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1 The University of Toledo The University of Toledo Digital Repository Theses and Dissertations 2011 Providing a template for future commodity flow on the Great Lakes : the use of an origin-constrained spatial interaction model to estimate the flow of coal by waterborne vessel Brett Porter The University of Toledo Follow this and additional works at: Recommended Citation Porter, Brett, "Providing a template for future commodity flow on the Great Lakes : the use of an origin-constrained spatial interaction model to estimate the flow of coal by waterborne vessel" (2011). Theses and Dissertations This Thesis is brought to you for free and open access by The University of Toledo Digital Repository. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of The University of Toledo Digital Repository. For more information, please see the repository's About page.

2 A Thesis entitled Providing a Template for Future Commodity Flow on the Great Lakes: The Use of an Origin-Constrained Spatial Interaction Model to Estimate the Flow of by Waterborne Vessel by Brett Porter Submitted to the Graduate Faculty as partial fulfillment of the requirements for the Master of Arts in Geography Advisor: Peter S. Lindquist, Ph. D. Committee: Daniel J. Hammel, Ph.D. Committee: Patrick L. Lawrence, Ph.D. Dean College of Graduate Studies: Dr. Patricia Komuniecki The University of Toledo August 2011 i

3 Copyright 2011, Brett Porter This document is copyrighted material. Under copyright law, no parts of this document may be reproduced without the expressed permission of the author.

4 An Abstract of Providing a Template for Future Commodity Flow on the Great Lakes: The Use of an Origin Constrained Spatial Interaction Model to Estimate the Flow of by Waterborne Vessel by Brett Porter Submitted to the Graduate Faculty as partial fulfillment of the requirements for the Master of Arts in Geography The University of Toledo August 2011 The purpose of this study was to develop a model that best estimates the flow of coal between origin-destination (O-D) pairs on the Great Lake. Included in this model were the values for the origin, destination, distance between each O-D pair, and the distance decay parameter beta. The scope of the study was to analyze the shipment of coal between origin and destination ports located in the United States and situated along the Great Lakes. What this research found was that the distance decay parameter when set at.01 is able to estimate shipments of coal on the Great Lakes with a high degree of accuracy. What this also indicates is that the shipment of commodities by waterborne vessel on the Great Lakes is different from that of other modes of transportation. This research identifies and utilizes sources of data used to run a model to estimate the shipment of coal on the Great Lakes. Key Words: Distance decay parameter, beta, coal, Great Lakes, waterborne vessel iii

5 Acknowledgements I would like to thank my advisor Dr. Peter Lindquist for his guidance and dedication in helping me complete this study. I would also like to thank my committee members Dr. Dan Hammel and Dr. Patrick Lawrence for their time and support throughout this process. I am grateful for the love and support of my family, especially my mother who has been very patient and understanding throughout this process. She along with Bryan has been very supportive, even during the times when I was tough to live with. I would especially like to thank my sister Dawn who has been a tremendous help throughout these last three years. It would have been a much tougher road had she not been there to guide me along. Lastly, I would like to thank my father who was very influential in my life and my path to graduate school. I know that he d be extremely proud of my accomplishment and that it would not have been possible without him. iv

6 Table of Contents Abstract Acknowledgements Table of Contents List of Figures iii iv v vii 1.1 Introduction Problem Statement Objectives History of the Industry Literature Review Distribution of from Origin to Destination on the Great Lakes Overview of the Great Lakes Distribution in Methodology: Origin-Constrained Spatial Interaction Model Distribution Scenarios Results: Scenario Results: Scenario Results: Scenario Conclusion.. 53 References.. 56 Appendix A: Data Sources. 59 Appendix B: Waterborne Commerce of the United States Part 3 Great Lakes Appendix C: GIS ArcView Files 61 Appendix D: Power Plant Data.. 61 Appendix E: Navigation Route Data.. 61 Appendix F: Goodness of Fit Table: Ashtabula. 62 Appendix G: Goodness of Fit Table: Midwest Energy Terminal.. 62 v

7 Appendix H: Goodness of Fit Table: Port of Chicago 63 Appendix I: Goodness of Fit Table: Sandusky Appendix J: Goodness of Fit Table: Port of Toledo 64 vi

8 List of Figures Production by -Producing Region Producing States (2010) Production of underground and surface coal mines Geographical relationships between the coal basins and the Great Lakes Shipments of 2008 coal tonnages from the Port of Toledo Shipments of 2008 coal tonnages from the Port of Sandusky Shipments of 2008 coal tonnages from Ashtabula Shipments of 2008 coal tonnages from Conneaut Shipments of 2008 coal tonnages from the Port of Chicago Shipments of 2008 coal tonnages from the Midwest Energy Terminal at the Port of Superior Duluth Great Lakes 2008 coal distribution Origin-Destination Flow Matrix Origin-Constrained Spatial Interaction Model Percent Root Mean Square Error Percent Difference Goodness of Fit Results (O-D) Scenario 1: Closing of Cobb Generating Station at Muskegon Scenario 1: Closing of coal Ashtabula Power Plant at Ashtabula Scenario 1: Closing of St. Clair Power Plant at St. Clair.. 38 vii

9 5-4 Scenario 1: Closing of Presque Isle Power Plant at Presque Isle Scenario 1: Closing of J.P. Pulliam Power Plant at the Port of Green Bay Scenario 1: Closing of Beach Power Plant at Beach Scenario 2: Replacing 15 percent of bituminous coal shipped from Ashtabula to each destination port with sub-bituminous coal for a two-year period Scenario 2: Map representing change in coal distribution from Ashtabula Scenario 2: Replacing 15 percent of bituminous coal shipped from Conneaut to each destination port with sub-bituminous coal for a two-year period Scenario 2: Map representing change in coal distribution from Conneaut Scenario 2: Shipping an additional 15 percent of sub-bituminous coal from the Midwest Energy Terminal to destination ports for a two-year period Scenario 2: Map representing change in coal distribution from Midwest Energy Terminal Scenario 2: Shipping an additional 15 percent of sub-bituminous coal from the Port of Chicago to destination ports for a two-year period Scenario 2: Map representing change in coal distribution from the Port of Chicago Scenario 2: Replacing 15 percent of bituminous coal shipped from Sandusky to each destination port with sub-bituminous coal for a two-year period Scenario 2: Map representing change in coal distribution from Sandusky Replacing 15 percent of bituminous coal shipped from the Port of Toledo to each destination port with sub-bituminous coal for a two-year period 46 viii

10 5-18 Scenario 2: Map representing change in coal distribution from the Port of Toledo Scenario 3: Estimated shipments of 2008 coal from Ashtabula for a two-year period Scenario 3: Estimated shipments of 2008 coal from Conneaut for a two-year period Scenario 3: Increase of 10 percent of sub-bituminous coal shipped from the Midwest Energy Terminal to each destination port for a two-year period Increase of 10 percent of sub-bituminous coal shipped from the Port of Chicago to each destination port for a two-year period Scenario 3: Estimated shipments of 2008 coal from Sandusky for a two-year period Scenario 3: Estimated shipments of 2008 coal from the Port of Toledo for a two-year period Scenario 3: Increase in the number of destination ports receiving coal from the Midwest Energy Terminal after a 10 percent increase in sub-bituminous coal Scenario 3: Increase in the number of destination ports receiving coal from the Port of Chicago after a 10 percent increase in sub-bituminous coal 52 ix

11 Chapter Introduction Freight movement, specifically coal, is an important component of the economy for port communities on the Great Lakes. The steady growth of industry and population near these port communities has added to the demand for electricity. This increased demand has led to an increase in the consumption of coal. In order to cope with this increase in freight transport, better planning models are necessary. The purpose of this research is to develop a planning model that predicts commodity flow on the Great Lakes. This study will give policy makers a better understanding of the flow of commodities on the Great Lakes, and the use of an estimation model for future maritime transportation decisions. Donald Cree, President of the Great Lakes Maritime Task Force, believes that the coal trade illustrates how the vitality and reliability of Great Lakes shipping contributes to the national economy (BusinessNorth.com, 2009). has been used to transport people, products, raw materials, and as energy to power and heat homes. The economic progress of the Great Lakes region is linked to the use and production of coal. In 1950, U.S. coal production was 560 million short tons, by 2003, coal production had increased to an estimated 1.07 billion short tons, an average annual increase of 1.2 percent per year (EIA, 2006). This increase in the production of coal can be attributed to its prominent 1

12 role in the energy production industry. Over 90 percent of coal production in the United States has been utilized at domestic electric power plants (EIA, 2006). While electricity generation is the largest consumer of coal on the Great Lakes cement and steel manufacturing have also become important industries in the coal trade. Annually an estimated 70 percent of the steel produced uses coking coal in the steel making process (World Institute). More than 1.5 million tons of coal was shipped to the Port of Detroit, Toledo, Cleveland, and Rouge River in 2008, second only to the electricity generation industry in coal consumption (Waterborne Commerce Data 2008). However, for the purposes of this study the main focus was on electricity generation, and more importantly on those power plants located in the United States that consume coal transported on the Great Lakes. The distribution and transportation of coal is an important component of the energy industry. is most valuable when transported in bulk, and its origins located in isolated regions of the country like West Virginia, Wyoming and Montana. With increased shipping distances the Great Lakes Maritime industry has become a valuable resource for the coal industry. is able to be shipped in larger quantities over greater distances and at inexpensive rates. Each year customers save an estimated 3.6 billion dollars by shipping on the Great Lakes when compared to the next least costly mode of transportation (Lake Carriers Association, 2009). The Great Lakes are located in the core of the industrial and manufacturing centers of the United States. These industries depend on the reliability and availability of all commodities including coal shipped using waterborne transportation. will continue to be an important resource for both the United States and the Great Lakes region. 2

13 1.2 Problem Statement Freight planning on the Great Lakes is non-existent, jeopardizing the economic progress of Great Lakes shipping. To avoid this inevitable problem planning models must be developed that consider future projections when planning in the present. Focusing on future projections this research will develop a model that best estimates the flow of coal between origin-destination (O-D) pairs on the Great Lakes using existing O- D data (Waterborne Commerce data, 2008) to calibrate the spatial interaction model. This model will include data for the origin and destination of each flow, the distance between each O-D pair, and the distance decay parameter beta. What will ultimately come from this study is a value of beta that will be useful in future freight transportation research. 1.3 Objectives What this research will seek to accomplish is: 1. Review and understand the coal industry from its history, to patterns, coal origins and destinations, new and existing technology, coal usage, role in the energy industry, and future trends. 2. Gather and analyze freight origin and destination data by mode and commodity group. This will include a geographical representation of the shipment of coal from its origin port to its destination power plant. 3. Provide policy makers with a better understanding of the shipment of coal on the Great Lakes. 3

14 4. Evaluate the effectiveness of an Origin-Constrained Spatial Interaction Model in freight transportation on the Great Lakes. 5. Estimate parameter values for future freight transportation research. 4

15 Chapter History of the Industry is the most abundant fossil fuel produced in the United States. Almost 94 percent of the coal used in the U.S. is for generating electricity. The U.S. is home to the largest recoverable reserves of coal in the world. There are about 600 coal-fired power plants spread throughout the United States that power almost half of the homes in the country (EIA). There is enough coal to last more than 200 years (EIA). It is a relatively inexpensive fuel when compared to natural gas. With the exception of 5 percent of this coal used for export the rest is used as a basic energy source in the production of steel, cement and paper (EIA). is produced in 25 states and spread across three regions which make up the core production of coal in the United States. Those regions are the interior, the Appalachian, and the western regions of the country. Each region produces a certain type of coal such as bituminous, sub-bituminous, anthracite, and lignite. The interior region of the United States produces about 15 percent of the nation s coal, spanning most of Illinois, southwest Indiana, northwest Missouri, and parts of Iowa, Kansas, Oklahoma, and Texas. Both sub-bituminous and bituminous coal is found in the region with Texas producing one-third of the region s coal. Mid-sized surface mines are 5

16 most common in this section of the country with mid to large-sized companies owning and operating the coal mines. Figure Production by -Producing Region The Appalachian region is located in the eastern U.S. lying across Western Pennsylvania, Western Maryland, Eastern Ohio, Eastern Kentucky, West Virginia, and small sections of Tennessee, Alabama, and Mississippi. Most common to this region is bituminous coal, a soft coal made up of high amounts of carbon, hydrogen, and sulfur. Due to its high heating value it is the most common type of coal used in electric power generation (EIA). This region was the epicenter for the coal mining industry until the mid 1970 s when more stringent restrictions on atmospheric emissions of sulfur dioxide were enforced on power plants (EIA, 2006). This region produced fewer than 340 million short tons of coal in 2008 (Freme, EIA, 2008). 6

17 By 2003, the annual growth in sub-bituminous coal production in the western United States had risen to just over 630 million short tons (Freme, EIA, 2008). Safer, less expensive mining techniques, accompanied with an abundance of sub-bituminous coal have made this region the largest producer of coal in the country. The lower carbon and sulfur content make sub-bituminous coal an attractive energy source for coal-fired power plants. Figure 2.2 Producing States All coal must be excavated in order to be utilized. The two most common forms of coal mining are underground and surface mining techniques. Underground mining is utilized in the Appalachian region where coal is extracted from enclosed rock by tunneling below the grounds surface (EIA). The room-and-pillar method of underground mining cuts rooms into the coal bed leaving columns or pillars of coal to support the roof. A grid-like pattern is formed as the mining progresses through the space. The two 7

18 techniques used for room-and-pillar are conventional and continuous mining methods. The conventional method requires drilling and blasting the coal seam, while the continuous method cuts the coal from the mine face (EIA, 2006). The longwall method accounts for 31 percent of underground coal mining and is more expensive but has higher extraction and recovery rates (EIA). Large access tunnels are created when machinery are put in place to shear away the coal from the sides of the tunnels in continuous passes. The coal is then loaded onto conveyor belts and transported to the surface of the mine opening. Once a section of the tunnel is excavated the roof is then allowed to collapse, replacing the opening created by the miners (EIA). In the Western United States surface or mountain top removal mining is the main practice of coal excavation. In this region of the country coal is located just below the earth s surface, making it much more accessible for extraction. Also called open cast, this technique has the ability to recover up to 90 percent of coal. The initial step requires the use of explosives to break up the soil and rock, which is then carefully removed by draglines to be used at the end of the process as backfill. The seam is then drilled and strip mined for coal extraction (World Institute). Once all of the coal has been removed from the strip mine, the access soil and rock are put back in place. Share of production from underground and surface coal mines Year Underground Percentage Surface Percentage Source: Energy Information Administration-Historical Overview Figure 2.3 Production of underground and surface coal mines 8

19 Once the coal has been excavated from surface or underground mines it is then shipped off to a coal preparation plant for treatment. Depending on the location of the prep plant a mine will have several different options for loading and transporting the coal. In many cases the coal is shipped by rail or conveyor to a coal preparation plant. A typical coal mine in Kentucky or West Virginia will use truck dumps to ship the recently excavated coal from the mine to rail cars, and then off to a treatment plant. On average one to 30 rail cars are loaded each day from truck dumps. Also used is a flood loader which uses miles of conveyors to ship coal to rail cars. This is a quick way of loading as much as one hundred rail cars in a single day for final destination (EIA). Most coal is not economically viable in raw form and must be broken down, treated and sorted for industrial use. A preparation plant will use gravity process equipment to separate the refuse from the coal. The final coal product is then transported to a transloader, which blends and stores the coal in silos for future hauling to a final destination (EIA) The spatial distribution of coal on the Great Lakes is determined by the origin and destination of the natural resource throughout the United States. The distribution patterns are typically determined by market areas and the spatial location of power plants and coal coke plants on the Great Lakes. Gulf Coast lignite is mainly transported over short distances from mine to power plants called mine-mouth power plants. Appalachian and Illinois Basin coals are transported over longer distances due to the separation from mine to power plant. The farthest reach for the demand of coal comes from the Powder River Basin which transports coal between 100 and 1,500 miles from mine to power plants, usually in the Midwest. 9

20 Figure 2.4 Geographical relationships between the coal basins and the Great Lakes The three main modes of transportation for coal are by rail, by truck, and by water. According to the EIA rail makes up 64 percent (684 million tons) of all coal transportation, while truck shipments makes up 12 percent (129 million tons) and water makes up 9 percent (98 million tons) (2007). More than one-third of all coal is transloaded with more than 223 million tons carried by waterborne transport at some point before reaching its final destination (US Army Corps of Engineers, 2006). Each of these modes of transportation is able to ship commodities in large quantities over long distances, making it ideal for the transportation of coal. This is due to coal s low economic worth, meaning it needs to be shipped in large quantities to keep it profitable. 10

21 is a vital ingredient in the production of steel. Around 70 percent of the steel produced today uses coking coal in the steel making process. An estimated 761 million tones of coking coal was used in the production of steel in 2009 (World Association). During the iron-making process, iron ore, coal coke, and small quantities of minerals are fed into a blast furnace where the coal coke produces carbon monoxide. The carbon monoxide reacts with the iron ore to heat and melt the iron, which is then drained off at the bottom of the furnace, creating iron and steel (World Association). The most common use for coal is in the production of electric generation used to heat homes, light streets, and even cook our food. -fired power plants have been used for many years in generating the electricity used in everyday tasks. How this is possible is by grinding the coal into a fine powder which is then blown into the combustion chamber of a boiler and burnt at high temperatures. The heat energy and hot gases produced from this converts water into steam, creating a high pressure system of steam that is then passed into a turbine, pushing the turbine shaft at high speeds. The magnetic field created from the rotating turbine produces electricity, which is then distributed to a final destination such as a neighborhood or a factory (World Association). 11

22 Chapter Literature Review The study of freight modeling is relatively new in transportation planning. A majority of the data required to complete these models is either aggregated or inaccessible do to private practices and competition within the private sector. Changes in the last 20 years in how data is acquired and released have altered the freight and transportation planning practices. Publications such as the Army Corps of Engineers Waterborne Commerce data and the National Cooperative Highway Research Programs statewide freight forecasting toolkit have given planners the ability to research and analyze freight flow both at the state and metropolitan level. Four-step freight planning models can be classified into two distinct models, commodity-based models and trip-based models. Commodity-based models generate the amount of commodity production and consumption instead of the number of vehicle trips. The focus is on the commodity being shipped, not the mode of transportation. Trip-based models directly estimate generation and attraction freight trips from size indicators for trip generation. The analysis is based on the mode of transportation and its movement as opposed to the concentration on the type of commodity being shipped (Wisetjindawat et al., 2006). 12

23 The statewide freight forecasting toolkit released by the National Cooperative Highway Research Program (NCHRP 606, 2008) gives a comprehensive review of current freight forecasting models practiced in the US. The first class label Class I is the direct facility flow factoring method, a shortterm forecasting method. This factoring method is used to rapidly apply existing data to determine one or more than one forecast volumes. Growth rates are applied to observed truck traffic volumes to obtain link-by-link flows. In 2005 the state of Minnesota developed a similar model to estimate truck travel demand for Truck Highway 10 (NCHRP 606, 2008). Class II is an O-D Factoring Method which utilizes mode split and traffic assignment with O-D factoring. This method takes into consideration O-D travel patterns of commercial vehicles. The main input is current and factored O-D information for freight. Economic models that provide growth rates to estimate future flows produce the factored O-D. Using mode split, Ohio developed a freight model, Ohio Freight Model Case Study to provide a better idea of current and future freight movements on the highway network. This model also produces estimates of freight truck volumes that match the patterns and magnitudes of existing truck volumes in Ohio (ODOT, 2008). Class III Truck Models assign generated aggregate truck trips to a specific road network. This method applies three of the four steps used in both commodity and passenger travel models. Steps include trip generation, trip distribution, and traffic assignment. Land use data, socioeconomic data, transport supply data, and demand data all produce the end product of truck O-D tables and truck flows on each facility. This model is usually part of a more comprehensive model that forecasts both passenger and 13

24 freight movements. The New Jersey study, Effects of Interstate Completion and Other Major Improvements on Regional Trip Making and Goods Movement uses a truck trip table to study truck trips as a component of the statewide transportation model (NCHRP 606, 2008). The city of Portland developed a truck model; Portland Metro Truck of the Tactical Model System, to forecast truck freight in the Portland metropolitan area (FHWA, 2007). The strategic model database (SMD) provides commodity flow data to the model as inputs, including both aggregate present and future freight flows for different commodity and mode combinations. Class IV Four-Step Commodity Models is very similar to the four-step urban travel demand model for passengers being; trip generation, trip distribution, mode split, and trip assignment. The connection of vehicle flows to commodity flow patterns is beneficial to regional planning and economic development. The Nashville Freight Model in the state of Tennessee models passenger movements and estimates truck movements with a similar four-step approach. Truck flow estimates are based on a function of households and total employment in each zone (Baker and Bostrom, 2008). In Southern California the Los Angeles Freight Forecasting Model (LAMTA) was developed to address the impacts of the growing volume of goods movement in and around the Los Angeles Metro area. The hybrid freight forecasting approach uses base year and forecast commodity flow data for inputs, and estimate multimodal freight trips on the transportation network. This four-step commodity based model utilized a linear regression model for trip generation, a gravity model with a negative exponential deterrence function for trip distribution and a multinomial logit model with a generalized 14

25 cost function for mode choice. Both long- and short-haul movements were forecasted using labor productivity, imports, and exports (FHWA, 2007). In the state of Texas the Texas State Analysis Model (SAM) covers both passenger and freight flow in Texas. Using TransCAD, SAM is a multimodal and intermodal travel demand modeling system focusing on passenger and freight movements. Highway, rail, air, and water systems were all integrated into the model while the state of Texas is divided into over 4,600 zones. Similar to LAMTA this model utilizes linear regression for trip generation, a gravity model for trip distribution, and a logit model for mode choice. Class V Economic Activity Models generate and distribute freight forecasts as a function of the economic activity in the region. This model develops modal facility flows by assigning modal O-D tables of commodity flow to modal networks. The application of a land use model creates the economic activity needed for the model. Any changes in both the economic activity and land use patterns influence the freight flow distribution on the transportation network. The state of Oregon developed a statewide passenger and freight forecasting model based on an economic and land use behavioral model. This model simulates commercial truck movements at the microscopic level. Individual trips are simulated instead of tours, and truck movements are the only freight movements accounted for (Hunt, et al., 2001). Fite, et al. (2002) used an approach that involves the use of stepwise multiple linear regression models that relate freight volume to economic, social and industrial indicators. With the use of multiple linear regression analysis they were able to model the cause and effect relationship between monthly values of the financial indicators and the monthly freight volume. 15

26 In freight transportation commodity-based models estimate the quantity moved between an origin and destination. This approach is similar to passenger mobility models. Oppenheim (1994) proposed an equilibrium model where a commodity flow s attraction to a destination is brought on by the need to support a given activity supported by consumers of that destination. Crocco, et al. (2010) designed a group of surveys to analyze the mobility of passengers for purchases and estimate the amount of different types of goods handled in the urban area of Cosenza Rende Castrolibero. A generation model was proposed and regression analysis utilized to assess the number of daily trips for purchases between O-D, the quantity of commodity consumed each day, and number of daily purchases. It was determined that the change in demand plays a key role in the modeling of transportation systems. There are two other model classes that have been primarily utilized in the private sector: supply chain/logistics models, and truck touring models. Supply chain models shift the focus from aggregate commodity flows to the private sector supply chains. Mode choice, size and frequency of shipments are all impacted by logistics and the relationship between shipper and carrier. Min and Zhou (2002) characterized supply chain planning as a synchronized series of inter-related business processes to (1) purchase raw materials; (2) develop these raw materials into finished products; (3) add value to the product; (4) sell these final products to a consumer base; (5) exchange ideas with all parties involved in this process. One of the first applications of a logistics supply chain freight model was the Strategic Model for Integrated Logistic Evaluations (SMILE), developed by Tavasszy, et al. (1998). Implemented in the Netherlands, SMILE featured a production layer that characterized 16

27 regional trade, a logistics layer which incorporated choice models for distribution, and a transport layer for handling modal and route choice behavior. Wisetjindawat, Sano, and Matsumoto (2006) proposed a commodity distribution model that extended the concept of the fractional split distribution model and applied it to urban freight movements. In the case study of the Tokyo Metropolitan Area they divided the study area into 56 zones to analyze the external trips. Regression analysis estimated commodity production and consumption using group size indicators such as employee totals and floor area. Commodity distribution between shipper and customer was determined by multiplying the fraction of a customer purchasing a commodity from a shipper by total consumption of the customer. De Jong and Ben-Akiva (2007) came up with a logistics model within a behavioral framework that includes the determination of shipment size and the use of consolidation and distribution centers, that when using disaggregated data can be forecasted. Developed for use in Norway and Sweden, the logistics model disaggregates the O-D commodity flow data down to a firm-to-firm level. Micro-simulation is conducted using minimization of the total annual logistics costs function with the output providing total logistics cost between zones. The gravity model is widely used in statewide freight distribution (LAMTA, SAM, Sorratini and Smith 2000, Park and Smith 1997). Developed and then refined over many years, this spatial interaction model focuses on origin-destination pairs of regions and the use of flow data (Nissi and Sarra, 2008). The application of these models have been used in many contexts to gain an understanding of the movement of people, commodities, information, migration flow, shopping behavior, distribution, and even traffic flow. 17

28 The trip distribution and average trip length are compared to observed values when calibrating the gravity model. In the case of the LAMTA, the gravity model parameters were calibrated using socioeconomic data. For their case study in 2006 of the Tokyo Metropolitan Area, Wisetjindawat, Sano, and Matsumoto performed a model calibration using the database of the Tokyo Metropolitan Goods Movement Survey. This database consisted of information regarding commodity and truck movements such as industry type, location, number of employees, and commodity type. In Sorratini and Smith s study of freight production and attractions for the state of Wisconsin using commodity flow data and Input-Output coefficients, Huang s trip length frequency distributions were used in the model calibration (2000). Sorretini and Smith were able to match assigned truck trips to actual truck volumes using a Selected Link Analysis iteration procedure in TRANPLAN. Evaluation measures were also uses in calibrating the gravity model. The use of Geographic Information Systems (GIS) is increasingly being used as a device for measuring, portraying and costing freight movements. It is an effective tool for manipulating a wide array of data for both spatial and non-spatial aspects of commodity flow logistics (Southworth and Peterson, 2000). The use of GIS along with the 1997 Commodity Flow Survey (CFS) was an important tool for Southworth and Peterson s work on the routing of tens of thousands of intermodal freight movements. GIS was a cost-effective tool that aided their work of constructing and maintaining a network of freight movements by mode, route, and connection. The GIS-based model Beuthe et al. (2001) uses for the multimodel network of freight transportation in Belgium analyze and assign the transport flow of each commodity to the cheapest combination of 18

29 modes, means and routes. They use a point-to-point O-D matrix for each commodity and assign cost functions to each virtual link, with cost being transport demand elasticity (COST). What this research will do differently from past research is to use one independent variable as opposed to multiple variables for the demand. The attraction will be the consumption of coal by power plant for electric power generation. A spatial interaction model will be used as the trip distribution model. GIS will also be utilized as a tool to display the distribution of coal on the Great Lakes. 3.2 Distribution of from Origin to Destination on the Great Lakes The distribution of coal by waterborne vessel on the Great Lakes is determined by its origin and destination throughout the United States. The distribution patterns are typically determined by market areas and the location of power plants and coal coke plants located in states that border the Great Lakes. In a typical supply chain the distribution centers would be located as close as possible to customer markets. In the case of the distribution of coal on the Great Lakes, selections of ports are considered the distribution centers. This is due in large part to the location of coal mines in the United States. The coal distribution centers or origin ports on the Great Lakes are located at six ports that cover three of the Great Lakes, the Detroit River, and St. Clair River. Lake Erie is home to four of the six origin ports, consisting of: Ashtabula, Conneaut, Sandusky, and the Port of Toledo. The Port of Chicago is located on Lake Michigan, and the Midwest Energy Terminal at the Port of Superior-Duluth, is located on 19

30 Lake Superior. According to the Army Corp of Engineers, in 2008, the combined tonnages of coal shipped on the Great Lakes from these six origin ports was 18,000,616 short tons, down from an estimated 19,854,098 short tons in 2007 and 19,667,225 short tons in 2006 (WC, 2008). In 2008 The Midwest Energy Terminal located at the Port of Superior-Duluth shipped an estimated 13,722,176 tons of coal, down from 13,857,708 the prior year. At the other end of the spectrum Conneaut shipped the least amount of coal at 111,409 tons in Each port ships coal to as little as five docks (Conneaut ), and to as many as 14 docks (Port of Chicago). In 2008 the greatest number of shipments leaving one port was 295 at the Midwest Energy Terminal. Situated along the shores of Lake Erie are four ports that exported coal to destinations within the Great Lakes. The Port of Toledo shipped the second highest amount of coal at 489,335 tons distributed over 35 shipments. Out of the eight ports in the U.S. that receive coal from Toledo, seven are from the state of Michigan. Those ports included Alpena (2), Marysville (6), Muskegon (4), Beach (7), Monroe (3), Escanaba (3), and Munising (4), totaling 29 shipments of 411,211 tons. The other destination was Green Bay, WI with six (6) shipments totaling 56,114 tons. 20

31 Figure 3.1 Shipments of 2008 coal tonnages from the Port of Toledo Sandusky is the largest origin port on Lake Erie in terms of coal tons shipped in 2008 at 630,523. Of the 10 destination, nine are located in the state of Michigan, totaling 27 shipments for 359,935 tons. Those ports included Gladstone (1), Ontonagon (8), Grand Haven (2), Manistee (8), Menominee (2), Monroe (2), Marysville (2), Muskegon (1), and Alpena (1). The last destination port is the Port of Green Bay with 19 shipments for 270,588 tons. 21

32 Figure 3.2 Shipments of 2008 coal tonnages from the Port of Sandusky Ashtabula distributed an estimated 408,333 tons of coal in 2008 over 28 shipments. Five U.S. ports received various amounts of coal including four ports located in Michigan totaling 27 shipments for an estimated 389,381 tons. Those destinations included Wyandotte (8), Muskegon (1), Grand Haven (10), and Alpena (8). In Wisconsin, Green Bay (1) collected approximately 18,952 tons. 22

33 Figure 3.3 Shipments of 2008 coal tonnages from Ashtabula Conneaut, located on the southern shore of Lake Erie, in Ohio, ships coal to a variety of Ports in the Great Lakes. In 2008, an estimated 111,409 tons of coal were distributed among 5 different destinations in the U.S. and for a total of 9 shipments. In the U.S. Alpena, MI (2), Marquette, MI (1), Green Bay, WI (2), Buffalo, NY (2), and Ashtabula, OH (2) received coal shipments for electric power generation. 23

34 Figure 3.4 Shipments of 2008 coal tonnages from Conneaut In 2008, the Port of Chicago shipped an estimated 2,638,840 tons of coal to four (4) of the Great Lakes, the St. Clair River, and the Detroit River. The state of Michigan received 97 of the 150 shipments of coal totaling 1,649,725 tons, reaching 11 destinations. Presque Isle received 27 of the shipments, followed by Manistee (25), Holland (13), 12 each to Escanaba and Muskegon, 3 shipments to Alpena, and then Marysville, Monroe, Marquette, Grand Haven, Wyandotte each received 1 shipment of coal. Wisconsin followed with a total of 54 shipments totaling 989,115 tons, and reaching 3 destinations. Green Bay received the most at 31 shipments, followed by Milwaukee at 18 and Manitowoc at 5. 24

35 Figure 3.5 Shipments of 2008 coal tonnages from the Port of Chicago The busiest coal terminal on the Great Lakes is the Midwest Energy Terminal at the Port of Superior-Duluth. Located at the western tip of Lake Superior, this terminal shipped a total of 13,722,176 tons of coal to four of the Great Lakes, the St. Clair, St. Mary s and Detroit River s. An estimated 295 shipments were distributed by the Midwest Energy Terminal for destinations in Michigan, Wisconsin and Minnesota in the United States. According to the Waterborne Commerce data provided by the Army Corps of Engineers, Michigan received 242 shipments in 2008 for a total of 12,005,545 tons, with St. Clair accounting for more than half at 144 shipments. The next highest 25

36 total went to Presque Isle at 31 shipments, followed by the Port of Monroe (28), Muskegon (15), Saginaw River (11), Marquette (10), and Marine City (4). In Wisconsin two ports received 17 shipments of coal for an estimated 434,311 tons. Milwaukee received the most shipments at 15, followed by Ashland at two. In Minnesota, Taconite and Silver Bay received a combined 36 shipments of coal for an estimated 1,282,320 tons. Figure 3.6 Shipments of 2008 coal tonnages from the Midwest Energy Terminal at the Port of Superior-Duluth 26

37 Ashtabula Conneaut Origin Midwest Energy Terminal Port of Chicago Sandusky Port of Toledo Alpena 115,432 20,841 36,717 10,008 21,170 Ashland 31,312 Ashtabula 35,357 Escanaba 221,648 48,493 Gladstone 16,585 Grand Haven 143,072 12,943 26,778 Beach 99,054 Holland 157,010 Manistee 315, ,291 Manitowoc 104,803 Marine City 234,189 Marquette 12, ,124 18,900 Marysville 18,463 16,551 57,061 Menominee 17,056 Munising 49,258 Muskegon 22, , ,276 13,673 76,132 Ontonagon 109,994 Port of Buffalo 9,193 Port of Green Bay 18,952 33, , ,588 78,124 Port of Milwaukee 402, ,246 Port of Monroe 822,121 9,614 43,999 60,043 Port of St. Clair 7,819,131 Presque Isle 1,690, ,565 Saginaw River 328,451 Silver Bay 509,633 Taconite 772,687 Wyandotte 108,486 15,742 Destination Figure Great Lakes coal distribution Origin-Destination Flow Matrix 27

38 3.3 Overview of the Great Lakes Distribution in 2008 In ports from five states received multiple shipments of more than 18 million tons of coal on the Great Lakes. Those five states included New York, Ohio, Michigan, Wisconsin, and Minnesota. Of those 27 ports, 21 were located in Michigan making it the largest beneficiary of waterborne shipment of coal on the Great Lakes. Michigan received an estimated 14.7 million tons of coal from waterborne transport that year, of which more than 90 percent came from the Western United States. According to the 2008 Waterborne Commerce data Southeast Michigan was the largest consumer of coal utilized for power plant consumption in the Great Lakes region. An estimated 9.2 of the 14.7 million tons of coal was consumed by power plants in this region, supplying power to more than 5.3 million residents (U.S. Census 2000). Monroe Power Plant, St. Clair Power Plant, and Belle River Power Plant have three of the largest generating capacities of all coal-fired power plants located on the Great Lakes. Combined both St. Clair and Belle River received 91 percent of total coal consumption for 2008 from coal shipped on the Great Lakes while Wyandotte received an estimated 80 percent of its coal consumption from Great Lakes shipments. On the other end of the spectrum Monroe Power Plant only received 10 percent of the 9.1 million tons of coal from the Great Lakes. All told more than half of all coal-fired power generation in Southeast Michigan in 2008 was fueled by Great Lakes shipped coal. Similar to that of Southeast Michigan, little if any trend exists when it comes to the rest of the Great Lakes region and Great Lakes coal consumption. Some power plants receive the majority of their coal from the Great Lakes while others receive very little. Manistee (Filer City Station), Manitowoc (Manitowoc Power Plant), 28

39 Muskegon (Cobb Generating Station), and Presque Isle (Presque Isle Power Plant) all received 100 percent of its coal consumption from Great Lakes shipments. Two logical explanations for the excess amount of coal are that these harbors are either stock piling for future usage or a steel mill is located nearby. Along with Monroe Power Plant, Saginaw River (Weadock Generating Station, Dan E. Karn Generating Station), Bay Front (Bay Front Station), Port of Buffalo (C.R. Huntley Generating Station), and Port of Green Bay (JP Pulliam Power Plant, Green Bay West Mill) all received less than 60 percent of their 2008 coal consumption from Great Lakes shipments. This was due to these power plants receiving a large portion of coal via the railroad system. is the most shipped commodity by rail in the U.S. with more than 47 percent of total rail tonnage (787 million tons) and 25 percent of total revenue ($12.1 billion) in 2009 (EIA). With its exceptional location and high concentration of people, the Great Lakes region is a valuable asset to the shipping industry. is a commodity that must be shipped in large quantities over long distances for it to be economically viable. was one of the most shipped commodities in 2008, with electric power generation as its main consumer. The methodological approach in past research in the field of freight forecast modeling has concentrated on the use of a wide array of independent variables including socioeconomic, transport supply and demand, truck O-D, and commodity flow survey data. (Yang, Regan and Son, 2010) The data is used as the attraction in the commodity flow. The purpose of the Economic Activity Model is to generate and distribute freight 29

40 forecasts as a function of the economic activity in the region. In a Four-Step Commodity Model socioeconomic data is used to estimate truck flows. Similar to statewide freight trip distribution models, this study will use a gravity model to estimate future coal flow. While statewide models have multiple road networks, multiple commodities, multiple modes of transportation, and multiple demand points to contend with the 2008 Waterborne Commerce data provides the origin, destination, and quantity transported. Only one commodity will be estimated, and only one mode of transportation will be utilized. 30

41 Chapter 4 Methodology The methodology for distributing freight flow on the Great Lakes is provided below. All forms of coal flow on the Great Lakes were initially considered for this study. The results of the calculations are discussed in future chapters. 4.1 Origin-Constrained Spatial Interaction Model Research initially indicated that the most appropriate model for freight flow distribution was an Origin-Constrained Spatial Interaction Model. Spatial interaction models are used to estimate the intensity of flows between two points that are spatially separated (origin-destination pairs). The closer two objects are to each other the more they will interact with each other, the intensity becomes greater. The spatial interaction model predicts that flows between two zones is directly proportional to the flow generations of each zone, and is inversely proportional to a function of the spatial separation between these two zones (Holguin-Veras and Thorson, 2000). The Origin- Constrained Spatial Interaction Model is formulated as follows: 31

42 F ij PS /C i m i 1 j j ij S /C ij (1) Where: F ij = flow from origin i to destination j S j = the attraction associated with facility j P i = the weight assigned to the origin i C ij = the cost of interaction from origin i to destination j =distance parameter related to the friction of distance between two points. The further the movement the greater the friction of distance Within the spatial interaction model the denominator is the balancing factor because it aids in determining the percentage of trips allocated to each destination j. Total consumption by each power plant is the attraction variable because they signify the demand for coal and attraction of each destination. When estimating commodity flow over water, distance becomes less of a factor due to the efficiency of shipping by waterborne vessel. Obtaining the friction factor distribution is critical to the spatial interaction model calibration process. The observed O-D flow data matrix was derived from the 2008 Waterborne Commerce Database. Without a recommended distance decay value for freight flow available, the initial friction factor values are assumed to be equal to one. 32

43 The values in table 4.1 represent the model calibration, which was determined as the best fit for the beta value. Calibration of the model was accomplished by choosing a high value of beta (2) where the friction of distance became more of the attraction, greatly influencing the distribution of coal. Destination ports closer in proximity to an origin port would receive significantly more coal than destination ports farther in distance. As the value of beta was decreased the friction of distance became less influential in the model and the demand for coal at each destination port increasingly became the attraction. The value of beta at.01 was determined to be the best fit for this model. Included in this model is the Root Mean Square Error which is the square root of the variance of the residuals. What this value indicates is how close the observed data are to the model s predicted values. The lower the values of RMSE the more indicative of a better fit. This measure is the most important criterion for how fit the model is if the end result is to predict or forecast a certain value such as the flow of coal from origin to destination. In figure 4.2 the percent RMSE is.023 percent, indicating the model will be very accurate in predicting future scenarios. This value increases slightly to percent when calculated for the total combined distribution of coal for 2008 on the Great Lakes. The percent difference is indicative of how close the estimated flow is to the actual flow. In figure 4.2 the percent difference is less than three percent for each O-D pair demonstrating an accurate estimation of coal flow. %RMSE (v ' v ) i i 2 V*100 /( N 1) (2) 33

44 %Differenc e [(v' i v i)/ v i]*100 (3) Where: V = Actual Flow V = Estimated Flow N = Number of OD Pairs Port Distance in miles Trip Distribution Origin Port: Conneaut S C F %Difference Alpena 20, , % Port of Green Bay 33, , % Port of Buffalo 9, , % Marquette 12, , % Ashtabula 35, , % Beta =.01 %RMSE =.023% Figure 4.4 Goodness of Fit Results (O-D) This version of the Origin-Constrained Spatial Interaction Model was the best model for this research because the focus of the study was on the mode of transportation used to ship coal on the Great Lakes. The use of beta in this model is different than with other modes of transportation such as by rail or by truck because the commodity is shipped over water and at a lower cost. In this model beta helps control the friction of distance so that the demand for coal at each power plant is the attraction. There are less distribution centers or origin ports when shipping over water so distance must play less of a role in the model. Traditional distribution centers would be placed as close to a market 34

45 as possible to cut down on shipment costs. Determining the value for beta increases the ability for the model to accurately predict the flow of coal. 35

46 Chapter Distribution Scenarios The U.S. Army Corps of Engineers provided the Waterborne Commerce data for This database provided freight flow on the Great Lakes and the St. Lawrence Seaway. The Navigation Data Center (NDC) supplied a National Waterway Network database that included route paths and freight movements on the Great Lakes. Each origin port was determined by the total tonnage shipped to other U.S. destination ports located on the Great Lakes who receive coal for use in the electric generation sector. For each origin port the destination port is numbered starting at 1 and increasing until each destination port is labeled (Example: Origin Port = the Port of Toledo, Destination Ports: 1. Port of Monroe 2. Marysville...8. Munising ). The O-D flows are illustrated in Figures 3.2 to 3.7 of chapter 3. With the 2008 Waterborne Commerce data providing the coal flow tonnage totals on the Great Lakes in disaggregate form it was fairly straightforward to determine each origin and destination point. An origin point is the source where the coal originates on the Great Lakes and ships coal out while a destination point receives coal for consumption by the electric generation sector. Only origins and destination on the Great Lakes and within the United States were considered for this study. Origin ports that ship coal to only one destination port in a calendar year were not considered for this study. 36

47 In order to analyze the sensitivity and effectiveness of the spatial interaction model several freight flow scenarios were developed to estimate future coal flow on the Great Lakes. Travel distance between origin and destination was used as the trip impedance and freight flow routes were determined using vessel route data from the Navigation Data Center. Three scenarios were developed and included all O-D groups. These Scenarios are: 1. Analyzing the impact of the closing of the power plant within each O-D group that received the largest tonnage of coal, based on the year Analyzing the impact of replacing 15 percent of the bituminous coal received at each destination port for sub-bituminous coal over a two-year period. 3. Analyzing the impact of an increase of 10 percent in sub-bituminous coal consumption at each destination port over a two-year period. These scenarios are illustrated in Tables 5.1 to Results: Scenario 1 In scenario 1 closing the power plant within each O-D GROUP consuming the largest tonnage of coal in 2008 completed the impact of an individual power plant on the overall distribution of coal. Travel distance is used as the trip impedance. Using the spatial interaction model the port receiving the largest tonnage of coal for power plant consumption in 2008 within each O-D group will be excluded from the model. This port will be represented within each table as having a value of zero in each column. The beta value will be set to.01 and the 2008 total tonnage of coal will be used as the weight assigned to origin i. 37

48 Origin Port: Ashtabula Distance in Miles Distribution Beta =.01 Destination Port S C F Trip Distribution Wyandotte 115, , Muskegon Port of Green Bay 22, , Grand Haven 18, , Alpena 108, , Figure 5.1 Scenario 1: Closing of Cobb Generating Station at Muskegon Origin Port: Conneaut Destination Port Distance in Miles Distribution S C F Beta =.01 Trip Distribution Alpena 20, , Port of Green Bay 33, , Port of Buffalo 9, , Marquette 12, , Ashtabula Figure 5.2 Scenario 1: Closing of Ashtabula Power Plant at Ashtabula Origin Port: Midwest Energy Terminal Distance in Miles Distribution Beta =.01 Destination Port S C F Trip Distribution Marine City 234, , Ashland 31, , Muskegon 964, , Saginaw River 328, , St. Clair Port of Monroe 822, , Presque Isle 1,690, ,694, Marquette 147, , Port of Milwaukee 402, , Silver Bay 509, , Taconite 772, , Figure 5.3 Scenario 1: Closing of St. Clair Power Plant at St. Clair 38

49 Origin Port: Port of Chicago Distance in Miles Distribution Beta =.01 Destination Port S C F Trip Distribution Wyandotte, MI 15, , Muskegon, MI 188, , Marysville, MI 18, , Monroe, MI 9, , Green Bay, WI 535, , Presque Isle Holland 157, , Alpena 36, , Marquette 18, , Port of Milwaukee 453, , Manistee 315, , Grand Haven 12, , Escanaba 221, , Manitowoc 104, , Figure 5.4 Scenario 1: Closing of Presque Isle Power Plant at Presque Isle Origin Port: Sandusky Distance in Miles Distribution Beta =.01 Destination Port S C F Trip Distribution Alpena 10, , Gladstone 16, , Grand Haven 26, , Manistee 105, , Marysville 16, , Menominee 17, , Muskegon 13, , Ontonagon 109, , Port of Green Bay Port of Monroe 43, , Figure 5.5 Scenario 1: Closing of J.P. Pulliam Power Plant at the Port of Green Bay 39

50 Origin Port: Port of Toledo Distance in Miles Distribution Beta =.01 Destination Port S C F Trip Distribution Alpena 21, , Escanaba 48, , Beach Marysville 57, , Munising 49, , Muskegon 76, , Port of Green Bay 78, , Port of Monroe 60, , Figure 5.6 Scenario 1: Closing of Beach Power Plant at Beach The results from scenario 1 suggest that the exclusion of the port with the highest tonnage of consumed coal will have little impact on the distribution of coal within each O-D group. When beta is set to.01 the model is very efficient at estimating the change in distribution while controlling for the loss in total tonnage of coal. Each O-D pair acts as an independent action therefore, is only effected by factors specific to that origin or destination. 5.3 Results: Scenario 2 Over the last 30 years there has been a shift of coal production from high-sulfur eastern coal (bituminous) to low-sulfur (sub-bituminous) western coal (EIA, 2006). This change in coal distribution has had an effect on ports situated along Lake s Erie and Ontario that receive coal from the Appalachian region of the US. Scenario 2 accounts for this pattern of change by running a model where there is an increase in the consumption of western coal at all destination ports for a two-year period. This increase in western coal will replace an existing amount of eastern coal at each destination port. An increase of 15 percent in western coal consumption was chosen arbitrarily. The Port of Chicago 40

51 and the Midwest Energy Terminal supply western coal for the Great Lakes and account for the increase in sub-bituminous coal. If a destination port did not originally receive sub-bituminous coal from either The Port of Chicago or the Midwest Energy Terminal then the closer of those two ports to that destination port will supply the future tonnage of sub-bituminous coal. The 2009 totals were calculated using the 2008 totals and the 2010 totals were calculated using the 2009 totals. If a cell has a zero that indicates the origin port has ceased shipping coal to that destination port. The Midwest Energy Terminal at the Port of Superior-Duluth and the Port of Chicago account for the increased shipments of western coal. Beta was set to.01 and travel distance is used as the trip impedance. Origin Port: Ashtabula Destination Port Wyandotte Muskegon Port of Green Bay Grand Haven 2008 Estimated Estimated Estimated 2010 S F S F S F 108, ,519 89,852 90,680 71,281 71,937 22,391 22, ,952 18, , , , , , ,299 Alpena 115, , , ,764 98,351 98,339 Figure 5.7 Scenario 2: Replacing 15 percent of bituminous coal shipped from Ashtabula to each destination port with sub-bituminous coal for a two-year period 41

52 Figure 5.8 Scenario 2: Map representing change in coal distribution from Ashtabula Origin Port: Conneaut 2008 Estimated Estimated Estimated 2010 Destination Port S F S F S F Alpena 20,841 20,659 13,185 12,914 3,760 3,668 Ashtabula 35,357 36,196 30,053 30,400 24,750 24,931 Marquette 12,499 12, Port of Buffalo 9,193 9,214 7,814 7,738 6,435 6,346 Port of Green Bay 33,519 33, Figure 5.9 Scenario 2: Replacing 15 percent of bituminous coal shipped from Conneaut to each destination port with sub-bituminous coal for a two-year period 42

53 Figure 5.10 Scenario 2: Map representing change in coal distribution from Conneaut Origin Port: Midwest Energy Terminal 2008 Estimated Estimated Estimated 2010 Destination Port S F S F S F Ashland 31,312 31,689 36,009 36,440 41,410 41,906 Ashtabula 0 0 5,304 5,279 10,607 10,556 Beach ,858 14,839 29,716 29,679 Marine City 234, , , , , ,837 Marquette 147, , , , , ,229 Munising 0 0 7,389 7,420 14,777 14,840 Muskegon 964, ,776 1,059,173 1,053,032 1,159,846 1,153,120 Ontonagon ,499 16,688 32,998 33,376 Port of Buffalo 0 0 1,379 1,371 2,758 2,741 Port of Milwaukee 402, , , , , ,537 Port of Monroe 822, , , , , ,653 Presque Isle 1,690,160 1,701,704 2,026,269 2,039,995 2,244,739 2,259,943 Saginaw River 328, , , , , ,770 Silver Bay 509, , , , , ,459 St. Clair 7,819,131 7,793,054 8,992,001 8,961,517 10,340,801 10,305,731 Taconite 772, , , ,480 1,021,879 1,041,301 Figure 5.11 Scenario 2: Shipping an additional 15 percent of sub-bituminous coal from the Midwest Energy Terminal to destination ports for a two-year period 43

54 Figure 5.12 Scenario 2: Map representing change in coal distribution from Midwest Energy Terminal Origin Port: Port of Chicago 2008 Estimated Estimated Estimated 2010 Destination Port S F S F S F Alpena 36,717 36,400 67,342 66,813 97,967 97,226 Escanaba 221, , , , , ,440 Gladstone Grand Haven 0 0 2,488 2,486 4,976 4,974 12,943 13,037 40,362 40,685 67,781 68,343 Green Bay, WI 535, , , , , ,047 Holland 157, , , , , ,643 Manistee 315, , , , , ,586 Manitowoc Marquette 104, , , , , ,228 18,900 18,705 32,293 31,985 46,758 46,326 Marysville, MI 18,463 18,296 25,369 25,159 38,144 37,839 Menominee 0 0 2,558 2,556 5,117 5,115 Monroe, MI 9,614 9,513 79,797 79, , ,265 Muskegon, MI 188, , , , , ,794 Port of Milwaukee Presque Isle 453, , , , , , , , , ,053 1,105,144 1,094,721 Wyandotte, MI 15,742 15,563 34,376 34,010 53,010 52,462 Figure 5.13 Scenario 2: Shipping an additional 15 percent of sub-bituminous coal from the Port of Chicago to destination ports for a two-year period

55 Figure 5.14 Scenario 2: Map representing change in coal distribution from the Port of Chicago Origin Port: Sandusky 2008 Estimated Estimated Estimated 2010 Destination Port S F S F S F Alpena 10,008 10,044 2,352 2, Gladstone Grand Haven 16,585 16,552 14,097 14,095 11,610 11,610 26,778 26,670 13,069 13, Manistee 105, ,016 42,241 42, Marysville Menominee Muskegon Ontonagon Port of Green Bay 16,551 16,761 9,645 9,785 3,258 3,306 17,056 17,019 14,498 14,493 11,939 11,937 13,673 13, , ,640 93,495 93,366 76,996 76, , , , , , ,923 Port of Monroe 43,999 45, Figure 5.15 Scenario 2: Replacing 15 percent of bituminous coal shipped from Sandusky to each destination port with sub-bituminous coal for a two-year period 45

56 Figure 5.16 Scenario 2: Map representing change in coal distribution from Sandusky Origin Port: Port of Toledo 2008 Estimated Estimated Estimated 2010 Destination Port S F S F S F Alpena 21,170 21,173 13,514 13,504 4,089 4,077 Escanaba 48,493 48,123 7,972 7, Beach 99,054 99,295 84,196 84,468 69,338 69,406 Marysville 57,061 57,500 50,155 50,582 43,768 44,041 Munising 49,258 48,857 41,869 41,562 34,481 34,151 Muskegon 76,132 75, Port of Green Bay 78,124 77,427 40,036 39, Port of Monroe 60,043 61, Figure 5.17 Replacing 15 percent of bituminous coal shipped from the Port of Toledo to each destination port with sub-bituminous coal for a two-year period 46

57 Figure 5.18 Scenario 2: Map representing change in coal distribution from the Port of Toledo Results from scenario 2 suggest that all ports are affected by a change in subbituminous coal for bituminous coal. This increase in coal from the western US effect the distribution of eastern US coal from those ports located on Lake Erie. Each of those four ports (Toledo, Sandusky, Conneaut, and Ashtabula) increasingly loses business each year to the Port of Chicago and the Midwest Energy Terminal. Results: Scenario 3 With skyrocketing gas prices and an ever-present focus on pollution and green technology people search for financial relief while still maintaining his and her driving habits. The demand for both hybrid cars and electric cars has given society that 47

58 alternative option to the standard automobile. These alternatives do require the use of electricity that is generated by coal-fired power plants. Electric cars are charged each day by connecting to a main electricity supply such as an individual s home. In scenario 3 this shift in technology is analyzed by accounting for an increase in electrical output brought on by an increased usage of electric automobiles. With an increase in clean-coal technology more coal-fired power plants are shifting to a blended mix that uses low sulfur sub-bituminous coal found in the western US. The Port of Chicago and the Midwest Energy Terminal supply western coal for the Great Lakes and account for the increase in sub-bituminous coal. Beta was set to.01 and travel distance is used as the trip impedance. Origin Port: Ashtabula 2008 Estimated Estimated Estimated 2010 Alpena 115, , , , , ,501 Grand Haven Muskegon Port of Green Bay Wyandotte 143, , , , , ,503 22,391 22,261 22,261 22,131 22,131 22,001 18,952 18,884 18,884 18,816 18,816 18, , , , , , ,598 Figure 5.19 Scenario 3: Estimated shipments of 2008 coal from Ashtabula for a two-year period Origin Port: Conneaut 2008 Estimated Estimated Estimated 2010 Alpena 20,841 20,659 20,659 20,473 20,473 20,282 Ashtabula 35,357 36,196 36,196 37,045 37,045 37,902 Marquette 12,499 12,305 12,305 12,107 12,107 11,910 Port of Buffalo 9,193 9,214 9,214 9,230 9,230 9,244 Port of Green Bay 33,519 33,035 33,035 32,542 32,542 32,047 Figure 5.20 Scenario 3: Estimated shipments of 2008 coal from Conneaut for a two-year period 48

59 Origin Port: Midwest Energy Terminal Ashland 2008 Estimated Estimated Estimated ,312 31,688 34,443 34,856 37,888 38,341 Marine City 234, , , , , ,564 Marquette Muskegon Port of Milwaukee 165, , , , , , , ,768 1,090,793 1,084,494 1,229,909 1,222, , , , , , ,780 Port of Monroe 822, , , ,397 1,018,634 1,013,819 Presque Isle 1,690,160 1,701,688 1,914,233 1,927,245 2,160,712 2,175,338 Saginaw River 328, , , , , ,840 Silver Bay 509, , , , , ,807 St. Clair 7,819,131 7,792,984 8,601,044 8,572,085 9,461,149 9,429,024 Taconite Ashtabula 772, , , , , , ,536 3,519 7,425 7,389 Port of Buffalo ,931 1,919 Beach 0 0 9,905 9,893 20,801 20,775 Ontonagon Munising ,999 11,125 23,099 23, ,926 4,947 10,344 10,388 Figure 5.21 Scenario 3: Increase of 10 percent of sub-bituminous coal shipped from the Midwest Energy Terminal to each destination port for a two-year period Origin Port: Port of Chicago Alpena Escanaba Grand Haven 2008 Estimated Estimated Estimated ,717 36,400 57,134 56,630 79,592 78, , , , , , ,749 12,943 13,037 31,222 31,441 51,330 51,682 Green Bay, WI 535, , , , , ,413 Holland Manistee Manitowoc Marquette 157, , , , , , , , , , , , , , , , , ,202 18,900 18,705 18,900 18,702 18,900 18,699 Marysville, MI 18,463 18,296 27,671 27,415 37,799 37,443 Monroe, MI 9,614 9,513 9,614 9,511 9,614 9,510 Muskegon, MI 188, , , , , ,469 Port of Milwaukee Presque Isle 453, , , , , , , , , , , ,591 Wyandotte, MI 15,742 15,563 28,165 27,838 41,830 41,338 Menominee Gladstone 0 0 1,706 1,703 3,582 3, ,659 1,656 3,483 3,477 Figure 5.22 Scenario 3: Increase of 10 percent of sub-bituminous coal shipped from the Port of Chicago to each destination port for a two-year period

60 Origin Port: Sandusky Alpena Gladstone Grand Haven Manistee Marysville Menominee Muskegon Ontonagon Port of Green Bay Port of Monroe 2008 Estimated Estimated Estimated ,008 10,044 10,044 10,079 10,079 10,114 16,585 16,552 16,552 16,518 16,518 16,483 26,778 26,670 26,670 26,561 26,561 26, , , , , , ,453 16,551 16,761 16,761 16,973 16,973 17,187 17,056 17,019 17,019 16,980 16,980 16,941 13,673 13,645 13,645 13,616 13,616 13, , , , , , , , , , , , ,219 43,999 45,034 45,034 46,090 46,090 47,169 Figure 5.23 Scenario 3: Estimated shipments of 2008 coal from Sandusky for a two-year period Origin Port: Port of Toledo 2008 Estimated Estimated Estimated 2010 Alpena 21,170 21,141 21,141 21,109 21,109 21,075 Escanaba 48,493 48,131 48,131 47,766 47,766 47,397 Beach 99,054 99,313 99,313 99,561 99,561 99,798 Marysville Munising Muskegon Port of Green Bay 57,061 57,511 57,511 57,957 57,957 58,400 49,258 48,867 48,867 48,473 48,473 48,076 76,132 75,430 75,430 74,726 74,726 74,019 78,124 77,439 77,439 76,750 76,750 76,059 Port of Monroe 60,043 61,504 61,504 62,993 62,993 64,511 Figure 5.24 Scenario 3: Estimated shipments of 2008 coal from the Port of Toledo for a two-year period Results from scenario 3 suggest that an increase in sub-bituminous coal would only affect the origin ports of the Port of Chicago and the Midwest Energy Terminal. Starting in 2009 The Port of Chicago would begin shipments to two new ports (Menominee and Gladstone ) while the Midwest Energy Terminal would begin shipments to five new ports (Ashtabula, Port of Buffalo, Beach, Ontonagon, and Munising ). Those origin ports located on Lake Erie 50

61 supply bituminous coal to destination ports and would continue similar shipments to that of the 2008 tonnage totals. Figure 5.25: Scenario 3: Increase in the number of destination ports receiving coal from the Midwest Energy Terminal after a 10 percent increase in sub-bituminous coal 51