Achieving Sustainable Development in Southern California: Collaborative Learning through System

Dynamics Modeling

Raymond Madachy, Benjamin Haas, Hilary Bradbury, Josh Newell, Mansour Rahimi, Robert Vos and Jennifer Wolch University of Southern California Center for Sustainable Cities

3620 S. Vermont Ave., KAP 416 Los Angeles, CA 90089-0255

(213) 821-1325 {madachy, bhaas, hbradbur, jnewell, mrahimi, vos, wolch}@usc.edu

Copyright © 2008 by Raymond Madachy, Benjamin Haas, Hilary Bradbury,

Josh Newell, Mansour Rahimi, Robert Vos and Jennifer Wolch. Published and used by INCOSE with permission.

Abstract. Southern California's gateway to international commerce is through major ports in

the San Pedro Bay. The University of Southern California is working with local business and other port stakeholders to enable collaborative learning about sustainable business practices. We are using system dynamics-based models for participants to locate leverage points that yield the highest CO2 footprint reduction for the lowest cost for the shipping of goods from China through one of the ports. As goods movement is a process in which numerous business entities connect, it also offers an opportunity for a collaborative learning approach. We model key leverage points in the supply chain to explore the affects of potential business decisions on the CO2 footprint of shipping containers. These include options for global route choices and clean technologies for ships, rails and trucks. We are expanding the models for refined route options, adding cost functions, and composite clean technology choices across the transport modalities.

Introduction Business leaders are becoming proactive in reducing CO2, and trade with China through the

port is on the rise. Carbon footprints are becoming increasingly important to track for a variety of reasons. CO2 is a greenhouse gas that is known to be proliferating in the atmosphere (US 2006), and contributes to global climate change. CO2 regulations are becoming a political issue at all levels of government and international alliances (UN 2007). One of the more famous of these initiatives is the international Kyoto Protocol of 1996. And there is much talk of a carbon tax / cap, and even a carbon market (Sperling 2007). These movements are leading business leaders to proactively look to reduce their CO2 emissions.

While businesses are moving to develop more sustainable and greener practices, trade with China continues to increase at an alarming rate. As a result, the tracking of goods shipped from China through local ports offers an opportunity to examine supply chain business decisions and their environmental impacts. Because the shipment of goods connects a number of businesses and governments, it also offers an excellent opportunity to examine collaborative learning between organizations through modeling.

The USC Center for Sustainable Cities is working with local business leaders and other users of the port in the Sustainable Enterprise Executive Roundtable (SEER) to enable collaborative learning about sustainable business practices. The mutual goal is to simultaneously benefit the environment and the bottom line, through the implementation of projects that promote

sustainable development. One of the key projects is to develop a set of tools for modeling the carbon footprint of an average shipping container from factories in China to destinations within the United States. Within SEER there is a primary sponsor of this project that has provided valuable data to help develop and test a simulation model.

The system dynamics model we have developed allows users to track the carbon footprint of a container that is being imported from China. The model was created to be modular enough to be expanded or modified to support numerous users with unique operating modes. Besides generating a carbon footprint, the model allows the user to study the effects of modifying shipping routes, land transportation modes, ship types, and many other supply chain decision points.

We want to evaluate supply chain options by quantifying the carbon footprint of importing a standard shipping container from a factory in China to its destination in the United States. A carbon footprint is typically defined as the amount of CO2 that is emitted into the atmosphere in a given year. In this case it is bounded as the amount of CO2 that is emitted during a given trip from factory to customer. An annual carbon footprint can be generated using this average trip carbon footprint as well. CO2 generation from the movement of goods is attributed to the type of movement as well as the duration of movement. Both of these items are directly influenced by supply chain decisions that are typically made without knowledge of the environmental impact.

Background The supply chain system under analysis is bounded from when the container leaves the

factory in China to when it reaches its destination within the United States. There are three primary contributors to the carbon footprint within this system. The first is the land contribution, which is partitioned into China and United States segments, and is further partitioned into truck and rail segments. The second contribution comes from the sea, which is portioned into cruising speed, and slow speed segments. The third contribution comes from port operations for loading and unloading containers.

Figure 1 shows the general system that is being modeled. These three contributors are broken down into six supply chain leverage points, which are considered to be points of influence for the user, and hence points of focus for the model developer. Each leverage point can be associated with a rate of CO2 generation that can be combined to determine the overall carbon footprint for a trip or an annual number of trips.

Figure 1. Supply Chain System Overview

System Dynamics Introduction. System dynamics is a simulation methodology for modeling continuous systems (Forrester 1961). Quantities are expressed as levels, rates and information links representing feedback loops. It provides a rich and integrative framework for capturing process phenomena and their relationships.

A simplified system dynamics rate and level model of emissions and its time output in Figure 3 demonstrates the behavior of each of these leverage points. The cumulative level is simply an integral of the rate over time. An increase in the import rate will lead to a steeper increase in the Cumulative Imports.

Cumulative Importscontainer import rate

Figure 3. Simple Rate and Level Model with Reference Behavior

Model Development Because the model is intended to be flexible for multiple users and their respective needs, it

was developed to generate multifaceted results using a modular approach. The model is able to break down the average and cumulative carbon footprint by contribution type and location, and allows users be able to compare the affects of supply chain decisions in real time.

A dynamic modeling tool, iThink©, was chosen for its ease of use and flexibility. The iThink interface layer graph pads and bar charts were used to display real time results of this model. “Ghosts” were used to repeat commonly used variables throughout the model, and “Sector Frames” were used to separate each module instantiation.

This initial model has one departure port, two destination ports, two US Distribution Centers, and thee possible shipping routes. Because only import data was available from the primary sponsor, this initial model footprint ends when the container reaches its last known destination. The model was created to be modular, so that it can easily be expanded to include additional ports or shipping routes. Each module can be used several times to replicate multiple ports or routes. One additional module was created to generate data and to help validate the model. The modules share common variables using “ghosts”, so that each module appears to be self containing.

The initial system boundary and leverage points were identified, using current supply chain data and expected opportunities for change. Six leverage points were initially identified to focus the model development, and are described in detail below. These leverage points were broken down in three categories of system carbon contributors, Sea, Land, and Port Operations. Each category was developed into a modular sub-model that could be applied to each of the leverage points.

1) Factory to Port Transportation. In order to support this leverage point, the sponsor provided data on the factory names and locations, port names and locations, and an estimate of the mileage

rate for local trucks. A sample of this data can be found in Appendix A. An estimate of distances was generated for each factor to port pair, and CO2 generation rates were developed for both truck and train usage. The land module sub-model was created based on this information.

Users may be able to influence this leverage point when making decisions in regards to awarding manufacturing contracts. Distance to port and method of shipment provide input for this leverage point and model module.

2) China Port Operations. A Port Operations module was created based on data gathered from the port. This module was re-used for port operations in China, however very little additional data could be gathered for this leverage point. The average CO2 emissions for typical Chinese electricity, and a typical port time were found, which helps to provide some real world data, however the average power consumption from terminal operations was re-used based on American port data. Port Operations are a small factor compared to the overall carbon footprint from importing a container; therefore this gap in data is unlikely to affect the overall model.

3) Port to Port. This leverage point will review shipping routes, ship types, and fuel types. The Port of Los Angeles has a wealth of information that was used to help generate a ship CO2 rate that is a primary factor in the Ship Module, used to model this leverage point. This module was repeated for several different routes and operating modes, including open ocean cruising for three different routs, and slow cruising for two different canals.

This leverage point is useful if individual model users are able to control which shipping routes are used, since the distance, ship efficiency and ship size all play important roles in CO2 generation.

4) United States Port Operations. This leverage point re-uses the port Module; however it uses actual US port energy and emissions data. The Port is an active member of SEER and is helping to refine this portion of the model.

Individual companies may use this leverage point by working with the ports to improve port operations, or by directing cargo to ports that have more efficient operations.

5) Terminal to Distribution Center. This leverage point is similar to leverage point one. Like leverage point one, the sponsor provided data on port traffic, distribution centers and fuel mileage data, which was used to populate a Land Module.

Unlike leverage point one, US customers should have greater control over the type of transportation used, and should be able to influence the distance of land transportation through port and distribution center selection.

6) Distribution Center to Destination. This leverage point is nearly identical to leverage point five, except that it is not affected by port usage.

Sponsor Data The project primary sponsor was able to provide some of their key supply chain information

that was used to help develop this model. The tables in Appendix A demonstrate some of the data that was collected and used. A trial version of NetPas Distance© 2.5 was used to generate shipping route distances for the routes of China to LA, China to Texas through the Panama Canal, and China to Texas through the Suez Canal.

All of the user data had to be modified to fit the modeling approach. Distances were calculated using the online mapping software, and then weighted against the volume of

containers per route. These weighted distances were used to determine the average truck and rail distances for each land module. A simple excel file was used to automatically generate the data.

To generate a Truck CO2 rate for each Land Module in the US and China, the average MPG had to be converted into CO2 per mile. According to the EIA, each gallon of diesel fuel creates 22 lbs. of CO2 (EIA 2007). A simple conversion finds that 9.98 kg of CO2 are generated per gallon.

The Train CO2 rate for each Land Module was generated with the average weight of containers. According to the UK Parliament “Minutes of Evidence,” freight generates CO2 at a rate of 15 grams / tonne-km (UK 2007). The average weight can then be used to create a rate using kg/miles. As expected, the rail rate is significantly lower than the truck rate.

A shipping CO2 rate of 0.670 kg / kilowatt-hour, which is commonly used by the port, was used for this model. Additional typical container service statistics used by the port are listed in Figure 4 (Nye 2007).

Figure 4. Typical Container Service Statistics

The Port was also able to provide Port Operations energy consumption and California

electricity CO2 rates, which are shown in Figure 5. Texas electricity CO2 rates were determined by using the Environmental Protection Agency’s emission rate comparison tool (EPA 2007).

Figure 5. Typical Container Service Statistics

Model Description The first module developed, was for leverage point 3, Port to Port, and is based on the CO2

emissions from a cargo ship, normalized per shipping container, as it transits from a port in China to a port in the United States. One of these modules was instantiated for each route

modeled. “Ghosts” were used to share many of the same variables. This was done to help maintain consistency and modularity. This module uses ship information for a typical container vessel, including Main Engine Power, Auxiliary Engine Power, Boiler Power, and Cargo Size. The power is used along with the Avg. Ship Cruise Time and a Ship CO2 Rate to determine the typical amount of CO2 generated per trip. This result was then normalized against the Ship Cargo Size to determine the average CO2 per container per trip. In order to determine the Cumulative CO2 from this module, the Panama Vs Suez and LA Vs TX settings are used to adjust the weighting for each route.

Figures 6 – 13 show the primary modules and are described in the following sections.

Figure 6. Ship Module (1 of 5)

The Ship module was further utilized to determine the effect of slowing the ship down to

transit canals. Two major canals, Panama and Suez, were also implemented using this module within this model.

The second module created was based on leverage points Two and Four, which both involve Port Operations, and was instantiated three times within the model. This module relies on local information such as Power Emissions, Avg. Berth Power, and Time in Port. This information is used to determine the local Terminal Emissions per visit. While in port, a ship will typically keep its auxiliary engine and boiler running, which also contribute to the carbon footprint. It is possible to replace the auxiliary engine power with shore based power, which can be cleaner and more efficient. This is known as Cold Ironing and can be switched on or off for each port. These emissions are captured in the local Port Ship Emissions and are added to the port carbon footprint. Like the first module, the Cargo Size is used to determine the Terminal CO2 per TEU and Port Ship CO2 per TEU, and the import rate is adjusted based on the usage of each particular port through the LA Vs TX setting.

Figure 7. Port Module (1 of 3)

We reused the Avg. Berth Power for each instantiation of the model, because we were unable to find average terminal or berth power for any of the ports at this time except for the port. Table 1 shows the local Port Ship Emissions and Ship energy emissions that were used within this module, and were collected from a variety of sources.

Table 1. CO2 Generation Rates 1 Port Los Angeles Houston China Ship

Kg CO2 / kilowatt 0.327 0.644 0.870 0.670

The third module developed, covers leverage points One, Five, and Six, which are all based

on emissions caused from shipping containers over land by truck or by rail. It is based on the local land shipping emissions captured in Avg. Truck CO2 per Mile and Avg. Train CO2 per Mile per TEU, and listed in Table 2. This emission standard can be upgraded by a Clean Truck Factor or Clean Train Factor if either the local Clean Truck or Clean Train switches are turned on. A clean technology factor of 30% was used throughout this model as a representative approximation. TX Avg. Truck Distance is used to capture the average distance of containers imported through the local port that are shipped by truck. TX Avg. Train Distance is used to capture the average distance of containers imported through the local port that are shipped by train. These two settings, along with the local Truck Vs Rail are used to determine the local CO2 per TEU. The cumulative CO2 is broken down into the local Truck CO2 and Train CO2 to give the user more flexibility in reporting results.

Figure 8. Land Module (1 of 2)

Table 2. CO2 Generation Rates 2

Port China Truck US Truck Train Ship * Kg CO2 / TEU-

Mile 2.222 1.996 0.246 0.105

This land module is instantiated once for each of the three modeled ports to cover leverage

points Two and Five, and again for each of the distribution centers within the United States to cover leverage point Six. These distribution center instantiations are slightly different, in that they track carbon emissions from distribution center to customer. They require two additional settings, which are the local DC CA_% and TX_%. These settings are used to set the percentage of traffic from each US port that is forwarded to this local distribution center. For instance DC1 CA_% percent of the traffic from the port is routed to distribution center 1. The local Avg. Truck Distance, Avg. Train Distance, and Truck Vs Rail are used to determine the type of traffic between the distribution center and the customer.

Figure 9. Land Module – Distribution Center (1 of 2)

The Avg. Truck Distance and Avg. Train Distance settings are weighted using the number of

containers per mile. Therefore the Truck Vs. Rail setting could easily be replaced with (Avg. Truck Distance)/(Avg. Truck Distance + Avg. Train Distance), however this additional setting provides the user with additional flexibility for theoretical study.

A final module was created to tie the modules together, to create additional data, and to validate the model. It consists of several sub-models that are explained below. At least two different methods were used to generate the required Total CO2 results within each module, and for the entire model. This was done to help validate that the results were consistent and catch any potential mistakes.

The first set of sub-models can be used to consolidate and validate the Ship Modules. The first method consists of adding the Cumulative CO2 from each instantiation of the module to determine the Total Cruise CO2 1. The second method determines the average Cruise CO2 per TEU by weighting each module CO2 per TEU rate by route usage, to determine the Total Cruise CO2 2. These results should be consistent if the weighting factors were accurate across each module. Similar methods were used to determine the Total Port CO2 and Port CO2 per TEU as well.

Figure 10. Ship & Port CO2 Results Generation

The Land Modules use one additional method to determine the Cumulative Truck CO2,

Cumulative Train CO2, and Total Land CO2.

Figure 11. Land CO2 Results Generation

The Cumulative CO2 and Total CO2 per TEU were calculated by combining these methods

yet again.

Figure 12. Total CO2 Results Generation

Additional sub-models were added to this module to develop reporting metrics related to the

average CO2 per TEU based on the user data.

Figure 13. CO2 per TEU Results Generation

Model Testing Each model could be tested separately because of the modular approach. The first test

compared the Cumulative CO2 calculations as described above. When the model modules were initially tested, many of these results showed varying slopes. This was the result of incorrect

weighting or mistypes within the various module instantiations. These were found and corrected easily to create consistent results in subsequent tests.

The overall model is very similar, using a series of ‘rate and level’ behaviors. There are no feedback mechanisms or distribution functions. Most of the model validation was based on switching the data from one extreme to the other to verify that the corresponding change in results was similar.

Sponsor Data. The sponsor data in Table 3 was used as input to the model, and was used as baseline results for experimentation. A summary of the baseline results is shown in Table 4.

Table 3. Baseline Sponsor Input Parameters Input Parameter Base Value Input Parameter Base Value

CA Truck Vs Train 68% DC1 CA% 60 %CA Avg. Train Distance 288 Miles DC1 TX% 0 %CA Avg. Truck Distance 600 Miles DC1 Truck Vs Rail 100 % DC1 Avg. Truck Distance 585 MilesTX Truck Vs Train 0% DC1 Avg. Train Distance N/A (0)TX Avg. Train Distance 1210 Miles TX Avg. Truck Distance (N/A) 0 DC2 CA% 40 % DC2 TX% 90 %China Truck Vs Train 100% DC2 Truck Vs Rail 0 %China Avg. Train Distance (N/A) 0 DC2 Avg. Truck Distance (N/A) 0China Avg. Truck Distance 212 DC2 Avg. Train Distance 1000 LA Vs. TX 100% US Clean Truck OffPanama Vs. Suez N/A (50%) US Clean Train Off CA Cold Ironing On China Clean Truck OffTX Cold Ironing Off China Clean Train OffChina Cold Ironing Off Container Input Rate 20742

Table 4. Baseline Sponsor Data Results

% of Total % of Total % of Total Portion

Land CO2 per

TEU Land Total Portion

Ship CO2 per

TEU Land Total Portion

Port CO2 per

TEU Land Total

DC1 759 38.3 26.9 Panama 0 0 0 China 96 60.6 3.4 DC2 99 5.0 3.5 Suez 0 0 0 CA 42 30.4 1.5 China 471 23.7 16.7 Route 1 666 100 23.6 TX 0 0 CA 719 36.2 25.5 Route 2 0 0 0 Total 138 100 4.9 TX 0 0 0 Route 3 0 0 0 Truck 1603 80.8 56.8 Total 666 100 23.6 Train 386 19.2 13.7 Land 1984 100 70.3 Total Carbon CO2 per TEU 2821

Port Operations. One thing that stands out based on this model, is that Port Operations play a very small part in contributing to the carbon footprint of an average container. It follows that Cold Ironing provides little benefit to the importer of goods from a carbon footprint perspective,

in fact Cold Ironing in China would actual create more CO2, since the local electricity is less efficient than the ship’s auxiliary engines (See Table 9). If this model were expanded to model other emissions, local ports might benefit from using cleaner energy; however Port Operations provide relatively little impact on the carbon footprint from China.

Ship Transportation. The sponsor currently ships nearly 100% of their goods through the port, which is the shortest shipping route. This results in the lowest shipping CO2 rates possible, however it also creates a need for additional land transportation within the United States, since most of the customers and distribution centers are East of the Rockies. The option to transfer shipping to TX is examined below.

If the customer were to use the alternative routes, they would find that the canals have even less impact on the carbon footprint, then the port Operations. Transiting the Panama Canal results in only 12 kg per TEU per Trip, while the transiting the Suez Canal results in only 6 kg per TEU per Trip. These modules will also be left in the model, in case it is ever expanded to include other environmental factors that might affect the local canal areas.

Land Transportation. It is no surprise that Trucking provides the largest impact to carbon footprint. According to Table 4, it is nearly 10 times less efficient per mile than a Train and nearly 22 times less efficient per mile than a Ship. Using these particular model settings compounds this fact, because all of the shipping is sent through CA which uses 68% Trucking.

Example Comparison of Shipping Options An alternative to the test cases described above is to transfer more goods through the port of

Houston. The first model run sent 40% of the CA imports to DC2 which is located closer to the port in Texas. If these goods are transported through Route 2 to the port in Texas, it should reduce the carbon footprint of these containers. The model was run by switching the LA Vs TX setting from 100% to 60%, the DC1_CA% from 60% to 100%, the DC2_CA% from 40% to 0%, the DC1_TX% to 0%, and the DC2_TX% to 100%. The results for these cases are shown in Figure 14 for total CO2 and allocations between ship, land and port. The second option has 82% of the initial carbon footprint with different emission allocations between the transport modalities.

Figure 14. Comparison of Shipping Options

Model Limitations The System Dynamic model has a number of limits that must be understood when using the

model to make decisions. The model is only as good as the data that is gathered, and some data may not be entirely accurate. For instance this model does not take into account the affect of traffic, which might drive up the land CO2 contribution associated with one or more of the supply routes.

Another limiting factor in this system model was discussed in leverage point two above. The average power consumption for terminal operations in a Chinese port could not be found. Because this is a relatively small factor in the carbon footprint, re-using the US data should be sufficient. In this particular model the port contribution to the carbon footprint is limited to the ship power used while in port and the average power of the terminal operations while the ship is in port. It does add in additional port CO2 emission that is related to upkeep and day to day operations of the terminal. These CO2 contributions could be added to terminal footprint by averaging these factors by the number of containers serviced over a year. This would add to the port CO2 footprint slightly, but since it is such a small factor in the overall model, it can be ignored.

Another limit to this model is that data from one typical container ship was used to populate the model; however it is likely that a number of different types of ships will be used for each route. These variations in ship types or operating modes could swing the carbon footprint of a container by several hundred kilograms in either direction. Future users of this model will want to use ship data based on the actual shipping companies and routes that they intend to use.

The train CO2 rate was used based on valid data; however one rate does not cover all freight services. A more accurate model would take a look at the fuel consumption for actual freight services that are being used or considered by the user.

The Chinese land contribution is another part of the model that used quite a bit of estimation for determining the distances from factory to port, however since there is little leverage for a user within this section of the model, it’s accuracy is less important.

Conclusions and Future Work The model is useful for calculating the carbon emissions of containers transported overseas

and within the US, and is being expanded for further capabilities. Because of its modularity, this model can be reused for multiple customers and operating modes, and could be easily expanded to consider additional environmental concerns.

A carbon footprint was created for a container transiting from factory to destination, based on the sponsor’s data. At nominal conditions for the primary sponsor, a carbon footprint of 2,821 kilograms per container per trip was determined.

It was found that the user’s operating model leads to a carbon footprint this is based primarily on trucking the container. To improve their carbon footprint, the goods being sent to a distribution center in Texas could be routed through the port of Houston. This, however, does not take into account timeliness of delivery, nor shipment costs, which will weigh heavily on any supply chain decisions. This model however will show a new cost, one that could not be considered until now. That cost is the environmental impact due to CO2 of the company’s supply chain decisions.

This model will continue to be developed and used by SEER for tracking additional environmental issues, and for use by other sponsors. The modularity and flexibility that was built into the model should provide easy modification for future modelers. One activity would

be to gather actual ship information based on the routes used by the customer. Small adjustments make the most impact within this module. Users should be able to request the appropriate data from their shipping services, and implementing these changes should not be difficult.

We are also expanding the model for refined route options, adding cost functions, and composite clean technology choices across the transport modalities. The important dimension of cost is being added to the model. With cost functions attached to the transport modalities, tradeoffs of cost and emissions can be evaluated by users. The time of transport is another consideration (and is a cost itself), but was not deemed as important to the stakeholders to tradeoff at this time.

Increased public accessibility of the model(s) is another goal of the USC Center for Sustainable Cities. We are looking into options for putting non-proprietary elements of the emissions model on the Internet for public domain usage and education. We will continue to report on the model extensions and its usage.

References Energy Information Administration, “Fuel & Energy Source Coefficients“,

http://www.eia.doe.gov/oiaf/1605/coefficients.html, last accessed November 2007. EPA, “Emissions Rate Comparison Charts,” Environmental Protection Agency,

http://oaspub.epa.gov/powpro/ept_pack.charts, last accessed November 2007. Forrester, J., Industrial Dynamics. Cambridge, MA, MIT Press, 1961. Nye, L., “Reducing Emissions at Ports through the use of cold-ironing,” Port of Los Angeles,

October 2007 Sperling, D., “A New Carbon Standard”, New York Times,

http://www.latimes.com/news/printedition/opinion/la-oe-sperling21jun21,1,6457401.story?coll=la-news-comment, June 2007.

UK, “Minutes of Evidence”, UK Parliament, http://www.publications.parliament.uk/pa/cm200607/cmselect/cmtran/61/6112013.htm, October 2006.

UN, “Kyoto Protocol Background”, United Nations Framework Convention on Climate Change, http://unfccc.int/kyoto_protocol/items/2830.php, last accessed November 2007.

US, “US Climate Action Report (CAR)”, US State Department, http://www.state.gov/g/oes/rls/rpts/car/90324.htm, 2006.

Appendix A – Sponsor Data Tables 5 - 9 list some of the sponsor data used in the model development.

Table 5. Average Usage of Container Types (Sponsor Data) Container Type D20 40 D40H D45 D48 40 NOR Average Usage 00.52% 15.19% 67.32% 16.70% 00.00% 00.26%

Table 6. Average Weight of Container Types (Sponsor Data)

Container Type Average Weight not including Container

Average Weight including Container

Average Weight of Empty Container

40H Maersk 15,556 lbs. 21,949 lbs. 6,393 lbs. 40 Dry Maersk 13,460 lbs. 20,259 lbs. 6,799 lbs.

20 Dry Maersk 26,460 lbs. 30,282 lbs. 3,822 lbs. Avg. by Usage 15,703 lbs. 22,642 lbs. 6,939 lbs.

Table 7. Estimated Truck Miles Per Gallon (Sponsor Data) USA MPG 5 China MPG 4.5

Table 8. Sample Supply Chain Data (Sponsor Data)

Location City POL POUL Destination Volume (FEUs)

HuMen DONGGUAN Yantian LA/Long Beach Ft. Worth 4 HuMen DONGGUAN Yantian LA/Long Beach San Bernardino 671 PingHu PINGHU Yantian Houston Ft. Worth 184

Table 9. Sample DC to Customer Distances (Sponsor Data)

Distribution Center Name: DC1 DC2 Avg. Distance to Customer DC: 587 miles 1000 miles

80538 72143 45014 Customer DC Zip Code: 85043 07836 24477

Biographies Raymond Madachy is a Research Assistant Professor in the USC Industrial and Systems

Engineering Department and a Principal of the USC Center for Systems and Software Engineering. Prior to USC he had 25 years of management and technical experience in industry. His research interests include modeling and simulation of processes for architecting and engineering of complex software-intensive systems; economic analysis and value-based engineering of software-intensive systems; systems and software measurement, process improvement, and quality; quantitative methods for systems risk management; integrating systems engineering and software engineering disciplines; and integrating empirical-based research with process simulation.

Benjamin Haas is a part time graduate student at USC working towards a MS in Systems

Architecture and Engineering. He is currently employed as a Systems Engineer working in a Systems Engineering & Integration role on a prominent Air Force Space Acquisition Program. He has previous experience working as a test and systems integration engineer for the Naval Surface Warfare Center.

Hilary Bradbury is the Director of Sustainable Business Research Programs at USC in the

Center for Sustainable Cities. Previously she was Associate Professor of Organizational Behavior at the Weatherhead School of Management at Case Western Reserve University. Her research, as well as her scholarly activism and teaching focus on organizational change, the human and organizational dimensions of sustainable development and action research. She is editor of the international, peer reviewed Sage journal, Action Research. She has published in Organization Science, Academy of Management Executive, Journal of Management Inquiry, and Organization Development Practitioner among other publications.

Josh Newell is a Program Manager at the Center for Sustainable Cities at University of Southern California. Educated at Brown University (History) and the University of Washington (Geography), Josh’s research areas include political ecology, industrial ecology, forestry, urban ecology/sustainable cities, environmental certification and corporate social responsibility, and supply chain and resource theory and modeling. From 1991–2000, Josh was a program director for Friends of the Earth-Japan, a large international environmental nongovernmental organization. His geographic areas of expertise include the Russian Far East, northeast China, and Japan. Josh has written journal articles for Landscape and Urban Planning and Ecological Economics, contributed chapters to edited book volumes, and has had two books published, including The Russian Far East: A Reference Guide for Conservation and Development (2004). Josh speaks Russian and Japanese.

Mansour Rahimi is an Associate Professor in the Epstein Department of Industrial and

Systems Engineering, USC. His research interests are in industrial ecology, sustainable transportation systems and life-cycle analysis of alternative fuels. His faculty affiliations include a year as AT&T Fellow in Industrial Ecology, an Urban Initiative Fellow at USC, a Collaborative Research Faculty in the NSF/USC Environmental Sciences, Policy, and Engineering program, and a Faculty Affiliate at the Center for Sustainable Cities, USC.

Robert Vos holds a Ph.D. in political science and focuses mostly on environmental politics

and policy. Since 1999, he has been with the Center for Sustainable Cities at the University of Southern California where he is a Research Assistant Professor of Geography. His research emphasis is in the area of industrial ecology, including projects on regional materials flow analysis, eco-industrial park planning, life-cycle assessment, and sustainability indicators. His co-edited book, Flashpoints in Environmental Policymaking: Controversies in Achieving Sustainability, won the Lynton K. Caldwell Prize from the Section on Technology and Environmental Policy in the American Political Science Association.

Jennifer Wolch is Professor of Geography and Urban Planning at the University of Southern

California, where she directs the Center for Sustainable Cities. Her research focuses on metropolitan sustainability, physical activity and urban design, urban open space and environmental justice. Wolch has received fellowships from the Guggenheim Foundation, Center for Advanced Study in the Social and Behavioral Sciences, and the Rockefeller Foundation’s Bellagio Study Center. She was awarded the Association of American Geographers’ award for research achievement in 2005.