ved biodiesel fuel in 2012. Th is comp ares favorably to the projected w holesale diesel fuel prices of 138 cents per gallon in 2012, signifying that the
econom ics for biodiesel are posi tive under the effects of the bl ender credit program , though the tax credit p r ogram will expire in 200 8 if it is no t extended. C ongress m a y later elect to extend the blend e r credit prog ram , though, following th e preced ence used for extending the ethanol blending su bsidies. Ad dition ally, th e Sm all Bio d iese l Blend ers Tax c r ed it prog ram and state tax and credit program s offe r som e additional subsidie s and credits, though the benefits are m odest in comp arison to the Blender’s T a x credit.
7.2.2 Inputs to and Results of USDA’s Model
We used USDA’s biodiesel m odel as a source to generate an estim ate for the cos tto produce biodiesel fuel. The m odel is in spre adsheet form at with inputs in 2005 dollars, and contains all of the capital and operating costs fo r a plant to produce 10 mi lli on gallons per year of biodiesel fuel.

7.2.2.1 Feedstock Costs Feedstock prices are the largest component in generating producti on costs for biodiesel fuel. For soy oil prices, we used prices based on USDA = s 2006 Outlook, which has forecasted soy oil prices considering productionof biodiesel under EPAct 2005. USDA = s Outlook is a national forecasting analysis that m o dels the e ffects of dem a nd for far m products and far m product prices for soy beans, soy bean oil, co rn and other farm comm odit i es. The 2006 Outlook estim ated so y oil prices considering the de m a nd of soy oil derived biodiesel fuel at approxim ately 160 MM gallons per year in 2006 and 312 MM gallons a year in 2007 8 5

. This is in clos e pro x imity to EIA = s soy oil derived biodiesel volum e projection of 135 MM gals and 265 MM gals in 2006 and 2007, respectively. W e ther efore used the soy oil prices from USDA = s Outlook to d e term ine bio d iesel produ ction co sts. The USDA does not forecast y e llow grease prices, so we assum e d that yellow grease feedst ocks costs w ould m aintain the sam e relative historical pricing differential to virgin soy oil. In the past, som e analysis has shown that yellow

85 Per US D A ph one di sc ussi on 6/ 2 2 / 0 6

274grease has sold for about half the price of soy oil 8 6

. The resulting feedstock costs to m ake a gallon of biodiesel under projected volum e s for RFS are in Table 7.2-3.
Table7.2-3. Projected Prices o f Feedstoc k (2004 Dollars per Gallon) Mar k eti n g Ye ar Soy Oil a Yello w Grea se 20 0 4 1. 71 0. 86 20 1 2 1. 56 0. 78

a Pro d u ct i o n of B i odi esel ass u m e d t o c o n s um e 7. 4 2 l b s of so y oi l pe r gal l o n . U S D A pri ces i n 2 0 1 2 are ad j u st edt o 2004 dollars to account for i n flation, using GDP i nde x of 109.7 in year 2004 an d 130.8 in year 2012.

7.2.2.2 Capital Costs For capital costs we usedUSDA = s total installed capital cost of $10.66 MM for a 10 MM gallon per year plant. This estim a te was determined by the USDA, using a detailed analysis to generate costs for equipm ent needs, insta llation, land, engineering and construction w ork, buildings, utility needs, contingencies, star tup costs etc. The U S DA model is based on 2005 dollars, so we adjusted the num bers to 2004 valu es using the GDP index. Per the USDA m e thod, the to tal installed capital costs on a p er gallon basis was am ortized on a 10 year straight line depreciation rate usi ng a facility dependent cost of 10 percent tim es the capita l costs. Maintenance charges, in surances an d facility supply costs w e re also calculated as percentages of the capital. The tota l 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 va lue s f o r utilities , f e ed reagents, m a npower and were based on the USDA = s m odel. The components of the operating costs are discussed below.
7.2.2.4 Utility and Labor Costs
We estim ated utility costs using energy requirements from USDA = s m ode l and adjusted the inpu ts to m atch the e n ergy a nd electricity prices for the M i dwest, using prices from EIA = s AEO. The cost for steam was estimated using the price of natural ga s. E ach pound of steam was produced from heating water, which requ ired 810 British Therm a l Units (BTUs) per pound of

steam

. Addition ally, the steam costs are estim a ted assumi ng that the BTUs to m ake steam are increased by a factor of two, to accou nt for steam distribution efficiency lo sses, treatm ent of boiler water to prevent fouli ng, m a intenance and other m i scellaneous costs. The utility requirem e nts per gallon of biodies el and energy prices are pres ented in T ables 7.2-4 and 7.2-5

86 Ener gy In fo rm at i on Admi ni strat i o n NEM S Petrol e u m M arket i n g M o del Doc u m e nt at i on page J- 2

275Table 7.2-4. Utility Requirements per Gallon Biodiesel a
Medium Press u re Steam , lbs 4. 0 Electricity, kWh 0. 10
Co o ling Tower W a ter, lb s 96 .1

a Utilities p e r USDA m o d e l fro m th e p r od u c ti o n of b i od iesel fro m so y o il.

Table 7.2-5. Midw est Energy Prices per Year (in 2004 $)
Year 2 004 2 012 Electricity, $/kWh 0. 04 6 0. 04 4 Natural g as, $ /MM BTU 7. 16 6. 16

Labor costs include the salaries and benefits for personnel to operate a biodiesel plant. This was estim a ted in the USDA model, though th e labor costs were in 2005 dollars, which we adjusted to 2 004 dollars using the G D P price in d ex. The res u lting labor costs are 5 cents per gallon of produced biodiesel fuel.
7.2.2.5 Chemical R e agents Another operating expense, the production of bi o d iesel also requires the u se of chemicals and chemical reag ents, a s these a ct a s a catalys t in the transes t erif ication process. Ad dition ally,
m e thanol is required as it is the feedstock thatis c h em ically c o m b ined with soy oil an d yellow grease during the transesterifi cation process, yielding the biod iesel product. The amount of chem icals and m e thanol required to m ake a ga llon of biodiesel are li sted in Table 7.2-6.
Table 7.2-6. Reagent Requirements
Rea g en t Annual Requirement, lbs pe r gallon
Water 0. 03 2 3 Hy dr oc hl o ri c a c i d 0. 01 8 5
Meth ano l 0. 80 0 6
So di um M e th o x i d e 0. 02 3 1
So di um Hy dr o x i d e 0. 00 3 1 For the prices of chemical reag ents, w e used prices that were supplied in USDA = s 2005 model and adjusted them to 2004 dollars. Addition a lly, sin ce we have no forecasting
m echanis m we assum e d that the chem ical r eagent prices rem a ined unchanged in 2012.

276However, we estim a ted m e thanol prices, as the co st for this f eedstock is a significant component of the total operating costs. For our analysis, we generated values by us e of a correlation that calcu lates methanol’s price as a f unction of the price of natural gas 8 7

. In 2004 and 2012, using Midwest natural gas prices, we estim ated m e thanol prices of 13.1 and 11.6 cents per pound, respectively. All other chem ical prices, we assum e d were consta nt over tim e and are in Table 7.2-7.
Table 7.2-7. Reagent Prices (in 2004 $) Reagent Prices, $/lb Hy dr oc hl o r i c a c i d 0. 16 7

So di um M e th o x i d e 1. 35 8

So di um Hy dr o x i d e 0. 27 3

7.2.2.6 Glycerol Byproduct
The feedsto c k cost credit for the gly cerin by product in our mode ling work was 5 cents per pound, based on recent pricing trends, assum i ng that additional glycerol generated from expansion of biodiesel production wi ll continue to keep prices low

. The model, like m a ny biodiesel plants, produces a crude 80% glycerin str eam , which is usually sold to glycerin refiners for purification. In the past, crude glycerinhas sold for around $0.15 / pound. Because of the increase in biodiesel production around the wo rld, however, the crude glycerin m arket has becom e saturated and the price is now ar ound $0.05 / pound. As m o re biodiesel capacity com e s on line, this price m a y very well drop further, though other m arkets for the use of glycerol are likely to dev e lop becau se glycerol is a platform chem ical used throughou t industry. We assum e d that the current glycerin pricing environm ent will continue inthe future. For our cost estim ation, the byproduct glycerin was sold at 5 cents per pound, reflecting cu rrent saturated m arket and low pricing conditions. The incom e fr om sale of the byproduct glycerin lowered biodiesel production costs by 2 percent and 4 percent for soy oil and yellow grease deri ved biodiesel fuel, respec tive l y.
The tota l bio d iese l produ ction co sts d erived us ing the USDA’s m odel are summarizedin Table 7.2-8

87 Per EIA p a p e r “MTBE Pr oductio n Econ o mi c s” Tan c r e d C

. M. Li d d e rd ale, m e th an o l p r i ce cen ts p e r gallo n = 15 .7 9 + 0 .0 9 9 *nat u ral gas p ri ce ($ pe r mi l lion B T U )

277Table7.2-8. Projected Production Costs for Bi odiesel by F eedstock per Gallon (2004 Dollars) Mar k eti n g Ye ar So y Oi l

20 0 4 2. 06

20 1 2 1. 89

7.2.3 Inputs to and Results of NREL’s Biodiesel Model
We used NREL’s biodiesel m odel as another so u r ce to generate an estim a te for the cos t to produce biodiesel fuel. Sim ilar to the USDA’s m odel, the NREL biodiesel m odel also represents a continuous transester ification process that uses s odium hydr oxide and methanol to convert soy oil to biodiesel and which has the fi nishing processes for biodiesel and glycerol. The model is in spreadsheet form at, and contains all o f the capital and operatin g costs for a plant to produce 10 million gallons per year of biodiesel f u el. W e again sim p lif ied our ana l y sis, and used the NREL model to estim a te production costs for an average biodiesel plant that m akes 10 MM gallon per year. To m a ke the results directly co mparable to USDA’s m o del, we usedenergy costs in the Midwest, and based the analys is on production of soy oil derived biodiesel.
Based on the results of the NREL model, we estim ate that th e tota l produ ction co sts to
m ake soy oil derived biodiesel fuel ar e $2.28 and $2.11 per gallon for years 2004 and 2012, respectively. This is 22 cents m ore per gallon than the estim ate derived from USDA’s model. The components that m ake up our NREL estim ate are discussed in the sections that follow.

7.2.3.1 Feedstock Costs
The feedstock costs increase because the NREL model assum e s 7.87 pounds of soy oil are required to m ake a gallon of biodiesel fuel. T his is slightly higher than the pounds required by the USDA m odel, though the dif f

erence m a y be due to each m odel being based on soy oils with differing chem ical structures, i.e. m o re este rs, differing densities. The higher amount of soy oil required by the NREL m odel raises the produc tion costs f o r biodiesel by about 10 cents per gallon for feedstock costs alone, versus the USDA m odel. The feedstock costs are su mmarized in Table7.2-9.
Table7.2-9. Projected Prices o f Feedstoc k (2004 Dollars per Gallon)
Mar k eti n g Ye ar So y Oi l

20 0 4 18 1. 0

20 1 2 16 5. 2

2787.2.3.2 Capital Costs
The tota l capita l cost in the NREL model accoun t f o r all of th e costs f o r b u ilding a pla n t, including but not lim ited to the expenses for e quipm ent, tanks, installation costs, engineering, tanks, cons truction, land and site dev e lopm ent, start up and p ermitting ch arges. Thes e costs do not account for expenses incurredf r o mm aintenance, insur an ce and taxes, however. The total capital coats for a plant are $14.8 m illion in 2002 dollars, which we adjusted to 2004 d ollars using the G D P price ind e x. The cap ital costs were am ortized assum i ng a seven percent return on investm e nt, resulting in a cost of 17 cents per gallon. All of the econom ic factors used for
am ortizing the capital costs are summ arized in Table 7.2-10.
Table 7.2-10. Economic F actors Used in Deriving th e Capital Cost Amortiz ation Factor Amo r tiz a tio n Scheme Depreciation Life E c on omi c a n d Projec t Life Federal and State T a x Rate Retu rn on Inves t ment (R OI) Resulting Ca pital Amo r tiz a tio n Factor Societal Cost 10 Year s 15 Year s 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 d i r ectly comp arable to thos e from the USDA model, as fixed operating co st are inclu d ed in the o p erating cos t s f o r the N R EL m odel, while the U S DA model accounts for fixed costs in the capital estim ate. The operating cost for the NREL analysis includes item s for utilities, reag ents, m a npower, insurance, taxe s, general adm i nistration and
m a intenance costs, thoug h do not account for capital co sts. A ddition ally, the sale of th e glycerol byproduct (80% strength) generated incom e of 4 cents per gallon of prod uced biodiesel, using glycerol price of 5 cents per pound. The cost associated with insura nce, taxes, general adm i nistration and supplies incur a cost of 2.4 cents per gallon of biodiesel. The rem a ining
components of operating costs for the NREL modeling analysis are discussed below

. 7.2.3.4 Utility and Labor The utility costs were estim a ted using the energy requirem e nts in the NREL m odel along with the sam e prices for energy, steam and el ectricity, as those used in our USDA analysis. The utility requirem e nts per gallon of biodi esel f u el are listedin Table 7.2 -11

279Table 7.2-11. Utility Requirements per Gallon of Biodiesel Natural Gas, SCF 2

. 0
M e di um Press u re St eam , l b s 3. 2
El ectri ci ty , k W h 0. 1
C ool i n g T o wer W a t e r , l b s 8. 3

The NREL model accou nts for the s a laries of 4 em ployees per shift to ru n and m aintain the plant. In addition to salaries for these pe rsonnel, the labor expens es also accounted for
employee fringe benefits and the cost for a plant supervisor. The resultin g labor costs are 6 cents for each gallon of biodiesel.
7.2.3.5 Chemical R e agents The NREL model also requires the use of the sam e chemicals and chemical reagents that are used in the USDA model. The amount of ch em ical reagents in the NREL m odel, however, reflect the use of diluted hydr ochloric acid (HCl) and sodiumm ethoxide for the biodiesel production process. Hydrochloric acid is list ed as being at 33 per cent strength, which we
assum e d also applied to the stre ngth of sodium methoxide, since the am ount of HCl in the m odel is reflective of about one third the value of the USDA’s m odel. For the chem ical and reagents prices, we used the sam e pricing values as thos e in our USDA m odeling analysis. The resulting total chem ical and reagent costs on a per gall on 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 T able 7.2-12.
Table 7.2-12. Reagent Requirements
Rea g en t Annual Requirement, lbs pe r gallon
Water 3. 46 4 6
HCl a 0. 00 9 8
Meth ano l 0. 60 3 7
NA OCH 3 a 0. 03 3 8
So di um Hy dr o x i d e 0. 19 0 1

a HC l i s Hy dr oc hl o ri c aci d, N AOC H 3 i s s o di um m e thoxi de.

The total biodiesel production costs derived fr om the NREL model are summ arized in Table 7.2-13.

280Table 7.2-13. Projected Production Costs for Bi odiesel by F eedstock per Gallon (2004 dollars) Mar k eti n g Ye ar So y Oi l a

20 0 4 2. 28

20 1 2 2. 11

a Pro d u ct i o n c o nsum es 7. 8 7 l b s o f s o y oi l per gal l o n of bi odi esel . U S D A pri ces i n 2 0 1 2 ar e ad ju st ed t o 20 04 dol l ars t o ac co unt f o r i n fl at i o n, usi n g G D P i nde x o f 10 9. 7 i n y ear 2 0 0 4 an d 13 0. 8 i n y ear 2 0 1 2

.

7.2.4 EIA NEMS Model for Biodiesel
We also estim ated production costs using th e biodiesel plant repr esentation in EIA’s NEMS m o del. This b i o d iese l m odel is in sp read sheet form at and has the aggregated cost
components for an average soy oil and yellow grease based biodiesel plant. W e could not locate written docu m entation th at describes the bas i s for these m odels, though we will assum e for our analys is that it repres ents an averag e biodies el plant.

EIA’s m ode l requires 7.65 lbs/gal of soy oil and yellow grease feedstock to produce a gallon of biodiesel fuel. Using the oil feedstoc k 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, re spectively, while yellow grease feedstock costs are $0.88/gal and $0.80/gal for years 2004 and 2012, respectively.

The EIA m o del does not provided specific in dividual cost components for biodiesel production, though it does have an estim a te for tota l energy, operating and capital costs for both plant types. Capital costs are es tim ated at 14.4 cents/gal in 2004 for both plant types, which we
assum e d contains all of costs as sociated with buildi ng a plant, along with the depreciation and capital payo ff costs. The energy co s t s are p r ovi ded in the m odel on an aggregated basis and do not contain the individual am ounts of natural gas, electricity and steam used by a plant. The model, though, has the total en ergy 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 en ergy cost by the EIA pro j ected pri ce change of natural ga s in the Midwest from 2004 to 2012, resulting in an energy cost of 16.0 a nd 16.9 cents/gal for soy oil and yellow grease, respectively. All of the other operating costs ar e represented by an aggregate num ber, which in year 2004 is 32.6 and 34.5 cent/gal for soy oil an d yellow grease, respectively. This cost repres ents all of the operating co sts n o t associatedwith energy and capital requirem e nts. For our analysis, we assum e that this cost does not ch ange in 2012. We used a glycerin price of 5 c/lb, which generates incom e and offs ets operating cost by 4 c/gal. The net production cost for yellow greases (m i nus feedstock costs) is about 3 c/gal more than the net production cost for soy based biodie sel, indicating the extr a cost incurred for the yellow grease process. T he resulting total production cost are pres ented in Table 7.2-14

281Table7.2-14. Projected Production Costs for Bi odiesel by F eedstock per Gallon (2004 Dollars) Mar k eti n g Ye ar So y Oi l aYello w Grea se
20 0 4 2. 33 1. 47
20 1 2 2. 15 1. 38

a Pro d u ct i o n c o nsum es 7. 6 5 l b s o f s o y oi l per gal l o n of bi odi esel . U S D A pri ces i n 2 0 1 2 ar e ad ju st ed t o 20 04 dol l ars t o ac co unt f o r i n fl at i o n, usi n g G D P i nde x o f 10 9. 7 i n y ear 2 0 0 4 an d 13 0. 8 i n y ear 2 0 1 2

.

7.2.5 Vendor Production Estimates for Biodiesel We used engineering vendor estim a tes as another source to generate the cost to produce biodiesel fuel. For this, we used engineering de tails from two firm s, Superior T echnologies and PSI-Lurgi E ngineering Inc. These engineeri ng vendors are engaged in the business of designing, constructing and building biodiesel plants. The biodiesel production proc esses provided by these firm s are also based on the continuous transest erification process, us ing sodium hydr oxide and
m e thanol to convert soy oil and yellow grease to biodiesel, along with the finishing processes for biodiesel and glycerol, sim ilar to the other m odels

. The vendors generated estim a tes of the total cost to build and operate a biodi esel plant, providing the require m ents for the equipm ent, energy, capital and operating. We adju sted these estim ates to a 2004 y ear costs basis for comparative purposes, and used energy costs in the Midwest.
The vendor estim ates we used for PSI-Lurgi are those listed in the report “Economic Feasibility o f Producing Biodiesel in Tennessee” 8 8

. The biodiesel plant in this an alys is was sized for soy oil feedstock, based on a 13 MM gallon pe r year plant, which we assum e d is directly
comp arable to the 10 MM gallon plant used in the USDA, NREL and EIA m odels. In m aking this comparison, we relied on a report 8 9 from Superior Process T e chnologies to generate the production costs to m ake biodiesel from soy oil a nd yellow grease. This report has the various cost components for a 10 MM gallon per year plant. The total soy oil based biodiesel producti on cost estim ate for year 2004 is $2.20/gal and $2.34/gal for PSI-Lurgi and SuperiorTechnology, respectively with an average cost of $2.27/gal. For 2012, we project the soy oil production cost from both vendors would average approxim a tely $2.09 gal. T he Superior Technology’s yellow grea se biodiesel production cost is $1.66/gal in 2004.

88 “Econo mi c Fe asib ility o f Prod u c i n g Bio

- d i esel in Ten n e ssee” AIM-AG Ag ri-In du stry M o d e ling & An alysis G r o u p , 2002

89 Su pe ri or P r oc ess Tec h n o l o gi es, “ B i o d i e sel Pl ant Ec on omics an d Pr ocess Descri pt i on” , 8 / 18/ 0 6

2827.2.5.1 PSI-Lurgi estimate of B i odiesel Costs
The feedstock costs assum e that 7.569 pounds of soy oil are required to m ake a gallon of biodiesel fuel. The resulting feedstock cost is 174.1 cents/gal for year 2 004 . W e adjusted all other cost item s in the PSI estim ate from a 2002 y ear to 2004 basis, scalin g by the relative change of GDP, though the costs for reagent needs and utilities are adjusted using the m e thods as discussed in the following sections.

7.2.5.2 Capital Costs The total capital cost provided in the estim a te account for all of the costs for building a plant, though excluding m a intenance costs, sim ilar to the capital requ irem ents in the NREL model. The total capital coats for a p l ant ar e $19. 7 m illion in 2004 dollars , which we adjusted from 2002 dollars. The capital costs were am ortized assum i ng a seven percent return on investm e nt, resulting in an a nnualized cost of 16.7 cents per gallon. The econom ic fac t ors used for a m ortizing the capital co sts are the sam e as thos e 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 adm i nistrations is 7.3 cents per gallon, while the cost for m a intenance is 2 cents/gal. T he sale of the glycerol byproduct at 80% strength generates incom e s of 4 cents per gallon of produced biodiesel, a ssumi ng a glycerol price of 5 cents per pound. The rem a ining component s of operating costs are discussed below

. 7.2.5.4 Utility and Labor The utility costs were estim a ted using th e energy requirem e nts presented in Table 3.1 of the report, along with the sam e prices for ener gy, steam and electricity, as those used in our 2004 m odel analysis. The total utility requirem e nts are 5.5 cents per gallon. The utility requirem e nts 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
M e di um Press u re St eam , l b s 3. 92
El ectri ci ty , k W h 0. 09 3
C ool i n g T o wer W a t e r , l b s 20 0. 7

The PSI-Lurgi estim a te accounts for the salari es of 4 e mployees per shift to run and
m a intain the plant. In ad dition to salaries fo r these personnel, the labo r expenses also accounted for e mployee fringe benefits. The resulting labor co sts are 6.3 cents for each gallon of biodiesel. In addition to these costs, t h e SG&A ex penses are est i m ated at 6.3 c/gal.

2837.2.5.5 Chemical R e agents The PSI-Lurgi estim ate also requ ires the sa m e chemicals and chemical reagents in the USDA/NREL m odels. We assum e d that the hydro chloric acid and the so dium m ethoxide used in the PSI estim ate is 33% strength, as the prices listed in the study are reflective of being on a diluted basis. W e also assum e d that the price for the am ount of caustic soda required is on an undiluted basis. W e adjusted the prices for the chem ical and reagents using the 2004 year pricing values used in our USDA/NREL m odeli ng analysis, though the price of phosphoric acid was adjusted using the GDP index. The resul tin g total chemical and reag ent cos t s on a per gallon basis are abou t 12 .6 cents for each gallon of biodiesel fuel produced. All of the required chemicals and reagen ts for the production of biodiesel are pres ented on an undiluted basis in Table 7.2-16.
Table 7.2-16. Reagent Requirements Reagent Annual Requirement, lbs pe r gallon Wat e r 1. 66 6 HCl a0. 02 6 M e th an ol 0. 70 0 NA OCH 3 a0. 03 8 C aust i c So da / So di um Hy dr o x i d e 0. 04 Ph osp h o ri c Ac i d 0. 01 3

a HC l i s Hy dr oc hl o ri c aci d, N AOC H 3 i s s o di um m e thoxi de.

The total soy oil based biodiesel production costs derived from th e PSI-Lurgi are $2.20 dollars in year 2004.
7.2.6 Superior Process Technologies Estimate The Superior feedstock costs assum e that 7.60 pounds of soy oil and yellow grease are required to m ake a gallon of biodi esel fuel, which results in a f eedstock cost of 175 cents/gal for year 2004 for soy oil and 87.5 c/gal f or yellow greas e, using the feedstock costs in section 7.2.2.1. We adjusted all other cost item s in the S uperior estim ate from a 2006 year to 2004 basis, adjusting the cost by the relativ e change of GDP, though the costs for reagent needs and utilities are adjusted using the m e thods as discussed below

.

7.2.6.1 Capital Costs The total capital coats for a soy oil based plan t are $10.7 m illion, while the costs for a yellow greas e plant are $ 13.4 m illion , in 2004 dollars. Thes e costs are in clusive of the am ount needed f o r a new plant, which is similar to th e other biod iesel estim ates. The capital costs were
am ortized assumi ng a seven percen t return on investm ent, resulti ng in a cost of 11.7 cents per gallon and 14.7 cents per gallon for a soy oil a nd yellow grease based plant, respectively.
7.2.6.2 Operating Costs

284The total op erating cos t s , excluding capital charges are 47 and 64 cents per gallon for soy oil and yellow grease plants in ye ar 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 m a intenan ce, plant overh ead cost s and supplies acco unt for about 16.0 and 2 1.5 cents/gal, for soy oil and yellow grease. The sa le of the glycerol bypr oduct at 80% strength generates incom e s of 4 cents per gallon of producedbiodiesel, assumi ng gly cerol price of 5 cents per pound. The rem a ining compone nts 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, elec tricity were not p r ovided f o r the 1 0 MM gallo n pl ant. W e adjusted the Superior u t ility cost estim ate from a 2006 year to a 2004 year basis, usi ng the relative price cha nge of natural gas and electricity, assum i ng that natural gas supplies 90 percent of the energy, and electricity supplies the rem a ining 10 percent. The resulting energy requirem e nt is 5.0 and 8.6 cents per gallon, for soy oil and yellow grease, respectively.
The estim ate accounts for personnel cost to run and m a intain the plant, including laboratory, plant supervisory and administration costs. The over all labor costs on a 2004 year basis are 7.6 and 12.8 cents for each gallon of bio diesel, for soy oil and y e llow grease, respectively.
7.2.6.4 Chemical R e agents The Superior vendor estim a te also requires th e sam e che m icals and chem ical reagents as used in the USDA/NREL m odels and Tennessee st udy. 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 p r ovided the pric es for each o f the chem icals, though the specific
am ounts of each chem ical were no t provided for the soy oil based estimate. W e therefore, adjusted the total chemical re agent cost to a 2004 year basis, assum i ng the dem a nds for reagent as docum ented in the PS I-Lurgi estim ate. The resulting reagent costs on a 2004 year basis are 16.5 and 18.3 cents/gal, for soy oil and yellow grease. The S uperior prices for the required chem icals and reagents are presented in Table 7.2-17.
Table 7.2-17. Superiors Reagent Prices Reag ent Do lla r / lb NA OCH 3 a(25% so l u tio n) 0

. 5 0 HCl a(32 % ) 0. 09 1 M e th an ol 0. 14 6 Ph osp h o ri c Ac i d ( 7 5% ) 0. 42 C aust i c So da / So di um Hy dr o x i d e ( 50% ) 0. 14

a HC l i s Hy dr oc hl o ri c aci d, N AOC H 3 i s s o di um m e thoxi de.

The total resulting biodiesel production costs de rived from Superior P r ocess description and engineering estim ate is $2.34 and $ 1.66 per ga llon for soy oil and yellow grease derived

285biodiesel respectively, in year 2004. Yellow greases net production cost (m inus feedstock costs) are about 20 c/gal m o re than the net production cost for soy based biodiesel.
7.2.7 YellowGrease Production Costs Yellow grease’s production cost is higher than soy oil produced biodiesel fuel, due to the extra capital and operating costs re quired to rem ove contam inants in the grease feeds t ock. In the prior sections, the EIA and Superi or analysis indicated that the yellow grease production cost for biodiesel is higher than the produc tion cost based on use of soy oil, excluding the feedstock costs. The E I A analysis showed that yellow grease’s production costis 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 provi de a m easure m ent of th e extra production costs (exclud i ng feedstock ) as sociatedwith m aking biodiesel from ye llow grease versus soy oil. In this section, we use the EIA and Superior resu lts to gen e rate yellow grease costs as inputs to the m odels, adjusting th e soy oil production co sts to reflect the ex tra cost for producing yellow grease. W e assum e the sa m e feedstock co sts 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. T able 7.2­ 18, contains the resulting yellow grease produc tion costs based on the EIA and Superior analyses.
Table 7.2-18. YellowGrease Costs Based on EIA and Superior Results EIA 2 004 Superio r 200 4 EIA 2 012 Superio r 201 2 US DA , c/ gal 12 4 14 1 11 1 12 8 NR EL, c/ gal 13 8 15 5 13 1 14 8 EIA , c/ gal 14 3 16 0 13 2 14 9 Ven d o r av g, c/ gal 14 2 15 9 13 9 15 6 Ave ra ge 13 6. 8 15 3. 8 12 8. 3 14 5. 2

We averaged all of the y e llow grease results, and generated an aver age production cost of $1.45/gal in 2004 and $1.37/gal in 2012 fo r yellow grease derived biodiesel.
7.2.8 Biodiesel Blending Credit Programs
There are nu m erous credit and incentive programs that encour age the b l en ding of biodiesel. T hese program s rei m burse blenders and producers for adding bi odies el to transpo r t diesel fuel, which acts to lowe r the production costs and m akes the production of biodiesel m ore econom ically comp etitiv e with petroleum de rived diesel fuel. There are s e veral federal/nationwide biodiesel credit program s that offer subsidies for blending or use of biodiesel as a transport dies el fuel which are d i scussed belo w.
The Comm odity Cred it Commi ssion Bio-energy Program is an existing program that expires at the end of fiscal year 2006, though due to a fundi ng shortfall the program wi ll term inate on July 31, 2006. This program was ad m i nistered by the USDA and pays biodiesel producers grants when the econom ics to produce bi odiesel are poor. The stipend is determ ined

286based on available funding and the volum e of rene wable fuel that can re ceive the credit. For historical purposes, the paym ents in 2004 a nd 2005 averaged about 107 and 50 c/gal of fuel produced, respectively. For the fi rst half of 2006, the credit on a per gallon basis is reduced further, as the paym ent is diluted by increased production volum e 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 Am erican Jobs Creation Act of 2004 which created an excise tax credit that can be cl aim e d by anyone who blends biodiesel into transport diesel fuel. Under this p r ogram , blenders m ay claim a credit against the ap plicab le fed eral m o tor fuels excise tax for blends containing biodies el. A ccording to IRS guidelines, the credit m a y be claim e d by anyone who adds biodiesel into dies el fuel at a level greater than 0.1 percent in the final blend. The full credit for biodiesel m ade from virgin ve getable oils and anim al fats is $1.0 per gallon, while biodiesel derived f r om recycled grease re ceives 50 cents/gallon. Ablender with m ore excise tax credits than taxes owed can receive a refund from t he IRS. Addition ally, u nder the current program , i mported biodiesel and fuel m a de from i mported feedstocks can also receive the credit.
The Incom e Tax Credit Alterna tive is a program that is also available. This program does not require any blending of biodies el, though it does offers allow a si m ilar excise tax credit as in the blend ers tax credit p r ogram
. The excise tax can only be taken agains t actual inco m e, however, which m akes the program less economica lly attractive than the blenders’ credit program
. The Energy Act also c r e ated th e Small Biod iese l Blenders Tax credit program . Under this program , a credit of 10 c/gal is available to sm a ll producers w ho m ake biodiesel fuel from virgin vegetable oils. This stipend is lim ited to comp anies with annual production volum es less than 60 MM gallons per year, using the aggregated capac ity from all production sites for an individual
comp any. The m a xi mu m paym ent per comp any 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 program s, there are state and local p r ogram s that offer state fuel tax exemptions, tax credits, and ince ntives that are m o re m o dest.



### 7.3 Distribution Costs

7.3.1. Ethanol Distribution C osts
There are tw o components to the costs asso ciated with distribu ting the volum es of ethanol n ecessary to m eet the requirem e nts of the Renewable Fu els Stand ard (RFS): 1) the capital cost of m aking the necessary upgrades to the fuel distribution infr astructure sy stem , and 2) the ongoing additional freight co sts associated with shipping et hanol to term inals. T h e m o st comprehensive study of the infrastructure requi rem e nts for an expanded fuel ethanol industry was conducted for the Departm e nt of Energy (DOE) in 2002

. W WWWW This study provides the foundation for our estim ates of the capital cost s associated w ith upgrading the distribution

287infrastru ctu re system as well as the freigh t co sts to handle the increased volum e 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 Distrib u tion System The 2002 DOE study exam ined two cases regarding the use of renewable fuels. The first
case assum e d that 5.1 B gal/yr of ethanol would be used in 2010, and the second case assum e d that 10 Bgal/yr of ethanol would be used in 2015. W e interpolated betw een these two cases to provide the foundation for our estim ate of the capital costs to s upport the use of 6.67 Bgal/yr of ethanol in 2012 (the RFS case). The 10 Bga l/yr case from the DOE study was used as the foundation to estim ate the capital costs under th e 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 estim a tes of the infr astructure changes and associated capital cos t s for the two ethanol u se scenarios ex amined in today’s ru le. 9 0

90 Th ese cap ital co sts will b e incu rred in crem e n tally d u ring t h e p eri o dof 2 0 0 7 -2 012 as et han o l vo lu m es in crease. Fo r th e pu rpo s e of th is an alysis, we assu m e d th at all cap ital co sts will b e i n curred i n 2 007

.

288Table 7.3-1. Ethanol Distribution Infrastructure Capital Costs Relative to a 3.9 BillionGallon per Year Reference Case RFS Case (6 .6 7 B g al/y r) EIA Case (9 .6 4 B g al/y r) New Terminal Blendin g Sy stem s for Etha n o l N u m b er o f t e rmi n al s 24 3 51 5 Cap ital co st $ 7 3 , 04 4,000 $ 154 ,5 30 ,00 0 New Ethan o l S t ora g e Tank s at Termi n al s N u m b er o f t a nk s 16 8 37 0 C a paci ty 1, 52 6, 0 0 0 ba rr el s 3, 41 5, 0 0 0 ba rr el s Cap ital co st $ 2 1 , 93 9,000 $ 4 8 , 80 3,000 Ter min al Sto r ag e Tank s C o nver t ed t o Et h a nol Num b er of t a nks 44 83 C apaci ty 31 9, 0 0 0 barrel s 59 2, 0 0 0 barrel s Cap ital co st $ 931 ,0 00 $ 1 ,739 ,0 00 Termin als Usin g Eth a no l fo r th e First Tim e a N u m b er of t e rmi n al s 21 2 45 3 Cap ital cost $ 4 ,238 ,0 00 $ 9 ,065 ,0 00 New Rail Deliv ery Facilities at Termin als N u m b er of t e rmi n al s 42 76 Cap ital cost $ 1 4 , 86 9,000 $ 2 7 , 12 7,000 Retail Facili tie s Usi n g Eth anol for Fi rst Tim e a

Nu m b er o f retail facilit ies 3 3 ,600 7 4 ,820 Cap ital co st $ 1 9 , 82 4,000 $ 4 4 , 14 6,000 New Tract or T r ai l e r Tr ans p ort Truc ks Num b er of T ruc ks 20 9 43 5 Cap ital Co st s $ 2 4 , 02 7,000 $ 5 0 , 07 5,000 New Bar g es

Num b er of n e w bar g es 11 23 Cap ital co st $ 2 1 , 47 5,000 $ 4 3 , 20 4,000 New Rail Cars N u m b er of ne w r a i l cares 2, 02 4 3, 49 1 Cap ital cost $ 172 ,0 12 ,00 0 $ 296 ,7 29 ,00 0 To tal Cap ital Co sts $ 352 ,3 61 ,00 0 $ 675 ,4 18 ,00 0 Cap ital Co sts Attribu t ed t o Termin al an d Retail (i.e. fi x ed) Facilities $ 134 ,8 47 ,00 0 $ 285 ,4 10 ,00 0 Cap ital Co sts Attribu t ed t o Mo b ile Facilities (tank t

ru c k s , rail cars, & barges) $ 217 ,5 14 ,00 0 $ 390 ,0 08 ,00 0

aTermin al an d retail facilities u s i n g eth a n o l for th e first ti me will n e ed t o mak e v ariou s m o d i ficatio n s t o en su re t h e co m p atib ilit y o f th eir system s with eth a no l.

Our estim ated capital co sts in this final ru le differ from thos e in the proposal for several
reasons. First, the volum e for the RFS case was updated to reflect the fu el rule provisions. Second, we adjusted our estim ate of capital costs fr om those in the proposal to reflect an increase in the cost of rail tank cars and barges si nce the DOE study was conducted. Third, we are
assumi ng a 30 percen t increas e in th e reliance onra il versus m arine trans port over that projected in the DOE study. The 2002 DOE s tudy estim a ted th at 53 percent of the increase in ethanol volum e shipped between PADDs would be carriedby barge and 47 percent by rail. F or the purposes of this analysis, we assum e d that 30 percent of the increas ed volum e in ethanol

289shipm e nts that were projected to be carried by barge in the D O E study would instead be carried by rail. This equates to 37 percent of the incr eas e in ethano l sh ipm ents being carried by barge and 63 percent by rail. T o provide a conservatively high estim a te of the potential econom i c impact, we assum ed that this sh if t tr a n slates in to a 30 percen t incre ase in rail inf r as tru cture costs. This inco rpo r ates the inc r eased cost to prepar e ad dition al terminals to re ceive e thano l by ra il, and to provide a sufficient number of additional rail tank cars for ethanol transport. The actual incre ase in r ail inf r astruc ture cos t s m ay be som e what lower given improvem ents in the efficiency of ethanol transport by rail.

Am ortized over 15 years at a 7 percent cost of capita l, the total cap ita l co sts (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 cas e and $74,157,000 under the EIA case. This translates to approxim a tely 1.4 cents per gallon of new ethanol volum e under the RFS case and 1.2 cents per gallon under the EIA case. Under bo th cases, approxim a tely 0.5 cents per gallon is attributed to mobile facilities and the rem a inder to fixed facilities.
7.3.1.2 Ethanol Freight Costs The 2002 DOE study contains esti mated ethanol freight cost s for each of the 5 PADDs

. These estimated costs are summ arized in the following Table 7.3-2. X XXXX 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

PA DD 5

. 1 b illio n g a llo n s per year (cen ts p e r g allo n) 1 0 .0 b illio n gallo n s p e r year (cen ts p e r g allo n) 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 Nat i onal A v era g e 7. 7 5. 7

290Figure 7.3-1. PAD District Definitions.

The Energy Inform ation Adm i nistration (EIA) tr anslated the cost estim a tes from the 2002 DOE study to a census division basis. Y YYYY A summary of the resulting (EIA) ethanol distribution cost estim ates ar e contained in the following Table 7.3-3. A m ap of the census divisions is contai ned in Figure 7.3-2.

291Table 7.3-3. EIA Estimated Ethanol Freight Costs

(derived from the 2002 DOE Study) C e nsus Di vi si o n Freight Cost (cen ts p e r g allo n) From To East N o rt h C e ntral New E ngl and 9. 8 East N o rt h C e ntral M i ddl e At l a nt i c 9. 8 East N o rt h C e ntral East N o rt h C e ntral 4 East N o rt h C e ntral So uth At l a nt i c 9. 8 East N o rt h C e ntral East So uthC e ntral 4. 7 East N o rt h Ce ntral Pacific 14 .0 West No rth C e ntral New E ngl and 11 .4 West No rth C e ntral M i ddl e At l a nt i c 11 .4 West No rth Ce ntral East N o rt h Ce ntral 4 West No rth Ce ntral We st No rth C e ntral 4 West No rth C e ntral So uth At l a nt i c 11 .4 West No rth C e ntral East So uthC e ntral 4. 7 West No rth C e ntral West S o uth C e ntral 4. 7 West No rth C e ntral M o u n t a i n 4. 5 West No rth Ce ntral Pacific 13 .0

292Figure 7.3-2. Census Divisions

We took the EIA projections and translated them into State-by-State ethanol freight costs. For the purposes of this analysis, all ethanol wa s assum e d to be produced in the East and W e st North Central Census Divisions (corresponding closely to PA DD 2). W e believe that this is a reasonable approach because the cost of shipping corn feedstock from PADD 2 to ethanol plants located outs i de of PADD 2 will typ i c ally neg ate a ny potential reduction in f r eight cos tf r om reduced shipping distances for ethanol or dried di stiller grains. The vast m a jority of ethanol plants planned for outside of PA DD 2 are projected to begin ope ration using corn supplied from PADD 2. 9 1 Many have stated plans to transition to local feedstocks. However, we believe that such a transition will typ i cally not be accomplis hed within th e tim efram e considered b y this

91 Hawaii is a special case b ecau s e p l an ts po ten tially lo cated th ere will u s e lo cal feed stock s fro m th eir in itial start up dat e
.

293analysis (i.e. 2012). Other local considerations m a y provide a unique costadvantage to locating a plant outside of PADD 2. One such considerati on m i ght be that if by locating a plant outside of PADD 2 t he ethanol producer coul d avoid the need to dry the di stiller grains it produces and sell the wet distiller grains to a local m arket. Although this m i ghtresult 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 m i ght be offset by the relatively lower price that can be negotiated for we t v ersus dry d i s tiller g r ain s. In any even t, the r e is ins u f f i cient data atthis tim e to evaluate th e exten tto whic h such local conditions m a y result in an advantage in lower freight costs for ethanol plants outside of PADD 2. Fu rther, our projection of where new ethanol production plants m i ght be located indicates thatonly 10 percent of production capacity could be located outside of PADD 2. Th us, any potential freight cost advantage that
m i ght be enjoyed by such plants would not like ly have a significant impact on our national analysis. Furtherm ore, to the extent that th e location of ethanol plan ts outside of PADD 2 imparts a savings in ethanol dist ribution costs, this would suggestthat our estim ates of ethanol freight costs in this rule are conservatively high.
Ethanol consum ed within census divisions belonging to PADD 2 was assu m e d to be transported by truck, while distri bution outside of these areas was assum e d to be by rail, ship, and/or barge. A single average d i stribution cos tfor each destination censu s divis i on was generated b y weighting together the 2012 frei ght costs giv e n for each m o de in both source census div i s i ons acco rding to their v o lum e share. These cents per gallon figures were first adjusted up ward by 10 percen t to reflect the increas ed cost of transportation fuels used to ship ethanol since the 2002 DOE study, and then add ition al ad jus t m ents were applied to so m e individual states based on their position withinthe census division. In the case of Alaska and Hawaii, differences in ethanol delivery prices from the m a inland were inf e rred from gasoline prices.

For som e states, different freight costs for ethanol supplied to large hub term inals versus
sm all satellite terminals was estim ated. The reas oning behind this is that large shipments of ethanol ship ped f r om the Midwest by barge, sh ip, and/or un it tra i n will of ten be initia lly unloaded at hub term inals for further distribution to satellite term inals. In cases where redistribution from a hub to a satellite terminal doesn’t take place, the volum e of ethanol shipped direc tly f r o mthe produ cer to a les s er volum e (“sate llite” ) te rminal will a l so incu r a h i gher f r e i ght rate than ethanol shipped to a larger-volum e “hub ” terminal. The large st adjustm ent was applied to the Rocky Mountain S t ates since they are gen e rally large inarea and ad dition al exp ense is required to transport freight th rough higher elevations and rugged terrain. Sm aller adjustm ents were applied to states th at are sm aller, flatter, or have access by navigable waterways

. The states to which an adjustm e nt was not applied were genera lly in the Midwest or we re so sm a ll as to not warrant different distribution costs. Given the large num ber of etha nol plants in the Midwest, w e do not believe that there are substa ntial d i f f

erences in th e cos tof distributing ethanol with that
area.
We m ade several adjustm ents in our estim ates of ethanol freight costs from those in the proposal. First, the differentia l cost of shipping ethanol to satellite term inals versus hub terminals was increased to better reflect th e add itional costs incurred in ei ther redistributing the ethanol from a hub to a satellite term inal, or of shipping ethanol directly from the producer to the

294sate llite terminal in a le sser volum e. The estim atedaddition al f r eigh t co st of shipping ethanol to satellite term inals versus hub term inals is contained in the following Table 7.3-4.
Table 7.3-4. Additional Freight Cost to Deliver Ethanol to a Satellite T ermina l Compared to a Hub Terminal States cents per gallon OH 2 AL, AR, FL , G A , K Y , L A , M D , ME , MS, N C , N H , N Y , OK , OR, P A , SC, T N , V A , VT, WA, WV 4

AK , AZ, C O , I D , NM, N V , T X , UT, WY 5

Another change that we m a de from the propos al was with respect the volum e of ethanol we estim ated would be delivered to hub versus sa tellite term inals. The proposal assum e d a 50/50 spit. For this final rule, we pr oject that all of the ethanol vo lum e blended into reformulated gasoline would be used in urban areas served by hub term inals. The percentage of ethanol blended into conventional gasolin e that is used in an urban area (and hence delivered to a hub term inal) versus that used in a rural area (and he n ce delivered to a satellite terminal) wa s based on our analysis of the percentage of vehicle m iles traveled in urban versus rural areas. 9 2
The final change from the proposal pertains to our consideration of the cost of shipping ethanol from the production plant to the rail he ad / m arine terminal either for large volum e shipm e nt by unit train or m arine shipm e nt to hub te rm inals, or for shipm e nt at single car rates via multiple -pro duct trains d i rec tly to satellite term inal s. Our rev i ew of curre nt ethano l f r eight rate s conducted in response to a comm ent on the proposedrule indicates that we did not adequately account for this added cost in the proposal. Ch icago is a prim ary 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 shipm e nts, loading costs, and other m i scellaneous fees. Based on this inform ation, we have added 4 cents per gallon to our etha nol freight estim ates.
Our estim ates of the State-by-S t ate ethanol fr eig ht costs und er the RFS a nd 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 inTable 7.3-7. W e are assum i ng that these freight costs do not include the costs asso ciated with the recovery of capital for the di stribution facility changes that are necessary to accom m odate the increas ed volum e of ethanol. This may tend to o v erstate distribution costs to some extentbecause som e capital recov e ry m a y be incorporated into the 4 cent per gallon conveyance fee. Th e inclusion of rail tank car leas e fees also suggests that these estim ated freight costs m a y be cons ervatively hi gh given that rail car l ease fees inco rporate a capital recovery and profit m argin.

92 See C h ap ter 2 o f th is RIA fo r add itio n a l d i scu ssion o f o u r esti m a te o f th e p e rcen tag e of eth a no l th at will b e u s ed i n u r ban ve rs us a ru ral ar eas

295Table 7.3-5. State-by-State Ethanol Freight Costs

State PA DD Eth a no l Fr eight Co st * : (cen ts p e r g a llon ) Hu b Satellite A v er ag e Fr e i gh t Co s t ** Terminal Terminal RFS Case EIA Case C o n n ect i c ut 1 15 .4 15 .4 15 .4 15 .4 M a i n e 1 17 .4 21 .4 20 .0 20 .0 M a ssach usett s 1 15 .4 15 .4 15 .4 15 .4 New Ham p shi r e 1 16 .4 16 .4 16 .4 16 .4 R h o d e Isl a n d 1 15 .4 15 .4 15 .4 15 .4

Verm ont 1 16 .4 16 .4 NA *** 16 .4 New Jersey 1 15 .4 15 .4 15 .4 15 .4 New Y ork 1 15 .4 19 .4 15 .4 15 .7 Pen n sy l v a n i a1 12 .4 16 .4 13 .8 13 .7

Del a ware 1 15 .4 15 .4 15 .4 15 .4 Di stri ct o f C o l u m b i a 1 15 .4 15 .4 NA *** 15 .4 Fl ori d a 1 12 .4 16 .4 14 .0 14 .0 Geo r gi a 1 15 .4 19 .4 NA *** 17 .4 M ary l a nd 1 15 .4 15 .4 NA *** 15 .4 No rthC ar ol i na 1 15 .4 19 .4 NA *** NA *** So uth C arol i na 1 15 .4 19 .4 NA *** NA *** Vi r gi ni a1 15 .4 19 .4 NA *** 15 .4 West Vi rgi n i a 1 15 .4 19 .4 NA *** NA *** Il l i noi s 2 4. 4 4. 4 4. 4 4. 4 In di ana 2 5. 4 5. 4 5. 4 5. 4 M i chi gan 2 6. 4 6. 4 6. 4 6. 4

Ohi o 2 5. 4 7. 4 6. 6 6. 6 W i sco nsi n 2 4. 4 4. 4 4. 4 4. 4 Io wa 2 3. 4 3. 4 3. 4 3. 4 Kans as 2 4. 4 4. 4 4. 4 4. 4 M i nnes ot a2 4. 4 4. 4 4. 4 4. 4 M i ssou ri 2 4. 4 4. 4 4. 4 4. 4 Neb ras ka 2 4. 4 4. 4 4. 4 4. 4 No rthDak ot a 2 5. 4 5. 4 5. 4 5. 4 So uth Dakot a 2 4. 4 4. 4 4. 4 4. 4

Kent ucky 2 6. 2 10 .2 7. 1 7. 3 Ten n essee 2 6. 2 10 .2 8. 7 8. 7 Okl ahom a 2 8. 3 12 .3 NA *** 11 .1 * Freigh t rates fro m PADD 2 p r od u c tion faci lities
* * Hub and satellite freig h t rates were vo lume we ig h t ed t o arriv e at an av erag e frei g h t rate. * * * No si gn ifican t eth a no l u s e. See Ch ap ter 2 of th is R I A r e gar d i n g o u r e s t i m a t e s of w h ere etha n o l wi l l be
use d

.

296Table 7.3-5. State-by-S tate Ethanol Freight Costs (con tinu ed) State PADD Eth a no l Fr eight Co st * : (cen ts p e r g a llon ) Hu b Satellite A v er ag e Fr e i gh t Co s t ** Terminal Terminal RFS Case EIA Case Al abam a 3 11 .2 15 .2 14 .0 14 .0 M i ssi ssi ppi 3 10 .2 14 .2 13 .9 13 .9

Arka nsas 3 11 .3 16 .3 15 .0 15 .0 Lo ui si ana 3 11 .3 15 .3 13 .8 13 .8

Texas 3 14 .3 19 .3 14 .3 16 .0 New M e xi c o 3 16 .4 21 .4 19 .0 19 .6

C ol ora d o 4 14 .4 19 .4 NA *** 17 .0

Idah o 4 19 .4 24 .4 NA *** 22 .8 M ont a n a 4 17 .4 22 .4 21 .8 21 .8

Ut ah 4 17 .4 22 .4 NA *** 20 .5 W y omi ng 4 16 .4 21 .4 NA *** 20 .8

Ari z ona 5 19 .4 24 .4 20 .3 20 .5 Neva da 5 20 .4 25 .4 21 .1 22 .3 Al aska 5 45 .5 50 .5 NA *** NA *** Hawai i 5 40 .5 40 .5 NA *** 40 .5 Ore g on 5 20 .5 24 .5 20 .5 20 .5 Was hi n gt o n 5 20 .5 24 .5 21 .9 21 .9

C a l i forni a 5 20 .5 20 .5 20 .5 20 .5 * Freigh t rates fro m PADD 2 p r od u c tion faci lities.
* * Hub an d sat e llite freig h t rates were vo lu m e we igh t ed to arriv e at th e av erag e freigh t rat e
. ** * N o si gni fi cant etha n o l us e. See C h ap t e r 2 of th is R I A reg ard ing ou r esti m a tes o f where eth a no l wil l b e u s ed

.

Table 7.3-6. National and PADD Average Ethanol Freight Costs Eth a no l Fr eight Co st * (cent s per gal l o n) RFS Case EIA Case Nat i onal A v era g e 11 .3 11 .9 PA DD 1 14 .9 15 .1 PA DD 2 5. 1 5. 3 PA DD 3 14 .6 15 .2 PA DD 4 21 .8 19 .8 PA DD 5 excl u d i n g AK & H I 20 .6 20 .7 PA DD 5 i n cl u d i n g AK & H I 20 .6 22 .1
* Freigh t rates fro m PADD 2 p r od u c tion facilities.

The national average etha nol freight cost of 11.3 cents per gallon under the RFS case and 11.9 cents p e r gallon un der the E I A case trans l ates to an ann u al freigh t cost for the addition al volum e of et hanol used in 2012 of $3 13,123,000 and $678,300,000 respectively. Adding in the annualized capital costs, results in a total a nnual ethanol distributi on 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 9 3 case.

2977.3.2 Biodiesel Distribution Costs
The volum e of biodiesel used by 2012 under th e RFS is estimated at 300 m illion gallons per year. The 2012 reference case against which we are estimating the cost of distributing the additional volum e of biodiesel needed to m eet the requirem e nts of the RFS is 30 m i l lion gallons. 9 4
The capital costs as sociated with d i s tribu tion of biodiesel are higher per gallon than those associated w ith the distribution of ethanol due to the need for storage tanks, blending system s, barges, tanker trucks and rail cars to be insula ted and in m a ny cases heated during the winter months. 9 5 In the proposal, we estim ated that thatthese capital costs w ould be approxim a tely $50,000,000. W e adjusted our estimate of these cap ital costs for this final rule based on additional infor m ation regarding the cost to inst all the necessary storag e and blending equipm ent at te rm inals and the needf o r addition al ra il tank c ars f o r biodiesel. 9 6 W e now estim ate that handling the incre ased biodiese l volu m e will re qu ire a tota l capita l cost in vestm ent of $145,500,000 which equates to about 6 cents per gallon of new biodiesel volum e. 9 7
In the propo sal, we estim ated that the freight co sts for ethan o l adequately reflect tho se for biodiesel as well. In response to comments, we sought additional information regarding the freight costs for biodiesel. This inform ation indicates that freight costs for biodiesel are typically 30 percent higher than those for ethanol wh ich translates into an estim a te of 15.5 cents per gallon for biodiesel freight costs. 9 8

Includ ing th e cost of cap ital recov ery for th e necessary distribution facility changes, we estim ate the cost of distributing biod iesel to be 21.5 cents per gallon.

93 All cap ital co sts were assu m e d to b e in cu rred in 200 7 an d were am ortized ove r 15 years at a 7 pe rce n t cost of cap ital.

94 See C h ap ter 1 o f th is RIA regard i n g the 2012 refe rence cas e.

95 See C h apter 1.3 of the Re gulat ory Im pact Analysis associ ated with tod ay’s ru le for a disc ussion of t h e s p ecial h a nd ling requ ire m en ts for b i od iesel und er cold cond itio n s
.

96 In fo rm atio n on b i od iesel faci lity co sts was ob tain ed fro m a n u m b e r o f b i od iesel b l en d e rs on th e cond itio n th at th e speci fi c s o urce o f s u chi n f o rm at i on w oul d not be i d ent i f i e d. B i o d i e sel ra i l t a nk cars ty p i cal l y have a c apaci ty of 25 ,5 0 0 gal l o n s as o p pose d t o 30 ,0 0 0 gal l o n s fo r a n etha n o l t a nk car
. T h us, ad di t i onal t a n k cars ar e needt o tr a n s p o r t a gi ve n v o l u m e o f bi o d i e sel r e l a t i v e t o th e s a m e vol um e of ethan o l

.

97 Cap ital co sts will b e in curred in crem en tall y o v e r t h e p eri o d of 20 07 -201 2 as b i od iesel vo lu m e s in crease. For th e p u rp o s e of t h is an alysis, all cap ital co sts were assu m e d to b e i n cu rre d i n 2 0 0 7 a n d were a m ort i zed ove r 15 y ears at a 7 p e rcen t co st o f cap ital.

Th is is b a sed o n our rev i ew o f pub licly av ailab l e b i od iesel and eth a no l freig h t rates from CSX an d BNSF rail at
ww w.cs x.c o m and www .b n s f

. co m , on i n fo rm at i on re gar d i n g th e l ease rat e s f o r

bi odi esel ve rsus etha n o l frei ght cars co nsid eri n g th e sm al ler size of b i od iesel tank cars, and o n di scussi o n s wi th bi o d i e sel di stri but ors
. The es t i m a t e d ethanol freight costs were increased by 30 pe rcent t o arrive at the estim a t e of bi odiesel fre ight c o sts.

298 98

### 7.4 Gasoline and Diesel Blendstock Costs The previous sections of this chapter have presented estim ates of the cost of producing and distributing ethanol and biodi esel. In this section, we su mmarize the results of refinery modeling conducted by Jacobs Consu ltancy under contract to EPA

. Jacobs’s used the Haverly Linear Programm ing (LP) m odel to conduct the an alysis. This m odel is widely used by the refining industry, consultants, engineering firm s and government agencies to analyze refinery
econom ics, refinery operations, fuel quality change s, refinery capital investm e nts, environm ental changes and dem a nd cha nges. The Haverly m odel uses Jacobs’s Refining Process Technology Database to represent refining operations
The m odeling was conducted to analyze the eff ect of the increased renewable fuel use on the production costs andcomposition of the nation’ s gasolin e and dies el fuel. The refinery modeling output described in th is section includ es the ch ang es in volum es and capital investm ents as well as th e resulting c apita l and f i xed operating costs, the variab le cos t s, and the total of all these costs. The costs are expresse d in 2006 dollars and capital costs are am ortized at 7% before tax return on investm e nt (ROI). Th e costs for the RFS and EIA cases are expressed increm ental to the reference case. We first re p o rt th e resu lts of the RFS case, f o llow ed by the results of th e EIA case.
7.4.1 Descriptionof Refinery Modeling Cases Modeled
The m odeling cases were set up to an alyze th e RFS and EIA cases described in Chapter 2. The primary renewable fuel m ode led was ethanol in gasoline, while considering a fixed production amount of biodiesel as projected by EI A in 2012. Along with the increased use of renewable f uels, th e analyses for the RFS and EI A cases both include the elim ination of the RFG oxygen content standard and the resulting rem ova l of MTBE from the U. S. gasoline m a rket. These scenarios both ass u m e the current Mobile Source Air Toxics standard (MSAT1) is in place. The effects of the MSAT2 standard are mode led in th at ru lem aking which has just recen tly been m ade final. Jacobs conducted a Linear Program m i ng (LP) modeling analysis of the refining industry for the various RFS scenarios using a m ode l developed by Haverly’s LP technology. The modeling was set up to analyze the extent to wh ich eth a nol will be us ed in CG versus RFG by region and the resulting effects on gasoline co mp osition. The refining industry was modeled based on five aggregate complex refining regions, representing PADD’s 1, 2, 3, 4 & 5 together
m i nus California and California separately. All of the PADDS were m odeled simultaneously together in the LP m odel, in order to balance and m eet the national gasoline and fuel dem a nds.
7.4.1.1 RVP The analysis m odeled summer and winter s easons, with all gaso line types including California R F G, Federal RFG, 7.0, 7.8 RVP controlled areas and 9.0 CG. The control cases consisted of the m i ni mu m renewable fuel vol ume as specified by EPAct and discussed in Chapter 2 an d the 2006 AEO projection of 9.6 billion g a llon s of ethanol p e r year in 20 12.

299W i nter gasoline RVP levels were ad justed h i gher than EPA data, to acco unt for refin e r RVP reporting in accuracies from use of the comp lex m odel in the winter season. (Som e refiners reported low e r RVP levels than actually produ ced, as the comp lex m odel has a fixed upper reporting lim it of 8.8 in the winter season.)
7.4.1.2 Base case (2004) The base case was established by m odeling fuel volum e s for 2004. Inform a tion was based on process capacities from Oil and Gas Journal, EIA data and gasoline em issions 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 assum e d sulfur s t andards based on gasoline data in 2004, not with fully phased in of Tier 2 gasoline standards at the 30 ppm level. In addition we assum e d the phase-in of 15 ppm s ulfur standards for highway, nonroad, locom o tive and m arine diesel fuel. The s upply/dem a nd balance for the U.S. was based on gasoline volum es from EIA and the California A i r Resources Board (CARB). Our decision to use 2004 rather than 200 5 as the baseline year wa s because of the refinery upset cond itions associated w ith the Gulf Coast hurricanes in 2005.
7.4.1.3 MSAT1 Pr ovisions for Refinery Cases in 2012 For CG and RFG, gasoline qualities were m odeled to assure Complex model Phase 2 calculations seasonal and annual compliance, taking into accountthe elim ination of the oxygen requirem e nt for RFG; (by PADD and Califor nia), and under MSAT1 gasoline standards. Increm ental gasolin e volum es above the 2004 base case vo lu m es for each PADD were allowed to conform t o less stringent toxics perform a n ce 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 incr em ental volum e s of gasoline above the 1998-2000 annual average would comply with MSAT1 provi sions, as predicated by EPact 2005. The following tables show the resul ting conventional and RFG ga solin e MSA T 1 baseline constraints, which was applied to gasoline produced for the cases m odeled in year 2012.
Table 7.4-1. Conventional Gasoline MSAT 1 2012 Baseline Data Exhaust Toxi cs m g /mi* NOxmg/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

California 99.85 1,414.00

*m g/m i i s mi lli gram per mi l e
.

300Table 7.4-2. RFG Gasoline MSAT 1 2012 Baseline Data Total Toxics mg/mi* Tox i cs P e r c e n t Redu ct ion PADD 1 75.11 27.39 PADD 2 80.11 22.56 PADD 3 74.74 27.75 PADD 4 & 5, excluding

California NA NA

*m g/m i i s mi lli gram per mi l e
.

7.4.1.4 Reference case (2012)
The reference case was based on m o deling th e base case, using 2012 fuel prices, and scaling the 2004 fuel volum es to 2012 based on grow th in fuel dem a nd. In addition, we scaled MTBE and ethanol upw ard, in proportion to ga soline 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 assum e d it should rema in at 0.5 wt%. Finally, we assum e d the MSAT 1 standards would rem a in in place as would the RFG oxygen m a ndate. W e assum e d 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 gall ons/yr (BGY) of ethanol in gasoline; it reflects the renewable fuel m a ndate. In a ddition, it is assum e d that no MTBE is in gasolin e, M S AT1 is in place, the 1 p si waiver for CG containing 10 volume percen t ethanol rem a ins in effect for all s t ates where it currently applies, the RFS is in effect, and there is no RFG oxygenate m a ndate.

• Control Case 2 (EIA case): Sam e as Control Case 1, except that the ethanol volum e 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 dem a nd for gasoline and other refinery fuels in the reference and control cases were based on EIA’s AEO 2006 project ions for year 2012 . The m odeling also was set up to m eet dem a nd based on terminals ’ sales in each refin i ng area, us in g EIA fuel sales da ta. The LP m o deling accounted for inter-PADD transfers of fini shed products and gasoline blendstocks from refiners, to m eet dem and at terminals, based on historical transfer data from EIA, including CBOB and RBOB. Both the R FS and EIA control cases did not m o del any production of biodiesel f uel in fulfilli ng transportation dies el fuel dem a nd.

3017.4.2.3 Ethanol
The control cases were based on fixed nationa l ethanol volum es as specified in Chapter 2. For the control cases, however, the LP m ode ling analysis used etha nol blending economi cs and ethanol distribution costs to allocate ethanol to each PADD, and to allocate ethanol use in CG and RFG grades of gasolin e. Addition a lly, the m odeling assum e d that all eth ano l added to gasoline is m a tch-blended for octane by refiners in the reference and cont rol cas es, while sp lash blending of ethanol was assum e d as appropriate for the base case using E P A gasoline data. The price of ethanol was based on the 2004 yearly average price spread between regular conventional gasoline sold on spot m arket in Houston and ethanol sold on spot m arket on Chicago Board of Trade (CBOT). This was usedto determ ine a Midwest ethanol production price. To derive ethanol pri ces for all other PADDs outside th e 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 subs ides.
The reference and contro l cases where m ode led assum i ng that ethanol CG blends are entitled to the 1.0 psi RVP waiver during the su m m e r (i.e., for all 9.0 RVP and low RVP control program s) so as to assess the impact on summertim e butane rem oval.
7.4.2.4 Processes and Capital All changes in refining capital was assessed a ta 1 5 % Return o n Investm e nt (ROI) afte r taxes, which was adjusted to 7 % ROI before taxe s. Crude and other input prices were based on Jacobs’ projection of ref i nery m argins and crude prices in 2012 cases, which was also based on the historical price spreads of fuels betweenPADDS, using infor m ation fro m EIA’s 2004 price inform ation 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 Ca se
There are a num b er of changes in in divi dual and overall volum e for specific gasoline blendsto cks between the RFS case and reference cas e based on the refinery m odel results. The changes in clude the in creased blending of ethanol, the rem o va l of MTBE, and the increased volum es of i sooctane, isooctene and alkylate from the reuse of isobutylene for m erly used to produce MT BE. The isooctane and isooctene ar e produced by m e rchant MTBE plants that for m erly produced MTBE from m i xe d butanes, et hylene crackers, and propylene oxide plants as determ ined 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 captiv e (refinery-bas e d) MTBE plants. The total volum e of these gasoli ne blend stocks is

302summarized in Table 7.4-3 for both the refere nce case and R FS case adju sting th e volum e of ethanol and MTBE to reflect their ga soline energy-equivalent volum es.
Table 7.4-3. Comparison of Ethanol, MTBE, Isooctane, Isooctene, and Alk ylate Volumes by PADD for the RFS Case and Re ference Case (barrels per day) Case G a so li ne B l en dstock PADD 1 PAD D 2 PAD D 3 PADD 4 /5 C A U SA Et ha no l 5 7, 620 11 4, 90 0 5, 2 42 20 ,6 76 5 8, 934 25 7, 37 2 Re f e r e nc e Case MT B E 5 4, 887 0 1 22, 4 74 0 0 17 7, 36 0 I s ooc t an e/ I s o oc t en e 200 20 0 2 00 2 00 200 1, 00 0 G a so li ne -e qu iv a l e nt Vo lu m e 8 2, 687 7 6, 03 4 1 02, 8 64 13 , 8 46 3 9, 096 31 4, 52 7 Et ha no l 13 9, 224 20 1, 98 9 23, 0 91 17 ,8 53 5 3, 004 43 5, 16 0 M T B E 0 0 0 0 0 0

RF S Cas e I s ooc t an e /I sooc t en e A l k y la te f r o m MT B E 1 1, 042 20 0 2 12, 1 77 2 00 2 1, 484 24 5, 10 3 G as o li ne -e qu i va l en t Vo lu m e 10 2, 930 13 3, 51 3 2 27, 4 17 11 , 9 83 5 6, 466 53 2, 30 8 Ch an ge in Gasol i n e Eq uivalent Volum e 2 0, 243 5 7, 47 9 1 24, 5 53

- 1 , 8 63 1 7, 370 21 7, 78 1

As the bottom row in Table 7.4-3 shows, the gasoline-equivalent volum e s for the aggregated volum e of these gaso line blendsto cks are expectedto increase as we com p are th e RFS case to the reference case. It is this net increase in gasoline blendstock volum e that is expected to result in a net redu ction in petroleum consumption.
The additionof ethanol to wintertim e gaso lin e, an d to summ ertim e RFG, will cau se a n increase of approxim a tely 1 psi in R V P that needs to be offset to m a intain constant RVP levels. An obvious m eans that refiners could choose to offset the increase in R V P is to reduce the butane levels in their gasoline. To som e exte nt, the m odeling results showed som e occurrences of that, but it also did not report an overall incr ease in butane sales as a r e sult of the in creas ed use of ethanol. To convert the captive MTBE over to alkylate , after the rejection of m e thanol, refiners will need to com b ine one m o lecule of refinery produced isob utane with the isobuty l ene that was the feedstock for MTBE. The use of the isobu tan e will reduce the RVP of the gasolin e pool from which it com e s, helping to offset the RVP impacts of ethanol. Also, the increased production of alkylate provides a low RVP gasolin e blendstock that offsets a portion of the cracked stocks produced by the flui dized catalytic cracker unit. Other m eans that the refinery model used to offset the high blending RVP of ethanol includes purch asing gasoline components with lower RVP, producing m ore poly gasoline which has low RVP and selling m ore high-RVP naphtha to petrochem i cal sales.
In Table 7.4-4, we summ arize the inputs in to and gasoline outputs from the refinery model separate from the ethanol and convertedMTBE blendstocks summ a r ized above. The summary shows that crude oil and vacuum gas oil and residu al fuel purch ases are exp ected to decrease about 1 percent averaged over all the PADDs. The re finery m odel also estim ates that the volum e of purchased gasolin e components will in crease in m o st PADDs. These gasolin e components include renewable blendstocks for ethanol blending (RBOB), which is a very low

303RVP gasoline blendstock. A lik ely reason for the increased gasoline blendstock volum es over purchased crude oil purchases is thatthe ref i ne ry model is seeking to purchase low RVP blendstocks to offset the volatili ty impacts of ethanol, as opposed to having to crack crude oil which produces m ore volatile four carbon compou nds. Table 7.4-4 also shows the volum e of gasoline projected to be produced for both the R FS case and reference case. W e adjusted th e gasolin e volum e for the RFS case to reflect the sam e energy density of the gasoline reported for the reference case. W h ile the national energy-ad justed gasoline production levels for RFS case are abou t th e sam e as the reference case, the energy adjusted gasolin e production levels vary significantly by PADD. The refine ries in PADDs 1 and 2 are projected to produce m o re gasoline in the RFS case compared to the reference case, while the refineries in PADD 3 are projected to produce less gasolin e in the RFS case.
Table 7.4-4. Summary of Refinery Model I nput and Output Volumes by PADD for the RFS Cas e and Reference Case (barrels per day) Cas e Cr u d e O il an d G a s o line P A D D 1 P ADD 2 P A DD 3 P ADD 4/5 CA US A Cr u de O i l 1, 82 3, 00 8 3 , 6 50 , 0 44 9 , 071 , 056 1, 52 9, 44 2 1 ,9 52 ,5 60 18 , 026 , 111 Ref e r e nc e Cas e VG O an d Res i dua l F ue l 15 2, 46 7 59 , 5 52 680 , 329 0 27 , 4 00 919 , 748 G a s o line Co m p one nt In pu t s 14 4, 29 3 69 ,2 33 144 , 782 4 9, 24 7 51 ,4 75 459 , 030 G a s o line Volu m e 1, 37 8, 81 1 2 ,3 98 ,1 79 4 , 004 , 675 77 8, 26 2 1 ,1 84 ,5 33 9 , 744 , 461 G as o line E ne r gy Con t ent 5. 01 2 4 .9 97 5

. 093 5. 01 9 5 .0 24 5

. 044 Cr u de O i l 1, 76 2, 01 8 3 , 5 79 , 2 32 9 , 071 , 056 1, 52 0, 70 9 1 ,9 52 ,5 60 17 , 885 , 576 VG O an d Res i dua l F ue l 7 5, 04 4 59 ,5 52 680 , 329 0 40 , 7 07 855 , 631 Cha n g e in Cr ude oil an d V GO/resi d p In pu t s - 1 38 ,4 13 20 0, 27 2 - 7 0, 81 2 69 ,2 33 0 178 , 080 - 8 ,7 33 4 9, 24 7 1 3, 30 7 67 ,1 46 - 2 0 4,6 5 1 563 , 979
RFS Cas e Ch an ge i n Ga s o l i n e Co m p on e n t In pu ts 55 ,9 79 0 3 3,2 9 8 0 1 5, 67 2 1 0 4,9 4 9 G a s o line 1, 48 3, 53 5 2 ,5 84 ,9 77 3 , 753 , 849 76 5, 88 0 1 ,1 84 ,5 33 9 , 772 , 775 Ga sol i n e En erg y Co nte n t 4. 95 1 4 .9 57 5

. 073 5. 01 5 5 .0 46 5

. 016 T o t a l G a s o line a t Co ns t a nt En er gy 1, 46 5, 38 5 2 ,5 64 ,3 30 3 , 739 , 352 76 5, 18 1 1 ,1 89 ,8 01 9 , 719 , 422 Vo lum e a t Co ns ta nt En er gy 8 6, 57 3 1 66 ,1 51

- 265 , 323

- 1 3, 08 1 5 ,2 68

- 25 , 039

The addition of ethanol, the phase out of MTBE a nd the reuse of for m er M T BE feedstocks to m ake other gasoline blendstocks is expected to change the c apita l inve stm ents that would otherwise occur if these changes were not m ade. Table 7.4-5 summ arizes the change in refinery unit throughputs by PADD c o m paring th e RFS case to the reference case.

304Table 7.4-5. Change in the Refinery Unit Capacities by PADD bet w een the RFS Case and Reference Case (thous and barrels per day) Uni t or Categ o r y PA DD 1 PA DD 2 P ADD 3 P ADD 4/5 C A US A Crud e T ow e r -61

- 7 1 0

- 9 0 -14 1 V a cuum T o w e r -22

- 3 2 0

- 2 0 -56 S a t s Gas Pl a n t

- 13 -1 2 6 -2 0

- 21 Unsat s Ga s Pl ant -38

- 1 9 0

- 4 3 -59 FCC DeC5 T o w e r -8 -3 56 -4 -9 33 FCC

- 130 -6 1 0 -16 2 -20 5 FCC S p li tte r -97

- 5 116 -8 3 9 Hy dro c ra c k e r 0 0 0 0 -6 -6 Del ay e d C o k e r -13

- 1 6 0 0 3 -27 Vis b r e ak e r 0 0 0 0 -3 -3 T h erm a l Nap h tha S p li tte r -2 -2 0 0 0 -3 CRU Re f orm e r 0 8 4 8 0 0 5 6 SR U R e f o r m e r 0 0 0 0 -4 -4 BT X R e f o r m e r 0 0 0 0 0 0 C4 Is o m e r iz ation

- 13 0 1 0

- 2

- 14 C5/C6 Is om e r iz ation 0 3

- 57 15 -26

- 65 H F A l k y l a t i o n 0

- 1 1 0 0 0 H2S O 4 A l k y la ti on -22 0 59 -1 -3 33 Di m e rs ol 0 0 0

- 2 0 -2 Cat Po l y 1 0 1 8 0 0 1 9 Is oo c t an e 0 0 0 0 0 0 DHT - T o ta l 0 1 2 2 5 -1 28 DHT 2nd RCT - T o tal 0 3 2 5 0 10 DHT Ar om Satur a tion 0 0 0 0

- 4

- 4 NHT - T o ta l F d -9 3

- 13 -1 -4 -23

NHT - I s o m /T herm a l F d -9 -5 -61 0 0 -75 NHT - Re f orm er Fd 0 8 48 0

- 4 52 CG H - Gene ric

- 81 -3 4

- 42 -4 0

- 16 1 CG H - Olef i n Sa t'n 0 0 0 0 2 8 2 8 FCCU Fd HD T

- 39 0 0 0 0 -39 LS R S p litt e r -12

- 3 2 0 0 -6 -50 L S R Bz S a t u ra to r -6 -1 5 0 0

- 2

- 22 Reform ate Sa tu rato r -2 -2 4 0 0

- 4

- 30 S D A 0 0 0 0 0 0 MT B E -5 0

- 114 0 0 -11 9 T A M E 0 0 0 0 0 0 Hy dro gen P l a n t - T o t a l BS CF -11 1 -75 3 -27

- 10 9 L ube Uni t 0 0 0 0 0 0 S ulfur Pla nt

- 276 -29 8 138 -13 81

- 36 8 F u el S y s t em - F u e l O i l 0 0 0 0 0 0 Fue l Sy ste m - CO2 (BL b / D ay )

- 18 -9 -5 -1 -2 -35 Ut i l i ti es - St ea m (B l b s ) -22

- 1 2 -17 0 -6 -58 Utili ti es - Stea m V en t (Bl bs) 0 0 0 0 0 0 Utili ti es - Po w e r (Mw h)

- 715 -22 6 749 1

- 13 3

- 32 4 Utili ti es - Cool ing H2 O (B ga l) -213 -9 3

- 156 -14

- 27 -50 2

Most of the capacity throughput changes are negative, reflec ting the decreased processing of crude oil and vacuum gas o il and d ecreased do wnstream refi ning units as projected by the refinery m odel. Of the negative throughput change s, the large reduced volum e of the fluidized catalytic cracker is important. As discussed above, the refinery m odel likely chose to decrease the fluidized catalytic crac ker 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 increas es, prim arily in PADD 3. PADD 3 refineries will have a s ubstantial lo ss in octane because of the rem oval of a substantial vo lum e

305of MTBE, but the refinery m odel did not choose to blend muc h ethanol in PADD 3. Instead, the refinery m odel chose to m ake additional alky late from the captive MTBE plants form erly operating in PADD 3, and blend in isooctene from the conversion of m e r c hant MTB E plants. The refinery m odel also added som e reform er cap acity to m a ke up the balance of octane loss. The refinery m odel added depent anizer capacity mostly in PADD 3 to e nable the blending of ethanol into RFG. Refiners can also control the gasoline pr oduction and quality by adjustm e nts they can
m ake to several of their refinery conversion unit s. Refiners can adjust the conversion of their FCC and hydrocracker units, and change the severity of their reform ers. Table 7.4-6 contains the percen t con v ersions andseverities of these un its.
Table 7.4-6. Comparison of Key Refinery UnitOperations by PADD betw een the RFS Case and Reference Case (percent) Cas e Ref i ner y Unit Oper ations P A DD 1 PAD D 2 P A DD 3 P A DD 4/5 C A F CCU Conver sion 73 74 74 71 75
Ref er enc e C on ti nuous R e f or m er Sever i t y 99 99 97 0 0 Cas e Sem i -Regen Severity 0 0 0 94 95 Hy dr oc r a ck er Conver s i on 80 80 85 85 85 F CCU Conver sion 72 74 74 71 75

RFS Cas e Cont inuous Ref or m er Sever i t y 100 96 97 0 0 Sem i -Regen Severity 0 0 0 93 96 Hy dr oc r a ck er Conver s i on 80 80 85 85 85

The refinery m odel m aintain s the sam e FCC uni t conversion percentage for the RFS case
compared to the reference case, ex cept for PADD 1 which showed a small decrease in FCC unit conversion. For all PA DDs, hydrocracker conversi on percentage rem a ins the sam e
. Continuous reform er severity is p r ojected to increase sli ghtly in PADD 1 likely becau s e of the octa ne loss caused by the rem oval of MTBE from the RFG pool which is not completely m a de up by the increased ethanol volum e there. In PADD 2 wher e a lot of ethanol is being blended, refor m er severity decreases signif i cantly from 99 RON to 96 RON. Refor m er severity rem a ins the sam e in PADD 3. Refor m er severity is projected to increase slightly in California due to an anticipated s m all decreas e in eth anol. Finally, reform er severity is projected to decrease slightly in PADDs 4 and 5 despite the sm all decrease in ethanol there. These changes in refinery unit throughputs are associated with changes in capital investm e nts. Table 7.4-7 summari zes the projected change in capital investm e nts between the reference case and the R FS control case. Table 7.4-7 shows that increm ental to the re f erence case, refiners are expected to reduce their capital investm e nts by $5.8 billion compared to business as usual. Most of the reduction occu rs in PADDs 1 and 2 where large volumes of ethanol, and other gasoline blends tocks, are expected to enter th e gasoline pool. Of course, this capital cost decrease is counter ed by the $2.3 billion in capital cost s being incurred to build new ethanol plants and put into plac e the distribution system required to distribute the new ethanol.

306Table 7.4-7. Comparison of Capital Expenditures by PADD betw een the RFS Case and Reference Case (million dollars) PA DD 1 PA DD 2 PA DD 3 PA DD 4/5 C A U .S . T o tal

Unit CA PE X v s Ref er enc e

Ca se CA PE X v s Ref er ence

Ca se CA PE X v s R e f e r ence Cas e C APE X vs Ref er enc e

Ca se CAPEX vs R ef er enc e Cas e CAPEX vs R ef er enc e Cas e Cr ude T o w e r - 228.6 0 .0 0.0 0 .0 0

. 0

- 228.6 V a c uum T o w e r - 141.4 0 .0 0.0 2 .6 0

. 0

- 138.7 S a ts G a s Plant

- 101.2

- 13.6

- 1.8 2 .6

- 2 2

. 7

- 136.8 Uns a ts G a s Plant

- 280.1

- 225.5

- 2.5

- 29.5

- 1

. 0

- 538.6 FCC D e C5 T o w e r 17.4

- 52.0 54.3

- 16.6 0 .0 3.1 FCC

- 1426.9

- 1160.4 0 .0

- 103.8 0 .0

- 2691.0 FCC S p l i tter

- 144.2

- 37.0 49.5

- 6.6 0 .0

- 138.3 Hy dr ocr a ck er 0.0 0 .0 0.0 0 .0 0

. 0 0 .0 H-O il Uni t 0.0 0 .0 0.0 0 .0 0.0 0 .0 Delay ed Cok e r 0 .0 0.0 0 .4 0.0 0 .0 0.4 V i s b r eak er - 0 .1

- 0 .1 0.0 0 .0 0

. 0

- 0.2 T her m a l N aphtha Splitter 0 .0 0.0 0 .0 0.0 0 .0 0.0 CR U R e f o r m er 0.0 0 .0 0.0 0 .0 0

. 0 0 .0 S R U R e f o r m er 0.0 0 .0 0.0 23.9 0 .0 23.9 B T X R e f o r m er 0.0 0 .0 2.4 0 .0 0

. 0 2 .4 C4 Is om er iz ation 0 .0 0.0 28.9 0 .0

- 6 0

. 6

- 31.7 C5/C 6 Is om er i z ation 0 .0 0.0 0 .0 153.7 0 .0 153.7 HF Alk y l a tion 0.0 0 .0 0.0 0 .0 0.0 0 .0 H2SO4 A l k y lation

- 698.3 0 .0 607.5 0 .0 0

. 0

- 90.9 Dim e r s ol 0.0 0 .0 0.0

- 23.3

- 25 .0

- 4 8.3 Cat Poly 29.0 0 .0 100.3 0 .0 0

. 0 129.3 Isooc tane 0.0 0 .0 0.0 0 .0 0

. 0 0 .0 DH T - T o tal 1 .6 0.0 217.8 32.0 1 1.2 262.6 DH T 2nd R C T - T o tal 0 .0 11.0 6 .1 21.1 0 .0 38.2 DHT A r om Saturation 0.0 0 .0 0.0 0 .0 0.0 0 .0 NH T - T o tal Fd

- 39.7 0 .0 0.0 69.3 0 .0 29.6 CG H - G ener ic

- 472.8

- 154.5

- 139.5

- 70.3 0 .0

- 837.2 CG H - O l ef in Sat'n 0 .0 0.0 0 .0 0.0 160 .5 160.5 FCC U Fd HD T

- 525.0 0 .0 0.0 0 .0 0

. 0

- 525.0 LS R S p litter 0 .0 -47.2 0 .0 0.0 0 .0 -47.2 LS R B z Satur a tor

- 44.7

- 151.7 0 .0 0.0 0 .4

- 196.0 Ref o r m ate Satur a tor

- 8.2

- 272.4 0 .0 0.0

- 47 .3

- 328.0 Ref o r m ate Spl i tter

- 4.4

- 142.7 0 .0 0.0

- 8

. 8

- 155.9 SD A 0.0 0 .0 0.0 0 .0 0.0 0 .0 MT B E 0.0 0 .0

- 175.4 0 .0 0

. 0

- 175.4 T A ME 0.0 0 .0 0.0 0 .0 0.0 0 .0 Hy dr ogen P l ant

- 109.6 2.2

- 196.1

- 7.4

- 58 .3

- 369.2 Lube Unit 0.0 0 .0 0.0 0 .0 0

. 0 0.0 S u lf urPl ant

- 1 .9

- 2 .8 0.0

- 0.1 0 .1

- 4 .7 Merox Jet 0 .0 0.0 0 .0 0.0 0 .0 0.0 Mer o x Dies el 0.0 0 .0 0.0 0 .0 0

. 0 0 .0 B T X R e f o r m er- T o w e rf eed 0.0 0 .0 0.0 0 .0 0

. 0 0 .0 B T X R e f o r m er- Extr act f eed 0.0 0 .0 0.0 0 .0 0 .0 0 .0 T otal Capital C ost s $MM

- 4,179

- 2,247 552 48

- 5 1

- 5,878

3077.4.3.2 Summary of Changes in Refinery Inputs and Outputs to the EIA Ca se
The EIA cas e has som e similarities to the RFS case. The MTBE is still es tim ated to n o longer be blended into gasoline, and the form er MTBE feedstocks are converted over to other low-RVP gasoline blendstocks. The annual volum e of ethanol blended into gasoline, however, is alm ost 3 billion gallons higher. This increased volum e of ethanol is expected to be spread over all the PADDs, although PADD 3 is projected to absorb the most

. The much increased volum e of very high octane ethanol is expected to slightly reduce the consumption of the gasoline blendstocks produced from for m er MTBE feedstocks. The ne t gasoline-equivalent volum e increase by ethanol and other ga soline blendstock changes is expected to be over 100 thousand barrels per day. Table 7.4-8 contains the vol um es of these gasoline blendstocks by PADD.
Table 7.4-8. Comparison of Ethanol, MTBE, Isooctane, Isooctene, and Alk ylate Volumes by PADD for the EIA Case and Re ference Case (barrels per day) Case Gasoline B l endsto c k PADD 1 PAD D 2 P ADD 3 P ADD 4/5 C A U SA E t han ol 57 ,620 114, 900 5,24 2 2 0,676 58, 934 2 57,3 72 Ref er ence Case MT B E 54 ,887 0 1 22,4 74 0 0 1 77,3 60 Isooctane /Isooctene 200 2 00 20 0 2 00 200 1 ,0 00 G asoline

- equi vale nt Vo lu m e 82 ,687 76, 034 1 02,8 64 1 3,846 39, 096 3 14,5 27 E t han ol 161 ,821 255, 512 1 17,7 22 3 2,113 59, 055 6 26,2 23 M T B E 0 0 0 0 0 0

EI A Ca s e Isooctane /Isooctene A l k y la te f r om M T BE 11 ,042 200 2 00,1 19 200 17, 010 2 28,5 71 Gasoline

- equi vale nt Vo lu m e 117 ,844 168, 838 2 77,8 16 2 1,395 55, 986 6 41,8 78 C hang e in G as olin e Eq u i v a le n t Vo lu m e 35 ,157 92, 804 1 74,9 52 7,548 16, 889 3 27,3 51

Table 7.4-9 summarizes the inputs into a nd gasoline outputs from the refinery m odel separate from the ethanol and converted MTBE blendstocks summarized in Table 7.4-12 above. Crude oil and vacuum ga s oil and residual fuel purchases are expected to decrease about 1.7 percent averaged over all the PADDs. The refine ry m odel also estim ates that the volum e o f purchased gasoline components will increase increm entally over the RFS case. It seem s that a likely reason for the increased gasoline blends tock volum es over purchased crude oil purchases is that the refinery m ode l is seeking to purch ase low RVP blendstocks to of f

set the volatility impacts of ethanol, as opposed to having to cr ack crude oil which produc es m ore volatile four carbon compounds. Table 7.4-15 also shows the en ergy-adjusted volum e of gasoline projected to be produced for both the RFS case and referen ce case. The national en ergy-adjusted gasoline production levels for EIA case is som e what lower th an the reference case wh ich suggests that the crude oil savings described above are som e what overstated. The refineries in PADDs 1 and 2 are projected to produce much m o re gasoline in the EIA case comp ared to the reference case, while the refineries in P ADD 3 are projected to produce mu ch less gasoline in the EIA case.

308Table 7.4-9. Summary of Refinery Model Input and Output Volumes by PADD for the EIA Case and Reference Case (barrels per day) Ca se Cr u de O i l a nd G asoli ne P ADD 1 PA DD 2 PA D D 3 PA D D 4/ 5 C A US A Cr u de O i l 1,823,0 08 3, 650,044 9,071,0 56 1 ,529,442 1,952, 560 18 ,026,111 R e f erenc e Ca se V G O and R e s i d ual F uel 152,4 67 59,552 680,3 29 0 27, 400 919,748 G asoli ne Com ponent In put s 144,2 93 69,233 144,7 82 49,247 51, 475 459,030 G a soli ne V o l u m e 1,378,8 11 2, 398,179 4,004,6 75 778,262 1,184, 533 9 , 744,461 G asoli ne E nergy C o ntent 5.0 12 4.997 5.0 93 5.019 5. 024 5.044 Cr u de O i l 1,667,8 93 3, 539,369 9,058,0 59 1 ,528,255 1,952, 560 17 ,746,134 V G O and R e s i d ual F uel 97,6 21 59,552 675,9 59 0 41, 194 874,325 Change in Crude oi l and V GO/ r e si d

- 209,9 62

- 1 10,675

- 17,3 68

- 1,188 13, 794 -325,398 G asoli ne Com ponent Inpu ts 208,8 09 69,233 166,0 23 49,247 62, 913 556,224

EIAC a s e Change in Gas ol i ne Com pon ent Inputs 64,5 16 0 21,2 40 0 11, 438 97,194 Ga s o l i n e 1,602,2 58 2, 584,977 3,657,5 19 775,512 1,184, 533 9 , 804,799 G asoli ne E nergy C o ntent 4.9 24 4.922 5.0 42 5.000 5. 042 4.988 T otal G a s ol i ne a t Cons tant E ner gy 1,574,0 07 2, 546,302 3,620,5 30 772,501 1,188, 742 9 , 695,751 Change in T o tal G a s o l i ne V o lum e at Cons ta nt E ner gy 195,1 96 148,123

- 384,1 45

- 5,761 4, 208 -48,710

The addition of ethanol, the phase out of MTBE a nd the reuse of for m er M T BE feedstocks to m ake other gasoline blendstocks is expected to change the c apita l inve stm ents that would otherwise occur if thes e changes were not m a de. Table 7.4-10 summarizes the change in refinery unit throughputs by PADD co mparing the EIA case to the reference case.

309Table 7.4-10. Change in the Refinery Unit Capacities by PADD bet w een the EIA Case and Reference Case (thous and barrels per day) Uni t or Categ o r y P ADD 1 PA DD 2 P ADD 3 P ADD 4/5 C A US A Crud e T ow e r -15 5 -1 11 -13

- 1 0 -28 0 Va c uu m T ower - 63

- 5 0 1 2 4 0

- 97 Sa ts G a s Plan t

- 17

- 2 0

- 2 0 0 1

- 5 6 Unsats G as P l ant -3 7

- 28

- 6 -7 3

- 7 4 FC C DeC5 T o w e r - 7 0 40 -6 -9 1 7 FC C

- 13 0

- 90 -18

- 24 3

- 26 0 FCC S p l i t t e r -9 9 0 82 -12 3 -2 7 Hy d r ocrac k e r 0 0 0 0

- 7

- 7 D e la y e d C o k e r - 10

- 2 5

- 7 0 3

- 39 Vis br e ak er 3 3 1 0

- 3 3 T h e r m a l Naph th a S p l i t t e r -1 -3

- 1 0 0 -5 CRU Ref o rm e r -1 5

- 19 -109 0 0 -14 3 S R U Ref o rm e r 0 0 0 -1 -3 -4 B T X R e f o r m e r 0 0 0 0 0 0 C4 Is om eri z atio n - 1 3 3 0 0

- 2

- 1 2 C5/C6 Is om eri z atio n 0 -14

- 93 -3 -30

- 14 0 H F A l k y l a t i o n 0

- 3 1 0 0

- 2 H2S O 4 A l k y l a t i o n -2 2 0 49 -1 -5 2 2 Di m e rsol 0 0 0 -2 3 1 Cat Po l y 0 0 1 8 0 0 1 9 I s o o c t a n e 0 0 0 0 0 0 DHT - T o t a l

- 2 4 -3 -66 1 6 0 -7 8 DHT 2 nd RCT - T otal 0 -15 17 15 0 1 7 DHT A r om Sa tu rat i o n 0 0 0 0

- 4

- 4 NHT - T o t a l Fd -3 2

- 27 -145 -1 -3 -20 8

NHT - Isom /T h e rm al Fd -1 7

- 7

- 36 0 0 -6 0 NHT - Ref o rm er Fd -1 5

- 19 -109 -1 -3 -14 7 CG H - G e neri c -8 1

- 53 26 -6 0

- 11 4 CG H - O l ef in Sa t'n 0 0 0 0 29 2 9 F C C U F d H D T

- 3 9 0 0 0 0

- 3 9 LSR Splitte r -1 2

- 32 0 0 -6 -5 0 LS R Bz Sa tu rato r -6 -15 0 1

- 2

- 2 1 Ref o r m at e S a t u ra to r - 3

- 24 0 0 -5 -3 2 SD A

- 3 0 0 0 0

- 3 MT B E -5 0

- 114 0 0 -11 9 T A M E 0 0 0 0 0 0 Hy d r og en P l a nt - T otal BS CF -3 4 7 4 4

- 30 -4 9 Lu be Uni t 0 23 0 0 0 2 3 Su lf urPlan t

- 3 4 0

- 55 1

- 2 6 1

- 4 8 6

- 1 ,0 70 Fuel Sy s t em - Fue l O i l 0 0 0 0 0 0 Fuel Sys t em - CO 2 (B Lb /Da y ) -2 0

- 13 -10

- 2

- 2

- 4 7 U t il it i e s - St e a m ( B lb s )

- 2 5

- 1 2

- 2 8

- 3

- 7

- 75 Uti l i t ies - S t eam Ve nt (B lb s ) 0 0 0 0 0 0 Uti l i t ies - P ow er (Mw h)

- 1,02 9

- 5 82

- 98 40 -168 -1,83 7 Uti l i t i e s - Coo l i n g H2 O (B ga l ) -24 6 -1 28 -256 -24

- 28 -68 2

Most of the capacity throughput changes are negative, reflec ting the decreased processing of crude oil and vacuum gas o il and d ecreased do wnstream refi ning units as projected by the refinery m odel. Of the negative throughput ch anges, the reduced volume of the fluidized catalytic cracker is important. As discussed above, the refinery m odel likely chose to decrease the fluidized catalytic crac ker throughput to crack less heavy hydrocarbons to light

310hydrocarbons, producing less four-carbon compounds to offset the volatility impacts of ethanol. The reduction in FCC unit throughput is relativel y less for the EIA case than it was for the RFS
case. There are several units which show thr oughput capacity increases, prim arily in PADD 3. PADD 3 refineries will have a s ubsta ntia l loss in octane because of the rem oval of a substantial volum e of MTBE. Unl i ke the RFS case, however, mu ch of t he ethanol is pr ojected to be blended into PADD 3’s gasoline pool m aking up for the octane loss. This can be seen in Table 7.4-15 as reform er and alkylation throughputs volum es are pr ojected to be lower for the EIA case. The refinery m odel added depentanizer capacity m os tly in PADD 3 to enable the blending of ethanol into RFG. Refiners can also control the gasoline pr oduction and quality by adjustm e nts they can
m ake to several of their refinery conversion unit s. Refiners can adjust the conversion of their FCC and hydrocracker units, and change the sever ity of their reform ers. Table 7.4-11 contains the percent conversions and severities of these units.
Table 7.4-11. Comparison of Key Refinery UnitOperations by PADD betw een The EIA Case and Reference Case (percent) Cas e Ref i ner y Unit Oper ations P A DD 1 PAD D 2 P A D D 3 P A DD 4/5 C A F CCU Conver sion 73 74 74 71 75
Ref er enc e C on ti nuous R e f or m er Sever i t y 99 99 97 0 0 Cas e Sem i -Regen Severity 0 0 0 94 95 Hy dr oc r a ck er Conver s i on 80 80 85 85 85 F CCU Conver sion 72 74 74 71 75

EIA Cas e Cont inuous Ref or m er Sever i t y 100 94 96 0 0 Sem i -Regen Severity 0 0 0 93 97 Hy dr oc r a ck er Conver s i on 80 80 85 85 85

The refinery m odel m aintain s the sam e FCC uni t conversion percentage for the RFS case
compared to the reference case, ex cept for PADD 1 which showed a small decrease in FCC unit conversion. For all PA DDs, hydrocracker conversi on percentage rem a ins the sam e
. Continuous ref o rm er severity is p r oje cted to incr e ase sli ghtly in PADD 1 despite the ethanol blended into that PADD’s gasoline. In PADD 2 where a lot of ethanol is be ing blended, reform er severity decreases significantly from 99 RO N to 94 RON. Refor m e r sever ity is projec ted to decrea se slightly in PADD 3 due to the large volum e of et hanol being ble nded into the gasoline in that PADD. Reform er severity is projected to decrea se sligh tly inCalif ornia d u e to an an ticipated
sm all increase in ethanol. Finally, refor m er severity is p r ojected to dec r ea se sligh tly inPADDs 4 and 5 due to the increase in ethanol there. These changes in refinery unit throughputs are associated with changes in capital investm e nts. Table 7.4-12 summarizes the projec ted change in capital investm e nts between the reference case and the E I A control case. Table 7 .4-12 shows that increm ental to the reference case, refiners are expected to reduce their capital investm e nts by $7.3 billion compared to business as usual. Most of the reduction occu rs in PADDs 1 and 2 where large volumes of

311ethanol, and other gasoline blends tocks are expected to enter the gasoline pool. Of course, this capital cost decrease is counter ed by the estim ated $6.5 billion in capital costs incurred to build new ethanol plants and put into place the distribution system that the new ethanol requires.

312Table 7.4-12. Comparison of Capital Expenditures by PADD betw een the EIA Case and Reference Case (million dollars) P A DD 1 P A DD 2 P A DD 3 P A DD 4 /5 CA U .S . T o ta l

Un i t CA PE X v s R ef e r enc e Ca s e CA PE X v s R ef er enc e Ca s e CAPEX v s R ef er enc e Ca s e CA PEX vs Ref er enc e Cas e CA P E X v s Ref er enc e Cas e CA PEX vs R ef er enc e Ca s e C r ude T ow er - 453. 8 0 .0 0

. 0 0.0 0.0

- 453. 8 Vac uum T ow er - 295. 0 0 .0 103 .7 8.9 0.0

- 182. 4 Sat s G as Plant

- 115. 9

- 13. 6

- 55 .8 0.1

- 23.4

- 208. 7 U ns at s G a s Plant

- 275. 6

- 261. 5

- 20 .1

- 4 9.6 2.5

- 604. 4 F C C D eC 5 T ow er 17. 7

- 58. 9 50

. 9

- 1 8.5 0.0

- 8. 8 F C C

- 1426. 9

- 1160. 4

- 68 .1

- 33 1.7 0.0

- 2987. 1 F C C Split ter

- 147. 0

- 48. 7 46

. 6

- 9.6 0.0

- 158. 7 H yd r oc r a c k er 0. 0 0 .0 0

. 0 0.0 0.0 0. 0 H

- O il Unit 0. 0 0 .0 0

. 0 0.0 0.0 0. 0 D elay ed Co k er 0. 0 0

. 0

- 185 .9 0.0 0.0

- 185. 9 Vis b r eak er 7. 2 6 .2 0 .0 0 .0 0.0 13. 4 T h er m al N apht ha Split t er 0. 0 0

. 0

- 0

. 3 0.0 0.0

- 0. 3 C R U Ref or m er 0. 0 0 .0 0 .0 0.0 0.0 0. 0 SR U R e f o r m er 0. 0 0 .0 0 .0

- 2.6 0 .0

- 2

. 6 B T X Re f o rm er 0

. 0 0. 0 1. 8 0

. 0 0

. 0 1 .8 C 4 I s o m er i z at i on 0. 0 0 .0 0

. 0 0.0

- 60.6

- 60. 6 C 5/C 6 Is om er iz ation 0. 0 0

. 0 0 .0

- 5 6.6 0.0

- 56. 6 H F Alk y latio n 0. 0 0 .0 0 .0 0 .0 0.0 0

. 0 H 2 SO 4 Alk y latio n - 715. 4 0 .0 497 .6 0.0 0 .0

- 217. 8 D i m er s ol 0. 0 0 .0 0

. 0

- 1 7.9 7.8

- 10. 1 C a t Poly 4. 3 0 .0 114 .9 0.0 0 .0 119. 2 Is ooc t ane 0. 0 0 .0 0

. 0 0.0 0.0 0. 0 D H T - T otal

- 169. 8 0 .0

- 219 .5 9 3.2 21.6

- 274. 6 D H T 2 nd R C T

- T ot al 0. 0

- 165. 3 105 .6 13 8.0 0.0 78. 3 D H T Ar om Sat ur at i on 0. 0 0 .0 0

. 0 0.0 0.0 0. 0 N H T - T otal F d

- 39. 7 0 .0 0

. 0

- 1.9 0.0

- 41. 7 C G H - G ener ic

- 471. 6

- 179. 5 102 .0

- 7 7.1 0.0

- 626. 1 C G H - O l ef in Sat' n 0. 0 0

. 0 0 .0 0.0 1 61.0 161. 0 F C C U F d HD T

- 525. 0 0 .0 0

. 0 0.0 0.0

- 525. 0 LSR Split ter 0

. 0

- 47. 2 0 .0 0.0 0 .0

- 47. 2 LSR Bz Sat u r a t o r - 44. 7

- 151. 7 0 .0 2 1 .9 0.4

- 174. 0 R ef or m at e Satu r ator

- 19. 2

- 272. 4 0 .0 0.0

- 49.9

- 341. 6 R ef or m at e Split ter

- 20. 1

- 142. 7 0 .0 0.0

- 10.1

- 172. 9 SD A 0. 0 0 .0 0 .0 0 .0 0.0 0

. 0 MT BE 0. 0 0 .0

- 175 .4 0.0 0.0

- 175. 4 T A ME 0. 0 0 .0 0 .0 0 .0 0.0 0

. 0 H yd r ogen Plan t

- 188. 5 22. 9 6 .6 5 4.2

- 58.3

- 163. 2 Lube U n it 0. 0 0 .0 0 .0 0 .0 0.0 0

. 0 S u l f u rP l a n t -2 .5 -2 .8 -0. 2 0

. 0 0

. 1 -5 .4 M e ro x Je t 0

. 0 0

. 0 0

. 0 0

. 0 0

. 0 0

. 0 Mer ox D i es el 0. 0 0 .0 0

. 0 0.0 0.0 0. 0 BT X R e f o r m e r- T o w e r f eed 0. 0 0 .0 0 .0 0 .0 0.0 0

. 0 BT X R e f o r m e r- Ex tr ac t f eed 0. 0 0 .0

- 0 .1 T o tal

- 4, 882

- 2, 476 304

- 249

- 9

- 7, 311

3137.4.3.3 Adjustments to the LP Refinery Model’s Cost Estimate

We m ade several adjustm ents to the costs di rectly estim ated by the LP refinery cost model for the RFS and EIA cases wh ich are incl uded in the costs reported below. One adjustm ent m ade was to adjust the costs based o n the eth ano l prices usedin the LP cost m odel to reflect the ethanol production costs estim atedand reported above in section 7.1.1. This adjustm e nt resulted in much lower ethanol cost s to refiners because Jacobs largely based its ethanol prices on ethanol’s octane costs instead of its historical price re lationship to gasoline, which is mu ch lower. We also adjusted th e ethanol distribution costs fro m those used in the LP refinery cost study, which roughl y corresponded to those used fo r the proposed rule cost analysis, to those estim a ted for the final rule as discussed above in section 7.3.1. In Table 7.4-13
we summ arize the ethanol production and distributi on costs used in the LP refinery cost m odel and those w e estim a ted for the final rule.
Table 7.4-13 Ethanol Price and Distribution Costs used in the LP Refinery Model versus Those used for the Fina l Rule Co st Analysis (cents per gallon) Case PADD 1 PADD 2 PADD 3 PADD 4/5 ex CA CA

Prices used in Ethanol Price in Midwest RFS and EIA Case 158 158 158 158 158

LP Refinery Cost Ethanol Distribution Cost RFS and EIA Case 12 0 10 17 18

Model Ethanol Price in PADD RFS and EIA Case 170 158 168 175 176

Ethanol Production RFS Case 126 126 126 126 126
Costs used in Cost EIA Case 131 131 131 131 131
Final Cost Analysis Ethanol Distribution Cost RFS and EIA Case 16 6.5 16 23 22

Ethanol Cost

in PADD RFS Case 142 132.5 142 149 148

EIA Case 147 137.5 147 154 153

314Another adjustm e nt we m a de to the costs directly estim ated by the LP refinery cost model was to add a cost for distributing gasolin e. The refinery cost m odel did not include distribution costs for gasoline for m o ving the gaso line f r om the ref i nery to the te rminal. W e assigned gasoline distribution costs to be 4 cents per gallon applied as a cost savings to the gasoline-equivalent volum e o f ethanol blended into each PADD’s gasoline, since this roughly correspond ed to the vo lu m e of gasoline displacedby the eth a nol.
7.4.3.4 Estimated Costs
7.4.3.4.1 Estimated Costs for th e RFS Case
Table 7.4-14 summarizes the costs for the R FS case excluding fede ral and state ethanol
consumption subsidies. The costs are reported by different cost component as well as aggregated tota l and the per-ga llon c o sts. 9 9 This e stim ate of costs ref l e cts the changes in gasoline that are occurring with the expanded use of ethanol, including the corresponding rem oval of MTBE and reuse of MTBE feedstocks. The operating cost s include the labor, utility and other operating costs and are a direct output from the refinery m odel. These costs are ad justed to reflect ethanol’s production cost plus distribution costs inst ead of the ethanol prices used in the refinery cost m odel. The fixed costs are 3 percent of the capital cos t s. The costs associated with lower energy density gasoline are accounted for using th e fractional change in energy density shown in Table 7.4-4, multiplied tim es the whol esale price of gasoline. By excludin g the federal and state ethanol consum ption subsidies in th e table, we av oid the tran sfer paym ent s caused by these subsidies that would hide a portion of the program ’s costs.
Table 7.4-14. Summary of RFS Case Costs w i thout Ethanol Consumption Subsidies (million dollars per year and c/gal, except as n oted; 2004 dollars, 7% ROI before taxes ) RFS Case 6

. 7 Billio n Gal s Inc r em ental to Refere nce Cas e Cap ital Co sts ($ MM)

- 5 ,878 Am o r tized Capital Co sts ($ M M /yr) -647 Fix e d Op er ating C o st ($ MM/yr )

- 178 Variab le Op eratin g C o st ($ M M /yr) -201 Lo wer Ene rgy Densi ty Gasol i ne ($M M /y r) 1, 84 8 Total Cost ($ M M /y r) 82 3 Cap ital Co sts (c/g al) -0.40 FixedO p e r atin g C o st (c/g al) -0 .1 1 Varia b le O p erating C o st (c/g a l ) -0 .1 2 Lo wer Ene r gy Densi ty Gasol i ne (c/ g al ) 1. 13 To tal Co st Ex clu d i ng Sub s i d ies (c/g al ) 0. 50

99 EPA typ i cally assesses so cial b en e fits and costs o f a ru lem a k i ng
. Howev e r, t h is an alysis is m o re li mi ted in its scop e b y ex amin in g th e av er ag e co st o f pr odu ctio n of et han o l an d gas o l i n e wi th out accoun tin g fo r th e effects of farm subsi d i e s th at t e ndt o di s t ort th e m arket pri ce of ag ri cul tural c o m m odi ti es.

315Our analysis shows that when consid ering all the costs as soci ated with th ese fuel changes resulting from the expanded use of ethanol that these various possible gasoline use scenarios will cost th e U.S. $820 m illio n in the year 2012. Expre ssed as per-gallon co sts, these fuel changes would cost the U.S. 0.50 cent per gallon of gasoline. Table 7.4-15 expresses the total and per-ga llon gasoline costs for the RFS case with the federal and state ethanol subsidie s included. The federal tax subs idy is 51 cents per gallon for each gallon of new ethanol blended into gaso lin e. The state tax subsid ies apply in 5 states and range from 1.6 to 29 cents per gallon. The cost re duction to the fuel industry and consum ers are estim ated by mu ltiply ing the subsidy tim es the volum e of new ethanol estim ated to be used in th e state.
Table 7.4-15. Estimated RFS Case Cost includin g Ethanol Consumption Subsidies (million dollars per year and cents per gallon; 2004 dollars, 7% ROI before taxes) RFS Case 6

. 7 Billio n Gal s Inc r em ental to Refere nce Cas e Total Cost ($ M M /y r) 82 3 Fede ral S ubsi d y ($M M /y r) -1 3 76 State Su bsidies ( $ M M /y r) -5 R e vi sed Tot a l C o st ( $ M M /y r )

- 558 Per- Gal l o n C o st Excl u d i n g S ubsi d i e s (c/ g al ) 0. 50 Fede ral S ubsi d y (c/gal) -0 .8 4 State Sub s id ies (c/g al) -0.003 To tal Co st Inclu d i n g Su bsid ies (c/g al ) -0 .3 4

The cost including subsidies better represen ts gasoline’s production cost as mi ght be reflected to the fuel indu stry as a whole and to consum ers “at the pump” because the federal and state subsidies tends to hide a portion of the actua l costs. Our analys is es tim ates that th e fuel industry andconsum ers will see a 0. 34 cent per gallon decrease in the ap parent cost o f producing gasolin e for the RFS case.
7.4.3.4.2 Estimated Costs for th e EIA Case
Table 7.4-16 summarizes the costs for the EI A 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 estim ate of costs reflects the changes in gasoline that are occu rring with the much expanded use of ethanol, including the rem oval of MTBE and reuse of MT BE feedstocks. The operating costs include the labor, utility and other operating costs and are a direct output from the refinery m odel, adjusted for ethanol’s production cost at this higher volu me including ethanol di stribution costs. The fixed costs are 3 percent of the capital costs. Th e costs as sociated with lo wer energy density gasoline, as shown in Table 7.4-9, are estim a ted by the fractional change in energy content tim es the wholesale price of gasoline. The increm ent of the EIA cas e to the RFS case ind i cates the
econom ic impact of the additional volum e of ethanol between the two cases.

316Table 7.4-16. Summary of EIA Case Costs w i thout Ethanol Consumption Subsidies (million dollars per year and c/gal, except as n oted; 2004 dollars, 7% ROI before taxes) EIA Case 9

. 6 Billio n Gal s Inc r em ental to Refere nce Cas e EIA Case 9

. 6 Billio n Gal s In crem en tal to RFS Case Cap ital Co sts ($ MM)

- 7 ,311 - 1 ,433 Am o r tized Capital Co sts ($ M M /yr) -804 -158 Fix e d Op er ating C o st ($ MM/yr )

- 222

- 43 Varia b le O p erating C o st ( $ M M /y r) -4 9 1 -2 9 0 Lo wer Ene r gy Density Gasoli ne ($M M /y r) 3, 25 5 14 0 7

Total Cost ($ M M /y r) 17 3 9 91 5 Cap ital Co sts (c/g al) -0.49 -0.10 FixedO p e r atin g C o st (c/g al) -0 .1 4 -0 .0 3 Varia b le O p erating C o st (c/g a l ) -0 .3 0 -0 .1 8 Lo wer Ene r gy Densi ty Gasol i ne (c/ g al ) 1. 98 0. 86 To tal Co st Ex clu d i ng Sub s i d ies (c/g al ) 1. 06 0. 56

Our analysis shows that when consid ering all the costs as soci ated with th ese fuel changes resulting from the expanded use of subsidized ethanol that these various possible gasoline use scenarios will cos tthe U. S. $1,740 m illion in the y ear 2012 for the EIA cas e. Expressed as pergallon costs, these fuel changes would cost the U.S. about 1.1 cents per gallon of gasoline. The increm ental volum e of et hanol added between th e RFS and EIA cases is expected to cost $915
million in th e year 2012, resulting in a 0.56 cent p er gallon cos t

. Table 7.4-17 expresses the to tal and per-gallon gasoline cost s for the EIA case with the federal and state ethanol subsidie s included. The federal tax subs idy is 51 cents per gallon for each gallon of new ethanol blended into gaso lin e. The state tax subsid ies apply in 5 states and range from 1.6 to 29 cents per gallon. The cost re duction to the fuel industry and consum ers are estim ated by mu ltiply ing the subsidy tim es the volum e of new ethanol estim ated to be used in th e state.

317Table 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 9

. 6 Billio n Gal s Inc r em ental to Refere nce Cas e EIA Case 9

. 6 Billio n Gal s In crem en tal to RFS Case Total Cost ($ M M /y r) 17 3 9 91 5 Fede ral S ubsi d y ($M M /y r) -2 8 65 -1 4 89 State Su bsidies ( $ M M /y r) -3 1 -2 6 R e vi sed Tot a l C o st ( $ M M /y r )

- 115 8 - 600 Per- Gal l o n C o st Excl u d i n g S ubsi d i e s (c/gal) 1. 06 0. 56

Fede ral S ubsi d y (c/gal) -1 .7 4 -0 .9 0 State Su bsidies (c/gal) -0 .0 2 -0 .0 2 To tal Co st Inclu d i n g Su bsid ies (c/gal) -0 .7 1 -0 .3 7

The cost including subsidies better represen ts gasoline’s production cost as mi ght be reflected to the fuel indu stry as a whole and to consum ers “at the pump” because the federal and state subsidies tends to hide a portion of the actua l costs. Our analys is es tim ates that th e fuel industry andconsum ers will see a 0. 71 cent per gallon decrease in the ap parent cost o f producing gasolin e for the EIA case. Increm ental to the RFS case, the consum er would be expected to see a 0.37 cent per gallon price decrease “at the pump.”
7.4.3.4.3 Sensitiv ity Cost Analys es for the RFS and EIA Cases In Table 7.1-5 above, we presented various corn-ethanol production cost estim ates based on varying corn and dried distille rs grain prices. We entered a range of low and high production ethanol cost estim ates from that tab l e into ou r co st spreadsheet created from the outp u t from the LP refinery cost m odeling. The range of etha nol production costs that w e chose represents a reasonable bound around the possible range of future ethanol production costs. This allowed us to estim a te the cost of using ethanol at thes e other possible ethanol production costs at the ethanol volum e s analyzed for the RFS and EIA cas es. W e present these costs in Table 7.4-18. We did not conduct sen sitiv ity analyses around higher or lo wer crude o i l prices. 1 00

Th is sen s itiv ity an alysis cond u c ted at l o wer an d h i g h e r et h a no l p r od u c ti o n costs can also b e u s ed as a surro g ate for a sen s itiv ity an alysis of h i g h e r and lower cru d e o il p r ices. An alyzing a lo wer et h a no l co st is similar t o an alyzin g a h i gh er crud e o il price with eth a no l pro d u c tio n costs at th e levels we anal yzed them at which was 126 an d 131 ce nts p e r g a llon , and v ice v e rsa fo r o u r sen s itiv ity an alysis at th e h igh er eth an o l p r od u c tion co st.

318 100Table 7.4-18. Summary of the Sensitivity Cost A n alysis atHigher and Low er Ethanol Production Costs (Costs in 2012, 2004 dollars, 7% ROI before taxes) Ethanol Production Cost Units Costs RFS Case Costs EIA Case Cost without $MM/yr -260 -846 0.86 Subsidies c/gal -0.16 -0.52 Cost with $MM/yr -1640 -3740 Subsidies c/gal -1.00 -2.28 Cost without $MM/yr 2930 5784 2.04 Subsidies c/gal 1.79 3.53 Cost with Subsidies $MM/yr 1546 2890 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 volum e and therefore the supply of diesel fuel and thereb y reduces th e dem a nd for refinery-produced diese l f u el. In this sec tio n, we estim ate the ov erall cost im pact, consid ering how mu ch refinery based diesel fuel is disp laced by the f o recasted production vo lum e of biodiesel fuel. T he cost impacts are evaluated considering the production cost of biodiese l with and without the subsidy from the Biodiesel Blenders Tax credit program

. Additionally, the diesel cost impacts are quantified w ith refinery diesel prices as fo recas ted by Jacobs’s which are based on EIA’s AEO 2006.
We estim ate the net effect that biodiesel produ ction has on overall cost for diesel fuel in year 2012 using total production co sts for biodiesel and diesel fu el. The costs are evaluated based on how much refinery based diesel fuel is displaced by th e biodiesel volum es as forecasted by EIA, accounting for energy density differences between th e fuels. The cost impact is estim ated from a 2012 year bas i s, by mu ltiply ing the production costs of each fuel by the respective changes in vo lum e s for biodiesel and estim ated displaced d i es el fuel. W e further
assum e that all of the forecasted bio-diesel fuel v o lum e is used as tran sport fuel, negle c ting
m i nor uses in the heating oil m arket.
For this an alysis, the p r o ducti on costs for biodiesel fuel ar e based on the costs generated using the U S DA, NREL, EIA and the design vendors estim ates in the preceding sections. W e average thes e results to d eveloped co sts f o r soy o il and ye llo w grease f ee d stocks. Ad dition ally, the production costs are based on EI A’s projection in 2012 that half of the total biodiesel volum e will be m ade from soy oil feedsto ck with th e remaining vo lu m e being produced from yellow grease. To these estim a tes, we add distribution costs of 21.5 c/gal to the biodiesel production costs, reflecting the distribution estim ates derive d in section 7.3.2. For the refinery diesel production costs in 2012, we used the projected w holesale national averag e diesel price of 160 c/gal p r ojected by the Jacobs’ pricin g forecast. Distribu tion costs of 4 c/gal were add ed to the Jacobs’s wholesale diesel price projection, to account for the additi onal costs to m ove diesel fuel from the wholesale m arket to end u se terminals.

319Our estim ate for the reduction in ref i nery produ ced diesel fuel is ba sed on EIA’s forecast for approxim a tely 300 MM gallons of biodiesel in 2012, along with the 2012 Reference Case year biodiesel production volum e of 28 MM gallons. W ith this and accounting for differences in energy density between biodiesel and diesel fuel, we estim at e in 2012 that the additional biodiesel production reduces the need for 250 MM ga llons of refinery produced diesel fuel. Table 7.4-19 contains the energy dens ities used in th is analysis.
Table 7.4-19. Energy Content of Fu els per Gallon Fuel Lo wer Hea t ing Va lue (BT U /g a llo n)

B i odi esel 11 7, 0 93

R e fi nery Pr od u ced Di esel 12 8, 7 00

For all RFS case scenarios, the net effect of biodiesel production on diesel fuel costs, including the biodiesel blenders’ su bsidy, is a reduction in the cost of transport diesel fuel costs by $114 MM per year, which equates to a fu el cost reduction of about 0.20 c/gal 1 01

. Without the subsidy, the transport diesel fuel costs are in creased 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 econom ic and environm ental impacts of increased ethanol use and decreased fossil fu el use in a holistic m a nner. Su ch an analysis is beyond the scope of this RIA. Howe ver, we can approxim a te som e of the im pacts of increas ed eth anol production and use, and we can discuss other impacts qualitatively. The p r eced ing 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 produc tion, as well as the di rect costs of state and federal tax subsidies for the renewable fuel s, which are financed through tax paym ents. There are many other economi c im p acts as sociated with the use of renewable fuels an d the fossil fuels they replace which go well bey ond the scop e of the analysis conducted for the RIA. W e have not attempted to quantify all of them here. For example, increased renewable fuel production and use m a y have adverse impact s on surface and ground water quality andsoil erosion, while decreased fossil fuel , distribution and use m ay have positive impacts. To quantify the econom ic impact associated with this would re quire extensive analysis of the likely responses of far m ers to the increased dem a nd for renewable fuels, the cost of actions taken to rem e dy the impacts, and the cost of any resu lting health and welfare im pacts.

101 Based on EI A’ s A E O 200 6, t h e to tal v o l u m e of h igh w a y an d of f- ro ad d iesel f u el con s umed in 2 012 was esti m ated at 5 8 .9 b illio n g allon s
.

320Furtherm ore, the renewable fuel production co sts assum e d in our analysis do not reflect the entire co st to society associated w ith th e pr oduction of the corn and soybean feedstocks used in their production due other state and federal ag ricultural policies. Direct paym ents, countercyclical paym ents, m arketing loans, and subsidiz ed crop insu rance are all examples of policies outside of this rulem aking that impac tthe price of corn and soybeans that are not ref l e cted in the production cost for ethanol and biodiesel, but do impact costs borne by consum ers indirectly through taxes. Quantifying the increm ental impa ct s of this rulem aking on the effects of these pre-existing program s would repres e n t a sign if ic ant cha lleng e. However, the cha lleng e is
comp licated even m o re by the direct and indi rect econom ic support provided for the production, supply, and distribution of the fo ssil fuels which would be replaced by th ese ren e wable fuels. Again, any assessm ent of the overall costs to soci ety for increase renewable use would have to look at the econom ic support provided across the en tire fuel supply. Such an analysis is well beyond the scope of this RIA. Despite our inability to f u lly cap ture all the poten tia l impacts on the cos tto socie t y of increased renewable fuel use, tw o potential impa cts were touched on briefly in our analysis, and these are discussed in this subsection. Economic Impacts of E m ission Changes As discussed in Chapters 4.1 and 5.1, we es tim ate that there m ay be an increase in
emissions and a corresponding sm all increase inozone resulting from the expanded use of renewable f u els. Our vehicle and equipm ent e mi ssion es tim ates are high ly uncertain, however, given the lack of data in particul ar on vehicles and engines comply ing with the latest standards. However, to the extent that there are em ission and ozone increases resulting from the expanded use of renewable fuels, there can b e a cost as sociat ed with th em
. In some cases, areas that s ee an incre ase in e missions res u lting f r om renewable fuel use m a y be forced to take other actions to offset these em ission increases. In other cas es, pa rticular ly in atta inm ent areas, th e impact, while not affecting attainm e nt, m a y adversely impact ai r quality and hum a n health. It is d i f f i cult to provide any quantitative estim ate of what the mitigation costs mi ght be to of f

set emission increases, or to quantify the health impacts resul ting from the air quality impacts. Not only are the em ission and air quality imp acts highly uncertain, but they are also very location dependent. While we have m a de projections on where the etha nol use m a y rise or fall for the purposes of estim ating nationwide fuel cost impa cts and poten tial emissions im pacts, these projections are much less re liable when trying to pre d ic t spe cif ic local a i r qua lity impacts. Despite all of the above caveats, we have attemp ted to provide a rough estim a te of the potential national-level cost im pacts; As a surrogate for addi tional emission control costs in nonattainm e nt areas and potential he alth impacts in attainm ent area s, we looked at the potential health costs associated w ith th e secon d ary ni trate PM resultin g from the decreases in NOx
em issions estim ated in previous EPA rules. W e note again that we actually expect an o v erall decrease in am bient PM 2. 5 for m ation due to the increased use of ethanol in fuel (See Chapter 5.2). 1 02 Thus, we expect most areas to have lo wer health impa ct costs and certainly lower abatem ent costs related to PM contro l.

102 Overall, we e x pect that t h e de crease in sec o ndary orga ni c PM is likely to e x ceed the i n cre a se in sec o ndary nitrate PM. I n 200 6, N O x emi ssi ons fr om gasol i n e
- fuel e d vehi cl es an d e qui pm ent com p ri se ab o u t 3 7 % o f nat i onal N O x

321In recent rulem akings we m onetized PM em i ssion im pacts, including those resulting from changes in secondarily form ed PM 2. 5 due to NOx em ission changes. Using this inform ation as a guide, we provide a screening- level estim ate of the m onetized PM-relate d health impacts associated w ith an increase in NOx em issions associ ated withthe f i nal rule. For this a n alysis, we derived a do llar-per-ton value based on recen t be nefits m odeling conducted for the Clean Air Nonroad Diesel Rule (CAND). Z ZZZZThis value ($8,000 in PM -related m one tized health impacts per ton of NOx reduced) is based on ai r quality m o deling condu cted in 2004 for the CAND rule. This benefits transfer method is co nsistent with approaches used in other recent mobile and stationary source rules. 1 03 W e refer the reade rto th e final CAND RIA for more details on this benefits transfer approach. Th e dollar-per-ton value repres ents m onetized health impacts in 2015 (in year 2000 dollars).

We com bined the dollar-per-ton estim ate of mo netized healtheffects withthe pro j ected 2015 em ission changes presented in Table 4.4-1, which includes emissions from gasoline vehicles and equipm ent and renewable fuel produ ction and distribution. We estim ate that the potential P M 2. 5 -related monetized impact associated withNOx e m issions from increased use of ethanol to b e up to $290 m illion for the RFS control scen ario, and up to $ 340 m illion for the EIA contro l scen ario. Note that th is impact is based on m onetized changes in health effects, includ ing ch anges in m o rtality risk, chronic bron chitis, nonfatal heart attacks, respirato r y hospital adm i ssions, asthm aattacks, and other m i nor health endpoints. It is also important to point out that this va lue does not repres ent th e cumu lative m onetized health impacts asso ciat e dwith the potential P M changes associated with the future use of ethanol described above.
This estim a te is subject to a num ber of additional caveats. The dollar-per-ton values reflect specific geograph i c pattern s o f emissions reductions and specific air quality and benefits modeling assumptions which are derived from pr evious analyses and will not m a tch those associated w ith in creasedethanol u se in fuel fo r two reasons. One, the geographical distribution of the em ission sources affected by increas ed ethanol use differs from that addressed in the CAND rulem aking. Two, the CAND rule was na tional in scope and the em ission reductions were spread out acros s th e entire nation. Incr eased ethanol u s e will be v e ry geographically focused. Many m a jor population centers will not e xperience an increase in ethanol use as their fuel already contains ethanol. Care should be taken when applying these estim ates to em ission reductions that occur in any specific location, si nce the dollars-per-ton fo r em ission reductions in specific locations m a y be very different than th e n a tional ave r age. Given these caveats and the

emi ssi ons f r o m m obi l e sou r ce s. In co ntrast , gas o l i n e- fuel e d vehi cl es a n d equi pm ent co m p ri se alm o st 90 % of nat i onal gase o u s ar om at i c VOC m obi l e so u r ce emi ssio n s
. Th e p e rcen tage in crease in natio n a l NO x e m i ssi ons due t o i n crease d ethanol use sh o u l d be sm al l e r th an th e perce n t a ge dec r ease i n n at i onal emi ssi ons o f gase ous ar om ati c s. Fi nal l y , i n m o st ur ban area s, am bi ent l e vel s o f sec o n d ar y o r gani c PM e x ce ed th ose o f sec o n d ar y ni tr at e PM

. T h us, d i rection a lly, we ex p ect a net redu ction in am b i en t PM lev e ls du e to in creased et h a no l u s e. Howe ver, we are unable to qu an tify th is red u c tion at t h is ti m e
.

103 See: Clean Air No nroad Diesel fin a l ru le (69 FR 389 58 , Jun e 2 9 , 2 004 ); Non r o a d Larg e Sp ark

- Ign itio n Eng in e s an d Recr eational Eng in e s stand ard s (6 7 FR 68 241 , No v e m b er 8 , 20 02 ) ; Fi n a l Ind u s t r ial Bo iler s an d Pro cess H eater s N E SH AP ( 6 9 FR 55 217 , Septe m b e r 13 , 2004 ) ; Fin a l Recipr o cating I n ter n al Co m b u s tio n Eng in e s N E SHA P ( 6 9 FR 3 347 3, Jun e 1 5 , 200 4); Fi n a l Clean Air Visi b ility Ru le (EPA-4 52 /R-05

- 00 4, Ju ne 1 5 , 2 0 0 5 ); Ozon e Imp l em en tatio n Ru le (7 0 FR 71 611 , No v e m b er2 9 , 2 005 )

.

322potential decreas e in am bient PM 2. 5 due to the d ecrea se in ar om atic fuel content, we can not say f

o r certa in in which dire ction the to tal m onetize d PM-relate d health impact will be. In reality there m a y be an overall reduction in PM-related health costs, despite the increas e due to increased N O x e m issions.

323105 

## Chapter 8: Agri cultural S ect or Im pacts

As described in the Preamble, we used the Fores tand Agricu ltur al Sec t or Optimizatio n Model (FASOM) to estim ate the U.S. agricu ltural im pacts o f increas ing renewable f u el volum es to 7.5 billion gallons per year (BGY) by 2012, as required by the Renewable Fuels Standard (RFS) and to 9.9 BGY, the volum e of renewabl e fuel the Energy Information Adm i nistration (EIA) predicts for the year 2012 in the Annual Energy Outlook 2006

. 1 04 Although these renewable f u el volum es are lower th an current market p r edictions, thes e assump tions were established during the NPRM and are used throughout this FRM. FASOM is a long term e c onom ic m o del of the U.S. agriculture sector that m a xi mi zes total revenues for producers wh ile meeting the dem a nds of co nsum ers. Using a num ber of inputs, FASOM determ ines which crops, lives toc k, and processed agricultural produ cts will be produced in the U.S. In each m odel simu lation, cr ops comp ete for price sensitive inp u ts such as land and labor at the regional level. The cost of these and other inputs are used to determ ine the price and level of production of prim ary commodities (e.g., fiel d crops, livestock, and biofuel products). FASOM also estim ates prices using co sts associated with the processing of prim ary commodities into secon dary products (e.g., conv erting liv estock to m eat and dairy, crushing soybeans to soybean m e al and oil). FASOM does not capture short-term fluctuations (i.e., month-to-m onth, annual) in prices and production, however, as it is designed to identify long term trends. 1 05

FASOM use s supply and dem a nd curves for the 11 m a jor U.S. dom e stic regions, 1 06 which are calibrated to historic price and pr oduction data. FASOM also includes detailed supply and dem a nd data for corn, wheat, soybeans, rice and sorghum acro ss 37 foreign regions. 1 07 FASOM m a inta ins trans portation co sts to a ll reg i ons and then uses all of this inform ation to determ ine U.S. exports to the point where prices are then equa ted in all m arkets. 1 08

104 W e a n alyzed the U.S. a g ric u ltural im pacts of prod uci n g re newa bl e fuel s dom est i cally after adjusting for equi val e nce va l u es of cel l u l o s i c ethan o l an d bi o d i e sel an d p r o j ect edU .S. i m port s
. F o r th e R FS C a se , w e assum e d 4 4 0 mill io n g a l lo n s of co rn b a sed eth a no l will b e im p o r te d, wh ile we assumed 63 0 mi l lio n g a llo ns o f corn b a sed eth a no l will b e i m p o r ted for t h e EIA Case. Fo r bo t h cases, we assu m e 2 5 0 mi l lio n g a llon s o f cellu lo sic eth a no l will b e p r od u c ed (with a 2

. 5 equ iv a len ce v a l u e), and 3 0 0 m illio n g a llo ns o f b i o d iesel will b e p r od u c ed (with a 1.5 equi val e nce va l u e).

FASOM calcu lates ou tpu t i n fiv e year in cre m en ts. Fo r this an alysis, 2 010 an d 201 5 d a t awere in terpo l ated to est i m a t e 201 2 val u es
.

106 U.S. reg i on s co n s ist of th e Pacific No rthwest (W est an d Ea st), Paci fic Southwes t, Ro ck y Mo un tain s, Great Pl ai ns, So uth w est , S out h C e nt ral , C o rn B e l t , Lake St at es, S out heast , a n d t h e No rtheast

.

107 FA SOM Fo rei g n R e gi ons i n c l ude: t h e E u ro pean Ec on omic C o m muni ty , No rthC e ntral Eur o pe, So uth w est Eur o pe, East ern E u r o pe, A d ri at i c , East ern M e di t e rranean , F o rm er Soviet Uni o n, North Africa, East Africa, West Africa, South Africa, Re d Se a, Iran, India, Taiwan, Japan, Sou th Kor ea, N o r th Ko r e a, Ch in a, Bang lad e sh , I ndo n e sia, Myanm ar, Pakistan, Philippi nes, T h ailand, Vietna m , W est Asia, Sou th east Asia, Au stralia, Caribb ean , Eastern M e xi co, East ern S o uth Am eri ca, W e st ern S o uth Am eri ca, Ar ge nt i n a, B r a z i l , C a nada , Other

.

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### 8.1 Commodity Prices

8.1.1 Corn and DDGS Prices FASOM predicts that as renewable f uel volum es increase, agri cultural 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 an ticip ated to rise. For consistency, all of the dollar estim ates 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). W ith the higher renewable fuels volum es in the EIA Case , corn prices rise to $2.71/bushel (2004$) by 2012. (See Table 8.1-1 ) To place th is difference in perspective, in 2012, corn prices are about 8 percent higher in the RFS Case and 17 percent higher in the EIA Ca se re lative to the Ref erence Case. 1 09

Table 8.1-1. Corn and DDGS Prices in 2012 Reference Case RFS Case EIA Case
Corn Price $2.32/bushel $2.50/bushel $2.71/bushel
Distille rs Dried Grains w ith Solubles (D DGS) Price $85.55/ton $83.35/ton $86.15/ton

The cost of producing ethanol is dependent upon, am ong 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 estim a te the f uture pri ces of the m a jor ethanol production byproduct: distillers dr ied grains with solubles ( DDGS)

. FASOM estim ates th at th e p r ice of DDGS will
rem ain relatively cons tan twith th e re newable volum e scenarios that we ar e examining in this rulem aking. An increase in DDGS s upply is antici pated to be offset by an increase in DDGS dem a nd as technology improves to pelletize and distribute DDGS to a wider m arket. 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 re m a ins within 3 percent of the DDGS Reference Case p r ice. Note that the DDGS price given here is the price an ethanol produ cer would expect to receive at the plant gate. FASO M pr edicts a higher value for the DDGS a tthe place of end use,

108 For add itio n a l d e tails on th e FASOM m o d e l, see th e r e por t by Pr of esso r Bru ce McCar l, Tex a s A & M Univ er sity, “The Im pacts of the Re newa ble Fuel Standard Program on th e U.S. Agr i cu ltu r a l Sector ,” Febru ar y 2 007 , i n clud ed in the doc k et.

109 The cu rre nt pri ce of co rn i n t h e U.S . i s ap pr oxi m a t e l y $3.5 0 per b u s h el ( 2 00 4 $ ) , w h i c h i s co nsi d e r a b l y hi g h er th an t h e FASOM p r ed ictio n an d is a lik ely a resu lt o f th e fact th at recen t de m a n d for co rn fo r et h a no l is hi g h er th a n th e curren tly av ailab l e sto c k s
. Th e m o d e l resu lts fo r 201 2 reflects m ed i u m -term sp atial eq u ilibriu m p r ices, wh ere rising dem a nd fo r c o rn is m e t by risin g su p p ly -- d u e to in c r eased ac res plan ted t o c o rn an d to inc r eased corn yields p e r acre. No te th at wh ile th e m o d e l assu m e s th at m ark ets fo r corn and rel a ted agricu ltural co mm o d ities will settle at a price of $2.50 per bus h el (i n the RFS cas e) b y 201 2, th at th is m a y b e a co n s erv a tiv e esti m a te to th e exten t th at th e ag ricu ltu ral secto ris ab le t o ad ju st t o th e in creased use o f co rn i n eth ano l p r od u c tion b y 2 012

.

325based on its nutritiona l ab ility to be s ubstitu ted a s half soy m e al and ha lf corn in an im al f eed. The difference between its feed value and the ethanol plant gate price is m a de up of the cost of handling and shipping, which m a y i nclude pelletiz ing or other m easures required to support a national commodity m a rket for DDGS.
8.1.2 Soybean and Soybean Byproducts FASOM predicts relativ ely m odest changes in so ybean prices as a resu lt o f the incre as es in the renew able fuel volum es exami n ed in this rulem aking. In the RFS Case, soybean prices rise to $5.44/bushel (2004$) by 2012 (compared to a Refe rence 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 exp ected to increase by ab o u t 3 perc ent (RFS Case) and 4 percen t (EIA Case) relative to the Reference Case by 2012. The slightly higher prices of soybeans reflect the consequences of the higher dem a nd for soybeans for renewable fuels as well as the slightly higher input costs (e.g., land prices). It is also expected that in m e dium -term the acres planted to soybeans will fall, due to increased corn plantin gs, which will also increase soybean prices.
Table 8.1-2. Soybean and Soybean Meal Prices in 2012 Reference Case RFS Case EIA 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 m e al is produced when crushing soybeans and extracting soybean oil, the prim ary feedstock of biodiesel in the U.S. Un der the RFS s cenario, FASOM estim ates th e price of soybean m eal will decreas e by ab out 3 perc en t in 2012, relative to the Reference Case. 1 10 (See Table 8.1-2) This decrease is slightly larger under the E I A scenario, with the price of soybean m e al dropping by about 4 percent. Se veral factors influence the sm all change in soybean m e al prices. First, m ore acres of soyb eans are being planted to ro tate with inc r eased planting of corn, and this leads to increased soybean supplies. Second, increased DDGS supplies can substitute for soybean m eal as a feed ingredient by reducing th e soybean m eal needed in feed rations using higher levels of DDG S. Third, the size of the livestoc k herd is sm aller due to higher
m eat prices, reducing the dem a nd for anim al feeds overall.

110 The curre nt price of soy b eans in t h e U.S. is consider ab ly h i g h e r t h an th e FASOM pred ictio n and is a likely a result of the fa ct that the m arket exp ects acres p l an ted t o so yb ean s i n th e sh ort term are l i k e ly to d e cline du e to in creased co rn p lan ting s . As with th e corn resu lts,.th e m o d e l reflects m e d i u m -ter m sp ati a l eq u ilibriu m p r ices, wh ere rising dem a nd fo r c o rn is m e t by risin g su p p ly -- d u e to in c r eased ac res plan ted t o c o rn an d to inc r eased corn yields p er acre b y 2 0 1 2 . Similarly, o v e r tim e, farmers will b e g i n to p lan t m o re so ybean s in resp on se to relativ ely h igh er sh ort-term p r ices. Th e m o d e l ex p ects so y b ean p r ices t o r each an eq u ilibrium p r ice o f $5 .44 p e r bu sh el (i n th e RFS case) by 2012.

326

### 8.2 Impact on U.S. Farm Income The increase in renewable fuel production provi d es a sign if ic ant inc r eas e in f arm incom e to the U.S. agricultural sector. FASOM predicts that in 2012, U.S. far m i n com e from the sale of agricu ltu ral commodities will in cr ease by $2.65 billion dollars in th e RFS Case and $5.41 billion in the EIA C a se. (See Figure 8.2-1) The RFS an d EIA far m incom e changes represent roughly a 5 and 10 per cent in crea s e, respe ctiv e l y, in U.S. f arm incom e from the sale of f

arm commodities over the Ref e rence Case of roughly $53 billion 1 11

. Most of the incre ase in n et incom e is like l y to be concentrated in rural areas, and m a y contribute to rural wealth creation. Figure 8.2-1. Change in Net Farm Inco me Relative to Reference Case in 2012

$2. 65 $5. 41

0. 0 0 1. 0 0 2. 0 0 3. 0 0 4. 0 0 5. 0 0 6. 0 0Billion2004Dollars

2 012

FA S O M R FS F A SO M EI A



### 8.3 Impact on Employment Agricultural employm e nt was not directly m odeled but is likely to be very sm all since modern farm practices are not la bor intensive and increases in pr oduction as m odeled here will have negligible im pact on direc tf arm em ployment. Som e addition al em ploym ent will r esult

111 Wh ile U.S. gov ern m en t farm p aym en ts are cu rren tly p art o f th e U.S. farm in co m e, wh at p r og ram s will b e i n place in 2012 a n d their impact on farm income is un clear. For our m odeling, we a ssum e d the s u pport program s were in place i n 2010 but none we re in place in 2015 ; interpolation bet w ee n 2010 a n d 2015 provi ded the assum ed i m p act in 201 2.

327from ethanol plant construction and operation. 30 to 50 people per ethanol production facility
seem s typical.



### 8.4 Commodity Use Change

8.4.1 Corn and Ethanol Byproducts For this analysis, U.S. corn uses are broke n down into four categories: dom e stic (i.e., household) consumption, ethanol production, livestock feed, and U. S. corn exports. (See Figure 8. 4-1) As th e dem and f o r corn in cre a ses to produ ce m o re renewable f u el, U.S. corn utilization patterns are expected to be alte red. In 2005, approxim a tely 13 percent of all corn produced in the U.S. was used for ethanol production. W ith th e two renewable fuel volum es that we are
exam ining, the percentage of corn feedstock usedfor 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 estim ates are similar to th e percen tage s includ ed inthe NPRM. The increasing use of corn for ethan ol raises the price of corn which has a direct impact on the other uses of corn. FASOM pr edicts higher U.S. corn prices leads to lower U.S. exports of corn. U.S. corn expo rts drop fro mabout 2 billion bush e ls 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 term s, U.S. exports of corn fall by $ 573 m illion in the RFS Case and by $1.29 billio n in the EIACase in 2012. Figure 8.4-1. Corn Us es in 2012

10 0%

80 %

60 %

40 %

20 %

0%

Fe e d F ASO M : R e f e r e n c e C a s e F ASO M : R F S F AS O M : E I A E t h ano l Ex p o r t s D o m e st i c co n su m p t i o nPercent CornUse

328Higher U.S. prices for corn due to increas ed dem a nd for ethanol production results in decreased use of corn for U.S. livestock feed. The am ount of co rn used for livestock f eeding decreases by about 320 m illion bushels in the R FS Case and by about 69 0 m illion bu shels in the EIA Case re lative to the Ref e re nce Case. Substitutes are available for co rn as a feedstock, and this m arket is highly price sensitive. One alte rnate feedstock is DDGS bec a use feed ration using increased levels of DDGS would need less corn. The relatively flat prices for DDGS predicted across all ethanol volum e scenarios results from the significant increase in the dem a nd for DDGS as a feed ingredient parallels the increase in supply of DDGS. FASOM estim ates that DDGS use for lives tock feeding for the RFS Case will alm ost double by 2012, increas ing from 8.5 million tons to 15.2 m illion tons. Under the EIA Case, FASOM predicts that DD GS will increase to 22.2 m illiontons by 201 2. (See Figu re 8.4-2) D o m e stic (i.e. , household) consumption of corn for food use declines slightly with the different renewable fu el volum es analyzed in this rulem aking. Figure 8.4-2. Livestock Feed Sources in 2012

600

500

400

300

200

100

0

Co r n FASO M R e f e r e n c e Ca s e F ASO M R F S F ASO M EI A

DDG S Ha y S i l age So y b e a n M e a l W h eat So r g h u m Ba r l e y Oa ts G l u t en M ealMillionTons

3298.4.2 Soybean and Soybean Byproducts As described previously, only a m odest incr ease in the dem a nd for soybeans are expe cted to be used to produce biodiesel in the renewable f u el scenarios analyzed in this rulem aking. Although changes in soybean uses inthis analysis are lim ited, U.S. exports are expected to drop by 41.8 m illion bushels (RFS Case) and 35.6 m illion bushels (EIA Case). In term s of export earnings, U. S. exports of soybeans fall by $220 m illion in the RFS Case and by $194 m illion 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 productio n of corn in the U. S. in 2012 will be 11.9 billion bushels under the RFS Scenario and 12.1 billion bushels under th e E I A Scenario (compared to 11.7 billion bushels in the Reference Case) 1 12

. (See Table 8.5-1) W ith higher renewable f u el volum es, 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 co rn 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. Improve m e nt in the productivity of growing corn on existing U.S. land is projected to grow by roughly 1 percent annually thr ough 2012. As a result, m ost of the inc r eas e ddem and f o r corn f r om increas ed use of renewable f u els will be m et f r om incre ased productivity on existing acres of corn relativ e to the 2005 baseline year. However, corn production from new acres plays an important role in corn supply. FASOM estim ates an increase in land devoted to corn prod uction of 1.6 m illion acres (RFS Case) and 2.6 m illion acres (EIA Case) in 2012 compared to the Reference Case.
Table 8.5-1. U.S. Corn Acres Harv ested, Corn Produc tion, and Agricultural Land Prices in 2012 Reference Case RFS Case EIA Case
Corn Acres Harvested (m illion acr es) 78.5 80.1 81.1

Total Corn Production (billion bushels) 11.7 11.9 12.1

Land Prices (percent

incre ase r ela tive to

Reference Case) N/A 8.4% 16.8%

112 FA SOM i n cl u d es c o rn e q ui v a l e nt fee d i n g o f by p r odu cts.i n th e estim a t e fo r to tal corn p r o d u c tion

.

330Higher ren e wable f u el v o lum es will have a dir ect impact on the value of U.S. agricultural land. As dem a nd for corn and other far m products increas es, the price of U.S. far m land will also increase. The FASOM anal ysis shows that in 2012, higher renewable fuel volumes increase average agricultural landprices in the U.S. by about 8 pe rcent (RFS Case) and 17 percent (E IA Case).
8.5.2 Soybean Ac reage Increasing use of biodiesel fuel in the renewable f uel scenarios does not cause a significant change in U.S. soybean production. Soybean production stays relatively flat at 3.3 billion bush els in all thr ee scenar ios analyze d. (S ee Table 8.5 -2) Soybeanacreag e increases modestly as well in the renewable fuel scen ario s examined. In the RFS Case, to tal so ybean acres are 74.6 m illion. For the EIA Case, total soyb eanacres are 74 .4 m illion acres, comp ared to 73.4
m illion acres of soybeans in the Reference Case.
Table 8.5-2. U.S. Soybe an Acres Harvested an d Soybean Production in 2012 Reference Case RFS Case EIA Case Soybean Acres Harvested (m illion acr es) 73.4 74.6 74.4

Total Soybean Production (billion bushels) 3.3 3.3 3.3

8.5.3 CRP Acreage
Current lands in the Conservation Reserve Pr og ra m (CRP) total appr oximately 40 m illion
acres. To q u alify for in clusion in th e CRP, the acres mu st have been at o n e tim e in active agricu ltu ral use. Farm ers are paid to take th es e lands out of production an d place them in CRP to provide environm ental benefits, including lim iti ng erosion and providing wildlife habitat. Farm ers put land into the CRP voluntarily, consid ering am ong other factors the value of the land if it were to rem a in in agricu ltural production versus the am ount paid under the CRP contract. The am ount of governm e nt paym ents can change over tim e
. For this analysis, we have assum e d current per-acre paym ent levels to landowners are
m a intained through 2012. However higher com m odity prices and higher land rents associated with higher renewable f uel volum es would likely require higher CRP paym ents to m a intain the
sam e level of CRP enrollm e nts. The RFS and EIA renewable fuel volum e s are estim ated to result in CRP withdrawals of 2.3 m illion and 2.5 m illion acres, respectiv ely, 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 m arginal lands due to their lo cation and productivity. For exam ple, only a relatively sm all portion of CRP lands are in the Corn Belt. Instead, additional corn or soybeans acres will probably be planted on lands that w e re previously used for other crops or pasture, for example, wheat, grain, sorghum orplanted forage crops. It is

331expected thatsom e of the land r em o ved f r om CRP will be u sed f o r these other ag ricu ltur al purposes. T able 8.5-3 depicts the estim ate of CRP i mpacts.
Table 8.5-3. CRP Acre age Changes Relative to Reference Case RFS Case EIA Case
Reduction in CRP Acre age (m illion acres ) 2.3 2.5



### 8.6 Fertiliz e r Use
Under the RFS scenario, the total am ount of nitrogen applied on all farm s increases by 1.2 percent, or 480,000 pounds, relative to the Refe rence Case in 2012. Under the E I A scenario, the total am ount of nitrogen applied on all farm s increases by 2 percent, or 790,000 pounds, relative to the Reference Case in2012. (See Table 8.6-1) W e note that this percent increase in fertilizer is largely accou n ted for by the 2 percen t increase in land used for corn production and 1 percent increase in land for soybean production. The fact that the am ount of nitrogen used increases at a sm aller percent than the am ount of land increase for cornproduction suggest that much of the corn production land is already in agricu ltural use (with fertiliz er applied) and is not likely to be land newly re leased from CRP.
Table 8.6-1. Nitrogen and Phosphorous Use in 2012 Reference Case RFS Case EIA Case Total Nitrog en Applied (million pou nds) 40.28 40.76 41.07

Total Phosphorous Applied (million pou nds) 4.24 4.27 4.29

Under the RFS scenario, the total am ount of phosphorous applied on all farm s increases by 0.7 percent, or 30,000 pounds, relative to th e Reference Case in 2012. Under the E I A scenario, the total am ount of phosphorous applie d on all farm s 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 notinclude a comp rehensive a nd integrated environm ental
assessm ent of the impacts in th e agr i cultu ral s ect or of higher renewable f u el volum es from this rulem aking, we looked at two fact ors directly im pacted by the pr oduction of agricultural crops

332that m ay relate to environm ental im pacts. FA SOM does estim ate the amount of fertilizer used and changes in CRP land, two indicators thatcould be associated with water pollution. 1 13

Marathon commented that it believes that EPA’s assessm ent of environmental im pacts does not consider all environm ental impacts and is therefore incomplete, especially with respect to water qu ality im pacts. As describ ed above, ou r analys is predicts a m o dest in crease in fertilizer use and m odest withdr awals of CRP lands due to the hi gher renewable fuel volum e s. W h ile incr e ased agri cultural dev elop m ent would likely increase pressure on environm entally sensitive areas such as w e tlands and prairie lands and rural ecosystem s in general, FASOM does not represent this level of la nd detail in the national m odel and therefore cannot quantify any potential impacts on these subset s of land types. To the exte nt that CRP withdrawals are
m a naged in an environm entally sustainable wa y, however, water polluti on impacts would be
m i ni m i zed. Increasing worldwide dem a nd for biofuels a nd decreasing U.S. exports of feedstocks used in producing renewable fuels will likely l ead 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 s upply curves for rest of world production of key agricultural pr oducts, but it does not contain a m echanism for appraising world environm ental implications since FA SOM is a dom estic m odel of the U.S. agricu ltu ral sector. Therefore, this analysis focuses only on impact s of the higher renewable f u els volum es 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 estim ates only a m ode st increase in U.S. household food costs. Annual wholesale U.S. food costs are estimated to increase by approxim ately $7 per person with the RF S renewable volum e s and by about $12 per personannually with the EIA renewable volum e s by 2012. (See Figure 8.8-1 ) Agricultural cos t s ar e only a portio n of ultim ate household food costs so significant increases in corn prices and, to a lesser degree, soybean pr ices results in a much
sm aller relative increase in household food costs.

113 Th e FASOM m o d e l can describ e t h e p r o portio n of fertili zer th at po ten tially will a ffect groun dwater quality an d surface water quality. FASOM also details the e x tent t o which shifts in agricultural production m ay affect soil erosi o n and ca rbon s e questrati o n. In th e short ti m e fra m e av ailab l e, we were no t ab le t o d e v o t e sign ificant effo rts t o th i s ty pe of a n a l y s i s , but t

h i s ar ea of i n q u i r y c oul d be i n vest i g at ed m o re e x t e nsi v el y i n th e fut u re. We do not e t h at we ca pture the sequestration i m pacts in the GREET a n alys is.

333Figure 8.8-1. Increase in Annual Food Costs Per Person Relative to Reference Case in 20122004 Dollars 14

12

10

8

6

4

2

0 $7. 0 $11. 9

2012
F ASO M R F S F ASO M EI A

FASOM estim ates a relatively m odest increase in U.S. prices for m e at and agricultural products associated with the higher renewable fu el volum es. When evaluating changes in overall U.S. food pr ices, FASOM uses the All Farm Pr oduc ts Price Index, which is a weighted average of prices received by farm ers at the "far m gate " for crop and livestock pr o ducts re la tiv e to the Reference Case. 1 14 FASOM estim ates a 4 percen t increas e in th e RFS Scenario and a 7 p ercen t increase in the EIA Scenario in the w e ighted price of all farm pr oducts. (See Table 8.8-1) To evaluate changes in U.S. m eat prices, FASOM uses the All Meat Products Price Index which is a w e ighted average of th e prices that farm ers receive for m eat products at the far m gate. This index is based upon changes in the weight ed average of beef, pork, chicken, and turkey prices. U.S. m eat prices that farm ers receive in 2012 are esti m a ted to increase by 0.3% in the RFS Case and by 1.3% in the EIA Case com pared to the Reference Case.

114 The All Farm Products Price Inde x incl udes: cotton, c o rn , s o ybea ns, w h eat, sorghum , rice, oats , barley, silage, h ay, sug arcan e, sug a r b eet, p o tato es, to m ato es, oran g e s, g r ap efru it, switch g r ass, h ybrid po p l ar, willo w, b eef, co ws, m i l k , pi gs , l a mb , w ool , h o r ses an d mu l e s, eg gs, chi c ken , a n d tu rk ey
.

334Table 8.8-1. Increase in the All Farm Products Price Index and the All Mea tPro ducts Price IndexRelative to the Referen ce Case in 2 012 RFS Case EIA Case
All Farm Pr oducts Price 3.8% increase 6.9% increase
All Meat Products Price 0.3% increase 1.3% increase

Because corn is a m a jor component of the A ll Crop Price Ind ex, a sign ificant chang e in corn prices will result ina pronounced change in this ind e x. The impact of corn price changes on the Meat Price Ind ex will be less pronounced for tw o reas ons. First, as corn prices rise, m eat producers w ill m odify fe ed rations an d production system s to reduce their corn usage. Second, there will also be substitution am ong m eats leadi ng to higher consum ption of m eat from anim als using less of the higher priced corn (e.g., incr eased production of poultry products relative to beef products).

335

## Chapter 9: Small-Busine ss Flexibility Analysis

This chapter presents our Sm all Business Fl exib ility Analys is (SBFA) which evalua tes the poten tia l im pacts of the new st an dards on small en tities. The Regulatory Flexibility Act, as
am ended by the Sm all Business Regulatory Enforcem ent Fairness Act of 1996 (SBREFA), generally requires an agency to prep are a regulatory flexibility an alysis of any rule subject to notice and comm ent rulem aking requirem e nts unde r the Administrative P r ocedure Act or any other s t atu t e unless th e agency certifies that the ru le will not have a sign ificant econo mi c i m pact on a substantial num ber of s m all entities. Prior to issuing a proposal for this rulem aking, we analyzed the potential impacts of these regulations on those entities that we believe ar e sm all entities (see section 9.3, below). As a part of this analysis, we conducted outreach with those entities to gather inf o rm ation and rec o mm enda tions f r om these entitie s on how to reduce the impact of the rule on sm all businesses.



### 9.1 Requirements of the Regulatory Flexibility Act We are generally required under the Regul atory Flexibility Act (RFA) to conduct a regulat ory f l exibility ana l ysis un less we certif y that the requir em ents of a regulation w ill no t cause a significant impact on a substantial num ber of sm all entities. The k ey elem ents of the RFA include:

• a description of and, where f easible, an estim a te of the num ber of sm all entities to which the proposed rule will apply;
• the projected reporting, record keeping, and other comp liance requirem e nts of the proposed ru le, includ ing an estim a te of the cl ass e s of sm all en tities which will be sub j ect to the requirem e nts and the type of professi onal s kills n eces sary for preparation of the report or record;
• an identif i cation to the e x tent p r actic able, of all other relevant Federal rules which m a y duplicate, overlap, or conflict with the proposed rule; and,
• any significant altern atives to th e proposed rule which acco mplish the stated objectives of applicable statutes and which m i ni m i ze any significant econom i c i m pact of the proposed rule on sm all entities.
The RFA was am ended by SBREFA to ensure that concerns regarding sm all entities are adequately considered during the developm ent of new regulations that affect them . Al though we are not required by the C l ean Air Act to provide s p ecia l tr eatm ent to sm all busin esses , the RFA requires us to carefully consider the economic i mpacts that our proposed rules will have on sm all entities. Spe cif ically, th e RFA requires us to de te rmine, to the extent f eas ible, our ru le ’s
econom ic impact on small entitie s, explore regulatory options for reducing any significant
econom ic impact on a substantial num b er of suchentities, and explain our ultim a te choice of regulatory approach.
We have concluded that the final RF S rule will not have a significant impact on a substantia l n u m b er of small en titie s. We based this conclusion on several criteria. First, the

336industry is expected to be ove rcomplying by a wide m argin independent of the standard, thus causing compliance costs to be m i nim a l. Second, the Energy Policy Act of 2005 (Energy Policy Act) already provides relief f

r om the renewable f u els standar d s until 2 0 1 1 f o r the m a jority of the
sm all entitie s; and lastly, we are exte nding this re lief to th e re m aining small en tities. This is discussed further below.



### 9.2 Need for the Rulemak ing and Rulemaking Objectives A detailed discussion on the need for and objec tives of this ru le ar e in th e pream ble to the final rule. As previously stated, EPA is requ ired to promulgate regulations imple m enting a renewable f u el program under S ection 1501 of the Energy Policy Act, which am ended the Clean Air Act by adding Section 211(o). T he Energy Po licy Act requires EPA to establish a program to ensure that U.S. gasoline contains specific vo lum e s of renewable fuel for each calendar year beginning in 2006, to increase the am ount 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

Sm all entities include sma ll businesses, sm all organizations, and sm all governm ental jurisd ic tions

. For the purposes of assessing the impacts of the rule on sm all en tities, a sm all entity is def i ned as: (1 ) a sm all business thatm eets the definition for business based on the Sm all Business Adm i nistration’s (SBA) size standard s (see Table 9-1); (2) a sm all governm e ntal jurisdiction that is a governm e nt of a city, county, town, school distri ct or special district with a population of less than 50,000; and (3) a sm all orga nization that is any not-for-profit enterprise which is independently owned and operated and is not dom inant in its field. Table 9.3.1-1 provides an overview of the prim ary SBA s m all bus iness c ate gories po ten tia lly af f

ecte dby this regulation.
Table 9.3.1-1. Small B u siness Definitions Industry Defined as small enti ty by SBA if: NAICS Co des a Gasol i ne refi ners ≤ 1,500 empl oyees 115 324110

aNort h Am erican Indust rial Classification Syste m

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 gasolin e. For our recent rulem aking “Control of Hazardous Air Pollutan t s from Mobile Sources”

115 I n th e D r aft R I A, we al so re f e rre d t o a 12 5, 00 0 barrel s of cru d e per day ( bpc d ) cru d e capaci ty l i mi t

. Thi s criterion wa s i n adve rtently us ed an d is n o t ap p licab le fo r this p r og ram (as it o n l y app lies in cases of g overn m e n t p r o c u r em en t). We n o t e t h at the nu m b er of small en tities re main s th e sam e wh eth e r th is criterio n is u s ed o r no t.

337(72 FR 8428, February 26, 2007), we perform e d a n industry characterization to determine the universe of potentially affectedgasoline refiners. Inform ati on about the characteristics of refiners comes from sour ces including the Ener gy Inform ation Adm i nistra tion within th e U.S. Departm e nt of Energy, and from Hoover’s (a di vision of Dun and Bradstreet). The refining industry is located prim arily in NAICS code 324110. The industry characterization was then usedto determ ine which refiners m e t the SBA definition of a sm all refiner. From the industr y characterization, and fu rther analysis following the Notice of Proposed Rulem aking (71 FR 55552, Septem ber 22, 2006), we determ ined that there we re 1 5 gasolin e re finers (o wning 16 refineries) that m e t the definition of a s m all refiner. It should b e noted that b ecause of th e dynam i cs in the refin i n g industry (e.g., m e rgers and acquis ition s) , the ac tual n u m b er of ref i ners that u l tim ately qualif y f o r sm all ref i ner s t atu s could be different from this estim a te. Title XV the Energy Policy Act provides, at Section 1501(a)(2) [42 U.S.C. 7545(o)(9 )(A)-(D)], special p r ovis i o n s for “sm a l l refineries ”, which inclu d es a tem porary exem ption from the standards until calendar year 2011. Further, the Energy Policy A c t states that EPA must use the definition of “sm a ll refi nery” and app l y the sp eci al provisions provided for s m all ref i neries in the RFS program

. Th e Energy Policy Act defines the term “small refinery” as “...a refinery for which the aver age aggregate daily crude oil throughput f o r a calendar year...does not exceed 75,0 00 barrels.”

A s m all refinery (as defined by the E nergy Po licy Act) is very different from a s m all refiner (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 sm all refiner is a sm all business that employs le ss than or equal to 1,500
employees. A s m all refinery , per the Energy Policy Act, is a sm all-capacity refinery and could be owned by a larg er r ef i ner thatexce eds the c r ite r ion spec if ie d in SBA’s sm all entity d ef i nition ; whereas small refiners generally only own a few (and m ore often than not, only one) refineries. In our analysis of the potentially affected sm all refiners, we found that 42 refineries m e t the Energy Policy Act’s definition of a s m all refinery. Of these, we determ ined that 13 of these refineries w e re owned by sm all refiners. Theref ore, 12 of the 15 sm all refiners owned refineries that also m e t the Energy Policy Act’s definition of a s m all refinery. As a result, we believe that all bu t thr ee sm all ref i ner s would auto m atically be granted relief by imple m enting the provisions specified in the Energy Policy Act.



### 9.4 Issues Raised By Public Comments During the public comment period we receiv ed num e rous comments regarding various aspects of the proposed rule, including our propos ed sm all refiner provisions. The following section prov ides a summary of the co mments that we receiv e don our prop osed provis i ons. More inform ation on these comments can be found in th e Final Summary and Analysis of Comm ents, which is a part of the rulem aking record.

3389.4.1 Extension of the Small Refinery Exemption to Small Refiners

Comm enters that supported the provision ex tending the sm all refinery exemption to sm all refiners g en erally stated that they believe th at a sm all refiner exemption is necessary as those entities that would qualif y as sm all refiners ar e generally at an econom ic disadvantage due to their compa ny size – whereas the Energy Policy Act only recognizes facilities, based on the size of each location. These comm enters also stated th at they h av e concern s with the co st and the availability of credits under the RFS program , a nd believe that provisions for s m all ref i ners are necessary to help mitigate any sign ificant adverse econom ic impact on th ese entities.

Comm enters that opposed the provision comment ed that they believe that EPA exceeded its discr etio nary autho r ity, thatthe r e appears to be no basis on which the Agency can legitim ately expand this statutory exemption to add sm all refi ners, and that Congress “clearly did not intend that the exemption be broadened to also include sm all refiners .” One comm enter also stated that it does no t b eliev e that s m all refiner provision s are necessary because the RFS program does not require costly capital investm e nts like previous f uel regulations.
9.4.2 Application Deadline We proposed that refiners would need to apply for the sm all refinery exemption, and that the exemption would be effective 60 days afte r receip tof the application by EPA (unless EPA notif ies the applic ant th at the applic ation was not approved or that additional docum entation is required). We receivedcomments on this prov is ion in whichcommenters stated that requiring
sm all ref i nery applic atio ns was inco nsisten twiththe langu ag e set ou t in the Energy Policy Act. The commenters s t ated that the Energy Policy Act intended that sm all refineries would autom a tically receive the sm all refinery exemp tion upon the effective date of the standard, and that these parties should not be considered obligated parties in 2007 even if they do not subm it a
sm all ref i nery applic atio n.
9.4.3 Provisions for Foreign Sma ll Refineries and Refiners For consistency with prior gasoline-related fu el program s, we also proposed to extend the RFS s m all refinery (and sm all refiner) exem ption to foreign refiners, and we requested comment on this prov ision. W e received som e comments in which commenters stated th at th ey believ e that there is no reason to extend th e s m all ref i ne ry exemption to thes e refiners. One comm enter even stated that it believ ed that such an allowance would be unlawful.
9.4.4 Other
We receivedsom e comments which s t ated th at EP A needed to clarify whether or not
exempt s m all refineries (and sm all refiners) co uld separate a RIN simply by owning a batch of fuel. W e also received a comment which stated th at it was not clear in the proposed rule whether or not sm all refineries (and sm all refiners) blendi ng ethanol at a term inal or any location without f

o rm ally opting into the pr ogram could separate RINs.

339

### 9.5 Related Federal Rules Other current and proposed Fede ral rules that are related to this rule are: the Mobile Source Air Toxics (MS A T2) rule (72 FR 8428, Fe bruary 26, 2007), the Tier 2 Vehicle/Gasoline Sulfur rulem aking (65 F R6698, February 10, 2000), a nd the fuel sulfur rules for highway diesel (66 FR 5002, January 18, 2001) and nonr oad diesel (69 FR 38958, June 29, 2004).



### 9.6 Projected Reporting, Record keeping, and Other Compliance Requirements For any fuel control program , EPA must ha ve th e assuran ce that refiners m eet the applicable standards. Thus, requirements are im posed to ensure that co mp lianc e oblig ations are
me t

. The recordkeeping, reporti ng and compliance provisions of this program are fairly consistent w ith those currently in place for our other 40 CFR part 80 fuel program s, including the highway and nonroad diesel and MS AT re gulations. These provisions include:

·

·

· Registr ation(the r egis tration num bers will a l so b e used in the RINs) Subm ission of annual reports summarizing a refiner’s annual ga soline production and a dem onstration of its complian ce with the renew able fuels standard and subm ission of annual reports detailing and tracking a refine r’s R I Ns; EPA’s Central Data Exchange will be used for report subm issions Recordkeeping will cons ist of the retenti on of all com p liance docum ents (such as Prod uct Transf er Docum ents and all reports s u bmitted to EPA) f o r at leas t f i ve ye ars
For a m ore detailed discussion of these provi s i on s, please see secti on IV of the preamble to this f i n al rule.



### 9.7 Steps to Minimiz e Significant Economic Impact on Small Entities
As stated above, we conducted outreach to a number of stakeholders that m e t the definition of a sm all entity to gain feedback and advice on the needs of sm all businesses and potential ch alleng es that these en tities m a y face. The feedback that we received from these entities as a result of these m eetings was usedduring the developm ent of the proposed rule for developing r egulato r y alternatives to mitigate the im pacts of the rulem aking on sm all businesse s. General con cerns rais ed by these en tities were the potential difficulty and costs of comp liance with the upcom ing standards given the other fuelcom p liance requirem ents that the fuel refin i ng industry is subject to. Below we discuss the regulatory flexibili ty alternatives and provisions which are being finali zedin this actio n. While we do not believe that the RFS progr am with just the s t atuto r ily -pre scribed temporary relief for sm a ll refineries would have a significant econom ic impact on a substantial
num b er of sm all entities, we continue to believe that som e refiners, due to their size, generally

340face greater challenges comp ared to larger refiners. These ref i ners gen e rally have g r eater difficulty in raising and securing capital for in v esting in cap ita l improve m e nts and in competing for engineering resources and projects. This rulem aking does not requir e that refiners m ake capital improvem e nts, however there are s t ill co st s associated with m eeting the standard. Thus, we find it appropriate to extend th e sm all refinery temporary exemption, as set out in the Energy Policy Act, to sm all refiners. Under this exemption, any gasoline produced at a refinery owned by a sm all refiner will not be counted in determining the renewable fuel obligation of a refiner until J anuary 1, 2011; further, the sm all refiner m a y exclude g asolin e prod uced at its refineries from its compliance calculations. Beginning in 2 011, refineries owned by sm all refiners will b e required to m eet the sam e renewable fu el obligation as all other refineries.
Past fuels rulem akings have included a provisi on that, for the purposes of the regulatory f l exibility p r ovisions f o r sm all entitie s, a ref i ne r 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 f i nalizing in this ru le thatref i n ers thatm eet this c r iterio n (in add itio n to having no m o re than 1,500 total co rporate em ployees) will b e con sid ered sm all refiners for the purposes of the regulatory flexibility p r o visions for RFS program

. Further, the refiner must have p r oduced gasoline at its refineries by pro cessing crude oil through refinery processing units. We are also finalizing th at eligib ility will be based on 2004 data. We agree with statem ents from comm enters that the Energy Policy Act d i d in f a ct intend to provide the sm all refinery exemption without the need for the subm ission of s m all refinery applications, and that these parties should rece ive the exemption upon the effective date of the rule. W e also believe that this should be the case for s m all ref i ners as well. Therefore, we are finalizing that sm all refiners will also receiv e the exemption immediately upon the effective date of the rule. However, to ensure th at only those refiners who m eet the criteria above receive this
exemption, we believe that it is necessary for refi ners to verif y that they do in fact m e et the criteria. Th erefore, th es e refiners will also be required to subm it a veri fication letter showing that they m e et the criteria f o r qualif ication as a s m all ref i ner f o r the regu latory f l ex ibility provision s. This le tte r will b e similar to the sm al l ref i ner s t atu s application s required u nder othe r EPA fuel program s (and mu st contain all the re quired elem ents specified at §80.1142 of the regulations ), except the lette r wi ll not be due prior to the program

. Sm all refiner status verif i cation letters f o r th is rule thatar e later f oun d to conta i n f alse or in ac curate inf o rm ation will be void as of the effective date of this rule. Sm all refiners who subsequently do not m eet all of the RFS program ’s regulatory flex ibility qu alifi cation criteria (i.e., cease p r oducing gasoline by processing crude oil, employ m ore than 1,500 people, or exceed the 155,000 bpcd crude oil capacity limit) as a result of a m e rger with or acquisition of or by anothe r entity, are d i squalif ied
as sm all refiners, except in the case o f a m e rger between two previously approved small refiners. As in other EPA program s, where such disquali fication occurs, the refiner must notify EPA in writing no later than 20 days follo wing the disqualifying event. We are finalizing the proposedprovision allowing foreign refiners to apply for a sm all refinery o r s m all refiner exem ption under the RF S program

. The Energy Policy Act d e finition of “sm all ref i nery” is no t limited to dom estic f acilitie s, and we believe that we have the discretion to apply the definition of sm all refinery, and th e s i milar relief that we are providing to sm all refiners, to foreign prod ucers. W e believ e that this provisio n is neces sary for consistency with

341prior fuel program s (anti-dumping, MSAT, and the fuel sulf ur rules) which allowed f oreign refiners to receive such exemptions. Unde r this provision, gasoline produced at approved f

o reign sm all ref i ne ries, and by appr oved f o reignsm all ref i ners, will b e e x em pt f r om the RFS standard such that obligated parties (importers or blenders ) would not count these volum es towards their renewable volum e obligations. We are also finalizing the pr oposed provision that the autom a tic five year exemption, and any sm all re f i nery exten d ed exem ptions (exten sions of the sm all ref i ners exem ption will only b e available to sm all refineries), m a y be waivedupon notification to EPA. Gasoline produced by a
sm all ref i ner who waives its ex em ption will be in cluded in th e RFS program and will be includ ed in the gasoline used to determ ine the refiner’s re newable fuel obligation. If a refiner waives the
exemption, the refiner will be able to separate an d transfer RINs like any other obligated party. However, exempt s m all refiners cannot separa te a RIN sim ply by owning a batch, a RIN can only be separated by these parties once the volum e of renewable fuel is blended with gasoline or diesel to produce a m otor vehicle fuel (as stat ed in the regulations at §80.1129). If a sm all refiner does not waive its sm all refiner exempti on, it can still separate and transfer RINs, but only for the renewable f u el that the refiner itself blends into gasoline (i .e., the refinery operates as an oxygenate blender facility). L astly, exempt sm all refiners who blend ethanol can separate RINs f r om batches witho u t f o rm ally opting in to the progr am
.



### 9.8 Conclusions After considering the econom i c impacts of today’s proposed rule on sm all entities, we do not believe that th is actio n will have a significa nt econom ic impact on a s ubstantial n u m b er of
sm all entitie s. W h ile the Energy Policy Act provided for a temporary exemption for sm all ref i neri es f r o m the requirem ents of today’s prop osed rule, th ese partie s will have to com p ly with the requ irem ents following the exem ption period. Therefore, we had to take into account the
econom ic effects of the program on sm all entities when they woul d need to comp ly with the standard. A s described in section VI of the prea mble to this final rule, the annual projections of ethanol production are greater than the annual renewable fuel volum es required by the Energy Policy Act. For example, in 2011, when the Ener gy Policy Act’s sm all refinery exemption ends, over one billion gallons in excess RINs are projecte d to b e av ailab l e. Further, excess RINs are anticipated for each year of the program
. Due to this pro j ec ted excess sup p ly in com p arison 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 sm al l entiti es, an d the pro j ected RIN availability, as w e ll as the temp orary relief provided to sm all refineries and sm all refiners, we do not believe that this program will impose a signif i cant e c onomi c impact on a sub stantial num ber of sm all entities.

342

## Endnotes

ARenewable Fuels Association (RFA), Ethanol B i orefinery Locations (Updated October 16, 2006).

B Ethanol Producer Magazine (EPM), Plant List (Downloaded October 18, 2006) m ont hly
m a gazine publications (June 2006 through October 2006).

C Ethanol Producer Magazine (EPM), m onthly m a gazine publications (June 2006, July 2006, August 2006, Septem be r 2006 and October 2006).

D ICF International, Ethanol Indus try Profile (Septem ber 30, 2006).

EBioFuels Journal, News & Inform ation for the Ethanol and BioFuels Indu stries (breaking news posted June 16, 2006 through October 18, 2006).

FRenewable Fuels Association (RFA), Ethanol B i orefinery Locations (Updated June 19, 2006).

GEthanol Producer Magazine (EPM), U.S. & Canada Fuel Ethanol Plant Map (Spring 2006).

H International Fuel Quality Center (IFQC) , Special Biofuels Report #75 (A pril 11, 2006).

I“Biodiesel Perform a nce , Costs, and Use”, Anthony Radich, EIA page 6.

J NBB Surve y Septem ber 13, 2006 “U.S . Biodiesel Production Capacity”.

K

From Independent Biod iesel Feasib ility Group P r esentation.

L“Pipeline Considerations for Ethanol”, Depart ment of Agricultural Econom ics, Kansas State University, August 2002. http://www.ag mr c.org/NR/rdonlyres/4EE0E81C-C607-4C3F-BBCFB75B7395C881/0/ksupipelineethl.pdf , and “Shipping Ethanol Th rough Pipelines”, Am erican Petroleum I nstitu te, April 6, 2006, http://apiep.api.org/printerform a t.cfm ? ContentI D=54FD1A11-95FA-4B7C-ACE1D7C6F121FB1C.

M Infrastructure Requirements for an E xpanded Fu el Ethanol Industry, Down stream Alternatives Inc., January 15, 2002.

N“Ethanol Industry’s Interest in River Sites Grows”, River Transport News, Vol. 15, No. 10, May 22, 2006.

343O Infrastructure Requirements for an E xpanded Fu el Ethanol Industry, Down stream Alternatives Inc., January 15, 2002.

PInfrastructure Requirements for an E xpanded Fu el Ethanol Industry, Down stream Alternatives Inc., January 15, 2002.

Q F.O. Licht, “ W orld Ethanol Markets, The Outlook to 2015” (2006).

REIA AEO 2006 Table 12: Petroleum Product Prices

S EIA Crude Oil Spot Pricing (http://tont o.eia.doe.gov/dnav/pet/pet_pri_spt_s1_d.htm )

TSources: U.S. EPA Office of Transportation & A i r Quality, L i st of Federal Reformulated Gasoline Areas (Updated February 23, 2004) an d U.S. EPA Office of Transportation & Air Quality, 2004 RFG Fuel Survey Results ( http://www.epa.gov/otaq/regs/fu els/rfg/properf/rfgperf.ht m ).

U Source: U.S. EPA Offic e of Transportation and Air Quality, State W i nter Oxygenated Fuel Program Re quirem e nts for Attainm e nt or Maintenance of CO NAAQS (Novem ber 2005).

V National E thanol Vehicle Coalition ( http://www.e85fuel.com / e85101/fa qs/num ber_ffvs.php ).

WEIA 2004 Petroleum M arketing Annually (Table 48: Prim e Supplier Sales Volum e s of Motor Gasoline by Grade, Formulati on, PAD District, and State).

X EIA Historical RFG MTBE Usage (file receiv ed from EIA represen tative on March 9, 2006).

Y EIA Monthly Energy Review, June 2006 (Tab le 10.1: Renewable Energy Consumption by Source, Appendix A: Ther m a l Conversion Factors).

ZFHWA Highway Statistics 2004: Es tim ated Use of Gasohol (April 2006).

AAEPA Reformulated Gas oline (RFG) Survey ( http://www.epa.gov/otaq/regs/fu els/rfg/properf/rfgperf.ht m ).

BB Alliance of Autom obile Manufacturers (AAM ) North Am erican Fuel Surv ey 2004. The report and data can be purchased through the AAM website, http://autoalliance.or g/fuel/fuel_surveys.php , or by contacting the Alliance at 1401 E ye Street, N.W ., Suite 900, W a shington, DC 20005, Tel: (202) 326-5500.

CC EIA Monthly Energy Review, June 2006 (Tab le 10.1: Renewable Energy Consumption by Source, Appendix A: Therm a l Conversion Factors).

DD EIA Historical RFG MTBE Usage (file receiv ed from EIA represen tative on March 9, 2006).

344EEU.S. EPA, State Actions Banni ng MTBE (Statewide), June 2004.

FFEIA 2004 Petroleum M arketing Annually (Table 48: Prim e Supplier Sales Volum e s of Motor Gasoline by Grade, Formulation, PADD, and State).

GGEIA Monthly Energy Review June 2006 (Tab le 10.1: Renewable Energy Consumption by Source, Appendix A: Ther m a l Conversion Factors).

HH EPA Reformulated Gas oline (RFG) Survey ( http://www.epa.gov/otaq/regs/fu els/rfg/properf/rfgperf.ht m )

IIAAM North Am erican Fuel Survey 2004 ( http://autoalliance.org/fuel/fuel_surveys.php ).

JJ EIA Annual Energy Outlook 2006 (Table 2: En ergy Consumption by Sector and Source).

KK International Fuel Quality Center (IFQC), Sp ecial Report: United States – State Renewable Content Standards (June 6, 2006).

LLRenewable Fuel News and W orld Re fining & N e ws Today (January 2006).

MM Modeling Impacts of Energy Bill RFS, Jacobs Consultancy Decem ber 2006.

NNThe Energy Act Section 1504, promulgated on May 8, 2006 at 71 FR 26691.

OO EIA 2004 Petroleum M arketing Annually (Table 32: Conventional Motor Gasoline Prices by Grade, Sales Type, PAD District, andState )

.

PPEIA NEMS Petroleum Market Model Docum e ntation, Appendix I, Table I4.

QQ Am erican Coalition for Ethanol, ST AT US : State by State Ethanol Handbook 2006 (supplem ented by inform ation obtained from stat e websites and conversations with state governm e nt officials).

RR Final Regulatory Impact Analysis fo r Reformula t ed Gasolin e , Table VI-A6, Dece m b er 13, 1993.

SS U.S. EPA, Final Regulatory Impact Analysis fo r Reformula t ed Gasolin e , Table VI-A6, EPA420-R-93-017, Decem ber 13, 1993. Available fr om RFG Final Rule, Docket No. A-92-12, February 1994.

TTU.S. EPA, EPA’s Natio nal Mobile Inventory Mo de l (NMIM) , A Consolid ated Emissions Modeling System for MOBILE6 and NONROAD , EPA420-R-05-024, Decem ber 2005, version NMIM20060310 with county datab a se NCD20060201 m o dified acco rding to discussion in Chapter 2.2.

345UU U.S. EPA, Guide on Federal and State Summer RVP Standards for Conventional Gasoline Only , EPA420-B-05-012, Nove m be r 2005.

VV Alliance of Automobile Manufactu rers North A m erican Fuel Survey 200 5

. The report and data can be purchased through the A A M website , http://autoalliance.org/fuel/fuel_surveys.php, or by contacting the Alliance at 1401 Eye Street, N.W ., Suite 900, W a shington, DC 20005, Tel: (202) 326-5500. Information about the data and m e thods is contained in the report Survey Sa mpling and Testing D e tails (May 2005), which m a y be downloaded from the AAM website at no charge.

WWU.S. EPA website www.epa.gov/otaq/rfg /whereyoulive.htm, as of Apr il 11, 2006.

XX U.S. EPA, A Comprehensive Analysis of Biodiese l Impacts on Exhaust E m issions, Draft Technical R eport , EPA420-P-02-001, October 2002.

YY Caf f r ey, Peter J., and P aul A. Mach iele , U.S. E P A, “In-Use Volatility I mpact of Comm ingling Ethanol and Non- Ethanol Fuels,” SAE 940765, 1994.

ZZ“Analysis of California’s Request for W a iver of the Reformulated Ga soline Oxygen Content Requirem e nt for California Covered Areas .” U.S. EPA, EPA420-R-01-016, June 2001.

AAA Durbin, T., Miller, J., Y ounglove, T., Huai, T ., and Cocker, K. “Effects of Ethanol and Volatility Param e ters on Exhaust Emissions”. CRC Project N o. E-67. Coordinating Research Council, Inc. 3650 Mansell Road, Suite 140. Alpharetta, GA 30022. January, 2006.

BBB “AAM and AIAM Study: Sulfur Oxygen Vehicl e Em issions Test Program ”. Available on the California Air Resources Board Website: http://www.arb.ca.gov/fuels/gas oline/carfg3/carf g3.htm

.
CCC “ExxonMobil: LEV/U LEV Gasoline Oxygenate Study”. Availab l e on the California Air Resources B oard’s W e bsite: http://www. arb.ca.gov/fuels/gasoline/carfg3/carfg3.htm

.

DDD “Toyota Study: Effects of Ethanol on Em issi ons of Gasoline LDVs”. Available on the California A i r Resources Board W e bsite: http://www.arb.ca.gov/fuels/g asoline/carfg3/carf g3.htm

.

EEESchifter, I., Diaz, L., and L opez-Salinas, E. “A Predictive Model to Correlate Fuel Specifications with On-Road Vehicles Em issions in Mexico”. Environm ental Science and Technology. 2006, 40, pp. 1270-1279.

FFF“California W a iver Decision Docum e nt,” US EPA, EPA420-S-05-005, June 2005.

GGG“Final Report, Effects of Gasoline Ethanol Blends on Perm ea tion Em issions Contribution to VOC Inventory from On-Road and Off-Road Sources,” Air Improvem e ntResource, Inc., for the Am erican Petro l eum Institu te, March 3, 2005.

346III HHH Haskew, Harold M., Thom as F. Liberty and Dennis McClem ent, “Fuel Perm eation from Autom o tive System s,” Final Report, f o r the Coor dinating Research Council and the California Air Resources Board, CRC Proj ect E-65, September 2004.

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JJJ Ragazzi, R on and Ken Nelson, “The Impact of a 10% Ethan o l Blended Fuel on the Exhaust Em issions of Tier 0 and Tier 1 Light-Duty Gasoline Vehicles at 35 F,” Colorado Departm e nt of Public Health and Environm ent, March 26, 1999. Fo r additional inform ation or copies, contact Gerald L. Gallagh er or Margie M. Perkins.

KKK Mulawa, Patricia.A. et. al. "Effect of Ambient Term perature and E-10 Fuel on Prim ary Exhaust Particulate Mater Em issions from Light -Duty Vehicles," Environ. Sci. Technol. 1997, 31, 1302-1307.

LLLStump, F., et. al., Characterization of Em issi on from Malfunctioning Vehicles Fueled with Oxygenated Gasoline - E 10 Fuel, Part II and Part III, EPA RTP.

MMM “Initial Mass Exhaust E missions from Refo r mul ated Gasolines”. Technical Bulletin No.1. Auto/Oil Air Quality Improvem e nt Research Pr ogram
. Coordinating Re search Coun cil Inc. Atlanta, Geo r gia. Decem ber 1990.

NNNHochhauser, A.M., et al. “Effects of O xygenated Fuels and RVP on Automotive Em is sions

–Auto/Oil Air Quality Improve m e nt Research Program .” Society of Autom o tive Engineers publication No. 920326.

OOO EPA Certification and Fuel Economy Inform ation System

. http://www.epa.gov/otaq/cfeis.htm

.

PPPBenson, J., Knepper, J., Reut er, R., Koehl,W ., Leppard, W ., Rippon, B., Burns, V., Painter, L., Rutherford, J., Hochhauser, A., and Rapp, L. “Em i ssions with E85 and Gasolines in Flexible/Variable Fue l Vehicles–The Auto/Oil Air Quality Improvem e nt Research Program .” SAE paper no. 952508. Warrendale, PA: SAE. October 1995.

QQQ Aakko, P. and Nylund, N.-O. Particle Em issions At Moderate and Cold Temperatures Using Different Fuels. SAE Technical Paper 2003-01-3285.

RRR“Exhaust E m ission Effects of Fuel Sulfur and Oxygen on Gasoline Nonroad Engines,” U.S. EPA, Dece mber 2005, EPA420-R-05-016, NR-003c.

347SSS “Nonroad Evaporative E m ission Rates,” Table 2, U.S. Environm ental Protection Agency, EPA420-R-05-020, NR-012c, December 2005. This document is available in docket EPA-HQOAR-2004-0008-0362.

TTT“EPA’s National Mobile Inventory Model (NMIM), A Consolidated Emissions Modeling System for MOBILE6 and NONROAD,” U.S. EPA, EPA420-R-05-024, Decem ber 2005.

UUU Jackson, Cleophas, “Ethanol Plant Em issions as Reported to the States,” EPA Me m or a ndum to the Docket, February 16, 2007.

VVV U.S. EPA, EPA’s Natio nal Mobile Inventory Mo de l (NMIM) , A Consolid ated Emissions Modeling System for MOBILE6 and NONROAD , EPA420-R-05-024, Decem ber 2005, version NMIM20061128 with county datab a se NCD20061227RfsFi nal m odified accord ing to discussio n in Chapter 2.2.

WWWU.S. EPA, User’s Guide to MOBIL E6.1 and MOBI LE6.2 Mobile Source Emission Factor Model , EPA420-R-03-010, August 2003, m ode l used version M6ChcOxFix.

XXX Gururaja ,P rashanth, U.S. EPA, "Fuel Tank Temperature generation for evaporative
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YYY Federal Highway Administration, Highway Statistics 2004 , Section V, Table VM-1 “Annual Vehicle Distance Traveled in Mile s and Related Data”, October 2005.

ZZZU.S. EPA, User’s Guide for the Final NONROAD2005 Model , EPA420-R-05-013, Decem ber 2005, m odel used version NR05c-Bond Base.

AAAAU.S. EPA. 2006. Technical Support Docum e nt for the Proposed Mobile Source Air Toxics Rule – Air Quality Mo deling. This docum ent is available in Do cket EPA-HQ-OAR-2005-0036.

BBBB U.S. EPA. 2005. Technical Support Docum e nt fo r the Final Clean Air In terstate Rule - Air Quality Modeling. Available from : http://www.ep a.gov/cair/technical.htm l

. This docum e nt is available in Dock et EPA-HQ-OAR-2003-0053.

CCCC U.S. EPA. 2005. Clean Air Interstate Ru le Em is sions Inventory Technical Support Docum ent. Available from : http://w ww.epa.gov/cai r/technical.htm l . This docum ent is available in Docket E P A-HQ-OAR-2003-0053.

348DDDD Pielke, R.A., W .R. Cotton, R.L. W a lko, et al. 1992. “A Comprehe nsive Meteorological Modeling S y stem – RAMS.” Meteor. Atmos. Phys., Vol. 49, pp. 69-91.

EEEEOzone Air Quality Ef f

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FFFFRagazzi, R. , Nelson, K. “The Impact of a 10% Ethanol Blend e d Fuel on the Exhaust Em issions of Tier 0 and Tier 1 Light Duty Gasoline Vehicles at35F.” Colorado Departm e nt of Public Health and Environm ent. March 26, 1999. For additional information or copies, contact Gerald L. Gallagh er or Margie M. Perkins.

GGGGMulawa, P.A. “Effect of Ambient Temperature and E-10 on Prim ary Exhaust Particulate Matter Em issions from Light-Duty V e hicles .” Environm ental Science and Technology 31, pp 1302-1307. 1997.

HHHH Grosjean, D

. “In Situ O r ganic Aerosol Form ation During a Smog Episode: Estim a ted Production and Che mical Functionality.” Atm o spheric Environm ent. Vol. 26A. No. 6. pp. 953­ 963: 1992.

IIIISchauer, J.J., Fraser, M.P., Cass, G.R., Si m oneit, B.R.T., “Source Reconciliation of At m ospheric Gas-Phase and Particle-Phase Po llutants during a Severe Photochem i cal Sm og Episode.” E nvironm ental Science and Technology 36, pp 3806-3814. 2002.

JJJJ Pun, Betty K. and Christian Seigneur, “I nvestigative Modeling of New Pathways for Secondary Organic Aerosol Form ation,” Fi nal R e port, CRC Project A-59, Coordinating Research Council, March 2006.

KKKK Edney. RTP PM 2.5 field study. Cu rrently unde rgoing peer review and will be pub lished shortly.

LLLLZheng, M., Cass, G.R., Schauer, J.J., Edgert on, E.S. “Source apportionm e nt of PM 2.5 in the Southeast U n ited States using so lvent-extractable orga nic compounds as trac ers.” Environm ental Science and Technology 36, pp. 2361–2371. 2002.

MMMM U.S. EPA, “ N ational Em issions Inventory (NEI) Air Pollutant Em issions Trends Data, 1970 - 2002 Average annual em issions.” Posted August 2005 on http://www.epa.gov/ttn/chief/trends/, f ilenam e “NOX07182 005.xls.” (A ccessed August 14, 2006). July 2005.

NNNNDelucchi, Mark A. (20 03) A Lifecycle Emissions Model (LEM): APPENDIX D: CO2 Equivalency Factors. Ins titu te of Tra n sportati on Studies, University of Calif ornia, Davis, Research Report UCD-ITS-RR-03-17D.

349OOOO Kwaitkowski, J.R., McAloon, A., Taylor, F., Johnston, D.B., Industrial Crops and Products 23 (2006) 288-296. A copy of the current US DA model can be obtained by contacting the corresponding author.

PPPPW a ng, Saricks, and W u , 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.

QQQQ Gervais andBaum el, 19XX, The Iowa Grain Flow Survey: Where and How Iowa Grain Producers Ship Corn and Soybeans , CTRE, IowaState University.

RRRRThe Energy Balance of Corn Ethanol: An U pdate, Shapouri, H, J. A. Duffield, and M. Wang, 2002 AER-813, Washington DC: USDA Office of the Chief Econom i st.

SSSS USDA’s 2002 Ethanol Cost-of-Production Surve y, Shapouri, H, P Gallagher, Agricultural Econom ic Report Num b er 841, July 2005.

TTTTThe USDA National Ag ricu ltur a l Statis tic s Serv ice (NASS) website, national statistics on field corn: http://www.nass.usda.gov:8080/Qu ickStats/Create_Federal_All.jsp.

UUUU USDA Agricultural Baseline P r ojections to 2015, USDA Office of the C hief Econom ist, World Agricultural Outlook Board, Baseline Report OCE-2006-1, Feb 2006.

VVVV Fossil Energy Use in th e Manufactu r e of CornEthanol, Graboski, M., Report Prepared for the National Corn Growers Association, August 2002.

WWWWLPG and diesel fuel p r ices from US DA Agricultural Statis tics report, Prices Paid by Farm ers, mu ltip le year s.

XXXX Ibid.

YYYY Shapouri, H., Duffield, J., Mcaloon, A.J. th e 2001 Net Energy Balance of Corn-Ethanol. 2004. Proceedings of the Conference on Agriculture As a Producer and Consum er of Energy, Arlington, V A ., June 24-25.

ZZZZFossil Energy Use in th e Manufactu r e of CornEthanol, Graboski, M., Report Prepared for the National Corn Growers Association, August 2002.

AAAAAShapouri, H., Duffield, J., Mcaloon, A.J. th e 2001 Net Energy Balance of Corn-Ethanol. 2004. Proceedings of the Conference on Agriculture As a Producer and Consum er of Energy, Arlington, V A ., June 24-25.

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.

FFFFFNet imports of petroleum include imports of crude oil, petroleum products, unfinished oils, alcohols, ethers, and blending compon ents m i nus exports of the sam e
.
GGGGGPetroleum products, according to the Annua l Energy Outlook 2006, includes imports of finished petroleum products, unfinished oils, alcohols, ethers, and blending components.

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NNNNNEnergy Policy Act of 2005, Section 1501(a)(2).

OOOOO Historical data at http://tonto.eia.doe.gov/dnav/pet/pet_pri_ allm g_d_nus_PTA_cpgal_m .htm (gasoline), http://tonto.eia.doe.gov/dnav/ng/ng_pr i_sum _dcu_nus_m .ht m (natural gas), http://www.eia.doe.gov/cneaf/e lectricity/page/sales_reve nue.xls (electricity), http://www.eia.doe.gov/cneaf/coal/ page/acr/table28.htm l (coal).

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RRRRR Lignocellulosic Biomass to Ethanol Process Design and Economics Utilizing Co-Current Dilute Acid Prehydrolysis and Enzymatic Hy drolysis Current and Futuristic Scenarios , Robert Wooley, Mark Ruth, John Sheehan, and Kelly Ibsen, Biotechnology Center for Fuels and Che m icals Henry Majdeski and Adrian Galvez, Delta-T Corporation; National Renewable Energy Laboratory, Golden, CO, July 1999, NREL/TP-580-26157.

SSSSS 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, W i nnie Yee, USDA, Eastern Regional Research Center Agricultu ral Res earch Service; Kelly Ibsen, Robert Wooley, National Renewable Energy Laboratory, Biotechnology Center for Fuels and Che m icals, 1617 Cole Boulevard, G olden, CO, 80401-3393; NREL is a USDOE Operated by Midwest Research Institute · Battelle · B echtel; Contract No. DE-AC36- 99-GO10337.

TTTTT Lignocellulosic Biomass to Ethanol Process Design and Economics Utilizing Co-Current Dilute Acid Prehydrolysis and Enzymatic Hy drolysis Current and Futuristic Scenarios , Robert Wooley, Mark Ruth, John Sheehan, and Kelly Ibsen, Biotechnology Center for Fuels and Che m icals Henry Majdeski and Adrian Galvez, Delta-T Corporation; National Renewable Energy Laboratory, Golden, CO, July 1999, NREL/TP-580-26157.

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