ANALYSIS OF FUEL ETHANOL TRANSPORTATION

ACTIVITY AND POTENTIAL DISTRIBUTION 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 2009

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 CONTENTS

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.........................................................................................24

3.7.3 Truck.................................................................................................25

3.8 Congestion/Delay.............................................................................................25

3.8.1 Rail....................................................................................................26

3.8.2 Waterway ..........................................................................................26

3.8.3 Highway............................................................................................30

4. GENERATION OF ETHANOL TRANSPORTATION ACTIVITY DATA –

MODELING APPROACH....................................................................................33

4.1 Origin –Destination Matrix (ODM).................................................................33

4.2 Traffic Assignment ..........................................................................................36

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 Cellulosic 

Ethanol Plants for Various Forecast Years................................................85

APPENDIX D Projected Cellulosic Ethanol Production ..................................................92
TABLES

Table 1. Annual Ethanol Projections (in billion gallons) under Various Scenarios............4

Table 2. Projected 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, Based on FAF2 .....27

Table 6. Estimated Future Commodity Flow (in thousand tons) and Growth 

 Factors for this Study..........................................................................................27

Table 7. Estimated Future Growth Rates for Lock Delay .................................................28

Table 8. Future Growth Factors for 2006 ADT/Lane ........................................................32

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.....................................................................42

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 Level under “2012B Scenario”...........58

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

of Various Categories of Quantity Level under “2014B Scenario”...........59

Table 16. Estimated Number of Miles Experiencing an Increase in Ethanol Traffic 

of Various Categories of Quantity Level under “2022B Scenario”...........60

Table 17. Estimated Number of Miles Experiencing an Increase in Ethanol Traffic 

of Various Categories of Quantity Level under “2022 Controlled” ..........61
FIGURES

Figure 1. Projected corn ethanol refineries in 2017 and 2022 .............................................6

Figure 2. Cellulosic ethanol refineries in 2022....................................................................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 2022 ................................................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 ...................................................................................21

Figure 15. Distribution of potential ethanol distribution terminals...................................23

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

Figure 17. Average tow delays for 2007............................................................................29

Figure 18. Average tow delays for 2022............................................................................29

Figure 19. Average daily traffic per lane trends for rural U.S. .........................................31

Figure 20. Average daily traffic per land trends for urban U.S. ........................................32

Figure 21. Projected ethanol movements (Ktons) under the 2022 RFS1 Reference 

scenario ......................................................................................................43

Figure 22. Projected ethanol movements (Ktons) under the 2022 AEO Reference 

scenario ......................................................................................................44

Figure 23. Projected ethanol movements (Ktons) under the 2022 Controlled

scenario ......................................................................................................45

Figure 24. Projected county-level total Kton-mile ethanol movements under 

the 2022 RFS1 Reference Scenario ...........................................................46

Figure 25. Projected county-level total Kton-mile ethanol movements under 

the 2022 RFS1 AEO Reference Scenario ..................................................47

Figure 26 Projected county-level total Kton-mile ethanol movements under 

the 2022 Controlled Scenario.....................................................................48

Figure 27. Projected county-level total ethanol loading/unloading activities

(Ktons) under 2022 RFS1 Reference Scenario.........................................49

Figure 28 Projected county-level total ethanol loading/unloading activities

(Ktons) under 2022 AEOference Scenario ...............................................50

Figure 29 Projected county-level total ethanol loading/unloading activities

(Ktons) under 2022 Controlled Scenario ..................................................51

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 % increase in base flows for 2012B scenario............63
Figure 32. Projected rail ethanol movements in terms of Ktons and % increase in 

base flows for 2014B scenario...................................................................64

Figure 33. Projected waterways and highways combined ethanol movements in 

terms of Ktons and % increase in base flows for 2014B scenario............65

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 ..............67

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............69
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 by 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, EISA 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. EISA further requires that of the total 36 billion gallons of renewable fuel use 

required by 2022, 16 billion gallons be cellulosic biofuel and 1 billion gallon be biomassbased 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 imported ethanol. EPA 

projected that the balance of the renewable fuel volume required by EISA 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 billion gallons of coprocessed renewable diesel. Ethanol currently cannot typically be shipped by pipeline due 

to concerns over materials compatibility and product contamination. Product 

contamination and other concerns currently also has prevented widespread shipment of 

FAME-based biodiesel by pipeline. In addition, the location of ethanol and to a lesser 

extent FAME biodiesel plants is typically 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 fungibly with petroleumbased diesel fuel and thus be fundamentally 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 by rail, barge, and tank truck to the terminals because the points of 

production may not be in proximity to the existing petroleum fuel distribution system. 

Projected biofuels production increases under the EISA will likely 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 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 

systemic delays in biofuels distribution. The country is lacking the infrastructure to most 
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efficiently transport liquid biofuels that are produced primarily in the midwest and plains

states to population centers in the East and elsewhere that account for 80% of the U.S. 

population. It is estimated that about one-third to one-half of domestic ethanol produced 

today 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 by 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 transport 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 faces capacity issues, labor shortages, and 

public opposition to increased truck traffic on the nation’s highway system. 

Transportation is typically the third largest expense to an ethanol producer—after 

feedstock and energy. Spatial variation in infrastructure development, especially refinery

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. It is important to assess how increased biofuels 

production levels would change freight flow patterns and related transportation costs, 

which are influenced by 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 less 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 by 

2016.

2

Transportation impacts 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.
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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 

transportation requirements for ethanol production and use of 5 billion gallons per year 

(BGY) by 2012 that would result from a “Renewable Fuels 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 was to conduct an analysis of 

ethanol transport by domestic truck, marine, and rail distribution systems from ethanol 

refineries to blending terminals using ORNL’s North American Transportation 

Infrastructure Network Model (hereafter referred to as the “Infrastructure Network 

Model”). As noted above, the ethanol (the biofuel on which this study focuses) that EPA 

projects will be used as a result of the EISA cannot currently be transported from the 

point of production to the terminal-level using the existing petroleum distribution system. 

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 terminallevel on a county-by-county basis. In addition, EPA must assess the potential constraints 

that may exist in the distribution of ethanol under EISA—hereafter referred to as 

distribution constraint analysis. An infrastructure analysis 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 analytical issues are addressed by ORNL

Infrastructure 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 EISA. 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 specific 

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 ethanol sales increases are highly 

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.
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2. SCENARIOS DESCRIPTION

Seven scenarios were considered across five projection years (i.e., 2010, 2012, 2014, 

2017, and 2022) using the EPA assumptions for major parameters based on EISA, 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 seven scenarios are considered 

for the distribution constraint analyses. Detailed, 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” cases indicate a substantially lower level of supply, particularly 

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 by using the historical import data and 

potential state-level import estimates, which were taken into consideration in the final 

analysis 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

Four scenarios are considered in the distribution constraint analysis. Additional demand 

for fatty acid methyl esters (FAME) biodiesel and non-co-processed 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 scenarios considered in the 

constraint analysis 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 years have not been 

considered for the analysis since they do not provide any additional information than the 

extreme cases already considered for the analysis. 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 diesel
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demand specific supply sources and imports have been increased proportionately. The 

following section discusses in detail the methodology used in to prepare the EPA data 

and the other necessary data used in the analysis.

3. DATA PREPARATION

3.1 Corn Ethanol Refineries

EPA furnished the corn ethanol refinery list and refinery production outputs for the years 

2010, 2012, 2014, 2017, and 2022. For the 2022 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. GIS-based 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 Figure 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 by the EPA. EPA did not identify locations 

of the proposed refineries by longitude and latitude, but by county and state. 

ORNL used a geographic information system (GIS) tool to locate potential refinery sites 

within the counties. ORNL assembled street, rail, and waterway map layers. Earth 

Science Research Institute’s (ESRI) StreetMap USA4is used for the highway/street 

network. The US Army Corps of Engineers’ Inland and Costal Waterway Network5

dataset prepared by 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 downloaded from http://www.iwr.usace.army.mil/NDC/data/datanwn.htm on February 10, 2008.
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Figure 1. Projected corn ethanol refineries in 2017 and 2022

In addition to transportation infrastructure networks for street, rail, and waterway, the 

following map layers are also used to help locate the cellulosic plants. A terrain map 

layer, i.e., the shaded relief based on US Geological Survey (USGS) National Elevation 

Dataset6(NED) is used. This map layer ensures that cellulosic plants will not be located 

in areas with terrain not suitable for industrial development. An aerial imagery map layer, 

the I-Cubed7 Nationwide Prime 1-meter or better resolution imagery8for 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 plants. 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 refinery, the cellulosic 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, efforts are made to locate 

the cellulosic ethanol refinery close to both rail and waterway if possible. 

Otherwise, the cellulosic 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.
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 If the county has access to rail only, the cellulosic ethanol refinery is to be located 

close to the railroad.

 If the county has access to waterway only, 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 outskirts of a small town.

GIS-based shape files have been generated for the cellulosic 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 plants 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 years 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 refineries 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

Information Administration (EIA) import locations. The imports, in terms of billion 

gallons per year, from Brazil and the Caribbean Basin Initiative (CBI) countries, are 
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listed in Table 2 along with domestic production estimates. The relatively small volume 

of ethanol imports under the AEO 2022 reference case has been assumed to originate 

from corn ethanol plants to simplify the analysis, 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

2017 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 ports of entry. The ports were identified by

reviewing the EIA historical ports of entry for imports of ethanol and gasoline. After 

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 based on the vehicle-miles-traveled. 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 by 

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 Table 3.

3.3.2 Geographic Information for 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 (GIS) 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.
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Table 3. Ports of Entry and Projected Imported Ethanol Volumes (MGY)

Port Name

2010 2012 2014 2017 2022

Seattle, WA 13.85 7.36 17.34 64.92 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

Tampa, 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 76.99 130.25
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Figure 3. Ports of entry for ethanol fuel in 2022

3.4 Ethanol Demand

EPA provided the state-level ethanol demand for the 2010, 2012, 2014, 2017, 2022 

controlled 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 ORNLdeveloped Transferability Travel Rates10

, and their distribution at the census tract level is 

assumed to be the same for all scenarios. 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 reliable 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 sources 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 locale-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 Figure 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.
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Table 4. State-Level Annual Ethanol Fuel Demand Projections (in million gallons)

Controlled Cases 2022 

AEO

2022 

STATE 2010 2012 2014 2017 2022 RFS1

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 50 142 139 235 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 of 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 197 199 201 197 558 194 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 97
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Table 4. State-Level Annual Ethanol Fuel Demand Projections (in million gallons)

Controlled Cases 2022 

AEO

2022 

STATE 2010 2012 2014 2017 2022 RFS1

Pennsylvania 223 368 518 624 720 350 123

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 distribution at census tract level for Hawaii
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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 Voronoi 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 service region level are then shared to individual terminals evenly 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 centroids is the partition of the US states which all points within a 

partition(i) are closer to the terminal i than any other terminals.
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Figure 6. Ethanol demand at the terminal level for 2010

Figure 7. Ethanol demand at the terminal level for 2012
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Figure 8. Ethanol demand at the terminal level for 2014

Figure 9. Ethanol demand at the terminal level for 2017
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Figure 10. Ethanol demand at the terminal level for 2022 controlled case

Figure 11. Ethanol demand at terminal level for 2022 AEO Reference Case
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3.5 Gasoline Fuel Terminals

The gasoline fuel terminals database is based on the OPIS/STALSBY Petroleum 

Terminal Encyclopedia, 2008 edition12, 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 handle gasoline and/or ethanol. These 1056 gasoline and/or ethanol terminals 

are depicted in Figure 12.

Figure 12: Gasoline and/or ethanol terminals

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 type, such as asphalt, crude, jet fuel, lube oil, petrochemicals, natural gas 

liquids, and refined products terminals.

A thematic map of total storage capacity at the terminals is presented in Figure 13. Four 

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.asp
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Figure 13. Gasoline and/or total storage capacity distribution

For the purpose of this project, a terminal is considered a gasoline terminal if the terminal 

in the database is of a refined products type with no detailed specification 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

 Ethanol
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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 generally located 

at multiple terminals in every metropolitan area and are operated by 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 

generally 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, however, are located adjacent to navigable water, so water is 

available only as part of a multi-modal trip.

So far, petroleum products pipelines typically cannot batch ethanol with their other 

products. Should pipelines 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 facilities are specifically 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, rarely, 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 waybill 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 miles long, and may be replaced eventually by dedicated ethanol pipelines 

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 by pipeline will be developed.

Several regional dedicated ethanol terminals have been established within the last few 

years. They are identified here:

1. Albany, NY. Receives unit trains, distributes ethanol by truck locally, but 

primarily by barge for blending in northern NJ (New York metro area) and the 

New England coast (Providence and Boston).
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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.

Distributes 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 originate ethanol 

rail shipments.

6. Arlington, TX. A new rail receiving facility separate from pipeline terminals in 

the Dallas Fort Worth 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. Location 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 waybills shows significant ethanol shipments 

terminating in Columbia, SC, Wilmington, NC, Kingsport, TN, and Ogden, UT (not 

associated with any known terminal, possibly for industrial 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 Southern terminal in 

Alexandria, VA where trucks presumably 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 probably 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 by rail, and 

generally distribute to California blending sites by truck.

Because ethanol distribution terminals in metro areas are preferentially located where 

product distribution terminals are already located, our analysis of future ethanol terminal 

operations will not be seriously limited by assuming they will occur at existing petroleum 

terminals. However, the analysis 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 analysis 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 appear. 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 

Sect. 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 by 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 considered 

as the intermediate ethanol demand locations, from where ethanol is shipped by trucks to 

existing petroleum product terminals. In case, there was only single petroleum product 

terminal in a given cluster, ethanol terminal was assumed to be collocated with the 

existing product terminal and thereby there was no final trucking distance in this case. 
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Figure 15. Distribution of potential ethanol distribution terminals

3.7 Transportation Cost

3.7.1 Rail 

Available rail transportation cost information is based on 2006 waybill 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 traveled per trip.13 In addition, there is a monthly maintenance and leasing cost of 

$900 per car13 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 analysis assuming an average rail distance of 700 miles based on the 2006 waybill 

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.
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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, last year fuel outlays were about 22% 

of its revenues of 53 G$, compared to 24% for labor.15 Thereby, 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 only been used for the determination of various modal distribution costs 

(discussed under Sect. 5.2). In the initial development of O-D (supply-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 by railroads. The EIA projections of wholesale price of residential distillate 

fuel oil/heating oil, which is quite similar to the proposed price index, has been used for 

our analysis.16 Distillate fuel oil price is assumed to be $2.00/gallon by 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 costs 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.13 For our analysis, 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 gallons 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, 20th 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. Department of Energy, 

Washington, DC. Accessed at the website: http://www.eia.doe.gov/oiaf/aeo/supplement/sup_ogc.xls
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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 large number of singleunit light package deliveries, 2006 trucking cost is assumed to be 14 cents/ton-mile. It is 

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 fuel 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 currently 22 mph.17 Unlike the other two major modes 

of transportation, rail corridors are rarely 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 classification, about 1 day for each 600 miles.

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 fuel price (as

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.
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Figure 16. Current train volumes compared to current train capacity18

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 Framework19 (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 type 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.
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Table 5. Commodity Movements (in thousand tons) by Waterways, Based on FAF2

Commodity

movement

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 thousand tons) and Growth Factors 

for this Study

2010 2012 2014 2017 2022

Flow 613,541 631,189 649,344 673,709 717,638

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 currently are the major bottlenecks for overall marine traffic operations. The 

average 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

Information from United States Army Corps of Engineers’ Lock Performance Monitoring 

System20 (LPMS) is used to describe the current waterway traffic operations. Information 

from LPMS is used to formulate regression models to estimate the future waterway

network constraints in terms of average delay 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 year 2007 are used to estimate the average delays at locks.

Estimate Future Lock Delays

Based on lock statistics for calendar year 2007 information from LPMS, two regression 

models (with R2= 0.7231 and 0.6747 respectively) have been formulated:

 

20 Fleming, Marilyn V.; Wood, Donna E.; Goodwin, Robert J. Lock Performance Monitoring System 

User’s Manual for Data Analysis, Corps Of Engineers Fort Belvoir VA Water Resources Support Center, 

1985
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Number of Tows: 0.4909 No Tows  0.4661 (Tons) , and

1.6502 Total Delay (hours)  0.0041 (No Tows) .

The average tow delay is the quotient of the total delay and number of tows.

Mathematically, it can be expressed as

No Tows

Total Delay Average Delay (hours) 

Instead of estimating the future average tow delay utilizing these equations directly, a 

growth factor has been estimated. That is 

0.4909

2007

0.4909

2007 2010

2007

2010

2010 0.4661 ( )

0.4661 ( )

Tons

Tons Ton Growth

Tows

Tows Tow Growth



 

 

Using the similar procedure, the future total delay and average delay growth factors for 

different target years 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 Figures 17 and 18, respectively. Future total delay and average 

delay growth factors in reference to 2007 are presented in Table 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 analysis of the data indicates that the waterway delay relative to year 2010 

would increase by 0.013 hrs, 0.027 hrs, 0.045 hrs, and 0.076 hrs in 2012, 2014, 2017, and 

2022, respectively. 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.
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Figure 17. Average tow delays for 2007

Figure 18. Average tow delays for 2022
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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 use realistic travel speed information with congested traffic in the 

scenario analyses for future years. Information presented in the 2007 Urban Mobility 

Report21 is used to approximate the travel speeds.

The Texas Transportation Institute’s 2007 Urban Mobility 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 by 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 Daily 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 Daily 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 formulated. Mathematically, they are

 1.7251  75.975

1000

ADT/Lane Freeway Speed , and

 1.52   37.118

1000

ADT/Lane Arterial Speed .

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 Lomax, 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.pdf
Ethanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory

31

highway lane-miles statistics from the Highway Statistics 2006 22published 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-year 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, respectively.

Based on the 10-year historical trends, the future Average Daily Traffic per Lane 

(ADT/Lane) growth factors can be estimated. The growth factors for the future analysis 

years 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 2006

ADT/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 DOT). 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 2006

ADT/Lane X 1000

Interstate Other Freeways Other Principal Arterial Minor Arterial

Figure 20. Average daily traffic per lane trends for urban 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 applying 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 applying 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 ORNL Transportation Infrastructure 

Network Model for three 2022 scenarios (i.e., 2 reference and 1controlled) 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 

year. Corn refineries from EPA (Sect. 3.1), cellulosic refineries as detailed 

above under Sect. 3.2, including import supplies as outlined under Sect. 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), by 

year 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 

exogenously 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 

currently not served by either rail or waterways, ethanol movement will be 

limited to trucks only.

The goal for each year was to fill in the ODM matrix between refineries and terminals 

consistent with these production and supply margins. 

“Location” here means a longitude/latitude that can be examined on a map. The analysis 

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 ODM routes, refineries and terminals must be attached to 

the network in an initial step. Existing locations were examined to see whether the 
Ethanol 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 by 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 destinations, and their 

quantities as discussed below.

2. A spatial interaction model (SIA) (or gravity model) 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 flows, 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. Its 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 

waybill sample) and projected 2010 supply-demand scenario considered in our 

analysis. Due to lack of data for other modes, the use of a single calibrated beta 

value across all modes 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 years 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) by 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 Sects. 3.7.1 and 3.7.2, the 

ratio of relative rates of 2.4 to 7 cents per ton-mile between inland barge and rail 

are used, thereby 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 tank 
Ethanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory

35

cars in manifest trains with their en route classification. Manifest train rates are 

generally 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 irrelevant to the outcome – it is merely a scaling factor for all 

t(o,d,m) entries in the OD 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 terminals, 

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 by 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 waterways to rail have also been 

considered in the analysis.

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 by travel time, perhaps stratified by socioeconomic variables, in determining 

the probability of trip making. The distance decay factor of 1/distance is generally 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 decline of the interaction, i.e., the impedance factor has to be empirically

measured, and varies by 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 discussed under items (a) and (b) above. This results in every cell having some non-

 

23 Levinson, D. and Kumar, A. 1995. A Multi-modal Trip Distribution Model,” 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. Instead, 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 rerun on the next cycle – thereby 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. It is 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 threshold 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 over the 

Intermodal Network containing the 1998 FAF1 base commodity county-by-county routes

by various modes – the only publicly 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 analysis is an extensive and integrated public 

domain geographically-based representation of the North American transportation 

infrastructure suitable for individual or multimodal studies.24 It is a combination of its 

data, the CTA intermodal network, and its traffic assignment models to load the network 

with commodity flows. The network is composed of independently constructed singlemode 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 analysis 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 by vertical access links. 

Traffic generators would typically also be located at a different level and similarly 

attached to an arbitrary modal network by vertical access links. Several types of links are 

considered, i.e., line haul links, railroad interlines, terminal links, access links, and 

shadow links where by various modes each has an impedance function based on its 

 

24 “CTA 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 intentionally share a common set of 

attributes, although the attributes of routing impedance and length and endpoint nodes 

have meaning only for links. Principal uses to date have included regional and modal 

competition effects of policies and investment in infrastructure (Marine input/output 

model,25 regional routing model, national rail freight infrastructure26), 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 system (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 analytic 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 Atlasbased 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 GIS versions easily 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 GIS 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 by 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 physical attributes of 

signal system and number of tracks. 

 

25Southworth, 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 analysis represents the latest 

updates made in 2006. The best test for the adequacy of the assignment is to compare line 

volumes produced by the program using the national O-D data with independent data on 

observed rail flows, which is available anecdotally and from the Federal Railroad 

Administration volume classes. Major discrepancies have been handled by 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 analyst 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 by rail, water, truck, or any intermodal 

combination. If by truck, the long distance trip terminates directly 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 sites in the same metro area –

considered by 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 typically be multiple blending sites 

operated by 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 

presently 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 today. 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 by constructing access links from their geographic 

locations to all the nearby modes (within 1000 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 similarly 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 by 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 mode 
Ethanol 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 by Rail Traffic Atlas by Harry Ladd 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 transportation cost, thereby causing only

additional ton-mile movements existing corridors due to ethanol 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, congestion costs are different on highway and inland water modes, 

where capital investment decisions are generally not optimal for minimizing total system 

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 by the growth in other traffic. FAF2 projections are 

for approximately 25% growth up to 2022, so congestion will rise slightly. 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 discussed 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 likely to increase due to fuel costs in about the same proportion as rail costs 

will, leaving their relative costs nearly 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. More 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 year 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 roughly 

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, when we know 

that currently New England is served by barge from intermodal terminals in New Jersey 

and Albany. To overcome this, intermodal transfer impedances were reduced to the 

approximate equivalent of 100 miles. It could be that the costs of intermodal transfers 

really 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. Amounts (in terms of Ktons) 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, doublecounting occurs under those Kton-mile movement cases . The local Ktons of truck 

movements 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 directly 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, 72% 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 considerably 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 considerably less 

for the long-distance ethanol movements. Local truck ton-mile movements would be 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): Manifest train

there would be a 167% increase in total rail ton-mile movements compared to the 2022 

RFS1 scenario. The total volume of domestic freight by rail in 2005 is estimated to be 

1734 billion ton-miles27; the ethanol shipments in the 2022 controlled case increase rail 

transport about 2.8%. Similarly, additional stresses from the 2022 controlled scenario on 

waterways and highways are estimated to be 0.6% and 0.13%, respectively 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 relatively less 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 by rail, and one each by waterways and truck) 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 not 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

County, 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 Summary for the 2022 Scenarios

Scenario

Ethanol

Demand

(Bgal)

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 1498K 3131 290K 18431 132K

2022 AEO 

Reference

13.18 28913 25362K 6182 5288K 4178 2539K 5597 491K 36736 253K

2022 

Controlled

34.14 73656 46711K 2481 2705K 11391 3770K 17508 1153K 82260 532K
Ethanol 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, 

particularly 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 Movements 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 147 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 0 875 0 3 89

Tazewell, IL Lorain, OH 0 478 0 3 82

St. Joseph, IN Mercer, NJ 0 771 0 3 80

Hendry, FL Manatee, FL 0 0 147 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 scenarios 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 blending are co-located. Unloading occurs at first at the 

intermodal transfer facility (if necessary), followed by 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 equally 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, thereby dictated by the 
43

Figure 21. Projected ethanol movements (Ktons) under the 2022 RFS1 Reference scenario
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Figure 22. Projected ethanol movements (Ktons) under the 2022 AEO Reference scenario
Ethanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory

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Figure 23. Projected ethanol movements (Ktons) under the 2022 Controlled scenario
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Figure 24. Projected county-level total Kton-mile ethanol movements under the 2022 RFS1 Reference Scenario
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Figure 25. Projected county-level total Kton-mile ethanol movements under the 2022 AEO Reference Scenario
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Figure 26. Projected county-level total Kton-mile ethanol movements under the 2022 Controlled Scenario
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Figure 27. Projected county-level total ethanol loading/unloading activities (Ktons) under 2022 RFS1 Reference Scenario
Ethanol Transport Activity & Potential Distribution Constraints Oak Ridge National Laboratory

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Figure 28. Projected total ethanol loading/unloading activities (Ktons) under the 2022 AEO Reference Scenario
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Figure 29. Projected total ethanol loading/unloading activities (Ktons) under 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 costs 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 analysis of operating cost of various railroads as reported in the 2006 waybill 

data, to which monthly maintenance and leasing costs were added to estimate the total 

rail cost in terms of $/ton-mile as a function of distance traveled and fuel cost. It was

further assumed that current diesel fuel cost of around $2.00/gallon will not be 

significantly different by the 2022 forecast year. The unit train distribution cost is 

assumed to be 35% less than for the manifest train. In addition, 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 14 cents/ton-mile—not a function of fuel cost as 

discussed earlier. The sum of mode-specific costs 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 11 shows the estimated distribution cost 

both in terms of total ($M) and cents/gal at the state-level for the three 2022 scenarios 

considered.

It is 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 cost 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 states (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

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Table 11. Estimated Ethanol Distribution Cost for the Three 2022 Scenarios

State RFS1 2022 AEO 2022 Controlled 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 2.2
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Table 11. Estimated Ethanol Distribution Cost for the Three 2022 Scenarios

State RFS1 2022 AEO 2022 Controlled 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 comparatively shorter distance for 

Massachusetts to import from the Midwest than it is for New Hampshire. For the two 

other reference 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, respectively. Under 2022 RFS1 scenario, a lack of ethanol demand in quite a 

few states causes thereby there no distribution cost.

Total distribution cost is mainly dictated by 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 $255M 

and $160M, respectively, under the 2022 controlled scenario. Total ethanol distribution 

cost expenditure is estimated to be $2,335M under the 2022 controlled scenario, 

compared to $1,269M and $617M for the 2022 AEO and RFS1 scenarios, respectively.

5.3 Rolling Stock Requirements 

Rolling stock requirements, i.e., number of rail cars, barges, and trucks, were estimated 

for the three 2022 scenarios 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 generally make annually 15-18 trips and 10-12 trips, 
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respectively.

29 We assume the higher value of the range in each case due to anticipated 

efficiency in the future ethanol transportation network Lower number of annual trips in 

rail cars is mainly 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, 

particularly 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 by 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 average 

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 only under highly 

congested and strong ethanol demand environment. The effect of assumed speed and 

average rail route distance is minimum on the estimated annual operating hours, since 

highly 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 annually, compared to double 

the amount of total yearly hours of operation for waterways modes of transportation.

Increased ethanol demand is anticipated to increase the demand for rail tank cars by a 

maximum of 41,301 cars under the 2022 controlled scenario as shown in Table 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 It is 

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 Estimation

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 3500
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Table 13. Estimated Rolling Stock Requirements under 2022 Scenarios

Scenario Rail Cars Barges Trucks

2022 Controlled 41,301 170 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 thereby total 

number of new ethanol rail requirements 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 

Mississippi 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 

recently 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 last five years as non-double-hull vessels built during the 

1978-1983 boom period reached their OPA90 phase-out dates. As of year-end 2007, 73% 

of total U.S. flagged tanker barge vessels were double hulled.32 It is unlikely 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—considerably 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 

analysis, out of which three scenarios were simulated by incorporating additional 

biodiesel demand in the range of 3-7% of ethanol demand dictated by EISA 

requirements. The three “with biodiesel” scenarios were for the years 2012, 2014, and 

2022 denoted here by 2012B, 2014B, and 2022B, respectively. The fourth scenario 

 

31 U.S. Army Corps of Engineers (2009). “Waterborne Transportation Lines 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.
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considered was for the year 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 biodiesel will be co-located 

along with original ethanol locations. For simplicity’s sake, movement of biodiesel was 

assumed to follow the same route as ethanol, only with amounts 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 by 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 Carload 

waybill sample traffic. The waybill 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 type of service train used to carry the 

shipment; the car equipment used to carry the shipment; and the number of carloads per 

year. For highways and waterways, nationwide background traffic levels are quite 

limited—particularly at the local street level in the former case. So, a typical level of 

traffic that a waterway and highway can support is used, and then future years’ ethanol 

traffic is shown as a percent of that “nominal capacity.”

Tables 14 through 17 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 analysis. 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 categories. 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 significantly higher in the cases of 

rail and waterways since they also include Canadian rail and worldwide waterways 

networks, respectively. 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 waterways is minimal, less than 2% carrying ethanol 

volume in the range of 50-100 million tons. For the 2022 controlled scenario, 10,626 

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 17.

As one would expect, the number of miles most affected would be rail, followed by

highways and waterways, respectively. 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

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respectively. It is likely that the percentage miles affected value for waterways would be 

significantly 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, 10,015 miles of rail network would be affected under the 2022B scenario 

compared to 9,942 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 by additional ethanol flows would be similar, 

14% and 15%, 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. For 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 feeder 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 increases 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 only 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 all. Note that for 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. Congestion 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.
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Table 14. Estimated Number of Miles Experiencing an Increase in Ethanol Traffic of 

Various Categories of Quantity Level 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

Total 

(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

20-50Mt 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

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

Various Categories of Quantity Level 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 549 486 155 265 135 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

Traffic 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

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Table 16. Estimated Number of Miles Experiencing an Increase in Ethanol Traffic of 

Various Categories of Quantity Level 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 675 662 794 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%

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 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

20-50Mt 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

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Table 17. Estimated Number of Miles Experiencing an Increase in Ethanol Traffic of 

Various 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) 2602860 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%
Figure 30. Projected rail ethanol movements in terms of Ktons and % increase in base flows for 2012B scenario.
63

Figure 31. Projected waterways and highways combined ethanol movements in terms of Ktons and % increase in base flows for 2012B 

scenario.
64

Figure 32. Projected rail ethanol movements in terms of Ktons and % increase in base flows for 2014B scenario.
65

Figure 33. Projected waterways and highways combined ethanol movements in terms of Ktons and % increase in base flows

for 2014B scenario.
66

Figure 34. Projected rail ethanol movements in terms of Ktons and % increase in base flows for 2022 scenario.
67

Figure 35. Projected waterways and highways combined ethanol movements in terms of Ktons and % increase

in base flows for 2022 scenario.
68

Figure 36. Projected rail ethanol movements in terms of Ktons and % increase in base flows for 2022B scenario
69

Figure 37. Projected waterways and highways combined ethanol movements in terms of Ktons 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 EISA

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 systemic delays in 

biofuels distribution. This study provides an analysis 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., 

Distribution 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 EISA. 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 by AEO and RFS1, respectively, and one 

based on the EISA of 34.14 billion gallons ethanol), focusing on the transportation 

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 analysis activity related to 

the estimation of emissions associated with the transportation of ethanol. The constraint

analysis (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 renewable diesel fuel demand. Total biofuel 

demand assumed for these three forecast years are 14.6, 17.5, and 35.1 billion gallons, 

respectively. The fourth scenario is exactly the same as the 34.14 billion gallons of 

ethanol volume projected to be used in 2022 under EISA—one of the scenarios 

considered under the first set of scenarios.

The analysis of fuel ethanol transportation and activity and potential distribution 

constrains using the ORNL Infrastructure 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 renewable 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 analysis, 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 ORNL model. 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 by 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 distance. 

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 by 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 by 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 

updated and validated ORNL intermodal network and its traffic assignment models for 

both long-distance and local ethanol transport, where the network is composed of 

independently 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 mode.

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 production 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 considerably 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 the controlled (i.e. EISA) scenario. The percentage increases in ton-mile 

movements by rail, waterways, and highways are estimated to be 2.8%, 0.6%, and 0.13%, 

respectively, compared to the corresponding 2005 total domestic flows 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, particularly southwest quadrant of the United States indicate a 

significantly higher level of ton-mile movements because of larger distance covered. 

Concentrations of loading/unloading activities are estimated to be in counties where 

ethanol supply centers (Midwest) and blending terminals coincide 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 1.2-33.2 cents/gallon under the 2022 

controlled scenario. Lower ethanol distribution cost values are mostly in midwestern 

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 estimates. Total 

ethanol distribution cost expenditure is estimated to be $2,335M under the controlled 

scenario, compared to $1,269M and $617M for the 2022 AEO and RFS1 scenarios, 

respectively. States with comparatively 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

analysis at the state level based on its demand. Increased ethanol demand is anticipated to 

increase the demand for rail tank cars by 41,301 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 about double the total new orders placed in 2007. The increase in 

number of barges is considerably 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 years 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 

2005.
73

The distribution constraint analysis considered for four scenarios encompassing three 

different forecast years 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 ethanol 

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 is 

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 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 all. Congestion will surely increase, but highway congestion is dominated 

by non-freight traffic, and long-distance ethanol movements 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 likely that the 

impacts would be more on feeder lines and different supply chain network representing 

biodiesel if considered in the analysis.

In summary, significant future ethanol demand would have minimal impacts on

transportation infrastructure overall. In addition, the simplified analysis of biodiesel/nonco-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 EISA 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 

both refineries and ethanol distribution terminals would impact significantly not only 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 citing of ethanol distribution terminals close 

to existing petroleum product terminals into the future will affect the future ethanol flows 

and thereby transportation infrastructure requirements. 
74

APPENDIX A

Location Details and Production Capacity (MGY) of Corn Ethanol Plants for 

Various Forecast Years
75

Location Details and Production Capacity (MGY) of Corn Ethanol Plants for Various Forecast Years

ID FEEDSTOCK

LONGITUDELATITUDE CITY COUNTYVol

2010

Vol

2012

Vol

2014

Vol

2017

2022

Vol

2022

Ref. 

AEO

Vol

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) -97.60 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 Fuel -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 92.00 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 41.42 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 Walhalla 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 Mount 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
76

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2012

Vol

2014

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2017

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Vol

2022

Ref. 

AEO

Vol

2022

Ref. 

RFS1

33

Aventine Renewable Energy, Inc. (formerly 

Williams 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 West Burlington Des Moines 92.00 92.00 92.00 92.00 92.00 52

36 Big River Resources, LLC - Galva -90.03 41.16 Galva 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 Bridgeport 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 Wapello 35.00 35.00 35.00 35.00 35.00 35

45 Cargill, Inc. -96.10 41.54 Blair Washington 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 Winnebago 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 Wentworth Lake 48.00 48.00 48.00 48.00 48.00 48

59 Dean CEG LLC -99.18 35.36 Burns Flat Washita 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 Agri-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 Wichita 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
77

ID FEEDSTOCK

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2010

Vol

2012

Vol

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Vol

2022

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AEO

Vol

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 Windsor Weld 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 120.00 120.00 150.00 150.00 120.00 120

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 Falls 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 Plains 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 Winthrop 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 BioEnergy, 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 Plainview 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 Wells 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 LLC -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
78

ID FEEDSTOCK

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Vol

2012

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2014

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Vol

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AEO

Vol

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 102.00 102.00 102.00 102.00 102.00 102

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 114.00 114.00 114.00 114.00 114.00 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 50.00 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 Pioneer Trail Energy -98.61 40.82 Wood 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 50.00 50.00 50.00 50.00 0
79

ID FEEDSTOCK

LONGITUDELATITUDE CITY COUNTYVol

2010

Vol

2012

Vol

2014

Vol

2017

2022

Vol

2022

Ref. 

AEO

Vol

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 52.00 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 40.97 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 Biorefining - 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 West -93.31 43.63

Glenville / Albert 

Lea Freeborn 65.00 65.00 65.00 0

150 POET Biorefining - Gowrie -94.29 42.33 Gowrie Webster 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 - Hamlontown -93.39 43.29 Hanlontown Worth 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 Manchester -85.80 40.94

North 

Manchester Wabash 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
80

ID FEEDSTOCK

LONGITUDELATITUDE CITY COUNTYVol

2010

Vol

2012

Vol

2014

Vol

2017

2022

Vol

2022

Ref. 

AEO

Vol

2022

Ref. 

RFS1

172 Show Me Ethanol, LLC -93.49 39.36 Carrollton Carroll 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 Energy (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 - Wheatland -101.54 40.85 Madrid Perkins 44.00 44.00 44.00 44.00 44.00 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 Walsh Baca 3.00 3.00 3.00 3.00 3.00 3

180 SW Energy, LLC. -100.62 40.20 McCook Red Willow 0.10 0.10 0.10 0.10 0.10 0.1

181 Tate & Lyle -94.31 42.50 Fort Dodge Webster 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 Andersons 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 Wisconsin Grain Producers, LLC. (UWGP) -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 Winnebago 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 110.00 110.00 110.00 110.00 110.00 110

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 - Bloomingburg (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 Webster 110.00 110.00 110.00 110.00 110.00 110

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 Bio) -93.71 44.11 Janesville Waseca 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 110.00 110.00 110.00 110.00 110.00 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 - Welcome -94.65 43.66 Welcome Martin 110.00 110.00 110.00 110.00 0

206 Verasun - Woodbury (formerly US Bio) -85.07 42.77 Lake Odessa Ionia 50.00 50.00 50.00 50.00 50.00 50

207 Western New York Energy, LLC -78.30 43.19 Shelby Orleans 50.00 50.00 50.00 50.00 50.00 0

208 Western Plains Energy, LLC -100.85 39.23 Campus / Oakley Gove 45.00 45.00 45.00 45.00 45.00 45
81

ID FEEDSTOCK

LONGITUDELATITUDE CITY COUNTYVol

2010

Vol

2012

Vol

2014

Vol

2017

2022

Vol

2022

Ref. 

AEO

Vol

2022

Ref. 

RFS1

209 Western Wisconsin Energy, LLC (WWE) -91.98 45.05 Boyceville Dunn 45.00 45.00 45.00 45.00 45.00 45

210 White Energy -102.52 34.87 Hereford Deaf Smith 100.00 100.00 100.00 100.00 100.00 0

211

White Energy (formerly Plainview BioEnergy, 

LLC.) -101.66 34.18 Plainview Hale 100.00 100.00 100.00 100.00 100.00 0

212 White Energy (formerly USEP) -98.86 38.89 Russell Russell 50.00 50.00 50.00 50.00 50.00 50

213 Wind Gap Farms (Anheuser/Miller Brewery) -84.18 31.35 Baconton Mitchell 0.40 0.40 0.40 0.40 0.40 0.4

214 Wyoming 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

TOTAL 11595 12890 14438 14994 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 2012
84

Figure B-3. Corn Ethanol Production in 2014

Figure B-4. Corn Ethanol Production AEO Reference Case in 2022
85

APPENDIX C

Location Details and Production Capacity (MGY) of Cellulosic Ethanol Plants for 

Various Forecast Years
86

Appendix C

Location details and production capacity (MGY) of cellulosic ethanol plants for various forecast years

ID FIPS FEEDSTOCK

LONGITUDELATITUDECOUNTY/

STATE

Transport

Mode

Vol

2010

Vol

2012

Vol

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 DEWITT 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 TAZEWELL 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 18163 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

LONGITUDELATITUDECOUNTY/

STATE

Transport

Mode

Vol

2010

Vol

2012

Vol

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 SEDGWICK 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 Residue 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 Stover 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 CHICKASAW IA R 82.21 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 Forest 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 Stover 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 87.69

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 SIBLEY 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

LONGITUDELATITUDECOUNTY/

STATE

Transport

Mode

Vol

2010

Vol

2012

Vol

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 and 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

76 29143 Corn Stover with some Forest Residue and MSW -89.65 36.59

NEW MADRID 

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. LOUIS 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 Forest 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 Stover 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 Residue and MSW -83.62 41.36 WOOD 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 Forest 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 46045 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 WI R 94.09 94.09

103 55025 Corn Stover with some Forest Residue and MSW -89.42 43.07 DANE WI R 75.76

104 55043 Corn Stover with some Forest Residue and MSW -90.71 42.87 GRANT WI 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

LONGITUDELATITUDECOUNTY/

STATE

Transport

Mode

Vol

2010

Vol

2012

Vol

2014

Vol

2017

Vol

2022

106 55015 Corn Stover with some Forest Residue and MSW -88.22 44.08 CALUMET WI R 91.42

107 55141 Corn Stover with some Forest Residue and MSW -90.04 44.46 WOOD WI R 59.11

108 55035 Corn Stover with some Forest Residue and MSW -91.29 44.73 EAU CLAIRE WI R 64.59

109 55033 Corn Stover with some Forest Residue and MSW -91.90 44.95 DUNN WI 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 CARROLL 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 WOODRUFF 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

Vol

2014

Vol

2017

Vol

2022

ROUGE LA

139 28035 Forestry and MSW-89.26 31.19 FORREST MSR 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 ALR 95.98

142 01053 Forestry and MSW-87.16 31.13 ESCAMBIA ALR 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 GAR 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 Forestry and MSW-82.07 33.36 RICHMOND GAR 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 

SCR 107.99

149 37067 Forestry and MSW-80.26 36.13 FORSYTH NCR 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 VAR 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 TNR 37.00 82.85 96.64 96.64

155 48005 Forestry and MSW-94.61 31.26 ANGELINA TXR 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 ARR 98.91

158 01121 Forestry and MSW-86.17 33.38 TALLADEGA ALR 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 

FLR 100.00

162 12099 Bagasse-80.47 26.65

PALM BEACH 

FLR 100.00

163 12099 Bagasse-80.47 26.65

PALM BEACH 

FLR 100.00

164 48215 Bagasse-98.18 26.40 HIDALGO TXR 100.00

165 48061 Bagasse-97.53 26.13 CAMERON TX RW 100.00

166 48489 Sorghum-97.68 26.47 WILLACY TXR 100.00

167 48113 MSW-Dallas, TX-96.78 32.77 DALLAS TXR 44.58 52.20 52.00

168 48029 MSW-San Antonio, TX-98.52 29.45 BEXAR TXR 16.20 16.00

169 48453 MSW-Austin, TX-97.78 30.33 TRAVIS TXR 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

LONGITUDELATITUDECOUNTY/

STATE

Transport

Mode

Vol

2010

Vol

2012

Vol

2014

Vol

2017

Vol

2022

172 22057 Bagasse -90.44 29.58

LAFOURCHE 

LA RW 50.00

173 22097 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 York, 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

TOTAL 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
94

Figure D-3. Cellulosic Ethanol Production in 2014

Figure D-4. Cellulosic Ethanol Production in 2017