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Carbon Footprint of Supply Chains: A Scoping Study (2013)

Chapter: 5 Developing a Decision Tool

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Suggested Citation:"5 Developing a Decision Tool." National Academies of Sciences, Engineering, and Medicine. 2013. Carbon Footprint of Supply Chains: A Scoping Study. Washington, DC: The National Academies Press. doi: 10.17226/22524.
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Suggested Citation:"5 Developing a Decision Tool." National Academies of Sciences, Engineering, and Medicine. 2013. Carbon Footprint of Supply Chains: A Scoping Study. Washington, DC: The National Academies Press. doi: 10.17226/22524.
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Suggested Citation:"5 Developing a Decision Tool." National Academies of Sciences, Engineering, and Medicine. 2013. Carbon Footprint of Supply Chains: A Scoping Study. Washington, DC: The National Academies Press. doi: 10.17226/22524.
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Suggested Citation:"5 Developing a Decision Tool." National Academies of Sciences, Engineering, and Medicine. 2013. Carbon Footprint of Supply Chains: A Scoping Study. Washington, DC: The National Academies Press. doi: 10.17226/22524.
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Suggested Citation:"5 Developing a Decision Tool." National Academies of Sciences, Engineering, and Medicine. 2013. Carbon Footprint of Supply Chains: A Scoping Study. Washington, DC: The National Academies Press. doi: 10.17226/22524.
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Suggested Citation:"5 Developing a Decision Tool." National Academies of Sciences, Engineering, and Medicine. 2013. Carbon Footprint of Supply Chains: A Scoping Study. Washington, DC: The National Academies Press. doi: 10.17226/22524.
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Suggested Citation:"5 Developing a Decision Tool." National Academies of Sciences, Engineering, and Medicine. 2013. Carbon Footprint of Supply Chains: A Scoping Study. Washington, DC: The National Academies Press. doi: 10.17226/22524.
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Suggested Citation:"5 Developing a Decision Tool." National Academies of Sciences, Engineering, and Medicine. 2013. Carbon Footprint of Supply Chains: A Scoping Study. Washington, DC: The National Academies Press. doi: 10.17226/22524.
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Suggested Citation:"5 Developing a Decision Tool." National Academies of Sciences, Engineering, and Medicine. 2013. Carbon Footprint of Supply Chains: A Scoping Study. Washington, DC: The National Academies Press. doi: 10.17226/22524.
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Suggested Citation:"5 Developing a Decision Tool." National Academies of Sciences, Engineering, and Medicine. 2013. Carbon Footprint of Supply Chains: A Scoping Study. Washington, DC: The National Academies Press. doi: 10.17226/22524.
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Suggested Citation:"5 Developing a Decision Tool." National Academies of Sciences, Engineering, and Medicine. 2013. Carbon Footprint of Supply Chains: A Scoping Study. Washington, DC: The National Academies Press. doi: 10.17226/22524.
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Suggested Citation:"5 Developing a Decision Tool." National Academies of Sciences, Engineering, and Medicine. 2013. Carbon Footprint of Supply Chains: A Scoping Study. Washington, DC: The National Academies Press. doi: 10.17226/22524.
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Suggested Citation:"5 Developing a Decision Tool." National Academies of Sciences, Engineering, and Medicine. 2013. Carbon Footprint of Supply Chains: A Scoping Study. Washington, DC: The National Academies Press. doi: 10.17226/22524.
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Suggested Citation:"5 Developing a Decision Tool." National Academies of Sciences, Engineering, and Medicine. 2013. Carbon Footprint of Supply Chains: A Scoping Study. Washington, DC: The National Academies Press. doi: 10.17226/22524.
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Suggested Citation:"5 Developing a Decision Tool." National Academies of Sciences, Engineering, and Medicine. 2013. Carbon Footprint of Supply Chains: A Scoping Study. Washington, DC: The National Academies Press. doi: 10.17226/22524.
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Suggested Citation:"5 Developing a Decision Tool." National Academies of Sciences, Engineering, and Medicine. 2013. Carbon Footprint of Supply Chains: A Scoping Study. Washington, DC: The National Academies Press. doi: 10.17226/22524.
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Suggested Citation:"5 Developing a Decision Tool." National Academies of Sciences, Engineering, and Medicine. 2013. Carbon Footprint of Supply Chains: A Scoping Study. Washington, DC: The National Academies Press. doi: 10.17226/22524.
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Suggested Citation:"5 Developing a Decision Tool." National Academies of Sciences, Engineering, and Medicine. 2013. Carbon Footprint of Supply Chains: A Scoping Study. Washington, DC: The National Academies Press. doi: 10.17226/22524.
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Suggested Citation:"5 Developing a Decision Tool." National Academies of Sciences, Engineering, and Medicine. 2013. Carbon Footprint of Supply Chains: A Scoping Study. Washington, DC: The National Academies Press. doi: 10.17226/22524.
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Suggested Citation:"5 Developing a Decision Tool." National Academies of Sciences, Engineering, and Medicine. 2013. Carbon Footprint of Supply Chains: A Scoping Study. Washington, DC: The National Academies Press. doi: 10.17226/22524.
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Suggested Citation:"5 Developing a Decision Tool." National Academies of Sciences, Engineering, and Medicine. 2013. Carbon Footprint of Supply Chains: A Scoping Study. Washington, DC: The National Academies Press. doi: 10.17226/22524.
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Suggested Citation:"5 Developing a Decision Tool." National Academies of Sciences, Engineering, and Medicine. 2013. Carbon Footprint of Supply Chains: A Scoping Study. Washington, DC: The National Academies Press. doi: 10.17226/22524.
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Suggested Citation:"5 Developing a Decision Tool." National Academies of Sciences, Engineering, and Medicine. 2013. Carbon Footprint of Supply Chains: A Scoping Study. Washington, DC: The National Academies Press. doi: 10.17226/22524.
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Suggested Citation:"5 Developing a Decision Tool." National Academies of Sciences, Engineering, and Medicine. 2013. Carbon Footprint of Supply Chains: A Scoping Study. Washington, DC: The National Academies Press. doi: 10.17226/22524.
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Suggested Citation:"5 Developing a Decision Tool." National Academies of Sciences, Engineering, and Medicine. 2013. Carbon Footprint of Supply Chains: A Scoping Study. Washington, DC: The National Academies Press. doi: 10.17226/22524.
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Suggested Citation:"5 Developing a Decision Tool." National Academies of Sciences, Engineering, and Medicine. 2013. Carbon Footprint of Supply Chains: A Scoping Study. Washington, DC: The National Academies Press. doi: 10.17226/22524.
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Suggested Citation:"5 Developing a Decision Tool." National Academies of Sciences, Engineering, and Medicine. 2013. Carbon Footprint of Supply Chains: A Scoping Study. Washington, DC: The National Academies Press. doi: 10.17226/22524.
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Suggested Citation:"5 Developing a Decision Tool." National Academies of Sciences, Engineering, and Medicine. 2013. Carbon Footprint of Supply Chains: A Scoping Study. Washington, DC: The National Academies Press. doi: 10.17226/22524.
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Suggested Citation:"5 Developing a Decision Tool." National Academies of Sciences, Engineering, and Medicine. 2013. Carbon Footprint of Supply Chains: A Scoping Study. Washington, DC: The National Academies Press. doi: 10.17226/22524.
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5 DEVELOPING A DECISION TOOL In this chapter the proposed three-tier architecture for the decision tool is presented along with the specific elements it requires. Next, a number of example scenarios for calculation are provided to illustrate some of the issues that must be considered when designing the tool. Finally, a work plan that describes the discrete tasks that must be performed to build the tool is developed and timelines to complete development for two possible versions of the tool are given. DECISION TOOL The proposed tool presented in this chapter is designed as a decision tool to support measuring and incorporating greenhouse gas emissions in the supply chain decision process. It is assumed that the users of such a tool will primarily be the shippers, carriers, and logistics providers that make transportation decisions, and the tool is designed as a way to provide information for both historical accounting of emissions and future decisions. Explicit consideration is given to the fact that different users may have access to different types of information at different levels of detail. The focus on decision support means the tool as presented is flexible and designed to estimate emissions under a wide array of scenarios. As such, it may not be suitable for some uses currently employed by existing tools. The focus on flexibility and a supply chain view of emissions makes the tool less well suited to regulatory approaches or those specifically designed for corporate level reporting. Tools such as EMFAC and the EPA MOVES tool can be used to estimate greenhouse gas emissions related to transportation, and for some situations the use of these tools is required. At their core these tools employ conceptually similar approach to the proposed decision tool, taking a set of input activity data and using that to produce emissions estimates. Include the emissions factors from those tools and allowing for the input of the same data, the tool could conceivably produce the same results. Similarly, some approaches to calculate emissions are focused on preventing double counting of emissions. Double counting may occur in situations where both the shipper and carrier measure and report emissions for the same shipment. From a supply chain perspective this behavior is not necessarily problematic, and may in fact be beneficial as it incentivizes both firms to work to reduce the emissions from transportation. Some programs, such as those designed for corporate reporting or when emissions reductions are used to claim carbon credits, may explicitly wish to avoid double counting. The approach outlined in this chapter does not provide any specific mechanism to guarantee compliance with regulatory approaches or to avoid double counting. Rather, it is assumed that such mechanisms can be handled by the appropriate choices of emissions factors, input data, and use of the tool. A decision support tool 56

for supply chains may not be the ideal tool for use in specific programs, and thus the decision of whether the tool should support such approaches is a question for the implementation of the design, and is left outside the scope of this report. THREE-TIER APPROACH Three-tier software architecture divides software in to three layers to allow developers to modify and change the tiers independently96. The tiers consist of a control tier that provides the interaction for the user; a model tier that provides the functionality and detailed processing; and a data tier that stores and retrieves information. These tiers may also be referred to as the presentation, logic, and database tiers. By separating the functions across three tiers, each individual tier can be modified and improved without requiring changes to the others. CONTROL TIER The control tier provides the interface and control for the user. The primary role of the control tier is to define how data is input to the tool and what results are returned to the user. Based on capabilities of current tools and the proposed network model framework for calculation, two methods of data input are proposed: direct input and network building. In direct input the user enters the necessary information directly without requiring support from the logic provided by the tool. In a network builder mode, the user locates the nodes of the network and describes the flow of goods on the links between the nodes, but the tool provides the capabilities of calculating the distances and routes between nodes. This is necessary for situations where the user may have only limited information related to the actual transportation, or for estimating future flows and what-if scenarios. MODEL TIER The model tier is responsible for the actual calculation of the emissions within the tool. It must support the types of measurements required for the control tier as well as interfacing with the data tier. The model tier may need to be capable of modeling each node and link in a supply chain, from the transportation of goods through multiple types of modes to the facilities needed to support that movement such as ports, terminals, airports, and warehouses. It must support the ability to link each of these types of nodes via transportation links and calculate emissions from each link using data pulled from the data tier. In some cases this may require the ability to calculate distances between two given locations in a network. DATA TIER The data tier must contain all the data needed to perform the actual calculations. The data tier must support emissions factors and data for each of the aspects of supply chain and do so at multiple levels of detail to support the types of decisions specified in the control tier — from high level strategic planning to low level operational decisions such as carrier assignment. In addition to the emissions 57

factors, the data must store the necessary information for the model tier to calculate distances, including the ability to locate points and calculate a route between them. ELEMENTS Together the specifications for each tier describe the workings of the tool. Within a given tier, a number of functions may be performed, and the separate functions are referred as elements. A representation of the various elements identified for inclusion in the tool is shown in Figure 17. More detail on the specific purpose and requirement of each element is given in the following sections. Figure 17: Proposed Three-Tier Architecture CONTROL ELEMENTS There are two primary elements of the control layer: data entry and output of results. Together these elements control how the user interfaces with the tool, both inputting data and viewing the results. DATA ENTRY The data entry element determines what information the user is required to provide in order for the tool to calculate emissions and how that information is entered. Two primary methods are possible for entering data. The first is direct entry of the relevant information by the user. The second allows the user to construct the network using nodes and links. Direct Entry The primary input method for most current carbon footprint tools is manual entry via web interface or through a Microsoft Excel spreadsheet. The GHG Protocol and SmartWay, two of the most popular and widely used tools, both rely on Excel spreadsheets. Both tools provide columns specifying the necessary information, and 58

users enter data in the rows for each entry. A screenshot of version 2.3 of the GHG Protocol Mobile Combustion97 tool is shown in Figure 18. Figure 18: GHG Protocol Tool Screenshot In this tool, each row allows the user to enter a separate source of activity data. Users select the mode of transportation, the type of activity data (fuel use, vehicle distance, or weight and distance), emissions factor, and enter the relevant data. The GHG emissions associated with each row are then calculated. The SmartWay 98 program provides similar capabilities through multiple tools designed for shippers, carriers, drayage, rail, and multi-modal operators. Though implemented in Excel, the tool uses Visual Basic code to provide forms for data entry. Users list the carriers they do business with, and then enter activity data for each carrier. Activity data is typically based on total ton-miles and miles by carrier, though default values related to payload, density, and loaded percentage may be used to estimate that data when it is not available. Emissions are calculated for each carrier, and summed to present a total. A screenshot of the activity data entry screen is shown in Figure 19. Figure 19: SmartWay Shipper Tool Activity Data Entry Screen Other popular tools employ a web-based interface that allows for similar types of data entry. The NTM99 basic freight calculator allows users to build up a list of movements by entering distance, weight, mode, and vehicle information. Emissions are calculated for each entry, as well as the total for all movements. A screenshot of this web interface is shown in Figure 20. 59

Figure 20: NTM Basic Freight Calculator Data Entry Each of these interfaces represents a method of direct entry. The user inputs all the information necessary to calculate the emissions, and the tool performs no additional processing. This is in contrast to other forms of data entry, where the user provides location information, but the tool must determine other input needed for the calculation, such as distance. Network Builder This ability is referred to as a network builder approach, as the user is able to construct a network by providing origins and destinations, with the tool calculating the distance and route. This removes the need of the user to have specific knowledge of the fuel consumed or exact distance. This approach provides a useful method for users with only limited knowledge of the exact shipment routing, or for forward-looking situations where the exact information will not be known until a future time. The EcoTransIT World100 calculator offers a simple web interface that uses the network approach. Users are able to enter data on the amount of goods (by weight or TEU), the type of goods, the transport mode, and the shipment origin and destination. Locations may be entered in a number of ways, including by city, airport code, railway station, harbor, zip, or through a Google Maps interface. After entering the information and clicking calculate, a route between origin and destination is calculated, and, along with the mode and goods information, used to calculate emissions. The extended interface can be used to calculate more complicated trips using a transport chain. At this time only one shipment or transport chain can be calculated at a time. A screenshot of this web interface is shown in Figure 21. 60

Figure 21: EcoTransIT World Web Interface The network builder approach, combined with the ability to do direct data entry when the exact details are known, provide the necessary capabilities for users to calculate emissions for transportation given a wide range of possible data types and availability. The interface of these data entry capabilities with the actual calculations is covered in the section on the model elements. OUTPUT The output element determines what results are returned to the user after the data has been entered and the calculations are performed. Most current tools provide only rudimentary reporting results. Often this is as limited as the total amount of CO2e. Some tools do provide more detailed information display capabilities. The EcoTransIT World tool provides not only data on total CO2e emissions, but also energy consumption, route visualization, distances, and modes. A screenshot of the results overview is shown in Figure 22. Figure 22: EcoTransIT World Results The output element must specify what specific metrics are to be reported, the format (charts, tables, maps, etc.) for display, and any selections the user may wish to make. When calculations are performed at a high level of precision, the results may be aggregated to include not just overall totals, but also summaries broken out 61

by factors such as mode, lanes, or even in/out of specific destinations. In addition a number of activity parameters, such as tons shipped, miles traveled, and ton-miles, can be reported and used to provide KPIs related to overall efficiency. To output these results to the user requires interaction with the model layer to aggregate results and calculate KPIs based on the data entered by the user. MODEL ELEMENTS The model tier is concerned with executing the logic required to support the control and data tiers. It provides the link between the two layers and is responsible for performing calculations requested by the user and returning the appropriate results. Given the proposed capabilities of the control tier, the model tier has three primary functions: 1. Providing distance calculations in the network model2. Calculating the greenhouse gas emissions associated with shipments3. Calculating the key performance indicators DISTANCE CALCULATION A strength of the network modeling approach is that it allows for the calculation of emissions when little data about the specific routing of a shipment is known. This may be particularly useful for shippers that use 3PLs to manage a large number of shipments across a variety of modes. In these situations the shipper may know little more than the origin, destination, and general mode of transportation. In the network modeling approach, the shipper can provide the origin and destination, and the model layer can determine the appropriate route and distance. This requires two steps: geocoding and route determination. Geocoding In the geocoding step, the origin and destination must be located given the input from the user. Depending on the interface implemented in the control layer, this could involve direct entering of locations through a Google Maps style interface, text entry, or selection from a predetermined list. Regardless of the means of data entry, the element must determine the appropriate geographic locations from the entered data, a process referred to as geocoding. Once the origin and destination have been determined in this manner, the distance can be calculated by determining a route between them. Route Determination After the origin and destination locations are determined, the model layer must find a route between the locations and calculate the distance. At the simplest level this can involve a great circle distance calculation between the origin and destination. This provides an approximation of the straight-line distance between two points of latitude and longitude over the Earth’s surface. This distance can be modified by applying a circuity factor based on the mode of transportation used to better estimate actual travel distance. 62

More complex route determinations can be made through the addition of detailed Geographic Information System (GIS) data. This data can contain information related to roads, railways, waterways, ports, terminal, interchanges, and other points that can be used to determine routes between locations. For example, the Dataloy Data Table101 is a web service that calculates ocean-shipping distances. The service makes use of a database with 7,200 port locations and more than 69,000 waypoints to calculate distances between ports based on typical sailing routes. The results of such systems can provide more accurate representations than a typical great circle distance at the cost of increased complexity. Geocoding and routing can be complicated procedures, and several current tools interface with specialized software in order to make use of their geocoding and routing software. The EcoTransIT World calculator works with Google Maps to provide geocoding and basic distance calculation. The GIFT model102, developed by the University of Delaware and the Rochester Institute of Technology, interfaces with ESRI’s GIS software in order to provide multi-modal routing capabilities. In cases similar to these, the model tier must handle the interface with outside software programs in order to provide these services. Regardless of the chosen level of complexity and accuracy provided by the system, the element must be able to provide some distance calculation between two points in order to support making distance-based GHG calculations from limited data. CARBON FOOTPRINT CALCULATION The model layer must support the three primary methods of GHG emissions calculations identified in practice: fuel-based methods, distance-weight methods, and vehicle-distance methods. Based on the data entered by the user, the calculation element must determine the appropriate calculation methodology, retrieve the relevant emissions factors from the database, and perform the calculation. None of the general methods for calculation are particularly complex, and thus the calculations are straightforward given the appropriate data and emissions factors. PERFORMANCE INDICATORS The last element of the model layer provides for the aggregation of results from many individual GHG calculations and calculates the relevant KPIs needed by the control layer. This may involve aggregation of data from thousands of individual shipment and calculation of the KPIs at the level of precision requested by the user. In addition, the element may need to interface with the database layer to store certain KPIs in the emissions factor database. That is, in a manner similar to how results from carriers that use the EPA SmartWay tool are made available to shippers, it may be advantageous for certain results of the KPI calculation step to be stored in the database and made available for other users (or potentially the same user at a later time). 63

DATA LAYER The data layer is concerned with storing information required to support the logic of the model layer. It provides the data requested by the elements of the model, but does not provide any logic of its own. Given the proposed capabilities of the model layer the data layer has three primary elements: 1. A list of locations used for geocoding points2. GIS data that may be used to determine routes3. Emissions factors used to calculate the carbon footprint of shipmentsOptionally, the layer could also support an archive capability used to store calculation data remotely. This would allow previous calculations to be saved and accessed from multiple locations, facilitating the sharing of information. Some firms may not wish to store proprietary data on a remote server, and therefore this would be in addition to the ability to output the results to local storage. LOCATIONS The location data specifies the list of points and their associated geographic coordinates, typically given by latitude and longitude. This element must define what points are stored, their coordinates, and possibly a data hierarchy. The points may consist of locations such as cities, but also points relevant to supply chains such as airports, seaports, terminals, switching yards, etc. The available points determine what kinds of data users should enter, as the data must eventually be matched with a point to determine the appropriate coordinates. Establishing a type of hierarchy in the data may also be useful, as points could be categorized by their type or by features such as country, state, and city. The existence of such a hierarchy may allow the data entry elements to perform functions such as providing an easily searchable list of points for the user to choose from, potentially making the data entry steps easier and more reliable. GIS DATA As discussed in the section on route determination, the process can be complicated in practice, and a number of methods exist to implement this step. The type of data available in the database layer limits the choice of methods. If no data related to routing is stored in the database, then a method such as great circle distance must be used to calculate distances, while a full GIS database makes complicated multi-modal routing possible. Unfortunately, detailed data may not be available for all locations in the world, thus the data and route determination elements must be constructed such that the model layer is capable of calculating distances based on whatever results the data layer is able to provide. This element must be constructed such that data is stored in a way that detailed data can be accessed where available, but that the model layer is capable of handling situations when it is not. 64

EMISSIONS FACTORS The most important data element for the actual calculation of emissions is the available emissions factors. The model layer supports three methods of calculation, and the data layer must provide emissions factors appropriate to each method. In addition the emissions factors must be available at a number of levels of precision to support the needs of different users. This could include average global data, averages specific to nations or regions, company specific emissions factors, or even detailed emissions factors appropriate for individual shipments. A number of current tools and programs offer different approaches to emissions factors. The NTM program uses defined scenarios for road transportation to calculate emissions factors specific to different vehicle models and load factors. For example, the emissions for a given shipment can vary based on vehicle type, load utilization, road type, fuel type, and abatement equipment, in addition to distance and the specific fuel energy content and emission factor. Conceivably this approach could be used to generate a large number of emissions factors specific to the choice of vehicle, load, road, fuel, and abatement equipment. The EPA SmartWay program provides factors in a different manner, capturing data from carriers to produce emissions factors for individual companies. These emissions factors can be specific to the company, mode, and category type. Their current database contains more than 3,000 specific emissions factors. Given the importance of the emissions factors in the calculation steps, and the large number of potential factors, this element must define how individual emissions factors are stored and the information necessary for the model layer to choose the appropriate emissions factor. The data layer must work with the model and control layers such that the information provided by the user can be used to unambiguously select the appropriate emissions factor and perform the calculation. DATA ARCHIVE The data archive provides the ability to save data for use at a later time. This could include storing previous year’s data, allowing multiple users access to the same data, or saving work in progress to be updated later. This capability would be in addition to the ability to store work locally. The data archive could also include functionality to share results with the emissions factor database, for example by allowing carriers to have their custom emissions factors made available to shippers. EXAMPLE SCENARIOS Calculations based on fuel data represent the most straightforward method of emissions calculation, and are the preferred approach when the data is available. The IPCC guidelines recommend using an emissions factor based on the amount of CO2 per unit of energy to account for differences in temperature or density, but in practice many calculators make emissions factors available based on volume. The emissions factors are derived by assuming a certain carbon content of the fuel, a heating value, and the amount of carbon oxidized during combustion. Emissions 65

factors may further differ based on the specific country, as the IPCC recommends countries develop specific emissions factors that account for the technology and quality of the oil specific to that country. This leads to a range of possible emissions factors depending on the assumptions made. Fuel based methods can be further distinguished by the range of fuels for which factors are provided, the depth of the emissions considered, and the greenhouse gases included in the calculation. In order to provide a comprehensive carbon calculator, a range of fuel based emissions factors must be considered that account for the necessary greenhouse gases, cover a full range of possible fuel sources, and the portion of the fuel life cycle considered. FUEL BASED SCENARIOS At the most basic level the calculator might provide an emissions factor for common fuels such as diesel. The EPA provides a default emission factor of 10.15 kg CO2/gallon for diesel fuel based on 100% oxidation and assumptions regarding the heat content of the fuel, the carbon content of the fuel, and the carbon factor per gallon103. Using a similar process Defra provides an emission factor for the UK of 9.99841 kg CO2/gallon104. If we consider a company that consumed 1000 gallonsiii of diesel fuel, the choice of emissions factors provides two different calculation results. 1000 gallons x 10.15 kg CO2/gallon = 10,150 kg CO2 1000 gallons x 9.99841 kg CO2/gallon = 9,998.41 kg CO2 In general the range of emissions factors for the same type of fuel are fairly consistent. In a review of country specific emissions factor in Europe, the range of diesel values were within 0.3% of the IPCC default factor on average. Other fuels showed greater ranges, with bitumen and refinery gas showing the greatest difference at around 12%105. CH4 AND N2O The default factors for CO2 neglect two other greenhouse gases typically produced during consumption of diesel fuel for transportation— CH4 and N2O. In addition to emissions factors for CO2, the EPA produces emissions factors for CH4 and N2O based on engine testing. These emissions factors are produced in terms of grams of CH4 and N2O per mile driven, based on vehicle type, emissions control technology, and fuel type. The GHG Protocol converts these into emissions factors in terms of CH4 and N2O per gallon based on assumptions regarding the MPG of different vehicle types. Using a default heavy-duty articulated diesel freight truck achieving 5.9 MPG this produces emissions factors of 0.03009 g CH4/gallon and 0.02832 g N2O/gallon. iii Throughout this document, the term gallons shall be used to reference a US Gallon (~3.79 liters). 66

Using the previous example of 1000 gallons of diesel fuel consumed this produces the following results. 1000 gallons x of 0.03009 g CH4/gallon = 30.09 g CH4 1000 gallons x 0.02832 g N2O/gallon = 28.32 g N2O The values can be converted to carbon dioxide equivalents by multiplying each value by the global warming potential of the gases. The IPCC 4th Assessment defines the 100-year GWP of CH4 and N2O to be 25 and 298, respectively106. Applying the values to the previous calculations we have the following results. 30.09 x 25 = 752.25 g CO2e 28.32 x 298 = 8,439.36 g CO2e Combining these with the results from the CO2 produced by 1000 gallons of diesel we can calculate the total CO2e produced as 10,159.2 kg. In general, the non-CO2 gases produce relatively little contribution to the total for standard transportation fuel (less than 2%). As such, many tools exclude their calculation and focus only on CO2. If CH4 and N2O are included it may be necessary to include additional activity data (such as miles traveled and emissions control technologies), or combine the assumptions regarding CO2, N2O, and CH4 to create a single emissions factor. For the example of US diesel in a default heavy-duty articulated truck the factor would be 10.1592 CO2e/gallon. In addition to the greenhouse gases considered, the range of possible fuel types creates a need for a variety of emissions factors. Some fuels require emissions factor represented in different units, such as standard cubic feet for CNG. A comprehensive GHG calculator must supply emissions factors for a variety of different fuel types in factors that represent their typical usage. The default emissions factors used in the GHG Protocol based on factors developed by the EPA is shown in Table 7. Fuel Region CO2 CO2 Biomass CO2 Unit - Numerato CO2 Unit - Denominator Jet Fuel US 9.57 0.00 kg Gallon Aviation Gasoline US 8.32 0.00 kg Gallon Gasoline/Petrol US 8.81 0.00 kg Gallon On-Road Diesel Fuel US 10.15 0.00 kg Gallon Residual Fuel Oil (3s 5 and 6) US 11.80 0.00 kg Gallon LPG US 5.79 0.00 kg Gallon CNG US 0.05 0.00 kg Std Cubic Foot LNG US 4.46 0.00 kg Gallon Ethanol US 0.00 5.56 kg Gallon 100% Biodiesel US 0.00 9.46 kg Gallon E85 Ethanol/Gasoline US 1.32 4.73 kg Gallon B20 Biodiesel/Diesel US 8.12 1.89 kg Gallon Table 7: Fuel Emission Factors 67

The inclusion of biofuels introduces a second complication—the need to separate emissions from fossil fuels from biomass. This can be seen explicitly in the factor forE85 Ethanol, where the 15% assumed to come from standard gasoline produces 1.3215 kg of CO2, while the remaining 85% ethanol is assumed to produce 4.726 kg of CO2. These are tracked separately because the CO2 emissions from biomass do not represent new emissions of CO2 to the atmosphere, but rather the release of CO2 that had been sequestered from the atmosphere during production of the biomass. The focus only on the direct emissions produced during combustion (tank-to-wheel) make comparisons between traditional fuels, biofuels, and electric vehicles difficult. The net contribution of biofuels to global warming is dependent on the share of biomass used in the fuel and the emissions generated producing the biomass used to make the fuel. Electric vehicles produce no tailpipe emissions, but do produce emissions during the upstream electricity generation phase. In order to provide a true comparison of the effect of different fuel sources, the use of emissions factors that consider both the direct emissions and the indirect emissions from fuel production is needed. UPSTREAM EMISSIONS The GREET 107 model produced by Argonne National Lab uses a Life Cycle Assessment approach to produce emissions factors for a variety of fuels that includes the upstream portion of the fuel cycle. The fleet calculator provides factors for 12 different vehicle and fuel types, and based on their modeling assumptions produces factors in terms of CO2e per unit of fuel, shown in Table 8. Fuel Type kg CO2e Denominator Gasoline 11.151 gallons Diesel 12.93 gallons Diesel HEV 12.93 gallons B20 10.82 gallons B100 2.96 gallons E85 6.13 gallons CNG 0.09 cubic feet LNG 6.54 gallons LPG 7.52 gallons Electricity 0.68 kilowatt-hours G.H2 0.04 cubic feet L.H2 6.45 gallons Table 8: Well-to-Wheel Emissions Factors 68

The use of emission factors that consider a greater level of depth in the measurement increase the total impact of transportation by including the emissions related to the production of fuel. Using the default emission factor for diesel we calculated earlier and comparing it to the WTW numbers produced by GREET provide the following results for the combustion of 1000 gallons of diesel. 1000 gallons x 10.1592 CO2e/gallon = 10,159.2 kg CO2e 1000 gallons x 12.9336 CO2e/gallon = 12,933.6 kg CO2e The greater depth of the GREET number produce results that are 27% greater than in the tank-to-wheel scenario. Using the GREET factors approximately 20% of total emissions are the result of upstream production in the case of diesel. The numbers are more complex when biofuels are taken into account. The GHG Protocol factors for biodiesel, taken from the EPA, account for no non-biomass CO2 emissions. Using those numbers for 1000 gallons of biodiesel produces results that indicate 0 kg of CO2 and 9,460 kg of biomass CO2. Applying the factor for B100 supplied by GREET produces at estimated 2,964 kg of CO2e. SUMMARY OF FUEL BASED SCENARIOS Based on the scenarios considered, the results of a fuel-based calculation can differ significantly based on the breadth, depth, and precision of the emissions factors considered. Breadth includes the range of GHGs (CO2, CH4, N2O) included in the emissions factor and the available types of fuels. Precision accounts for the level of detail in the factor—such as whether country-specific factors are considered or the range of assumptions built into the factor (carbon content, heating value, oxidation %, vehicle MPG efficiency, emissions control technology). Depth is primarily based on whether a WTW or TTW analysis is used, and is of particular importance when comparing non-conventional transport fuels. The choice of emissions factors to include in any tool limits the available choices that users may make and the types of analysis that may be performed. In some cases users may not have the specific knowledge needed to determine the best emissions factors to use and simpler emissions factors that make use of standard default values may be easier to use in practice. Table 9 summarizes the results from a number of different emissions factors used in the previous discussion for consumption of 1000 gallons of fuel. The results highlight the impact that the choice of emissions factor has on the output of the tool. Fuel GHGs Source Scope Results Units Diesel CO2 Defra Pump-to-wheel 9,998 kg CO2 Diesel CO2 EPA Pump-to-wheel 10,150 kg CO2 Diesel CO2, CH4, N2O GHG Protocol (EPA) Pump-to-wheel 10,159 kg CO2e Biodiesel CO2 GHG Protocol (EPA) Pump-to-wheel 0 kg CO2 Biodiesel CO2 (biomass) GHG Protocol (EPA) Pump-to-wheel 9,460 kg CO2 Diesel CO2, CH4, N2O GREET Well-to-wheel 12,933 kg CO2e Biodiesel CO2, CH4, N2O GREET Well-to-wheel 2,964 kg CO2e Table 9: Comparison of Results for 1000 Gallons Consumed 69

ACTIVITY BASED METHODS When direct fuel consumption data is not available a number of activity-based methods are available. While considered less accurate than fuel-based methods for CO2 calculations, they offer advantages in terms of more easily acquired data and the ability to estimate future emissions from predicted transportation demand. Activity-based methods generally work by estimating the fuel consumed during transportation based on vehicle characteristics, or combining fuel consumption data with activity data to calculate average efficiency numbers. Like fuel-based methods these methods will be sensitive to the choice of fuel emissions factors, but our focus here is on how the fuel consumption is estimated, rather than the emissions from the fuel itself. VEHICLE DISTANCE BASED The simplest approach to estimating emissions from activity data is to use the distance traveled multiplied by the average fuel consumption of the vehicle. Together these produce an estimate of the fuel consumed, which can then be used to estimate GHG emissions by choosing an appropriate factor as discussed in the fuel-based methods. A number of different approaches have been used in practice to estimate vehicle-distance emissions factors, generally varying in the level of precision they provide. The GHG Protocol provides default emissions factors per mile for a number of vehicle types using both US and UK numbers. The emissions factors for US vehicles are based on assumed average vehicle efficiency for a variety of vehicle types (Heavy Duty, Light Duty, Passenger Cars, Motorbikes, etc.) to determine fuel consumption, and the standard factors for CO2, CH4, and N2O from the EPA discussed in the fuel-based section. Numbers in the UK are based on surveys of fuel consumption in vehicle fleets. The fuel consumption data is combined with Defra’s standard CO2 factor to produce an emission factor consider only CO2 on a per kilometer basis. Other sources have focused more on a single mode type to provide more precise levels of emissions factors. The EPA’s SmartWay program collects data from a number of different carriers. They employ a fuel-based methodology to calculate emissions from the carriers, and combine this with activity data supplied by the carriers to calculate distance based emission factors at the individual carrier level. The tool also allows the carriers to enter data not just at the company level, but also for various fleets or operating sectors within the company. This is used to create a hierarchy of emissions factors, where a user can select emission factors from a mode (truck, rail, multi-modal, logistics), a category within the mode (such as package, tl/dry van, refrigerated, and others within the truck category), and finally a specific carrier within that category. Likewise, a single company may have a number of different emissions factors, one for each category of business they reported data for. The NTM program does not collect specific data from carriers, but rather uses the ARTEMIS simulation tool to calculate fuel consumption for a number of different 70

The NTM program does not collect specific data from carriers, but rather uses the ARTEMIS simulation tool to calculate fuel consumption for a number of different scenarios108. These scenarios account for different sizes of vehicles, % loaded, road type, and driving conditions. Using these scenarios and an associated fuel-based emissions factor a range of emissions factors can be calculated. In each case the emissions are calculated using a straightforward multiplication of the distance and the vehicle-specific emissions factor. Table 10 shows a summary of the results of using a number of different types of factors to calculate the emissions from a 1,000 mile trip. Source Emission Factor Value Units GHGs Total Units GHG Protocol Heavy Duty Vehicle - Articulated - Diesel - Year 1960-present (US EPA) 1.722 kg CO2e/mile CO2, CH4, N2O 1,722 kg CO2e GHG Protocol HGV - Articulated - Engine Size Unknown (UK Defra) 1.560 kg CO2/mile CO2 1,560 kg CO2 GHG Protocol HGV - Rigid - Engine Size 7.5 - 17 tonnes - 50% Weight Laden (UK Defra) 1.235 kg CO2/mile CO2 1,235 kg CO2 SmartWay Flatbed, Carrier Aa 1.700 kg CO2/mile CO2 1,700 kg CO2 SmartWay TL/Dry Van, Carrier Ab 1.750 kg CO2/mile CO2 1,750 kg CO2 SmartWay TL/Dry Van, Carrier B* 1.550 kg CO2/mile CO2 1,550 kg CO2 NTM Small lorry/truck, Motorway, 100% loaded 0.583** kg CO2/mile CO2 583 kg CO2 NTM Lorry/Truck + Semi-trailer, Motorway, 100% loaded 2.296** kg CO2/mile CO2 2,296 kg CO2 NTM Lorry/Truck + Semi-trailer, Urban roads, 0% loaded 1.569** kg CO2/mile CO2 1,569 kg CO2 Table 10: Estimated Emissions for a 1000 Mile Distance a. Specific carrier names and factors are available for download b. Assumes default Defra factor for diesel fuel Despite little variation between emissions factors for diesel fuel, the emissions estimated for a specific trip can vary considerably. This is true even for vehicles in the same class, as the NTM factors shown for a truck + semi-trailer range from 1.569 to 2.296 depending on the load factor and road type. The SmartWay factors show that the results can vary depending on the specific carrier and type of freight as well. This demonstrates important points about the precision of the emissions factors used. Estimations of fuel consumed can vary considerably, and therefore even if consistent fuel-based factors are used the results obtained from activity-based data are sensitive to the assumptions regarding vehicle operating conditions. Providing emissions factors at a variety of levels of detail allow users to make best estimates based on their level of knowledge of the system, improving estimated values. WEIGHT DISTANCE BASED Despite the ease of using vehicle-distance factors and the availability of a wide range of emissions factors, is it inappropriate when used for shared modes or when only the bare minimum of information is known about the shipment. In first case, the emissions of the vehicle as a whole are not of concern, rather the share of emissions 71

related to a specific amount of goods are considered. In the second case, the shipper may not know the specific vehicle and distance that were used. In these situations weight-distance methods are generally used, though in some cases a volume-distance method may be more appropriate. Emissions factors for weight-distance methods are generally expressed in terms of ton-miles of goods moved (or perhaps TEU-miles for ocean containers where volume may be more important than weight). These methods provide a quick and easy method of calculating emissions, relying only on the weight of the goods shipped, the distance, and a general knowledge of the mode of transport used. They are also useful in comparing between modes, where efficiency is measured not just in the amount of emissions produced but the total amount of goods moved. The GHG Protocol provides emissions factors in terms of ton-miles for a variety of transportation modes, using factors derived from both the EPA and Defra. Other calculators, such as NTM or EcoTransIT, also provide similar capabilities. These factors introduce another layer of assumptions beyond those of fuel-based and vehicle-distance based methods, as now the factors must include assumption regarding the total amount of goods on the vehicle. This can lead to a wide range of emissions factors depending on the assumptions used. This is illustrated in Table 11, where emissions factors for different modes and types of transportation are compared for a shipment consisting of 10,000 short ton-miles (equivalent to a 10 ton shipment being moved 1,000 miles). Source Emission Factor Value Units GHGs Total (kg CO2) GHG Protocol Air – Long Haul (US EPA) 1.527 kg CO2/ton-mile CO2 15,270 GHG Protocol Air – Long Haul (UK Defra) 0.346 kg CO2/ton-mile CO2 3,460 GHG Protocol Air – Domestic (US EPA) 1.527 kg CO2/ton-mile CO2 15,270 GHG Protocol Air – Domestic (UK Defra) 1.105 kg CO2/ton-mile CO2 11,050 GHG Protocol Watercraft – Shipping – Large Container Vessel (20000 tonnes deadweight) (US EPA) 0.048 kg CO2/ton-mile CO2 480 GHG Protocol Watercraft – Shipping – Large Container Vessel (20000 tonnes deadweight) (UK Defra) 0.007 kg CO2/ton-mile CO2 70 GHG Protocol Watercraft – Shipping – Small Tanker (844 tonnes deadweight) (US EPA) 0.048 kg CO2/ton-mile CO2 480 GHG Protocol Watercraft – Shipping – Small Tanker (844 tonnes deadweight) (UK Defra) 0.019 kg CO2/ton-mile CO2 190 GHG Protocol Road Vehicle – HGV – Articulated – Engine Size > 33 tonnes (US EPA) 0.297 kg CO2/ton-mile CO2 2,970 GHG Protocol Road Vehicle – HGV – Articulated – Engine Size > 33 tonnes (UK Defra) 0.049 kg CO2/ton-mile CO2 490 72

GHG Protocol Road Vehicle – Light Goods Vehicle – Petrol – Engine Size 1.305 – 1.74 tonnes (US EPA) 0.297 kg CO2/ton-mile CO2 2,970 GHG Protocol Road Vehicle – Light Goods Vehicle – Petrol – Engine Size 1.305 – 1.74 tonnes (UK Defra) 0.462 kg CO2/ton-mile CO2 4,620 GHG Protocol Rail (US EPA) 0.025 kg CO2/ton-mile CO2 250 GHG Protocol Rail (UK Defra) 0.016 kg CO2/ton-mile CO2 160 Table 11: Results for a 10000 Short Ton-Mile Shipment The table shows the wide variation not just between modes, where ocean shipping may be as much as 200 times more efficient than air transport, but also between sources. The EPA’s numbers are based on high level, and do not distinguish between types of transport within a mode. Thus, there is no distinction between heavy-duty trucks or light-duty vehicles within road transport, or between large container ships and small tankers in watercraft. This is in contrast to the Defra numbers that are generated at a greater level of precision and show the range of values that can exist between different types of transport. DISTANCE CALCULATION The final step necessary to calculate emissions using activity data is a method to estimate distance traveled when the exact details are not known. The simplest method of estimating the distance between two points on the Earth is through a great circle calculation. The great circle calculation estimates the distance between two points on a sphere, measured along the surface of the sphere rather than going through it. Using latitude and longitude to mark a location’s spot, and assuming the Earth is a sphere, the great circle distance provides a rough estimate of the travel distance between two points. Actual travel distance between points varies depending on the actual route of travel (see Table 12 for an example for road and rail). This ratio of the actual distance to the great circle distance is referred to as the circuity factor, and varies depending on the mode of travel and the structure of the network. Estimates for the United States put network circuity at 1.21 for road109, 1.45 for rail, and 1.94 for barge110. Calculations for ocean distances are more complicated, as vessels must navigate around land rather than over a specific route network. Circuity factors can also vary by country, further complicating distance calculation. A number of services are available that can perform more sophisticated distance calculations. Distances between locations are estimated using models of actual road, rail, and water networks. Using these services a better distance estimate can be obtained, but does not account for any deviations due to the actual route taken. Sophisticated systems that bring together all the networks and model intermodal transfer points are capable of generating multi-modal trips. Without knowledge of the actual route; however, all of these methods must make assumption regarding the route and transfer points, and thus may not model the actual route chosen. Further, network models are not available for all global locations, so a 73

comprehensive solution capable of calculating distances for all possible shipments is not currently available. Origin Destination Mode Method Distance (miles) Circuity Los Angeles Chicago Road Great Circle111 1,745 NA Los Angeles Chicago Road Google Maps112 2,029 1.16 Los Angeles Chicago Road MapQuest113 2,031 1.16 Los Angeles Chicago Rail Great Circle 1,745 NA Los Angeles Chicago Rail BNSF Calculator114 2,120 1.21 Los Angeles Chicago Rail CSX Calculator115 2,218 1.27 Boston Miami Rail Great Circle 1,258 NA Boston Miami Rail CSX Calculator 1,636 1.30 Table 12: Distance Comparison The issue of distance calculation can be particularly important in ocean and airfreight, where the details of the routing may be of increased importance. In airfreight, the LTO phase can consume a significant amount of fuel. Since each flight must take off and land, regardless of the overall distance of the flight, this can cause shorter flights to emit more CO2 per km than longer flights. This is illustrated in Table 13, showing illustrative data for a Boeing 737-400 under different flight distances116. Standard flight distances (nm) [1 nm = 1.852 km] 125 250 500 750 1,000 1,500 2,000 Fuel (kg) Flight total 1,603 2,268 3,613 4,960 6,303 9,187 12,168 LTO 825 825 825 825 825 825 825 Non-LTO 778 1,443 2,787 4,135 5,477 8,362 11,342 Emissions (kg CO2/km) 21.9 15.5 12.3 11.3 10.8 10.5 10.4 Table 13: Data for Boeing 737-400 A shipment traveling 1,000 nm by making two 500 nm flights could emit 14% more CO2 than if it was made using a single 1,000 nm flight. Similarly, two 250 nm flights would emit 26% more CO2 than a single 500 nm flight. The combination of higher average emissions from shorter flights and differences in aircraft type and utilization can produce drastically different emissions factors for freight. Using surveys regarding aircraft type and utilization, along with data on fuel consumption from the European Environment Agency (EEA), Defra estimated that emissions for freight on domestic flights emitted 2.41 kg CO2/tonne-km while freight on long-haul flights emitted 0.62 kg CO2/tonne-km. These differences in emissions factors highlight the need for getting accurate flight data to estimate emissions from airfreight. In a hub and spoke network it is possible for a shipment to make multiple short-haul flights rather than a single long-haul flight directly from the origin to the destination. With short-haul and domestic 74

OTHER ISSUES In addition to the issues related to the development of appropriate emissions factors and methods there is also the question of how such methods can be combined for more complicated scenarios. There are two particular scenarios worthy of further attention. First, how should emissions from multi-modal moves be combined to produce a calculation for the movement as a whole. Second, how should the emissions from shipments carrying the goods of multiple users be allocated between the different users. INTERMODAL The simplest version of a multi-modal move may be a combined road-rail intermodal shipment. In an intermodal shipment the goods are picked up and delivered by truck, referred to as drayage movements. In between the drayage movements the goods are loaded on a railway to provide a rail line haul. This method combines the point-to-point service of trucking with the efficiency of rail in order to provide a single seamless movement to the shipper. Calculating emissions from intermodal shipments requires knowledge of the distances of the drayage movements and the rail haul, as well as the relative efficiencies of the modes. When these are known the total carbon footprint of the shipment can be calculated using standard methods, treating the total journey as three separate movements. However, this may be difficult in practice. Different companies may perform the drayage movements and rail haul, and the overall movement may be coordinate by an intermodal operator117. Table 14 shows a comparison of the CO2 calculated for an intermodal shipment between San Diego, CA and Bloomington, MN using three different methods. The first uses data supplied by the intermodal operator regarding drayage distances, length of the rail haul, average drayage efficiency calculated by the operator, and rail efficiency supplied by the railway. The second approach uses the average CO2 per ton-mile for all intermodal movements performed by the operator, along with the shipment weight and great circle distance between the origin and destination to estimate emissions. The third approach uses the locations of the origin, destination, and the intermodal ramps to calculate distances (via Google maps for drayage and the CSX distance calculator for rail). This is combined with standard emissions factors from the GHG Protocol mobile calculator to estimate emissions from the drayage movements and rail haul. 75 flights emitting two or three times the amount of CO2 as long-haul flights this can lead to significant errors in estimation in incorrect data is used.

Calculation Method Estimated Travel Distance (miles) Estimated CO2 (tonnes) % Difference Intermodal Operator Data 2,721 2.48 NA Average Intermodal Efficiency 1,524 1.90 -23% Movement distances + average mode efficiency 2,348 1.88 -24% Table 14: Comparison of Intermodal CO2 Estimates Using the intermodal operator’s actual data and the full details of the shipment produces significantly higher total emissions than estimates using average efficiency or standardized factors. The average efficiency number does not account for the higher-than-average amount of drayage required for this shipment, and the resulting lower level of efficiency achieved. Using publicly available data underestimates the total distance traveled on the rail haul. The use of the shipment weight in the calculations also underestimates the emissions from rail due to failure to include the weight of the chassis required for intermodal movement. As movements involve multiple modes they become more complex, and assumptions regarding how the movement is made can affect the calculated carbon. This must be considered when creating a tool that estimates carbon for all types of shipments. ALLOCATION Finally, a method of allocation must be identified to separate emissions from shared modes of transport. The EN 16258 standard provides a number of methods for separating emissions from freight and passengers, as well as between shipments on the same vehicle. At its core the allocation process must calculate the emissions for the vehicle as a whole, and then assign those emissions to each of the shipments it carries. This could be done based on volume, weight, distance, value, or some combination of these. One of the simplest scenarios that illustrates the issue is shown in Figure 23. A truck leaves the depot with 25 tons worth of goods to deliver to three customers, visited in order. After delivery to Customer 3 the truck returns empty back to the depot. During the course of the 80 mile round trip the truck burns 15 gallons of fuel and produces approximately 150 kg of CO2. The allocation process must specify how those 150 kg should be assigned to the different customer shipments. 76

Figure 23: Delivery Scenario A number of possible approaches could be used. The emissions could be divided equally, with each customer being charged for 50 kg CO2. It could be allocated by weight, such that Customer 1 is charged 60 kg CO2, Customer 2 30 kg CO2, and Customer 3 60 kg CO2. The emissions could be allocated by how far away each customer is, or by the combined ton-miles required to serve them. Customer 1 is 10 miles away and received 10 tons, for 100 total ton-miles. Customer 3 is clearly 20 miles away and received 10 tons, for 200 total ton-miles. It is not clear which distance to use for Customer 2. The truck drove 20 miles to reach the customer, but only after stopping at Customer 1. Using the great circle distance the customer is perhaps 15 miles away, resulting in 75 ton-miles. That produces a total of 375 total ton-miles for the trip. Allocation on this basis would be 40 kg CO2 to Customer 1, 30 kg CO2 for Customer 2, and 80 kg CO2 for Customer 3. ALLOCATION IN COMBINED PASSENGER AND FREIGHT SERVICE In some cases allocation must be performed to calculate emissions for freight that is moved along with passengers in the same vehicle. The EN16258 standards specifically discuss the scenario where freight is carried in the belly of a passenger plane. In these situations an allocation method must be specified that allows the emissions to be shared between the two purposes of moving passengers and moving freight. The ISO standards for LCA call for allocation to be performed based on the underlying physical relationships between inputs and outputs, but where that cannot be established the economic value or another relationship may be used. The EN16258 standards specify the use of mass as the method of allocation between passengers and freight. Passengers, including their baggage, are assumed to have a mass of 100 kg. The number of passengers is multiplied by this number to get the total mass of passengers. The total mass of freight is then calculated and assigned a share of emissions based on the share of total mass, passengers plus freight, represented by the freight. The remaining emissions are allocated towards passenger movement. The use of a basic physical allocation method like mass represents one type of a non-economic relationship. Economic allocation uses the value of the outputs as the 77

means of allocation. In some cases this may be more representative of the true drivers of system behavior, and may be preferred. In the airfreight example, the total value of passenger tickets sold could be used to determine the value of the passenger travel, while the revenue from freight carried in the plane could be used to estimate the value of freight. Emissions would be allocated between passengers and freight based on their share of total revenue. The choice of allocation method can have significant impact on calculated emissions. No allocation method can ever be considered right for all situations, so the trade-off among different choices must be considered. To provide consistency it should be clear that all emissions, including those from empty movements, must be allocated. In addition, allocations that are independent of arbitrary choices such as which customer is delivered to first should be avoided. No choice of method will necessarily satisfy all stakeholders perfectly, so a focus on consistency and transparency is recommended. SUMMARY The process of estimating emissions using fuel-based and activity-based data is simple in concept, but often remains complicated in practice. Assumptions regarding fuel, distance, vehicle efficiency, and utilization can introduce uncertainty into estimates. Capturing data at a level of detail needed for more precise estimates is often not possible. In the next section we present a specific set of tasks required to develop the elements of a decision support tool. As seen by the examples in this section, many of the functions of the tool can operate at different levels of sophistication, requiring a flexible tool capable of taking advantage of more detailed data when it is available. TASK LIST Based on the architecture defined in this chapter, there are six primary tasks composed of 11 sub-tasks that need to be completed to create a decision tool. Some of the tasks involve surveying current programs and other available technologies to identify data and best practices that can be integrated with a new tool. The example scenarios are intended to help clarify the issues involved in assessing how well those current practices can serve the needs of a new decision tool. The remaining tasks generally involve developing the back-end software support needed by the tool, at varying levels of sophistication depending on the type of tool envisioned. TASK 1—DEFINE CALCULATION METHODOLOGIES The review of methodologies in Chapter 2 identified two primary methodologies: fuel-based and activity-based. Activity-based methodologies generally consist of vehicle-distance and weight-distance methods, though other activity data can also be used (for example, dollar value spent for EIO-LCA methods). The first task is to define the calculation methodologies that will be used in the tool. The results from this task define the necessary emissions factors for Task 2 and the acceptable forms of data entry for Task 3. 78

TASK 2—COMPILE EMISSIONS FACTOR DATABASE TASK 2.1 – COLLECT EXISTING EMISSIONS FACTORS Based on the review of methods and proposed definition in Chapter 1, a database of emissions factors must be compiled to support the calculation methodologies. Based on the working definition of the carbon footprint of the supply chain, these emissions factors should consider a well-to-wheel system boundary. At a minimum, this includes emissions factors for a wide variety of fuel types and activity-based factors for all four main transport modes. Emissions factors in terms of energy consumed and TTW emissions scope may also be included in order to provide compatibility with requirements of EN 16258. TASK 2.2 – DEFINE A HIERARCHY OF EMISSIONS FACTORS As the available emissions factors define the precision with which the carbon footprint can be calculated, this task must also include a review of existing emissions factor databases to determine the appropriate range of factors within a category. This includes the appropriate regional emissions factors for fuel-based methods, with a primary focus on electricity generation. For activity-based factors this includes developing a hierarchy of data precision that might include modes, sub-modes, vehicle types, company, lane, or shipment specific factors. TASK 3—DEVELOP A USER INTERFACE AND DATA ENTRY SYSTEM TASK 3.1—DEFINE DIRECT DATA ENTRY METHODS When specific data related to fuel use or distance traveled is available, users may enter this data directly. The user interface must specify the method of data entry and define the required data. The interface must connect with the emissions factor database to allow user selection of appropriate factors. The interface should support automated data input through saved data archive files created by the tool. TASK 3.2—CREATE AN INTERFACE FOR A NETWORK VIEW When distance and fuel are unknown, the tool should support a network view of data entry. The system allows users to enter shipment origin and destinations and automatically performs distance calculation. The system must interface with the route calculation service to provide the distances. TASK 4—IMPLEMENT A ROUTE CALCULATION SERVICE TASK 4.1—EVALUATE EXISTING TECHNOLOGIES The tool must be capable of calculating the distance between two entered points. Existing routing technologies should be reviewed for their suitability based on cost, accuracy, and ease of use. The selected technology or technologies must support all four major modes (road, rail, air, and water) at the global level. At a minimum the system should support calculation of great circle distance between points. 79

TASK 4.2—INTEGRATE SELECTED TECHNOLOGY WITH CALCULATION TOOL Based on the technology or technologies defined in Task 4.1, an interface to the data entry system of Task 3.2 must be implemented. The service shall take the origin, destination, and modes entered by the user and return the calculated distance between the points. TASK 5—CREATE A PERFORMANCE DASHBOARD TASK 5.1—IDENTIFY KEY PERFORMANCE INDICATORS The work identified in this report has indicated total CO2e and CO2e per ton-mile as the primary performance indicators for the calculator. Possible secondary performance indicators include CO2e per mile, CO2e per ton, and CO2e per unit of volume. Each of these performance indicators can be calculated at an individual shipment level, or aggregated at mode, company, lane, or other level. Using the programs identified in this project, the indicators identified in NCFRP Report 10, and other literature, a review should be conducted to determine the specific series of performance indicators that should be calculated by the tool and the appropriate level of aggregation for those indicators. TASK 5.2—CREATE PERFORMANCE DASHBOARD Based on the KPIs identified in Task 5.1 and the calculation methodologies defined in Task 1, a performance dashboard shall be created to compile the results of the calculations and display the resulting indicators to the user. Existing performance dashboards and best practices should be reviewed to determine the appropriate information and display format. TASK 6—UPDATE AND MAINTAIN DATA ARCHIVE TASK 6.1—CREATE ARCHIVE FORMAT The results of the tool, both in terms of data entered and calculated results, should be saved in an appropriate data archive format. The format should allow for transfer of data between users on separate systems, or storage on a network location. The format should be readable by the tool such that the archived format can be read as input to the tool. A centralized network location should be created that can accept and store archived data. TASK 6.2—UPDATE EMISSIONS FACTORS DATABASE The emissions factor database shall be updateable to receive calculated results from the tool and store new emissions factors. This should allow data supplied by users of the tool to create company-specific emissions factors. These factors should be stored in a centralized repository, and the tool shall regularly update emissions factors from the repository as they become available. 80

TIMELINE Given the tasks outlined for a future tool, there is significant flexibility in the time and cost required to implement the tool based on the desired level of sophistication. The GHG Protocol tool is perhaps one of the most popular tools in use, but is little more than a Microsoft Excel spreadsheet. The EPA SmartWay tool is also implemented in Excel, though with some increased functionality due to the use of macros. At the other end of the spectrum are tools like the GIFT tool that use a multi-modal, GIS based approach and represents a years long research process. Two possible development paths and their associated development timelines are presented below. The first is a simplified tool that could be developed in several months. It would be a static tool that serves mainly to provide a consistent set of emissions factors and methods that meets the needs identified in this report. The second is a more advanced tool that provides a more dynamic, robust set of features. This tool would require professional software development, and is designed to be delivered by a web application or stand-alone software application. BASIC TOOL A basic tool would require little more than a form for data entry linked to data tables of emissions factors and locations. This tool could be developed in a three-month timeframe and could be developed with little professional software experience. The tool could be implemented in standard business software such as Microsoft Excel, or through a basic web interface. The tool could be made available for download, and would serve as a standalone calculation tool that does not require an interface with other programs or services. The primary work related to this tool would be contained in Task 2 and Task 3. After defining the appropriate calculation methodologies, a consistent set of emissions factors must be developed. These emissions factors should provide a consistent system boundary for the emissions included, and may require creation of custom emissions factors by combining WTW fuel emissions factors with fuel consumption estimates from other sources. At a minimum, emissions factors for different fuel types and averages by ton-mile for each mode type should be provided. Distance calculation would be provided through a pre-determined list of locations. This would allow users to choose origins and destinations from the list of locations, and perform basic great circle distance calculations between those points or lookup distances from a data table. This would make the tool self-contained, and remove any need for other software services or an internet connection. The user interface would use relatively simple data entry and selections. Data entry would collect the necessary fuel and activity data, while the selections would allow user to choose the appropriate emissions factors and select locations for distance calculations. The output would be summarized in a set of standardized tables and charts. The results of the calculations would be savable to a local file. The saved files would be capable of being read by the tool to allow sharing of data without the need for reentering data. 81

This tool would meet the needs of a basic carbon calculator suitable for wide use, but would be limited due to the static nature of the tool. Users would be limited by the available choices of factors and locations. A proposed schedule for a three month (12 week) development plan is shown in Figure 24. Figure 24: Schedule for Basic Tool Development ADVANCED TOOL The advanced tool would expand on the capabilities of the basic tool through a more advanced user interface, actual route calculations, and a dynamic set of emissions factors that could be updated based on data provided by users. Ideally, the tool would be a web-based application to allow connection to other software services, though a standalone software application with updates delivered automatically through the internet is also a possibility. The increased capabilities necessitate the use of professional software development, and a longer one year development time is anticipated. The primary differences between the tools are the expansion of Task 4 and Task 6, as well as a general increase in complexity and capability. Task 4 will now require implementation of actual routing through integration with road, rail, and water routing services. Great circle distance calculation would be included only for regions where no routing data was available. This requires additional time to study potential services and integrate the chosen service with the tool. Task 3.2 will also increase in complexity, as a graphical user interface and other capabilities may be needed to harness the more powerful routing capabilities. Task 6 requires more work to allow the tool to capture data from users and use this to provide expanded emissions factors. The capabilities would be similar to those provided by the EPA SmartWay tool that allows data entered by carriers to be 82

shared and used by shippers to calculate their own emissions. This capability requires the ability to calculate and store company specific emissions factors, make these factors available to users, and protect any sensitive information. The longer development time for the remaining tasks represents an increase in the scope and complexity of the tool. The emissions factors database should be more comprehensive, and allow a greater level of precision through inclusion of additional factors. The user interface should include a more intuitive GUI and allow for modeling several types of what-if scenarios based on the data input. The performance dashboard should have the capability of generating more extensive metrics, reports, and analytics for output. Together these changes represented a more polished user interface, easier analysis of scenarios, and better reporting to aid in decision-making. A proposed schedule for a one year (12 month) development plan is shown in Figure 25. Figure 25: Schedule for Advanced Tool Development The goal for both tools is to provide a consistent methodology, a set of WTW emissions factors across all modes, and provide output that can be easily compared with other organizations on a standardized basis. The capabilities of the advanced tool provide for better functionality than the basic tool, but also the possibility to provide better levels of precision. The advanced tool more closely aligns with the needs identified through the application of the criteria developed in Chapter 3 to current tools in Chapter 4. Tools currently exist that are capable of providing WTW emissions factors across all modes, but none that make use of carrier or shipment-level emissions factors. The combination of capturing user data to create updated emissions factors with a consistent set of emissions factors across all modes would represent an improvement on the current tools available. 83

96 Ramirez, A. O. (2000). "Three-tier architecture." Linux Journal 2000(75es): 7. 97 WRI (2011). GHG Protocol tool for mobile combustion. Version 2.3, The Greenhouse Gas Protocol. 98 http://www.epa.gov/smartway/partnership/shippers.htm 99 http://www.ntmcalc.org/index.html 100 http://www.ecotransit.org/calculation.en.html 101 http://www.dataloy.com/ 102 http://www.rit.edu/gccis/lecdm/index.php 103 EPA (2008). Direct Emissions from Mobile Combustion Sources. Washington, D.C., U.S. Environmental Protection Agency. 104 Defra (2010). 2010 Guidelines to Defra/DECC's GHG Conversion Factors for Company Reporting, Defra. 105 Herold, A. (2003). Comparison of CO2 emission factors for fuels used in Greenhouse Gas Inventories and consequences for monitoring and reporting under the EC emissions trading scheme, ETC/ACC Technical Paper 2003/10. 106 IPCC (2007). Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, and M. T. a. H. L. Miller. Cambridge, UK, Cambridge University Press. 107 http://greet.es.anl.gov/fleet_footprint_calculator 108 NTM (2010). Road Transport Europe, Network for Transport and Environment. 109 Ballou, R. H., H. Rahardja, et al. (2002). "Selected country circuity factors for road travel distance estimation." Transportation Research Part A: Policy and Practice 36(9): 843-848. 110 Strogen, B., A. Horvath, et al. (2012). "Fuel Miles and the Blend Wall: Costs and Emissions from Ethanol Distribution in the United States." Environ. Sci. Technol 46(10): 5285-5293. 111 http://www.gcmap.com/ 112 http://maps.google.com 113 http://www.mapquest.com/ 114 http://www.bnsf.com/bnsf.was6/RailMiles/RMCentralController 115 http://www.csx.com/index.cfm/customers/tools/carbon-calculator-v2/ 116 EEA (2009). EMEP/EEA air pollutant emissions inventory guidebook—2009. European Environment Agency. Copenhagen. 117 Craig, A. J., E. E. Blanco, et al. (2012). Estimating the CO2 of Intermodal Freight Transportation. ESD Working Paper Series, Massachusetts Institute of Technology. 84

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TRB’s National Cooperative Freight Research Program (NCFRP) Web-Only Document 5: Carbon Footprint of Supply Chains: A Scoping Study defines a standardized, conceptual approach to assessing global greenhouse gas (GHG) emissions of the transportation component of supply chains, critiques current methods and data used to quantify greenhouse gas (GHG) emissions, and outlines a work plan to develop a decision tool to help estimate the carbon footprint of the transportation component of supply chains.

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