## ANALYSIS OF FUEL ETHANOL TRANSPORTATION

A CTIVITY AND POTENTIAL D ISTRIBUTION CONSTRAINTS

Bruce Peterson Shih - Miao Chin Sujit Das Energy and Transportation Science Division Oak Ridge National Laboratory Prepared for Office of Transportation and Air Quality U. S. Environmental Protection Agency



### March 200 9

OAK RIDGE NATIONAL LABORATORY Oak Ridge, Tennessee 37831 managed and operated by UT -Battelle, LLC for the U.S. DEPARTMENT OF ENERGY under contract No. DE -AC05 - 00OR22725

### TABLE OF CON TENTS

1. INTRODUCTION ................................ ................................ ................................ .......... 1 2. SCENARIOS DESCRIPTION ................................ ................................ ........................ 4 3. DATA PREPARATION ................................ ................................ ................................ .. 5 3.1 Corn Ethanol Refineries ................................ ................................ ..................... 5 3.2 Cellulosic Ethanol Refineries ................................ ................................ ............ 5 3.3 Ethanol Imports ................................ ................................ ................................ .. 7 3.3.1 Ports of Entry ................................ ................................ ...................... 8 3.3.2 Geographic Information for Ports ................................ ....................... 8 3.4 Ethanol Demand ................................ ................................ ............................... 10 3.5 Gasoline Fuel Terminals ................................ ................................ .................. 17 3.6 Ethanol Intermodal Terminals ................................ ................................ ......... 19 3.7 Transportation Cost ................................ ................................ .......................... 23 3.7.1 Rail ................................ ................................ ................................ .... 23 3.7.2 Waterways ................................ ................................ ......................... 2 4 3.7.3 Truck ................................ ................................ ................................ . 2 5 3.8 Congestion/Delay ................................ ................................ ............................. 2 5 3.8.1 Rail ................................ ................................ ................................ .... 2 6 3.8.2 Waterway ................................ ................................ .......................... 2 6 3.8.3 Highway ................................ ................................ ............................ 30 4. GENERATION OF ETHANOL TRANSPORTATION ACTIVITY DATA

– MODELING APPROACH ................................ ................................ .................... 3 3 4.1 Origin

–Destination Matrix (ODM) ................................ ................................ . 3 3 4.2 Traffic Assignment ................................ ................................ .......................... 3 6 5. RESULTS ................................ ................................ ................................ ........ 40 5.1 Transportation Activity ................................ ................................ .................... 40 5.2 Distribution Cost ................................ ................................ .............................. 51 5 .3 Rolling Stock Requirements ................................ ................................ ............ 53 5.4 Distribution Constraint Analysis ................................ ................................ ...... 55 6. SUMMARY ................................ ................................ ................................ ........ 70 APPENDIX A Location Details and Production Capacity (MGY) of Corn

Ethanol Plants for Various Forecast Year ................................ .................. 74 APPENDIX B Projected Corn Ethanol Production ................................ ........................... 82 APPENDIX C Location Details and Production Capacity (MGY) of C ellulosic

Ethanol Plants for Various Forecast Y ears ................................ ................ 85 APPENDIX D Projected Cellulosic Ethanol Production ................................ .................. 92

### TABLES

Table 1. Annual Ethanol Projections (in billion gallons) under Various Scenarios ............ 4 Table 2. P rojected U.S. Ethanol Imports (billion gallons per year) ................................ ..... 8 Table 3. Ports of Entry

and Projected Imported Ethanol Volumes (MGY) ......................... 9 Table 4. State - Level Annual Ethanol Fuel Demand Projection (in million gallons) ......... 11 Table 5. Commodity

Movements (in thousand tons) by Waterways, B ased on FAF 2 ..... 2 7 T able 6. Estimated Future Commodity Flow ( in thousand tons) and Growth Factors for this Study ................................ ................................ .......................... 2 7 Table 7. Estimated Future Growth Rates for Lock Delay ................................ ................. 2 8 Table 8. Future Growth Factors for 2006 ADT/Lane ................................ ........................ 3 2 Table 9. Transportation Activity Summary

for the 2022 Scenarios ................................ .. 41 Table 10. A Sample of High - Volume Ethanol Movements by Different Modes for the Three 2022 Scenarios ................................ ................................ ..... 4 2 Table 11. Estimated Ethanol Distribution Cost for the Three 2022 Scenarios .................. 52 Table 12. Assumptions Used for Rolling Stock Requirements Estimation ....................... 54 Table 13. Estimated Rolling Stock Requirements under 2022 Scenarios ......................... 55 Table 14. Estimated Number of Miles Experiencing an Increase in Ethanol Traffic

of Various Categories of Quantity L evel under “2012B Scenario” ........... 58 Table 15 . Estimated Number of Miles Experiencing an Increase in Ethanol Traffic

of Various Categories of Quantity L evel under “2014 B Scenario” ........... 59 Table 16 . Estimated Number of Miles Experiencing an Increase in Ethanol Traffic

of Various Categories of Quantity L evel under “20 2 2B Scenario” ........... 60 Table 17 . Estimated Number of Miles Experiencing an Increase in Ethanol Traffic

of Various Categories of Quantity L evel under “202 2 Controlled” .......... 61

### FIGURES

Figure 1. Projected corn ethanol refineries in 2017 and 2022 ................................ ............. 6 F igure 2 . Cellulosic ethanol refineries in 202 2 ................................ ................................ .... 7 Figure 3. Ports of entry

for ethanol fuel in 2022 ................................ ................................ 10 Figure 4. VMT distribution at census tract level for Hawaii ................................ ............. 12 Figure 5. The Voronoi diagram for the state of Hawaii ................................ ..................... 13 Figure 6. Ethanol demand at the terminal level for 2010 ................................ .................. 14 Figure 7. Ethanol demand at the terminal level for 2012 ................................ .................. 14 Figure 8. Ethanol demand at the terminal level for 2014 ................................ .................. 15 Figure 9. Ethanol demand at the terminal level for 2017 ................................ .................. 15 Figure 10. Ethanol demand at the terminal level for 20 22 ................................ ................ 16 Figure 11. Ethanol demand at the terminal level for 2022 AEO Reference Case ............. 16 Figure 12. Gasoline and/or ethanol terminals ................................ ................................ .... 17 Figure 13. Gasoline and/ore ethanol total storage capacity ................................ ............... 18 Figure 14. Ethanol rail flows, 2006 ................................ ................................ ................... 2 1 Figure 15. Distribution of potential ethanol distribution terminals ................................ ... 2 3 Figure 16. Current train volumes compared to current train capacity ............................... 2 6 Figure 17. Average tow delays for 2007 ................................ ................................ ............ 2 9 Figure 18. Average tow delays for 2022 ................................ ................................ ............ 2 9 Figure 19. Average dail y traffic per lane trends for rural U.S. ................................ ......... 3 1 Figure 20. Average dail y traffic per land trends for urban U.S. ................................ ........ 3 2 Figure 21. Projected ethanol movements (Ktons) under the 2022 RFS1 Reference

s cenario ................................ ................................ ................................ ...... 4 3 Figure 2 2 . Projected ethanol movements (Ktons) under the 2022 AEO Reference

s c enario ................................ ................................ ................................ ...... 4 4 Figure 2 3 . Projected ethanol movements (Ktons) under the 2022 Controlled

s cenario ................................ ................................ ................................ ...... 4 5 Figure 24. Projected county- level total Kton- mile ethanol movements under

the 2022 RFS1 Reference Scenario ................................ ........................... 4 6 Figure 2 5 . Projected county- level total Kton- mile ethanol movements under

the 2022 RFS1 AEO Reference Scenari o ................................ .................. 4 7 Figure 2 6 Projected county- level total Kton- mile ethanol movements under

the 2022 Controlled Scenari o ................................ ................................ ..... 4 8 Figure 27. Projected county- level total ethanol loading/unloading activities

(Ktons) under 2022 RFS1 Reference Scenario ................................ ......... 4 9 Figure 2 8 Projected county- level total ethanol loading/unloading activities

(Ktons) under 2022 AEO ference Scenario ................................ ............... 50 Figure 29 Projected county- level total ethanol loading/unloading activities

(Ktons ) under 2022 Controlled Scenario ................................ .................. 5 1 Figure 30. Projected rail ethanol movements in terms of Ktons and % increase in

base flows for 2012B scenario ................................ ................................ ... 62 Figure 31 . Projected waterways and highways combined ethanol movements in

terms of Ktons and % increa se in base flows for 2012B scenario ............ 6 3

### Figure 32 . Projected rail ethanol movements in terms of Ktons and % increase in base flows for 2014 B scenario ................................ ................................ ... 64 Figure 33 . Projected waterways and highways combined ethanol movements in

terms of Ktons and % increase in base flows for 2014 B scenario ............ 6 5 Figure 34 . Projected rail ethanol movements in terms of Ktons and % increase in

base flows for 2022 scenario ................................ ................................ ...... 66 Figure 35 . Projected waterways and highways combined ethanol movements in

terms of Ktons and % increase in base flows for 2022 scenario .............. 6 7 Figure 36 . Projected rail ethanol movements in terms of Ktons and % increase in

base flows for 2022B scenario ................................ ................................ ... 68 Figure 37 . Projected waterways and highways combined ethanol movements in

terms of Ktons and % increase in base flows for 2022B scenario ............ 6 9

### 1 Analysis of Fuel Ethanol Transportation Activity and

Potential Distribution Constraints 1. INTRODUCTION The Energy

Independence and Security Act (EISA) of 2007 mandates that the Environmental Protection Agency (EPA) establish regulations to ensure the use of 36 billion gallons of renewable fuel b y 2022. EISA established renewable fuel categories and eligibility

requirements, including setting mandatory greenhouse gas ( GHG) reduction thresholds for the various categories of fuels . Of the total 36 billion gallons of renewable fuel use required by 2022, EI SA requires that 21 billion gallons be advanced biofuels . This limits the volume of ethanol produced from grain-based feedstocks that can be used to satisfy

the requirement of the EISA to no more than 15 billion gallons by 2022. EI SA further requires that of the total 36 billion gallons of renewable fuel use required b y 2022, 16 billion gallons be cellulosic biofuel and 1 billion gallon be biomass base d diesel fuel. EPA estimated that 34.14 of the 36 billion gallons or renewable fuels required by 2022 would be ethanol, of which 15 billion gallons would be corn ethanol, 16 billion gallons would be cellulosic ethanol, and 3.14 billion gallons would be im ported ethanol. EPA projected that the balance of the renewable fuel volume required b y E IS A in 2022 would be made up of 0.81 billion gallons of fatty acid methyl ester (FAME) biodiesel, 0.19 billion gallons of non- co -processed renewable diesel, and 0.19 b illion gallons of co processed renewable diese l. Ethanol currently cannot ty picall y be shipped by pipeline due to concerns over materials compatibility and product contamination. Product contamination and other concerns currently also ha s prevented widespread shipment of FAME -based biodiesel by pipeline. In addition, the locati on of ethanol and to a lesser extent FAME biodiesel plants is ty picall y dictated by

the need to be close to the source of the feedstocks used rather than to fuel demand centers or to take advantage of the existing petroleum fuel pipeline distribution system. EPA projected that renewable diesel fuel that is co -processed at petroleum refineries would be distributed fungibl y with petroleum based diesel fuel and thus be fundamentall y transparent with respect to its handling in the fuel distribution system. Renewable diesel fuel that is not co -processed at refineries may need to be transported b y rail, barge, and tank truck to the terminals because the points of production may

not be in prox imity

to the existing petroleum fuel distribution system. Projected biofuels production increases under the EI SA will likel y spur demand for transportation services that face capacity constraints. The extent of infrastructure incompatibilities that may result due to a shift from the current petroleum pipeline system to rail -based transportation of ethanol is not fully known. Incongruity between the geographic locations of production facilitie s and feedstock suppl y networks and the locations of the petroleum distribution system and consumption demand centers would require adequate infrastructure development to avoid serious logistical bottlenecks and systemic delays in biofuels distribution. The country is lacking the infrastructure to mostEthanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory



### 2 efficientl y tran sport liquid bio fuels that are produced primaril y in the midwest and p lain s states to population centers in the East and elsewhere that account for 80% of the U.S. population . Itis estimated that about one - third to one -half of domestic ethanol produced to day travels through Chicago, which is already proving to be a “ choke point . ” Several new primary

gathering points, unit train loading facilities, barge receiving and transloading capabilities are either being built or planned as ethanol demand continues to grow. 1 

### Today, approximately

60 percent of all corn -based ethanol is transported b y the U.S. rail system because the corrosive properties of ethanol prevent transporting the product by pipeline. Trucks transport 30 percent of the ethanol, and barges tran sport the remaining 10 percent . 2 

### The rail infrastructure for transporting ethanol from biorefineries is not well developed, and being a major mode of biofuels transportation requires adequate infrastructure investment for efficient distribution. A lack of excess capacity increases the sensitivity

of the transportation system to sudden changes in transportation demand and distribution patterns. Water-borne transportation via barge and ship faces limitations under the Jones Act, while truck transportation fac es capacity issues, labor shortages, and public opposition to increased truck traffic on the nation ’ s highway s ys tem. Transportation is ty

pically the third largest expense to an ethanol producer

—after feedstock and energy . Sp atial variation in infrastructure development, especially re finery location , will influence transportation costs of renewable fuels . Transportation costs, along with feedstock costs, production technologies, and economies of scale of refineries influence biofuels ’ competitiveness. Itis important to assess how increased biofuels production levels would change freight flow patterns and related transportation costs, which are influenced b y many factors including rail accessibility, railcar price differentiation (unit trains vs. single cars ), and network connectivity ; rail capacity and congestion; highway

accessibility

and congestion; proximity to waterways; plant location and transportation infrastructure capacity ; and modal competition. At current levels of shipments, biofuels represent l ess than 1% of total railcar ton- miles shipped in the United States . 1 

### It is anticipated that renewable fuels are unlikely to have more than marginal impact on rail capacity or congestion. However, the rail system itself is subject to increasing capacity and congestion constraints that will impact all commodities shipped by

rails, including biofuels. A lack of rail accessibility at biorefinery

sites, the type of rail infrastructure accessible to biofuels producers, the current state of capacity and congestion on the U.S. rail network, and logistics and supply- chain management capabilities at biofuels producers are some of the issues related to the U.S. rail system that need to be addressed. A recent study

evaluated the impact of ethanol production on grain transportation under two scenarios, i.e., annual ethanol production increases to 15 and 20 billion gallons b y 2016 . 2 

### Transportation impact s varied under each scenario due in part to modal share

1 Boudreaux, T. (2007). Ethanol and Biodiesel: What You Need to Know . Hart Energy Publishing LLP. McLean, VA. 2 U.S. Department of Agriculture (USDA) (2007). “Ethanol Transportation Backgrounder: Expansion of U.S. Corn-based Ethanol from the Agricultural Perspective,” Agricultural Marketing Service, Sept.Ethanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory



### 3 differences. It is estimated that rail and barge demand could decrease if corn exports decrease, but in the short - term increased ethanol and by-product shipments could offset decreases in rail grain shipments. However, truck transportation, the dominant mode of conveying corn to ethanol plants, would increase by about 104% by

2016 under all scenarios . Similarly, an earlier study looking at the increased infrastructure needs and tran sportation requirements for ethanol production and use of 5 billion gallons per year (BGY) by 2012 that would result from a “Renewable Fuel s Standard” has found negligible impacts on the transportation infrastructure. The study found that additional movements by

barge, rail, and truck represent a very small amount of total movements (less than 1%) and would have no major impact on these transportation sectors. 3 

### The objective of the study documented in this report wa s to conduct an analysis of ethanol transport b y domestic truck, marine, and rail distribution systems from ethanol refineries to blending terminals using ORNL ’ s North American Transportation In frastructure Network Model (hereafter referred to as the “ Infrastructure Network Model ” ). As noted above, the ethanol (the biofuel on which this study focus es ) that EPA projects will be used as a result of the EISA cannot currentl y be transported from the point of production to the terminal - level using the existing petroleum distribution syst em. As a result, ethanol must be transported to terminals by truck, marine, and/or rail. As part of the rulemaking to implement the EISA, EPA must estimate the increased emissions and costs associated with the transportation of ethanol. To estimate these impacts, EPA needs transportation activity data for domestic truck, marine, and rail modes of transportation employed to convey ethanol from the point of production to the terminal level on a county-by- county basis. In addition, EPA must assess the potential constraints that may exist in the distribution of ethanol under EI SA

— hereafter referred to as distribution constraint analysis . An infrastructure anal ys is is necessary to identify

the potential location and timing of such constraints and the extent to which the constraints may represent a barrier to the use of volumes of ethanol that EPA projects will be used in response to the EISA. Both of these EPA analy tical issues are addressed b y ORNL In frastructure Network Model based on the projected major inputs such as estimated location of production facilities, sources of imports, and areas of consumption for annual volumes of ethanol that EPA estimates will be consumed in response to the EI SA. The following sections at first discuss the major input data preparation, followed by the estimation procedure and results obtained. The projections in this study are a representation of what might happen given the speci fic premises, assumptions, and methodologies used. Real world data were used as a starting point. Such information includes historic gasoline/ethanol blend sales, current ethanol production, and actual product terminal locations. However, the actual projections of the locations of new ethanol production facilities and future ethano l sales increases are highl y dependent on the premises, assumptions, and methodologies used.

3 Downstream Alternatives, Inc. (2002). “Transportation and Infrastructure Requirements for a Renewable Fuels Standard,” Phase III Project Deliverable Report prepared for Oak Ridge National Laboratory, Oak Ridge, TN, Aug. 20.Ethanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory



### 4 2. SCENARIOS DESCRIPTION S even scenarios were considered across five projection years (i.e., 2010, 2012, 2014, 2017, and 2022) using the EPA assumptions for major parameters based on EI SA , as shown in Table 1 . For the detailed county-by- county transportation activity

data, three 2022 scenarios have been the focus, whereas five of the s even scenarios are considered for the distribution constraint anal ys es. D etailed, specific - refinery- level productioncapacity data were provided by

EPA for both corn and cellulosic ethanol. Estimates of ethanol production under the “ Controlled” case reflect the EISA supply requirements, whereas the “Reference ” case s indicate a substantial ly lower level of suppl y, particularl y for cellulosic ethanol, in the absence of EISA. The two 2022 reference cases differ with respect to assumptions about imports and demand . Annual demand estimates were provided by

EPA at the state level and were mapped to specific existing product terminal locations . Under controlled cases, average ethanol content is assumed to increase from 8.22% in 2010 to 22.19% in 2022 . The average ethanol content under the 2022 AEO reference case ethanol content is assumed to be 8.96%, and under the 2022 RFS1 reference case it is assumed to be 4.6% . Similarly, total ethanol imports data was provided at the level of specific domestic ports b y using the historical import data and potential state - level import estimates, which were taken into consideration in the final anal ys is by

allocating these imports to specific product terminals. Table 1. Annual Ethanol Projections (in billion gallons) under Various Scenarios

2010 2012 2014 2017 2022 2022

Controlled AEO Reference RFS1

Reference Supply

- Corn

- Cellulosic 11.6

0.10 12.9

0.5 14.4 1.75 15.0

5.5 15.0 16.039 12.29

0.25 6.69

0.0 Imports 0.29 0.18 0.36 1.78 3.14 0.64 0.0 Demand

-w/o Biodiesel

-With Biodiesel 11.9 13.62 16.5 22.28 34.14 13.18 6.69

12.59 14.62 17.51 23.28 35.14 NA NA 

### F our scenarios are considered in the distribution constraint anal ys is. Additional demand for fatty acid methyl esters (FAME) biodiesel and non- co -processe d renewable diesel has been taken into consideration under these scenarios by treating the volumes of these fuels as additional ethanol volume. The projected biodiesel and non- co -processed renewable diesel ranged from 3 to 7% of the ethanol demand. The sc enarios considered in the constraint anal ys is are the controlled cases with simulated biodiesel/non - co -processed demand for 2012, 2014, and 2022 and the controlled case without simulated biodiesel/non - co -processed demand for 2022. The remaining ye ars have not been considered for the anal ys is since they do not provide any

additional information than the extreme cases already considered for the anal ys is. Information on supply in Table 1 is based on ethanol demand without additional biodiesel/non- co -processed renewable diesel demand, and so to simulate the additional biodesel/ non - co -processed renewable dieselEthanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory



### 5 demand specific supply

sources and imports have been increased proportionately . The following section discusses in detail the methodology used in to prep are the EPA data and the other necessary

data used in the analysis. 3. D ATA P REPARATION 3 .1 C orn Ethanol Refineries EPA furnished the corn ethanol refinery list and refinery production outputs for the ye ars 2010, 2012, 2014, 2017, and 2022 . For the 202 2 AEO reference case of corn ethanol production outputs , EPA assumed that necessary small volume of cellulosic ethanol and imports under this case would be produced and co - located at corn ethanol plants . The RFS1 reference case does not include cellulosic ethanol production. Based on the information within the corn ethanol refinery list, the location of each refinery is determined by its latitude and longitude. The geo - location is estimated by searching for refinery

address information on the internet and geo - coding the address using Google Earth. GI S - base d shape files have been generated for the corn ethanol refineries. The corn-based ethanol production output information for the years 2010, 2012, 2014, 2017 and 2022 is then added to the shape file ’ s attribute table. A thematic map of the corn ethanol refineries with their 2017 and controlled 2022 output (annual facility

capacity shown in terms of million gallons) is depicted in F igure 1 since both years have the same total maximum corn ethanol supply volume of 15 BGY. As shown in this figure, the largest share of corn ethanol supply is projected to come from the Midwest region as it is today . Detailed location information of corn ethanol plants and maps for other forecast years are shown in Appendix A and B, respectively . 3. 2 Cellulosic Ethanol Refineries The cellulosic ethanol refinery list and refinery

production outputs for the years 2010, 2012, 2014, 2017, and 2022 were furnished b y the EPA. EPA did not identif y locations of the proposed refineries by longitude and latitude, but by county and state. ORNLused a geographic information system (GI S) tool to locate potential refinery sites within the counties. ORNL assembled street, rail, and waterway map layers. Earth Science Research Institute’s (ESRI) StreetMap USA4 

### is used for the highway/street network. The US Army

Corps of Engineers’ Inland and Costal Waterway Network 5 

### dataset prepared b y the Navigation Data Center is used for the waterway network map layer. The Railway

map layer published by

the Center for Transportation Analysis, Oak Ridge National Laboratory is used for the railroad network.

4 ARCGIS 9, Data, Maps and StreetMap USA, DVD, Redland Ca, 2006. 5 Data dow

nloaded from http://www.iw r.usace.army.mil/NDC/data/datanwn.htm on February 10 , 2008.Ethanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory



### 6 Figure 1 . Projected c orn ethanol r efineries in 2017 and 2022 In addition to transportation infrastructure networks for street, rail , and waterway, the following map layers are also used to he lp locate the cellulosic plants. A terrain map layer, i.e., the shaded relief based on US Geological Survey (USGS) National Elevation Dataset 6 

### (NED) is used . This map layer ensure s that cellulosic plants will not be located in areas with terrain not suitable for industrial development. An aeri al imagery map l ayer, the I - Cubed 7 

### Nationwide Prime 1 -meter or better resolution imagery 8 

### for the contiguous United States , is also used under this task. This map layer enables transportation analysts to visualize the existing potential locations to build cellulosic plants. There is no formal algorithm developed to locate new ethanol plant s. Locations are assigned using engineering judgment on a case -by- case basis. The following basic principles , listed in order of priority, are employed.  If the county already has a corn-based ethanol refinery, the cellulosic refinery is to be located close to the corn-based ethanol refinery.  If the county has more than one corn -based ethanol ref inery, the cel lulosic ethanol refinery is to be located close to any one of the corn -based ethanol refineries .  If the county has access to both railroad and waterway, effort s are made to locate the cellulosic ethanol refinery close to both rail and waterway if possible. Otherwise, the cel lulosic ethanol refinery is to be located close to the railroad.

6 More information can be found at http://ned.usgs.gov/ 7 More information can be found at http://www.i3.com/ . 8 More information on this map layer can be found at http://www1.arcwebservices.com/v2006/content/publisher/service_summary.do?name=ArcWeb%3AI3.Im agery_Tiles.World&service=MapImage&publisher=i - cubed&show=true .Ethanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory



### 7  If the county has access to rail only, the cel lulosic ethanol refinery is to be located close to the railroad.  If the county has access to waterway onl y, the cellulosic ethanol refinery is to be located close to the waterway .  If the county does not have access to rail or waterway, the cellulosic refinery is to be located on the outskirt s of a small town. GI S -base d shape files have been generated for the cellul osic ethanol refineries. The cellulosic ethanol production output information for the years 2010, 2012, 2014, 2017 and 2022 is added to the shape file ’ s attribute table. Appendix C shows the detailed location and production capacity of 181 cellulosic plant s considered for the various forecast years in this analysis , including the location of new cellulosic ethanol plants close to any specific transportation mode . As shown in this table, most cellulosic ethanol plants are located in proximity to rail. A thematic map of the cellulosic ethanol refineries with their 2022 output is depicted in Figure 2, whereas maps of other ye ars are shown in Appendix D. Unlike the case of corn ethanol plants, the location of cellulosic ethanol plants is somewhat distributed, with several plants located in the northeast, southeast, and west coast regions.

Figure 2 . Cellulosic ethanol r efineries in 2022 3 . 3 Ethanol Imports EPA provided estimates of the total amount of ethanol likely to be imported as a motor fuel from 2010 to 2022. This supply was first allocated among three U.S. regions, i.e., California/West Coast, Hawaii, and East/Gulf Coast based on the 2006 Energy In formation Administration (EIA) import locations. The imports, in terms of billion gallons per year, from Brazil and the Caribbean Basin Initiative (CBI) countries, areEthanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory



### 8 listed in Table 2 along with domestic production estimates. The relatively s mall volume of ethanol imports under the AEO 2022 reference case has been assumed to originate from corn ethanol plants to simplify the anal ys is , whereas no imports have been assumed under the RFS1 2022 scenario . Table 2. Projected U.S. Ethanol Imports (billion gallons per year)
Year Total

Domestic Ethanol Total

Ethanol Total

Imports From Brazil From CBI 2009 9.85 10.35 0.50 0.00 0.50 2010 11.65 11.94 0.29 0.00 0.29 2011 12.54 12.70 0.16 0.00 0.16 2012 13.44 13.62 0.18 0.00 0.18 2013 14.75 14.94 0.19 0.00 0.19 2014 16.15 16.51 0.36 0.00 0.36 2015 18.00 18.83 0.83 0.00 0.83 2016 19.25 20.56 1.31 0.18 1.13 20 17 20.50 22.28 1.78 0.57 1.21 2018 22.00 24.25 2.25 0.96 1.29 2019 23.50 26.22 2.72 1.33 1.39 2020 25.50 28.20 2.70 1.22 1.48 2021 28.50 31.17 2.67 1.06 1.61 2022 31.00 34.14 3.14 1.34 1.80 

### 3.3.1 Ports of Entry EPA provided the information about p orts of entry . The ports were identified by reviewing the EIA historical ports of entry for imports of ethanol and gasoline. A fter estimating the total ethanol imports demand by state based on its share of total regional demand , total state level imports demand was then allocated among various ports within that state b ased on the vehicle -miles - trave led. Two ports, i.e., Hampton Roads in Virginia and Port of Baltimore in Maryland have also been included as potential ethanol import ports although these currently do not receive any

gasoline imports due to being served b y pipelines. The projected imports, in terms of million gallons per year, at these ports of entry for years 2010, 2012, 2014, 2017 and 2022 are listed in T able 3 . 3.3.2 Geographic Information fo r Ports The geographic location information for ports of entry is based on the U.S. Army Corps of Engineers , Navigation Data Center’s Principal Ports of the United States database. 9 

### A geographic information system (GI S) based shape file has been created for port of entry locations. The ports of entry and their ethanol imports in 2022 are displayed in the Figure 3.

9 The information can be found at http://www.ndc.iwr.usace.army.mil/data/datappor.htm .Ethanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory



### 9 Table 3. Ports of Entry and Projected Imported Ethanol Volumes (M GY) Port Name 2010 2012 2014 2017 2022 Seattle, WA 13.85 7.36 17.34 6 4.92 73.26 Vancouver, WA 2.54 1.35 3.18 13.81 15.58 Portland, OR 0.00 0.00 0.00 0.00 13.13 San Francisco, CA 4.23 2.69 5.20 15.70 25.45 Selby, CA 10.06 6.39 12.36 49.87 80.81 Los Angeles, CA 75.01 47.69 92.23 332.84 539.40 Honolulu / Pearl Harbor, HI 2.78 1.84 4.33 15.30 19.36 Belfast / Searsport, ME 0.29 0.19 0.42 2.76 0.21 Portland, ME 2.00 1.33 2.85 15.96 1.20 Boston, MA 8.85 5.88 12.58 74.27 125.65 Providence, RI 1.46 0.97 2.08 12.38 20.94 New Haven, CT 2.40 1.60 3.42 18.92 32.01 Bridgeport, CT 2.67 1.78 3.80 22.04 37.30 New York, NY 16.06 9.72 20.90 115.21 263.38 Newark, NJ 3.95 2.63 5.62 29.24 49.47 Paulsboro / Gloucester, NJ 2.12 1.41 3.02 20.52 34.72 Perth Amboy, NJ 6.29 4.18 8.94 53.22 90.04 Philadelphia, PA 16.09 9.94 21.28 120.58 216.19 Wilmington, NC 12.62 8.38 14.01 81.51 134.48 Charleston, SC 7.26 4.82 10.32 32.99 69.17 Savannah, GA 15.88 7.43 12.53 73.73 173.90 Jacksonville, FL 7.35 4.71 9.39 56.79 93.28 Pt Canaveral, FL 4.43 2.83 5.65 34.50 56.68 Pt Everglade, FL 11.64 7 .45 14.86 95.98 157.65 Tam pa, FL 8.27 5.29 10.56 65.11 106.95 Port Arthur, TX 1.74 1.02 2.08 10.16 21.45 Houston, TX 26.35 15.45 31.46 177.21 373.97 Brownsville, TX 2.10 1.23 2.51 14.34 30.26 Hampton Roads, VA 12.66 8.41 14.24 83.14 153.85 Baltimore, MD 9.04 6.01 12.85 76.99 130.25Ethanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory



### 10 Figure 3 . Po rts of entry for ethanol f uel in 2022 3.4 Ethanol Demand EPA provided the state - level ethanol demand for the 2010, 2012, 2014, 2017, 2022 control led and 2022 reference cases (as shown in Table 4) . The data have been disaggregated at the level of existing petroleum product terminals . The vehicle -miles - traveled (VMT) at census tract level is estimated based on the ORNL developed Transferability Travel Rates 10 

### , and their distribution at the census tract level is assumed to be the same for all s cenarios. The U.S. Department of Transportation sponsors and ORNL conducts a recurring survey of traffic statistics including VMT. The data are too aggregate in nature to provide any reliab le estimate within small geographic areas such as Census tract and transportation analysis zone , i.e., from sample households to geographic areas . An alternative statistical method was developed using the original data in conjunction with other data source s to transfer estimated travel rates from sample households geographic areas with similar socioeconomic and demographic characteristics. Travel estimates using the new method were found to be second best only to travel parameters estimated with data from l ocale - specific household surveys. The state - level ethanol demand for various future years is shared to the census tract level. A thematic map of VMT distribution at census tract level for the state of Hawaii is depicted in F igure 4 .

10 Hu, P., T. Reuscher, and R.L. Schmoyer, Transferring 2001 National Household Travel Survey , Oak Ridge, TN 2007. Document can be found at http://fmip.ornl.gov/nhts/TransferabilityReport.pdf .Ethanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory



### 11 Table 4. State - Level Annual Ethanol Fuel Demand Projections (in million gallons) Controlled Cases 2022 AEO 2022 RFS1 STATE 2010 2012 2014 2017 2022 Alabama 126 281 285 275 509 267 0 Alaska 0 14 27 30 29 16 0 Arizona 293 295 298 343 695 338 257 Arkansas 0 50 142 139 2 35 36 0 California 1723 1738 2313 3664 5138 1787 1739 Colorado 246 248 303 462 607 274 177 Connecticut 161 162 215 312 437 152 152 Delaware 44 44 45 43 122 42 0 District o f Columbia 19 19 19 39 54 19 19 Florida 947 1011 1022 1526 1677 1056 0 Georgia 320 550 556 589 1219 390 0 Hawaii 47 47 63 95 133 46 46 Idaho 74 74 99 158 222 77 0 Illinois 508 513 620 832 1087 501 337 Indiana 365 368 372 398 770 359 157 Iowa 150 152 170 301 422 147 147 Kansas 143 144 145 143 312 140 3 Kentucky 242 244 269 303 673 234 102 Louisiana 131 216 255 356 612 213 0 Maine 44 73 74 114 200 70 0 Maryland 247 288 291 481 755 286 250 Massachusetts 280 282 285 565 793 276 276 Michigan 454 458 564 747 980 429 294 Minnesota 260 263 349 525 737 256 256 Mississippi 197 19 9 201 197 558 194 0 Missouri 331 334 374 436 934 325 325 Montana 0 5 52 51 60 5 0 Nebraska 88 89 109 178 249 87 87 Nevada 92 93 99 190 257 105 81 New Hampshire 66 66 67 66 188 65 65 New Jersey 391 394 525 784 1100 382 382 New Mexico 122 123 137 259 363 126 126 New York 397 582 827 1079 1391 558 388 North Carolina 0 126 406 418 420 0 0 North Dakota 35 35 46 66 93 32 32 Ohio 534 539 544 522 1256 511 0 Oklahoma 137 239 241 246 696 242 0 Oregon 168 170 226 348 487 170 97Ethanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory



### 12 Table 4. State - Level Annual Ethanol Fuel Demand Projections (in million gallons) Controlled Cases 2022 AEO 2022 RFS1 STATE 2010 2012 2014 2017 2022 Pennsylvania 223 368 518 6 24 720 350 123 Rhode Island 46 47 47 94 132 46 0 South Carolina 0 113 234 237 434 0 0 South Dakota 41 41 55 81 114 40 40 Tennessee 329 332 442 681 955 332 332 Texas 1003 1063 1217 1281 2332 1113 0 Utah 0 95 134 145 143 103 0 Vermont 53 53 54 109 153 53 53 Virginia 221 268 408 415 825 219 0 Washington 277 279 282 600 842 293 0 West Virginia 24 87 88 79 124 78 0 Wisconsin 299 302 355 610 855 297 297 Wyoming 43 43 44 42 42 41 41

TOTAL 11943 13620 16514 22281 34142 13177 6697



### Figure 4. VMT dis tribution at census tract level for HawaiiEthanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory



### 13 Petroleum products terminals tend to cluster in the same general location. There are about 413 clusters of petroleum product terminals, of which 48 of them had at least 5 petroleum product terminals or more. The centroids of the existing refined products terminal clusters are used to develop a Thiessen Polygons or Voronoi Diagram. 11 

### This Voronoi diagram provides the terminal cluster service regions. The Voronoi diagram for the state of Hawaii is depicted in Figure 5 .

Figure 5. The Vor o noi diagram for the state of Hawaii By aggregating from the census tract level, the ethanol demand can be estimated for the terminal cluster service regions (Voronoi diagram level). The ethanol demands at the terminal cluster ser

vice region level are then shared to individual terminals evenl y if there is more than one terminal in the cluster. Figures 6 thru 11 show projected ethanol demand at petroleum product terminal levels for six scenarios considered in this analysis.

11 The Voronoi Diagram for the terminal centro ids is the partition of the US states which all points within a partition(i) are closer to the terminal i than any other terminals.Ethanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory



### 14 Figu re 6 . Ethanol d emand at the terminal l evel for 2010

Figure 7 . Ethanol demand at the terminal level for 2012Ethanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory



### 15 Figure 8 . Ethanol d emand at the terminal l evel for 2014

Figure 9 . Ethanol d emand at the t erminal l evel for 2017Ethanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory



### 16 Figure 10 . Ethanol d eman d at the terminal l evel for 2022 controlled case

Figure 1 1 . Ethanol d emand at t erminal l evel for 2022 AEO Reference CaseEthanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory



### 17 3.5 Gasoline Fuel Terminals The gasoline fuel terminals database is based on the OPI S/STALSBY Petroleum Terminal Encyclopedia , 200 8 edition 12 

### , which lists 1,495 petroleum terminals in the United States. Efforts have been made to isolate terminals that handle gasoline or ethanol for this project. There are 1056 petroleum terminals in the United States located as 413 clusters that handl e gasoline and/or ethanol. These 1056 gasoline and/or ethanol terminals are depicted in Figure 12.

Figure 12: Gasoline and/or ethanol t erminals It should be noted that the information in the database is not comprehensive. Most terminals have detailed information on what type of petroleum products are stored along with their storage tank capacity information. However, certain terminals only specify their general ty

pe , such as asphalt, crude, jet fuel, lube oil, petrochemicals, natural gas l iquids , and refined products terminals. A thematic map of total storage capacity at the terminals is presented in Figure 13. F our terminals in the database have very large total storage capacities (more than hundreds of million barrels).

12 More information can be found at http://opisnet.com/directories/pte.aspEthanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory



### 18 Figure 13 . Gasoline and/or total storage capacity distribution For the purpose of this proj ect, a terminal is considered a gasoline terminal if the terminal in the database is of a refined products ty pe with no detailed specificati on on which product it stored. In addition, a terminal is also considered as a gasoline terminal if the database specifies that any of the following products are stored at the terminal:  CA Reformulated Blendstock for Oxygenate Blending (gasoline production) (RBOB)  CA RBOB Regular  RVP Midgrade  RVP Premium  RVP Regular  Sub - Octane  Conventional Regular  Conventional Midgrade  Conventional Premium  Premium RBOB  Refiners Blendstocks  Regular RBOB  RFG Mid Grade  RFG Premium  RFG Regular  Gasoline  EthanolEthanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory



### 19 Out of these 1056 gasoline terminals , ethanol distribution intermodal terminals in major metropolitan areas were further examined , as discussed in the following section. 3.6 Ethanol Intermodal Terminals Ethanol must travel from refineries to blending sites. Blending sites are generall y located at multiple terminals in every metropolitan area and are operated b y competing companies that receive petroleum products from pipeline or by water transport , have tank storage capacity, and distribute products by truck to retail outlets. Ethanol refineries are generall y located near agricultural production, so the distance between refineries and blending sites is significant, and the expense of long - distance truck movements is prohibitive, leaving rail, inland barge, and marine tanker as modes of choice. Few domestic refineries, how ever, are located adjacent to navigable water, so water is available onl y as part of a multi -modal trip. So far, petroleum products pipelines ty picall y cannot batch ethanol with their other products. Should pipeline s become a widespread option , it would massively rearrange the transportation locations , favoring a Gulf Coast hub to collect and distribute ethanol. In principle, each blending site could receive its ethanol directly from refineries if it had rail access or could receive waterborne imported ethanol if it had deep berths. At current low levels of consumption, less than 10% of gasoline, there are several difficulties with this pattern. First, few products terminals have rail access, and when they do, it is a single spur that can handle only a handful of cars at a time. Unit trains can be accommodated only when unit train fac ilities are specificall y built and when sufficient background storage and adjacent land is available. For the foreseeable low level of consumption, an alternate transportation paradigm is emerging to establish one (or, rarel y, two) dedicated ethanol terminals in a large region which will receive cross country unit train shipments, and then distribute that ethanol by truck to each company ’ s existing products terminals in the region , as shown in Figure 14 based on 2006 way

bill data . The largest ethanol flow has been 3.2 million tons of alcohol from Houston, Texas to New Orleans, Louisiana. Those terminals may be hundreds of miles away, but the dedicated ethanol terminal will often be located adjacent to an existing concentration of pipeline terminals in a metro area, so truck trips are more commonly

10 mi les long, and may

be replaced eventually by dedicated ethanol pipel ines within the petro complex. This pattern will probably last unless ethanol becomes as common as gasoline, at which time more products terminals will construct their own unit train facilities, or the ability to move ethanol b y pipeline will be developed. Several regional dedicated ethanol terminals have been estab l ished within the last few ye ars. They

are identified here: 1. Albany, NY . Receives unit trains, distributes ethanol by truck locall y, but primarily by barge for blending in northern NJ (New York metro area) and the New England coast (Providence and Boston).Ethanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory



### 20 2. Northern NJ . Linden and Sewaren (Motiva). Receives unit trains and seaborne imports, distributes primarily by truck and private pipeline to local blending sites, and secondarily

by barge to the New England coast. 3. Philadelphia, PA . Receives unit trains, and in the future seaborne imports. D istributes to blending sites mostly on the lower Delaware River by truck or barge. 4. Baltimore, MD . Receives by unit train or seaborne imports. 5. Houston, TX . At least two sites receive seaborne imports and ori ginate ethanol rail shipments. 6. Arlington, TX . A new rail receiving facility separate from pipeline terminals in the D allas F ort W orth area. 7. Carson, CA (south Los Angeles ) . A new rail receiving facility, adjacent to Shell refinery . Truck shipments go to the LA basin, San Diego, and all of southern California. 8. Sacramento or Stockton, CA . Lo cation unidentified. 2006 rail ethanol shipments terminated in Sacramento, but references were found to a Stockton terminal. Truck shipments go to all of northern California and the Bay Area. In addition, an examination of the 2006 rail way bills shows significant ethanol shipments terminating in Columbia, SC, Wilmington, NC, Kingsport, TN, and Ogden, UT (not associated with any known terminal, possibly for industri al use). The largest concentrated originations of rail shipments are at Houston. There are transload facilities (that do not include storage, sometimes called “TransFlo” for liquids, and often depend on shipper- supplied pumps) that transfer ethanol directly from rail cars to trucks for distribution to blending sites. Although multiple rail cars may be parked while waiting for trucks to drive up, these transload facilities do not have unit train capability. The most notable transload facility is a Norfolk S outhern terminal in Alexandria, VA where trucks presumabl y make a short drive to the petroleum product terminals (i.e., tank farms) in Newington and Fairfax. This transfer operation would make no sense if the terminals had rail facilities. Fairfax is too distant from a rail line, but Newington is probabl y achievable. Transload operations also occur in Richmond, VA. Essentially

every city has a transload terminal where a small number of rail cars may be parked awaiting a direct transfer to trucks, and small ethanol transfers may be occurring there now. We expect transload operations to be temporary until more efficient facilities are constructed as ethanol consumption increases. A mirror- image collection function can be performed at the beginning of the trip , collecting ethanol in trucks from refineries not served by rail, and using on- site storage to load unit trains or barges. The only two known collection terminals are at Manly, IA (associated with the Iowa Northern Railways) and at Sauget, IL, for barges. They can also collect single carloads by rail from refineries without unit train facilities. However, single - to multiple - car rail shipments do not require a specific terminal. A railroad can cooperate with a group of shippers to collect single carloads at any rail yard to form a unit train provided those shippers have a common destination.

### 21

Note: Dark blue stars are dedicated ethanol terminals, small purple stars are petroleum products intermodal terminals, and red markers are ethanol refinery locations. 

### Figure 14. Ethanol rail flows, 2006

### 22

Note that California ethanol producers import corn from the Midwest b y rail, and generall y distribute to California blending sites b y truck. Because ethanol distribution terminals in metro areas are preferentially located where product distribution terminals are already located, our anal ys is of future ethanol terminal operations will not be seriously limited by

assuming they

will occur at existing petroleum terminals. However, the anal ys is must allow for a single concentrated ethanol rail destination that accommodates unit trains, followed by truck movements to possibly distant terminals in the same metro region. The anal ys is of transportation options will allow essentially unlimited throughput at any terminal, in effect assuming that capacity will rise as demand increases. In cities that have no dedicated ethanol terminal now, an existing products distribution terminal will be allowed to handle that function with whatever costs are involved in adding unit train or barge facilities. The location of the facility is based on the assumption that dedicated ethanol terminals in the future will be located adjacent to existing product terminals That will likely

be the terminal with the best apparent access to existing rail lines , however capability of unit train facilities will be determined for each existing product terminal based on the access to the existing rail network . Most likely, as ethanol demand grows, multiple receiving terminals will appe ar. The assumption of a concentrated single metro terminal will tend to overstate slightly the actual amount of truck traffic in the distribution to blending sites. Based on the list of existing 1056 petroleum product terminals (as discussed earlier in Se ct. 3.5, ) each of them was examined if they already had rail or waterways access needed for ethanol movement. If they do not have rail access, the distance for a reasonable route that would link the fuel terminal by constructing spur line to the nearest rail line was estimated. A single ethanol distribution terminal was then selected for each metropolitan area from the 413 cluster of existing petroleum product terminals (as determined b y the Voronoi diagram and discussed in Sect. 3.4) based on having the minimum access rail distance. No petroleum product terminal was found to have existing unit train facilities. Figure 15 shows the distribution of potential ethanol distribution terminals in major metropolitan areas. These ethanol distribution terminals are c onsidered as the intermediate ethanol demand locations, from where ethanol is shipped by trucks to existing petroleum product terminals. In case, there was onl y single petroleum product terminal in a given cluster, ethanol terminal was assumed to be colloc ated with the existing product terminal and thereb y there was no final trucking distance in this case.Ethanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory



### 23 Figure 15. Distribution of potential ethanol distribution terminals 3.7 Tr ansportation Cost 3.7.1 Rail Available rail transportation cost information is based on 2006 way bill data . Indications are that operating cost varies in the range of 3.8 cents/ton - mile

– 17.1 cents/ton - mile depending on the railroad company used , existence of long - term contracts , and average miles trave led per trip. 13 

### Inaddition, there is a monthly maintenance and leasing cost of $900 per car 13 

### which when taken into consideration results in total shipment cost of 5.6 cents/ton -mile

– 18.4 cents/ton -mile. Detailed available ethanol shipment cost data were regressed as a function of distance , and a cost of 7 cents/ton - mile has been considered in our anal ys is assuming an average rail distance of 700 miles based on the 2006 way bill data. In order to include the economies of scale associated with unit train movements, a 35 % reduction in cost, i.e., 4. 6 cents/ton- mile has been assumed in that case.

13 Dager, C. (2008). Tennessee Valley Authority, Knoxville, TN, Personal communication with Sujit Das, Oak Ridge National Laboratory, Oak Ridge, TN, Sept. 22.Ethanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory



### 24 Average rail fuel consumption in 2006 is estimated to be 330 Btu/ton- mile or 0.0023 gal/ton- mile. 14 

### For the railroad industry in general, l ast year fuel outlays were about 22% of its revenues of 53 G$, compared to 24% for labor. 15 

### Thereb y, fuel outlays in general contribute to a smaller share of the total rail transportation cost. For a relatively small share of fuel cost to total rail and waterways (discussed below under Sect. 3.7.2) transportation cost, future cost projections for these two modes using the future fuel price trend have onl y been used for the determination of various modal distribution costs (discussed under Sect. 5.2) . In the initial development of O - D (suppl y- demand) matrix and assignment of specific routes, relative transportation mode costs are important since the cost differences among various modes are quite substantial as discussed here. The future fuel price trend has been assumed to be based on EIA ’ s projections for U.S. No.2 Diesel Retail Sales by All Sellers , which index has also been suggested by the recent ruling of the Surface Transportation Board (STB) for estimating fuel surcharges to be charged b y railroads. The EIA projections of whole sale price of residential distillate fuel oil/heating oil , which is quite similar to the proposed price index , has been used for our anal ys is. 16 

### Distillate fuel oil price is assumed to be $2.0 0/gallon b y 2022

– about the same as today ’s price for all three transportation modes . The EPA diesel fuel sulfur requirements for locomotive and C1/C2 marine engines to be aligned with those for highway diesel fuel starting this year may result only

in a small cost difference between these two fuels. It is likely that with larger economies of scale for diesel fuels used in rail and waterways, may

further help in reducing the fuel cost differentials among the various modes. 3.7.2 Waterways Current ethanol shipment cost s by barge in upper Mississippi and Illinois waterways are estimated to be in the range of 2.35 cents/ton- mile

– 2.43 cents/ton- mile based on the Tennessee Valley

Authority barge costing model, having an average hauling distance of around 1200 miles. 1 3 

### For our anal ys is, we assume an average value of 2.4 cents/ton- mile. For barges, average energy consumption is the least, i.e., 0.0020 gal/ton - mile, close to the estimate for rail but significantly higher than for trucks. Average energy consumption is based on the fact that a one standard tow consumes 44 gal lons of fuel per mile with a carrying capacity

of 22,500 tons. For future barge shipment cost projections, we vary only the fuel cost component by using the EIA distillate fuel cost projections as was done in the case of rail mode of shipment above.

14 Davis, S. C., Diegel, S. W. and Boundy, R. G. (2008). “Transportation Energy Data Book: Edition 27, Oak Ridge National Laboratory, Oak Ridge, TN. 15 Eno Foundation, Transportation in America, 20 th Edition, p. 32 and 40. 16 Energy Information Administration (EIA) 2008. “Annual Energy Outlook 2008 , ” supplemental tables 92 - 117, Petroleum, Natural Gas, Coal, Macroeconomic, Petroleum, and Import , U.S. D epartment of Energy, Washington, DC. Accessed at the website: http://www.eia.doe.gov/oiaf/aeo/supplement/sup_ogc.xlsEthanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory



### 25 3. 7. 3 Truck The average trucking cost in 2003 is estimated to be about 13.3 cents/ton- mile, based on annual revenues and total ton -mile movements . 16 

### . With the consideration of escalation in diesel fuel price and since this average cost estimate includes a l arge number of single unit light package deliveries, 2006 trucking cost is assumed to be 14 cents/ton- mile. Itis estimated that 900 trucks with a carrying capacity of 22,500 tons consume 381 gallons of fuel per mile, or 0.0169 gallons/ton- mile of diesel f uel consumption. As in other transportation modes, future diesel retail price for trucking in 2022 is assumed to be the same as today, without explicit consideration of fuel cost component in total transportation cost . Fortunately in our case , the truck component in ethanol distribution will be small and inelastic, so accuracy is not determinative. 3.8 Congestion/Delay 3.8.1 Rail Average line haul rail speeds are currentl y 22 mph. 17 

### Unlike the other two major modes of transportation, rail corridors are rarel y at widespread over capacity, since in most cases an optimal balance is achieved between the level of the capital expenditures for capacity expansions with the anticipated revenues. As shown in Figure 16, 88 percent of today ’ s primary corridor mileage is operating below practical capacity (Level of Service (LOS) A/B/C), 12 percent is near or at practical capacity (LOS D/E), and less than 1 percent is operating above capacity (LOS F). LOS (A/B/C) represents below capacity and volume/capacity in the range of 0.0 - 0.7 ; LOS D at near capacity with a value of 0.7 - 0.8; LOS E at capacity

with a value of 0.8 - 1.0 and LOS F above capacity with a value of greater than 1. It is assumed in the future that rail management will increase capacity in line with the total traffic on a route to maintain an optimal balance between congestion costs and capital costs . Also, there will not be much change occurring in non - container movements in the future . Manifest traffic has an additional delay for switching and en route classi fication, about 1 day for each 600 mi les . The presumption for rail traffic is that rates cover both the operating costs and capital costs for the provision of capacity . Therefore, except for change in f uel price (a s discussed earlier in Sect. 3.7.1) , rail costs can be expected not to change significantly in the future, along with travel speeds.

17 American Association of Railroads (AAR) (2004). Accessed from http://findarticles.com/p/articles/mi_m1215/is_12_205/ai_n8967508 on 1/26/09.Ethanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory



### 26 Figure 16. Current train volumes compared to current train capacity 18 

### 3.8.2 Waterway 3.8.2.1 Commodity

Flows For analyzing scenarios that involve future ethanol shipments for the years 2010, 2012, 2014, 2107 and 2022, future waterway traffic operation information other than ethanol needs to be forecasted for the corresponding years. The future waterway traffic operation forecasts are based on information from the Freight Analysis Framework 19 

### (FAF). The FAF integrates data from a variety of sources to estimate commodity flows by different modes of transportation and related freight transportation activity among states, regions, and major international gateways. The Commodity Origin - Destination Database of FAF estimates tonnage and value of goods shipped by the ty pe of commodity

and mode of transportation among and within 114 areas, as well as to and from seven international trading regions. The original version, FAF1, provides estimates for 1998 and forecasts for 2010 and 2020. The new version, FAF2, provides estimates for 2002 and the most recent year 2007 , in addition to forecasts through 2035 (see Table 5) . Future national freight demands growth factors in reference to 2007, by water mode, are estimated by

interpolating future freight demand information in the FAF2 database and are presented in Table 6.

18 Cambridge Systematics (2007). “National Rail Freight Infrastructure Capacity and Investment Study: Final Report,” prepared for Association of American Railroads, Cambridge, MA, Sept. 19 Freight Analysis Framework (FAF) Version 2.2, User Guide, Federal Highway Administration, 2006.Ethanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory



### 27 Table 5. Commodity Movements (in thousand tons) by Waterways , B ased on FAF2 Commodity mo ve ment 2007 2010 2015 2020 2025 2030 2035

575,010 613,541 658,618 696,996 749,777 807,244 873,763 

### Table 6. Estimated Future Commodity Flow (in thousan d tons) and Growth Factors for this Study 2010 2012 2014 2017 2022 Flow 613,541 631,189 649,344 6 73,709 717,638 Growth Factor 6.70% 9.77% 12.93% 17.16% 24.80% 

### 3.8.2.2 Estimation of Operation Constraints on National Inland and Coastal Waterway Networks In order to archive hydro power generation and deeper navigation channels, a series of dams have been constructed along national inland waterways. With the dams is a series of locks constructed to facilitate marine traffic and regional commerce. These locks and dams currentl y are the major bottlenecks for overall marine traffic operations. The averag e delay

can be as high as thirty hours per tow operation. The Lock Performance Monitoring System has been used as the base for estimating the existing and future operation delays. Lock Performance Monitoring System In formation from United States Army Cor ps of Engineers ’ Lo ck Performance Monitoring System 20 

### (LPMS) is used to describe the current waterway traffic operations. In formation from LPMS is used to formulate regression models to estimate the future waterway network constraints in term s of average de lay in hours per tow. LPMS collects, edits, maintains, analyzes, and disseminates data collected at all Corps - owned and/or Corps operated locks. Data collection began in 1975 and consists of information about waterway traffic usage and performance of each lock in the Corps ’ national system. Lock statistics for calendar ye ar 2007 are used to estimate the average delays at locks. Estimate Future Lock Delays Based on lock statistics for calendar ye ar 2007 information from LPMS, two regression models (with R2 

### = 0.7231 and 0.6747 respectively) have been formulated:

20 Fleming, Marilyn V.; W ood, Donna E.; Goodwin, Robert J. Lock Performance Monitoring System User ’ s Manual for Da ta Analysis , Corps Of Engineers Fort Belvoir VA Water Resources Support Center , 1985Ethanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory



### 28 Number of Tows: 4909 . 0 

### ) ( 4661 . 0 Tons Tows No   , and 6502 . 1 

### ) ( 0041 . 0 ) ( Tows

No hours Delay Total 

 . The average tow delay

is the quotient of the total delay and number of tows. Mathematicall y, it can be expressed as Tows No Delay Total hours Delay Average 

) ( 

### Instead of estimating the future average tow delay utilizing these equations directly, a growth factor has been estimated. That is

4909 . 0 2007 4909

. 0 2010 2007 2007 2010 2010 

### ) ( 4661 . 0 ) ( 4661 . 0 Tons Growth

Ton Tons Tows Tows Growth Tow      Using the similar procedure, the future total delay and average delay growth factors for different target ye ars have been estimated. The future average delay for a lock is estimated by

multiplying average delay growth factor by

target year to average tow delay at 2007. The existing and 2022 average tow delays for locks in the nation ’ s waterway system are depicted in F igure s 17 and 18, respectively . Future total delay and average delay growth factors in reference to 2007 are presented in T able 7 . Table 7. Estimated Future Growth Rates for Lock Delay

2010 2012 2014 2017 2022 Total Delay 105.39% 107.84% 110.35% 113.69% 119.66% Average Delay 102.09% 103.02% 103.96% 105.18% 107.33% 

### A histogram anal ys is of the data indicates that the waterway delay relative to year 2010 would increase b y 0.013 hrs, 0.027 hrs, 0.045 hrs, and 0.076 hrs in 2012, 2014, 2017, and 2022, respectivel y. The mode estimate for the waterway delay distribution in 2010 is estimated to be 1.498 hrs, which would increase to 1.562 hrs by 2022. Lower estimated delay is due to the fact with an increase in freight demand will increase the number of tows, and thus not introduce a big change in an average delay per tow.Ethanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory



### 29 Figure 17 . Average tow d elays for 2007

Figure 18. Average tow d elays for 2022Ethanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory



### 30 3.8.3 Highway With the ever- increasing demand for freight and people movement on our relatively stabilized highway

infrastructure, traffic congestion and delays have become one of the major operation constraints that impede economic growth and degrade our quality of life. It is important to us e realistic travel speed information with congested traffic in the scenario anal ys es for future years. Information presented in the 2007 Urban Mobility Report

21 

### is used to approximate the travel speeds. The Texas Transportation Institute ’ s 2007 Urban Mobi lity Report is an annual publication which studies urban highway congestion and associated problems from 437 urban areas. The report presents various measures of mobility in travel demand, available highway facilities, traffic delays, excessive fuel consumptions, congestion costs, and effectiveness of congestion relief counter measures. One of the measures of urban mobility estimated b y the 2007 Urban Mobility Report is area-wide travel speeds for freeways and arterial streets. This project adapts the travel speed estimation procedure used in the 2007 Urban Mobility Report to approximate the travel speeds for different interstates, other freeways and expressways, and arterials in different states for both urban and rural areas. The travel speed estimation procedure is based on the Average Dail y Traffic per Lane (ADT/Lane) statistics. The description of the speed estimation procedure in the 2007 Urban Mobility Report is incomplete. Therefore, direct calculation of travel speed based on ADT/Lane statistics is impossible. However, the 2007 Urban Mobility Report has estimated travel speed information and Average Dail y Traffic per Lane (ADT/Lane) statistics by freeway and arterial for the top 85 major metropolitan areas. Based on the data for freeway and arterial from the 85 metropolitan areas, two linear travel speed models (with R2 

### = 0.8371 and 0.5284) have been f ormulated. Mathematically, they are 975

. 75 7251 . 1     1000

ADT/Lane Speed Freeway 

### , and 118 . 37 52 . 1     1000

ADT/Lane Speed Arterial 

### . The Average Daily

Traffic per Lane (ADT/Lane) statistics for 2006 by state and by highway functional classification are calculated based on the vehicle - miles - traveled and

21 David Schrank, and Lo max , T. (2007) The 2007 Urban Mobility Report, Texas Transportation Institute , The Texas A&M University System . Copy of the report is available at http://tti.tamu.edu/documents/mobility_report_2007_wappx.pdfEthanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory



### 31 highway lane -miles statistics from the Highway Statistics 2006 22

### published by the Federal Highway Administration (FHWA), US Department of Transportation (US DOT). In order to forecast the future Average Daily Traffic per Lane (ADT/Lane) statistics, vehicle -miles - traveled and highway lane - miles statistics from 1997 to 2006 have been assembled. The 10 - ye ar trends for national level Average Daily Traffic per Lane (ADT/Lane) for different freeways and arterials are presented in Figures 19 and 20 for rural and urban areas , respectivel y. Based on the 10 - ye ar historical trends, the future Average Dail y Traffic per Lane (ADT/Lane) growth factors can be estimated. The growth factors for the future analys is ye ars by highway functional classification and for rural and urban areas are presented in Table 8. National Trends - Rural

0 1 2 3 4 5 6 7

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006ADT/Lane X 1000

Interstate Other Principal Arterial Minor Arterial 

### Figure 19 . Average daily traffic per lane trends for rural U . S.

22Highway Statistics Annual Publications , Federal Highway Administration (FHWA), US Department of Transportation (US DO T). Tables from Highway Statistics series are available through http://www.fhwa.dot.gov/policy/ohpi/hss/hsspubs.cfm .Ethanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory



### 32 National Trends - Urban

0 2 4 6 8 10 12 14 16

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006ADT/Lane X 1000

Interstate Other Freew ays Other Principal Arterial Minor Arterial 

### Figure 20 . Average d aily traff ic per lane trends for u rban U . S. Table 8 . Future Growth Factors for 2006 ADT/Lane

Year Rural Urban Interstate Other

Principal Arterial Minor

Arterial Interstate Other

Freeways Other

Principal Arterial Minor

Arterial 2010 1.0532 1.0029 1.0085 1.0333 1 .0471 1.0054 1.0337 2012 1.0797 1.0043 1.0127 1.0500 1.0706 1.0081 1.0505 2014 1.1063 1.0058 1.0170 1.0666 1.0941 1.0108 1.0674 2017 1.1462 1.0079 1.0234 1.0916 1.1294 1.0148 1.0926 2022 1.2126 1.0115 1.0340 1.1333 1.1882 1.0215 1.1347 

### By appl ying these growth factors to the 2006 Average Daily Traffic per Lane (ADT/Lane) statistics, ADT/Lane statistics for 2010, 2012, 2014, 2017, and 2022 can be estimated. By

appl ying the future ADT/Lane statistics to the freeway and arterial travel speed models, the travel speeds for 2010, 2012, 2014, 2017, and 2022 can be approximated.Ethanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory



### 33 4. GENERATION OF ETHANOL TRANSPORTATION ACTIVITY DATA
– MODELING APPROACH Estimates of ethanol transport activities using the ORNLTransportation Infrastructure Network Model for three 2022 scenarios (i.e., 2 reference and 1 controlled) were based on the following two steps: a. ODM (origin - destination -mode) matrix b. Traffic assignment 4.1 Origin -Destination Matrix (ODM) At first, an initial origin- destination matrix was developed based on the following inputs: ( a) A vector of ethanol production by plant location and by

base or projection ye ar. Corn refineries from EPA (Sect. 3.1), cellulosic refineries as detailed above under Sect. 3.2, including import supplies as outlined under Se ct. 3.3. It is assumed that sufficient storage capacity exists for a continuous flow over the year. (b ) A vector of ethanol demand by terminal location (where blending occurs), b y ye ar as detailed under Sect. 3.4. It is assumed that some of these terminals are ethanol distribution terminals, one in each metro area, the volume of which is exogenousl y determined as discussed in Sect. 3.6. Local distribution by truck would then occur from the 413 metro ethanol distribution terminal to blending sites at existing 1056 products terminals (also termed as “tank farms ” and as discussed earlier under Sect.3.5), which have tank storage and currently receive products from pipeline, ship, or barge, and distribute products by truck to retail or wholesale customers. Existing product terminals which are currentl y not served b y ei ther rail or waterways, ethanol movement will be limited to trucks only . The goal for each ye ar was to fill in the ODM matrix between refineries and terminals consistent with these production and supply margins.

“ Lo cation” here means a longitude/latitude that can be examined on a map. The analys is was done at the county- level resolution, by accounting for local transportation infrastructure. As discussed earlier, most refineries have been assumed to be located in proximity to either rail or waterways mode of transportation. Procedure : 1. Attach ethanol facilities to modal networks. As part of an examination of OD M routes, refineries and terminals must be attached to the network in an initial step. Existing locations were examined to see whether theEthanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory



### 34 various modal attachments are already existent or feasible. By and large, assumptions were made that feasible connections will be built when they make economic sense, for instance, that one unit train per week will justify building the tracks and storage facilities at existing locations to accommodate long trains. The routing b y various modes was then generated to produce a matrix of distances and costs , which were used in the spatial interaction model to determine which origins serve which destinati ons, and their quantities as discussed below. 2 . A spatial interaction model (SIA) (or gravity m odel) was applied to the cost matrix to produce an initial ODM activity matrix. The model is: t(o,d,m) = K *O(o)* D(d) * exp[ -beta*c(o,d,m)] where beta is a distance decay factor. A larger value of beta indicates more distance sensitive movements, and it is more likely that demand will be met by nearby suppliers. A smaller beta value produces more cross flow s, that is, more suppliers located in distant parts of the country . It also incorporates an implicit scale factor for cost units. As it approaches infinity, the SIA approaches a transportation cost minimization model. Every choice of beta will result in a different average shipment distance. It s value was calibrated at 0.85 (a low value characteristic of high value commodities (like chemicals) that are not very distance sensitive and which encompass substantial cross -hauling) based on the average ethanol shipment distance of 700 miles for rail in 2006 (according to the latest 2006 way bill sample) and projected 2010 suppl y- demand scenario considered in our analysis . Due to lack of data for other modes, the use of a single calibrated beta value across all mode s resulted in average shipment lengths for 500 miles and 100 miles for barge and truck mode of transport , respectively . The beta value remained unchanged for future forecast ye ars due to lack of empirical data , though as volumes increased and demand became more dispersed, average shipment distances increased. c is the transportation cost from origin (o) to destination (d) b y mode (m) . At first the generalized cost of travel between every mode combination of origin O(i) and destination D(j) is calculated, and from which the minimum cost per ton among feasible mode choices is used for c(o,d,m). For the mode specific cost, it is only the relative cost that matters. As discussed earlier under Sect s . 3.7.1 and 3.7.2 , the ratio of relative rates of 2.4 to 7 cent s per ton- mile between inland barge and rail are used, thereb y favoring the waterways mode whenever available. As noted before, truck costs are much higher 14 cents/ton- mile. Many of the costs that a shipper faces are not included in the carrier ’ s charges, such as reliability, inventory costs while a shipment is in transit, and scale economies, which will often make truck a more desirable alternative. These are usually modest for bulk commodities like ethanol, and so those have been ignored here. In addition, we distinguished between unit train movements and the shipment of individual tankEthanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory



### 35 cars in manifest trains with their en route classification . Manifest train rates are generall y about 40% higher, which was used in the cost calculation. K is the calibrated constant, ensuring that total flows match. That is, the matrix (i.e., t(o,d,m)) that most closely matches the initial SIA internal distribution while meeting the constraints on individual supplies and demands for ethanol. In this way, K is actually

irre levant to the outcome

– it is merel y a scaling factor fo r all t(o,d,m) entries in the O D matrix. O(o) total amount to be shipped from origin (o), D (d) total amount demanded at destination (d), and t is the amount of ethanol to be shipped from (o) to (d) by mode (m) m is the mode consisting of the following options -- truck, manifest train (loose cars), unit train, and barge. It is assumed that in most cases, either one of these modes would be used for ethanol delivery from refineries to ethanol terminal s, from where truck delivery will be used for the final destination of product terminals. Only

in some instances, e.g., Alaska and Hawaii, initial shipment would be by

rail to the port in the west coast, from where ethanol will be delivered b y barges for the eventual truck delivery to product terminals. In the case of the Northeast

—to be consistent with the existing flow pattern

—final ethanol delivery

is assumed to be by

barges. Intermodal transfers between highways and rail, highways and waterways, and water ways to rail have also been considered in the anal ys is. The gravity model used here for the generation of ODM is commonly used for trip distribution in the traditional four- step transportation forecasting model before mode choice and route assignment are made. 23 

### This step matches tripmakers ’ origins and destinations to develop a “trip table ” a matrix that displays the number of trips going from each origin to each destination. It is based on the principle that the interaction between two locations declines with increasing distance, time, and cost between them, but is positively

associated with the amount of activity at each location. It uses impedance b y travel time, perhaps stratified b y socioeconomic variables, in determining the probability

of trip making. The distance decay factor of 1/distance is generall y a more comprehensive function of generalized cost, which is not necessarily linear

– a negative exponential tends to be the preferred form as also used in our case illustrated above . The rate of dec line of the interaction, i.e., the impedance factor has to be empirically measured, and varies b y context. Limiting the usefulness of the gravity model is its aggregate nature. The resulting matrix is then fitted to the exogenous origin and destination marginal totals as dis cussed under items (a) and (b) above . This results in every ce ll having some non- 23 Levinson, D. and Kumar, A. 1995. A Multi - modal Trip Distribution Mo del,” Transportation Research Record #1466, pp. 124 - 131.Ethanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory



### 36 zero volume. This is adjusted by converting the lowest volume cells to zero, provided they were less than 0.11 M gal/yr and 10 % of that origin ’ s total volume and 10 % of the destination ’ s total. In stead, those suppliers redistributed their shipments to customers that already had a significant relationship with them. On each cycle one destination for each origin and one origin for each destination were permanently zeroed, and the problem re run on the next cycle

– thereb y forcing the resulting matrix into compliance with the input origin and destination marginal controls through Iterative Proportional Fitting (IPF). Costs were initially assumed to be by

unit train, but if insufficient volumes were obtained, costs for unit trains and water on the next cycle were adjusted upward until manifest train costs appeared superior. The minimum threshold was 2 unit trains/year (15 Ktons or 5 Mgal assuming a unit train has 80 tank cars each having an ethanol capacity of 100 tons or 30,000 gallons) for each possible O - D pair for the full benefit of unit train pricing. Itis further assumed that at both ethanol supply source (i.e., refineries) and demand destination (i.e., metro ethanol terminal) meet the minimum threshold of 1 unit train/month (or 12 unit trains/year or 30Mgal) in order to justify the necessary

capital investments for unit train facilities including sidings. It is important that not only the minimum threshold between every possible O - D pair on a specific trip be met, but also every demand and supply

centers should also meet the total annual minimum threshold movements which includes either various supplies coming to a single demand point or vice versa. The thre shold for barge is assumed to be the same as unit trains, whereas truck is the last default mode used having an ethanol capacity of 8,000 gallons per truck. 4.2 Traffic Assignment To determine route - specific flows, the ODM shipment tonnages are routed ov er the Intermodal Network containing the 1998 FAF1 base commodity county-by- county routes by various modes

– the only publicl y available detailed data to date . Since commodity flows remain fairly

unchanged, use of the older flow data is inconsequential. The ORNL infrastructure network model used for the anal ys is is an extensive and integrated public domain geographically- based representation of the North American transportation infrastructure suitable for individual or multimodal studies. 24 

### Itis a combinati on of its data, the CTA intermodal network, and its traffic assignment models to load the network with commodity

flows. The network is composed of independentl y constructed single mode networks for highway, rail, and water, along with a set of intermodal terminals and a terminal model to connect them. Terminals connect mode - specific subnetworks, and each terminal is a link in this network with an impedance that represents the generalized cost of terminal activities. The result is a unified routable network, with a single node list, a single link list, and a topology defined by

the links ’ endpoint nodes, a structure common to most network anal ys is programs. It may be easiest to imagine that each modal network occupies a horizontal plane, while intermodal terminals connecting 2 modes lie between the planes and are attached above and below b y vertical access links. Traffic generators would ty picall y also be located at a different level and similarly attached to an arbitrary

modal network by

vertical access links . Several ty pes of links are considered, i.e., line haul links, railroad interlines, terminal links, access links, and shadow links where b y various modes each has an impedance function based on its 24 “CT A Transportation Networks,” can be accessed at http://cta.ornl.gov/transnet/Ethanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory



### 37 characteristics and context. Both links and nodes intenti onally share a common set of attributes, although the attributes of routing impedance and length and endpoint nodes have meaning onl y for links. Principal uses to date have included regional and modal competition effects of policies and investment in infra structure (Marine input/output model, 25 

### regional routing model, national rail freight infrastructure 26 

### ), optimal investment strategies for waterways (Ohio River Navigation Investment Model), simulating the routes of shipments in the Commodity Flow Survey, routing and scheduling military convoy movements (MOBCON), and risk assessments of hazardous materials movements. The three networks, i.e., the Oak Ridge national highway network, the CTA rail network, and the composite intermodal transportation network

— representing the North American transportation infrastructure are well documented and available for open distribution. 22 

### The Oak Ridge national highway network is a database of major highways in the United States using data from several sources such as USGS national Atlas digital line graphs, state maps, FHWA highway performance monitoring s ys tem (HPMS), TIGER/line files, and USGS 1:100,000 digital line graphs etc. The CTA railroad network is a geographically- based representation of the North American railroad system which is designed foremost to support anal ytic transportation studies, such as traffic assignment, capacity, optimal investment, intermodal terminals, and cost estimation. The base data for the U.S. portion of the network is the Federal Railroad Administration ’ s National Atlas based strategic rail network. The intermodal transportation network connects the four individual modal networks having the modification option in order to estimate different link impedances. These data are also available in GI S versions easil y importable into commercial software such as TransCAD or ARC/Info. The other network, i.e., the national waterway

network representing the North American transportation infrastructure is a customized network with input from the National Waterway GI S Design Committee containing members from several federal agencies and using data from several sources. The majority

of the inland links are at 1:100,000 scale as is at least the case with most other networks . Most software is now written to use the intermodal network to achieve the maximum flexibility in studying intermodal as well as mode - specific problems. The core routine is an incremental traffic assignment, which accepts an origin- destination matrix and finds multiple paths through the network used b y each O - D pair. Traffic is assigned to each of the paths in order to minimize a generalized cost, or impedance, which increases as congestion, or the volume/capacity ratio, on each facility

increases. The capacity

measure on rail lines is determined by an approximate function of current traffic (transcribed from the Federal Railroad Administration), subjective line quality, and phys ical attributes of signal s ys tem and number of tracks.

25 Southworth, F. (2005). “A Multimodal Regional Routing and Multi -port Analysis Model, Prepared for the Institute for Water Resources, United States Army Corps of Engineers, Fort Belvoir, Alexandria VA 22315 - 3868, Nov. 26 Cambridge Systematics, Inc. (2007). “National Rail Freight Infrastructure Capacity and Investment Study,” prepared for Association of American Railroads , Cambridge, MA, Sept.Ethanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory



### 38 The ORNL intermodal network is updated on an average cycle of every three years using press reports and road atlases for updating capacity and linkage information for rail and highways, respectively . The intermodal network used for the analys is represents the latest updates made in 2006. The best test for the ade quacy of the assignment is to compare line volumes produced b y the program using the national O - D data with independent data on observed rail flows, which is available anecdotall y and from the Federal Railroad Administration volume classes. Major discrepancies have been handled b y manual editing of the capacity

measures to enforce better compliance (e.g., limiting Cascade Tunnel to 25 trains/day). For waterways, there are few choices of routes, so the accuracy of flows is essentially

the accuracy

of the input O - D matrix. For trucks, there is little reliable intercity flow data, and the reasonableness of results depends on mostly anecdotal evidence of actual routing choices. Therefore, the knowledge and judgment of an anal ys t for the reasonableness of routes is essential, absent an extensive data effort to collect and interpret truck activity by route. There are separate traffic assignment models for the long- distance and local components of ethanol transport. The long - distance component of the model considers receipts of long - distance shipments from refineries or, in the case of imported ethanol, from seaports at each metro area assumed to have an ethanol terminal. The movement from ethanol source to metro receiving terminal could be b y rail, water, truck, or any intermodal combination. If b y truck, the long distance trip terminates directl y at the blending site without the use of a dedicated receiving terminal. After receipt and intermediate storage, the ethanol is distributed by truck to local blending site s in the same metro area

– considered b y the second component of the traffic assignment model . Those blending sites are existing petroleum products distribution facilities (sometimes referred to as pipeline terminals or “tank farms ” ). There will ty pically be multiple blending sites operated b y competing distributors. In cities that do not have existing ethanol distribution terminals, we assume that one will be built adjacent to the blending site with the best rail or water access. That tank farm may or may not have a rail siding, and surely cannot presentl y accommodate unit trains, but we assume that unit train facilities will be built as demand in the area increases. Note that all existing ten ethanol receiving terminals or so can accommodate unit trains to day . The distance of local truck trips is taken as the direct distance between ethanol terminal and blending site, plus 20% circuity. The t( o,d,m) OD matrix (as discussed above in Sec. 4.1) describes every flow from source to consumption. Shipments using the same source and metro - area receiving terminal are consolidated, and routed over the ORNL intermodal network. The ethanol sources are attached to the network b y constructing access links from their geographic locations to all the nearby modes (within 10 00 m). Cellulosic plants are located at county centroids, and use existing county access links to connect to every mode within that county, regardless of distance. Ports of entry for imported ethanol are already existing network nodes. Receiving terminals are similarl y attached so that they, too, become network nodes. The S -network containing lines representing shorelines, US county boundaries, and North American provinces define county boundaries for destination and origin ports b y extending 3 miles into the water from shorelines. A minimum impedance path is then found from each record ’ s origin node to destination node, allowing any modeEthanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory



### 39 or mode combination. The only connections allowed between modes are existing ethanol and petroleum products terminals. Impedances are chosen to reproduce actual routes used by carriers such as the data provided b y Rail Traffic Atlas by Harry L add and Highway

Performance Monitoring System by

Federal Highway

Administration. For the best, high volume routes (rural Interstates , rail A -mainlines), impedances in terms of cost for highway, rail, and water are in the ratio described above in Sect. 3.7, i.e., 14:7:2.4 . This is where the preponderance of ton - miles will occur. All waterways are treated equally . Lower class highways and rail lines (surface arterials and branch lines) receive substantial impedance penalties to shift traffic to the major routes, in accordance with actual practice. Impedances are defined only in terms of cost here since they include optimal balance between congestion and infrastructure cost. As further discussed below and in Sect. 3.8, congestion/delay may not add significant additional impedances beyond transportati on cost, thereb y causing onl y additional ton-mile movements existing corridors due to ethano l movements. The expectation for the future routing of ethanol (or other commodity) is that over an intermediate time range of 2 to 4 years the assurance of a steady stream of revenues will finance any needed capacity improvements along existing routes, and they will continue to be used. In other words, existing capacities will not constrain future flows. The interesting question is not whether expansion is economically justified, but where it will occur, which our analysis attempts to address by reporting traffic increases by each county and mode. Unlike rail mode, c ongestion costs are different on highway and inland water modes, where capital investment decisions are generally not optimal for minimizing total s ys tem costs. By assuming optimal investments in rail will continue to be made in the future, congestion cost is not an issue in this case. Ethanol represents a small proportion of total water traffic, but it will be affected b y the growth in other traffic. FAF2 projections are for approximately

25% growth up to 2022, so congestion will rise slightl y. Current congestion delays at each lock on the Upper Mississippi are in the neighborhood of 1.4 hours . This growth, without capacity improvements, should lead to delays in the neighborhood of 1.6 hr as dis cussed earlier in Sect. 3.8.2 above. This is still a small percentage penalty

for the future use of barges relative to rail. Water carriage costs in the future are likel y to increase due to fuel costs in about the same proportion as rail costs will, leaving their relative costs nearl y equivalent. Highway congestion is expected to increase, and average speeds decline, even in rural areas, over the next two decades as discussed above in Sect. 3.8.3 . This will slightly increase truck costs relative to rail. M ore importantly, fuel represents a larger proportion of truck than rail operating costs, where trucks consume about 9 times as much fuel per ton - mile as discussed earlier under Sect. 3.7.3. This will further increase the truck price disadvantage relative to rail. By rights, this growth should have been incorporated in the future ye ar cost calculations, but the truck proportion of long - distance ethanol movement is so small that the effect would have been barely perceptible. Truck dominates the local ethanol distribution from metro - area receiving terminals to blending sites, but this activity is price inelastic as the only effective modal competition is from pipeline.Ethanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory



### 40 Impedance penalties for using intermodal transfer terminals in other studies was roughl y equivalent to 400 mi of travel, that is, a shipper or carrier would be willing to accept a route 400 mi longer on a single mode in order to avoid the costs of an intermodal transfer. This penalty

level resulted in no intermodal ethanol transfers occurring, wh en we know that currentl y New England is served b y barge from intermodal terminals in New Jersey and Albany. To overcome this, intermodal transfer impedances were reduced to the approximate equivalent of 100 mi les . It could be that the costs of intermodal transfers reall y are very low for ethanol; it could also be that the marginal costs for water shipments to New England are low for tank farms that already have well developed water unloading facilities and no rail facilities. During the assignment process traffic is accumulated on each link for each O - D pair whose path traverses it, and is then reported as ton- mi in the geographic area that contains the link. Traffic is also accumulated at the county level as the sum of long - distance haul (i.e., from production refinery /import facilities to metro ethanol terminal) including intermodal transfers and local truck movements from ethanol metro terminal to blending terminal as the final ethanol destination point. 5. RESULTS 5.1 Transportation Activity Transportation activity

data by domestic truck, waterways, and rail have been generated using the ORNL Infrastructure Network model for the three 2022 scenarios, i.e., controlled and AEO and RFS1 reference cases, summary results of which are shown in Table 9. Amount s (in terms of K tons) and Kton- mile movements are listed in this table. The Kton-mile movements are based on ethanol quantity loaded at the origination point for each mode of transportation used ; thus double - counting occurs from this perspective . Since same ethanol quantity may be used several times depending on the number of times different transportation modes are used to reach the final destination point, double counting occurs under those Kton-mile movement cases . The l ocal Ktons of truck movement s are somewhat less than the final demand quantity that is to be delivered from dedicated ethanol terminals to blending terminals since a small amount of ethanol is moved directl y from refineries to the blending terminal . As observed today, major ethanol movements from production centers to blending terminals will continue to be by rail. Under the 2022 controlled scenario, 7 2 % of total quantity (i.e., in terms of Ktons) of long - distance ethanol movements are estimated to be by rail. By ton - mile movement, the estimate is 91% by

rail. Unit- train movements would dominate under the 2022 controlled scenario, to the extent that manifest train movements become considerabl y less than under the two reference scenarios. Ethanol ton- mile movements by waterways are estimated to be quite low, i.e., about 6.9% of the total under the 2022 controlled scenario. Because of higher costs, the share of highway mode of transportation is considerabl y less for the long - distance ethanol movements. Local truck ton - mile movements would b e the least among various modes considered here due to the small distance to be covered from the metro ethanol terminal to the blending terminal. Under the 2022 controlled scenario,Ethanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory



### 41 Note: 1: Ktons; 2: Ktons - mile; (U): Unit train; (M): Mani fest train 

### there would be a 167% increase in total rail ton- mile movements c ompared to the 2022 RFS1 scenario. The total volume of domestic freight by rail in 2005 is estimated to be 1734 billion ton -miles 27 

### ; the ethanol shipments in the 2022 controlled case increase rail transport about 2. 8 % . Similarly, additional stresses from the 2022 controlled sc enario on waterways and highways are es timated to be 0.6% and 0.13%, re spectivel y of the corresponding 2005 ton- mile movements . Figures 21 through 23 show Ktons of ethanol movements , including both long - distance haul and local truck movements, for the three scenarios considered here. Truck movements in most cases are local, with the exception of the case where ethanol truck movement is the preferred mode from a production refinery /import facility directly

to the blending terminal . Because of relativel y les s Kton movements by waterways and truck, those movements in some cases overlap and are invisible in the figures with higher rail Kton movements. Table 10 shows a sample of high - volume ethanol movements (top three b y rail, and one each by waterways and truc k) by different modes for the three 2022 scenarios. As one would expect, top high - volume shipments are by unit train for all three scenarios covering long - distance movements. In addition, most movements are from the Midwest producing regions to high- demand regions, i.e., northeast, west, and south. Long - distance highway

ethanol movements are mainly limited to within the same state. Because of the location of dedicated ethanol terminals in proximity to ethanol blending terminals, local highway

mile movements are estimated to be the least in each case . Total ton-mile movements for the three 2022 scenarios at the county- level are shown in Figures 24 thru 26, but no t mode - specific ton- mile movements. As one would expect with no ethanol demand in 21 states under the 2022 RFS1 scenario, no ton- mile movements are estimated to occur in most southern and southwest states as shown in Figure 24. The largest ton -mile movements are estimated to occur in San Bernardino C ounty, CA and quite a few counties in Wyoming. From the county area perspective, the most intense ton -mile movements is estimated to occur in the southwest of the United

27U.S. Department of Transportation (2008). “Pocket Guide to Transportation 2008,” Bureau of Transportation Statistics, Research & Innovative Technology Administration, Washington, DC, Feb.

Table 9. Transportation Activity S ummary for the 2022 Scenarios Scen ario Ethanol

Demand (B gal) Long- Distance Local

Rail (U) Rail (M) Barge Truck Truck 1 2 1 2 1 2 1 2 1 

### 2 2022 RFS1 Reference 6.69 16800 17218K 1492 1413K 2816 1498 K 3131 290 K 18431 132K 2022 AEO Reference 13.18 28913 25362K 6182 5288K 4178 253 9K 55 97 491 K 36736 253 K 2022 Controlled 34.14 7 3656 4 67 1 1 K 2481 2705 K 1 139 1 3 770K 17 508 115 3K 822 60 532 KEthanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory



### 42 States. Although demand is relatively less in those regions, larger county size causes total ton - mile movements to be significantly more intense compared to other regions, particularl y northeast regions with a concentrated level of ethanol demand. As anticipated, Midwest ethanol producing regions would also have significant ethanol movements. Table 10. A Sample of High -Volume Ethanol Moveme nts by Different Modes for the Three 2022 Scenarios
Source County Destination County LongDistance Hwy (miles) Unit

Train (miles) Waterways (miles) Local

Hwy (miles) Ethanol

Quantity Shipped (MGal) 2022 RFS1 Scenario Floyd, IA Chittenden, VT 0 1193 1 47 3 63 Linn, IA Prince George's, MD 0 999 0 3 51 Hamilton, NE Butte, CA 0 1607 0 3 48 St.Joseph, IN Middlesex, CT 0 0 1542 3 19 Green Lake, WI Fond du Lac, WI 31 0 0 0 30 2022 AEO Scenario Linn, IA Orange, FL 0 1437 0 3 86 Putnam, IL Prince George' s, MD 0 870 0 3 85 Macon, IL Manatee, FL 0 1229 0 3 74 Des Moines, IA Colbert, AL 0 0 700 3 59 Green Lakes, WI Fond du Lac, WI 31 0 0 0 33 2022 Controlled Scenario Bureau , IL Prince George’s, MD 0 87 5 0 3 89 Tazewell , IL Lorain , OH 0 47 8 0 3 82 St. Joseph, IN Mercer, NJ 0 77 1 0 3 80 Hendry, FL Manatee, FL 0 0 1 4 7 3 67 Lane, OR Lane, OR 35 0 0 0 126 

### Transportation activity

data in terms of total loading/unloading in Ktons has also been aggregated at the county level and are shown for the three sc enarios in Figures 27 thru 29. Loading activity

for the long - distance haul mainly occurs at the production refinery /import facilities, and at any intermodal transfer sites that may be used on the way to metro ethanol terminal. Final ethanol loading activity occurs at the metro ethanol terminal for transferring to the blending terminal by truck with the exception being a terminal were ethanol and bl ending are co - located. Unloading occurs at first at the intermodal transfer facility (if necessary), followed b y at the metro ethanol terminal, and finally at the blending terminal if both ethanol and blending terminals are not co - located. In cases for local truck trips, where ethanol receiving and blending terminals are located in two neighboring counties, ktons moved between them were equall y split between the two terminals. However, ton -mile movements for the two - county local truck trips are all accumulated in the blending county, not resulting in much error in overall estimates because it is a small share of total ton- mile movements. Most total loading/unloading activities shown in these figures are the ethanol blending sites, thereb y dictated b y the

### 43 Figure 21. Projected ethanol movements (Ktons) under the 2022 RFS1 Reference scenarioEthanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory



### 44 Figure 2 2 . Projected et hanol movements (Ktons) under the 2022 AEO Reference scenarioEthanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory



### 45 Figure 2 3 . Projected ethanol movements (Ktons) under the 2022 Controlled scenarioEthanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory



### 46 Figure 24. Projected county-level total Kton - mile ethanol movements under the 2022 RFS1 Reference Scen arioEthanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory



### 47 Figure 25. Projected county-level total Kton - mile ethanol movements under the 2022 AEO Reference ScenarioEthanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory



### 48 Figure 2 6 . Projected county-level total Kton - mile ethanol movements under the 2022 Controlled ScenarioEthanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory



### 49 Figure 2 7 . Projected county-level total ethanol loading/unloading activities (Kton s) under 2022 RFS1 Reference ScenarioEthanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory



### 50 Figure 2 8 . Projected total ethanol loading/unloading activities (Kton s) u nder the 2022 AEO Reference ScenarioEthanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory



### 51 Figure 2 9 . Projected total ethanol loading/unloading activities (Kton s) u nder 2022 Controlled Scenario

### 51

level of demand assumed at these sites. Due to the limited level of estimated intermodal transfers occurring, loading/unloading activities are not widespread among a large number of counties, as one would expect. For example, about 4269 Ktons intermodal transfers are estimated to occur out of total 380,021 Ktons loading/unloading activity under the controlled 2022 scenario. Southern California and upper Midwest counties dominate the most activities under all scenarios. The activity distribution is similar to the distribution of supply

centers (i.e., Figures 1 and 2) and demand centers (i.e., existing petroleum product terminals) as shown earlier in Figure 12. Note that the focus of the ethanol distribution analysis here has been from refineries to the blending terminals. More activities would have been observed in these figures with the consideration of final ethanol delivery

from blending terminals to retail outlets. 5.2 Distribution Cost State -by- state and national average ethanol distribution cost s for the three 2022 scenarios were estimated based on the cost of ton- mile movements to satisfy the projected demand. Cost of ton-mile movements were determined by specific mode using the latest available data. 28 

### The data sources and methodology used for the cost of various transportation modes are those discussed earlier under Sect. 3.7. For example, rail cost was based on the regression anal ys is of operating cost of various railroads as reported in the 2006 way bill data, to which monthly

maintenance and leasing cost s were added to estimate the total rail cost in terms of $/ton-mile a s a function of distance travel ed and fuel cost . It was further assumed that current diesel fuel cost of around $2.00/gallon will not be significantl y different by

the 2022 forecast year. The unit train distribution cost is assumed to be 35% less than for the manifest train . Inaddition, minimum threshold for rail cost has been assumed to be 1.5 cents/ton - mile. Unlike rail and waterways , the trucking cost is assumed to be fixed at 1 4 cent s/ton - mile

— not a function of fuel cost as discussed earlier . The sum of mode - specific cost s of ton- mile movements when divided by projected total demand level provided the composite ethanol distribution cost per gallon both at the state and national level. Table 1 1 show s the estimated distribution cost both in terms of total ($M) and cents/gal at the state - level for the three 2022 scenarios considered. It i s estimated that the national average ethanol distribution cost would be in the range of 6.80 - 9.63 cents/gallon, where the lower end of the value range reflects the economies of scale obtained due to a significantly higher level of demand under the 2022 controlled scenario. At the state level, the distribution cost ranges between 1.2

– 33.2 cents/gallon under the same scenario. It is important to note here that ethanol distribution cost is purely dictated by

the assumed refinery

and ethanol distribution terminal locations. Lower ethanol distribution co st values are mostly in midwestern states, as most demand is met by local refineries imports causing thereby a significant lower level of ethanol transportation. Alaska, Hawaii, some Southwestern states (for example New Mexico, Utah etc.) and eastern state s (e.g., Massachusetts, Vermont etc.) have higher ethanol distribution costs because of their greater distances from production locations. New 28 Dager, C. (2008). Tennessee Valley Authority, Knoxville, TN. Personal communication with Sujit Das, Oak Ridge National Laboratory, Oak Ridge, TN, Sept. 22.Ethanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory



### 52 Table 11. Estimated Ethanol Distribution Cost for the Three 2022 Scenarios State RFS1 2022 AEO 2022 Controlle d 2022

Total ($ M) Rate

(¢ /gal) Total

($ M) Rate

( ¢/gal) Total

($ M) Rate

( ¢ /gal) AL - - 23 8.7 32 6.0 AK - - 11 33.8 17 33.2 AZ 7.5 5.7 29 10.7 52 7.8 AR - - 9.2 18.6 21 7.6 CA 129 7.8 106 6.7 255 6.3 CO 18 10.1 28 8.3 71 10.1 CT 15 14.1 2.5 4.2 7. 5 3.0 DE - - 6.1 20.4 6.2 7.1 FL - - 64 8.0 45 3.5 GA - 19 8.0 79 7.1 HI 41 44.5 39 41.2 26 31.9 ID - - 2.5 4.7 16 6.6 IL 28 8.5 53 9.6 108 7.1 IN 9.6 6.9 35 9.0 45 5.4 IA 5.0 3.1 5.9 3.2 9.2 1.5 KS 0.2 10.7 13 7.0 24 5.4 KY 8.5 10.6 20 9.7 56 8 .3 LA - - 36 14.7 12 2.2 ME - - 17 10.8 5.7 4.1 MD 28 12.3 32 13.3 52 9.5 MA 33 10.2 31 13.1 102 12.5 MI 32 10.5 61 11.3 144 10.0 MN 24 4.9 23 4.5 71 4.6 MS - - 21 9.9 33 5.8 MO 30 8.9 33 8.9 91 7.2 MT - - 1.9 19.9 10 13.4 NE 6.5 5.5 7.1 5.5 13 3 .8 NV 6.0 9.8 6.3 7.2 12 6.3 NH 10 13.1 4.9 9.7 2.5 1.9 NJ 35 11.8 39 15.3 66 8.5 NM 16 13.8 13 12.6 49 12.4 NY 49 16.5 51 13.0 78 7.3 NC - - - - 11 3.7 ND 2.1 3.7 2.8 4.8 4.9 3.7 OH - - 19.6 6.2 58 5.1 OK - - 38 12.4 93 9.7 OR 5.5 6.2 4.6 4.7 3. 7 1.2 PA 18 19.9 46 18.4 70 12.2 RI - - 1.5 7.5 2.5 2.2Ethanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory



### 53 Table 11. Estimated Ethanol Distribution Cost for the Three 2022 Scenarios State RFS1 2022 AEO 2022 Controlle d 2022

Total ($ M) Rate

(¢ /gal) Total

($ M) Rate

( ¢/gal) Total

($ M) Rate

( ¢ /gal) SC - - - - 7.0 2.1 SD 0.8 2.4 1.2 3.0 30 1.3 TN 22 8.8 39 12.1 94 9.6 TX - - 167 12.1 160 6.8 UT - - 14 13.3 16 11.9 VT 6.3 10.0 8.3 13.1 27 11.0 VA 2.4 18.7 26 16.3 50 8.0 WA - - 23 6.5 42 4.7 WV - - 2.7 5.4 6.6 6.4 WI 24 6.3 2.6 6.2 71 5.2 WY 4.9 12.1 7.5 13.3 5.3 12.4 TOTAL 617 9.2 1269 9.6 2335 6.8 Hampshire has a significantly lower distribution cost since it is cheaper for it to import from neighboring state Massachusetts, instead of comparativel y shorter distance for Massachusetts to import from the Midwest than it is for New Hampshire. For the two other referenc e scenarios, similar discrepancies in the ethanol distribution cost at the state - level have been observed , however the distribution cost is significantly higher . For example, in New Hampshire, ethanol distribution cost is estimated to be 1.9 cents/gallon under the controlled scenario, compared to 13.1 and 9.7 cents/gallon for AEO and RFS1 scenarios, respectivel y. Under 202 2 RFS1 scenario, a lack of ethanol demand in quite a few states causes thereb y there no distribution cost. Total distribution cost is mainly dictated b y the ethanol demand level, thereby larger demand states such as the California and Texas would have the most distribution cost. Total annual ethanol distribution cost for those states has been estimated to be $2 55 M and $16 0 M, respectively, under the 2022 controlled scenario. Total ethanol distribution cost expenditure is estimated to be $2, 335 M under the 20 22 controlled scenario, compared to $1,269M and $617M for the 2022 AEO and RFS1 scenarios, respectivel y. 5.3 Rolling Stock Requirements Rolling stock requirements, i.e., number of rail cars, barges, and trucks , were estimated for the three 2022 scenario s at the state level based on that state’ s demand. Total estimated ton-mile movements to satisfy the state level demand when divided by the mode specific speed, capacity, and total yearly hours of operation (as shown in Table 12) provided the mode - specific rolling stock requirements. Assumptions for the number of rail car requirements are based mostly on the assumed annual number of trips per car. Unit and manifest trains generall y make annuall y 15 - 18 trips and 10 - 12 trips,Ethanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory



### 54 respectivel y. 29 

### We assume the higher value of the range in each case due to anticipated efficiency in the future ethanol transportation network Lo wer number of annual trips in rail cars is mainl y due to long times now used for loading, unloading, and idle time. For example, it is estimated that loading and idle each may take in the range of 7 - 10 days depending on how long it takes to gather all shipments from various ethanol refineries, particularl y in the case of manifest trains. Annual rail car operating time is estimated based on the difference between total number of annual operating hours and estimated total rail idle time, where the latter is dictated b y assumed train speed as shown in the table below. For example, for unit trains assuming 18 trips/year, 15 mph train speed, and average route distance of 700 miles lead to about 20 days for a round- trip (i.e., load to load), out of which 4 days when train will be running. The corresponding numbers for manifest trains are estimated to be 12 trips/year, 10 mph train speed, 600 miles of averag e route distance, 5 days train running out of 30 days of a total round- trip. It is likely that assumed train speeds are on the conservative side and applicable onl y under highl y congested and strong ethanol demand environment. The effect of assumed speed a nd average rail route distance is minimum on the estimated annual operating hours, since highl y uncertain total non- operating rail time is the major component of it. It is assumed that trucks will be operating 10 hours a day for 350 days annuall y, compared to double the amount of total yearly hours of operation for waterways modes of transportation. In creased ethanol demand is anticipated to increase the demand for rail tank cars b y a maximum of 4 1 ,3 01 cars under the 2022 controll ed scenario as shown in Ta ble 13 . This additional maximum demand for new rail cars is estimated to be almost double the total new orders placed in 2007. New orders for rail cars have steadily increased by 12% annually during 2005 - 2007 period, with a maximum value of 36,292 in 2006. 30 

### Itis estimated that 60 - 65% of new rail tank car orders are from the increased ethanol demand. There continues to be a backlog between the new orders and deliveries, and the current backlog is estimated to be 25,900 tank cars. As maximum demand for tank cars under the 2022 controlled scenario is estimated to be more than double of total 2007 new orders, supply may be an issue in that case if the high level current backlog continues in the future. Even under the least constrained 2022 ethanol demand scenario, i.e., 2022 RFS1, rail car demand is estimated to be 65% of total new orders. Rail tank cars are nearly all

29Hadenfeldt, K. (2009). GATX Corporation, Chicago, IL. Personal communication with Bruce Peterson and Sujit Das of Oak Ridge National Laboratory, Oak Ridge, TN on 1/26/09. 30 Tom Simpson (2009). Railway Supply Institute, Washington, DC. Personal communication with Sujit Das, Oak Ridge National Laboratory, Oak Ridge, TN, Jan. 5. 

### Table 12. Assumptions Used for Rolling Stock Requirements E stimation Mode Speed (mph) Capacity (ton/vehicle) Operating

Time (hours/year) Rail (Unit Train) 15 90 1848 Rail (Manifest) 10 90 1560 Waterways 8 800 6500 Highways 35 27 3500Ethanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory



### 55 Table 13 . Estimated Rolling Stock Requirements under 2022 Scenarios Scenario Rail Cars Barges Trucks 2022 Controlled 4 1 ,3 01 170 1,643 2022 AEO 27,863 122 736 2022 RFS1 15,815 72 403 privately owned, either by

leasing companies or shippers. It is likely that by streamlining the ethanol rail movement in the future, a significant reduction in delay and thereb y total number of new ethanol rail re quirements can be substantially reduced. It is estimated that total number of barges with each carrying capacity of 800 tons necessary under the 2022 controlled scenario would be 170, which is about 4% of total barges used in 2007. 31 

### Almost 85% of the total barges used in 2007 were along the Missis sippi River system and the Gulf of Mexico intracoastal waterways. Tank barges used to move petroleum products and chemicals in the coastal and short -haul (Gulf/South Atlantic) inter- coastal trades do not face the shortage of double -hulled vessels as required by the Oil Pollution Act of 1990 (OPA90). OPA90 requires petroleum products and certain petrochemicals to be shipped in double hulled vessels and a time line to phase out the use of single hulled vessels. Several single hulled vessels built prior to 1970 were recentl y retired from petroleum products service. The double -hulling of the coastal tank vessel fleets will be virtually complete over the next three years. The double -hulling process accelerated over the l ast five years as non- double -hull vessels built during the 1978 - 1983 boom period reached their OPA90 phase - out dates. As of ye ar- end 2007, 73% of total U.S. flagged tanker barge vessels were double hulled. 32 

### Itis unlikel y that the availability

of double -hulled vessels would be an issue to meet the increased ethanol demand in 2022. It is estimated that about 1643 maximum more trucks will be on the road due to increased ethanol demand under the 2022 controlled scenario

—considerabl y less than other transportation needs, as trucking will mainly be used for short- distance hauling from dedicated ethanol terminals to blending terminals. This additional truck demand will contribute to less than 0.02% of total registered trucks in 2005. 5.4 Distribution Constraint Analysis As discussed in Section 2, four scenarios were considered for distribution constraint anal ys is, out of which three scenarios were simulated by incorporating additional biodiesel demand in the range of 3 - 7% of ethanol demand dictated by EI SA requirements. The three “with biodiesel ” scenarios were for the ye ars 2012, 2014, and 2022 denoted here b y 2012B, 2014B, and 2022B, respectivel y. The fourth scenario

31 U.S. Army Corps of Engineers (2009). “Waterborne Transportation Li nes of the United States: Calendar Year 2007

– Volume 1: National Summaries,” Alexandria, VA. Accessed from the website: http://www.iwr.usace.army.mil/ndc/publicatons.htm on 1/5/09. 32 U.S. Army Corps of Engineers (2009). “The U.S. Waterway System Facts: Fact Card 2008

– Volume 1: National Summaries,” Alexandria, VA. Accessed from the website: http://www.iwr.usace.army. mil/ndc/factcard/fc07/factcard.pdf on 1/5/09.Ethanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory



### 56 considered was for the ye ar 2022 without consideration of additional biodiesel demand and is denoted as 2022 controlled scenario . To simplify the analysis, it is assumed here that the location of both supply and demand of additional b iodiesel will be co - located along with original ethanol locations. For simplicity ’s sake, movement of biodiesel was a ssumed to follow the same route as ethanol , only with amount s increased by the additional level of biodiesel demand. This simplified assumption, thus, explicitly does not consider the strain on feeder lines and other specific routes that may be used due to different types of feedstocks and location of production facilities suitable for biodiesel. The constraint analysis is based on at first updating the ORNL intermodal network with latest information on all commodity flows (i.e., base), and then determining the stress generated in terms of the percentage increase in base flows due to ethanol flows under various scenarios b y extent (in terms of miles) of various transportation modal networks. For railroads where the preponderance of movements will occur, the network was loaded with current traffic levels from flowing all 2006 Surface Transportation Board Carlo ad way bill sample traffic. The way bill records provide shipment information about the originating county, the terminating county, the originating railroad, terminating railroad, and up to six bridge railroads involved in carrying the shipment; the interchange junction if the shipment is exchanged between railroads; the ty pe of service train used to carry the shipment; the car equipment used to carry the shipment; and the number of carloads per ye ar. For highways and waterways, nationwide background traffic levels are quite limited

— particularly

at the local street level in the former case. So, a ty pical level of traffic that a waterway

and highway

can supp ort is used, and then future years’ ethanol traffic is shown as a percent of that “nominal capacity . ” Tables 1 4 through 1 7 show the estimated number of miles of rail, waterway and highway that will experience an increase in traffic given various ethanol quantities in the four scenarios considered in the distribution constraint anal ys is. As discussed above, the increase in traffic due to ethanol has been estimated based on the reference 2006 flows of all commodities and have been grouped under eight major c ategories. The traffic flow increase under the category “ 5% ” indicates a traffic flow increase in the range of 2 - 5% , and the number of miles under the “ 0% ” category indicates along where no ethanol flows is estimated to occur . Total number of miles under “ 0% ” is the total number of miles considered in our network for the analysis, which is significantl y higher in the cases of rail and waterways since they also include Canadian rail and worldwide waterways networks, respectivel y. Most number of miles would experience an increase in the traffic level of less than 10% due to additional ethanol flow. For rail, most affected miles are where ethanol flow is in the range 10 - 100 million tons, compared to 0 - 10 million tons for highways. The effect on domestic waterwaysis minimal, less than 2% carryingethanol volume in the range of 50 - 100 million tons. For the 2022 controlled scenario, 10 , 6 26 miles of U.S. rail network would have an additional traffic flow in the range of 0 - 2% due to transportation of 20 - 50 million tons loads of ethanol as shown in Table 1 7 . As one would expect, the number of miles most affected would be rail, followed by highways and waterways, respectivel y. Under the 2022 controlled scenario, 17% rail miles would be affected, compared to 2% and 0.1 % for highways and waterways,Ethanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory



### 57 respectivel y. It is likel y that the percentage miles affected value for waterways would be significantl y higher when considered on the basis of U.S. - controlled waterways miles. An additional biodiesel demand in 2022, wouldn’t cause much change on the total basis in various ethanol transportation modes, but under various categories of increased traffic percentages, total number of affected miles would increase. For example for 2 - 5% traffic flow increase, 1 0,015 miles of rail network would be affected under the 2022B scenario compared to 9 , 94 2 miles for the 2022 controlled scenario. In prior forecast years, the impacts would be less than in 2022, as one would expect. For both forecast years, i.e., 2012 and 2014, total rail miles affected b y additional ethanol flows would be similar, 1 4 % and 1 5 %, respectively, compared to 1% for highway miles for both forecast years . It is more important to evaluate the spatial distribution of affected network miles due to additional ethanol flows (shown in Figures 30 - 37) , rather than total percentage of affected network miles. F or each scenario , two figures are shown: one for rail and the other one waterways and highways combined. These figures are very similar in nature to Figures 21 - 23, with the major difference being different colors show different “ % increase in base flows ” categories, while the thickness of lines indicates the actual ethanol flow volume as before. Since end values of various ranges indicating ethanol flow volumes are the same, thickness of flow lines becomes larger from 2012B scenario to 2022B scenario with the increased ethanol ton- mile movements due to higher ethanol demand. The notable features of rail flow (as shown in Figures 30, 32, 34, and 36) are the increased loadings of the Overland Corridor from the Corn Belt to the West Coast, and local loadings of fee der lines within the Corn Belt. Note, however, that long - distance loadings seldom exceed 10% of base, and that transportation fees will easily cover the modest increa se s in infrastructure required. For the controlled case, ethanol flows are not that great as imports satisfy a large extent of total ethanol demand, particularly coastal lying states. Figures 31, 33, 35, and 37 indicate the spatial distribution of affected waterways and highways network miles combined for the four scenarios considered here, since each of these transportation modes contribute onl y a small share of total ethanol movements. For waterways, although congestion levels are currently significant on the upper Mississippi and Ohio Rivers, ethanol will still be a small proportion of all traffic, other traffic is not expected to grow, and at worst lock delays will increase from the current half hour to the hour range, and possibly

will increase not at al l. Note that f or highways , local trucking from centralized distribution terminals to petroleum blending terminals is not considered, and so due to limited long - distance expensive highway mode of transportation, not much highway ethanol flows are estimated for the right half of the U.S. regions as shown in these figures. C ongestion will surely increase, but highway

congestion is dominated by non - freight traffic, and long - distance ethanol movements will be very modest. The exception is local traffic in corn-producing areas, with often a longer- distance movement of ethanol to transshipment points than the corn shipments to elevators that they displace. There is a possibility

of consequential impacts on local roads in the vicinity of refineries, both for incoming feedstock and outgoing ethanol.Ethanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory



### 58 Table 14 . Estimated N umber of Miles Experiencing an Increase in Ethanol Traffic of

Various Categories of Quantity L evel under “2012B Scenario” RAIL Traffic 0% 2% 5% 10% 20% 50% 100% >100% 0Mt 4413 0 0 0 0 0 0 10 0 - 2Mt 60291 299 316 358 271 320 37 0 2 - 5Mt 15174 617 311 306 156 123 3 0 5 - 10Mt 26247 1101 757 773 143 197 0 0 10 - 20Mt 32226 4377 1216 312 397 20 0 0 20 - 50Mt 45157 8592 3053 895 48 0 0 0 50 - 100Mt 11055 5194 2146 610 0 0 0 0 100 - 500Mt 0 0 0 0 0 0 0 0 Tot al (miles) 194563 20180 7799 3254 1015 660 40 10 % Rail miles affected = 14% WATERWAYS Traffic 0% 2% 5% 10% 20% 50% 100% >100% 0Mt 2070 0 0 0 0 0 0 5 0 - 2Mt 0 0 0 0 0 0 0 0 2 - 5Mt 0 0 0 0 0 0 0 0 5 - 10Mt 0 0 0 0 0 0 0 0 10 - 20Mt 0 0 0 0 0 0 0 0 20 - 50 Mt 0 0 0 0 0 0 0 0 50 - 100Mt 2601879 430 0 0 0 0 0 0 100 - 500Mt 0 0 0 0 0 0 0 0 Total (miles) 2603949 430 0 0 0 0 0 5 HIGHWAYS Traffic 0% 2% 5% 10% 20% 50% 100% >100% 0Mt 3457 0 0 0 0 0 0 1 0 - 2Mt 295290 2151 746 401 190 38 1 0 2 - 5Mt 138939 2144 520 45 0 0 0 0 5 - 10Mt 27397 959 30 2 0 0 0 0 10 - 20Mt 10031 32 0 0 0 0 0 0 20 - 50Mt 45189 193 0 0 0 0 0 0 50 - 100Mt 109 0 0 0 0 0 0 0 100 - 500Mt 0 0 0 0 0 0 0 0 Total (miles) 520412 5479 1296 448 190 38 1 1 % Highway miles affected = 1%Ethanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory



### 59 Table 15 . Estimated Number of Miles Experiencing an Increase in Ethanol Traffic at

V ariou s Categories of Quantity L evel under “2014B Scenario” RAIL Traffic 0% 2% 5% 10% 20% 50% 100% >100% 0Mt 4415 0 0 0 0 0 0 8 0 - 2Mt 60442 110 290 448 278 286 35 3 2 - 5Mt 15095 549 48 6 155 265 135 5 0 5 - 10Mt 26087 1076 1360 343 181 165 7 0 10 - 20Mt 32306 3960 1381 524 304 74 0 0 20 - 50Mt 44677 8849 3110 1057 53 0 0 0 50 - 100Mt 10522 5536 2099 848 0 0 0 0 100 - 500Mt 0 0 0 0 0 0 0 0 Total (miles) 19354

4 20080 8726 3375 1081 660 47 11 % Rail miles affected = 15% WATERWAYS T raffic 0% 2% 5% 10% 20% 50% 100% >100% 0Mt 2071 0 0 0 0 0 0 5 0 - 2Mt 0 0 0 0 0 0 0 0 2 - 5Mt 0 0 0 0 0 0 0 0 5 - 10Mt 0 0 0 0 0 0 0 0 10 - 20Mt 0 0 0 0 0 0 0 0 20 - 50Mt 0 0 0 0 0 0 0 0 50 - 100Mt 2601808 501 0 0 0 0 0 0 100 - 500Mt 0 0 0 0 0 0 0 0 Total (miles) 2603879 501 0 0 0 0 0 5 HIGHWAYS Traffic 0% 2% 5% 10% 20% 50% 100% >100% 0Mt 3457 0 0 0 0 0 0 1 0 - 2Mt 295004 1964 937 624 276 12 1 0 2 - 5Mt 138914 1954 705 75 0 0 0 0 5 - 10Mt 27458 895 34 2 0 0 0 0 10 - 20Mt 10027 36 0 0 0 0 0 0 20 - 50Mt 45282 100 0 0 0 0 0 0 50 - 100Mt 109 0 0 0 0 0 0 0 100 - 500Mt 0 0 0 0 0 0 0 0 Total (miles) 520251 4949 1676 701 276 12 1 1 % Highway miles affected = 1%Ethanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory



### 60 Table 16 . Estimated Number of Miles Experiencing an Increase in Et hanol Traffic of

Various Categories of Quantity L evel under “2022B Scenario” RAIL Traffic 0% 2% 5% 10% 20% 50% 100% >100% 0Mt 4408 0 0 0 0 0 0 15 0 - 2Mt 60165 174 228 315 497 411 85 17 2 - 5Mt 14752 301 680 319 379 202 56 1 5 - 10Mt 25620 1238 675 662 7 94 64 165 0 10 - 20Mt 32042 2455 2195 1385 273 199 0 0 20 - 50Mt 40815 10575 3548 2419 388 0 0 0 50 - 100Mt 10603 3933 2689 877 902 0 0 0 100 - 500Mt 0 0 0 0 0 0 0 0 Total (miles) 188405 18676 10015 5977 3233 876 306 33 % Rail miles affected = 17% WATERWAY S Traffic 0% 2% 5% 10% 20% 50% 100% >100% 0Mt 2067 0 0 0 0 0 0 8 0 - 2Mt 0 0 0 0 0 0 0 0 2 - 5Mt 0 0 0 0 0 0 0 0 5 - 10Mt 0 0 0 0 0 0 0 0 10 - 20Mt 0 0 0 0 0 0 0 0 20 - 50Mt 0 0 0 0 0 0 0 0 50 - 100Mt 2600715 1593 0 0 0 0 0 0 100 - 500Mt 0 0 0 0 0 0 0 0 Total (miles) 2602782 1593 0 0 0 0 0 8 HIGHWAYS Traffic 0% 2% 5% 10% 20% 50% 100% >100% 0Mt 3458 0 0 0 0 0 0 0 0 - 2Mt 294018 2154 952 961 412 288 34 0 2 - 5Mt 138163 2074 529 458 418 6 0 0 5 - 10Mt 27017 1156 164 51 0 0 0 0 10 - 20Mt 9984 64 15 0 0 0 0 0 20 - 50 Mt 44931 451 0 0 0 0 0 0 50 - 100Mt 109 0 0 0 0 0 0 0 100 - 500Mt 0 0 0 0 0 0 0 0 Total (miles) 517680 5899 1660 1470 830 294 34 0 % Highway miles affected = 2%Ethanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory



### 61 Table 1 7 . Estimated N umber of Miles Experiencing an Increase in Ethanol Traffic of V ariou s Categories of Quantity Level under the “2022 Controlled Scenario” RAIL Traffic 0% 2% 5% 10% 20% 50% 100% >100% 0Mt 4408 0 0 0 0 0 0 15 0 - 2Mt 60179 160 240 302 584 325 85 16 2 - 5Mt 14752 391 592 330 414 155 56 1 5 - 10Mt 25620 1241 673 663 797 60 165 0 10 - 20Mt 32042 2614 2366 1098 277 152 0 0 20 - 50Mt 40955 10626 3366 2411 387 0 0 0 50 - 100Mt 10603 3950 2705 844 902 0 0 0 100 - 500Mt 0 0 0 0 0 0 0 0 Total (miles) 188559 18982 9942 5648 3361 692 306 32 % Rail miles affected = 17% WATERWAYS Traffic 0% 2% 5% 10% 20% 50% 100% >100% 0Mt 2067 0 0 0 0 0 0 8 0 - 2Mt 0 0 0 0 0 0 0 0 2 - 5Mt 0 0 0 0 0 0 0 0 5 - 10Mt 0 0 0 0 0 0 0 0 10 - 20Mt 0 0 0 0 0 0 0 0 20 - 50Mt 0 0 0 0 0 0 0 0 50 - 100Mt 2600793 1516 0 0 0 0 0 0 100 - 500Mt 0 0 0 0 0 0 0 0 Total (miles) 26028 60 1516 0 0 0 0 0 8 HIGHWAYS Traffic 0% 2% 5% 10% 20% 50% 100% >100% 0Mt 3458 0 0 0 0 0 0 0 0 - 2Mt 294037 2181 999 908 371 288 34 0 2 - 5Mt 138223 2022 541 447 409 6 0 0 5 - 10Mt 27028 1147 170 44 0 0 0 0 10 - 20Mt 9984 77 2 0 0 0 0 0 20 - 50Mt 44965 417 0 0 0 0 0 0 50 - 100Mt 109 0 0 0 0 0 0 0 100 - 500Mt 0 0 0 0 0 0 0 0 Total (miles) 517804 5844 1712 1399 780 294 34 0 % Highway miles affected = 2%F igure 30. Projected rail ethanol movements in terms of Ktons and % increase in base flows fo r 2012 B scenario.

### 63

Figure 31. Projected waterways and highways combined ethanol movements in terms of Kto ns and % increase in base flows fo r 2012B scenario.

### 64 Figure 32. Projected rail ethanol movements in terms of Ktons and % increase in base flows fo r 2014B scenario.

### 65

Figure 33. Projected waterways and highways combined ethanol movements in terms of Kto ns and % increase in base flows for 2014B scenario.

### 66 Figure 34. Projected rail ethanol movements in terms of Kto ns and % increase in base flows fo r 202 2 scenario.

### 67 Figure 35. Projected waterways and highways combined ethanol movements in terms of Kto ns and % increase in base flows for 2022 scenario.

### 68 Figure 36. Projected rail ethanol movements in terms o f Kto ns and % increase in base flows fo r 2022B s cenario

### 69

Figure 37. Projected waterways and highways combined ethanol movements in terms of Kto ns and % increase in base flows for 2022B scenario.Ethanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory



### 70 6. Summary The projected need for 34 .14 billion gallons of ethanol by 2022 under the EI SA compared to the 2007 production level of 6.5 billion gallons will likely spur demand for transportation services that face capacity constraints. Incongruity between the geographic locations of production facilities and feedstock supply networks and the locations of the petroleum distribution system and consumption demand centers would require adequate infrastructure development to avoid serious logistical bottlenecks and s ys temic delays in biofuels distribution. This study provides an anal ysis of ethanol transport by domestic rail, waterways, and truck distribution systems from ethanol refineries to blending terminals using ORNL’s Infrastructure Network model. The scope of this study was limited to the intermediate portion of the entire ethanol supply chain from the ethanol producer/point of importation to the terminal where ethanol is blended with gasoline. , D istribution issues related to transportation of feedstocks for ethnaol production and of ethanol from blending terminals to retail gas outlets was not considered in this study . 33 

### Although the focus of this study has been on ethanol, several scenarios have been considered that include simulating additional demand for biodiesel and non- co -processed renewable diesel fuel , in the range of 3 - 7% of total ethanol demand. 34 

### Two scenario sets have been considered to facilitate EPA ’s to evaluation of the adequacy of the domestic biofuel transportation system to deliver the volumes of biofuels projected to be used in response to the requirements of the EI SA. The first set of scenarios considered three 2022 scenarios (i.e. two reference cases having a demand level of 13.2 billion gallons and 6.7 billion gallons denoted b y AEO and RFS1, respectively, and one based on the EISA of 34 .14 billion gallons ethanol) , focus ing on the transpo rtation activity analysis of ethanol from the point of production to the blending terminal - level on a county-by- county

basis. This analysis is to support the EPA anal ys is activity related to the estimation of emissions associated with the transportation of ethanol. The constraint anal ys is (the focus of the second set of scenario analyses ) was conducted to evaluate potential constraints in the biofuel distribution system which may limit the ability

of the system to cope with the projected increase in biofuel volumes under the EISA . Four scenarios have been considered here, where three scenarios represent three forecast years under EISA , i.e., 2012, 2014, and 2022 and for each forecast year total biofuels demand includes also biodiesel/non- co -processed renewab le diesel fuel demand. Total biofuel demand assumed for these three forecast ye ars are 14.6, 17.5, and 35.1 billion gallons, respectivel y. The fourth scenario is exactl y the same as the 34 .14 billion gallons of ethanol volume projected to be used in 2022 under EI SA

—one of the scenarios considered under the first set of scenarios . The anal ys is of fuel ethanol transportation and activity and potential distribution constrains using the ORNLInfrastructure Network model are based on the data provided

33 An additional overall motor fuel volume will need to be trucked to retail facilities to satisfy demand due to the lower energy density of ethanol relative to gasoline. 34 EPA assumed that co -processed rene wable diesel would be distributed fungibly with petroleum-based diesel fuel from petroleum refineries, whereas biodiesel and non -processed renewable diesel fuel would not be able to take advantage of the established petroleum fuel distribution system.

### 71

both by EPA and available commercial sources. EPA provided major data inputs by each forecast year including the estimated location of ethanol production facilities, sources of ethanol imports, and state - level consumption for the annual volumes of ethanol that EPA estimates will be consumed in response to the EISA. EPA also provided the projected volumes of biodiesel and non - co -processed renewable diesel fuel that would be used under the EISA. To simplify the anal ys is, biodiesel and non- co -processed diesel fuel volumes were assumed to originate from the ethanol production facilities and follow projected ethanol use patterns. Several geographic information systems (GIS) were used for the geo - location of refineries needed by the ORNLmodel. EPA provided state - level ethanol imports were geo - coded at the specific port level and ethanol demand were mapped to the existing petroleum product terminals using the vehicle - miles - traveled data at the census tract level. Commercial data source was used for geo - coding the 1056 existing U.S. petroleum product terminals. It was assumed that the concept of existing 10+ ethanol distribution terminals in metro areas will continue, most likely occurring close to existing petroleum terminals as found today that would accommodate unit trains , followed b y truck movements to possibly distant in the same metro region. A single ethanol distribution terminal was selected for each metropolitan area from the 413 cluster of existing petroleum product terminals based on the minimum access rail distanc e. Hence, all three modes of transportation, i.e., rail, waterways, and highways were considered for the initial ethanol distribution from refineries to the ethanol distribution terminal, followed b y local trucking to the final blending terminal. Estimation of transportation activity is based on the development of an initial origindestination matrix using the spatial interaction model , followed b y the actual ethanol route selection using the traffic assignment process. Relative transportation cost (where waterways the most preferred mode of transportation), total supply and demand level to be met at various locations, and the distance between dictated the linkage between various supply

demand margins points. The traffic assignment is based on the latest u pdated and validated ORNL intermodal network and its traffic assignment models for both long - distance and local ethanol transport, where the network is composed of independentl y constructed single -mode networks of highways, rail, and water, along with a set of intermodal terminals and a terminal mode to connect them. Ethanol traffic assignment on the network is based on the minimum impedance principle, i.e., relative transportation costs by

various transportation modes in addition to the reproduction of data available of actual routes used by various carriers. The expectation for the future routing of ethanol along existing routes presumes that the assurance of a steady stream of revenues will finance any needed capacity

movements and thereby

existing capacities will not constrain future flows. Impedance penalties for using intermodal transfer terminals was assumed to be equivalent to 400 miles of additional travel, and in some cases lower where intermodal facilities provide the most optimal transportation mo de. Transportation activity

analysis of the three 2022 scenarios indicate a significant increase in the level of ton-mile activity under the controlled scenario compared to two other reference scenarios. As observed today, major ethanol shipments from pro duction centers to blending terminals will continue to be by rail

– 91% in terms of ton- mile movements under the controlled scenario. Unit- train movements would dominate under the controlled scenario to the extent that manifest train movements become consi derabl y lower than

### 72

those under two reference scenarios. Under the controlled scenario, there would also be an increase of 167% in total rail movements compared to the 2022 RFS1 scenario. However, the additional stress on U.S. transportation network is estimated to be minimal even under th e controlled (i.e. EI SA) scenario. The percentage increases in ton - mile movements by

rail, waterways, and highways are estimated to be 2. 8 %, 0.6%, and 0. 13%, respectivel y, compared to the corresponding 2005 total domestic f lows by various modes. Most high -volume ethanol movements are estimated to occur from the Midwest producing regions to high - demand regions, i.e., northeast, west, and south. At the countylevel, larger county sizes, particularl y southwest quadrant of the United States indicate a significantl y higher level of ton-mile movements because of larger distance covered. Concentration s of lo ading/unloading activities are estimated to be in counties where ethanol supply

centers (Midwest) and blending terminals coinci de with the locations of existing petroleum product terminals . State -by- state ethanol distribution cost shows the effect of economies of scale, as demand level increases considerably in 2022 from RFS1 scenario to controlled scenario. It is estimated that the national average ethanol distribution cost under the controlled scenario is 6. 8 cents/gallon, compared to 9.2 cents/gallon under the RFS1 scenario. At the state level, the distribution cost ranges between 1 . 2 - 3 3.2 cents/gallon under the 2022 controlled scenario. Lower ethanol distribution cost values are mostl y in midwe stern states as most demand is met by local refineries causing thereby a significantly

lower level of ethanol transportation. Some of the southwestern states and eastern states have higher ethanol distribution cost as most ethanol distribution occurs by the long - distance unit rail mode of transportation. Assumptions made regarding locations of refineries and ethanol distribution terminals are key factors to these distribution cost estimate s. Total ethanol distribution cost expenditure is estimated to be $2, 335 M under the controlled scenario, compared to $1,269M and $617M for the 2022 AEO and RFS1 scenarios, respectivel y. States with comparativel y a higher level of ethanol demand as well as having fewer local refineries such as California and Texas indicate the most annual ethanol distribution cost expenditures. Rolling stock requirements, i.e., number of rail cars, barges, and trucks were estimated for the first set of three 2022 scenario considered under the ethanol transportation activity anal ys is at the state level based on its demand. Increased ethanol demand is anticipated to increase the demand for rail tank cars b y 41,3 0 1 under the 2022 controlled scenario, compared to 15,815 under the 2022 RFS1 scenario. This maximum demand for rail tank cars is estimated to be a

bout double the total new orders placed in 2007. The increase in number of barges is considerabl y less, about 4% total barges used in 2007 under the 2022 controlled scenario . It is unlikely that the availability

of double -hulled vessels under OPA90 would be an issue to meet the increased ethanol demand, since the double -hulling process has accelerated over the last five ye ars and as of year- end 2007, 73% of total U.S. flagged tanker barge vessels were double -hulled. Since trucking is limited to shortdistance hauling from dedicated ethanol terminals to blending terminals, the additional truck demand is estimated to contribute to less than 0.02% of total registered trucks in 20 05.

### 73

The distribution constraint analysis considered for four scenarios encompassing three different forecast ye ars indicate the most network miles affected compared to 2006 base commodity

flows would be the rail as it’d be used as the major mode of ethano l transportation. The percentage miles affected would be 17% for the 2022 scenarios, compared to 2 - 3% lower in 2012 and 2014 scenarios. The effect would be considerably less in the case of highways, i.e., 1 - 2%, range depending on the scenario as trucking i s used mainly

for the ethanol transportation from central ethanol distribution terminals to existing petroleum product terminals. The notable features of rail flow are the increased loadings of the Overland Corridor from the Corn Belt to the West Coast, and local loadings of feeder lines within the Corn Belt. For waterways, although congestion levels are currentl y significant on the upper Mississippi and Ohio Rivers, ethanol will still be a small proportion of all traffic, other traffic is not expected to grow, and at worst lock delays will increase from the current half hour to the hour range, and possibly will increase not at all. Congestion will surely increase, but highway

congestion is dominated by non - freight traffic, and long - distance ethanol movement s will be very modest. The difference in additional stress on the U.S. transportation network due to biodiesel demand consideration was minimal (as observed between the 2022 controlled vs. 2022B cases) since the difference in demand between them was only 4 %. It is also likel y that the impacts would be more on feeder lines and different supply chain network representing biodiesel if considered in the anal ys is. In summary, significant future ethanol demand would have minimal impacts on transportation infrastructure overall . In addition, the simplified analys is of biodiesel/non co -processed renewable fuel transportation indicates that the additional burden on the distribution system from the increased volumes of these fuels (relatively small compared to ethanol) under the EI SA also would have minimal impacts. However, there will be spatial impacts due to a considerable increase in rail traffic from refineries to ethanol distribution terminals requiring a significant level of investment . Assumed locations of bo th refineries and ethanol distribution terminals would impact significantly not onl y the projected ethanol flows but also distribution cost. Feedstock availability and the relative cost of transporting ethanol vs. feedstock are some of the major parameters that need to be considered for estimating refinery locations. Similarly, intermodal ethanol terminal locations may

be different from existing petroleum -based intermodal terminals, depending on ethanol supply locations and on the extent of availability of pipelines (not considered in this study) as one of the modes of future ethanol transportation. Further more, any change in the current trend of the c iting of ethanol distribution terminals close to existing petroleum product terminals into the future will affect the future ethanol flows and thereb y transportation infrastructure requirements.

### 74 APPENDIX A Location D etails and Production C apacity (MGY) of C orn Ethanol Plants for Various Forecast Y ears

### 75 Location D etails and Production C apacity (MGY) of C orn Ethanol Plants for Various Forecast Y ear s

ID FEEDSTOCK LONGI TUDE LATI TUDE CITY COUNTY Vol 2010 Vol

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2022 Ref. RFS1 1 Abengoa Bioenergy Corporation - 97.53 37.79 Colwich Sedgwick 25.00 25.00 25.00 25.00 25.00 25 2 Abengoa Bioenergy Corporation - 101.39 37.18 Hugoton Stevens 88.00 0 3 Abengoa Bioenergy Corporation - 103.37 34.17 Portales Roosevelt 30.00 30.00 30.00 30.00 30.00 30 4 Abengoa Bioenergy Corporation - 98.87 41.02 Ravenna Buffalo 95.00 95.00 95.00 95.00 95.00 0 5 Abengoa Bioenergy Corporation - 97.54 40.87 York York 55.00 55.00 55.00 55.00 55.00 55 6 Aberdeen Energy, LLC - 98.78 45.43 Mina Edmunds 100.00 100.00 100.00 100.00 100.00 0 7 Absolute Energy, LLC - 92.92 43.38 St. Ansgar Mitchell 100.00 100.00 100.00 100.00 100.00 0 8 ACE Ethanol - 90.96 44.96 Stanley Chippewa 39.60 39.60 39.60 39.60 39.60 39.6 9 Adkins Energy, LLC - 89.80 42.36 Lena Stephenson 42.50 42.50 42.50 42.50 42.50 42.5 10 Advanced BioEnergy (ABE) - 97.6 0 40.61 Fairmont Fillmore 110.00 110.00 110.00 110.00 110.00 0 11 Ag Processing Inc. (AGP) - 98.34 40.59 Hastings Adams 52.00 52.00 52.00 52.00 52.00 52 12 Agri - Energy, LLC - 96.22 43.64 Luverne Rock 21.00 21.00 21.00 21.00 21.00 21 13 Al - Corn Clean Fu el - 93.00 44.04 Claremont Dodge 50.00 50.00 50.00 50.00 50.00 50 14 AltraBiofuels Coshocton Ethanol LLC. - 81.86 40.21 Coshocton Coshocton 60.00 60.00 60.00 60.00 60.00 0 15 AltraBiofuels Indiana, LLC. - 86.80 39.54 Cloverdale Putnam 92.00 92.00 92.00 9 2.00 92.00 0 16 AltraBiofuels Nebraska, LLC. - 97.69 40.30 Carleton Thayer 113.00 113.00 113.00 113.00 113.00 0 17 AltraBiofuels Phoenix Bio Industries, LLC. - 119.43 36.36 Goshen Tulare 31.50 31.50 31.50 31.50 31.50 31.5 18 Amaizing Energy - 95.40 41.98 Denison Crawford 60.00 60.00 60.00 60.00 60.00 60 19 Amaizing Energy Atlantic, LLC (formerly Cassco & EK- SEN) - 95.03 41.42 Atlantic Cass 100.00 100.00 0 20 Archer Daniels Midland (ADM) - 91.69 41.93 Cedar Rapids Linn 250.00 250.00 250.00 250.00 250 .00 250 21 Archer Daniels Midland (ADM) - 91.69 41.93 Cedar Rapids Linn 275.00 275.00 275.00 275.00 275.00 0 22 Archer Daniels Midland (ADM) - 90.21 41.82 Clinton Clinton 190.00 190.00 190.00 190.00 190.00 190 23 Archer Daniels Midland (ADM) - 97.29 41.4 2 Columbus Platte 275.00 275.00 275.00 275.00 275.00 0 24 Archer Daniels Midland (ADM) - 97.29 41.42 Columbus Platte 95.00 95.00 95.00 95.00 95.00 95 25 Archer Daniels Midland (ADM) - 88.90 39.86 Decatur Macon 290.00 290.00 290.00 290.00 290.00 290 26 Archer Daniels Midland (ADM) - 95.79 44.46 Marshall Lyon 40.00 40.00 40.00 40.00 40.00 40 27 Archer Daniels Midland (ADM) - 89.61 40.68 Peoria Peoria 210.00 210.00 210.00 210.00 210.00 210 28 Archer Daniels Midland (ADM) - 97.89 48.91 Wa lhalla Pembina 25 .00 25.00 25.00 25.00 25.00 25 29 Arkalon Energy - 100.92 37.04 Liberal Seward 110.00 110.00 110.00 110.00 110.00 0 30 Aventine Aurora West - 98.04 40.86 Aurora Hamilton 113.00 113.00 0 31 Aventine Renewable Energy - Mt. Vernon, LLC. - 87.85 37.92 Mou nt Vernon Posey 113.00 113.00 0 32 Aventine Renewable Energy, Inc. - 89.66 40.56 Pekin Tazewell 56.50 56.50 56.50 56.50 56.50 56.5

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2022 Ref. RFS1 33 Aventine Renewable Energy, Inc. (formerly Wi lliams Energy) - 89.66 40.56 Pekin Tazewell 100.00 100.00 100.00 100.00 100.00 100 34 Badger State Ethanol, LLC - 89.66 42.60 Monroe Monroe 52.00 52.00 52.00 52.00 52.00 52 35 Big River Resources, LLC - 91.23 40.84 We st Burlington Des Moines 92.00 92.00 92.00 92.00 92.00 52 36 Big River Resources, LLC - Galva - 90.03 41.16 G alva Henry 100.00 100.00 0 37 Blue Flint Ethanol LLC - 101.19 47.46 Underwood McLean 50.00 50.00 50.00 50.00 50.00 50 38 Bonanza BioEnergy - 100.83 37.95 Garden City Finney 55.00 55.00 55.00 55.00 55.00 0 39 Bridgeport Ethanol, LLC. - 103.14 41.71 Br idgeport Morrill 45.00 45.00 0 40 Buffalo Lake Energy, LLC - 94.49 43.67 Fairmont Martin 115.00 115.00 115.00 115.00 115.00 0 41 Bushmills Ethanol - 94.79 45.13 Atwater Kandiyohi 49.00 49.00 49.00 49.00 49.00 49 42 Calgren Renewable Fuels - 119.30 36 .00 Pixley Tulare 55.00 55.00 55.00 55.00 55.00 0 43 Cardinal Ethanol - 84.86 40.19 Harrisville / Union City Randolph 100.00 100.00 100.00 100.00 0 44 Cargill Inc. - 92.65 41.14 Eddyville Wa pello 35.00 35.00 35.00 35.00 35.00 35 45 Cargill, Inc. - 96.1 0 41.54 Blair Wa shington 85.00 85.00 85.00 85.00 85.00 85 46 Cascade Grain Products - 123.16 46.17 Clatskanie Columbia 113.00 113.00 113.00 113.00 113.00 0 47 Castlerock Renewable Fuels, LLC. - 90.07 43.99 Necedah Juneau 50.00 50.00 50.00 50.00 50.00 0 48 Center Ethanol Co. - 90.18 38.60 Sauget St. Clair 54.00 54.00 54.00 54.00 54.00 0 49 Central Indiana Ethanol, LLC - 85.71 40.56 Marion Grant 48.00 48.00 48.00 48.00 48.00 48 50 Central Minnesota Ethanol Co - op (CMEC) - 94.34 46.02 Little Falls Morrison 21.50 21.50 21.50 21.50 21.50 21.5 51 Chief Ethanol Fuels Inc. - 98.34 40.58 Hastings Adams 62.00 62.00 62.00 62.00 62.00 62 52 Chippewa Valley Ethanol Co., LLC (CVEC) - 95.61 45.31 Benson Swift 46.00 46.00 46.00 86.00 46.00 46 53 Cilion - 120.92 37.56 Keyes Stanislaus 55.00 55.00 55.00 55.00 55.00 0 54 Commonwealth Agri - Energy, LLC - 87.41 36.82 Hopkinsville Christian 35.00 35.00 35.00 35.00 35.00 35 55 Corn LP - 93.92 42.73 Goldfield Wright 50.00 50.00 50.00 50.00 50.00 50 56 Corn Plus, LLP - 94.16 43.76 Wi nnebago Faribault 46.00 46.00 46.00 46.00 46.00 46 57 Cornhusker Energy Lexington (CEL) - 99.73 40.77 Lexington Dawson 40.00 40.00 40.00 150.00 40.00 40 58 Dakota Ethanol, LLC - 96.96 43.98 We ntworth Lake 48.00 48.00 48.00 48.00 48.00 48 59 De an CEG LLC - 99.18 35.36 Burns Flat Wa shita 2.00 2.00 2.00 2.00 2.00 2 60 Didion Milling / Grand River Distribution - 89.10 43.54 Cambria / Courtland Columbia 50.00 50.00 50.00 50.00 50.00 0 61 Diversified Energy Company (DENCO), LLC - 95.91 45.57 Morris Stevens 25.00 25.00 25.00 25.00 25.00 25 62 E Caruso Ethanol (part of Goodland Energy Center) - 101.71 39.37 Goodland Sherman 20.00 20.00 20.00 20.00 20.00 0 63 E Energy Adams - 96.55 40.49 Adams Adams 50.00 50.00 50.00 50.00 50.00 0 64 East Kansas Agr i - Energy, LLC (EKAE) - 95.24 38.27 Garnett Anderson 35.00 35.00 35.00 35.00 35.00 35 65 Elkhorn Valley Ethanol - 97.39 42.06 Norfolk Madison 40.00 40.00 40.00 40.00 40.00 0 66 ESE Alcohol Inc. - 101.41 38.48 Leoti Wi chita 1.50 1.50 1.50 1.50 1.50 1.5 67 Ethanol Grain Processors, LLC (EGP) - 89.15 36.29 Obion / Rives Obion 100.00 100.00 100.00 0

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2022 Ref. RFS1 68 First United Ethanol LLC (FUEL) - 84.16 31.17 Camilla Mitchell 100.00 100.00 100.00 100.00 0 69 Front Range Energy, LLC - 104.91 40.46 Wi ndsor We ld 48.00 48.00 48.00 48.00 48.00 48 70 Glacial Lakes Energy, LLC (GLE) - 97.11 44.88 Watertown Codington 100.00 100.00 100.00 100.00 100.00 50 71 Global Ethanol - 94.09 43.38 Lakota Kossuth 100.00 100.00 100.00 200.00 100.00 100 72 Global Ethanol / Midwest Grain Processors - 83.85 41.82 Riga Lenawee 57.00 57.00 114.00 114.00 57.00 57 73 Golden Cheese Company of CA - 117.58 33.89 Corona Riverside 5.00 5.00 5.00 5.00 5.00 5 74 Golden Grain Energy LLC - 93.20 43.16 Mason City Cerro Gordo 120.00 120.00 150.00 150.0 0 120.00 120 75 Golden Triangle Energy, LLC - 95.37 40.20 Craig Holt 20.00 20.00 20.00 20.00 20.00 20 76 Grain Processing Corp - 91.06 41.40 Muscatine Muscatine 20.00 20.00 20.00 20.00 20.00 20 77 Granite Falls Energy, LLC (GFE) - 95.52 44.77 Granite Fal ls Chippewa 50.00 50.00 50.00 50.00 50.00 50 78 Greater Ohio Ethanol (GOE) - 84.11 40.74 Lima Allen 55.00 55.00 55.00 55.00 55.00 0 79 Green Plains Renewable Energy, Inc. (GPRE) - 95.40 40.76 Shenandoah Page 50.00 50.00 50.00 50.00 50.00 0 80 Green Pla ins Renewable Energy, Inc. (GPRE) (formerly Superior Ethanol) - 94.95 43.43 Superior Dickinson 50.00 50.00 50.00 50.00 50.00 0 81 Hawkeye Menlo, LLC - 94.38 41.52 Menlo Guthrie 115.00 115.00 0 82 Hawkeye Renewables, LLC - 92.03 42.64 Fairbank Buchannan 115.00 115.00 115.00 115.00 115.00 115 83 Hawkeye Renewables, LLC - 93.29 42.51 Iowa Falls Hardin 115.00 115.00 115.00 115.00 115.00 115 84 Hawkeye Shell Rock, LLC - 92.63 42.74 Shell Rock Butler 115.00 115.00 0 85 Heartland Corn Products - 94.34 44 .54 Wi nthrop Sibley 99.00 99.00 99.00 99.00 99.00 99 86 Heartland Grain Fuels, LP - 98.54 45.46 Aberdeen Brown 55.30 55.30 55.30 55.30 55.30 10 87 Heartland Grain Fuels, LP - 98.25 44.37 Huron Beadle 32.00 32.00 32.00 32.00 32.00 32 88 Heron Lake BioEn ergy, LLC - 95.30 43.81 Heron Lake Jackson 50.00 50.00 50.00 50.00 50.00 0 89 Holt County Ethanol - 98.66 42.45 O ’ Neill Holt 100.00 0 90 Homeland Energy Solutions - 92.14 43.07 Lawler Chickasaw 100.00 100.00 0 91 Husker Ag, LLC - 97.77 42.32 Plain view Pierce 26.00 66.00 66.00 66.00 66.00 26 92 Idaho Ethanol Processing, LLC (former J.R. Simplot plant) - 116.73 43.67 Caldwell Canyon 5.00 5.00 5.00 5.00 5.00 3 93 Illinois River Energy LLC (IRE) - 89.07 41.91 Rochelle Ogle 50.00 50.00 115.00 115.00 50.00 50 94 Indiana Bio - Energy, LLC - 85.17 40.76 Bluffton We lls 100.00 100.00 100.00 100.00 0 95 Iroquois BioEnergy Company, LLC (IBEC) - 87.06 40.93 Rensselaer Jasper 40.00 40.00 40.00 40.00 40.00 40 96 KAAPA Ethanol, LLC - 98.95 40.51 Minden Kearney 60.00 60.00 60.00 60.00 60.00 60 97 Kansas Ethanol, LLC - 98.22 38.35 Lyons Rice 55.00 55.00 55.00 55.00 55.00 0 98 Land O ’ Lakes / Melrose Dairy Proteins - 94.80 45.67 Melrose Stearns 2.60 2.60 2.60 2.60 2.60 2.6 99 Levelland/Hockley County Ethanol LL C - 102.27 33.59 Levelland Hockley 40.00 40.00 40.00 40.00 40.00 0 100 LifeLine Foods / ICM - 94.85 39.74 St. Joseph Buchanan 45.00 45.00 45.00 45.00 45.00 0 101 Lincolnland Agri - Energy - 87.63 39.01 Palestine / Robinson Crawford 48.00 48.00 48.00 48.00 48.00 48 102 Lincolnway Energy LLC - 93.51 42.02 Nevada Story 50.00 50.00 50.00 50.00 50.00 50

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2022 Ref. RFS1 103 Little Sioux Corn Processors - 95.76 42.82 Marcus Cherokee 100.00 100.00 100.00 100.00 100.00 88 104 Marquis Energy LLC. - 89.34 41.25 Hennepin Putnam 100 .00 100.00 100.00 100.00 100.00 0 105 Marysville Ethanol - 82.49 42.88 Marysville St. Clair 50.00 50.00 50.00 50.00 50.00 0 106 Merrick & Company (Coors Brewery) - 105.20 39.76 Golden Jefferson 3.00 3.00 3.00 3.00 3.00 3 107 MGP Ingredients, Inc. - 95.13 39.56 Atchison Atchison 25.00 25.00 25.00 25.00 25.00 25 108 MGP Ingredients, Inc. - 89.67 40.56 Pekin Tazewell 90.00 90.00 90.00 90.00 90.00 90 109 Mid - Missouri Energy (MME) - 93.45 39.20 Malta Bend Saline 45.00 45.00 45.00 45.00 45.00 45 110 Midwest Renewable Energy, LLC (MRE) - 101.09 41.16 Sutherland Lincoln 20.00 20.00 20.00 20.00 20.00 20 111 Minnesota Energy - 94.62 44.74 Buffalo Lake Renville 18.00 18.00 18.00 18.00 18.00 18 112 Nebraska Energy - 98.04 40.86 Aurora Hamilton 50.00 50.00 50.00 50.00 50.00 50 113 NEDAK Ethanol - 98.98 42.53 Atkinson Holt 44.00 44.00 44.00 44.00 44.00 0 114 Nesika Energy - 97.82 39.79 Scandia Republic 10.00 10.00 10.00 10.00 10.00 0 115 New Energy Corp. - 86.29 41.65 South Bend St. Joseph 102.00 102.00 102.00 1 02.00 102.00 102 116 Nexsun Ethanol - 101.36 37.58 Ulysses Grant 40.00 40.00 0 117 North Country Ethanol (NCE) - 96.74 45.88 Rosholt Roberts 25.00 25.00 25.00 25.00 25.00 25 118 Northeast Biofuels, LP (NEB) - 76.37 43.30 Volney Oswego 114.00 114.00 1 14.00 114.00 114.00 0 119 Northwest Renewable, LLC. - 122.98 46.14 Longview Cowlitz 55.00 55.00 55.00 0 120 One Earth Energy, LLC - 88.38 40.46 Gibson City Ford 100.00 100.00 0 121 Orion Ethanol (formerly Gateway Ethanol) - 98.72 37.60 Pratt Pratt 55.00 55.00 55.00 55.00 55.00 0 122 Otter Tail Ag Enterprises (OTAE) - 96.09 46.30 Fergus Falls Otter Tail 55.00 55.00 55.00 55.00 55.00 0 123 Pacific Ethanol - Columbia - 119.77 45.81 Boardman Morrow 40.00 40.00 40.00 40.00 40.00 0 124 Pacific Ethanol - Madera - 119.97 36.92 Madera Madera 40.00 40.00 40.00 40.00 40.00 40 125 Pacific Ethanol - Magic Valley - 113.81 42.52 Burley Cassia 50.00 50.00 50.00 50.00 50.00 0 126 Pacific Ethanol - Stockton - 121.34 37.94 Stockton San Joaquin 50.00 50.00 50.00 5 0.00 0 127 Panda Ethanol - Hereford - 102.40 34.82 Hereford Deaf Smith 115.00 115.00 115.00 115.00 0 128 Parallel Products - 117.54 34.10 Rancho Cucamonga San

Bernardino 4.00 4.00 4.00 4.00 4.00 4 129 Parallel Products - 85.77 38.22 Shively / Louisville Jefferson 5.40 5.40 5.40 5.40 5.40 5.4 130 Patriot Renewable Fuels - 89.90 41.40 Annawan / Mineral Henry 100.00 100.00 100.00 100.00 100.00 0 131 Penford Products Co. - 91.67 41.97 Cedar Rapids Linn 45.00 45.00 45.00 45.00 0 132 Permeate Refining - 91 .25 42.34 Hopkinton Delaware 1.50 1.50 1.50 1.50 1.50 1.5 133 Pinal Energy, LLC - 112.03 33.05 Maricopa Pinal 50.00 50.00 50.00 50.00 50.00 50 134 Pine Lake Corn Processors, LLC - 93.06 42.46 Steamboat Rock Hardin 20.00 20.00 20.00 20.00 20.00 20 135 P ioneer Trail Energy - 98.61 40.82 Wo od River Hall 115.00 115.00 115.00 115.00 115.00 0 136 Platinum Ethanol , LLC. (formerly Willmark) - 95.34 42.28 Arthur Ida 110.00 110.00 110.00 110.00 0 137 Plymouth Energy, LLC - 96.25 42.72 Merrill Plymouth 50.00 5 0.00 50.00 50.00 50.00 0

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2022 Ref. RFS1 138 POET Biorefining - Alexandria - 85.65 40.29 Alexandria Madison 65.00 65.00 65.00 65.00 65.00 0 139 POET Biorefining - Ashton - 95.81 43.27 Ashton Osceola 55.00 55.00 55.00 55.00 55.00 55 140 POET Biorefining - Big Stone - 96. 55 45.30 Big Stone City Grant 75.00 75.00 75.00 75.00 75.00 75 141 POET Biorefining - Bingham Lake - 95.06 43.98 Bingham Lake Cottonwood 33.00 33.00 33.00 33.00 33.00 33 142 POET Biorefining - Caro - 83.41 43.47 Caro Tuscola 52.00 52.00 52.00 52.00 52.0 0 52 143 POET Biorefining - Chancellor - 96.96 43.37 Chancellor Turner 100.00 100.00 100.00 100.00 100.00 50 144 POET Biorefining - Coon Rapids - 94.63 41.86 Coon Rapids Carroll 54.00 54.00 54.00 54.00 54.00 54 145 POET Biorefining - Corning - 94.80 40.9 7 Corning Adams 60.00 60.00 60.00 60.00 60.00 60 146 POET Biorefining - Emmetsburg - 94.64 43.10 Emmetsburg Palo Alto 56.00 56.00 56.00 93.75 56.00 56 147 POET Biorefining - Fostoria - 83.39 41.17 Fostoria Seneca 65.00 65.00 65.00 0 148 POET Biorefin ing - Glenville East - 93.31 43.63 Glenville / Albert Lea Freeborn 45.00 45.00 45.00 45.00 45.00 45 149 POET Biorefining - Glenville W est - 93.31 43.63 Glenville / Albert Lea Freeborn 65.00 65.00 65.00 0 150 POET Biorefining - Gowrie - 94.29 42.33 Gowri e We bster 62.00 62.00 62.00 62.00 62.00 62 151 POET Biorefining - Groton - 98.14 45.46 Groton Brown 53.00 53.00 53.00 53.00 53.00 53 152 POET Biorefining - Ham lontown - 93.39 43.29 Hanlontown Wo rth 45.00 45.00 45.00 45.00 45.00 45 153 POET Biorefining - Hudson - 96.48 43.10 Hudson Lincoln 55.00 55.00 55.00 55.00 55.00 55 154 POET Biorefining - Jewell - 93.66 42.33 Jewell Hamilton 62.00 62.00 62.00 62.00 62.00 62 155 POET Biorefining - Laddonia - 91.65 39.25 Laddonia Audrain 50.00 50.00 50.00 50.00 50. 00 50 156 POET Biorefining - Lake Crystal - 94.27 44.14 Lake Crystal Blue Earth 56.00 56.00 56.00 56.00 56.00 56 157 POET Biorefining - Leipsic - 83.99 41.10 Leipsic Putnam 65.00 65.00 65.00 65.00 65.00 0 158 POET Biorefining - Macon - 92.38 39.75 Macon Macon 42.00 42.00 42.00 42.00 42.00 42 159 POET Biorefining - Marion - 83.16 40.62 Marion Marion 65.00 65.00 65.00 65.00 0 160 POET Biorefining - Mitchell - 98.10 43.80 Mitchell Davison 60.00 60.00 60.00 60.00 60.00 60 161 POET Biorefining - North Man chester - 85.80 40.94 North Manchester Wa bash 65.00 65.00 65.00 0 162 POET Biorefining - Portland - 85.02 40.42 Portland Jay 65.00 65.00 65.00 65.00 65.00 0 163 POET Biorefining - Preston - 92.09 43.69 Preston Fillmore 42.00 42.00 42.00 42.00 42.00 42 164 POET Research Center - 97.72 43.15 Scotland Bon Homme 9.00 9.00 9.00 9.00 9.00 9 165 Prairie - Horizon Agri - Energy, LLC (PHAE) - 99.33 39.76 Phillipsburg Phillips 40.00 40.00 40.00 40.00 40.00 40 166 Quad - County Corn Processors - 95.42 42.47 Galva Ida 27.00 27.00 27.00 27.00 27.00 27 167 Red Trail Energy, LLC - 102.31 46.88 Richardton Stark 50.00 50.00 50.00 50.00 50.00 50 168 Redfield Energy - 98.71 44.91 Redfield Spink 50.00 50.00 50.00 50.00 50.00 50 169 Reeve Agri - Energy - 100.88 37.95 Garden City Finney 12.00 12.00 12.00 12.00 12.00 12 170 Renew Energy - 88.79 43.03 Jefferson Junction Jefferson 130.00 130.00 130.00 130.00 130.00 0 171 Renova Energy - 113.74 42.55 Heyburn Minidoka 20.00 0

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2017 2022 Vol

2022 Ref. AE O Vol

2022 Ref. RFS1 172 Show Me Ethanol, LLC - 93.49 39.36 Carrollton Ca rroll 60.00 0 173 Siouxland Energy & Livestock Coop (SELC) - 96.23 43.09 Sioux Center Sioux 57.00 57.00 57.00 57.00 57.00 22 174 Siouxland Ethanol, LLC - 96.59 42.45 Jackson Dakota 50.00 50.00 50.00 50.00 50.00 50 175 Southwest Iowa Renewable Energ y (SIRE) - 95.83 41.16 Council Bluffs Pottawatta mie 110.00 110.00 0 176 Standard Ethanol - Horizon - 100.16 40.29 Cambridge Furnas 44.00 44.00 44.00 44.00 44.00 0 177 Standard Ethanol - Wh eatland - 101.54 40.85 Madrid Perkins 44.00 44.00 44.00 44.00 4 4.00 0 178 Sterling Ethanol, LLC - 103.19 40.64 Sterling Logan 42.00 42.00 42.00 42.00 42.00 42 179 Sun Energy - 102.39 37.22 Wa lsh Baca 3.00 3.00 3.00 3.00 3.00 3 180 SWEnergy, LLC. - 100.62 40.20 McCook Red W illow 0.10 0.10 0.10 0.10 0.10 0.1 181 Ta te & Lyle - 94.31 42.50 Fort Dodge We bster 115.00 115.00 0 182 Tate & Lyle - 84.32 35.74 Loudon Loudon 126.00 126.00 126.00 126.00 126.00 66 183 The Andersons Albion Ethanol, LLC - 84.79 42.26 Albion Calhoun 55.00 55.00 55.00 55.00 55.00 55 184 The A ndersons Clymers Ethanol LLC - 86.44 40.72 Clymers Cass 110.00 110.00 110.00 110.00 110.00 110 185 The Andersons Marathon Ethanol, LLC - 84.59 40.08 Greenville Darke 110.00 110.00 110.00 110.00 110.00 0 186 Theraldson Ethanol - 97.26 46.90 Casselton Cass 100.00 100.00 0 187 Trenton Agri - Products, LLC. (TAP) - 101.01 40.17 Trenton Hitchcock 40.00 40.00 40.00 40.00 40.00 40 188 United Ethanol, LLC - 88.92 42.76 Milton Rock 50.00 50.00 50.00 50.00 50.00 50 189 United W isconsin Grain Producers, LLC. (UW GP) - 89.07 43.59 Friesland Coumbia 60.00 60.00 80.00 80.00 60.00 50 190 Utica Energy, LLC - 88.65 43.98 Oshkosh / Utica Wi nnebago 52.00 52.00 52.00 52.00 52.00 52 191 Verasun - Albert City (formerly US Bio) - 94.95 42.78 Albert City Buena Vista 110.00 1 10.00 110.00 110.00 110.00 110 192 Verasun - Albion (formerly ASAlliances) - 97.99 41.68 Albion Boone 110.00 110.00 110.00 110.00 110.00 0 193 VeraSun - Aurora - 96.69 44.32 Aurora Brookings 120.00 120.00 120.00 120.00 120.00 120 194 Verasun - Bloomingb urg (formerly ASAlliances) - 83.39 39.60 Bloomingburg Fayette 110.00 110.00 110.00 110.00 110.00 0 195 Verasun - Central City (formerly US Bio) - 97.96 41.11 Central City Merrick 100.00 100.00 100.00 100.00 100.00 100 196 VeraSun - Charles City - 92.74 43 .10 Charles City Floyd 110.00 110.00 110.00 110.00 110.00 110 197 Verasun - Dyersville (formerly US Bio) - 91.15 42.49 Dyersville Dubuque 110.00 110.00 110.00 110.00 0 198 VeraSun - Fort Dodge - 94.31 42.51 Fort Dodge We bster 110.00 110.00 110.00 110.0 0 110.00 110 199 Verasun - Hankinson (formerly US Bio) - 96.89 46.08 Hankinson Richland 110.00 110.00 110.00 110.00 110.00 0 200 VeraSun - Hartley - 95.51 43.18 Hartley O ’ Brien 110.00 110.00 110.00 110.00 110.00 0 201 Verasun - Janesville (formerly US B io) - 93.71 44.11 Janesville Wa seca 110.00 110.00 0 202 Verasun - Linden (formerly ASAlliances) - 86.89 40.21 Linden Montgomer y 110.00 110.00 110.00 110.00 110.00 0 203 Verasun - Marion (formerly US Bio) - 97.26 43.43 Marion Turner 110.00 110.00 110.0 0 110.00 110.00 0 204 Verasun - Ord (formerly US Bio) - 98.92 41.58 Ord Valley 50.00 50.00 50.00 50.00 50.00 50 205 VeraSun - We lcome - 94.65 43.66 We lcome Martin 110.00 110.00 110.00 110.00 0 206 Verasun - Wo odbury (formerly US Bio) - 85.07 42.77 Lake Odessa Ionia 50.00 50.00 50.00 50.00 50.00 50 207 We stern New York Energy, LLC - 78.30 43.19 Shelby Orleans 50.00 50.00 50.00 50.00 50.00 0 208 We stern Plains Energy, LLC - 100.85 39.23 Campus / Oakley Gove 45.00 45.00 45.00 45.00 45.00 45

### 81

ID FEEDSTOCK LONGI TUDE LATI TUDE CITY COUNTY Vol 2010 Vol

2012 Vo l

2014 Vol

2017 2022 Vol

2022 Ref. AE O Vol

2022 Ref. RFS1 209 We stern Wi sconsin Energy, LLC (WWE) - 91.98 45.05 Boyceville Dunn 45.00 45.00 45.00 45.00 45.00 45 210 Wh ite Energy - 102.52 34.87 Hereford Deaf Smith 100.00 100.00 100.00 100.00 100.00 0 211 Wh ite Energy (formerly Plainview BioEnergy, LLC.) - 101.66 34.18 Plainv iew Hale 100.00 100.00 100.00 100.00 100.00 0 212 Wh ite Energy (formerly USEP) - 98.86 38.89 Russell Russell 50.00 50.00 50.00 50.00 50.00 50 213 Wi nd Gap Farms (Anheuser/Miller Brewery) - 84.18 31.35 Baconton Mitchell 0.40 0.40 0.40 0.40 0.40 0.4 214 Wyom ing Ethanol - 104.35 42.13 Torrington Goshen 9.00 9.00 9.00 9.00 9.00 9 215 Xethanol - 92.09 41.89 Blairstown Benton 5.00 5.00 5.00 5.00 5.00 5 216 Yuma Ethanol - 102.68 40.25 Yuma Yuma 50.00 50.00 50.00 50.00 50.00 0 TOTA L 11595 12890 14438 149 94 12540 6697

### 82 APPENDIX B Projected Corn Ethanol Production

### 83 Figure B - 1. Corn Ethanol Production in 2010

Figure B - 2. Corn Ethanol Production in 201 2

### 84 Figure B -3. Corn Ethanol Production in 2014

Figure B - 4. Corn E thanol Production AEO Reference Case in 2022

### 85 APPENDIX C Location Details and P roduction Capacity (MGY) of Cellulosic Ethanol Plants for Various Forecast Y ears

### 86 Appendix C Location details and production capacity (MGY) of cellulosic ethanol p lants for various forecast years

ID FIPS FEEDSTOCK LONGI TUDE LATI TUDE COUNTY/

STATE Transport

Mode Vol

2010 Vol

2012 Vo l

2014 Vol 2017 Vol

2022 1 17189 Corn Stover with some Forest Residue and MSW - 89.41 38.35 WASHINGTON IL R 84.77 2 17159 Corn Stover with some Forest Residue and MSW - 88.09 38.71 RICHLAND IL R 80.96 3 17029 Corn Stover with some Forest Residue and MSW - 88.22 39.52 COLES IL R 77.24 4 17019 Corn Stover with some Forest Residue and MSW - 88.20 40.14 CHAMPAIGN IL R 88.63 5 17075 Corn Stover with some Forest Residue and MSW - 87.82 40.75 IROQUOIS IL R 80.39 6 17063 Corn Stover with some Forest Residue and MSW - 88.42 41.29 GRUNDY IL RW 76.57 7 17039 Corn Stover with some Forest Residue and MSW - 88.90 40.17 DEW ITT IL R 99.83 8 17173 Corn Stover with some Forest Residue and MSW - 88.81 39.39 SHELBY IL R 67.69 9 17135 Corn Stover with some Forest Residue and MSW - 89.48 39.23 MONTGOMERY IL R 77.62 10 17129 Corn Stover with some Forest Residue and MSW - 89. 80 40.03 MENARD IL R 99.11 11 17137 Corn Stover with some Forest Residue and MSW - 90.20 39.72 MORGAN IL R 67.11 12 17179 Corn Stover with some Forest Residue and MSW - 89.51 40.51 TAZEW ELL IL RW 107.42 107.42 13 17011 Corn Stover with some Forest Residue and MSW - 89.53 41.40 BUREAU IL R 129.66 14 17095 Corn Stover with some Forest Residue and MSW - 90.21 40.93 KNOX IL R 88.82 15 17043 Corn Stover with some Forest Residue and MSW - 88.09 41.85 DUPAGE IL R 109.44 127.65 127.65 16 17141 Corn Stover with some Forest Residue and MSW - 89.32 42.04 OGLE IL R 94.58 94.58 17 17015 Corn Stover with some Forest Residue and MSW - 89.93 42.07 CARROLL IL R 77.43 18 18179 Corn Stover with some Forest Residue and MSW - 85.22 40.73 WELLS IN R 77.14 77.14 19 18095 Corn Stover with some Forest Residue and MSW - 85.72 40.16 MADISON IN R 78.05 78.05 20 18079 Corn Stover with some Forest Residue and MSW - 85.63 39.00 JENNINGS IN R 94.39 21 18027 Corn Stover with some Forest Residue and MSW - 87.07 38.70 DAVIESS IN R 93.09 22 18109 Corn Stover with some Forest Residue and MSW - 86.45 39.48 MORGAN IN R 100.12 100.12 23 18023 Corn Stover with some Forest Residue and MSW - 86.48 40.30 CLINTON IN R 80.43 24 18049 Corn Stover with some Forest Residue and MSW - 86.26 41.05 FULTON IN R 73.56 25 18161 Corn Stover with some Forest Residue and MSW - 84.92 39.63 UNION IN R 82.29 26 18033 Corn Stover with some Forest Residue and MSW - 85.00 41.40 DEKALB IN R 91.46 27 18121 Corn Stover with some Forest Residue and MSW - 87.21 39.77 PARKE IN R 91.79 28 18007 Corn Stover with some Forest Residue and MSW - 87.31 40.61 BENTON IN R 91.62 29 18073 Corn Stover with some Forest Residue and MSW - 87.12 41.02 JASPER IN R 81.53 30 18 163 Corn Stover with some Forest Residue and MSW - 87.59 38.03 VANDERBURG H IN RW 73.94 31 20109 Corn Stover with some Forest Residue and MSW - 101.15 38.92 LOGAN KS R 74.88

### 87

ID FIPS FEEDSTOCK LONGI TUDE LATI TUDE COUNTY/

STATE Transport

Mode Vol

2010 Vol

2012 Vo l

2014 Vol 2017 Vol

2022 32 20189 Corn Stover with some Forest Residue and MSW - 101.31 37.19 STEVENS KS R 30.00 74.42 86.80 86.80 33 20173 Corn Stover with some Forest Residue and MSW - 97.46 37.68 SEDGWIC K KS R 70.57 34 20131 Corn Stover with some Forest Residue and MSW - 96.01 39.78 NEMAHA KS R 78.02 35 19061 Corn Stover with some Forest Resi due and MSW - 90.88 42.47 DUBUQUE IA R 70.43 70.43 36 19011 Corn Stover with some Forest Residue and MSW - 92.07 42.08 BENTON IA R 68.91 37 19019 Corn Stover with some Forest Residue and MSW - 91.84 42.47 BUCHANAN IA R 83.34 83.34 38 19123 Corn S tover with some Forest Residue and MSW - 92.64 41.34 MAHASKA IA R 80.29 39 19075 Corn Stover with some Forest Residue and MSW - 92.79 42.40 GRUNDY IA NT 82.64 40 19037 Corn Stover with some Forest Residue and MSW - 92.32 43.06 CHICKASAWIA R 82.2 1 82.21 41 19033 Corn Stover with some Forest Residue and MSW - 93.26 43.08 CERRO GORDO IA R 79.37 79.37 42 19069 Corn Stover with some Forest Residue and MSW - 93.26 42.73 FRANKLIN IA R 79.54 43 19169 Corn Stover with some Forest Residue and MSW - 93.47 42.04 STORY IA R 89.27 89.27 44 19175 Corn Stover with some Forest Residue and MSW - 94.24 41.03 UNION IA R 75.54 45 19077 Corn Stover with some Forest Residue and MSW - 94.50 41.68 GUTHRIE IA R 85.35 85.35 46 19187 Corn Stover with some F orest Residue and MSW - 94.18 42.43 WEBSTER IA R 85.83 85.83 47 19155 Corn Stover with some Forest Residue and MSW - 95.54 41.34 POTTAWATTA MIE IA R 84.39 84.39 48 19093 Corn Stover with some Forest Residue and MSW - 95.51 42.39 IDA IA R 87.72 87.72 49 19021 Corn Stover with some Forest Residue and MSW - 95.15 42.74 BUENA VISTA IA R 83.63 83.63 50 19147 Corn Stover with some Forest Residue and MSW - 94.68 43.08 PALO ALTO IA R 31.00 64.58 75.33 75.33 51 19141 Corn Stover with some Forest Residue and MSW - 95.62 43.08 O ’ BRIEN IA R 80.45 80.45 52 19167 Corn Stover with some Forest Residue and MSW - 96.18 43.08 SIOUX IA R 72.10 72.10 53 19139 Corn Stover with some Forest Residue and MSW - 91.11 41.48 MUSCATINE IA RW 82.58 54 19057 Corn Stover with some Forest Residue and MSW - 91.18 40.92 DES MOINES IA R 87.22 87.22 55 19145 Corn Stover with some Forest Residue and MSW - 95.15 40.74 PAGE IA R 80.88 80.88 56 21233 Corn Stover with some Forest Residue and MSW - 87.68 37.52 WEBSTER KY R 69.54 57 26163 Corn Stover with some Forest Residue and MSW - 83.29 42.29 WAYNE MI RW 84.68 84.68 58 26025 Corn Stover with some Forest Residue and MSW - 85.01 42.25 CALHOUN MI R 108.67 108.67 59 26157 Corn Stover with some Forest Residue and MSW - 83.42 43.46 TUSCOLA MI R 105.33 105.33 60 26067 Corn Stover with some Forest Residue and MSW - 85.07 42.95 IONIA MI R 100.62 117.37 117.37 61 26159 Corn Stover with some Forest Residue and MSW - 86.02 42.25 VAN BUREN MI R 89.12 62 27133 Corn Sto ver with some Forest Residue and MSW - 96.25 43.67 ROCK MN R 72.56 63 27091 Corn Stover with some Forest Residue and MSW - 94.55 43.67 MARTIN MN R 94.64 94.64 64 27043 Corn Stover with some Forest Residue and MSW - 93.95 43.67 FARIBAULT MN R 87.6 9 65 27039 Corn Stover with some Forest Residue and MSW - 92.86 44.02 DODGE MN R 85.90 85.90 66 27083 Corn Stover with some Forest Residue and MSW - 95.84 44.41 LYON MN R 83.94 67 27143 Corn Stover with some Forest Residue and MSW - 94.23 44.58 SIBL EY MN R 101.73 68 27037 Corn Stover with some Forest Residue and MSW - 93.07 44.67 DAKOTA MN R 113.56 113.56

### 88

ID FIPS FEEDSTOCK LONGI TUDE LATI TUDE COUNTY/

STATE Transport

Mode Vol

2010 Vol

2012 Vo l

2014 Vol 2017 Vol

2022 69 27145 Corn Stover with some Forest Residue and MSW - 94.61 45.55 STEARNS MN R 67.81 70 27023 Corn Stover with some Forest Residue an d MSW - 95.57 45.02 CHIPPEWA MN R 91.66 71 27149 Corn Stover with some Forest Residue and MSW - 96.00 45.59 STEVENS MN R 76.01 76.01 72 29007 Corn Stover with some Forest Residue and MSW - 91.84 39.22 AUDRAIN MO R 86.23 86.23 73 29041 Corn Stover with some Forest Residue and MSW - 92.96 39.51 CHARITON MO R 73.73 74 29177 Corn Stover with some Forest Residue and MSW - 93.99 39.35 RAY MO RW 99.91 99.91 75 29075 Corn Stover with some Forest Residue and MSW - 94.41 40.21 GENTRY MO R 95.44 7 6 29143 Corn Stover with some Forest Residue and MSW - 89.65 36.59 NEWM ADRID MO R 84.19 77 29045 Corn Stover with some Forest Residue and MSW - 91.74 40.41 CLARK MO R 89.40 78 29189 Corn Stover with some Forest Residue and MSW - 90.44 38.64 ST. LOU IS MO RW 124.77 79 31179 Corn Stover with some Forest Residue and MSW - 97.12 42.21 WAYNE NE R 95.77 80 31089 Corn Stover with some Forest Residue and MSW - 98.78 42.46 HOLT NE R 91.48 81 31155 Corn Stover with some Forest Residue and MSW - 96. 64 41.23 SAUNDERS NE R 99.87 82 31011 Corn Stover with some Forest Residue and MSW - 98.07 41.71 BOONE NE R 98.35 98.35 83 31109 Corn Stover with some Forest Residue and MSW - 96.69 40.78 LANCASTER NE R 73.93 84 31129 Corn Stover with some Fore st Residue and MSW - 98.05 40.18 NUCKOLLS NE R 75.78 85 31185 Corn Stover with some Forest Residue and MSW - 97.60 40.87 YORK NE R 93.51 93.51 86 31041 Corn Stover with some Forest Residue and MSW - 99.73 41.39 CUSTER NE R 84.18 87 31111 Corn St over with some Forest Residue and MSW - 100.75 41.05 LINCOLN NE R 81.45 88 31087 Corn Stover with some Forest Residue and MSW - 101.04 40.18 HITCHCOCK NE R 83.41 89 31083 Corn Stover with some Forest Residue and MSW - 99.40 40.18 HARLAN NE R 78. 47 90 39133 Corn Stover with some Forest Residue and MSW - 81.20 41.17 PORTAGE OH R 97.84 97.84 91 39139 Corn Stover with some Forest Residue and MSW - 82.54 40.77 RICHLAND OH R 83.33 92 39049 Corn Stover with some Forest Residue and MSW - 83.01 39. 97 FRANKLIN OH R 77.48 93 39027 Corn Stover with some Forest Residue and MSW - 83.81 39.41 CLINTON OH R 99.83 99.83 94 39091 Corn Stover with some Forest Residue and MSW - 83.77 40.39 LOGAN OH R 74.83 95 39173 Corn Stover with some Forest Resid ue and MSW - 83.62 41.36 WO OD OH R 84.96 96 39011 Corn Stover with some Forest Residue and MSW - 84.22 40.56 AUGLAIZE OH R 79.87 97 46037 Corn Stover with some Forest Residue and MSW - 97.61 45.37 DAY SD R 84.73 98 46077 Corn Stover with some F orest Residue and MSW - 97.49 44.37 KINGSBURY SD R 98.02 99 46079 Corn Stover with some Forest Residue and MSW - 97.13 44.02 LAKE SD R 83.21 83.21 100 46125 Corn Stover with some Forest Residue and MSW - 97.15 43.31 TURNER SD R 88.50 88.50 101 46 045 Corn Stover with some Forest Residue and MSW - 99.22 45.42 EDMUNDS SD R 79.94 102 55055 Corn Stover with some Forest Residue and MSW - 88.78 43.02 JEFFERSON W I R 94.09 94.09 103 55025 Corn Stover with some Forest Residue and MSW - 89.42 43.07 DAN E W I R 75.76 104 55043 Corn Stover with some Forest Residue and MSW - 90.71 42.87 GRANT W I RW 68.08 105 55077 Corn Stover with some Forest Residue and MSW - 89.40 43.82 MARQUETTE WI R 65.48

### 89

ID FIPS FEEDSTOCK LONGI TUDE LATI TUDE COUNTY/

STATE Transport

Mode Vol

2010 Vol

2012 Vo l

2014 Vol 2017 Vol

2022 106 55015 Corn Stover with some Forest Residue and MSW - 88.22 44.08 CALUMET W I R 91.42 107 55141 Corn Stover with some Forest Residue and MSW - 90.04 44.46 WO OD W I R 59.11 108 55035 Corn Stover with some Forest Residue and MSW - 91.29 44.73 EAU CLAIRE W I R 64.59 109 55033 Corn Stover with some F orest Residue and MSW - 91.90 44.95 DUNN W I R 62.85 110 40035 Corn Stover with some Forest Residue and MSW - 95.21 36.76 CRAIG OK R 130.00 111 40101 Corn Stover with some Forest Residue and MSW - 95.38 35.62 MUSKOGEE OK R 118.00 112 40063 Corn Stover with some Forest Residue and MSW - 96.25 35.05 HUGHES OK R 91.00 113 40113 Corn Stover with some Forest Residue and MSW - 96.40 36.63 OSAGE OK NW 116.00 114 40081 Corn Stover with some Forest Residue and MSW - 96.88 35.70 LINCOLN OK R 120 .00 115 40073 Corn Stover with some Forest Residue and MSW - 97.94 35.95 KINGFISHER OK R 110.00 116 40051 Corn Stover with some Forest Residue and MSW - 97.88 35.02 GRADY OK R 108.00 117 40027 Corn Stover with some Forest Residue and MSW - 97.33 35 .20 CLEVELAND OK R 118 54033 Corn Stover with some Forest Residue and MSW - 80.38 39.28 HARRISON WV R 149.00 119 06007 Forestry and MSW - 121.60 39.67 BUTTE CA R 12.00 80.92 94.39 94.39 120 06077 Forestry and MSW - 121.27 37.93 SAN JOAQUIN CA RW 103.05 120.21 120.21 121 06093 Forestry and MSW - 122.54 41.59 SISKIYOU CA R 102.20 122 06059 MSW - 117.76 33.70 ORANGE CA R 22.00 22.00 114.02 133.20 133.00 123 41039 Forestry and MSW - 122.85 43.94 LANE OR R 125.91 124 41071 Forestry and MSW - 123.31 45.23 YAMHILL OR RW 16.00 43.00 101.25 118.11 118.11 125 53067 Forestry and MSW - 122.83 46.92 THURSTON WA R 97.11 126 53007 Forestry and MSW - 120.62 47.87 CHELAN WA RW 78.04 127 30089 Forestry and MSW - 115.13 47.68 SANDERS MT R 91.54 128 23021 Forestry and MSW - 69.29 45.84 PISCATAQUIS ME R 91.11 129 23019 Forestry and MSW - 68.65 45.40 PENOBSCOT ME R 99.88 130 33003 Forestry and MSW - 71.20 43.87 CARROLL NH R 136.10 131 33003 Switchgrass NE (NH, CT &MA) - 71.20 43.87 CARRO LL NH R 34.92 132 05061 Forestry and MSW - 93.99 34.09 HOWARD AR R 96.80 133 05147 Forestry and MSW - 91.24 35.18 WO ODRUFF AR RW 102.09 134 28043 Forestry and MSW - 89.80 33.77 GRENADA MS R 106.98 135 22035 Forestry and MSW - 91.24 32.73 EAST CARROLL LA RW 36.00 88.25 102.93 102.93 136 22013 Forestry and MSW - 93.06 32.35 BIENVILLE LA R 115.21 137 22053 Forestry and MSW - 92.81 30.27 JEFFERSON DAVIS LA R 50.00 74.59 87.01 87.01 138 22033 Forestry and MSW - 91.10 30.54 EAST BATON RW 105.54 105.54

### 90

ID FIPS FEEDSTOCK LONGI - TUDE LATI -

TUDE COUNTY/

STATE Transport

Mode Vol

2010 Vol

2012 Vo l

2014 Vol 2017 Vol

2022 ROUGE LA 139 28035 Forestry and MSW - 89.26 31.19 FORREST MS R 106.78 140 01063 Forestry and MSW - 87.96 32.86 GREENE AL RW 108.33 141 01103 Forestry and MSW - 86.85 34.45 MORGAN AL R 95.98 142 01053 Forestry and MSW - 87.16 31.13 ESCAMBIA AL R 112.00 143 01113 Forestry and MSW - 85.19 32.29 RUSSELL AL RW 100.52 144 13131 Forestry and MSW - 84.23 30.87 GRADY GA R 130.00 145 13283 Forestry and MSW - 82.57 32.40 TREUTLEN GA RW 43.00 43.00 84.07 98.06 98.06 146 13245 Fore stry and MSW - 82.07 33.36 RICHMOND GA R 50.00 86.74 101.18 101.18 147 45015 Forestry and MSW - 79.95 33.20 BERKELEY SC RW 104.58 104.58 148 45083 Forestry and MSW - 81.99 34.93 SPARTANBURG SC R 107.99 149 37067 Forestry and MSW - 80.26 36.13 FORSYT H NC R 89.09 103.92 103.92 150 37051 Forestry and MSW - 78.83 35.05 CUMBERLAND NC RW 109.58 151 37117 Forestry and MSW - 77.11 35.84 MARTIN NC RW 101.80 152 51083 Forestry and MSW - 78.94 36.77 HALIFAX VA R 97.57 153 51149 Forestry and MSW - 77.23 37.18 PRINCE GEORGE VA RW 85.19 99.38 99.38 154 47123 Forestry and MSW - 84.25 35.44 MONROE TN R 37.00 82.85 96.64 96.64 155 48005 Forestry and MSW - 94.61 31.26 ANGELINA TX R 114.10 156 13127 Forestry and MSW - 81.55 31.24 GLYNN GA RW 107 .63 157 05025 Forestry and MSW - 92.19 33.90 CLEVELAND AR R 98.91 158 01121 Forestry and MSW - 86.17 33.38 TALLADEGA AL R 98.40 114.78 114.78 159 48201 Forestry and MSW - 95.39 29.86 HARRIS TX RW 79.92 79.92 160 12051 Bagasse - 81.16 26.55 HENDRY FL NW 37.00 77.16 90.00 90.00 161 12099 Bagasse - 80.47 26.65 PALM BEACH FL R 100.00 162 12099 Bagasse - 80.47 26.65 PALM BEACH FL R 100.00 163 12099 Bagasse - 80.47 26.65 PALM BEACH FL R 100.00 164 48215 Bagasse - 98.18 26.40 HIDALGO TX R 100.00 165 48061 Bagasse - 97.53 26.13 CAMERON TX RW 100.00 166 48489 Sorghum - 97.68 26.47 WILLACY TX R 100.00 167 48113 MSW - Dallas, TX - 96.78 32.77 DALLAS TX R 44.58 52.20 52.00 168 48029 MSW - San Antonio, TX - 98.52 29.45 BEXAR TX R 16.20 16.00 169 48453 MSW -Austin, TX - 97.78 30.33 TRAVIS TX R 14.00 14.00 170 22055 Bagasse - 92.06 30.21 LAFAYETTE LA NW 100.00 171 22047 Bagasse - 91.35 30.26 IBERVILLE LA RW 90.00

### 91

ID FIPS FEEDSTOCK LONGI TUDE LATI TUDE COUNTY/

STATE Transport

Mode Vol

2010 Vol

2012 Vo l

2014 Vol 2017 Vol

2022 172 22057 Bagasse - 90.44 29.58 LAFOURCHE LA RW 50.00 173 2209 7 Bagasse - 92.01 30.60 ST. LANDRY LA R 100.00 174 22077 Bagasse - 91.60 30.71 POINTE COUPEE LA RW 100.00 175 22009 Bagasse - 92.00 31.08 AVOYELLES LA R 100.00 176 22003 Bagasse - 92.83 30.65 ALLEN LA R 42.87 50.00 50.00 177 36119 MSW - New Yor k, NY - 73.75 41.17 WESTCHESTE R NY R 16.00 63.00 61.73 72.00 72.00 178 32003 MSW - Las Vegas, NV - 115.02 36.21 CLARK NV R 17.20 17.00 179 12011 MSW - Miami, FL - 80.49 26.15 BROWARD FL RW 26.58 31.20 31.00 180 08001 MSW - Denver, CO - 104.34 39.88 ADAMS CO R 3.00 10.00 24.00 28.00 28.00 181 42091 MSW - Phillidelphia, PA - 75.37 40.21 MONTGOMERY PA R 36.00 36.01 42.00 42.00 TOTA L 100 500 1750 5500 16039 Note: R= Rail; RW= Rail and Water; NT= Near Town; and NW= Near Water

### 92 APPENDIX D Projected Cellulosic Ethanol Production

### 93 Figure D - 1. Cellulosic Ethanol Production in 2010

Figure D -2. Cellulosic Ethanol Production in 2012

### 9 4 Figure D -3. Cellulosic Ethanol Production in 2014

Figure D - 4. Cellulosic Ethanol Production in 2017