FRA-RRD30-2022-0001 Final Report | December 2022

Carbon Dioxide Emissions from Four Real World InterCity Passenger Trips: A Comparison of Rail, Air, and 

Road Travel Modes by City Pair

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August 2021 – September 2022

4. TITLE AND SUBTITLE

Carbon Dioxide Emissions from Four Real World Inter-City Passenger Trips: A 

Comparison of Rail, Air, and Road Travel Modes by City Pair

5a. CONTRACT NUMBER

693JJ621N000043

5b. GRANT NUMBER

5c. PROGRAM ELEMENT NUMBER

6. AUTHOR(S)

Matthew C. Simon (ORCID: 0000-0002-0924-8630), Anjuliee Mittelman (ORCID: 

0000-0002-0064-2148), Gina Solman (ORCID: 0000-0002-4428-4397), Scott 

Gilman (ORCID: 0000-0002-5966-8377), Olivia Henning, Abigail Raymond

(ORCID: 0000-0001-5158-1931), Rebecca Blatnica

5d. PROJECT NUMBER

RR41A221

5e. TASK NUMBER

VM641

5f. WORK UNIT NUMBER

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)

U.S. Department of Transportation

John A. Volpe National Transportation Systems Center

Environmental Measurement and Modeling Division

Cambridge, MA 02142

8. PERFORMING ORGANIZATION 

REPORT NUMBER

DOT-VNTSC-FRA-22-02

9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES)

U.S. Department of Transportation

Federal Railroad Administration

Office of Railroad Development

Office of Environmental Program Management

Washington, DC 20590

10. SPONSOR/MONITOR'S ACRONYM(S)

FRA

11. SPONSOR/MONITOR'S REPORT 

NUMBER(S)

FRA-RRD30-2022-0001

12. DISTRIBUTION/AVAILABILITY STATEMENT

This document is available to the public through the FRA website. 

13. SUPPLEMENTARY NOTES

FRA Project Sponsors: Michael Johnsen, Andrea Martin

14. ABSTRACT

This study compares the operational carbon dioxide (CO2) emissions of three main travel modes (rail, air, and road – both passenger

vehicle and bus) for four real-world trips between different city pairs across the U.S. For each city pair and mode, operational emissions 

were estimated by calculating the roundtrip per-passenger-mile fuel efficiency and emissions. Operational emissions were bounded by 

emissions from the movement of passengers only (i.e., a pump-to-wheels analysis). First- and last-mile emissions for traveling to and 

from transit centers were calculated for five different inter-city modes. For the four scenarios analyzed,travel by car (i.e., single 

occupancy vehicle or SOV) or air were the most carbon intensive modes. Traveling by rail or bus was found to have substantially lower 

operational CO2 emissions than either SOV or air. The electric train had the lowest CO2 emissions of themodes compared.

15. SUBJECT TERMS

Carbon, greenhouse gas, operational emissions, transportation mode, Amtrak

16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF 

ABSTRACT

UU

18. NUMBER 

OF 

PAGES

38

19a. NAME OF RESPONSIBLE PERSON

a. REPORT Michael Johnsen

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b. ABSTRACT

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c. THIS PAGE

U 19b. TELEPHONE NUMBER (Include area code)

202-450-8540

Standard Form 298 (Rev. 8/98)

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ENGLISH TO METRIC METRIC TO ENGLISH

LENGTH (APPROXIMATE) LENGTH (APPROXIMATE)

1 inch (in) = 2.5 centimeters (cm) 1 millimeter (mm) = 0.04 inch (in)

1 foot (ft) = 30 centimeters (cm) 1 centimeter (cm) = 0.4 inch (in)

1 yard (yd) = 0.9 meter (m) 1 meter (m) = 3.3 feet (ft)

1 mile (mi) = 1.6 kilometers (km) 1 meter (m) = 1.1 yards (yd)

1 kilometer (km) = 0.6 mile (mi)

AREA (APPROXIMATE) AREA (APPROXIMATE)

1 square inch (sq in, in2) = 6.5 square centimeters (cm2) 1 square centimeter (cm2) = 0.16 square inch (sq in, in2)

1 square foot (sq ft, ft2) = 0.09 square meter (m2) 1 square meter (m2) = 1.2 square yards (sq yd, yd2) 

1 square yard (sq yd, yd2) = 0.8 square meter (m2) 1 square kilometer (km2) = 0.4 square mile (sq mi, mi2)

1 square mile (sq mi, mi2) = 2.6 square kilometers (km2) 10,000 square meters (m2) = 1 hectare (ha) = 2.5 acres

1 acre = 0.4 hectare (he) = 4,000 square meters (m2)

MASS - WEIGHT (APPROXIMATE) MASS - WEIGHT (APPROXIMATE)

1 ounce (oz) = 28 grams (gm) 1 gram (gm) = 0.036 ounce (oz)

1 pound (lb) = 0.45 kilogram (kg) 1 kilogram (kg) = 2.2 pounds (lb)

1 short ton = 2,000 pounds (lb) = 0.9 tonne (t) 1 tonne (t) =

=

1,000 kilograms (kg)

1.1 short tons

VOLUME(APPROXIMATE) VOLUME(APPROXIMATE)

1 teaspoon (tsp) = 5 milliliters (ml) 1 milliliter (ml) = 0.03 fluid ounce (fl oz)

1 tablespoon (tbsp) = 15 milliliters (ml) 1 liter (l) = 2.1 pints (pt)

1 fluid ounce (fl oz) = 30 milliliters (ml) 1 liter (l) = 1.06 quarts (qt)

1 cup (c) = 0.24 liter (l) 1 liter (l) = 0.26 gallon (gal)

1 pint (pt) = 0.47 liter (l)

1 quart (qt) = 0.96 liter (l)

1 gallon (gal) = 3.8 liters (l)

1 cubic foot (cu ft, ft3) = 0.03 cubic meter (m3) 1 cubic meter (m3) = 36 cubic feet (cu ft, ft3) 

1 cubic yard (cu yd, yd3) = 0.76 cubic meter (m3) 1 cubic meter (m3) = 1.3 cubic yards (cu yd, yd3)

TEMPERATURE(EXACT) TEMPERATURE(EXACT)

[(x-32)(5/9)] °F = y °C [(9/5) y + 32] °C = x °F

QUICK INCH - CENTIMETER LENGTH CONVERSION

0 1 2 3 4 5

Inches

Centimeters 0 1 2 3 4 5 6 987 1110 1312

QUICK FAHRENHEIT - CELSIUS TEMPERATURE CONVERSIO

 -40° -22° -4° 14° 32° 50° 68° 86° 104° 122° 140° 158° 176° 194° 212°

 

°F

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For more exact and or other conversion factors, see NIST Miscellaneous Publication 286, Units of Weights and 

Measures. Price $2.50 SD Catalog No. C13 10286 Updated 6/17/98
iii

Acknowledgements 

The authors would like to thank Christopher Witt (FRA) and Amtrak for providing ridership and 

diesel consumption data.
iv

Contents 

Scenario 1: Boston – New York............................................................................................... 1

Scenario 2: Los Angeles – San Diego....................................................................................... 2

Scenario 3: Washington – Orlando........................................................................................... 3

Scenario 4: St. Louis – Chicago ............................................................................................... 4

Executive Summary................................................................................................................. 5

1. Introduction .......................................................................................................... 6

2. Methodology......................................................................................................... 8

2.1 Routes.................................................................................................................. 8

2.2 Ridership .............................................................................................................. 8

2.3 Modal CO2 Emissions........................................................................................... 9

2.4 Last Mile CO2 Emissions.....................................................................................13

2.5 Scenario Emissions.............................................................................................14

3. Results and Discussion........................................................................................15

4. Summary and Recommendations......................................................................... 18

5. References..........................................................................................................20

Appendix A. Literature Review..............................................................................................21

Appendix B. Power Grid Assumptions...................................................................................29

Abbreviations and Acronyms................................................................................................. 30
v 

Illustrations 

Figure 1. Summary of Scenario 1 – Suitcase Full of Laundry. ................................................... 1

Figure 2. Summary of Scenario 2 – Reach the Beach. ............................................................... 2

Figure 3. Summary of Scenario 3 – Family Disney Vacation..................................................... 3

Figure 4. Summary of Scenario 4 – Reunion in the Windy City................................................. 4
vi

Tables

Table 1. Summary of literature reviewed.................................................................................. 7

Table 2. City pair, terminal-to-terminal details by travel mode. ................................................. 8

Table 3. MOVES Run Spec for passenger vehicle and transit bus CO2 emissions by county..... 11

Table 4. City pair, county, and month mappings for MOVES runs........................................... 12

Table 5. Operational CO2 emission for last-mile modes on a per-passenger-mile basis............. 14

Table 6. Origin and destination points used in the full scenario analysis................................... 14

Table 7. Operational CO2 emissions by route and mode – roundtrip emissions, terminal to 

terminal.........................................................................................................................15

Table 8. Operational CO2 emissions (total) by scenario and main mode of travel – last-mile 

emissions included.........................................................................................................16




5 

Executive Summary

This study compares the operational carbon dioxide (CO2) emissions of three main travel modes 

(rail, air, and road – both passenger vehicle and bus) for four real-world trips between different 

city pairs across the U.S. The city pairs selected for the comparative analysis include Boston, 

MA – New York, NY; Los Angeles, CA – San Diego, CA; Washington, DC – Orlando, FL; and

St. Louis, MO – Chicago, IL. For each city pair and mode, operational emissions were estimated 

by calculating the roundtrip per-passenger-mile fuel efficiency and emissions. Operational 

emissions were bounded by emissions from the movement of passengers only (i.e., a pump-towheels analysis). First- and last-mile emissions for traveling to and from transit centers were 

calculated for five different inter-city modes. Total emissions for one passenger making the 

journey described in each scenario were then calculated. For the four scenarios analyzed, travel 

by car (i.e., single occupancy vehicle or SOV) or air were the most carbon intensive modes. For 

the shorter routes, air travel resulted in higher emissions than SOV; for the longer routes, SOV 

produced higher emissions. Traveling by rail or bus was found to have substantially lower 

operational CO2 emissions than either SOV or air. The emissions from traveling by bus were 

slightly better than the equivalent route by diesel train; however, the electric train (for the 

Boston-New York scenario) had the lowest CO2 emissions of the modes compared and has the 

potential to have no CO2 emissions if powered by 100 percent renewable energy.
6 

1. Introduction 

The transportation sector in the United States is the largest contributor to greenhouse gas (GHG) 

emissions.1 The majority of carbon dioxide (CO2) and other GHG emissions are due to the 

combustion of fossil fuels. Even electric vehicles are contributors when considering the GHGs 

released from generating the electricity and other upstream emissions such as construction and 

maintenance. A goal of the U.S. Department of Transportation (DOT) is to substantially reduce 

GHG emissions, with a target of economy-wide net-zero emissions by 2050. One simple strategy 

available, that requires no scientific advances or breakthroughs to reduce emissions, is shifting 

trips (whether people or freight) from carbon-intensive transportation modes to those that are less 

intensive.

As a first step in exploring how rail can contribute towards the goal of economy-wide net-zero

emissions, the Federal Railroad Administration (FRA) asked the John A. Volpe National 

Transportation System Center (Volpe) to compare the operational CO2 emissions of three main 

travel modes (rail, air, and road – both passenger vehicle and bus) for four real-world trips 

between different city pairs across the U.S. The city pairs selected for the comparative analysis 

include Boston, MA – New York, NY; Los Angeles, CA – San Diego, CA; Washington, DC – 

Orlando, FL; and St. Louis, MO – Chicago, IL. 

While emissions models are well-established for both road and air travel – for example, U.S. 

Environmental Protection Agency’s (EPA) Motor Vehicle Emissions Simulator (MOVES)2 for 

road and the International Civil Aviation Organization (ICAO) Carbon Emissions Calculator3 for 

air – equivalent estimator tools for U.S. rail are lacking. Tools do exist that estimate rail 

emissions, such as Argonne National Laboratory’s Greenhouse gases, Regulated Emissions, and 

Energy use in Technology (GREET) model, 4 but emissions data may need to be updated and 

estimates are system wide (i.e., the full rail network).

To understand how researchers have approached estimating rail emissions compared to other 

travel modes, a literature search was conducted resulting in 18 recent (year 2009+) studies. These 

were reviewed in further detail, and 10 of the studies deemed to be the most relevant for this 

analysis effort are highlighted in Table 1. More detailed summaries of each are available in 

Appendix A. In brief, the literature search highlighted the reliance on study-specific emissions 

estimates over established emissions models or tools much of the time. As such, this analysis 

used a similar approach for rail.

The section below details the methodology used for all travel modes, including key data sources 

and assumptions. For the analysis to be representative of origin to destination travel scenarios, 

last-mile emissions were included and are also described in the methodology. This report 

summarizes Volpe’s findings on the operational CO2 emissions by travel mode for the four 

scenarios investigated.

1 https://www.epa.gov/air-emissions-inventories/2017-national-emissions-inventory-nei-data. 

2 https://www.epa.gov/moves.

3 https://www.icao.int/environmental-protection/CarbonOffset/Pages/default.aspx.

4 https://greet.es.anl.gov/.
7 

Recommendations for future research on GHG emissions from passenger travel are included at 

the conclusion of the report.

Table 1. Summary of literature reviewed.

Citation Emissions Estimate 

Approach for Rail

Emissions Estimate 

Approach for Air

Emissions Estimate 

Approach for Road

(Miller, 2020) Study-specific 

calculations Emissions calculator N/A

(Chester & Horvath, 

2010) Emissions calculator Study-specific calculations

Study-specific 

calculations

(Baumeister & 

Leung, 2021)

Study-specific 

calculations

Study-specific 

calculations

Study-specific 

calculations

(Baumeister, 2019) Study-specific 

calculations

Study-specific 

calculations

Study-specific 

calculations

(Graver & Frey, 

2016)

Study-specific

calculations N/A Emissions calculator

(Meynerts et al., 

2018) Emissions calculator N/A N/A

(Trevisan & 

Bordignon, 2020) Literature review Literature review Literature review

(Kapetanović et al., 

2019) Literature review N/A N/A

(Inderbitzin, 2019) Emissions calculator Emissions calculator N/A

(Dimoula et al., 

2016)

Study-specific 

calculations N/A Study-specific calculations
8 

2. Methodology 

2.1 Routes

Characteristics of the four scenarios are summarized in Table 2. Route selection was based on a 

variety of criteria, including: 1) geographically diverse, 2) of varying distance, 3) origins and 

destinations were metropolitan areas with Amtrak stations, 4) include both diesel and electric 

Amtrak routes, and 5) at least one airport and bus station nearby to serve as a realistic travel 

option between the city pairs.

Table 2. City pair, terminal-to-terminal details by travel mode. 

City Pair Amtrak Station 

Pair

Airport 

Pair

Rail 

Distance 

(miles)

Air 

Distance 

(miles)*

Car 

Distance 

(miles)

Bus 

Distance 

(miles)

Boston – 

New York

South Station 

(BOS) - 

Moynihan Train 

Hall at Penn 

Station (NYP)

BOS – 

JFK 231 186 211 216

Los Angeles 

– San Diego

Union Station 

(LAX) - Santa Fe 

Depot (SAN)

LAX – 

SAN 130 109 128 112

Washington 

– Orlando

Auto Train 

Station (LOR) - 

Auto Train 

Station (SFA)

IAD – 

MCO 860 759 848 936

St. Louis – 

Chicago

Gateway Station 

(STL) - Union 

Station (CHI)

STL – 

ORD

284 258 300 319

*Great-circle distance – the shortest distance between two points along the surface of a sphere (e.g., Earth).

Rail and car distances were based on the route connecting the Amtrak station pairs, air distance 

was based on the great-circle route connecting the airport pairs, and bus distance was based on 

the route connecting the bus station pairs that were nearest the Amtrak stations. Road routes were 

the fastest, shortest routes between the stations and did not consider route modifications due to 

traffic conditions.

2.2 Ridership

To ensure a fair comparison across modes, the analysis assumes a single passenger for all 

scenarios and modes. The Washington-Orlando scenario (the Auto Train route) considered a 

family of four traveling together. Information about the total number of passengers (or ridership) 


11

2.3.2 Highway

Highway CO2 emission rates were generated using EPA’s Motor Vehicle Emissions Simulator 

(MOVES), 7 specifically MOVES3. MOVES is a modeling system that estimates emissions from 

mobile sources at the national, county, and project-levels. It provides estimates of the criteria air 

pollutants and GHGs, including CO2. Specific emission rates can be developed for vehicle type, 

road type (rural and urban, unrestricted and restricted8), fuel type, and model year. 

MOVES was run for each origin and destination county associated with the city pair assuming a 

project year of 2022. Emission rates (g/mile) were calculated for passenger vehicles and transit 

buses and delineated by county, road type, and month to align with each scenario. The MOVES 

default national mix of vehicle types, fuel types, and model years was assumed for both the 

passenger vehicle and transit bus fleets. The MOVES run spec parameters are summarized in 

Table 3. 

Table 3. MOVES Run Spec for passenger vehicle and transit bus CO2 emissions by county.

Category Variable Input

Description ----- <blank>

Scale Model Onroad

Scale Domain/Scale

Default Scale (uses the national 

database with default state and local 

allocation factors)

Scale Calculation Type Inventory

Time Spans Time Aggregation Level Year

Time Spans Year 2022

Time Spans Months All Selected

Time Spans Days All Selected

Time Spans Hours All Selected

Geographic Bounds ----- 

Cook County, IL (17031)

Fairfax County, VA (51059)

Los Angeles County, CA (06037)

New York County, NY (36061)

San Diego County, CA (06073)

Seminole County, FL (12117)

St. Louis City, MO (29510)

Suffolk County, MA (25025)

7 MOVES and Other Mobile Source Emissions Models | US EPA. 

8 Road types in MOVES include urban restricted, urban unrestricted, rural restricted, and rural unrestricted. 

Restricted access refers to a roadway that has beendesigned for high-speed traffic. 
12

Category Variable Input

Vehicles/Equipment On-Road Vehicle

Equipment

Passenger Car – Diesel Fuel

Passenger Car – Electricity

Passenger Car – Ethanol (E-85)

Passenger Car – Gasoline

Passenger Truck – Diesel Fuel

Passenger Truck – Electricity

Passenger Truck – Ethanol (E-85)

Passenger Truck – Gasoline

Transit Bus – Compressed Natural 

Gas (CNG)

Transit Bus – Diesel Fuel

Transit Bus – Gasoline 

Road Type Road Types All Selected

Pollutants and Processes Atmospheric CO2

Running Exhaust, Running 

Crankcase Exhaust, Start Exhaust, 

Crankcase Start Exhaust 

Manage Input Data 

Series ----- <blank>

Strategies Rate of Progress <blank>

General Output Units Mass: kilograms, Energy: Million 

BTU, Distance: miles

General Output Activity Distance Traveled

Output Emissions Detail Always Month, County

Output Emissions Detail On Road/Off Road Road Type, Source Use Type

Output Emissions Detail

For All 

Vehicle/Equipment 

Combinations

None (aggregate fleet by model year, 

fuel type, emission process)

Advanced Features ----- <blank>

The specific counties and months used for each city pair are detailed in Table 4. 

Table 4. City pair, county, and month mappings for MOVES runs. 

City Pair Origin County Destination County Month

Boston –

New York Suffolk County, MA New York County, NY December

Los Angeles –

San Diego Los Angeles County, CA San Diego County, CA August

Washington –

Orlando Fairfax County, VA Seminole County, FL April

St. Louis –

Chicago St. Louis City, MO Cook County, IL September
13

The output from the MOVES run was post-processed to obtain county-specific emission rates by 

road type and month for passenger vehicles and transit buses:

1. Activity rates – To obtain activity rates, vehicle miles traveled (VMT) were extracted 

from the results for (1) all passenger cars and trucks and (2) all transit buses by month, 

road type, and county.

2. Emissions associated with VMT – Emission rates were generated on a per-mile basis 

for passenger vehicles and transit buses by month, road type, and county. This involved 

joining emission inventories from the movesoutput table and activity from the 

movesactivityoutput table. Emissions were summed across all vehicle types, fuel types, 

and emissions processes to generate inventories for each scenario. Emission rates were 

calculated as an average of the origin and destination emission rates.

Additionally, it was assumed buses travel with 25 passengers,9 which is equivalent to a load 

factor of 0.5 on a 50-person bus.

2.3.3 Air

Operational CO2 emissions from civil aircraft were estimated with the International Civil 

Aviation Organization (ICAO) Carbon Emissions Calculator.

10 The ICAO Calculator estimates 

fuel consumption based on the great-circle distance between airport pairs and expected aircraft 

types flown (by route). Passenger load factors and passenger-to-cargo ratios were used to obtain 

the proportion of total fuel used attributable only to the passengers on board. Databases that 

support these CO2 emissions estimates are updated periodically as new data are available, or on 

an annual basis. This includes ICAO traffic data, air carriers’ schedule data, aircraft mappings 

(actual to model), and aircraft fuel consumption. The ICAO Calculator only estimates CO2

emissions for aircraft operations and does not include other GHGs or emissions from 

maintenance, ground support equipment, or infrastructure. 

2.4 Last Mile CO2 Emissions

Last-mile CO2 emissions considered five different modes: light rail, commuter rail, singleoccupancy vehicle (SOV), city bus, and walking or biking. Emissions for the two rail modes 

were based on the Federal Transit Administration’s Transit GHG Emissions Estimator,11 which 

used data from GREET. Since emissions from the tool were on a per train basis, assumptions 

were made as to the average number of passengers per train to be able to estimate emissions on a 

per-passenger basis. For light rail, it was assumed there were 100 people per train; for the 

commuter rail, it was assumed there were 500 people per train. The two road modes (SOV and 

city bus) used MOVES to estimate CO2 emissions. All inputs to the model were the same as 

reported in section 2.3.2, except the unrestricted urban roadway was used as the road type, which 

has higher CO2 emissions per mile than restricted highway due to the lower speeds and more 

starts and stops. As with rail, an assumed number of passengers per vehicle was needed to 

9 DEVELOPING REFINED ESTIMATES OF INTERCITY BUS RIDERSHIP (dot.gov).

10 ICAO Carbon Emissions Calculator. 

11 FTA's Transit Greenhouse Gas Emissions Estimator v3.0 | FTA (dot.gov).
14

estimate emissions on a per passenger basis. For SOV, the assumption was one; for city bus, the 

assumption was 15. No modeling was needed for walking or biking as CO2 emissions for this 

mode are zero; electric bicycles were not considered, and neither was the potential increase in 

respiration (versus a person sitting on one of the other modes). Table 5 highlights the various 

CO2 emission rates on a per-passenger-mile basis. 

Table 5. Operational CO2 emission for last-mile modes on a per-passenger-mile basis. 

Last-Mile Mode Emissions

(kg CO2/passenger mile)

# Passengers Assumed 

Per Vehicle/Train

Light Rail 0.036 100

Commuter Rail 0.056 500

Car (single 

occupancy)

0.383 1

City Bus 0.112 15

Walking, Biking 0.000 1

2.5 Scenario Emissions

Scenario emissions were estimated as roundtrip excursions and only considered the travel 

components to and from the main destination. Additional emissions while at the destination, for 

example, sightseeing by bus or other activities with CO2 emissions, were not included. Last-mile 

emissions were added to the terminal-to-terminal emissions at both ends of the main route. It was 

assumed travelers went directly to the terminal from their starting point and, similarly, directly to 

their end-point destination from the terminal. To differentiate the higher emissions of driving on 

urban streets versus the highway, the terminal points for the car mode were the beginning and 

end of the highway segment for the overall trip. Thus, the last-mile components (with the higher 

emissions per mile) for traveling by car were the start-to-highway and highway-to-destination 

segments. For all modes, start-to-destination emissions were multiplied by two to represent the 

roundtrip CO2 emissions by overall travel mode for each scenario. Last-mile emissions added to 

the terminal-to-terminal emissions were based on the scenario origin and destination points 

provided in Table 6. 

Table 6. Origin and destination points used in the full scenario analysis. 

City Pair Origin Destination

Boston –

New York

Upper Residential Quad, Medford, 

MA Sterling Rd., Elmont, NY

Los Angeles –

San Diego Santa Monica Blvd., Santa Monica, CA Mission Beach, San Diego, CA

Washington –

Orlando Hackberry St., Springfield, VA Epcot Resorts Blvd., Lake Buena Vista, FL

St. Louis –

Chicago Caroline St., St. Louis, MO N. Rush St., Chicago, IL
15

3. Results and Discussion

For the four scenarios analyzed, travel by car (i.e., SOV) or air were the most carbon intensive 

modes. For the shorter routes, air was worse than SOV; for the longer routes, SOV was worse. 

Traveling by rail or bus was found to have substantially fewer operational CO2 emissions than 

either SOV or air. The emissions from traveling by bus were slightly better than the equivalent 

route by diesel train; however, the electric train (for the Boston-New York scenario) had the 

lowest CO2 emissions of the modes compared and has a high potential to produce very low or no 

CO2 emissions if powered 100 percent from renewable energy. It should be noted that emissions 

reported for the electric train were based on the CO2 emitted from the generation of the 

electricity – direct CO2 emissions from the train are zero. For the unique scenario of traveling by 

Auto Train (the Washington-Orlando scenario), it was found that taking the train and bringing 

along one’s car as cargo was less carbon intensive than driving the car. This benefit was 

observed even when considering two passengers traveling together (253 kg CO2/person on the 

Auto Train versus 272 kg CO2/person driving). Table 7 highlights the terminal-to-terminal 

roundtrip emissions for each of the four scenarios (last-mile emissions excluded), by mode.

Table 7. Operational CO2 emissions by route and mode – roundtrip emissions, terminal to 

terminal. 

City Pair Car

(kg CO2/per.)

Busǂ

(kg CO2/per.)

Air

(kg CO2/per.)

Diesel Train

(kg CO2/per.)

Train (w/ car)

(kg CO2/per.)

Electric Train

(kg CO2/per.)

Boston –

New York 132.6 24.8 129.0 N/A N/A 23.4+

Los Angeles –

San Diego 84.0 12.8 92.0 41.8 N/A N/A

Washington –

Orlando 543.2 107.2 237.6 175.8 330.4 N/A

Washington –

Orlando

(Family of 4)

135.8* 107.2 237.6 175.8 214.5* N/A

St. Louis –

Chicago 193.2 36.6 158.4 46.8 N/A N/A

ǂ Assumes load factor of 0.5 on a 50-person bus. + Emissions from electricity generation; emissions at the vehicle are zero.

*Assumes car emissions are split among four people.

For the Washington-Orlando scenario, emissions based on traveling alone versus traveling as a 

family of four are included. Only emissions from traveling by car or traveling by train with a car 

as cargo are impacted by the number of people traveling together. This is because emissions 

associated with the car are split across the people traveling – the more people traveling together, 

the fewer emissions per person. This points to why carpooling in general is a great strategy for 

reducing emissions. The passenger load factor is independent of the number of people traveling 

together by bus, air, and train (without a car). Note that air emissions for the Washington to 
16

Orlando route do not include transporting a car as such a service is unavailable by air. Adding 

last-mile emissions to each of these terminal-to-terminal emissions did impact the results, but 

trends observed in Table 7 were largely unchanged. Total CO2 emissions by mode and scenario 

(including last-mile emissions) are provided in Table 8. 

Table 8. Operational CO2 emissions (total) by scenario and main mode of travel – last-mile 

emissions included. 

City Pair Carǂ

(kg CO2/per.)

Bus+

(kg CO2/per.)

Airǂ,+

(kg CO2/per.)

Diesel Train

(kg CO2/per.)

Train (w/ car)ǂ

(kg CO2/per.)

Electric Train

(kg CO2/per.)

Boston –

New York 135.0 26.7 141.1 N/A N/A 25.3

Los Angeles –

San Diego 87.0 15.2 105.0 44.2 N/A N/A

Washington –

Orlando 548.0 121.9 273.5 210.6 369.2 N/A

Washington –

Orlando

(Family of 4)

137.0* 111.1* 246.6*+ 185.5* 224.2* N/A

St. Louis –

Chicago 193.9 37.1 184.4 47.3 N/A N/A

ǂ Assumes last-mile mode was a car for all scenarios.

*Assumes car emissions are split among four people.

+ Air and bus service only includes passengers as air service does not allow for car transportation.

The impact to full-trip CO2 emissions was greatest by assuming a car as the mode for the lastmile segments in each scenario. For this analysis, a car was assumed for the car, air, and train 

(with car) modes. For the car and train (with car) modes it was necessary for the car to be present 

at the beginning of the terminal-to-terminal segment of the trip, thus it was not realistic to 

assume any other last-mile mode in those instances. For air travel, it was assumed travelers 

would be seeking the fastest mode to getting from the starting point to the terminal and terminal 

to the end point since they had opted to fly already. It was assumed a car would be the fastest 

mode for all four scenarios. Additionally, for the Washington-Orlando scenario, all modes 

assumed a car would be rented either at the bus terminal or train station and driven to the 

destination. For the other three scenarios, bus and train modes assumed public transportation 

(i.e., city bus or light rail) in combination with some walking as the last-mile modes.

This analysis includes a few assumptions and limitations. First, the analysis did not consider the 

impact on emissions due to traffic or other delays (e.g., aircraft departure delays and subsequent 

queueing emissions). Other scenarios were also excluded, such as indirect travel routes, which

would increase the total trip distance and thus total CO2 emissions. While these complexities 

would be expected to increase operational CO2 emissions and may be more representative of 

typical travel conditions, it was unclear what the equivalent delays are between modes and how 

to compare different indirect routes that still allowed the comparison by mode to be fair. As such, 

only unimpeded travel by different modes were included in this analysis. However, since the rail 
17

analysis was based on fuel-use data, enroute delays were included by default, which would result 

in slightly higher emissions as compared to air and road travel modes. Second, both rail and air 

modes excluded emissions related to ground support during loading and unloading. For 

passenger-only related emissions (i.e., passenger cargo), these emissions are expected to be low 

and would not change the overall emissions trends observed. For cases with freight movement 

mixed with passenger travel, as is often the case with larger airplanes, these emissions would 

likely be more substantial ground support emissions; however, freight emissions were already 

excluded from the analysis. Third, for air travel specifically, aircraft emissions from taxiing were 

also excluded.

Thus, air emissions reported by scenario are likely underestimates of the true CO2 emissions due 

to air travel. This is a limitation of using the ICAO Calculator; however, its accuracy for the 

flight portions of each route still supported its use and are the bulk of the operational emissions. 

Some have reported taxi emissions can be estimated as a percentage of fuel burn on the full 

flight, with an average of approximately 6 percent. It was unclear how applicable this estimate 

would be for the eight airports included in this analysis. Instead of adding a fixed 6 percent to the 

estimates reported at the risk of the addition being in error, it was decided to only report CO2

emissions as estimated by the ICAO Calculator.

This analysis also excluded upstream emissions from the production, transport, and distribution 

of aviation and motor vehicle fuels. Upstream emissions were included in the case of the Acela 

route to determine emissions from the electricity used to move the train. Upstream emissions can 

vary as a percentage of overall fossil fuel emissions and is largely dependent on the source of the 

fossil fuel.12 The source of the oil itself tends to dictate the upstream emissions more than the 

refined end product, thus upstream emissions would be relative to the amount of fuel used, and 

thus be comparable percentages between the modes.

12 Upstream Emissions as a Percentage of Overall Lifecycle Emissions | World Resources Institute (wri.org), 2016. 
18

4. Summary and Recommendations

This analysis was a first step in exploring the role rail can play in achieving U.S. DOT’s goal of 

economy-wide net-zero emissions by 2050, including the specific strategy of reducing GHG 

emissions from the transportation sector. The evaluation assumed current fleets and 

infrastructure and the present-day grid mix of sources for power generation, and only considered 

the impact of passengers traveling by different modes to the same destinations. Even within this 

limited scope, it is evident that rail can play a significant role in reducing operational CO2

emissions in the U.S., where policies and programs that promote shifts to less carbon intensive 

modes could tap into this existing potential right now.

While shifting passenger travel from road and air to rail would reduce GHG emissions, it is 

important to realize traveler decision making when it comes to mode switch is complex. There 

are several factors that a person may consider when opting to drive instead of taking the train 

besides impact on the environment – travel time, cost, accessibility, convenience, among others. 

To fully understand the potential rail can play on reducing GHGs in the U.S., cost-benefit 

analyses could consider these other decision-influencing factors at a personal level. Additionally, 

more accurate representations of the true travel experience should be included in the analysis. 

For example, traveling by road through high-traffic corridors or in urban environments with lots 

of starts and stops, or connecting flights and trains. Not only could these real-life experiences 

amplify or dampen the potential GHG savings, but they could also feed into a person’s decision 

to take one mode over another.

Shifting to less carbon intensive modes with present-day fleets and infrastructure can have 

immediate benefits, but to understand where the true GHG savings are in the transportation 

sector, the full lifecycle of each mode should be considered. This includes, but is not limited to, 

emissions from the construction and maintenance of infrastructure, supporting equipment (e.g., 

baggage tractors at airports and train stations), manufacture of vehicles (for any mode), fuel 

production or electricity generation, and the recycling or disposal of vehicles and infrastructure 

at the end of life. Sensitivity analyses may be conducted to understand how alternative fuels 

(e.g., biofuels and hydrogen) and use of renewable technologies for power generation (e.g., wind 

and solar), or new technologies (e.g., battery-electric or fuel cells) impact lifecycle GHG 

emissions by mode. Additionally, GHG goals outside of U.S. DOT, such as Amtrak’s goal of 

100 percent carbon-free electricity by 2030 and net-zero GHG emissions across their network by 

2045, may speed up the adoption of cleaner technologies. Emerging travel modes are also worth 

evaluating, such as urban air mobility, the hyperloop, and high-speed electric rail in the U.S. 

Additionally, travel demand modeling may elucidate the potential impact these new technologies 

and travel options could have on transportation based GHG emissions.

Other near-future factors and changes will alter this analysis as the fuel sources and efficiency of 

vehicles are undergoing dramatic change. For example, Amtrak purchased Tier 4 locomotives, 

that, while focusing on EPA criteria emission reductions, may produce efficiencies not reflected 

in the 2019 data, as the locomotives only recently went into service (in the Chicago area). Air 

carriers are tracking fossil-fuel replacements with more sustainable aviation fuels with the goal 

of eliminating GHG emissions. Also, the fleet of U.S. vehicles continues to become more 

efficient with an increasing number of electric cars entering the mix, which could reduce GHG 

emissions estimates from the MOVES model. And to note, sources for electricity are 

increasingly coming from renewable energy sources and electrification is widely seen as the 
19

pathway for zero emissions in the transportation sector. Once all modes are using renewable 

fuels, the CO2 emissions comparison would result in very small differences in CO2 emissions. 

However, for the near and medium term, modal shift to rail from road and air can produce a 

substantial reduction in passenger GHG emissions for intercity travel.

Rail will certainly have a role to play in transitioning the U.S. to economy-wide net-zero 

emissions, with new technologies potentially amplifying the GHG savings operationally and over 

the lifecycle. Rail also has several other sustainability benefits over roadway travel including a 

smaller footprint, fewer resources for the production of vehicles, safety, and promoting denser, 

infrastructure-oriented development. Future research would help refine how best to realize rail’s 

GHG emissions reduction potential and additional sustainability benefits.
20

5. References

Baumeister, S. (2019). Replacing short-haul flights with land-based transportation modes to 

reduce greenhouse gas emissions: The case of Finland. Journal of Cleaner Production, 

225, 262–269. https://doi.org/10.1016/j.jclepro.2019.03.329

Baumeister, S., & Leung, A. (2021). The emissions reduction potential of substituting short-haul 

flights with non-high-speed rail (NHSR): The case of Finland. Case Studies on Transport 

Policy, 9(1), 40–50. https://doi.org/10.1016/j.cstp.2020.07.001

Chester, M., & Horvath, A. (2010). Life-cycle assessment of high-speed rail: The case of 

California. Environmental Research Letters, 5(1), 014003. https://doi.org/10.1088/1748-

9326/5/1/014003

Dimoula, V., Kehagia, F., & Tsakalidis, A. (2016). A Holistic Approach for Estimating Carbon 

Emissions of Road and Rail Transport Systems. Aerosol and Air Quality Research, 16(1), 

61–68. https://doi.org/10.4209/aaqr.2015.05.0313

Graver, B. M., & Frey, H. C. (2016). Highway Vehicle Emissions Avoided by Diesel Passenger 

Rail Service Based on Real-World Data. Urban Rail Transit, 2(3), 153–171. 

https://doi.org/10.1007/s40864-016-0044-y 

Inderbitzin, R. (2019). Switzerland: Railway or aviation nation? Emission saving potential from 

replacing air by train travel between Switzerland and Europe and the possibilities for the 

Swiss government to foster this mode shift. Master Thesis Series in Environmental 

Studies and Sustainability Science. http://lup.lub.lu.se/student-papers/record/8998563

Kapetanović, M., van Oort, N., Núñez, A., & Goverde, R. (2019). Sustainability of Railway 

Passenger Services – A Review of Aspects, Issues, Contributions and Challenges of Life 

Cycle Emissions. RailNorrköping 2019, 21.

Meynerts, L., Brito, J., Ribeiro, I., Peças, P., Claus, S., & Götze, U. (2018). Life Cycle 

Assessment of a Hybrid Train – Comparison of Different Propulsion Systems. Procedia 

CIRP, 69, 511–516. https://doi.org/10.1016/j.procir.2017.11.035

Miller, C. A. (2020). Savings in per-passenger CO2 emissions using rail rather than air travel in 

the northeastern U.S. Journal of the Air & Waste Management Association, 0(0), 1–14. 

https://doi.org/10.1080/10962247.2020.1837996

Trevisan, L., & Bordignon, M. (2020). Screening Life Cycle Assessment to compare CO2 and 

Greenhouse Gases emissions of air, road, and rail transport: An exploratory study. 

Procedia CIRP, 90, 303–309. https://doi.org/10.1016/j.procir.2020.01.100
21

Appendix A. 

Literature Review

Source: C.A. Miller (2020). Savings in per-passenger CO2 emissions using rail rather than 

air travel in the northeastern U.S. Journal of the Air & Waste Management Association

Overview: This study examined 120 city pairs in the Northeastern U.S. and compared CO2 

emissions associated with air travel versus emissions from rail travel. The report compared flight 

emissions (calculated using the International Civil Aviation Organization (ICAO) flight 

emissions calculator) with Amtrak emissions calculated using the Environmental Protection 

Agency’s (EPA) Simplified GHG Emissions Calculator. 

Description of model and method development: Air travel CO2 emissions were calculated using 

the ICAO Carbon Emissions Calculator. The ICAO flight emissions calculator was used to 

determine emissions from air travel based on flight origin and destination. The ICAO calculator 

estimates CO2 mass for a single passenger on the journey, assuming one-way direct flights, 

economy-class, and great circle distance for each city pairs. Miller divided CO2 mass by flight 

distance to determine the pounds (lb) of CO2 emitted per passenger-mile. For rail travel, an 

average emission factor for electric locomotives was calculated using electricity generation by 

state, multiplied by the Bureau of Transportation Statistics’ estimate of energy used (in kilowatt 

hours, or kWh) for Amtrak travel. An average emission factor for diesel locomotives was 

calculated using reported diesel fuel consumption and an average CO2 per gallon diesel (i.e., 

22.40 lb CO2). The allocations of passenger miles on electrified and diesel routes were calculated 

using Rail Passenger Association statistics. Then, an average emission factor was calculated by 

assuming the ratio of passenger-miles on electric-powered Amtrak routes to passenger-miles on 

diesel-powered Amtrak routes was equivalent to the ratio between miles of electric-powered 

routes to the miles of diesel-powered routes. An adjustment factor was used to address longer rail 

distances than flight distances.

Emissions factors and data sources: Amtrak timetables for train distances;13 Amtrak reported 

diesel fuel used in revenue operation;14 Annual electricity consumption by Amtrak, passengermiles traveled on Amtrak;15 EPA electricity generation and emissions by state;16 Rail ridership 

by state.17

Assumptions and limitations: This analysis only explored CO2 emissions (no additional GHGs) 

and did not include travel to and from airports and train stations (i.e., “last mile” calculations). 

Additionally, all flights were assumed to be one-way direct flights and economy class. Road 

13 https://www.amtrak.com/train-schedules-timetables.

14 https://www.amtrak.com/content/dam/projects/dotcom/english/public/documents/environmental1/AmtrakSustainability-Report-FY18.pdf.

15 https://www.bts.gov/content/rail-profile. 

16 https://www.epa.gov/egrid. 

17 https://www.railpassengers.org/resources/ridership-statistics/. 
22

travel was not included in this analysis. The scope of the report was limited to only the 

northeastern United States.

Source: M. Chester and A. Horvath (2010). Life-Cycle Assessment of High-Speed Rail: The 

Case of California. Environmental Research Letters

Overview: This analysis compared lifecycle emissions from air, rail, and road travel in 

California. For each mode of travel, emissions were calculated in CO2 equivalents (CO2e) from 

separate emissions calculations of sulfur dioxide, carbon monoxide (CO), nitrogen oxides, 

volatile organic compounds, and particulate matter (PM) ≤10 micrometers in diameter.

Description of model and method development: For rail emissions estimates, a California High 

Speed Rail (CAHSR) inventory was developed by estimating energy consumption and 

subsequent emissions from vehicle, infrastructure, and electricity production components. 

Electricity consumption was based on the German InterCity Express (ICE) HSR. Lifecycle 

emissions were estimated using the SimaPro life-cycle assessment (LCA) calculator and 

Ecoinvent database, which includes emissions from station construction, station energy usage, 

maintenance, and other components of the rail lifecycle. Both low and high occupancy scenarios 

were evaluated. Ridership numbers were based on CAHSR statistics and estimates. For aircraft 

emissions, small, mid-size, and large aircraft sizes were modeled. For each LCA component, 

environmental performance was calculated per passenger-kilometer traveled. Total emissions 

from all actives were calculated using an equation from Chester and Hovath 2009.18 For road 

travel, emissions were estimated by evaluating vehicle, infrastructure, and fuel components. A 

lifecycle inventory was provided which broke down the components of rail emissions. Both low 

and high occupancy scenarios were evaluated. 

Emissions factors and data sources: Lifecycle inventory of automobiles and aircraft;19 SimaPro

LCA calculator;20 Ecoinvent lifecycle inventory database.21

Assumptions and limitations: This analysis used a consistent vehicle electricity consumption 

estimate of 170 kWh for rail travel, based on the electricity consumption of the German ICE 

HSR. The Western Electricity Coordinating Council electricity mix factors were used to 

calculate emissions from electricity usage. 

18 https://iopscience.iop.org/article/10.1088/1748-9326/4/2/024008/pdf. 

19 https://iopscience.iop.org/article/10.1088/1748-9326/4/2/024008. 

20 https://simapro.com/. 

21 https://ecoinvent.org/. 
23

Source: S. Baumeister and A. Leung (2021). The Emissions Reduction Potential of 

Substituting Short-Haul Flights with Non-High-Speed Rail (NHSR): The Case of Finland. 

Case Studies on Transport Policy

Overview: This analysis evaluated CO2e emissions for short-haul flights and non-high-speed rail 

in Finland. Sixteen city pairs were analyzed and emissions as well as travel time were included 

from door-to-door (last mile). 

Description of model and method development: All trains in this study were electric, and the 

only emissions considered were those emitted from electricity production. The LIPASTO 

(Finnish acronym for Liikenteen Päästöinventaario, which translates to ‘Transport Emissions 

Inventory’ in English) unit emissions database was used to find the electricity consumption per 

passenger-kilometer of different train types. Calculations were made for both CO2 and methane 

(CH4) emissions from electricity production based on predetermined estimates of grams released 

per kWh. A real travel time was calculated by adding additional time to account for arriving at 

the station and finding the correct platform. CO2e emissions were calculated based on aircraft 

type, route, and great circle distance to the base airport. A lengthening factor was applied for 

short-haul flights to account for stacking, traffic, and weather. Aircraft fuel data were extracted 

from the European Environment Agency Air Pollutant Emissions Inventory Guidebook, using 

emissions factors based on emissions per kilogram fuel burned. Separate calculations were made 

for CO2, CH4, and nitrous oxide (N2O) emissions based on assumed values from the LIPASTO 

unit emissions database. Per-passenger emissions were calculated by dividing by number of seats 

on the plane and multiplying by the average load factor. Emissions from buses were calculated 

using an average vehicle mass and capacity. Car occupancy was set to an average number of 

passengers, and diesel car mileage was set to the country average. Distance and travel time were 

calculated using the Google Maps route planner, assuming the fastest and most direct routes. 

Emissions data were provided by the LIPASTO database. The CO2e emissions per passenger in 

kilograms for all transportation modes were calculated based on the Intergovernmental Panel on 

Climate Change Fifth Assessment Report.

Emissions factors and data sources: LIPASTO unit emissions database for passenger and 

freight transport in Finland;22 Intergovernmental Panel on Climate Change Fifth Assessment 

Report.23

Assumptions and limitations: This analysis was based on rail travel in Finland, so specific 

emissions factors may not be directly applicable to transportation systems in the United States. 

The calculations were focused on non-high-speed rail. Average load factors were used for each 

mode, and diesel car mileage was assumed to be the country average. There was a heavy reliance 

on the LIPASTO unit emissions database, which provides data specific to Finland.

22 http://lipasto.vtt.fi/yksikkopaastot/index.htm. 

23 https://www.ipcc.ch/report/ar5/syr/. 
24

Source: S. Baumeister (2019). Replacing Short-Haul Flights with Land-Based 

Transportation Modes to Reduce Greenhouse Gas Emissions: The Case of Finland, Journal 

of Cleaner Production

Overview: This analysis evaluated the GHG reduction potential of replacing air travel with travel 

by rail and road in Finland. Emissions and travel time were included for door-to-door (last mile) 

travel.

Description of model and method development: CO2e emissions per passenger were calculated 

based on LIPASTO unit emissions database (specific to Finland). The emissions from rail were 

based on emissions released from electricity production. Both CO2 and CH4 emissions were 

considered. A real travel time was calculated by adding additional time to account for arriving at 

the station and finding the correct platform. The analysis used per passenger-kilometer CO2e 

emissions values based on flight route length (classified as short or long distance). Real travel 

time was added for check-in. CO2e emissions per passenger were calculated based on LIPASTO 

unit emissions database (specific to Finland).

Emissions factors and data sources: LIPASTO unit emissions database for passenger and 

freight transport in Finland;24 Intergovernmental Panel on Climate Change Fifth Assessment 

Report.25

Assumptions and limitations: This analysis was based on rail travel in Finland, so specific 

emissions factors may not be equivalent to transportation systems in the United States.

Source: B.M. Graver and H.C. Frey (2016). Highway Vehicle Emissions Avoided by Diesel 

Passenger Rail Service Based on Real-World Data. Urban Rail Transit

Overview: This analysis evaluated CO2, CO, hydrocarbons, nitrogen oxide, and PM emissions 

associated with passenger rail service between Raleigh and Charlotte, NC. Rail emissions were 

calculated directly from exhaust pipe emissions measurements and compared to road emissions 

estimated from the EPA’s Motor Vehicle Emissions Simulator (MOVES). 

Description of model and method development: Per passenger-kilometer locomotive emission 

factors were quantified based on measured exhaust concentrations (using a portable emissions 

measurement system), engine activity data, and locomotive duty cycles observed during 

passenger rail service. Exhaust emissions concentrations were measured from the exhaust pipe 

during rail service. EPA’s MOVES was used to estimate fleet average emission factors from 

light-duty gasoline vehicles. Input data related to the distributions of vehicle type and age, fuel 

type, emissions inspection compliance, and meteorology were obtained from the Division of Air 

Quality at the North Carolina Department of Environment and Natural Resources. Data were 

assumed to be representative of the state average for vehicle type, vehicle age, and fuel type.

24 http://lipasto.vtt.fi/yksikkopaastot/index.htm.

25 https://www.ipcc.ch/report/ar5/syr/.
25

Emissions factors and data sources: tailpipe measurements; MOVES.26

Assumptions and limitations: This analysis did not include a calculation of emissions from air 

travel. State averages were used for vehicle type, age, and fuel type in road travel calculations. 

Source: L. Meynerts, J. Brito, I. Ribeiro, P. Peças, S. Claus, and U. Götze (2018). Life Cycle 

Assessment of a Hybrid Train – Comparison of Different Propulsion Systems. Procedia 

CIRP 

Overview: This analysis developed an LCA for diesel trains, hybrid trains, and hybrid trains with 

recharging stations. Train types were compared using vehicle kilometers traveled over a period 

of 15 years.

Description of model and method development: This study evaluated the lifecycle impacts of 

three types of rail (diesel, hybrid, and hybrid with recharging stations). Lifecycle phases included 

raw material extraction, production, use, and end-of-life. For operational emissions, energy 

consumption quantities were calculated using timetables, route profile, performance/capacity,

and energy conversion efficiency. Emissions were estimated from energy quantities using 

SimaPro software and the Ecoinvent database. For emissions from electricity used by hybrid 

trains, the German electricity mix from 2014 was used (686 g CO2e/kWh). Emissions from diesel 

fuel generation and combustion were estimated to be 3,120 g CO2e/liter. The environmental 

impact analysis was used to measure total environmental impact for specific categories, such as 

climate change and ozone depletion.

Emissions factors and data sources: SimaPro LCA calculator;27 Ecoinvent life cycle inventory 

database.28

Assumptions and limitations: This analysis only examined rail travel. Assumptions about energy 

consumption were based on European energy consumption factors.

Source: L. Trevisan and M. Bordignon (2020). Screening Life Cycle Assessment to 

Compare CO2 and Greenhouse Gases Emissions of Air, Road, and Rail Transport: An 

Exploratory Study. Procedia CIRP

Overview: This literature review analyzed ten existing studies on GHG emissions from road, air, 

and trail transport, including lifecycle emissions. Studies were selected based on use of 

comparable vehicle types, indicators (i.e., energy consumption in megajoules and emissions in 

CO2e), and units expressed in passenger-kilometers.

26 MOVES and Other Mobile Source Emissions Models | US EPA.

27 https://simapro.com/.

28 https://ecoinvent.org/.
26

Description of model and method development: For each travel mode, a literature review of 

LCA emissions was developed. The analysis compared studies based on percent of total 

emissions calculated at each stage in the life cycle (operation, embedded vehicle, infrastructure, 

and upstream energy supply). Other factors that contribute to operational emissions were 

considered (e.g., type of vehicle, type of journey, gradients, and driving style) as well as 

demand/load factors by mode. The impact of infrastructure was also included for lifecycle 

analysis. 

Emissions factors and data sources: Online emissions calculators: EcoPassenger, 2020;29

Mobiltool, 2020;30 Transport Direct, 2020.31

Assumptions and limitations: This analysis focused on percent of emissions associated with 

each component of the life cycle of the transportation mode, rather than consistencies in overall 

generated emissions between studies. It was found that there was a heavy reliance on “ecocalculators” between studies, which were noted to have minimal detail for load factor 

calculations.

Source: M. Kapetanović, N. van Oort, A. Núñez, and R. Goverde (2019). Sustainability of 

Railway Passenger Services: A Review of Aspects, Issues, Contributions and Challenges of 

Life Cycle Emissions. RailNorrköping 2019

Overview: This analysis evaluated the holistic impact of railway service through an LCA. All 

GHG emissions were considered, with a focus on CO2 emissions. Detailed steps for a life cycle 

"Well to Wheel" (WTW) analysis was included.

Description of model and method development: Emissions were categorized into direct 

emissions (from diesel consumption) and indirect emissions (from energy carrier production, 

maintenance, construction, etc.). Consumption emissions from studies which used direct 

emissions measurements from testing engines were compared. No specific emissions factors 

were listed from these studies. An additional analysis compared various numerical emissions 

calculations. Researchers found resistance to be the main calculation component for energy 

consumption, representing inertial and grade resistances as well as running resistances based on 

train characteristics. The energy consumption needed to overcome resistance was multiplied by 

emissions factors to get total emissions. Three quantitative emissions models were compared: 

International Union of Railways Method, Rail Safety Standards and Board Method, and the 

Assessment and Reliability of Transport Emission Models and Inventory Systems (ARTEMIS) 

Rail Emissions model. The comparison included equations used for energy calculations in each 

method. A review of rail LCA and its challenges was also included. 

29 http://www.ecopassenger.org/bin/query.exe/en?L=vs_uic. 

30 https://www.mobitool.ch/. 

31 https://www.transportdirect.info/. 
27

Emissions factors and data sources: Emissions models reviewed: International Union of 

Railways Method, Rail Safety Standards and Board Method, and the ARTEMIS Rail Emissions 

model.

Assumptions and limitations: This analysis only examined rail emissions. Note, some of the 

information required for the emissions models used in this analysis could be challenging to 

obtain for a comparable U.S.-based analysis. 

Source: R. Inderbitzin (2019). Switzerland: Railway or Aviation Nation? Emission Saving 

Potential from Replacing Air by Train Travel between Switzerland and Europe and the 

Possibilities for the Swiss Government to Foster This Mode Shift. Master Thesis Series in

Environmental Studies and Sustainability Science

Overview: This analysis examined the emission-saving potential of replacing air travel with rail 

travel in Switzerland and Europe.

Description of model and method development: Rail distances and travel times were calculated 

using the Google Distance Matrix application programming interface. Emissions were estimated 

using the Ecopassenger emissions calculator, which uses data from the International Union of 

Railways CO2 and Energy Database. The calculator separates rail into high-speed, intercity, and 

regional/urban travel. Air travel calculations used data from the Swiss Federal Statistical Office 

for all air travel statistics from 2018. Travel time was calculated by adding time for the 

train/drive to the airport, waiting time at the airport, flight time, check-out time at arrival airport, 

and train/drive to the destination. These were calculated using Google Maps, from city center to 

city center. The Ecopassenger emissions calculator was used to calculate flight emissions. This 

calculator uses flight distances and accounts for the average fleet mix from German airports as 

well as aircraft types, load factor, and radiative forcing.

Emissions factors and data sources: Ecopassenger.32

Assumptions and limitations: This analysis did not include road travel. Emission calculations 

were based on European-specific consumption factors and statistics. 

Source: V. Dimoula, F. Kehagia, and A. Tsakalidis (2016). A Holistic Approach for 

Estimating Carbon Emissions of Road and Rail Transport Systems. Aerosol and Air Quality 

Research

Overview: Analysis of emissions from construction and operation of road and rail infrastructure 

in Greece.

Description of model and method development: Rail analysis included 

construction/infrastructure (track, ballast, stations, tunnels, bridges, signaling, 

telecommunications, manufacturing) and use/operations for a rail system. The calculations used 

32 http://www.ecopassenger.org/bin/query.exe/en?L=vs_uic.
28

the length of the track and the materials used for the track. Infrastructure and construction 

analysis synthesized previous studies to calculate an average tons of CO2e/km per year from 

previous research. The analysis divided the rail line into smaller sections, treated all trips as 

nonstop, and used a simplified fuel consumption equation for passenger trains based on the 

locomotive type. Gradient of the vertical alignment of the route was considered when looking at 

fuel consumption. An average train occupancy of 75 percent was used to estimate emissions per 

km. Freight trains were analyzed separately from passenger trains. Road emissions analysis 

included road construction, extraction of raw materials, processing/transport of materials, 

operation and maintenance, and disposal after use. Infrastructure and construction analysis 

synthesized previous studies to calculate an average tons of CO2e/km per year. For operations, an 

annual traffic volume was estimated. Buses were not included in the analysis; only gasoline cars 

were included as they were considered at the top of consumers' preferences in Greece. Engines 

were placed into three displacement categories, and CO2 emissions per category were estimated 

based on data from manufacturing companies. Total passenger-kilometer estimates were based 

on observatory records using a consistent passenger load factor of 1.67. Freight road transport 

was included, with fuel consumption per vehicle taken from McKinnon (2009). Vehicles were 

categorized as passenger transport, multi-axel vehicles, and three-axel vehicles. 

Emissions factors and data sources: McKinnon (2009) road freight transport stats.33

Assumptions and limitations: This study focused on rail and road emissions. Emission 

calculations were based on consumption factors and statistics in Greece.

33 https://www.semanticscholar.org/paper/Benchmarking-road-freight-transport%3A-Review-of-aMcKinnon/95fceb80f1b9c7a42ea080a47c45723449dacf47. 
29

Appendix B.

Power Grid Assumptions

Similar to Miller (2020), this analysis assumed that the electricity used for electrified portion of 

the Amtrak system was generated within the states through which the electrified rails ran. Table 

B1 below reports data provided from the Environmental Protection Agency’s (EPA) eGRID data 

used in this analysis – reported in megawatt-hours (MWh). These data were used to calculate the

average emission factor for electricity along the electrified portion of the Amtrak rail system. 

Table B1. Net electricity generation (2019) and associated CO2 emission rate from states 

along Amtrak’s Northeast Corridor route, from EPA’s eGRID.

State Output emission 

rate (kg CO2/MWh)

Net generation 

(MWh)

CT 216 40,050,038

DC 362 174,080

DE 323 5,258,538

MA 351 21,513,220

MD 334 39,325,596

NJ 247 70,988,176

NY 171 131,568,023

PA 343 228,994,207

RI 387 7,624,238
30

Abbreviations and Acronyms

ACRONYMS EXPLANATION

ARTEMIS Assessment and Reliability of Transport Emission Models and 

Inventory Systems

BTS Bureau of Transportation Statistics

CH4 Methane

CO Carbon monoxide

CO2 Carbon dioxide

CO2e Carbon dioxide equivalents

DOT Department of Transportation

eGRID Emissions & Generation Resource Integrated Database

EPA Environmental Protection Agency

FRA Federal Railroad Administration

GHG Greenhouse gas

GREET Greenhouse gases, Regulated Emissions, and Energy use in 

Technology

ICAO International Civil Aviation Organization

ICE InterCity Express

kWh Kilowatt hour

lb Pound

LCA Life-cycle assessment

LIPASTO Liikenteen Päästöinventaario (Finnish for ‘Transport Emissions 

Inventory’)

MOVES Motor Vehicle Emissions Simulator

MWh Megawatt hour

N2O Nitrous oxide

PM Particulate matter

SOV Single occupancy vehicle

VMT Vehicle miles traveled

Volpe John A. Volpe National Transportation Systems Center