SmartWay 2.0.10 Shipper Tool – 

Technical Documentation:

United States Version
Transportation and Climate Division

Office of Transportation and Air Quality

U.S. Environmental Protection Agency

Office of Transportation and Air Quality

EPA-420-B-12-005

January 2012

SmartWay 2.0.10 Shipper Tool – 

Technical Documentation:

United States Version
1

SmartWay 2.0.10

Shipper Tool Technical Documentation

United States Version

1-3-12

1.0 Overview

The SmartWay Shipper Tool is intended to help shippers estimate and assess their

carbon, PM, and NOx emissions associated with goods movement in the U.S. freight

rail and trucking sectors1 with a greater degree of sophistication than was possible with

previous SmartWay tools. Shippers can track their freight-related emissions

performance from year to year using the tool and assess a range of strategies to

improve the emissions performance of their freight operations, including selection of

low-emissions carriers and implementation of operational strategies such as (but not

limited to) packaging improvements, load optimization and logistical improvements.

The new SmartWay truck carrier emissions performance data that EPA has included in

the tool, along with publically available Class I rail CO2 data, will allow shippers to

generate more accurate emissions inventories. The new data will also help shippers

optimize their emissions performance by allowing them to better estimate the emissions

impact of individual carriers, modal shifts, and operational strategies.

1 While this tool is primarily focused on freight movements in the U.S. rail and trucking sectors, our longterm vision is to provide shippers with tools to help them evaluate the emissions performance of their full

freight transportation supply chain.
2

2.0 Tool Inputs and Calculations

After shippers enter their company and contact information, they provide basic

information about each company they operate, including name and NAICS code.

Shippers then identify each carrier that they use for each company and the service that

the carrier provides (e.g., Inbound or Outbound hauls, International and/or Domestic

service, etc.). These parameters serve as “tags” which allows shippers to filter their

emission data as desired using the screen tools discussed in Section 3 below.

Next, if shippers have annual mileage-related activity data by carrier (miles or tonmiles), they select the “Emissions Footprint and % SmartWay” option on the second

data entry screen, and proceed to input activity data for each carrier. Otherwise, they

select the “% SmartWay” option, which only requires them to report the portion of goods

they move with SmartWay carrier partners based on money spent, weight shipped,

packages shipped, or another custom metric. If shippers select the “% SmartWay

Only”, they will not be able to calculate an emissions inventory or develop

emissions performance metrics (e.g. g/mile or g/ton-mile) for their freight

operations. All shippers – regardless of whether they select the “Emissions Footprint”

option or the “% SmartWay Only” option – will be able to see the bin-level emissions

performance data for truck, logistics and multi-modal carriers as well as available Class

1 and rail-industry-wide emissions factors.

Emission Inventory and Performance Metric Calculations

If shippers choose the “Emissions Footprint” option, the tool will calculate their total

mass emissions (i.e., an emissions inventory) based on the mileage-related activity data

they entered for each carrier, as well as various emission performance metrics (e.g.,

composite grams/mile and grams/ton-mile – see below) . The tool offers two options for

calculating mass emissions, based on either the annual mileage or ton-mileage data

that shippers enter for each carrier. We encourage shippers to select the unit of activity

data that is most appropriate for characterizing each truck carrier operation (e.g., use

grams per mile for TL and grams per ton-mile for LTL and rail.)

The emissions inventory for each carrier/mode combination displayed on the

Emissions Summary and Carrier Performance screens is calculated by multiplying

the appropriate unit of activity data (i.e., miles/railcar-miles, or ton-miles) by the

corresponding carrier emissions performance data. To calculate composite, companywide emissions performance metrics on the Carrier Performance screen (i.e., overall

g/mile and g/ton-mile performance), the tool weights the emissions performance of each

of the shipper company’s carriers by the percentage of the company’s overall freight

activity that the carrier moves. An example composite performance calculation is

provided below.
3

Table 1. Example Compositing Calculation

CO2 g/mi Mi/yr Weighting Factor Weighted CO2 g/mi

Carrier 1 1,700 2,000,000 0.667 1,134 (0.667 x 1,700)

Carrier 2 1,500 1,000,000 0.333 500 (0.333 x 1,500)

Weighted composite g/mi 1,633 (1,134 + 500)

This compositing process proceeds in an identical fashion for ton-miles.

Likewise, if a shipper selects one or more filters (e.g., inbound domestic carriers-only),

the tool adjusts the weighting factors to ensure that they sum to 100% for the selected

subset of carriers. The following provides a simplified example calculation.

o Shipper selects three Truck carriers (T1, T2, T3)

o T1 has a CO2 g/mile of 1

o T2 has a CO2 g/mile of 2

o T3 has a CO2 g/mile of 3

o T1 is Inbound

o T2 and T3 are both Outbound

o Shipper enters miles for the three carriers of 2,000, 4,000, and 2,000,

respectively

o When Inbound/Outbound combo = All:

 Composite CO2 g/mile = [(1*2,000 + 2*4,000 + 3*2,000) / 8,000] = 2

o When Inbound/Outbound combo = Inbound:

 Composite CO2 g/mile = [1*2,000 / 2,000] = 1

o When Inbound/Outbound combo = Outbound:

 Composite CO2 g/mile = [(2*4,000 + 3*2,000) / 8,000] = 2.3
4

Carrier Emissions Performance Data

The current SmartWay program provides CO2, NOx and PM gram per mile, and gram

per ton-mile emission factors for truck and rail freight transport providers. These data

are provided in the SmartWayCarrierData.xls file, which should be downloaded to the

user’s computer using the button on the tool’s Home page.

It is envisioned that SmartWay will incorporate emission factors from marine and air

transport providers, and gram per volume-mile emission factors for all modes, in the

near future.

Truck Carrier Performance

Truck carrier performance data utilized by the Shipper Tool is based on 2010 truck

partner tool submittals. Performance data includes g/mile, g/ton-mile, and bin ranking

(1-5) for each truck carrier, with a bin ranking of 1 indicating top 20 percent performance

for a given pollutant/bin category. Note that g/mile and g/ton-mile values represent

midpoints for the appropriate bin category, rather than exact performance levels for a

given carrier. Truck bin categories include:

• TL Dry Van

• LTL Dry Van

• Refrigerated

• Flatbed

• Tanker

• Dray

For Future Tools: Addressing Empty Miles, Out-of-Route Miles, Return Miles

Ideally, total emissions should be adjusted to account for possible under-reporting of

system miles. Shippers generally only see the miles they are invoiced by the carrier

moving the freight, or use a point-to-point mileage calculation. In many cases,

shippers may be charged for all miles traveled by the carrier, including empty miles

and out-of-route miles, but shippers may only be aware of the point-to-point mileage.

Therefore a shipper will base its emissions inventory calculations only on the miles of

which they are aware, and will therefore not account for all of the emissions

associated with their freight.

EPA is investigating potential adjustment factors that can help ensure that shippers

report their full emissions inventory. These factors may be specific to each carrier

mode or possibly each carrier fleet type (e.g., distinguishing TL and LTL). While there

are many ways to calculate this factor, it will likely follow this basic form:

emissions adjustment factor = total carrier fleet odometer miles / miles invoiced to shippers
5

• Package

• Specialized

• Mixed

The following provides an overview of the truck carrier binning processed used to

estimate the carrier-specific performance bins.

Truck Performance Binning

In the 2010 SmartWay Truck Tool, data is collected at the individual company fleet

level. Fleets are characterized by A.) business type: for-hire or private, B.) operational

type: truckload/expedited, less than truckload, dray, or package delivery, and C.)

equipment type: dry van, refrigerated van, flatbed, tanker, chassis (container), or

specialized (auto carrier, bulk, heavy haul, others.)2 The possible categories are shown

below.

For-Hire

Dry Van Refrigerated Flatbed Tanker Chassis Specialized

TL & Exp

LTL

Dray

PD

Private

Dry Van Refrigerated Flatbed Tanker Chassis Specialized

TL & Exp

LTL

Dray

PD

As originally developed, truckload and expedited were treated as separate categories,

and specialized was broken out into bulk, auto, heavy haul, and moving. For purposes

of simplification, truckload and expedited are treated as similar operations, and due to

small numbers of trucking fleets, bulk, auto, heavy haul and moving were aggregated

into one category. Specialized fleets will have disparate operations/equipment types

and thus do not compare well, however they are also unlikely to compete with one

another, so it was deemed possible to aggregate these into one bin.

For-hire and private fleets are combined in SmartWay categories/bins. There are

relatively few private fleets compared to for-hire fleets. Because owners of private fleets

generally hire their own fleets exclusively, it was determined that ranking for-hire and

2

Additional operation and body type categories have been added to the 2011 Truck Tool and will be reflected in

next year’s Shipper Tool.
6

private fleets together would not be detrimental to for-hire fleets, and the simplicity of

one for-hire and private category outweighed the benefits of listing fleets separately.

Ranking for-hire and private separately would have doubled the number of categories.

Fleets can thus be categorized as shown below.

For-Hire and Private Fleets

Dry Van Refrigerated Flatbed Tanker Chassis Specialized

TL & Exp

LTL

Dray

PD

To be categorized in a particular category, a fleet must have at least 75% of its

operations by mileage in a single category, otherwise it is classed as a “Mixed” fleet.

Fleets could be mixed via their operational or equipment type. Fleets are generally

segregated by their operational type, but some mixing does occur via equipment type,

especially with smaller carriers that do not differentiate their fleet. Fleets that do not

have 75% of their operations in a specific category are placed in the Mixed category.

Individual fleets were then placed into categories. The following graphic illustrates the

population of the various categories. The darker the shade of the intersection, the

higher the number of fleets in that category.

For-Hire and Private

Dry Van Refrigerated Flatbed Tanker Chassis Specialized Mixed

TL & Exp

LTL

Dray

PD

Mixed

SmartWay then looked at combining categories that exhibited similar characteristics for

simplification purposes. One prerequisite was that there needed to be a minimum

number of fleets in each category. SmartWay determined that a category needed a

minimum of 25 fleets to be created. It was also determined that dry van and chassis

(intermodal container) functioned primarily as dry van transport, so these categories

were combined. While most refrigerated carriers were truckload, a few less than

truckload refrigerated fleets exist, so these two categories were combined. A similar

situation was identified with flatbed, and flatbed truckload and less than truckload were

combined. Although no less than truckload tanker fleets were identified, tanker

truckload and less than truckload were combined into one category so that no

intersections would be left undefined. All dray was collapsed into one category. Any

fleet that had mixed operation and/or mixed equipment was placed into a single mixed

category. This produces the final bin categories illustrated below.
7

For-Hire and Private

Dry Van/Chassis Refrigerated Flatbed Tanker Specialized Mixed

TL & Exp

LTL

PD

Dray/Package

Mixed

It is possible that SmartWay will expand these categories based on in-use experience or

as a result of further data analysis, and/or requests from industry.

Companies within a category have been ranked from lowest emission factor (best) to

highest emission factor (worst) for each of the following metrics: CO2 g/mile, CO2 g/tonmile, NOx g/mile, NOx g/ton-mile, PM10 g/mile and PM10 g/ton-mile. Companies within

a category were then separated into 5 groups (bins) such that an equal number of

companies were in each bin. Each bin thus represents a range of emission factors.

This range, and bin cutpoints (transition points from one bin to the next) were then

modified so that each bin had an equal range, and the new bin cutpoints remained as

close to the originals as possible. The new range cutpoint is displayed as a number

with significant digits appropriate to emission factors in that category. The midpoint of

the range is used as the emission factor for all companies in a bin.

It would be simpler and more straightforward to use company-specific emission factors,

however the trucking industry expressed concern with revealing exact data that could

be used to back-calculate mile per gallon numbers. The above described methodology

prevents a determination of an exact mpg figure, while at the same time attributing an

emission factor much more exact than a modal default number. Given the large number

of trucking companies, and thus opportunity for companies to be very close to each

other in performance (for example 0.001 g/mile of CO2), SmartWay believes it is

acceptable and appropriate to break truck fleets into 5 performance bins. The table

below illustrates the bins in the For Hire/Private Truckload/Expedited Dry Van/Container

category, using 2010 truck partner data.

Table 2. Example Binning Results for One Bin Category (2010 Data)

For-Hire/Private Truckload/Expedited Dry Van/Container CO2 g/mile

Bin ID Fleets

Per Bin

Grams Per

Mile Min

Grams Per

Mile Max

Grams Per

Mile Avg

Grams Per Mile

Midpoint

Grams Per

Mile Std Dev

1 159 602 1,600 1,503 1,550 141

2 241 1,601 1,699 1,654 1,650 28

3 204 1,700 1,799 1,746 1,750 28

4 139 1,800 1,899 1,853 1,850 28
8

5 55 1,900 3,701 2,064 2,801 302

Similar tables were developed for all categories. The midpoint of each bin is the data

that a Shipper will download into their SmartWay Shipper tool to represent the emission

performance of a specific fleet that is in the associated bin. Once the categories and

bins have been established, the fleets of any new companies joining SmartWay will fall

into one of the predefined categories/bins. SmartWay expects to update the

category/bin structure approximately every three years.

Performance estimates for Non-SmartWay truck carriers were calculated based on the

lowest performing truck partners. Since no data exists to define non-SmartWay fleets,

SmartWay believes the prudent approach is to assign conservative emission factors to

non-SmartWay companies. Also, this policy makes it likely that any company joining

SmartWay will see better emission factors displayed than the non-SmartWay default

emission factors.

The Non-SmartWay performance metrics were calculated by taking the standard

performance bin delta (min/max) for each bin category, and using the delta to calculate

a non-SmartWay carrier midpoint for each bin category. This midpoint was the midpoint

for Bin 5 plus the standard bin delta. For example, if the Bin 5 midpoint was 10.5 and

the Bin Categories standard delta was 1, then the non-SmartWay midpoint was

calculated to be 11.5. Once the non-SmartWay midpoints for each pollutant were

calculated for all Bin Categories, the worst performance value was selected to be the

midpoint for the non-SmartWay Truck carriers. This approach does not require the

shipper to identify the appropriate bin category for their Non-SmartWay carrier(s), which

they may not know.

As discussed in the Shipper Tool User Guide, depending upon the type of data

available for a given carrier, the user may input ton-miles or miles, and rely on carrier

data to back-calculate the other value. For example, providing ton-miles and average

payload allows the tool to estimate total miles, by dividing the former by the latter.

Alternatively, freight density and cargo volume utilization information can also be used

to estimate average payloads. For this reason, average payload and volume

information are provided for each carrier in the SmartWayCarrierData.xls file. For NonSmartWay truck carriers, the values for average payload (17.1 tons) and average

volume (3,334 cubic feet) were derived from the average values for all truck partners

(2010 data), weighted by miles.

Logistics and Multi-modal Carrier Performance

Logistic and multi-modal carriers have their own performance bins based on the carrier

tool submittals for the 2010 calendar year. The Shipper Tool modifies the Bin 5 values

for each of these bin categories (logistics and multi-modal) to estimate non-SmartWay

carrier performance in the same way as was done for non-SmartWay Truck carriers

(i.e., adding the standard bin delta value to the Bin 5 midpoints).
9

Rail Carrier Performance

Rail carrier performance data are collected and displayed in the Shipper Tool at the

individual company level for Class 1 rail companies. Gram per ton-mile factors were

determined by dividing total fuel use by total ton-miles and multiplied by a rail diesel

CO2factor (10,084 g CO2/gal diesel fuel), from publicly available data submitted in the

2010 railroad R-1 reports to the Department of Transportation. 2010 R-1 data was also

used to obtain total railcar miles per year for each Class 1 carrier, in order to estimate

gram per railcar-mile factors. Industry average values are used for non-class 1 and

Non-SmartWay rail carriers. It is anticipated that Class 2 and 3 rail companies will have

an opportunity to provide company specific data in the future. The R-1 data and

corresponding CO2 performance data are presented in Table 3 below.

Table 3. Rail Carrier Performance Metric Calculation Inputs and Results (2010 R-1

Data)

Rail Company

Gal/Yr

(‘000)Sch.

750 Line 4

Freight TonMi/Yr (‘000)

Sch .755 line

110

Railcar-Mi/Yr

(‘000) Sch.

755 sum of

lines 30, 46,

64 & 82

g CO2/railcarmileg CO2/short

ton-mile

BNSF Railway 1,295,147 646,549,059 11,230,994 1,163 20.20

CSX Transportation 490,050 230,507,431 4,720,293 1,047 21.44

Grand Trunk 88,290 50,586,328 1,206,818 738 17.60

Kansas City Southern 62,354 31,025,588 609,929 1,031 20.76

Norfolk Southern* 440,159 183,104,320 4,081,893 1,087 24.24

Soo Line 65,530 33,473,544 771,033 857 19.74

Union Pacific 1,063,201 525,297,747 10,336,081 1,037 20.41

Non Class 1 and

Non-SmartWay

3,504,731 1,700,544,017 32,957,041 1,072 20.78

* and combined subsidiaries

NOx and PM emission factors for rail carriers are based on industry averages. Please

see the “Background on Illustrative (Modal Average) U.S. Truck and Rail Factors”

section below for details regarding the calculation of industry average performance

levels for different modes.

Average payload per loaded railcar were calculated for each Class 1 carrier by dividing

the value for annual ton-miles hauled by an estimate for loaded railcar miles, based on

2008 R-1 data. The calculation uses the Total Revenue and Non-Revenue Ton-Miles as

listed In the R-1 Report on line 114 of schedule 755 divided by the Total loaded Railcar

Miles (the sum of lines 30 and 64 of schedule 755) along with the factor for fuel gallons

consumed for loaded freight that is created based on the percentage of loaded freight to

total freight multiplied by the total diesel fuel value listed on schedule 750 Line 4. The

following table summarizes the estimated average payload per railcar, by carrier.
10

Table 4. Rail Carrier Average Payload

Carrier

Avg Payload/Loaded

Railcar (tons)

BNSF Railway 108

CSX Transportation 85

Grand Trunk 80

Kansas City Southern 91

Norfolk Southern 76

Soo Line 77

Union Pacific 91

Non Class 1 and Non-SmartWay 93

Average railcar volumes were calculated for each carrier by first estimating an average

volume for each major railcar type listed in the R-1 forms (schedule 755, lines 15-81).

The assumptions used to estimate these volumes are provided in Table 8 below. The

railcar-miles reported for each railcar type were multiplied by these average volumes to

estimate annual cubic foot-miles travelled by car type for each company. The

distribution of cubic foot-miles across car types was used as the weighting factor to

estimate a single average railcar volume for each company. These values and the

resulting volume estimates are presented in Table 5 below.
11

Table 5. Rail Carrier Average Volume Determination

BNSF

Freight Car Types (R1 - Schedule 755) Avg. Cu Ft. Railcar Miles (x1K) Cu Ft Miles (x1K)

Box-Plain 40-Foot 4,555 1 4,555

Box-Plain 50-Foot & Longer 7,177 9,338 67,018,826

Box-Equipped 7,177 147,226 1,056,641,002

Gondola-Plain 5,190 379,762 1,970,964,780

Gondola-Equipped 5,190 75,894 393,889,860

Hopper-Covered 4,188 758,442 3,176,355,096

Hopper-Open Top-General Service 4,220 65,077 274,624,940

Hopper-Open Top-Special Service 4,220 137,449 580,034,780

Refrigerator-Mechanical 6,202 19,272 119,524,944

Refrigerator-Non-Mechanical 6,202 32,910 204,107,820

Flat-TOFC/COFC 6,395 520,521 3,328,731,795

Flat-Multi-Level 13,625 38,624 526,252,000

Flat-General Service 6,395 357 2,283,015

Flat-All Other 6,395 71,826 459,327,270

All Other Car Types-Total 5,772 20,146 116,282,712

Average Railcar Cubic Feet 5,811
12

CSX

Freight Car Types (R1 - Schedule 755) Railcar Miles (x1K) Cu Ft Miles (x1K)

Box-Plain 40-Foot - -

Box-Plain 50-Foot & Longer 6,987 50,145,699

Box-Equipped 144,631 1,038,016,687

Gondola-Plain 137,256 712,358,640

Gondola-Equipped 64,532 334,921,080

Hopper-Covered 153,315 642,083,220

Hopper-Open Top-General Service 78,412 330,898,640

Hopper-Open Top-Special Service 35,451 149,603,220

Refrigerator-Mechanical 17,117 106,159,634

Refrigerator-Non-Mechanical 11,923 73,946,446

Flat-TOFC/COFC 125,828 804,670,060

Flat-Multi-Level 29,956 408,150,500

Flat-General Service 162 1,035,990

Flat-All Other 31,913 204,083,635

All Other Car Types-Total 19,861 114,637,692

Average Railcar Cubic Feet 6,389
13

Grand Trunk

Freight Car Types (R1 - Schedule 755) Railcar Miles (x1K) Cu Ft Miles (x1K)

Box-Plain 40-Foot 0 -

Box-Plain 50-Foot & Longer 2,119 15,208,063

Box-Equipped 66,110 474,471,470

Gondola-Plain 6,467 33,563,730

Gondola-Equipped 19,201 99,653,190

Hopper-Covered 44,239 185,272,932

Hopper-Open Top-General Service 9,114 38,461,080

Hopper-Open Top-Special Service 32,621 137,660,620

Refrigerator-Mechanical 312 1,935,024

Refrigerator-Non-Mechanical 205 1,271,410

Flat-TOFC/COFC 2,779 17,771,705

Flat-Multi-Level 4,831 65,822,375

Flat-General Service 20 127,900

Flat-All Other 31,744 203,002,880

All Other Car Types-Total 4,755 27,445,860

Average Railcar Cubic Feet 6,309
14

Kansas City Southern

Freight Car Types (R1 - Schedule 755) Railcar Miles (x1K) Cu Ft Miles (x1K)

Box-Plain 40-Foot 0 -

Box-Plain 50-Foot & Longer 3,383 24,279,791

Box-Equipped 39,792 285,587,184

Gondola-Plain 16,628 86,299,320

Gondola-Equipped 11,150 57,868,500

Hopper-Covered 50,346 210,849,048

Hopper-Open Top-General Service 626 2,641,720

Hopper-Open Top-Special Service 943 3,979,460

Refrigerator-Mechanical 21 130,242

Refrigerator-Non-Mechanical 52 322,504

Flat-TOFC/COFC 10,736 68,656,720

Flat-Multi-Level 629 8,570,125

Flat-General Service 12 76,740

Flat-All Other 2,321 14,842,795

All Other Car Types-Total 247 1,425,684

Average Railcar Cubic Feet 5,938
15

Norfolk Southern

Freight Car Types (R1 - Schedule 755) Railcar Miles (x1K) Cu Ft Miles (x1K)

Box-Plain 40-Foot 0 -

Box-Plain 50-Foot & Longer 7,622 54,703,094

Box-Equipped 136,745 981,418,865

Gondola-Plain 193,214 1,002,780,660

Gondola-Equipped 111,320 577,750,800

Hopper-Covered 116,848 489,359,424

Hopper-Open Top-General Service 84,557 356,830,540

Hopper-Open Top-Special Service 30,078 126,929,160

Refrigerator-Mechanical 3,512 21,781,424

Refrigerator-Non-Mechanical 5,392 33,441,184

Flat-TOFC/COFC 114,928 734,964,560

Flat-Multi-Level 20,349 277,255,125

Flat-General Service 145 927,275

Flat-All Other 24,563 157,080,385

All Other Car Types-Total 212,408 1,226,018,976

Average Railcar Cubic Feet 6,065
16

Soo Line

Freight Car Types (R1 - Schedule 755) Railcar Miles (x1K) Cu Ft Miles (x1K)

Box-Plain 40-Foot 0 -

Box-Plain 50-Foot & Longer 725 5,203,325

Box-Equipped 17,972 128,985,044

Gondola-Plain 1,203 6,243,570

Gondola-Equipped 8,856 45,962,640

Hopper-Covered 94,146 394,283,448

Hopper-Open Top-General Service 3,077 12,984,940

Hopper-Open Top-Special Service 20 84,400

Refrigerator-Mechanical 159 986,118

Refrigerator-Non-Mechanical 742 4,601,884

Flat-TOFC/COFC 11,178 71,483,310

Flat-Multi-Level 2,973 40,507,125

Flat-General Service 12 76,740

Flat-All Other 10,068 64,384,860

All Other Car Types-Total 428 2,470,416

Average Railcar Cubic Feet 5,667
17

Union Pacific

Freight Car Types (R1 - Schedule 755) Railcar Miles (x1K) Cu Ft Miles (x1K)

Box-Plain 40-Foot 0 -

Box-Plain 50-Foot & Longer 12,311 88,356,047

Box-Equipped 238,241 1,709,855,657

Gondola-Plain 206,370 1,071,060,300

Gondola-Equipped 91,775 476,312,250

Hopper-Covered 370,929 1,553,450,652

Hopper-Open Top-General Service 188,027 793,473,940

Hopper-Open Top-Special Service 104,969 442,969,180

Refrigerator-Mechanical 82,874 513,984,548

Refrigerator-Non-Mechanical 27,009 167,509,818

Flat-TOFC/COFC 1,026,251 6,562,875,145

Flat-Multi-Level 46,889 638,862,625

Flat-General Service 350 2,238,250

Flat-All Other 72,371 462,812,545

All Other Car Types-Total 16,769 96,790,668

Average Railcar Cubic Feet 6,248
18

Total (for Industry Average)

Freight Car Types (R1 - Schedule 755) Railcar Miles (x1K) Cu Ft Miles (x1K)

Box-Plain 40-Foot 1 4,555

Box-Plain 50-Foot & Longer 42,485 304,914,845

Box-Equipped 790,717 5,674,975,909

Gondola-Plain 940,900 4,883,271,000

Gondola-Equipped 382,728 1,986,358,320

Hopper-Covered 1,588,265 6,651,653,820

Hopper-Open Top-General Service 428,890 1,809,915,800

Hopper-Open Top-Special Service 341,531 1,441,260,820

Refrigerator-Mechanical 123,267 764,501,934

Refrigerator-Non-Mechanical 78,233 485,201,066

Flat-TOFC/COFC 1,812,221 11,589,153,295

Flat-Multi-Level 144,251 1,965,419,875

Flat-General Service 1,058 6,765,910

Flat-All Other 244,806 1,565,534,370

All Other Car Types-Total 274,614 1,585,072,008

Average Railcar Cubic Feet 6,091
19

% SmartWay Value

The % SmartWay screen tracks the portion of goods that shippers move with

SmartWay partners (expressed as a percentage between 0 and 100). Shippers select

the basis for calculating the percentage shipped with SmartWay partners, including the

following options:

• Total annual miles (the tool will automatically populate the % SmartWay screen

with any carrier activity data that shippers entered in the freight Activity Data

screen). Miles correspond to truck miles for trucks, and railcar miles for rail;

• Total annual ton-miles (the tool will automatically populate the % SmartWay

screen with any truck or rail carrier activity data that shippers entered on the

freight Activity Data screen);

• Total annual CO2 emissions, based on the preferred calculation metric specified

for each carrier on the Activity Data screen (miles or ton-miles)

• Percent Spent;

• Percent Weight Shipped;

• Percent Packages Shipped;

• Other Custom Metric (as defined by Shipper).

3.0 Calculator Tools

In addition to estimating a shipper’s emissions inventory and performance metrics, the

current Shipper Tool also allows shippers to estimate the emissions impact of

operational strategies as well as modal shifts, if they provide mileage-related activity

data under the “Emissions Footprint” option.

Shipper Operational Strategies3

The Shipper Strategies screen is optional and is intended for scoping purposes only.

On the Shipper Strategies screen, shippers may estimate emission reduction benefits

for the following options:

• Miles Removed from the System

o Distribution center relocation

o Retail sales relocation

o Routing optimization

o Cube optimization

o Larger vehicles and/or multiple trailers

• Weight Removed from System

o Product weight reduction

3

This “Shipper Strategies” calculation sheet cannot be used if shippers do not provide mileage-related

activity data, since the tool will be unable to determine the shippers’ baseline mass emissions.
20

o Package weight reduction

o Vehicle weight reduction

For each shipper strategy selected, shippers must provide an estimate of the

percentage reduction in freight activity (in miles or weight), for each mode of interest,

along with a text description of the strategy. The tool assumes that total mass

emissions are reduced in direct proportion with the specified mileage or weight

reduction.4 Mass emission reductions are calculated by using the appropriate

emissions inventory for the Emissions Summary screen (based on reported activity

data and associated carrier emissions performance data) as shown below:

S = EF x (1 / (1 – Reduction) - 1)

Where:

S = Savings (tons of CO2, NOx, or PM)

EF = Emissions inventory value (tons of CO2, NOx, or PM)

Reduction = the reduction in total miles or weight as a result of the

strategy (expressed as fraction)

Fractional reduction estimates must be documented with the Shipper Tool. An example

calculation is provided below:

A shipper changes the shape of your milk cartons from round to square. As a result, the

shipper can pack 20% more milk cartons per truck trailer than the rounded milk cartons.

This reduces 20% of the loads associated with that product line (corresponding to the

""Cube Optimization"" activity selection for the ""Miles removed from system""

category). However, the company sells many products, and the total truckloads

associated with milk shipments is 1,000 out of 50,000 overall truckloads. The efficiency

gain is thus 20% x (1,000/50,000), or a 0.4% system improvement. Therefore the

shipper would enter “0.4” in the Percent Improvement column. This assumes that all

loads on average travel an equivalent distance. If milk loads were significantly shorter

than other loads, then a mileage-based weighting per trip would need to be added to

arrive at a percent improvement. The burden of proof on demonstrating an accurate

percent reduction and modal allocation is the shipper’s. The data sources and

methodology should be briefly described in the tool under Data Source/Methodology.

The shipper should, at a minimum, keep detailed records electronically within the

company to document the estimate upon EPA request. The shipper can also submit

any documentation in electronic text format along with the tool to their Partner Account

Manager.

4

This assumption should be accurate for weight reduction strategies when applied to truckload

shipments that weigh out. Additional uncertainty arises in the case of LTL and package delivery

shipments, where weight reductions may not result in one-to-one reductions in miles hauled.

Uncertainties are even greater for non-truck modes, where the shipper commonly does not control the

entire content of the container. Likewise, this assumption may not hold if shippers reduce freight by

loading more products (i.e., more weight) on trucks that were previously cubing out, since the increase in

payload will negatively impact the truck’s fuel economy and g/mile emissions performance.
21

Modal Shift

Overview

The Modal Shift screen in the tool is optional and is intended for scoping purposes

only. Shippers should develop their carrier emissions inventories (and associated

emissions factors for their companies) by inputting activity data in the Activity Data

screen.

Shippers wishing to conduct scenario analyses can use the Modal Shift screen to

estimate the emissions impacts associated with modal shifts by specifying the mode

from which they are considering shifting their freight (“From Mode”), as well as the

target mode (“To Mode”). Shippers have several options for selecting an emissions

factor for both the “From Mode” and “To Mode”. First, the tool automatically calculates

and displays the average emission factors for truck and rail modes corresponding to the

company-specific data from the Activity Data screen (corresponding to the “Shipper’s

Carrier Average” Emission Factor Source selection). In this case partners can also

adjust their estimates of emission impacts from modal shifts by applying different filters

for the “From” Mode (e.g., just considering inbound international freight). Second,

partners may select illustrative rail and/or trucking industry average emission factors

(discussed in the section below) from the drop-down menu (corresponding to the “Modal

Average” selection). Third, the shipper can input an alternative emissions factor of their

choice (corresponding to the “User Input” selection).

Note that the emissions factors that automatically appear on the Modal Shift screen do

not include all potential emissions impacts; for example the factors do not include

emissions associated with drayage (i.e., short-distance trips often required to move

freight from one mode to another), or the operation of intermodal facilities.

While we have populated the tool with illustrative modal average freight rail and truck

emission factors, we recommend that partners use more representative emission

factors to analyze scenarios whenever possible. For example, partners may wish to

evaluate the emissions impact from moving freight from rail to a specific truck fleet by

consulting the average emissions factors associated with the fleet (available on the

SmartWay website) or by inputting data that partners receive directly from a carrier. For

better estimates of emission impacts from modal shifts, partners are encouraged to use

a factor that reflects the full emissions impact (e.g., including likely drayage emissions)

and that best represents the fleet equipment and operational type that they are most

likely to work with for their unique freight movement.

Partners may also evaluate modal shifts to and from the marine and aviation sectors by

inputting an emissions factor of their choice (“User Input” option only). While we have

not provided illustrative marine and aviation freight factors in the tool, there are several

external resources that partners can consult. We have included some selected sources

of marine and aviation factors in the following section.
22

In order to calculate the emissions impact associated with a modal shift, shippers input

the activity data corresponding with their modal shift scenario expressed in a given unit

(miles or ton-miles) and the tool combines that data with a corresponding emission

factor (described above) in the same unit. The tool then displays the change in

emissions (as calculated below) in tons per year.

Total Emission Impact (tons/yr) =

[(Efficiency Before x “From Mode” Amount) –

(Efficiency After x “To Mode” Amount)] x grams to short tons conversion factor5

If the shipper is evaluating a mode shift between truck and rail, and if the available

activity units are in miles rather than ton-miles, then an adjustment factor must be

applied to the above equation converting railcar miles (the activity unit for the rail mode)

to equivalent truck miles (the unit for the truck mode).

The railcar-to-truck equivalency factor is calculated by first identifying the average cargo

volume for a given carrier (see Table 5 above). These volumes estimates are contained

in the Carrier File, and are weighted by the miles associated with each rail carrier on the

Activity Data screen in order to estimate a single weighted-average railcar volume for

the shipper company in question. Similarly, weighted average volumes are also

calculated for the different truck carriers associated with the given shipping company.

The weighting calculations involve all carriers used by the company if no filters are

selected on the Modal Shift screen (only relevant for the “From” mode). Otherwise the

weighted average calculation is only performed for the filtered subset (e.g., inbound

domestic truck carriers).

Once the weighted average volumes are determined for both rail and truck modes, the

tool calculates the ratio of the average railcar volume to the average truck volume (R).

Next, the tool converts the g/railcar-mile values (g/RC-mi) to g/truck-equivalent mile

values by dividing g/RC-mi by the ratio R. The tool then shows these values in the

appropriate efficiency column, and uses these values to calculate emission changes as

shown in the equation above. This approach does not require the shipper to develop

and apply equivalency factors, relying on simple railcar-mile activity estimates.

Background on Illustrative (Modal Average) U.S. Truck and Rail Factors

Modal Average performance metrics have been developed for rail and truck modes

(both gram per mile and gram per ton-mile), for estimating emission impacts using the

Modal Shift screen. We developed the freight truck g/ton-mile factors with 2010 CO2,

NOx, and PM2.56inventory data on short-haul single unit, short-haul combination unit,

5

1.1023 x 10-6 short tons/gram

6 Corresponding PM10 emission factors were estimated assuming PM2.5 values were 97% of PM10 values, based

on MOVES model outputs for diesel fueled trucks.
23

long-haul single unit, and long-haul combination unit truck categories7in EPA’s 2010a

version of the Motor Vehicle Emissions Simulator (MOVES2010a) model8. MOVES

does not contain ton-mile data, so we then divided the MOVES-based inventories by

2002 ton-mile data from the Federal Highway Administration’s 2009 Freight Facts and

Figures9, which we determined was the most recent, comprehensive national freight

truck ton-mile dataset available.

For the freight truck g/mile factors, we used the same emissions inventory data as the

g/ton-mile factors described above and divided them by the corresponding 2010 VMT

data in MOVES2010a.

Table 6 presents the illustrative freight truck emissions factors in the tool and Table 7

presents the key underlying data. (Note that the modal average factors calculated for

truck carriers were assumed valid for logistics carriers as well.)

Table 6: Illustrative U.S. Freight Truck Industry Average Factors in Modal Shift

CO2 NOx PM2.5

gram/short ton-mile 161.8 1.114 0.0480

gram/mile 1,661 11.44 0.4925

gram/TEU-mile 597.4 4.113 0.1772

Table 7: Underlying Emissions Inventories and Activity Data for Illustrative U.S.

Freight Truck Industry Average Factors in Modal Shift

CO2 (grams) 341,986,421,100,000

NOx (grams) 2,354,767,660,000

PM2.5 (grams) 101,411,195,611

short ton-miles 2,114,115,022,573

miles 205,918,984,400

We developed the freight rail gCO2/ton-mile factors with 2008 inventory data from

EPA's most recent Inventory of U.S. Greenhouse Gas Emissions and Sinks (1990-

2008)

10, which is based on Class I rail fuel consumption data from the Association of

American Railroads and estimates of Class II and III rail fuel consumption by the

American Short Line and Regional Railroad Association. We divided this emissions

inventory by the latest rail ton-mile data (2007) presented in Table 1-46b in the Bureau

7 These four truck categories are coded as 52, 53, 61, and 62 in the MOVES model, respectively.

8 EPA’s MOVES model and accompanying resources, including technical documentation, are available at:

www.epa.gov/otaq/models/moves/index.htm.

9 U.S. DOT, Federal Highway Administration, 2009. Freight Facts and Figures 2009, FHWA-HOP-10-007, Ton

Miles of Truck Shipments by State: 2002 (Table 3-10). Available at:

http://ops.fhwa.dot.gov/freight/freight_analysis/nat_freight_stats/docs/09factsfigures/table3_10.htm

10 U.S. EPA, 2010. Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990 – 2008 , Washington DC (EPA

430-R-10-006), available at: http://www.epa.gov/climatechange/emissions/usgginv_archive.html. Total freight rail

GHG emissions are presented in Table A-110 of the inventory. Table 10 in this document presents CO2-only data.

In order to isolate the CO2-only emissions data, we accessed spreadsheets that are not publically available.
24

of Transportation Statistics’ (BTS) National Transportation Statistics11, which is intended

to encompass all freight rail ton-miles, including Classes I, II, and III.

We developed the freight rail gNOx/ton-mile and gPM2.5/ton-mile factors with 2010

inventory data from Tables 3-82 and 3-83, respectively, in EPA’s 2008 Regulatory

Impact Analysis for a locomotive diesel engine rule12. This inventory data represents

2010 emission projections for all U.S. rail except for passenger and commuter rail (i.e.,

large line-haul, large switch, and small railroads), which we determined would very

closely align with the freight rail sector. We divided this emissions inventory data by the

2007 BTS ton-mile data described above.

We developed the freight rail g/mile factors by using 2008 railcar mileage data from

lines 15 through 81 of R-1 forms that Class I railroad companies submitted to the

Surface Transportation Board13. We then converted the railcar miles into “truckequivalent” railcar miles, so that partners can more readily estimate emissions impacts

from shifting freight between truck and rail modes, by estimating the average volume

capacity of Class I railcars and dividing it by an average freight truck volume capacity.

This results in a very crude estimate that does not take into consideration the utilized

volume of railcars or the comparative freight truck, but we determined that this was the

best available data and method to estimate modal average truck-equivalent railcar

miles. (As discussed above, carrier-specific volumes and truck-equivalent conversion

factors can be applied by selecting the “Select Carriers” data source option on the

Modal Shift screen, if available.)

To estimate the average volume capacity of Class I railcars, we multiplied the railcar

miles reported by each company for each railcar type in their respective 2008 R-1

reports (lines 15-81) by the volume-per-railcar assumptions in Table 8 to get total Class

I TEU-miles. We then divided the total railcar TEU-miles by the total railcar miles to

estimate the average railcar volume capacity. We then divided this average railcar

volume capacity (3.92) by the average freight truck volume capacity that we developed

for the truck g/TEU-mile factor discussed above (2.78 TEUs) to develop the conversion

factor - 1.41 railcar-miles-to-truck-miles. (Note the 1.41 factor was also used to convert

modal average g/truck-equivalent mile performance metrics to the g/railcar-mile factors

displayed on the Modal Shift screen of the Shipper Tool.)

11 U.S. DOT, Research and Innovative Technology Administration, Bureau of Transportation Statistics, 2009.

National Transportation Statistics, Table 1-46b - U.S. Ton-Miles of Freight (BTS Special Tabulation) (Updated

September 2009). Available at:

www.bts.gov/publications/national_transportation_statistics/html/table_01_46b.html

12 U.S. EPA, Office of Transportation and Air Quality, 2008. Regulatory Impact Analysis: Control of Emissions of

Air Pollution from Locomotive Engines and Marine Compression Ignition Engines Less than 30 Liters Per Cylinder,

EPA420-R-08-001a, Washington DC. Available at: www.epa.gov/otaq/regs/nonroad/420r08001a.pdf

13 Surface Transportation Board (STB) , Industry Data, Economic Data, Financial and Statistical Reports, Class 1

Annual Report, Form R-1. Available at: http://www.stb.dot.gov/stb/industry/econ_reports.html
25

We developed the CO2inventory for the rail g/mile factors by using 2008 Class I rail fuel

consumption reported in the R-1 reports and an emissions factor of 10,180 gCO2/gallon,

which corresponds to the diesel emissions factor in the current version of the SmartWay

Truck Tool.14 We developed the NOx and PM inventories in a similar fashion using the

average 2010 locomotive gPM10/gal and gNOx/gal factors from Tables 5 and 6,

respectively, in EPA's 2009 Technical Highlights: Emissions Factors for Locomotives15.

To calculate gPM2.5/gal, we assumed 95% of PM10 is PM2.5, which we determined

was a good approximation of the share of overall PM10 emissions represented by

particulate matter that is 2.5 micrometers in diameter or smaller.

Table 9 presents the illustrative freight rail emissions factors in the tool and Table 10

presents the key underlying data.

14 The source of the diesel factor is the fuel economy calculations in 40 C.F.R 600.113 available at

http://edocket.access.gpo.gov/cfr_2004/julqtr/pdf/40cfr600.113-93.pdf.

15 U.S. EPA, Office of Transportation and Air Quality, 2009. Technical Highlights: Emission Factors for

Locomotives, EPA-420-F-09-025, Washington DC. Available at:

http://www.epa.gov/oms/regs/nonroad/locomotv/420f09025.pdf.
26

Table 8: Railcar Volume Assumptions and Sources

Railcar Type Cubic

Feet

Source/Method

Key: Norfolk Southern Railroad (NS)16, Union Pacific Railroad (UP)17,

Burlington Northern Santa Fe Railroad (BNSF)18, CSX Transportation Railroad

(CSX)19, World Trade Press Guide to Railcars (GTRC)20, Chicago Rail Car

Leasing (CRCL)21, Union Tank Car Company (UTCC)22, U.S Department of

Agriculture (USDA)23

Boxcar 50 ft and longer

including equipped

boxcars

7,177 Based on the average of the following boxcar types:

50ft assumed to be 5694 [reflecting the average of 5355 (NS), 5431 (UP), 5238

(CSX), 6175 (BSNF), 6269 (GTRC)].

60ft assumed to be 6,648 [reflecting the average of 6618 (NS), 6389 (UP), 6085

(CSX), 7500 (BNSF)].

50ft high cube assumed to be 6,304 [reflecting the average of 6339 (NS) and

6269 (CSX)].

60 ft high cube assumed to be 6917 [reflecting the average of 7499 (NS) , 6646

(CSX), and 6607 (GTRC)].

86ft assumed to be 9999 (NS).

Autoparts assumed to be 7499 (NS).

Boxcar 40ft 4,555 Based on estimate of 50ft boxcar volume described above. Assumed 40ft

length would result in 20% reduction in volume.

Flat car – all types

except for multi-level

6,395 Based on the average of the following flat car types:

60ft assumed to be 6739 (BNSF).

89ft assumed to be 9372(BNSF).

Coil assumed to be 3387(NS).

Covered coil assumed to be 5294 [reflecting the average of 8328 (NS) and

2260 (BNSF)].

Centerbeam assumed to be 6546 [reflecting the average of 5857 (UP) and

7236 (BNSF)].

Bulkhead assumed to be 7030 (BNSF).

Multi-level flat car 13,625 Based on the average of the following multi-level flat car types:

Unilevel (that carry very large cargo, such as vehicles/tractors) assumed to be

12183 (NS).

Bi-level assumed to be 14381(NS).

Tri-level assumed to be 14313 (based on average of 15287 (NS) and 13339

(BNSF).

Flat Car – all typesincluding multi-level

[not used in analysis,

except for estimating

volume of “All Other

Cars”]

7,428 Based on the average volumes of the flatcar types described above including

multi-level as a single flat car type.

Gondola – all types

Including equipped

5,190 Based on the average of the following gondala car types:

52-53ft assumed to be 2626 [based on average of 2665 (NS), 2743 (CSX),

2400 (BNSF), and 2697(CRLC)].

60-66ft assumed to be 3372 [based on average of 3281 (NS), 3242 (CSX),

16 http://www.nscorp.com/nscportal/nscorp/Customers/Equipment_Guide

17 http://www.uprr.com/customers/equip-resources/cartypes/index.shtml

18http://www.bnsf.com/customers/how-can-i-ship/individual-railcar/#%23subtabs-3

19 http://www.csx.com/index.cfm/customers/equipment/railroad-equipment/#boxcar_specs

20 http://www.worldtraderef.com/WTR_site/Rail_Cars/Guide_to_rail_Cars.asp

21 http://www.crdx.com/railcar.html

22 http://www.utlx.com/bdd_tank.html

23 U.S. Department of Agriculture (USDA), 1992, Weights, Measures, and Conversion Factors for Agricultural

Commodities and Their Products, Agricultural Handbook Number 697, Economic Research Service, Washington,

DC. Available at: http://www.ers.usda.gov/publications/ah697/ah697.pdf
27

Railcar Type Cubic

Feet

Source/Method

Key: Norfolk Southern Railroad (NS)16, Union Pacific Railroad (UP)17,

Burlington Northern Santa Fe Railroad (BNSF)18, CSX Transportation Railroad

(CSX)19, World Trade Press Guide to Railcars (GTRC)20, Chicago Rail Car

Leasing (CRCL)21, Union Tank Car Company (UTCC)22, U.S Department of

Agriculture (USDA)23

3350 (BNSF), CRCL-3670,and 3366 (GTRC)].

Municipal Waste assumed to be 7999 (NS).

Woodchip assumed to be 7781[based on average of 7862 (NS) and 7700

(CRCL)].

Coal assumed to be 4170 [based on average of 3785 (NS) and 4556 (BNSF)].

Refrigerated -

Mechanical /nonMechanical

6,202 Based on the average of the following refrigerated car types:

48-72ft assumed to be 6963 [based on average of 6043 (UP) and 7883

(BNSF)].

50ft assumed to be 5167(GTRC).

40-90 ft assumed to be 6476 [based on average of 6952 (UP) and 6000

(BNSF)].

Open Top Hopper 4,220 Based on the average of the following open top hopper car types:

42ft assumed to be 3000 (UP).

54ft assumed to be 3700 (UP).

60ft assumed to be 5188 [based on average of 5125 (UP) and 5250 (GTRC)].

45ft+ assumed to be 4105 [based on average of 4500 (UP) and 3710 (BNSF).

Woodchip assumed to be 7075 [based on average of 7525 (NS), 5999 (UP),

and 7700 (CRCL)].

Small Aggregate assumed to be 2252 [based on average of 2150 (NS), 2106

(BNSF), and 2500 (CRCL)].

Covered Hopper 4,188 Based on the average of the following covered top hopper car types:

45ft assumed to be 5250 (GTRC).

Aggregate assumed to be 2575 [based on average of 2150 (NS) and 3000

(CRCL)].

Small Cube Gravel assumed to be 2939 [based on average of 2655 (NS), 3100

(CSX), and 3063 (BNSF).

Med-Large Cube Ores and Sand assumed to be 4169 [based on average of

3750 (NS) and 4589 (BNSF)].

Jumbo assumed to be 5147 [based on average of 4875 (NS), 4462 (CSX),

5175 (BNSF), and 6075 (CRCL)].

Pressure Differential (flour) assumed to be 5050 [based on average of 5124

(NS) and 4975 (CRCL)].

Tank Cars under

22,000 gallons

2,314 Assumes 1 gallon=0.1337 cubic foot (USDA).

Based on small tank car average volume of 17304 gallons, which is the average

of the following currently manufactured tank car volume design capacities of

13470, 13710, 15100, 15960, 16410,17300,19900,20000,20590, and 20610

gallons (GTRC).

Tank Cars over 22,000

gallons

3,857 Assumes 1 gallon=0.1337 (USDA).

Based on large tank car volume of 28851 gallons, which is the average of the

following currently manufactured tank car volume design capacities of 23470,

25790, 27200, 28700, 30000, 33000, and 33800 gallons (GTRC).

All Other Cars 5,014 Based on average volume presented above for each of the nine railcar types

(all flatcars are represented by the line item that includes multi-level flatcars -

7428).

Table 9: Illustrative U.S. Freight Rail Industry Average Factors in Modal Shift

CO2 NOx PM2.5

gram/short ton-mile 22.94 0.4270 0.0120

gram/truck-equivalent mile 813.8 13.19 0.3569

gram/TEU-mile 292.8 4.745 0.1284
28

Table 10: Underlying Emissions Inventories and Activity Data for Illustrative U.S.

Freight Rail Industry Average Factors in Modal Shift

CO2 (grams) 41,736,353,990,153

short ton-miles 1,819,633,000,000

Class I-only diesel fuel consumption (gallons) 3,905,310,865

Class I-only railcar miles (total) 34,611,843,000

50' and Larger Box Plain + Box Equipped 2,223,402,000

40' Box Plain 22,000

Flat TOFC/COFC, General, and Other 5,057,466,000

Flat Multi Level 1,725,998,000

Gondola Plain and Equipped 7,893,684,000

Refrigerated Mechanical and Non-Mechanical 495,311,000

Open Top Hopper General and Special Service 5,913,012,000

Covered Hopper 7,210,656,000

Tank under 22,000 gallons 1,295,482,000

Tank 22,000 gallons and over 2,394,565,000

All Other Car Types 402,245,000

Note that NOx and PM emission factors were not available at the carrier level for the rail

mode. Accordingly, the modal average emission factors for NOx and PM were

assumed to apply equally for all rail carriers, even though the corresponding factors for

CO2 emissions are company-specific. Also, note that the g/truck-equivalent mile factors

for NOx and PM were converted back to a g/railcar mile basis for inclusion in the carrier

file, to be consistent with the Modal Shift calculation methodology described above.

Outside Sources of Marine and Air Emission Factors

There are many sources of marine and aviation emission factors available in research

literature and other GHG estimation tools. For reference, we have included below:

 gCO2/ton-mile marine and aviation factors from the Business for Social

Responsibility’s (BSR) Clean Cargo Tool gCO2/ton-mile marine and aviation factors

from a study prepared for the International Maritime Organization (IMO)24

 multi-pollutant g/ton-mile barge factors from a study prepared by the Texas

Transportation Institute (TTI) for the U.S. Maritime Administration25

Note that the factors from BSR and IMO are published in units of kgCO2/metric ton-km,

so we converted this data into gCO2/ton-mile by first multiplying by 1,000 (to convert

from kilograms to grams), then multiplying by 0.9072 (to convert from metric tonnes to

24 Buhaug, Ø., et al. for the International Maritime Organization (IMO), 2009. Second IMO GHG Study 2009,

International Maritime Organization (IMO), London, UK, April 2009. Available at:

http://www.imo.org/OurWork/Environment/PollutionPrevention/AirPollution/Documents/GHGStudyFINAL.pdf

25 U.S. Maritime Administration and the National Waterways Foundation (U.S. MARAD), amended March 2009. A

Modal Comparison of Domestic Freight Transportation Effects on the General Public . Prepared by Center for

Ports & Waterways, Texas Transportation Institute. Available at:

www.waterwayscouncil.org/study/public%20study.pdf
29

short tons), and then multiplying by 1.609 (to convert from kilometers to miles) to

prepare the tables below.

BSR developed average 2009 marine emission factors for various shipping corridors, as

well as global defaults that are applicable outside those corridors, based on surveys

from marine carriers. BSR also included two aviation factors in their tool from DEFRA

for short and long international trips, which reflect higher CO2 emissions rates from

shorter trips because landing and take-off operations consume more fuel than cruising

at altitude. The BSR aviation and marine factors in Tables 11 and 12 below are from

the “Emission Factors & Distances” tab in their tool.

Table 11: BSR Marine Emission Factors (gCO2/short ton-mile)

Ship_general International 13.0678

Ship_Barge International 29.1937

Ship_Feeder International 29.1937

Ship_inland_Germany Germany 41.5280

Ship_inland_China China 35.0578

Ship_Asia-Africa Asia--Africa 11.9227

Ship_Asia-South America (EC/WC) Asia--South America (EC/WC) 13.1897

Ship_Asia-Oceania Asia--Oceania 13.4028

Ship_Asia-North Europe Asia--North Europe 10.8586

Ship_Asia-Mediterranean Asia--Mediterranean 12.1358

Ship_Asia-North America EC Asia--North America EC 12.9854

Ship_Asia-North America WC Asia--North America WC 12.0818

Ship_Asia-Middle East/India Asia--Middle East/India 13.5459

Ship_North Europe-North America EC North Europe--North America EC (incl. Gulf) 14.1823

Ship_North Europe-North America WC North Europe--North America WC 13.0642

Ship_Mediterranean-North America EC Mediterranean--North America EC (incl.

Gulf)

12.6788

Ship_Mediterranean-North America WC Mediterranean--North America WC 10.1433

Ship_Europe (North & Med)-Middle

East/India

Europe (North & Med)--Middle East/India 13.4276

Ship_Europe (North & Med)-Africa Europe (North & Med)--Africa 15.8361

Ship_Europe (North & Med)-Oceania (via

Suez / via Panama)

Europe (North & Med)--Oceania (via Suez /

via Panama)

14.4056

Ship_Europe (North & Med)-Latin

America/South America

Europe (North & Med)--Latin America/South

America

12.6146

Ship_North America-Africa North America--Africa 17.4549

Ship_North America EC-Middle East/India North America EC--Middle East/India 12.8788

Ship_North America-South America

(EC/WC)

North America--South America (EC/WC) 13.4379

Ship_North America-Oceania North America--Oceania 15.0552

Ship_South America (EC/WC)-Africa South America (EC/WC)--Africa 11.7432

Ship_Intra-Americas (Caribbean) Intra-Americas (Caribbean) 15.9222

Ship_Intra-Asia Intra-Asia 15.2012

Ship_Intra-Europe Intra-Europe 17.1790
30

Table 12: BSR Air Emission Factors (gCO2/short ton-mile)

Air_freight_long_>3700km International 868.3227

Air_freight_short_<3700km International 2049.9959

The marine and aviation factors in the IMO study reflect commonly-used equipment

sizes and types. The factors in Tables 13 and 14 below come from Table 9.1 and 9.4 in

the IMO study, respectively.

Table 13: IMO Marine Emission Factors

TYPE SIZE AVERAGE

CARGO

CAPACITY

(metric

tonne)

Average

yearly

capacity

utilizatio

n

Average

service

speed

(knots)

Transport work

per ship (tonne

NM)

Loaded

efficiency

(g of CO2/

ton-mile)

Total

efficiency

(g of

CO2/tonmile)

Crude oil

tanker

2000,000+dwt 295,237 48% 15.4 14,197,046,742 2.34 4.23

Crude oil

tanker

120,000-199,99

dwt

151,734 48% 15 7,024,437,504 3.21 6.42

Crude oil

tanker

80,000-119,999

dwt

103,403 48% 14.7 4,417,734,613 4.38 8.61

Crude oil

tanker

60,000-79,999 dwt 66,261 48% 14.6 2,629,911,081 6.28 10.95

Crude oil

tanker

10,000-59,999 dwt 38,631 48% 14.5 1,519,025,926 7.59 13.28

Crude oil

tanker

0-9,999 dwt 3668 48% 12.1 91,086,398 30.22 48.61

Products

tanker

60,000+ dwt 101,000 55% 15.3 3,491,449,962 4.82 8.32

Products

tanker

20,000-59,999 dwt 40,000 55% 14.8 1,333,683,350 10.51 15.03

Products

tanker

10,000-19,999 dwt 15,000 50% 14.1 464,013,471 16.49 27.30

Products

tanker

5,000-9,999 dwt 7,000 45% 12.8 170,712,388 21.60 42.62

Products

tanker

0-49,999 dwt 1,800 45% 11 37,598,072 38.68 65.69

Chemical

tanker

20,000 + dwt 32,200 64% 14.7 1,831,868,715 8.32 12.26

Chemical

tanker

10,000-19,999 dwt 15,000 64% 14.5 820,375,271 10.66 15.76

Chemical

tanker

5,000-9,999 dwt 7,000 64% 14.5 382,700,554 15.62 22.04

Chemical

tanker

0-4,999 dwt 1,800 64% 14.5 72,147,958 27.15 32.41

LPG tanker 50,000 + m3 46,656 48% 16.6 2,411,297,106 7.59 13.14

LPG tanker 0-49,999 m3 3,120 48% 14 89,631,360 39.41 63.50

LNG tanker 200,00 + m3 97,520 48% 19.6 5,672,338,333 7.88 13.58

LNG tanker 0-199,999 m3 62,100 48% 19.6 3,797,321,655 12.26 21.17

Bulk carrier 200,000 +dwt 227,000 50% 14.4 10,901,043,017 2.19 3.65

Bulk carrier 100,000-199,999

dwt

163,000 50% 14.4 7,763,260,284 2.63 4.38

Bulk carrier 60,000-99,999 dwt 74,000 55% 14.4 3,821,361,703 3.94 5.98

Bulk carrier 35,000-59,999 dwt 45,000 55% 14.4 2,243,075,236 5.55 8.32
31

TYPE SIZE AVERAGE

CARGO

CAPACITY

(metric

tonne)

Average

yearly

capacity

utilizatio

n

Average

service

speed

(knots)

Transport work

per ship (tonne

NM)

Loaded

efficiency

(g of CO2/

ton-mile)

Total

efficiency

(g of

CO2/tonmile)

Bulk carrier 10,000-34,999 dwt 26,000 55% 14.3 1,268,561,872 7.74 11.53

Bulk carrier 0-9,999 dwt 2,400 60% 11 68,226,787 33.43 42.62

General cargo 10,000 + dwt 15,000 60% 15.4 866,510,887 11.09 17.37

General cargo 5,000-9,999 dwt 6,957 60% 13.4 365,344,150 14.74 23.06

General cargo 0-4,999 dwt 2,545 60% 11.7 76,645,792 15.91 20.29

General cargo 10,000+ dwt, 100+

TEU

18,000 60% 15.4 961,054,062 12.55 16.06

General cargo 5,000-9,999 dwt,

100+TEU

7,000 60% 13.4 243,599,799 20.14 25.54

General cargo 0-4,999 dwt,

dwt+TEU

4,000 60% 11.7 120,938,043 22.63 28.90

Refrigerated

cargo

All 6,400 50% 20 392,981,809 18.83 18.83

Container 8000+TEU 68,600 70% 25.1 6,968,284,047 16.20 18.25

Container 5,000-7,999 TEU 40,355 70% 25.3 4,233,489,679 22.19 24.23

Container 3,000-4,999 TEU 28,784 70% 23.3 2,280,323,533 22.19 24.23

Container 2,000-2,999 TEU 16,800 70% 20.9 1,480,205,694 26.71 29.19

Container 1,000-1,999 TEU 7,000 70% 19 578,339,367 42.91 46.86

Container 0-999 TEU 3,500 70% 17 179,809,363 48.61 52.99

Vehicle 4000 +ceu 7,908 70% 19.4 732,581,677 36.78 46.71

Vehicle 0-3999 ceu 2,808 70% 17.7 226,545,399 68.90 84.08

Ro-Ro 2,000 + lm 5,154 70% 19.4 368,202,021 66.12 72.25

Ro-Ro 0-1,999 lm 1432 70% 13.2 57,201,146 80.57 88.02

Note: “Loaded efficiency” is the theoretical maximum efficiency when the ship is fully loaded at service speed/85% load.

Since engine load at the fully loaded condition is higher than the average including ballast and other voyages, the difference

between the columns “loaded efficiency” and “total efficiency cannot be explained by differences in utilization only.

Table 14: IMO Air Emission Factors (gCO2/short ton-mile)

Boeing 747F high 691.89

Boeing 747F low 634.96

Ilyushin IL 76T high 2,627.43

Ilyushin IL 76T low 1,605.65

The barge emissions factors presented in Table 15 are from Table 10 in the TTI study

and reflect inland waterway towing operations in the U.S. We converted the PM10 factor

in the TTI study into PM2.5 by assuming 95% of PM10 is PM2.5, which we determined

was a good approximation of the share of overall PM10 emissions represented by

particulate matter that is 2.5 micrometers in diameter or smaller.
32

Table 15: TTI Barge Emission Factors

CO2 NOx PM2.5

gram/short ton-mile 17.48 0.4691 0.0111

4.0 Data Validation

The Shipper Tool also contains data validation checks designed to identify missing and

potentially erroneous data. Validation messages are presented for the following

conditions:

• Average truck/logistics/multi-modal payloads are less than 9.4 tons (error – red)

• Average truck/logistics payloads are greater than 33.8 tons (error – red)

• Average multi-modal payloads are greater than 90 tons (error – red)

• Average railcar payloads are less than 9.4 tons or greater than 125 tons (error –

red)

• Average truck/logistics/multi-modal payloads are between 9.4 and 15.5 tons

(warning – yellow)

• Average truck/logistics payloads are between 27.7 and 33.7 tons (warning –

yellow)

• Average multi-modal payloads are between 60 and 90 tons (warning – yellow)

• Payload is less than or equal to zero (error)

The cutpoints for Truck and Logistics carriers (and the lower bound cutpoints for multmodal and rail carriers) are based on the payload validation ranges used in the Truck

Tool for Class 8b trucks. (See the Truck Tool Technical Documentation for additional

information.) The upper bound cutpoints for multi-modal payloads are based on a

qualitative review of 2010 multi-modal carrier tool submittals. The upper bound

cutpoints for rail payloads are based on the distribution of average values estimated for

Class 1 carriers (see Table 4 above). These cutpoints were set with the intention of

identifying those payloads that are somewhat outside typical industry values (yellow flag

warnings) and those that are far outside industry averages (red flag warnings).