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

May 2008

Assessment and Standards Division 

Office of Transportation and Air Quality 

U.S. Environmental Protection Agency

Regulatory Impact Analysis:

Control of Emissions of Air Pollution 

from Locomotive Engines and Marine 

Compression Ignition Engines Less than 

30 Liters Per Cylinder 
Table of Contents 

Executive Summary 

Chapter 1: Industry Characterization 

Chapter 2: Air Quality and Resulting Health and Welfare Effects 

Chapter 3: Emission Inventory 

Chapter 4: Technological Feasibility 

Chapter 5: Engineering Cost Estimates 

Chapter 6: Cost-Benefit Analysis 

Chapter 7: Economic Impact Analysis 

Chapter 8: Alternatives 

ii
List of Acronyms 

μm Micrometers 

bext Light-Extinction Coefficient 

μg Microgram 

μg/m3 Microgram per Cubic Meter 

AAR Association of American Railroads 

ABT Average Banking and Trading 

ACS American Cancer Society 

AEO Annual Energy Outlook (an EIA publication) 

AESS Automatic Engine Stop/Start System 

AIM Aerosol Inorganics Model 

AIRS Aerometric Information Retrieval System 

APHEA Air Pollution and Health: A European Approach 

APU Auxiliary Power Unit 

AQ Air Quality 

AQCD Air Quality Criteria Document 

AQMTSD Air Quality Modeling Technical Support Document 

ARB Air Resources Board (California) 

ASLRRA American Short Line and Regional Railroad Association 

ASPEN Assessment System for Population Exposure Nationwide 

ATAC Average Total Cost 

avg Average 

BenMAP Benefits Mapping and Analysis Program 

bhp Brake Horsepower 

BNSF Burlington Northern Santa Fe 

BSFC Brake Specific Fuel Consumption 

BTS Bureau of Transportation 

C Celsius 

C1 Category 1 

C2 Category 2 

C3 Category 3 

CA California 

CAA Clean Air Act 

CAIR Clean Air Interstate Rule (CAIR) (70 FR 25162, May 12, 2005) 

CAMR Clean Air Mercury Rule 

CAND Clean Air Nonroad Diesel rule (69 FR 38957, June 29, 2004) 

CARB California Air Resources Board 

CASAC Clean Air Scientific Advisory Committee 

CAVR Clean Air Visibility Rule 

CB Chronic Bronchitis 

CCV Closed Crankcase Ventilation 

CDC Centers for Disease Control 

CDPF Catalyzed Diesel Particulate Filter 

CEA Cost Effective Analysis 

CES Constant Elasticity of Substitution 

CFR Code of Federal Regulations 

CI Compression Ignition (i.e., diesel engines) 

CI Confidence Interval 

CIMT Carotid Intima-Media Thickness 

CITT Chemical Industry Institute of Toxicology 

CMAQ Community Multiscale Air Quality 

CMB Chemical Mass Balance 

CN Canadian National Railroad 

CO Carbon Monoxide 

CO2 Carbon Dioxide 

iii
COI Cost of Illness 

COPD Chronic Obstructive Pulmonary Disease 

CPI-U Consumer Price Index - All Urban Consumers 

C-R Concentration Response 

CSS Coastal Sage Scrub 

CUA Cost Utility Analysis 

cyl Cylinder 

D Demand 

DE Diesel Exhaust 

DEM Domestic Engine Manufacturer 

DDHS Diesel Driven Heating System 

diff Difference 

disp Displacement 

DOC Diesel Oxidation Catalyst 

DOE Department of Energy 

DOT Department of Transportation 

DPF Diesel Particulate Filter 

DPM Diesel Particulate Matter 

DR Discount Rate 

DRIA Draft Regulatory Impact Analysis 

DV Design Values 

EAC Early Action Component 

EC Elemental Carbon 

EDHS Electric Driven Heating System 

EF Emission Factor 

EGR Exhaust Gas Recirculation 

EIA Energy Information Administration (part of the U.S. Department of Energy) 

EIA Economic Impact Analysis 

EIM Economic Impact Model 

EMD Electromotive Diesel 

EMS-HAP Emissions Modeling System for Hazardous Air Pollution 

EO Executive Order 

EPA Environmental Protection Agency 

EPAct Energy Policy Act of 2005 

ESPN EPA speciation network 

F Fahrenheit 

FEM Foreign Engine Manufacturer 

FEV Functional Expiratory Volume 

FR Federal Register 

FRA Federal Railroad Administration 

FRM Final Rulemaking 

FRP Fiberglass-Reinforced Plastic 

g Gram 

g/bhp-hr Grams per Brake Horsepower Hour 

g/kW-hr Grams per Kilowatt Hour 

gal Gallon 

GAO Government Accountability Office 

GDP Gross Domestic Product 

GEOS Goddard Earth Observing System 

GETS General Electric Transportation Systems 

GIS Geographic Information System 

H2 Hydrogen Gas 

HAD Diesel Health Assessment Document 

HAP Hazardous Air Pollutant 

HC Hydrocarbon 

HD Heavy-Duty 

iv
HEI Health Effects Institute 

HEP Head End Power 

HES Health Effects Subcommittee 

hp Horsepower 

hp-hrs Horsepower Hours 

hrs Hours 

IARC International Agency for Research on Cancer 

ICD International Classification of Diseases 

IMO International Maritime Organization 

IMPROVE Interagency Monitoring of Protected Visual Environments 

IRIS Integrated Risk Information System 

ISCST3 Industrial Source Complex Short Term Model 

ISORROPIA Inorganic Aerosol Thermodynamic Model 

JAMA Journal of the American Medical Association 

K Kelvin 

k Thousand 

km Kilometer 

kW Kilowatt 

kWH Kilowatt Hour 

L Liter 

lb Pound 

LM Locomotive and Marine 

LRS Lower Respiratory Symptoms 

LSD Low Sulfur Diesel fuel 

m3 Cubic Meters 

MARAD U.S. Maritime Administration 

MARPOL The International Convention for the Prevention of Pollution of Ships 

MC Marginal Cost 

MCIP Meteorology-Chemistry Interface Processor 

MECA Manufacturers of Emission Controls Association 

mg Milligram 

MI Myocardial Infarction 

MILY Morbidity Inclusive Life Years 

min Minute 

MM Million 

MM-1 Inverse Megameter 

MOBILE6 Vehicle Emission Modeling Software 

MRAD Minor Restricted Activity Days 

MSAT Mobile Source Air Toxic 

MSAT1 2001 Mobile Source Air Toxics Rule 

MSB Major Shipbuilding Base 

MVUS Merchant Vessels of the U. S. 

MW Megawatt 

MW-hrs Megawatt Hours 

N Nitrogen 

N2 Nitrogen Molecule 

NA Not Applicable 

NAAQS National Ambient Air Quality Standards 

NAICS North American Industry Classification System 

NAS National Academy of Sciences 

NASA National Aeronautics and Space Administration 

NATA National Air Toxic Assessment 

NBER National Bureau of Economic Research 

NCDC National Clean Diesel Campaign 

NCI National Cancer Institute 

NCLAN National Crop Loss Assessment Network 

v
NEI National Emissions Inventory 

NESCAUM Northeast States for Coordinated Air Use Management 

NESHAP National Emissions Standards for Hazardous Air Pollutants 

NH3 Ammonia 

NIOSH National Institute of Occupational Safety and Health 

NLEV National Low Emission Vehicle 

NMHC Nonmethane Hydrocarbons 

NMIM National Mobile Inventory Model (EPA software tool) 

NMIM2005 National Mobile Inventory Model Released in 2005 

NMMA National Marine Manufacturers Association 

NMMAPS National Morbidity, Mortality, and Air Pollution Study 

NO Nitrogen Oxide 

NO2 Nitrogen Dioxide 

NOAA National Oceanic and Atmospheric Administration 

NONROAD EPA’s Non-road Engine Emission Model 

NONROAD2005 EPA’s Non-road Engine Emission Model Released in 2005 

NOx Oxides of Nitrogen 

NPRM Notice of Proposed Rulemaking 

NPV Net Present Value 

NRC National Research Council 

NREC National Railway Equipment Co 

NRLM Nonroad, Locomotive and Marine diesel fuel 

NRT4 Nonroad Tier 4 Rule 

NSTC National Science and Technology Council 

NTE Not To Exceed 

O&M Operating and maintenance 

O3 Ozone 

OAQPS Office of Air Quality Planning and Standards 

OC Organic Carbon 

oCA Degree Crank Angle 

OEHHA Office of Environmental Health Hazard Assessment 

OEM Original Equipment Manufacturer 

OMB Office of Management and Budget 

OTAQ Office of Transportation and Air Quality 

P Price 

PAH Polycyclic Aromatic Hydrocarbons 

PCB Polychlorinated Biphenyls 

PGM Platinum Metals Group 

PM Particulate Matter 

PM AQCD EPA Particulate Matter Air Quality Criteria Document 

PM/NMHC Particulate Matter to Non-Methane Hydrocarbon Ratio 

PM10 Coarse Particulate Matter (diameter of 10 μm or less) 

PM2.5 Fine Particulate Matter (diameter of 2.5 μm or less) 

PMM Post-Manufacturer Marinizer 

PMNAAQS Particulate Matter National Ambient Air Quality Standards 

POM Polycyclic Organic Matter 

ppb Parts per Billion 

PPI Producer Price Index 

ppm Parts per Million 

psi Pounds per Square Inch 

PSR Power Systems Research 

Q Quantity 

QALY Quality Adjusted Life Years 

R&D Research and Development 

RfC Reference Concentration 

RFA Regulatory Flexibility Analysis 

vi
RFS Renewable Fuels Standard 

RIA Regulatory Impact Analysis 

rpm Revolutions per Minute 

RPO Regional Planning Organization 

RRF Relative Reduction Factors 

RV Revision 

RVP Reid Vapor Pressure 

S Sulfur 

S Supply 

SAB Science Advisory Board 

SAB-HES Science Advisory Board - Health Effects Subcommittee 

SAE Society of Automotive Engineers 

SAPS Sulfated-Ash, Phosphorus, and Sulfur Content 

SBA Small Business Administration 

SBREFA Small Business Regulatory Enforcement Fairness Act 

SCC Source Classification Code 

SCR Selective Catalyst Reduction 

SI Spark Ignition 

SIC Standard Industrial Classification 

SiC Silicon Carbide 

SMAT Speciated Modeled Attainment Test 

SO2 Sulfur Dioxide 

SOx Oxides of Sulfur 

SOA Secondary Organic Carbon Aerosols 

SOF Soluble Organic Fraction 

STB Surface Transportation Board 

SVOC Semi-Volatile Organic Compound 

SwRI Southwest Research Institute 

TBN Total Base Number 

TCC Total Compliance Cost 

TCM Total Carbon Mass 

TDC Top Dead Center 

TFM Transportacion Ferroviaria Mexicana 

THC Total Hydrocarbon 

TSD Technical Support Document 

TVCC Total Variable Compliance Cost 

ULSD Ultra Low Sulfur Diesel fuel 

UP Union Pacific Railroad 

URS Upper Respiratory Symptoms 

USDA United States Department of Agriculture 

UV Ultraviolet 

UV-b Ultraviolet-b 

VOC Volatile Organic Compound 

VOF Volatile Organic Fraction 

VSL Value of Statistical Life 

WLD Work Loss Days 

WTP Willingness-to-Pay 

$2,005 U.S. Dollars in calendar year 2005 

vii
viii

This Page Intentionally Left Blank 
Executive Summary 

Executive Summary 

The Environmental Protection Agency (EPA) is finalizing a comprehensive three-part 

program to reduce emissions of particulate matter (PM) and oxides of nitrogen (NOx) from 

locomotives and marine diesel engines below 30 liters per cylinder displacement. 

Locomotives and marine diesel engines designed to these more stringent standards will 

achieve PM reductions of 90 percent and NOx reductions of 80 percent, compared to engines 

meeting the current Tier 2 standards. These standards will also yield sizeable reductions in 

emissions of nonmethane hydrocarbons (NMHC), carbon monoxide (CO), and hazardous 

compounds known as air toxics. 

This program is part of EPA’s ongoing National Clean Diesel Campaign (NCDC) to 

reduce harmful emissions from diesel engines of all types. The anticipated emission 

reductions will significantly reduce exposure to harmful pollutants and also provide assistance 

to states and regions facing ozone and particulate air quality problems that are causing a range 

of adverse health effects, especially in terms of respiratory impairment and related illnesses. 

We project that by 2030, this program will reduce annual emissions of NOx and PM 

by 800,000 and 27,000 tons, respectively. The annual monetized PM2.5- and ozone-related 

health benefits of this rule in 2030 will range from $9.2 billion to $11 billion, assuming a 3 

percent discount rate, or between $8.4 billion to $10 billion, assuming a 7% discount rate. 

The estimated annual social cost of the program in 2030 is projected to be significantly less, at 

$740 million. 

This Regulatory Impact Analysis provides technical, economic, and environmental 

analyses of the emission standards. Chapter 1 provides industry characterization for both the 

locomotive and marine industry. Chapter 2 presents air quality modeling results and 

describes the health and welfare effects associated with particulate matter (PM), ozone, and 

air toxics. Chapter 3 provides our estimates of the current emission inventories and the 

reductions that can be expected from implementation of the more stringent standards. 

Chapter 4 contains our technical feasibility justification for the emission limits, and Chapter 5 

contains the estimated costs of complying with those standards. Chapter 6 presents the 

estimated societal benefits of the rulemaking. Chapter 7 contains our estimates of the market 

impacts of the more stringent standards and the distribution of costs among stakeholders. 

Finally, Chapter 8 contains our analysis of several alternative control scenarios we considered 

during the development of this rulemaking. 

ES-1 
Regulatory Impact Analysis 

1. Emission Standards 

The program we are finalizing addresses emissions from all types of diesel 

locomotives, including line-haul, switch, and passenger rail, and all types of marine diesel 

engines below 30 liters per cylinder displacement (collectively called “marine diesel 

engines.”).A These include marine propulsion engines used on vessels from recreational and 

small fishing boats to super-yachts, tugs and Great Lakes freighters, and marine auxiliary 

engines ranging from small gensets to large generators on ocean-going vessels. Each of these 

markets is described in Chapter 1. 

We are finalizing a comprehensive three-part emission control program for 

locomotives and for marine diesel engines that will dramatically reduce the emissions from 

these sources. The standards and our technical feasibility justification are contained in 

Chapter 4. 

The first part consists of near-term engine-out emission standards, referred to as Tier 3 

standards, for newly-built locomotives and marine diesel engines. These standards reflect the 

application of engine-out PM and NOx reduction technologies and begin to phase in starting 

in 2009. The second part consists of longer-term standards, referred to as Tier 4 standards, for 

newly-built locomotives and over 600 kW marine diesel engines. These standards begin to 

take effect in 2015 for locomotives and in 2014 for marine diesel engines. . For most 

engines, these standards are similar in stringency to the final standards included in the 2007 

highway diesel and Clean Air Nonroad Diesel programs and are expected to require the use of 

high-efficiency aftertreatment systems to ensure compliance. These standards will be enabled 

by the availability of ultra-low sulfur diesel fuel (ULSD). Third, we are adopting more 

stringent emission standards for existing locomotives when they are remanufactured. Also 

included in this rulemaking are provisions to eliminate emissions from unnecessary 

locomotive idling and standards which apply to existing marine diesel engines over 600 kW 

when they are remanufactured. 

Locomotive Standards

The standards for newly-built line-haul, passenger, and switch locomotives and for 

existing 1973 and later Tier 0, Tier 1, and Tier 2 locomotives are set out in Tables 1 and 2. 

With some exceptions, these standards will apply to all locomotives that operate extensively 

within the United States. Exceptions include historic steam-powered locomotives and 

locomotives powered solely by an external source of electricity. 

 

A In this RIA, Amarine diesel engine@ refers to compression-ignition marine engines below 30 liters per cylinder 

displacement unless otherwise indicated. Engines at or above 30 liters per cylinder are being addressed in 

separate EPA actions. 

ES-2 
Executive Summary 

Table ES-1 – Standards for Line-Haul and Passenger Locomotives (g/bhp-hr) 

standards apply 

to: take effect in year: PM NOx HC 

Remanufactured 

Tier 0 & 1 

2008 as available, 

2010 required 0.22 7.4a 0.55 

Remanufactured 

Tier 2 

2008 as available, 

2013 required 0.10 5.5 0.30 

New Tier 3 2012 0.10 5.5 0.30 

New Tier 4 2015 0.03 1.3 0.14 

a For Tier 0 locomotives originally manufactured without a separate loop intake air cooling system, these 

standards are 8.0 and 1.00 g/bhp-hr for NOx and HC, respectively. 

Table ES-2 – Standards for Switch Locomotives (g/bhp-hr) 

standards apply 

to: take effect in year: PM NOx HC 

Remanufactured 

Tier 0 

2008 as available, 

2010 required 0.26 11.8 2.10 

Remanufactured 

Tier 1 

2008 as available, 

2010 required 0.26 11.0 1.20 

Remanufactured 

Tier 2 

2008 as available, 

2013 required 0.13 8.1 0.60 

New Tier 3 2011 0.10 5.0 0.60 

New Tier 4 2015 0.03 1.3 0.14 

Marine Standards

 The standards for newly-built marine diesel engines are set out in Tables 3, 4, 5, and 6. 

The Tier 3 standards will apply to all marine diesel engines with per cylinder displacement up 

to 30 liters. The Tier 4 standards will apply only to commercial marine diesel engines above 

600 kW. 

 For the purposes of this emission control program, Category 1 marine diesel engines 

are those with per cylinder displacement up to 7 liters. Category 2 marine diesel engines are 

those with per cylinder displacement from 7 to 30 liters. High power density engines are 

those with a power density above 35 kW/liter). 

ES-3 
Regulatory Impact Analysis 

 Table ES-3 – Tier 3 Standards for Marine Diesel C1 Commercial Standard Power Density 

MAXIMUM 

ENGINE 

POWER

L/CYLINDER PM

g/bhp-hr 

(g/kW-hr)

NOx+HCd

g/bhp-hr 

(g/kW-hr)

MODEL 

YEAR 

<19 kW <0.9 0.30 (0.40) 5.6 (7.5) 2009 

19 to <75 kW <0.9 0.22 (0.30) 5.6 (7.5) 2009 a

 0.22 (0.30)b 3.5 (4.7)b 2014 

<0.9 0.10 (0.14) 4.0 (5.4) 2012 

0.9- <1.2 0.09 (0.12) 4.0 (5.4) 2013 

1.2- <2.5 0.08 (0.11)c 4.2 (5.6) 2014 

2.5- <3.5 0.08 (0.11)c 4.2 (5.6) 2013 

75 to 3700 

kW 

3.5- <7.0 0.08 (0.11)c 4.3 (5.8) 2012 

Notes: 

a <75 kW engines at or above 0.9 L/cylinder are subject to the corresponding 75-3700 kW standards. 

b Option: 0.15 g/bhp-hr (0.20 g/kW-hr) PM / 4.3 g/bhp-hr (5.8 g/kW-hr) NOx+HC in 2014. c This standard level drops to 0.07 g/bhp-hr (0.10 g/kW-hr) in 2018 for <600 kW engines. 

d

 Tier 3 NOx+HC standards do not apply to 2000-3700 kW engines. 

Table ES-4 Tier 3 Standards for Marine Diesel C1 Recreational and Commercial High Power Density 

MAXIMUM 

ENGINE 

POWER

L/CYLINDER PM

g/bhp-hr 

(g/kW-hr)

NOx+HC 

g/bhp-hr 

(g/kW-hr)

MODEL 

YEAR 

<19 kW <0.9 0.30 (0.40) 5.6 (7.5) 2009 

19 to <75 kW <0.9 0.22 (0.30) 5.6 (7.5) 2009 a

 0.22 (0.30)b 3.5 (4.7)b 2014 

<0.9 0.11 (0.15) 4.3 (5.8) 2012 

0.9- <1.2 0.10 (0.14) 4.3 (5.8) 2013 

1.2- <2.5 0.09 (0.12) 4.3 (5.8) 2014 

2.5- <3.5 0.09 (0.12) 4.3 (5.8) 2013 

75 to <3700 

kW 

3.5- <7.0 0.08 (0.11) 4.3 (5.8) 2012 

Notes: 

a

 <75 kW engines at or above 0.9 L/cylinder are subject to the corresponding 75-3700 kW standards. 

b

 Option: 0.15 g/bhp-hr (0.20 g/kW-hr) PM / 4.3 g/bhp-hr (5.8 g/kW-hr) NOx+HC in 2014. 

ES-4 
Executive Summary 

Table ES-5 – Tier 3 Standards for Marine Diesel C2a

MAXIMUM 

ENGINE 

POWER 

L/CYLINDER PM

g/bhp-hr 

(g/kW-hr)

NOx+HCb

g/bhp-hr 

(g/kW-hr)

MODEL 

YEAR 

7- <15 0.10 (0.14) 4.6 (6.2) 2013 

15- <20 0.20 (0.27)c 6.5 (8.7)c 2014 

20- <25 0.20 (0.27) 7.3 (9.8) 2014 

< 3700 kW 

25- <30 0.20 (0.27) 8.2 (11.0) 2014 

Notes: 

a

 See note (c) of Table ES-6 for optional Tier 3/Tier 4 standards. 

b

 Tier 3 NOx+HC standards do not apply to 2000-3700 kW engines. 

c

 For engines below 3300 kW in this group, the PM Tier 3 standard is 0.25 g/bhp-hr (0.34 g/kW-hr). 

Table ES-6 – Tier 4 Standards for Marine Diesel C1 and C2 

MAXIMUM 

ENGINE POWER 

PM

g/bhp-hr 

(g/kW-hr)

NOx+HC 

g/bhp-hr 

(g/kW-hr)

HC 

g/bhp-hr 

(g/kW-hr)

MODEL 

YEAR 

 0.09 (0.12)a 1.3 (1.8) 0.14 (0.19) 2014c at or above 3700 kW 

0.04 (0.06) 1.3 (1.8) 0.14 (0.19) 2016b,c

2000 to <3700 kW 0.03 (0.04) 1.3 (1.8) 0.14 (0.19) 2014c,d

1400 to <2000 kW 0.03 (0.04) 1.3 (1.8) 0.14 (0.19) 2016c

600 to <1400 kW 0.03 (0.04) 1.3 (1.8) 0.14 (0.19) 2017b

Notes: 

a

 This standard is 0.19 g/bhp-hr (0.25 g/kW-hr) for engines with 15-30 liter/cylinder displacement. 

b

 Optional compliance start dates can be used within these model years; see discussion below. c

 Option for C2: Tier 3 PM / NOx+HC at 0.10 / 5.8 g/bhp-hr (0.14/7.8 g/kW-hr) in 2012, and Tier 4 in 2015. 

d

 The Tier 3 PM standards continue to apply for these engines in model years 2014 and 2015 only.

 We are also finalizing standards for remanufactured marine diesel engines that will 

apply to engines above 600 kW (800 hp). This program requires the use of a certified 

remanufacture system when an engine is remanufactured if a certified system is available. 

The standard we are finalizing for these systems is a 25 percent reduction in PM emissions, 

compared to the engine’s baseline emissions level. We expect that this PM reduction will be 

met by using incrementally-improved components that are replaced when an engine is 

remanufactured. The remanufacture systems sets a cost cap of $45,000 per ton of PM 

reduced, based on the incremental cost of the remanufacture system. We intend to assess the 

effectiveness of this voluntary program as early as 2012 to ascertain the extent to which 

engine manufacturers are providing certified remanufacture systems. If remanufacture system 

ES-5 
Regulatory Impact Analysis 

are not available or are not in the process of being developed and certified at that time for a 

significant number of engines, we may consider changes to the program. 

2. Projected Inventory and Cost Impacts

 Our analysis of the projected impacts of these standards can be found in Chapter 2 (air 

quality impacts), Chapter 3 (inventory impacts) and Chapter 6 (benefits). 

Inventory Reductions

A discussion of the estimated current and projected inventories for several key air 

pollutants are contained in Chapter 3. Nationally, in 2007 these engines account for about 20 

percent of mobile source NOx emissions and 25 percent of mobile source diesel PM2.5 

emissions. Absent new emissions standards, we expect overall emissions from these engines 

to remain relatively flat over the next 10 to 15 years due to existing regulations such as lower 

fuel sulfur requirements and the phase-in of locomotive and marine diesel Tier 1 and Tier 2 

engine standards, but starting in about 2025, emissions from these engines would begin to 

grow. Without new controls, by 2030, these engines would have become a large portion of 

the total mobile source emissions inventory constituting 35 percent of mobile source NOx

emissions and 65 percent of diesel PM emissions. 

We estimate that these standards will reduce annual NOx emissions by about 800,000 

tons and PM2.5 and 27,000 tons in 2030. Table 7 shows the emissions reductions associated 

with today’s rulemaking for selected years, and the cumulative reductions through 2040 

discounted at 3 and 7 percent. These reductions in PM and NOx levels will produce 

nationwide air quality improvements. 

Table ES-7 – Estimated Emissions Reductions Associated with the Locomotive and Marine Standards 

(Short tons) 

YEAR PM2.5 PM10a NOx VOC 

2015 7,000 7,000 161,000 15,000 

2020 14,000 15,000 371,000 28,000 

2030 27,000 27,000 795,000 43,000 

2040 37,000 38,000 1,144,000 55,000 

a

 Note that, PM2.5 is estimated to be 97 percent of the more inclusive PM10 emission inventory. In Section II 

we generate and present PM2.5 inventories since recent research has determined that these are of greater 

health concern. Traditionally, we have used PM10 in our cost effectiveness calculations. Since cost 

effectiveness is a means of comparing control measures to one another, we use PM10 in our cost 

effectiveness calculations for comparisons to past control measures. 

ES-6 
Executive Summary 

Engineering Costs

The engineering cost analysis for these standards can be found in Chapter 5. The total 

engineering costs associated with this rulemaking are the summation of the engine and 

equipment compliance costs, both fixed and variable, the operating costs, and the costs 

associated with the locomotive and marine remanufacturing programs. These costs are 

summarized in Table 8. 

Table ES-8 – Total Engineering Costs of the Program ($Millions) 

YEAR ENGINE 

COSTS 

EQUIPMENT 

COSTS 

OPERATING 

COSTS 

COSTS OF 

REMANUFACTURING 

PROGRAM 

TOTAL 

COSTS 

2011 $138 $0 $0 $143 $281 

2012 $80 $0 $0 $135 $215 

2015 $123 $24 $30 $89 $266 

2020 $82 $17 $187 $63 $349 

2030 $99 $20 $535 $105 $759 

2040 $98 $17 $806 $161 $1,082 

NPV at 3% $1,764 $260 $5,264 $2,120 $9,407 

NPV at 7% $974 $122 $2,057 $1,153 $4,307 

These engineering costs are allocated to NOx and PM reductions in Table 9. About 

half of the costs of complying with the program are operating costs, with the bulk of those 

being reductant-related costs associated with SCR technology. Since SCR is a technique for 

reducing NOx emissions, this means that most of the operating costs and, therefore, the 

majority of the total engineering costs of the program are associated with NOx control. 

Table ES-9 – Total Engineering Costs, Allocated by Pollutant ($Millions) 

YEAR PM COSTS NOx COSTS 

2011 $121 $160 

2012 $94 $121 

2015 $116 $150 

2020 $106 $242 

2030 $181 $578 

2040 $240 $842 

NPV at 3% $2,680 $6,727 

NPV at 7% $1,333 $2,973 

Cost per Ton of Reduced Emissions

Using the inventory and engineering cost information, we can estimate the cost per ton 

of pollutant reduced as a result of the more stringent standards. Table 10 contains the 

estimated cost per ton of pollutant reduced based on the net present value of the engineering 

costs and inventory reductions from 2006 through 2040. This estimate captures all of the 

engineering costs and emissions reductions including those associated with the locomotive 

and marine remanufacturing programs. Table 10 also presents the estimated cost per ton of 

pollutant reduced for 2030 using the annual costs and emissions reductions in that year alone. 

ES-7 
Regulatory Impact Analysis 

That estimates includes engineering costs and emission reductions that will occur from the 

new engine standards and locomotive and marine remanufacturing programs in that year. 

Table ES-10 – Program Cost per Ton Estimates 

POLLUTANT 2006 THRU 2040 

DISCOUNTED LIFETIME 

COST PER TON AT 3% 

2006 THRU 2040 

DISCOUNTED LIFETIME 

COST PER TON AT 7% 

LONG-TERM 

COST PER TON IN 

2030 

NOx+NMHC $720 $750 $690 

PM $8,440 $9,620 $6,620 

3. Estimated Benefits and Economic Impacts

Estimated Benefits

We estimate that the requirements in this rulemaking will result in substantial benefits 

to public health and welfare and the environment, as described in Chapter 6. The benefits 

analysis performed for this rulemaking uses sophisticated air quality and benefit modeling 

tools and is based on peer-reviewed studies of air quality and health and welfare effects 

associated with improvements in air quality and peer-reviewed studies of the dollar values of 

those public health and welfare effects. 

The range of benefits associated with this program are estimated based on the risk of 

several sources of PM- and ozone-related mortality effect estimates, along with all other PM 

and ozone non-mortality related benefits information. These benefits are presented in Table 

ES-11. The benefits reflect two different sources of information about the impact of 

reductions in PM on reduction in the risk of premature death, including estimates of mortality 

derived from the epidemiological literature (using both the American Cancer Society (ACS) 

cohort study and the Six Cities study) and an expert elicitation study conducted by EPA in 

2006. In order to provide an indication of the sensitivity of the benefits estimates to 

alternative assumptions, in Chapter 6 of the RIA we present a variety of benefits estimates 

based on two epidemiological studies (including the ACS Study and the Six Cities Study) and 

the expert elicitation. EPA intends to ask the Science Advisory Board to provide additional 

advice as to which scientific studies should be used in future RIAs to estimate the benefits of 

reductions in PM. 

The range of ozone benefits associated with the final standards is also estimated based 

on risk reductions estimated using several sources of ozone-related mortality effect estimates. 

There is considerable uncertainty in the magnitude of the association between ozone and 

premature mortality. This analysis presents four alternative estimates for the association 

based upon different functions reported in the scientific literature. We use the National 

Morbidity, Mortality and Air Pollution Study (NMMAPS), which was used as the primary 

basis for the risk analysis in the ozone Staff Paper and reviewed by the Clean Air Science 

Advisory Committee (CASAC). We also use three studies that synthesize ozone mortality 

data across a large number of individual studies. Note that there are uncertainties within each 

study that are not fully captured by this range of estimates. Chapter 6 of the RIA presents the 

results of each of the ozone mortality studies separately. 

ES-8 
Executive Summary 

Recognizing that additional research is needed to more fully establish underlying 

mechanisms by which such effects occur, we also consider the possibility that the observed 

associations between ozone and mortality may not be causal in nature. EPA has requested 

advice from the National Academy of Sciences on how best to quantify uncertainty in the 

relationship between ozone exposure and premature mortality in the context of quantifying 

benefits associated with ozone control strategies. 

The range of total ozone- and PM-related benefits associated with the final standards 

is presented in Table ES-11. We present total benefits based on the PM- and ozone-related 

premature mortality function used. The benefits ranges therefore reflect the addition of each 

estimate of ozone-related premature mortality (each with its own row in Table ES-11) to 

estimates of PM-related premature mortality, derived from either the epidemiological 

literature or the expert elicitation. 

Table ES-11– Estimated Monetized PM- and Ozone-Related Health Benefits of the Locomotive and 

Marine Engine Standards

2030 Total Ozone and PM Benefits – PM Mortality Derived from Epidemiology Studiesa

Premature Ozone Mortality 

Function or Assumption 

Reference Mean Total Benefits 

(Billions, 2006$)c,d

NMMAPS Bell et al., 2004 $9.7 to $20 

Bell et al., 2005 $11 to $21 

Meta-analysis Ito et al., 2005 $11 to $21 

Levy et al., 2005 $11 to $22 

Assumption that association is not causal $9.2 to $20 

2030 Total Ozone and PM Benefits – PM Mortality Derived from Expert Elicitationb

Premature Ozone Mortality 

Function or Assumption 

Reference Mean Total Benefits 

(Billions, 2006$)c,d

NMMAPS Bell et al., 2004 $5.2 to $37 

Bell et al., 2005 $6.2 to $38 

Ito et al., 2005 $6.7 to $39 

Meta-analysis 

Levy et al., 2005 $6.7 to $39 

Assumption that association is not causal $4.7 to $37 

Notes: 

a

 Total includes ozone and PM2.5 benefits. Range was developed by adding the estimate from the ozone 

premature mortality function to both estimates of PM2.5-related premature mortality derived from the ACS 

(Pope et al., 2002) and Six-Cities (Laden et al., 2006) studies, respectively. 

b

 Total includes ozone and PM2.5 benefits. Range was developed by adding the estimate from the ozone 

premature mortality function to both the lower and upper ends of the range of the PM2.5 premature mortality 

functions characterized in the expert elicitation. The effect estimates of five of the twelve experts included in the 

elicitation panel fall within the empirically-derived range provided by the ACS and Six-Cities studies. One of 

the experts fall below this range and six of the experts are above this range. Although the overall range across 

experts is summarized in this table, the full uncertainty in the estimates is reflected by the results for the full set 

of 12 experts. The twelve experts’ judgments as to the likely mean effect estimate are not evenly distributed 

across the range illustrated by arraying the highest and lowest expert means. c Note that total benefits presented here do not include a number of unquantified benefits categories. A detailed 

listing of unquantified health and welfare effects is provided in Table VI-5. 

d

 Results reflect the use of a 3 percent discount rate. Monetary results presented in Table VI-3 use both a 3 and 

7 percent discount rate, as recommended by EPA’s Guidelines for Preparing Economic Analyses and OMB 

Circular A-4. Results are rounded to two significant digits for ease of presentation and computation. 

ES-9 
Regulatory Impact Analysis 

We estimate that by 2030, the annual emission reductions associated with these more 

stringent standards will annually prevent 1,100 PM-related premature deaths (based on the 

ACS cohort study), between 50 and 250 ozone-related premature deaths (assuming a causal 

relationship between ozone and mortality), 120,000 work days lost, and approximately 

1,000,000 minor restricted-activity days. 

Benefit-Cost Analysis

Using the ACS-based estimate of PM-related premature mortality incidence, we 

estimate that the monetized benefits of this rule in 2030 will range between approximately 

$9.2 and $11 billion, assuming a 3 percent discount rate. Using the range of results derived 

from the expert elicitation, we estimate that the monetized benefits in 2030 will range from 

approximately $4.7 billion to $39 billion, assuming a 3 percent discount rate. These estimates 

reflect the remanufactured marine engine program that we are finalizing. 

The annual cost of the program in 2030 are estimated to be significantly less, at 

approximately $740 million.

Economic Impact Analysis 

We also performed an economic impact analysis to estimate the market-level changes 

in prices and outputs for affected markets, the social costs of the program, and the expected 

distribution of those costs across stakeholders.. This analysis can be found in Chapter 7. We 

estimate the social costs of the new program to be approximately $738 million in 2030.B, C 

The rail sector is expected to bear about 62.5 percent of the social costs of the program in 

2030, and the marine sector is expected to bear about 37.5 percent. In each of these two 

sectors, these social costs are expected to be born primarily by producers and users of 

locomotive and marine transportation services (62 and 36 percent, respectively). The 

remaining 2 percent is expected to be borne by locomotive, marine engine, and marine vessel 

manufacturers and fishing and recreational vessel users. 

The impact of these costs on society are expected to be minimal, with the prices of rail 

and marine transportation services in 2030 estimated to increase by less about 0.6 percent for 

locomotive transportation services and about 1.1 percent for marine transportation services. 

 

B

 All estimates presented in this section are in 2005$. 

C

 The estimated 2030 social welfare cost of $738 million is based on an earlier version of the engineering costs 

developed for this rule, which estimated $740 million engineering costs in 2030. The final engineering cost 

estimate for 2030 is $760 million. 

ES-10 
Executive Summary 

4. Alternative Program Options

In the course of designing our rulemaking, we investigated several alternative 

approaches to both the engine and fuel programs. Chapter 8 contains a description of these 

alternatives and an analysis of their potential costs and benefits. 

ES-11 
Regulatory Impact Analysis 

ES-12 

This Page Intentionally Left Blank 
Industry Characterization 

CHAPTER 1: INDUSTRY CHARACTERIZATION ............................................ 2

1.1 Marine..............................................................................................................................2

1.1.1 Introduction ..........................................................................................................2

1.1.2 Marine Diesel Engine Manufacturers ................................................................6

1.1.3 Marine Vessel Manufacturers...........................................................................24

1.1.4 Vessel Operators.................................................................................................41

1.2 Locomotive.....................................................................................................................42

1.2.1 Introduction ........................................................................................................42

1.2.2 Current Emission Regulations ..........................................................................43

1.2.3 Supply: Locomotive Manufacturing and Remanufacturing ..........................45

1.2.4 Demand: Railroads.............................................................................................54

1.2.5 Existing Regulations...........................................................................................67

1.2.6 Foreign Railroads in US.....................................................................................69

1-1 
Regulatory Impact Analysis 

CHAPTER 1: Industry Characterization 

In order to assess the impacts of emission regulations upon the affected industries, it is 

important to understand the nature of the industries impacted by the regulations. These 

industries include marine diesel engine manufacturers and marinizers, boat or marine vessel 

builders which have marine diesel engines installed on them, vessel operators which either 

purchase new diesel engines or remanufacture existing engines greater than 600kW, the 

manufacturers of locomotives and locomotive engines, the owners and operators of 

locomotives (i.e., railroads), and remanufacturers of locomotives and locomotive engines. 

This chapter provides market information for each of these affected industries for background 

purposes. 

1.1 Marine 

1.1.1 Introduction 

The regulations for marine diesel engines will directly impact five industries: 1) 

manufacturers of marine diesel engines, 2) diesel engine marinizers, 3) marine diesel engine 

remanufacturers, 4) boat or vessel builders which install marine diesel engines installed on 

their vessels, and 5) vessel operators who own existing marine diesel engines greater than 600 

kW. Much of this marine industry characterization was taken from a report done for us by 

RTI, International.1

1.1.1.1 Marine Diesel Market Overview 

Marine diesel engines include both engines used for propulsion on marine vessels, and 

those used for marine vessel auxiliary power needs. Diesel marine engines are generally 

derived from engines originally designed and manufactured for land-based nonroad 

applications. These nonroad engines are then adapted for use in marine applications through 

the process of marinization, either by the original engine manufacturer, or by a postmanufacturer marinizer (PMM). The marinization process is discussed in further detail in 

section 1.1.2.2.2. 

 Propulsion engines can vary dramatically in size and power, from the smallest 

engines used in recreational sailboats, to very large engines used in ocean-going commercial 

vessels. Similarly, auxiliary engines cover a very broad range of sizes and rated power. 

Auxiliary engines can be used for a variety of purposes, including primary or emergency 

electrical power generation, and the powering of onboard equipment such as pumps, winches, 

cable and pipe laying machinery, and dredging equipment. A description of the various 

engine categories used for regulatory purposes is contained in section 1.1.2.1. 

As with marine diesel engines, marine vessels include a very broad range of vessel 

sizes and types. These include small recreational vessels, as well as commercial vessels such 

as tow and tug boats, patrol boats, commercial fishing vessels, research vessels, passenger 

vessels (tour boats and ferries), offshore support vessels which service offshore drilling 

platforms, and a variety of other specialized commercial vessels. 

1-2 
Industry Characterization 

Figure 1-1-1 shows the links between the various market segments of the marine 

diesel engine industry and the marine vessel industry, as discussed further in the following 

sections. 

Figure 1-1-1 Marine Diesel Market Segment Flow Chart 

1-3 
Regulatory Impact Analysis 

1.1.1.2 Current US Emission Regulations 

We adopted Tier 2 emission standards for Category 1 (C1) marine diesel engines over 

37 kW and for category 2 (C2) marine diesel engines in 1999 (64 CFR 73300, December 29, 

1999). These standards are shown in Table 1-1. 

Table 1-1 Tier 2 Emission Standards for C1 (over 37 kW) and C2 Commercial Marine Diesel Engines 

Category Displacement 

(liters/cylinder) 

Starting 

Date 

NOx+THC 

(g/kW-hr) 

PM 

(g/kW-hr) 

CO 

(g/kW-hr) 

Power ≥37 kW, disp. 2005 7.5 0.40 5.0 

0.9 ≤ disp. < 1.2 2004 7.2 0.30 5.0

1.2 ≤ disp. < 2.5 2004 7.2 0.20 5.0

1 

2.5 ≤ disp. < 5.0 2007 7.2 0.20 5.0 

5.0 ≤ disp. < 15.0 2007 7.8 0.27 5.0

15.0 ≤ disp. < 20.0, 

and power < 3300 

2007 8.7 0.50 5.0 

15.0 ≤ disp. < 20.0, 

and power ≥ 3300 

2007 9.8 0.50 5.0 

20.0 ≤ disp. < 25.0 2007 9.8 0.50 5.0

2 

25.0 ≤ disp. < 30.0 2007 11.0 0.50 5.0 

We applied the Tier 2 emission standards for C1 engines shown in Table 1-1 to 

recreational marine diesel engines, but with applicable dates two years behind those for the 

corresponding commercial marine diesel engines (67 FR 68242, November 8, 2002). 

Following the initial adoption of the Tier 2 standards just discussed, we adopted the 

Tier 1 emission standards in 2003. These standards became effective with the 2004 model 

year (68 FR 9746, February 28, 2003). These NOx-only standards apply to commercial 

marine diesel engines with a per-cylinder displacement of greater than 2.5 liters per cylinder. 

As shown in Table 1-2 the standards vary depending on the rated speed of the engine. 

Table 1-2 Tier 1 Standards for Commercial Marine Diesel Engines over 2.5 Liters per Cylinder 

Rated engine speed (rpm) NOx (g/kW-hr) 

<130 17 

45 X rpm-0.2 130-2000 

>2000 9.8 

Prior to today’s action, there were no emission regulations specifically for marine 

diesel engines less than 37 kW. Rather, these engines were covered by the Tier 2 standards 

for nonroad compression ignition (CI) engines, as shown in Table 1-3 (63 FR 56968, October 

23, 1998). 

1-4 
Industry Characterization 

Table 1-3 Tier 2 Emission Standards for Marine Diesel Engines Below 37 kW 

Engine Power NMHC+NOx 

(g/kW-hr) 

PM 

(g/kW-hr) 

CO 

(g/kW-hr) 

kW < 8 7.5 0.80 8.0 

8 ≤ kW < 19 7.5 0.80 6.6 

19 ≤kW < 37 7.5 0.60 5.5 

1.1.1.3 Current European Standards and International Maritime Organization (IMO) 

Standards 

 The European Union (EU) has a program in place dealing with marine fuels: marine 

fuel Directive 2005/33. This directive, which limits the sulfur content of distillate fuels used 

in EU territory has four components. First, until August 10, 2006, the fuel sulfur content of 

distillate fuel cannot exceed 2,000 ppm. This applies to DMA, DMB, DMC, and DMX 

grades.A From August 11, 2006 to December 31, 2007, this requirement is relaxed for DMB 

and DMC grades, which are then pegged to the 15,000 ppm SECA limit. That requirement 

applies to fuels placed on the market during that period. From January 1, 2008 to December 

31, 2009, the fuel sulfur limit for DMA and DMX grades falls to 1,000 ppm. Finally, 

beginning January 1, 2010, a fuel sulfur limit of 1,000 ppm applies to all marine gas oils 

(DMA, DMB, DMC, and DMX) placed on the market, and to all types of marine fuels used 

by ships at berth in EU ports and by inland waterway vessels. These last limits apply to any 

fuel used onboard a vessel. Exemptions apply for ships that spend less than 2 hours at berth, 

ships that use shore-side electricity while at berth, and hybrid sea-river vessels while they are 

at sea. 

Annex VI to the International Convention for the Prevention of Pollution from Ships 

(MARPOL) addresses air pollution from ships or ocean-going vessels, as opposed to the 

Category 1 and 2 marine diesel engines which are the subject of this final rule. Annex VI was 

adopted by the Parties to MARPOL at a Diplomatic Conference on September 26, 1997, and 

it went into force May 20, 2005. As of July 31, 2007, the Annex has been ratified by 44 

countries, representing 74.1 percent of the world’s merchant shipping tonnage.2

 

 

A ASTM specifications for marine fuels identify four kinds of marine distillate fuels: DMX, DMA, DMB, and 

DMC. DMX is a special light distillate intended mainly for use in emergency engines. DMA (also called MGO) 

is a general purpose marine distillate that must contain no traces of residual fuel. These fuels can be used in all 

marine diesel engines but are primarily used by Category 1 engines. DMB, also called marine diesel oil, is not 

typically used with Category 1 engines, but is used for Category 2 and 3 engines. DMB is allowed to have a 

trace of residual fuel, which can be high in sulfur. DMC is a grade of marine fuel that may contain some 

residual fuel and is often a residual fuel blend. Residual fuel is typically designated by the prefix RM (e.g., 

RMA, RMB, etc.). These fuels are also identified by their nominal viscosity (e.g., RMA10, RMG35, etc.). Most 

residual fuels require treatment by a purifier-clarifier centrifuge system, although RMA and RMB do not require 

this. 

1-5 
Regulatory Impact Analysis 

Globally harmonized regulation of ship emissions is generally recognized to be the 

preferred approach for addressing air emissions from ocean-going vessels. It reduces costs 

for ship owners, since they would not be required to comply with a patchwork of different 

standards that could occur if each country was setting its own standards, and it can simplify 

environmental protection for port and coastal states. 

The significance of international shipping to the United States can be illustrated by 

port entrance statistics. In 1999, according to U.S. Maritime Administration (MARAD) data, 

about 90 percent of annual entrances to U.S. ports were made by foreign-flagged vessels 

(75,700 total entrances; 67,500 entrances by foreign vessels; entrances are for vessels engaged 

in foreign trade and do not include Jones Act3 vessels). 

The emission control program contained in Annex VI was the first step for the 

international control of air pollution from ships. However, as early as the 1997 conference, 

many countries “already recognized that the NOx emission limits established in Regulation 13 

were very modest when compared with current technology developments.”4 Consequently, a 

Conference Resolution was adopted at the 1997 conference that invited the Marine 

Environment Protection Committee (MEPC) to review the NOx emission limits at a minimum 

of five-year intervals after entry into force of the protocol and, if appropriate, amend the NOx 

limits to reflect more stringent controls. 

 The United States began advocating a review of the NOx emission limits in 1999.5 

However, MEPC did not formally consider the issue until 2005, after the Annex went into 

effect. Negotiations for amendments to the Annex VI standards, including NOx and SOx 

emission limits, officially began in April 2006, with the most recent round of negotiations 

taking place in April 2007. The United States submitted a paper to that meeting (April 2007 

Bulk Liquids and Gases Sub-Committee meeting, referred to as BLG-11) setting out an 

approach for new international engine and fuel standards. That approach forms the basis of 

the program outlined in the recently published Advance notice of proposed rulemaking for 

Control of Emissions from New Marine Compression-Ignition Engines at or Above 30 Liters 

per Cylinder.6

 Discussions are expected to continue through Summer 2008 and are expected 

to conclude at the October 2008 MEPC meeting. We will continue to coordinate our national 

rule for Category 3 emission limits with our activities at IMO. 

1.1.2 Marine Diesel Engine Manufacturers 

Diesel (compression-ignition) engines are designed to be quite robust in order to 

withstand the very high temperatures and pressures associated with compression-ignition. As 

a result, they tend to be very reliable and have very long service lives. Their energy 

efficiency and simple design result in low operating and maintenance costs. As a result, 

diesel engines tend to dominate commercial marine applications, where cost and reliability are 

key purchase decisions for the vessel operator. Diesel engines account for only a small 

portion of the recreational marine market, however, as their initial purchase price is high 

relative to gasoline (spark-ignition) engines. The benefits of lower operating costs are not 

nearly as important in the recreational market, where engines tend not to get much use as 

compared to commercial applications. 

1-6 
Industry Characterization 

The terms "commercial" and "recreational" are defined in 40 CFR Part 94, Control of 

Emissions for Marine Compression-Ignition Engines (Code of Federal Regulations, 2006). 

The definitions in section 94.2 state that a commercial engine is an engine installed on a 

commercial vessel. Likewise, a recreational engine is an engine installed on a recreational 

vessel. As adopted in this final rule in 40 CFR Part 1042, “recreational vessel” is defined as a 

vessel that is intended by its manufacturer to be operated primarily for pleasure purposes, 

although such a vessel could be chartered, rented or leased. Further, a recreational vessel 

includes only those vessels less than 100 gross registered tons carrying six or fewer 

passengers, and cannot be used solely for competition. 

This industry characterization is concerned with the U.S. market for marine diesel 

engines, which encompasses all diesel marine engines installed on marine vessels to be 

flagged (registered) in the United States. This includes engines made in the U.S., engines 

imported for installation in vessels made in the U.S., and engines included in vessels made 

overseas and imported into the U.S. Unless otherwise noted, the production and engine 

characteristics data presented in the following sections were obtained from the Power Systems 

Research OELink database.7

1.1.2.1 Engine Categories and Characteristics 

For the purposes of this industry characterization, we looked at four broad categories 

of diesel marine engines, based on the categories that currently exist for emission regulation 

purposes. These categories are shown in Table 1-4. 

Table 1-4 Diesel Marine Engine Categories and Applications 

Category Power Displacement per 

Cylinder 

Applications 

Small ≤37 kW Any Auxiliary, Recreational Propulsion 

Recreational 

Category 1 

>37 kW < 5 liters Recreational Propulsion 

Commercial 

Category 1 

>37 kW < 5 liters Auxiliary, Commercial Propulsion 

Commercial 

Category 2 

>37 kW ≥ 5 liters and < 30 liters Auxiliary, Commercial Propulsion 

Commercial 

Category 3 

>37 kW ≥ 30 liters Commercial Propulsion 

Given the broad range of commercial and recreational marine vessels types, it is 

difficult to identify typical applications for each engine category. Nonetheless, the following 

paragraphs provide an overview of the general characteristics and typical applications of 

engines in each category. 

Small: Engines in this category range from 4 to 43 horsepower (hp) and are 

characterized by low costs and high sales volumes. Most small engines are used for auxiliary 

purposes on marine vessels or for propulsion on recreational sailboats. In 2002 they 

accounted for approximately 26 percent of the marine diesel engines produced or imported in 

1-7 
Regulatory Impact Analysis 

the U.S. market. They are typically marinized land-based nonroad diesel engines; we are not 

aware of any marine engines of this size made solely for marine application. 

Category 1 (C1) Recreational: Engines in this category range from 52 to 3,155 hp and 

are characterized by high power density (power to weight ratio) and low annual hours of 

operation relative to commercial engines. Such engines are typically operated no more than 

200 to 250 hours per year, and often less. These engines are used for propulsion in 

recreational vessels, which are designed for speed and planing operation. In 2002 they 

accounted for approximately 34 percent of the marine diesel engines produced or imported in 

the U.S. market. 

Recreational vessels are designed primarily for speed, and this imposes certain 

constraints on the type of engine they can use. For a marine vessel to reach high speeds, it is 

necessary to reduce the surface contact between the vessel and the water, and consequently 

these vessels typically operate in a planing mode. However, the accompanying high engine 

speeds are sustained for only short periods of time compared to the total operation of the 

vessel (i.e., long enough for the vessel to get up on plane), and the duty cycle on which these 

engines are certified reflects these operations. 

Planing imposes two important design requirements. First, the vessel needs to have a 

very high power, but lightweight, engine to achieve the speeds necessary to push the vessel 

onto the surface of the water. Therefore, recreational engine manufacturers have focused on 

achieving higher power output with lighter engines (this is also referred to as high power 

density). The tradeoff is less durability, and recreational engines are warranted for fewer 

hours of operation than commercial marine engines. The shorter warranty period is not a 

great concern, however, since recreational vessels, and therefore their engines, are typically 

used for fewer hours per year than commercial engines, and spend much less time operating at 

higher engine loads. Second, the vessel needs to be as light as possible, with vertical and 

horizontal centers of gravity carefully located to allow the hull of the vessel to be lifted onto 

the surface of the water. Therefore, recreational vessel manufacturers have focused on 

designing very lightweight hulls. They are typically made out of fiberglass, using precisely 

designed molds. The tradeoff is a reduced ability to accommodate any changes to the 

standard design. For these reasons, recreational vessels are typically designed around a 

specific engine or group of engines, and engines that are heavier or that are physically larger 

cannot be used without jeopardizing the vessel's planing abilities or, in many cases, designing 

a new fiberglass mold for a modified hull. 

Category 1 (C1) Commercial: Engines in this category are very similar to engines in 

the C1 recreational category in displacement, but tend to have lower hp ratings than 

recreational marine diesel engines in order to provide increased durability required in 

commercial applications. In contrast to C1 recreational engines, C1 commercial engines are 

typically used 750 to 4,000 hours per year. They are typically used for propulsion in vessels 

with displacement hull designs. They are also used for a wide variety of auxiliary power 

needs on marine vessels. In 2002 they accounted for approximately 39 percent of the marine 

diesel engines produced or imported in the U.S. market. 

1-8 
Industry Characterization 

In contrast to recreational marine vessels, commercial vessels are typically larger 

displacement hull vessels, and instead of operating on the surface of the water, for speed, they 

are pushed through the water. The speed at which a displacement vessel can operate is 

limited by its hull design and above that limit, there are quickly diminishing returns on power: 

little vessel speed increase is achieved by increasing power. Because vessel speed is limited 

by the hull design, there is little incentive to over power the vessel, and engines on these types 

of commercial vessels tend to be lower power when compared to recreational vessels of 

similar size. Commercial engines operate for long periods at about 80-90% of rated power 

and are designed primarily with durability and fuel consumption in mind. 

Category 2 (C2): Engines in this category are typically derived from engines 

originally designed for use in locomotives or for land-based stationary power generation. 

Such engines typically operate 3,000 to 5,000 hours or more per year, and are designed to be 

durable and have a very long service life. Under our current program, all C2 marine diesel 

engines are handled the same way; there is no distinction between recreational or commercial 

engines in this category. In 2002 they accounted for approximately one percent of the marine 

diesel engines produced or imported in the U.S. market. 

As we were developing this rule, engine manufacturers brought to our attention 

another category of marine diesel engines that do not fit neatly in the above scheme. These 

are high power-density marine diesel engines used in some commercial vessels, including 

certain kinds of crew boats, research vessels, and fishing vessels. Unlike most commercial 

vessels, these vessels are built for higher speed, planing operation, which allows them to reach 

research fields, oil platforms, or fishing beds more quickly. These engines may have smaller 

service lives because of operation at these higher speeds. Our current program does not 

distinguish between these commercial engines and those used on displacement vessels with 

respect to useful life periods. Further, this industry characterization does not specifically 

address these engines as a unique group. 

A final category of marine diesel engines, Category 3 (C3) engines, have 

displacements of 30 liters per cylinder or greater. Such engines are typically only used in 

large ocean-going vessels, and are not considered in this industry characterization; these 

engines are not covered by the final rule. Table 1-5 shows a summary of the general 

characteristics of engines in each of the four categories considered in this industry 

characterization. 

1-9 
Regulatory Impact Analysis 

Table 1-5 Engine Characteristics for the Considered Engine Categories 

Small Recreational 

Category 1 

Commercial 

Category 1 

Category 2 

Cylinders 1–4 3–16 3–24 5–20 

Horsepower 4.2–42.4 52–3,155 37.5–2,500 300–9,190a

Engine Speed (rpm) 1,800 – 3,000 1,800 – 3,000 1,800 – 3,000 750 – 1,500 

Weight (lbs) 26–246 156–7,491 106–7,900 7,850–35,000 

Cycle: 

 2 0.0% 10.2% 9.5% 41.0% 

 4 100.0% 89.8% 90.5% 59.0% 

Configuration: 

 H-Block 8.1% 0.0% 0.0% 0.0% 

 Inline 91.9% 65.3% 73.3% 33.7% 

 V-Block 0.0% 34.7% 26.7% 66.3% 

Cooling: 

 Air 5.9% 0.0% 0.4% 0.0% 

 Oil 0.0% 0.0% 0.1% 0.0% 

 Water 94.1% 100.0% 99.5% 100.0% a

 While the PSR database shows one C2 engine family with a 300 hp rating, C2 engines are generally over 1000 

hp at minimum. 

Table 1-6 shows the total number of engines in each category which were sold in the 

United States in 2002. 

Table 1-6 Marine Diesel Engine Sales by Engine Category in 2002 

Application Category Sales in 2002 Percent of Total 

Small 10,761 26.4% 

Recreational C1 13,952 34.2% 

Commercial C1 15,826 38.8% 

Commercial C2 277 0.7% 

Total 40,816 

1.1.2.2 Supply Side 

Marine diesel engines are typically derived from land-based nonroad engines. These 

engines are adapted for use in the marine environment through a process known as 

marinization. In this section we will discuss nonroad engine design, production and costs, 

followed by descriptions of the marinization process and the companies engaged in this 

activity. Finally we will discuss engine dressing and rebuilding practices for marine diesel 

engines. 

1-10 
Industry Characterization 

1.1.2.2.1 Nonroad Diesel Engine Design and Production 

Engine blocks are cast in a foundry, most often from gray iron. Depending on the size 

and complexity of the engine, the block may be formed by impression molding or two-piece 

sand-casting. Smaller, more complex parts, including cylinder heads, exhaust manifolds, and 

cylinder liners, are cast from ductile iron, typically using sand cores to allow formation of the 

complicated shapes. All castings must be cleaned and deburred prior to further processing. In 

addition, ductile iron parts will also usually be heat treated to relieve stress and harden the 

alloys. Table 1-7 lists the materials and primary production processes for various engine 

components.8

Table 1-7 Engine Component Materials and Production Processes 

Component Primary Materials Primary Process 

Block Iron, aluminum Casting 

Cylinder head Iron, aluminum Casting, machining 

Intake manifold Plastic, aluminum Casting, machining 

Connecting rods Powder metal, steel Molding, forging, machining 

Pistons Aluminum Forging, machining 

Crankshaft Iron, steel, powder metal Molding, forging, machining 

Valves Steel, magnesium Stamping, machining 

Exhaust systems Stainless steel, aluminum, iron Extruding, stamping 

The cast block, cylinder head, and cylinder liners, along with crankshafts, gears, 

connecting rods, and other engine parts, are next machined to exact specifications in a 

machining center. Holes are drilled, parts reshaped, excess metal removed, and the metal 

surfaces polished in the machining area. The operation of the finished engine depends 

critically on the precision of the machining work at this stage. 

The third major step in engine manufacturing is assembly. This area is usually 

physically isolated from the dirty upstream operations so that contaminants are not introduced 

into the completed engines, thus affecting their operation or shortening the engine’s life. In a 

typical plant, subassemblies are first put together on separate lines or in separate bays; then 

the subassemblies are brought together for final assembly. The completed engines are 

visually inspected and then evaluated on-line on a test bench or in a test cell to ensure their 

performance will meet expectations. 

1.1.2.2.2 Engine Marinization 

Land-based nonroad diesel engines generally need to be modified in some ways to 

make them suitable for installation on marine vessels. The process by which this is done is 

known as marinization. The marinization process results in changes to the emission 

characteristics of the nonroad engine. For this reason, a marinized nonroad engine must be 

certified to marine diesel engine emission standards even though the base nonroad engine is 

certified to the nonroad diesel engine emission standards. Sometimes, land-based nonroad 

diesel engines can be adapted for use in marine applications without changing the emission 

characteristics of the engine. This process is called engine dressing, and is discussed in 

section 1.1.2.2.5. Marinization typically involves three significant modifications: choosing 

1-11 
Regulatory Impact Analysis 

and optimizing the fuel management system, configuring a marine cooling system, and 

making other peripheral changes. These changes are detailed in the following paragraphs. 

Fuel and Air Management: High-performance engines are preferred for most 

recreational and some light duty commercial applications. These engines are built to 

maximize their power-to-weight ratio (provide more power with less added weight), which is 

typically done by increasing power from a given cylinder displacement. This is usually 

accomplished by installing a new fuel injection system, which injects more fuel directly into 

the cylinder to increase power. This can require changes to the camshaft, cylinder head, and 

the injection timing and pressure. Currently, the design limits for increased fuel to the 

cylinder are smoke and durability. Modifications made to the cooling system also help 

enhance performance. By cooling the charge, more air can be forced into the cylinder. As a 

result, more fuel can be injected and burned efficiently because of the increase in available 

oxygen. In addition, changes are often made to the pistons, cylinder head components, and 

the lubrication system. For example, aluminum piston skirts can be used to reduce the weight 

of the pistons. Cylinder head changes include changing valve timing to optimize engine 

breathing characteristics. Marinizers do not typically go as far as to physically modify the 

cylinder head. 

Cooling System: To mitigate performance problems, engine manufacturers 

historically used cooling systems that cooled by circulating seawater through the engine that 

was pumped from outside the boat. Even though many currently operating marine diesel 

engines still use seawater to cool the engine, almost all newly built engines use a closed 

cooling system that recirculates coolant through the engine block. These engines still use raw 

seawater by using it to draw heat out of the engine coolant. These closed systems help prevent 

corrosion and allow the engine to operate at higher temperatures. As part of the cooling 

system, water-jacketed exhaust manifolds, pumps, and heat exchangers are added. Marine 

diesel engines may also have larger oil pans to help keep oil temperatures down. 

Other Additions and Modifications: Marine engines are often installed in engine 

compartments without much air flow for cooling, which can result in a number of exposed hot 

surfaces (leading to safety concerns) or performance problems from overheating the engine. 

To address safety concerns and to comply with U.S. Coast Guard regulations, marine diesel 

engines are designed to keep engine and exhaust component (exhaust manifold, turbocharger 

and exhaust pipe) temperatures cool. Recreational and light duty commercial engines can 

accomplish this by running cool water through a jacket around the exhaust system 

components. Larger engines generally use a thick insulation around the exhaust pipes. 

Marinization might also include replacing some engine parts with parts made of 

materials more durable in a marine environment. These changes include more use of chrome 

and brass to prevent corrosion. Because of the unique marine engine designs, marinizers also 

add their own front accessory drive assembly. Finally, marine engines must also be coupled 

with the lower drive unit to be applicable to a specific vessel. 

1-12 
Industry Characterization 

1.1.2.2.3 Nonroad Diesel Engine Costs of Production 

The U.S. Census Bureau does not differentiate cost of production figures for marine 

diesel engines (North American Industry Classification System [NAICS] 333618B106). 

However, because small, recreational C1, commercial C1, and commercial C2 engines are 

derived from nonroad diesel engines, costs of production for nonroad engines could be used to 

illustrate costs of production of marine diesel engines (NAICS 3336183). Costs of production 

figures are divided into major input categories of labor, materials, and capital expenditures. 

Of these categories, purchased materials account for the largest share of total costs. Based on 

data from the most recent Economic Census, costs of materials represent about 64 percent of 

the value of shipments, followed by labor at about 11 percent and capital expenditures at 

about 3 percent. (These numbers correspond with the broader “other engine manufacturing” 

category [NAICS 333618].) 

Table 1-8 lists the primary materials used in engine components.9 No breakdown of 

cost of materials used in production is available from the 2002 Economic Census for the 

specific category of marine diesel engines (NAICS 333618B106) nor for nonroad diesel 

engines (NAICS 3336183), but based on the broader “other engine manufacturing” category 

(NAICS 333618), cost of materials are dominated by cast and formed metal. Iron and steel 

accounted for 13 percent of material costs; aluminum accounted for 7 percent; injection fuel 

pumps for 5.6 percent; pistons, valves, and piston rings for 3.5 percent; and engine electrical 

equipment for 3.5 percent. All other materials and components, parts, containers, and 

supplies accounted for 52 percent; no single material accounted for more than 2 percent of 

material costs. 

Table 1-8 Nonroad and “Other Engine” Costs of Production and Materials Consumed in 2002 

NAICS Value of 

Shipments 

($106

) 

Labor ($106

)

a Cost of 

Materials 

($106

)

a

Capital 

Expenditures 

($106

)

a

333618 Other engine 

equipment manufacturing 

18,586 2,145 11,800 730 

11.5% 63.5% 3.9% 

3336183 Diesel, semi- 2,003 215 1,287 59 

diesel, and dual-fuel 

engines (except 

automobile, highway 

truck, bus, tank) 

10.7% 64.3% 2.9% 

Materials Consumed by 

333618 

Cost ($106) Share of Cost 

of Materials 

Iron and steelb 1,449 13.1% 

Aluminumc 770 6.9% 

Notes: 

a

 Percentages refer to the share of the total value of shipments. 

b

 NAICS codes 33211101, 33151001, 33120007, 33120016, 33120033. 

c

 NAICS codes 33152005, 33152003, 33631100. 

1-13 
Regulatory Impact Analysis 

1.1.2.2.4 Nonroad Diesel Engine Manufacturers and Marinizers 

As was previously discussed, marine diesel engines are typically derived from similar 

size land-based diesel engines through the marinization process. Marinization is normally 

performed by two types of firms, and has an impact on the engine’s emission characteristics. 

First, there are large engine manufacturers such as Cummins, Caterpillar, and Deere 

that marinize their land-based nonroad engines. They are referred to as domestic engine 

manufacturers (DEMs), and they are usually involved in every step of the manufacturing 

process of a marine engine. Foreign engine manufacturers (FEMs) are similar to DEM, but 

they are owned by foreign parent companies (this also pertains to DDC and EMD, which are 

owned by foreign investment companies now). Production of marine engines begins on the 

nonroad production line; however, at some stage of the production process, an engine is 

moved to a different assembly line or area where production is completed using parts and 

processes specifically designed for marine applications. 

Second, postmanufacture marinizers (PMMs), or simply marinizers, are smaller 

manufacturers that purchase complete or semi-complete land-based engines from engine 

manufacturers and complete the marinization process themselves using specially designed 

parts, potentially modifying fuel and cooling systems. 

Table 1-9 lists DEM, FEM, and PMM companies. Only four U.S.-based engine 

manufacturers produce and marinize their marine diesel engines. Cummins is the only 

company involved in two types of production. In addition to marinizing their own, Cummins 

(through its subsidiary Onan) produces generators using Kubota engines and therefore is 

included in both the DEM and postmanufacture marinizers categories. 

1-14 
Industry Characterization 

Table 1-9 Marine Engine Manufacturers 

Domestic Engine 

Manufacturers 

Foreign Engine Manufacturers Postmanufacture Marinizers 

Bombardiera Caterpillar Deutz 

Cummins EQT (parent to MTU) Brunswick 

Deere & Company Greenbriar Equity, LLC (parent 

to EMD) 

Cummins 

Daytona Marinea General Electric MAN 

Rumo Fairbanks Morsea

Volvo Klassen 

Yanmar Kohler 

Marine Corp. of Americaa

Marine Power 

NREC Power Systems 

Peninsular Diesel 

Reagan Equipmenta

Stewart & Stevenson 

Sword Marine Technology 

Valley Power Systems (parent to 

Alaska Diesel) 

Westerbeke a

 These companies’ production is not included in the 2004 PSR database. 

1.1.2.2.5 Marine Engine Dressing 

Marine engine dressing refers to the modifications made to a land-based engine that 

enable it to be installed on a marine vessel. Unlike PMMs, however, the changes made by 

marine dressers do not affect the emission characteristics of the engine. These modifications 

can be made by engine manufacturers or marine dressing firms. Modifications typically 

include installing mounting supports and a generator (in the case of an auxiliary engine) or 

propeller gears (in the case of propulsion engines). Other modifications consist of adding 

adaptors, water-cooled exhaust manifolds, water tanks, electronic instrumentation, and alarm 

systems. There are many manufacturers of this type. However, because these companies do 

not do anything to the engines to change their emission characteristics, they are exempted 

from the regulations. Thus, their coverage will be omitted in this profile. 

1.1.2.2.6 Marine Engine Remanufacturing 

Marine remanufacturers are engaged in the manufacture or assembly of 

remanufactured marine diesel engines. We have identified about 10 U.S. companies 

(aftermarket suppliers or aftermarket parts manufacturers and engine manufacturers) that 

could potentially remanufacture marine engines. Some of these companies are only involved 

in the remanufacture of locomotives. Two companies are both locomotive and marine 

remanufacturers that have already certified models.

1-15 
Regulatory Impact Analysis 

1.1.2.3 Demand Side 

Marine diesel engines can be distinguished according to whether they are used on 

commercial or recreational applications. As discussed above, the basic difference derives 

from the nature of the requirements on the engine in each application: more power density in 

recreational applications and more durability in commercial applications. In this section, we 

look at the characteristics of the four key segments of this industry; Recreational marine C1 

and small (at or below 37 kW), Commercial C1, and C2 diesel engine markets. 

Table 1-10 presents the total number of engines produced in and imported to the 

United States broken down by application category. According to the data in the PSR 

database, the largest single category is marine engines produced for propulsion purposes in 

recreational applications (17,954). A slightly smaller number was produced for all auxiliary 

functions (16,377) and the rest for propulsion purposes in commercial applications (6,524). 

Based on the engine category, the majority of the engines produced or imported were 

classified as commercial C1, followed by recreational C1 and small. Category 2 is the 

smallest category with 277 engines produced in 2002. 

Table 1-10 Marine Diesel Engine Production by Application and Use Type (2002) 

Use Type Small (≤37 

kW) 

C1 Recreational C1 Commercial C2 

Commercial 

propulsion 

NA NA 6,389 135 

Marine auxiliary 6,798 NA 9,437 142 

Pleasure 

propulsion 

3,963 13,952 NA NA 

Total 10,761 13,952 15,826 277 

A further look into the characteristics of a commercial fleet was accomplished by 

taking a random sample of nearly 400 vessels from the Inland River Record (2006).10 That 

sample suggests that the average age of vessels in that fleet is 30 years (with vessels built 

between 1944 and 2004), and the average horsepower of these vessels is 840 hp (with a range 

of 160-4,400 hp). About 68% of the engines in these vessels were built after 1972, and 31% 

were both greater than 800 hp and built after 1972. In addition, about 65% of the engines that 

are greater than 800 hp were derived from locomotive engines. Although this sample reflects 

only the characteristics of the vessels included in the Inland River Record (primarily tug and 

towboats), it provides a detailed look into one database of existing vessels. 

1.1.2.3.1 Recreational Applications 

Recreational boats (especially the larger ones powered by diesel engines) are generally 

considered discretionary goods; demand for them is typically price elastic. 

There are several reasons why consumers might choose diesel engines over gasoline 

engines for recreational applications. First, diesel engines are more durable and reliable. 

Second, diesel engines have better fuel consumption. 

1-16 
Industry Characterization 

Based on the National Marine Manufacturers Association (NMMA) sales data, there 

were approximately 5,760 diesel-powered (out of a total 10,200 diesel and gas-powered 

inboard cruiser boats) recreational boats sold in 2002. NMMA also estimated that among 

10,200 boats, 92.2 percent had a twin engine.11 Under these ratios, we estimated 11,070 

recreational marine diesel engines were sold for propulsion purposes in the United States in 

2002. This number differs from 13,952 engines imported or produced in the United States in 

2002, as reported in the PSR database. Some of the engines produced are used as the 

replacement engines; however, the PSR OELink database is probably not entirely accurate. 

Because the NMMA estimate is derived from surveying a large portion of the industry 

stakeholders, their consumption estimate seems more reliable. 

Not included in that estimate are small marine diesel engines. PSR data indicate that 

10,761 small marine diesel engines were produced in 2002, with approximately 64 percent of 

those being used for auxiliary purposes and the remainder used as maneuvering engines on 

recreational applications and as cruising engines on sailboats. 

1.1.2.3.2 Commercial C1 Applications 

Engines in this category are inputs into various commercial applications, such as 

seasonal and commercial fishing vessels, emergency rescue vessels, ferries, and coastal 

freighters. 

Commercial vessels are inputs into a wide range of production processes that generate 

products and services. As a result, the demand for C1 engines is linked directly to the demand 

for boats, and indirectly through the supply chain to the demand for final products and 

services produced with commercial ships and boats. 

No data are readily available on the volumes of commercial boats produced annually 

in the United States. However, based on the 2004 Workboat Construction survey of 

approximately 400 commercial boats scheduled to be delivered in 2005, we estimate that 40 

percent of them were C1, 55 percent were C2, and 5 percent were C3 (Workboat, 2005). 

Using these estimates, we find that 160 C1 engine-powered commercial vessels were 

produced in the United States in 2004. Once again, this number does not correspond with 

6,389 engines listed by PSR. More than likely Workboat Construction journal’s survey lists 

the largest commercial ships and boats, and many smaller commercial boats are unaccounted 

for. 

1.1.2.3.3 Commercial C2 Applications 

Commercial C2 engines might be used on crew and supply boats, trawlers, and tug 

and tow boats. Many of the engines are also used as large auxiliary engines on ocean-going 

vessels. Based on the Workboat Construction survey estimate, there were 220 C2 enginepowered commercial vessels built in the United States in 2004.12 This number is lower 

compared with 2002 production volume (277 engines) listed by PSR. 

Like commercial C1 engines, commercial C2 engines are inputs in vessels, which are 

in turn inputs in production processes that generate products and services. Therefore, demand 

1-17 
Regulatory Impact Analysis 

for commercial C2 engines is linked directly to the demand for commercial C2 vessels and 

indirectly to the demand for products and services produced with these vessels. 

1.1.2.4 Market Structure 

Figure 1-1-2 and Figure 1-1-3 present small and recreational C1 marine diesel engine 

market breakdown by the type of a supplier. In 2002, a majority of the small marine diesel 

engines (60 percent) were supplied by engine marinizers, with about half of that value 

supplied by engine dressers, and only 11 percent by foreign engine manufacturers (FEM) that 

oversee the entire production process. No domestic engine manufacturers (DEM) supplied 

engines to this market. The situation is opposite for the recreational C1 market, where DEMs 

supply 45 percent of engines, and FEMs supply 26 percent. Marinizers accounted for 28 

percent, and dressers for less than 1 percent of the recreational C1 market supply. 

Table 1-11 details the top three engine manufacturers and marinizers in the small (at 

or below 37 kW) and C1 recreational categories. The majority of the engines in the small 

category are supplied by U.S.-based marinizer Westerbeke (48 percent). In 2002, Japanese 

manufacturer Yanmar and U.S.-based marinizer Kohler both had approximately 10 percent of 

the market share. Cummins, a DEM, serves as a marinizer in this market. Kubota engines, 

marinized by Cummins, accounted for approximately 3.5 percent of small marine diesel 

engine market supply in 2002. 

1-18 
Industry Characterization 

Figure 1-1-2 Small (≤37 kW) Marine Diesel Engine Market Supply by Manufacturer Type (2002) 

Figure 1-1-3 C1 Recreational Marine Diesel Engine Market Supply by Manufacturer Type (2002) 

1-19 
Regulatory Impact Analysis 

Table 1-11 Top Three Small and Recreational C1 Marine Diesel Engine Manufacturers and Marinizers 

(2002) 

2002 Production Market Share 

C1 

Engine Manufacturers 

 Caterpillar 

 Cummins 

 Yanmar 

 Top 3 Firms’ Production 9,524 68.3% 

Engine Marinizers 

 Westerbeke 

 Peninsular Diesel 

 Brunwick Corporation 

 Top 3 Firms’ Production 2,800 20.1% 

Total Dressers 23 0.2% 

Total C1 Market 13,952 

Small (≤37 kW) 

Engine Manufacturers (D) (D) 

 Yanmar 

Engine Marinizers 

 Westerbeke 

 Valley Power Systems, Inc. 

 Kohler 

 Top 3 Firms’ Production 7,136 66.3% 

 Total Dressers 2,000–3,000a 25%–30%a

 Total Small Market 10,761 a

 The range is provided to avoid disclosing proprietary information of individual companies. 

(D) = Data have been withheld to avoid disclosing proprietary information of individual companies. 

1.1.2.4.1 C1 Commercial Applications 

The supply structure of the commercial C1 marine diesel engines market resembles 

the supply structure of the recreational C1 market, with DEMs and PMMs supplying 76 

percent of the engines to the market (Figure 1-1-4). As opposed to the recreational C1 

market, dressers supply a larger portion of the commercial C1 market (19 percent), with 

FEMs supplying 5 percent. 

1-20 
Industry Characterization 

Figure 1-1-4 Commercial C1 Marine Diesel Engine Market Supply by Manufacturer Type (2002) 

Commercial C1 marine diesel engine market shares are listed by the type of 

manufacturer in Table 1-12. DEMs Caterpillar and Deere and engine marinizer Kohler have 

approximately equal market shares of 15 percent each. They are followed by U.S.-based 

marinizer Westerbeke with an 11 percent market share. Even though engine dressers are not 

covered by this rule, it is worth noting that the vast majority of the engines supplied in the 

commercial C1 market by these companies are auxiliary engines. 

Table 1-12 Top Three Commercial C1 Marine Diesel Engine Manufacturers and Marinizers (2002) 

C1 2002 Production Market Share 

Engine Manufacturers 

 Caterpillar 

 Deere & Company 

 Cummins 

 Top 3 Firms’ Production 6,452 40.8% 

Engine Marinizers 

 Kohler 

 Westerbeke 

 Valley Power Systems, Inc. 

 Top 3 Firms’ Production 5,690 36.0% 

Total Dressers 1,383 8.7% 

Total C1 Market 15,826 

1-21 
Regulatory Impact Analysis 

1.1.2.4.2 Commercial C2 Applications 

The commercial C2 marine diesel market is not supplied by dresser companies; most 

of the supply comes from marinizers, which supply approximately half of its volume. U.S.-

based companies are dominant in the commercial C2 marine diesel engine market. Among 

engine manufacturers, Caterpillar, and among marinizers, General Motors and Stewart and 

Stevenson, together compose 78.4 percent of the market. Caterpillar is followed by Japanese 

manufacturer Yanmar and German MAN B&W with 11 and 6 percent, respectively (Table 

1-13). 

Table 1-13 Top Three Commercial C2 Marine Diesel Engine Manufacturers and Marinizers (2002) 

C2 2002 Production Market Share 

Engine Manufacturers 

 Caterpillar 87 

 Greenbriar Equity LLC 73 

 Yanmar 31 

Top 3 Firms’ Production 191 69.0% 

Engine Marinizers 

 Stewart and Stevenson (D)a (D) 

Total Dressers — 0.0% 

Total C2 Market 277 a

 (D) = Data have been withheld to avoid disclosing proprietary information of individual companies. 

1.1.2.4.3 Pricing Behavior of Marine Diesel Engine Markets 

Discussions about market competitiveness usually focus on two types of pricing 

behavior: perfect competition (price-taking behavior) and imperfect competition (lack of 

price-taking behavior). Under the former scenario, buyers and sellers take (and thus are 

“price takers”) the market price set in a competitive equilibrium: the market price equals the 

value consumers place on the marginal product, as well as the marginal cost to producers. 

Under this scenario, firms have some ability to influence the market price of the output they 

produce. For example, a firm might produce a commodity with unique qualities that 

differentiate its product from its competitors’ product. The value consumers place on the 

marginal product, the market price, is greater than the cost to producers. Thus, the social 

welfare is reduced under this scenario. 

As evident from the market share information presented in this report, marine diesel 

engine markets are moderately (small and commercial C1) to highly (recreational C1 and 

commercial C2) concentrated and thus have a potential for emergence of imperfect 

competition. Nevertheless, our analysis suggests mitigating factors will limit prices from 

rising above the marginal cost; therefore, the assumption of perfect competition is justified. 

First, the threat of entry encourages price-taking behavior. Industries with high profits 

provide incentives to new firms to enter the market and lower the market price to their 

competitive levels. In all of the marine diesel markets, domestic and foreign candidates can 

enter any of these markets without incurring significant costs. 

1-22 
Industry Characterization 

Second, the data on capacity utilization rates published by the Federal Reserve (for 

machinery, NAICS 333) suggest that excess capacity exists in the broad category that also 

includes converted internal combustion engines industry (NAICS 333618B106). February 

2006 data present an industry utilization rate of 82.6 percent. If these data do, in fact, indicate 

excess capacity in the marine diesel engine industry, then the ability to raise prices is limited 

by excess idle capacity. 

Third, other theories place less value on market shares as a determinant of pricing 

behavior and examine the role of potential competition instead. For instance, three conditions 

of perfectly contestable markets demonstrate how potential competition may lead to perfect 

competition:13

• New firms have access to the same production technology, input prices, products, and 

demand information as existing firms 

• All costs associated with entry can be fully recovered 

• After learning about new firms’ entry, existing firms cannot adjust prices before these 

new firms supply the market 

Although the extent to which these conditions apply to marine diesel engine markets is 

not clear, the theory suggests that market shares alone should not necessarily be considered as 

an indicator of imperfect competition in the market. 

1.1.2.5 Historical Market Data 

1.1.2.5.1 Recreational Applications 

The historical market statistics are presented as a means to assess the future of marine 

diesel engine production. Information on production trends is presented here. 

Historical production volumes for recreational C1 and small marine diesel engine 

markets are presented in Table 1-14. The small marine diesel engine market demonstrated 

continuous growth in production between 1998 and 2002, growing by 37 percent since 1998. 

The recreational C1 market experienced a slight peak in 2000 with 7 percent growth and then 

leveled off in 2002 at a slightly higher volume than it was in 1998. 

Table 1-14 Historical Market Trends for Small and Recreational C1 Marine Diesel Markets 

 Recreational C1 Small 

2002 13,952 10,761 

2001 13,754 9,833 

2000 14,408 9,576 

1999 13,836 7,997 

1998 13,446 7,853 

Percentage Change 3.8% 37.0% 

1-23 
Regulatory Impact Analysis 

1.1.2.5.2 Commercial C1 Applications 

The commercial C1 engine market demonstrated a strong steady growth in the past 5 

years. Starting at 10,508 engines produced and imported into the United States in 1998, it 

grew by more than 50 percent and equaled 15,826 engines in 2002 (Table 1-15). 

Table 1-15 Historical Market Trends Commercial C1 Marine Diesel Market 

Year Production 

2002 15,826 

2001 14,078 

2000 12,838 

1999 12,178 

1998 10,508 

Percent Change 50.6% 

1.1.2.5.3 Commercial C2 Applications 

The commercial C2 market has a relatively small volume of sales compared to the 

recreational and commercial C1 markets. Nevertheless, the commercial C2 market 

experienced significant growth in the past 5 years. In the period from 1998 to 2002, market 

volume more than doubled and equaled 277 engines in 2002 (Table 1-16). 

Table 1-16 Historical Market Trends Commercial C1 Marine Diesel Market 

Year Production 

2002 277 

2001 231 

2000 200 

1999 138 

1998 134 

Percentage Change 106.7% 

1.1.3 Marine Vessel Manufacturers 

Marine vessels include a wide variety of ships and boats. Several alternative 

definitions exist to distinguish between ships and boats. For this profile, ships are defined as 

those marine vessels exceeding 400 feet in length. They are built to purchasers’ specifications 

in specialized “Main Shipyard Base” ship yards, and typically powered by Category 3 diesel 

engines. Under this definition most of the vessels powered by small, C1 or C2 diesel engines 

would be considered boats. In this section, the terms “vessel” and “boat” will be used 

interchangeably. Vessels powered by C1 and C2 engines vary widely; they may be made 

from fiberglass-reinforced plastic (FRP or fiberglass), aluminum, wood, or steel. Some 

vessels are serially produced using assembly line methods; others are individually built to 

meet purchasers’ specifications in boatyards or in the same yards that build ships. Small 

boats may be powered by small spark-ignition (gasoline) engines. Vessels covered by this 

profile include a small share of recreational boats: inboard cruisers, especially those over 40 

feet in length. In addition the profile covers diesel-powered commercial and governmental 

1-24 
Industry Characterization 

vessels such as tug/tow boats, fishing vessels, passenger vessels, cargo vessels, offshore 

service vessels and crew boats, patrol boats, and assorted other commercial vessels. 

The Economic Census includes two industry sectors, NAICS 336611 Ship Building 

and Repairing and NAICS 336612 Boat Building, that together cover the marine vessel types 

addressed in this profile. Each NAICS includes some vessels not included in this profile. 

NAICS 336612 defines boats as “watercraft not built in shipyards and typically of the type 

suitable or intended for personal use.”; thus, NAICS 336612 includes essentially recreational 

vessels; within this NAICS, NAICS 3366123 covers inboard motor boats, including those 

powered by diesel engines. Thus, the diesel-powered recreational vessels covered by this 

profile represent only a relatively small share of NAICS 336612. NAICS 336611 comprises 

establishments primarily engaged in operating a shipyard, fixed facilities with drydocks and 

fabrication equipment capable of building a “watercraft typically suitable or intended for 

other than personal or recreational use.”14 Commercial and governmental vessels powered by 

small, C1 and C2 diesel engines are included in NAICS 336611, along with larger ships that 

are powered by C3 engines and thus not covered by this profile. 

1.1.3.1 Overview of Vessels 

This profile covers a wide variety of vessels, including recreational vessels and 

smaller commercial, service, and industrial vessels, generally less than 400 feet in length. 

Commercial vessels under 400 feet long dominate inland and coastal waters where shallow 

drafts restrict access by larger ships. Depending on their mission, C1- and C2-powered 

vessels also may operate in the Great Lakes, coastwise, intercoastal, noncontiguous, and/or 

transoceanic environments. The principal commercial boat types are tugboats, towboats, 

offshore supply boats, fishing and fisheries vessels, passenger boats, and industrial boats, such 

as cable- and pipe-laying boats, oceanographic boats, dredges, and drilling boats. Passenger 

boats include crewboats, excursion boats, and smaller ferries. 

Most commercial vessels covered by this profile are U.S.-built, U.S.-owned and U.S.-

operated. Under provision of the Jones Act (Section 27, Merchant Marine Act, 1920), vessels 

transporting merchandise between U.S. ports must be built in and documented under the laws 

of the United States and owned and operated by persons who are citizens of the United States. 

Because C1 and C2 diesel engines are frequently used to power vessels that operate in inland 

waters or coastwise, they are generally operating between U.S. ports. Thus, many cargo 

vessels powered by C1 and C2 diesel engines are required to be U.S.-built, -owned, and -

operated, unless a waiver is granted by the Secretary of the Treasury. 

Generally excluded from this profile, because they are powered by C3 engines, are 

larger merchant and military vessels, typically exceeding 400 feet in length, that engage in 

waterborne trade and/or passenger transport or military operations. Commercial and 

government-owned (e.g., military) ships operate in Great Lakes, coastwise, intercoastal, 

noncontiguous (between United States mainland and its noncontiguous territories, such as 

Alaska, Hawaii, and Puerto Rico), and/or transoceanic routes. The principal commercial ship 

types are dry cargo ships, tankers, bulk carriers, and passenger ships. Dry cargo ships include 

break bulk, container, and roll-on/roll-off vessels. Passenger ships include cruise ships and 

the largest ferries. Military ships include aircraft carriers, battleships, and destroyers. Also 

1-25 
Regulatory Impact Analysis 

excluded from the profile are the smallest recreational, commercial, and government vessels, 

which are powered by gasoline outboard, stern-drive, or inboard engines. Figure 1-1-5

illustrates the size of the U.S. commercial fleet over time from 1980 to 2003 and the 

distribution between larger and smaller vessels. Compared with smaller commercial vessels, 

larger commercial vessels represent a small fraction of the U.S. commercial fleet. 

Figure 1-1-5 includes vessels as small as 1,000 gross tons in the ship, rather than boat 

population, and omits key categories of boats (smaller vessels), such as supply boats and 

fishing boats.15 It is very difficult to develop useful criteria which will allow the separation of 

vessel populations into those powered by the various engine categories. Nonetheless, this 

analysis provides some insight as to the relative proportion of vessels in the U.S. fleet 

powered by C1/C2 engines versus C3 engines. 

Figure 1-1-5 U.S. Commercial Fleet (1980 to 2003) 

1.1.3.2 Overview of Vessel Manufacturers 

This report classifies vessel manufacturing facilities (“yards”), according to the types 

of vessels manufactured. The Economic Census reports on two industry segments that are 

related to vessel manufacture—shipbuilding and repairing (NAICS 336611) and boatbuilding 

(NAICS 336612). Shipbuilding facilities typically have drydocks. NAICS 336612 

encompasses facilities that build “watercraft suitable for personal or recreational use,” which 

corresponds closely to recreational boats, and NAICS 336611 includes facilities that build 

larger commercial and government vessels. Both NAICS codes include vessels not covered by 

this profile. 

NAICS 336611 includes generally one-of-a-kind vessels built in a shipyard with 

drydock facilities, including vessels powered by Category 1 and 2 diesel engines, as well as 

the larger Category 3 engines. Most vessels manufactured by this NAICS code are for 

commercial or governmental applications (e.g., Coast Guard, military, Army Corps of 

Engineers, municipal harbor police). 

1-26 
Industry Characterization 

NAICS 336612 covers generally recreational vessels. These may be built using 

repetitive methods, such as an assembly line process or individually; it includes those 

powered by gasoline, alcohol, and diesel engines. Within NAICS 336612, only larger (over 

40 feet) inboard cruisers are predominantly powered by diesel engines. This segment of 

NAICS 336612 (NAICS 3366123 Inboard Motorboats) includes only 82 establishments, less 

than 7 percent of the total in the NAICS code. Because most of the smaller inboard 

motorboats are SI-powered, the number of facilities manufacturing diesel-powered 

recreational vessels is even smaller. The information summarized in Table 1-17 shows 

information about establishments and companies in NAICS 336611 and 336612, and indicates 

that there are a large number of small establishments in both of these industry segments.16 

Most companies in both NAICS codes are single-establishment companies. 

Table 1-17 2002 Economic Census Data on Shipbuilding and Boatbuilding Industries 

NAICS 336611 

(shipbuilding) 

NAICS 336612 

(boatbuilding) 

Number of establishments 639 1,123 

Number of companies 586 1,063 

Establishments with 100+ 

employees 

91 134 

Establishments with 500+ 

employees 

21 16 

Within NAICS 336611, the U.S. Maritime Administration (MARAD) classifies yards 

as either first-tier or second-tier according to building capacity. In the Report on Survey of 

U.S. Shipbuilding and Repair Facilities, MARAD (2003) identifies 24 first-tier yards, which 

form the “major shipbuilding base” (MSB) in the United States. The 24 MSB yards satisfy 

several requirements, including at least one construction position capable of accommodating a 

vessel that is 400 feet in length or over and an unobstructed waterway leading to open water 

(i.e., locks, bridges) and the channel water must be a minimum of 12 feet deep. While MSB 

yards are the only ones to manufacture large ships, many of them also produce smaller 

commercial vessels. Second-tier yards do not meet these criteria and include many small- and 

medium-sized yards that construct and repair boats.17

1.1.3.3 Recreational Vessels 

This section describes the recreational boat manufacturing industry, with special 

attention to the segment of the industry using diesel engines. 

1.1.3.3.1 Types of Recreational Vessels 

U.S. boatbuilders construct a variety of recreational boats, including ski/wakeboard 

boats, powerboats, racing boats, sailboats, recreational fishing boats, and yachts. Only a small 

segment of recreational boats are powered by diesel engines and thus addressed by this 

profile. Diesel-powered types of vessels include inboard cruisers and most of the larger 

yachts. 

1-27 
Regulatory Impact Analysis 

1.1.3.3.2 Supply of Recreational Vessels 

Boats for personal and recreational use can be manufactured from many different 

materials, including fiberglass-reinforced plastic (FRP), aluminum, rotationally molded 

(rotomolded) polyethylene or other thermoplastic materials, and wood. Only relatively large 

(over 40 foot) inboard cruisers commonly use diesel engines; diesel engines used in 

recreational vessels are almost exclusively C1 engines, although C2 engines may be used on 

the largest yachts. Among recreational boats, large inboard cruisers are less likely to be 

serially produced; because they are quite costly, they tend to be customized to buyers’ 

specifications. Like smaller serially produced boats, the most common hull material is FRP. 

1.1.3.3.3 Production Process 

The most common material used in boat manufacturing is FRP. Boats made from FRP 

are typically manufactured serially. Using FRP makes it very difficult to incorporate 

purchaser preferences into a vessel’s design because 1) many features are designed into 

fiberglass molds, making customization time consuming and expensive and 2) vessels 

constructed from FRP are very sensitive to changes in their vertical or horizontal centers of 

gravity, making it difficult to change a particular design. In some cases, boat manufacturers 

produce the FRP hulls and decks used in constructing their boats; in other cases the FRP hulls 

and decks of boats are manufactured by a contractor for the boat manufacturer. 

The process typically used to manufacture these boats is known as open molding. In 

this process, separate molds are used for the boat hull, deck, and miscellaneous small FRP 

parts such as fuel tanks, seats, storage lockers, and hatches. The parts are built on or inside 

the molds using glass roving, cloth, or mat that is saturated with a thermosetting liquid resin 

such as unsaturated polyester or vinylester resin. The liquid resin is mixed with a catalyst 

before it is applied to the glass. The catalyzed resin hardens to form a rigid shape consisting 

of the plastic resin reinforced with glass fibers. 

The FRP boat manufacturing process generally follows the following production 

steps: 

• Before each use, the molds are cleaned and polished and then treated with a mold 

release agent that prevents the part from sticking to the mold 

• The open mold is first spray coated with a pigmented polyester resin known as a 

gel coat that will become the outer surface of the finished part. The gel coat is 

mixed with a catalyst as it is applied so that it will harden 

• After the gel coat has hardened, the inside of the gel coat is coated with a skin coat 

of polyester resin and short glass fibers and then rolled with a metal or plastic 

roller to compact the fibers and remove air bubbles. The fibers are applied in the 

form of a chopped strand mat or chopped roving from a chopper gun; the skin coat 

is about 90 mils (0.09 inches) thick and is intended to prevent distortion of the gel 

coat (known as “print through”) from the subsequent layers of fiberglass and resin 

1-28 
Industry Characterization 

• After the skin coat has hardened, additional glass reinforcement in the form of 

chopped roving, chopped strand mat, woven roving, or woven cloth is applied to 

the inside of the mold and saturated with catalyzed polyester resin. The resin is 

usually applied with either spray equipment or by hand using a bucket and brush 

or paint-type roller. The saturated fabric is then rolled with a metal or plastic 

roller to compact the fibers and remove air bubbles 

• More layers of woven glass or glass mat and resin are applied until the part is the 

desired thickness; the part is then allowed to harden while still in the mold. As the 

part cures, it generates heat from the exothermic reactions that take place as the 

resin hardens; very thick parts may be built in stages to allow this heat to dissipate 

to prevent heat damage to the mold 

• After the resin has cured, the part is removed from the mold and the edges are 

trimmed to the final dimensions 

• The different FRP parts of the boat are assembled using small pieces of woven 

glass or glass mat and resin, adhesives, or mechanical fasteners 

• After the assembly of the hull is complete, the electrical and mechanical systems 

and the engine are installed along with carpeting, seat cushions, and other 

furnishings and the boat is prepared for shipment 

• Some manufacturers paint the topsides of their boats to obtain a superior finish; the 

larger boats generally also require extensive interior woodwork and cabin 

furnishings to be installed 

As noted above, only the larger inboard cruisers are likely to have diesel propulsion 

engines. Of all inboard cruisers, 56 percent are diesel-powered. For boats less than 40 feet in 

length, less than 35 percent are diesel-powered; for those over 40 feet in length, 85 percent are 

diesel-powered. Table 1-18 provides estimates of inboard cruiser retail sales by engine type 

and length of boat. In 2003, 5,191 diesel-powered inboard cruisers were sold; of these, 3,032 

were 41 feet or longer. Another 988 diesel-powered cruisers ranged from 36 to 40 feet in 

length. Only 454 were 30 feet long or less.18

Table 1-18 Estimates of Inboard Cruiser Retail Unit Sales by Engine Type and Length of Boat 

1997 1999 2001 2003 

Boat Length Gas Diesel Gas Diesel Gas Diesel Gas Diesel 

30' and under 917 178 1,064 435 1,059 495 279 454 

31'–35' 1,525 309 2,199 673 2,458 953 1,294 717 

36'–40' 1,048 492 1,142 804 1,280 991 1,984 988 

41' and over 529 1,302 428 2,655 420 3,144 572 3,032 

Total 4,019 2,281 4,833 4,567 5,217 5,583 4,109 5,191 

Table 1-19 summarizes the sales data from 1997 through 2003 for recreational boats. 

In 2003, an estimated 9,200 inboard cruisers were sold; 97 percent of inboard cruisers over 31 

feet long were powered by twin engines. Sales in the United States are expected to continue 

1-29 
Regulatory Impact Analysis 

to decrease as more and more of the larger recreational boats are being built overseas (e.g., 

Taiwan).19

Table 1-19 Estimates of Inboard Cruiser Retail Unit Sales by Single vs. Twin Engine and Length of Boat 

1997 1999 2001 2003 

Boat Length Single Twin Single Twin Single Twin Single Twin 

30' and under 789 306 1,028 471 1,004 550 463 271 

31'–35' 91 1,742 97 2,775 155 3,256 86 1,925 

36'–40' 51 1,490 112 1,834 233 2,038 136 2,815 

41' and over 30 1,801 23 3,060 32 3,532 20 3,584 

Total 961 5,339 1,260 8,140 1,424 9,376 705 8,595 

While not all inboard cruisers are diesel-powered, the production costs for inboard 

cruisers as a group are likely representative of the relative costs of various inputs used in 

producing diesel-powered inboard cruisers. Production costs for builders of inboard cruisers 

include the costs of materials, labor, and capital equipment. Materials costs are more than 

double the cost of labor for these producers and represent roughly half of the value of 

shipments of inboard cruisers (see Table 1-20).20 Because diesel engines are generally more 

expensive than gasoline engines, materials may represent an even larger share of dieselpowered inboard cruiser costs. 

Table 1-20 Costs of Production for NAICS 3366123, Inboard Motorboats, Including Commercial and 

Military, Except Sailboats and Lifeboats 

Establishments Number Payroll 

($1,000) 

Number Hours 

(1,000) 

Wages 

($1,000) 

Cost of 

Materials 

($1,000) 

Capital 

Expenditure

s ($1,000) 

Value of 

Shipments 

($1,000) 

82 13,412 427,949 10,457 20,773 299,815 1,197,464 39,900 2,384,478

1.1.3.3.4 Demand for Recreational Vessels 

Recreational boats are final consumer goods, and are generally considered 

discretionary purchases. Demand for recreational boats is typically characterized by elastic 

demand. 

1.1.3.3.5 Industrial Organization for Recreational Vessel Manufacturers 

Recreational boat builders are located along all coasts and major waterways. Table 1-

21 provides sales and employment information of recreational diesel boat builders.21,22,23 Of 

the 36 companies for which data were identified, only 9 employ more than 500 employees. 

Two large, multi-facility companies (Genmar and Brunswick) employ 21,000 and 6,000 

employees respectively. Companies with fewer than 500 employees would be considered 

small businesses under the criteria of the Small Business Administration for NAICS 336612. 

Based on that definition, the majority of firms producing recreational diesel boats would thus 

be considered small entities. 

1-30 
Industry Characterization 

Table 1-21 Employment Distribution of Companies that Build Recreational Boats 

Employment Range Number of Firms Revenue Range ($Millions) 

0–100 11 1.3 – 8.5 

101–250 9 9.2 – 45.0 

251–500 7 20.2 – 101.7 

501–1,000 4 63.2 – 131.0 

1,000+ 5 45.60 – 5,229 

Total number of firms 36 

Although there are a few large companies in the recreational diesel boat building 

industry, there are many more small companies. The boat yards are located on water bodies 

throughout the country, and many serve somewhat regional markets. Because there are a 

relatively large number of suppliers, because there is increasing competition from foreign 

suppliers, and because barriers to entry and exit are low, it is reasonable to characterize the 

markets for recreational diesel vessels as competitive. As described in section 1.1.2.4.3, the 

potential for competition and entry (contestable markets) forces existing producers to behave 

in a competitive manner. 

1.1.3.3.6 Markets and Trends in the Recreational Vessel Manufacturing Industry 

As summarized in Table 1-22, prices for inboard cruisers 41 feet and longer have 

displayed no clear trend during the period 2001–2003.24 Prices in most categories dipped in 

2003, reaching prices below 2001 levels. This may result from increased competition from 

foreign suppliers. 

Table 1-22 Estimated Average Retail Selling Price of Recreational Inboard Boats by Length of Boat 

Boat Length 1997 1998 1999 2000 2001 2002 2003 

41' and over $490,409 $475,869 $469,866 $516,146 — — — 

41'–49' — — — — $449,990 $419,873 $384,329 

50'–59' — — — — $963,197 $898,256 $842,578 

60'–65' — — — — $2,166,030 $2,280,029 $2,220,833 

66' and over — — — — $3,627,189 $4,464,111 $2,816,731 

Information from NMMA indicates that the number of larger recreational boats being 

built abroad, in places like Taiwan, has increased significantly in the last few years.25 A 

recent NMMA report on recreational boat sales compiled U.S. Department of Commerce 

import and export data, as reported in the U.S. International Trade Commission database. The 

2003 data confirmed that the trade imbalance continues to grow. Factors affecting this growth 

include the rising cost of shipping, trade disputes between the U.S. and Europe, and the 

strength of the dollar, which makes it difficult for U.S. boatbuilders to offer competitive 

pricing overseas. 

Table 1-23 shows that exports of vessels declined from 1997 to 2001, then increased, 

posting a substantial increase between 2002 and 2003.26 Imports continue to outpace exports, 

with the trade balance deficit roughly tripling between 1997 and 2003. However, because of 

the substantial increase in exports, the deficit actually fell between 2002 and 2003. 

1-31 
Regulatory Impact Analysis 

Table 1-23 Value of Imported and Exported Vessels (in $Millions) 

1997 1998 1999 2000 2001 2002 2003 

Boats export $678.6 $674.8 $698.5 $662.0 $560.4 $600.5 $746.5 

Boats import $835.0 $874.7 $984.2 $1,074.8 $1,113.1 $1,157.7 $1,207.2 

Trade balance –$156.40 –$199.90 –$285.70 –$412.80 –$552.70 –$557.20 –$460.70 

1.1.3.4 Commercial Vessels 

This section builds on earlier work by EPA to characterize commercial vessels and 

identify how many of each type are powered by C1 and C2 diesel engines. U.S. boat builders 

construct a wide variety of commercial vessels. Most of these boat builders are singleestablishment companies and manufacture a limited number of boat designs. A handful of 

yards (e.g., Halter Marine) also have the capacity to build ships that would be powered by C3 

engines. Most commercial and government boats are manufactured individually or 

customized to purchaser’s specifications. 

U.S. boatyards build boats primarily used on inland and coastal waterways between 

U.S. ports. This is because cargo vessels on these routes must satisfy Jones Act requirements 

(U.S. Department of Transportation, 1998)(Section 27 of the Merchant Marine Act of 1920) 

that any vessel transporting merchandise between U.S. ports be built in the U.S., and be 

owned and operated by U.S. citizens. For this reason, the U.S. commercial boatbuilding 

industry has a protected local market and does not face the intense foreign competition that 

recreational boat builders or shipbuilders constructing large C3 vessels for international trade 

do. Clients include waterways operators (e.g., tugboats and pushboats), offshore petroleum 

exploration and drilling companies (e.g., liftboats, crewboats, supply boats), fisheries 

companies (e.g., fishing and fish processing boats), industrial companies, (e.g., cable-laying 

boats), and research organizations (e.g., oceanographic research vessels). 

The markets for commercial and governmental vessels can be modeled as if they were 

competitive. While the Jones Act prohibits foreign manufacture of cargo vessels trading 

between U.S. ports and the Passenger Services Act imposes a fee of $200 per passenger on 

carriers transporting passengers between U.S. ports unless the vessels are U.S.-built, -owned, 

and -operated, most U.S. markets for commercial vessels have relatively low barriers to entry 

and exit. There are a significant number of U.S. firms in each market segment, and they 

compete for both government and commercial contracts. 

For the commercial boat market, we collected much of the background information in 

a separate report.27 Although the objective of that report was to develop inputs for emissions 

inventory modeling, the report provides a general characterization of commercial vessels, and 

estimates both C1 and C2 vessel counts for various applications. This report adopts the same 

commercial/governmental vessel categories and definitions. 

1.1.3.4.1 Tug and Towboats 

Towboats, also known as tugboats, include boats with rounded bows used for pulling 

(towboats) and boats with square bows for pushing barges, known as pushboats. Towboats 

that pull or push barges are referred to as line-haul boats, and are the largest category of 

1-32 
Industry Characterization 

towboats. Specialized towboats may also be used for maneuvering ships in harbors, channel 

dredging, and construction activities. Towboats vary widely in size and configuration, 

ranging from small harbor tugs less than 30 feet in length to large ocean-going tugs over 100 

feet. 

Data from WorkBoat Magazine’s annual construction survey are shown in Table 

1-24.

28 Participating in this survey is voluntary, and only 56 of more than 500 companies that 

build commercial boats and ships responded. The voluntary nature of the survey may result 

in some selection bias such that the respondents are not fully representative of the 

nonrespondents. This effect may be relatively stable over time, however, so that trends in the 

data may be indicative of trends in the industry as a whole. 

Table 1-24 shows that the number of towboats (including towboats, pushboats, tugs, 

and AHTS) in production increased from 39 in 2003 to 57 in 2004, and 73 in 2005. The 

Category 2 Vessel Census estimated that 3,164 of 4,337 towboats in existing databases had 

C1 engines.29 Thus, it is likely that the majority of the newbuilt towboats are also powered by 

C1 engines. According to the Vessel Census, the majority of these towboats operate in the 

U.S. Gulf and U.S. Inland areas. 

1-33 
Regulatory Impact Analysis 

Table 1-24 U.S. Commercial Boat Orders, 1993, 1994, 1997 and 2003, 2004, 2005 

Number of Boats Produced 

Vessel Type 1993 1994 1997 2003 2004 2005 

Number of survey respondents 85 83 84 40 46 56 

Casino/gaming 34 27 6 

Passenger (dive, dinner, 

excursion, ferries, sightseeing, 

water taxi, charter) 

102 95 68 44 31 40 

Crew, crew/supply pilot, 

personnel launch 

27a 41 44 17 31 18 

Supply/service 5 81 37 25 29 

Liftboat, utility 26b 34 5 7 8 

Pushboat, towboat, tug 28 60 88c 39 57 73 

Fire, rescue 5 7 2 12 2 

Boom, spill response 60 33 38 4 10 6 

Small craft (assorted), tender 44d 124e 38 17 7 14 

Patrol (military, nonmilitary) 99f 89 48 74 69 92 

Other military 79 27 6 24 

Others 26 33 38 110g 149 155 

Total number of boats 446 512h 569 376 405 460 

Notes: 

a

 Supply boats were consolidated with crew/supply boats and pilot boats. 

b

 General workboats were consolidated with utility boats in the 1993 survey. 

c

 AHTSs were consolidated with pushboats, towboats, and tugs. 

d

 Research and survey boats were consolidated with tenders in the.1993 survey and in the table for 2004 and 

2005. 

e

 Research, survey, and utility boats were consolidated with the assorted small craft and tenders in the 1994 

survey. 

f

 Fireboats were consolidated with the patrol boats in the 1993 survey. 

g

 The total number of “other” boats in included nonself-propelled vessels (2003–42 vessels, 2004–92 vessels, 

2005–80 vessels). 

h

 The total number of boats in 1994 did not include the 111 RIBS, skiffs, or small utility, or the 26 support, 

minehunter, or landing craft reported. 

1.1.3.4.1.1 Supply of Tugs and Towboats 

The majority of towboats are manufactured individually according to buyer 

specifications. Some of the smallest ones may be serially produced. Towboats are durable, 

all but the smallest are made of steel, and have relatively large engines for their dimensions. 

Commercial shipyards and boatyards building towboats use a variety of manufacturing 

processes, including assembly, metal finishing operations, welding, abrasive blasting, 

painting, and the use of engines for crane operation and boilers. The typical ship construction 

process begins with steel plate material. The steel is formed into shapes, abrasively cleaned 

(blasted), and then coated with a preconstruction primer for corrosion protection. This is 

usually done indoors at the bigger shipyards and most facilities have automated these steps. 

Using the preformed steel plates, small subassemblies are then constructed and again a primer 

coat is applied. Larger subassemblies are similarly put together and primed to protect the 

1-34 
Industry Characterization 

steel substrate material. At some point in the construction, components are moved outdoors 

to work areas adjacent to the drydock. Final assembly and engine installation are done at the 

drydock. 

Based on statistics for the shipbuilding NAICS code, NAICS 336611, materials 

account for more than 50 percent of the production costs, and labor for approximately 40 

percent. Energy costs, investment in capital equipment, rental payments, and business 

services all account for smaller shares of total value of shipments. 

1.1.3.4.1.2 Demand for Tugs and Towboats 

Towboats are purchased by towing companies that move cargo on barges on coastal 

routes or on the nation’s rivers. According to the American Waterways Operators (AWO), 

the tugboat, towboat, and barge industry include more than 4000 operating tugs/towboats and 

more than 27,000 barges. These vessels move more than 800 million tons of raw materials 

and finished goods each year, including more than 20 percent of the nation’s coal, more than 

60 percent of the nation’s grain exports, and most of New England’s home heating oil and 

gasoline.30 In addition to commodity transportation, tugs are needed within harbors to 

maneuver ships to and from their berths, and to assist with bunkering and lightering. The 

demand for towboats is thus derived from the demand for commodity transportation services, 

which in turn is derived from the demand for the commodities being transported. According 

to the AWO, mandated replacement of single hulled barges with double hulled barges in the 

oils transportation sector will result in some replacement of the towing vessel fleet as larger 

tugs are required to move heavier barges. Further, the harbor tug fleet will continue to be 

modernized to include alternatives to conventional tugs for shipdocking and tanker escort 

services. In general, the size of the tow and tug fleet is shrinking somewhat, but industry 

capacity is increasing as older vessels are being replaced with larger, more powerful vessels.31

1.1.3.4.2 Commercial Fishing Vessels 

Commercial fishing vessels are dedicated to procuring fish for market and may be 

distinguished by whether they tow nets or are engaged in “hook and line” fishing, or are 

multipurpose vessels that support a variety of fishing activities. Fishing vessels vary widely 

in size and configuration. Smaller fishing vessels may be serially produced using fiberglass, 

similar to recreational boats. Larger fishing vessels are generally built individually to buyer’s 

specifications. The largest fishing vessels also serve as factory ships with the capacity to sort, 

clean, gut, and freeze large quantities of fish. 

The Vessel Census, based on the Coast Guard’s Merchant Vessels of the U. S. 

(MVUS) database, estimates that there are more than 30,000 commercial fishing vessels 

operating in the U.S., with the largest number being in Alaska, followed by Washington and 

Texas. Other states with large numbers of commercial fishing vessels include California, 

Florida, Louisiana, and Maine. Of the roughly 30,000 commercial fishing vessels identified, 

8,130 are listed as definitely C1 and another 21,300 are characterized by the report’s authors 

as probably C1.32 If accurate, this means that all but 700 or so commercial fishing vessels are 

powered by C1 engines, and that the remaining 700 are powered by C2 engines. The C2 

vessel census suggests that the actual number of C2 powered fishing vessels may be less than 

1-35 
Regulatory Impact Analysis 

half this number.33 Less than 1 percent of commercial fishing vessels were identified as 

gasoline-powered. 

Given that the vast majority of commercial fishing vessels are powered by C1 engines, 

it seems reasonable to assume that the majority of these vessels are also similar to recreational 

vessels in construction. Small commercial fishing vessels must be able to travel rapidly to 

and from fishing grounds given that their operations have them going to fishing grounds and 

returning to port each day. Thus, many of these vessels have fiberglass hulls and are designed 

for planning operation, much like recreational vessels. 

1.1.3.4.2.1 Supply of Commercial Fishing Vessels 

Smaller commercial fishing vessels are generally produced using fiberglass with a 

production method similar to that used for recreational boats. Mid-size fishing boats may be 

made of fiberglass, aluminum, or steel, and are likely produced individually to buyers’ 

specifications. The largest fishing boats, factory ships, are produced individually at shipyards 

and a few exceed the 400 foot length that is covered by this profile. Serial and individual 

production methods are described above. 

1.1.3.4.2.2 Demand for Commercial Fishing Vessels 

Commercial fishing boats are inputs into the production of fish for sale to consumers, 

restaurants, retailers, and processors. Reduced catch in many of the nations’ fisheries has 

resulted in lower returns for fishermen, and thus in a declining number of commercial 

fisherman and declining demand for commercial fishing vessels. This decline is projected to 

continue.34 To the extent that governmental efforts to replenish stocks and increase catch are 

successful, some increase in the number of commercial fishermen and fishing boats may 

occur in the future. 

1.1.3.4.3 Patrol Vessels 

Patrol boats such as Coast Guard vessels (government, Department of Homeland 

Security), include small boats used by harbor police and other law enforcement agency 

patrols, as well as larger vessels such as cutters. Small boats used by the Coast Guard include 

approximately 1,400 boats ranging from 12 to 64 feet, which operate close to shore. Coast 

Guard cutters are at least 65 feet in length, and range up to more than 400 feet in length. The 

Vessel Census identified 158 of 235 cutters that were powered by C2 engines.35 The smaller 

boats operated by the Coast Guard were determined to be powered by C1 engines. Fast 

pursuit boats may be powered by gasoline engines. The majority of patrol boats not operated 

by the Coast Guard are relatively small and thus most likely powered by C1 engines, or SI 

outboards for the smallest patrol boats. 

1.1.3.4.3.1 Supply of Patrol Boats 

Patrol boats are generally manufactured from aluminum (two major manufacturers of 

patrol boats, Seaark Marine and SAFE Boats, Inc., both manufacture aluminum boats in large 

numbers). Other aluminum boatbuilders with government work, including military as well as 

state and local agencies, include Kvichak Marine, Northwind Marine, Rozema, All American 

1-36 
Industry Characterization 

Marine, ACB, Almar, Munson and Workskiff. While their designs can be customized, these 

aluminum boats are largely serially produced. Significant inputs include aluminum, engines, 

and labor. Some small patrol boats are inflatable, with reinforced rigid hulls made of steel. 

Larger patrol boats such as Coast Guard cutters are made of steel. 

1.1.3.4.3.2 Demand for Patrol Boats 

Government agencies, including the Coast Guard, the Military, the Army Corps of 

Engineers, as well as harbor police and municipalities are the major purchasers of patrol 

boats. The need to increase vigilance along our coasts and in our harbors since the September 

11 attacks has led to a tremendous increase in demand for Coast Guard patrol boats, which is 

likely to continue to be strong for several more years as the fleet is built up.36 The Workboat 

Construction Survey shows that contracts have risen from 48 in 1997 to 92 in 2005. 

1.1.3.4.4 Passenger Vessels 

Passenger vessels powered by C1 or C2 diesel engines include ferries, excursion 

boats, and water taxis. Ferries are self-propelled vessels that carry passengers from one 

location to another, either with or without their automobiles. Ferries may be owned by states 

or private companies, and generally operate over set routes according to regular schedules. 

Water taxis are generally smaller than ferries and operate on a for-hire basis. The Vessel 

Census studied ferries, and identified 106 that were powered by C2 engines and 508 powered 

by C1 engines. Water taxis are generally powered by spark-ignition (SI) engines, although 

some may be powered by C1 inboard engines. Excursion boats are generally powered by C1 

engines, although some of the larger ones that approach small cruise ships in size, are 

powered by C2 engines. 

1.1.3.4.4.1 Supply of Passenger Vessels 

Passenger vessels may be made of aluminum or steel. For example, Derektor 

Shipyards had orders to deliver three aluminum ferries ranging from a 92 foot high speed 

catamaran ferry to a passenger/vehicle ferry that was 239 feet long. Two other companies had 

orders for large steel ferries, including two 310-foot Staten Island Ferries. Larger ferries and 

other passenger vessels are likely powered by C2 engines, while smaller ones are likely C1 or 

even SI outboard or sterndrive for the smallest and lightest ones. 

1.1.3.4.4.2 Demand for Passenger Vessels 

Ferries and water taxis are needed for transportation services, and are generally used 

in urban areas. Other types of passenger vessels, including excursion boats, dinner boats, and 

floating casinos, are needed for recreational purposes. Some of these, such as whale watching 

boats, are very small; others such as floating casinos and some excursion boats may be more 

than 100 feet in length. Workboat’s 2005 Construction Survey showed orders for 19 dinner, 

excursion, or sightseeing boats and also for 19 ferries or water taxis. Both types of passenger 

boats are likely to respond to cyclical patterns in the economy, as both commuting and 

recreation increase when the economy is strong. 

1-37 
Regulatory Impact Analysis 

1.1.3.4.5 Research Vessels 

Research vessels include vessels equipped with scientific monitoring equipment used 

to track wildlife, map geological formations, monitor coastal water quality, measure 

meteorological conditions, and conduct other scientific investigations. They vary widely in 

size and complexity and may be made of aluminum, fiberglass, or steel. They may be 

powered by SI outboard engines, C1, or C2 inboard engines, depending on their size. While 

they may be built on a standard hull design, the fittings are highly individualized based on 

their task, and may be technically complex. Of 12 research vessels reported in the Workboat 

2005 Construction Survey, most are made of aluminum and are less than 80 feet in length. 

Two are made of steel and are about 150 to 200 feet in length. Of the purchasers listed, three 

of the vessels were ordered by the National Oceanic and Atmospheric Administration 

(NOAA) and one by a university. The instruments and other scientific equipment are a 

special and potentially expensive cost element for these vessels. Demand for the vessels is a 

function of demand for the research products that they support. 

1.1.3.4.6 Offshore Support Vessels 

Offshore support vessels (OSVs) include a variety of vessels used to construct, 

operate, maintain, and service offshore oil platforms. Of the categories listed in Table 1-24, 

crew, crew/supply, personnel, supply/service and liftboat/utility vessels are all vessel types 

that support the offshore oil industry. This is a diverse category, including a wide range of 

sizes, materials, and configurations. Platform supply boats and crew/supply boats tend to be 

over 150 feet in length and may be made of steel or aluminum. Lift boats tend to be about 

150 feet in length and made of steel. OSVs listed in Workboat’s 2005 Construction Survey 

range from 145 feet to 280 feet and are made of steel. At the other end of the spectrum are 

smaller aluminum crew and utility boats. Most offshore oil activity in the U.S. is in the Gulf 

of Mexico; thus, most offshore support vessels operate there. 

Demand for offshore support vessels depends largely on the status of the offshore oil 

industry. Changes in that industry over the past 15 years have resulted in reduced numbers of 

rigs farther from shore. Thus, while fewer support vessels may be needed, they may be 

required to be larger and more seaworthy. The Gulf Coast hurricanes of 2005 had a 

substantial impact on the offshore oil industry and offshore support vessels. Many platforms 

and offshore support vessels suffered damage due to the storms. Demand for offshore support 

vessels has increased drastically, and day rates have more than doubled. This will likely 

result in an increase in construction of offshore support vessels in the next few years, relative 

to recent years. 

Table 1-25 gives a summary of the types of boats currently under contract to be built 

at U.S. boatyards based on information taken from the Marine Log website and Workboat’s 

2005 Construction Survey, using the commercial boat categories described above.37

1-38 
Industry Characterization 

Table 1-25 Boats Under Construction by Type and Client, December 2005 Contracts 

Type of Boat Commercial Clients Government Clients Total 

Tow/Tug 31 7 38 

Fishing 0 1 1 

Coast Guard 0 92 92 

Ferry 19 2 21 

Cargo 75 0 75 

Research 1 2 3 

Offshore Support 31 0 31 

Great Lake/Others 3 1 4 

Military 0 64 64 

Total 140 169 329 

1.1.3.5 Industry Organization 

This section examines the organization of the boat building industry, including 

characterizing firms in the industry, and examining market structure. 

1.1.3.5.1 Location and Number of Vessel Manufacturers 

There are several hundred yards that build many different types of boats powered with 

small engines (≤37 kW) as well as larger C1 and C2 engines. Boat builders are located along 

all coasts and major inland waterways of the United States. Figure 1-1-6 shows the 

geographic distribution of boat builders in the United States. A majority of them are located 

in the Gulf Coast, the Northeast, and the West Coast, and account for approximately 30 

percent, 25 percent, and 26 percent of the boatbuilding industry, respectively. Collectively, 

these three regions represent 345 boat builders, including 128 builders on the Gulf Coast, 107 

in the Northeast and 110 on the West Coast - 80 percent of all companies in the 1998 Boat 

builder Database. 

Figure 1-1-6 Major Boatbuilding Regions of the United States 

1-39 
Regulatory Impact Analysis 

1.1.3.5.2 Firm Characteristics 

Table 1-26 summarizes company financial data for companies that produce 

commercial vessels powered by C1 and C2 engines.21,22,23 The available data capture total 

company employment and sales figures including any subsidiaries and operations including 

boat repair that may not be related to boatbuilding. Because many companies may produce 

boats powered by both SI and CI engines, or may produce larger vessels powered by C3 

engines, not all of the boatbuilding employment and revenues listed in Table 1-26 are 

associated with vessels powered by C1 and C2 engines. 

Table 1-26 Employment Distribution of Companies that Build Commercial and Government Boats 

Employment Range Number of Firms Revenue Range ($Millions) 

100 or fewer 29 0.15 – 7.0 

101–250 12 12.0 – 50.0 

251–500 5 11.0 – 30.9 

501–1,000 3 42.0 – 73.0 

1,001 or more 13 82.0 – 29.9 

Total number of firms 62 

 Almost all companies that produce commercial or governmental vessels powered by 

C1 or C2 engines are classified under NAICS 336611. Of an estimated 589 firms in that 

NAICS code, company names, employment, and sales data were obtained for only 62. Using 

the Small Business Administration’s small business criterion for NAICS 336611 (1,000 

employees), 49 of the 62 (79 percent) of the companies for which data were obtained would 

qualify as small entities. 

1.1.3.5.3 Markets and Trends in Commercial Vessel Manufacturing 

Markets for commercial and governmental vessels can be modeled as competitive. 

While products are differentiated rather than homogeneous, there are many yards that produce 

similar types of vessels, and compete for both commercial and governmental contracts. 

Barriers to entry and exit are relatively low, at least domestically. For commercial cargo 

vessels working between U.S. ports, foreign competition is limited by the Jones Act. 

Similarly, passenger vessels plying exclusively domestic routes are constrained by the U.S. 

Passenger Services Act. Nevertheless, because the technology and materials for boat building 

are widely available, costs of entry into the market are fully recoverable, and barriers to entry 

and exit are thus low which results in domestic commercial boat manufacturers facing 

markets that are contestable and therefore competitive. 

The U.S. boatbuilding industry is currently influenced by several key factors. These 

factors suggest a continued increase in the number of commercial boats built in the United 

States: 

• Increasing demand for the T-class vessels. (The U.S. Coast Guard defines T-class 

vessels as boats not designed to see the open ocean, such as cruise boats, dinner and 

gambling boats, crew boats in the Gulf of Mexico, and off-shore vessels); 

1-40 
Industry Characterization 

• Increasing demand for offshore supply vessels to repair and service offshore oil rigs, 

including repairing or replacing rigs and OSVs damaged or destroyed by Gulf Coast 

hurricanes in 2005; 

• Increasing demand for oil (e.g., drillships and semisubmersible rigs); 

• Expansion of demand for casino boats; 

• Decisions by leading boat builders to reopen facilities and expand their labor forces 

provide strong indications that they anticipate continued growth in the market for 

commercial and governmental vessels. An increase in demand for new boats will 

mean more business for the commercial U.S. boatbuilding industry, as foreign builders 

are ineligible to build for segments of this market. Some of the larger boat builders in 

the United States also build boats for foreign owners/operators, particularly for foreign 

militaries. As noted in the table summarizing current shipyard/boatyard contracts, 

there are at least three yards doing work with foreign governments (e.g., Egypt and 

Oman) 

In summary, U.S. boat builders are cautiously optimistic about the future because 

almost every segment of the U.S. flag fleet is facing significant replacement requirements. 

The commercial boat builders are expected to continue to be a major consumer of marine 

diesel engines. 

1.1.4 Vessel Operators 

The U.S. Economic Census provides an overview of the Water Transportation 

subsector and the subsectors which provide water transporation for passengers and cargo 

using water crafts such as ships, barges, and boats. While there is no database that directly 

links these firms to type of Commercial C1 and C2 marine diesel engines they use in their 

businesses, the survey data does provide a very helpful overview of the industry where these 

engines predominate. Information is organized by the North American Industry Classification 

System ( NAICS) Codes and the data was gathered in 2002 Economic Census. 

The Water Transporation Subsector is composed of two industry groups: (1) one for 

deep sea, coastal, and Great Lakes- NAICS 4831; and (2) one for inland water transportationNAICS 4832. This split typically reflects the difference in equipment used. Scenic and 

sightseeing water transportation services are not included in this subsector but rather are taken 

account of in subsector 487, Scenic and Sightseeing Transportation. Harbor Tug Services are 

included in subsector 488330 and Fishing is captured in subsector 1141. Table 1-27 provides 

the breakdown for each of the subsectors. 

1-41 
Regulatory Impact Analysis 

Table 1-27 Industry Subsectors with Employment Level defining Small Business Standards 

NAICS 

CODE Industry Total Firms 

 Deep Sea 

483111 Freight 235 

483112 Passenger 77 

 

Coastal & Great 

Lakes 

483113 Freight 443 

483114 Passenger 124 

 Inland 

4832112 Freight (Towing) 310 

4832121 Passenger (Ferry) 250 

1141 Fishing 2084 

487 

Scenic & 

Sightseeing 1609 

488330 

Harbor Tugs 

Service 657 

 TOTAL 5789 

Approximately 5800 firms are represented in the Water Transporation subsector and 

affiliated sectors which purchase and use commercial vessels propelled by either C1 or C2 

marine diesel engines. U.S. economic census data reveals that in 2002 about 65 percent of 

these firms had annual revenues of less than $1 million while 87 percent had revenues less 

than $5 million. In summary, when taking into account all firms in these subsectors about 88 

percent of all revenue was generated by 12 percent (~720) of the 5800 firms. 

1.2 Locomotive 

1.2.1 Introduction 

The regulations for locomotives and locomotive engines are expected to directly 

impact three industries. These industries are: (1) locomotive and locomotive engine original 

equipment manufacturers (OEMs); (2) owners and operators of locomotives (railroads); and 

(3) remanufacturers of locomotives and locomotive engines including OEMs, railroads, and 

independent remanufacturers. Locomotive manufacturers are companies that make or import 

complete “freshly” manufactured locomotives.B Remanufacturers are companies that certify 

kits for remanufactured locomotives.C A brief overview of these industries follows, along 

 

B

 Freshly manufactured locomotives are those which are powered by freshly manufactured engines, and contain 

fewer than 25 percent previously used parts (weighted by the dollar value of the parts). 

C

 Remanufactured locomotives are locomotives in which all of the power assemblies are replaced with freshly 

manufactured (containing no previously used parts) or refurbished power assemblies. Remanufacturing includes 

1-42 
Industry Characterization 

with descriptions of the national economic impact of railroads and current regulations in 

effect for railroads. 

1.2.2 Current U.S. Emission Regulations 

The Agency’s 1998 Locomotive Rule (63 FR 18978; April 16, 1998) created a 

comprehensive emission control program that subjected manufacturers and railroads 

(including all Class I and some small Class II and III railroads) to emission standards, test 

procedures and a full compliance program. The unique feature of this program was the 

regulation of the engine remanufacturing process, including the remanufacture of locomotives 

originally manufactured prior to the effective date of that rulemaking. Regulation of the 

remanufacturing process was critical because locomotives are generally remanufactured four 

to eight times during their total service lives of approximately 40+ years. Locomotives 

powered by an external source of electricity, historic steam-powered locomotives, and 

locomotives freshly manufactured prior to 1973 were not covered by the 1998 regulations. 

Three separate sets of emission standards (Tiers) were adopted in the 1998 

rulemaking, with applicability of the standards dependent on the date a locomotive was 

manufactured. The first set of standards (Tier 0) applied to locomotives and locomotive 

engines originally manufactured from 1973 through 2001. The second set of standards (Tier 

1) applied to locomotives and locomotive engines originally manufactured from 2002 to 2004, 

and the final set of standards (Tier 2) applied to locomotives and locomotive engines 

originally manufactured in 2005 or later. All of these standards must be met when a 

locomotive is “freshly manufactured” and at each subsequent remanufacture. The emission 

standards set in 1998 for line-haul and switch duty-cycles are shown in Table 1-28. 

Table 1-28 Maximum Permissible NOx, CO, HC, and PM Rates by Tier 

(g/bhp/hr) 

Tier 0 LineHaul DutyCycle 

Tier 0 

Switch 

Duty-Cycle 

Tier 1 LineHaul DutyCycle 

Tier 1 

Switch 

Duty-Cycle 

Tier 2 LineHaul DutyCycle 

Tier 2 Switch 

Duty-Cycle 

NOx 9.5 14.0 7.4 11.0 5.5 8.1 

CO 5.0 8.0 2.2 2.5 1.5 2.4 

HC 1.00 2.10 0.55 1.20 0.30 0.60 

PM 0.60 0.72 0.45 0.54 0.20 0.24 

In addition to the separate sets of emissions standards established by the 1998 

rulemaking, additional requirements for compliance with these emission standards were 

promulgated, and are described in 40 CFR Part 92. These provisions apply to manufacturers, 

remanufacturers, and owners and operators of locomotives, and locomotive engines 

manufactured on or after January 1, 1973. The three most significant requirements for 

 

the following: replacing an engine, upgrading an engine, and converting an engine to enable it to operate using a 

fuel other than it was originally manufactured to use. 

1-43 
Regulatory Impact Analysis 

railroads relate to: 1) remanufacture of locomotives 2) maintenance of locomotives, and 3) 

testing of locomotives. 

The regulations require that post-1972 locomotives be covered by an EPA Certificate 

of Conformity when they are remanufactured. (See Applicability of Locomotive Emission 

Standards, EPA420-F-99-037, for more information about which locomotives are covered by 

these regulations). The certificate certifies that the locomotive was remanufactured in a 

specific manner such that it complies with EPA’s emission standards. Each certificate covers 

a group of similar locomotives that is referred to as an “engine family.” A railroad may apply 

directly to EPA to obtain a certificate, or may rely on a supplier or remanufacturer that has 

obtained a certificate. The company that obtains the certificate is referred to as the certificate 

holder, and is responsible for ensuring that the locomotive complies with EPA’s emission 

standards. 

The regulations also require that railroads perform emission-related maintenance on 

all regulated locomotives. This requirement is described in 40 CFR 92.1004. Emissionrelated maintenance is specified by the certificate holder and approved by EPA at the time of 

certification. The certificate holder is required to provide the emission-related maintenance 

instructions to the railroads. Emission-related maintenance generally includes regular 

replacement of fuel injectors and air filters, as well as the use of fuels and lubricants meeting 

the specifications of the certificate holder. In most cases, it will also include frequent 

inspection of other emission-related components to ensure that they are functioning properly. 

This section of the regulations also prohibits any maintenance that would reasonably be 

expected to adversely affect the emission performance of the locomotive. 

EPA also established two testing programs to monitor the in-use emissions of 

locomotives. The fist program is run by the certificate holders, the second program is run by 

the Class I freight railroads and is described in 40 CFR 92.1003. Under this program, which 

began on January 1, 2005, each Class I freight railroad is required to test 0.15 percent of its 

locomotive fleet each year using the specified EPA test procedure (40 CFR Part 92 Subpart 

B). This railroad testing program focuses on the locomotives in the fleet that have exceeded 

their useful life values. (Useful life values are defined as the period specified in a certificate 

during which the locomotive is designed to comply with the standards; it is generally 

equivalent to 750,000 miles or more.) 

1.2.2.1 Certification 

Locomotive manufacturers must produce compliant locomotives, and they must be 

certified. In order for a locomotive to be certified, a company must certify the engine together 

with the locomotive. An engine manufacturer can certify, but it must certify the complete 

locomotive. Railroads must purchase all new locomotives with a valid certificate of 

conformity, and when remanufacturing a locomotive must have a valid certificate of 

conformity. However, small railroads are generally provided an exemption for the existing 

uncertified locomotives in their fleet, as well as any uncertified locomotives that they may 

purchase from other railroads in the future. 

1-44 
Industry Characterization 

1.2.3 Supply: Locomotive Manufacturing and Remanufacturing 

1.2.3.1 Locomotive Manufacturing 

1.2.3.1.1 Types of Locomotives 

Locomotives generally fall into three broad categories based on their intended use: 

switcher, passenger, and line-haul locomotives. Switch locomotives, typically 2000 hp or 

less, are the least powerful locomotives, and are used in freight yards to assemble and 

disassemble trains, or for short hauls of trains that are made up of only a few cars. Some 

larger switchers can be rated as high as 2300 hp. Passenger locomotives are powered by 

engines of approximately 3000 hp, with high-speed electric passenger locomotives powered 

by engines with 6000 hp or more. Freight or line-haul locomotives are used to power freight 

train operations over long distances. Older line-haul locomotives are typically powered by 

engines with 2000-3000 hp, while newer line-haul locomotives are powered by engines with 

3500-5000 hp. In some cases, older line-haul locomotives (especially those with lower 

powered engines) are used in switch applications. The development of line-haul locomotives 

with even higher horsepower ratings, such as 6000 hp or more continues, but it is not clear if 

this will be the future of locomotive engines. 

1.2.3.1.2 Type of Propulsion Systems 

Locomotives can be subdivided into three general groups on the basis of the source of 

energy powering the locomotive: 1) “all-electric” 2) “engine-powered” 3) “hybrid”. In the 

"all-electric" group, externally generated electrical energy is supplied to the locomotive by 

means of overhead lines, a third rail that runs between or alongside the rails, or an onboard 

electric storage device such as a battery. Locomotives of this type have existed for over 125 

years.38 An example of this type of locomotive is commonly seen on commuter trains. 

Emission control requirements for all-electric locomotives would be achieved at the point of 

electrical power generation, and thus are not included in this rulemaking. 

In the "engine-powered" group of locomotives, fuel (usually diesel in the U.S., 

although natural gas options are also being pursued) is carried on the locomotive. The energy 

contained in the fuel is converted to power by burning the fuel in the locomotive engine. A 

small portion of the engine output power is normally used directly to drive an air compressor 

to provide brakes for the locomotive and the train. However, the vast majority of the output 

power from the engine is converted to electrical energy in an alternator or generator which is 

directly connected to the engine. This electrical energy is transmitted to electric motors 

(traction motors) connected directly to the drive wheels of the locomotive for propulsion, as 

well as to motors which drive the cooling fans, pumps, etc., necessary for operation of the 

engine and the locomotive.D In the case of passenger locomotives, electrical energy is also 

supplied to the train’s coaches to provide heat, air conditioning, lights, etc. (i.e., "hotel 

 

D Essentially all "engine powered" locomotives used in the U.S. employ a diesel engine and the electrical drive 

system described above. The term "diesel-electric" has therefore become the most common terminology for 

these locomotives. 

1-45 
Regulatory Impact Analysis 

power"). In some passenger trains, electrical energy required for the operation of the 

passenger coaches is supplied by an auxiliary engine mounted either on the locomotive or 

under the floor of a passenger car. 

The third category, "hybrid" is a combination of the "electric" and "engine-powered" 

groups, and was first developed and used in the 1920’s, although at the time it wasn’t very 

successful due to a lag in battery technology. Today’s hybrid locomotive technology is 

considered to be “battery dominant” and uses a small diesel engine and generator to charge a 

battery pack; the battery pack then supplies energy on demand to the traction motors.39 The 

engine can be 250-640 hp (200-480 kW) and typically operates at a constant speed, which 

allows the engine to be optimized for efficiency. Further fuel savings are achieved by running 

this engine only during times when it is needed to generate power to keep the batteries at a 

certain charge level.40 This technology is currently only available for switcher locomotives, 

although it is being developed for use on line-haul locomotives.41

1.2.3.1.3 Locomotive Design Features and Operation 

1.2.3.1.3.1 Sizing Constraints 

Similar to the variation in horsepower, locomotive size determines the work it may 

perform. Switch locomotives tend to be about 40 to 55 feet long, while line-haul locomotives 

are typically 60 to 76 feet long. Locomotive length is roughly correlated with engine size, 

and thus the difference in length has become more significant as locomotive engines have 

become larger and more powerful. Locomotive length is also related to the number of axles 

found on a locomotive. In the past, a typical locomotive had four axles (two trucks with two 

axles each). While there still are a large number of four-axle locomotives in service, all 

newly manufactured line-haul locomotives have six axles (two trucks with three axles each). 

There are two primary advantages of having more axles on the locomotive. First, additional 

axles allow locomotives to be heavier without increasing the load on each individual axle (and 

thus the load on the rail). Second, six-axle locomotives typically have greater tractive power 

at low speeds, which can be critical when climbing steep grades. The use of six-axles on a 

locomotive does increase its overall length, and continues to lead to the discontinuation of the 

practice of converting old line-haul locomotives into switch locomotives, as these larger sixaxle locomotives are too long to be practical in most switch applications. 

1.2.3.1.3.2 Operational Characteristics 

One unique feature of locomotives that makes them different than other currently 

regulated mobile sources is the way power is transferred from the engine to the wheels. Most 

mobile sources utilize mechanical means (i.e., a transmission) to transfer energy from the 

engine to the wheels (or other point where the power is applied). Because there is a 

mechanical connection between the road, vehicle engine and the wheels, the relationship 

between engine rotational speed and vehicle speed is mechanically dictated by the gear ratios 

in the transmission and final drive (e.g., the differential and rear axle). This results in engine 

operation which is very transient in nature, with respect to changes in both speed and load. In 

contrast, locomotive engines are typically connected to an electrical alternator or generator to 

convert the mechanical energy to electricity. As noted above, this electricity is then used to 

1-46 
Industry Characterization 

power traction motors which turn the wheels. The effect of this arrangement is that a 

locomotive engine can be operated at a desired power output and corresponding engine speed 

without being constrained by vehicle speed. The range of possible combinations of 

locomotive speed and engine power vary from a locomotive speed approaching zero with the 

engine at rated power and speed, to the locomotive at maximum speed and the engine at idle 

speed producing no propulsion power. This lack of a direct, mechanical connection between 

the engine and the wheels allows the engine to operate in an essentially steady-state mode, in 

a number of discrete power settings, or notches. Notches are throttle positions that load the 

engine at different power levels. There are typically eight power notches on a locomotive, as 

well as idle positions. 

Dynamic braking is another unique feature of locomotives setting them apart from 

other mobile sources. Dynamic braking is especially important given the traction problems 

that locomotives must overcome. Locomotives generate an enormous amount of power that 

can be applied to the wheels when they start to roll, however, the use of steel wheels (which 

provide less rolling resistance) also make it difficult to start moving a locomotive. On straight 

sections of rail, some locomotives have a built-in system that will put sand on the rails in 

order to increase traction. The ridges on the sides of the wheels provide traction during 

cornering to keep the wheels on the rails, and some locomotives are equipped with an oil 

system that puts oil on the sides of the rails to reduce friction on the sides of the wheels during 

turns and cornering. 

 In dynamic braking the traction motors act as generators, where the generated power 

is dissipated as heat through an electric resistance grid; this feature decreases overall braking 

distance and wear on the wheels. While the engine is not generating motive power (i.e., 

power to propel the locomotive, also known as tractive power) in the dynamic brake mode, it 

is generating power to operate resistance grid cooling fans. The power generated from 

braking then heats up these resistance grids and is dissipated into the air as heat. As such, the 

engine is operating in a power mode that is different than the power notches or idle settings 

discussed above. While most diesel-electric locomotives have a dynamic braking mode, some 

do not (generally switch locomotives). The potential energy that could be recovered during 

dynamic braking and utilized by the locomotive is one area researchers are focusing on to 

increase locomotive efficiency. GE noted that “the energy dissipated in braking a 207-ton 

locomotive during the course of one year is enough to power 160 households for that year.”41

However, it is very difficult to capture and store this energy. The power generated from 

dynamic breaking is instantaneous and high enough that it cannot be effectively used by the 

locomotive at the time it is generated. If the energy could be stored in batteries, or a 

mechanical device such as a flywheel, tremendous fuel savings could be gained, and therefore 

development of these types of systems continues.42,43 

Hotel power or "Head End Power" (HEP) is power used to operate lighting, heating, 

ventilation and air conditioning, and all other electrical needs of the crew and passengers alike 

on locomotives equipped with this feature. This power can be provided by the lead 

locomotive or by an additional engine, and is distributed to the rest of the cars as needed. The 

design of locomotives for use in passenger train service (without additional engines used to 

provide HEP) provides for a locomotive to be operated in either of two distinct modes. In one 

mode, the locomotive engine provides only propulsion power for the train. In this mode, the 

1-47 
Regulatory Impact Analysis 

engine speed changes with changes in power output, resulting in operation similar to freight 

locomotives. In the second mode, the locomotive engine supplies HEP to the passenger cars, 

in addition to providing propulsion power for the train. Hotel power provided to the 

passenger cars can amount to as much as 800 kW (1,070 hp). In contrast to operation in the 

non-hotel power mode, the engine speed remains constant with changes occurring in power 

output when operating in hotel power mode. Thus, the two modes of operation utilize 

different speed and load points to generate similar propulsion power. These differences in 

speed and load points mean that locomotive engines will have different emissions 

characteristics when operating in hotel power mode than when operating in non-hotel power 

mode. 

1.2.3.1.3.3 Design Characteristics 

In 1909 Rudolph Diesel helped construct the first diesel locomotive, and in 1918 the 

first diesel-electric switch locomotives were put into service.44 By the 1950’s diesel-electric 

locomotives had replaced steam powered locomotives because they required less fuel, 

maintenance, and man-power.45 Locomotives use diesel engines because they are much more 

efficient, reliable, and can generate tremendous power. The diesel engine is one of the most 

efficient transportation powerplant available today. Thermal efficiency of locomotive diesel 

engines is 40% or higher, which results from high power density (via high turbocharger 

boost), high turbocharger efficiencies, direct fuel injection with electronic timing control, high 

compression ratios, and low thermal and mechanical losses. Many locomotive engines 

achieve the equivalent of one million miles before overhaul.43 Durability is critical as a 

locomotive breakdown on the tracks can bottleneck the entire system; these failures are very 

costly to the railroads because of the importance of timeliness to their customers, and the 

difficulty in getting replacement locomotives to the location of the failure. The trend toward 

higher power locomotives is naturally resulting in a trend of fewer locomotives per train, 

thereby increasing the likelihood that a train would become immobilized by the failure of a 

single locomotive. 

Another unique design feature of locomotives is the design of the engine cooling 

system and procedures used to control engine coolant temperature. Normal practice in 

locomotive design has been to mount the radiator on the roof of the locomotive and not to use 

a thermostat. Control of coolant temperature is achieved by controlling the heat rejection rate 

at the radiator. The rate of heat rejection at the radiator can be controlled by means such as 

turning fans on and off or employing a variable speed fan drive, or by controlling the amount 

of coolant flow to the radiator (using non-thermostat controls). A related point of difference 

between road vehicle and locomotive engine cooling systems is that antifreeze is not generally 

used in locomotives. Locomotives use water, not antifreeze to cool their engines because 

water is much more efficient at removing heat. Using antifreeze would require a cooling 

system approximately 20% larger than the current design (which holds approximately 450 

gallons of water).46 The size of a locomotive is limited by the existing track and tunnel 

infrastructure which restricts the height, width, and length of a locomotive. Locomotives 

usually run in consists (groups) which means that any locomotive following the lead 

locomotive will not have the same effective cooling as the one in front because the air it 

encounters will be warmer. The practice of “following” creates additional cooling problems, 

especially in tunnels which call for special design considerations. 

1-48 
Industry Characterization 

The final unique design feature noted here is the manner in which new designs and 

design changes are developed. The initial design of any new locomotive model and the 

production of prototype models are done in much the same manner as is the case with other 

mobile sources. Locomotive manufacturers have indicated that this process can be expected 

to require from 12 to 24 months for significant changes such as those required to comply with 

Tier 0 standards. Unlike most other mobile sources, prototype locomotives are typically sold 

or leased to their customers (the railroads) for extended field reliability testing. Only after 

this testing is completed is the new design/design change certified and placed into normal 

production. 

1.2.3.2 Line-Haul Manufacturing 

1.2.3.2.1 Manufacturers 

Locomotives used in the United States are primarily produced by two manufacturers: 

Electromotive Diesel (EMD) and General Electric Transportations Systems (GETS). EMD 

manufactures its locomotives primarily in London, Ontario and their engines in La Grange, 

Illinois. The GETS locomotive manufacturing facilities are located in Erie, Pennsylvania, 

while their engine manufacturing facilities are located in Grove City, Pennsylvania. These 

manufacturers produce both the locomotive chassis and propulsion engines; they also 

remanufacture engines. MotivePower’s Wabtec division, headquartered in Wilmerding, 

Pennsylvania, has produced some mid-horsepower locomotives suited for commuter or longdistance service using engines manufactured by Caterpillar, Inc.47 They also manufacture a 

switcher locomotive that runs on liquefied natural gas. The Cummins Engine Company, Inc., 

headquartered in Columbus, Indiana, produces V12 and V16 diesel engines for use in 

locomotives.48 The EPA has identified four diesel locomotive manufacturers, one of which 

can be considered a small business according to SBA guidelines.49 There are also a few 

companies such as Steward and Stevenson or Brookville Mining Equipment that manufacture 

small switch locomotives (under 700 bhp and not covered by this rulemaking) for use in 

mines or other specialized purposes.50

EMD was founded in 1922 and acquired by General Motors in 1930 and subsequently 

sold by General Motors in 2005 to the Greenbriar Equity Group and Berkshire Partners, and is 

now called Electro-Motive Diesel, Inc. While they primarily manufacture a 2-stroke diesel 

locomotive engine, they began manufacturing a 4-stroke engine in 1997. They currently 

produce five national models ranging from 3000-6000hp, and offer international models as 

well as custom built locomotives. EMD employs approximately 2,600 people and designs, 

manufactures, market, sells, and services freight and passenger diesel-electric locomotives 

worldwide.51 GE was formed by Thomas Edison who developed his first experimental 

electrical locomotive in 1880, GE also built and put into the service the world’s first dieselelectric switcher locomotive in 1924 that remained in service until 1957. GE currently 

produces at least five national models, two international models, passenger locomotives, and 

is developing a hybrid locomotive. GE’s Transportation division employs approximately 

8,000 people and also engineers, manufactures, markets and services their diesel locomotive 

products worldwide.52

1-49 
Regulatory Impact Analysis 

1.2.3.2.2 Production 

Due to the long total life span of locomotives and their engines, annual replacement 

rates of existing locomotives with freshly-manufactured units are very low. Table 1-29

illustrates the historical replacement rates for locomotives in the Class I railroad industry. 

Sales of new locomotives have averaged approximately 780 units per year over the last ten 

years. This replacement rate indicates a fleet turnover time of about 30 years for Class I 

railroads. Fleet turnover is the time required for the locomotive fleet to be entirely composed 

of locomotives that were not in service in the applicable base year. Class II and III railroads 

generally buy used locomotives from Class I railroads, although occasionally purchase new 

switchers or line-haul locomotives. 

Table 1-29 Class I New Locomotive Turnover Rates53

Year Number of New 

Locomotives Installed 

Total Number of 

Locomotives in Service 

Percent Turnover 

of New 

1995 928 18,812 4.9% 

1996 761 19,269 3.9% 

1997 743 19,684 3.8% 

1998 889 20,261 4.4% 

1999 709 20,256 3.5% 

2000 640 20,028 3.2% 

2001 710 19,745 3.6% 

2002 745 20,506 3.6% 

2003 587 20,774 2.8% 

2004 1,121 22,015 5.1% 

1.2.3.2.3 Cost 

The cost of a locomotive can vary between $1.5 million to $2.2 million, depending on 

the configuration and options installed. Figure 1-1-7 shows data from the AAR’s Railroad 

Ten-Year Trends 1995-2004 publication. Some of the variation from year to year can be 

attributed to differences in features, but it appears the overall trend in the price of AC 

locomotives is downward, while DC locomotive pricing remains steady. 

1-50 
Industry Characterization 

Figure 1-1-7 Cost of New Locomotives53

Cost of Diesel-Electric Locomotives

1000000

1500000

2000000

2500000

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

Year

Cost ($)

dc-traction ac-traction

1.2.3.3 Switcher Manufacturing 

1.2.3.3.1 Manufacturers 

The majority of switchers in operation today are former line-haul locomotives that 

have been assigned to a yard, and they are usually quite old. This trend will most likely abate 

over time because the size and power of most new locomotives can make them unsuitable for 

switching operations. While EMD does offer a traditional new switch locomotive, other 

companies are offering switchers with alternative power plants (such as hybrid or gen-set 

locomotives) that are usually built off of an old switcher platform. 

Motive Power, headquartered in Wilmerding, PA offers a switching locomotive fueled 

by liquefied natural gas, which they build on cores supplied by a railroad. A core is a 

locomotive that is no longer in service which will be completely torn-down and reconfigured 

reusing any of its own salvageable parts as well as new parts. Motive Power is a large 

company with nearly 5,000 employees; they service other industries such as marine, transit 

and power generation. National Railway Equipment Co. (NREC) based in Houma, Louisiana 

with facilities also in Illinois manufactures a “gen-set” switcher locomotive (powered by 

multiple smaller diesel engines) that is built from the ground up. They employ approximately 

150 employees.54 RailPower Technologies is headquartered in Brossard, Quebec but also has 

an American office in Erie, Pennsylvania. They employ approximately 100 people, and 

manufacture the GreenGoat hybrid yard switcher and are developing a natural gas switcher 

locomotive as well. Railpower also uses an old switcher locomotive core to build their 

platform on.55 

1-51 
Regulatory Impact Analysis 

1.2.3.3.2 Production 

The existing fleet of retired line-haul switcher locomotives turns over very slowly. 

However, production of alternative technology switchers is beginning to increase. NREC is 

working with Union Pacific Railroad (UP) to build sixty 2,100 hp GS21B gensets equipped 

with three four-cycle, six-cylinder, 700hp Cummins QSK-19 engines.56 These switchers are 

believed to reduce NOx and PM by 80% and to reduce fuel consumption by up to 16% as 

compared to a conventional switcher. UP is also evaluating the GreenGoat hybrid switcher, 

which is also expected to reduce fuel consumption by up to 16% and NOx and PM emissions 

by 80%. Norfolk Southern has recently ordered two gen-set switchers from from RailPower 

in the form of retrofit kits where their own maintenance staff will install this triple-engine 

system during a switcher rebuild.57 While new switchers can cost upwards of $1.5 million 

dollars, the GreenGoat hybrid switcher can cost as little as $700,000 if a customer supplies a 

completely reconditioned GP-9 locomotive.58 Note that the price of these and other switchers 

depends on whether or not a core is supplied and what features it will be built with. 

1.2.3.3.3 Trends 

Remote control locomotives (RCL) have been used in Canada and the U.S. for many 

years; however, Class I railroads have recently begun to implement this technology on a wider 

scale according to the Federal Railroad Administration (FRA).59 Although RCL technology 

is mainly used in switch yards, this type of operation may be applied on line-hauls in the 

future. RCL requirements may affect cab design and require that special equipment is bu

into future switchers. Another growing trend is the retrofit and use of idle reduction 

technology on locomotives to decrease fuel consumption and increase the railroads efficiency,

especially as the cost of fuel continues to in 60

ilt 

crease. 

 

1.2.3.4 Remanufactured Locomotives 

Since most locomotive engines are designed to be remanufactured a number of times, 

they generally have extremely durable engine blocks and internal parts. Parts or systems that 

experience inherently high wear rates (irrespective of design and materials used) are designed 

to be easily replaced so as to limit the time that the unit is out of service for repair or 

remanufacture. The prime example of a part that is designed to be readily replaceable on 

locomotive engines is the power assembly, which is composed of: pistons, piston rings, 

cylinder liners, fuel injectors and controls, fuel injection pump(s) and controls, and valves. 

Within the power assemblies, parts such as the cylinder head generally do not experience high 

wear rates, and may be reused after being inspected and requalified (i.e. determined to be 

within manufacturers specifications). The power assemblies can be remanufactured to bring 

them back to as-new condition, or they can be upgraded to incorporate the latest design 

configuration for that engine. In addition to the power assemblies, there are numerous other 

parts or systems that are also designed to be easily replaced on locomotives.E

 Engine 

E

 Bottom end components, such as crankshafts and bearings, are often remanufactured only during every other 

remanufacture event. Remanufacture events that do not include these bottom end components are sometimes 

referred to as "partial remanufactures" 

1-52 
Industry Characterization 

remanufacturing may be performed by the railroad that owns the locomotive, or by the 

original manufacturer of the locomotive. Remanufacturing is also performed by companies 

that specialize in performing this work. 

During its forty-plus year total life span, a locomotive engine could be remanufactured 

as many as ten times (although this would not be considered the norm). Locomotive engine 

remanufacturing events are thus routine, and are usually part of scheduled maintenance. It is 

standard practice for the Class I railroads to remanufacture a line-haul locomotive engine 

every four to eight years. Typically newer locomotives, which have very high usage rates, are 

remanufactured every four years. Older locomotives are usually remanufactured less 

frequently because they are used less within each year. Such remanufacturing is necessary to 

ensure the continued proper functioning of the engine. Remanufacturing is performed to 

correct losses in power or fuel economy, and to prevent catastrophic failures, which may 

cause a railroad line to be blocked by an immobile train. 

When a locomotive engine is remanufactured, it receives replacement parts which are 

either freshly-manufactured or remanufactured to as-new condition (in terms of their 

operation and durability).F

 This includes the emission-related parts which, if not part of the 

basic engine design, are also generally designed to be periodically replaced. The replacement 

parts are often updated designs, which are designed to either restore or improve the original 

performance of the engine in terms of durability, fuel economy and emissions. Because of a 

locomotive engine's long life, a significant overall improvement in the original design of the 

parts, and therefore of the engine, is possible over the total life of the unit. Since these 

improvements in design usually occur in the power assemblies (i.e., the components where 

fuel is burned and where emissions originate), remanufacturing of the engine essentially also 

makes the locomotive or locomotive engine a new system in terms of emission performance. 

A remanufactured locomotive would therefore be like-new in terms of emissions generation 

and control. 

While Class I locomotives are remanufactured on a relatively frequent and scheduled 

basis of 4 to 8 years, Class II and III locomotives may be remanufactured on a longer 

schedule or may not be remanufactured at all. The typical service life of a locomotive (40 

years) is often exceeded by small railroads that continue to use older locomotives. It is 

important to note that there is no inherent limit on how many times a locomotive can be 

remanufactured, or how long it can last, rather, the service life of a locomotive or locomotive 

engine is limited by economics. For example, in cases, where it is economical to cut out 

damaged sections of a frame, and weld in new metal, an old locomotive may be salvaged 

instead of being scrapped. Remanufacturers can also replace other major components, such as 

trucks or traction motors, to allow an older locomotive to stay in service. However, at some 

point, most railroads decide that the improved efficiency of newer technologies justifies the 

additional cost, and thus scrap the entire locomotive. Nevertheless, many smaller railroads, 

 

F

 In some cases, some components are remanufactured by welding in new metal and remachining the component 

to the original specifications. 

1-53 
Regulatory Impact Analysis 

especially switching and terminal railroads, are still using locomotives that were originally 

manufactured in the 1940s. 

1.2.3.4.1 Remanufacturers 

While the original manufacturers provide much of the remanufacturing services to 

their customers, there are several smaller entities that also provide remanufacturing services 

for locomotive engines. These businesses can be rebuilders licensed by the OEMs, in 

addition to the OEMs themselves. Moreover, some of the Class I and II railroads 

remanufacture locomotive engines for their own units and on a contractual basis for other 

railroads. EPA has been able to identify nine independent locomotive remanufacturers, four 

of which are small business entities. Many of these businesses are full service operations that 

remanufacture locomotive assemblies (such as trucks or air brake systems), sell new and used 

parts, repair wrecked locomotives or provide routine maintenance. A few of these operations 

remanufacture locomotives primarily for resale or lease, while others remanufacture engines 

for operating railroads or industrial customers. A few also offer contract maintenance; this 

may be tied to a locomotive lease, or may be offered separately to owners of locomotives. The 

size of these companies can vary tremendously. Some have as few as two employees, while 

others can have as many as 5,000 employees. The cost of a remanufacturing kit can vary 

depending on the model of locomotive and year of manufacture; an estimated range is 

$15,000 - $30,000 per kit. 

1.2.4 Demand: Railroads 

Railroads are said to transport freight more efficiently than other modes of surface 

transportation because they require less energy and emit fewer pollutants.43 The 2006 

Transportation Energy Data Book shows that rail transportation used approximately 7% of all 

diesel fuel used in transportation to move nearly 40% of all freight ton-miles (miles one ton of 

freight is moved). It is important to recognize, however, that this 7% represents the total 

amount of fuel used in all rail sectors including: line-haul, switcher, Amtrak, commuter rail, 

and transit rail, as shown in Figure 1-1-8. 

1-54 
Industry Characterization 

Figure 1-1-8 Rail Energy Use 

Source: Linda Gains, "Reduction of Impacts from Locomotive Idling", Argonne National Laboratory, 2003 

There are many other unique characteristics of the railroad industry, such as: track 

sharing, locomotive sharing, and fleet age. Track sharing is when a locomotive owned by one 

company travels over track that belongs to another company. This is not an inherent right and 

must be negotiated between the railroads. Locomotive sharing occurs when locomotives 

owned by different companies form one consist that hauls a train. This enables a company’s 

locomotives to be fully utilized. Unlike most other methods of shipping, railroads are 

responsible for maintaining their own infrastructure such as tracks, and bridges, which is a 

very expansive network. The Class I railroads spent more than $320 billion or approximately 

44% of their operating revenue between 1980-2003 to maintain and improve their 

infrastructure and equipment.61 As locomotives grow larger and heavier, and as cars are 

designed to hold more weight, track is required that can handle this increased load. To date, 

of the 549 short line and regional railroads in existence, 333 have track that cannot handle 

these increased loads.62 

1.2.4.1 Railroad Classification System (Class I, II and III) 

In the United States, freight railroads are subdivided into three classes based on annual 

revenue by the federal government's Surface Transportation Board (STB) (STB regulations 

for the classification of railroads are contained in 49 CFR Chapter X). The STB regulations 

divide the railroads into three classes based on their annual carrier operating revenue.63 As of 

2005, Class I railroads had annual carrier operating revenues of at least $319 million, Class II 

railroads had annual carrier operating revenues between $40-$319 million, and Class III 

railroads had operating revenues of $40 million or less. The AAR further subdivides Class II 

and III railroads into regional and local railroads based on the miles of track over which they 

operate, in addition to their revenue. A regional railroad is a line-haul railroad that operates 

on at least 350 miles of track and/or earns revenue between $40 million and the Class I 

revenue threshold. A local railroad is a line-haul railroad which operates over less than 350 

miles of track and has revenues less than $40 million. Class III also includes switching and 

terminal railroads. These types of railroads usually belong to the American Short Line and 

1-55 
Regulatory Impact Analysis 

Regional Railroad Association (ASLRRA). Figure 1-9 shows the differences in fuel 

consumption, number of railroads and the amount of track miles traveled for these different 

categories of railroads. 

Figure 1-1-9 Freight Rail Industry Overview 

Freight Rail Industry Overview

(% of Industry Totals in 2003)

(Source: Assoc. of American Railroads. Overview of U.S. Freight Railroads, Feb.2005)

1%

6%

56%

37%

70%

11%

14%

5%

95%

1.70% 1.70% 1.70%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Class I Regional Local Linehaul Switching & Terminal

Number of Railroads Track Miles Operated Fuel Consumption

1.2.4.2 Class I Characteristics 

Current railroad networks (rail lines) are geographically widespread across the United 

States, serving every major city in the country. Approximately one-sixth of the freight hauled 

in the United States is hauled by train.64 There are few industries or citizens in the country 

who are not ultimate consumers of services provided by American railroad companies. 

According to statistics compiled by AAR, Class I rail revenue accounted for 0.36 percent of 

the Gross National Product in 2004. Combined with the value of the freight they haul (which 

is nearly 40% of all freight as measured in ton-miles), it is obvious that efficient train 

transportation is a vital factor in the strength of the U.S. economy. 

In order for Class I railroads to operate nationally, they need unhindered rail access 

across all state boundaries. If different states regulated locomotives differently, a railroad 

could conceivably be forced to change locomotives at state boundaries, and/or have 

state-specific locomotive fleets. Currently, facilities for such changes do not exist, and even if 

switching areas were available at state boundaries, it would be a costly and time consuming 

disruption of interstate commerce. A disruption in the efficient interstate movement of trains 

throughout the U.S. could have an impact on the health and well-being of not only the rail 

industry, but the entire U.S. economy as well. 

Class I railroads are nationwide, long-distance, line-haul railroads which carry the 

bulk of the railroad commerce. There are currently 7 Class I freight railroads operating in the 

1-56 
Industry Characterization 

country, two of which are Canadian owned.65 Class I railroads operated approximately 

22,400 locomotives in the U.S., over 97,662 milesG of track, accounted for approximately 90 

percent of the ton-miles of freight hauled by rail annually, and consumed 4.1 billion gallons of 

diesel fuel in 2004.66,64 Of these, the two largest Class I railroads, Burlington Northern and 

Santa Fe (BNSF), and UP, accounted for the vast majority (63%) of the Class I locomotives in 

service in the U.S as of the end of 2004.67 According to the 2004 AAR “Analysis of Class I 

Railroads,” Class I railroads paid on average over $1.06 for a gallon of fuel in 2004 for a total 

expenditure of $4.2 billion which was 11% of their operating revenue. U.S. Class I railroads 

employ approximately 177,000 people, the vast majority of who are unionized, and as of 2004 

received an average compensation of $65,000.

The Bureau of Transportation Statistics 2006 report shows that in terms of ton-miles 

of freight, railroads haul 36.8% of total ton-miles, followed by trucking (29%), pipline 

(19.9%), river/canal/barge (13.9%), and air (0.3%), also shown in Figure 1-1-10 . Rail is a 

primary means of transport for many bulk commodities, according to AAR, 65% of all coal 

produced in the U.S., 33% of all grain harvested in the U.S. and 75% of all new automobiles 

manufactured in the U.S. were transported by rail. As a primary mode of transportion for 

these items, the railroad industry normally sets the industry standard price ($/ton-mile). Rail 

transport is typically more fuel efficient and less expensive than other land-based sources of 

transport. In terms of BTUs of energy expended per ton-mile of freight hauled, Department 

of Energy statistics indicate that rail transport can be as much as three to four times more 

efficient than truck transport. The AAR has asserted that one double-stack train can carry the 

equivalent of 280 truckloads of freight.68.69

Figure 1-1-10 U.S. Freight Transportation Share by Mode 

 

G This is the road length of track or the aggregate length of track excluding sidings and parallel tracks, actual 

track miles are 167,312. 

1-57 
Regulatory Impact Analysis 

Figure 1-12 and Table 1-30 show the long term growth trends in the amount of freight 

carried by Class I railroads and the amount of fuel consumed in carrying that freight.66 Note 

that the ton miles of freight carried have almost tripled, while total fuel consumption has risen 

only 10-20%, showing an approximate 250% improvement in freight hauling efficiency since 

1960. Efficiency increases have occurred for a number of reasons including: locomotive 

manufacturers have made continual progress in improving the fuel efficiency of their engines 

and the electrical efficiency of their alternators and motors, and railroads have made 

significant improvements to their operational efficiency. Fuel efficiency of the railroad 

industry overall has improved 16% over the last decade.43 It is reasonable to project that the 

growth in the amount of freight hauled will continue in the future. It is less certain, however, 

whether fuel consumption will increase significantly in the near future. 

Table 1-30 Annual Fuel Consumption and Revenue Freight For Class I Railroads 

Figure 1-1-11 Fuel Consumption and Revenue Ton-Miles for Class I Railroads 

Annual Fuel Consumption and Revenue Freight 

For Class I Railroads 

Year Revenue Freight Fuel Consumption Ton-Miles of Freight 

(Million Ton-Miles) (Million Gallons) moved per gallon of fuel 

1960 572,000 3,500 160 

1970 765,000 3,500 220 

1980 919,000 3,900 240 

1990 1,030,000 3,100 330 

1995 1,300,000 3,400 380 

2000 1,460,000 3,700 390 

2001 1,500,000 3,700 410 

2002 1,510,000 3,700 410 

2003 1,550,000 3,800 410 

2004 1,660,000 4,100 410 

1-58 
Industry Characterization 

1.2.4.2.1 Class I Market Share 

UP operates over the most miles of track (32,616), has the largest number of 

employees (49,511), the greatest operating revenue ($12,180 million), but is surpassed in 

revenue ton-miles by BNSF (569 billion).H UP owns more miles of track than any other 

Class I (27,123), and operates the most locomotives (7,680), as shown in Table 1-31. 

Table 1-31 Class I Railroads - Number of Locomotives 

Class I Railroads - Number of Locomotives

(2003 Data)

(Source: https://www.aar.org/AboutTheIndustry/RailroadProfiles.asp)

0

1000

2000

3000

4000

5000

6000

7000

8000

Union Pacific

Railroad

Company

Burlington

Northern and

Santa Fe

Railway

Company

CSX

Transportation

Norfolk

Southern

Combined

Railroad

Subsidiaries

Candian

National

Kansas City

Southern

Railway

Company

Canadian

Pacific Railway

1.2.4.2.2 Locomotive Fleet 

Historically, Class I railroads have purchased virtually all of the freshly-manufactured 

locomotives sold. As the Class I railroads replace their equipment with freshly-manufactured 

units, the older units are either sold by the Class I railroads to smaller railroads, are scrapped, 

or are purchased for remanufacture and ultimate resale (or leasing) by companies specializing 

in this work. The industry-wide replacement rate for locomotives is therefore actually lower 

than those indicated for the Class I railroads only. This would mean that the time required for 

the total locomotive fleet to turn over is longer. 

Additionally, independent of cyclic changes in the industry, future locomotive 

replacement rates may actually decrease as higher powered locomotives pull longer trains 

leading to fewer locomotives in a consist. Locomotive manufacturers are now producing 

locomotives that have significantly more horsepower than older locomotives. Railroads have 

requested this change so that fewer locomotives are needed to pull a train. Placing more 

horsepower on a locomotive chassis increases overall train fuel efficiency. For example, it is 

 

H A revenue ton-mile is calculated by dividing freight revenue by total freight ton-miles, it is a measure of the 

level of revenue received by a railroad for hauling weight over distance. (AAR Railroad Facts, 2006) 

1-59 
Regulatory Impact Analysis 

more fuel-efficient to use two 6000 hp locomotives, rather than three 4000 hp locomotives, to 

pull the same size train, because the weight of an entire locomotive can be eliminated. Thus, 

while three old locomotives may be scrapped, only two new locomotives need to be 

purchased as replacements. 

On the other hand, the business outlook for the railroad industry has been improving in 

the last few years. As railroads have become increasingly cost-competitive with other 

shipping methods like trucking, they are attracting more business. This in turn increases 

demand for locomotive power to move the additional freight. Thus, while purchases of new 

locomotives may increase in the next few years, these locomotives will likely supplement, 

rather than replace, existing locomotives. Moreover, if freight demands continue to increase, 

it may become cost-effective to operate locomotives for longer periods than are estimated 

here. 

1.2.4.2.3 Operation Profile 

1.2.4.2.3.1 Fuel consumption70

Class I railroads consumed over 531 trillion BTUs in 2003. Locomotives traveled 

1,538 million unit-miles in 2004, and averaged 69,900 miles per locomotive in 2004. The 

Surface Transportation Board reported that Class I railroads consumed 4.1 billion gallons of 

diesel fuel in 2004, for an average mile traveled per gallon of 0.13. The 4.1 billion gallons of 

diesel fuel used by the Class I railroads is 96% of all locomotive fuel used in the U.S. and 

7.4% of all diesel fuel used for transportation in the United States. Class I railroads spent 

$4.2 billion which is nearly 11% of total operating expenses on fuel in 2004. Railroads are 

continually trying to reduce their fuel consumption through efforts such as idle reduction, and 

other operational improvements.71 In a study done by the Department of Energy, the 

aerodynamic drag of coal cars has been shown to account for 15% of total round-trip fuel 

consumption on a coal train; intermodal cars that are double stacked also carry an 

aerodynamic fuel consumption penalty of approximately 30% due to drag. Experiments have 

been done to develop equipment such as fairings and foil that can reduce this drag loss on coal 

cars by up to 5% which would save 75 million gallons or 2% of total Class I fuel consumption 

in 2002.43 

1.2.4.2.3.2 Maintenance Practices 

Locomotive maintenance practices also present some unique features. As is the case 

with other mobile sources, locomotive maintenance activities can be broken down into a 

number of subcategories including: routine servicing, scheduled maintenance, and breakdown 

maintenance. Routine servicing consists of providing the fuel, oil, water, sand (which is 

applied to the rails for added traction), and other expendables necessary for day-to-day 

operation. Scheduled maintenance can be classified as light (e.g., inspection and cleaning of 

fuel injectors) or heavy, which can range from repair or replacement of major engine 

components (such as power assemblies) to a complete an engine remanufacture. Wherever 

possible, scheduled maintenance (particularly the lighter maintenance) is timed to coincide 

with periodic federally-required safety inspections, which normally occur at 92-day intervals. 

Breakdown maintenance, which may be required to be done in the field, consists of the 

1-60 
Industry Characterization 

actions necessary to get a locomotive back into service. Because of the high cost of a 

breakdown in terms of lost revenue that could result from a stalled train or blocked track, 

every effort is made to minimize the need for this type of maintenance. In general, railroads 

strive to maintain a high degree of reliability, which results in more rigorous maintenance 

practices than would be expected for most other mobile sources. However, the competitive 

nature of the business also results in close scrutiny of costs to achieve the most cost-effective 

approach to achieving the necessary reliability. This has resulted in a variety of approaches to 

providing maintenance. 

Maintenance functions were initially the purview of the individual railroads. Some 

major railroads with extensive facilities have turned to providing this service for other 

railroads. A few of the smaller railroads have also done the same, in particular for other small 

railroads. However, the tendency in recent years has been toward a diversification of 

maintenance providers. A number of independent companies have come into existence to 

provide many of the necessary, often specialized services involved in locomotive repair (e.g., 

turbocharger repair or remanufacture). The trend toward outside maintenance has also been 

accelerated by the policies of some of the larger railroads to divest themselves of not only 

maintenance activities, but ownership of locomotives as well. The logical culmination of this 

trend is the "power by the mile" concept, whereby a railroad can lease a locomotive with all 

the necessary attendant services for an agreed-upon rate. 

1.2.4.2.4 Leasing 

Locomotives are available for lease from OEMs, remanufacturers, and a small number 

of specialized leasing companies formed for that purpose. Leasing practices appear to be 

fairly standardized throughout the industry. Although lease contracts can be tailored on an 

individual basis, most leases seem to incorporate standard boilerplate language, terms and 

conditions. Under a typical lease, the lessee takes on the responsibility for safety certification 

and maintenance (parts and scheduled service) of the locomotive (including the engine), 

although these could be made a part of the lease package if desired. The lease duration ranges 

between 30 days and 5 years, with the average being 3 years Figure 1-12 shows that leasing 

has been a continuing trend among Class I railroads, with almost two-thirds of the 

locomotives placed into service in 2004 leased locomotives. Leasing among Class II and III 

railroads is not nearly as widespread. 

1-61 
Regulatory Impact Analysis 

Figure 1-1-12 Source: AAR Railroad Ten-year Trends 1995-2004: Number of Purchased and Leased Class 

I Locomotives 

0

200

400

600

800

1000

1200

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

leased

new

1.2.4.2.5 Traffic70

The value of goods transported by all modes of transportation between 1993 and 2003 

increased by 43.6% to $8,397.2 billion, and the ton miles increased over that period by 29.6% 

to 3,138 billion ton-miles. The railroads share of the value market increased during that time 

by 25.7%, and the percent increase in their ton-miles shipped over that time was 33.8%. Tonmiles shipped using multiple modes of transportation also increased over this period such as 

Truck and Rail (20.8%), and Rail and Water (63.8%). 

Figure 1-1-13 shows that the overall Class I traffic volumes are still increasing, and as 

the car miles and train miles converge, this means they are optimizing the number of cars a 

locomotive can carry most likely by using fewer more powerful locomotives to haul more 

cars.64 The average length of a haul for Class I railroads has generally increased every year, 

and has almost doubled since 1960 when 461 miles was the average haul as compared to 2004 

when 862 miles was the average haul length. Commuter rail has generally not increased its 

average haul length over this same time period. Class I train-miles, (a train-mile is the 

movement of a train, which can consist of multiple cars, the distance of one mile) were 535 

million in 2004, Class I car-miles (a car-mile measures the distance traveled by every car in a 

train) were 37,071 million miles in 2004.72

1-62 
Industry Characterization 

Figure 1-1-13 Class I Train Miles and Car Miles Source: AAR Ten Year Trends 1995-2004 

20000

22000

24000

26000

28000

30000

32000

34000

36000

38000

1975

1980

1985

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

300

350

400

450

500

550

Class I car miles Class I train miles

1.2.4.3 Hauling Statistics70

Class I railroads hauled more than 1.6 million ton-miles of freight in 2003, which was 

37% of all freight hauled in the US. Class I railroads also carried nearly 20 billion ton-miles 

of crude oil and petroleum products, which represented 2.2% of the total moved, while trucks 

carried 3.8%, pipeline 67%, and water carried 27%. As of 2002, railroads transported over 72 

billion ton miles of hazardous materials, or 22% of all hazardous material which was shipped 

an average of 695 miles per shipment.70 While railroads and trucks carry roughly equal 

hazmat ton-miles, trucks have nearly 16 times more accidental hazmat releases than do 

railroads.61

The 2006 FRA Freight Railroad Overview indicates that intermodal shipping is the 

fastest growing segment of rail traffic. Doublestack containers were introduced in the 1980's 

and since that time the number of trailer and container loadings has risen from 3.4 million to 

11 million in 2004. Figure 1-1-14 shows the near doubling of this traffic in each of the past 

two decades. The Staggers Act of 1980 also legalized railroad-shipper contracts, and 

according to the STB, at least 55% of all traffic moves under contract, which allows railroads 

to increase efficiency by permitting better planning. 

1-63 
Regulatory Impact Analysis 

Figure 1-1-14 Class I Intermodal Traffic 

Intermodal Traffic (AAR Railroad Facts 2006)

0

2000000

4000000

6000000

8000000

10000000

12000000

1980 1985 1990 1995 2000 2004

year

Number of Trailers and Containers

1.2.4.4 Track Statistics52 

As of 2004, Class I owned 97,662 miles of trackage. Since 1980, capital expenditures 

on trackage and other structures has increased 88% from $2.6 billion in 1990 to $4.9 billion in 

2004 as railroad tracks have been upgraded to 130 pound per yard weighted rail to 

accommodate heavier loads being hauled per car. Class I railroads have increased their traffic 

(ton-miles) by approximately 81%, while they have decreased the miles of track they own by 

41%. This has increased traffic density, and although double-stacking containers has helped 

to reduce traffic to some degree, this is still a concern due to the continual growth in tonmiles.73 

1.2.4.5 Class II & III Characteristics74,75 

In the 1970's, deregulation allowed the Class I railroads to stop operating many 

smaller lines that were unprofitable. This allowed many small independent railroads to take 

over that portion of the line and run it more efficiently and sometimes at a lower cost due to 

their enhanced flexibility as a small business. In 2004 there were 549 Class II and Class III 

railroads. In many cases, these smaller railroads are also able to receive financial assistance 

from local governments or associations of customers to help them upgrade their infrastructure 

(in many cases, the tracks are quite old and are not rated for the loads that today's cars 

typically carry). 

In 2004, short lines originated or terminated one out of every four carloads moved by 

the domestic rail industry, and operated over 50,000 miles of track, which is nearly 29% of all 

U.S. rail mileage. They had over 19,000 employees and served over 11,700 customers and 

facilities. Of the track they operate, only 43% is capable of handling the heavier 286,000 ton 

axle weight cars. The total revenue for the Class II and III railroads in 2004 was almost $3 

billion, while they spent nearly $433 million on capital expenditures, $397 million on 

maintenance of equipment, road and structures, and $221 million on fuel. More than half of 

1-64 
Industry Characterization 

the short line and regional railroads connect to two or more other railroads, and over 80% 

operate in only one state. 

Statistics compiled by the ASLRRA in 2004 show that there are approximately 549 

Class II and III railroads (not including commuter or insular railroads that serve a track within 

a factory). A more detailed breakdown of these small railroads can be found in Table 1-32. 

They consist primarily of regional and local line-haul and switching railroads, which operate 

in a much more confined environment than do the Class I railroads.I

 Class II and III railroads 

operated approximately 3,777 locomotives in 2004, and in survey taken in 2004 by the 

ASLRRA, locomotive fleet age data shows that over 92% of the locomotives owned by the 

Class II and III railroads are over twenty years old, slightly over 5% are 10-19 years old, and 

2% are newer than 10 years old. Class II and III railroads used 552 million gallons of fuel in 

2004, which is about 13% of the amount of diesel fuel used by Class I locomotives, in 2004. 

Employment has declined for all railroads including Class I railroads substantially since the 

1990's, but nearly all railroads are predicting growth in hiring. 

Table 1-32 Profile of Railroad Industry -200453

Type of Railroad Number of Railroads Number of Employees 

Class I Freight Railroad 7 157699 

National Passenger 

Railroads 

1 18,909 

Regional Railroads 31 7422 

Local/Line-Haul Railroads 314 5349 

Switching and Terminal 204 6429 

Class I Subsidiaries 102 3687 

Commuter Railroads 21 25,29676

Shipper-Owned Railroads 68 NA 

Government Owned 

Railroads 

28 NA 

Some of the smaller railroads are owned and operated by Class I railroads, many of 

which are operated as formal subsidiaries for financial purposes, but are run as standalone 

entities. In 2004, there were 31 regional railroads, 314 local line-haul railroads and 204 

switching and terminal railroads, including subsidiaries (regional and local railroads may also 

have subsidiaries). A few of these are publicly held railroads and some are shipper-owned. 

Insular (in-plant) railroads are not included in this total. ASLRRA estimated that there are 

probably about 1,000 insular railroads in the U.S. These railroads are not common carriers, 

but rather are dedicated to in-plant use. They typically operate a single switch locomotive 

powered by an engine with less than 1000 hp. Finally, there are a handful of very small 

passenger railroads that are primarily operated for tours. These tourist railroads are included 

within the Class II and III railroads. 

 

I

 "Regional railroad" and "local railroad" are terms used by AAR that are similar, but not identical, to "Class II" 

and "Class III", respectively. 

1-65 
Regulatory Impact Analysis 

1.2.4.6 Passenger Rail 

1.2.4.6.1 Amtrak 

Amtrak was formed in 1971 by Congress through the Rail Passenger Service Act of 

1970 (P.L. 91-518, 84 Stat.1327) to relieve the railroads of the financial burden of providing 

passenger railway service. In return for government permission to leave the passenger rail 

business and avoid continued losses, many of the freight railroads donated equipment to 

Amtrak as well as $200 million in startup capital.61 Amtrak is operated by the National 

Railroad Passenger Corporation of Washington, D.C. The U.S. Secretary of Transportation 

has the authority to designate Amtrak's destinations, which as of 2004 included 527 cities; 

other transit rail serve 2,909 destinations, some of which may be shared with Amtrak.70 

Amtrak traveled over 37 million train miles in 2004, and served on average, 777,000 people 

each day that depend on commuter rail services operated under contract by Amtrak, or that 

use Amtrak-owned infrastructure, shared operations and dispatching. On average, 69,000 

people ride on up to 300 Amtrak trains each day77. Amtrak relies on receiving federal 

subsidies in order to operate, although it continually works to become independent and 

profitable. 

Although Amtrak's rates are not regulated, it does depend on the amount of subsidies 

received from the Federal government; this is not unlike most other forms of passenger rail in 

the U.S that receive federal, state, or local subsidies. Amtrak competes with other modes of 

transportation, and this also affects its ability to charge higher rates. Fuel costs can 

dramatically affect rates and Amtrak’s need for subsidies. Between 2004 and 2005, Amtrak’s 

fuel costs increased 149% or by $43 million, and continue to increase substantially, despite 

efforts for improved fuel conservation methods that reduced its fuel consumption by nearly 

10% during that same period.78 

Amtrak is the sole large-scale provider of inter-city passenger transport. Its fleet 

includes 436 locomotives, 360 of which are diesel locomotives that used a reported 69.9

million of gallons of fuel in 2005. It also owns 76 electric locomotives. Recently, the FRA 

provided Amtrak with funding to purchase Acela locomotives, which are 4,000 horsepower 

gas turbine locomotives. These locomotives consume about the same amount of fuel as a 

diesel locomotive but produce about 1/10th of the NOx.

Amtrak offers service to 46 states on 21,000 miles of routes, only 745 miles of which 

are actually owned by Amtrak and are located primarily in Michigan, and between Boston and 

Washington DC.Error! Bookmark not defined. Based on gross revenue, Amtrak is classified as a 

Class I railroad by the STB. However, unlike the Class I freight railroads Amtrak's current 

operating expenses exceed its gross revenue. 

The average age of a passenger train from Amtrak is quite young; in fact, since 1980 it 

has remained under 14.5 years old. Amtrak was on-time 74% of the time in 2003, and 65% of 

that delay was caused by a host railroad. A host railroad is a freight or commuter railroad over 

which track Amtrak operates on for all or part of a trip, and delays can include signal delays, 

train interference, routing delays or power outages. Amtrak must pay these host railroads for 

use of this track and any other resources. In 2005, those payments were based on more than 

1-66 
Industry Characterization 

25 million train miles (one train-mile is a mile of track usage by each train) which totaled 

more than $92 million. 

The average Amtrak city-to-city fare was $55.15, with an average trip of 231 miles, 

and average revenue per passenger-mile is $0.251 for Amtrak.70 In 2006, Amtrak was able to 

obtain an additional subsidy in order to remain operational in 2006, in the amount of $1.1 

billion.79 The future of Amtrak is uncertain, and may change if the Passenger Rail Reform 

Act is passed. This bill is currently in the House Subcommittee on Railroads, and would split 

Amtrak up into three different entities, two privately owned and one government owned 

corporation. 

1.2.4.6.2 Commuter80

There are also 21 independent commuter rail systems operating in sixteen U.S. cities, 

consuming 72 million gallons of diesel fuel annually, operating over 6,785 miles of track.

They employed approximately 25,000 employees in 2004. Many of these commuter railroads 

rely on Federal subsidies to improve their infrastructure, in some cases they also rely on state 

and local government subsidies to support their operations. 

The average commuter fare in 2004 was $3.90, for an average trip length of 23.5 

miles, with average revenue per passenger-mile of $0.154. An estimated 414 million people 

use commuter rail each year to result in over 9.7 billion passenger-miles. Like Amtrak, 

commuter rail operations also maintain a young fleet that has remained younger than 17 years 

old since 1985. 

1.2.5 Existing Regulations 

1.2.5.1 Safety 

Achieving and maintaining the safe operation of commercial (common carrier) 

railroads in the U.S. falls under the jurisdiction of the Federal Railroad Administration (FRA), 

which is a part of the Department of Transportation. The FRA was created in 1966 to 

perform a number of disparate functions, including rehabilitating the Northeast Corridor rail 

passenger service, supporting research and development for rail transportation, and promoting 

and enforcing safety regulations throughout the railway system. 

FRA safety regulations apply to railroads on a nationwide basis. In 49 CFR section 

229 the regulations require safety inspections of each locomotive used in commercial 

operations: daily, every 92 days (i.e. the periodic inspection), annually, and biennial. Each 

subsequent inspection increases in complexity. The inspections are usually performed by the 

railroad which owns or leases the locomotive. FRA personnel review the findings of these 

inspections and any corrective actions identified and taken. Since each locomotive is required 

to be out of revenue service for inspection every 92 days, railroads commonly schedule their 

performance of preventive maintenance during these times. It appears likely that each 

locomotive is out of service for 12 to 24 hours during each FRA safety inspection and 

1-67 
Regulatory Impact Analysis 

preventative maintenance period.J To limit the time that locomotives are out of service for 

these safety inspections and preventive maintenance, railroads maintain suitable facilities 

distributed across the nation. Thus, it appears that the railroads have had a long history of 

compliance with federal regulations, and have developed strategies to live within the 

regulations and to minimize any adverse business impacts that may have resulted. 

1.2.5.2 Federal81

In 1980 Congress passed the Staggers Act (USCA 49 § 10101) which laid out the 

government's statutory objectives for the railroad industry which are to balance the efficiency 

and viability of the industry with the need for: reasonable rates, fair wages, public health and 

safety, and energy conservation. The railroads are governed by two separate federal agencies 

directly, both under the Department of Transportation, a cabinet-level department. The 

Federal Railroad Administration (FRA) regulates safety issues. The FRA sets safety 

standards for rail equipment and operation, and also investigates accidents on rail lines and at 

rail crossings. The FRA also plays a role in labor disputes to a small degree, by monitoring 

the progress of negotiations, projecting the economic impact of a strike and assisting the 

Secretary in briefing Congress if necessary. The STB is an adjudicatory body that was 

formed in 1966 to settle disputes and regulate the various modes of surface transportation 

within the U.S. Organizationally, the STB is part of the Department of Transportation (DOT), 

the STB deals with railway rate and service issues, railway restructuring and various other 

issues, including classification of railroads. The Surface Transportation Board (STB) 

regulates economic issues such as rates. The STB can also mandate access to locations in 

order to maintain competition in areas where mergers reduced the number of available carriers 

1.2.5.3 Rates 

Rail transportation accounts for 8.7% of all for-hire transportation services that are a 

measured in the GDP. The average freight revenue per ton-mile for Class I rail in 2004 was 

$0.0235, with average operating revenue of $40.5 billion. Freight rates, when adjusted for 

inflation have declined by an average of 1.1% a year between 1990 and 2004, due in large 

part to the passage of the Staggers Act, as shown in Figure 1-1-15.

82

If a shipper believes a rate is unreasonable (only if that shipper does not have access to 

another railroad, and waterway or highway modes are not feasible), they can complain to the 

STB, which has a stand-alone rate standard. This means that they determine what a 

hypothetical new carrier to serve that shipper would need to charge to cover all of its costs 

including capital and construction. If this hypothetical rate is less than what the shipper is 

being charged, that charged rate is considered to be “unreasonably high” and the railroad can 

be ordered to reduce the challenged rate and pay reparations to the shipper. Complaints such 

as these are typically made by bulk shippers, such as coal or chemicals, who cannot use other 

modes of transportation such as highway or can't access other railroads. 

 

J

 Values are an approximate estimate by FRA personnel. 

1-68 
Industry Characterization 

Figure 1-1-15 Railroad Rate Trends Before and After Staggers Act of 198082

1.2.6 Foreign Railroads in US 

Locomotives that operate extensively within the U.S. are subject to the existing 

provisions of 40 CFR Part 92. 

1.2.6.1 Mexico 

In 2004, the Bureau of Transportation Statistics (BTS) says there were a total of 

675,305 US/Mexico railcar crossings, that's an average of almost 1900 crossings a day, or one 

every minute. The Mexican Railroads and their 16,415 miles of track have been privately 

owned since a Constitutional amendment was passed in 1995 (see FRA "Border Issues"). 

They primarily haul NAFTA generated goods, such as cars, automobile parts, and other 

manufactured products. Mexico has two railroads, Ferrocarril Mexicano, which has a joint 

venture with UP, and Transportacion Ferroviaria Mexicana (TFM) of which Kansas City 

Southern has controlling interest.83

1.2.6.2 Canada 

In 2004, the BTS says there were 1,950,909 border crossings into Canada by railcars. 

Canada is also home to two Class I railroads that operate extensively in the U.S., Grand Trunk 

Corporation which includes almost all of Canadian National’s (CN) U.S. operations, and 

Canadian Pacific Railway which operates its Soo Line primarily in the U.S. 

1-69 
Regulatory Impact Analysis 

1-70 

References

 

1

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Vessels,” Final Report, May 2006. Prepared for U.S Environmental Protection Agency, Office of Transportation 

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2

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 46 USCS Appx § 688 

4

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Sweden and the United Kingdom. MEPC 53/4/4, 15 April 2005. Marine Environment Protection Committee, 

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Cylinder, December 7, 2007 (72 FR 69522). 

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9

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DC: U.S. Census Bureau. Tables 1, 5, and 7. 

10 Docket ID# EPA-HQ-OAR-2003-0190-0847 11 National Marine Manufacturers’ Association (NMMA). January 12, 2006. Facsimile from John McKnight 

(NMMA) to Dave Reeves (RTI). 

12 Workboat. January 2005. Workboat Construction Survey. Last obtained March 15, 2006. 

(http://www.workboat.com/pdfs/WB_2004const_survey.pdf) 

13 Viscusi, W.K., J.M. Vernon and J.E. Harrington. 1992. Economics of Regulation and Antitrust. Lexington, 

MA: D.C. Heath and Co. 

14 U.S. Census Bureau. 2004. Economic Manufacturing Industry Series: 2002. EC02-31I-333618. Washington, 

DC: U.S. EPA. 

15 U.S. Department of Transportation. 1998 and 2006. National Transportation Statistics 1997 and 2005. 

Washington, DC: U.S Department of Transportation. 

16 U.S. Bureau of the Census. 2002 Economic Census. http://www.census.gov/prod/ec02/ec0231i336611.pdf and 

http://www.census.gov/prod/ec02/ec0231i336612.pdf. 

17 U.S. Maritim Administration (MARAD). 2003. Report on the survey of U.S. Shipbuilding and Repair 

Facilities. U.S. Department of Transportation, Maritime Administration. 18 National Marine Manufacturers Association (NMMA). 2004. 2004 Recreational Boating Statistical Abstract. 

Chicago, IL. 

19 National Marine Manufacturers Association (NMMA). 2004. 2004 Recreational Boating Statistical Abstract. 

Chicago, IL. 

20 U.S. Bureau of the Census. 2002 Economic Census. http://www.census.gov/prod/ec02/ec0231i336612.pdf. 21 Hoover’s Online. 2006. Online company database. <http://www.hoovers.com/>. 22 Dun & Bradstreet. 2006a. D&B Million Dollar Directory. Bethlehem, Pennsylvania: Dun & Bradstreet, Inc. 23 Dun & Bradstreet. 2006b. Small Business Database. <http://smallbusiness.dnb.com/>. 24 National Marine Manufacturers Association (NMMA). 2004. 2004 Recreational Boating Statistical Abstract. 

Chicago, IL. 

25 National Marine Manufacturers Association (NMMA). 2004. 2004 Recreational Boating Statistical Abstract. 

Chicago, IL. 

26 National Marine Manufacturers Association (NMMA). 2004. 2004 Recreational Boating Statistical Abstract. 

Chicago, IL. 

27 Eastern Research Group, Inc. (ERG). “Category 2 Vessel Census, Activity, and Spacial Allocation Assessment 

and Category 1 and Category 2 In-port/At-sea Splits,” Final Report, Feb. 16, 2007. Prepared for U.S 

Environmental Protection Agency, Office of Transportation and Air Quality. 
Industry Characterization 

1-71 

 

28 WorkBoat. Annual Construction Survey. January/February 1992, January/February 1994, January/February 

1995, January 1998, and January 2004. Portland, ME: Workboat. 

29 Eastern Research Group, Inc. (ERG). “Category 2 Vessel Census, Activity, and Spacial Allocation Assessment 

and Category 1 and Category 2 In-port/At-sea Splits,” Final Report, Feb. 16, 2007. Prepared for U.S 

Environmental Protection Agency, Office of Transportation and Air Quality. 

30 American Waterways Operators (AWO). 2006. Facts About the American Tugboat, Towboat, and Barge 

Industry. http://www.americanwaterways.com/. Arlington, VA. 31 Letter from Jennifer Carpenter, Senior Vice President of Government Affairs and Policy Analysis, American 

Waterways Operators, to EPA Docket EPA-HQ-OAR-2003-0190, July 2, 2007. 32 Eastern Research Group, Inc. (ERG). “Category 2 Vessel Census, Activity, and Spacial Allocation Assessment 

and Category 1 and Category 2 In-port/At-sea Splits,” Final Report, Feb. 16, 2007. Prepared for U.S 

Environmental Protection Agency, Office of Transportation and Air Quality. 

33 Eastern Research Group, Inc. (ERG). “Category 2 Vessel Census, Activity, and Spacial Allocation Assessment 

and Category 1 and Category 2 In-port/At-sea Splits,” Final Report, Feb. 16, 2007. Prepared for U.S 

Environmental Protection Agency, Office of Transportation and Air Quality. 

34 U.S. Department of Labor Statistics. 2006. Occupational Outlook Handbook. 

http://www.bls.gov/oco/ocos177.htm. 35 Eastern Research Group, Inc. (ERG). “Category 2 Vessel Census, Activity, and Spacial Allocation Assessment 

and Category 1 and Category 2 In-port/At-sea Splits,” Final Report, Feb. 16, 2007. Prepared for U.S 

Environmental Protection Agency, Office of Transportation and Air Quality. 

36 WorkBoat.com. February 2006. 2005 Construction Survey. 

<http://www.workboat.com/pdfs/wb06_constructionsurvey.pdf> 

37 WorkBoat.com. February 2006. 2005 Construction Survey. 

<http://www.workboat.com/pdfs/wb06_constructionsurvey.pdf> 

38 “Locomotive Facts & History” GE Transportation, www.getransportation.com/na/en/locofacts.html 

39 Frank W. Donnelly, Raymond L. Cousineau, R. Nigel M. Horsley, "Hybrid Technology for the Rail Industry" 

ASME/IEEE Document RTD2004-66041, April 2004. 

40 www.railpower.com/products_hl_howitworks.html 

41 https://www.getransportation.com/general/locomotives/hybrid/hybrid_default.asp 

42 “Prospects for Dynamic Brake Energy Recovery on North American Freight Locomotives” 

43 "Railroad and Locomotive Technology Roadmap" Argonne National Laboratory, December 2002 44 “American Diesel-Electric Locomotives” www.nps.gov/history/history/online_books/steamtown/shs5.htm 45 www.uprr.com/aboutup/history/loco/locohs03.shtml 

46 Steven Fritz, "On Track Toward Cleaner Large Engines: New emissions reduction strategies focus on 

locomotives and ferry boats" Southwest Research Technology Today, Spring 2004. 47 http://www.wabtec.com/corp/fast_facts.asp 48 http://www.everytime.cummins.com/every/applications/rail.jsp 

49 Docket ID# EPA-HQ-OAR-2003-0190-0367[1] 

50 www.stewartand Stevenson.com, http://brookvilleequipment.com/ 51 http://www.emdiesels.com/lms/en/company/history 

52 www.getransportation.com/general/freight_rail/models 

53 Association of American Railroads (AAR) Railroad Ten-Year Trends 1995-2004 

54 http://www.nationalrailway.com/ 

55 http://www.railpower.com/products_td.html 

56 “New Ultra-Low Emission Locomotive Goes to Work in Union Pacific’s Los Angeles Basin Rail Yards” 

http://www.uprr.com/newsinfo/releases/environment/2007/0131_ultralow.shtml 57 William C. Vantuono, "New power plays to watch" Railway Age, August 2006, www.nyab.com/news/NYABRA-10-06-Power%20Plays.pdf 

58 “Green $ave$ Green” http://www.railpower.com/dl/greensavesgreen.pdf 

59 http://www.fra.dot.gov/us/printcontent/94 

60 “Stop Your Engines” http://www.roadstartonline.com/2004/11/026a0411.asp 61 “Overview of U.S. Freight Railroads” nationalatlas.gov/articles/transportation/a_freightrr.html 
Regulatory Impact Analysis 

1-72 

 

62 American Short Line and Regional Railroad Association 2005 Facts and Figures. 

63 STB Railroad Revenue Deflator Formula: Current Year's Revenues x (1991 Average Index/Current Year's 

Average Index) 

64 Transportation Energy Data Book, Edition 25, 2006 

65 The five U.S. Class I Railroads are: Burlington Northern and Santa Fe (BNSF), CSX Transportation, Kansas 

City Southern Railway Company, Norfolk Southern Railway (and subsidiaries), Union Pacific Railroad 

Company (UP). The two Canadian owned Class I Railroads are: Grand Trunk Corporation and the Soo Line 

Railroad Company 

66 Association of American Railroads (AAR) Railroad Facts 2005 67 Based on 2005 Railroad Annual Reports filed with STB. 

68 “CPR showcases success in greenhouse gas reductions” http:www.prnewswire.com 69 Quoted from May 15, 1997 testimony by Bruce Wilson representing AAR. Docket item #A-94-31-IV-D-7. 

70 National Transportation Statistics 2006, Bureau of Transportation 

71 “Fuel Savings Solutions” www.getransportation.com/na/en/docs/806349_Fuel Savings Broch-L.pdf 72 Table 4-17: Class I Rail Freight Fuel Consumption and Travel 

http://www.bts.gov/publications/national_transportation_statistics/2006/csv/table_04_17.csv 73 “Freight Railroads: Industry Health Has Improved, but Concerns about Competition and Capacity Should be 

Addressed” GAO Report to Congressional Requesters, October, 2006. 

74 Short Line and Regional Railroad Facts and Figures, 2005 Edition 

75 “An Industry in Uncertain Territory” Presentation by Richard F. Timmons, President of the Transportation 

Research Forum, www.aslrra.org/images/news_file/Transportation_Research.Forum_October_6_2006.pdf 

76 “Commuter Rail National Totals, Fiscal Year 2004” http://www.apta.com/research/stats/rail/crsum.cfm 77 “Amtrak National Fact Sheet” Amtrak Media Relations: July, 2006. 

78 ”Amtrak: Energy Efficient Travel” Amtrak Media Reltaions: June, 2006. 79 Andrew Taylor, "Amtrak Supporters Aim to Ease Budget Cuts" 06/14/06, Associated Press. 

80 "2006 Transit Fact Book", American Public Transportation Association. 81 http://www.fra.dot.gov/us/printcontent/955 

82 “Freight Railroads Background" Federal Railroad Administration, 2003: 

www.fra.dot.gov/downloads/policy/freight2003.pdf 83 “Competition Agencies Clear the Way for Kansas City Southern’s NAFTA Railway” The Transportation 

Antitrust Update, Winter 2005 – Issue No. 14 
Air Quality and Resulting Health and Welfare Effects 

2-1

CHAPTER 2: Air Quality and Resulting Health and Welfare Effects of Air Pollution from 

Mobile Sources...................................................................................................................... 2-2 

2.1 Particulate Matter ..............................................................................................2-4 

2.1.1 Science of PM Formation......................................................................... 2-4 

2.1.2 Health Effects of PM Pollution .............................................................. 2-11 

2.1.3 Current PM2.5 Levels .............................................................................. 2-14 

2.1.4 Projected PM2.5 Levels ........................................................................... 2-15 

2.1.5 Environmental Effects of PM Pollution ................................................. 2-19 

2.2 Ozone...............................................................................................................2-28 

2.2.1 Science of Ozone Formation .................................................................. 2-28 

2.2.2 Health Effects of Ozone Pollution.......................................................... 2-29 

2.2.3 Current 8-Hour Ozone Levels ................................................................ 2-30 

2.2.4 Projected 8-Hour Ozone Levels ............................................................. 2-32 

2.2.5 Environmental Effects of Ozone Pollution.......................................... 2-40 

2.3 Air Quality Modeling Methodology................................................................2-42 

2.3.1 Air Quality Modeling Overview............................................................. 2-42 

2.3.2 Model Domain and Configuration.......................................................... 2-43 

2.3.3 Model Inputs........................................................................................... 2-44 

2.3.4 CMAQ Evaluation.................................................................................. 2-45 

2.3.5 Model Simulation Scenarios................................................................... 2-45 

2.3.6 Visibility Modeling Methodology.......................................................... 2-46 

2.4 Air Toxics........................................................................................................2-48 

2.4.1 Diesel Exhaust PM ................................................................................. 2-48 

2.4.2 Other Air Toxics—benzene, 1,3-butadiene, formaldehyde, acetaldehyde, 

acrolein, POM, naphthalene ............................................................................ 2-65 
Regulatory Impact Analysis 

2-2

CHAPTER 2: Air Quality and Resulting Health and Welfare 

Effects of Air Pollution from Mobile Sources 

The locomotive and marine diesel engines subject to this rulemaking generate 

significant emissions of particulate matter (PM) and nitrogen oxides (NOx) that contribute to 

nonattainment of the National Ambient Air Quality Standards (NAAQS) for PM2.5 and ozone. 

These engines also emit hazardous air pollutants, or air toxics, that are associated with serious 

adverse health effects. Emissions from locomotive and marine diesel engines also cause harm 

to public welfare by contributing to visibility impairment and other harmful environmental 

impacts across the U.S. Therefore, EPA is adopting new standards to control these emissions. 

The health and environmental effects associated with these emissions are a classic 

example of a negative externality (an activity that imposes uncompensated costs on others). 

With a negative externality, an activity’s social cost (the cost borne to society imposed as a 

result of the activity taking place) exceeds its private cost (the cost to those directly engaged 

in the activity). In this case, as described in this chapter, emissions from locomotives and 

marine diesel engines and vessels impose public health and environmental costs on society. 

However, these added costs to society are not reflected in the costs of those using these 

engines and equipment. The current market and regulatory scheme do not correct this 

externality because firms in the market are rewarded for minimizing their production costs, 

including the costs of pollution control, and do not benefit from reductions in emissions. In 

addition, firms that may take steps to use equipment that reduces air pollution may find 

themselves at a competitive disadvantage compared to firms that do not. The emission 

standards EPA is finalizing help address this market failure and reduce the negative 

externality from these emissions by providing a regulatory incentive for engine and 

locomotive manufacturers to produce engines and locomotives that emit fewer harmful 

pollutants and for railroads and vessel builders and owners to use those cleaner engines. 

Today millions of Americans continue to live in areas with air quality that may 

endanger public health and welfare (i.e., levels not requisite to protect the public health with 

an adequate margin of safety). As of October 10, 2007 there are 88 million people living in 

39 areas (which include all or part of 208 counties) that either do not meet the PM2.5 NAAQS 

or contribute to violations in other counties. These numbers do not include the people living 

in areas where there is a significant future risk of failing to maintain or achieve the current or 

future PM2.5 NAAQS. Currently, ozone concentrations exceeding the level of the 8-hour 

ozone NAAQS occur over wide geographic areas, including most of the nation’s major 

population centers. As of October 10, 2007 there are approximately 144 million people living 

in 81 areas (which include all or part of 366 counties) designated as not in attainment with the 

8-hour ozone NAAQS. These numbers do not include the people living in areas where there 

is a future risk of failing to attain or maintain the 8-hour ozone NAAQS. Figure 2-1 presents 

the counties currently designated as nonattainment for the PM2.5 or 8-hour ozone NAAQS as 

well as mandatory class I federal areas for visibility. This figure illustrates the widespread 

nature of these air quality problems. 
Air Quality and Resulting Health and Welfare Effects 

2-3

Figure 2-1 Air Quality Problems are Widespread 

Emissions from locomotive and marine diesel engines account for substantial portions 

of today’s ambient PM2.5 and NOx levels (20 percent of total mobile source NOx emissions 

and 25 percent of total mobile source diesel PM 2.5 emissions). Unless EPA takes action to 

reduce their pollution levels, the relative contribution of these engines to air quality problems 

will increase over time. By 2030 locomotive and marine diesel engines could constitute more 

than 65 percent of mobile source diesel PM2.5 emissions and 35 percent of mobile source NOx

emissions. 

Under the emission standards finalized in this action, in 2030 annual NOx emissions 

will be reduced by about 800,000 tons and annual PM2.5 emissions by about 27,000 tons. We 

estimate that the reduction in PM2.5 will produce nationwide air quality improvements. 

According to air quality modeling performed in conjunction with this rule, all current PM2.5

nonattainment areas will experience a resulting decrease in their 2030 annual PM2.5 design 

values (DV). In addition, all 133 modeled mandatory class I federal areas will experience 

improved visibility. For the current 39 PM2.5 nonattainment areas (annual DVs greater than 

15μg/m3

) the average population-weighted modeled future-year annual PM2.5 DVs will on 

average decrease by 0.16 μg/m3 in 2030. The maximum decrease for future-year annual 

PM2.5 DVs over the U.S. will be 0.81 μg/m3

 in 2030. 
Regulatory Impact Analysis 

2-4

According to air quality modeling performed for this rulemaking, the locomotive and 

marine diesel engine emissions controls are expected to provide nationwide improvements in 

ozone levels. On a population-weighted basis, the average modeled future-year 8-hour ozone 

design values will decrease by 0.85 ppb in 2030. Within projected ozone nonattainment 

areas, the average decrease will be 0.62 ppb in 2030. The maximum decrease for future-year 

8-hour ozone DVs over the U.S. will be 4.6 ppb in 2030. 

While EPA has already adopted many emission control programs that are expected to 

reduce both ambient ozone and PM levels over the next two decades, including the Clean Air 

Interstate Rule (CAIR) (70 FR 25162, May 12, 2005) and the Clean Air Nonroad Diesel rule 

(69 FR 38957, June 29, 2004), the additional PM2.5 and NOx emissions reductions from this 

rule will be important to a number of states efforts to attain and maintain the ozone and PM2.5

NAAQS near term and in the decades to come. 

2.1 Particulate Matter 

In this section we review the health and welfare effects of PM2.5. We also describe air 

quality monitoring and modeling data that indicate many areas across the country continue to 

be exposed to high levels of ambient PM2.5. Emissions of hydrocarbons (HCs) and NOx from 

the engines subject to this rule contribute to these PM concentrations. Information on air 

quality was gathered from a variety of sources, including monitored PM concentrations, air 

quality modeling done for this rulemaking as well as state and local air quality information. 

2.1.1 Science of PM Formation 

Particulate matter (PM) represents a broad class of chemically and physically diverse 

substances. It can be principally characterized as discrete particles that exist in the condensed 

(liquid or solid) phase spanning several orders of magnitude in size. PM10 refers to particles 

generally less than or equal to 10 micrometers (μm) in diameter. PM2.5 refers to fine particles, 

generally less than or equal to 2.5 μm in diameter. Inhalable (or “thoracic”) coarse particles 

refer to those particles generally greater than 2.5 μm but less than or equal to 10 μm in 

diameter. Ultrafine PM refers to particles less than 100 nanometers (0.1 μm) in diameter. 

Larger particles tend to be removed by the respiratory clearance mechanisms, whereas smaller 

particles are deposited deeper in the lungs. 

Particles span many sizes and shapes and consist of hundreds of different chemicals. 

Particles are emitted directly from sources and are also formed through atmospheric chemical 

reactions; the former are often referred to as “primary” particles, and the latter as “secondary” 

particles. In addition, there are also physical, non-chemical reaction mechanisms that 

contribute to secondary particles. Particle pollution also varies by time of year and location 

and is affected by several weather-related factors, such as temperature, clouds, humidity, and 

wind. A further layer of complexity comes from a particle’s ability to shift between 

solid/liquid and gaseous phases, which is influenced by concentration, meteorology, and 

temperature. 

Particles are made up of different chemical components. The major chemical 

components include carbonaceous materials (carbon soot and organic compounds), inorganic 
Air Quality and Resulting Health and Welfare Effects 

2-5

compounds (including, sulfate and nitrate compounds that usually include ammonium) and a 

mix of substances often apportioned to crustal materials such as soil and ash (Figure 2-2). 

The different components that make up particle pollution come from specific sources and are 

often formed in the atmosphere. As mentioned above, particulate matter includes both 

“primary” PM, which is directly emitted into the air, and “secondary” PM. Primary PM 

consists of carbonaceous materials emitted from cars, trucks, heavy equipment, forest fires, 

some industrial processes and burning waste. It also includes both combustion and processrelated fine metals and larger crustal material from unpaved roads, stone crushing, 

construction sites, and metallurgical operations. Secondary PM forms in the atmosphere from 

gases. Some of these reactions require sunlight and/or water vapor. Secondary PM includes: 

sulfates formed from sulfur dioxide emissions from power plants and industrial facilities; 

nitrates formed from nitrogen oxide emissions from cars, trucks, industrial facilities, and 

power plants; and organic carbon formed from reactive organic gas emissions from cars, 

trucks, industrial facilities, forest fires, and biogenic sources such as trees. 

Source: The Particulate Matter Report, USEPA 454-R-04-002, Fall 2004. Carbon reflects both organic 

carbon and elemental carbon. Organic carbon accounts for emissions from a wide range of sources including 

locomotive and marine diesel engines as well as automobiles, biogenic, gas-powered off-road vehicles, and 

wildfires. Elemental carbon is formed from both diesel and gasoline powered sources. 

2.1.1.1 Composition of PM2.5 in Selected Urban Areas 

Note that fine particles can be transported long distances by wind and weather and can 

be found in the air thousands of miles from where they formed. The relative contribution of 

various chemical components to PM2.5 varies by region of the country, as illustrated in Figure 

Cars, trucks, industrial 

combustion and 

processes, heavy 

equipment, wildfires, 

wood/waste burning,

Cars, trucks,

industrial combustion, and

power generation 

Suspended soils, industrial 

metallurgical operations 

Mobile power generation, 

industrial combustion and 

processes 

Figure 2-2 Common Sources Contributing to Fine Particle Levels 
Regulatory Impact Analysis 

2-6

2-3. Data on PM2.5 composition are available from the EPA Speciation Trends Network and 

the IMPROVE Network, covering both urban and rural locations across the U.S. 

These data show that carbonaceous PM2.5 makes up the major component for PM2.5 in 

both urban and rural areas in the Western U.S. Carbonaceous PM2.5 includes both elemental 

and organic carbon. Nitrates formed from NOx also play a major role in the western U.S., 

especially in the California area where nitrates are responsible for about a quarter of the 

ambient PM2.5 concentrations. Sulfate plays a lesser role in these regions by mass, but it 

remains important to visibility impairment. Data for the Eastern and Central U.S. show that 

both sulfates and carbonaceous PM2.5 are major contributors to ambient PM2.5 in urban and 

rural areas. In some eastern areas, carbonaceous PM2.5 is responsible for up to half of ambient 

PM2.5 concentrations. Sulfate is also a major contributor to ambient PM2.5 in the Eastern U.S. 

and in some areas sulfate makes greater contribution than carbonaceous PM2.5. 

Figure 2-3 Average PM2.5 Composition in Urban areas by Region, 2003 

2.1.1.2 Source Apportionment Studies of PM2.5

Determining sources of fine particulate matter is complicated in part because the 

concentrations of various components are influenced by both primary emissions and 

secondary atmospheric reactions. As described earlier, when attempting to characterize the 

sources affecting PM2.5 concentrations, it is important to note that both regional and local 

sources impact ambient levels. In the eastern US, regional fine particles are often dominated 

by secondary particles including sulfates, organics (primary and secondary) and nitrates. 

These are particles which form through atmospheric reactions of emitted sulfur dioxide, 

oxides of nitrogen and ammonia, and are transported over long distances. Conversely, local 
Air Quality and Resulting Health and Welfare Effects 

2-7

contributions to fine particles are likely dominated by directly emitted particulate matter from 

sources such as gasoline and diesel mobile sources, including locomotive and marine diesel 

enginesA, industrial facilities (e.g., iron and steel manufacturing, coke ovens, or pulp mills), 

and residential wood and waste burning. 

Development of effective and efficient emission control strategies to lower PM2.5

ambient concentrations can be aided by determining the relationship between the various 

types of emissions sources and elevated levels of PM2.5 at ambient monitoring sites. Source 

apportionment analyses such as receptor modeling are useful in this regard by estimating 

potential fine particulate regional and local source impacts on a receptor’s ambient 

concentrations. The goal is to apportion the mass concentrations into components attributable 

to the most significant source categories. Receptor modeling techniques are observationbased models which utilize measured ambient concentrations of PM2.5 species to quantify the 

contribution that regional and local sources have at a given receptor which, in this case, is an 

ambient monitoring location.B

 These techniques may be useful in helping to characterize fine 

particulate source contributions to ambient PM2.5 levels; however, there are inherent 

limitations including but not limited to the adequacy (e.g., vintage and representativeness) of 

existing source profiles in identifying source groups or specific sources, availability, 

completeness and representativeness of ambient datasets to fully inform these techniques, and 

current scientific understanding and measured data to relate tracer elements to specific 

sources, production processes, or activities. Additionally, commingling of similar species 

from different sources in one "factor” can make it difficult to relate the "factor" to a particular 

source. Furthermore, long-range transport of particles may alter the chemical composition of 

specific source emissions, making it more difficult to differentiate sources.1

A literature compilation summarizing source apportionment studies was conducted as 

part of a research and preparation program for the CAIR, which was focused on PM2.5

transport.2

 Literature selected in this compilation represented key source apportionment 

research, focusing primarily on recent individual source apportionment studies in the eastern 

U.S. The sources identified are grouped into seven categories: secondary sulfates, mobile, 

secondary nitrates, biomass burning, industrial, crustal and salt, and other/not identified. 

Some of these studies are based on older ambient databases and more recent ambient data 

have shown improvement and reduced levels of ambient PM2.5 concentrations across the U.S., 

especially in the East, which affects the quantitative conclusions one may draw from these 

studies. Notably, the relative fraction of sulfates has continued to decrease with the 

implementation of the acid rain program and removal of sulfur from motor vehicle fuels. 

More routine monitoring for specific tracer compounds that are unique to individual sources 

or more time-resolved measurements can lead to better separation of blended “factors” such 

 

A Note that while we believe that the mobile source sector is a substantial contributor to total PM2.5 mass; our 

current mobile source inventory is likely underestimated and information on control measures is incomplete. 

B

 Currently, three established receptor models are widely used for source apportionment studies: the Chemical 

Mass Balance (CMB) model, UNMIX and Positive Matrix Factorization (PMF). The CMB receptor model relies 

on measured source profiles as well as ambient species measurements to produce a source contribution estimate 

at the receptor location, while the PMF and UNMIX techniques decompose the ambient measurement data 

matrix into source profiles and contributions by utilizing the underlying relationship (i.e., correlations) between 

the individually measured species. 
Regulatory Impact Analysis 

2-8

as secondary commingled sulfates and organic aerosols, the latter which are more attributed to 

emissions from vehicles and vegetation. Western studies have focused on sources impacting 

both high population areas such as Seattle, Denver, the San Joaquin Valley, Los Angeles, San 

Francisco as well as national parks.3,4,5,6,7,8,9,10,11,12 

As mentioned previously, the sources of PM2.5 can be categorized as either direct 

emissions or contributing to secondary formation. The results of the studies showed that 

approximately 20 to 60% of the fine particle mass comes from secondarily formed inorganic 

nitrates and sulfates depending on the area of the country, with nitrates predominantly 

affecting the West, sulfates in the East and a mixture of the two in the Industrial Midwest. 

The precursors of secondary inorganic particles are generally gaseous pollutants such 

as sulfur dioxide or oxides of nitrogen, which react with ammonia in the atmosphere to form 

ammonium salts. Dominant sources of SO2 include power generation facilities, which are 

also sources of NOx, of which mobile sources including locomotive and marine diesel engines 

are also major sources. The result of recent and future reductions in precursor emissions from 

electrical generation utilities and mobile sources, however, will lead to a reduction in 

precursor contributions which would aid in limiting the production of secondary sulfates and 

nitrates. Also, reductions in gasoline and diesel fuel sulfur will reduce mobile source SO2

emissions. 

In addition, secondary organic carbon aerosols (SOA) also make a large contribution 

to the overall total PM2.5 concentration in both the Eastern and Western United States. For 

many of the receptor modeling studies, the majority of organic carbon is attributed to mobile 

source emissions (including both gasoline and diesel). While vehicles emit organic carbon 

particulate, the various organic gases also emitted by these sources react in the atmosphere to 

form SOA which shows a correlation to the other secondarily formed aerosols due to common 

atmospheric reactions. As Section 2.1.1.4 of this RIA discusses, based on current data, 

locomotives and larger marine diesel engines which have similar engine characterizations 

emit a relatively large amount of organic PM. Other common sources of the organic gases 

which form SOA include vegetation, vehicles, and industrial volatile organic compound 

(VOC) and semi-volatile organic compound (SVOC) emissions. However, due to some limits 

on data and a lack of specific molecular markers, current receptor modeling techniques have 

some difficulty attributing mass to SOA. Therefore, currently available source apportionment 

studies may be attributing an unknown amount of SOA in ambient PM to direct emissions of 

mobile sources; concurrently, some secondary organic aerosol found in ambient samples may, 

as mentioned above, be coming from mobile sources and not be fully reflected in these 

assessments. Research is underway to improve estimates of the contribution of SOA to total 

fine particulate mass. 

While gaseous precursors of PM2.5 are important contributors, urban primary sources 

still influence peak local concentrations that exceed the NAAQS, even if their overall 

contributions are smaller. The mixture of industrial source contributions to mass vary across 

the nation and include emissions from heavy manufacturing such as metal processing (e.g., 

steel production, coke ovens, foundries), petroleum refining, and cement manufacturing, 

among others. Mobile source contributions from facilities like ports, rail yards, truck stops, 

and heavily-trafficked roads can also have strong, localized air quality impacts. Other sources 
Air Quality and Resulting Health and Welfare Effects 

2-9

of primary PM2.5 are more seasonal in nature. One such source is biomass burning, which 

usually contributes more during the winter months when households burn wood for heat, but 

also contributes episodically during summer as a result of forest fires. Other seasonal sources 

of primary PM include soil, sea salt and road salting operations that occur in winter months. 

The extent of these primary source contributions to local PM2.5 problems varies across the 

U.S. and can even vary within an urban area. 

2.1.1.3 Regional and Local Source Contributions to Formation of PM2.5

Both local and regional sources contribute to particle pollution. Figure 2-4 shows how 

much of the PM2.5 mass can be attributed to local versus regional sources for 13 selected 

urban areas. The “urban excess” is estimated by subtracting the measured PM2.5 species at a 

regional monitor location C (assumed to be representative of regional background) from those 

measured at an urban location. 

As shown in Figure 2-4, total carbon mass is the main contributor to urban excess 

concentrations in all regions, with Fresno, CA and Birmingham, AL having the largest 

observed measures. Larger urban excess of nitrates is seen in the western U.S. and northern 

tier cities with Fresno, CA and Salt Lake City, UT having values significantly higher than all 

other areas across the nation. These results indicate that local sources of these pollutants are 

indeed contributing to the PM2.5 air quality problem in these areas. 

Urban and nearby rural PM2.5 concentrations suggest substantial regional contributions 

to fine particles in the East. 

 

C

 Regional concentrations are derived from the rural IMPROVE monitoring network Interagency Monitoring of 

Protected Visual Environments. See http://vista.cira.colostate.edu/improve. 
Regulatory Impact Analysis 

2-10

Figure 2-4. Estimated "Urban Excess" of 13 Urban Areas by PM2.5 Species Component 

Note: Total Carbon Mass (TCM) is the sum of Organic Carbon (OC) and Elemental Carbon (EC). In 

this graph, the light grey is OC and the dark grey is EC. See: Turpin, B. and H-J, Lim, 2001: Species 

contributions to PM2.5 mass concentrations: Revisiting common assumptions for estimating organic mass, 

Atmospheric Environment, 35, 602-610. 

Regional pollution contributes more than half of total PM2.5 concentrations. As 

expected for a predominately regional pollutant, only a modest urban excess is observed for 

sulfates. Rural background PM2.5 concentrations are high in the East and are somewhat 

uniform over large geographic areas. These regional concentrations come from emission 

sources such as power plants, natural sources, and urban pollution and can be transported 

hundreds of miles and reflect to some extent the denser clustering of urban areas in the East as 

compared to the West. The local and regional contributions for the major chemical 

components that make up urban PM2.5 are sulfates, carbon, and nitrates. 

2.1.1.4 Composition of PM2.5 from Locomotive and Marine Diesel Engines 

Locomotive and Marine Diesel engines contribute significantly to ambient PM2.5

levels, largely through emissions of carbonaceous PM2.5. As discussed in the previous 

section, carbonaceous PM2.5 is a major portion of ambient PM2.5, especially in populous urban 

areas. For the medium speed diesel engine commonly used in locomotive and Category 2 

marine applications, the majority of the total carbon PM is organic carbon. Locomotive and 

marine diesels also emit high levels of NOx which react in the atmosphere to form secondary 

PM2.5 (namely ammonium nitrate). Locomotive and marine diesel engines also emit SO2 and 

HC which form secondary PM2.5 (namely sulfates and organic carbonaceous PM2.5). Figure 
Air Quality and Resulting Health and Welfare Effects 

2-11

2-5 shows the relative contribution of elemental and organic carbon to PM emissions for 

seven Tier 0, Tier 1, and Tier 2 locomotives (four locomotive engines were 2-stroke while 3 

locomotive engines were 4- stroke). This recent data, while limited to seven locomotives, 

suggest that locomotives, regardless of when they were built, tend to emit a very high level of 

organic carbon PM, precisely the type of carbon that appears to be responsible for a high 

percentage of the urban excess PM2.5 species across the US. 

 Figure 2-5: PM emissions for 7 locomotives tested using 2800 ppm sulfur nonroad diesel fuel. 

2006 AAR Data

0.000

0.050

0.100

0.150

0.200

0.250

0.300

0.350

0.400

0.450

0.500

0.550

0.600

16-645F3 7FDL16 645 (Roots-blown) 16-710G3B 7FDL16 16-710G3C GEVO12LDB5

EMD SD50 Tier 0 GE DASH9-44CW

Tier 0

EMD GP38-2 Tier

0

EMD SD70M Tier

1

GE AC4400 Tier 1 EMD SD70Ace

Tier 2

GE ES44DC Tier 2

KCS7017 CN2508 CP4404 NS2630 CSXT595 UP8353 BNSF7736

Line-Haul Brake-specific PM Emissions (g/bhp-hr)

Sulfate PM (mostly sulfuric acid+associated water)

Organic PM (mostly lube oil)

Elemental Carbon PM (soot)

0.20 g/bhp-hr (Tier 2 PM Standard)

0.10 g/bhp-hr (Tier 3 PM Standard)

0.03 g/bhp-hr (Tier 4 PM Standard)

0.45 g/bhp-hr (Tier 1 PM Standard)

0.60 g/bhp-hr (Tier 0 PM Standard)

2.1.2 Health Effects of PM Pollution 

As stated in EPA’s Particulate Matter Air Quality Criteria Document (PM AQCD), 

available scientific findings “demonstrate well that human health outcomes are associated 

with ambient PM.”D We are relying on the data and conclusions in the PM AQCD and PM 

Staff Paper, which reflects EPA’s analysis of policy-relevant science from the PM AQCD, 

regarding the health effects associated with particulate matter.13,14 We also present additional 

recent studies published after the cut-off date for the PM AQCD.E15 Taken together this 

 

D Personal exposure includes contributions from many different types of particles, from many sources, and in 

many different environments. Total personal exposure to PM includes both ambient and nonambient 

components; and both components may contribute to adverse health effects. 

E

 These additional studies are included in the 2006 Provisional Assessment of Recent Studies on Health Effects 

of Particulate Matter Exposure. The provisional assessment did not and could not (given a very short timeframe) 

undergo the extensive critical review by EPA, CASAC, and the public, as did the PM AQCD. The provisional 

assessment found that the “new” studies expand the scientific information and provide important insights on the 
Regulatory Impact Analysis 

2-12

information supports the conclusion that PM-related emissions such as those controlled in this 

action are associated with adverse health effects. Information on PM-related mortality and 

morbidity is presented first, followed by information on near-roadway exposure studies, 

marine ports and rail yard exposure studies. 

2.1.2.1 Short-term Exposure Mortality and Morbidity Studies 

As discussed in the PM AQCD, short-term exposure to PM2.5 is associated with 

mortality from cardiopulmonary diseases (PM AQCD, p. 8-305), hospitalization and 

emergency department visits for cardiopulmonary diseases (PM AQCD, p. 9-93), increased 

respiratory symptoms (PM AQCD, p. 9-46), decreased lung function (PM AQCD Table 8-34) 

and physiological changes or biomarkers for cardiac changes (PM AQCD, Section 8.3.1.3.4). 

In addition, the PM AQCD describes a limited body of new evidence from epidemiologic 

studies for potential relationships between short term exposure to PM and health endpoints 

such as low birth weight, preterm birth, and neonatal and infant mortality. (PM AQCD, 

Section 8.3.4). 

Among the studies of effects from short-term exposure to PM2.5, several specifically 

address the contribution of mobile sources to short-term PM2.5 effects on daily mortality. 

These studies indicate that there are statistically significant associations between mortality 

and PM related to mobile source emissions (PM AQCD, p.8-85). The analyses incorporate 

source apportionment tools into daily mortality studies and are briefly mentioned here. 

Analyses incorporating source apportionment by factor analysis with daily time-series studies 

of daily death indicated a relationship between mobile source PM2.5 and mortality.16,17 

Another recent study in 14 U.S. cities examined the effect of PM10 exposures on daily hospital 

admissions for cardiovascular disease. This study found that the effect of PM10 was 

significantly greater in areas with a larger proportion of PM10 coming from motor vehicles, 

indicating that PM10 from these sources may have a greater effect on the toxicity of ambient 

PM10 when compared with other sources.18 These studies provide evidence that PM-related 

emissions, specifically from mobile sources, are associated with adverse health effects. 

2.1.2.2 Long-term Exposure Mortality and Morbidity Studies 

Long-term exposure to elevated ambient PM2.5 is associated with mortality from 

cardiopulmonary diseases and lung cancer (PM AQCD, p. 8-307), and effects on the 

respiratory system such as decreased lung function or the development of chronic respiratory 

disease (PM AQCD, pp. 8-313, 8-314). Of specific importance to this rulemaking, the PM 

AQCD also notes that the PM components of gasoline and diesel engine exhaust represent 

one class of hypothesized likely important contributors to the observed ambient PM-related 

increases in lung cancer incidence and mortality (PM AQCD, p. 8-318). 

The PM AQCD and PM Staff Paper emphasize the results of two long-term studies, 

the Six Cities and American Cancer Society (ACS) prospective cohort studies, based on 

 relationship between PM exposure and health effects of PM. The provisional assessment also found that “new” 

studies generally strengthen the evidence that acute and chronic exposure to fine particles and acute exposure to 

thoracic coarse particles are associated with health effects. 
Air Quality and Resulting Health and Welfare Effects 

2-13

several factors – the inclusion of measured PM data, the fact that the study populations were 

similar to the general population, and the fact that these studies have undergone extensive 

reanalysis (PM AQCD, p. 8-306, Staff Paper, p.3-18).19,20,21 These studies indicate that there 

are significant associations for all-cause, cardiopulmonary, and lung cancer mortality with 

long-term exposure to PM2.5. One analysis of a subset of the ACS cohort data, which was 

published after the PM AQCD was finalized but in time for the 2006 Provisional Assessment, 

found a larger association than had previously been reported between long-term PM2.5 

exposure and mortality in the Los Angeles area using a new exposure estimation method that 

accounted for variations in concentration within the city.22

As discussed in the PM AQCD, the morbidity studies that combine the features of 

cross-sectional and cohort studies provide the best evidence for chronic exposure effects. 

Long-term studies evaluating the effect of ambient PM on children’s development have 

shown some evidence indicating effects of PM2.5 and/or PM10 on reduced lung function 

growth (PM AQCD, Section 8.3.3.2.3). In another recent publication included in the 2006 

Provisional Assessment, investigators in southern California reported the results of a crosssectional study of outdoor PM2.5 and measures of atherosclerosis in the Los Angeles basin.23 

The study found significant associations between ambient residential PM2.5 and carotid 

intima-media thickness (CIMT), an indicator of subclinical atherosclerosis, an underlying 

factor in cardiovascular disease. 

2.1.2.3 Roadway-Related Exposure and Health Studies 

A recent body of studies reinforces the findings of these PM morbidity and mortality 

effects by looking at traffic-related exposures, PM measured along roadways, or time spent in 

traffic and adverse health effects. While many of these studies did not measure PM 

specifically, they include potential exhaust exposures which include mobile source PM 

because they employ indices such as roadway proximity or traffic volumes. One study with 

specific relevance to PM2.5 health effects is a study that was done in North Carolina looking at 

concentrations of PM2.5 inside police cars and corresponding physiological changes in the 

police personnel driving the cars. The authors report significant elevations in markers of 

cardiac risk associated with concentrations of PM2.5 inside police cars on North Carolina state 

highways.24 A number of studies of traffic-related pollution have shown associations between 

fine particles and adverse respiratory outcomes in children who live near major 

roadways.25,26,27

2.1.2.4 Marine Ports and Rail Yard Studies 

Recently, new studies from the State of California provide evidence that PM2.5

emissions within marine ports and rail yards can contribute significantly to elevated ambient 

concentrations near these sources28,29 and that a substantial number of people experience 

exposure to fresh locomotive and marine diesel engine emissions, raising potential health 

concerns. Additional information on marine port and rail yard emissions and potential health 

effects can be found in Section 2.4 of this RIA. 
Regulatory Impact Analysis 

2-14

2.1.3 Current PM2.5 Levels 

EPA has recently amended the NAAQS for PM2.5 (71 FR 61144, October 17, 2006). 

The final PM NAAQS rule addressed revisions to the primary and secondary NAAQS for 

PM2.5 to provide increased protection of public health and welfare, respectively. The primary 

PM2.5 NAAQS includes a short-term (24-hour) and a long-term (annual) standard. The level 

of the 24-hour PM2.5 NAAQS has been revised from 65 μg/m3to 35 μg/m3 to provide 

increased protection against health effects associated with short-term exposures to fine 

particles. The current form of the 24-hour PM2.5 standard was retained (e.g., based on the 

98th percentile concentration averaged over three years). The level of the annual PM2.5 

NAAQS was retained at 15μg/m3, continuing protection against health effects associated with 

long-term exposures. The current form of the annual PM2.5 standard was retained as an 

annual arithmetic mean averaged over three years, however, the following two aspects of the 

spatial averaging criteria were narrowed: (1) the annual mean concentration at each site will 

now be within 10 percent of the spatially averaged annual mean, and (2) the daily values for 

each monitoring site pair will now yield a correlation coefficient of at least 0.9 for each 

calendar quarter. 

With regard to the secondary standards for PM2.5, EPA has revised these standards to 

be identical in all respects to the revised primary standards. Specifically, EPA has revised the 

current 24-hour PM2.5 secondary standard by making it identical to the revised 24-hour PM2.5

primary standard and retained the annual PM2.5 secondary standard. This suite of secondary 

PM2.5 standards is intended to provide protection against PM-related public welfare effects, 

including visibility impairment, effects on vegetation and ecosystems, and material damage 

and soiling. 

A nonattainment area is defined in the Clean Air Act (CAA) as an area that is 

violating an ambient standard or is contributing to a nearby area that is violating the standard. 

In 2005, EPA designated 39 nonattainment areas for the 1997 PM2.5 NAAQS based on air 

quality design values and a number of other factors (70 FR 943, January 5, 2005; 70 FR 

19844, April 14, 2005).F

 These areas are comprised of 208 full or partial counties with a total 

population exceeding 88 million. The 1997 PM2.5 nonattainment counties, areas and 

populations, as of October 2007, are listed in Appendix 2A to this RIA. The 1997 PM2.5

NAAQS was recently revised and the 2006 PM2.5 NAAQS became effective on December 18, 

2006. Nonattainment areas will be designated with respect to the 2006 PM2.5 NAAQS in 

early 2010. Table 2-1 provides an estimate, based on 2003-05 air quality data, of the counties 

violating the 2006 PM2.5 NAAQS. 

 

F

 The full details involved in calculating a PM2.5 design value are given in Appendix N of 40 CFR Part 50. 
Air Quality and Resulting Health and Welfare Effects 

2-15

Table 2-1 Counties violating the 2006 PM2.5 NAAQS based on 2003-2005 Air Quality Data 

Fine Particle Standards: 

Current Nonattainment Areas and Other Violated Counties 

 Number of Counties Populationa

1997 PM2.5 Standards: 39 areas currently 

designated 

208 88,394,000

2006 PM2.5 Standards: Counties with violating 

monitorsb

49 18,198,676

Total 257 106,592,676

Notes: 

a

 Population numbers are from 2000 census data. 

b

 The areas designated as nonattainment for the 2006 PM2.5 NAAQS will be based on three years of air quality 

data. Also, the county numbers in the summary table include only the counties with monitors violating the 2006 

PM2.5 NAAQS. The monitored county violations may be an underestimate of the number of counties and 

populations that will eventually be included in areas with multiple counties designated nonattainment. 

As can be seen in Figure 2-1 ambient PM2.5 levels exceeding the 1997 PM2.5 NAAQS 

are widespread throughout the country. States with PM2.5 nonattainment areas will be 

required to take action to bring those areas into compliance in the future. Most PM2.5

nonattainment areas will be required to attain the 1997 PM2.5 NAAQS in the 2010 to 2015 

time frame and then be required to maintain the 1997 PM2.5 NAAQS thereafter.G The 

attainment dates associated with the potential nonattainment areas based on the 2006 PM2.5

NAAQS would likely be in the 2015 to 2020 timeframe. The emission standards being 

finalized in this action become effective between 2008 and 2015. Therefore, the PM2.5 and 

PM2.5 precursor inventory reductions in this action will be useful for states to attain or 

maintain the PM2.5 NAAQS. 

2.1.4 Projected PM2.5 Levels 

In conjunction with this rulemaking, we performed a series of PM2.5 air quality 

modeling simulations for the continental U.S. The model simulations were performed for 

several emissions scenarios including the following: 2002 baseline projection, 2020 baseline 

projection, 2020 baseline projection with locomotive/marine diesel engine controls, 2030 

baseline projection, and 2030 baseline projection with locomotive/marine diesel engine 

controls. Information on the air quality modeling methodology is contained in Section 2.3 as 

well as the air quality modeling technical support document (AQ TSD). In the following 

sections we describe projected PM2.5 levels in the future with and without the controls being 

finalized in this action. 

2.1.4.1 Projected PM2.5 Levels without this Rulemaking 

Even with the implementation of all current state and federal regulations, including the 

CAIR Rule, the NOx SIP call, nonroad and on-road diesel rules and the Tier 2 rule, there are 

 

G The EPA finalized PM2.5 attainment and nonattainment areas in April 2005. The EPA finalized the PM 

Implementation rule in November 2005 (70 FR 65984, Nov. 5, 2005). 
Regulatory Impact Analysis 

2-16

projected to be U.S. counties violating the PM2.5 NAAQS well into the future. The model 

outputs from the 2002, 2020 and 2030 baselines, combined with current air quality data, were 

used to identify areas expected to exceed the PM2.5 NAAQS in the future. 

The baseline air quality modeling conducted for this final rule projects that in 2020, 

with all current controls in effect, up to 11 counties, with a population of 24 million people,

may not attain the annual standard of 15 μg/m3

. This does not account for additional areas 

that have air quality measurements within 10 percent of the PM2.5 standard. These areas, 

although not violating the standard, will also benefit from the emissions reductions, ensuring 

long term maintenance of the PM NAAQS. For example, in 2020, an additional 16 million 

people are projected to live in 13 counties that have air quality measurements within 10 

percent of the 2006 PM NAAQS. This modeling supports the conclusion that there are a 

substantial number of counties across the US projected to experience PM2.5 concentrations at 

or above the PM2.5 NAAQS into the future. A number of state governments and state 

organizations have told EPA that they need the reductions from this rule in order to be able to 

attain or maintain the 1997 PM2.5 standards as well as to attain the 2006 PM2.5 NAAQS.30 

Emission reductions from locomotive and marine diesel engines will be helpful for these 

counties in attaining and maintaining the PM2.5 NAAQS. 

2.1.4.2 Projected PM2.5 Levels With this Rulemaking 

The impacts of the locomotive/marine diesel engine controls were determined by 

comparing the model results in the future year control runs against the baseline simulations of 

the same year. According to air quality modeling performed for this rulemaking, the 

locomotive and marine diesel engine standards are expected to provide nationwide 

improvements in PM2.5 levels. On a population-weighted basis, the average modeled futureyear annual PM2.5 design value (DV) for all counties is expected to decrease by 0.06 μg/m3 in 

2020 and 0.12 μg/m3 in 2030. In counties predicted to have annual PM2.5 design values 

greater than 15 μg/m3

 the average decrease will be somewhat higher: 0.11 μg/m3 in 2020 and

0.21 μg/m3 in 2030. In addition, those counties that are within 10 percent of the annual PM2.5

design value will see their average DV decrease by 0.09 μg/m3

 in 2020 and 0.18 μg/m3 in 

2030. The maximum decrease for future-year annual PM2.5 design values will be 0.38 μg/m3

in 2020 and 0.81 μg/m3

 in 2030. Note that for the current 39 PM2.5 nonattainment areas the 

average population weighted future-year annual PM2.5 design value will on average decrease 

by 0.08 μg/m3

 in 2020 and by 0.16 μg/m3 in 2030. 

Figure 2-6 illustrates the geographic impact of the locomotive and marine diesel 

engine controls on annual PM2.5 design values in 2030. The greatest PM2.5 reductions are 

projected to occur in the gulf coast region where in 2030 four counties will experience 

reductions in their annual PM2.5 design values of 0.50 to 1.00 μg/m3. The twenty counties 

experiencing PM2.5 reductions between 0.25 and 0.49 μg/m3

 are geographically dispersed 

along the midwest, the pacific northwest, the gulf coast and southern California An 

additional 143 counties spread across the US will see a decrease in their projected annual 

PM2.5 DV ranging from 0.10 to 0.24 μg/m3

. A complete set of maps illustrating the 

geographic impact of various alternatives explored as part of this rulemaking are available in 

the Air Quality Modeling TSD for this rulemaking. 
Air Quality and Resulting Health a

2-17

Number of Counties

4

20

143193196

0 Differences due to scenario 2030cc Loco

Legend

-1.00 to -0.50-0.49 to -0.25-0.24 to -0.10-0.09 to -0.05-0.04 to 0.00> 0.00

Figure 2-6 Impact of Locomotive/Marine controls on annual PM2.5 Design Values (DV) in 2030 
Regulatory Impact Analysis 

2-18

Table 2-2 lists the counties with projected annual PM2.5 design values that violate or 

are within 10 percent of the annual PM2.5 standard in 2020. Counties are marked with a “V” 

in the table if their projected design values are greater than or equal to 15.05 μg/m3. Counties 

are marked with an “X” in the table if their projected annual design values are greater than or 

equal to 13.55 μg/m3, but less than 15.05 μg/m3. The counties marked “X” are not projected 

to violate the standard, but to be close to it, so the rule will help assure that these counties 

continue to meet the standard. The current design values are also presented in Table 2-2. 

Recall that we project future design values only for counties that have current design values, 

so this list is limited to those counties with ambient monitoring data sufficient to calculate 

current 3-year design values. 

Table 2-2 Counties with 2020 Projected Annual PM2.5Design Values in Violation or within 10 percent of 

the Annual PM2.5 Standard 

State County 

2000-

2004 

Average 

annual 

PM2.5

DV 

(ug/m3

) 

2020 modeling 

projections of 

annual PM2.5 DV 

(ug/m3

) 

2020 

Population

 base control 

Alabama Jefferson Co 18.36 V V 673,910

California Fresno Co 20.02 X X 1,012,929

California Imperial Co 14.44 V V 183,835

California Kern Co 21.77 X X 851,948

California Kings Co 18.77 X X 172,415

California Los Angeles Co 23.16 X X 10,067,663

California Merced Co 16.47 X X 263,184

California Orange Co 18.27 X X 3,690,329

California Riverside Co 27.15 X X 2,173,672

California San Bernardino Co 24.63 X X 2,302,697

California San Diego Co 15.65 V V 3,715,268

California San Joaquin Co 14.84 V V 711,938

California Stanislaus Co 16.49 V V 579,349

California Tulare Co 21.33 X X 464,651

Georgia Fulton Co 18.29 V V 898,342

Illinois Cook Co 17.06 V V 5,369,914

Illinois Madison Co 17.27 V V 276,838

Kentucky Jefferson Co 16.58 V V 717,730

Michigan Wayne Co 19.32 X X 1,879,876

Montana Lincoln Co 15.85 V V 20,078

New York New York Co 17.16 V V 1,560,060

Ohio Cuyahoga Co 18.36 V V 1,305,880

Pennsylvania Allegheny Co 20.99 X X 1,234,865

West Virginia Hancock Co 17.30 V V 30,461
Air Quality and Resulting Health and Welfare Effects 

2-19

2.1.5 Environmental Effects of PM Pollution 

In this section we discuss public welfare effects of PM and its precursors including 

visibility impairment, atmospheric deposition, and materials damage and soiling. 

2.1.5.1 Visibility Impairment 

Visibility can be defined as the degree to which the atmosphere is transparent to 

visible light.31 Visibility impairment manifests in two principal ways: as local visibility 

impairment and as regional haze.32 Local visibility impairment may take the form of a 

localized plume, a band or layer of discoloration appearing well above the terrain as a result 

of complex local meteorological conditions. Alternatively, local visibility impairment may 

manifest as an urban haze, sometimes referred to as a “brown cloud.” This urban haze is 

largely caused by emissions from multiple sources in the urban area and is not typically 

attributable to only one nearby source or to long-range transport. The second type of 

visibility impairment, regional haze, usually results from multiple pollution sources spread 

over a large geographic region. Regional haze can impair visibility over large regions and 

across states. 

Visibility is important because it has direct significance to people’s enjoyment of daily 

activities in all parts of the country. Individuals value good visibility for the well-being it 

provides them directly, where they live and work and in places where they enjoy recreational 

opportunities. Visibility is also highly valued in significant natural areas such as national 

parks and wilderness areas, and special emphasis is given to protecting visibility in these 

areas. 

Fine particles are the major cause of reduced visibility in parts of the United States. 

To address the welfare effects of PM on visibility, EPA sets secondary PM2.5 standards which 

work in conjunction with the regional haze program. The secondary (welfare-based) PM2.5

NAAQS is equal to the suite of primary (health-based) PM2.5 NAAQS. The regional haze 

rule (64 FR 35714, July 1999) was put in place to protect the visibility in mandatory class I 

federal areas. These areas are defined in Section 162 of the Act as those national parks 

exceeding 6,000 acres, wilderness areas and memorial parks exceeding 5,000 acres, and all 

international parks which were in existence on August 7, 1977. A list of the mandatory class I 

federal areas is included in Appendix 2D. Visibility is impaired in both PM2.5 nonattainment 

areas and mandatory class I federal areas. 

Control of locomotive and marine diesel engine emissions will improve visibility 

across the nation. The locomotive and marine diesel engines subject to this rule either 

directly emit PM2.5 or emit PM precursors which contribute to the formation of secondary 

PM2.5 and thus contribute to visibility impairment. In the next sections we present current 

information and projected estimates about visibility impairment related to ambient PM2.5

levels across the country and visibility impairment in mandatory class I federal areas. We 

conclude that visibility will continue to be impaired in the future and the emission reductions 

from this rule will help improve visibility conditions across the country and in mandatory 

class I federal areas. For more information on visibility see the PM AQCD as well as the 

2005 PM Staff Paper.33,34 
Regulatory Impact Analysis 

2-20

2.1.5.1.1 Current Visibility Impairment in PM2.5 Nonattainment Areas 

As mentioned above, the secondary PM2.5 standards were set as equal to the suite of 

primary PM2.5 standards. Almost 90 million people live in the 208 counties that are in 

nonattainment for the 1997 PM2.5 NAAQS, (see Appendix 2A for the complete list of current 

nonattainment areas). These populations, as well as large numbers of individuals who travel 

to these areas can experience visibility impairment. 

2.1.5.1.2 Current Visibility Impairment at Mandatory Class I Federal Areas 

Detailed information about current and historical visibility conditions in mandatory 

class I federal areas is summarized in the EPA Report to Congress and the 2002 EPA Trends 

Report.35,36 The conclusions draw upon the Interagency Monitoring of Protected Visual 

Environments (IMPROVE) network data. One of the objectives of the IMPROVE monitoring 

network program is to provide regional haze monitoring representing all mandatory class I 

federal areas where practical. The National Park Service report also describes the state of 

national park visibility conditions and discusses the need for improvement.37 

The regional haze rule requires states to establish goals for each affected mandatory 

class I federal area that 1) improves visibility on the haziest days (20% most impaired days), 

2) ensures no degradation occurs on the cleanest days (20% least impaired days), and 3) 

achieves natural background visibility levels by 2064. Although there have been general 

trends toward improved visibility, progress is still needed on the haziest days. Specifically, as 

discussed in the 2002 EPA Trends Report, without the effects of pollution a natural visual 

range in the United States is approximately 75 to 150 km in the East and 200 to 300 km in the 

West. In 2001, the mean visual range for the worst days was 29 km in the East and 98 km in 

the West.38 

2.1.5.1.3 Future Visibility Impairment 

Additional emission reductions will be needed from a broad set of sources, including 

those in this action, as part of the overall strategy to achieve the visibility goals of the Act and 

the regional haze program. 

Modeling was used to project visibility conditions in 133 mandatory class I federal 

areas across the US in 2020 and 2030 as a result of the locomotive and marine diesel engine 

standards. The AQ modeling TSD and Section 2.3 of this RIA provide information on the 

modeling methodology. The results indicate that improvements in visibility will occur in all 

133 mandatory class I federal areas, although these areas will continue to have annual average 

deciview levels above background. Table 2-3 below indicates the current monitored deciview 

values, the natural background levels each area is attempting to reach, and also the projected 

deciview values in 2020 and 2030 with and without the standards. In 2030, the greatest 

visibility improvement due to this rule will occur at San Gorgonio (0.24 deciview) located in 

San Bernadino County, California. 
Air Quality and Resulting Health and Welfare Effects 

2-21

Table 2-3 Current (2002) and Future (2020 and 2030) Projected Visibility Conditions With and Without 

Locomotive and Marine Diesel Rule in Mandatory Class I Federal Areas (20% Worst Days) 

Mandatory Class I 

Federal Area Name 

State 2002 

Baseline 

Visibility 

(dv)a

2020 

Base 

Case 

(dv) 

2020 

Control 

Case 

(dv) 

2030 

Base 

Case 

(dv) 

2030 

Control 

Case 

(dv) 

Natural 

Bckgrnd 

(dv) 

Acadia NP ME 22.89 19.79 19.77 19.86 19.81 12.43 

Agua Tibia Wilderness CA 23.50 21.23 21.14 21.16 20.94 7.64 

Alpine Lake Wilderness WA 17.84 16.77 16.71 16.72 16.60 8.43 

Anaconda-Pintler 

Wilderness MT 13.41 13.15 13.14 13.13 13.11 7.43 

Arches NP UT 11.24 11.14 11.11 11.05 11.03 6.43 

Badlands NP SD 17.14 15.87 15.84 15.80 15.75 8.06 

Bandelier NM NM 12.22 11.43 11.41 11.38 11.34 6.26 

Big Bend NP TX 17.30 16.15 16.13 16.18 16.15 7.16 

Black Canyon of the 

Gunnison NM CO 10.33 9.80 9.79 9.79 9.77 6.24 

Bosque del Apache NM 13.80 12.96 12.90 12.94 12.81 6.73 

Bob Marshall 

Wilderness MT 14.48 14.14 14.13 14.11 14.08 7.74 

Bryce Canyon NP UT 11.65 11.36 11.34 11.34 11.31 6.86 

Bridger Wilderness WY 11.12 10.81 10.81 10.80 10.80 6.58 

Brigantine NJ 29.01 24.88 24.85 24.99 24.91 12.24 

Cabinet Mountains 

Wilderness MT 14.09 13.57 13.54 13.52 13.46 7.53 

Caney Creek 

Wilderness AR 26.36 22.11 22.05 22.06 21.92 11.58 

Canyonlands NP UT 11.24 10.84 10.81 10.83 10.82 6.43 

Caribou Wilderness CA 14.15 13.62 13.60 13.55 13.51 7.31 

Carlsbad Caverns NP TX 17.19 15.93 15.92 15.92 15.90 6.68 

Chassahowitzka FL 26.09 21.96 21.94 21.96 21.91 11.21 

Chiricahua NM AZ 13.43 13.09 13.09 13.10 13.09 7.21 

Chiricahua Wilderness AZ 13.43 13.09 13.09 13.10 13.09 7.21 

Cohutta Wilderness GA 30.30 23.36 23.33 23.34 23.28 11.14 

Crater Lake NP OR 13.74 13.29 13.27 13.26 13.20 7.84 

Craters of the Moon NM ID 14.00 13.00 12.97 12.90 12.82 7.53 

Cucamonga Wilderness CA 19.94 17.42 17.36 17.14 17.10 7.06 

Desolation Wilderness CA 12.63 12.15 12.13 12.15 12.12 6.12 

Diamond Peak 

Wilderness OR 13.74 13.25 13.20 13.22 13.12 7.84 

Dome Land Wilderness CA 19.43 18.37 18.34 18.20 18.11 7.46 

Dolly Sods Wilderness WV 29.04 22.38 22.35 22.38 22.33 10.39 

Eagle Cap Wilderness OR 18.57 17.86 17.83 17.79 17.71 8.92 

Eagles Nest Wilderness CO 9.61 9.05 9.03 8.99 8.96 6.54 

Emigrant Wilderness CA 17.63 17.22 17.21 17.24 17.19 7.64 

Everglades NP FL 22.30 19.75 19.77 19.97 19.94 12.15 

Fitzpatrick Wilderness WY 11.12 10.86 10.85 10.86 10.84 6.58 

Flat Tops Wilderness CO 9.61 9.31 9.31 9.32 9.31 6.54 

Galiuro Wilderness AZ 13.43 13.08 13.07 13.11 13.09 7.21 
Regulatory Impact Analysis 

2-22

Mandatory Class I 

Federal Area Name 

State 2002 

Baseline 

Visibility 

(dv)a

2020 

Base 

Case 

(dv) 

2020 

Control 

Case 

(dv) 

2030 

Base 

Case 

(dv) 

2030 

Control 

Case 

(dv) 

Natural 

Bckgrnd 

(dv) 

Gates of the Mountains 

Wilderness MT 11.29 10.92 10.91 10.89 10.87 6.45 

Gearhart Mountain 

Wilderness OR 13.74 13.39 13.37 13.36 13.33 7.84 

Gila Wilderness NM 13.11 12.55 12.54 12.55 12.54 6.69 

Glacier Peak 

Wilderness WA 13.96 13.62 13.60 13.69 13.67 8.01 

Goat Rocks Wilderness WA 12.76 12.06 12.05 12.07 12.03 8.36 

Grand Canyon NP AZ 11.66 11.13 11.09 11.15 11.08 7.14 

Great Gulf Wilderness NH 22.82 19.48 19.45 19.50 19.46 11.99 

Great Sand Dunes NM CO 12.78 12.36 12.36 12.37 12.36 6.66 

Great Smoky Mountains 

NP TN 30.28 23.96 23.93 23.93 23.86 11.24 

Grand Teton NP WY 11.76 11.36 11.35 11.33 11.31 6.51 

Guadalupe Mountains 

NP TX 17.19 15.89 15.88 15.89 15.86 6.68 

Hells Canyon 

Wilderness OR 18.55 17.26 17.20 17.18 17.04 8.32 

Hercules-Glades 

Wilderness MO 26.75 23.00 22.93 22.96 22.81 11.30 

Hoover Wilderness CA 12.87 12.73 12.72 12.75 12.74 7.91 

Isle Royale NP MI 20.74 19.15 19.10 19.16 19.04 12.37 

Jarbidge Wilderness NV 12.07 11.86 11.86 11.86 11.85 7.87 

James River Face 

Wilderness VA 29.12 23.43 23.34 23.43 23.26 11.13 

Joshua Tree NM CA 19.62 17.95 17.93 17.85 17.71 7.19 

Joyce-Kilmer-Slickrock 

Wilderness TN 30.28 23.46 23.43 23.43 23.37 11.24 

Kalmiopsis Wilderness OR 15.51 15.00 14.98 14.97 14.93 9.44 

Lava Beds NM CA 15.05 14.45 14.42 14.40 14.32 7.86 

La Garita Wilderness CO 10.33 9.90 9.89 9.89 9.88 6.24 

Lassen Volcanic NP CA 14.15 13.56 13.54 13.48 13.43 7.31 

Linville Gorge 

Wilderness NC 28.77 22.48 22.45 22.47 22.41 11.22 

Lostwood ND 19.57 17.73 17.70 17.67 17.60 8.00 

Lye Brook Wilderness VT 24.45 21.10 21.08 21.17 21.11 11.73 

Maroon BellsSnowmass Wilderness CO 9.61 9.24 9.24 9.24 9.24 6.54 

Mammoth Cave NP KY 31.37 25.53 25.48 25.54 25.44 11.08 

Mazatzal Wilderness AZ 13.35 12.74 12.72 12.77 12.73 6.68 

Medicine Lake MT 17.72 16.25 16.22 16.18 16.12 7.90 

Mesa Verde NP CO 13.03 12.40 12.39 12.40 12.37 6.83 

Mission Mountains 

Wilderness MT 14.48 14.06 14.04 14.02 13.99 7.74 

Mount Hood Wilderness OR 14.86 14.19 14.13 14.28 14.14 8.44 

Mount Jefferson 

Wilderness OR 15.33 14.80 14.77 14.82 14.76 8.79 
Air Quality and Resulting Health and Welfare Effects 

2-23

Mandatory Class I 

Federal Area Name 

State 2002 

Baseline 

Visibility 

(dv)a

2020 

Base 

Case 

(dv) 

2020 

Control 

Case 

(dv) 

2030 

Base 

Case 

(dv) 

2030 

Control 

Case 

(dv) 

Natural 

Bckgrnd 

(dv) 

Mokelumne Wilderness CA 12.63 12.32 12.30 12.34 12.31 6.12 

Mountain Lakes 

Wilderness OR 13.74 13.26 13.24 13.23 13.17 7.84 

Moosehorn ME 21.72 18.65 18.63 18.68 18.64 12.01 

Mount Rainier NP WA 18.24 17.27 17.24 17.27 17.21 8.55 

Mount Washington 

Wilderness OR 15.33 14.77 14.75 14.78 14.72 8.79 

Mount Zirkel Wilderness CO 10.52 10.06 10.05 10.07 10.04 6.44 

North Absaroka 

Wilderness WY 11.45 11.17 11.16 11.15 11.13 6.86 

North Cascades NP WA 13.96 13.58 13.57 13.68 13.67 8.01 

Okefenokee GA 27.13 23.46 23.42 23.50 23.40 11.44 

Olympic NP WA 16.74 15.85 15.82 15.95 15.89 8.44 

Otter Creek Wilderness WV 29.04 22.31 22.29 22.32 22.27 10.39 

Pasayten Wilderness WA 15.23 14.85 14.84 14.83 14.81 8.26 

Pecos Wilderness NM 10.41 10.01 10.00 10.02 10.01 6.44 

Petrified Forest NP AZ 13.21 12.90 12.83 12.87 12.75 6.49 

Pine Mountain 

Wilderness AZ 13.35 12.59 12.58 12.59 12.54 6.68 

Pinnacles NM CA 18.46 17.37 17.36 17.16 17.09 7.99 

Point Reyes NS CA 22.81 22.01 21.99 21.87 21.79 15.77 

Presidential Range-Dry 

River Wilderness NH 22.82 19.48 19.45 19.50 19.46 11.99 

Rawah Wilderness CO 10.52 10.04 10.04 10.06 10.04 6.44 

Red Rock Lakes WY 11.76 11.44 11.43 11.42 11.39 6.51 

Redwood NP CA 18.45 17.89 17.86 17.86 17.79 13.91 

Roosevelt Campobello 

International Park ME 21.72 18.47 18.45 18.51 18.47 12.01 

Cape Romain SC 26.48 22.77 22.74 22.77 22.71 12.12 

Rocky Mountain NP CO 13.83 13.10 13.08 13.06 13.01 7.24 

Salt Creek NM 18.03 16.61 16.59 16.58 16.52 6.81 

San Gabriel Wilderness CA 19.94 17.30 17.25 17.01 16.93 7.06 

San Gorgonio 

Wilderness CA 22.17 20.28 20.22 19.94 19.70 7.30 

Saguaro NM AZ 14.83 14.50 14.47 14.49 14.44 6.46 

San Jacinto Wilderness CA 22.17 19.92 19.87 19.61 19.55 7.30 

St. Marks FL 26.03 21.84 21.82 21.88 21.83 11.53 

San Pedro Parks 

Wilderness NM 10.17 9.53 9.52 9.53 9.52 6.08 

Sawtooth Wilderness ID 13.78 13.64 13.63 13.64 13.63 6.43 

Scapegoat Wilderness MT 14.48 14.17 14.16 14.14 14.12 7.74 

Selway-Bitterroot 

Wilderness MT 13.41 13.06 13.04 13.02 12.99 7.43 

Seney MI 24.16 21.77 21.72 21.78 21.66 12.65 

Shenandoah NP VA 29.31 22.83 22.80 22.83 22.76 11.35 

Sierra Ancha AZ 13.67 13.22 13.20 13.18 13.15 6.59 
Regulatory Impact Analysis 

2-24

Mandatory Class I 

Federal Area Name 

State 2002 

Baseline 

Visibility 

(dv)a

2020 

Base 

Case 

(dv) 

2020 

Control 

Case 

(dv) 

2030 

Base 

Case 

(dv) 

2030 

Control 

Case 

(dv) 

Natural 

Bckgrnd 

(dv) 

Wilderness 

Sipsey Wilderness AL 29.03 23.78 23.73 23.77 23.66 10.99 

South Warner 

Wilderness CA 15.05 14.61 14.59 14.57 14.52 7.86 

Strawberry Mountain 

Wilderness OR 18.57 17.77 17.73 17.69 17.60 8.92 

Swanquarter NC 25.49 21.17 21.15 21.20 21.15 11.94 

Sycamore Canyon 

Wilderness AZ 15.25 14.96 14.94 14.98 14.93 6.69 

Teton Wilderness WY 11.76 11.41 11.40 11.39 11.36 6.51 

Three Sisters 

Wilderness OR 15.33 14.84 14.82 14.84 14.79 8.79 

Thousand Lakes 

Wilderness CA 14.15 13.54 13.52 13.46 13.41 7.31 

Theodore Roosevelt NP ND 17.74 16.65 16.54 16.61 16.42 7.79 

UL Bend MT 15.14 14.66 14.64 14.62 14.58 8.16 

Upper Buffalo 

Wilderness AR 26.27 22.41 22.35 22.34 22.19 11.57 

Ventana Wilderness CA 18.46 17.67 17.64 17.67 17.62 7.99 

Voyageurs NP MN 19.27 17.62 17.58 17.53 17.43 12.06 

Washakie Wilderness WY 11.45 11.18 11.17 11.16 11.14 6.86 

West Elk Wilderness CO 9.61 9.24 9.24 9.24 9.23 6.54 

Weminuche Wilderness CO 10.33 9.86 9.86 9.86 9.86 6.24 

White Mountain 

Wilderness NM 13.70 13.07 13.05 13.07 13.04 6.86 

Mount Adams 

Wilderness WA 12.76 12.03 12.01 12.02 11.97 8.36 

Wheeler Peak 

Wilderness NM 10.41 9.96 9.95 9.97 9.96 6.44 

Wind Cave NP SD 15.84 14.94 14.91 14.94 14.87 7.71 

Wichita Mountains OK 23.81 20.67 20.62 20.68 20.55 7.53 

Wolf Island GA 27.13 23.40 23.37 23.40 23.32 11.44 

Yellowstone NP WY 11.76 11.39 11.38 11.36 11.34 6.51 

Yosemite NP CA 17.63 17.16 17.14 17.15 17.11 7.64 

Zion NP UT 13.24 12.96 12.92 12.89 12.81 6.99 

a

 The level of visibility impairment in an area is based on the light-extinction coefficient and a unitless visibility 

index, called a “deciview”, which is used in the valuation of visibility. The deciview metric provides a scale for 

perceived visual changes over the entire range of conditions, from clear to hazy. Under many scenic conditions, 

the average person can generally perceive a change of one deciview. The higher the deciview value, the worse 

the visibility. Thus, an improvement in visibility is a decrease in deciview value. 

2.1.5.2 Particulate Matter Deposition 

Particulate matter contributes to adverse effects on vegetation and ecosystems, and to 

soiling and materials damage. These welfare effects result predominately from exposure to 

excess amounts of specific chemical species, regardless of their source or predominant form 
Air Quality and Resulting Health and Welfare Effects 

2-25

(particle, gas or liquid). Reflecting this fact, the PM AQCD concludes that regardless of size 

fractions, particles containing nitrates and sulfates have the greatest potential for widespread 

environmental significance, while effects are also related to other chemical constituents found 

in ambient PM, such as trace metals and organics. The following characterizations of the 

nature of these welfare effects are based on the information contained in the PM AQCD and 

PM Staff Paper. 

2.1.5.2.1 Deposition of Nitrates and Sulfates 

At current ambient levels, risks to vegetation from short-term exposures to dry 

deposited particulate nitrate or sulfate are low. However, when found in acid or acidifying 

deposition, such particles do have the potential to cause direct leaf injury. Specifically, the 

responses of forest trees to acid precipitation (rain, snow) include accelerated weathering of 

leaf cuticular surfaces, increased permeability of leaf surfaces to toxic materials, water, and 

disease agents; increased leaching of nutrients from foliage; and altered reproductive 

processes—all which serve to weaken trees so that they are more susceptible to other stresses 

(e.g., extreme weather, pests, pathogens). Acid deposition with levels of acidity associated 

with the leaf effects described above are currently found in some locations in the eastern 

U.S.39 Even higher concentrations of acidity can be present in occult depositions (e.g., fog, 

mist or clouds) which more frequently impacts higher elevations. Thus, the risk of leaf injury 

occurring from acid deposition in some areas of the eastern U.S. is high. Nitrogen deposition 

has also been shown to impact ecosystems in the western U.S. A study conducted in the 

Columbia River Gorge National Scenic Area (CRGNSA), located along a portion of the 

Oregon/Washington border, indicates that lichen communities in the CRGNSA have shifted 

to a higher proportion of nitrophilous species and the nitrogen content of lichen tissue is 

elevated.40 Lichens are sensitive indicators of nitrogen deposition effects to terrestrial 

ecosystems and the lichen studies in the Columbia River Gorge clearly show that ecological 

effects from air pollution are occurring. 

Some of the most significant detrimental effects associated with excess reactive 

nitrogen deposition are those associated with a syndrome known as nitrogen saturation. 

These effects include: (1) decreased productivity, increased mortality, and/or shifts in plant 

community composition, often leading to decreased biodiversity in many natural habitats 

wherever atmospheric reactive nitrogen deposition increases significantly and critical 

thresholds are exceeded; (2) leaching of excess nitrate and associated base cations from soils 

into streams, lakes, and rivers, and mobilization of soil aluminum; and (3) fluctuationof 

ecosystem processes such as nutrient and energy cycles through changes in the functioning 

and species composition of beneficial soil organisms.41 

In the U.S. numerous forests now show severe symptoms of nitrogen saturation. 

These forests include: the northern hardwoods and mixed conifer forests in the Adirondack 

and Catskill Mountains of New York; the red spruce forests at Whitetop Mountain, Virginia, 

and Great Smoky Mountains National Park, North Carolina; mixed hardwood watersheds at 

Fernow Experimental Forest in West Virginia; American beech forests in Great Smoky 

Mountains National Park, Tennessee; mixed conifer forests and chaparral watersheds in 

southern California and the southwestern Sierra Nevada in Central California; the alpine 
Regulatory Impact Analysis 

2-26

tundra/subalpine conifer forests of the Colorado Front Range; and red alder forests in the 

Cascade Mountains in Washington. 

Excess nutrient inputs into aquatic ecosystems (i.e. streams, rivers, lakes, estuaries or 

oceans) either from direct atmospheric deposition, surface runoff, or leaching from nitrogen 

saturated soils into ground or surface waters can contribute to conditions of severe water 

oxygen depletion; eutrophication and algae blooms; altered fish distributions, catches, and 

physiological states; loss of biodiversity; habitat degradation; and increases in the incidence 

of disease. 

Severe and persistent eutrophication often directly impacts human activities. For 

example, losses in the nation’s fishery resources may be directly caused by fish kills 

associated with low dissolved oxygen and toxic blooms. Declines in tourism occur when low 

dissolved oxygen causes noxious smells and floating mats of algal blooms create unfavorable 

aesthetic conditions. Risks to human health increase when the toxins from algal blooms 

accumulate in edible fish and shellfish, and when toxins become airborne, causing respiratory 

problems due to inhalation. According to a NOAA report, more than half of the nation’s 

estuaries have moderate to high expressions of at least one of these symptoms – an indication 

that eutrophication is well developed in more than half of U.S. estuaries. 42 

2.1.5.2.2 Deposition of Heavy Metals 

Heavy metals, including cadmium, copper, lead, chromium, mercury, nickel and zinc, 

have the greatest potential for influencing forest growth (PM AQCD, p. 4-87).43 Investigation 

of trace metals near roadways and industrial facilities indicate that a substantial load of heavy 

metals can accumulate on vegetative surfaces. Copper, zinc, and nickel have been 

documented to cause direct toxicity to vegetation under field conditions (PM AQCD, p. 4-75). 

Little research has been conducted on the effects associated with mixtures of contaminants 

found in ambient PM. While metals typically exhibit low solubility, limiting their 

bioavailability and direct toxicity, chemical transformations of metal compounds occur in the 

environment, particularly in the presence of acidic or other oxidizing species. These chemical 

changes influence the mobility and toxicity of metals in the environment. Once taken up into 

plant tissue, a metal compound can undergo chemical changes, accumulate and be passed 

along to herbivores or can re-enter the soil and further cycle in the environment. Although 

there has been no direct evidence of a physiological association between tree injury and heavy 

metal exposures, heavy metals have been implicated because of similarities between metal 

deposition patterns and forest decline (PM AQCD, p. 4-76). This hypothesized 

relationship/correlation was further explored in high elevation forests in the northeastern U.S. 

These studies measured levels of a group of intracellular compounds found in plants that bind 

with metals and are produced by plants as a response to sublethal concentrations of heavy 

metals. These studies indicated a systematic and significant increase in concentrations of 

these compounds associated with the extent of tree injury. These data strongly imply that 

metal stress causes tree injury and contributes to forest decline in the northeastern United 

States (PM AQCD 4-76,77).44 Contamination of plant leaves by heavy metals can lead to 

elevated soil levels. Trace metals absorbed into the plant frequently bind to the leaf tissue, 

and then are lost when the leaf drops (PM AQCD, p. 4-75). As the fallen leaves decompose, 

the heavy metals are transferred into the soil.45,46 
Air Quality and Resulting Health and Welfare Effects 

2-27

The environmental sources and cycling of mercury are currently of particular concern 

due to the bioaccumulation and biomagnification of this metal in aquatic ecosystems and the 

potent toxic nature of mercury in the forms in which is it ingested by people and other 

animals. Mercury is unusual compared with other metals in that it largely partitions into the 

gas phase (in elemental form), and therefore has a longer residence time in the atmosphere 

than a metal found predominantly in the particle phase. This property enables mercury to 

travel far from the primary source before being deposited and accumulating in the aquatic 

ecosystem. The major source of mercury in the Great Lakes is from atmospheric deposition, 

accounting for approximately eighty percent of the mercury in Lake Michigan.47,48 Over fifty 

percent of the mercury in the Chesapeake Bay has been attributed to atmospheric 

deposition.49 Overall, the National Science and Technology Council identifies atmospheric 

deposition as the primary source of mercury to aquatic systems.50 Forty-four states have 

issued health advisories for the consumption of fish contaminated by mercury; however, most 

of these advisories are issued in areas without a mercury point source. 

Elevated levels of zinc and lead have been identified in streambed sediments, and 

these elevated levels have been correlated with population density and motor vehicle use.51,52 

Zinc and nickel have also been identified in urban water and soils. In addition, platinum, 

palladium, and rhodium, metals found in the catalysts of modern motor vehicles, have been 

measured at elevated levels along roadsides.53 Plant uptake of platinum has been observed at 

these locations. 

2.1.5.2.3 Deposition of Polycyclic Organic Matter 

Polycyclic organic matter (POM) is a byproduct of incomplete combustion and 

consists of organic compounds with more than one benzene ring and a boiling point greater 

than or equal to 100 degrees centigrade.54 Polycyclic aromatic hydrocarbons (PAHs) are a 

class of POM that contains compounds which are known or suspected carcinogens. 

Major sources of PAHs include mobile sources. PAHs in the environment may be 

present as a gas or adsorbed onto airborne particulate matter. Since the majority of PAHs are 

adsorbed onto particles less than 1.0 μm in diameter, long range transport is possible. 

However, studies have shown that PAH compounds adsorbed onto diesel exhaust particulate 

and exposed to ozone have half lives of 0.5 to 1.0 hours.55 

Since PAHs are insoluble, the compounds generally are particle reactive and 

accumulate in sediments. Atmospheric deposition of particles is believed to be the major 

source of PAHs to the sediments of Lake Michigan.56,57 Analyses of PAH deposition in 

Chesapeake and Galveston Bay indicate that dry deposition and gas exchange from the 

atmosphere to the surface water predominate.58,59 Sediment concentrations of PAHs are high 

enough in some segments of Tampa Bay to pose an environmental health threat. EPA funded 

a study to better characterize the sources and loading rates for PAHs into Tampa Bay.60 

PAHs that enter a water body through gas exchange likely partition into organic rich particles 

and can be biologically recycled, while dry deposition of aerosols containing PAHs tend to be 

more resistant to biological recycling.61 Thus, dry deposition is likely the main pathway for 

PAH concentrations in sediments while gas/water exchange at the surface may lead to PAH 

distribution into the food web, leading to increased health risk concerns. 
Regulatory Impact Analysis 

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Trends in PAH deposition levels are difficult to discern because of highly variable 

ambient air concentrations, lack of consistency in monitoring methods, and the significant 

influence of local sources on deposition levels.62 Van Metre et al. noted PAH concentrations 

in urban reservoir sediments have increased by 200-300% over the last forty years and 

correlate with increases in automobile use.63 

Cousins et al. estimate that more than ninety percent of semi-volatile organic 

compound (SVOC) emissions in the United Kingdom deposit on soil.64 An analysis of PAH 

concentrations near a Czechoslovakian roadway indicated that concentrations were thirty 

times greater than background.65

2.1.5.2.4 Materials Damage and Soiling 

The effects of the deposition of atmospheric pollution, including ambient PM, on 

materials are related to both physical damage and impaired aesthetic qualities. The deposition 

of PM (especially sulfates and nitrates) can physically affect materials, adding to the effects of 

natural weathering processes, by potentially promoting or accelerating the corrosion of 

metals, by degrading paints, and by deteriorating building materials such as concrete and 

limestone. Only chemically active fine particles or hygroscopic coarse particles contribute to 

these physical effects. In addition, the deposition of ambient PM can reduce the aesthetic 

appeal of buildings and culturally important articles through soiling. Particles consisting 

primarily of carbonaceous compounds cause soiling of commonly used building materials and 

culturally important items such as statues and works of art. 

2.2 Ozone 

In this section we review the health and welfare effects of ozone. We also describe the 

air quality monitoring and modeling data which indicate that people in many areas across the 

country continue to be exposed to high levels of ambient ozone and will continue to be into 

the future. Emissions of nitrogen oxides (NOx) and VOCs, of which HC are the major subset, 

from the locomotive and marine diesel engines subject to this rule have been shown to 

contribute to these ozone concentrations. Information on air quality was gathered from a 

variety of sources, including monitored ozone concentrations, air quality modeling forecasts 

conducted for this rulemaking, and other state and local air quality information. 

The emission reductions from this rule will assist 8-hour ozone nonattainment and 

maintenance areas in reaching the standard by each area’s respective attainment date, and 

maintaining the 8-hour ozone standard in the future. The emission reductions will also help 

continue to lower ambient ozone levels and reduce health impacts. 

2.2.1 Science of Ozone Formation 

Ground-level ozone pollution is formed by the reaction of VOCs and nitrogen oxides 

(NOx) in the atmosphere in the presence of heat and sunlight. These pollutants, often referred 

to as ozone precursors, are emitted by many types of pollution sources such as highway 

vehicles and nonroad engines, power plants, chemical plants, refineries, makers of consumer 

and commercial products, and smaller area sources. 
Air Quality and Resulting Health and Welfare Effects 

2-29

The science of ozone formation, transport, and accumulation is complex.66 Groundlevel ozone is produced and destroyed in a cyclical set of chemical reactions, many of which 

are sensitive to temperature and sunlight. When ambient temperatures and sunlight levels 

remain high for several days and the air is relatively stagnant, ozone and its precursors can 

build up and result in more ozone than typically would occur on a single high-temperature 

day. Ozone also can be transported into an area from pollution sources found hundreds of 

miles upwind, resulting in elevated ozone levels even in areas with low VOC or NOx

emissions. 

The highest levels of ozone are produced when both VOC and NOx emissions are 

present in significant quantities on clear summer days. Relatively small amounts of NOx

enable ozone to form rapidly when VOC levels are relatively high, but ozone production is 

quickly limited by removal of the NOx. Under these conditions NOx reductions are highly 

effective in reducing ozone while VOC reductions have little effect. Such conditions are 

called “NOx-limited”. Because the contribution of VOC emissions from biogenic (natural) 

sources to local ambient ozone concentrations can be significant, even some areas where manmade VOC emissions are relatively low can be NOx -limited. 

Ozone concentrations in an area can be lowered by the reaction of nitric oxide with 

ozone, forming nitrogen dioxide (NO2); as the air moves downwind and the cycle continues, 

the NO2 forms additional ozone. The importance of this reaction depends, in part, on the 

relative concentrations of NOx, VOC, and ozone, all of which change with time and location. 

When NOx levels are relatively high and VOC levels are relatively low, NOx forms inorganic 

nitrates (i.e., particles) but relatively little ozone. Such conditions are called “VOC-limited.” 

Under these conditions, VOC reductions are effective in reducing ozone, but NOx reductions 

can actually increase local ozone under certain circumstances. Even in VOC-limited urban 

areas, NOx reductions would not be expected to increase ozone levels if the NOx reductions 

are sufficiently large. 

Rural areas are usually NOx-limited, due to the relatively large amounts of biogenic 

VOC emissions in many rural areas. Urban areas can be either VOC- or NOx -limited, or a 

mixture of both, in which ozone levels exhibit moderate sensitivity to changes in either 

pollutant. 

2.2.2 Health Effects of Ozone Pollution 

Exposure to ambient ozone contributes to a wide range of adverse health effectsH. 

These health effects are well documented and are critically assessed in the EPA ozone air 

quality criteria document (ozone AQCD) and EPA staff paper.67,68 We are relying on the data 

and conclusions in the ozone AQCD and staff paper, regarding the health effects associated 

with ozone exposure. 

 

H Human exposure to ozone varies over time due to changes in ambient ozone concentration and because people 

move between locations which have notable different ozone concentrations. Also, the amount of ozone 

delivered to the lung is not only influenced by the ambient concentration but also by the individuals breathing 

route and rate. 
Regulatory Impact Analysis 

2-30

Ozone-related health effects include lung function decrements, respiratory symptoms, 

aggravation of asthma, increased hospital and emergency room visits, increased asthma 

medication usage, and a variety of other respiratory effects. Cell-level effects such as, 

inflammation of lungs, have been documented as well. In addition, there is suggestive 

evidence of a contribution of ozone to cardiovascular-related morbidity and highly suggestive 

evidence that short-term ozone exposure directly or indirectly contributes to non-accidental 

and cardiopulmonary-related mortality, but additional research is needed to clarify the 

underlying mechanisms causing these effects. People who appear to be more susceptible to 

effects associated with exposure to ozone include children, asthmatics and the elderly. Those 

with greater exposures to ozone, for instance due to time spent outdoors (e.g., children and 

outdoor workers), are also of concern. 

Based on a large number of scientific studies, EPA has identified several key health 

effects associated with exposure to levels of ozone found today in many areas of the country. 

Short-term (1 to 3 hours) and prolonged exposures (6 to 8 hours) to higher ambient ozone 

concentrations have been linked to lung function decrements, respiratory symptoms, increased 

hospital admissions and emergency room visits for respiratory problems.69, 70, 71, 72, 73, 74 

Repeated exposure to ozone can increase susceptibility to respiratory infection and lung 

inflammation and can aggravate preexisting respiratory diseases, such as asthma.75, 76, 77, 78, 79

Repeated exposure to sufficient concentrations of ozone can also cause inflammation of the 

lung, impairment of lung defense mechanisms, and possibly irreversible changes in lung 

structure, which over time could lead to premature aging of the lungs and/or chronic 

respiratory illnesses, such as emphysema and chronic bronchitis.80, 81, 82, 83

Children and adults who are outdoors and active during the summer months, such as 

construction workers, are among those most at risk of elevated ozone exposures.84 Children 

and outdoor workers tend to have higher ozone exposure because they typically are active 

outside, working, playing and exercising, during times of day and seasons (e.g., the summer) 

when ozone levels are highest.85 For example, summer camp studies in the Eastern United 

States and Southeastern Canada have reported significant reductions in lung function in 

children who are active outdoors.86, 87, 88, 89, 90, 91, 92, 93 Further, children are more at risk of 

experiencing health effects from ozone exposure than adults because their respiratory systems 

are still developing. These individuals (as well as people with respiratory illnesses such as 

asthma, especially asthmatic children) can experience reduced lung function and increased 

respiratory symptoms, such as chest pain and cough, when exposed to relatively low ozone 

levels during prolonged periods of moderate exertion.94, 95, 96, 97

2.2.3 Current 8-Hour Ozone Levels 

The locomotive and marine engine emission reductions will assist 8-hour ozone 

nonattainment areas in reaching the standard by each area’s respective attainment date and/or 

assist in maintaining the 8-hour ozone standard in the future. The current ozone National 

Ambient Air Quality Standard (NAAQS) has an 8-hour averaging time.I

 The 8-hour ozone 

 

I

 EPA’s review of the ozone NAAQS is underway, the proposal was published in June 2007 and the final rule is 

scheduled for March 2008. 
Air Quality and Resulting Health and Welfare Effects 

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NAAQS is met at an ambient air quality monitoring site when the average of the annual 

fourth-highest daily maximum 8-hour average ozone concentration over three years is less 

than or equal to 0.08 ppm. In the following section we present information on current and 

model-projected future 8-hour ozone levels. 

A nonattainment area is defined in the CAA as an area that is violating a NAAQS or is 

contributing to a nearby area that is violating the NAAQS. EPA designated nonattainment 

areas for the 8-hour ozone NAAQS in June 2004. The final rule on Air Quality Designations 

and Classifications for the 8-hour Ozone NAAQS (69 FR 23858, April 30, 2004) identifies 

the criteria that EPA considered in making the 8-hour ozone nonattainment designations, 

including 2001-2003 measured data, air quality in adjacent areas, and other factors.J

As of October 10, 2007 there are approximately 144 million people living in 81 areas 

designated as nonattainment with the 8-hour ozone NAAQS. There are 366 full or partial 

counties that make up the 8-hour ozone nonattainment areas. These numbers do not include 

the people living in areas where there is a future risk of failing to maintain or attain the 8-hour 

ozone NAAQS. The current 8-hour ozone nonattainment areas, nonattainment counties, and 

populations are listed in Appendix 2C to this RIA. 

States with 8-hour ozone nonattainment areas are required to take action to bring those 

areas into compliance in the future. The maximum attainment date assigned to an ozone 

nonattainment area is based on the area’s classification. Most 8-hour ozone nonattainment 

areas will be required to attain the 8-hour ozone NAAQS in the 2007 to 2013 time frame and 

then be required to maintain the 8-hour ozone NAAQS thereafter.K We expect many of the 8-

hour ozone nonattainment areas will need to adopt additional emissions reduction programs to 

attain and maintain the 8-hour ozone NAAQS. The expected NOx and VOC reductions from 

these standards, which take effect between 2008 and 2017, will be useful to states as they seek 

to either attain or maintain the 8-hour ozone NAAQS. 

EPA’s review of the ozone NAAQS is currently underway, the proposal was published 

in June 2007 (72 FR 37818, July 11, 2007) and the final rule is scheduled for March 2008. If 

the ozone NAAQS is revised then new nonattainment areas could be designated. While EPA 

 

J

 An ozone design value is the concentration that determines whether a monitoring site meets the NAAQS for 

ozone. Because of the way they are defined, design values are determined based on three consecutive-year 

monitoring periods. For example, an 8-hour ozone design value is the fourth highest daily maximum 8-hour 

average ozone concentration measured over a three-year period at a given monitor. The full details of these 

determinations (including accounting for missing values and other complexities) are given in Appendices H and 

I of 40 CFR Part 50. Due to the precision with which the standards are expressed (0.08 parts per million (ppm) 

for the 8-hour), a violation of the 8-hour standard is defined as a design value greater than or equal to 0.085 ppm 

or 85 parts per billion (ppb). For a county, the design value is the highest design value from among all the 

monitors with valid design values within that county. If a county does not contain an ozone monitor, it does not 

have a design value. However, readers should note that ozone design values generally represent air quality 

across a broad area and that absence of a design value does not imply that the county is in compliance with the 

ozone NAAQS. 

K The Los Angeles South Coast Air Basin 8-hour ozone nonattainment area is designated as severe and will have 

to attain before June 15, 2021. The South Coast Air Basin has recently applied to be redesignated as an extreme 

nonattainment area which will make their attainment date June 15, 2024. 
Regulatory Impact Analysis 

2-32

is not relying on it for purposes of justifying this rule, the emission reductions from this 

rulemaking will also be helpful to states if EPA revises the ozone NAAQS to be more 

stringent. 

2.2.4 Projected 8-Hour Ozone Levels 

In the following sections we describe our modeling of 8-hour ozone levels in the 

future with and without the controls being finalized in this action. 

2.2.4.1 Projected 8-Hour Ozone Levels without this Rulemaking 

EPA has already adopted many emission control programs that are expected to reduce 

ambient ozone levels. These control programs include the Clean Air Interstate Rule (70 FR 

25162, May 12, 2005), the Clean Air Nonroad Diesel rule (69 FR 38957, June 29, 2004), and 

the Heavy Duty Engine and Vehicle Standards and Highway Diesel Fuel Sulfur Control 

Requirements (66 FR 5002, Jan. 18, 2001). As a result of these programs, the number of 

areas that continue to violate the 8-hour ozone NAAQS in the future is expected to decrease. 

The baseline air quality modeling completed for this rule predicts that without 

additional local, regional or national controls there will continue to be a need for reductions in 

8-hour ozone concentrations in some areas in the future. The determination that an area is at 

risk of exceeding the 8-hour ozone standard in the future was made for all areas with current 

design values greater than or equal to 85 ppb (or within a 10 percent margin) and with 

modeling evidence that concentrations at and above these levels will persist into the future.L

 

Those interested in greater detail should review the air quality modeling TSD. 

The baseline inventories that underlie the modeling conducted for this rulemaking 

include emission reductions from existing federal, state and local controls. There was no 

attempt to examine the prospects of areas attaining or maintaining the standard with future 

possible controls. The results should therefore be interpreted as indicating what counties are 

at risk for violating the ozone NAAQS in the future without additional federal, state or local 

measures that may implemented after this rulemaking is finalized. We expect many of the 

areas to adopt additional emission reduction programs, but we are unable to quantify or rely 

upon future reductions from additional programs since they have not yet been promulgated. 

With reductions from programs already in place (but excluding the emission 

reductions from this rule), the number of counties with projected 8-hour ozone design values 

at or above 85 ppb in 2020 is expected to be 9 counties where 22.5 million people are 

projected to live. In addition, in 2020, 39 counties where 28.6 million people are projected to 

live, will be within 10 percent of violating the 8-hour ozone NAAQS. 

As discussed in the next section, the air quality modeling conducted for this rule 

indicates that the almost 300,000 tons of annual NOx reductions in 2020 will be important for 

ensuring that air quality in these areas meets the 8-hour ozone standard. 

 

L

 Ozone design values are reported in parts per million (ppm) as specified in 40 CFR Part 50. Due to the scale of 

the design value changes in this action results have been presented in parts per billion (ppb) format. 
Air Quality and Resulting Health and Welfare Effects 

2-33

2.2.4.2 Projected 8-Hour Ozone Levels with this Rulemaking 

This section summarizes the results of our modeling of ozone air quality impacts in 

the future due to the reductions in locomotive and marine diesel emissions finalized in this 

action. Specifically, we compare baseline scenarios to scenarios with controls. Our modeling 

indicates that the reductions from this rule will contribute to reducing ambient ozone 

concentrations and minimizing the risk of exposures in future years. Since some of the VOC 

and NOx emission reductions from this rule go into effect during the period when some areas 

are still working to attain the 8-hour ozone NAAQS, the projected emission reductions will 

assist state and local agencies in their effort to attain the 8-hour ozone standard and help 

others maintain the standard. Emissions reductions from this rule will also help to counter 

potential ozone increases due to climate change, which are expected in many urban areas in 

the United States, but are not reflected in the modeling shown here.98 

 According to air quality modeling performed for this rulemaking, the locomotive and 

marine diesel engine standards provide improvements in ozone levels for the vast majority of 

areas. There are three nonattainment areas in southern California, the Los Angeles-South 

Coast Air Basin nonattainment area, the Riverside Co. (Coachella Valley) nonattainment area 

and the Los Angeles–San Bernardino (W. Mojave) nonattainment area, which will experience 

8-hour ozone design value increases due to the NOx disbenefits which occur in these VOClimited ozone nonattainment areas. Briefly NOx reductions can at certain times and in some 

areas cause ozone levels to increase slightly. Section 2.2.4.2.1 provides additional detail 

about NOx disbenefits. 

Despite these areas which experience ozone increases, the overall effect of this rule is 

positive with 573 counties (of 579 that have monitored data) experiencing at least a 0.1 ppb 

decrease in their 2030 ozone design values. On a population-weighted basis, the average 

modeled future-year 8-hour ozone design values over these counties will decrease by 0.30 ppb 

in 2020 and 0.85 ppb in 2030. Within areas with the highest projected 8-hour ozone design 

values, greater than 85 ppb, the average decrease will be 0.13 ppb in 2020 and 0.62 ppb in 

2030. The maximum decrease for future-year design values will be 1.8 ppb in 2020 and 4.6 

ppb in 2030. 

Table 2-4 identifies the full list of counties projected to have design values at or above 

85 ppb as well as the counties within 10 percent of violating the 8-hour ozone NAAQS in 

2020. The design value for Kenosha, WI goes from being above the standard in the base case 

to being lower than the 8-hour ozone NAAQS with the controls being finalized in this 

rulemaking. 
Regulatory Impact Analysis 

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Table 2-4 Counties with 2020 8-hour Ozone Design Values in Violation or within 10 percent of the Ozone 

Standard in the Base and Control Cases

State County 

2000-2004 

Average 8-Hour 

Ozone DV 

(ppb)a

2020 modeling projections of 

8-Hour Ozone DV (ppb) 

2020 

Population 

 base control 

CA Calaveras 91.0 V V 58,261

CA El Dorado 105.0 X X 236,310

CA Fresno 110.0 X X 1,066,878

CA Kern 114.3 X X 876,131

CA Kings 95.7 V V 173,390

CA Los Angeles 121.3 X X 10,376,013

CA Madera 91.0 V V 173,940

CA Merced 101.7 V V 277,863

CA Nevada 97.7 V V 131,831

CA Orange 85.3 V V 3,900,599

CA Placer 98.3 V V 451,620

CA Riverside 115.0 X X 2,252,510

CA Sacramento 99.0 V V 1,640,590

CA San Bernardino 128.7 X X 2,424,764

CA San Diego 92.3 V V 3,863,460

CA Stanislaus 95.0 V V 607,766

CA Tulare 105.7 X X 477,296

CA Tuolumne 91.0 V V 70,570

CA Ventura 94.7 V V 1,023,136

CT Fairfield 98.3 V V 962,824

CT New Haven 98.3 V V 898,415

IN Lake 88.3 V V 509,293

LA East Baton Rouge 87.0 V V 522,399

MD Harford 100.3 V V 317,847

MI Allegan 94.0 V V 141,851

MI Macomb 92.3 V V 894,095

NJ Camden 99.7 V V 547,817

NJ Gloucester 98.0 V V 304,105

NJ Mercer 97.7 V V 392,236

NJ Ocean 105.7 V V 644,323

NY Erie 95.7 V V 959,145

NY Niagara 91.7 V V 220,989

NY Suffolk 97.0 V V 1,598,742

OH Ashtabula 95.7 V V 108,355

OH Geauga 99.0 V V 114,438

PA Bucks 99.0 V V 711,275

PA Philadelphia 96.7 V V 1,394,176

TX Brazoria 94.0 V V 322,385
Air Quality and Resulting Health and Welfare Effects 

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State County 

2000-2004 

Average 8-Hour 

Ozone DV 

(ppb)a

2020 modeling projections of 

8-Hour Ozone DV (ppb) 

2020 

Population 

TX Galveston 89.7 V V 318,966

TX Harris 102.0 X X 4,588,812

TX Jefferson 91.0 V V 272,075

TX Montgomery 88.3 V V 526,335

TX Tarrant 98.7 V V 2,137,957

WI Kenosha 98.3 X V 184,825

WI Milwaukee 91.0 V V 927,845

WI Ozaukee 93.0 V V 110,294

WI Racine 91.7 V V 212,351

WI Sheboygan 97.0 V V 128,777

a

 Ozone design values are reported in parts per million (ppm) as specified in 40 CFR Part 50. Due to the scale of 

the design value changes in this action results have been presented in parts per billion (ppb) format. 

Table 2-5 shows the average change in future year eight-hour ozone design values. 

Average changes are shown for: (1) all counties with 2002 baseline design values, (2) 

counties with baseline design values that exceeded the standard in 2000-2004 (“violating” 

counties), and (3) counties that did not exceed the standard, but were within 10 percent of it in 

2000-2004. This last category is intended to reflect counties that meet the standard, but will 

likely benefit from help in maintaining that status in the face of growth. All of these metrics 

show a decrease in 2020 and a larger decrease in 2030, indicating in three different ways the 

overall improvement in ozone air quality. 

 

Table 2-5 Average change in projected future year 8-hour ozone design value as a result of the 

locomotive and marine diesel controls 

Averagea Number of US 

Counties 

Change in 2020 

design valueb

 (ppb) 

Change in 2030 

design valueb (ppb) 

All 579 -0.45 -1.15 

All, population-weighted 579 -0.30 -0.85 

Violating countiesc 261 -0.45 -1.18 

Violating countiesc, population-weighted 261 -0.27 -0.78 

Counties within 10 percent of the standardd 477 -0.46 -1.18 

Counties within 10 percent of the standardd, 

population-weighted 

477 -0.31 -0.86 

Notes: 

a averages are over counties with 2002 modeled design values 

b

 Ozone design values are reported in parts per million (ppm) as specified in 40 CFR Part 50. Due to the scale of the 

design value changes in this action results have been presented in parts per billion (ppb) format. c

 counties whose 2002 baseline design values exceeded the 8-hour ozone standard (>= 85 ppb) 

d

 counties whose 2002 baseline values were less than but within 10 percent of the 8-hour ozone standard. 
Regulatory Impact Analysis 

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Figure 2.7 shows those US counties in 2030 which experience a change in their ozone 

design values as a result of this rule. Some of the most significant decreases will occur in the 

following counties: St. Mary (3.1 ppb) and Lafayette (2.6 ppb) Counties in Louisiana; 

Brazoria (2.9 ppb) and Jefferson (3.0 ppb) Counties in Texas; Warren County (3.2 ppb) in 

Mississippi; and Santa Barbara County (4.6 ppb) in California. 338 counties will see 8-hour 

ozone design value reductions from between 1.0 to 1.9 ppb while an estimated 190 additional 

counties will see design value reductions from 0.5 to 0.9 ppb. Note that 5 counties, Cook 

County (0.2 ppb) in Illinois; Lake County (0.1 ppb) in Indiana; and San Bernardino (0.1 ppb), 

Riverside (0.5 ppb) and Orange (5.5 ppb) counties in California are projected to experience 

increases in their ozone design values because of the NOx disbenefit that occurs in these 

VOC-limited areas.
Air Quality and Resulting Health a

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Number of Counties

27

338190

18

14

Effect of Locomarine in 2030 1

05

Legend

<= -2.0> -2.0 to <= -1.0> -1.0 to <= -0.5> -0.5 to <= -0.1= 0.0> 0.0 to <= 1.0> 1.0

Figure 2-7 Impact of Locomotive/Marine controls on Ozone Design Values in 2030
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2.2.4.2.1 Potentially Counterproductive Impacts on Ozone Concentrations from NOx

Emissions Reductions 

 While this rule reduces ozone levels generally and provides national ozone-related 

health benefits, this is not always the case at the local level. Due to the complex 

photochemistry of ozone production, NOx emissions lead to both the formation and 

destruction of ozone, depending on the relative quantities of NOx, VOC, and ozone formation 

catalysts such as the OH and HO2 radicals. In areas dominated by fresh emissions of NOx, 

ozone catalysts are removed via the production of nitric acid which slows the ozone formation 

rate. Because NOx is generally depleted more rapidly than VOC, this effect is usually shortlived and the emitted NOx can lead to ozone formation later and further downwind. The 

terms “NOx disbenefits” or “ozone disbenefits” refer to the ozone increases that result when 

reducing Ox emissions in localized areas. According to the NARSTO Ozone Assessment, 

disbenefits are generally limited to small regions within specific urban cores and are 

surrounded by larger regions in which NOx control is beneficial.99

 EPA believes that the best available approach for determining the value of a particular 

national emission reduction strategy is the net air quality change projected to result from a 

rule, evaluated on a nationwide basis for all pollutants of concern. The primary tool for 

assessing the net health and welfare impacts at this time is the Community Multiscale Air 

Quality (CMAQ) model. Model scenarios for 2020 and 2030 with and without the emission 

control strategies from this rule were compared to determine the expected changes in future 

pollutant levels resulting from this rule. A wide variety of ozone metrics were considered in 

assessing emissions reductions. Three of the most important are: 1) the effect of the rule on 

projected future-year ozone design values, 2) the effect of the rule in assisting local areas in 

attainment and maintenance of the NAAQS, and 3) an economic assessment of the rule 

benefits based on existing health studies. 

 When considering NOx disbenefit results for these local areas several factors related 

to both the model inputs and the air quality modeling should be considered. First, our future 

year modeling does not contain any local governmental actions which have not already been 

promulgated in a finalized State SIP. The inability to account for future local controls is a 

function of the SIP development process. EPA typically does not include local plans to 

reduce ozone precursors in our future baseline projections until those reductions are part of a 

fully promulgated State or local regulation. However, significant local controls of VOC 

and/or NOx that are not reflected in the air quality modeling for this rule could modify the 

conclusions regarding ozone changes in some areas. 

Second, due to the ozone disbenefit chemistry described above, modeling only the 

final rule-related NOx reductions in an area that is VOC-limited can give an inaccurate 

representation of future air quality. In an area such as this, marginal NOx reductions modeled 

independently will likely lead to ozone disbenefits. However, there is a level of NOx 

reduction, even in VOC-limited areas, where enough NOx will have been controlled to result 

in NOx-limited conditions and as a result ambient ozone concentrations will decrease. 
Air Quality and Resulting Health and Welfare Effects 

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The majority of the projected NOx disbenefits from this rule occur in Southern 

California, specifically Orange, Riverside and San Bernardino counties. California’s South 

Coast Air Quality Management District (SCAQMD) includes the southern two-thirds of Los 

Angeles County, all of Orange County, and the western, urbanized portions of Riverside and 

San Bernardino counties. The SCAQMD has recently completed an air quality modeling 

exercise as part of their ozone attainment demonstration. This modeling indicates that with 

substantial NOx and VOC reductions (~90% and ~45% respectively), the entire south coast 

basin will be in attainment for the 8-hour ozone NAAQS by 2024.M 

The SCAQMD attainment demonstrations for both PM2.5 and 8-hour ozone were 

conducted using photochemical dispersion and meteorological modeling tools developed in 

response to U.S EPA modeling guidelines100, and recommendations from air quality modeling 

experts. The air quality modeling has undergone scientific peer review and was made 

available for public review. Air Resources Board (ARB) and South Coast District staffs 

worked together on the modeling, including development of a gridded modeling inventory 

and meteorological and geological data inputs, model performance analysis, and validation of 

the attainment demonstrations. 

It is important to note that the NOx emission reductions associated with this final 

locomotive and marine rule play an important role in the South Coast's demonstration of 

future-year ozone attainment. The SCAQMD modeling projections are based on emissions 

reductions from many different state and local programs, the majority of which are not yet 

finalized. The results of the SCAQMD attainment demonstration modeling illustrate the fact 

that with additional NOx and VOC controls, beyond those being finalized in this rule, ambient 

ozone would be reduced in Southern California. 

Finally, although a VOC-heavy control strategy can be an effective means to reduce 

NOx disbenefits, there are reasons why NOx reductions can still be the preferred route to 

reducing ozone in local areas. One reason is because NOx is not only an ozone precursor but 

a PM precursor. Based on modeling and cost/benefit analyses completed by SCAQMD they 

have concluded that due to the magnitude of emissions reductions needed for ozone and PM 

attainment, as well as the readiness of NOx control technologies, a NOx-heavy control 

approach provides the most efficient path to attainment for both pollutants in California’s 

south coast.. 

 Historically, NOx reductions have been very successful at reducing regional/national 

ozone levels. Consistent with that fact, the photochemical modeling completed for this rule 

indicates that the emission reductions resulting from the locomotive and marine engine rule 

assist in the attainment and maintenance of the ozone NAAQS at the national level. 

Furthermore, NOx reductions also result in reductions in PM and its associated health and 

welfare effects. This rule is one important element of the overall emission reductions that 

States, local governments, and Tribes need to reach their clean air goals. It is expected that 

future local and national controls that decrease VOC, CO, and regional ozone will mitigate 

 

M Note that the NOx reductions modeled in the south coast attainment demonstration include NOx reductions 

that are projected to occur due to new technology that does not currently exist. 
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localized disbenefit. EPA continues to rely on local attainment measures to ensure no future 

violations of the NAAQS. Many states and environmental organizations with an interest in 

improved air quality have urged EPA to finalize this rule because of the significant NOx

reductions that will reduce both ozone and PM.101 EPA believes that a balanced air quality 

management approach, which includes NOx reductions from locomotive and marine engines, 

is needed as part of the Nation’s progress toward clean air. 

 Another effect of ozone reduction strategies is the potential impact these reductions 

may have on the shielding provided by ozone from ultraviolet radiation (UV-B) derived from 

the sun. The majority of this shielding results from naturally occurring ozone in the 

stratosphere, but a variable portion of this tropospheric fraction of UV-B shielding is derived 

from ground level ozone. Therefore, strategies that reduce ground level ozone could, in some 

small measure, increase exposure to UV-B from the sun, thus potentially increasing skin 

cancer. 

While it is possible to provide quantitative estimates of benefits associated with 

globally based strategies to restore the far larger and more spatially uniform stratospheric 

ozone layer, the changes in UV-B exposures associated with ground level ozone reduction 

strategies are much more complicated and uncertain. Comparatively smaller changes in 

ground-level ozone (compared to the total ozone in the troposphere) and UV-B are not likely 

to measurably change long-term risks of adverse effects. 

2.2.5 Environmental Effects of Ozone Pollution 

There are a number of public welfare effects associated with the presence of ozone in 

the ambient air.102 In this section we discuss the impact of ozone on plants, including trees, 

agronomic crops and urban ornamentals. 

2.2.5.1 Impacts on Vegetation 

 The Air Quality Criteria Document for Ozone and related Photochemical Oxidants 

notes that “ozone affects vegetation throughout the United States, impairing crops, native 

vegetation, and ecosystems more than any other air pollutant. Like carbon dioxide (CO2) and 

other gaseous substances, ozone enters plant tissues primarily through apertures (stomata) in 

leaves in a process called “uptake”.103 Once sufficient levels of ozone, a highly reactive 

substance, (or its reaction products) reaches the interior of plant cells, it can inhibit or damage 

essential cellular components and functions, including enzyme activities, lipids, and cellular 

membranes, disrupting the plant's osmotic (i.e., water) balance and energy utilization 

patterns.104,105 This damage is commonly manifested as visible foliar injury such as chlorotic 

or necrotic spots, increased leaf senescence (accelerated leaf aging) and/or reduced 

photosynthesis. All these effects reduce a plant’s capacity to form carbohydrates, which are 

the primary form of energy used by plants.106 With fewer resources available, the plant 

reallocates existing resources away from root growth and storage, above ground growth or 

yield, and reproductive processes, toward leaf repair and maintenance. Studies have shown 

that plants stressed in these ways may exhibit a general loss of vigor, which can lead to 

secondary impacts that modify plants' responses to other environmental factors. Specifically, 

plants may become more sensitive to other air pollutants, more susceptible to disease, insect 
Air Quality and Resulting Health and Welfare Effects 

2-41

attack, harsh weather (e.g., drought, frost) and other environmental stresses. Furthermore, 

there is evidence that ozone can interfere with the formation of mycorrhiza, essential 

symbiotic fungi associated with the roots of most terrestrial plants, by reducing the amount of 

carbon available for transfer from the host to the symbiont.107,108

Ozone can produce both acute and chronic injury in sensitive species depending on the 

concentration level and the duration of the exposure. Ozone effects also tend to accumulate 

over the growing season of the plant, so that even lower concentrations experienced for a 

longer duration have the potential to create chronic stress on sensitive vegetation. Not all 

plants, however, are equally sensitive to ozone. Much of the variation in sensitivity between 

individual plants or whole species is related to the plant’s ability to regulate the extent of gas 

exchange via leaf stomata (e.g., avoidance of O3 uptake through closure of stomata).109,110,111 

Other resistance mechanisms may involve the intercellular production of detoxifying 

substances. Several biochemical substances capable of detoxifying ozone have been reported 

to occur in plants including the antioxidants ascorbate and glutathione. After injuries have 

occurred, plants may be capable of repairing the damage to a limited extent.112

Because of the differing sensitivities among plants to ozone, ozone pollution can also 

exert a selective pressure that leads to changes in plant community composition. Given the 

range of plant sensitivities and the fact that numerous other environmental factors modify 

plant uptake and response to ozone, it is not possible to identify threshold values above which 

ozone is consistently toxic for all plants. The next few paragraphs present additional 

information on ozone damage to trees, ecosystems, agronomic crops and urban ornamentals. 

Ozone also has been conclusively shown to cause discernible injury to forest 

trees.113,114 In terms of forest productivity and ecosystem diversity, ozone may be the 

pollutant with the greatest potential for regional-scale forest impacts. Studies have 

demonstrated repeatedly that ozone concentrations commonly observed in polluted areas can 

have substantial impacts on plant function.115, 116

Because plants are at the center of the food web in many ecosystems, changes to the 

plant community can affect associated organisms and ecosystems (including the suitability of 

habitats that support threatened or endangered species and below ground organisms living in 

the root zone). Ozone impacts at the community and ecosystem level vary widely depending 

upon numerous factors, including concentration and temporal variation of tropospheric ozone, 

species composition, soil properties and climatic factors.117 In most instances, responses to 

chronic or recurrent exposure in forested ecosystems are subtle and not observable for many 

years. These injuries can cause stand-level forest decline in sensitive ecosystems.118,119,120 It 

is not yet possible to predict ecosystem responses to ozone with much certainty; however, 

considerable knowledge of potential ecosystem responses has been acquired through longterm observations in highly damaged forests in the United States. 

Laboratory and field experiments have also shown reductions in yields for agronomic 

crops exposed to ozone, including vegetables (e.g., lettuce) and field crops (e.g., cotton and 

wheat). The most extensive field experiments, conducted under the National Crop Loss 

Assessment Network (NCLAN) examined 15 species and numerous cultivars. The NCLAN 

results show that “several economically important crop species are sensitive to ozone levels 
Regulatory Impact Analysis 

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typical of those found in the Unites States.”121 In addition, economic studies have shown 

reduced economic benefits as a result of predicted reductions in crop yields associated with 

observed ozone levels.122, 123, 124

Urban ornamentals represent an additional vegetation category likely to experience 

some degree of negative effects associated with exposure to ambient ozone levels. It is 

estimated that more than $20 billion (1990 dollars) are spent annually on landscaping using 

ornamentals, both by private property owners/tenants and by governmental units responsible 

for public areas.125 This is therefore a potentially costly environmental effect. However, in 

the absence of adequate exposure-response functions and economic damage functions for the 

potential range of effects relevant to these types of vegetation, no direct quantitative analysis 

has been conducted. 

2.3 Air Quality Modeling Methodology 

In this section we present information on the air quality modeling, including the model 

domain and modeling inputs. Further discussion of the modeling methodology, including 

evaluations of model performance, is included in the Air Quality Modeling Technical Support 

Document (AQM TSD).126

2.3.1 Air Quality Modeling Overview 

A national scale air quality modeling analysis was performed to estimate future year 

annual PM2.5 concentrations, 8-hour ozone concentrations and visibility levels. These 

projections were used as inputs to the calculation of expected benefits from the locomotive 

and marine emissions controls considered in this assessment. The 2002-based CMAQ 

modeling platform was used as the tool for the air quality modeling of future baseline 

emissions and control scenarios. It should be noted that the 2002-based modeling platform 

has recently been finalized and the 2001-based modeling platform was used as the tool for the 

air quality modeling performed for the proposal. In the next paragraph we discuss some of 

the differences between the 2001-based platform used for the proposal and the 2002-based 

platform used for this final rule. 

The 2002-based modeling platform includes a number of updates and improvements to 

data and tools compared to the 2001-based platform that was used for the proposal modeling. 

For the final rule modeling we used the new 2002 National Emissions Inventory along with 

updated versions of the models used to project future emissions from electric generating units 

(EGUs) and onroad and nonroad vehicles. The proposal modeling was based on the 2001 

National Emissions Inventory. The new platform also includes 2002 meteorology and more 

recent ambient design values which were used as the starting point for projecting future air 

quality. For proposal, we used meteorology for 2001 for modeling the East and 2002 for 

modeling the West. The updates to CMAQ between proposal and final include (1) an incloud sulfate chemistry module that accounts for the nonlinear sensitivity of sulfate formation 

to varying pH; (2) improved vertical convective mixing; (3) heterogeneous reaction involving 

nitrate formation; (4) an updated gas-phase chemistry mechanism, Carbon Bond 2005 

(CB05); and (5) an aqueous chemistry mechanism that provides a comprehensive simulation 

of aerosol precursor oxidants. 
Air Quality and Resulting Health and Welfare Effects 

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The CMAQ model is a three-dimensional grid-based Eulerian air quality model 

designed to estimate the formation and fate of oxidant precursors, primary and secondary 

particulate matter concentrations and deposition over regional and urban spatial scales (e.g., 

over the contiguous U.S.).127,128,129 Consideration of the different processes that affect 

primary (directly emitted) and secondary (formed by atmospheric processes) PM at the 

regional scale in different locations is fundamental to understanding and assessing the effects 

of pollution control measures that affect PM, ozone and deposition of pollutants to the 

surface. In addition to the CMAQ model, the modeling platform includes the emissions, 

meteorology, and initial/boundary condition data which are inputs to this model. 

The CMAQ model was peer-reviewed in 2003 for EPA as reported in “Peer Review of 

CMAQ Model”.130 The latest version of CMAQ (Version 4.6.1) was employed for this 

modeling analysis. This version reflects updates, as mentioned above, in a number of areas to 

improve the underlying science which include (1) use of a state-of-the science inorganic and 

organic aerosol module, (2) an in-cloud sulfate chemistry module that accounts for the 

nonlinear sensitivity of sulfate formation to varying pH, (3) improved vertical convective 

mixing, (4) heterogeneous reaction involving nitrate formation and (5) an updated Carbon 

Bond 05 (CB05) gas-phase chemistry mechanism and aqueous chemistry mechanism that 

provides a comprehensive simulation of aerosol precursor oxidants. 

2.3.2 Model Domain and Configuration 

As shown in Figure 2-8 the CMAQ modeling domain encompasses all of the lower 48 

States and portions of Canada and Mexico. The modeling domain is made up of a large 

continental U.S. 36 km grid and two 12 km grids (an Eastern US and a Western US domain), 

as shown in Figure 2-8. The modeling domain contains 14 vertical layers with the top of the 

modeling domain at about 16,200 meters, or 100 millibars (mb). 
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Figure 2-8. Map of the CMAQ modeling domain. 

2.3.3 Model Inputs 

The key inputs to the CMAQ model include emissions from anthropogenic and 

biogenic sources, meteorological data, and initial and boundary conditions. The CMAQ 

meteorological input files were derived from a simulation of the Pennsylvania State 

University / National Center for Atmospheric Research Mesoscale Model131 for the entire 

year of 2002. This model, commonly referred to as MM5, is a limited-area, nonhydrostatic, 

terrain-following system that solves for the full set of physical and thermodynamic equations 

which govern atmospheric motions. The meteorology for the national 36 km grid and the 12 

km Eastern U.S. grid were developed by EPA and are described in more detail within the 

AQM TSD. The meteorology for the 12 km Western U.S. grid was developed by the Western 

Regional Air Partnership (WRAP) Regional Planning Organization. The meteorological 

outputs from MM5 were processed to create model-ready inputs for CMAQ using the 

Meteorology-Chemistry Interface Processor (MCIP) version 3.1 to derive the specific inputs 

to CMAQ, for example: horizontal wind components (i.e., speed and direction), temperature, 

moisture, vertical diffusion rates, and rainfall rates for each grid cell in each vertical layer.132

The lateral boundary and initial species concentrations are provided by a threedimensional global atmospheric chemistry model, the GEOS-CHEM model.133 The global 

GEOS-CHEM model simulates atmospheric chemical and physical processes driven by 

assimilated meteorological observations from the NASA’s Goddard Earth Observing System 
Air Quality and Resulting Health and Welfare Effects 

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(GEOS). This model was run for 2002 with a grid resolution of 2 degree x 2.5 degree 

(latitude-longitude) and 20 vertical layers. The predictions were used to provide one-way 

dynamic boundary conditions at three-hour intervals and an initial concentration field for the 

36 km CMAQ simulations. The future base conditions from the 36 km coarse grid modeling 

were used as the initial/boundary state for all subsequent 12 km finer grid modeling. 

The emissions inputs used for the 2002 base year and each of the future year base 

cases and control scenarios are summarized in Chapter 3 of this RIA. 

2.3.4 CMAQ Evaluation 

An operational model performance evaluation for PM2.5 and its related speciated 

components (e.g., sulfate, nitrate, elemental carbon, organic carbon, etc.) was conducted using 

the 2002 data in order to estimate the ability of the CMAQ modeling system to replicate base 

year concentrations. In summary, model performance statistics were calculated for 

observed/predicted pairs of daily/monthly/seasonal/annual concentrations. Statistics were 

generated for the following geographic groupings: domain wide, Eastern vs. Western (divided 

along the 100th meridian), and each Regional Planning Organization (RPO) region.134 The 

“acceptability” of model performance was judged by comparing our results to those found in 

recent regional PM2.5 model applications for other, non-EPA studies135. Overall, the 

performance for the 2002 modeling platform is within the range of these other applications. 

A detailed summary of the 2002 CMAQ model performance evaluation is available within the 

AQM TSD. 

2.3.5 Model Simulation Scenarios 

As part of our analysis for this rulemaking the CMAQ modeling system was used to 

calculate annual PM2.5 concentrations, 8-hour ozone concentrations and visibility estimates 

for each of the following emissions scenarios: 

2002 base year 

2020 base line projection 

2020 base line projection with diesel marine only controls 

2020 base line projection with locomotive and diesel marine controls 

2030 base line projection 

2030 base line projection with diesel marine only controls 

2030 base line projection with locomotive and diesel marine controls 

 It should be noted that the emission control scenarios used in the air quality and 

benefits modeling are slightly different than the emission control program being finalized. 

The differences reflect further refinements of the regulatory program since we performed 

the air quality modeling for this rule. Chapter 3 of this RIA describes the changes in the 
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inputs and resulting emission inventories between the preliminary assumptions used for 

the air quality modeling and the final regulatory scenario. These refinements to the 

program would not significantly change the results summarized here or our conclusions 

drawn from this analysis. 

We use the predictions from the model in a relative sense by combining the 2002 baseyear predictions with predictions from each future-year scenario and applying these modeled 

ratios to ambient air quality observations to estimate annual PM2.5 concentrations, 8-hour 

ozone concentrations, and visibility levels for each of the 2020 and 2030 scenarios. The 

ambient air quality observations are average conditions, on a site by site basis, for a period 

centered around the model base year (i.e., 2000-2004). After completing this process, we then 

calculated the effect of changes in PM, ozone and visibility air quality metrics resulting from 

this rulemaking on the health and welfare impact functions of the benefits analysis 

The projected annual PM2.5 design values were calculated using the Speciated 

Modeled Attainment Test (SMAT) approach. The SMAT uses an Federal Reference Method 

FRM mass construction methodology that results in reduced nitrates (relative to the amount 

measured by routine speciation networks), higher mass associated with sulfates (reflecting 

water included in FRM measurements), and a measure of organic carbonaceous mass that is 

derived from the difference between measured PM2.5 and its non-carbon components. This 

characterization of PM2.5 mass also reflects crustal material and other minor constituents. The 

resulting characterization provides a complete mass balance. It does not have any unknown 

mass that is sometimes presented as the difference between measured PM2.5 mass and the 

characterized chemical components derived from routine speciation measurements. However, 

the assumption that all mass difference is organic carbon has not been validated in many areas 

of the US. The SMAT methodology uses the following PM2.5 species components: sulfates, 

nitrates, ammonium, organic carbon mass, elemental carbon, crustal, water, and blank mass (a 

fixed value of 0.5 μg/m3). More complete details of the SMAT procedures can be found in 

the report "Procedures for Estimating Future PM2.5 Values for the CAIR Final Rule by 

Application of the (Revised) Speciated Modeled Attainment Test (SMAT)".136 For this latest 

analysis, several datasets and techniques were updated. These changes are fully described 

within the AQM TSD. The projected 8-hour ozone design values were calculated using the 

approach identified in EPA's guidance on air quality modeling attainment demonstrations. 

2.3.6 Visibility Modeling Methodology 

The modeling platform described in this section was also used to project changes in 

visibility. The estimate of visibility benefits was based on the projected improvement in 

annual average visibility at mandatory class I federal areas. There are 156 Federally 

mandated Class I areas which, under the Regional Haze Rule, are required to achieve natural 

background visibility levels by 2064. These mandatory class I federal areas are mostly 

national parks, national monuments, and wilderness areas. There are currently 116 

Interagency Monitoring of Protected Visual Environments (IMPROVE) monitoring sites 

(representing all 156 mandatory class I federal areas) collecting ambient PM2.5 data at 

mandatory class I federal areas, but not all of these sites have complete data for 2002. For 

this analysis, we quantified visibility improvement at the 133 mandatory class I federal areas 
Air Quality and Resulting Health and Welfare Effects 

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which have complete IMPROVE ambient data for 2002 or are represented by IMPROVE 

monitors with complete data.N

Visibility impairment is quantified in extinction units. Visibility degradation is 

directly proportional to decreases in light transmittal in the atmosphere. Scattering and 

absorption by both gases and particles decrease light transmittance. To quantify changes in 

visibility, our analysis computes a light-extinction coefficient (bext) and visual range. The 

light extinction coefficient is based on the work of Sisler, which shows the total fraction of 

light that is decreased per unit distance. This coefficient accounts for the scattering and 

absorption of light by both particles and gases and accounts for the higher extinction 

efficiency of fine particles compared to coarse particles. Fine particles with significant lightextinction efficiencies include sulfates, nitrates, organic carbon, elemental carbon, and soil.137

Visual range is a measure of visibility that is inversely related to the extinction 

coefficient. Visual range can be defined as the maximum distance at which one can identify a 

black object against the horizon sky. Visual range (in units of kilometers) can be calculated 

from bext using the formula: Visual Range (km) = 3912/bext (bext units are inverse megameters 

[Mm-1]) 

The future year visibility impairment was calculated using a methodology which 

applies modeling results in a relative sense similar to the Speciated Modeled Attainment Test 

(SMAT). 

In calculating visibility impairment, the extinction coefficient is made up of individual 

component species (sulfate, nitrate, organics, etc). The predicted change in visibility is 

calculated as the percent change in the extinction coefficient for each of the PM species (on a 

daily average basis). The individual daily species extinction coefficients are summed to get a 

daily total extinction value. The daily extinction coefficients are converted to visual range 

and then averaged across all days. In this way, we can calculate annual average extinction 

and visual range at each IMPROVE site. Subtracting the annual average control case visual 

range from the base case visual range gives a projected improvement in visual range (in km) 

at each mandatory class I federal area. This serves as the visibility input for the benefits 

analysis (See Chapter 6). 

For visibility calculations, we are continuing to use the IMPROVE program species 

definitions and visibility formulas which are recommended in the draft modeling guidance. 

Each IMPROVE site has measurements of PM2.5 species and therefore we do not need to 

estimate the species fractions in the same way that we did for FRM sites (using interpolation 

techniques and other assumptions concerning volatilization of species). 

 

N There are 100 IMPROVE sites with complete data for 2002. Many of these sites collect data that is 

“representative” of other nearby unmonitored mandatory class I federal areas. There are a total of 133 

mandatory class I federal areas that are represented by the 100 sites. The matching of sites to monitors is taken 

from “Guidance for Tracking Progress Under the Regional Haze Rule”. 
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2.4 Air Toxics

People experience elevated risk of cancer and other noncancer health effects from 

exposure to air toxics. Mobile sources are responsible for a significant portion of this risk. 

According to the National Air Toxic Assessment (NATA) for 1999, mobile sources were 

responsible for 44 percent of outdoor toxic emissions and almost 50 percent of the cancer risk. 

Benzene is the largest contributor to cancer risk of all 133 pollutants quantitatively assessed in 

the 1999 NATA and mobile sources were responsible for 68 percent of benzene emissions in 

1999. In response, EPA has recently finalized mobile source and fuel controls that address 

this public health risk.O Although the 1999 NATA did not quantify cancer risks associated 

with exposure to diesel exhaust, EPA has concluded that diesel exhaust ranks with the other 

emissions that the 1999 NATA suggests pose the greatest relative risk. 

According to the 1999 NATA, nearly the entire U.S. population was exposed to an 

average concentration of air toxics that has the potential for adverse noncancer respiratory 

health effects. This will continue to be the case in 2030, even though toxics concentrations 

will be lower. Mobile sources were responsible for 74 percent of the noncancer (respiratory) 

risk from outdoor air toxics in 1999. The majority of this risk was from exposure to acrolein. 

The confidence in the RfC for acrolein is medium and confidence in NATA estimates of 

population noncancer hazard from ambient exposure to this pollutant is low.138,139 

The NATA modeling framework has a number of limitations which prevent its use as 

the sole basis for setting regulatory standards. These limitations and uncertainties are 

discussed on the 1999 NATA website.140 Even so, this modeling framework is very useful in 

identifying air toxic pollutants and sources of greatest concern, setting regulatory priorities, 

and informing the decision making process. 

The following section provides an overview of air toxics which are associated with 

nonroad engines, including locomotive and marine diesel engines, and provides a discussion 

of the health risks associated with each air toxic. 

2.4.1 Diesel Exhaust PM 

Locomotive and marine diesel engines emit diesel exhaust (DE), a complex mixture 

comprised of carbon dioxide, oxygen, nitrogen, water vapor, carbon monoxide, nitrogen 

compounds, sulfur compounds and numerous low-molecular-weight hydrocarbons. A number 

of these gaseous hydrocarbon components are individually known to be toxic including 

aldehydes, benzene and 1,3-butadiene. The diesel particulate matter (DPM) present in diesel 

exhaust consists of fine particles (< 2.5μm), including a subgroup with a large number of 

ultrafine particles (< 0.1 μm). These particles have large surface areas which make them an 

excellent medium for adsorbing organics, and their small size makes them highly respirable 

and able to deposit deep in the lung. Diesel PM contains small quantities of numerous 

mutagenic and carcinogenic compounds associated with the particles (and also organic gases). 

In addition, while toxic trace metals emitted by locomotive and marine diesel engines 

 

O U.S. EPA (2006) Control of Hazardous Air Pollutants from Mobile Sources. 71 FR 15804; March 29, 2006. 
Air Quality and Resulting Health and Welfare Effects 

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represent a very small portion of the national emissions of metals (less than one percent) and 

are a small portion of diesel PM (generally much less than one percent of diesel PM), we note 

that several trace metals of potential toxicological significance and persistence in the 

environment are emitted by diesel engines. These trace metals include chromium, 

manganese, mercury and nickel. In addition, small amounts of dioxins have been measured in 

highway engine diesel exhaust, some of which may partition into the particulate phase. 

Dioxins are a major health concern but diesel engines are a minor contributor to overall dioxin 

emissions. 

Diesel exhaust varies significantly in chemical composition and particle sizes between 

different engine types (heavy-duty, light-duty), engine operating conditions (idle, accelerate, 

decelerate), and fuel formulations (high/low sulfur fuel). 141 Also, there are emission 

differences between on-road and nonroad engines because the nonroad engines are generally 

of older technology. After being emitted diesel exhaust undergoes dilution as well as 

chemical and physical changes in the atmosphere. The lifetime for some of the compounds 

present in diesel exhaust ranges from hours to days. 

A number of health studies have been conducted regarding diesel exhaust. These 

include epidemiologic studies of lung cancer in groups of workers and animal studies 

focusing on non-cancer effects specific to diesel exhaust exposure. Diesel exhaust PM 

(including the associated organic compounds which are generally high molecular weight 

hydrocarbon types but not the more volatile gaseous hydrocarbon compounds) is generally 

used as a surrogate measure for diesel exhaust. 

2.4.1.1 Potential Cancer Effects of Exposure to Diesel Exhaust 

 Exposure to diesel exhaust is of specific concern because it has been judged by EPA to 

pose a lung cancer hazard for humans at environmental levels of exposure. 

EPA’s 2002 final “Health Assessment Document for Diesel Engine Exhaust” (the 

EPA Diesel HAD) classified exposure to diesel exhaust as likely to be carcinogenic to 

humans by inhalation at environmental exposures, in accordance with the revised draft 

1996/1999 EPA cancer guidelines.142,143 In accordance with earlier EPA guidelines, exposure 

to diesel exhaust would similarly be classified as probably carcinogenic to humans (Group 

B1).144,145 A number of other agencies (National Institute for Occupational Safety and 

Health, the International Agency for Research on Cancer, the World Health Organization, 

California EPA, and the US Department of Health and Human Services) have made similar 

classifications.146, 147,148,149,150 The Health Effects Institute has prepared numerous studies and 

reports on the potential carcinogenicity of exposure to diesel exhaust.151,152,153 In addition 

many animal and bioassay/genotoxic tests have been done on diesel exhaust154,155 and casecontrol and cohort studies have been conducted on railroad worker exposures to diesel 

exhaust from railroad engines156,157,158 in addition to studies on truck workers.159,160,161,162 

Also, there are numerous other epidemiologic studies including some studying mine workers 

and fire fighters.163,164

More specifically, the EPA Diesel HAD states that the conclusions of the document 

apply to diesel exhaust in use today including both onroad and nonroad engines. The EPA 
Regulatory Impact Analysis 

2-50

Diesel HAD acknowledges that the studies were done on engines with generally older 

technologies and that “there have been changes in the physical and chemical composition of 

some DE [diesel exhaust] emissions (onroad vehicle emissions) over time, though there is no 

definitive information to show that the emission changes portend significant toxicological 

changes.” In any case, the diesel technology used for locomotive and marine diesel engines 

typically lags that used for onroad engines which have been subject to PM standards since 

1998. Thus it is reasonable to assume that the hazards identified from older technologies may 

be largely applicable to locomotive and marine engines. 

For the Diesel HAD, EPA reviewed 22 epidemiologic studies on the subject of the 

carcinogenicity of exposure to diesel exhaust in various occupations, finding increased lung 

cancer risk, although not always statistically significant, in 8 out of 10 cohort studies and 10 

out of 12 case-control studies which covered several industries, including railroad workers. 

Relative risk for lung cancer, associated with exposure, ranged from 1.2 to 1.5, although a few 

studies show relative risks as high as 2.6. Additionally, the Diesel HAD also relied on two 

independent meta-analyses, which examined 23 and 30 occupational studies respectively, and 

found statistically significant increases of 1.33 to 1.47 in smoking-adjusted relative lung 

cancer risk associated with diesel exhaust. These meta-analyses demonstrate the effect of 

pooling many studies and in this case show the positive relationship between diesel exhaust 

exposure and lung cancer across a variety of diesel exhaust-exposed occupations.165,166,167

Retrospective health studies of railroad workers have played an important part in 

finding that exposure to diesel exhaust is a likely to be carcinogenic to humans by inhalation 

at environmental levels of exposure. Key evidence of the diesel exhaust exposure linkage to 

lung cancer comes from two retrospective case-control studies of railroad workers. The 

Garshick railroad study168 looked at more than 55,000 railroad workers post-1959 which 

coincided with the widespread dieselization of the railroads. The study found that the risk of 

lung cancer increased with increasing duration of employment, and that the youngest workers 

had the highest risk of dying. The second railroad study, authored by Swanson et al.169, found 

statistically significant excess risks, when adjusted for age, smoking, and race, among railroad 

workers employed for more than 10 years and heavy truck drivers employed for more than 20 

years. In addition, a 1988 industrial hygiene study documented the increased lung cancer 

risks associated with different railroad worker job classifications.170 Thirty-nine job titles 

were originally identified and were then collapsed, for statistical analyses, into 5 categories 

including clerks, signal maintainers, engineers/firers, brakers/conductors/hostlers, and shop 

workers. The study documented that those in closest contact with diesel exhaust exhibited the 

highest level of lung cancer risk. Train workers (engineers/firers etc.) had the highest risk, 

shop workers an intermediate level, and clerks the lowest lung cancer risk. 

EPA generally derives cancer unit risk estimates to calculate population risk more 

precisely from exposure to carcinogens. In the simplest terms, the cancer unit risk is the 

increased risk associated with average lifetime exposure of 1 μg/m3

. EPA concluded in the 

Diesel HAD that it is not currently possible to calculate a cancer unit risk for diesel exhaust 

due to a variety of factors that limit the current studies, such as a lack of standard exposure 

metric for diesel exhaust and the absence of quantitative exposure characterization in 

retrospective studies. 
Air Quality and Resulting Health and Welfare Effects 

2-51

In the absence of a cancer unit risk, the Diesel HAD sought to provide additional 

insight into the significance of the diesel exhaust-cancer hazard by estimating possible ranges 

of risk that might be present in the population. An exploratory analysis was used to 

characterize a possible risk range by comparing a typical environmental exposure level for 

highway diesel sources to a selected range of occupational exposure levels. The 

occupationally observed risks were then proportionally scaled according to the exposure ratios 

to obtain an estimate of the possible environmental risk. If the occupational and 

environmental exposures are similar, the environmental risk would approach the risk seen in 

the occupational studies whereas a much higher occupational exposure indicates that the 

environmental risk is lower than the occupational risk.A comparison of environmental and 

occupational exposures showed that for certain occupations the exposures are similar to 

environmental exposures while, for others, they differ by a factor of about 200 or more. 

A number of calculations are involved in the exploratory analysis of a possible risk 

range, and these can be seen in the EPA Diesel HAD. The outcome was that environmental 

risks from diesel exhaust exposure could range from a low of 10-4 to 10-5 to as high as 10-3, 

reflecting the range of occupational exposures that could be associated with the relative and 

absolute risk levels observed in the occupational studies. Because of uncertainties, the 

analysis acknowledged that the risks could be lower than 10-4 or 10-5, and a zero risk from 

diesel exhaust exposure was not ruled out. 

EPA recently assessed air toxic emissions and their associated risk (the National-Scale 

Air Toxics Assessment or NATA for 1996 and 1999), and we concluded that diesel exhaust 

ranks with other emissions that the national-scale assessment suggests pose the greatest 

relative risk.171,172 This national assessment estimates average population inhalation 

exposures to diesel PM for nonroad as well as on-highway sources. These are the sum of 

ambient levels in various locations weighted by the amount of time people spend in each of 

the locations. 

In summary, even though EPA does not have a specific carcinogenic potency with 

which to accurately estimate the carcinogenic impact of exposure to diesel exhaust, the likely 

hazard to humans together with the potential for significant environmental risks leads us to 

conclude that diesel exhaust emissions from locomotive and marine engines present public 

health issues of concern to this rule. 

2.4.1.2 Other Health Effects of Diesel Exhaust 

 Noncancer health effects of acute and chronic exposure to diesel exhaust emissions are 

also of concern to the EPA. The Diesel HAD established an inhalation Reference 

Concentration (RfC) specifically based on animal studies of diesel exhaust exposure. An RfC 

is defined by EPA as “an estimate of a continuous inhalation exposure to the human 

population, including sensitive subgroups, with uncertainty spanning perhaps an order of 

magnitude, which is likely to be without appreciable risks of deleterious noncancer effects 

during a lifetime.” EPA derived the RfC from consideration of four well-conducted chronic 

rat inhalation studies showing adverse pulmonary effects.173,174,175,176 The diesel RfC is based 

on a “no observable adverse effect” level of 144 μg/m3

 that is further reduced by applying 

uncertainty factors of 3 for interspecies extrapolation and 10 for human variations in 
Regulatory Impact Analysis 

2-52

sensitivity. The resulting RfC derived in the Diesel HAD is 5 μg/m3

 for diesel exhaust as 

measured by diesel PM. This RfC does not consider allergenic effects such as those 

associated with asthma or immunologic effects. There is growing evidence that exposure to 

diesel exhaust can exacerbate these effects, but the exposure-response data is presently 

lacking to derive an RfC. The EPA Diesel HAD states, “With DPM [diesel particulate 

matter] being a ubiquitous component of ambient PM, there is an uncertainty about the 

adequacy of the existing DE [diesel exhaust] noncancer database to identify all of the 

pertinent DE-caused noncancer health hazards” (p. 9-19). 

While there have been relatively few human studies associated specifically with the 

noncancer impact of exposure to diesel PM alone, diesel PM is a component of the ambient 

particles studied in numerous epidemiologic studies. The conclusion that health effects 

associated with ambient PM in general are relevant to diesel PM is supported by studies that 

specifically associate observable human noncancer health effects with exposure to diesel PM. 

As described in the Diesel HAD, these studies identified some of the same health effects 

reported for ambient PM, such as respiratory symptoms (cough, labored breathing, chest 

tightness, wheezing), and chronic respiratory disease (cough, phlegm, chronic bronchitis and 

suggestive evidence for decreases in pulmonary function). Symptoms of immunological 

effects such as wheezing and increased allergenicity are also seen. Studies in rodents, 

especially rats, show the potential for human inflammatory effects in the lung and 

consequential lung tissue damage from chronic diesel exhaust inhalation exposure. The 

Diesel HAD concludes “that acute exposure to DE [diesel exhaust] has been associated with 

irritation of the eye, nose, and throat, respiratory symptoms (cough and phlegm), and 

neurophysiological symptoms such as headache, lightheadedness, nausea, vomiting, and 

numbness or tingling of the extremities.”177 There is also evidence for an immunologic effect 

such as the exacerbation of allergenic responses to known allergens and asthma-like 

symptoms.178,179,180 

Exposure to diesel exhaust has been shown to cause serious noncancer effects in 

occupational exposure studies. One recent study181 of a small group of railroad workers and 

electricians found that exposure to diesel exhaust resulted in neurobehavioral impairments in 

one or more areas including reaction time, balance, blink reflex latency, verbal recall, and 

color vision confusion indices. Pulmonary function tests also showed that 10 of the 16 

workers studied had airway obstruction and another group of 10 of 16 workers had chronic 

bronchitis, chest pain, tightness, and hyperactive airways. Finally, a variety of studies have 

been published subsequent to the completion of the Diesel HAD. One study, published in 

2006182 found that railroad engineers and conductors with diesel exhaust exposure from 

operating trains had an increased incidence of chronic obstructive pulmonary disease (COPD) 

mortality. The odds of COPD mortality increased with years on the job so that those who had 

worked more than 16 years as an engineer or conductor after 1959 had an increased risk of 

1.61 (95% confidence interval, 1.12 - 2.30). EPA is assessing the significance of this study 

within the context of the broader literature. 

The Diesel HAD briefly summarizes health effects associated with ambient PM and 

discusses the PM2.5 NAAQS. There is a much more extensive body of human data, which is 

also mentioned earlier in the health effects discussion for PM2.5 (Section 2.1.2 of this RIA), 

showing a wide spectrum of adverse health effects associated with exposure to ambient PM, 
Air Quality and Resulting Health and Welfare Effects 

2-53

of which diesel exhaust is an important component. The PM2.5 NAAQS is designed to 

provide protection from the non-cancer and premature mortality effects of PM2.5 as a whole. 

A number of recent studies have associated living near roadways with adverse health 

effects. A Dutch study of a population of people 55-69 years old found that there was an 

elevated risk of heart and lung related mortality among populations living near high traffic 

roads.183 In a review of studies of the respiratory health of people living near roadways, 

another publication indicated that the risk of asthma and related respiratory disease appeared 

elevated in people living near heavy traffic.184 These studies offer evidence that people 

exposed most directly to emissions from mobile sources, including diesels, face an elevated 

risk of illness or death. 

2.4.1.3 Ambient Levels of Diesel Exhaust PM 

Because diesel PM is part of overall ambient PM and cannot be easily distinguished 

from overall PM, we do not have direct measurements of diesel PM in the ambient air. Diesel 

PM concentrations are estimated here using ambient air quality modeling based on diesel PM 

emission inventories. 

2.4.1.3.1 Toxics Modeling and Methods 

In addition to the general ambient PM modeling conducted for this rulemaking, diesel 

PM concentrations were recently estimated as part of the 1999 National-Scale Air Toxics 

Assessment.185 Ambient impacts of mobile source emissions were predicted using the 

Assessment System for Population Exposure Nationwide (ASPEN) dispersion model. 

Concentrations of diesel PM were calculated at the census tract level in the 1999 

NATA. The median diesel PM concentration calculated nationwide is 0.91 μg/m3 with levels 

of 1.06 μg/m3

 in urban counties and 0.43 μg/m3 in rural counties. Table 2-6 below 

summarizes the distribution of ambient diesel PM concentrations at the national scale. Over 

half of the diesel PM and diesel exhaust organic gases can be attributed to nonroad diesels. A 

map of median ambient concentrations is provided in Figure 2-9. Areas with high median 

concentrations are clustered in the Northeast, Great Lake States, California, and the Gulf 

Coast States, and are also distributed throughout the rest Assessment of the U.S. 

 Table 2-6 Distribution of Census Tract Ambient Concentrations of Diesel PM at the National Scale in 1999 NATAa

 Nationwide (μg/m3) Urban (μg/m3) Rural (μg/m3) 

5th Percentile 0.22 0.33 0.08 

25th Percentile 0.54 0.70 0.28 

Median 0.91 1.06 0.43 

75th Percentile 1.41 1.56 0.62 

95th Percentile 2.91 3.21 0.96 

Onroad Contribution to Mean 0.43 0.49 0.20 

Nonroad Contribution to Mean 0.78 0.90 0.28 

a

 This table is generated from data contained in the diesel particulate matter Microsoft Access database file found in the 

County-Level Ambient Concentration Summaries section of the 1999 NATA webpage 

(http://www.epa.gov/ttn/atw/nata1999/tables.html). 
Regulatory Impact Analysis 

2-54

Figure 2-9 Estimated County Median Ambient Concentration of Diesel Particulate Matter 
Air Quality and Resulting Health and Welfare Effects 

2-55

2.4.1.4 Exposure to Diesel Exhaust PM 

Exposure of people to diesel exhaust depends on their various activities, the time spent 

in those activities, the locations where these activities occur, and the levels of diesel exhaust 

pollutants in those locations. The major difference between ambient levels of diesel 

particulate and exposure levels for diesel particulate is that exposure levels account for a 

person moving from location to location, the proximity to the emission source, and whether 

the exposure occurs in an enclosed environment. 

2.4.1.4.1 Occupational Exposures

Occupational exposures to diesel exhaust from mobile sources, including locomotive 

engines and marine diesel engines, can be several orders of magnitude greater than typical 

exposures in the non-occupationally exposed population. 

Over the years, diesel particulate exposures have been measured for a number of 

occupational groups resulting in a wide range of exposures from 2 to 1280 μg/m3 for a variety 

of occupations. Studies have shown that miners and railroad workers typically have higher 

diesel exposure levels than other occupational groups studied, including firefighters, truck 

dock workers, and truck drivers (both short and long haul).186 A 1988 study187 estimated that 

U.S. railroad workers received an estimated occupational exposure/concentration of between 

39 -191 μg/m3 (measured as smoking adjusted respirable particles) which resulted in an 

equivalent environmental exposure of 8-40 μg/m3. As discussed in the Diesel HAD, the 

National Institute of Occupational Safety and Health (NIOSH) has estimated a total of 

1,400,000 workers are occupationally exposed to diesel exhaust from on-road and nonroad 

vehicles including locomotive and marine diesel engines. 

2.4.1.4.2 Elevated Concentrations and Ambient Exposures in Mobile Source-Impacted 

Areas 

While occupational studies indicate that those working in closest proximity to diesel 

exhaust experience the greatest health effects, recent studies are showing that human 

populations living near large diesel emission sources such as major roadways, 188 rail yards,

189 and marine ports190 are also likely to experience greater exposure to PM and other 

components of diesel exhaust than the overall population, putting them at a greater health risk. 

Regions immediately downwind of rail yards and marine ports may experience 

elevated ambient concentrations of directly-emitted PM2.5 from diesel engines. Due to the 

unique nature of rail yards and marine ports, emissions from a large number of diesel engines 

are concentrated in a small area. Furthermore, emissions occur at or near ground level, 

allowing emissions of diesel engines to reach nearby receptors without fully mixing with 

background air. 

A study conducted by the California Air Resources Board (CARB) in 2004 examined 

the air quality impacts of railroad operations at the J.R. Davis Rail Yard, the largest service 

and maintenance rail facility in the western United States.191 The yard occupies 950 acres 

along a one-quarter mile wide and four mile long section of land in Roseville, CA. The study 

developed an emissions inventory for the facility for the year 2000 and modeled ambient 
Regulatory Impact Analysis 

2-56

concentrations of diesel PM using the ISCST3 dispersion model. The study found 

substantially elevated concentrations in an area 5,000 meters from the facility, with higher 

concentrations closer to the rail yard. Using local meteorological data, annual average 

contributions from the rail yard to ambient diesel PM concentrations under prevailing wind 

conditions were 1.74, 1.18, 0.80, and 0.25 μg/m3

 at receptors located 200, 500, 1000, and 

5000 meters from the yard, respectively. Several tens of thousands of people live within the 

area experiencing substantial increases in annual average ambient PM2.5 as a result of 

emissions from the yard. As part of an agreement between CARB and the Union Pacific 

Railroad and BNSF Railway, similar assessments are being prepared for 16 other large 

railyards. The details and results of these additional assessments can be found in their 

respective reports.192

Another study from CARB evaluated air quality impacts of diesel engine emissions 

within the Port of Long Beach and Los Angeles in California, one of the largest ports in the 

U.S.193 Like the earlier rail yard study, the port study employed the ISCST3 dispersion 

model. Also using local meteorological data, annual average concentrations of diesel PM 

were substantially elevated over an area exceeding 200,000 acres. Because the Ports are 

located near heavily-populated areas, the modeling indicated that over 700,000 people lived in 

areas with at least 0.3 μg/m3

 of port-related diesel PM in ambient air, about 360,000 people 

lived in areas with at least 0.6 μg/m3 of diesel PM, and about 50,000 people lived in areas 

with at least 1.5 μg/m3 of ambient diesel PM emitted directly from the port. Figure 2-10 

provides an aerial shot of the Port of Long Beach and Los Angeles in California. 

Figure 2-10 Aerial Shot – Port of LA and Long Beach, California 

Together these railyard and port studies highlight the substantial contribution these 

facilities make to ambient concentrations of diesel PM in large, densely populated areas. 
Air Quality and Resulting Health and Welfare Effects 

2-57

The US EPA recently conducted a screening-level analysis to better understand the 

populations including minority, low-income, and children that are exposed to diesel particulate 

matter (DPM) from these facilities. The results of this studyP are discussed here and are also 

available in the public docket.194,195 In the proposal, EPA committed to finalize this study as 

part of an ongoing obligation to children’s health and environmental justice (EJ). 

 This screening-level analysis focused on a representative selection of national marine 

ports and rail yards.Q Of the 47 marine ports and 37 rail yards selected, the results indicate 

that at least 13 million people, including a disproportionate number of low-income 

households, African-Americans, and Hispanics, living in the vicinity of these facilities, are 

being exposed to ambient DPM levels that are 2.0 μg/m3

 and 0.2 μg/m3 above levels found in 

areas further from these facilities. Because those populations exposed to DPM emissions 

from marine ports and rail yards are more likely to be low-income and minority residents, 

these populations will receive a significant benefit from this rule. 

With regard to children, this analysis shows that of the 13 million people living in the 

vicinity of the marine ports and rail yards 3.5 million are children. The age composition of the 

total affected population in the screening analysis matches closely with the age composition 

of the overall US population. However, for some individual facilities the young (0-4 years) 

appear to be over-represented in the affected population compared to the overall US 

population. Detailed results for individual harbors and rail yards are presented in the 

Appendices of the memorandum in the docket. 

As part of this study, a computer geographic information system was used to identify 

the locations and boundaries of a sampling of 47 US harbor areas and 37 US rail yards and 

terminals, and determined the size and demographic characteristics of the populations living 

near these facilities. These facilities are listed in Tables 2-7 and 2-8 for harbor areas and rail 

yards, respectively. Figures 2-11 to 2-14 provide examples of digitized footprints of the rail 

yards and marine harbor areas included in this study. 

 

P

 This type of screening-level analysis is an inexact tool and not appropriate for regulatory decision-making; it is 

useful in beginning to understand potential impacts and for illustrative purposes. Additionally, the emissions 

inventories used as inputs for the analyses are not official estimates and likely underestimate overall emissions 

because they are not inclusive of all emission sources at the individual ports in the sample. For example, most 

inventories did include emissions from ocean-going vessels (powered by Category 3 engines), as well as some 

commercial vessel categories, including harbor crafts, (powered by Category 1and 2 engines), cargo handling 

equipment, locomotives, and heavy-duty vehicles. This final rule will not address emissions from ocean-going 

vessels, cargo handling equipment, or heavy-duty vehicles. 

Q The Agency selected a representative sample of the top 150 U.S. ports including coastal, inland, and Great 

Lake ports. In selecting a sample of rail yards the Agency identified a subset from the hundreds of rail yards 

operated by Class I Railroads. 
Regulatory Impact Analysis 

2-58

Table 2-7 Marine Harbor Areas 

Harbor Location 

Baltimore, MD 

Boston, MA 

Charleston, SC 

Chicago, IL 

Cincinnati, OH 

Cleveland, OH 

Corpus Christi, TX 

Detroit, MI 

Duluth-Superior, MN 

Freeport, TX 

Gary, IN 

Helena, AR 

Houston, TX 

Jacksonville, FL 

Lake Charles, LA 

Long Beach, CA 

Los Angeles, CA 

Louisville, KY 

Miami, FL 

Mobile, AL 

Mount Vernon, IN 

Nashville, TN 

New Orleans, LA 

New York, NY 

Norfolk Harbor, VA 

Oakland, CA 

Panama City, FL 

Paulsboro, NJ 

Philadelphia, PA 

Pittsburgh, PA 
Air Quality and Resulting Health and Welfare Effects 

2-59

Port Arthur, TX 

Port Everglades, FL 

Port of Baton Rouge, LA 

Port of Plaquemines, LA 

Portland, ME 

Portland, OR 

Richmond, CA 

Savannah, GA 

Seattle, WA 

South Louisiana, LA 

St. Louis, MO 

Tacoma, WA 

Tampa, FL 

Texas City, TX 

Tulsa - Port of Catoosa, OK

Two Harbors, MN 

Wilmington, NC 

Table 2-8 Rail Yards and Terminals 

Yard Name Location Railroad 

Argentine Kansas City, KS BNSF 

Avon Yard Indianapolis, IN CSXT 

Bailey Yard North Platte, NE UP 

Barr Yard Chicago, IL CSXT 

Barstow Yard Barstow, CA BNSF 

Bellevue Yard Bellevue, OH NS 

Bensenville Yard Bensenville, IL CP 

Blue Island Yard Blue Island, IL IHB 

Boyles Yard Birmingham, AL CSXT 

Buckeye Yard Columbus, OH CSXT 

Clearing Yard Chicago, IL BRC 

Conway Yard Conway, PA NS 
Regulatory Impact Analysis 

2-60

Corwith Yard Chicago, IL BNSF 

DeButts Yard Chattanooga, TN NS 

Frontier Yard Buffalo, NY CSXT 

Frontier Yard Intermodal Terminal Buffalo, NY CSXT 

Galesburg Galesburg, IL BNSF 

Hinkle Hermiston, OR UP 

Inman Yard Atlanta, GA NS 

J.R. Davis Yard Roseville, CA UP 

Jenks Shop North Little Rock, AR UP 

Locomotive Maintenance Facility Alliance, NE BNSF 

Locomotive Repair Facility Topeka, KS BNSF 

Madison Yard East St. Louis, MO TRRA 

Moncrief Yard Jacksonville, FL CSXT 

Philadelphia PA Railyard Philadelphia, PA CSXT 

Pig's Eye Yard Minneapolis, MN CP 

Proviso Yard Chicago, IL UP 

Queensgate Yard Cincinnati, OH CSXT 

Radnor Yard Nashville, TN CSXT 

Rice Yard Waycross, GA CSXT 

Schiller Park Schiller Park, IL CP 

Selkirk Yard Selkirk, NY CSXT 

Shaffers Crossing Roanoke, VA NS 

Spencer Yard Linwood, NC NS 

Stanley/Walbridge Yard Toledo, OH CSXT 

West Colton Yard West Colton, CA UP 
Air Quality and Resulting Health and Welfare Effects 

2-61

Figure 2-11 Digitized footprint of New York, NY harbor area. 

Figure 2-12 Digitized footprint of Portland, OR harbor area. 
Regulatory Impact Analysis 

2-62

Figure 2-13 Digitized footprint of Argentine Rail Yard, Kansas City, Kansas. 

Figure 2-14. Digitized footprint of DeButts Rail Yard, Chattanooga, Tennessee. 

In order to better understand the populations that are living in the vicinity of rail yards 

and marine harbor areas and their potential exposures to DPM, DPM concentration isopleths 
Air Quality and Resulting Health and Welfare Effects 

2-63

surrounding the facilities were identified and digitized. The concentration isopleths of interest 

were selected to correspond to two DPM concentrations above urban background, 2.0 μg/m3

and 0.2 μg/m3. For marine harbor areas, the isopleths were estimated using the AERMOD air 

dispersion model. For rail yards and terminals, the isopleths were estimated using a process 

for scaling from published rail yard modeling reports. Both estimation methods are subject to 

important uncertainties that are discussed in the memorandum. Figures 2-15 to 2-16 provide 

examples of concentration isopleths surrounding the New York, NY harbor area and DeButts 

Rail Yard in Chattanooga, TN. 

Figure 2-15 Concentration isopleths of New York, NY harbor area. 
Regulatory Impact Analysis 

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Figure 2-16. Concentration isopleths of DeButts Rail Yard, Chattanooga, Tennessee. 

The size and characteristics of populations and households that reside within the area 

encompassed by the two DPM concentration isopleths were determined. In addition to the 

total population and number of households residing within each isopleth, the demographic 

compositions were assessed, including age, income level, and race/ethnicity. 

In summary, the population analysis suggests that for the 47 US marine ports and 37 

US rail yards analyzed, at least 13 million people living in the vicinity of these facilities are 

being exposed to ambient DPM levels that are 2.0 μg/m3

 and 0.2 μg/m3 above those found in 

areas further from these facilities. 
Air Quality and Resulting Health and Welfare Effects 

2-65

2.4.2 Other Air Toxics—benzene, 1,3-butadiene, formaldehyde, acetaldehyde, 

acrolein, POM, naphthalene 

Locomotive and marine diesel engine emissions contribute to ambient levels of other 

air toxics known or suspected as human or animal carcinogens, or that have non-cancer health 

effects. Noncancer health effects can result from chronicR

, subchronicS, or acuteT inhalation 

exposures, and include neurological, cardiovascular, liver, kidney, and respiratory effects as 

well as effects on the immune and reproductive systems. 

These other compounds include, but are not limited to, benzene, 1,3-butadiene, 

formaldehyde, acetaldehyde, acrolein, polycyclic organic matter (POM), and naphthalene. 

These compounds, except acetaldehyde, were identified as national or regional risk drivers in 

the 1999 National-Scale Air Toxics Assessment (NATA) and have significant inventory 

contributions from mobile sources. Table 2–9 provides the mobile source inventory 

contributions associated with these compounds.196 The reductions in locomotive and marine 

diesel engine emissions in this rulemaking will help reduce exposure to these harmful 

substances. 

Table 2-9 Mobile Source Inventory Contribution to 1999 Emissions of NATA Risk Driversa 

1999 NATA Risk Driver Percent of Emissions 

Attributable to All 

Mobile Sources 

Percent of Emissions 

Attributable to Nonroad Sources 

Benzene 68% 19% 

1,3-Butadiene 58% 17% 

Formaldehyde 47% 20% 

Acrolein 25% 11% 

Polycyclic organic matter (POM)b 5% 2% 

Naphthalene 27% 6% 

Diesel PM and Diesel exhaust 

organic gases 

100% 62% 

a

 This table is generated from data contained in the pollutant specific Microsoft Access database files found in 

the County-Level Emission Summaries section of the 1999 NATA webpage 

(http://www.epa.gov/ttn/atw/nata1999/tables.html). 

b This POM inventory includes the 15 POM compounds: benzo[b]fluoranthene, benz[a]anthracene, indeno(1,2,3-

c,d)pyrene, benzo[k]fluoranthene, chrysene, benzo[a]pyrene, dibenz(a,h)anthracene, anthracene, pyrene, 

benzo(g,h,i)perylene, fluoranthene, acenaphthylene, phenanthrene, fluorine, and acenaphthene. 

 

R

 Chronic exposure is defined in the glossary of the Integrated Risk Information (IRIS) database 

(http://www.epa.gov/iris) as repeated exposure by the oral, dermal, or inhalation route for more than 

approximately 10 of the life span in humans (more than approximately 90 days to 2 years in typically laboratory 

animal species). 

S

 Defined in the IRIS database as exposure to a substance spanning approximately 10 of the lifetime of an 

organism. 

T

 Defined in the IRIS database as exposure by the oral, dermal, or inhalation route for 24 hours or less. 
Regulatory Impact Analysis 

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 Benzene: The EPA’s IRIS database lists benzene as a known human carcinogen 

(causing leukemia) by all routes of exposure, and that exposure is associated with additional 

health effects, including genetic changes in both humans and animals and increased 

proliferation of bone marrow cells in mice.197,198,199 EPA states in its IRIS database that data 

indicate a causal relationship between benzene exposure and acute lymphocytic leukemia and 

suggests a relationship between benzene exposure and chronic non-lymphocytic leukemia and 

chronic lymphocytic leukemia. A number of adverse noncancer health effects including 

blood disorders, such as preleukemia and aplastic anemia, have also been associated with 

long-term exposure to benzene.200, 201 The most sensitive noncancer effect observed in 

humans, based on current data, is the depression of the absolute lymphocyte count in 

blood.202,203 In addition, recent work, including studies sponsored by the Health Effects 

Institute (HEI), provides evidence that biochemical responses are occurring at lower levels 

of benzene exposure than previously known.204,205, 206,207 EPA’s IRIS program has not yet 

evaluated these new data. 

1,3-Butadiene: EPA has characterized 1,3-butadiene as carcinogenic to humans by 

inhalation.208, 209 There are numerous studies consistently demonstrating that 1,3-butadiene is 

metabolized into genotoxic metabolites by experimental animals and humans. The specific 

mechanisms of 1,3-butadiene-induced carcinogenesis are unknown; however, the scientific 

evidence strongly suggests that the carcinogenic effects are mediated by genotoxic 

metabolites. Animal data suggest that females may be more sensitive than males for cancer 

effects; while there are insufficient data in humans from which to draw conclusions about 

sensitive subpopulations. 1,3-Butadiene also causes a variety of reproductive and 

developmental effects in mice; no human data on these effects are available. The most 

sensitive effect was ovarian atrophy observed in a lifetime bioassay of female mice.210 

Formaldehyde: Since 1987, EPA has classified formaldehyde as a probable human 

carcinogen based on evidence in humans and in rats, mice, hamsters, and monkeys.211 EPA is 

currently reviewing recently published epidemiological data. For instance, research 

conducted by the National Cancer Institute (NCI) found an increased risk of nasopharyngeal 

cancer and lymphohematopoietic malignancies such as leukemia among workers exposed to 

formaldehyde.212,213 NCI is currently performing an update of these studies. A recent 

National Institute of Occupational Safety and Health (NIOSH) study of garment workers also 

found increased risk of death due to leukemia among workers exposed to formaldehyde.214 

Extended follow-up of a cohort of British chemical workers did not find evidence of an 

increase in nasopharyngeal or lymphohematopoeitic cancers, but a continuing statistically 

significant excess in lung cancers was reported.215

In the past 15 years there has been substantial research on the inhalation dosimetry for 

formaldehyde in rodents and primates by the CIIT Centers for Health Research (formerly the 

Chemical Industry Institute of Toxicology), with a focus on use of rodent data for refinement 

of the quantitative cancer dose-response assessment.216, 217,218 CIIT’s risk assessment of 

formaldehyde incorporated mechanistic and dosimetric information on formaldehyde. 
Air Quality and Resulting Health and Welfare Effects 

2-67

Based on the developments of the last decade, in 2004, the working group of the 

International Agency for Research on Cancer (IARC) concluded that formaldehyde is 

carcinogenic to humans (Group 1), on the basis of sufficient evidence in humans and 

sufficient evidence in experimental animals - a higher classification than previous IARC 

evaluations. After reviewing the currently available epidemiological evidence, the IARC 

(2006) characterized the human evidence for formaldehyde carcinogenicity as “sufficient,” 

based upon the data on nasopharyngeal cancers; the epidemiologic evidence on leukemia was 

characterized as “strong”.219 EPA is reviewing the recent work cited above from the NCI and 

NIOSH, as well as the analysis by the CIIT Centers for Health Research and other studies, as 

part of a reassessment of the human hazard and dose-response associated with formaldehyde. 

Formaldehyde exposure also causes a range of noncancer health effects, including 

irritation of the eyes (tearing of the eyes and increased blinking) and mucous membranes. 

Acetaldehyde: Acetaldehyde is classified in EPA’s IRIS database as a probable 

human carcinogen, based on nasal tumors in rats, and is considered toxic by the inhalation, 

oral, and intravenous routes.220 The primary noncancer effects of exposure to acetaldehyde 

vapors include irritation of the eyes, skin, and respiratory tract.221 Some asthmatics have been 

shown to be a sensitive subpopulation to decrements in functional expiratory volume (FEV1 

test) and bronchoconstriction upon acetaldehyde inhalation.222

In short-term (4 week) rat studies, degeneration of olfactory epithelium was observed 

at various concentration levels of acetaldehyde exposure.223,224 Data from these studies were 

used by EPA to develop an inhalation reference concentration. The agency is currently 

conducting a reassessment of the health hazards from inhalation exposure to acetaldehyde. 

Acrolein: EPA determined in 2003 that the human carcinogenic potential of acrolein 

could not be determined because the available data were inadequate. No information was 

available on the carcinogenic effects of acrolein in humans and the animal data provided 

inadequate evidence of carcinogenicity.225

Acrolein is extremely acrid and irritating to humans when inhaled, with acute 

exposure resulting in upper respiratory tract irritation, mucus hypersecretion and congestion. 

Levels considerably lower than 1 ppm (2.3 mg/m3

) elicit subjective complaints of eye and 

nasal irritation and a decrease in the respiratory rate.226,227 Lesions to the lungs and upper 

respiratory tract of rats, rabbits, and hamsters have been observed after subchronic exposure 

to acrolein. Based on animal data, individuals with compromised respiratory function (e.g., 

emphysema, asthma) are expected to be at increased risk of developing adverse responses to 

strong respiratory irritants such as acrolein. This was demonstrated in mice with allergic 

airway-disease by comparison to non-diseased in a study of the acute respiratory irritant 

effects of acrolein.228

The Agency is currently in the process of conducting an assessment of acute exposure 

effects for acrolein. The intense irritancy of this carbonyl has been demonstrated during 

controlled tests in human subjects who suffer intolerable eye and nasal mucosal sensory 

reactions within minutes of exposure.229
Regulatory Impact Analysis 

2-68

Polycyclic Organic Matter (POM): POM is generally defined as a large class of 

organic compounds which have multiple benzene rings and a boiling point greater than 100 

degrees Celsius. Many of the compounds included in the class of compounds known as POM 

are classified by EPA as probable human carcinogens based on animal data. One of these 

compounds, naphthalene, is discussed separately below. Recent studies have found that 

maternal exposures to PAHs ( a subclass of POM), in a population of pregnant women, were 

associated with several adverse birth outcomes, including low birth weight and reduced length 

at birth as well as impaired cognitive development at age three.230,231 EPA has not yet 

evaluated these recent studies. 

Naphthalene: Naphthalene is found in small quantities in gasoline and diesel fuels but 

is primarily a product of combustion. Naphthalene emissions have been measured in larger 

quantities in both gasoline and diesel exhaust and evaporative emissions from mobile sources. 

EPA recently released an external review draft of a reassessment of the inhalation 

carcinogenicity of naphthalene based on a number of recent animal carcinogenicity studies.232 

The draft reassessment recently completed external peer review.233 Based on external peer 

review comments received to date, additional analyses are being undertaken. This external 

review draft does not represent official agency opinion and was released solely for the 

purposes of external peer review and public comment. Once EPA evaluates public and peer 

reviewer comments, the document will be revised. The National Toxicology Program listed 

naphthalene as "reasonably anticipated to be a human carcinogen" in 2004 on the basis of 

bioassays reporting clear evidence of carcinogenicity in rats and some evidence of 

carcinogenicity in mice.234 California EPA has released a new risk assessment for 

naphthalene, and the IARC has reevaluated naphthalene and re-classified it as Group 2B: 

possibly carcinogenic to humans.235 Naphthalene also causes a number of chronic non-cancer 

effects in animals, including abnormal cell changes and growth in respiratory and nasal 

tissues.236

In addition to reducing substantial amounts of NOx and PM2.5 emissions from 

locomotive and marine diesel engines the standards being finalized today will also reduce air 

toxics emitted from these engines thereby helping to mitigate some of the adverse health 

effects associated with operation of these engines. 
Air Quality and Resulting Health and Welfare Effects 

A2-1

Appendix 2A - PM2.5 Nonattainment 

Table 2A 1997 PM2.5 Nonattainment Areas and Populations (Data is current through October 2007 and 

Population Numbers are from 2000 Census Data)

County Area Name County 

NAWhole/Part 

Design Value 

(μg/m3

) 

Pop (2000) 

ALABAMA 

Jackson Co Chattanooga, AL-TN-GA Part 16.1 1,578

Jefferson Co Birmingham, AL Whole 17.3 662,047

Shelby Co Birmingham, AL Whole 17.3 143,293

Walker Co Birmingham, AL Part 17.3 2,272

CALIFORNIA 

Fresno Co San Joaquin Valley, CA Whole 21.8 799,407

Kern Co San Joaquin Valley, CA Part 21.8 550,220

Kings Co San Joaquin Valley, CA Whole 21.8 129,461

Los Angeles Co Los Angeles-South Coast Air 

Basin, CA 

Part 27.8 9,222,280

Madera Co San Joaquin Valley, CA Whole 21.8 123,109

Merced Co San Joaquin Valley, CA Whole 21.8 210,554

Orange Co Los Angeles-South Coast Air 

Basin, CA 

Whole 27.8 2,846,289

Riverside Co Los Angeles-South Coast Air 

Basin, CA 

Part 27.8 1,194,859

San Bernardino Co Los Angeles-South Coast Air 

Basin, CA 

Part 27.8 1,330,159

San Joaquin Co San Joaquin Valley, CA Whole 21.8 563,598

Stanislaus Co San Joaquin Valley, CA Whole 21.8 446,997

Tulare Co San Joaquin Valley, CA Whole 21.8 368,021

CONNECTICUT 

Fairfield Co New York-N. New JerseyLong Island, NY-NJ-CT Whole 17.7 882,567

New Haven Co New York-N. New JerseyLong Island, NY-NJ-CT Whole 17.7 824,008

DELAWARE 

New Castle Co Philadelphia-Wilmington, PANJ-DE Whole 16.2 500,265

DISTRICT OF COLUMBIA 

Entire District Washington, DC-MD-VA Whole 15.8 572,059

GEORGIA 

Barrow Co Atlanta, GA Whole 18 46,144

Bartow Co Atlanta, GA Whole 18 76,019

Bibb Co Macon, GA Whole 15.2 153,887

Carroll Co Atlanta, GA Whole 18 87,268

Catoosa Co Chattanooga, AL-TN-GA Whole 16.1 53,282

Cherokee Co Atlanta, GA Whole 18 141,903
Regulatory Impact Analysis 

A2-2

County Area Name County 

NAWhole/Part 

Design Value 

(μg/m3

) 

Pop (2000) 

Clayton Co Atlanta, GA Whole 18 236,517

Cobb Co Atlanta, GA Whole 18 607,751

Coweta Co Atlanta, GA Whole 18 89,215

De Kalb Co Atlanta, GA Whole 18 665,865

Douglas Co Atlanta, GA Whole 18 92,174

Fayette Co Atlanta, GA Whole 18 91,263

Floyd Co Rome, GA Whole 15.6 90,565

Forsyth Co Atlanta, GA Whole 18 98,407

Fulton Co Atlanta, GA Whole 18 816,006

Gwinnett Co Atlanta, GA Whole 18 588,448

Hall Co Atlanta, GA Whole 18 139,277

Heard Co Atlanta, GA Part 18 170

Henry Co Atlanta, GA Whole 18 119,341

Monroe Co Macon, GA Part 15.2 950

Newton Co Atlanta, GA Whole 18 62,001

Paulding Co Atlanta, GA Whole 18 81,678

Putnam Co Atlanta, GA Part 18 3,088

Rockdale Co Atlanta, GA Whole 18 70,111

Spalding Co Atlanta, GA Whole 18 58,417

Walker Co Chattanooga, AL-TN-GA Whole 16.1 61,053

Walton Co Atlanta, GA Whole 18 60,687

ILLINOIS 

Cook Co Chicago-Gary-Lake County, 

IL-IN 

Whole 17.7 5,376,741

DuPage Co Chicago-Gary-Lake County, 

IL-IN 

Whole 17.7 904,161

Grundy Co Chicago-Gary-Lake County, 

IL-IN 

Part 17.7 6,309

Kane Co Chicago-Gary-Lake County, 

IL-IN 

Whole 17.7 404,119

Kendall Co Chicago-Gary-Lake County, 

IL-IN 

Part 17.7 28,417

Lake Co Chicago-Gary-Lake County, 

IL-IN 

Whole 17.7 644,356

Madison Co St. Louis, MO-IL Whole 17.5 258,941

Mc Henry Co Chicago-Gary-Lake County, 

IL-IN 

Whole 17.7 260,077

Monroe Co St. Louis, MO-IL Whole 17.5 27,619

Randolph Co St. Louis, MO-IL Part 17.5 3,627

St Clair Co St. Louis, MO-IL Whole 17.5 256,082

Will Co Chicago-Gary-Lake County, 

IL-IN 

Whole 17.7 502,266

INDIANA 
Air Quality and Resulting Health and Welfare Effects 

A2-3

County Area Name County 

NAWhole/Part 

Design Value 

(μg/m3

) 

Pop (2000) 

Clark Co Louisville, KY-IN Whole 16.9 96,472

Dearborn Co Cincinnati-Hamilton, OH-KYIN Part 17.8 10,434

Dubois Co Evansville, IN Whole 16.2 39,674

Floyd Co Louisville, KY-IN Whole 16.9 70,823

Gibson Co Evansville, IN Part 16.2 3,698

Hamilton Co Indianapolis, IN Whole 16.7 182,740

Hendricks Co Indianapolis, IN Whole 16.7 104,093

Jefferson Co Louisville, KY-IN Part 16.9 16,770

Johnson Co Indianapolis, IN Whole 16.7 115,209

Lake Co Chicago-Gary-Lake County, 

IL-IN 

Whole 17.7 484,564

Marion Co Indianapolis, IN Whole 16.7 860,454

Morgan Co Indianapolis, IN Whole 16.7 66,689

Pike Co Evansville, IN Part 16.2 4,633

Porter Co Chicago-Gary-Lake County, 

IL-IN 

Whole 17.7 146,798

Spencer Co Evansville, IN Part 16.2 5,092

Vanderburgh Co Evansville, IN Whole 16.2 171,922

Warrick Co Evansville, IN Whole 16.2 52,383

KENTUCKY 

Boone Co Cincinnati-Hamilton, OH-KYIN Whole 17.8 85,991

Boyd Co Huntington-Ashland, WV-KYOH Whole 17.2 49,752

Bullitt Co Louisville, KY-IN Whole 16.9 61,236

Campbell Co Cincinnati-Hamilton, OH-KYIN Whole 17.8 88,616

Jefferson Co Louisville, KY-IN Whole 16.9 693,604

Kenton Co Cincinnati-Hamilton, OH-KYIN Whole 17.8 151,464

Lawrence Co Huntington-Ashland, WV-KYOH Part 17.2 1,050

MARYLAND 

Anne Arundel Co Baltimore, MD Whole 16.6 489,656

Baltimore (City) Baltimore, MD Whole 16.6 651,154

Baltimore Co Baltimore, MD Whole 16.6 754,292

Carroll Co Baltimore, MD Whole 16.6 150,897

Charles Co Washington, DC-MD-VA Whole 15.8 120,546

Frederick Co Washington, DC-MD-VA Whole 15.8 195,277

Harford Co Baltimore, MD Whole 16.6 218,590

Howard Co Baltimore, MD Whole 16.6 247,842

Montgomery Co Washington, DC-MD-VA Whole 15.8 873,341
Regulatory Impact Analysis 

A2-4

County Area Name County 

NAWhole/Part 

Design Value 

(μg/m3

) 

Pop (2000) 

Prince George's Co Washington, DC-MD-VA Whole 15.8 801,515

Washington Co Martinsburg, WV-Hagerstown, 

MD 

Whole 16.3 131,923

MICHIGAN 

Livingston Co Detroit-Ann Arbor, MI Whole 19.5 156,951

Macomb Co Detroit-Ann Arbor, MI Whole 19.5 788,149

Monroe Co Detroit-Ann Arbor, MI Whole 19.5 145,945

Oakland Co Detroit-Ann Arbor, MI Whole 19.5 1,194,156

St Clair Co Detroit-Ann Arbor, MI Whole 19.5 164,235

Washtenaw Co Detroit-Ann Arbor, MI Whole 19.5 322,895

Wayne Co Detroit-Ann Arbor, MI Whole 19.5 2,061,162

MISSOURI 

Franklin Co St. Louis, MO-IL Whole 17.5 93,807

Jefferson Co St. Louis, MO-IL Whole 17.5 198,099

St Charles Co St. Louis, MO-IL Whole 17.5 283,883

St Louis St. Louis, MO-IL Whole 17.5 348,189

St Louis Co St. Louis, MO-IL Whole 17.5 1,016,315

MONTANA 

Lincoln Co Libby, MT Part 16.2 2,626

NEW JERSEY 

Bergen Co New York-N. New JerseyLong Island, NY-NJ-CT Whole 17.7 884,118

Burlington Co Philadelphia-Wilmington, PANJ-DE Whole 16.2 423,394

Camden Co Philadelphia-Wilmington, PANJ-DE Whole 16.2 508,932

Essex Co New York-N. New JerseyLong Island, NY-NJ-CT Whole 17.7 793,633

Gloucester Co Philadelphia-Wilmington, PANJ-DE Whole 16.2 254,673

Hudson Co New York-N. New JerseyLong Island, NY-NJ-CT Whole 17.7 608,975

Mercer Co New York-N. New JerseyLong Island, NY-NJ-CT Whole 17.7 350,761

Middlesex Co New York-N. New JerseyLong Island, NY-NJ-CT Whole 17.7 750,162

Monmouth Co New York-N. New JerseyLong Island, NY-NJ-CT Whole 17.7 615,301

Morris Co New York-N. New JerseyLong Island, NY-NJ-CT Whole 17.7 470,212

Passaic Co New York-N. New JerseyLong Island, NY-NJ-CT Whole 17.7 489,049

Somerset Co New York-N. New JerseyLong Island, NY-NJ-CT Whole 17.7 297,490

Union Co New York-N. New Jersey- Whole 17.7 522,541
Air Quality and Resulting Health and Welfare Effects 

A2-5

County Area Name County 

NAWhole/Part 

Design Value 

(μg/m3

) 

Pop (2000) 

Long Island, NY-NJ-CT 

New York 

Bronx Co New York-N. New JerseyLong Island, NY-NJ-CT Whole 17.7 1,332,650

Kings Co New York-N. New JerseyLong Island, NY-NJ-CT Whole 17.7 2,465,326

Nassau Co New York-N. New JerseyLong Island, NY-NJ-CT Whole 17.7 1,334,544

New York Co New York-N. New JerseyLong Island, NY-NJ-CT Whole 17.7 1,537,195

Orange Co New York-N. New JerseyLong Island, NY-NJ-CT Whole 17.7 341,367

Queens Co New York-N. New JerseyLong Island, NY-NJ-CT Whole 17.7 2,229,379

Richmond Co New York-N. New JerseyLong Island, NY-NJ-CT Whole 17.7 443,728

Rockland Co New York-N. New JerseyLong Island, NY-NJ-CT Whole 17.7 286,753

Suffolk Co New York-N. New JerseyLong Island, NY-NJ-CT Whole 17.7 1,419,369

Westchester Co New York-N. New JerseyLong Island, NY-NJ-CT Whole 17.7 923,459

NORTH CAROLINA 

Catawba Co Hickory, NC Whole 15.5 141,685

Davidson Co Greensboro-Winston SalemHigh Point, NC Whole 15.8 147,246

Guilford Co Greensboro-Winston SalemHigh Point, NC Whole 15.8 421,048

OHIO 

Adams Co Huntington-Ashland, WV-KYOH Part 17.2 2,374

Ashtabula Co Cleveland-Akron-Lorain, OH Part 18.3 23,239

Belmont Co Wheeling, WV-OH Whole 15.7 70,226

Butler Co Cincinnati-Hamilton, OH-KYIN Whole 17.8 332,807

Clark Co Dayton-Springfield, OH Whole 15.2 144,742

Clermont Co Cincinnati-Hamilton, OH-KYIN Whole 17.8 177,977

Coshocton Co Columbus, OH Part 16.7 1,286

Cuyahoga Co Cleveland-Akron-Lorain, OH Whole 18.3 1,393,978

Delaware Co Columbus, OH Whole 16.7 109,989

Fairfield Co Columbus, OH Whole 16.7 122,759

Franklin Co Columbus, OH Whole 16.7 1,068,978

Gallia Co Huntington-Ashland, WV-KYOH Part 17.2 3,625

Greene Co Dayton-Springfield, OH Whole 15.2 147,886

Hamilton Co Cincinnati-Hamilton, OH-KY- Whole 17.8 845,303
Regulatory Impact Analysis 

A2-6

County Area Name County 

NAWhole/Part 

Design Value 

(μg/m3

) 

Pop (2000) 

IN 

Jefferson Co Steubenville-Weirton, OH-WV Whole 17.8 73,894

Lake Co Cleveland-Akron-Lorain, OH Whole 18.3 227,511

Lawrence Co Huntington-Ashland, WV-KYOH Whole 17.2 62,319

Licking Co Columbus, OH Whole 16.7 145,491

Lorain Co Cleveland-Akron-Lorain, OH Whole 18.3 284,664

Medina Co Cleveland-Akron-Lorain, OH Whole 18.3 151,095

Montgomery Co Dayton-Springfield, OH Whole 15.2 559,062

Portage Co Cleveland-Akron-Lorain, OH Whole 18.3 152,061

Scioto Co Huntington-Ashland, WV-KYOH Whole 17.2 79,195

Stark Co Canton-Massillon, OH Whole 17.3 378,098

Summit Co Cleveland-Akron-Lorain, OH Whole 18.3 542,899

Warren Co Cincinnati-Hamilton, OH-KYIN Whole 17.8 158,383

Washington Co Parkersburg-Marietta, WV-OH Whole 16 63,251

PENNSYLVANIA 

Allegheny Co Liberty-Clairton, PA Part 21.2 21,600

Allegheny Co Pittsburgh-Beaver Valley, PA Part 16.9 1,260,066

Armstrong Co Pittsburgh-Beaver Valley, PA Part 16.9 3,691

Beaver Co Pittsburgh-Beaver Valley, PA Whole 16.9 181,412

Berks Co Reading, PA Whole 16.4 373,638

Bucks Co Philadelphia-Wilmington, PANJ-DE Whole 16.2 597,635

Butler Co Pittsburgh-Beaver Valley, PA Whole 16.9 174,083

Cambria Co Johnstown, PA Whole 15.8 152,598

Chester Co Philadelphia-Wilmington, PANJ-DE Whole 16.2 433,501

Cumberland Co Harrisburg-Lebanon-Carlisle, 

PA 

Whole 15.7 213,674

Dauphin Co Harrisburg-Lebanon-Carlisle, 

PA 

Whole 15.7 251,798

Delaware Co Philadelphia-Wilmington, PANJ-DE Whole 16.2 550,864

Greene Co Pittsburgh-Beaver Valley, PA Part 16.9 1,714

Indiana Co Johnstown, PA Part 15.8 11,833

Lancaster Co Lancaster, PA Whole 17 470,658

Lawrence Co Pittsburgh-Beaver Valley, PA Part 16.9 1,198

Lebanon Co Harrisburg-Lebanon-Carlisle, 

PA 

Whole 15.7 120,327

Montgomery Co Philadelphia-Wilmington, PANJ-DE Whole 16.2 750,097

Philadelphia Co Philadelphia-Wilmington, PA- Whole 16.2 1,517,550
Air Quality and Resulting Health and Welfare Effects 

A2-7

County Area Name County 

NAWhole/Part 

Design Value 

(μg/m3

) 

Pop (2000) 

NJ-DE 

Washington Co Pittsburgh-Beaver Valley, PA Whole 16.9 202,897

Westmoreland Co Pittsburgh-Beaver Valley, PA Whole 16.9 369,993

York Co York, PA Whole 17 381,751

TENNESSEE 

Anderson Co Knoxville, TN Whole 16.4 71,330

Blount Co Knoxville, TN Whole 16.4 105,823

Hamilton Co Chattanooga, AL-TN-GA Whole 16.1 307,896

Knox Co Knoxville, TN Whole 16.4 382,032

Loudon Co Knoxville, TN Whole 16.4 39,086

Roane Co Knoxville, TN Part 16.4 737

VIRGINIA 

Alexandria Washington, DC-MD-VA Whole 15.8 128,283

Arlington Co Washington, DC-MD-VA Whole 15.8 189,453

Fairfax Washington, DC-MD-VA Whole 15.8 21,498

Fairfax Co Washington, DC-MD-VA Whole 15.8 969,749

Falls Church Washington, DC-MD-VA Whole 15.8 10,377

Loudoun Co Washington, DC-MD-VA Whole 15.8 169,599

Manassas Washington, DC-MD-VA Whole 15.8 35,135

Manassas Park Washington, DC-MD-VA Whole 15.8 10,290

Prince William Co Washington, DC-MD-VA Whole 15.8 280,813

WEST VIRGINIA 

Berkeley Co Martinsburg, WV-Hagerstown, 

MD 

Whole 16.3 75,905

Brooke Co Steubenville-Weirton, OH-WV Whole 17.8 25,447

Cabell Co Huntington-Ashland, WV-KYOH Whole 17.2 96,784

Hancock Co Steubenville-Weirton, OH-WV Whole 17.8 32,667

Kanawha Co Charleston, WV Whole 17.1 200,073

Marshall Co Wheeling, WV-OH Whole 15.7 35,519

Mason Co Huntington-Ashland, WV-KYOH Part 17.2 2,774

Ohio Co Wheeling, WV-OH Whole 15.7 47,427

Pleasants Co Parkersburg-Marietta, WV-OH Part 16 1,675

Putnam Co Charleston, WV Whole 17.1 51,589

Wayne Co Huntington-Ashland, WV-KYOH Whole 17.2 42,903

Wood Co Parkersburg-Marietta, WV-OH Whole 16 87,986

TOTAL 208 Counties 88,394,361
Regulatory Impact Analysis 

A2-8

Appendix 2B - Current 8-Hour Ozone Nonattainment Areas 

Table 2B 1997 8-Hour Ozone Nonattainment Areas and Populations (Data is current through October 

2007 and Population Numbers are from 2000 Census Data) 

Area Name Category/ 2000 Pop

Class

 Albany-Schenectady-Troy, NY Subpart 1 923,778

 Allegan Co, MI Subpart 1 105,665

 Allentown-Bethlehem-Easton, PA Subpart 1 637,958

 Amador and Calaveras Cos (Central Mtn), CA Subpart 1 75,654

 Atlanta, GA Marginal 4,228,492

 Baltimore, MD Moderate 2,512,431

 Baton Rouge, LA Marginal 636,214

 Beaumont-Port Arthur, TX Marginal 385,090

 Berkeley and Jefferson Counties, WV Subpart 1 EAC 118,095

 Boston-Lawrence-Worcester (E. MA), MA Moderate 5,534,130

 Boston-Manchester-Portsmouth(SE),NH Moderate 696,713

 Buffalo-Niagara Falls, NY Subpart 1 1,170,111

 Charlotte-Gastonia-Rock Hill, NC-SC Moderate 1,476,564

 Chattanooga, TN-GA Subpart 1 EAC 372,264

 Chicago-Gary-Lake County, IL-IN Moderate 8,757,808

 Chico, CA Subpart 1 203,171

 Cincinnati-Hamilton, OH-KY-IN Subpart 1 1,891,518

 Clearfield and Indiana Cos, PA Subpart 1 172,987

 Cleveland-Akron-Lorain, OH Moderate 2,945,831

 Columbia, SC Subpart 1 EAC 494,518

 Columbus, OH Subpart 1 1,541,930

 Dallas-Fort Worth, TX Moderate 5,030,828

 Denver-Boulder-Greeley-Ft Collins-Love., CO Subpart 1 EAC 2,811,580

 Detroit-Ann Arbor, MI Marginal 4,932,383

 Door Co, WI Subpart 1 27,961

 Erie, PA Subpart 1 280,843

 Essex Co (Whiteface Mtn), NY Subpart 1 1,000

 Fayetteville, NC Subpart 1 EAC 302,963

 Frederick Co, VA Subpart 1 EAC 82,794

 Greater Connecticut, CT Moderate 1,543,919

 Greene Co, PA Subpart 1 40,672

 Greensboro-Winston Salem-High Point, NC Marginal EAC 1,285,879

 Greenville-Spartanburg-Anderson, SC Subpart 1 EAC 799,147

 Haywood and Swain Cos (Great Smoky NP), NC Subpart 1 288

 Hickory-Morganton-Lenoir, NC Subpart 1 EAC 309,512

 Houston-Galveston-Brazoria, TX Moderate 4,669,571

 Imperial Co, CA Marginal 142,361

 Indianapolis, IN Subpart 1 1,607,486

 Jamestown, NY Subpart 1 139,750

 Jefferson Co, NY Moderate 111,738

 Johnson City-Kingsport-Bristol, TN Subpart 1 EAC 206,611

 Kern Co (Eastern Kern), CA Subpart 1 99,251

 Kewaunee Co, WI Subpart 1 20,187

 Knoxville, TN Subpart 1 713,755

 Las Vegas, NV Subpart 1 1,348,864

 Los Angeles-San Bernardino Cos(W Mojave),CA Moderate 656,408
Air Quality and Resulting Health and Welfare Effects 

A2-9

Area Name Category/ 2000 Pop

Class

 Los Angeles South Coast Air Basin, CA Severe 17 14,593,587

 Macon, GA Subpart 1 153,937

 Manitowoc Co, WI Subpart 1 82,887

 Mariposa and Tuolumne Cos (Southern Mtn),CA Subpart 1 71,631

 Memphis, TN-AR Marginal 948,338

 Milwaukee-Racine, WI Moderate 1,839,149

 Murray Co (Chattahoochee Nat Forest), GA Subpart 1 1,000

 Nashville, TN Subpart 1 EAC 1,097,810

 Nevada Co. (Western Part), CA Subpart 1 77,735

 New York-N. New Jersey-Long Island,NY-NJ-CT Moderate 19,634,122

 Philadelphia-Wilmin-Atlantic Ci,PA-NJ-MD-DE Moderate 7,333,475

 Phoenix-Mesa, AZ Subpart 1 3,086,045

 Pittsburgh-Beaver Valley, PA Subpart 1 2,431,087

 Poughkeepsie, NY Moderate 717,262

 Providence (All RI), RI Moderate 1,048,319

 Raleigh-Durham-Chapel Hill, NC Subpart 1 1,244,053

 Riverside Co, (Coachella Valley), CA Serious 324,750

 Roanoke, VA Subpart 1 EAC 235,932

 Rochester, NY Subpart 1 1,098,201

 Sacramento Metro, CA Serious 1,978,348

 San Antonio, TX Subpart 1 EAC 1,559,975

 San Diego, CA Subpart 1 2,813,431

 San Francisco Bay Area, CA Marginal 6,541,828

 San Joaquin Valley, CA Serious 3,191,367

 Scranton-Wilkes-Barre, PA Subpart 1 699,312

 Sheboygan, WI Moderate 112,646

 Springfield (Western MA), MA Moderate 814,967

 St Louis, MO-IL Moderate 2,504,603

 State College, PA Subpart 1 135,758

 Sutter Co (Sutter Buttes), CA Subpart 1 1

 Ventura Co, CA Moderate 753,197

 Washington, DC-MD-VA Moderate 4,452,498

 Washington Co (Hagerstown), MD Subpart 1 EAC 131,923

 York, PA Subpart 1 473,043

 Youngstown-Warren-Sharon, OH-PA (PA Portion) Subpart 1 120,293

Total (81 areas) 144,349,183 
Regulatory Impact Analysis 

A2-10

References 

 

1 Robinson, A.L.; Donahue, N.M.; Rogge, W.F. J Geophys Res 2006, 111: D03302. 

2 Second Draft Technical Report (Revision 1), Compilation of Existing Studies on Source 

Apportionment for PM2.5, August 22, 2003 (Contract No. 68-D-02-061; Work Assignment 1-

05). http://www.epa.gov/oar/oaqps/pm25/docs/compsareports.pdf

3 Chow, J. C.; Fairley, D.; Watson, J. G.; de Mandel, R.; Fujita, E. M.; Lowenthal, D. H.; Lu, 

Z.; Frazier, C. A.; Long, G.; Cordova, J. J. Environ. Eng. 1995, 21, 378-387.

4 Magliano, K. L.; Hughes, V. M.; Chinkin, L. R.; Coe, D. L.; Haste, T. L.; Kumar, N.; 

Lurmann, F. W. Atmos. Environ. 1999, 33 (29), 4757-4773.

5 Schauer, J. J.; Cass, G. R. Environ. Sci. Technol. 2000, 34 (9), 1821-1832.

6

Chow, J. C.; Watson, J. G.; Lowenthal, D. H.; Countess, R. J. Atmos. Environ. 1996, 30 (9), 

1489-1499.

7 South Coast Air Quality Management District. 1997 air quality maintenance plan: Appendix 

V, Modeling and attainment demonstrations. Prepared by South Coast Air Quality 

Management District: Diamond Bar, CA, 1996.

8 Chow, J. C.; Watson, J. G.; Green, M. C.; Lowenthal, D. H.; Bates, B. A.; Oslund, W.; 

Torres, G. Atmos. Environ. 2000, 34 (11), 1833-1843.

9 Chow, J. C.; Watson, J. G.; Green, M. C.; Lowenthal, D. H.; DuBois, D. W.; Kohl, S. D.; 

Egami, R. T.; Gillies, J. A.; Rogers, C. F.; Frazier, C. A.; Cates, W. JAWMA 1999, 49 (6), 

641-654.

10 Watson, J. G.; Fujita, E. M.; Chow, J. C.; Zielinska, B.; Richards, L. W.; Neff, W. D.; 

Dietrich, D. Northern Front Range Air Quality Study. Final report. Prepared for Colorado 

State University, Fort Collins, CO, by Desert Research Institute: Reno, NV, 1998.

11 Malm, W. C.; Gebhart, K. A. JAWMA 1997, 47 (3), 250-268.

12 Eatough, D. J.; Farber, R. J.; Watson, J. G. JAWMA 2000, 50 (5), 759-774. 

13 U.S. EPA (2004) Air Quality Criteria for Particulate Matter (Oct 2004), Volume I 

Document No. EPA600/P-99/002aF and Volume II Document No. EPA600/P-99/002bF.

14 U.S. EPA (2005) Review of the National Ambient Air Quality Standard for Particulate 

Matter: Policy Assessment of Scientific and Technical Information, OAQPS Staff Paper. 

EPA-452/R-05-005.

15 U.S. EPA 2006. Provisional Assessment of Recent Studies on Health Effects of Particulate 

Matter Exposure. EPA/600/R-06/063.

16 Laden F; Neas LM; Dockery DW; et al. 2000. “Association of fine particulate matter from 

different sources with daily mortality in six U.S. cities.” Environ Health Perspectives 

108(10):941-947.

17 Schwartz J; Laden F; Zanobetti A. 2002. “The concentration-response relation between 

PM(2.5) and daily deaths.” Environ Health Perspect 110(10): 1025-1029.
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18 Janssen NA; Schwartz J; Zanobetti A.; et al. 2002. “Air conditioning and source-specific 

particles as modifiers of the effect of PM10 on hospital admissions for heart and lung 

disease.” Environ Health Perspect 110(1):43-49.

19 Dockery, DW; Pope, CA, III; Xu, X; et al. 1993. “An association between air pollution and 

mortality in six U.S. cities.” N Engl J Med 329:1753-1759.

20 Pope, CA, III; Burnett, RT; Thun, MJ; Calle, EE; et al. 2002. “Lung cancer, 

cardiopulmonary mortality, and long-term exposure to fine particulate air pollution.” J Am 

Med Assoc 287: 1132-1141.

21 Krewski, D; Burnett, RT; Goldberg, M S; et al. 2000. “Reanalysis of the Harvard Six Cities 

study and the American Cancer Society study of particulate air pollution and mortality. A 

special report of the Institute's Particle Epidemiology Reanalysis Project.” Cambridge, MA: 

Health Effects Institute.

22 Jerrett, M; Burnett, RT; Ma, R; et al. 2005. “Spatial Analysis of Air Pollution and Mortality 

in Los Angeles.” Epidemiology. 16(6):727-736.

23 Künzli, N.; Jerrett, M.; Mack, W.J.; et al.(2004) Ambient air pollution and atherosclerosis in 

Los Angeles. Environ Health Perspect doi:10.1289/ehp.7523 [Available at http://dx.doi.org/].

24 Riediker, M.; Cascio, W.E.; Griggs, T.R.; et al. 2004. “Particulate matter exposure in cars is 

associated with cardiovascular effects in healthy young men.” Am J Respir Crit Care Med 

169: 934–940.

25 Van Vliet, P.; Knape, M.; de Hartog, J.; Janssen, N.; Harssema, H.; Brunekreef, B. (1997). 

Motor vehicle exhaust and chronic respiratory symptoms in children living near freeways. 

Env. Research 74: 122-132.

26 Brunekreef, B., Janssen, N.A.H.; de Hartog, J.; Harssema, H.; Knape, M.; van Vliet, P. 

(1997). Air pollution from truck traffic and lung function in children living near roadways. 

Epidemiology 8:298-303.

27 Kim, J.J.; Smorodinsky, S.; Lipsett, M.; Singer, B.C.; Hodgson, A.T.; Ostro, B (2004). 

Traffic-related air pollution near busy roads: The East Bay children’s respiratory health 

study. Am. J. Respir. Crit. Care Med. 170: 520-526.

28 State of California Air Resources Board. Roseville Rail Yard Study. Stationary Source 

Division, October 14, 2004. This document is available electronically at: 

http://www.arb.ca.gov/diesel/documents/rrstudy.htm 

29 State of California Air Resources Board. Diesel Particulate Matter Exposure Assessment 

Study for the Ports of Los Angeles and Long Beach, April 2006. This document is available 

electronically at: 

ftp://ftp.arb.ca.gov/carbis/msprog/offroad/marinevess/documents/portstudy0406.pdf
Regulatory Impact Analysis 

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30 For example, see letter dated September 23, 2006 from Northeast States for Coordinated Air 

Use Management to Administrator Stephen L. Johnson; September 7, 2006 letter from 

Executive Officer of the California Air Resources Board to Acting Assistant Administrator 

William L. Wehrum; August 9, 2006 letter from State and Territorial Air Pollution Program 

Administrators and Association of Local Air Pollution Control Officials (and other 

organizations) to Administrator Stephen L. Johnson; January 20, 2006 letter from Executive 

Director, Puget Sound Clean Air Agency to Administrator Stephen L. Johnson; June 30, 

2005 letter from Western Regional Air Partnership to Administrator Stephen L. Johnson.

31 National Research Council, 1993. Protecting Visibility in National Parks and Wilderness 

Areas. National Academy of Sciences Committee on Haze in National Parks and Wilderness 

Areas. National Academy Press, Washington, DC. This book is available electronically at 

http://www.nap.edu/books/0309048443/html/ and is available in Docket EPA-HQ-OAR2004-0008.

32 U.S. EPA , National Ambient Air Quality Standards for Particulate Matter; Proposed Rule; 

January 17, 2006, Vol71 p 2676. This information is available electronically at: 

http://epa.gov/fedrgstr/EPA-AIR/2006/January/Day-17/a177.pdf 

33 U.S. EPA (2004) Air Quality Criteria for Particulate Matter (Oct 2004), Volume I 

Document No. EPA600/P-99/002aF and Volume II Document No. EPA600/P-99/002bF. 

This document is available in Docket EPA-HQ-OAR-2004-0008.

34 U.S. EPA (2005) Review of the National Ambient Air Quality Standard for Particulate 

Matter: Policy Assessment of Scientific and Technical Information, OAQPS Staff Paper. 

EPA-452/R-05-005. This document is available in Docket EPA-HQ-OAR-2004-0008.

35 U.S. EPA. 1993. Effects of the 1990 Clean Air Act Amendments on Visibility in Class I 

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36 U.S. EPA (2002) Latest Findings on National Air Quality – 2002 Status and Trends. EPA 

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37 National Park Service. Air Quality in the National Parks, Second edition. NPS, Air 

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38 U.S. EPA (2002) Latest Findings on National Air Quality – 2002 Status and Trends. EPA 

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39 Environmental Protection Agency (2003). Response Of Surface Water Chemistry to the 

Clean Air Act Amendments of 1990. National Health and Environmental Effects Research 

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40 Fenn, M.E. and Blubaugh, T.J. (2005) Winter Deposition of Nitrogen and Sulfur in the 

Eastern Columbia River Gorge National Scenic Area, USDA Forest Service. 

41 Galloway, J. N.; Cowling, E. B. (2002). Reactive nitrogen and the world: 200 years of 

change. Ambio 31: 64–71.
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A2-13

 

42 Bricker, Suzanne B., et al., National Estuarine Eutrophication Assessment, Effects of 

Nutrient Enrichment in the Nation’s Estuaries, National Ocean Service, National Oceanic and 

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43 Smith, W.H. 1991. “Air pollution and Forest Damage.” Chemical Engineering News, 

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44 Gawel, J.E.; Ahner, B.A.; Friedland, A.J.; and Morel, F.M.M. 1996. “Role for heavy metals 

in forest decline indicated by phytochelatin measurements.” Nature, 381: 64-65. 

45 Cotrufo, M.F.; DeSanto, A.V.; Alfani, A.; et al. 1995. “Effects of urban heavy metal 

pollution on organic matter decomposition in Quercus ilix L. woods.” Environmental 

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46 Niklinska, M.; Laskowski, R.; Maryanski, M. 1998. “Effect of heavy metals and storage 

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47 Mason, R.P. and Sullivan, K.A. 1997. “Mercury in Lake Michigan.” Environmental Science 

& Technology, 31: 942-947. (from Delta Report “Atmospheric deposition of toxics to the 

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48 Landis, M.S. and Keeler, G.J. 2002. “Atmospheric mercury deposition to Lake Michigan 

during the Lake Michigan Mass Balance Study.” Environmental Science & Technology, 21: 

4518-24.

49 U.S. EPA. 2000. EPA453/R-00-005, “Deposition of Air Pollutants to the Great Waters: 

Third Report to Congress,” Office of Air Quality Planning and Standards, Research Triangle 

Park, North Carolina.

50 NSTC 1999 

51 Callender, E. and Rice, K.C. 2000. “The Urban Environmental Gradient: Anthropogenic 

Influences on the Spatial and Temporal Distributions of Lead and Zinc in Sediments.” 

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52 Rice, K.C. 1999. “Trace Element Concentrations in Streambed Sediment Across the 

Conterminous United States.” Environmental Science & Technology, 33: 2499-2504. 

53 Ely, JC; Neal, CR; Kulpa, CF; et al. 2001. “Implications of Platinum-Group Element 

Accumulation along U.S. Roads from Catalytic-Converter Attrition.” Environ. Sci. Technol. 

35: 3816-3822.

54 U.S. EPA. 1998. EPA454/R-98-014, “Locating and Estimating Air Emissions from Sources 

of Polycyclic Organic Matter,” Office of Air Quality Planning and Standards, Research 

Triangle Park, North Carolina.

55 U.S. EPA. 1998. EPA454/R-98-014, “Locating and Estimating Air Emissions from Sources 

of Polycyclic Organic Matter,” Office of Air Quality Planning and Standards, Research 

Triangle Park, North Carolina.
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56 Simcik, M.F.; Eisenreich, S.J.; Golden, K.A.; et al. 1996. “Atmospheric Loading of 

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57 Simcik, M.F.; Eisenreich, S.J.; and Lioy, P.J. 1999. “Source apportionment and source/sink 

relationship of PAHs in the coastal atmosphere of Chicago and Lake Michigan.” Atmospheric 

Environment, 33: 5071-5079.

58 Arzayus, K.M.; Dickhut, R.M.; and Canuel, E.A. 2001. “Fate of Atmospherically Deposited 

Polycyclic Aromatic Hydrocarbons (PAHs) in Chesapeake Bay.” Environmental Science & 

Technology, 35, 2178-2183. 

59 Park, J.S.; Wade, T.L.; and Sweet, S. 2001. “Atmospheric distribution of polycyclic 

aromatic hydrocarbons and deposition to Galveston Bay, Texas, USA.” Atmospheric 

Environment, 35: 3241-3249.

60 Poor, N.; Tremblay, R.; Kay, H.; et al. 2002. “Atmospheric concentrations and dry 

deposition rates of polycyclic aromatic hydrocarbons (PAHs) for Tampa Bay, Florida, USA.” 

Atmospheric Environment 38: 6005-6015.

61 Arzayus, K.M.; Dickhut, R.M.; and Canuel, E.A. 2001. “Fate of Atmospherically Deposited 

Polycyclic Aromatic Hydrocarbons (PAHs) in Chesapeake Bay.” Environmental Science & 

Technology, 35, 2178-2183.

62 U.S. EPA. 2000. EPA453/R-00-005, “Deposition of Air Pollutants to the Great Waters: 

Third Report to Congress,” Office of Air Quality Planning and Standards, Research Triangle 

Park, North Carolina.

63 Van Metre, P.C.; Mahler, B.J.; and Furlong, E.T. 2000. “Urban Sprawl Leaves its PAH 

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64 Cousins, I.T.; Beck, A.J.; and Jones, K.C. 1999. “A review of the processes involved in the 

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the Total Environment, 228: 5-24.

65 Tuhackova, J. et al. (2001) Hydrocarbon deposition and soil microflora as affected by 

highway traffic. Environmental Pollution, 113: 255-262. 

66 U.S. EPA. 1996. Air Quality Criteria for Ozone and Related Photochemical Oxidants, 

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67 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. 

EPA, Washington, DC, EPA/600/R-05/004aF-cF, 2006.

68 U.S. EPA, Review of the National Ambient Air Quality Standards for Ozone: Policy 

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69 Bates, D.V.; Baker-Anderson, M.; Sizto, R. (1990) Asthma attack periodicity: a study of 

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70 Thurston, G.D.; Ito, K.; Kinney, P.L.; Lippmann, M. (1992) A multi-year study of air 

pollution and respiratory hospital admissions in three New York State metropolitan areas: 

results for 1988 and 1989 summers. J. Exposure Anal. Environ. Epidemiol. 2:429-450.

71 Thurston, G.D.; Ito, K.; Hayes, C.G.; Bates, D.V.; Lippmann, M. (1994) Respiratory 

hospital admissions and summertime haze air pollution in Toronto, Ontario: consideration of 

the role of acid aerosols. Environ. Res. 65: 271-290.

72 Lipfert, F.W.; Hammerstrom, T. (1992) Temporal patterns in air pollution and hospital 

admissions. Environ. Res. 59: 374-399.

73 Burnett, R.T.; Dales, R.E.; Raizenne, M.E.; Krewski, D.; Summers, P.W.; Roberts, G.R.; 

Raad-Young, M.; Dann,T.; Brook, J. (1994) Effects of low ambient levels of ozone and 

sulfates on the frequency of respiratory admissions to Ontario hospitals. Environ. Res. 65: 

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74 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. 

EPA, Washington, DC, EPA/600/R-05/004aF-cF, 2006.

75 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. 

EPA, Washington, DC, EPA/600/R-05/004aF-cF, 2006.

76 Devlin, R. B.; McDonnell, W. F.; Mann, R.; Becker, S.; House, D. E.; Schreinemachers, D.; 

Koren, H. S. (1991) Exposure of humans to ambient levels of ozone for 6.6 hours causes 

cellullar and biochemical changes in the lung. Am. J. Respir. Cell Mol. Biol. 4: 72-81.

77 Koren, H. S.; Devlin, R. B.; Becker, S.; Perez, R.; McDonnell, W. F. (1991) Timedependent changes of markers associated with inflammation in the lungs of humans exposed 

to ambient levels of ozone. Toxicol. Pathol. 19: 406-411.

78 Koren, H. S.; Devlin, R. B.; Graham, D. E.; Mann, R.; McGee, M. P.; Horstman, D. H.; 

Kozumbo, W. J.; Becker, S.; House, D. E.; McDonnell, W. F.; Bromberg, P. A. (1989a) 

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79 Schelegle, E.S.; Siefkin, A.D.; McDonald, R.J. (1991) Time course of ozone-induced 

neutrophilia in normal humans. Am. Rev. Respir. Dis. 143:1353-1358.

80 U.S. EPA (1996). Air Quality Criteria for Ozone and Related Photochemical Oxidants, 

EPA600-P-93-004aF; (See page 7-171).

81 Hodgkin, J.E.; Abbey, D.E.; Euler, G.L.; Magie, A.R. (1984) COPD prevalence in 

nonsmokers in high and low photochemical air pollution areas. Chest 86: 830-838.

82 Euler, G.L.; Abbey, D.E.; Hodgkin, J.E.; Magie, A.R. (1988) Chronic obstructive 

pulmonary disease symptom effects of long-term cumulative exposure to ambient levels of 

total oxidants and nitrogen dioxide in California Seventh-day Adventist residents. Arch. 

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83 Abbey, D.E.; Petersen, F.; Mills, P.K.; Beeson, W.L. (1993) Long-term ambient 

concentrations of total suspended particulates, ozone, and sulfur dioxide and respiratory 

symptoms in a nonsmoking population. Arch. Environ. Health 48: 33-46.

84 U.S. EPA, Review of the National Ambient Air Quality Standards for Ozone: Policy 

Assessment of Scientific and Technical Information, OAQPS Staff Paper, Washington, DC, 

EPA-452/R-07-003, January 2007. 

85 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. 

EPA, Washington, DC, EPA/600/R-05/004aF-cF, 2006.

86 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. 

EPA, Washington, DC, EPA/600/R-05/004aF-cF, 2006.

87 Avol, E. L.; Trim, S. C.; Little, D. E.; Spier, C. E.; Smith, M. N.; Peng, R.-C.; Linn, W. S.; 

Hackney, J. D.; Gross, K. B.; D'Arcy, J. B.; Gibbons, D.; Higgins, I. T. T. (1990) Ozone 

exposure and lung function in children attending a southern California summer camp. 

Presented at: 83rd annual meeting and exhibition of the Air & Waste Management 

Association; June; Pittsburgh, PA. Pittsburgh, PA: Air & Waste Management Association; 

paper no. 90-150.3.

88 Higgins, I. T. T.; D'Arcy, J. B.; Gibbons, D. I.; Avol, E. L.; Gross, K. B. (1990) Effect of 

exposures to ambient ozone on ventilatory lung function in children. Am. Rev. Respir. Dis. 

141: 1136-1146.

89 Raizenne, M. E.; Burnett, R. T.; Stern, B.; Franklin, C. A.; Spengler, J. D. (1989) Acute 

lung function responses to ambient acid aerosol exposures in children. Environ. Health 

Perspect. 79: 179-185.

90 Raizenne, M.; Stern, B.; Burnett, R.; Spengler, J. (1987) Acute respiratory function and 

transported air pollutants: observational studies. Presented at: 80th annual meeting of the Air 

Pollution Control Association; June; New York, NY. Pittsburgh, PA: Air Pollution Control 

Association; paper no. 87-32.6.

91 Spektor, D. M.; Lippmann, M. (1991) Health effects of ambient ozone on healthy children 

at a summer camp. In: Berglund, R. L.; Lawson, D. R.; McKee, D. J., eds. Tropospheric 

ozone and the environment: papers from an international conference; March 1990; Los 

Angeles, CA. Pittsburgh, PA: Air & Waste Management Association; pp. 83-89. (A&WMA 

transaction series no. TR-19).

92 Spektor, D. M.; Thurston, G. D.; Mao, J.; He, D.; Hayes, C.; Lippmann, M. (1991) Effects 

of single- and multiday ozone exposures on respiratory function in active normal children. 

Environ. Res. 55: 107-122. 

93 Spektor, D. M.; Lippman, M.; Lioy, P. J.; Thurston, G. D.; Citak, K.; James, D. J.; Bock, 

N.; Speizer, F. E.; Hayes, C. (1988a) Effects of ambient ozone on respiratory function in 

active, normal children. Am. Rev. Respir. Dis. 137: 313-320.

94 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. 

EPA, Washington, DC, EPA/600/R-05/004aF-cF, 2006. 
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95 Hazucha, M. J.; Folinsbee, L. J.; Seal, E., Jr. (1992) Effects of steady-state and variable 

ozone concentration profiles on pulmonary function. Am. Rev. Respir. Dis. 146: 1487-1493. 

96 Horstman, D.H.; Ball, B.A.; Folinsbee, L.J.; Brown, J.; Gerrity, T. (1995) Comparison of 

pulmonary responses of asthmatic and nonasthmatic subjects performing light exercise while 

exposed to a low level of ozone. Toxicol. Ind. Health. 

97 Horstman, D.H.; Folinsbee, L.J.; Ives, P.J.; Abdul-Salaam, S.; McDonnell, W.F. (1990) 

Ozone concentration and pulmonary response relationships for 6.6-hour exposures with five 

hours of moderate exercise to 0.08, 0.10, and 0.12 ppm. Am. Rev. Respir. Dis. 142: 1158-

1163.

98 Intergovernmental Panel on Climate Change (2007). Fourth Assessment Report of the 

Intergovernmental Panel on Climate Change. Cambridge University Press, NY.

99 NARSTO Synthesis Team (2000). An Assessment of Tropospheric Ozone Pollution: A 

North American Perspective. 

100 Attainment demonstration modeling guidelines 

101 For example, see letters in the Air Docket for this rule from American Lung Association, 

Clean Air Trust, California Environmental Protection Agency, New York State Department of 

Environmental Conservation, Texas Commission on Environmental Quality (TCEQ, formerly 

Texas Natural Resources Conservation Commission), State and Territorial Air Pollution 

Program Administrators and the Association of Local Air Pollution Control Officials 

(STAPPA/ALAPCO), Natural Resources Defense Council, Sierra Club, and Union of 

Concerned Scientists.). 

102 U.S. EPA. 1999. The Benefits and Costs of the Clean Air Act, 1990-2010. Prepared for 

U.S. Congress by U.S. EPA, Office of Air and Radiation, Office of Policy Analysis and 

Review, Washington, DC, November; EPA report no. EPA410-R-99-001.

103 Winner, W.E., and C.J. Atkinson. 1986. “Absorption of air pollution by plants, and 

consequences for growth.” Trends in Ecology and Evolution 1:15-18. 

104 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). 

U.S. EPA, Washington, DC, EPA/600/R-05/004aF-cF, 2006.This document is available in 

Docket EPA-HQ-OAR-2005-0036.

105 Tingey, D.T., and Taylor, G.E. 1982. “Variation in plant response to ozone: a conceptual 

model of physiological events.” In: Effects of Gaseous Air Pollution in Agriculture and 

Horticulture (Unsworth, M.H., Omrod, D.P., eds.) London, UK: Butterworth Scientific, 

pp.113-138.

106 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). 

U.S. EPA, Washington, DC, EPA/600/R-05/004aF-cF, 2006.This document is available in 

Docket EPA-HQ-OAR-2005-0036.

107 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). 

U.S. EPA, Washington, DC, EPA/600/R-05/004aF-cF, 2006. This document is available in 

Docket EPA-HQ-OAR-2005-0036.
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108 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). 

U.S. EPA, Washington, DC, EPA/600/R-05/004aF-cF, 2006. This document is available in 

Docket EPA-HQ-OAR-2005-0036.

109 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). 

U.S. EPA, Washington, DC, EPA/600/R-05/004aF-cF, 2006. This document is available in 

Docket EPA-HQ-OAR-2005-0036.

110 Ollinger, S.V., J.D. Aber and P.B. Reich. 1997. “Simulating ozone effects on forest 

productivity: interactions between leaf canopy and stand level processes.” Ecological 

Applications 7:1237-1251. 

111 Winner, W.E., 1994. “Mechanistic analysis of plant responses to air pollution.” Ecological 

Applications, 4(4):651-661.

112 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). 

U.S. EPA, Washington, DC, EPA/600/R-05/004aF-cF, 2006. This document is available in 

Docket EPA-HQ-OAR-2005-0036.

113 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). 

U.S. EPA, Washington, DC, EPA/600/R-05/004aF-cF, 2006. This document is available in 

Docket EPA-HQ-OAR-2005-0036.

114 Fox, S., and R. A. Mickler, eds. 1996. Impact of Air Pollutants on Southern Pine Forests. 

Springer-Verlag, NY, Ecol. Studies, Vol. 118, 513 pp.

115 De Steiguer, J., J. Pye, C. Love. 1990. “Air Pollution Damage to U.S. Forests.” Journal of 

Forestry, Vol 88 (8) pp. 17-22.

116 Pye, J.M. 1988. “Impact of ozone on the growth and yield of trees: A review.” Journal of 

Environmental Quality 17 pp.347-360.

117 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). 

U.S. EPA, Washington, DC, EPA/600/R-05/004aF-cF, 2006. This document is available in 

Docket EPA-HQ-OAR-2005-0036.

118 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). 

U.S. EPA, Washington, DC, EPA/600/R-05/004aF-cF, 2006. This document is available in 

Docket EPA-HQ-OAR-2005-0036.

119 McBride, J.R., P.R. Miller, and R.D. Laven. 1985. “Effects of oxidant air pollutants on 

forest succession in the mixed conifer forest type of southern California.” In: Air Pollutants 

Effects On Forest Ecosystems, Symposium Proceedings, St. P, 1985, p. 157-167. 

120 Miller, P.R., O.C. Taylor, R.G. Wilhour. 1982. Oxidant air pollution effects on a western 

coniferous forest ecosystem. Corvallis, OR: U.S. Environmental Protection Agency, 

Environmental Research Laboratory (EPA600-D-82-276).

121 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). 

U.S. EPA, Washington, DC, EPA/600/R-05/004aF-cF, 2006. This document is available in 

Docket EPA-HQ-OAR-2005-0036.
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122 Kopp, R. J.; Vaughn, W. J.; Hazilla, M.; Carson, R. 1985. “Implications of environmental 

policy for U.S. agriculture: the case of ambient ozone standards.” J. Environ. Manage. 

20:321-331.

123 Adams, R. M.; Hamilton, S. A.; McCarl, B. A. 1986. “The benefits of pollution control: 

the case of ozone and U.S. agriculture.” Am. J. Agric. Econ. 34: 3-19.

124 Adams, R. M.; Glyer, J. D.; Johnson, S. L.; McCarl, B. A. 1989. “A reassessment of the 

economic effects of ozone on U.S. agriculture.” JAPCA 39:960-968.

125 Abt Associates, Inc. 1995. Urban ornamental plants: sensitivity to ozone and potential 

economic losses. U.S. EPA, Office of Air Quality Planning and Standards, Research Triangle 

Park. Under contract to RADIAN Corporation, contract no. 68-D3-0033, WA no. 6. pp. 9-

10.

126 U.S. EPA, Technical Support Document for the Final Locomotive/Marine Rule: Air 

Quality Modeling, Research Triangle Park, NC, EPA 454 R-07-XXX. 

127 U.S. EPA, Byun, D.W., and Ching, J.K.S., Eds, 1999. Science algorithms of EPA Models3 Community Multiscale Air Quality (CMAQ modeling system, EPA/600/R-99/030, Office 

of Research and Development). 

128 Byun, D.W., and Schere, K.L., 2006. Review of the Governing Equations, Computational 

Algorithms, and Other Components of the Models-3 Community Multiscale Air Quality 

(CMAQ) Modeling System, J. Applied Mechanics Reviews, 59 (2), 51-77. 

129 Dennis, R.L., Byun, D.W., Novak, J.H., Galluppi, K.J., Coats, C.J., and Vouk, M.A., 1996. 

The next generation of integrated air quality modeling: EPA’s Models-3, Atmospheric 

Environment, 30, 1925-1938. 

130 Amar, P., Bornstein, R., Feldman, H., Jeffries, H., Steyn, D., Yamartino, R., Zhang, Y., 

2004. Final Report Summary: December 2003 Peer Review of the CMAQ Model. p. 7. 

131 Grell, G., J. Dudhia, and D. Stauffer, 1994: A Description of the Fifth-Generation Penn 

State/NCAR Mesoscale Model (MM5), NCAR/TN-398+STR., 138 pp, National Center for 

Atmospheric Research, Boulder CO. 

132 U.S. Environmental Protection Agency, Byun, D.W., and Ching, J.K.S., Eds, 1999. 

Science algorithms of EPA Models-3 Community Multiscale Air Quality (CMAQ modeling 

system, EPA/600/R-99/030, Office of Research and Development). Please also see: 

http://www.cmascenter.org/ 

133 Yantosca, B., 2004. GEOS-CHEMv7-01-02 User’s Guide, Atmospheric Chemistry 

Modeling Group, Harvard University, Cambridge, MA, October 15, 2004. 

134 Regional Planning Organization regions include: Mid-Atlantic/Northeast Visibility Union 

(MANE-VU), Midwest Regional Planning Organization – Lake Michigan Air Directors 

Consortium (MWRPO-LADCO), Visibility Improvement State and Tribal Association of the 

Southeast (VISTAS), Central States Regional Air Partnership (CENRAP), and Western 

Regional Air Partnership (WRAP). 
Regulatory Impact Analysis 

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135 These other modeling studies represent a wide range of modeling analyses which cover 

various models, model configurations, domains, years and/or episodes, chemical mechanisms, 

and aerosol modules. 

136 U.S. EPA, (2004), “Procedures for Estimating Future PM2.5 Values for the CAIR Final 

Rule by Application of the (Revised) Speciated Modeled Attainment Test (SMAT)- Updated 

11/8/04”. 

137 Sisler 1996

138 U.S. EPA (2003) Integrated Risk Information System File of Acrolein. National Center 

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2003. This material is available electronically at http://www.epa.gov/iris/subst/0364.htm. 

139 U.S. EPA (2006) National-Scale Air Toxics Assessment for 1999. This material is 

available electronically at http://www.epa.gov/ttn/atw/nata1999/risksum.html.

140 U.S. EPA (2006) National-Scale Air Toxics Assessment for 1999. 

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141 U.S. EPA (2002) Health Assessment Document for Diesel Engine Exhaust. EPA/600/8-

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available electronically at http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=29060. 

142 U.S. EPA (2002) Health Assessment Document for Diesel Engine Exhaust. EPA/600/8-

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143 U.S. EPA. (1999). Guidelines for Carcinogen Risk Assessment. Review Draft. NCEA-F0644, July. Risk Assessment Forum, Washington, DC. 

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148 World Health Organization International Program on Chemical Safety (1996). 

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149 California Environmental Protection Agency. (Cal EPA, OEHHA) (1998). Health risk 

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159 Steenland, K., Silverman, D, Hornung, R. (1990). Case-control study of lung cancer and 

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164 Saverin, R. (1999). German potash miners: cancer mortality. Health Effects Institute 

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166 Bhatia, R., Lopipero, P., Smith, A. (1998) Diesel exposure and lung cancer. Epidemiology 

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170 Woskie, SR; Smith, TJ; Hammond, SK; et al. (1988a) Estimation of the diesel exhaust 

exposures of railroad workers: I. Current exposures. Am J Ind Med 13:381-394; II National 

and historical exposures. Am J Ind Med 13:395-404.

171 U.S. EPA (2002), National-Scale Air Toxics Assessment for 1996. This material is 

available electronically at http://www.epa.gov/ttn/atw/nata/.

172 U.S. EPA (2006), National-Scale Air Toxics Assessment for 1999. This material is 

available electronically at http://www.epa.gov/ttn/atw/nata1999/.

173 Ishinishi, N; Kuwabara, N; Takaki, Y; et al. (1988) Long-term inhalation experiments on 

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174 Heinrich, U; Fuhst, R; Rittinghausen, S; et al. (1995) Chronic inhalation exposure of 

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175 Mauderly, JL; Jones, RK; Griffith, WC; et al. (1987) Diesel exhaust is a pulmonary 

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176 Nikula, KJ; Snipes, MB; Barr, EB; et al. (1995) Comparative pulmonary toxicities and 

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177 “Health Assessment Document for Diesel Engine Exhaust,” U.S. Environmental Protection 

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178 Reger, R; Hancock, J; Hankinson, J; et al. (1982) Coal miners exposed to diesel exhaust 

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179 Attfield, MD. (1978) The effect of exposure to silica and diesel exhaust in underground 

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180 Wade, JF, III; Newman, LS. (1993) Diesel asthma: reactive airways disease following 

overexposure to locomotive exhaust. J Occup Med 35:149-154.

181 Kilburn (2000) See HAD Chapter 5-7.

182 Hart, JE, Laden F; Schenker, M.B.; and Garshick, E. Chronic Obstructive Pulmonary 

Disease Mortality in Diesel-Exposed Railroad Workers; Environmental Health Perspective 

July 2006: 1013-1016.

183 Hoek, G.; Brunekreef, B.; Goldbohm, S.; et al. (2002) Association between mortality and 

indicators of traffic-related air pollution in the Netherlands: a cohort study. Lancet 360: 1203-

1209. 

184 R. Delfino

185 U.S. EPA (2006), National-Scale Air Toxics Assessment for 1999. This material is 

available electronically at http://www.epa.gov/ttn/atw/nata1999/.

186 Diesel HAD Page 2-110, 8-12; Woskie, SR; Smith, TJ; Hammond, SK: et al. (1988a) 

Estimation of the DE exposures of railroad workers: II. National and historical exposures. 

Am J Ind Med 12:381-394.

187 Woskie, SR; Smith, TJ; Hammond, SK: et al. (1988a) Estimation of the DE exposures of 

railroad workers: II. National and historical exposures. Am J Ind Med 12:381-394. 

188 Mobile Source Air Toxics Rule (Control of Hazardous Air Pollutants from Mobile 

Sources; 72 FR 8428, February 26, 2007) Regulatory Impact Analysis Chapter 3, “Air Quality 

and Resulting Health and Welfare Effects of Air Pollution from Mobile Sources.” This 

document is available electronically at: http://www.epa.gov/otaq/regs/toxics/420r07002.pdf

189 Sate of California Air Resources Board. Rail Yard Health Risk Assessments and 

Mitigation Measures. This material is available electronically at: 

http://www.arb.ca.gov/railyard/hra/hra.htm.

190 State of California Air Resources Board. Diesel Particulate Matter Exposure Assessment 

Study for the Ports of Los Angeles and Long Beach, April 2006. This document is available 

electronically at: http://www.arb.ca.gov/regact/marine2005/portstudy0406.pdf

191 Hand, R.; Pingkuan, D.; Servin, A.; Hunsaker, L.; Suer, C. (2004) Roseville rail yard 

study. California Air Resources Board. This document is available electronically at: 

http://www.arb.ca.gov/diesel/documents/rrstudy.htm

192 Sate of California Air Resources Board. Rail Yard Health Risk Assessments and 

Mitigation Measures. This material is available electronically at: 

http://www.arb.ca.gov/railyard/hra/hra.htm. 
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193 Di, P.; Servin, A.; Rosenkranz, K.; Schwehr, B.; Tran, H. (2006) Diesel particulate matter 

exposure assessment study for the Ports of Los Angeles and Long Beach. California Air 

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http://www.arb.ca.gov/msprog/offroad/marinevess/marinevess.htm.

194 ICF International. September 28, 2007. Estimation of diesel particulate matter 

concentration isopleths for marine harbor areas and rail yards. Memorandum to EPA under 

Work Assignment Number 0-3, Contract Number EP-C-06-094. This memo is available in 

Docket EPA-HQ-OAR-2003-0190. 

195 ICF International. September 28, 2007. Estimation of diesel particulate matter population 

exposure near selected harbor areas and rail yards. Memorandum to EPA under Work 

Assignment Number 0-3, Contract Number EP-C-06-094. This memo is available in Docket 

EPA-HQ-OAR-2003-0190.

196 http://www.epa.gov/ttn/atw/nata1999/tables.html

197 U.S. EPA. 2000. Integrated Risk Information System File for Benzene. This material is 

available electronically at: http://www.epa.gov/iris/subst/0276.htm.

198 International Agency for Research on Cancer, IARC monographs on the evaluation of 

carcinogenic risk of chemicals to humans, Volume 29, Some industrial chemicals and 

dyestuffs, International Agency for Research on Cancer, World Health Organization, Lyon, 

France, p. 345-389, 1982. 

199 Irons, R.D.; Stillman, W.S.; Colagiovanni, D.B.; Henry, V.A. (1992) Synergistic action of 

the benzene metabolite hydroquinone on myelopoietic stimulating activity of 

granulocyte/macrophage colony-stimulating factor in vitro, Proc. Natl. Acad. Sci. 89:3691-

3695.

200 Aksoy, M. (1989). Hematotoxicity and carcinogenicity of benzene. Environ. Health 

Perspect. 82: 193-197. 

201 Goldstein, B.D. (1988). Benzene toxicity. Occupational medicine. State of the Art 

Reviews. 3: 541-554. 

202 Rothman, N., G.L. Li, M. Dosemeci, W.E. Bechtold, G.E. Marti, Y.Z. Wang, M. Linet, 

L.Q. Xi, W. Lu, M.T. Smith, N. Titenko-Holland, L.P. Zhang, W. Blot, S.N. Yin, and R.B. 

Hayes (1996) Hematotoxicity among Chinese workers heavily exposed to benzene. Am. J. 

Ind. Med. 29: 236-246.

203 U.S. EPA 2002 Toxicological Review of Benzene (Noncancer Effects). Environmental 

Protection Agency, Integrated Risk Information System (IRIS), Research and Development, 

National Center for Environmental Assessment, Washington DC. This material is available 

electronically at http://www.epa.gov/iris/subst/0276.htm.

204 Qu, O.; Shore, R.; Li, G.; Jin, X.; Chen, C.L.; Cohen, B.; Melikian, A.; Eastmond, D.; 

Rappaport, S.; Li, H.; Rupa, D.; Suramaya, R.; Songnian, W.; Huifant, Y.; Meng, M.; 

Winnik, M.; Kwok, E.; Li, Y.; Mu, R.; Xu, B.; Zhang, X.; Li, K. (2003). HEI Report 115, 

Validation & Evaluation of Biomarkers in Workers Exposed to Benzene in China. 
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205 Qu, Q., R. Shore, G. Li, X. Jin, L.C. Chen, B. Cohen, et al. (2002). Hematological changes 

among Chinese workers with a broad range of benzene exposures. Am. J. Industr. Med. 42: 

275-285.

206 Lan, Qing, Zhang, L., Li, G., Vermeulen, R., et al. (2004). Hematotoxically in Workers 

Exposed to Low Levels of Benzene. Science 306: 1774-1776.

207 Turtletaub, K.W. and Mani, C. (2003). Benzene metabolism in rodents at doses relevant to 

human exposure from Urban Air. Research Reports Health Effect Inst. Report No.113.

208 U.S. EPA. 2002. Health Assessment of 1,3-Butadiene. Office of Research and 

Development, National Center for Environmental Assessment, Washington Office, 

Washington, DC. Report No. EPA600-P-98-001F. This document is available electronically 

at http://www.epa.gov/iris/supdocs/buta-sup.pdf.

209 U.S. EPA. 2002 “Full IRIS Summary for 1,3-butadiene (CASRN 106-99-0)” 

Environmental Protection Agency, Integrated Risk Information System (IRIS), Research and 

Development, National Center for Environmental Assessment, Washington, DC 

http://www.epa.gov/iris/subst/0139.htm.

210 Bevan, C.; Stadler, J.C.; Elliot, G.S.; et al. (1996) Subchronic toxicity of 4-

vinylcyclohexene in rats and mice by inhalation. Fundam. Appl. Toxicol. 32:1-10. 

211 U.S. EPA. 1987. Assessment of Health Risks to Garment Workers and Certain Home 

Residents from Exposure to Formaldehyde, Office of Pesticides and Toxic Substances, April 

1987.

212 Hauptmann, M..; Lubin, J. H.; Stewart, P. A.; Hayes, R. B.; Blair, A. 2003. Mortality 

from lymphohematopoetic malignancies among workers in formaldehyde industries. Journal 

of the National Cancer Institute 95: 1615-1623.

213 Hauptmann, M..; Lubin, J. H.; Stewart, P. A.; Hayes, R. B.; Blair, A. 2004. Mortality 

from solid cancers among workers in formaldehyde industries. American Journal of 

Epidemiology 159: 1117-1130.

214 Pinkerton, L. E. 2004. Mortality among a cohort of garment workers exposed to 

formaldehyde: an update. Occup. Environ. Med. 61: 193-200.

215 Coggon, D, EC Harris, J Poole, KT Palmer. 2003. Extended follow-up of a cohort of 

British chemical workers exposed to formaldehyde. J National Cancer Inst. 95:1608-1615. 

216 Conolly, RB, JS Kimbell, D Janszen, PM Schlosser, D Kalisak, J Preston, and FJ Miller. 

2003. Biologically motivated computational modeling of formaldehyde carcinogenicity in the 

F344 rat. Tox Sci 75: 432-447.

217 Conolly, RB, JS Kimbell, D Janszen, PM Schlosser, D Kalisak, J Preston, and FJ Miller. 

2004. Human respiratory tract cancer risks of inhaled formaldehyde: Dose-response 

predictions derived from biologically-motivated computational modeling of a combined 

rodent and human dataset. Tox Sci 82: 279-296.
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218 Chemical Industry Institute of Toxicology (CIIT).1999. Formaldehyde: Hazard 

characterization and dose-response assessment for carcinogenicity by the route of inhalation. 

CIIT, September 28, 1999. Research Triangle Park, NC.

219 International Agency for Research on Cancer (2006) Formaldehyde, 2-Butoxyethanol and 

1-tert-Butoxypropan-2-ol. Monographs Volume 88. World Health Organization, Lyon, 

France.

220 U.S. EPA (1988). Integrated Risk Information System File of Acetaldehyde. Research and 

Development, National Center for Environmental Assessment, Washington, DC. This 

material is available electronically at http://www.epa.gov/iris/subst/0290.htm.

221 U.S. EPA (1988). Integrated Risk Information System File of Acetaldehyde. This 

material is available electronically at http://www.epa.gov/iris/subst/0290.htm.

222 Myou, S.; Fujimura, M.; Nishi K.; Ohka, T.; and Matsuda, T. (1993) Aerosolized 

acetaldehyde induces histamine-mediated bronchoconstriction in asthmatics. Am. Rev. 

Respir.Dis.148(4 Pt 1): 940-943.

223 U.S. EPA. 2003. Integrated Risk Information System File of Acrolein. Research and 

Development, National Center for Environmental Assessment, Washington, DC. This 

material is available electronically at http://www.epa.gov/iris/subst/0364.htm.

224 Appleman, L.M., R.A. Woutersen, and V.J. Feron. (1982). Inhalation toxicity of 

acetaldehyde in rats. I. Acute and subacute studies. Toxicology. 23: 293-297.

225 Integrated Risk Information System File of Acrolein. Research and Development, National 

Center for Environmental Assessment, Washington, DC. This material is available at 

http://www.epa.gov/iris/subst/0364.htm

226 Weber-Tschopp, A; Fischer, T; Gierer, R; et al. (1977) Experimentelle reizwirkungen von 

Acrolein auf den Menschen. Int Arch Occup Environ Hlth 40(2):117-130. In German

227 Sim, VM; Pattle, RE. (1957) Effect of possible smog irritants on human subjects. J Am 

Med Assoc 165(15):1908-1913. 

228 Morris JB, Symanowicz PT, Olsen JE, et al. 2003. Immediate sensory nerve-mediated 

respiratory responses to irritants in healthy and allergic airway-diseased mice. J Appl Physiol 

94(4):1563-1571. 

229 Sim VM, Pattle RE. Effect of possible smog irritants on human subjects JAMA165: 1980-

2010, 1957. 

230 Perera, F.P.; Rauh, V.; Tsai, W-Y.; et al. (2002) Effect of transplacental exposure to 

environmental pollutants on birth outcomes in a multiethnic population. Environ Health 

Perspect. 111: 201-205. 

231 Perera, F.P.; Rauh, V.; Whyatt, R.M.; Tsai, W.Y.; Tang, D.; Diaz, D.; Hoepner, L.; Barr, 

D.; Tu, Y.H.; Camann, D.; Kinney, P. (2006) Effect of prenatal exposure to airborne 

polycyclic aromatic hydrocarbons on neurodevelopment in the first 3 years of life among 

inner-city children. Environ Health Perspect 114: 1287-1292.
Air Quality and Resulting Health and Welfare Effects 

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232 U. S. EPA. 2004. Toxicological Review of Naphthalene (Reassessment of the Inhalation 

Cancer Risk), Environmental Protection Agency, Integrated Risk Information System, 

Research and Development, National Center for Environmental Assessment, Washington, 

DC. This material is available electronically at http://www.epa.gov/iris/subst/0436.htm. 

233 Oak Ridge Institute for Science and Education. (2004). External Peer Review for the IRIS 

Reassessment of the Inhalation Carcinogenicity of Naphthalene. August 2004. 

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=84403

234 National Toxicology Program (NTP). (2004). 11th Report on Carcinogens. Public Health 

Service, U.S. Department of Health and Human Services, Research Triangle Park, NC. 

Available from: http://ntp-server.niehs.nih.gov.

235 International Agency for Research on Cancer (IARC). (2002). Monographs on the 

Evaluation of the Carcinogenic Risk of Chemicals for Humans. Vol. 82. Lyon, France.

236 U. S. EPA. 1998. Toxicological Review of Naphthalene, Environmental Protection 

Agency, Integrated Risk Information System, Research and Development, National Center for 

Environmental Assessment, Washington, DC. This material is available electronically at 

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This Page Intentionally Left Blank 
Emission Inventory 

3-1

CHAPTER 3: Emission Inventory......................................................................... 3-2 

3.1 Commercial Marine Diesel Engines............................................................. 3-2 

3.1.1 General Methodology............................................................................. 3-3 

3.1.2 Baseline (Pre-Control) Inventory Development .................................. 3-7 

3.1.3 Control Inventory Development ......................................................... 3-34 

3.1.4 Projected Commercial Marine Emission Reductions of Final Rule 3-57 

3.2 Recreational Marine Diesel Engines.......................................................... 3-61 

3.2.1 General Methodology........................................................................... 3-61 

3.2.2 Baseline (Pre-Control) Inventory Development ................................ 3-63 

3.2.3 Control Inventory Development ......................................................... 3-68 

3.2.4 Projected Recreational Marine Emission Reductions of Final Rule 3-71 

3.3 Locomotives.................................................................................................. 3-73 

3.3.1 General Methodology........................................................................... 3-73 

3.3.2 Baseline (Pre-Control) Inventory Development ................................ 3-77 

3.3.3 Control Inventory Development ......................................................... 3-88 

3.3.4 Projected Locomotive Emission Reductions from the Final Rule ... 3-95 

3.4 Projected Total Emission Reductions from the Final Rule ..................... 3-99 

3.5 Contribution of Marine Diesel Engines and Locomotives to Baseline 

National Emission Inventories.................................................................... 3-105 

3.5.1 Categories and Sources of Data......................................................... 3-105 

3.5.2 PM2.5 Contributions to Baseline........................................................ 3-106 

3.5.3 NOx Contributions to Baseline .......................................................... 3-106 

3.5.4 VOC Contributions to Baseline......................................................... 3-106 

3.5.5 CO Contributions to Baseline............................................................ 3-106 

3.5.6 SO2 Contributions to Baseline........................................................... 3-107 

3.6 Contribution of Marine Diesel Engines and Locomotives to NonAttainment Area Emission Inventories ......................................................... 3-112 

3.7 Emission Inventories Used for Air Quality Modeling............................ 3-115 

3.7.1 Comparison of Air Quality and Final Rule Inventories ................. 3-115 

3.7.2 Locomotive Inventory Changes......................................................... 3-116 

3.7.3 Marine Diesel Inventory Changes..................................................... 3-116 
Regulatory Impact Analysis 

3-2

CHAPTER 3: Emission Inventory 

This chapter presents our analysis of the emission impact of the final rule for the three 

source categories affected: commercial marine diesel engines, recreational marine diesel 

engines, and locomotives. The final control requirements include NOx, HC, and PMA

emission standards for Category 1 and Category 2 commercial marine diesel engines (both 

above and below 37 kilowatts [kW]). New NOx, HC, and PM emission standards would also 

apply to all recreational marine diesel engines and locomotives. There are no new standards 

for CO. In addition, there is a remanufacturing program requirement for locomotives and for 

selected Category 1 and Category 2 engines. 

Section 3.1 describes the methodology and presents the resulting baseline and 

controlled inventories for commercial marine diesel engines, including the projected emission 

reductions from the final rule. Sections 3.2 and 3.3 present similar information for 

recreational marine diesel engines and locomotives, respectively. The baseline inventories 

represent current and future emissions with only the existing standards. The controlled 

inventories incorporate the new standards in the final rule. Section 3.4 follows with the total 

projected emission reductions from all three affected source categories. Section 3.5 and 

section 3.6 then describe the contribution of these source categories to national and selected 

local inventories, respectively. Section 3.7 concludes the chapter by describing the changes in 

the inputs and resulting emission inventories between the baseline and control scenarios used 

for the air quality modeling and the updated baseline and control scenarios in this final rule. 

The inventory estimates reported in this chapter are for the 50-state geographic area. 

Inventories are presented for the following pollutants: particulate matter (PM2.5 and PM10), 

oxides of nitrogen (NOx), sulfur dioxide (SO2), volatile organic compounds (VOC), carbon 

monoxide (CO), and mobile source air toxics. The specific air toxics are benzene, 

formaldehyde, acetaldehyde, 1,3-butadiene, acrolein, napthalene, and 15 other compounds 

grouped together as polycyclic organic matter (POM). The PM inventories include directly 

emitted PM only, although secondary sulfates are taken into account in the air quality 

modeling. Inventories are provided for calendar years 2002 through 2040. 

3.1 Commercial Marine Diesel Engines 

This section describes the methodology and presents the resulting baseline and 

controlled inventories for commercial marine diesel engines, including the projected emission 

reductions from the final rule. Separate inventories were developed for the following 

commercial marine diesel engine categories: Category 1 commercial propulsion, Category 1 

marine auxiliary, Category 2 commercial propulsion, less than (<) 37kW commercial 

propulsion, and <37kW marine auxiliary. Category 1 and 2 only include engines greater than 

or equal to (≥) 37kW, so it was necessary to include separate categories for those engines less 

than 37kW. Note that the auxiliary categories include engines used on either commercial or 

recreational vessels; however, given the expected small number of recreational auxiliary 

 

A PM in this document refers to PM10, which are particles less than 10 microns in diameter. 
Emission Inventory 

3-3

engines in comparison to commercial auxiliary engines, and our inability to separate the 

auxiliary categories by end use, the auxiliary categories have been included in the broader 

commercial marine category. Category 2 marine auxiliary engines are not included here, 

since they are used on Category 3 ocean-going vessels that are primarily foreign-flagged and 

not subject to U.S. regulations. Emissions from Category 2 auxiliary engines are therefore 

part of the Category 3 inventories. 

3.1.1 General Methodology 

For the Category 1 and Category 2 commercial marine categories, inventories are 

estimated using spreadsheet models. Since the less than 37kW commercial marine engines 

are subject to existing EPA nonroad diesel regulations, emissions were estimated for this 

category using a special version of the NONROAD2005 model, with Source Classification 

Codes (SCCs) and associated inputs added for both the commercial and auxiliary engines. 

The general methodology for calculating commercial marine diesel engine inventories 

for HC, CO, NOx, and PM is first described. This is followed by the methodologies used to 

calculate fuel consumption, SO2, VOC, PM2.5, and air toxic inventories. 

Commercial marine diesel engine inventories for HC, CO, NOx, and PM are estimated 

using the equation: 

Equation 3-1 = ( )× ( )× ( )× ( )× (EFALPNI )

where each term is defined as follows: 

I = the emission inventory (gram/year) 

N = engine population (units) 

P = average rated power (kW) 

L = load factor (average fraction of rated power used during operation; unitless) 

A = engine activity (operating hours/year) 

EF = emission factor (gram/kW-hr) 

Emissions are then converted and reported as short tons/year. 

The average rated power, load factor, and activity inputs remain constant across all 

simulation years. However, populations and emission factors vary by year and age. 

Populations for a given base calendar year are first calculated, along with the corresponding 

age distribution, and then projected from that base year into the future. For most of the 

commercial marine diesel categories, the base year is 2002. The pollutant emission factors 

vary by age to account for the current and final regulations, as well as emissions deterioration. 

PM emission factors also have an additional adjustment to account for the in-use fuel sulfur 

level, which is described in more detail below. 

Three variables are used to project emissions over time: the annual population growth 

rate, the engine median life/scrappage, and the relative deterioration rate. Collectively, these 

variables represent population growth, changes in the population age distribution, and 

emission deterioration. 
Regulatory Impact Analysis 

3-4

Annual Population Growth Rate (percent/year). The population growth rate 

represents the percentage increase in the total calendar year engine population from year (n) 

to year (n+1). It is a compound growth rate. These growth rates vary by category. The 

compound growth rates are used for Category 1 and 2 engines. Since the NONROAD model 

uses linear growth inputs, the compound growth rates are converted to a form consistent with 

the NONROAD model for the less than 37kW commercial marine engines. 

Engine Median Life (years) and Scrappage. The engine median life defines the 

length of time engines remain in service. Engines persist in the population over two median 

lives; during the first median life, 50 percent of the engines are scrapped, and over the second, 

the remaining 50 percent of the engines are scrapped. Engine median lives also vary by 

category. The age distribution is defined by the median life and the scrappage algorithm. For 

commercial marine diesel engines, the scrappage algorithm in the NONROAD model was 

used for all categories.1

Relative Deterioration Rate (percent increase in emission factor/percent median life 

expended). A deterioration factor can be applied to the emission factor to account for in-use 

deterioration. The deterioration factor varies by age and is calculated as: 

Equation 3-2 ⎟

⎠

⎞ ⎜

⎝

⎛ ×+=

ML

age 1 ADF

where each term is defined as follows: 

DF = the deterioration factor for a given pollutant at a given age 

A = the relative deterioration rate for a given pollutant (percent increase in emission 

factor/percent useful life expended) 

age = the age of a specific model year group of engines in the simulation year (years) 

ML = the median life of the given model year cohort (years) 

A given model year cohort is represented as a fraction of the entire population. The 

deterioration factor adjusts the emission factor for engines in a given model year cohort in 

relation to the proportion of median life expended. Deterioration is linear over one median 

life. Following the first median life, the deteriorated emission factor is held constant over the 

remaining life for engines in the cohort. This is consistent with the diesel deterioration 

applied in the NONROAD model.2

Sulfur Adjustment for PM Emissions. For Tier 2 and prior engines, a sulfate 

adjustment is added to the PM emissions to account for differences in fuel sulfur content 

between the certification fuel and the episodic (calendar year) fuel, using the following 

equation from the NONROAD model: 
Emission Inventory 

3-5

Equation 3-3 ( )( )( ) ( ) ⎟

⎠

⎞ ⎜

⎝

⎛ ⎟ ×−×

⎠

⎞ ⎜

⎝

⎛

××= ×

2000

1

32

224 FCS 02247.01.7 soxdsl soxbas PMadj

where each term is defined as follows: 

SPM adj = PM sulfate adjustment (tons) 

FC = fuel consumption (gallons) 

7.1 = fuel density (lb/gal) 

0.02247 = fraction of fuel sulfur converted to sulfate 

224/32 = grams PM sulfate/grams PM sulfur 

soxdsl = episodic fuel sulfur weight fraction (varies by calendar year) 

soxbas = certification fuel sulfur weight fraction 

2000 = conversion from lb to ton 

The certification fuel sulfur term is the fuel sulfur level associated with a base 

emission factor in the model. If the episodic fuel sulfur level is less than the certification fuel 

sulfur level, the PM adjustment is negative and PM emissions will decrease. 

For engines prior to Tier 2 the base fuel sulfur (soxbas) is assumed to be 3300 ppm. 

For Tier 2 engines less than or equal to 50 hp (37 kW) it is set at 2000 ppm, as described in 

the Clean Air Nonroad Diesel Rule3

, since these smaller engines are subject to the same 

standards as land-based diesel engines. For Tier 2 engines greater than 50 hp (37 kW) it is set 

at 350 ppm, based on the most recent certification data for these engines. For Tier 3 and later 

engines, no sulfur adjustment is applied. These engines will be certified to a fuel sulfur level 

at or lower than the episodic fuel sulfur levels expected when these engines are introduced. 

The fraction of fuel sulfur converted to sulfate is based on an analysis of nonroad 

engine data conducted at various fuel sulfur levels. The derivation is described in the 

NONROAD documentation.4

Estimation of fuel consumption. Annual fuel consumption is estimated using the 

following equation: 

Equation 3-4 ( )

( ) × 4541.7

× × × × = ALPNBSFC FC

where each term is defined as follows: 

FC = fuel consumption (gallons) 

BSFC = brake specific fuel consumption (g/kW-hr) 

N = engine population (units) 

P = average rated power (kW) 

L = load factor (average fraction of rated power used during operation; unitless) 

A = engine activity (operating hours/year) 

7.1 = fuel density (lb/gal) 

454 = conversion from lb to g 

Estimation of SO2 emissions. Annual SO2 inventories are estimated using the 

following equation: 
Regulatory Impact Analysis 

3-6

Equation 3-5 ( )( )( ) ( ) ⎟

⎠

⎞ ⎜

⎝

⎛ ⎟ ××

⎠

⎞ ⎜

⎝

⎛

−××= ×

2000

1

32

64

2 FCSO 02247.011.7 soxdsl

where each term is defined as follows: 

SO2 = sulfur dioxide inventory (tons) 

FC = fuel consumption (gallons) 

7.1 = fuel density (lb/gal) 

(1-0.02247) = fraction of fuel sulfur converted to SO2

64/32 = grams SO2/grams sulfur 

soxdsl = episodic fuel sulfur weight fraction (varies by calendar year) 

2000 = conversion from lb to ton 

The calendar year fuel sulfur levels (soxdsl) were taken from the Clean Air Nonroad 

Diesel Rule and are reproduced in Table 3-1 below.5

Table 3-1 Modeled 50-State In-Use Diesel Fuel Sulfur Content for Marine Engines 

Calendar 

Year(s) 

Modeled In-Use Fuel 

Sulfur Content, ppm 

Through 2000 2640 

2001 2635 

2002-2005 2637 

2006 2588 

2007 1332 

2008-2009 435 

2010 319 

2011 236 

2012 124 

2013 44 

2014 52 

2015-2017 56 

2018-2040 55 

Estimation of VOC and PM2.5 emissions. To estimate VOC emissions, an adjustment 

factor of 1.053 is applied to the HC output. Similarly, to estimate PM2.5 emissions, an 

adjustment factor of 0.97 is applied to the PM10 output. These adjustment factors are 

consistent with those used in the NONROAD model6,7 and the Clean Air Nonroad Diesel 

Rule.8

Estimation of air toxic emissions. The air toxic baseline emission inventories for this 

rule are based on information developed for EPA’s Mobile Source Air Toxics (MSAT) final 

rulemaking.9

 That rule calculated air toxic emission inventories for all nonroad engines. The 

gaseous air toxics are correlated to VOC emissions, while POM is correlated to PM10

emissions. To calculate the air toxics emission inventories and reductions for this rule, the 

percent reductions in VOC and PM10 emissions will be applied to the baseline gaseous and 

POM air toxic inventories, respectively. 
Emission Inventory 

3-7

3.1.2 Baseline (Pre-Control) Inventory Development 

This section describes the inputs and provides the resulting baseline inventories for 

commercial marine engines. 

3.1.2.1 Category 1 Propulsion 

The inventory inputs of base year population, average power, load factor, and activity 

for Category 1 commercial propulsion engines are given in Table 3-2 and Table 3-3. These 

inventory inputs are used to develop both baseline and control inventories. As a result, there 

are displacement, power density, and kilowatt subcategories, which are required to model 

both the current and final standards in this rule. 

The current emission standards vary only by displacement (disp) category, which is 

expressed as liters per cylinder (L/cyl). There are four displacement categories for Category 1 

engines: 1) less than 0.9 L/cyl (and power greater than or equal to 37kW), 2) greater than or 

equal to 0.9 L/cyl and less than 1.2 L/cyl, 3) greater than or equal to 1.2 L/cyl and less than 

2.5 L/cyl, and 4) greater than or equal to 2.5 L/cyl and less than 5 L/cyl. For simplification, 

these will be referred to as 1) disp <0.9, 2) 0.9≤ disp <1.2, 3) 1.2≤ disp <2.5, and 4) 2.5≤ disp 

<5. 

In order to model the final Tier 3 standards, the 2.5≤ disp <5 category is further 

broken out into 2.5≤ disp <3.5 and 3.5≤ disp <5 categories. The Tier 3 standards also have 

cut points at 75kW and 3700kW, so it was necessary to break out the disp<0.9 category into 

37<kW≤75 and >75kW categories. Since there are no Category 1 engines greater than 

3700kW, this cut point was not necessary to include. Finally, there are different Tier 3 

standards for standard power density and high power density engines. Standard power 

density engines are less than 35 kW per liter (kW/L), and the high power density engines are 

greater than or equal to 35 kW/L. The inputs for the standard power density engines are given 

in Table 3-2 and the inputs for the high power density engines in Table 3-3. 

The final Tier 4 standards that apply to Category 1 engines vary by the following kW 

categories: <600kW, 600≤kW<1000, 1000≤kW<1400, 1400≤kW<2000, 2000≤kW<3700, and 

≥3700kW. As a result, these power categories were also added, with the exception of the 

≥2000kW categories, since there are no Category 1 engines in this power range. 

The base year populations by displacement category are generated using historical 

sales estimates in conjunction with the scrappage algorithm described above. Other inventory 

inputs that affect scrappage are load factor, activity, and median life. The historical sales 

estimates for calendar years 1973-2002 were obtained from Power Systems Research (PSR). 

These populations by displacement category were further broken out into power density and 

kilowatt categories using the 2002 population and engine data from PSR. 

The average power estimates were population-weighted, using the 2002 engine and 

population data from PSR. The load factor and activity estimates were 0.45 and 943 hours 

per year, respectively for engines <560 kW (750 hp). These are the estimates for commercial 

marine propulsion engines provided by PSR. For engines >560 kW, the load factor and 
Regulatory Impact Analysis 

3-8

activity estimates used were 0.79 and 4,503 hours per year. These latter estimates were taken 

from the 1999 Marine Diesel FRM.10 Higher load factors and activities were assigned to 

these larger engines based on information provided by the manufacturers for the previous 

rule, and supported by more recent discussions with the American Waterways Operators 

about how these larger engines typically operate.11 This power break point is not related to 

the kW categories in the final standards. 

Load factors for each subcategory were developed by first identifying the engines in 

the PSR population dataset corresponding to each subcategory. Load factors for each engine 

in a subcategory were assigned based on the criteria above. An average load factor for each 

subcategory was then obtained by weighting the individual engine load factors by population 

and power. A similar approach was followed to obtain activity estimates for each 

subcategory, with the exception that the weightings were population, power, and load factor. 

The average power, load factors and activities needed to be estimated using these weightings 

to ensure that the total inventory from this source category is correctly calculated. Note that 

the load factor and hours of use only varied for C1 engines in the <600 kW and 

1.2<=disp<2.5 standard power density category. 

The median life for all C1 propulsion engines used is 13 years, which is the estimate 

provided by PSR. The annual population growth rate is 1.009, which is the estimate from the 

Energy and Information Administration (EIA) for domestic shipping.12
3-9

Table 3-2 Inventory Inputs for C1 Propulsion Standard Power Density Engines 

<35 W/L 

<=600KW 600<KW≤1000 DISPLACEMENT CATEGORY 

2002 

POPULATION 

AVG 

KW 

LOAD 

FACTOR 

ACTIVITY, 

HOURS 

2002 

POPULATION 

AVG 

KW 

LOAD 

FACTOR 

A

DISP<0.9 AND 37<KW<=75 1,665 43 0.45 943 0 DISP<0.9 AND >75KW 1,102 154 0.45 943 0 0.9<=DISP<1.2 19,255 128 0.45 943 0 1.2<=DISP<2.5 23,561 294 0.51 1,905 795 781 0.79 2.5<=DISP<3.5 5,898 397 0.45 943 675 832 0.79 3.5<=DISP<5.0 205 404 0.45 943 308 748 0.79 TOTAL 51,687 1,777 

<35 KW/L 

1000<KW≤1400KW >1400KWa DISPLACEMENT CATEGORY 

2002 

POPULATION 

AVG 

KW 

LOAD 

FACTOR 

ACTIVITY, 

HOURS 

2002 

POPULATION 

AVG 

KW 

LOAD 

FACTOR 

A

DISP<0.9 AND 37<KW<=75 0 0 DISP<0.9 AND >75KW 0 0 0.9<=DISP<1.2 0 0 1.2<=DISP<2.5 1,013 1,065 0.79 4,503 0 2.5<=DISP<3.5 186 1,194 0.79 4,503 0 3.5<=DISP<5.0 212 1,119 0.79 4,503 1,264 1,492 0.79 TOTAL 1,411 1,264 

Grand Total 53,098 3,041 56,139 a No populations ≥2000 kW 
Regulatory Impact Analysis 

3-10

Table 3-3 Inventory Inputs for C1 Propulsion High Power Density Engines 

≥35 KW/L 

<=600KW 600<KW≤1000 DISPLACEMENT CATEGORY 

2002 

POPULATION 

AVG 

KW 

LOAD 

FACTOR 

ACTIVITY, 

HOURS 

2002 

POPULATION 

AVG 

KW 

LOAD 

FACTOR 

A

DISP<0.9 AND 37<KW<=75 0 0 DISP<0.9 AND >75KW 3,151 165 0.45 943 0 0.9<=DISP<1.2 21 313 0.45 943 0 1.2<=DISP<2.5 1,338 341 0.45 943 102 678 0.79 2.5<=DISP<3.5 0 0 3.5<=DISP<5.0 0 0 TOTAL 4,510 102 

≥35 KW/L 

1000<KW≤1400KW >1400KWa DISPLACEMENT CATEGORY 

2002 

POPULATION 

AVG 

KW 

LOAD 

FACTOR 

ACTIVITY, 

HOURS 

2002 

POPULATION 

AVG 

KW 

LOAD 

FACTOR 

A

DISP<0.9 AND 37<KW<=75 0 0 DISP<0.9 AND >75KW 0 0 0.9<=DISP<1.2 0 0 1.2<=DISP<2.5 0 0 2.5<=DISP<3.5 0 0 3.5<=DISP<5.0 214 1,176 0.79 4,503 361 1,765 0.79 TOTAL 214 361 

Grand Total 4,724 463 5,187 a No populations ≥2000 kW 
Emission Inventory 

3-11

The baseline emission factors are given in Table 3-4 and Table 3-5. The emission 

factors are provided for three technology types: Base, Tier 1, and Tier 2. The base 

technology type includes all pre-control engines. Tier 1 refers to the first round of existing 

standards for NOx only that began in 2000. Tier 2 refers to the second round of existing 

standards for HC+NOx and PM that began in 2004 to 2007, depending on the displacement 

category. 

Table 3-4 Baseline PM10 and NOx Emission Factors for C1 Propulsion Enginesa

DISPLACEMENT PM10 G/KW-HR NOX G/KW-HR 

CATEGORY BASE TIER 1 TIER 2 BASE TIER 1 TIER 2 

DISP<0.9 0.54 0.54 0.23 10 9.8 5.7 

0.9<=DISP<1.2 0.47 0.47 0.12 10 9.8 6.1 

1.2<=DISP<2.5 0.34 0.34 0.13 10 9.8 6.0 

2.5<=DISP<3.5 0.30 0.30 0.13 10 9.1 6.0 

3.5<=DISP<5.0 0.30 0.30 0.13 11 9.2 6.0 

a

 Deterioration is applied to the PM emission factors (EFs); see text for details. The NOx EFs are not subject to 

deterioration. 

Table 3-5 Baseline HC and CO Emission Factors for C1 Propulsion Enginesa

DISPLACEMENT HC G/KW-HR CO G/KW-HR 

CATEGORY BASE TIER 1 TIER 2 BASE TIER 1 TIER 2 

DISP<0.9 0.41 0.41 0.41 1.6 1.6 1.6 

0.9<=DISP<1.2 0.32 0.32 0.32 1.6 1.6 0.9 

1.2<=DISP<2.5 0.27 0.27 0.19 1.6 1.6 1.1 

2.5<=DISP<3.5 0.27 0.27 0.19 1.6 1.6 1.1 

3.5<=DISP<5.0 0.27 0.27 0.19 1.8 1.8 1.1 

a

 The HC and CO emission factors (EFs) are not subject to deterioration. 

The base emission factors were taken from the 1999 Marine Diesel rulemaking, and 

are based on emission data for uncontrolled engines.13 For Tier 1, the NOx emission factors 

were estimated using 2006 certification data. The certification data for engines using the E3 

cycleB

 were sales-weighted to obtain Tier 1 NOx emission factors for each displacement 

category. Since the Tier 1 standards only affect NOx, the Tier 1 emission factors for the other 

pollutants are equal to the base emission factors. For Tier 2, the same 2006 certification data 

were used to estimate PM, NOx, and HC emission factors. 

 

B

 The E3 duty cycle is designated for propulsion marine diesel engines. 
Regulatory Impact Analysis 

3-12

For C1 engines, PM is the only pollutant for which deterioration factors are applied. 

The relative deterioration rate (A) is 0.473, which is used for both pre-control and all 

regulatory tiers. As a result, the maximum PM deterioration factor is 1.473. This is 

consistent with the diesel deterioration assumed in the NONROAD model.14 Not enough 

information is available at this time to develop deterioration factors for the other pollutants. 

The certification fuel sulfur levels, which are used to estimate the PM sulfate 

adjustments, are 3300ppm for the Base (pre-control) technology type, and 350ppm for Tier 1 

and Tier 2. The Base level was taken from the NONROAD model.2

 The Tier 1 and Tier 2 

levels were estimated from reviewing the marine certification data and fuel requirements. 

For calculating fuel consumption, estimates of brake specific fuel consumption 

(BSFC) are also required. For this analysis, a value of 213 g/kW-hr was used. This value is 

consistent with published estimates of BSFC and those for heavy-duty diesel engines.15

The resulting baseline 50-state emission inventories for Category 1 propulsion engines 

are given in Table 3-6. 
Emission Inventory 

3-13

Table 3-6 Baseline (50-State) Emissions for C1 Propulsion Engines (short tons) 

YEAR PM10 PM2.5 NOX VOC HC CO SO2

2002 13,328 12,928 335,561 9,488 9,010 55,303 36,201 

2003 13,690 13,279 336,369 9,573 9,091 55,801 36,528 

2004 13,807 13,393 332,798 9,561 9,080 55,722 36,862 

2005 13,873 13,457 328,810 9,550 9,069 55,582 37,192 

2006 13,872 13,456 324,900 9,540 9,060 55,450 36,827 

2007 12,230 11,863 316,663 9,415 8,941 54,423 19,121 

2008 10,961 10,632 308,524 9,291 8,824 53,405 6,299 

2009 10,710 10,388 300,509 9,170 8,708 52,401 6,355 

2010 10,304 9,995 292,651 9,051 8,595 51,414 4,705 

2011 9,916 9,619 284,979 8,934 8,484 50,445 3,513 

2012 9,471 9,187 277,551 8,821 8,377 49,497 1,862 

2013 9,003 8,733 270,764 8,711 8,273 48,574 664 

2014 8,587 8,330 264,634 8,606 8,173 47,680 799 

2015 8,155 7,910 258,879 8,507 8,079 46,827 857 

2016 7,718 7,487 253,538 8,415 7,992 46,023 865 

2017 7,346 7,126 249,327 8,347 7,927 45,368 872 

2018 7,058 6,846 246,339 8,304 7,886 44,879 879 

2019 6,805 6,601 243,964 8,272 7,855 44,482 886 

2020 6,632 6,433 242,764 8,269 7,852 44,301 893 

2021 6,538 6,342 242,677 8,293 7,876 44,329 900 

2022 6,470 6,276 242,990 8,326 7,907 44,423 907 

2023 6,422 6,229 243,640 8,367 7,946 44,571 915 

2024 6,388 6,197 244,563 8,414 7,990 44,760 923 

2025 6,368 6,177 245,736 8,466 8,040 44,987 931 

2026 6,359 6,168 247,141 8,523 8,094 45,248 939 

2027 6,363 6,173 248,720 8,584 8,152 45,539 946 

2028 6,381 6,190 250,474 8,649 8,214 45,861 954 

2029 6,410 6,218 252,384 8,719 8,280 46,209 962 

2030 6,451 6,258 254,450 8,792 8,349 46,583 970 

2031 6,499 6,304 256,608 8,868 8,421 46,975 978 

2032 6,552 6,356 258,851 8,946 8,495 47,385 986 

2033 6,611 6,413 261,181 9,026 8,572 47,811 995 

2034 6,671 6,471 263,532 9,107 8,649 48,241 1,006 

2035 6,731 6,529 265,903 9,189 8,727 48,675 1,015 

2036 6,791 6,588 268,297 9,272 8,805 49,114 1,023 

2037 6,852 6,647 270,711 9,356 8,885 49,556 1,032 

2038 6,914 6,707 273,148 9,440 8,965 50,002 1,040 

2039 6,976 6,767 275,606 9,525 9,045 50,452 1,050 

2040 7,039 6,828 278,086 9,610 9,127 50,906 1,059 
Regulatory Impact Analysis 

3-14

3.1.2.2 Category 1 Auxiliary 

The methodology and data sources for Category 1 marine auxiliary engines are 

essentially the same as those for Category 1 propulsion engines. For this source category, 

however, the PSR data for marine auxiliary engines and the certification data with the D2 

auxiliary cycleC

 were used instead. Weighted load factor and activity values were calculated, 

although the load factor and hours of use only varied in the <600 kW and 1.2<=disp<2.5 

standard power density category. The inventory inputs of base year population, average 

power, load factor, and activity for C1 auxiliary engines are given in Table 3-7 and Table 3-8. 

The baseline emission factors are given in Table 3-9 and Table 3-10. 

For auxiliary engines, the load factor and activity estimates are 0.56 and 724 hours per 

year, respectively, for engines <560kW. These are the estimates for auxiliary marine engines 

provided by PSR. For engines >560kW, the load factor and activity estimates used are 0.65 

and 2,500 hours per year, taken from the 1999 FRM.10 The cut point of 560kW is that used 

for propulsion engines and is not related to the kW categories in the final standards. 

The median life for all C1 auxiliary engines is 17 years, which is the estimate provided 

by PSR. Estimates for the annual growth rate, PM deterioration factor, certification fuel 

sulfur levels, and BSFC are assumed to be the same as those for C1 propulsion engines. 

The resulting baseline 50-state emission inventories for Category 1 auxiliary engines 

are given in Table 3-11. 

 

C

 The D2 steady-state duty cycle is designated for constant-speed engines. 
3-15

Table 3-7 Inventory Inputs for C1 Auxiliary Standard Power Density Engines 

<35 KW/L 

<=600KW 600<KW≤1000 DISPLACEMENT CATEGORY 

2002 

POPULATION 

AVG 

KW 

LOAD 

FACTOR 

ACTIVITY, 

HOURS 

2002 

POPULATION 

AVG 

KW 

LOAD 

FACTOR 

A

DISP<0.9 AND 37<KW<=75 9,786 44 0.56 724 0 DISP<0.9 AND >75KW 1,251 83 0.56 724 0 0.9<=DISP<1.2 11,933 109 0.56 724 0 1.2<=DISP<2.5 14,119 324 0.57 925 512 741 0.65 2.5<=DISP<3.5 785 332 0.56 724 74 882 0.65 3.5<=DISP<5.0 347 356 0.56 724 408 746 0.65 TOTAL 38,221 994 

<35 KW/L 

1000<KW≤1400 >1400KWa DISPLACEMENT CATEGORY 

2002 

POPULATION 

AVG 

KW 

LOAD 

FACTOR 

ACTIVITY, 

HOURS 

2002 

POPULATION 

AVG 

KW 

LOAD 

FACTOR 

A

DISP<0.9 AND 37<KW<=75 0 0 DISP<0.9 AND >75KW 0 0 0.9<=DISP<1.2 0 0 1.2<=DISP<2.5 0 0 2.5<=DISP<3.5 14 1,194 0.65 2,500 0 3.5<=DISP<5.0 268 1,119 0.65 2,500 96 1,527 0.65 TOTAL 282 96 

Grand Total 38,503 1,090 a No populations ≥2000KW 
Regulatory Impact Analysis 

3-16

Table 3-8 Inventory Inputs for C1 Auxiliary High Power Density Engines 

≥35 KW/L 

<=600KW 600<KW≤1000 DISPLACEMENT CATEGORY 

2002 

POPULATION 

AVG 

KW 

LOAD 

FACTOR 

ACTIVITY, 

HOURS 

2002 

POPULATION 

AVG 

KW 

LOAD 

FACTOR 

A

DISP<0.9 AND 37<KW<=75 215 75 0.56 724 0 DISP<0.9 AND >75KW 218 141 0.56 724 0 0.9<=DISP<1.2 0 0 1.2<=DISP<2.5 0 0 2.5<=DISP<3.5 0 0 3.5<=DISP<5.0 0 0 TOTAL 433 0 

≥35 KW/L 

1000<KW≤1400 >1400KWa DISPLACEMENT CATEGORY 

2002 

POPULATION 

AVG 

KW 

LOAD 

FACTOR 

ACTIVITY, 

HOURS 

2002 

POPULATION 

AVG 

KW 

LOAD 

FACTOR 

A

DISP<0.9 AND 37<KW<=75 0 0 DISP<0.9 AND >75KW 0 0 0.9<=DISP<1.2 11 1,231 0.65 2,500 0 1.2<=DISP<2.5 0 39 1,531 0.65 2.5<=DISP<3.5 0 0 3.5<=DISP<5.0 0 0 TOTAL 11 39 

Grand Total 444 39 a No populations ≥2000KW 
Emission Inventory 

17

Table 3-9 Baseline PM10 and NOx Emission Factors for C1 Auxiliary Enginesa

DISPLACEMENT PM10 G/KW-HR NOX G/KW-HR 

CATEGORY BASE TIER 1 TIER 2 BASE TIER 1 TIER 2 

DISP<0.9 0.84 0.84 0.23 11 9.8 5.7 

0.9<=DISP<1.2 0.53 0.53 0.21 10 9.8 5.4 

1.2<=DISP<2.5 0.34 0.34 0.15 10 9.8 6.1 

2.5<=DISP<3.5 0.32 0.32 0.15 10 9.1 6.1 

3.5<=DISP<5.0 0.30 0.30 0.15 11 9.2 6.1 

a

 Deterioration is applied to the PM emission factors (EFs); see text for details. The NOx EFs are not subject to 

deterioration. 

Table 3-10 Baseline HC and CO Emission Factors for C1 Auxiliary Enginesa

DISPLACEMENT HC G/KW-HR CO G/KW-HR 

CATEGORY BASE TIER 1 TIER 2 BASE TIER 1 TIER 2 

DISP<0.9 0.41 0.41 0.41 2.0 2.0 1.6 

0.9<=DISP<1.2 0.32 0.32 0.32 1.7 1.7 0.8 

1.2<=DISP<2.5 0.27 0.27 0.21 1.5 1.5 0.9 

2.5<=DISP<3.5 0.27 0.27 0.21 1.5 1.5 0.9 

3.5<=DISP<5.0 0.27 0.27 0.21 1.8 1.8 0.9 

a

 The HC and CO emission factors (EFs) are not subject to deterioration. 
Regulatory Impact Analysis 

18

Table 3-11 Baseline (50-State) Emissions for C1 Auxiliary Engines (short tons) 

YEAR PM10 PM 2.5 NOX VOC HC CO SO2

2002 2,714 2,632 60,641 1,767 1,678 9,624 6,553 

2003 2,773 2,690 60,959 1,783 1,693 9,710 6,613 

2004 2,791 2,708 60,482 1,785 1,696 9,668 6,673 

2005 2,786 2,703 59,774 1,788 1,698 9,585 6,733 

2006 2,769 2,686 59,073 1,791 1,700 9,503 6,667 

2007 2,482 2,407 58,048 1,787 1,697 9,331 3,461 

2008 2,263 2,195 57,030 1,783 1,693 9,160 1,140 

2009 2,230 2,163 56,020 1,779 1,690 8,989 1,150 

2010 2,170 2,105 55,022 1,776 1,686 8,820 852 

2011 2,115 2,052 54,038 1,773 1,684 8,654 636 

2012 2,052 1,990 53,069 1,770 1,681 8,489 337 

2013 1,993 1,933 52,118 1,767 1,678 8,327 120 

2014 1,952 1,893 51,185 1,765 1,676 8,167 145 

2015 1,907 1,850 50,277 1,763 1,674 8,010 155 

2016 1,860 1,805 49,399 1,761 1,673 7,857 157 

2017 1,806 1,752 48,589 1,760 1,672 7,708 158 

2018 1,746 1,693 47,849 1,759 1,671 7,563 159 

2019 1,685 1,634 47,160 1,759 1,671 7,426 160 

2020 1,625 1,576 46,531 1,760 1,672 7,298 162 

2021 1,576 1,528 46,079 1,764 1,675 7,198 163 

2022 1,543 1,497 45,840 1,771 1,681 7,134 164 

2023 1,520 1,474 45,706 1,778 1,689 7,088 166 

2024 1,504 1,459 45,683 1,788 1,698 7,066 167 

2025 1,495 1,451 45,756 1,799 1,709 7,067 169 

2026 1,489 1,445 45,875 1,811 1,720 7,077 170 

2027 1,486 1,441 46,035 1,824 1,732 7,094 171 

2028 1,484 1,440 46,228 1,837 1,745 7,117 173 

2029 1,484 1,440 46,452 1,851 1,758 7,145 174 

2030 1,486 1,441 46,703 1,865 1,771 7,178 176 

2031 1,489 1,444 46,980 1,880 1,785 7,215 177 

2032 1,493 1,448 47,283 1,895 1,800 7,257 179 

2033 1,499 1,454 47,611 1,911 1,815 7,303 180 

2034 1,506 1,461 47,962 1,927 1,830 7,353 182 

2035 1,514 1,469 48,332 1,943 1,845 7,407 184 

2036 1,524 1,478 48,721 1,960 1,861 7,464 185 

2037 1,535 1,489 49,126 1,977 1,878 7,524 187 

2038 1,547 1,501 49,553 1,995 1,894 7,588 188 

2039 1,561 1,514 49,991 2,013 1,911 7,654 190 

2040 1,574 1,527 50,436 2,031 1,928 7,721 192 
Emission Inventory 

19

3.1.2.3 Category 2 Propulsion 

The methodology used for C2 propulsion engines is the same as that used for C1 

propulsion engines, as described in section 3.1.1. For activity however, separate estimates 

were made for underway and idling activity, using the following equations: 

Equation 3-6 Underway kW-hr = (Likely kW) x (Total Engines) x (Likely Annual Transit Days) x (Likely 

Load Factor) x (24 hours/day) 

Equation 3-7 Idling kW-hr = (Likely kW) x (Total Engines) x (Likely Annual Idling Days) x (Minimum 

Load Factor) x (24 hours/day) 

The Category 2 activity inputs for U.S. flag vessels by vessel type are provided in 

Table 3-12. These inputs were largely developed by Eastern Research Group, Inc. (ERG), 

under contract to EPA, with the exception of ferry vessels.16 For ferries, a diesel fuel 

consumption estimate of 35,120,000 gallons for fiscal year 2004 published by the American 

Public Transportation Association was used, since a published value was deemed more 

reliable.17 The diesel fuel gallons consumed by ferries were then converted to kW-hr using a 

fuel density of 7.1 lb/gallon and a brake specific fuel consumption estimate of 0.35 lb/hp-hr. 

As part of its comment submittal to EPA, the American Waterways Operators (AWO) 

provided propulsion engine population data for towboats that can be directly compared with 

the estimates in this rule.18 Table 3-13 below provides a comparison of the data submitted by 

AWO to that developed by ERG for towboats. The AWO data represent current populations, 

although the reference year was not provided. For this rule, population data for 2004 were 

used. 
Regulatory Impact Analysis 

20

Table 3-12 Category 2 U.S. Flag Engine Activity Inputs 

Horsepower Annual Transit Days 

Vessel Type Total C2 Engines Utilization Rate Equivalent C2 Enginesa Min Likely Max Total Min Likely Max An

Idlin

Li

Deep Water Cargo 92 100% 92 1,860 3,603 7,200 329,920 219 Tow Boats 2,071 74% 1,533 900 2,207 7,420 3,382,781 219 Ferries 218 85% 185 865 2,412 4,400 446,529 152 174 243 Commercial Fishing 403 85% 342 1,000 1,924 4,313 658,293 250 271 292 Great Lake 272 85% 232 518 2,505 3,600 579,847 136 Coast Guard 320 100% 320 1,250 2,289 3,650 732,734 29 88 157 Offshore 1,339 97% 1,303 740 2,016 7,502 2,626,265 280 299 317 Research 70 100% 70 600 1,622 3,750 113,717 220 Total 4,785 4,076 8,870,086 711 1,625 1,009 2

Activity (HP-HR) Chosen Activityb Total U.S. F

2

Vessel Type 

Underway Idling Total HP-HR Total HP-HR 

Reference 

Year(s) 

Chosen 

Reference 

Year Total HP

Deep Water Cargo 1,387,249,658 0 1,387,249,658 1,387,249,658 2004 2004 1,362,612Tow Boats 15,112,911,191 0 15,112,911,191 15,112,911,191 2002-2004 2004 14,844,507Ferries 1,267,998,585 0 1,267,998,585 712,434,286 2000-2004 2004 699,781Commercial Fishing 2,995,235,100 346,591,490 3,341,826,590 3,341,826,590 2000-2004 2004 3,282,476Great Lake 1,588,928,635 0 1,588,928,635 1,588,928,635 2004 2004 1,560,709Coast Guard 1,238,027,096 94,962,306 1,332,989,402 1,332,989,402 2004 2004 1,309,315Offshore 15,992,380,323 413,920,432 16,406,300,754 16,406,300,754 2005 2005 15,971,186Research 510,360,006 24,016,941 534,376,948 534,376,948 2004 2004 524,886Total 40,093,090,594 879,491,169 40,972,581,763 40,417,017,464 39,555,475

a Equivalent C2 Engines = (Total C2 Engines) x (Utilization Rate). b Different activity used for ferries; see text. 
Emission Inventory 

3-21

Table 3-13 Comparison of Towboat Propulsion Engine Populations 

Total Propulsion Engines Category 2 Propulsion Engines Vessel Type ERG AWO ERG AWO 

Inland Towing 6,091 5,228 1,356 1,516 

Coastal Towing 2,181 2,582 715 904 

Total 8,272 7,810 2,071 2,420 

AWO estimates that 31 percent of the towboat propulsion engines are Category 2, 

based on the assumption that inland towing vessels greater than 2,000 hp and coastal towing 

vessels greater than 3,000 hp are generally equipped with C2 engines. For the ERG analysis, 

a percentage of 25 percent was applied, using both vessel horsepower and hull displacement 

as category indicators. Applying both percentages to the AWO engine total of 7,810 engines, 

the range of Category 2 towboat propulsion engines is 1,952 to 2,420 engines. Since the ERG 

estimate falls within this range, it was retained for the final rule. 

Since the reference years for the data collection varied by vessel type, it was necessary 

to adjust the activity estimates to the 2002 calendar year, which is the base year for this 

analysis. This was done by backcasting the Category 2 growth rate. The underway and idling 

kW-hr estimates for all vessel types were then consolidated into a single term for total kWhr/year. For U.S. flag vessels, the total activity estimate in 2002 is 29,496,517,905 kW-hr. 

The foreign-flag inputs are given in Table 3-14. All foreign-flag activity is assumed to 

come from off-shore vessels. These estimates were also generated by ERG. The total activity 

estimate is then multiplied by 0.15 to account for the fraction of time these vessels are spent 

in U.S. waters. The resulting foreign-flag activity in U.S. waters is estimated to be 

750,291,634 kW-hr. 

Table 3-14 Category 2 Foreign Flag Engine Activity Inputs 

Horsepower Total Activity 

Vessel Type Total 

Engines Likely Total 

Annual 

Transit 

Days 

Annual 

Idling 

Days 

Load 

Factor 

Total HP-HR Total KW-HR 

Deep Water 

Cargo Foreign 508 2,576 1,308,465 267 0 80% 6,707,716,548 5,001,994,230 

Time spent in U.S. waters: 0.15 

Total activity in U.S. waters: 750,291,634 kW-hr

The total kW-hr values for U.S. flag and foreign flag vessels were then allocated to the 

necessary displacement and horsepower categories, using the PSR engine data. The 

allocation fractions are provided in Table 3-15. For U.S. vessels, inventories were then 

developed by applying current and future emission factors, whereas only base (noncontrolled) emission factors are used for foreign flag vessels. The same growth rate is used 

for each. 
Regulatory Impact Analysis 

3-22

Table 3-15 Category 2 Activity Allocation Fractions by Displacement and Power 

Displacement/kW Category 

Fraction of 

Total Activity 

5.0<=disp<15 and <600kW 0.0066 

5.0<=disp<15 and 600<=kW<1000 0.0046 

5.0<=disp<15 and 1000<=kW<1400 0.0191 

5.0<=disp<15 and 1400<=kW<2000 0.0881 

5.0<=disp<15 and 2000<=kW<3700 0.2021 

5.0<=disp<15 and >=3700kW 0.2049 

15.0<=disp<20.0 and <600kW 0.0000 

15.0<=disp<20.0 and 600<=kW<1000 0.0000 

15.0<=disp<20.0 and 1000<=kW<1400 0.0000 

15.0<=disp<20.0 and 1400<=kW<2000 0.0284 

15.0<=disp<20.0 and 2000<=kW<3300 0.1229 

15.0<=disp<20.0 and 3300<=kW<3700 0.0000 

15.0<=disp<20.0 and >=3700kW 0.3234 

The activity estimates in Table 3-12 were based on estimates provided by ERG prior 

to release of the latest version of the report. The estimates in the latest version contain some 

revised inputs for deep water cargo and research vessels. Incorporating these revisions would 

change the total U.S. flag activity from 29,496,517,905 kW-hr to 25,665,967,253 kW-hr. 

ERG also provides a total activity estimate of 34,869,677,700 kW-hr, using a Monte Carlo 

analysis. Since the activity estimates in Table 3-12 fall between these two updated estimates, 

the decision was made to retain the previous activity estimates used for the NPRM. 

The median life for all C2 propulsion engines is 23 years.19 The emission factors used 

for all C2 propulsion engines are largely those we used for the original commercial marine 

rulemaking analysis.20 The one exception to this is for Tier 1 NOx, which was updated based 

on an analysis of 2006 certification data. The C2 emission factors are shown in Table 3-16. 

Estimates for the annual growth rate, PM deterioration factor, and certification fuel sulfur 

levels are assumed to be the same as those for C1 propulsion engines.

Table 3-16 Baseline Emission Factors for C2 Engines (g/kW-hr)a

Tier PM10 NOx HC CO 

BASE 0.32 13.36 0.134 2.48 

TIER 1 0.32 10.55 0.134 2.48 

TIER 2 0.32 8.33 0.134 2.00 a

 Deterioration is applied to the PM emission factors (EFs); see text for details. The NOx, HC and CO EFs are 

not subject to deterioration. 

The resulting baseline 50-state emission inventories for Category 2 propulsion engines are 

given in Table 3-17..
Emission Inventory 

3-23

Table 3-17 Baseline (50-State) Emissions for C2 Propulsion Engines (short tons) 

YEAR PM10 PM 2.5 NOX VOC HC CO SO2

2002 12,850 12,464 432,306 4,701 4,464 82,621 36,868 

2003 13,112 12,719 431,973 4,743 4,504 83,364 37,193 

2004 13,376 12,975 431,683 4,786 4,545 84,115 37,528 

2005 13,641 13,232 431,417 4,829 4,586 84,872 37,866 

2006 13,907 13,490 431,195 4,872 4,627 85,635 38,207 

2007 14,174 13,748 427,380 4,916 4,669 85,621 38,550 

2008 14,436 14,003 423,601 4,960 4,711 85,611 38,837 

2009 14,706 14,264 419,857 5,005 4,753 85,605 39,204 

2010 14,975 14,525 416,169 5,050 4,796 85,609 39,559 

2011 15,245 14,787 412,537 5,096 4,839 85,621 39,920 

2012 15,515 15,050 408,943 5,141 4,883 85,639 40,278 

2013 15,727 15,255 405,428 5,188 4,927 85,665 39,905 

2014 14,475 14,041 401,970 5,234 4,971 85,701 21,334 

2015 13,635 13,226 398,593 5,281 5,016 85,746 7,888 

2016 13,883 13,466 395,295 5,329 5,061 85,800 7,958 

2017 13,986 13,566 392,101 5,377 5,106 85,864 6,238 

2018 14,127 13,703 388,988 5,425 5,152 85,937 4,998 

2019 14,228 13,801 386,000 5,474 5,199 86,020 3,277 

2020 14,365 13,934 383,155 5,523 5,245 86,116 2,031 

2021 14,613 14,175 380,458 5,573 5,293 86,222 2,185 

2022 14,850 14,405 377,990 5,623 5,340 86,341 2,258 

2023 15,059 14,607 376,313 5,674 5,388 86,475 2,279 

2024 15,243 14,786 375,430 5,725 5,437 86,626 2,299 

2025 15,423 14,960 374,784 5,777 5,486 86,790 2,319 

2026 15,599 15,131 374,343 5,829 5,535 86,974 2,339 

2027 15,772 15,299 374,086 5,881 5,585 87,178 2,359 

2028 15,943 15,465 374,039 5,934 5,635 87,406 2,379 

2029 16,114 15,630 374,219 5,987 5,686 87,672 2,399 

2030 16,283 15,794 375,126 6,041 5,737 88,078 2,421 

2031 16,451 15,957 376,727 6,096 5,789 88,623 2,442 

2032 16,618 16,120 378,567 6,150 5,841 89,207 2,463 

2033 16,786 16,282 380,573 6,206 5,893 89,820 2,485 

2034 16,952 16,444 382,749 6,262 5,946 90,457 2,507 

2035 17,119 16,605 385,076 6,318 6,000 91,119 2,529 

2036 17,286 16,767 387,519 6,375 6,054 91,799 2,551 

2037 17,453 16,929 390,097 6,432 6,108 92,500 2,573 

2038 17,620 17,091 392,794 6,490 6,163 93,219 2,595 

2039 17,787 17,253 395,609 6,549 6,219 93,956 2,618 

2040 17,954 17,416 398,527 6,607 6,275 94,707 2,641 
Regulatory Impact Analysis 

3-24

3.1.2.4 Under 37 kW Propulsion and Auxiliary 

Category 1 commercial marine engines are defined as being greater than or equal to 

(≥) 37kW and less than (<) 5.0 liters/cylinder; however, there are commercial marine engines 

<37kW. The majority of these small power engines are used as auxiliary engines, although 

there are some propulsion engines that fall into this category. Commercial marine engines 

<37kW are covered under this proposal; therefore, inventories have been estimated. 

Emissions were estimated using a special version of the NONROAD2005 model, with 

Source Classification Codes (SCCs) and associated inputs added for both the commercial and 

auxiliary engines. An SCC of 2280002030 was assigned to the <37kW propulsion engines, 

with an SCC of 2280002040 assigned to the <37kW auxiliary engines. 

The inventory inputs of base year population, average power, load factor, activity, and 

median life are given in Table 3-18 below. These inputs were generated using the same 

methodology and data sources as the C1 propulsion and C1 auxiliary categories. Horsepower 

(hp) is used as the unit for power in the NONROAD model, so the inputs for power and 

emission factors are hp and g/hp-hr, respectively. The 2002 base year populations are 

assigned to one or more of the following hp categories in NONROAD: 0-11, 11-16, 16-25, 

25-40, and 40-50. The propulsion engines all fall within the 25-40hp category, whereas there 

are auxiliary engines in each hp category. The average power values in the table below are 

population-weighted estimates. 

Table 3-18 Inventory Inputs for <37kW Commercial Marine Diesel Engines 

INPUTS PROPULSION AUXILIARY 

2002 

POPULATION 1,232 67,708 

AVG HP 34.8 24.9 

LOAD 

FACTOR 0.45 0.56 

ACTIVITY, 

HOURS 943 724 

MEDIAN 

LIFE, YEARS 13 17 

The baseline emission factors are given in Table 3-19 and Table 3-20. These engines 

are subject to EPA nonroad diesel regulations that have established two tiers of emission 

standards.21 Tier 1 phased in from 1999-2000, depending on the horsepower category, with 

Tier 2 phased in from 2004-2005. The “Base” entries in the tables refer to emissions from 

pre-controlled engines. These emission factors are used for both propulsion and auxiliary 

engines. 
Emission Inventory 

3-25

Table 3-19 Baseline PM10 and NOx Emission Factors and Deterioration Factors for <37kW Commercial 

Marine Diesel Engines 

HP PM10 G/HP-HR NOX G/HP-HR 

RANGE BASE TIER 1 TIER 2 BASE TIER 1 TIER 2 

0-11 1.00 0.45 0.38 10.00 5.23 4.39 

11-16 0.90 0.27 0.19 8.50 4.44 3.63 

16-25 0.90 0.27 0.19 8.50 4.44 3.63 

25-50 0.80 0.34 0.23 6.90 4.73 3.71 

DF ("A") 0.473 0.473 0.473 0.024 0.024 0.009 

Table 3-20 Baseline HC and CO Emission Factors and Deterioration Factors for <37kW Commercial 

Marine Diesel Engines 

HP HC G/HP-HR CO G/HP-HR 

RANGE BASE TIER 1 TIER 2 BASE TIER 1 TIER 2 

0-11 1.50 0.76 0.68 5.00 4.11 4.11 

11-16 1.70 0.44 0.21 5.00 2.16 2.16 

16-25 1.70 0.44 0.21 5.00 2.16 2.16 

25-50 1.80 0.28 0.54 5.00 1.53 1.53 

DF ("A") 0.047 0.036 0.034 0.185 0.101 0.101 

The emission factors for the base and Tier 1 technology types are consistent with those 

used in the NONROAD model.22 Tier 2 emission factors were estimated using nonroad 

engine certification data. The deterioration factors by pollutant and technology type are also 

given in the tables above. The deterioration factors are those used for diesel engines in the 

NONROAD model.23

The certification fuel sulfur levels are 3300ppm for the base and Tier 1 technology 

type and 350ppm for Tier 2. Brake specific fuel consumption (BSFC) values were taken from 

the NONROAD model and are 0.408 lb/hp-hr for all hp categories.24 The compound 

population growth rate is 1.009, which is the growth rate used for all commercial diesel 

engines. Since the NONROAD model uses linear growth inputs, the compound growth rates 

are converted to a form consistent with the NONROAD model for this category. 

The resulting baseline 50-state emission inventories for <37kW commercial marine 

engines (propulsion and auxiliary combined) are given in Table 3-21. 
Regulatory Impact Analysis 

3-26

Table 3-21 Baseline (50-State) Emissions for <37kW Commercial Marine Engines (short tons) 

YEAR PM10 PM 2.5 NOX VOC HC CO SO2

2002 728 706 5,517 1,273 1,209 3,783 731 

2003 710 689 5,448 1,222 1,161 3,680 738 

2004 692 671 5,350 1,179 1,120 3,576 745 

2005 671 651 5,229 1,128 1,071 3,460 752 

2006 648 629 5,101 1,075 1,021 3,339 745 

2007 596 578 4,973 1,022 970 3,216 387 

2008 551 534 4,846 969 920 3,093 128 

2009 526 511 4,719 916 870 2,970 129 

2010 499 484 4,594 864 821 2,846 95 

2011 472 458 4,472 813 772 2,724 71 

2012 444 431 4,351 763 725 2,603 38 

2013 417 404 4,234 715 679 2,484 14 

2014 392 381 4,120 668 634 2,369 16 

2015 368 357 4,011 624 592 2,259 18 

2016 348 337 3,917 588 559 2,170 18 

2017 332 322 3,846 564 535 2,109 18 

2018 320 311 3,790 546 518 2,063 18 

2019 310 301 3,744 531 504 2,027 18 

2020 301 292 3,704 519 493 1,997 18 

2021 294 285 3,675 507 482 1,972 18 

2022 288 279 3,659 497 472 1,952 18 

2023 284 275 3,654 491 466 1,940 19 

2024 280 272 3,654 485 461 1,932 19 

2025 278 269 3,658 481 457 1,926 19 

2026 276 268 3,670 479 455 1,926 19 

2027 275 267 3,685 478 454 1,929 19 

2028 275 267 3,703 478 454 1,934 19 

2029 275 267 3,723 478 454 1,942 20 

2030 275 267 3,746 479 455 1,952 20 

2031 276 268 3,771 481 457 1,963 20 

2032 278 269 3,798 484 460 1,977 20 

2033 279 271 3,828 488 463 1,992 20 

2034 282 273 3,859 492 467 2,009 21 

2035 284 275 3,891 496 471 2,026 21 

2036 286 278 3,924 500 475 2,044 21 

2037 289 280 3,958 504 479 2,061 21 

2038 291 282 3,992 509 483 2,079 21 

2039 294 285 4,026 513 487 2,097 21 

2040 296 287 4,061 517 491 2,115 22 
Emission Inventory 

3-27

3.1.2.5 Commercial Marine Diesel Baseline Inventory Summary 

3.1.2.5.1 PM10, PM 2.5, NOx, VOC, CO, and SO2 Emissions 

Table 3-22 thru Table 3-27 present the resulting 50-state consolidated commercial 

marine baseline inventories by pollutant and category, for calendar years 2002-2040. 

3.1.2.5.2 Air Toxics Emissions 

The baseline air toxics inventories for the consolidated commercial marine diesel 

engines were taken from the Mobile Source Air Toxics Rule (MSAT)25 and are provided in 

Table 3-28. Inventories are provided for calendar years 1999, 2010, 2015, 2020, and 2030. 

The air toxics inventories were developed independently and prior to this final rule 

development. The purpose of presenting these inventories is to show the incidental hazardous 

air pollutant (HAP) reductions from this rule. 
Regulatory Impact Analysis 

3-28

Table 3-22 Baseline (50-State) PM10 Emissions for Commercial Marine Diesel Engines 

(short tons) 

YEAR C1 

PROPULSION 

C1 

AUXILIARY 

C1 

TOTAL 

C2 

PROPULSION <37KW TOTAL 

2002 13,328 2,714 16,041 12,850 728 29,619 

2003 13,690 2,773 16,463 13,112 710 30,285 

2004 13,807 2,791 16,598 13,376 692 30,666 

2005 13,873 2,786 16,659 13,641 671 30,972 

2006 13,872 2,769 16,641 13,907 648 31,196 

2007 12,230 2,482 14,712 14,174 596 29,481 

2008 10,961 2,263 13,224 14,436 551 28,211 

2009 10,710 2,230 12,940 14,706 526 28,172 

2010 10,304 2,170 12,474 14,975 499 27,948 

2011 9,916 2,115 12,031 15,245 472 27,748 

2012 9,471 2,052 11,522 15,515 444 27,482 

2013 9,003 1,993 10,996 15,727 417 27,140 

2014 8,587 1,952 10,539 14,475 392 25,406 

2015 8,155 1,907 10,062 13,635 368 24,066 

2016 7,718 1,860 9,579 13,883 348 23,809 

2017 7,346 1,806 9,152 13,986 332 23,470 

2018 7,058 1,746 8,804 14,127 320 23,250 

2019 6,805 1,685 8,490 14,228 310 23,028 

2020 6,632 1,625 8,257 14,365 301 22,923 

2021 6,538 1,576 8,114 14,613 294 23,021 

2022 6,470 1,543 8,013 14,850 288 23,151 

2023 6,422 1,520 7,942 15,059 284 23,284 

2024 6,388 1,504 7,893 15,243 280 23,416 

2025 6,368 1,495 7,864 15,423 278 23,564 

2026 6,359 1,489 7,849 15,599 276 23,724 

2027 6,363 1,486 7,849 15,772 275 23,897 

2028 6,381 1,484 7,865 15,943 275 24,083 

2029 6,410 1,484 7,895 16,114 275 24,283 

2030 6,451 1,486 7,937 16,283 275 24,495 

2031 6,499 1,489 7,988 16,451 276 24,715 

2032 6,552 1,493 8,045 16,618 278 24,941 

2033 6,611 1,499 8,110 16,786 279 25,175 

2034 6,671 1,506 8,177 16,952 282 25,411 

2035 6,731 1,514 8,245 17,119 284 25,648 

2036 6,791 1,524 8,315 17,286 286 25,887 

2037 6,852 1,535 8,387 17,453 289 26,129 

2038 6,914 1,547 8,461 17,620 291 26,372 

2039 6,976 1,561 8,537 17,787 294 26,617 

2040 7,039 1,574 8,613 17,954 296 26,864 
Emission Inventory 

3-29

Table 3-23 Baseline (50-State) PM2.5 Emissions for Commercial Marine Diesel Engines 

(short tons) 

YEAR C1 

PROPULSION 

C1 

AUXILIARY 

C1 

TOTAL 

C2 

PROPULSION <37KW TOTAL 

2002 12,928 2,632 15,560 12,464 706 28,730 

2003 13,279 2,690 15,969 12,719 689 29,377 

2004 13,393 2,708 16,100 12,975 671 29,746 

2005 13,457 2,703 16,159 13,232 651 30,042 

2006 13,456 2,686 16,142 13,490 629 30,260 

2007 11,863 2,407 14,270 13,748 578 28,596 

2008 10,632 2,195 12,827 14,003 534 27,364 

2009 10,388 2,163 12,552 14,264 511 27,327 

2010 9,995 2,105 12,100 14,525 484 27,109 

2011 9,619 2,052 11,670 14,787 458 26,916 

2012 9,187 1,990 11,177 15,050 431 26,657 

2013 8,733 1,933 10,666 15,255 404 26,326 

2014 8,330 1,893 10,223 14,041 381 24,644 

2015 7,910 1,850 9,760 13,226 357 23,344 

2016 7,487 1,805 9,291 13,466 337 23,095 

2017 7,126 1,752 8,878 13,566 322 22,766 

2018 6,846 1,693 8,539 13,703 311 22,553 

2019 6,601 1,634 8,235 13,801 301 22,337 

2020 6,433 1,576 8,009 13,934 292 22,236 

2021 6,342 1,528 7,871 14,175 285 22,330 

2022 6,276 1,497 7,773 14,405 279 22,457 

2023 6,229 1,474 7,703 14,607 275 22,585 

2024 6,197 1,459 7,656 14,786 272 22,714 

2025 6,177 1,451 7,628 14,960 269 22,857 

2026 6,168 1,445 7,613 15,131 268 23,012 

2027 6,173 1,441 7,614 15,299 267 23,180 

2028 6,190 1,440 7,629 15,465 267 23,361 

2029 6,218 1,440 7,658 15,630 267 23,555 

2030 6,258 1,441 7,699 15,794 267 23,760 

2031 6,304 1,444 7,748 15,957 268 23,973 

2032 6,356 1,448 7,804 16,120 269 24,193 

2033 6,413 1,454 7,867 16,282 271 24,420 

2034 6,471 1,461 7,932 16,444 273 24,648 

2035 6,529 1,469 7,998 16,605 275 24,879 

2036 6,588 1,478 8,066 16,767 278 25,111 

2037 6,647 1,489 8,136 16,929 280 25,345 

2038 6,707 1,501 8,207 17,091 282 25,581 

2039 6,767 1,514 8,281 17,253 285 25,819 

2040 6,828 1,527 8,355 17,416 287 26,058 
Regulatory Impact Analysis 

3-30

Table 3-24 Baseline (50-State) NOx Emissions for Commercial Marine Diesel Engines 

(short tons) 

YEAR C1 

PROPULSION 

C1 

AUXILIARY 

C1 

TOTAL 

C2 

PROPULSION <37KW TOTAL 

2002 335,561 60,641 396,202 432,306 5,517 834,025 

2003 336,369 60,959 397,328 431,973 5,448 834,749 

2004 332,798 60,482 393,280 431,683 5,350 830,313 

2005 328,810 59,774 388,583 431,417 5,229 825,229 

2006 324,900 59,073 383,973 431,195 5,101 820,269 

2007 316,663 58,048 374,710 427,380 4,973 807,063 

2008 308,524 57,030 365,554 423,601 4,846 794,001 

2009 300,509 56,020 356,529 419,857 4,719 781,105 

2010 292,651 55,022 347,673 416,169 4,594 768,436 

2011 284,979 54,038 339,017 412,537 4,472 756,026 

2012 277,551 53,069 330,621 408,943 4,351 743,915 

2013 270,764 52,118 322,882 405,428 4,234 732,544 

2014 264,634 51,185 315,819 401,970 4,120 721,910 

2015 258,879 50,277 309,156 398,593 4,011 711,760 

2016 253,538 49,399 302,937 395,295 3,917 702,150 

2017 249,327 48,589 297,916 392,101 3,846 693,862 

2018 246,339 47,849 294,188 388,988 3,790 686,966 

2019 243,964 47,160 291,123 386,000 3,744 680,867 

2020 242,764 46,531 289,295 383,155 3,704 676,154 

2021 242,677 46,079 288,756 380,458 3,675 672,889 

2022 242,990 45,840 288,831 377,990 3,659 670,480 

2023 243,640 45,706 289,346 376,313 3,654 669,313 

2024 244,563 45,683 290,245 375,430 3,654 669,329 

2025 245,736 45,756 291,492 374,784 3,658 669,934 

2026 247,141 45,875 293,016 374,343 3,670 671,029 

2027 248,720 46,035 294,755 374,086 3,685 672,525 

2028 250,474 46,228 296,703 374,039 3,703 674,445 

2029 252,384 46,452 298,836 374,219 3,723 676,778 

2030 254,450 46,703 301,153 375,126 3,746 680,025 

2031 256,608 46,980 303,588 376,727 3,771 684,087 

2032 258,851 47,283 306,134 378,567 3,798 688,500 

2033 261,181 47,611 308,792 380,573 3,828 693,193 

2034 263,532 47,962 311,494 382,749 3,859 698,103 

2035 265,903 48,332 314,236 385,076 3,891 703,203 

2036 268,297 48,721 317,017 387,519 3,924 708,460 

2037 270,711 49,126 319,838 390,097 3,958 713,892 

2038 273,148 49,553 322,701 392,794 3,992 719,486 

2039 275,606 49,991 325,597 395,609 4,026 725,233 

2040 278,086 50,436 328,522 398,527 4,061 731,111 
Emission Inventory 

3-31

Table 3-25 Baseline (50-State) VOC Emissions for Commercial Marine Diesel Engines 

(short tons) 

YEAR C1 

PROPULSION 

C1 

AUXILIARY 

C1 

TOTAL 

C2 

PROPULSION <37KW TOTAL 

2002 9,488 1,767 11,255 4,701 1,273 17,229 

2003 9,573 1,783 11,356 4,743 1,222 17,321 

2004 9,561 1,785 11,346 4,786 1,179 17,311 

2005 9,550 1,788 11,338 4,829 1,128 17,295 

2006 9,540 1,791 11,331 4,872 1,075 17,278 

2007 9,415 1,787 11,202 4,916 1,022 17,140 

2008 9,291 1,783 11,074 4,960 969 17,003 

2009 9,170 1,779 10,949 5,005 916 16,870 

2010 9,051 1,776 10,826 5,050 864 16,741 

2011 8,934 1,773 10,707 5,096 813 16,615 

2012 8,821 1,770 10,591 5,141 763 16,495 

2013 8,711 1,767 10,479 5,188 715 16,381 

2014 8,606 1,765 10,371 5,234 668 16,273 

2015 8,507 1,763 10,270 5,281 624 16,175 

2016 8,415 1,761 10,176 5,329 588 16,094 

2017 8,347 1,760 10,107 5,377 564 16,048 

2018 8,304 1,759 10,063 5,425 546 16,034 

2019 8,272 1,759 10,031 5,474 531 16,036 

2020 8,269 1,760 10,029 5,523 519 16,071 

2021 8,293 1,764 10,057 5,573 507 16,137 

2022 8,326 1,771 10,097 5,623 497 16,218 

2023 8,367 1,778 10,145 5,674 491 16,310 

2024 8,414 1,788 10,202 5,725 485 16,412 

2025 8,466 1,799 10,265 5,777 481 16,523 

2026 8,523 1,811 10,334 5,829 479 16,642 

2027 8,584 1,824 10,408 5,881 478 16,767 

2028 8,649 1,837 10,487 5,934 478 16,898 

2029 8,719 1,851 10,570 5,987 478 17,035 

2030 8,792 1,865 10,657 6,041 479 17,178 

2031 8,868 1,880 10,748 6,096 481 17,325 

2032 8,946 1,895 10,841 6,150 484 17,476 

2033 9,026 1,911 10,937 6,206 488 17,631 

2034 9,107 1,927 11,034 6,262 492 17,788 

2035 9,189 1,943 11,133 6,318 496 17,947 

2036 9,272 1,960 11,232 6,375 500 18,107 

2037 9,356 1,977 11,333 6,432 504 18,269 

2038 9,440 1,995 11,435 6,490 509 18,433 

2039 9,525 2,013 11,537 6,549 513 18,599 

2040 9,610 2,031 11,641 6,607 517 18,766 
Regulatory Impact Analysis 

3-32

Table 3-26 Baseline (50-State) CO Emissions for Commercial Marine Diesel Engines (short tons) 

YEAR C1 

PROPULSION 

C1 

AUXILIARY 

C1 

TOTAL 

C2 

PROPULSION <37KW TOTAL 

2002 55,303 9,624 64,927 82,621 3,783 151,331 

2003 55,801 9,710 65,511 83,364 3,680 152,556 

2004 55,722 9,668 65,390 84,115 3,576 153,080 

2005 55,582 9,585 65,167 84,872 3,460 153,499 

2006 55,450 9,503 64,954 85,635 3,339 153,928 

2007 54,423 9,331 63,754 85,621 3,216 152,591 

2008 53,405 9,160 62,565 85,611 3,093 151,269 

2009 52,401 8,989 61,391 85,605 2,970 149,966 

2010 51,414 8,820 60,235 85,609 2,846 148,690 

2011 50,445 8,654 59,099 85,621 2,724 147,444 

2012 49,497 8,489 57,986 85,639 2,603 146,227 

2013 48,574 8,327 56,901 85,665 2,484 145,050 

2014 47,680 8,167 55,847 85,701 2,369 143,917 

2015 46,827 8,010 54,837 85,746 2,259 142,842 

2016 46,023 7,857 53,880 85,800 2,170 141,851 

2017 45,368 7,708 53,076 85,864 2,109 141,049 

2018 44,879 7,563 52,443 85,937 2,063 140,443 

2019 44,482 7,426 51,908 86,020 2,027 139,954 

2020 44,301 7,298 51,599 86,116 1,997 139,712 

2021 44,329 7,198 51,527 86,222 1,972 139,720 

2022 44,423 7,134 51,557 86,341 1,952 139,851 

2023 44,571 7,088 51,659 86,475 1,940 140,073 

2024 44,760 7,066 51,827 86,626 1,932 140,384 

2025 44,987 7,067 52,054 86,790 1,926 140,771 

2026 45,248 7,077 52,325 86,974 1,926 141,226 

2027 45,539 7,094 52,633 87,178 1,929 141,740 

2028 45,861 7,117 52,978 87,406 1,934 142,318 

2029 46,209 7,145 53,354 87,672 1,942 142,968 

2030 46,583 7,178 53,761 88,078 1,952 143,791 

2031 46,975 7,215 54,191 88,623 1,963 144,776 

2032 47,385 7,257 54,642 89,207 1,977 145,825 

2033 47,811 7,303 55,114 89,820 1,992 146,926 

2034 48,241 7,353 55,595 90,457 2,009 148,060 

2035 48,675 7,407 56,082 91,119 2,026 149,227 

2036 49,114 7,464 56,577 91,799 2,044 150,419 

2037 49,556 7,524 57,079 92,500 2,061 151,640 

2038 50,002 7,588 57,589 93,219 2,079 152,887 

2039 50,452 7,654 58,105 93,956 2,097 154,158 

2040 50,906 7,721 58,627 94,707 2,115 155,449 
Emission Inventory 

3-33

Table 3-27 Baseline (50-State) SO2 Emissions for Commercial Marine Diesel Engines (short tons) 

YEAR C1 

PROPULSION 

C1 

AUXILIARY 

C1 

TOTAL 

C2 

PROPULSION <37KW TOTAL 

2002 36,201 6,553 42,754 36,868 731 80,353 

2003 36,528 6,613 43,141 37,193 738 81,073 

2004 36,862 6,673 43,535 37,528 745 81,808 

2005 37,192 6,733 43,925 37,866 752 82,543 

2006 36,827 6,667 43,493 38,207 745 82,445 

2007 19,121 3,461 22,583 38,550 387 61,520 

2008 6,299 1,140 7,440 38,837 128 46,404 

2009 6,355 1,150 7,506 39,204 129 46,838 

2010 4,705 852 5,557 39,559 95 45,212 

2011 3,513 636 4,148 39,920 71 44,139 

2012 1,862 337 2,199 40,278 38 42,515 

2013 664 120 784 39,905 14 40,702 

2014 799 145 943 21,334 16 22,293 

2015 857 155 1,012 7,888 18 8,917 

2016 865 157 1,021 7,958 18 8,997 

2017 872 158 1,030 6,238 18 7,286 

2018 879 159 1,038 4,998 18 6,054 

2019 886 160 1,046 3,277 18 4,342 

2020 893 162 1,055 2,031 18 3,104 

2021 900 163 1,063 2,185 18 3,267 

2022 907 164 1,072 2,258 18 3,348 

2023 915 166 1,081 2,279 19 3,378 

2024 923 167 1,090 2,299 19 3,408 

2025 931 169 1,099 2,319 19 3,437 

2026 939 170 1,109 2,339 19 3,466 

2027 946 171 1,118 2,359 19 3,496 

2028 954 173 1,127 2,379 19 3,526 

2029 962 174 1,136 2,399 20 3,555 

2030 970 176 1,146 2,421 20 3,586 

2031 978 177 1,155 2,442 20 3,617 

2032 986 179 1,165 2,463 20 3,649 

2033 995 180 1,175 2,485 20 3,680 

2034 1,006 182 1,188 2,507 21 3,716 

2035 1,015 184 1,198 2,529 21 3,748 

2036 1,023 185 1,208 2,551 21 3,780 

2037 1,032 187 1,218 2,573 21 3,812 

2038 1,040 188 1,228 2,595 21 3,845 

2039 1,050 190 1,240 2,618 21 3,880 

2040 1,059 192 1,251 2,641 22 3,913 
Regulatory Impact Analysis 

3-34

Table 3-28 Air Toxics Emissions for Commercial Marine Diesel Engines (short tons) 

HAP 1999 2010 2015 2020 2030 

BENZENE 530 556 559 572 624 

FORMALDEHYDE 3,897 4,091 4,112 4,208 4,587 

ACETALDEHYDE 1,937 2,033 2,044 2,091 2,280 

1,3-BUTADIENE 6 6 6 6 7 

ACROLEIN 75 79 79 81 89 

NAPHTHALENE 43 39 37 36 40 

POM 11 10 9 9 10 

3.1.3 Control Inventory Development 

This section describes how the control emission inventories were developed for the 

commercial marine diesel categories: Category 1 propulsion, Category 1 auxiliary, Category 2 

propulsion, and less than (<) 37kW. This section will only describe the modifications to the 

emission factors, since the other inventory inputs are unchanged. 

3.1.3.1 Control Scenario(s) Modeled 

For commercial marine diesel engines, there are two tiers of final PM standards and 

either combined HC+NOx or NOx and HC only standards for the control scenario that were 

modeled. 

The final emission standards for Category 1 engines are summarized in Table 3-29 and 

Table 3-30. These standards apply to both propulsion and auxiliary engines. There are 

separate emission levels for standard and high power density engines. Standard power 

density engines are less than 35 kW per liter (kW/L), and the high power density engines are 

greater than or equal to 35 kW/L. Within these power density categories, there are also 

separate standards that vary by power and displacement. There are no Tier 4 standards for 

engines less than 600 kW. Standards are not shown in cases where there is zero engine 

population. 

The final emission standards for Category 2 engines are summarized in Table 3-31. 

The standards vary by displacement and power. All Category 2 engines are considered to be 

standard power density engines. These engines are subject to both Tier 3 and Tier 4 emission 

standards. 

The final emission standards for <37kW propulsion and auxilliary engines are given in 

Table 3-32. This category is subject to Tier 3 standards which begin in 2009. 
3-35

Table 3-29 Final Standards (g/kW-hr) for C1 Standard Power Density Engines 

<35 KW/L 

<=600KW 600<KW≤1

TIER 3 TIER 4 TIER 3 

DISPLACEMENT CATEGORY 

YEAR NOX+HC PM YEAR HC NOX PM YEAR NOX+HC PM YE

DISP<0.9 AND 2009 7.5 0.30 37<KW<=75 2014 4.7 DISP<0.9 AND >75KW 2012 5.4 0.13 0.9<=DISP<1.2 2013 5.4 0.12 

NO ENGINES IN THES

1.2<=DISP<2.5 2014 5.6 0.11 

2018 0.09 

2014 5.6 0.11 20

2.5<=DISP<3.5 2013 5.6 0.11 

2018 0.09 

2013 5.6 0.11 20

3.5<=DISP<5.0 2012 5.8 0.11 

2018 0.09 

NO TIER 4 STANDARDS 

2012 5.8 0.11 20

<35 KW/L 

1000<KW≤1400 1400<KW≤

TIER 3 TIER 4 TIER 3 

DISPLACEMENT CATEGORY 

YEAR NOX+HC PM YEAR HC NOX PM YEAR NOX+HC PM YE

DISP<0.9 AND 37<KW<=75 DISP<0.9 AND >75KW 0.9<=DISP<1.2 

NO ENGINES IN THESE CATEGORIES 

1.2<=DISP<2.5 2014 5.6 0.11 2017 0.19 1.7 0.04 2.5<=DISP<3.5 2013 5.6 0.11 2017 0.19 1.7 0.04 

NO ENGINES IN THES

3.5<=DISP<5.0 2012 5.8 0.11 2017 0.19 1.7 0.04 2012 5.8 0.11 20
Regulatory Impact Analysis 

3-36

Table 3-30 Final Standards (g/kW-hr) for C1 High Power Density Engines 

≥35 KW/L 

<=600KW 600<KW≤

TIER 3 TIER 4 TIER 3 

DISPLACEMENT CATEGORY 

YEAR NOX+HC PM YEAR HC NOX PM YEAR NOX+HC PM YE

DISP<0.9 AND 2009 7.5 0.30 37<KW<=75 2014 4.7 DISP<0.9 AND >75KW 2012 5.8 0.15 0.9<=DISP<1.2 2013 5.8 0.13 

NO ENGINES IN THES

1.2<=DISP<2.5 2014 5.8 0.12 2014 5.6 0.11 202.5<=DISP<3.5 3.5<=DISP<5.0 NO ENGINES 

NO TIER 4 STANDARDS 

NO ENGINES IN THES

≥35 KW/L 

1000<KW≤1400 1400<KW≤

TIER 3 TIER 4 TIER 3 

DISPLACEMENT CATEGORY 

YEAR 

NOX+H

C PM YEAR HC 

NOX

PM YEAR 

NOX+H

C PM YE

DISP<0.9 AND 37<KW<=75 DISP<0.9 AND >75KW 

NO ENGINES IN THESE CATEGORIES NO ENGINES IN THE

0.9<=DISP<1.2 2013 5.4 0.12 2017 0.19 1.7 0.04 1.2<=DISP<2.5 2014 5.6 0.11 22.5<=DISP<3.5 NO ENGINES IN THESE CATEGORIES NO ENGINES IN TH3.5<=DISP<5.0 2012 5.8 0.11 2017 0.19 1.7 0.04 2012 5.8 0.11 2
Emission Inventory 

3-37

Table 3-31 Final Standards (g/kW-hr) for C2 Engines 

DISPLACEMENT CATEGORY TIER 3 TIER 4 

YEAR NOX+HC PM YEAR HC NOX PM 

5.0<=DISP<15 AND <600KW 2013 6.2 0.13 

5.0<=DISP<15 AND 600≤KW<1000 2013 6.2 0.13 2018 0.19 1.7 0.04 

5.0<=DISP<15 AND 1000≤KW<1400 2013 6.2 0.13 2017 0.19 1.7 0.04 

5.0<=DISP<15 AND 1400≤KW<2000 2013 6.2 0.13 2016 0.19 1.7 0.04 

2014 0.19 1.7 5.0<=DISP<15 AND 2000≤KW<3700 2013 0.13 2016 0.04 

2014 0.19 1.7 0.12 5.0<=DISP<15 AND ≥3700KW 2017 0.05 

15.0<=DISP<20.0 AND <1400KW NO ENGINES IN THIS CATEGORY 

15.0<=DISP<20.0 AND 1400≤KW<2000 2014 7.0 0.34 2016 0.19 1.7 0.04 

2014 0.19 1.7 15.0<=DISP<20 AND 2000≤KW<3300 2014 0.34 2016 0.04 

15.0<=DISP<20.0 AND 3300≤KW<3700 NO ENGINES IN THIS CATEGORY 

2014 0.19 1.7 0.25 15.0<=DISP<20.0 AND ≥3700KW 2017 0.05 

20.0<=DISP<30.0 NO ENGINES IN THIS CATEGORY 

Table 3-32 Final Standards (g/hp-hr) for <37kW Commercial Marine Diesel Engines 

HP TIER 3 

RANGE YEAR NOX+HC PM 

0-25 2009 5.6 0.30 

2009 5.6 0.22 25-50 2014 3.5 0.22 

In addition to the new emission standards, Category 1 and Category 2 engines larger 

than 600 kW and newly manufactured after January 1, 1973 are subject to a remanufacturing 

program. A description of the remanufacturing program modeled is provided in Table 3-33. 

The remanufacturing program affects PM emissions from Category 1 engines and both NOx 

and PM emissions from Category 2 engines. The remanufacturing program exempts those 

vessel owners who earn less than $5 million per year in gross annual revenues. 

Table 3-33 Description of Remanufacturing Program Modeled 

Percent 

Category Reduction Power 

Cutoff 

Calendar 

Year Start 

Model 

Years Engine Model Series Tiers 

NOx PM 

Category 1 >600 kW 2012 1972+ 

Cat 3500 

DOC/MTU 149 

Cummins KTA 38 

Tiers 0/1/2 None 25%

Category 2 >600 kW 2008 1972+ 

EMD 645 

EMD 710 

GE (all models) 

Tiers 0/1 for NOx 

Tiers 0/1/2 for PM 30% 25%
Regulatory Impact Analysis 

3-38

3.1.3.2 Category 1 Propulsion 

The modeled Tier 3 and Tier 4 emission factors corresponding to the emission 

standards are shown in Table 3-34 and Table 3-35. These emission factors are derived by 

applying the appropriate relative reductions from the Tier 2 standard to the Tier 2 emission 

factors, using the following equations: 

Equation 3-8 Tier 3 EF = (Tier 3 std/Tier 2 std) x Tier 2 EF 

Equation 3-9 Tier 4 EF = (Tier 4 std/Tier 2 std) x Tier 2 EF 

For NOx, the standards used in the above equations are the combined HC+NOx

standards. For HC and PM, the PM standards are used. 

Once inventories were estimated using the control emission factors, the additional 

NOx emissions reductions of the remanufacturing program were calculated. The total NOx 

inventories were first separated by tier and by power category. The NOx emissions from Tier 

0, Tier 1, and Tier 2 engines greater than 600 kW that are subject to the remanufacturing 

program were then summed for each calendar year. The following equation was then applied 

to those affected tons to calculate the reduced tons under the remanufacturing program: 

Equation 3-10 Remfr TonsCY = [Frac ctlCY x (1-Red) + (1-Frac ctlCY)] x TonsCY

Where: 

Remfr TonsCY = Tons for >600kW pre-Tier 3 engines with remanufacturing program, 

by calendar year 

Fract ctlCY = Fraction of the fleet >600kW subject to the remanufacturing program, by 

calendar year (from Table 3-36) 

Red = Fractional reduction (0.25 from Table 3-33) 

Tons = Tons for >600kW pre-Tier 3 engines, by calendar year 

The fraction of the fleet subject to the remanufacturing program was estimated based 

on an evaluation of a random sample of inland river vessels.26 A seven year phase-in was 

allowed to account for the typical rebuild cycle for these engines. The resulting fleet fractions 

by calendar year are provided in Table 3-36. 

The remanufacture tons (Remfr Tons) was then subtracted from the Tons value to 

obtain the delta tons for the remanufacturing program for each calendar year. The resulting 

delta tons value was decreased by 12 percent to account for the small business exemption. 

This delta tons value was then subtracted from the total Category 1 propulsion inventory for 

each calendar year to obtain the resulting tons for the combined emission standard and marine 

remanufacturing control program. 

The resulting control case 50-state emission inventories for Category 1 propulsion 

engines are given in Table 3-37. 
3-39

Table 3-34 Control PM10, NOx, and HC Emission Factors (g/kW-hr) for C1 Propulsion Standard Power Density 

<35 KW/L 

<=600KW 600<KW≤1000 

TIER 3 TIER 3 

DISPLACEMENT CATEGORY 

YEAR 

HC NOX PM 

YEAR 

HC NOX PM 

YEAR 

HC 

DISP<0.9 AND 2009 0.30 5.70 0.17 37<KW<=75 2014 3.56 DISP<0.9 AND >75KW 2012 0.14 4.08 0.08 0.9<=DISP<1.2 2013 0.13 4.54 0.05 

NO ENGINES IN THESE CATEGORIES 

2014 0.10 4.69 0.07 1.2<=DISP<2.5 

2018 0.061 2014 0.10 4.69 0.07 2018 0.04 

2013 0.10 4.69 0.07 2.5<=DISP<3.5 2018 0.061 2013 0.10 4.69 0.07 2018 0.04 2012 0.10 4.81 0.07 3.5<=DISP<5.0 

2018 0.061 

2012 0.10 4.81 0.07 2018 0.04 

<35 KW/L 

1000<KW≤1400 >1400K

TIER 3 TIER 4 TIER 3 

DISPLACEMENT CATEGORY 

YEAR 

HC NOX PM 

YEAR 

HC NOX PM 

YEAR 

HC NOX PM 

Y

DISP<0.9 AND 37<KW<=75 DISP<0.9 AND >75KW 0.9<=DISP<1.2 

NO ENGINES IN THESE CATEGORIES 

1.2<=DISP<2.5 2014 0.10 4.69 0.07 2017 0.04 1.3 0.03 2.5<=DISP<3.5 2013 0.10 4.69 0.07 2017 0.04 1.3 0.03 

NO ENGINES IN THES

3.5<=DISP<5.0 2012 0.10 4.81 0.07 2017 0.04 1.3 0.03 2012 0.10 4.81 0.07 
Regulatory Impact Analysis 

3-40

Table 3-35 Control PM10, NOx, and HC Emission Factors (g/kW-hr) for C1 Propulsion High Power Density E

≥35 KW/L 

<=600KW 600<KW≤1000 

TIER 3 TIER 3 

DISPLACEMENT CATEGORY 

YEAR 

HC NOX PM 

YEAR 

HC NOX PM 

YEAR 

HC 

DISP<0.9 AND 37<KW<=75 NO ENGINES DISP<0.9 AND >75KW 2012 0.15 4.38 0.08 0.9<=DISP<1.2 2013 0.14 4.89 0.05 

NO ENGINES IN THESE CATEGORIES 

1.2<=DISP<2.5 2014 0.11 4.81 0.08 2014 0.10 4.69 0.07 2018 0.04 2.5<=DISP<3.5 3.5<=DISP<5.0 NO ENGINES NO ENGINES IN THESE CATEGORIES 

≥35 KW/L 

1000<KW≤1400 >1400K

TIER 3 TIER 4 TIER 3 

DISPLACEMENT CATEGORY 

YEAR 

HC NOX PM 

YEAR 

HC NOX PM 

YEAR 

HC NOX PM 

Y

DISP<0.9 AND 37<KW<=75 DISP<0.9 AND >75KW 0.9<=DISP<1.2 1.2<=DISP<2.5 2.5<=DISP<3.5 

NO ENGINES IN THESE CATEGORIES NO ENGINES IN THES

3.5<=DISP<5.0 2012 0.10 4.81 0.07 2017 0.04 1.3 0.03 2012 0.10 4.81 0.07 
Emission Inventory 

3-41

Table 3-36 Percent of Fleet Subject to Remanufacturing Standards* 

Calendar Year Category 1 Category 2 

2008 0.0% 8.8% 

2009 0.0% 17.6% 

2010 0.0% 26.4% 

2011 0.0% 35.2% 

2012 8.5% 44.0% 

2013 16.9% 52.8% 

2014 25.4% 61.6% 

2015 33.9% 61.6% 

2016 42.3% 61.6% 

2017 50.8% 61.6% 

2018+ 59.3% 61.6% 

* Note that these percentages were based on an earlier draft analysis of Inland River Record samples. 

The percentages in the final analysis differ by less than two percent from those in this table. 
Regulatory Impact Analysis 

3-42

Table 3-37 Control Case (50-State) Emissions for C1 Propulsion Engines (short tons) 

YEAR PM10 PM2.5 NOX VOC HC CO SO2

2002 13,328 12,928 335,561 9,488 9,010 55,303 36,201 

2003 13,690 13,279 336,369 9,573 9,091 55,801 36,528 

2004 13,807 13,393 332,798 9,561 9,080 55,722 36,862 

2005 13,873 13,457 328,810 9,550 9,069 55,582 37,192 

2006 13,872 13,456 324,900 9,540 9,060 55,450 36,827 

2007 12,230 11,863 316,663 9,415 8,941 54,423 19,121 

2008 10,961 10,632 308,524 9,291 8,824 53,405 6,299 

2009 10,709 10,388 300,509 9,169 8,708 52,401 6,355 

2010 10,304 9,995 292,651 9,050 8,594 51,414 4,705 

2011 9,916 9,618 284,979 8,933 8,483 50,445 3,513 

2012 9,274 8,996 276,209 8,708 8,270 49,497 1,862 

2013 8,610 8,351 267,453 8,433 8,008 48,574 664 

2014 7,949 7,710 257,691 8,042 7,637 47,680 799 

2015 7,288 7,070 248,317 7,658 7,273 46,827 857 

2016 6,608 6,410 236,292 7,228 6,864 46,023 865 

2017 5,981 5,802 223,265 6,784 6,443 45,368 872 

2018 5,422 5,259 209,717 6,334 6,015 44,879 879 

2019 4,970 4,821 196,847 5,898 5,601 44,482 886 

2020 4,582 4,445 185,242 5,496 5,219 44,301 893 

2021 4,259 4,132 174,843 5,126 4,868 44,329 900 

2022 3,956 3,837 164,971 4,772 4,532 44,423 907 

2023 3,668 3,558 155,589 4,433 4,210 44,571 915 

2024 3,393 3,292 146,696 4,111 3,904 44,760 923 

2025 3,143 3,048 138,521 3,826 3,634 44,987 931 

2026 2,922 2,834 131,195 3,589 3,408 45,248 939 

2027 2,739 2,657 124,763 3,400 3,229 45,539 946 

2028 2,591 2,513 119,185 3,252 3,089 45,861 954 

2029 2,471 2,397 114,708 3,134 2,976 46,209 962 

2030 2,387 2,315 111,660 3,049 2,896 46,583 970 

2031 2,330 2,260 109,766 2,991 2,841 46,975 978 

2032 2,295 2,226 108,624 2,953 2,804 47,385 986 

2033 2,273 2,205 107,896 2,927 2,780 47,811 995 

2034 2,258 2,190 107,443 2,911 2,764 48,241 1,006 

2035 2,249 2,182 107,233 2,902 2,756 48,675 1,015 

2036 2,246 2,179 107,236 2,901 2,755 49,114 1,023 

2037 2,249 2,181 107,444 2,906 2,760 49,556 1,032 

2038 2,256 2,188 107,834 2,919 2,772 50,002 1,040 

2039 2,268 2,200 108,376 2,936 2,788 50,452 1,050 

2040 2,282 2,214 109,054 2,957 2,808 50,906 1,059 
Emission Inventory 

3-43

3.1.3.3 Category 1 Auxiliary 

The modeled Tier 3 and Tier 4 emission factors for Category 1 auxiliary engines are 

shown in Table 3-38 and Table 3-39. The methodology described above for Category 1 

propulsion engines was used to derive these emission factors and calculate the emissions 

impact of the remanufacturing program. 

The resulting control case 50-state emission inventories for Category 1 auxiliary 

engines are given in Table 3-40. 
Regulatory Impact Analysis 

3-44

Table 3-38 Control PM10, NOx, and HC Emission Factors (g/kW-hr) for C1 Auxiliary Standard Power Density 

<35 KW/L 

≤600KW 600<KW≤1000 

TIER 3 TIER 3 

DISPLACEMENT CATEGORY 

YEAR HC NOX PM YEAR HC NOX PM YEAR HC 

DISP<0.9 AND 2009 0.30 5.70 0.17 37<KW<=75 2014 3.56 DISP<0.9 AND >75KW 2012 0.14 4.08 0.08 0.9<=DISP<1.2 2013 0.13 4.02 0.08 

NO ENGINES IN THESE CATEGORI

1.2<=DISP<2.5 2014 0.11 4.77 0.08 

2018 0.070 

2014 0.11 4.77 0.08 2018 0.04 

2.5<=DISP<3.5 2013 0.11 4.77 0.08 

2018 0.070 

2013 0.11 4.77 0.08 2018 0.04 

3.5<=DISP<5.0 2012 0.11 4.89 0.08 

2018 0.070 

2012 0.11 4.89 0.08 2018 0.04 

<35 KW/L 

1000<KW≤1400 >1400K

TIER 3 TIER 4 TIER 3 

DISPLACEMENT CATEGORY 

YEAR 

HC NOX PM 

YEAR 

HC NOX PM 

YEAR 

HC NOX PM 

Y

DISP<0.9 AND 37<KW<=75 DISP<0.9 AND >75KW 0.9<=DISP<1.2 1.2<=DISP<2.5 

NO ENGINES IN THESE CATEGORIES 

2.5<=DISP<3.5 2013 0.11 4.77 0.08 2017 0.04 1.3 0.03 

NO ENGINES IN THES

3.5<=DISP<5.0 2012 0.11 4.89 0.08 2017 0.04 1.3 0.03 2012 0.11 4.89 0.08 
3-45

Table 3-39 Control PM10, NOx, and HC Emission Factors (g/kW-hr) for C1 Auxiliary High Power Density E

≥35 KW/L 

≤600KW 600<KW≤1000 

TIER 3 TIER 3 

DISPLACEMENT CATEGORY 

YEAR HC NOX PM YEAR HC NOX PM YEAR HC 

DISP<0.9 AND 2009 0.30 5.70 0.17 37<KW<=75 2014 3.56 DISP<0.9 AND >75KW 2012 0.15 4.38 0.08 0.9<=DISP<1.2 1.2<=DISP<2.5 2.5<=DISP<3.5 3.5<=DISP<5.0 

NO ENGINES IN THESE 

CATEGORIES 

NO ENGINES IN THESE CATEGORI

≥35 KW/L 

1000<KW≤1400 >1400K

TIER 3 TIER 4 TIER 3 

DISPLACEMENT CATEGORY 

YEAR 

HC NOX PM 

YEAR 

HC NOX PM 

YEAR 

HC NOX PM 

Y

DISP<0.9 AND 37<KW<=75 DISP<0.9 AND >75KW 

NO ENGINES IN THESE CATEGORIES 

0.9<=DISP<1.2 2013 0.13 4.02 0.08 2017 0.04 1.3 0.03 

NO ENGINES IN THES

1.2<=DISP<2.5 2014 0.11 4.77 0.08 2.5<=DISP<3.5 3.5<=DISP<5.0 

NO ENGINES IN THESE CATEGORIES NO ENGINES IN THES
Regulatory Impact Analysis 

3-46

Table 3-40 Control Case (50-State) Emissions for C1 Auxiliary Engines (short tons) 

YEAR PM10 PM2.5 NOX VOC HC CO SO2

2002 2,714 2,632 60,641 1,767 1,678 9,624 6,553 

2003 2,773 2,690 60,959 1,783 1,693 9,710 6,613 

2004 2,791 2,708 60,482 1,785 1,696 9,668 6,673 

2005 2,786 2,703 59,774 1,788 1,698 9,585 6,733 

2006 2,769 2,686 59,073 1,791 1,700 9,503 6,667 

2007 2,482 2,407 58,048 1,787 1,697 9,331 3,461 

2008 2,263 2,195 57,030 1,783 1,693 9,160 1,140 

2009 2,229 2,162 56,020 1,778 1,688 8,989 1,150 

2010 2,169 2,104 55,022 1,773 1,684 8,820 852 

2011 2,113 2,049 54,038 1,768 1,679 8,654 636 

2012 2,028 1,967 52,949 1,753 1,664 8,489 337 

2013 1,943 1,884 51,796 1,727 1,640 8,327 120 

2014 1,862 1,806 50,317 1,677 1,593 8,167 145 

2015 1,778 1,725 48,863 1,628 1,546 8,010 155 

2016 1,691 1,641 47,349 1,577 1,497 7,857 157 

2017 1,597 1,549 45,754 1,523 1,446 7,708 158 

2018 1,490 1,445 43,895 1,463 1,389 7,563 159 

2019 1,393 1,351 42,089 1,403 1,333 7,426 160 

2020 1,296 1,258 40,347 1,345 1,278 7,298 162 

2021 1,210 1,173 38,787 1,290 1,225 7,198 163 

2022 1,138 1,104 37,444 1,239 1,176 7,134 164 

2023 1,075 1,043 36,210 1,188 1,129 7,088 166 

2024 1,019 988 35,096 1,141 1,083 7,066 167 

2025 968 939 34,089 1,095 1,040 7,067 169 

2026 919 892 33,138 1,052 999 7,077 170 

2027 873 847 32,243 1,010 959 7,094 171 

2028 829 804 31,399 970 921 7,117 173 

2029 788 765 30,630 935 888 7,145 174 

2030 752 730 29,948 905 859 7,178 176 

2031 723 702 29,388 882 838 7,215 177 

2032 700 679 28,939 866 823 7,257 179 

2033 680 660 28,572 853 810 7,303 180 

2034 664 644 28,303 843 801 7,353 182 

2035 652 632 28,159 836 794 7,407 184 

2036 644 625 28,117 832 790 7,464 185 

2037 638 619 28,123 830 788 7,524 187 

2038 635 616 28,176 829 787 7,588 188 

2039 634 615 28,259 829 788 7,654 190 

2040 634 615 28,367 831 789 7,721 192 
Emission Inventory 

3-47

3.1.3.4 Category 2 Propulsion 

The modeled Tier 3 and Tier 4 emission factors for Category 2 propulsion engines are 

shown in Table 3-41. The methodology described above for Category 1 propulsion engines 

was used to derive these emission factors. The emissions impact of the remanufacturing 

program was calculated using Equation 3-10 and the inputs provided in Table 3-33 and Table 

3-36. Similarly to Category 1, the emissions benefit of the remanufacturing program was 

reduced by 12 percent to account for the small business exemption. 

The resulting control case 50-state emission inventories for Category 2 propulsion 

engines are given in Table 3-42. 

Table 3-41 Control PM10, NOx, and HC Emission Factors (g/kW-hr) for C2 Engines 

DISPLACEMENT CATEGORY TIER 3 TIER 4 

YEAR HC NOX PM YEAR HC NOX PM 

5.0<=DISP<15 AND <600KW 2013 0.07 5.97 0.11 

5.0<=DISP<15 AND 600≤KW<1000 2013 0.07 5.97 0.11 2018 0.02 1.3 0.03 

5.0<=DISP<15 AND 1000≤KW<1400 2013 0.07 5.97 0.11 2017 0.02 1.3 0.03 

5.0<=DISP<15 AND 1400≤KW<2000 2013 0.07 5.97 0.11 2016 0.02 1.3 0.03 

2014 0.02 1.3 5.0<=disp<15 AND 2000≤KW<3700 2013 0.11 2016 0.03 

5.0<=DISP<15 AND ≥3700KW 2014 0.06 1.3 0.10 

2017 0.03 1.3 0.04 

15.0<=DISP<20.0 AND 1400≤KW<2000 2014 0.09 6.77 0.30 2016 0.01 1.3 0.04 

2014 0.01 1.3 15.0<=DISP<20.0 AND 2000≤kW<3300 2014 0.30 2016 0.04 

15.0<=DISP<20.0 AND >3700KW 2014 0.07 1.3 0.23 

2017 0.01 1.3 0.05 
Regulatory Impact Analysis 

3-48

Table 3-42 Control Case (50-State) Emissions for C2 Propulsion Engines 

YEAR PM10 PM2.5 NOX VOC HC CO SO2

2002 12,850 12,464 432,306 4,701 4,464 82,621 36,868 

2003 13,112 12,719 431,973 4,743 4,504 83,364 37,193 

2004 13,376 12,975 431,683 4,786 4,545 84,115 37,528 

2005 13,641 13,232 431,417 4,829 4,586 84,872 37,866 

2006 13,907 13,490 431,195 4,872 4,627 85,635 38,207 

2007 14,174 13,748 427,380 4,916 4,669 85,621 38,550 

2008 14,164 13,739 414,725 4,960 4,711 85,611 38,837 

2009 14,151 13,726 402,915 5,005 4,753 85,605 39,204 

2010 14,127 13,703 391,965 5,050 4,796 85,609 39,559 

2011 14,094 13,671 381,869 5,096 4,839 85,621 39,920 

2012 14,051 13,630 372,614 5,141 4,883 85,639 40,278 

2013 13,813 13,399 363,742 5,174 4,914 85,665 39,905 

2014 12,231 11,864 345,213 5,069 4,814 85,701 21,334 

2015 11,378 11,037 333,586 4,964 4,714 85,746 7,888 

2016 11,293 10,954 320,992 4,846 4,602 85,800 7,817 

2017 10,973 10,644 308,346 4,680 4,445 85,864 5,901 

2018 10,680 10,360 295,746 4,514 4,287 85,937 4,574 

2019 10,361 10,051 283,222 4,348 4,129 86,020 2,963 

2020 10,067 9,765 270,832 4,182 3,972 86,116 1,888 

2021 9,831 9,536 258,585 4,018 3,816 86,222 1,976 

2022 9,580 9,293 246,543 3,855 3,661 86,341 1,995 

2023 9,298 9,019 235,176 3,692 3,506 86,475 1,975 

2024 8,990 8,720 224,475 3,530 3,352 86,626 1,954 

2025 8,670 8,410 213,984 3,369 3,200 86,790 1,934 

2026 8,340 8,090 203,629 3,209 3,047 86,974 1,913 

2027 8,001 7,761 193,441 3,050 2,896 87,178 1,894 

2028 7,653 7,424 183,404 2,891 2,746 87,406 1,874 

2029 7,301 7,082 173,555 2,734 2,597 87,672 1,855 

2030 6,943 6,735 164,024 2,579 2,450 88,078 1,836 

2031 6,581 6,383 154,845 2,427 2,305 88,623 1,818 

2032 6,216 6,030 145,870 2,276 2,162 89,207 1,800 

2033 5,852 5,676 137,176 2,129 2,022 89,820 1,783 

2034 5,488 5,323 128,777 1,986 1,886 90,457 1,766 

2035 5,128 4,974 120,726 1,848 1,755 91,119 1,750 

2036 4,795 4,651 113,237 1,718 1,632 91,799 1,735 

2037 4,504 4,369 107,705 1,616 1,535 92,500 1,721 

2038 4,244 4,116 104,042 1,539 1,462 93,219 1,709 

2039 4,022 3,902 101,058 1,472 1,398 93,956 1,700 

2040 3,864 3,748 98,614 1,420 1,349 94,707 1,699 
Emission Inventory 

3-49

3.1.3.5 Less than 37 kW Propulsion and Auxiliary 

The modeled Tier 3 emission factors for less than (<) 37kW commercial marine diesel 

engines are given in Table 3-43. These emission factors apply to both propulsion and 

auxiliary engines. For HC, the methodology described for Category 1 propulsion engines was 

used. For PM, a 20 percent compliance margin was applied to the Tier 3 standard; however, 

if the resulting emission factor was greater than the corresponding Tier 2 emission factor, the 

Tier 2 value was used for Tier 3. Since the final rule does not result in NOx control for this 

category, the Tier 3 NOx emission factors were set equal to Tier 2. 

Table 3-43 Control PM10, NOx, and HC Emission Factors (g/hp-hr) for <37kW Commercial Marine Diesel 

Engines 

HP TIER 3 

RANGE YEAR HC NOX PM 

0-11 2009 0.43 4.39 0.24 

2009 0.21 3.63 0.19 11-16 2014 0.21 2.32 0.19 

2009 0.21 3.63 0.19 16-25 2014 0.21 2.32 0.19 

2009 0.41 3.71 0.18 25-50 2014 0.41 2.32 0.18 

The resulting control case 50-state emission inventories for <37kW propulsion and 

auxiliary engines are given in Table 3-44. 

3.1.3.6 Commercial Marine Diesel Control Inventory Summary 

3.1.3.6.1 PM10, PM2.5, NOx, VOC, CO, and SO2 Emissions 

Table 3-45 thru Table 3-50 present the resulting 50-state consolidated commercial 

marine control case inventories for each pollutant and category, for calendar years 2002-2040. 

3.1.3.6.2 Air Toxics Emissions 

The control case air toxics inventories for commercial marine diesel engines are 

provided in Table 3-51. The gaseous air toxics are assumed to be controlled proportionately 

to VOC, whereas POM is controlled proportionately to PM. 
Regulatory Impact Analysis 

3-50

Table 3-44 Control Case (50-State) Emissions for <37kW Commercial Marine Engines 

(short tons) 

YEAR PM10 PM2.5 NOX VOC HC CO SO2

2002 728 706 5,517 1,273 1,209 3,783 731 

2003 710 689 5,448 1,222 1,161 3,680 738 

2004 692 671 5,350 1,179 1,120 3,576 745 

2005 671 651 5,229 1,128 1,071 3,460 752 

2006 648 629 5,101 1,075 1,021 3,339 745 

2007 596 578 4,973 1,022 970 3,216 387 

2008 551 534 4,846 969 920 3,093 128 

2009 524 509 4,719 911 865 2,970 129 

2010 495 480 4,594 853 810 2,846 95 

2011 466 452 4,472 797 757 2,724 71 

2012 437 424 4,351 741 704 2,603 38 

2013 409 397 4,234 688 653 2,484 14 

2014 383 371 4,073 636 604 2,369 16 

2015 357 346 3,917 586 556 2,259 18 

2016 334 324 3,777 545 518 2,170 18 

2017 317 308 3,658 515 489 2,109 18 

2018 303 294 3,556 492 467 2,063 18 

2019 291 282 3,462 472 448 2,027 18 

2020 280 272 3,377 454 432 1,997 18 

2021 271 263 3,301 438 416 1,972 18 

2022 263 255 3,240 423 402 1,952 18 

2023 257 249 3,188 411 390 1,940 19 

2024 252 244 3,144 401 381 1,932 19 

2025 248 240 3,103 393 373 1,926 19 

2026 244 237 3,070 387 368 1,926 19 

2027 242 235 3,042 383 364 1,929 19 

2028 241 234 3,018 381 361 1,934 19 

2029 240 233 2,998 379 360 1,942 20 

2030 240 233 2,982 378 359 1,952 20 

2031 240 233 2,978 378 359 1,963 20 

2032 241 234 2,983 380 360 1,977 20 

2033 242 235 2,993 381 362 1,992 20 

2034 244 236 3,007 384 365 2,009 21 

2035 245 238 3,022 387 367 2,026 21 

2036 247 240 3,040 389 370 2,044 21 

2037 249 242 3,058 392 372 2,061 21 

2038 251 244 3,079 395 375 2,079 21 

2039 253 246 3,100 398 378 2,097 21 

2040 255 248 3,123 402 381 2,115 22 
Emission Inventory 

3-51

Table 3-45 Control Case (50-State) PM10 Emissions for Commercial Marine Diesel Engines (short tons) 

YEAR 

C1 

PROPULSION 

C1 

AUXILIARY 

C1 

TOTAL 

C2 

PROPULSION <37KW TOTAL 

2002 13,328 2,714 16,041 12,850 728 29,619 

2003 13,690 2,773 16,463 13,112 710 30,285 

2004 13,807 2,791 16,598 13,376 692 30,666 

2005 13,873 2,786 16,659 13,641 671 30,972 

2006 13,872 2,769 16,641 13,907 648 31,196 

2007 12,230 2,482 14,712 14,174 596 29,481 

2008 10,961 2,263 13,224 14,164 551 27,938 

2009 10,709 2,229 12,939 14,151 524 27,614 

2010 10,304 2,169 12,472 14,127 495 27,095 

2011 9,916 2,113 12,029 14,094 466 26,589 

2012 9,290 2,029 11,320 14,051 437 25,808 

2013 8,640 1,946 10,586 13,813 409 24,808 

2014 7,990 1,866 9,856 12,231 383 22,470 

2015 7,338 1,784 9,122 11,378 357 20,857 

2016 6,663 1,699 8,362 11,293 334 19,989 

2017 6,039 1,605 7,644 10,973 317 18,934 

2018 5,480 1,499 6,979 10,680 303 17,962 

2019 5,020 1,401 6,421 10,361 291 17,073 

2020 4,625 1,303 5,928 10,067 280 16,275 

2021 4,296 1,216 5,512 9,831 271 15,614 

2022 3,986 1,144 5,130 9,580 263 14,973 

2023 3,692 1,080 4,772 9,298 257 14,328 

2024 3,412 1,023 4,435 8,990 252 13,677 

2025 3,157 971 4,128 8,670 248 13,046 

2026 2,933 922 3,855 8,340 244 12,440 

2027 2,747 876 3,623 8,001 242 11,866 

2028 2,597 831 3,428 7,653 241 11,323 

2029 2,476 790 3,266 7,301 240 10,807 

2030 2,390 754 3,144 6,943 240 10,327 

2031 2,333 725 3,057 6,581 240 9,878 

2032 2,297 701 2,998 6,216 241 9,455 

2033 2,274 681 2,955 5,852 242 9,049 

2034 2,259 665 2,923 5,488 244 8,655 

2035 2,250 652 2,902 5,128 245 8,276 

2036 2,246 644 2,891 4,795 247 7,933 

2037 2,249 639 2,887 4,504 249 7,641 

2038 2,256 636 2,892 4,244 251 7,387 

2039 2,268 634 2,902 4,022 253 7,177 

2040 2,282 634 2,916 3,864 255 7,035 
Regulatory Impact Analysis 

3-52

Table 3-46 Control Case (50-State) PM2.5 Emissions for Commercial Marine Diesel Engines (short tons) 

YEAR 

C1 

PROPULSION 

C1 

AUXILIARY 

C1 

TOTAL 

C2 

PROPULSION <37KW TOTAL 

2002 12,928 2,632 15,560 12,464 706 28,730 

2003 13,279 2,690 15,969 12,719 689 29,377 

2004 13,393 2,708 16,100 12,975 671 29,746 

2005 13,457 2,703 16,159 13,232 651 30,042 

2006 13,456 2,686 16,142 13,490 629 30,260 

2007 11,863 2,407 14,270 13,748 578 28,596 

2008 10,632 2,195 12,827 13,739 534 27,100 

2009 10,388 2,162 12,551 13,726 509 26,785 

2010 9,995 2,104 12,098 13,703 480 26,282 

2011 9,618 2,049 11,668 13,671 452 25,792 

2012 9,012 1,968 10,980 13,630 424 25,034 

2013 8,380 1,888 10,268 13,399 397 24,064 

2014 7,750 1,810 9,561 11,864 371 21,796 

2015 7,118 1,731 8,848 11,037 346 20,231 

2016 6,463 1,648 8,111 10,954 324 19,389 

2017 5,858 1,557 7,414 10,644 308 18,366 

2018 5,316 1,454 6,770 10,360 294 17,423 

2019 4,869 1,359 6,228 10,051 282 16,561 

2020 4,486 1,264 5,750 9,765 272 15,787 

2021 4,167 1,179 5,346 9,536 263 15,145 

2022 3,867 1,109 4,976 9,293 255 14,524 

2023 3,582 1,048 4,629 9,019 249 13,898 

2024 3,310 992 4,302 8,720 244 13,266 

2025 3,062 942 4,005 8,410 240 12,654 

2026 2,845 895 3,740 8,090 237 12,067 

2027 2,665 850 3,514 7,761 235 11,510 

2028 2,519 806 3,325 7,424 234 10,983 

2029 2,402 767 3,168 7,082 233 10,483 

2030 2,318 731 3,050 6,735 233 10,017 

2031 2,263 703 2,966 6,383 233 9,582 

2032 2,228 680 2,908 6,030 234 9,171 

2033 2,206 661 2,867 5,676 235 8,778 

2034 2,191 645 2,836 5,323 236 8,395 

2035 2,182 633 2,815 4,974 238 8,028 

2036 2,179 625 2,804 4,651 240 7,695 

2037 2,181 619 2,801 4,369 242 7,411 

2038 2,188 616 2,805 4,116 244 7,165 

2039 2,200 615 2,815 3,902 246 6,962 

2040 2,214 615 2,828 3,748 248 6,824 
Emission Inventory 

3-53

Table 3-47 Control Case (50-State) NOx Emissions for Commercial Marine Diesel Engines 

(short tons) 

YEAR 

C1 

PROPULSION 

C1 

AUXILIARY 

C1 

TOTAL 

C2 

PROPULSION <37KW TOTAL 

2002 335,561 60,641 396,202 432,306 5,517 834,025 

2003 336,369 60,959 397,328 431,973 5,448 834,749 

2004 332,798 60,482 393,280 431,683 5,350 830,313 

2005 328,810 59,774 388,583 431,417 5,229 825,229 

2006 324,900 59,073 383,973 431,195 5,101 820,269 

2007 316,663 58,048 374,710 427,380 4,973 807,063 

2008 308,524 57,030 365,554 414,725 4,846 785,125 

2009 300,509 56,020 356,529 402,915 4,719 764,163 

2010 292,651 55,022 347,673 391,965 4,594 744,232 

2011 284,979 54,038 339,017 381,869 4,472 725,358 

2012 276,209 52,949 329,158 372,614 4,351 706,123 

2013 267,453 51,796 319,249 363,742 4,234 687,225 

2014 257,691 50,317 308,007 345,213 4,073 657,294 

2015 248,317 48,863 297,181 333,586 3,917 634,684 

2016 236,292 47,349 283,640 320,992 3,777 608,409 

2017 223,265 45,754 269,020 308,346 3,658 581,023 

2018 209,717 43,895 253,612 295,746 3,556 552,914 

2019 196,847 42,089 238,936 283,222 3,462 525,620 

2020 185,242 40,347 225,589 270,832 3,377 499,798 

2021 174,843 38,787 213,630 258,585 3,301 475,517 

2022 164,971 37,444 202,415 246,543 3,240 452,197 

2023 155,589 36,210 191,800 235,176 3,188 430,164 

2024 146,696 35,096 181,792 224,475 3,144 409,411 

2025 138,521 34,089 172,610 213,984 3,103 389,698 

2026 131,195 33,138 164,333 203,629 3,070 371,033 

2027 124,763 32,243 157,006 193,441 3,042 353,489 

2028 119,185 31,399 150,584 183,404 3,018 337,006 

2029 114,708 30,630 145,338 173,555 2,998 321,891 

2030 111,660 29,948 141,608 164,024 2,982 308,614 

2031 109,766 29,388 139,154 154,845 2,978 296,977 

2032 108,624 28,939 137,563 145,870 2,983 286,416 

2033 107,896 28,572 136,468 137,176 2,993 276,637 

2034 107,443 28,303 135,746 128,777 3,007 267,530 

2035 107,233 28,159 135,392 120,726 3,022 259,140 

2036 107,236 28,117 135,352 113,237 3,040 251,629 

2037 107,444 28,123 135,566 107,705 3,058 246,330 

2038 107,834 28,176 136,009 104,042 3,079 243,131 

2039 108,376 28,259 136,635 101,058 3,100 240,793 

2040 109,054 28,367 137,421 98,614 3,123 239,157 
Regulatory Impact Analysis 

3-54

Table 3-48 Control Case (50-State) VOC Emissions for Commercial Marine Diesel Engines (short tons) 

YEAR 

C1 

PROPULSION 

C1 

AUXILIARY 

C1 

TOTAL 

C2 

PROPULSION <37KW TOTAL 

2002 9,488 1,767 11,255 4,701 1,273 17,229 

2003 9,573 1,783 11,356 4,743 1,222 17,321 

2004 9,561 1,785 11,346 4,786 1,179 17,311 

2005 9,550 1,788 11,338 4,829 1,128 17,295 

2006 9,540 1,791 11,331 4,872 1,075 17,278 

2007 9,415 1,787 11,202 4,916 1,022 17,140 

2008 9,291 1,783 11,074 4,960 969 17,003 

2009 9,169 1,778 10,947 5,005 911 16,863 

2010 9,050 1,773 10,823 5,050 853 16,726 

2011 8,933 1,768 10,701 5,096 797 16,594 

2012 8,708 1,753 10,461 5,141 741 16,344 

2013 8,433 1,727 10,160 5,174 688 16,022 

2014 8,042 1,677 9,719 5,069 636 15,424 

2015 7,658 1,628 9,286 4,964 586 14,836 

2016 7,228 1,577 8,805 4,846 545 14,196 

2017 6,784 1,523 8,307 4,680 515 13,502 

2018 6,334 1,463 7,796 4,514 492 12,802 

2019 5,898 1,403 7,302 4,348 472 12,121 

2020 5,496 1,345 6,841 4,182 454 11,478 

2021 5,126 1,290 6,416 4,018 438 10,872 

2022 4,772 1,239 6,010 3,855 423 10,288 

2023 4,433 1,188 5,621 3,692 411 9,724 

2024 4,111 1,141 5,252 3,530 401 9,183 

2025 3,826 1,095 4,922 3,369 393 8,684 

2026 3,589 1,052 4,640 3,209 387 8,236 

2027 3,400 1,010 4,410 3,050 383 7,843 

2028 3,252 970 4,223 2,891 381 7,494 

2029 3,134 935 4,068 2,734 379 7,182 

2030 3,049 905 3,953 2,579 378 6,911 

2031 2,991 882 3,874 2,427 378 6,679 

2032 2,953 866 3,819 2,276 380 6,475 

2033 2,927 853 3,781 2,129 381 6,291 

2034 2,911 843 3,754 1,986 384 6,124 

2035 2,902 836 3,738 1,848 387 5,973 

2036 2,901 832 3,733 1,718 389 5,841 

2037 2,906 830 3,736 1,616 392 5,744 

2038 2,919 829 3,748 1,539 395 5,682 

2039 2,936 829 3,765 1,472 398 5,636 

2040 2,957 831 3,787 1,420 402 5,609 
Emission Inventory 

3-55

Table 3-49 Control Case (50-State) CO Emissions for Commercial Marine Diesel Engines 

(short tons) 

YEAR 

C1 

PROPULSION 

C1 

AUXILIARY 

C1 

TOTAL 

C2 

PROPULSION <37KW TOTAL 

2002 55,303 9,624 64,927 82,621 3,783 151,331 

2003 55,801 9,710 65,511 83,364 3,680 152,556 

2004 55,722 9,668 65,390 84,115 3,576 153,080 

2005 55,582 9,585 65,167 84,872 3,460 153,499 

2006 55,450 9,503 64,954 85,635 3,339 153,928 

2007 54,423 9,331 63,754 85,621 3,216 152,591 

2008 53,405 9,160 62,565 85,611 3,093 151,269 

2009 52,401 8,989 61,391 85,605 2,970 149,966 

2010 51,414 8,820 60,235 85,609 2,846 148,690 

2011 50,445 8,654 59,099 85,621 2,724 147,444 

2012 49,497 8,489 57,986 85,639 2,603 146,227 

2013 48,574 8,327 56,901 85,665 2,484 145,050 

2014 47,680 8,167 55,847 85,701 2,369 143,917 

2015 46,827 8,010 54,837 85,746 2,259 142,842 

2016 46,023 7,857 53,880 85,800 2,170 141,851 

2017 45,368 7,708 53,076 85,864 2,109 141,049 

2018 44,879 7,563 52,443 85,937 2,063 140,443 

2019 44,482 7,426 51,908 86,020 2,027 139,954 

2020 44,301 7,298 51,599 86,116 1,997 139,712 

2021 44,329 7,198 51,527 86,222 1,972 139,720 

2022 44,423 7,134 51,557 86,341 1,952 139,851 

2023 44,571 7,088 51,659 86,475 1,940 140,073 

2024 44,760 7,066 51,827 86,626 1,932 140,384 

2025 44,987 7,067 52,054 86,790 1,926 140,771 

2026 45,248 7,077 52,325 86,974 1,926 141,226 

2027 45,539 7,094 52,633 87,178 1,929 141,740 

2028 45,861 7,117 52,978 87,406 1,934 142,318 

2029 46,209 7,145 53,354 87,672 1,942 142,968 

2030 46,583 7,178 53,761 88,078 1,952 143,791 

2031 46,975 7,215 54,191 88,623 1,963 144,776 

2032 47,385 7,257 54,642 89,207 1,977 145,825 

2033 47,811 7,303 55,114 89,820 1,992 146,926 

2034 48,241 7,353 55,595 90,457 2,009 148,060 

2035 48,675 7,407 56,082 91,119 2,026 149,227 

2036 49,114 7,464 56,577 91,799 2,044 150,419 

2037 49,556 7,524 57,079 92,500 2,061 151,640 

2038 50,002 7,588 57,589 93,219 2,079 152,887 

2039 50,452 7,654 58,105 93,956 2,097 154,158 

2040 50,906 7,721 58,627 94,707 2,115 155,449 
Regulatory Impact Analysis 

3-56

Table 3-50 Control Case (50-State) SO2 Emissions for Commercial Marine Diesel Engines 

(short tons) 

YEAR 

C1 

PROPULSION 

C1 

AUXILIARY 

C1 

TOTAL 

C2 

PROPULSION <37KW TOTAL 

2002 36,201 6,553 42,754 36,868 731 80,353 

2003 36,528 6,613 43,141 37,193 738 81,073 

2004 36,862 6,673 43,535 37,528 745 81,808 

2005 37,192 6,733 43,925 37,866 752 82,543 

2006 36,827 6,667 43,493 38,207 745 82,445 

2007 19,121 3,461 22,583 38,550 387 61,520 

2008 6,299 1,140 7,440 38,837 128 46,404 

2009 6,355 1,150 7,506 39,204 129 46,839 

2010 4,705 852 5,557 39,559 95 45,212 

2011 3,513 636 4,148 39,920 71 44,139 

2012 1,862 337 2,199 40,278 38 42,515 

2013 664 120 784 39,905 14 40,702 

2014 799 145 943 21,334 16 22,293 

2015 857 155 1,012 7,888 18 8,917 

2016 865 157 1,021 7,817 18 8,855 

2017 872 158 1,030 5,901 18 6,949 

2018 879 159 1,038 4,574 18 5,630 

2019 886 160 1,046 2,963 18 4,028 

2020 893 162 1,055 1,888 18 2,961 

2021 900 163 1,063 1,976 18 3,058 

2022 907 164 1,072 1,995 18 3,085 

2023 915 166 1,081 1,975 19 3,074 

2024 923 167 1,090 1,954 19 3,063 

2025 931 169 1,099 1,934 19 3,052 

2026 939 170 1,109 1,913 19 3,041 

2027 946 171 1,118 1,894 19 3,031 

2028 954 173 1,127 1,874 19 3,020 

2029 962 174 1,136 1,855 20 3,010 

2030 970 176 1,146 1,836 20 3,002 

2031 978 177 1,155 1,818 20 2,993 

2032 986 179 1,165 1,800 20 2,985 

2033 995 180 1,175 1,783 20 2,978 

2034 1,006 182 1,188 1,766 21 2,975 

2035 1,015 184 1,198 1,750 21 2,969 

2036 1,023 185 1,208 1,735 21 2,964 

2037 1,032 187 1,218 1,721 21 2,961 

2038 1,040 188 1,228 1,709 21 2,958 

2039 1,050 190 1,240 1,700 21 2,962 

2040 1,059 192 1,251 1,699 22 2,971 
Emission Inventory 

3-57

Table 3-51 Control Case (50-State) Air Toxic Emissions for Commercial Marine Diesel Engines (short 

tons) 

HAP 2010 2015 2020 2030 

BENZENE 556 513 409 251 

FORMALDEHYDE 4,088 3,772 3,005 1,846 

ACETALDEHYDE 2,032 1,875 1,494 917 

1,3-BUTADIENE 6 5 4 3 

ACROLEIN 79 73 58 36 

NAPHTHALENE 38 34 26 16 

POM 9 8 6 4 

3.1.4 Projected Commercial Marine Emission Reductions of Final Rule 

The PM2.5, NOx, and VOC emission reductions for each category and calendar year 

are presented in Table 3-52 thru Table 3-54. The air toxic emission reductions by pollutant 

and calendar year are given in Table 3-55. 
Regulatory Impact Analysis 

3-58

Table 3-52 Projected Commercial Marine PM2.5 Emission Reductions (short tons) 

YEAR 

C1 

PROPULSION 

C1 

AUXILIARY 

C1 

TOTAL 

C2 

PROPULSION <37KW TOTAL 

2008 0 0 0 264 0 264 

2009 0 1 1 538 2 541 

2010 0 2 2 822 4 828 

2011 0 2 3 1,116 5 1,124 

2012 175 22 197 1,420 6 1,623 

2013 353 45 398 1,856 8 2,262 

2014 580 83 662 2,177 9 2,848 

2015 792 120 912 2,189 11 3,112 

2016 1,023 157 1,180 2,512 13 3,706 

2017 1,268 196 1,463 2,922 15 4,400 

2018 1,530 240 1,770 3,343 16 5,130 

2019 1,732 275 2,007 3,750 18 5,776 

2020 1,947 312 2,259 4,170 20 6,448 

2021 2,175 349 2,524 4,639 22 7,185 

2022 2,409 387 2,797 5,112 24 7,933 

2023 2,647 427 3,074 5,587 26 8,687 

2024 2,887 467 3,354 6,066 28 9,447 

2025 3,115 508 3,623 6,550 29 10,203 

2026 3,323 550 3,873 7,041 31 10,945 

2027 3,508 592 4,099 7,538 32 11,670 

2028 3,671 633 4,304 8,041 33 12,378 

2029 3,816 673 4,489 8,549 33 13,072 

2030 3,939 710 4,649 9,059 34 13,743 

2031 4,041 741 4,782 9,574 35 14,391 

2032 4,128 768 4,896 10,090 35 15,022 

2033 4,207 793 5,000 10,606 36 15,642 

2034 4,280 816 5,096 11,121 37 16,253 

2035 4,347 836 5,183 11,631 37 16,851 

2036 4,409 853 5,262 12,116 38 17,416 

2037 4,466 869 5,335 12,560 38 17,933 

2038 4,518 884 5,402 12,975 39 18,416 

2039 4,568 899 5,466 13,352 39 18,857 

2040 4,614 912 5,526 13,667 40 19,233 
Emission Inventory 

3-59

Table 3-53 Projected Commercial Marine NOx Emission Reductions (short tons) 

YEAR 

C1 

PROPULSION 

C1 

AUXILIARY 

C1 

TOTAL 

C2 

PROPULSION <37KW TOTAL 

2008 0 0 0 8,876 0 8,876 

2009 0 0 0 16,942 0 16,942 

2010 0 0 0 24,204 0 24,204 

2011 0 0 0 30,668 0 30,668 

2012 1,342 121 1,463 36,330 0 37,792 

2013 3,311 322 3,633 41,685 0 45,319 

2014 6,944 868 7,812 56,757 47 64,616 

2015 10,562 1,414 11,976 65,007 94 77,077 

2016 17,246 2,051 19,297 74,303 141 93,741 

2017 26,061 2,835 28,896 83,755 188 112,839 

2018 36,621 3,954 40,576 93,242 235 134,052 

2019 47,117 5,071 52,187 102,778 281 155,247 

2020 57,522 6,184 63,705 112,324 328 176,357 

2021 67,833 7,292 75,126 121,873 374 197,373 

2022 78,019 8,397 86,416 131,448 420 218,283 

2023 88,051 9,495 97,546 141,137 465 239,149 

2024 97,867 10,586 108,453 150,954 510 259,918 

2025 107,215 11,667 118,882 160,800 555 280,237 

2026 115,946 12,737 128,683 170,714 599 299,996 

2027 123,957 13,792 137,749 180,644 643 319,036 

2028 131,290 14,829 146,119 190,636 685 337,439 

2029 137,676 15,822 153,498 200,664 726 354,888 

2030 142,790 16,755 159,545 211,102 764 371,411 

2031 146,842 17,592 164,434 221,882 794 387,110 

2032 150,228 18,343 168,571 232,697 815 402,084 

2033 153,285 19,039 172,324 243,398 835 416,556 

2034 156,089 19,659 175,748 253,972 852 430,573 

2035 158,671 20,173 178,844 264,350 869 444,063 

2036 161,061 20,604 181,665 274,282 884 456,832 

2037 163,268 21,004 184,271 282,393 899 467,563 

2038 165,314 21,377 186,692 288,751 913 476,356 

2039 167,230 21,732 188,962 294,551 926 484,440 

2040 169,033 22,069 191,102 299,914 938 491,954 
Regulatory Impact Analysis 

3-60

Table 3-54 Projected Commercial Marine VOC Emission Reductions (short tons) 

YEAR 

C1 

PROPULSION 

C1 

AUXILIARY 

C1 

TOTAL 

C2 

PROPULSION <37KW TOTAL 

2008 0 0 0 0 0 0 

2009 0 2 2 0 5 7 

2010 1 3 4 0 11 14 

2011 1 5 6 0 16 22 

2012 113 17 130 0 22 152 

2013 279 40 319 14 27 359 

2014 564 88 652 166 32 850 

2015 849 135 984 318 38 1,339 

2016 1,187 185 1,372 483 43 1,897 

2017 1,563 237 1,800 697 49 2,546 

2018 1,970 297 2,267 912 54 3,232 

2019 2,374 356 2,730 1,127 59 3,915 

2020 2,773 415 3,188 1,341 64 4,593 

2021 3,167 474 3,640 1,555 70 5,265 

2022 3,555 532 4,087 1,769 75 5,930 

2023 3,934 590 4,524 1,982 79 6,586 

2024 4,303 647 4,950 2,195 84 7,229 

2025 4,639 704 5,343 2,407 89 7,839 

2026 4,934 760 5,694 2,620 92 8,405 

2027 5,184 814 5,998 2,831 95 8,924 

2028 5,397 867 6,264 3,043 97 9,404 

2029 5,585 917 6,501 3,253 99 9,854 

2030 5,743 961 6,704 3,462 101 10,267 

2031 5,876 998 6,874 3,669 103 10,646 

2032 5,993 1,029 7,022 3,874 105 11,001 

2033 6,099 1,058 7,157 4,076 106 11,339 

2034 6,197 1,084 7,281 4,275 108 11,664 

2035 6,287 1,107 7,394 4,470 109 11,974 

2036 6,371 1,128 7,499 4,657 111 12,267 

2037 6,449 1,147 7,596 4,816 112 12,525 

2038 6,521 1,166 7,687 4,951 114 12,751 

2039 6,589 1,183 7,772 5,076 115 12,963 

2040 6,654 1,200 7,854 5,187 116 13,157 
Emission Inventory 

3-61

Table 3-55 Projected Commercial Marine Air Toxic Emission Reductions (short tons) 

HAP 2010 2015 2020 2030 

BENZENE 0 46 164 373 

FORMALDEHYDE 4 341 1,203 2,742 

ACETALDEHYDE 2 169 598 1,363 

1,3-BUTADIENE 0 0 2 4 

ACROLEIN 0 7 23 53 

NAPHTHALENE 0 3 10 24 

POM 0 1 3 6 

3.2 Recreational Marine Diesel Engines 

This section describes the methodology and presents the resulting baseline and 

controlled inventories for recreational marine (pleasure craft) diesel propulsion engines, 

including the projected emission reductions from the final rule. These engines are already 

subject to existing emission control standards, so the baseline inventories presented here 

account for those existing standards. Emissions from any diesel auxiliary engines used on 

recreational marine vessels are covered above in the section on engines less than 37 kW or the 

section on Category 1 engines, if they are over 37 kW. 

3.2.1 General Methodology 

The general methodology for calculating recreational marine diesel engine inventories 

for HC, CO, NOx, PM10, SO2, VOC, PM2.5, and fuel consumption uses the EPA 

NONROAD2005 model with inputs modified to reflect the final standards as well as updated 

baseline data.27 Air toxic inventories are not generated by the NONROAD model, so those 

are calculated separately. NONROAD separates recreational diesel engines into two basic 

categories: inboard and outboard engines. NONROAD also subdivides these by power 

range. There are relatively few outboard diesels, and they are all in the 25 - 40 hp range. 

The actual calculation methodology used by the NONROAD model is the same as 

described above in section 3.1.1 for all other marine diesel engines. Following is a summary 

of that. 

Equation 3-11 = ( )× ( )× ( )× ( )× (EFALPNI )

where each term is defined as follows: 

I = the emission inventory (gram/year) 

N = engine population (units) 

P = average rated power (kW) 

L = load factor (average fraction of rated power used during operation; unitless) 

A = engine activity (operating hours/year) 

EF = emission factor (gram/kW-hr) 
Regulatory Impact Analysis 

3-62

Emissions are then converted and reported as short tons/year. In NONROAD the 

inputs are expressed in terms of horsepower (hp) instead of kW, and gram/bhp-hr instead of 

gram/kW-hr. 

Three variables are used to project emissions over time: the engine population growth, 

the engine median life/scrappage, and the relative emissions deterioration rate. 

Engine Population Growth. Unlike the commercial marine methodology which uses 

a compound population growth rate, the NONROAD model uses a linear growth assumption 

for recreational diesel engines, which is represented by a set of growth indexes that provide a 

ratio of estimation year population relative to the base year population. 28 The growth used 

for recreational diesel engines is 3.3 percent per year relative to a 1996 base year; i.e., each 

year the population grows by the same number of engines, and that number is 3.3 percent of 

the 1996 population. 

Engine Median Life (years) and Scrappage. The engine median life defines the 

length of time engines remain in service. Engines persist in the population over two median 

lives; during the first median life, 50 percent of the engines are scrapped, and over the second, 

the remaining 50 percent of the engines are scrapped. Engine median lives also vary by 

category. The median life of both inboard and outboard engines is assumed to be 20 years, 

but due to the different activities used for these two categories (200 and 150 hours/year, 

respectively), the corresponding median life inputs for the model are 1400 and 1050 hours at 

full load. The age distribution is defined by the median life and the scrappage algorithm. The 

same basic scrappage algorithm is used for recreational and commercial marine diesel 

engines.29

Relative Deterioration Rate (percent increase in emission factor/percent median life 

expended). A deterioration factor can be applied to the emission factor to account for in-use 

deterioration. The deterioration factor varies by age and is calculated as: 

Equation 3-12 ⎟

⎠

⎞ ⎜

⎝

⎛ ×+=

ML

age 1 ADF

where each term is defined as follows: 

DF = the deterioration factor for a given pollutant at a given age 

A = the relative deterioration rate for a given pollutant (percent increase in emission 

factor/percent useful life expended) 

age = the age of a specific model year group of engines in the simulation year (years) 

ML = the median life of the given model year cohort (years) 

A given model year cohort is represented as a fraction of the entire population. In the 

NONROAD model the deterioration factor adjusts the emission factor for engines in a given 

model year cohort in relation to the proportion of median life expended.30 Deterioration is 

linear over one median life. Following the first median life, the deteriorated emission factor is 

held constant over the remaining life for engines in the cohort. 
Emission Inventory 

3-63

Sulfur Adjustment for PM Emissions. For Tier 2 and prior engines, a sulfate 

adjustment is added to the PM emissions to account for differences in fuel sulfur content 

between the certification fuel and the episodic (calendar year) fuel, using the following 

equation: 

Equation 3-13 ( )( )( ) ( ) ⎟

⎠

⎞ ⎜

⎝

⎛ ⎟ ×−×

⎠

⎞ ⎜

⎝

⎛

××= ×

2000

1

32

224 FCS 02247.01.7 soxdsl soxbas PMadj

where each term is defined as follows: 

SPM adj = PM sulfate adjustment (tons) 

FC = fuel consumption (gallons) 

7.1 = fuel density (lb/gal) 

0.02247 = fraction of fuel sulfur converted to sulfate 

224/32 = grams PM sulfate/grams PM sulfur 

soxdsl = episodic fuel sulfur weight fraction (varies by calendar year) 

soxbas = certification fuel sulfur weight fraction 

2000 = conversion from lb to ton 

For engines prior to Tier 2 the base fuel sulfur (soxbas) is assumed to be 3300 ppm. 

For Tier 2 engines less than or equal to 50 hp (37 kW) it is set at 2000 ppm, as described in 

the Clean Air Nonroad Diesel Rule31, since these smaller engines are subject to the same 

standards as land-based diesel engines. For Tier 2 engines greater than 50 hp (37 kW) it is set 

at 350 ppm, based on the most recent certification data for these engines. For Tier 3 and later 

engines, no sulfur adjustment is applied. These engines will be certified to a fuel sulfur level 

at or lower than the episodic fuel sulfur levels expected when these engines are introduced. 

The calendar year fuel sulfur levels (soxdsl) were taken from the Clean Air Nonroad 

Diesel Rule and are provided in Table 3-1.32

Estimation of air toxic emissions. The air toxic baseline emission inventories for this 

proposal are based on information developed for EPA’s Mobile Source Air Toxics (MSAT) 

final rulemaking.33 That rule calculated air toxic emission inventories for all nonroad 

engines. The gaseous air toxics are correlated to VOC emissions, while POM is correlated to 

PM10 emissions. To calculate the air toxics emission inventories and reductions for this 

proposal, the percent reductions in VOC and PM10 emissions will be applied to the baseline 

gaseous and POM air toxic inventories, respectively. 

3.2.2 Baseline (Pre-Control) Inventory Development 

3.2.2.1 Baseline Inventory Inputs 

This section describes the NONROAD model inputs that were used to generate the 

baseline emission inventories for recreational marine diesel engines. 

Table 3-56 and Table 3-57 list the base engine populations, average hp by power 

range, annual activity, load factor, and median life. These also apply to the control case, and 

are unchanged from the default inputs in the NONROAD model. 
Regulatory Impact Analysis 

3-64

Table 3-56 Recreational Marine Diesel Modeling Inputs 

NONROAD MODEL INPUT RECREATIONAL MARINE DIESEL 

INBOARD OUTBOARD 

POPULATION (year 2000) 291,387* 9,819 

HP AVERAGE * 32.25 

ACTIVITY HRS/YEAR 200 150 

LOAD FACTOR 0.35 0.35 

MEDIAN LIFE (hrs at full load) 1400 1050 

MEDIAN LIFE (years) 20 20 

* See TABLE 3-57 for breakout by individual power ranges. 

Table 3-57 Recreational Marine Inboard Diesel Population 

POWER RANGE DIESEL REC MARINE INBOARD 

MIN < HP <= MAX HP AVG POPULATION 

0 – 11 9.736 9,126 

11 – 16 14.92 4,478 

16 – 25 21.41 9,908 

25 – 40 31.2 5,421 

40 – 50 42.4 1,002 

50 – 75 56.19 8,784 

75 – 100 94.22 7,397 

100 - 175 144.9 60,632 

175 - 300 223.1 99,703 

300 - 600 387.1 73,546 

600 - 750 677 2,902 

750 - 1000 876.5 5,502 

1000 - 1200 1154 448 

1200 - 2000 1369 1,573 

2000 - 3000 2294 964 

TOTAL 291,387 

The baseline emission factors are given in Table 3-58 and Table 3-59. "Zero Hour" 

emission factors represent the emissions from new engines that have been broken in, but 

before any significant deterioration occurs. The Deterioration Factor is used to calculate how 

emissions change as the engine and emission control system deteriorate over time, as 

explained above in Equation 3-2. Engines under 50 hp are subject to EPA nonroad diesel 

regulations that have established two tiers of emission standards.34 Tier 1 phased in from 

1999-2000, depending on the hp category, and Tier 2 phased in from 2004-2005. Engines 

above 50 hp are subject to separate standards (shown in the Tier 2 column) that take effect in 
Emission Inventory 

3-65

2008-2012, depending on hp category. The “Base” entries in the tables refer to emissions 

from pre-controlled engines. All these emission factors are used for both inboard and 

outboard diesel engines, although the outboards are all under 50 hp. 

The emission factors for the base and Tier 1 technology types are unchanged from 

what has been in the NONROAD model.35 Tier 2 emission factors were updated from those 

in the NONROAD model using all the nonroad engine certification data available in mid2006. The deterioration factors by pollutant and technology type are also given in the tables 

above, and they are unchanged from what has been in the NONROAD model.36

The certification fuel sulfur levels are 3300ppm for the base and Tier 1 technology 

type and 350ppm for Tier 2. Brake Specific Fuel Consumption (BSFC) values in the 

NONROAD model are 0.408 lb/hp-hr for all hp categories.37

Table 3-58 Baseline PM10 and NOx Zero Hour Emission Factors and Deterioration Factors for 

Recreational Marine Diesel Engines 

HP PM10 G/HP-HR NOX G/HP-HR 

RANGE BASE TIER1 TIER2 BASE TIER1 TIER2 

0-11 1.00 0.45 0.38 10.00 5.23 4.39 

11-16 0.90 0.27 0.19 8.50 4.44 3.63 

16-25 0.90 0.27 0.19 8.50 4.44 3.63 

25-50 0.80 0.34 0.23 6.90 4.73 3.71 

50-75 0.16 0.16 0.13 6.67 6.67 3.82 

75-100 0.16 0.16 0.13 6.67 6.67 3.82 

100-175 0.16 0.16 0.13 6.67 6.67 3.82 

175-300 0.16 0.16 0.090 6.67 6.67 4.46 

300-600 0.16 0.16 0.082 6.67 6.67 4.42 

600-750 0.16 0.16 0.082 6.67 6.67 4.42 

750-1200 0.16 0.16 0.082 6.67 6.67 4.42 

>1200 0.16 0.16 0.082 6.67 6.67 4.42 

DF ("A") 0.473 0.473 0.473 0.024 0.024 0.009 
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Table 3-59 Baseline HC and CO Zero Hour Emission Factors and Deterioration Factors for Recreational 

Marine Diesel Engines 

HP HC G/HP-HR CO G/HP-HR 

RANGE BASE TIER1 TIER2 BASE TIER1 TIER2 

0-11 1.50 0.76 0.68 5.00 4.11 4.11 

11-16 1.70 0.44 0.21 5.00 2.16 2.16 

16-25 1.70 0.44 0.21 5.00 2.16 2.16 

25-50 1.80 0.28 0.54 5.00 1.53 1.53 

50-75 0.22 0.22 0.20 0.95 0.95 0.95 

75-100 0.22 0.22 0.20 0.95 0.95 0.95 

100-175 0.22 0.22 0.20 0.95 0.95 0.95 

175-300 0.22 0.22 0.25 0.95 0.95 0.95 

300-600 0.22 0.22 0.33 0.95 0.95 0.95 

600-750 0.22 0.22 0.33 0.95 0.95 0.95 

750-1200 0.22 0.22 0.33 0.95 0.95 0.95 

>1200 0.22 0.22 0.33 0.95 0.95 0.95 

DF ("A") 0.047 0.047 0.034 0.185 0.101 0.101 

3.2.2.2 Recreational Marine Diesel Baseline Inventory 

3.2.2.2.1 PM10, PM2.5, NOx, VOC, CO, and SO2 Emissions 

Table 3-60 shows the baseline 50-state emission inventories for recreational marine 

diesel engines (inboard and outboard combined) resulting from the baseline model inputs 

presented above. 

3.2.2.2.2 Air Toxics Emissions 

The baseline air toxics inventories for recreational marine diesel engines were taken 

from the final MSAT rule38 and are summarized in Table 3-61. Inventories are provided for 

calendar year 1999, and are projected for 2010, 2015, 2020, and 2030. 
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Table 3-60 Baseline (50-State) Emissions for Recreational Marine Diesel Engines (short tons) 

YEAR PM10 PM2.5 NOX VOC HC CO SO2

2002 1,130 1,096 40,437 1,540 1,462 6,467 5,145 

2003 1,161 1,126 41,572 1,578 1,499 6,642 5,290 

2004 1,192 1,156 42,704 1,618 1,536 6,816 5,436 

2005 1,223 1,186 43,835 1,656 1,573 6,989 5,582 

2006 1,247 1,210 44,089 1,720 1,633 7,161 5,621 

2007 1,054 1,023 44,307 1,783 1,693 7,331 2,967 

2008 915 888 44,513 1,846 1,753 7,499 993 

2009 937 909 44,648 1,912 1,816 7,665 1,017 

2010 935 907 44,772 1,979 1,879 7,829 764 

2011 938 910 44,880 2,045 1,942 7,991 578 

2012 934 906 44,977 2,112 2,006 8,150 311 

2013 935 907 45,064 2,179 2,069 8,308 113 

2014 952 924 45,139 2,246 2,133 8,464 136 

2015 969 940 45,208 2,313 2,196 8,618 150 

2016 984 954 45,270 2,380 2,260 8,771 153 

2017 998 968 45,327 2,448 2,325 8,922 156 

2018 1,011 981 45,378 2,516 2,389 9,073 156 

2019 1,024 994 45,427 2,584 2,454 9,223 159 

2020 1,037 1,006 45,477 2,653 2,520 9,374 162 

2021 1,050 1,019 45,531 2,723 2,586 9,525 165 

2022 1,063 1,031 45,586 2,793 2,652 9,675 168 

2023 1,075 1,043 45,649 2,862 2,718 9,825 171 

2024 1,087 1,054 45,729 2,932 2,784 9,975 174 

2025 1,099 1,066 45,842 3,000 2,849 10,124 177 

2026 1,112 1,079 46,114 3,064 2,910 10,279 180 

2027 1,127 1,093 46,549 3,124 2,967 10,439 183 

2028 1,143 1,108 47,030 3,184 3,023 10,601 186 

2029 1,159 1,124 47,551 3,242 3,079 10,765 189 

2030 1,175 1,140 48,102 3,299 3,133 10,930 192 

2031 1,192 1,156 48,671 3,356 3,187 11,095 195 

2032 1,208 1,172 49,257 3,412 3,240 11,262 199 

2033 1,226 1,189 49,861 3,468 3,294 11,429 202 

2034 1,243 1,205 50,477 3,524 3,346 11,596 205 

2035 1,260 1,222 51,106 3,579 3,399 11,765 208 

2036 1,278 1,239 51,748 3,634 3,451 11,933 211 

2037 1,295 1,256 52,399 3,689 3,503 12,102 214 

2038 1,313 1,274 53,062 3,744 3,555 12,272 217 

2039 1,331 1,291 53,735 3,798 3,607 12,442 220 

2040 1,349 1,308 54,417 3,852 3,659 12,613 223 
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Table 3-61 Baseline Air Toxics Emissions for Recreational Marine Diesel Engines (short tons) 

HAP 1999 2010 2015 2020 2030 

BENZENE 30 34 34 34 35 

FORMALDEHYDE 176 199 197 195 201 

ACETALDEHYDE 79 89 88 87 90 

1,3-BUTADIENE 3 3 3 3 3 

ACROLEIN 5 5 5 5 5 

NAPHTHALENE 0 0 0 0 0 

POM 1 0 0 0 0 

3.2.3 Control Inventory Development 

3.2.3.1 Control Scenario(s) Modeled 

Table 3-62 shows the control case exhaust emission standards that were modeled for 

recreational marine diesel engines. 

Table 3-62 Modeled Standards (g/hp-hr) for Recreational Marine Diesel Engines 

HP TIER 3 

RANGE YEAR NOX+HC PM 

0-25 2009 5.6 0.30 

2009 5.6 0.22 25-100 2014 3.5 0.22 

100-175 2012 4.3 0.11 

175-300 2013 4.3 0.10 

300-750 2014 4.3 0.09 

750-1200 2013 4.3 0.09 

>1200 2012 4.3 0.08 

3.2.3.2 Control Inventory Inputs 

Table 3-63 shows the NONROAD model emission factor inputs that were used to 

generate the control case emission inventories for recreational marine diesel engines. These 

emission factors were applied to engines beginning with the model years shown in Table 

3-62. No sulfur adjustment is applied to the Tier 3 PM calculations, since these engines will 

be certified to a fuel sulfur level at or lower than the episodic fuel sulfur levels expected when 

these engines are introduced 

All other modeling inputs are the same as shown above for the base case inventory 

development. Table 3-56 and Table 3-57 list the base engine populations, average hp by 
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3-69

power range, annual activity, load factor, and median life. These are unchanged from the 

default inputs in the NONROAD model. 

Table 3-63 Control Emission Factors for Recreational Marine Diesel Engines 

TIER 3 EMISSION FACTORS 

G/HP-HR 

HP RANGE PM10 NOX HC CO 

0-11 0.24 4.39 0.43 4.11 

11-16 0.19 3.63 0.21 2.16 

16-25 0.19 3.63 0.21 2.16 

0.18 3.71 0.41 1.53 25-50 0.18 2.32 0.41 1.53 

0.13 3.82 0.2 0.95 50-75 0.13 2.39 0.2 0.95 

0.13 3.82 0.2 0.95 75-100 0.13 2.39 0.2 0.95 

100-175 0.088 3.34 0.13 0.95 

175-300 0.08 3.9 0.22 0.95 

300-600 0.072 3.98 0.29 0.95 

600-750 0.072 3.98 0.29 0.95 

750-1200 0.072 3.98 0.29 0.95 

>1200 0.064 3.98 0.29 0.95 

DF ("A") 0.473 0.009 0.034 0.101 

3.2.3.3 Recreational Marine Diesel Control Inventory 

3.2.3.3.1 PM10, PM2.5, NOx, VOC, CO, and SO2 Emissions 

The control case 50-state emission inventories for recreational marine diesel engines 

(inboard and outboard combined) resulting from the control case model inputs presented 

above are shown in Table 3-64. 

3.2.3.3.2 Air Toxics Emissions 

The control case air toxics inventories for recreational marine diesel engines are 

provided in Table 3-65. Gaseous air toxics and POM are reduced proportionately to VOC and 

PM2.5, respectively. 
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Table 3-64 Control Case (50-State) Emissions for Recreational Marine Diesel Engines 

(short tons) 

YEAR PM10 PM2.5 NOX VOC HC CO SO2

2002 1,130 1,096 40,437 1,540 1,462 6,467 5,145 

2003 1,161 1,126 41,572 1,578 1,499 6,642 5,290 

2004 1,192 1,156 42,704 1,618 1,536 6,816 5,436 

2005 1,223 1,186 43,835 1,656 1,573 6,989 5,582 

2006 1,247 1,210 44,089 1,720 1,633 7,161 5,621 

2007 1,054 1,023 44,307 1,783 1,693 7,331 2,967 

2008 915 888 44,513 1,846 1,753 7,499 993 

2009 937 909 44,649 1,912 1,816 7,665 1,017 

2010 935 907 44,772 1,978 1,878 7,829 764 

2011 938 910 44,880 2,044 1,941 7,991 578 

2012 931 903 44,938 2,104 1,998 8,150 311 

2013 929 901 44,889 2,159 2,051 8,308 113 

2014 943 915 44,724 2,206 2,095 8,464 136 

2015 956 927 44,551 2,252 2,139 8,618 150 

2016 966 937 44,368 2,298 2,183 8,771 153 

2017 976 947 44,178 2,345 2,227 8,922 156 

2018 986 956 43,982 2,391 2,271 9,073 156 

2019 994 965 43,780 2,438 2,316 9,223 159 

2020 1,003 973 43,579 2,486 2,361 9,374 162 

2021 1,011 981 43,380 2,534 2,406 9,525 165 

2022 1,018 988 43,181 2,582 2,452 9,675 168 

2023 1,025 995 42,989 2,629 2,497 9,825 171 

2024 1,032 1,001 42,814 2,676 2,542 9,975 174 

2025 1,038 1,007 42,672 2,723 2,586 10,124 177 

2026 1,046 1,015 42,688 2,765 2,626 10,279 180 

2027 1,056 1,024 42,868 2,803 2,662 10,439 183 

2028 1,065 1,033 43,096 2,840 2,697 10,601 186 

2029 1,075 1,043 43,364 2,877 2,732 10,765 189 

2030 1,086 1,053 43,665 2,913 2,766 10,930 192 

2031 1,097 1,064 43,989 2,948 2,800 11,095 196 

2032 1,108 1,075 44,343 2,985 2,835 11,262 199 

2033 1,121 1,088 44,741 3,024 2,872 11,429 202 

2034 1,135 1,101 45,189 3,065 2,911 11,596 205 

2035 1,149 1,114 45,672 3,108 2,952 11,765 208 

2036 1,163 1,128 46,177 3,151 2,993 11,933 211 

2037 1,178 1,143 46,697 3,195 3,034 12,102 214 

2038 1,193 1,157 47,234 3,239 3,076 12,272 217 

2039 1,208 1,172 47,785 3,282 3,117 12,442 220 

2040 1,223 1,187 48,347 3,326 3,159 12,613 223 
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Table 3-65 Control Case Air Toxic Emissions for Recreational Marine Diesel Engines 

(short tons) 

HAP 2010 2015 2020 2030 

BENZENE 34 33 31 31 

FORMALDEHYDE 198 192 182 178 

ACETALDEHYDE 89 86 82 80 

1,3-BUTADIENE 3 3 3 3 

ACROLEIN 5 5 5 5 

NAPHTHALENE 0 0 0 0 

POM 0 0 0 0 

3.2.4 Projected Recreational Marine Emission Reductions of Final Rule 

The PM2.5, NOx, and VOC emission reductions by calendar year are shown in Table 

3-66. The air toxic emission reductions by pollutant and calendar year are given in Table 

3-67. 
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Table 3-66 Projected Recreational Marine Emission Reductions (short tons) 

YEAR PM 2.5 NOX VOC 

2008 0 0 0 

2009 0 0 1 

2010 0 0 1 

2011 1 0 2 

2012 3 39 8 

2013 5 174 20 

2014 9 415 40 

2015 13 657 61 

2016 17 902 82 

2017 21 1,148 103 

2018 25 1,397 124 

2019 29 1,647 146 

2020 34 1,898 167 

2021 38 2,151 189 

2022 43 2,405 211 

2023 48 2,659 233 

2024 53 2,915 255 

2025 59 3,171 277 

2026 64 3,426 299 

2027 70 3,681 321 

2028 75 3,935 343 

2029 81 4,187 365 

2030 86 4,437 386 

2031 92 4,682 407 

2032 97 4,915 427 

2033 101 5,120 444 

2034 105 5,288 459 

2035 108 5,434 471 

2036 111 5,570 483 

2037 114 5,702 494 

2038 116 5,828 505 

2039 119 5,951 516 

2040 122 6,070 526 
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Table 3-67 Projected Air Toxic Reductions from Recreational Marine Diesel Engines (short tons) 

HAP 2010 2015 2020 2030 

BENZENE 0 1 2 4 

FORMALDEHYDE 0 5 12 24 

ACETALDEHYDE 0 2 6 11 

1,3-BUTADIENE 0 0 0 0 

ACROLEIN 0 0 0 1 

NAPHTHALENE 0 0 0 0 

POM 0 0 0 0 

3.3 Locomotives 

3.3.1 General Methodology 

Given the quality of the data available, it was possible to develop more detailed 

estimates of fleet composition and emission rates. As described in this chapter, detailed data 

on fuel consumption, fleet size, and fleet composition were available from industry sources. 

Load factors and emission factors were developed in the previous rulemaking. However, 

usage and scrappage rates had to be assumed based on less detailed information. These 

assumptions were made available to the railroads and the rest of the public in the NPRM, and 

we received no information to contradict them. It is important to note that the overall analysis 

is much less sensitive to these assumptions than to the estimates of fuel consumption and 

emission factors. 

Locomotive emissions were calculated based on estimated current and projected fuel 

consumption rates. Emissions were calculated separately for the following locomotive 

categories: 

• Large Railroad Line-Haul Locomotives 

• Large Railroad Switching (including Class III Switch railroads owned by Class I 

railroads) 

• Other Line-Haul Locomotives (such as local railroads) 

• Other Switch/Terminal Locomotives 

• Passenger/Commuter Locomotives 

We used the following approach for all categories, except for the small railroads (see 

3.3.2.3). For each calendar year, locomotives were tracked separately by model year and then 

the activity was summed (in terms of work, fuel, and emissions) for all model years in the 

fleet. Seven basic steps were used to determine emissions in any calendar year: 
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1. Start with the fleet from the previous calendar year. 

2. Determine which model years would be due to be remanufactured or scrapped. 

3. Update the fleet to remove locomotives that would be scrapped. 

4. Determine the amount of work that would be done by the remaining locomotives from the 

previous year’s fleet. 

5. Determine the number of freshly manufactured (i.e., brand new) locomotives that would 

be purchased, and add them to the fleet. 

6. Determine the total amount of work that would be done by all the locomotives in the fleet. 

7. Determine total emissions from the work and brake-specific emission factors. 

3.3.1.1 Base Fleet 

As is described later, the base fleet was estimated for 2005 from a variety of industry 

sources. A new base fleet is calculated for each subsequent calendar year based on the 

scrappage rates and sales. The base fleet is a sum of multiple model years that are described 

by the number of locomotives in the fleet, the average work that has been accumulated since 

the last time it was remanufactured (in megawatt-hours or MW-hr), the average horsepower, 

and the Tier of standards to which they are certified. 

3.3.1.2 Useful Life 

In this analysis, all locomotives are assumed to be either remanufactured or scrapped 

when they reach or exceed their useful life. The useful life in MW-hrs is set equal to the rated 

horsepower of the locomotive multiplied by 7.5. Thus a 4000 horsepower locomotive would 

have a useful life of 30,000 MW-hrs. Annual accumulation of MW-hrs is projected based on 

the assumed rated horsepower of the locomotive and the relative use rate (which is a function 

of locomotive age). At the end of this second step, the projected fleet is adjusted to reflect a 

year's worth of use beyond the previous base fleet. 

3.3.1.3 Scrappage 

For each future calendar year, there will generally be some locomotive model years 

that will be projected to have reached the end of their current useful life. For example, we 

estimate that there will be 243 line-haul freight locomotives in use in 2010 that: 

• Were originally manufactured in model year 1986. 

• Will be accumulating about 2000 MW-hrs per year. 

• Will reach the end of their useful lives during 2011. 
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According to our scrappage curve, we estimate that 15 of these locomotives will be 

scrapped in 2011. The remaining 228 are projected to be remanufactured. We perform this 

analysis for each model year, then update that fleet to remove locomotives that would be 

scrapped and change the emission levels for locomotives that are remanufactured to new 

standards. 

3.3.1.4 Work Done by Old Fleet 

Once the existing fleet is adjusted for each new calendar year, we determine the 

amount of work that would be done by the remaining locomotives from the previous year’s 

fleet. First we calculate the amount of work done by each model year's fleet as follows: 

Equation 3-14 ( ) ( ) ( ) ( ) ( ) i = × × × ii × RUFPNLFHW i

Wi = Combined annual work output for all locomotives remaining in the fleet that 

were originally manufactured in model year i. 

H = Number of hours per year that a newly manufactured locomotive is projected to 

be used (approximately 4000 to 5000 hrs/yr). 

L = Typical average load factor. 

Ni = Number of locomotives remaining in the fleet that were originally manufactured 

in model year i. 

Pi = Average rated power of locomotives remaining in the fleet that were originally 

manufactured in model year i.

RUFi = Relative use factor for locomotives remaining in the fleet that were originally 

manufactured in model year i. 

The total work done by the remaining fleet (Wr) is calculated by summing the work 

done by each model year (Wi). 

3.3.1.5 New Sales 

Sales of freshly manufactured locomotives are projected for each calendar year after 

the remaining fleet has been analyzed. These newly manufactured locomotives are added to 

the remaining locomotives to comprise a new total fleet. The number is calculated based on 

the amount of fuel that is projected to be used in that calendar year: 

Equation 3-15 SalesNew ( ) PLFHWBSFCFuelTotal r = / − ///

Where BSFC is the estimated brake specific fuel consumption rate (Gal/MW-hr) 
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3.3.1.6 Total Work 

The total amount of work that would be done by all the locomotives in the fleet is 

calculated for each calendar year by summing the work projected to be done by the newly 

manufactured locomotives and the work projected to be done by the remaining locomotives. 

The total work is calculated separately for each tier of locomotives. 

3.3.1.7 Emissions 

Emissions are determined from the work calculated in section 3.3.1.6 (converted to 

hp-hrs) and brake-specific emission factors: 

Equation 3-16 Total Emissions = ( WorkTotal )× (Emission Factor)

The emission factors used are the estimated average in-use emissions for each tier of 

standards, which are shown in Table 3-68 and Table 3-69. They take into account 

deterioration of emissions throughout the useful life, production variations, and the 

compliance margins that manufacturers incorporate into their designs. For this analysis, we 

are generally assuming that average in-use emission levels will be 10 percent below the 

applicable standards. 

Table 3-68 Baseline Line-Haul Emission Factors (g/bhp-hr) 

 PM10 HC NOx CO 

UNCONTROLLED 0.32 0.48 13.0 1.28 

TIER 0 0.32 0.48 8.60 1.28 

TIER 1 0.32 0.47 6.70 1.28 

TIER2 0.18 0.26 4.95 1.28 

Table 3-69 Baseline Switch Emission Factors (g/bhp-hr) 

 PM10 HC NOx CO 

UNCONTROLLED 0.44 1.01 17.4 1.83 

TIER 0 0.44 1.01 12.6 1.83 

TIER 1 0.43 1.01 9.9 1.83 

TIER 2 0.19 0.51 7.3 1.83 

These PM10 emission factors reflect the emission rates expected from locomotives 

operating on current in-use fuel with sulfur levels at 3000 ppm. The emission inventories 

described in this chapter, however, account for the reductions in sulfate particulate expected 

to result from using lower sulfur fuels after 2007. We estimate that the PM10 emission rate for 
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locomotives operating on nominally 500 ppm sulfur fuel will be 0.05 g/bhp-hr lower than the 

PM10 emission rate for locomotives operating on 3000 ppm sulfur fuel. Similarly we estimate 

that the PM10 emission rate for locomotives operating on nominally 15 ppm sulfur fuel will be 

0.06 g/bhp-hr lower than the PM10 emission rate for locomotives operating on 3000 ppm 

sulfur fuel. This is higher than the estimates used for the NPRM because they are based on 

newer data. 

To estimate VOC emissions, an adjustment factor of 1.053 is applied to the HC 

output.39 Similarly, to estimate PM2.5 emissions, an adjustment factor of 0.97 is applied to the 

PM10 output.40 These adjustment factors are the same as those used for marine engines. 

3.3.2 Baseline (Pre-Control) Inventory Development 

In developing the baseline inventory, we collected fuel consumption estimates from 

the regulated industries, including publicly available estimates for Class I and commuter 

railroads. We used the same estimated average in-use emission factors and load factors as we 

used in the previous rulemaking. 

We are using a projection by the Energy Information Administration (EIA) that 

locomotive fuel consumption will grow 1.6 percent annually.41 We are assuming that this 

fuel growth applies equally across all categories of locomotives and is directly proportional to 

engine work performed by the fleet. 
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Table 3-70 Summary of Locomotive Emission Analysis Inputs 

 Large 

Line-Haul 

Large 

Switch 

Small 

Line-Haul 

Small 

Switch 

Passenger/ 

Commuter 

2005 FUEL 

CONSUMPTION 

(GAL/YR) 

3.981 

BILLION 

315 

MILLION 

34 

MILLION 

34 

MILLION 

142 

MILLION 

HOURS USED PER 

YEAR WHEN NEW 

4350 4450 NA NA 3900 

YEARS AFTER 

WHICH USAGE 

BEGINS TO 

DECLINE 

8 50 NA NA 20 

HOURS PER YEAR 

AT END OF LIFE 

1740 @ 40 

YRS 

3115 @ 70 

YRS 

NA NA 2340@30YRS 

AGE AFTER WHICH 

SCRAPPAGE 

BEGINS 

20 50 NA NA 20 

AGE AFTER WHICH 

NO LOCOMOTIVES 

REMAIN IN FLEET 

40 70 NA NA 30 

LOAD FACTOR 

(AVG HP/RATED 

HP) 

0.275 0.100 0.275 0.100 0.275 

AVG HP-HR/GAL 20.8 15.2 18.2 15.2 20.8 

3.3.2.1 Large Line-Haul 

The large line-haul category includes line-haul freight locomotives that are fully 

subject to the standards being final. Class III locomotives that are owned and operated by 

railroads that qualify as small businesses are addressed separately, as described in 3.3.2.3. 

The large line-haul analysis is based primarily on data collected for Class I and Class II 

railroads. However, as described in 3.3.2.3, the total fuel includes one-third of the estimated 

Class III fuel use to account for those Class III railroads that do not qualify as small 

businesses. The estimate of current Class I total fuel use came from the AAR Railroad Facts 

booklet. The estimate of Class II railroad fuel consumption is based on the survey 

information provided by the American Shortline Railroad Association for Class II and Class 

III railroads. These results had to be adjusted upward to correct for a response rate of 

approximately 85 percent. The combined total for Class I and Class II railroads was reduced 

by 7 percent to reflect fuel used in switching rather than line-haul operation.42

The fleet composition for all large railroads was estimated based on a contractor 

analysis. The contractor estimated that this fleet included 19,757 locomotives with more than 

2500 hp. (Locomotives with 2500 hp or less were assumed to be used primarily in switching 

operations.) To be precise, this estimate was intended to reflect the locomotives that were 

subject to the Part 92 regulations and thus excluded Class II locomotives owned by small 

businesses. An additional 878 locomotives were added to account for the previously excluded 

Class II locomotives. Usage and scrappage patterns were developed to fit the fuel use and 
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fleet composition data. The average in-use load factor was assumed to be the same as the 

load factor for a typical line-haul duty cycle test. 

3.3.2.2 Large Switch 

We generally used the same approach to calculate switch emissions as we used to 

calculate line-haul emissions, but we used different inputs. We also made one change to the 

analysis of future sales. We assumed that the majority of growth in switching activity will be 

achieved by using switch locomotives more rather than by adding new switch locomotives to 

the fleet. More specifically, we assumed that 1.2 percent of the annual 1.6 percent growth in 

activity will be achieved by using the existing switchers more, while only 0.4 percent of the 

growth will be achieved by increasing the number of switchers in the fleet. 

As shown in Table 3-70, we believe that switch locomotives tend to last longer in the 

fleet and have a lower in-use load factor than line-haul locomotives. Thus the average age of 

switch locomotives is much older than for line-haul. We also estimate that switching 

operation will use approximately 7 percent of total large railroad fuel, and will grow at the 

same rate as line-haul operation. The switch fleet composition for all large railroads was 

estimated based on the same contractor analysis used for the line-haul fleet. The contractor 

estimated that this fleet included 5206 locomotives with 2500 hp or less. This included 1645 

locomotives with 2250 to 2500 hp. While we recognize that some of these locomotives will 

be used in branch serviceD, for this analysis they are assumed to be used primarily in 

switching operations. The FRM analysis includes an additional 66 switch locomotives owned 

by previously excluded Class II railroads. 

3.3.2.3 Small Railroads 

We used a simplified approach for small railroads (that is, railroads that are not 

required to retrofit their locomotive with new emission controls because they qualify as 

"small railroads" under the regulatory definition). We assume that these small railroads are 

unlike the larger railroads in the following ways: 

• They do not purchase newly manufactured locomotives. 

• They use their locomotives at a constant rate. 

• They replace their existing locomotives at a constant rate of 3 percent per year. 

• Brake-specific PM emissions are 0.03 g/bhp-hr higher for locomotives owned by 

small railroads (relative to Class I/II locomotives) because of their higher fuel 

consumption rates. 

 

D Branch service includes short-haul operations that would be considered intermediate to intercity line-haul 

service and switch service. 
Regulatory Impact Analysis 

3-80

For this analysis, we considered small railroad activity in the same two categories as 

the larger railroads: line-haul and switch. For small line-haul operations, we are projecting 

that railroads will scrap and replace their oldest locomotives with 25 year-old locomotives 

purchased from the larger railroads. Thus, the inventory analysis has these railroads obtaining 

Tier 1 locomotives starting in 2026, and Tier 2 locomotives in 2030. For small switch 

operations, the railroads are projected to replace their scrapped locomotives with only 

uncontrolled or Tier 0 locomotives purchased from the larger railroads. This analysis runs 

through 2040 and we consider it unlikely that any significant number of Tier 1 or later switch 

locomotives will be available for small railroads before 2040. 

The analysis of small railroads is based on the survey information provided by the 

American Shortline Railroad Association for Class III railroads. These results had to be 

adjusted upward to correct for a response rate of approximately 85 percent. We also had to 

adjust these estimates because not all Class III railroads qualify as small railroads under the 

regulations. We estimate that one-third of these railroads are owned by Class I railroads or 

other large businesses. Finally, we estimated that Class III railroads use 50 percent of their 

fuel in switching service. We thus estimate that small Class III railroads used a total of 34 

million gallons of diesel fuel in line-haul service in 2005, and 34 million gallons of diesel fuel 

in switching service, as shown in Table 3-64. 

3.3.2.4 Passenger/Commuter 

We used the same approach to calculate passenger and commuter emissions as we 

used to calculate large line-haul emissions, but we used different inputs. As shown in Table 

3-1, we believe that passenger/commuter locomotives tend to have an average age that is 

slightly newer than for line-haul. We used estimates from AMTRAK and APTA for current 

fuel consumption rates, and project that these will grow at the same rate as line-haul 

operation. 

3.3.2.5 Locomotive Baseline Inventory Summary 

The baseline locomotive inventory is shown separately for PM10, PM2.5, NOx, VOC, 

HC, CO, and SO2 in Table 3-71 through Table 3-77. 

The baseline air toxics inventories for locomotives were taken from the MSAT rule 

and are provided in Table 3-78. Inventories are provided for calendar years 1999, 2010, 2015, 

2020, and 2030. 
Emission Inventory 

3-81

Table 3-71 Baseline (50-State) PM10 Emissions for Locomotives (short tons) 

Calendar 

Year 

Large Linehaul Large Switch Small 

Railroads 

Passenger/ 

Commuter Total 

2006 28,477 2,304 492 1,023 32,296 

2007 28,401 2,329 500 1,011 32,241 

2008 23,293 2,071 442 822 26,629 

2009 23,152 2,093 449 807 26,502 

2010 22,989 2,115 456 793 26,353 

2011 22,815 2,137 464 778 26,193 

2012 22,635 2,159 471 762 26,028 

2013 21,759 2,141 469 723 25,093 

2014 21,582 2,163 477 708 24,930 

2015 21,394 2,185 484 692 24,755 

2016 21,193 2,208 491 676 24,567 

2017 20,990 2,231 498 660 24,379 

2018 20,792 2,254 505 644 24,195 

2019 20,593 2,277 513 628 24,010 

2020 20,388 2,301 520 612 23,821 

2021 20,178 2,324 528 596 23,626 

2022 19,964 2,348 535 581 23,428 

2023 19,748 2,353 543 565 23,209 

2024 19,542 2,350 551 549 22,992 

2025 19,332 2,342 559 533 22,766 

2026 19,151 2,329 567 520 22,567 

2027 18,996 2,310 575 510 22,391 

2028 18,868 2,286 584 501 22,239 

2029 18,768 2,260 592 496 22,115 

2030 18,692 2,227 597 492 22,007 

2031 18,645 2,188 601 489 21,923 

2032 18,625 2,143 606 489 21,863 

2033 18,629 2,097 610 490 21,827 

2034 18,662 2,046 615 493 21,816 

2035 18,727 1,996 620 497 21,840 

2036 18,827 1,945 624 501 21,898 

2037 18,953 1,909 629 505 21,996 

2038 19,105 1,879 633 509 22,127 

2039 19,287 1,850 638 513 22,287 

2040 19,497 1,819 642 517 22,476 
Regulatory Impact Analysis 

3-82

Table 3-72 Baseline (50-State) PM2.5 Emissions for Locomotives (short tons) 

Calendar 

Year 

Large Linehaul Large Switch Small 

Railroads 

Passenger/ 

Commuter 

Total 

2006 27,622 2,235 478 992 31,327 

2007 27,549 2,259 485 981 31,274 

2008 22,595 2,009 429 797 25,830 

2009 22,458 2,030 436 783 25,707 

2010 22,300 2,051 443 769 25,562 

2011 22,130 2,073 450 754 25,407 

2012 21,956 2,094 457 740 25,247 

2013 21,107 2,077 455 702 24,340 

2014 20,935 2,098 462 686 24,182 

2015 20,752 2,120 469 671 24,012 

2016 20,557 2,142 476 656 23,830 

2017 20,361 2,164 483 640 23,648 

2018 20,168 2,186 490 625 23,469 

2019 19,975 2,209 497 609 23,290 

2020 19,776 2,232 504 594 23,106 

2021 19,572 2,255 512 578 22,917 

2022 19,365 2,278 519 563 22,725 

2023 19,156 2,282 527 548 22,513 

2024 18,955 2,280 534 533 22,302 

2025 18,752 2,271 542 517 22,083 

2026 18,576 2,259 550 505 21,890 

2027 18,426 2,241 558 494 21,719 

2028 18,302 2,218 566 486 21,572 

2029 18,205 2,192 574 481 21,452 

2030 18,131 2,160 579 477 21,347 

2031 18,086 2,122 583 475 21,266 

2032 18,066 2,079 588 474 21,207 

2033 18,070 2,034 592 476 21,172 

2034 18,102 1,985 597 478 21,162 

2035 18,166 1,936 601 482 21,185 

2036 18,262 1,887 605 486 21,241 

2037 18,384 1,852 610 490 21,336 

2038 18,532 1,823 614 494 21,463 

2039 18,708 1,794 619 498 21,619 

2040 18,913 1,765 623 502 21,802 
Emission Inventory 

3-83

Table 3-73 Baseline (50-State) NOx Emissions for Locomotives (short tons) 

Calendar Year Large Linehaul Large Switch Small 

Railroads 

Passenger/ 

Commuter 

Total 

2006 802,958 88,191 18,256 38,466 947,871 

2007 791,683 89,152 18,548 36,409 935,793 

2008 781,652 89,244 18,845 34,361 924,102 

2009 775,692 90,220 19,146 32,338 917,397 

2010 765,957 90,298 19,453 30,370 906,078 

2011 756,495 91,286 19,764 28,459 896,004 

2012 751,219 90,636 20,080 27,212 889,147 

2013 747,643 91,627 20,402 26,017 885,689 

2014 744,236 90,873 20,728 24,872 880,710 

2015 740,810 91,866 20,960 24,382 878,018 

2016 737,040 91,523 21,195 23,325 873,083 

2017 733,648 92,522 21,431 22,922 870,523 

2018 730,943 92,775 21,670 22,559 867,947 

2019 729,027 93,779 21,851 22,197 866,854 

2020 727,038 91,309 22,032 21,836 862,215 

2021 725,264 92,294 22,214 21,477 861,249 

2022 723,450 91,215 22,396 21,119 858,178 

2023 721,953 91,400 22,578 20,797 856,728 

2024 720,934 89,923 22,760 20,510 854,127 

2025 720,243 87,571 22,942 20,256 851,012 

2026 720,287 87,376 23,124 20,066 850,854 

2027 720,940 87,055 23,254 19,935 851,184 

2028 722,270 86,654 23,383 19,860 852,167 

2029 724,347 86,197 23,509 19,836 853,890 

2030 727,188 85,620 23,584 19,859 856,251 

2031 730,771 84,916 23,656 19,926 859,269 

2032 735,073 84,125 23,723 20,033 862,953 

2033 740,040 83,298 23,787 20,160 867,284 

2034 745,745 82,395 23,847 20,305 872,292 

2035 752,326 81,507 23,902 20,468 878,203 

2036 759,819 80,635 23,953 20,631 885,038 

2037 768,001 80,347 24,000 20,797 893,145 

2038 776,894 80,345 24,042 20,963 902,243 

2039 786,558 80,358 24,079 21,131 912,125 

2040 797,012 80,353 24,111 21,300 922,775 
Regulatory Impact Analysis 

3-84

Table 3-74 Baseline (50-State) VOC Emissions for Locomotives (short tons) 

Calendar Year Large Linehaul Large Switch Small 

Railroads 

Passenger/ 

Commuter Total 

2006 44,756 5,585 928 1,609 52,878 

2007 44,595 5,650 943 1,589 52,777 

2008 44,431 5,730 958 1,568 52,685 

2009 44,293 5,797 973 1,546 52,609 

2010 44,122 5,865 989 1,523 52,498 

2011 43,933 5,934 1,004 1,500 52,371 

2012 43,739 6,004 1,020 1,476 52,239 

2013 43,560 6,074 1,037 1,453 52,124 

2014 43,392 6,146 1,053 1,429 52,020 

2015 43,211 6,218 1,070 1,404 51,904 

2016 43,010 6,292 1,087 1,380 51,769 

2017 42,809 6,366 1,105 1,356 51,635 

2018 42,616 6,441 1,122 1,332 51,511 

2019 42,425 6,517 1,140 1,307 51,390 

2020 42,227 6,595 1,159 1,283 51,263 

2021 42,022 6,673 1,177 1,259 51,131 

2022 41,813 6,752 1,196 1,235 50,996 

2023 41,605 6,790 1,215 1,212 50,822 

2024 41,416 6,814 1,235 1,188 50,653 

2025 41,226 6,825 1,254 1,165 50,471 

2026 41,085 6,829 1,274 1,146 50,335 

2027 40,991 6,819 1,295 1,132 50,237 

2028 40,942 6,801 1,316 1,121 50,180 

2029 40,943 6,777 1,337 1,114 50,170 

2030 41,000 6,740 1,358 1,110 50,208 

2031 41,107 6,690 1,380 1,109 50,286 

2032 41,261 6,630 1,402 1,111 50,405 

2033 41,460 6,567 1,424 1,116 50,567 

2034 41,708 6,494 1,447 1,123 50,772 

2035 42,014 6,423 1,470 1,132 51,038 

2036 42,379 6,352 1,494 1,141 51,366 

2037 42,791 6,311 1,518 1,150 51,770 

2038 43,249 6,285 1,542 1,159 52,236 

2039 43,759 6,261 1,566 1,169 52,755 

2040 44,321 6,234 1,592 1,178 53,325 
Emission Inventory 

3-85

Table 3-75 Baseline (50-State) HC Emissions for Locomotives (short tons) 

Calendar Year Large Linehaul Large Switch Small 

Railroads 

Passenger/Co

mmuter 

Total 

2006 42,503 5,304 881 1,528 50,216 

2007 42,350 5,366 895 1,509 50,121 

2008 42,194 5,441 909 1,489 50,034 

2009 42,064 5,505 924 1,468 49,961 

2010 41,901 5,570 939 1,446 49,856 

2011 41,722 5,635 954 1,424 49,735 

2012 41,537 5,701 969 1,402 49,610 

2013 41,368 5,769 985 1,379 49,500 

2014 41,208 5,837 1,000 1,357 49,402 

2015 41,036 5,905 1,016 1,334 49,292 

2016 40,845 5,975 1,033 1,311 49,163 

2017 40,654 6,046 1,049 1,288 49,036 

2018 40,471 6,117 1,066 1,265 48,919 

2019 40,290 6,189 1,083 1,242 48,804 

2020 40,101 6,263 1,100 1,219 48,683 

2021 39,907 6,337 1,118 1,196 48,557 

2022 39,709 6,412 1,136 1,173 48,430 

2023 39,511 6,448 1,154 1,151 48,264 

2024 39,331 6,471 1,172 1,129 48,103 

2025 39,151 6,482 1,191 1,107 47,931 

2026 39,017 6,485 1,210 1,089 47,801 

2027 38,928 6,476 1,230 1,075 47,709 

2028 38,882 6,459 1,249 1,064 47,654 

2029 38,882 6,436 1,269 1,058 47,645 

2030 38,936 6,401 1,290 1,054 47,681 

2031 39,038 6,353 1,310 1,053 47,755 

2032 39,184 6,297 1,331 1,055 47,868 

2033 39,373 6,236 1,352 1,060 48,022 

2034 39,609 6,168 1,374 1,067 48,217 

2035 39,899 6,099 1,396 1,075 48,469 

2036 40,246 6,032 1,418 1,084 48,780 

2037 40,637 5,994 1,441 1,092 49,164 

2038 41,072 5,969 1,464 1,101 49,607 

2039 41,557 5,946 1,488 1,110 50,100 

2040 42,090 5,920 1,511 1,119 50,641 
Regulatory Impact Analysis 

3-86

Table 3-76 Baseline (50-State) CO Emissions for Locomotives (short tons) 

Calendar Year Large 

Line-haul Large Switch Small Railroads 

Passenger/ 

Commuter Total 

2006 118,701 9,788 1,863 4,201 134,553 

2007 120,601 9,944 1,893 4,234 136,672 

2008 122,530 10,103 1,923 4,268 138,825 

2009 124,491 10,265 1,954 4,302 141,012 

2010 126,483 10,429 1,985 4,337 143,234 

2011 128,506 10,596 2,017 4,371 145,491 

2012 130,562 10,766 2,049 4,406 147,784 

2013 132,651 10,938 2,082 4,442 150,113 

2014 134,774 11,113 2,116 4,477 152,480 

2015 136,930 11,291 2,149 4,513 154,884 

2016 139,121 11,472 2,184 4,549 157,326 

2017 141,347 11,655 2,219 4,585 159,806 

2018 143,609 11,842 2,254 4,622 162,327 

2019 145,906 12,031 2,290 4,659 164,887 

2020 148,241 12,224 2,327 4,696 167,488 

2021 150,613 12,419 2,364 4,734 170,130 

2022 153,023 12,618 2,402 4,772 172,814 

2023 155,471 12,820 2,440 4,810 175,541 

2024 157,958 13,025 2,480 4,849 178,311 

2025 160,486 13,233 2,519 4,887 181,125 

2026 163,054 13,445 2,559 4,926 183,984 

2027 165,662 13,660 2,600 4,966 186,889 

2028 168,313 13,879 2,642 5,006 189,839 

2029 171,006 14,101 2,684 5,046 192,837 

2030 173,742 14,326 2,727 5,086 195,882 

2031 176,522 14,555 2,771 5,127 198,975 

2032 179,346 14,788 2,815 5,168 202,118 

2033 182,216 15,025 2,860 5,209 205,310 

2034 185,131 15,265 2,906 5,251 208,553 

2035 188,093 15,510 2,953 5,293 211,848 

2036 191,103 15,758 3,000 5,335 215,196 

2037 194,161 16,010 3,048 5,378 218,596 

2038 197,267 16,266 3,097 5,421 222,050 

2039 200,423 16,526 3,146 5,464 225,560 

2040 203,630 16,791 3,196 5,508 229,125 
Emission Inventory 

3-87

Table 3-77 Baseline (50-State) SO2 Emissions for Locomotives (short tons) 

Calendar Year Large 

Line-haul Large Switch Small Railroads 

Passenger/ 

Commuter Total 

2006 84,429 6,685 1,429 2,988 95,531 

2007 85,780 6,792 1,452 3,012 97,036 

2008 10,168 805 172 354 11,499 

2009 10,330 818 175 357 11,680 

2010 10,496 831 178 360 11,864 

2011 10,664 844 181 363 12,051 

2012 10,834 858 183 366 12,241 

2013 315 25 5 11 355 

2014 320 25 5 11 361 

2015 325 26 5 11 367 

2016 330 26 6 11 372 

2017 335 27 6 11 378 

2018 340 27 6 11 384 

2019 346 27 6 11 390 

2020 351 28 6 11 396 

2021 357 28 6 11 403 

2022 363 29 6 11 409 

2023 369 29 6 11 415 

2024 375 30 6 11 422 

2025 380 30 6 12 429 

2026 387 31 7 12 435 

2027 393 31 7 12 442 

2028 399 32 7 12 449 

2029 405 32 7 12 456 

2030 412 33 7 12 464 

2031 419 33 7 12 471 

2032 425 34 7 12 478 

2033 432 34 7 12 486 

2034 439 35 7 12 494 

2035 446 35 8 13 501 

2036 453 36 8 13 509 

2037 460 36 8 13 517 

2038 468 37 8 13 526 

2039 475 38 8 13 534 

2040 483 38 8 13 542 
Regulatory Impact Analysis 

3-88

Table 3-78 Baseline (50-State) Air Toxics Emissions for Locomotives (short tons) 

HAP 1999 2010 2015 2020 2030 

BENZENE 92 84 82 80 76 

FORMALDEHYDE 1,467 1,339 1,318 1,280 1,214 

ACETALDEHYDE 640 584 575 558 530 

1,3-BUTADIENE 107 98 96 93 88 

ACROLEIN 104 94 93 90 86 

NAPHTHALENE 58 42 40 38 34 

POM 35 25 24 23 20 

3.3.3 Control Inventory Development 

Control inventories were developed in the same manner as the baseline inventories. 

The only change was in the emission factors. 

3.3.3.1 Control Scenario Modeled 

The final regulations will apply in largely the same manner as the existing program. 

Thus, the control scenario can be defined simply by the final standards and the model years 

for which they become effective. Two new sets of emission standards are being finalized: 

line-haul locomotive standards and switch locomotive standards. The line-haul standards 

apply for freight and passenger line-haul locomotives, while the switch standards apply for 

freight and passenger switch locomotives. Note; we are not changing the emission standards 

for CO. 

As in the baseline analysis, average in-use emission factors for the analysis of the final 

standards were generally assumed to be 10 percent below the applicable standards, to account 

for deterioration of emissions throughout the useful life, production variations, and the 

compliance margins that manufacturers incorporate into their designs. The exceptions to this 

general rule are the HC emissions for all locomotives and the NOx emissions for Tier 4 

locomotives. While we are changing the Tier 3 or earlier HC standards, we expect the 

emission controls for PM10 will generally achieve proportional reductions in HC. For Tier 4 

NOx standards, we expect that manufacturers will need to have lower zero-hour emission rates 

to account for potential deterioration and include larger compliance margins (expressed as a 

percentage of the standards). 

The emission factors used to generate the control case inventories are given in Table 

3-79 and Table 3-80. 
Emission Inventory 

3-89

Table 3-79 Projected Line-Haul Emission Factors with Final Standards 

Tier Initial Model 

Year 

NOx

(g/bhp-hr) 

PM10

(g/bhp-hr) 

HC 

(g/bhp-hr) 

TIER 0 2008/2010A 7.20 0.20 0.30 

TIER 1 2008/2010A 6.70 0.20 0.29 

TIER 2 2013 4.95 0.08 0.13 

TIER 3 2012 4.95 0.08 0.13 

TIER 4 2015 1.00 0.015 0.04 

A The new Tier 0 standard would apply in 2008 where kits are available, and for all locomotives in 2010. 

This is modeled as apply the new Tier 0/1 standards to 660 locomotives in 2008 and 2009.

Table 3-80 Projected Switch Emission Factors with Final Standards 

Tier Initial Model 

Year 

NOx

(g/bhp-hr) 

PM10

(g/bhp-hr) 

HC 

(g/bhp-hr) 

TIER 0 2008 10.62 0.23 0.57 

TIER 1 2008 9.90 0.23 0.57 

TIER 2 2013 7.30 0.11 0.26 

TIER 3 2012 5.40 0.08 0.26 

TIER 4 2015 1.00 0.015 0.08 

3.3.3.2 Locomotive Control Inventory Summary 

The control locomotive inventory is shown separately for PM10, PM2.5, NOx, VOC, 

and HC in Table 3-81 through Table 3-85. See section 3.3.2.5 for CO and SO2 inventories 

which are not projected to change as a result of the final standards. 

The control air toxic inventory for locomotives is provided in Table 3-86. The gas 

phase air toxics are assumed to be controlled proportionately to VOC, while POM is 

controlled proportionately to PM. 
Regulatory Impact Analysis 

3-90

Table 3-81 Control Case PM10 Emissions for Locomotives (short tons) 

Calendar Year Large 

Line-haul Large Switch Small Railroads 

Passenger/ 

Commuter Total 

2006 28,477 2,304 492 1,023 32,296 

2007 28,401 2,329 500 1,011 32,241 

2008 23,287 2,019 442 822 26,569 

2009 22,804 2,039 449 807 26,100 

2010 22,248 2,019 456 774 25,498 

2011 21,234 2,037 464 741 24,476 

2012 20,203 1,987 471 701 23,362 

2013 18,945 1,972 469 647 22,034 

2014 18,313 1,928 477 611 21,329 

2015 17,451 1,942 481 574 20,448 

2016 16,329 1,891 485 532 19,237 

2017 15,214 1,904 490 490 18,097 

2018 14,363 1,883 494 448 17,188 

2019 13,540 1,895 498 407 16,341 

2020 12,938 1,798 502 375 15,613 

2021 12,324 1,809 507 350 14,990 

2022 11,675 1,752 511 325 14,263 

2023 11,016 1,732 515 300 13,563 

2024 10,367 1,655 520 275 12,817 

2025 9,712 1,543 524 250 12,029 

2026 9,091 1,505 528 227 11,351 

2027 8,492 1,460 533 207 10,692 

2028 7,915 1,412 537 188 10,053 

2029 7,363 1,361 542 172 9,438 

2030 6,844 1,305 543 157 8,849 

2031 6,349 1,244 544 144 8,281 

2032 5,879 1,179 545 132 7,735 

2033 5,431 1,111 546 121 7,209 

2034 5,026 1,040 547 111 6,723 

2035 4,653 969 547 101 6,270 

2036 4,326 897 548 93 5,864 

2037 4,033 840 548 86 5,508 

2038 3,775 801 549 81 5,205 

2039 3,556 761 549 76 4,941 

2040 3,375 720 549 72 4,717 
Emission Inventory 

3-91

Table 3-82 Control Case PM2.5 Emissions for Locomotives (short tons) 

Calendar Year Large 

Line-haul Large Switch Small Railroads 

Passenger/ 

Commuter Total 

2006 27,622 2,235 478 992 31,327 

2007 27,549 2,259 485 981 31,274 

2008 22,588 1,958 429 797 25,772 

2009 22,120 1,978 436 783 25,317 

2010 21,580 1,959 443 751 24,733 

2011 20,597 1,976 450 718 23,741 

2012 19,597 1,928 457 680 22,661 

2013 18,377 1,913 455 628 21,373 

2014 17,764 1,870 462 593 20,689 

2015 16,928 1,883 467 557 19,835 

2016 15,839 1,835 471 516 18,660 

2017 14,757 1,847 475 475 17,554 

2018 13,933 1,826 479 435 16,673 

2019 13,134 1,838 483 395 15,850 

2020 12,550 1,744 487 364 15,145 

2021 11,954 1,755 492 340 14,540 

2022 11,325 1,700 496 315 13,835 

2023 10,685 1,680 500 291 13,156 

2024 10,056 1,606 504 266 12,433 

2025 9,421 1,497 508 242 11,668 

2026 8,818 1,459 513 220 11,010 

2027 8,237 1,416 517 200 10,371 

2028 7,678 1,370 521 183 9,751 

2029 7,143 1,320 525 167 9,155 

2030 6,638 1,266 526 153 8,584 

2031 6,159 1,207 528 140 8,033 

2032 5,703 1,143 529 128 7,503 

2033 5,268 1,078 529 117 6,993 

2034 4,875 1,009 530 107 6,521 

2035 4,513 940 531 98 6,082 

2036 4,196 871 532 91 5,689 

2037 3,912 815 532 84 5,342 

2038 3,662 777 532 78 5,049 

2039 3,449 738 533 74 4,793 

2040 3,274 698 533 70 4,575 
Regulatory Impact Analysis 

3-92

Table 3-83 Control Case NOx Emissions for Locomotives (short tons) 

Calendar Year Large 

Line-haul Large Switch Small Railroads 

Passenger/ 

Commuter Total 

2006 802,958 88,191 18,256 38,466 947,871 

2007 791,683 89,152 18,548 36,409 935,793 

2008 777,326 88,424 18,845 34,361 918,955 

2009 769,265 89,386 19,146 32,338 910,135 

2010 748,176 88,920 19,453 29,845 886,393 

2011 719,947 89,750 19,764 27,408 856,869 

2012 706,002 88,058 20,080 25,933 840,074 

2013 691,886 88,873 20,402 24,545 825,706 

2014 685,692 87,106 20,728 23,239 816,764 

2015 664,161 87,722 20,929 22,225 795,037 

2016 634,126 86,130 21,131 20,406 761,792 

2017 604,818 86,724 21,334 19,264 732,140 

2018 584,819 86,169 21,538 18,185 710,710 

2019 566,469 86,754 21,636 17,127 691,985 

2020 548,958 82,365 21,732 16,351 669,405 

2021 531,295 82,893 21,825 15,591 651,605 

2022 512,821 80,480 21,915 14,833 630,049 

2023 490,714 79,595 22,003 14,074 606,387 

2024 468,890 76,258 22,089 13,316 580,553 

2025 446,847 71,540 22,171 12,558 553,116 

2026 425,402 69,917 22,250 11,833 529,401 

2027 404,319 68,040 22,313 11,182 505,853 

2028 383,636 65,996 22,372 10,555 482,558 

2029 363,473 63,825 22,427 9,948 459,673 

2030 344,049 61,413 22,428 9,355 437,245 

2031 326,929 58,747 22,423 8,775 416,875 

2032 310,357 55,897 22,413 8,204 396,872 

2033 294,261 52,957 22,396 7,641 377,256 

2034 279,046 49,854 22,374 7,082 358,355 

2035 264,662 46,750 22,344 6,527 340,282 

2036 251,877 43,649 22,308 6,048 323,882 

2037 240,339 41,172 22,266 5,623 309,400 

2038 230,075 39,498 22,216 5,270 297,059 

2039 221,180 37,824 22,159 4,986 286,149 

2040 213,678 36,091 22,094 4,765 276,629 
Emission Inventory 

3-93

Table 3-84 Control Case VOC Emissions for Locomotives (short tons) 

Calendar Year Large 

Line-haul 

Large Switch Small 

Railroads 

Passenger/ 

Commuter 

Total 

2006 44,756 5,585 928 1,609 52,878 

2007 44,595 5,650 943 1,589 52,777 

2008 43,784 5,574 958 1,568 51,884 

2009 42,751 5,639 973 1,546 50,909 

2010 41,527 5,572 989 1,470 49,558 

2011 39,032 5,624 1,004 1,395 47,055 

2012 36,485 5,455 1,020 1,299 44,259 

2013 34,151 5,505 1,037 1,205 41,898 

2014 32,533 5,347 1,053 1,114 40,048 

2015 30,731 5,386 1,070 1,032 38,218 

2016 28,146 5,207 1,087 933 35,374 

2017 25,595 5,244 1,105 837 32,781 

2018 23,874 5,164 1,122 742 30,902 

2019 22,241 5,200 1,140 648 29,229 

2020 21,372 4,861 1,159 582 27,974 

2021 20,495 4,892 1,177 542 27,107 

2022 19,568 4,691 1,196 503 25,958 

2023 18,629 4,637 1,215 463 24,944 

2024 17,708 4,387 1,235 423 23,752 

2025 16,780 3,999 1,254 384 22,416 

2026 15,904 3,889 1,274 348 21,416 

2027 15,061 3,767 1,295 317 20,440 

2028 14,252 3,636 1,316 288 19,492 

2029 13,481 3,499 1,337 263 18,580 

2030 12,758 3,366 1,358 240 17,722 

2031 12,074 3,220 1,380 219 16,892 

2032 11,427 3,065 1,402 200 16,093 

2033 10,812 2,905 1,424 184 15,325 

2034 10,264 2,738 1,447 168 14,617 

2035 9,764 2,570 1,470 154 13,959 

2036 9,332 2,404 1,494 142 13,371 

2037 8,952 2,274 1,518 131 12,875 

2038 8,624 2,193 1,542 123 12,481 

2039 8,353 2,112 1,566 115 12,147 

2040 8,141 2,029 1,592 110 11,871 
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Table 3-85 Control Case HC Emissions for Locomotives (short tons) 

Calendar Year Large 

Line-haul 

Large Switch Small 

Railroads 

Passenger/ 

Commuter 

Total 

2006 42,503 5,304 881 1,528 50,216 

2007 42,350 5,366 895 1,509 50,121 

2008 41,581 5,293 909 1,489 49,272 

2009 40,600 5,355 924 1,468 48,347 

2010 39,437 5,291 939 1,396 47,064 

2011 37,067 5,341 954 1,325 44,687 

2012 34,648 5,180 969 1,233 42,031 

2013 32,432 5,228 985 1,144 39,789 

2014 30,896 5,077 1,000 1,058 38,032 

2015 29,184 5,114 1,016 980 36,295 

2016 26,729 4,945 1,033 886 33,593 

2017 24,307 4,980 1,049 795 31,131 

2018 22,672 4,904 1,066 704 29,346 

2019 21,121 4,938 1,083 615 27,757 

2020 20,297 4,616 1,100 553 26,566 

2021 19,464 4,646 1,118 515 25,743 

2022 18,583 4,455 1,136 477 24,652 

2023 17,691 4,404 1,154 440 23,688 

2024 16,817 4,166 1,172 402 22,557 

2025 15,935 3,797 1,191 364 21,288 

2026 15,104 3,694 1,210 330 20,338 

2027 14,303 3,578 1,230 301 19,411 

2028 13,535 3,453 1,249 274 18,511 

2029 12,803 3,323 1,269 249 17,644 

2030 12,116 3,196 1,290 228 16,830 

2031 11,466 3,058 1,310 208 16,042 

2032 10,851 2,910 1,331 190 15,283 

2033 10,268 2,759 1,352 174 14,554 

2034 9,747 2,600 1,374 160 13,881 

2035 9,273 2,441 1,396 146 13,256 

2036 8,862 2,283 1,418 135 12,698 

2037 8,501 2,160 1,441 125 12,227 

2038 8,190 2,083 1,464 116 11,853 

2039 7,933 2,006 1,488 110 11,536 

2040 7,731 1,926 1,511 104 11,273 
Emission Inventory 

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Table 3-86 Control Case Air Toxic Emissions for Locomotives (short tons) 

HAP 2010 2015 2020 2030 

BENZENE 79 61 44 27 

FORMALDEHYDE 1,264 971 698 429 

ACETALDEHYDE 551 424 305 187 

1,3-BUTADIENE 92 71 51 31 

ACROLEIN 89 69 49 30 

NAPHTHALENE 40 30 21 12 

POM 25 20 15 8 

3.3.4 Projected Locomotive Emission Reductions from the Final Rule 

The projected emission reductions for PM 2.5, NOx and VOC for each category of 

locomotives and calendar year are given in Table 3-87 through Table 3-89. Table 3-90 

presents the air toxic emission reductions. 
Regulatory Impact Analysis 

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Table 3-87 Projected Locomotive PM2.5 Emission Reductions (short tons) 

Calendar Year Large 

Line-haul 

Large Switch Small 

Railroads 

Passenger/ 

Commuter 

Total 

2008 6 51 0 0 58 

2009 338 52 0 0 390 

2010 719 93 0 18 830 

2011 1,533 96 0 36 1,666 

2012 2,360 167 0 60 2,586 

2013 2,730 164 0 74 2,967 

2014 3,171 228 0 93 3,492 

2015 3,825 236 3 114 4,178 

2016 4,718 307 5 140 5,170 

2017 5,603 317 8 165 6,094 

2018 6,235 360 11 190 6,796 

2019 6,841 370 14 214 7,439 

2020 7,226 488 17 230 7,961 

2021 7,618 500 20 239 8,377 

2022 8,040 578 23 248 8,890 

2023 8,470 603 27 257 9,357 

2024 8,899 674 30 266 9,869 

2025 9,331 775 34 275 10,415 

2026 9,758 800 37 285 10,880 

2027 10,189 824 41 294 11,348 

2028 10,624 848 45 304 11,821 

2029 11,062 872 49 314 12,297 

2030 11,493 894 52 324 12,764 

2031 11,927 915 56 335 13,233 

2032 12,363 936 59 347 13,705 

2033 12,802 956 63 359 14,180 

2034 13,227 976 66 371 14,640 

2035 13,652 996 70 384 15,102 

2036 14,067 1,016 74 395 15,552 

2037 14,473 1,037 78 406 15,993 

2038 14,870 1,046 82 416 16,414 

2039 15,259 1,056 86 424 16,826 

2040 15,638 1,066 90 432 17,227 
Emission Inventory 

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Table 3-88 Projected Locomotive NOx Emission Reductions (short tons) 

Calendar Year Large 

Line-haul 

Large Switch Small 

Railroads 

Passenger/ 

Commuter 

Total 

2008 4,327 820 0 0 5,147 

2009 6,428 834 0 0 7,261 

2010 17,781 1,378 0 526 19,684 

2011 36,548 1,535 0 1,051 39,135 

2012 45,217 2,578 0 1,278 49,073 

2013 55,757 2,754 0 1,472 59,983 

2014 58,545 3,767 0 1,634 63,946 

2015 76,649 4,144 31 2,157 82,981 

2016 102,915 5,393 64 2,919 111,291 

2017 128,830 5,798 97 3,658 138,383 

2018 146,125 6,605 132 4,374 157,237 

2019 162,559 7,025 215 5,071 174,869 

2020 178,080 8,944 301 5,486 192,811 

2021 193,969 9,401 389 5,885 209,644 

2022 210,629 10,734 480 6,286 228,129 

2023 231,239 11,805 574 6,723 250,341 

2024 252,043 13,665 671 7,195 273,574 

2025 273,396 16,030 771 7,699 297,895 

2026 294,886 17,460 873 8,233 321,452 

2027 316,621 19,015 941 8,753 345,331 

2028 338,635 20,659 1,011 9,305 369,609 

2029 360,874 22,372 1,083 9,888 394,217 

2030 383,139 24,207 1,157 10,504 419,006 

2031 403,841 26,169 1,232 11,151 442,394 

2032 424,715 28,228 1,310 11,828 466,082 

2033 445,778 30,341 1,391 12,519 490,029 

2034 466,699 32,542 1,473 13,223 513,937 

2035 487,665 34,757 1,558 13,941 537,920 

2036 507,942 36,986 1,645 14,584 561,156 

2037 527,662 39,175 1,734 15,173 583,744 

2038 546,818 40,847 1,826 15,693 605,184 

2039 565,378 42,533 1,920 16,145 625,976 

2040 583,334 44,261 2,017 16,534 646,146 
Regulatory Impact Analysis 

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Table 3-89 Projected Locomotive VOC Emission Reductions (short tons) 

Calendar Year Large 

Line-haul 

Large Switch Small 

Railroads 

Passenger/ 

Commuter 

Total 

2008 638 143 0 0 802 

2009 1,477 144 0 0 1,700 

2010 2,476 279 0 52 2,940 

2011 4,727 296 0 105 5,316 

2012 7,002 534 0 175 7,980 

2013 9,102 554 0 242 10,226 

2014 10,527 784 0 307 11,972 

2015 12,054 817 0 359 13,686 

2016 14,349 1,067 0 428 16,395 

2017 16,617 1,104 0 495 18,855 

2018 18,078 1,259 0 561 20,610 

2019 19,468 1,299 0 625 22,162 

2020 20,122 1,714 0 661 23,289 

2021 20,778 1,760 0 674 24,024 

2022 21,480 2,040 0 687 25,038 

2023 22,194 2,126 0 699 25,878 

2024 22,908 2,395 0 713 26,900 

2025 23,629 2,780 0 726 28,055 

2026 24,346 2,887 0 740 28,919 

2027 25,077 2,993 0 754 29,797 

2028 25,819 3,100 0 770 30,688 

2029 26,570 3,207 0 786 31,590 

2030 27,329 3,297 0 803 32,486 

2031 28,099 3,387 0 822 33,393 

2032 28,878 3,477 0 841 34,312 

2033 29,667 3,566 0 861 35,242 

2034 30,439 3,656 0 882 36,156 

2035 31,220 3,745 0 904 37,080 

2036 31,992 3,835 0 924 37,994 

2037 32,759 3,926 0 943 38,895 

2038 33,521 4,016 0 960 39,755 

2039 34,276 4,107 0 976 40,608 

2040 35,026 4,198 0 990 41,454 

Table 3-90 Projected Locomotive Air Toxic Emission Reductions (short tons) 

HAP 2010 2015 2020 2030 

BENZENE 5 22 36 49 

FORMALDEHYDE 75 348 581 786 

ACETALDEHYDE 33 152 254 343 

1,3-BUTADIENE 5 25 42 57 

ACROLEIN 5 25 41 55 

NAPHTHALENE 2 11 17 22 

POM 1 4 8 12 
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3.4 Projected Total Emission Reductions from the Final Rule 

The total base and control inventories, as well as emission reductions by calendar year, 

for PM2.5, NOx, and VOC are given in Table 3-91. The totals include emissions from the 

three major categories affected by this final rule: commercial marine diesel engines, 

recreational marine diesel engines, and locomotives. The results for PM2.5 and NOx are also 

illustrated in Figure 1 and Figure 2. Reductions by pollutant and category are also provided in 

Table 3-92 through Table 3-94. 

The total air toxics reductions are provided in Table 3-95. 

Calendar year 2040 was chosen as the end date for the analysis; however, additional 

reductions are expected to occur beyond this date. 

Figure 1 Estimated PM2.5 Reductions from Locomotive and Marine Diesel Engine Standards (short tons) 

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

2005 2010 2015 2020 2025 2030 2035 2040

Base 50-State

Control 50-State
Regulatory Impact Analysis 

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Figure 2 Estimated NOx Reductions from Locomotive and Marine Diesel Engine Standards (short tons) 

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

1,600,000

1,800,000

2,000,000

2005 2010 2015 2020 2025 2030 2035 2040

Base 50-State

Control 50-State
Emission Inventory 

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Table 3-91 Total Emissions and Projected Reductions (short tons) 

PM2.5 NOX VOC Year Base Control Reduction Base Control Reduction Base Control Reduction 

2006 62,797 62,797 0 1,812,229 1,812,229 0 71,875 71,875 0 

2007 60,893 60,893 0 1,787,163 1,787,163 0 71,699 71,699 0 

2008 54,082 53,760 322 1,762,615 1,748,592 14,023 71,535 70,733 802 

2009 53,942 53,011 931 1,743,151 1,718,947 24,204 71,391 69,684 1,708 

2010 53,579 51,921 1,658 1,719,287 1,675,398 43,889 71,218 68,262 2,956 

2011 53,233 50,443 2,790 1,696,911 1,627,108 69,803 71,032 65,692 5,340 

2012 52,810 48,598 4,212 1,678,040 1,591,136 86,904 70,846 62,707 8,139 

2013 51,572 46,338 5,234 1,663,297 1,557,820 105,476 70,683 60,078 10,605 

2014 49,750 43,400 6,350 1,647,759 1,518,782 128,976 70,539 57,677 12,862 

2015 48,296 40,993 7,303 1,634,987 1,474,272 160,715 70,392 55,306 15,086 

2016 47,879 38,986 8,893 1,620,503 1,414,569 205,934 70,243 51,869 18,374 

2017 47,381 36,867 10,514 1,609,712 1,357,341 252,371 70,131 48,628 21,504 

2018 47,002 35,052 11,951 1,600,291 1,307,606 292,686 70,061 46,095 23,966 

2019 46,620 33,376 13,245 1,593,148 1,261,385 331,763 70,011 43,788 26,223 

2020 46,348 31,905 14,443 1,583,847 1,212,782 371,065 69,988 41,938 28,049 

2021 46,266 30,666 15,600 1,579,669 1,170,501 409,167 69,991 40,513 29,478 

2022 46,213 29,347 16,866 1,574,245 1,125,428 448,817 70,007 38,828 31,179 

2023 46,141 28,049 18,092 1,571,689 1,079,540 492,149 69,994 37,297 32,697 

2024 46,070 26,700 19,370 1,569,185 1,032,778 536,407 69,996 35,612 34,384 

2025 46,006 25,330 20,676 1,566,788 985,485 581,303 69,994 33,823 36,171 

2026 45,981 24,092 21,889 1,567,997 943,123 624,875 70,041 32,417 37,624 

2027 45,993 22,905 23,087 1,570,259 902,211 668,048 70,128 31,086 39,042 

2028 46,042 21,768 24,274 1,573,643 862,660 710,983 70,262 29,827 40,435 

2029 46,130 20,681 25,449 1,578,219 824,927 753,292 70,447 28,638 41,809 

2030 46,247 19,654 26,593 1,584,378 789,523 794,855 70,685 27,546 43,140 

2031 46,395 18,679 27,716 1,592,027 757,840 834,186 70,966 26,520 44,447 

2032 46,572 17,749 28,823 1,600,710 727,630 873,080 71,293 25,553 45,739 

2033 46,781 16,858 29,923 1,610,338 698,634 911,704 71,666 24,640 47,025 

2034 47,016 16,017 30,999 1,620,871 671,073 949,798 72,084 23,806 48,278 

2035 47,285 15,224 32,061 1,632,511 645,094 987,417 72,564 23,039 49,525 

2036 47,591 14,512 33,079 1,645,247 621,689 1,023,558 73,107 22,363 50,744 

2037 47,937 13,896 34,040 1,659,436 602,427 1,057,009 73,728 21,814 51,914 

2038 48,318 13,371 34,947 1,674,792 587,424 1,087,368 74,413 21,402 53,011 

2039 48,729 12,927 35,802 1,691,093 574,726 1,116,367 75,152 21,066 54,087 

2040 49,168 12,586 36,582 1,708,303 564,133 1,144,170 75,943 20,806 55,137 
Regulatory Impact Analysis 

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Table 3-92 Projected Total PM2.5 Emission Reductions (short tons) 

YEAR COMMERCIAL 

MARINE 

RECREATIONAL 

MARINE 

LOCOMOTIVES TOTAL 

2008 264 0 58 322 

2009 541 0 390 931 

2010 828 0 830 1,658 

2011 1,124 1 1,666 2,790 

2012 1,623 3 2,586 4,212 

2013 2,262 5 2,967 5,234 

2014 2,848 9 3,492 6,350 

2015 3,112 13 4,178 7,303 

2016 3,706 17 5,170 8,893 

2017 4,400 21 6,094 10,514 

2018 5,130 25 6,796 11,951 

2019 5,776 29 7,439 13,245 

2020 6,448 34 7,961 14,443 

2021 7,185 38 8,377 15,600 

2022 7,933 43 8,890 16,866 

2023 8,687 48 9,357 18,092 

2024 9,447 53 9,869 19,370 

2025 10,203 59 10,415 20,676 

2026 10,945 64 10,880 21,889 

2027 11,670 70 11,348 23,087 

2028 12,378 75 11,821 24,274 

2029 13,072 81 12,297 25,449 

2030 13,743 86 12,764 26,593 

2031 14,391 92 13,233 27,716 

2032 15,022 97 13,705 28,823 

2033 15,642 101 14,180 29,923 

2034 16,253 105 14,640 30,999 

2035 16,851 108 15,102 32,061 

2036 17,416 111 15,552 33,079 

2037 17,933 114 15,993 34,040 

2038 18,416 116 16,414 34,947 

2039 18,857 119 16,826 35,802 

2040 19,233 122 17,227 36,582 
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Table 3-93 Projected Total NOx Emission Reductions (short tons) 

YEAR COMMERCIAL 

MARINE 

RECREATIONAL 

MARINE 

LOCOMOTIVES TOTAL 

2008 8,876 0 5,147 14,023 

2009 16,942 0 7,261 24,204 

2010 24,204 0 19,684 43,889 

2011 30,668 0 39,135 69,803 

2012 37,792 39 49,073 86,904 

2013 45,319 174 59,983 105,476 

2014 64,616 415 63,946 128,976 

2015 77,077 657 82,981 160,715 

2016 93,741 902 111,291 205,934 

2017 112,839 1,148 138,383 252,371 

2018 134,052 1,397 157,237 292,686 

2019 155,247 1,647 174,869 331,763 

2020 176,357 1,898 192,811 371,065 

2021 197,373 2,151 209,644 409,167 

2022 218,283 2,405 228,129 448,817 

2023 239,149 2,659 250,341 492,149 

2024 259,918 2,915 273,574 536,407 

2025 280,237 3,171 297,895 581,303 

2026 299,996 3,426 321,452 624,875 

2027 319,036 3,681 345,331 668,048 

2028 337,439 3,935 369,609 710,983 

2029 354,888 4,187 394,217 753,292 

2030 371,411 4,437 419,006 794,855 

2031 387,110 4,682 442,394 834,186 

2032 402,084 4,915 466,082 873,080 

2033 416,556 5,120 490,029 911,704 

2034 430,573 5,288 513,937 949,798 

2035 444,063 5,434 537,920 987,417 

2036 456,832 5,570 561,156 1,023,558 

2037 467,563 5,702 583,744 1,057,009 

2038 476,356 5,828 605,184 1,087,368 

2039 484,440 5,951 625,976 1,116,367 

2040 491,954 6,070 646,146 1,144,170 
Regulatory Impact Analysis 

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Table 3-94 Projected Total VOC Emission Reductions (short tons) 

YEAR COMMERCIAL 

MARINE 

RECREATIONAL 

MARINE 

LOCOMOTIVES TOTAL 

2008 8,876 0 5,147 14,023 

2009 16,942 0 7,261 24,204 

2010 24,204 0 19,684 43,889 

2011 30,668 0 39,135 69,803 

2012 37,792 39 49,073 86,904 

2013 45,319 174 59,983 105,476 

2014 64,616 415 63,946 128,976 

2015 77,077 657 82,981 160,715 

2016 93,741 902 111,291 205,934 

2017 112,839 1,148 138,383 252,371 

2018 134,052 1,397 157,237 292,686 

2019 155,247 1,647 174,869 331,763 

2020 176,357 1,898 192,811 371,065 

2021 197,373 2,151 209,644 409,167 

2022 218,283 2,405 228,129 448,817 

2023 239,149 2,659 250,341 492,149 

2024 259,918 2,915 273,574 536,407 

2025 280,237 3,171 297,895 581,303 

2026 299,996 3,426 321,452 624,875 

2027 319,036 3,681 345,331 668,048 

2028 337,439 3,935 369,609 710,983 

2029 354,888 4,187 394,217 753,292 

2030 371,411 4,437 419,006 794,855 

2031 387,110 4,682 442,394 834,186 

2032 402,084 4,915 466,082 873,080 

2033 416,556 5,120 490,029 911,704 

2034 430,573 5,288 513,937 949,798 

2035 444,063 5,434 537,920 987,417 

2036 456,832 5,570 561,156 1,023,558 

2037 467,563 5,702 583,744 1,057,009 

2038 476,356 5,828 605,184 1,087,368 

2039 484,440 5,951 625,976 1,116,367 

2040 491,954 6,070 646,146 1,144,170 
Emission Inventory 

3-105

Table 3-95 Projected Total Air Toxic Emission Reductions (short tons) 

HAP 2010 2015 2020 2030 

BENZENE 5 69 202 426 

FORMALDEHYDE 79 693 1,796 3,551 

ACETALDEHYDE 35 323 857 1,716 

1,3-BUTADIENE 5 26 44 62 

ACROLEIN 5 31 65 109 

NAPHTHALENE 2 14 28 45 

POM 1 5 11 18 

3.5 Contribution of Marine Diesel Engines and Locomotives to Baseline 

National Emission Inventories 

This section provides the contribution of marine diesel engines and locomotives to 

baseline nationwide emission inventories in 2001, 2020, and 2030. The baseline represents 

current and future emissions with the existing standards. The calendar years correspond to 

those chosen for the air quality modeling. 

The pollutants included in this section are directly emitted PM 2.5, NOx, VOC, CO, and 

SO2. While we do not provide estimates for other pollutants here, it should be noted that the 

affected engines also contribute to national ammonia (NH3) and air toxics inventories. 

3.5.1 Categories and Sources of Data 

As described more fully earlier in this chapter, our current inventories for marine 

diesel engines and locomotives were developed using multiple methodologies, but they all are 

based on combining engine populations, hours of use, average engine loads, and in-use 

emissions factors. Locomotive emissions were calculated based on estimated current and 

projected fuel consumption rates. Emissions were calculated separately for the following 

locomotive categories: Large Railroad Line-Haul Locomotives, Large Railroad Switching 

(including Class III Switch railroads owned by Class I railroads), Other Line-Haul 

Locomotives (i.e., local railroads), Other Switcher/Terminal Locomotives and Passenger 

Locomotives. The inventories for marine diesel engines were created separately for Category 

1 and 2 propulsion and auxiliary engines, including those less than or equal to 37 kW, and 

diesel fueled recreational marine propulsion engines. 

The locomotive, commercial marine (C1 & C2), and diesel recreational marine values 

given for 2001 are actually 2002 estimates, since that is the base year that was used for air 

quality modeling. The stationary, aircraft, onroad diesel, and C3 commercial marine values 

are from the PM NAAQS 2001 air quality modeling platform, which is more recent than, but 

essentially the same as CAIR (2001 platform) for these sources. The 2030 stationary source 

values are set equal to 2020, since no specific estimates for 2030 stationary source emissions 

are available. All the stationary source values exclude "non-manmade" sources, such as fires 

and fugitive dust. Onroad gasoline vehicle values are from the National Mobile Inventory 
Regulatory Impact Analysis 

3-106

Model (NMIM) outputs for the final Mobile Source Air Toxics rulemaking, which includes 

the assumed implementation of Renewable Fuels Standards (RFS) and corrections for coldstart HC effects. Nonroad land-based diesel values are from the latest publicly released 

version of EPA's nonroad model (NONROAD2005a). Nonroad spark-ignition (SI) values in 

these tables (small SI, SI recreational marine, large SI, and SI recreational vehicles) are also 

from NONROAD2005a. The NONROAD2005 model runs were all done at the 

nationwide/annual level using single default nationwide temperature & RVP and the full 50-

state equipment population including all California equipment. 

3.5.2 PM2.5 Contributions to Baseline 

Table 3-96 provides the contribution of locomotives and diesel-fueled recreational and 

commercial marine engines to mobile source diesel and to total man-made PM2.5 emissions. 

PM2.5 emissions from these sources are 18 percent of the mobile source diesel PM2.5

emissions in 2001, and this percentage increases to about 65 percent by 2030. PM2.5 emissions 

from the affected sources decreases from almost 60,000 tons in 2002 to 46,000 tons in 2020 

due to the existing emission standards. From 2020 to 2025 emissions remain relatively 

constant as growth offsets the effect of continued turnover of older engines to engines 

meeting the existing emission standards. These emissions begin to increase again around 2025 

and exceed 2015 levels by 2035. 

3.5.3 NOx Contributions to Baseline 

Table 3-97 provides the contribution of locomotives and diesel-fueled recreational and 

commercial marine engines to mobile source NOx and to total man-made NOx emissions. 

NOx emissions from these sources are 16 percent of the mobile source NOx emissions in 

2001, and this percentage increases to 35 percent by 2030. NOx emissions from affected 

sources decrease from about 2 million tons in 2002 to almost 1.6 million tons in 2020 due to 

the existing emission standards. From 2020 to 2025 emissions remain relatively constant as 

growth offsets the effect of continued turnover of older engines to engines meeting the 

existing emission standards. These emissions begin to increase again in 2025 and by 2035 

exceed 2015 emission levels. 

3.5.4 VOC Contributions to Baseline 

Table 3-98 provides the contribution of locomotives and diesel-fueled recreational and 

commercial marine engines to mobile source VOC and to total man-made VOC emissions. 

Due to the efficient combustion in diesel engines, mobile source VOC emissions are 

dominated by spark-ignition engines, and the VOC emissions from the affected sources are 

only 0.8 percent of the mobile source VOC in 2001, increasing to 1.3 percent by 2030. VOC 

emissions from affected sources increase from 69,000 tons in 2002 to 70,000 tons in 2020 and 

71,000 tons in 2030, since the existing emission standards are not aimed at controlling VOC. 

3.5.5 CO Contributions to Baseline 

Table 3-99 provides the contribution of locomotives and diesel-fueled recreational and 

commercial marine engines to mobile source carbon monoxide (CO) and to total man-made 
Emission Inventory 

3-107

CO emissions. As with VOC, mobile source CO emissions are dominated by spark-ignition 

engines, so the CO emissions from the affected sources are only 0.3 percent of the mobile 

source CO in 2001, increasing to 0.5 percent by 2030. CO emissions from affected sources 

increase from 281,000 tons in 2002 to 317,000 tons in 2020 and 350,000 tons in 2030, since 

the existing emission standards are not aimed at controlling CO. 

3.5.6 SO2 Contributions to Baseline 

Table 3-100 provides the contribution of locomotives and diesel-fueled recreational 

and commercial marine engines to mobile source SO2 and to total man-made SO2 emissions. 

SO2 emissions from these sources are 21 percent of the mobile source SO2 emissions in 2001, 

and this percentage decreases significantly to about one percent in 2020 and 2030 due to 

existing diesel fuel sulfur standards. SO2 emissions from affected sources decrease from 

162,000 tons in 2002 to 3,700 tons in 2020. From 2020 to 2030 emissions increase to 4,200 

tons due to continued projected growth in these sectors. 
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Table 3-96 50-State Annual PM 2.5 Baseline Emission Levels for Mobile and Other Source Categories 

2001a Category 2020 2030 

short tons % of 

diesel 

mobile 

% of 

total 

short tons % of 

diesel 

mobile 

% of 

total 

short tons % of 

diesel 

mobile 

% of 

total 

Locomotive 29,660 8.9% 1.2% 23,106 21.4% 1.1% 21,347 28.7% 1.0% 

Recreational Marine Diesel 1,096 0.3% 0.0% 1,006 0.9% 0.0% 1,140 1.5% 0.1% 

Commercial Marine (C1 & C2) 28,730 8.6% 1.2% 22,236 20.5% 1.1% 23,760 32.0% 1.2% 

Land-Based Nonroad Diesel 164,180 49.2% 6.8% 46,075 42.6% 2.2% 17,934 24.2% 0.9% 

Commercial Marine (C3)b 20,023 - 0.8% 36,141 - 1.7% 52,682 - 2.6% 

Small Nonroad SI 25,575 1.1% 31,083 1.5% 35,761 1.7% 

Recreational Marine SI 17,101 0.7% 6,595 0.3% 6,378 0.3% 

SI Recreational Vehicles 12,301 0.5% 11,773 0.6% 9,953 0.5% 

Large Nonroad SI (>25hp) 1,610 0.1% 2,421 0.1% 2,844 0.1% 

Aircraft 5,664 0.2% 7,044 0.3% 8,569 0.4% 

Total Off Highway 305,941 12.6% 187,480 9.1% 180,368 8.7% 

Highway Diesel 109,952 33.0% 4.5% 15,800 14.6% 0.8% 10,072 13.6% 0.5% 

Highway non-diesel 50,277 2.1% 47,354 2.3% 56,734 2.7% 

Total Highway 160,229 6.6% 63,154 3.1% 66,806 3.2% 

Total Diesel (distillate) Mobile 333,619 100% 13.7% 108,223 100% 5.2% 74,253 100% 3.6% 

Total Mobile Sources 466,170 19.2% 250,634 12.1% 247,174 12.0% 

Stationary Point and Area 

Sources 

1,963,264 80.8% 1,817,722 87.9% 1,817,722 88.0% 

Total Man-Made Sources 2,429,434 100% 2,068,356 100% 2,064,896 100% 

Notes: 

a

 The locomotive, commercial marine (C1 & C2), and diesel recreational marine estimates are for calendar year 2002. 

b

 This category includes emissions from Category 3 (C3) propulsion engines and C2/3 auxiliary engines used on oceangoing vessels. 
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Table 3-97 50-State Annual NOx Baseline Emission Levels for Mobile and Other Source Categories 

2001a Category 2020 2030 

short tons % of 

mobile 

source 

% of 

total 

short tons % of 

mobile 

source 

% of 

total 

short tons % of 

mobile 

source 

% of 

total 

Locomotive 1,118,786 9.0% 5.1% 862,215 17.3% 7.8% 856,251 19.1% 8.1% 

Recreational Marine Diesel 40,437 0.3% 0.2% 45,477 0.9% 0.4% 48,102 1.1% 0.5% 

Commercial Marine (C1 & C2) 834,025 6.7% 3.8% 676,154 13.5% 6.1% 680,025 15.1% 6.4% 

Land-Based Nonroad Diesel 1,548,236 12.5% 7.1% 678,377 13.6% 6.1% 434,466 9.7% 4.1% 

Commercial Marine (C3)b 224,100 1.8% 1.0% 369,160 7.4% 3.3% 531,641 11.8% 5.0% 

Small Nonroad SI 100,319 0.8% 0.5% 98,620 2.0% 0.9% 114,287 2.5% 1.1% 

Recreational Marine SI 42,252 0.3% 0.2% 83,312 1.7% 0.8% 92,188 2.1% 0.9% 

SI Recreational Vehicles 5,488 0.0% 0.0% 17,496 0.4% 0.2% 20,136 0.4% 0.2% 

Large Nonroad SI (>25hp) 321,098 2.6% 1.5% 46,319 0.9% 0.4% 46,253 1.0% 0.4% 

Aircraft 83,764 0.7% 0.4% 105,133 2.1% 0.9% 118,740 2.6% 1.1% 

Total Off Highway 4,318,505 34.8% 19.8% 2,982,264 59.8% 26.9% 2,942,089 65.5% 27.7% 

Highway Diesel 3,750,886 30.2% 17.2% 646,961 13.0% 5.8% 260,915 5.8% 2.5% 

Highway non-diesel 4,354,430 35.0% 20.0% 1,361,276 27.3% 12.3% 1,289,780 28.7% 12.2% 

Total Highway 8,105,316 65.2% 37.2% 2,008,237 40.2% 18.1% 1,550,695 34.5% 14.6% 

Total Diesel (distillate) Mobile 7,292,308 58.7% 33.5% 2,907,578 58.3% 26.2% 2,277,735 50.7% 21.5% 

Total Mobile Sources 12,423,821 100% 57.0% 4,990,501 100% 44.9% 4,492,784 100% 42.4% 

Stationary Point and Area 

Sources 

9,355,659 - 43.0% 6,111,866 - 55.1% 6,111,866 - 57.6% 

Total Man-Made Sources 21,779,480 - 100% 11,102,367 - 100% 10,604,650 - 100% 

Notes: 

a

 The locomotive, commercial marine (C1 & C2), and diesel recreational marine estimates are for calendar year 2002. 

b

 This category includes emissions from Category 3 (C3) propulsion engines and C2/3 auxiliary engines used on oceangoing vessels. 
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Table 3-98 50-State Annual VOC Baseline Emission Levels for Mobile and Other Source Categories 

2001a Category 2020 2030 

short tons % of 

mobile 

source 

% of 

total 

short tons % of 

mobile 

source 

% of 

total 

short tons % of 

mobile 

source 

% of 

total 

Locomotive 50,665 0.6% 0.3% 51,263 1.0% 0.4% 50,208 0.9% 0.4% 

Recreational Marine Diesel 1,540 0.0% 0.0% 2,653 0.0% 0.0% 3,299 0.1% 0.0% 

Commercial Marine (C1 & C2) 17,229 0.2% 0.1% 16,071 0.3% 0.1% 17,178 0.3% 0.1% 

Land-Based Nonroad Diesel 188,884 2.3% 1.1% 76,047 1.4% 0.5% 63,144 1.1% 0.4% 

Commercial Marine (C3)b 9,572 0.1% 0.1% 18,458 0.3% 0.1% 27,582 0.5% 0.2% 

Small Nonroad SI 1,314,015 15.9% 7.3% 999,810 18.6% 7.2% 1,156,408 19.7% 8.1% 

Recreational Marine SI 1,212,446 14.7% 6.8% 688,774 12.8% 5.0% 697,712 11.9% 4.9% 

SI Recreational Vehicles 512,059 6.2% 2.9% 454,979 8.5% 3.3% 391,541 6.7% 2.7% 

Large Nonroad SI (>25hp) 132,888 1.6% 0.7% 12,429 0.2% 0.1% 10,276 0.2% 0.1% 

Portable Fuel Containers 244,545 3.0% 1.4% 254,479 4.7% 1.8% 288,630 4.9% 2.0% 

Aircraft 22,084 0.3% 0.1% 27,644 0.5% 0.2% 30,331 0.5% 0.2% 

Total Off Highway 3,705,926 44.9% 20.7% 2,602,608 48.4% 18.8% 2,736,309 46.7% 19.1% 

Highway Diesel 223,519 2.7% 1.2% 123,449 2.3% 0.9% 138,758 2.4% 1.0% 

Highway non-diesel 4,316,615 52.3% 24.1% 2,646,363 49.3% 19.1% 2,987,562 51.0% 20.8% 

Total Highway 4,540,134 55.1% 25.3% 2,769,812 51.6% 20.0% 3,126,320 53.3% 21.8% 

Total Diesel (distillate) Mobile 479,285 5.8% 2.7% 270,844 5.0% 2.0% 274,189 4.7% 1.9% 

Total Mobile Sources 8,246,060 100% 46.0% 5,372,420 100% 38.8% 5,862,629 100% 40.9% 

Stationary Point and Area 

Sources 

9,692,344 - 54.0% 8,475,443 - 61.2% 8,475,443 - 59.1% 

Total Man-Made Sources 17,938,404 - 100% 13,847,863 - 100% 14,338,072 - 100% 

Notes: 

a

 The locomotive, commercial marine (C1 & C2), and diesel recreational marine estimates are for calendar year 2002. 

b

 This category includes emissions from Category 3 (C3) propulsion engines and C2/3 auxiliary engines used on oceangoing vessels. 
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Table 3-99 50-State Annual CO Baseline Emission Levels for Mobile and Other Source Categories 

2001a Category 2020 2030 

short tons % of 

mobile 

source 

% of 

total 

short tons % of 

mobile 

source 

% of 

total 

short tons % of 

mobile 

source 

% of 

total 

Locomotive 123,210 0.1% 0.1% 167,488 0.3% 0.2% 195,882 0.3% 0.2% 

Recreational Marine Diesel 6,467 0.0% 0.0% 9,374 0.0% 0.0% 10,930 0.0% 0.0% 

Commercial Marine (C1 & C2) 151,331 0.2% 0.2% 139,712 0.2% 0.2% 143,791 0.2% 0.2% 

Land-Based Nonroad Diesel 893,320 1.0% 0.9% 310,258 0.5% 0.4% 155,625 0.2% 0.2% 

Commercial Marine (C3)b 19,391 0.0% 0.0% 37,459 0.1% 0.1% 56,713 0.1% 0.1% 

Small Nonroad SI 18,843,914 21.4% 19.4% 27,269,797 41.7% 36.8% 31,623,016 42.5% 38.1% 

Recreational Marine SI 2,816,005 3.2% 2.9% 2,136,234 3.3% 2.9% 2,178,413 2.9% 2.6% 

SI Recreational Vehicles 1,229,707 1.4% 1.3% 1,922,020 2.9% 2.6% 1,902,925 2.6% 2.3% 

Large Nonroad SI (>25hp) 1,801,679 2.0% 1.9% 304,532 0.5% 0.4% 281,993 0.4% 0.3% 

Aircraft 263,232 0.3% 0.3% 327,720 0.5% 0.4% 358,012 0.5% 0.4% 

Total Off Highway 26,148,256 29.6% 26.9% 32,624,593 49.9% 44.1% 36,907,299 49.6% 44.4% 

Highway Diesel 1,098,213 1.2% 1.1% 248,689 0.4% 0.3% 149,784 0.2% 0.2% 

Highway non-diesel 60,985,008 69.1% 62.7% 32,503,404 49.7% 43.9% 37,399,211 50.2% 45.0% 

Total Highway 62,083,221 70.4% 63.8% 32,752,093 50.1% 44.2% 37,548,995 50.4% 45.2% 

Total Diesel (distillate) Mobile 2,272,530 2.6% 2.3% 877,583 1.3% 1.2% 658,428 0.9% 0.8% 

Total Mobile Sources 88,231,477 100% 90.7% 65,376,686 100% 88.3% 74,456,294 100% 89.6% 

Stationary Point and Area 

Sources 

9,014,249 - 9.3% 8,641,678 - 11.7% 8,641,678 - 10.4% 

Total Man-Made Sources 97,245,726 - 100% 74,018,364 - 100% 83,097,972 - 100% 

Notes: 

a

 The locomotive, commercial marine (C1 & C2), and diesel recreational marine estimates are for calendar year 2002. 

b

 This category includes emissions from Category 3 (C3) propulsion engines and C2/3 auxiliary engines used on oceangoing vessels. 
Regulatory Impact Analysis 

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 Table 3-100 50-State Annual SO2 Baseline Emission Levels for Mobile and Other Source Categories 

2001a Category 2020 2030 

short tons % of 

mobile 

source 

% of 

total 

short tons % of 

mobile 

source 

% of 

total 

short tons % of 

mobile 

source 

% of 

total 

Locomotive 76,727 9.7% 0.5% 396 0.1% 0.0% 464 0.1% 0.0% 

Recreational Marine Diesel 5,145 0.7% 0.0% 162 0.0% 0.0% 192 0.0% 0.0% 

Commercial Marine (C1 & C2) 80,353 10.2% 0.5% 3,104 0.9% 0.0% 3,586 0.7% 0.0% 

Land-Based Nonroad Diesel 167,615 21.2% 1.1% 999 0.3% 0.0% 1,078 0.2% 0.0% 

Commercial Marine (C3)b 166,739 21.1% 1.1% 272,535 79.9% 3.2% 400,329 83.2% 4.6% 

Small Nonroad SI 6,723 0.9% 0.0% 8,620 2.5% 0.1% 9,990 2.1% 0.1% 

Recreational Marine SI 2,755 0.3% 0.0% 2,980 0.9% 0.0% 3,160 0.7% 0.0% 

SI Recreational Vehicles 1,241 0.2% 0.0% 2,643 0.8% 0.0% 2,784 0.6% 0.0% 

Large Nonroad SI (>25hp) 925 0.1% 0.0% 905 0.3% 0.0% 1,020 0.2% 0.0% 

Aircraft 7,890 1.0% 0.0% 9,907 2.9% 0.1% 11,137 2.3% 0.1% 

Total Off Highway 516,113 65.4% 3.3% 302,251 88.7% 3.5% 433,741 90.2% 5.0% 

Highway Diesel 103,632 13.1% 0.7% 3,443 1.0% 0.0% 4,453 0.9% 0.1% 

Highway non-diesel 169,125 21.4% 1.1% 35,195 10.3% 0.4% 42,709 8.9% 0.5% 

Total Highway 272,757 34.6% 1.7% 38,638 11.3% 0.5% 47,162 9.8% 0.5% 

Total Diesel (distillate) Mobile 433,465 54.9% 2.7% 8,108 2.4% 0.1% 9,777 2.0% 0.1% 

Total Mobile Sources 788,870 100% 5.0% 340,889 100% 4.0% 480,903 100% 5.5% 

Stationary Point and Area 

Sources 

15,057,420 - 95.0% 8,215,016 - 96.0% 8,215,016 - 94.5% 

Total Man-Made Sources 15,846,290 - 100% 8,555,905 - 100% 8,695,919 - 100% 

Notes: 

a

 The locomotive, commercial marine (C1 & C2), and diesel recreational marine estimates are for calendar year 2002. 

b

 This category includes emissions from Category 3 (C3) propulsion engines and C2/3 auxiliary engines used on oceangoing vessels. 

3.6 Contribution of Marine Diesel Engines and Locomotives to NonAttainment Area Emission Inventories 

Table 3-101 and Table 3-102 show the percent contribution to mobile source diesel 

PM2.5 and total mobile source NOx for certain non-attainment areas where there are large rail 

yards and/or commercial marine ports. The county-level inventories were estimated by 

allocating the nationwide baseline inventories to the counties using the same county:national 

ratios as used in the 2002 National Emissions Inventory (NEI).43 It can be seen that 

locomotives and diesel marine vessels make up a substantial portion of the PM2.5 and NOx

mobile source inventories in these areas. For instance, the combination of rail and 

commercial marine activity in the Huntington-Ashland WV-KY-OH area yields a 

contribution over 50% of mobile source diesel PM2.5 in 2002, increasing to 90% in 2030. 

Additional details, including the annual tons of PM2.5 and NOx from locomotives, 

diesel marine engines, and all mobile sources within each of the counties of these 

metropolitan areas are provided in Appendix 3A of this chapter. 
Emission Inventory 

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Table 3-101 Locomotive and Diesel Marine Engine Contributions to Non-Attainment Area Mobile Source 

Diesel PM2.5 Emissions 

PM 2002 2020 2030 2.5 Metropolitan Area 

LM % LM % LM % 

Huntington-Ashland WV-KY-OH 52.9% 82.1% 90.4% 

Houston, TX 41.9% 72.9% 84.6% 

Los Angeles, CA 31.3% 49.3% 72.1% 

Cleveland-Akron-Lorain, OH 25.1% 56.0% 72.0% 

Chicago, IL 24.6% 54.9% 70.0% 

Cincinnati, OH 23.2% 53.6% 69.5% 

Chattanooga, TN 21.1% 56.3% 69.5% 

Kansas City, MO 20.6% 51.3% 68.0% 

Baltimore, MD 22.5% 52.6% 67.8% 

St. Louis, MO 21.4% 51.3% 67.5% 

Philadelphia, PA 19.6% 47.0% 63.9% 

Seattle, WA 17.0% 43.3% 60.4% 

Birmingham, AL 16.3% 46.6% 57.5% 

Minneapolis-St. Paul, MN 10.7% 31.3% 47.8% 

Boston, MA 7.8% 22.9% 40.5% 

San Joaquin Valley, CA 8.8% 19.4% 38.2% 

Atlanta, GA 5.2% 19.6% 29.9% 

Indianapolis, IN 5.0% 17.5% 29.3% 

Phoenix-Mesa, AZ 4.9% 17.3% 26.8% 

Detroit, MI 4.1% 15.3% 26.0% 

New York, NY 3.5% 11.1% 20.3% 
Regulatory Impact Analysis 

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Table 3-102 Locomotive and Diesel Marine Engine Contributions to Non-Attainment Area Total Mobile 

Source NOx Emissions 

NOx Metropolitan Area 2002 2020 2030 

LM % LM % LM % 

Houston, TX 31.5% 46.3% 44.8% 

Kansas City, MO 19.3% 39.3% 43.2% 

Birmingham, AL 16.7% 38.3% 42.6% 

Chicago, IL 19.9% 37.8% 41.1% 

Cleveland-Akron-Lorain, OH 18.8% 37.2% 39.5% 

Chattanooga, TN 15.6% 35.7% 39.1% 

Cincinnati, OH 17.5% 35.7% 38.3% 

Los Angeles, CA 18.1% 30.8% 37.2% 

St. Louis, MO 15.7% 33.8% 36.9% 

Huntington-Ashland WV-KY-OH 38.1% 41.9% 36.2% 

Seattle, WA 13.2% 27.7% 30.3% 

San Joaquin Valley, CA 8.4% 16.0% 25.7% 

Minneapolis-St. Paul, MN 8.1% 17.5% 19.4% 

Philadelphia, PA 13.4% 19.7% 18.8% 

Phoenix-Mesa, AZ 5.1% 11.7% 14.6% 

Atlanta, GA 4.2% 10.7% 12.8% 

Indianapolis, IN 4.3% 10.7% 12.7% 

Boston, MA 6.3% 10.6% 10.8% 

Baltimore, MD 7.1% 10.4% 9.7% 

Detroit, MI 2.8% 7.2% 8.2% 

New York, NY 4.7% 7.4% 7.3% 
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3.7 Emission Inventories Used for Air Quality Modeling 

3.7.1 Comparison of Air Quality and Final Rule Inventories 

This section describes the differences in the inventories used for the final rule air 

quality analysis and the inventories used for the final rule. Chapter 2 of this document 

discusses the air quality analysis results and addresses the likely impact of these differences 

(if any) on the air quality outcomes from the final rule. 

For the commercial marine vessel and diesel recreational marine categories affected 

by this rule, the baseline inventories for the air quality analysis are unchanged from those 

used for the final rule. For the diesel locomotive category, minor changes have been made to 

the baseline inventories. Minor changes to the control case have also been made for all three 

categories since the air quality analysis; these changes are described in this section. 

In addition to the diesel locomotive, commercial marine vessel, and diesel recreational 

marine sources, the air quality inventories include emission contributions from all sources, 

including the following sources not directly affected by the final rule: 

• Stationary and area sources 

• Aircraft 

• Oceangoing commercial marine vessels (Category 3) 

• Onroad (highway) mobile sources 

• Nonroad mobile sources other than diesel pleasure craft 

The emission inventory estimates used in the air quality analysis for these sources are 

those included in the 2002-based CMAQ modeling platform. In comparison, the inventory 

estimates presented in section 3.5 were taken from the recent Clean Air Interstate Rule 

(CAIR)44. 
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3-116

Table 3-103 through Table 3-105 summarize the differences between the air quality 

inventories and the final rule inventories for baseline VOC, NOx, and PM2.5. Similarly, Table 

3-106 through Table 3-108 summarize the differences between the air quality inventories and 

the final rule inventories for control case VOC, NOx, and PM2.5. Lastly, Table 3-109 through 

Table 3-111 summarize the differences in ton reductions for these pollutants between the air 

quality inventories and the more updated final rule inventories. Only the years 2020 and 2030 

are shown for the latter two sets of tables, since this rule has no benefits prior to 2008. 

3.7.2 Locomotive Inventory Changes 

Since the air quality analysis, changes were made to the baseline inputs, primarily to 

more accurately account for PM sulfate reductions from reduced future fuel sulfur levels. In 

addition, changes have been made in the control case to include all Class II railroads, exclude 

small businesses, and accelerate the Tier 4 NOx standard for the line-haul category. The net 

effect of these updates in 2030 is roughly a 4 percent increase in VOC and NOx tons reduced 

and a 10 percent decrease in PM2.5 tons reduced. 

3.7.3 Marine Diesel Inventory Changes 

Since the air quality analysis, changes have been made to expand the remanufacturing 

program requirements for Category 1 engines to include Tier 2 engines. Also, a 12 percent 

reduction in benefits for the remanufacturing program, affecting both Category 1 and 

Category 2 engines, was applied to account for the small business exclusion. For Category 2 

engines, the Tier 3 and Tier 4 standards for the 2000-3700 kW category have been revised. 

These changes collectively affect NOx, PM, and VOC emissions for the control case 

beginning in 2008. 

Since the air quality analysis, the Tier 4 NOx and PM standards affecting 2000 hp and 

greater recreational marine engines have been eliminated. In addition, there have been minor 

changes to the Tier 3 NOx + HC and PM standards for engines greater than 1200 hp. 

There was also an error applying the PM sulfur adjustment factor for recreational 

marine engines, which only affects the 2020 and 2030 baseline PM emissions used in the air 

quality analysis. 

The net effect of these updates for marine diesel engines in 2030 is a 3 percent 

decrease in VOC tons reduced, a 2 percent increase in NOx tons reduced, and a 9 percent 

increase in PM2.5 tons reduced. 
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3-117

Table 3-103 50-State Annual VOC Baseline Emission Levels for Mobile and Other Source Categories 

2002 2020 2030 

CATEGORY AQ 

MODELING FRM* % DIFF AQ MODELING FRM % DIFF AQ MODELING FRM % DIFF 

LOCOMOTIVE 50,665 50,665 0.0% 52,634 51,263 -2.6% 51,813 50,208 -3.1% 

MARINE DIESEL 18,839 18,768 -0.4% 18,927 18,724 -1.1% 20,780 20,477 -1.5% 

ALL OTHER SOURCES 

(MOBILE & 

STATIONARY) 

16,917,345 17,868,970 5.6% 12,415,007 13,777,876 11.0% 12,330,869 14,267,387 15.7% 

TOTAL MAN-MADE 

SOURCES 16,986,849 17,938,403 5.6% 12,486,568 13,847,864 10.9% 12,403,462 14,338,072 15.6% 

* THE FRM VALUE FOR ALL OTHER SOURCES IN THE "2002" COLUMN IS ACTUALLY A 2001 ESTIMATE. 

Table 3-104 50-State Annual NOx Baseline Emission Levels for Mobile and Other Source Categories 

2002 2020 2030 

CATEGORY AQ 

MODELING FRM* % DIFF AQ MODELING FRM % DIFF AQ MODELING FRM % DIFF 

LOCOMOTIVE 1,118,788 1,118,786 0.0% 860,463 862,215 0.2% 854,238 856,251 0.2% 

MARINE DIESEL 868,315 874,462 0.7% 717,259 721,632 0.6% 724,061 728,127 0.6% 

ALL OTHER SOURCES 

(MOBILE & 

STATIONARY) 

18,820,689 19,786,232 5.1% 9,198,907 9,518,521 3.5% 8,722,491 9,020,273 3.4% 

TOTAL MAN-MADE 

SOURCES 20,807,792 21,779,480 4.7% 10,776,629 11,102,368 3.0% 10,300,790 10,604,651 2.9% 

* THE FRM VALUE FOR ALL OTHER SOURCES IN THE "2002" COLUMN IS ACTUALLY A 2001 ESTIMATE. 

Table 3-105 50-State Annual PM2.5 Baseline Emission Levels for Mobile and Other Source Categories 

2002 2020 2030 

CATEGORY AQ 

MODELING FRM* % DIFF AQ 

MODELING FRM % DIFF AQ 

MODELING FRM % 

DIFF 

LOCOMOTIVE 29,214 29,660 1.5% 25,901 23,106 -10.8% 24,726 21,347 -13.7% 

MARINE DIESEL 29,184 29,827 2.2% 22,300 23,242 4.2% 23,674 24,900 5.2% 

ALL OTHER SOURCES 

(MOBILE & 

STATIONARY) 

2,228,545 2,369,947 6.3% 2,066,257 2,022,009 -2.1% 2,062,331 2,018,649 -2.1% 

TOTAL MAN-MADE 

SOURCES 2,286,943 2,429,434 6.2% 2,114,458 2,068,357 -2.2% 2,110,731 2,064,896 -2.2% 

* THE FRM VALUE FOR ALL OTHER SOURCES IN THE "2002" COLUMN IS ACTUALLY A 2001 ESTIMATE. 
Regulatory Impact Analysis 

3-118

Table 3-106 50-State Annual VOC Control Case Emission Levels for Mobile and Other Source Categories 

2020 2030 

CATEGORY AQ 

MODELING FRM % DIFF AQ MODELING FRM % DIFF 

LOCOMOTIVE 30,135 27,974 -7.2% 20,383 17,722 -13.1% 

MARINE DIESEL 13,991 13,964 -0.2% 9,800 9,824 0.2% 

ALL OTHER SOURCES 

(MOBILE & STATIONARY) 12,415,007 13,777,876 11.0% 12,330,869 14,267,387 15.7% 

TOTAL MAN-MADE 

SOURCES 12,459,133 13,819,814 10.9% 12,361,052 14,294,933 15.6% 

Table 3-107 50-State Annual NOx Control Case Emission Levels for Mobile and Other Source Categories 

2020 2030 

CATEGORY AQ 

MODELING FRM % DIFF AQ MODELING FRM % DIFF 

LOCOMOTIVE 712,492 669,405 -6.0% 453,651 437,245 -3.6% 

MARINE DIESEL 543,668 543,377 -0.1% 354,310 352,279 -0.6% 

ALL OTHER SOURCES 

(MOBILE & STATIONARY) 9,198,907 9,518,521 3.5% 8,722,491 9,020,273 3.4% 

TOTAL MAN-MADE 

SOURCES 10,455,067 10,731,303 2.6% 9,530,452 9,809,796 2.9% 

Table 3-108 50-State Annual PM2.5 Control Case Emission Levels for Mobile and Other Source Categories 

2020 2030 

CATEGORY AQ 

MODELING FRM % DIFF AQ MODELING FRM % DIFF 

LOCOMOTIVE 16,368 15,145 -7.5% 10,512 8,584 -18.3% 

MARINE DIESEL 16,767 16,760 0.0% 10,997 11,071 0.7% 

ALL OTHER SOURCES 

(MOBILE & STATIONARY) 2,066,257 2,022,009 -2.1% 2,062,331 2,018,649 -2.1% 

TOTAL MAN-MADE 

SOURCES 2,099,392 2,053,914 -2.2% 2,083,840 2,038,303 -2.2% 
Emission Inventory 

3-119

Table 3-109 50-State Annual VOC Ton Reductions for Mobile and Other Source Categories 

2020 2030 

CATEGORY AQ 

MODELING FRM % DIFF AQ MODELING FRM % DIFF 

LOCOMOTIVE 22,499 23,289 3.5% 31,430 32,486 3.4% 

MARINE DIESEL 4,936 4,760 -3.6% 10,980 10,653 -3.0% 

ALL OTHER SOURCES 

(MOBILE & STATIONARY) 0 0 0.0% 0 0 0.0% 

TOTAL MAN-MADE 

SOURCES 27,435 28,049 2.2% 42,410 43,140 1.7% 

Table 3-110 50-State Annual NOx Ton Reductions for Mobile and Other Source Categories 

2020 2030 

CATEGORY AQ 

MODELING FRM % DIFF AQ MODELING FRM % DIFF 

LOCOMOTIVE 147,971 192,811 30.3% 400,587 419,006 4.6% 

MARINE DIESEL 173,591 178,255 2.7% 369,751 375,848 1.6% 

ALL OTHER SOURCES 

(MOBILE & STATIONARY) 0 0 0.0% 0 0 0.0% 

TOTAL MAN-MADE 

SOURCES 321,562 371,065 15.4% 770,338 794,855 3.2% 

Table 3-111 50-State Annual PM2.5 Ton Reductions for Mobile and Other Source Categories 

2020 2030 

CATEGORY AQ 

MODELING FRM % DIFF AQ MODELING FRM % DIFF 

LOCOMOTIVE 9,533 7,961 -16.5% 14,214 12,764 -10.2% 

MARINE DIESEL 5,533 6,482 17.2% 12,677 13,829 9.1% 

ALL OTHER SOURCES 

(MOBILE & STATIONARY) 0 0 0.0% 0 0 0.0% 

TOTAL MAN-MADE 

SOURCES 15,066 14,443 -4.1% 26,891 26,593 -1.1% 
Regulatory Impact Analysis 

3-120

APPENDIX 3A 

Locomotive and Diesel Marine Contributions to County-Specific Mobile Source 

Emissions in Non-attainment Areas 
Emission Inventory 

3-121

Table 3-112 2002 Locomotive and Diesel Marine PM2.5 Tons/Year and Percent of Total Diesel Mobile 

Sources 

2002 PM2.5 

FIPS MSA County ST Diesel 

Locomotive

Diesel 

Marine 

Total 

Diesel 

Mobile 

LM 

Percent 

13013 Atlanta Barrow GA 5.77 0.01 41 14.3%

13015 Atlanta Bartow GA 20.64 0.20 109 19.1%

13045 Atlanta Carroll GA 5.65 0.08 92 6.2%

13057 Atlanta Cherokee GA 0.00 0.19 118 0.2%

13063 Atlanta Clayton GA 10.87 0.03 164 6.7%

13067 Atlanta Cobb GA 28.66 0.08 504 5.7%

13077 Atlanta Coweta GA 14.35 0.06 123 11.8%

13089 Atlanta DeKalb GA 13.29 0.05 440 3.0%

13097 Atlanta Douglas GA 5.22 0.01 68 7.7%

13113 Atlanta Fayette GA 3.71 0.04 86 4.4%

13117 Atlanta Forsyth GA 0.00 0.39 114 0.3%

13121 Atlanta Fulton GA 39.07 0.11 857 4.6%

13135 Atlanta Gwinnett GA 9.95 0.07 476 2.1%

13139 Atlanta Hall GA 6.62 0.65 146 5.0%

13149 Atlanta Heard GA 0.00 0.09 11 0.8%

13151 Atlanta Henry GA 14.63 0.04 154 9.5%

13217 Atlanta Newton GA 1.65 0.05 80 2.1%

13223 Atlanta Paulding GA 12.13 0.03 86 14.2%

13237 Atlanta Putnam GA 0.35 0.30 15 4.3%

13247 Atlanta Rockdale GA 2.35 0.03 71 3.4%

13255 Atlanta Spalding GA 0.62 0.03 53 1.2%

13297 Atlanta Walton GA 1.99 0.01 47 4.2%

24003 Baltimore Anne Arundel MD 14.68 1.82 302 5.5%

24005 Baltimore Baltimore MD 39.65 1.22 576 7.1%

24013 Baltimore Carroll MD 6.14 0.04 158 3.9%

24025 Baltimore Harford MD 11.40 1.18 186 6.8%

24027 Baltimore Howard MD 17.07 0.41 203 8.6%

24510 Baltimore Baltimore MD 46.07 313.45 590 60.9%

1073 Birmingham Jefferson AL 80.24 1.08 631 12.9%

1117 Birmingham Shelby AL 41.96 0.29 157 26.9%

1127 Birmingham Walker AL 17.15 1.08 81 22.4%

9007 Boston Middlesex CT 0.00 1.70 114 1.5%

25001 Boston Barnstable MA 7.23 20.34 179 15.4%

25005 Boston Bristol MA 13.57 14.82 311 9.1%

25007 Boston Dukes MA 0.00 133.61 143 93.4%

25009 Boston Essex MA 17.74 4.90 424 5.3%

25019 Boston Nantucket MA 0.00 19.79 29 67.4%

25021 Boston Norfolk MA 21.42 6.80 460 6.1%

25023 Boston Plymouth MA 11.20 4.99 256 6.3%

25025 Boston Suffolk MA 11.57 57.64 2,518 2.7%

25027 Boston Worcester MA 43.94 1.04 556 8.1%

33011 Boston Hillsborough NH 1.33 0.42 266 0.7%
Regulatory Impact Analysis 

3-122

2002 PM2.5 

FIPS MSA County ST Diesel 

Locomotive

Diesel 

Marine 

Total 

Diesel 

Mobile 

LM 

Percent 

33015 Boston Rockingham NH 1.00 36.02 263 14.1%

47065 Chattanooga Hamilton TN 40.53 29.56 283 24.7%

47115 Chattanooga Marion TN 5.67 5.70 63 18.1%

47153 Chattanooga Sequatchie TN 0.00 0.00 7 0.0%

13047 Chattanooga Catoosa GA 12.28 0.01 52 23.6%

13083 Chattanooga Dade GA 11.66 0.00 46 25.3%

13295 Chattanooga Walker GA 0.00 0.01 48 0.0%

17031 Chicago Cook IL 708.71 209.67 3,661 25.1%

17043 Chicago DuPage IL 200.17 0.14 812 24.7%

17063 Chicago Grundy IL 13.55 6.45 114 17.6%

17089 Chicago Kane IL 70.19 0.10 371 19.0%

17093 Chicago Kendall IL 8.97 0.01 78 11.5%

17097 Chicago Lake IL 37.26 22.02 406 14.6%

17111 Chicago McHenry IL 20.29 0.16 189 10.8%

17197 Chicago Will IL 186.94 4.74 498 38.5%

18089 Chicago Lake IN 129.22 14.34 541 26.5%

18127 Chicago Porter IN 45.64 12.55 216 26.9%

18029 Cincinnati Dearborn IN 6.21 22.72 92 31.3%

21015 Cincinnati Boone KY 8.45 34.08 133 31.9%

21037 Cincinnati Campbell KY 16.05 23.57 95 41.5%

21117 Cincinnati Kenton KY 30.93 11.78 147 29.1%

39017 Cincinnati Butler OH 45.48 0.05 279 16.3%

39025 Cincinnati Clermont OH 1.96 44.98 181 25.9%

39061 Cincinnati Hamilton OH 44.25 133.23 737 24.1%

39165 Cincinnati Warren OH 6.75 0.09 192 3.6%

39007 Cleveland Ashtabula OH 30.49 178.56 310 67.4%

39035 Cleveland Cuyahoga OH 83.10 122.90 1,119 18.4%

39085 Cleveland Lake OH 21.22 26.15 190 25.0%

39093 Cleveland Lorain OH 50.28 113.72 414 39.6%

39103 Cleveland Medina OH 15.82 0.06 166 9.6%

39133 Cleveland Portage OH 31.34 0.24 198 15.9%

39153 Cleveland Summit OH 25.49 0.17 392 6.5%

26093 Detroit Livingston MI 2.47 0.07 174 1.5%

26099 Detroit Macomb MI 3.83 5.35 437 2.1%

26115 Detroit Monroe MI 18.09 8.90 198 13.6%

26125 Detroit Oakland MI 15.09 4.59 781 2.5%

26147 Detroit St. Clair MI 7.39 21.37 224 12.8%

26161 Detroit Washtenaw MI 4.04 0.05 269 1.5%

26163 Detroit Wayne MI 29.94 10.03 1,140 3.5%

48039 Houston Brazoria TX 18.79 247.18 463 57.4%

48071 Houston Chambers TX 1.07 7.41 57 14.8%

48157 Houston Fort Bend TX 26.30 0.09 270 9.8%

48167 Houston Galveston TX 13.07 566.43 751 77.1%

48201 Houston Harris TX 68.97 1,477.09 3,940 39.2%

48291 Houston Liberty TX 28.79 3.02 112 28.3%
Emission Inventory 

3-123

2002 PM2.5 

FIPS MSA County ST Diesel 

Locomotive

Diesel 

Marine 

Total 

Diesel 

Mobile 

LM 

Percent 

48339 Houston Montgomery TX 22.38 0.27 300 7.5%

48473 Houston Waller TX 6.50 0.04 45 14.5%

21019 Huntington Boyd KY 11.13 18.28 65 45.2%

21127 Huntington Lawrence KY 10.86 5.94 33 51.6%

39001 Huntington Adams OH 0.39 52.61 88 60.0%

39053 Huntington Gallia OH 3.44 23.13 62 42.8%

39087 Huntington Lawrence OH 12.48 34.34 86 54.5%

39145 Huntington Scioto OH 27.95 33.28 124 49.5%

54011 Huntington Cabell WV 24.48 25.26 112 44.5%

54053 Huntington Mason WV 6.12 39.72 92 50.0%

54099 Huntington Wayne WV 30.53 60.21 133 68.1%

18011 Indianapolis Boone IN 6.78 0.06 120 5.7%

18057 Indianapolis Hamilton IN 0.16 0.62 224 0.3%

18059 Indianapolis Hancock IN 5.17 0.03 107 4.9%

18063 Indianapolis Hendricks IN 18.14 0.03 188 9.7%

18081 Indianapolis Johnson IN 0.91 0.21 115 1.0%

18095 Indianapolis Madison IN 16.17 0.12 156 10.5%

18097 Indianapolis Marion IN 31.30 1.34 662 4.9%

18109 Indianapolis Morgan IN 0.41 0.22 93 0.7%

18145 Indianapolis Shelby IN 7.35 0.02 102 7.2%

20091 Kansas City Johnson KS 55.73 0.04 481 11.6%

20103 Kansas City Leavenworth KS 14.29 0.50 84 17.6%

20121 Kansas City Miami KS 81.56 0.15 139 58.6%

20209 Kansas City Wyandotte KS 30.24 4.47 148 23.5%

29037 Kansas City Cass MO 16.72 0.12 110 15.3%

29047 Kansas City Clay MO 28.19 4.43 188 17.3%

29049 Kansas City Clinton MO 0.00 0.16 49 0.3%

29095 Kansas City Jackson MO 90.00 33.46 646 19.1%

29107 Kansas City Lafayette MO 23.25 4.16 124 22.1%

29165 Kansas City Platte MO 22.68 0.84 151 15.6%

29177 Kansas City Ray MO 44.83 3.97 108 45.2%

6037 Los Angeles Los Angeles CA 241.14 1,666.68 5,016 38.0%

6059 Los Angeles Orange CA 63.57 176.82 1,696 14.2%

6065 Los Angeles Riverside CA 109.12 1.01 872 12.6%

6071 Los Angeles San 

Bernardino 

CA 359.75 0.47 1,040 34.6%

6111 Los Angeles Ventura CA 12.49 231.21 524 46.5%

27003 Minneapolis Anoka MN 21.27 12.73 232 14.7%

27019 Minneapolis Carver MN 0.05 0.79 82 1.0%

27037 Minneapolis Dakota MN 12.70 11.89 278 8.9%

27053 Minneapolis Hennepin MN 31.68 35.83 870 7.8%

27123 Minneapolis Ramsey MN 12.03 11.31 349 6.7%

27139 Minneapolis Scott MN 2.70 1.38 94 4.3%

27163 Minneapolis Washington MN 23.15 50.70 237 31.1%

9001 New York Fairfield CT 0.00 44.84 705 6.4%
Regulatory Impact Analysis 

3-124

2002 PM2.5 

FIPS MSA County ST Diesel 

Locomotive

Diesel 

Marine 

Total 

Diesel 

Mobile 

LM 

Percent 

9005 New York Litchfield CT 0.00 0.89 109 0.8%

34003 New York Bergen NJ 26.97 3.48 512 6.0%

34013 New York Essex NJ 6.64 0.99 416 1.8%

34017 New York Hudson NJ 22.70 27.96 402 12.6%

34019 New York Hunterdon NJ 9.60 0.33 185 5.4%

34023 New York Middlesex NJ 12.54 4.94 421 4.1%

34025 New York Monmouth NJ 10.14 29.48 418 9.5%

34027 New York Morris NJ 6.96 0.53 300 2.5%

34029 New York Ocean NJ 0.52 13.26 256 5.4%

34031 New York Passaic NJ 6.11 0.51 233 2.8%

34035 New York Somerset NJ 13.21 0.02 195 6.8%

34037 New York Sussex NJ 0.99 0.63 113 1.4%

34039 New York Union NJ 11.04 17.95 355 8.2%

36005 New York Bronx NY 0.13 0.75 372 0.2%

36047 New York Kings NY 0.00 1.30 696 0.2%

36059 New York Nassau NY 0.00 11.73 518 2.3%

36061 New York New York NY 0.00 0.54 1,296 0.0%

36071 New York Orange NY 9.19 2.55 288 4.1%

36081 New York Queens NY 0.06 2.02 982 0.2%

36085 New York Richmond NY 0.00 2.29 166 1.4%

36087 New York Rockland NY 6.91 2.69 125 7.7%

36103 New York Suffolk NY 0.00 39.17 690 5.7%

36119 New York Westchester NY 0.00 3.76 479 0.8%

10003 Philadelphia New Castle DE 22.95 47.44 458 15.4%

24015 Philadelphia Cecil MD 9.27 1.70 125 8.7%

24029 Philadelphia Kent MD 0.07 1.41 42 3.6%

24031 Philadelphia Montgomery MD 28.82 0.53 485 6.0%

34005 Philadelphia Burlington NJ 0.00 54.50 328 16.6%

34007 Philadelphia Camden NJ 4.82 21.83 273 9.8%

34011 Philadelphia Cumberland NJ 0.57 55.22 155 36.0%

34015 Philadelphia Gloucester NJ 0.80 29.18 214 14.0%

34021 Philadelphia Mercer NJ 5.56 6.66 277 4.4%

34033 Philadelphia Salem NJ 0.27 16.91 86 19.9%

42017 Philadelphia Bucks PA 2.29 1.20 330 1.1%

42029 Philadelphia Chester PA 11.62 0.16 328 3.6%

42045 Philadelphia Delaware PA 4.55 193.17 409 48.4%

42101 Philadelphia Philadelphia PA 6.45 339.10 922 37.5%

4013 Phoenix Maricopa AZ 98.35 0.78 2,828 3.5%

4021 Phoenix Pinal AZ 52.54 0.17 256 20.6%

6019 San Joaquin Fresno CA 17.77 0.58 647 2.8%

6029 San Joaquin Kern CA 92.07 0.22 635 14.5%

6031 San Joaquin Kings CA 2.57 0.02 155 1.7%

6039 San Joaquin Madera CA 18.89 0.16 145 13.2%

6047 San Joaquin Merced CA 17.75 0.46 218 8.4%

6077 San Joaquin San Joaquin CA 29.94 30.32 437 13.8%
Emission Inventory 

3-125

2002 PM2.5 

FIPS MSA County ST Diesel 

Locomotive

Diesel 

Marine 

Total 

Diesel 

Mobile 

LM 

Percent 

6099 San Joaquin Stanislaus CA 12.07 0.24 267 4.6%

6107 San Joaquin Tulare CA 26.68 0.16 340 7.9%

53029 Seattle Island WA 0.00 19.63 69 28.5%

53033 Seattle King WA 28.95 191.88 1,568 14.1%

53035 Seattle Kitsap WA 0.00 1.27 134 0.9%

53045 Seattle Mason WA 0.00 0.58 37 1.6%

53053 Seattle Pierce WA 18.18 173.52 612 31.3%

53061 Seattle Snohomish WA 36.65 29.32 471 14.0%

53067 Seattle Thurston WA 10.80 12.02 179 12.7%

17027 St. Louis Clinton IL 23.14 0.08 99 23.5%

17083 St. Louis Jersey IL 1.86 19.07 65 32.1%

17119 St. Louis Madison IL 7.81 10.33 247 7.4%

17133 St. Louis Monroe IL 37.61 16.72 104 52.1%

17163 St. Louis St. Clair IL 8.93 19.78 229 12.5%

29055 St. Louis Crawford MO 5.23 0.04 45 11.6%

29071 St. Louis Franklin MO 31.20 2.36 153 21.9%

29099 St. Louis Jefferson MO 8.38 16.93 186 13.6%

29113 St. Louis Lincoln MO 13.80 6.69 87 23.4%

29183 St. Louis St. Charles MO 16.62 15.02 244 13.0%

29189 St. Louis St. Louis MO 26.77 19.32 831 5.5%

29219 St. Louis Warren MO 2.82 2.31 47 10.9%

29510 St. Louis St. Louis MO 23.28 261.28 456 62.4%
Regulatory Impact Analysis 

3-126

Table 3-113 2020 Locomotive and Diesel Marine PM2.5 Tons/Year and Percent of Total Diesel Mobile 

Sources 

2020 PM2.5 

FIPS MSA County ST Diesel 

Locomotive

Diesel 

Marine 

Total 

Diesel 

Mobile 

LM 

Percent 

13013 Atlanta Barrow GA 5.45 0.01 11 49.6%

13015 Atlanta Bartow GA 19.49 0.17 35 56.9%

13045 Atlanta Carroll GA 5.28 0.07 19 27.6%

13057 Atlanta Cherokee GA 0.00 0.17 23 0.7%

13063 Atlanta Clayton GA 10.27 0.02 41 25.2%

13067 Atlanta Cobb GA 26.96 0.07 137 19.8%

13077 Atlanta Coweta GA 13.55 0.05 33 41.2%

13089 Atlanta DeKalb GA 12.49 0.04 103 12.1%

13097 Atlanta Douglas GA 4.86 0.01 16 30.0%

13113 Atlanta Fayette GA 3.50 0.03 20 18.0%

13117 Atlanta Forsyth GA 0.00 0.35 24 1.5%

13121 Atlanta Fulton GA 36.74 0.09 224 16.4%

13135 Atlanta Gwinnett GA 9.33 0.06 118 8.0%

13139 Atlanta Hall GA 5.67 0.57 31 19.9%

13149 Atlanta Heard GA 0.00 0.08 2 4.4%

13151 Atlanta Henry GA 13.81 0.03 41 34.2%

13217 Atlanta Newton GA 1.56 0.04 15 10.4%

13223 Atlanta Paulding GA 11.45 0.02 24 47.5%

13237 Atlanta Putnam GA 0.31 0.26 3 17.2%

13247 Atlanta Rockdale GA 2.22 0.02 16 14.2%

13255 Atlanta Spalding GA 0.59 0.02 10 6.4%

13297 Atlanta Walton GA 1.88 0.01 10 18.8%

24003 Baltimore Anne Arundel MD 10.36 1.57 73 16.4%

24005 Baltimore Baltimore MD 34.68 1.03 154 23.1%

24013 Baltimore Carroll MD 5.34 0.03 37 14.6%

24025 Baltimore Harford MD 8.84 1.00 46 21.5%

24027 Baltimore Howard MD 12.62 0.32 56 22.9%

24510 Baltimore Baltimore MD 46.50 242.61 328 88.1%

1073 Birmingham Jefferson AL 75.36 0.86 188 40.6%

1117 Birmingham Shelby AL 39.49 0.26 65 61.4%

1127 Birmingham Walker AL 14.91 0.86 30 52.9%

9007 Boston Middlesex CT 0.00 1.50 22 6.7%

25001 Boston Barnstable MA 6.28 16.59 55 41.7%

25005 Boston Bristol MA 11.82 11.64 79 29.6%

25007 Boston Dukes MA 0.00 103.75 106 97.6%

25009 Boston Essex MA 15.42 4.13 101 19.4%

25019 Boston Nantucket MA 0.00 15.54 18 85.4%

25021 Boston Norfolk MA 18.39 5.32 114 20.9%

25023 Boston Plymouth MA 9.78 4.30 65 21.8%

25025 Boston Suffolk MA 9.83 44.70 688 7.9%

25027 Boston Worcester MA 37.77 0.92 142 27.3%

33011 Boston Hillsborough NH 1.15 0.37 56 2.7%
Emission Inventory 

3-127

2020 PM2.5 

FIPS MSA County ST Diesel 

Locomotive

Diesel 

Marine 

Total 

Diesel 

Mobile 

LM 

Percent 

33015 Boston Rockingham NH 0.87 28.09 74 39.1%

47065 Chattanooga Hamilton TN 38.28 22.98 103 59.3%

47115 Chattanooga Marion TN 5.36 4.45 18 53.8%

47153 Chattanooga Sequatchie TN 0.00 0.00 1 0.0%

13047 Chattanooga Catoosa GA 11.60 0.01 18 63.7%

13083 Chattanooga Dade GA 11.01 0.00 16 67.9%

13295 Chattanooga Walker GA 0.00 0.01 9 0.1%

17031 Chicago Cook IL 608.24 164.40 1,362 56.7%

17043 Chicago DuPage IL 162.78 0.12 317 51.4%

17063 Chicago Grundy IL 13.20 5.01 42 43.5%

17089 Chicago Kane IL 59.50 0.09 138 43.3%

17093 Chicago Kendall IL 8.18 0.01 27 30.5%

17097 Chicago Lake IL 30.80 19.13 138 36.2%

17111 Chicago McHenry IL 17.00 0.14 61 28.0%

17197 Chicago Will IL 163.07 3.70 242 68.9%

18089 Chicago Lake IN 132.19 12.15 232 62.3%

18127 Chicago Porter IN 40.84 10.56 85 60.4%

18029 Cincinnati Dearborn IN 5.59 17.59 35 65.7%

21015 Cincinnati Boone KY 7.99 26.40 54 63.8%

21037 Cincinnati Campbell KY 15.10 18.27 43 77.4%

21117 Cincinnati Kenton KY 29.20 9.12 59 65.4%

39017 Cincinnati Butler OH 40.67 0.04 92 44.2%

39025 Cincinnati Clermont OH 1.76 34.82 63 58.0%

39061 Cincinnati Hamilton OH 39.70 103.13 268 53.3%

39165 Cincinnati Warren OH 6.08 0.08 49 12.5%

39007 Cleveland Ashtabula OH 27.38 138.54 185 89.5%

39035 Cleveland Cuyahoga OH 76.82 96.57 379 45.7%

39085 Cleveland Lake OH 18.90 21.00 71 56.4%

39093 Cleveland Lorain OH 45.04 88.60 190 70.4%

39103 Cleveland Medina OH 14.17 0.05 45 31.8%

39133 Cleveland Portage OH 28.09 0.21 61 46.1%

39153 Cleveland Summit OH 22.96 0.15 101 22.8%

26093 Detroit Livingston MI 2.33 0.06 35 6.8%

26099 Detroit Macomb MI 3.62 4.26 96 8.2%

26115 Detroit Monroe MI 17.08 6.95 60 39.7%

26125 Detroit Oakland MI 14.21 3.58 188 9.5%

26147 Detroit St. Clair MI 6.90 16.67 64 37.1%

26161 Detroit Washtenaw MI 3.82 0.04 61 6.3%

26163 Detroit Wayne MI 28.47 7.97 253 14.4%

48039 Houston Brazoria TX 17.74 191.47 248 84.2%

48071 Houston Chambers TX 1.01 5.88 16 44.1%

48157 Houston Fort Bend TX 24.70 0.08 79 31.5%

48167 Houston Galveston TX 12.47 438.65 487 92.6%

48201 Houston Harris TX 65.54 1,143.23 1,727 70.0%

48291 Houston Liberty TX 27.14 2.35 45 65.6%
Regulatory Impact Analysis 

3-128

2020 PM2.5 

FIPS MSA County ST Diesel 

Locomotive

Diesel 

Marine 

Total 

Diesel 

Mobile 

LM 

Percent 

48339 Houston Montgomery TX 21.14 0.24 68 31.6%

48473 Houston Waller TX 6.14 0.04 15 42.5%

21019 Huntington Boyd KY 10.44 14.15 31 79.9%

21127 Huntington Lawrence KY 9.43 4.60 17 84.4%

39001 Huntington Adams OH 0.35 40.72 49 84.4%

39053 Huntington Gallia OH 3.09 17.90 28 73.8%

39087 Huntington Lawrence OH 11.20 26.58 44 85.6%

39145 Huntington Scioto OH 25.08 25.76 63 80.7%

54011 Huntington Cabell WV 22.84 19.57 54 78.0%

54053 Huntington Mason WV 5.31 30.79 47 76.4%

54099 Huntington Wayne WV 28.80 46.62 85 88.8%

18011 Indianapolis Boone IN 5.92 0.05 34 17.6%

18057 Indianapolis Hamilton IN 0.15 0.55 54 1.3%

18059 Indianapolis Hancock IN 4.58 0.03 28 16.2%

18063 Indianapolis Hendricks IN 16.27 0.03 55 29.9%

18081 Indianapolis Johnson IN 0.88 0.19 26 4.2%

18095 Indianapolis Madison IN 14.62 0.11 45 32.9%

18097 Indianapolis Marion IN 27.99 1.19 166 17.5%

18109 Indianapolis Morgan IN 0.40 0.20 19 3.1%

18145 Indianapolis Shelby IN 6.54 0.02 29 22.3%

20091 Kansas City Johnson KS 52.60 0.03 155 33.9%

20103 Kansas City Leavenworth KS 13.49 0.39 29 47.4%

20121 Kansas City Miami KS 77.03 0.14 92 83.9%

20209 Kansas City Wyandotte KS 28.47 3.46 56 57.5%

29037 Kansas City Cass MO 15.70 0.11 37 42.4%

29047 Kansas City Clay MO 26.78 3.48 64 47.6%

29049 Kansas City Clinton MO 0.00 0.14 12 1.2%

29095 Kansas City Jackson MO 85.15 25.94 223 49.8%

29107 Kansas City Lafayette MO 21.96 3.26 49 51.3%

29165 Kansas City Platte MO 21.42 0.67 51 42.9%

29177 Kansas City Ray MO 42.28 3.09 61 74.5%

6037 Los Angeles Los Angeles CA 217.08 1,290.10 2,697 55.9%

6059 Los Angeles Orange CA 56.50 136.94 729 26.6%

6065 Los Angeles Riverside CA 93.21 0.90 380 24.8%

6071 Los Angeles San 

Bernardino 

CA 321.96 0.42 574 56.2%

6111 Los Angeles Ventura CA 11.01 179.05 298 63.8%

27003 Minneapolis Anoka MN 19.93 10.06 72 41.9%

27019 Minneapolis Carver MN 0.05 0.67 20 3.6%

27037 Minneapolis Dakota MN 11.92 9.37 80 26.5%

27053 Minneapolis Hennepin MN 29.88 28.17 242 24.0%

27123 Minneapolis Ramsey MN 11.29 8.91 89 22.8%

27139 Minneapolis Scott MN 2.55 1.15 25 14.6%

27163 Minneapolis Washington MN 21.74 39.42 96 63.8%

9001 New York Fairfield CT 0.00 35.19 184 19.1%
Emission Inventory 

3-129

2020 PM2.5 

FIPS MSA County ST Diesel 

Locomotive

Diesel 

Marine 

Total 

Diesel 

Mobile 

LM 

Percent 

9005 New York Litchfield CT 0.00 0.79 23 3.4%

34003 New York Bergen NJ 22.36 2.76 146 17.2%

34013 New York Essex NJ 5.18 0.79 95 6.3%

34017 New York Hudson NJ 19.90 21.74 120 34.7%

34019 New York Hunterdon NJ 7.47 0.29 41 19.0%

34023 New York Middlesex NJ 11.14 3.88 106 14.1%

34025 New York Monmouth NJ 6.84 23.33 114 26.6%

34027 New York Morris NJ 5.01 0.47 73 7.6%

34029 New York Ocean NJ 0.41 11.45 58 20.3%

34031 New York Passaic NJ 4.28 0.45 54 8.7%

34035 New York Somerset NJ 11.03 0.01 51 21.8%

34037 New York Sussex NJ 0.96 0.55 23 6.6%

34039 New York Union NJ 8.53 13.90 97 23.1%

36005 New York Bronx NY 0.12 0.62 75 1.0%

36047 New York Kings NY 0.00 1.07 146 0.7%

36059 New York Nassau NY 0.00 9.33 139 6.7%

36061 New York New York NY 0.00 0.44 364 0.1%

36071 New York Orange NY 8.01 2.02 63 15.9%

36081 New York Queens NY 0.06 1.66 228 0.8%

36085 New York Richmond NY 0.00 1.84 39 4.8%

36087 New York Rockland NY 6.33 2.13 37 22.9%

36103 New York Suffolk NY 0.00 32.24 193 16.7%

36119 New York Westchester NY 0.00 3.02 123 2.5%

10003 Philadelphia New Castle DE 23.49 36.76 144 41.7%

24015 Philadelphia Cecil MD 7.73 1.40 30 30.2%

24029 Philadelphia Kent MD 0.06 1.21 12 10.5%

24031 Philadelphia Montgomery MD 21.88 0.43 127 17.6%

34005 Philadelphia Burlington NJ 0.00 42.25 100 42.2%

34007 Philadelphia Camden NJ 3.47 16.92 72 28.3%

34011 Philadelphia Cumberland NJ 0.55 43.19 65 67.8%

34015 Philadelphia Gloucester NJ 0.83 22.64 63 37.2%

34021 Philadelphia Mercer NJ 4.55 5.17 64 15.3%

34033 Philadelphia Salem NJ 0.25 13.18 28 47.6%

42017 Philadelphia Bucks PA 1.95 1.00 78 3.8%

42029 Philadelphia Chester PA 9.26 0.14 81 11.7%

42045 Philadelphia Delaware PA 3.76 149.53 199 77.0%

42101 Philadelphia Philadelphia PA 5.74 262.48 383 69.9%

4013 Phoenix Maricopa AZ 89.13 0.69 709 12.7%

4021 Phoenix Pinal AZ 48.94 0.15 92 53.5%

6019 San Joaquin Fresno CA 15.98 0.51 236 7.0%

6029 San Joaquin Kern CA 80.81 0.19 265 30.6%

6031 San Joaquin Kings CA 2.13 0.02 56 3.8%

6039 San Joaquin Madera CA 17.29 0.14 63 27.9%

6047 San Joaquin Merced CA 15.33 0.40 84 18.6%

6077 San Joaquin San Joaquin CA 26.62 23.51 184 27.3%
Regulatory Impact Analysis 

3-130

2020 PM2.5 

FIPS MSA County ST Diesel 

Locomotive

Diesel 

Marine 

Total 

Diesel 

Mobile 

LM 

Percent 

6099 San Joaquin Stanislaus CA 10.69 0.21 101 10.8%

6107 San Joaquin Tulare CA 24.00 0.14 133 18.1%

53029 Seattle Island WA 0.00 15.26 25 60.2%

53033 Seattle King WA 27.06 149.20 484 36.4%

53035 Seattle Kitsap WA 0.00 1.13 27 4.2%

53045 Seattle Mason WA 0.00 0.50 7 7.1%

53053 Seattle Pierce WA 16.97 134.63 238 63.7%

53061 Seattle Snohomish WA 33.68 23.01 140 40.4%

53067 Seattle Thurston WA 9.33 9.42 48 39.1%

17027 St. Louis Clinton IL 21.27 0.07 41 51.8%

17083 St. Louis Jersey IL 1.73 14.76 28 57.9%

17119 St. Louis Madison IL 8.44 8.01 67 24.7%

17133 St. Louis Monroe IL 33.99 12.95 60 77.8%

17163 St. Louis St. Clair IL 9.49 15.31 69 36.2%

29055 St. Louis Crawford MO 4.54 0.04 12 38.8%

29071 St. Louis Franklin MO 29.11 1.86 54 57.6%

29099 St. Louis Jefferson MO 7.90 13.13 49 43.3%

29113 St. Louis Lincoln MO 13.04 5.22 34 53.8%

29183 St. Louis St. Charles MO 15.70 11.75 73 37.4%

29189 St. Louis St. Louis MO 25.09 15.01 214 18.8%

29219 St. Louis Warren MO 2.66 1.81 14 32.7%

29510 St. Louis St. Louis MO 22.18 202.23 256 87.7%
Emission Inventory 

3-131

Table 3-114 2030 Locomotive and Diesel Marine PM2.5 Tons/Year and Percent of Total Diesel Mobile 

Sources 

2030 PM2.5 

FIPS MSA County ST Diesel 

Locomotive

Diesel 

Marine 

Total 

Diesel 

Mobile 

LM 

Percent 

13013 Atlanta Barrow GA 5.25 0.01 9 60.3%

13015 Atlanta Bartow GA 18.79 0.20 28 68.0%

13045 Atlanta Carroll GA 5.08 0.08 14 37.1%

13057 Atlanta Cherokee GA 0.00 0.19 14 1.3%

13063 Atlanta Clayton GA 9.90 0.03 27 36.7%

13067 Atlanta Cobb GA 25.96 0.08 85 30.8%

13077 Atlanta Coweta GA 13.06 0.06 25 53.1%

13089 Atlanta DeKalb GA 12.03 0.05 66 18.2%

13097 Atlanta Douglas GA 4.66 0.01 12 40.2%

13113 Atlanta Fayette GA 3.37 0.04 13 26.9%

13117 Atlanta Forsyth GA 0.00 0.40 14 2.9%

13121 Atlanta Fulton GA 35.38 0.11 130 27.4%

13135 Atlanta Gwinnett GA 8.98 0.07 69 13.2%

13139 Atlanta Hall GA 5.34 0.65 21 29.0%

13149 Atlanta Heard GA 0.00 0.09 1 8.2%

13151 Atlanta Henry GA 13.32 0.04 28 47.3%

13217 Atlanta Newton GA 1.50 0.05 9 16.7%

13223 Atlanta Paulding GA 11.04 0.03 19 58.6%

13237 Atlanta Putnam GA 0.29 0.30 2 26.8%

13247 Atlanta Rockdale GA 2.14 0.03 10 22.0%

13255 Atlanta Spalding GA 0.57 0.03 6 10.2%

13297 Atlanta Walton GA 1.81 0.01 6 28.3%

24003 Baltimore Anne Arundel MD 9.01 1.77 43 24.8%

24005 Baltimore Baltimore MD 32.54 1.15 94 36.0%

24013 Baltimore Carroll MD 5.05 0.04 22 23.1%

24025 Baltimore Harford MD 8.01 1.11 28 32.2%

24027 Baltimore Howard MD 11.21 0.35 35 33.5%

24510 Baltimore Baltimore MD 45.29 259.26 330 92.2%

1073 Birmingham Jefferson AL 72.52 0.94 143 51.5%

1117 Birmingham Shelby AL 38.03 0.29 52 73.3%

1127 Birmingham Walker AL 14.10 0.93 26 58.7%

9007 Boston Middlesex CT 0.00 1.70 14 12.3%

25001 Boston Barnstable MA 5.94 18.19 42 58.0%

25005 Boston Bristol MA 11.25 12.53 55 43.6%

25007 Boston Dukes MA 0.00 111.06 112 98.9%

25009 Boston Essex MA 14.59 4.59 63 30.3%

25019 Boston Nantucket MA 0.00 16.73 18 93.2%

25021 Boston Norfolk MA 17.74 5.72 72 32.7%

25023 Boston Plymouth MA 9.26 4.83 42 33.7%

25025 Boston Suffolk MA 9.40 47.81 295 19.4%

25027 Boston Worcester MA 35.83 1.04 91 40.4%

33011 Boston Hillsborough NH 1.09 0.42 31 4.9%
Regulatory Impact Analysis 

3-132

2030 PM2.5 

FIPS MSA County ST Diesel 

Locomotive

Diesel 

Marine 

Total 

Diesel 

Mobile 

LM 

Percent 

33015 Boston Rockingham NH 0.82 30.13 56 55.2%

47065 Chattanooga Hamilton TN 36.91 24.61 85 72.5%

47115 Chattanooga Marion TN 5.16 4.78 15 67.1%

47153 Chattanooga Sequatchie TN 0.00 0.00 1 0.0%

13047 Chattanooga Catoosa GA 11.18 0.01 15 74.0%

13083 Chattanooga Dade GA 10.61 0.00 14 76.7%

13295 Chattanooga Walker GA 0.00 0.01 5 0.1%

17031 Chicago Cook IL 583.11 176.82 1,069 71.1%

17043 Chicago DuPage IL 150.13 0.14 227 66.1%

17063 Chicago Grundy IL 12.86 5.35 29 62.4%

17089 Chicago Kane IL 55.63 0.10 91 61.3%

17093 Chicago Kendall IL 7.87 0.01 16 48.2%

17097 Chicago Lake IL 28.70 21.57 96 52.5%

17111 Chicago McHenry IL 15.86 0.16 37 42.9%

17197 Chicago Will IL 154.37 3.97 195 81.2%

18089 Chicago Lake IN 129.63 13.55 186 77.1%

18127 Chicago Porter IN 39.03 11.74 68 75.0%

18029 Cincinnati Dearborn IN 5.39 18.81 30 79.5%

21015 Cincinnati Boone KY 7.70 28.23 46 78.5%

21037 Cincinnati Campbell KY 14.53 19.53 40 85.6%

21117 Cincinnati Kenton KY 28.15 9.75 49 78.1%

39017 Cincinnati Butler OH 38.73 0.05 64 60.4%

39025 Cincinnati Clermont OH 1.68 37.21 53 72.9%

39061 Cincinnati Hamilton OH 38.03 110.20 216 68.7%

39165 Cincinnati Warren OH 5.88 0.09 26 23.0%

39007 Cleveland Ashtabula OH 26.16 148.22 185 94.4%

39035 Cleveland Cuyahoga OH 73.82 103.97 280 63.4%

39085 Cleveland Lake OH 17.97 22.85 57 71.2%

39093 Cleveland Lorain OH 42.97 94.99 165 83.8%

39103 Cleveland Medina OH 13.67 0.06 30 45.8%

39133 Cleveland Portage OH 26.81 0.24 45 60.8%

39153 Cleveland Summit OH 21.98 0.17 63 35.3%

26093 Detroit Livingston MI 2.25 0.06 20 11.7%

26099 Detroit Macomb MI 3.49 4.61 56 14.6%

26115 Detroit Monroe MI 16.47 7.45 42 56.9%

26125 Detroit Oakland MI 13.69 3.84 108 16.2%

26147 Detroit St. Clair MI 6.63 17.88 44 55.4%

26161 Detroit Washtenaw MI 3.80 0.04 35 11.1%

26163 Detroit Wayne MI 29.36 8.63 151 25.1%

48039 Houston Brazoria TX 17.11 204.68 242 91.5%

48071 Houston Chambers TX 0.97 6.36 12 59.3%

48157 Houston Fort Bend TX 23.77 0.09 52 45.9%

48167 Houston Galveston TX 13.22 468.88 500 96.4%

48201 Houston Harris TX 67.89 1,221.64 1,557 82.8%

48291 Houston Liberty TX 26.15 2.52 37 77.9%
Emission Inventory 

3-133

2030 PM2.5 

FIPS MSA County ST Diesel 

Locomotive

Diesel 

Marine 

Total 

Diesel 

Mobile 

LM 

Percent 

48339 Houston Montgomery TX 20.38 0.27 49 42.2%

48473 Houston Waller TX 5.92 0.04 10 58.2%

21019 Huntington Boyd KY 10.05 15.13 29 87.4%

21127 Huntington Lawrence KY 8.93 4.91 15 90.7%

39001 Huntington Adams OH 0.34 43.51 48 92.1%

39053 Huntington Gallia OH 2.94 19.13 26 85.9%

39087 Huntington Lawrence OH 10.68 28.40 43 91.2%

39145 Huntington Scioto OH 23.91 27.52 58 89.0%

54011 Huntington Cabell WV 21.94 20.93 49 86.9%

54053 Huntington Mason WV 5.03 32.92 42 89.3%

54099 Huntington Wayne WV 27.75 49.83 82 95.0%

18011 Indianapolis Boone IN 5.59 0.06 19 30.0%

18057 Indianapolis Hamilton IN 0.18 0.63 26 3.1%

18059 Indianapolis Hancock IN 4.35 0.03 16 28.1%

18063 Indianapolis Hendricks IN 15.52 0.03 33 46.7%

18081 Indianapolis Johnson IN 1.02 0.21 14 8.9%

18095 Indianapolis Madison IN 13.96 0.12 29 47.9%

18097 Indianapolis Marion IN 26.79 1.35 98 28.7%

18109 Indianapolis Morgan IN 0.46 0.22 10 6.6%

18145 Indianapolis Shelby IN 6.23 0.02 17 36.6%

20091 Kansas City Johnson KS 50.70 0.04 101 50.4%

20103 Kansas City Leavenworth KS 13.00 0.42 21 63.9%

20121 Kansas City Miami KS 74.26 0.15 81 91.7%

20209 Kansas City Wyandotte KS 27.42 3.70 43 71.6%

29037 Kansas City Cass MO 15.11 0.12 26 59.1%

29047 Kansas City Clay MO 27.23 3.74 48 64.9%

29049 Kansas City Clinton MO 0.00 0.16 6 2.9%

29095 Kansas City Jackson MO 85.76 27.74 171 66.5%

29107 Kansas City Lafayette MO 21.17 3.50 36 68.5%

29165 Kansas City Platte MO 20.65 0.74 35 61.6%

29177 Kansas City Ray MO 42.32 3.31 53 86.7%

6037 Los Angeles Los Angeles CA 214.05 1,378.65 2,053 77.6%

6059 Los Angeles Orange CA 57.93 146.38 433 47.2%

6065 Los Angeles Riverside CA 87.56 1.02 189 46.8%

6071 Los Angeles San 

Bernardino 

CA 306.89 0.48 400 76.8%

6111 Los Angeles Ventura CA 10.79 191.39 247 81.7%

27003 Minneapolis Anoka MN 19.16 10.87 51 59.0%

27019 Minneapolis Carver MN 0.05 0.75 10 7.6%

27037 Minneapolis Dakota MN 11.47 10.11 51 42.4%

27053 Minneapolis Hennepin MN 28.80 30.34 153 38.7%

27123 Minneapolis Ramsey MN 10.87 9.61 56 36.4%

27139 Minneapolis Scott MN 2.46 1.27 14 26.4%

27163 Minneapolis Washington MN 20.93 42.22 81 78.3%

9001 New York Fairfield CT 0.00 37.87 112 33.7%
Regulatory Impact Analysis 

3-134

2030 PM2.5 

FIPS MSA County ST Diesel 

Locomotive

Diesel 

Marine 

Total 

Diesel 

Mobile 

LM 

Percent 

9005 New York Litchfield CT 0.00 0.90 13 6.6%

34003 New York Bergen NJ 20.91 2.98 89 26.9%

34013 New York Essex NJ 4.68 0.85 50 11.0%

34017 New York Hudson NJ 18.73 23.28 79 53.1%

34019 New York Hunterdon NJ 6.78 0.33 25 28.1%

34023 New York Middlesex NJ 10.49 4.17 63 23.1%

34025 New York Monmouth NJ 5.78 25.21 75 41.6%

34027 New York Morris NJ 4.44 0.53 42 11.9%

34029 New York Ocean NJ 0.36 12.87 39 33.6%

34031 New York Passaic NJ 3.75 0.51 30 14.0%

34035 New York Somerset NJ 10.24 0.02 32 32.2%

34037 New York Sussex NJ 1.10 0.63 13 12.9%

34039 New York Union NJ 7.68 14.86 59 38.2%

36005 New York Bronx NY 0.12 0.69 38 2.1%

36047 New York Kings NY 0.00 1.18 77 1.5%

36059 New York Nassau NY 0.00 10.10 78 12.9%

36061 New York New York NY 0.00 0.48 168 0.3%

36071 New York Orange NY 7.53 2.18 39 25.2%

36081 New York Queens NY 0.05 1.83 103 1.8%

36085 New York Richmond NY 0.00 2.00 19 10.3%

36087 New York Rockland NY 6.09 2.31 23 36.8%

36103 New York Suffolk NY 0.00 35.49 118 30.1%

36119 New York Westchester NY 0.00 3.29 61 5.4%

10003 Philadelphia New Castle DE 23.04 39.30 105 59.6%

24015 Philadelphia Cecil MD 7.20 1.55 20 43.2%

24029 Philadelphia Kent MD 0.06 1.36 6 22.2%

24031 Philadelphia Montgomery MD 19.61 0.46 78 25.7%

34005 Philadelphia Burlington NJ 0.00 45.18 76 59.6%

34007 Philadelphia Camden NJ 3.05 18.09 49 43.0%

34011 Philadelphia Cumberland NJ 0.64 46.39 58 81.6%

34015 Philadelphia Gloucester NJ 0.83 24.22 45 55.3%

34021 Philadelphia Mercer NJ 4.21 5.53 38 25.5%

34033 Philadelphia Salem NJ 0.26 14.13 22 65.7%

42017 Philadelphia Bucks PA 1.82 1.10 44 6.7%

42029 Philadelphia Chester PA 8.44 0.16 46 18.8%

42045 Philadelphia Delaware PA 3.48 159.80 188 86.8%

42101 Philadelphia Philadelphia PA 5.93 280.49 347 82.6%

4013 Phoenix Maricopa AZ 85.15 0.79 425 20.2%

4021 Phoenix Pinal AZ 46.98 0.17 71 66.3%

6019 San Joaquin Fresno CA 15.23 0.58 101 15.7%

6029 San Joaquin Kern CA 76.62 0.22 145 52.9%

6031 San Joaquin Kings CA 1.98 0.02 21 9.3%

6039 San Joaquin Madera CA 16.55 0.16 32 52.1%

6047 San Joaquin Merced CA 14.48 0.46 41 36.8%

6077 San Joaquin San Joaquin CA 25.33 25.14 100 50.3%
Emission Inventory 

3-135

2030 PM2.5 

FIPS MSA County ST Diesel 

Locomotive

Diesel 

Marine 

Total 

Diesel 

Mobile 

LM 

Percent 

6099 San Joaquin Stanislaus CA 10.15 0.23 45 23.3%

6107 San Joaquin Tulare CA 22.89 0.16 65 35.4%

53029 Seattle Island WA 0.00 16.34 22 75.7%

53033 Seattle King WA 26.00 159.80 344 54.0%

53035 Seattle Kitsap WA 0.00 1.28 16 8.1%

53045 Seattle Mason WA 0.00 0.57 4 12.8%

53053 Seattle Pierce WA 16.30 144.03 206 77.8%

53061 Seattle Snohomish WA 32.24 24.77 102 56.0%

53067 Seattle Thurston WA 8.81 10.12 36 53.2%

17027 St. Louis Clinton IL 20.38 0.08 29 69.4%

17083 St. Louis Jersey IL 1.66 15.78 23 76.4%

17119 St. Louis Madison IL 8.64 8.56 43 40.3%

17133 St. Louis Monroe IL 32.45 13.84 52 88.4%

17163 St. Louis St. Clair IL 9.36 16.36 48 53.5%

29055 St. Louis Crawford MO 4.30 0.04 9 50.9%

29071 St. Louis Franklin MO 27.96 2.00 43 70.5%

29099 St. Louis Jefferson MO 7.62 14.04 38 57.1%

29113 St. Louis Lincoln MO 12.57 5.60 26 70.5%

29183 St. Louis St. Charles MO 15.14 12.62 51 54.2%

29189 St. Louis St. Louis MO 24.13 16.08 130 30.9%

29219 St. Louis Warren MO 2.56 1.95 9 49.3%

29510 St. Louis St. Louis MO 23.99 216.11 260 92.3%
Regulatory Impact Analysis 

3-136

Table 3-115 2002 Locomotive and Diesel Marine NOx Tons/Year and Percent of Total Mobile Sources 

2002 NOx 

FIPS MSA County ST Diesel 

Locomotive

Diesel 

Marine 

Total 

Mobile 

LM 

Percent 

13013 Atlanta Barrow GA 224.1 0.5 2,039 11.0%

13015 Atlanta Bartow GA 799.6 7.0 5,172 15.6%

13045 Atlanta Carroll GA 219.5 3.0 4,762 4.7%

13057 Atlanta Cherokee GA 0.0 6.8 5,828 0.1%

13063 Atlanta Clayton GA 420.9 1.0 9,512 4.4%

13067 Atlanta Cobb GA 1,110.1 2.8 23,542 4.7%

13077 Atlanta Coweta GA 555.6 2.0 5,727 9.7%

13089 Atlanta DeKalb GA 515.4 1.8 26,283 2.0%

13097 Atlanta Douglas GA 202.2 0.5 3,952 5.1%

13113 Atlanta Fayette GA 143.8 1.3 3,977 3.6%

13117 Atlanta Forsyth GA 0.0 14.1 4,418 0.3%

13121 Atlanta Fulton GA 1,512.7 3.8 39,991 3.8%

13135 Atlanta Gwinnett GA 385.8 2.5 21,343 1.8%

13139 Atlanta Hall GA 258.8 23.1 6,452 4.4%

13149 Atlanta Heard GA 0.0 3.3 465 0.7%

13151 Atlanta Henry GA 567.2 1.3 6,479 8.8%

13217 Atlanta Newton GA 64.4 1.8 3,584 1.8%

13223 Atlanta Paulding GA 470.2 1.0 3,801 12.4%

13237 Atlanta Putnam GA 14.1 10.6 630 3.9%

13247 Atlanta Rockdale GA 91.1 1.0 3,158 2.9%

13255 Atlanta Spalding GA 24.5 1.0 2,584 1.0%

13297 Atlanta Walton GA 77.1 0.5 2,211 3.5%

24003 Baltimore Anne Arundel MD 520.4 63.4 15,497 3.8%

24005 Baltimore Baltimore MD 1,243.0 41.5 24,021 5.3%

24013 Baltimore Carroll MD 199.2 1.3 5,995 3.3%

24025 Baltimore Harford MD 389.4 40.2 7,894 5.4%

24027 Baltimore Howard MD 594.5 12.7 8,160 7.4%

24510 Baltimore Baltimore MD 1,282.5 1,670.4 23,591 12.5%

1073 Birmingham Jefferson AL 4,615.9 268.9 32,416 15.1%

1117 Birmingham Shelby AL 1,156.1 10.4 6,159 18.9%

1127 Birmingham Walker AL 889.2 116.8 3,687 27.3%

9007 Boston Middlesex CT 160.2 121.4 282 99.8%

25001 Boston Barnstable MA 318.1 474.3 8,446 9.4%

25005 Boston Bristol MA 588.4 238.7 15,719 5.3%

25007 Boston Dukes MA 0.0 1,589.6 2,042 77.9%

25009 Boston Essex MA 777.6 197.2 21,303 4.6%

25019 Boston Nantucket MA 0.0 282.5 596 47.4%

25021 Boston Norfolk MA 902.6 163.4 22,498 4.7%

25023 Boston Plymouth MA 493.8 169.6 12,655 5.2%

25025 Boston Suffolk MA 489.2 855.0 38,095 3.5%

25027 Boston Worcester MA 1,860.6 36.5 26,614 7.1%

33011 Boston Hillsborough NH 49.0 15.0 12,444 0.5%

33015 Boston Rockingham NH 37.0 1,112.9 11,846 9.7%

47065 Chattanooga Hamilton TN 1,569.2 909.5 14,329 17.3%
Emission Inventory 

3-137

2002 NOx 

FIPS MSA County ST Diesel 

Locomotive

Diesel 

Marine 

Total 

Mobile 

LM 

Percent 

47115 Chattanooga Marion TN 220.0 176.6 2,998 13.2%

47153 Chattanooga Sequatchie TN 0.0 0.0 270 0.0%

13047 Chattanooga Catoosa GA 475.9 0.3 2,527 18.8%

13083 Chattanooga Dade GA 452.1 0.0 2,263 20.0%

13295 Chattanooga Walker GA 0.0 0.3 1,996 0.0%

17031 Chicago Cook IL 24,769.1 6,520.5 178,269 17.6%

17043 Chicago DuPage IL 7,028.5 5.0 31,241 22.5%

17063 Chicago Grundy IL 479.6 198.0 3,244 20.9%

17089 Chicago Kane IL 2,446.9 3.5 8,879 27.6%

17093 Chicago Kendall IL 310.8 0.3 1,789 17.4%

17097 Chicago Lake IL 1,301.3 774.4 16,423 12.6%

17111 Chicago McHenry IL 700.7 5.8 5,103 13.8%

17197 Chicago Will IL 6,401.5 146.5 16,000 40.9%

18089 Chicago Lake IN 4,656.8 490.6 23,491 21.9%

18127 Chicago Porter IN 1,588.7 425.4 8,840 22.8%

18029 Cincinnati Dearborn IN 216.3 696.0 3,628 25.1%

21015 Cincinnati Boone KY 327.3 1,044.5 5,966 23.0%

21037 Cincinnati Campbell KY 621.1 722.6 4,914 27.3%

21117 Cincinnati Kenton KY 1,197.5 360.8 7,316 21.3%

39017 Cincinnati Butler OH 1,581.9 1.7 10,604 14.9%

39025 Cincinnati Clermont OH 68.2 1,377.2 7,579 19.1%

39061 Cincinnati Hamilton OH 1,540.5 4,078.9 34,403 16.3%

39165 Cincinnati Warren OH 235.3 3.2 5,948 4.0%

39007 Cleveland Ashtabula OH 1,062.3 5,482.2 12,796 51.1%

39035 Cleveland Cuyahoga OH 2,914.2 3,832.5 49,767 13.6%

39085 Cleveland Lake OH 738.0 837.5 8,866 17.8%

39093 Cleveland Lorain OH 1,749.1 3,509.5 15,702 33.5%

39103 Cleveland Medina OH 551.8 2.1 6,896 8.0%

39133 Cleveland Portage OH 1,090.9 8.6 8,119 13.5%

39153 Cleveland Summit OH 888.7 6.0 18,330 4.9%

26093 Detroit Livingston MI 95.5 1.9 7,393 1.3%

26099 Detroit Macomb MI 148.2 169.4 24,046 1.3%

26115 Detroit Monroe MI 700.2 276.4 7,675 12.7%

26125 Detroit Oakland MI 584.1 140.9 38,601 1.9%

26147 Detroit St. Clair MI 285.7 662.4 9,871 9.6%

26161 Detroit Washtenaw MI 154.9 1.3 12,742 1.2%

26163 Detroit Wayne MI 1,133.9 318.6 68,502 2.1%

48039 Houston Brazoria TX 728.4 7,573.7 18,133 45.8%

48071 Houston Chambers TX 41.6 234.3 2,586 10.7%

48157 Houston Fort Bend TX 1,019.2 3.3 11,057 9.2%

48167 Houston Galveston TX 491.0 17,352.7 30,023 59.4%

48201 Houston Harris TX 2,609.1 45,215.7 165,530 28.9%

48291 Houston Liberty TX 1,115.9 93.4 4,073 29.7%

48339 Houston Montgomery TX 867.4 9.7 13,754 6.4%

48473 Houston Waller TX 252.5 1.5 1,574 16.1%

21019 Huntington Boyd KY 430.5 559.8 3,171 31.2%
Regulatory Impact Analysis 

3-138

2002 NOx 

FIPS MSA County ST Diesel 

Locomotive

Diesel 

Marine 

Total 

Mobile 

LM 

Percent 

21127 Huntington Lawrence KY 425.1 181.8 1,317 46.1%

39001 Huntington Adams OH 13.7 1,610.6 3,248 50.0%

39053 Huntington Gallia OH 119.7 708.1 2,184 37.9%

39087 Huntington Lawrence OH 433.9 1,051.2 3,946 37.6%

39145 Huntington Scioto OH 972.1 1,018.7 4,780 41.7%

54011 Huntington Cabell WV 946.3 774.1 9,978 17.2%

54053 Huntington Mason WV 239.7 1,218.0 2,909 50.1%

54099 Huntington Wayne WV 1,182.1 1,844.2 4,489 67.4%

18011 Indianapolis Boone IN 235.9 2.1 3,600 6.6%

18057 Indianapolis Hamilton IN 5.7 22.6 7,413 0.4%

18059 Indianapolis Hancock IN 179.7 1.2 3,342 5.4%

18063 Indianapolis Hendricks IN 630.8 1.2 5,968 10.6%

18081 Indianapolis Johnson IN 33.0 7.6 4,964 0.8%

18095 Indianapolis Madison IN 563.4 4.3 6,314 9.0%

18097 Indianapolis Marion IN 1,089.8 48.3 33,822 3.4%

18109 Indianapolis Morgan IN 15.0 8.0 3,634 0.6%

18145 Indianapolis Shelby IN 255.6 0.9 3,130 8.2%

20091 Kansas City Johnson KS 2,157.3 1.4 18,312 11.8%

20103 Kansas City Leavenworth KS 553.1 15.5 2,984 19.1%

20121 Kansas City Miami KS 3,157.4 5.5 4,481 70.6%

20209 Kansas City Wyandotte KS 1,170.2 137.0 7,329 17.8%

29037 Kansas City Cass MO 646.8 4.4 3,752 17.4%

29047 Kansas City Clay MO 1,073.0 137.9 8,204 14.8%

29049 Kansas City Clinton MO 0.0 5.8 1,517 0.4%

29095 Kansas City Jackson MO 3,434.0 1,026.2 30,133 14.8%

29107 Kansas City Lafayette MO 899.9 129.2 3,796 27.1%

29165 Kansas City Platte MO 878.0 26.9 5,793 15.6%

29177 Kansas City Ray MO 1,713.2 122.5 3,190 57.5%

6037 Los Angeles Los Angeles CA 9,771.2 42,754.8 257,574 20.4%

6059 Los Angeles Orange CA 2,374.1 2,363.7 68,174 6.9%

6065 Los Angeles Riverside CA 4,414.1 56.3 45,019 9.9%

6071 Los Angeles San 

Bernardino 

CA 14,261.8 26.3 56,392 25.3%

6111 Los Angeles Ventura CA 479.2 4,087.6 18,815 24.3%

27003 Minneapolis Anoka MN 822.8 399.5 10,508 11.6%

27019 Minneapolis Carver MN 2.0 27.0 2,563 1.1%

27037 Minneapolis Dakota MN 491.2 371.9 11,559 7.5%

27053 Minneapolis Hennepin MN 1,226.2 1,117.3 42,042 5.6%

27123 Minneapolis Ramsey MN 465.4 353.7 18,199 4.5%

27139 Minneapolis Scott MN 104.5 46.1 2,947 5.1%

27163 Minneapolis Washington MN 895.5 1,560.4 9,536 25.8%

9001 New York Fairfield CT 589.7 257.5 28,368 3.0%

9005 New York Litchfield CT 100.0 31.6 4,615 2.9%

34003 New York Bergen NJ 1,055.1 193.9 23,136 5.4%

34013 New York Essex NJ 228.1 51.3 21,624 1.3%

34017 New York Hudson NJ 777.7 1,486.3 16,558 13.7%
Emission Inventory 

3-139

2002 NOx 

FIPS MSA County ST Diesel 

Locomotive

Diesel 

Marine 

Total 

Mobile 

LM 

Percent 

34019 New York Hunterdon NJ 331.3 11.7 7,327 4.7%

34023 New York Middlesex NJ 481.9 282.2 19,497 3.9%

34025 New York Monmouth NJ 379.8 682.3 17,750 6.0%

34027 New York Morris NJ 234.4 18.7 13,461 1.9%

34029 New York Ocean NJ 19.6 435.6 12,234 3.7%

34031 New York Passaic NJ 229.2 18.1 11,334 2.2%

34035 New York Somerset NJ 509.9 0.6 8,259 6.2%

34037 New York Sussex NJ 36.0 22.2 4,546 1.3%

34039 New York Union NJ 420.7 1,084.1 14,897 10.1%

36005 New York Bronx NY 5.1 203.9 18,301 1.1%

36047 New York Kings NY 0.0 1,713.6 36,548 4.7%

36059 New York Nassau NY 0.0 586.4 22,268 2.6%

36061 New York New York NY 0.0 1,207.0 44,035 2.7%

36071 New York Orange NY 349.9 80.2 13,475 3.2%

36081 New York Queens NY 2.3 2,056.4 39,760 5.2%

36085 New York Richmond NY 0.0 2,386.5 8,667 27.5%

36087 New York Rockland NY 265.0 16.6 4,886 5.8%

36103 New York Suffolk NY 0.0 1,361.4 27,455 5.0%

36119 New York Westchester NY 0.0 127.5 16,193 0.8%

10003 Philadelphia New Castle DE 818.9 2,545.5 21,119 15.9%

24015 Philadelphia Cecil MD 306.8 56.0 5,150 7.0%

24029 Philadelphia Kent MD 2.4 48.8 984 5.2%

24031 Philadelphia Montgomery MD 987.2 16.9 23,771 4.2%

34005 Philadelphia Burlington NJ 0.0 1,178.2 13,449 8.8%

34007 Philadelphia Camden NJ 182.3 471.7 13,996 4.7%

34011 Philadelphia Cumberland NJ 20.8 1,242.9 5,472 23.1%

34015 Philadelphia Gloucester NJ 36.7 633.3 10,121 6.6%

34021 Philadelphia Mercer NJ 193.5 144.7 12,609 2.7%

34033 Philadelphia Salem NJ 10.3 374.9 3,009 12.8%

42017 Philadelphia Bucks PA 86.8 40.0 13,732 0.9%

42029 Philadelphia Chester PA 435.2 5.7 12,150 3.6%

42045 Philadelphia Delaware PA 171.7 5,914.4 18,361 33.1%

42101 Philadelphia Philadelphia PA 239.6 10,381.6 44,901 23.7%

4013 Phoenix Maricopa AZ 3,884.9 28.0 105,636 3.7%

4021 Phoenix Pinal AZ 2,030.8 6.2 10,844 18.8%

6019 San Joaquin Fresno CA 765.2 32.2 24,853 3.2%

6029 San Joaquin Kern CA 3,687.8 12.0 27,768 13.3%

6031 San Joaquin Kings CA 104.0 1.1 4,389 2.4%

6039 San Joaquin Madera CA 819.3 8.9 5,469 15.1%

6047 San Joaquin Merced CA 790.7 25.4 9,353 8.7%

6077 San Joaquin San Joaquin CA 1,287.6 603.0 18,977 10.0%

6099 San Joaquin Stanislaus CA 528.7 12.5 12,862 4.2%

6107 San Joaquin Tulare CA 1,172.3 8.9 13,310 8.9%

53029 Seattle Island WA 0.0 2,098.3 3,999 52.5%

53033 Seattle King WA 1,119.6 5,906.0 68,488 10.3%

53035 Seattle Kitsap WA 0.0 45.6 6,933 0.7%
Regulatory Impact Analysis 

3-140

2002 NOx 

FIPS MSA County ST Diesel 

Locomotive

Diesel 

Marine 

Total 

Mobile 

LM 

Percent 

53045 Seattle Mason WA 0.1 26.7 1,679 1.6%

53053 Seattle Pierce WA 703.0 5,327.1 27,443 22.0%

53061 Seattle Snohomish WA 1,279.7 912.6 20,798 10.5%

53067 Seattle Thurston WA 369.2 373.3 8,518 8.7%

17027 St. Louis Clinton IL 801.1 2.8 2,597 31.0%

17083 St. Louis Jersey IL 64.8 583.9 1,759 36.9%

17119 St. Louis Madison IL 287.0 316.7 10,200 5.9%

17133 St. Louis Monroe IL 1,288.0 512.0 3,122 57.7%

17163 St. Louis St. Clair IL 325.2 605.6 10,049 9.3%

29055 St. Louis Crawford MO 204.7 1.5 2,080 9.9%

29071 St. Louis Franklin MO 1,206.1 73.8 6,434 19.9%

29099 St. Louis Jefferson MO 324.2 519.4 9,205 9.2%

29113 St. Louis Lincoln MO 534.3 206.8 2,771 26.7%

29183 St. Louis St. Charles MO 643.6 465.6 10,406 10.7%

29189 St. Louis St. Louis MO 1,035.3 594.2 41,254 4.0%

29219 St. Louis Warren MO 109.1 71.9 1,692 10.7%

29510 St. Louis St. Louis MO 866.5 7,998.7 23,595 37.6%
Emission Inventory 

3-141

Table 3-116 2020 Locomotive and Diesel Marine NOx Tons/Year and Percent of Total Mobile Sources 

2020 NOx 

FIPS MSA County ST Diesel 

Locomotive

Diesel 

Marine 

Total 

Mobile 

LM 

Percent 

13013 Atlanta Barrow GA 189.4 0.6 682 27.9%

13015 Atlanta Bartow GA 675.7 8.5 1,838 37.2%

13045 Atlanta Carroll GA 183.4 3.6 1,404 13.3%

13057 Atlanta Cherokee GA 0.0 8.1 1,834 0.4%

13063 Atlanta Clayton GA 355.6 1.2 3,382 10.6%

13067 Atlanta Cobb GA 933.8 3.3 7,245 12.9%

13077 Atlanta Coweta GA 469.5 2.4 1,995 23.7%

13089 Atlanta DeKalb GA 433.1 2.1 7,494 5.8%

13097 Atlanta Douglas GA 168.0 0.6 1,353 12.5%

13113 Atlanta Fayette GA 121.5 1.5 1,333 9.2%

13117 Atlanta Forsyth GA 0.0 16.9 1,392 1.2%

13121 Atlanta Fulton GA 1,272.1 4.5 15,332 8.3%

13135 Atlanta Gwinnett GA 323.3 3.0 6,226 5.2%

13139 Atlanta Hall GA 186.3 27.8 1,919 11.2%

13149 Atlanta Heard GA 0.0 3.9 128 3.1%

13151 Atlanta Henry GA 479.3 1.5 2,241 21.5%

13217 Atlanta Newton GA 54.4 2.1 996 5.7%

13223 Atlanta Paulding GA 397.3 1.2 1,372 29.0%

13237 Atlanta Putnam GA 10.3 12.7 202 11.4%

13247 Atlanta Rockdale GA 77.0 1.2 1,026 7.6%

13255 Atlanta Spalding GA 20.7 1.2 728 3.0%

13297 Atlanta Walton GA 65.1 0.6 664 9.9%

24003 Baltimore Anne Arundel MD 306.6 71.4 8,342 4.5%

24005 Baltimore Baltimore MD 936.5 45.1 11,487 8.5%

24013 Baltimore Carroll MD 145.4 1.5 2,579 5.7%

24025 Baltimore Harford MD 251.7 42.9 3,608 8.2%

24027 Baltimore Howard MD 366.4 10.6 3,859 9.8%

24510 Baltimore Baltimore MD 1,186.5 1,357.0 15,594 16.3%

1073 Birmingham Jefferson AL 4,173.3 221.1 12,112 36.3%

1117 Birmingham Shelby AL 1,026.2 12.5 2,492 41.7%

1127 Birmingham Walker AL 649.1 97.7 1,530 48.8%

9007 Boston Middlesex CT 110.6 121.2 233 99.6%

25001 Boston Barnstable MA 232.2 490.2 4,681 15.4%

25005 Boston Bristol MA 436.1 214.3 7,364 8.8%

25007 Boston Dukes MA 0.0 1,332.0 1,732 76.9%

25009 Boston Essex MA 567.6 201.2 9,768 7.9%

25019 Boston Nantucket MA 0.0 256.8 530 48.4%

25021 Boston Norfolk MA 682.9 140.1 10,197 8.1%

25023 Boston Plymouth MA 363.1 191.5 6,163 9.0%

25025 Boston Suffolk MA 362.6 703.7 17,700 6.0%

25027 Boston Worcester MA 1,382.7 43.9 12,067 11.8%

33011 Boston Hillsborough NH 35.8 18.0 6,327 0.8%

33015 Boston Rockingham NH 27.0 928.5 6,652 14.4%

47065 Chattanooga Hamilton TN 1,326.0 749.8 5,500 37.7%
Regulatory Impact Analysis 

3-142

2020 NOx 

FIPS MSA County ST Diesel 

Locomotive

Diesel 

Marine 

Total 

Mobile 

LM 

Percent 

47115 Chattanooga Marion TN 185.9 148.4 1,048 31.9%

47153 Chattanooga Sequatchie TN 0.0 0.0 73 0.0%

13047 Chattanooga Catoosa GA 402.1 0.3 953 42.2%

13083 Chattanooga Dade GA 382.0 0.0 814 46.9%

13295 Chattanooga Walker GA 0.0 0.3 555 0.1%

17031 Chicago Cook IL 18,683.3 5,549.0 69,728 34.8%

17043 Chicago DuPage IL 4,853.4 5.9 11,856 41.0%

17063 Chicago Grundy IL 436.8 161.9 1,367 43.8%

17089 Chicago Kane IL 1,791.2 4.2 3,786 47.4%

17093 Chicago Kendall IL 253.3 0.3 774 32.7%

17097 Chicago Lake IL 930.4 886.7 6,916 26.3%

17111 Chicago McHenry IL 496.6 7.0 1,870 26.9%

17197 Chicago Will IL 4,767.5 122.8 7,685 63.6%

18089 Chicago Lake IN 4,582.7 527.6 12,632 40.5%

18127 Chicago Porter IN 1,239.8 449.0 4,478 37.7%

18029 Cincinnati Dearborn IN 172.0 565.9 1,708 43.2%

21015 Cincinnati Boone KY 276.6 850.5 3,457 32.6%

21037 Cincinnati Campbell KY 522.3 588.4 2,204 50.4%

21117 Cincinnati Kenton KY 1,011.3 293.3 2,771 47.1%

39017 Cincinnati Butler OH 1,225.0 2.0 3,504 35.0%

39025 Cincinnati Clermont OH 53.0 1,117.4 3,185 36.7%

39061 Cincinnati Hamilton OH 1,208.2 3,308.4 13,388 33.7%

39165 Cincinnati Warren OH 188.0 3.8 1,673 11.5%

39007 Cleveland Ashtabula OH 833.8 4,487.1 9,441 56.4%

39035 Cleveland Cuyahoga OH 2,405.6 3,286.2 18,923 30.1%

39085 Cleveland Lake OH 568.9 773.0 3,859 34.8%

39093 Cleveland Lorain OH 1,360.5 2,917.8 8,463 50.5%

39103 Cleveland Medina OH 438.3 2.5 1,945 22.7%

39133 Cleveland Portage OH 851.4 10.3 2,483 34.7%

39153 Cleveland Summit OH 702.5 7.2 4,985 14.2%

26093 Detroit Livingston MI 80.7 2.3 2,010 4.1%

26099 Detroit Macomb MI 125.2 151.8 7,234 3.8%

26115 Detroit Monroe MI 591.7 231.4 2,799 29.4%

26125 Detroit Oakland MI 492.0 117.6 12,011 5.1%

26147 Detroit St. Clair MI 238.4 552.6 4,414 17.9%

26161 Detroit Washtenaw MI 137.0 1.6 3,811 3.6%

26163 Detroit Wayne MI 1,064.0 284.1 23,915 5.6%

48039 Houston Brazoria TX 615.5 6,160.6 12,492 54.2%

48071 Houston Chambers TX 35.1 208.0 1,047 23.2%

48157 Houston Fort Bend TX 855.7 4.0 4,021 21.4%

48167 Houston Galveston TX 481.5 14,101.9 24,831 58.7%

48201 Houston Harris TX 2,463.1 36,663.4 88,044 44.4%

48291 Houston Liberty TX 940.8 77.6 1,866 54.6%

48339 Houston Montgomery TX 732.9 11.6 4,332 17.2%

48473 Houston Waller TX 213.4 1.8 593 36.3%

21019 Huntington Boyd KY 361.2 454.4 1,599 51.0%
Emission Inventory 

3-143

2020 NOx 

FIPS MSA County ST Diesel 

Locomotive

Diesel 

Marine 

Total 

Mobile 

LM 

Percent 

21127 Huntington Lawrence KY 310.3 147.8 706 64.9%

39001 Huntington Adams OH 10.6 1,305.9 2,379 55.3%

39053 Huntington Gallia OH 93.0 574.4 1,310 50.9%

39087 Huntington Lawrence OH 337.7 852.5 2,252 52.8%

39145 Huntington Scioto OH 755.3 826.2 2,737 57.8%

54011 Huntington Cabell WV 789.0 630.5 10,401 13.6%

54053 Huntington Mason WV 175.0 993.3 2,088 56.0%

54099 Huntington Wayne WV 997.3 1,498.1 3,047 81.9%

18011 Indianapolis Boone IN 178.0 2.5 1,171 15.4%

18057 Indianapolis Hamilton IN 6.4 27.1 2,259 1.5%

18059 Indianapolis Hancock IN 138.0 1.4 1,042 13.4%

18063 Indianapolis Hendricks IN 490.3 1.4 1,989 24.7%

18081 Indianapolis Johnson IN 37.1 9.1 1,445 3.2%

18095 Indianapolis Madison IN 444.2 5.2 2,073 21.7%

18097 Indianapolis Marion IN 851.8 58.0 11,238 8.1%

18109 Indianapolis Morgan IN 16.9 9.6 1,015 2.6%

18145 Indianapolis Shelby IN 197.5 1.0 1,011 19.6%

20091 Kansas City Johnson KS 1,821.4 1.7 6,851 26.6%

20103 Kansas City Leavenworth KS 467.1 13.3 1,177 40.8%

20121 Kansas City Miami KS 2,667.9 6.6 3,085 86.7%

20209 Kansas City Wyandotte KS 985.3 111.7 2,919 37.6%

29037 Kansas City Cass MO 543.2 5.2 1,476 37.1%

29047 Kansas City Clay MO 984.8 118.0 3,214 34.3%

29049 Kansas City Clinton MO 0.0 7.0 435 1.6%

29095 Kansas City Jackson MO 3,099.6 837.4 12,014 32.8%

29107 Kansas City Lafayette MO 760.4 109.5 1,724 50.5%

29165 Kansas City Platte MO 741.9 25.2 2,964 25.9%

29177 Kansas City Ray MO 1,528.5 101.4 2,106 77.4%

6037 Los Angeles Los Angeles CA 8,078.6 34,699.8 126,737 33.8%

6059 Los Angeles Orange CA 2,064.2 1,935.3 27,820 14.4%

6065 Los Angeles Riverside CA 3,206.9 67.6 18,781 17.4%

6071 Los Angeles San 

Bernardino 

CA 10,808.1 31.6 26,747 40.5%

6111 Los Angeles Ventura CA 380.6 3,334.9 9,593 38.7%

27003 Minneapolis Anoka MN 688.9 350.5 4,088 25.4%

27019 Minneapolis Carver MN 1.7 29.2 848 3.6%

27037 Minneapolis Dakota MN 412.2 322.9 4,372 16.8%

27053 Minneapolis Hennepin MN 1,034.8 960.2 16,513 12.1%

27123 Minneapolis Ramsey MN 390.6 306.8 6,337 11.0%

27139 Minneapolis Scott MN 88.3 47.8 1,053 12.9%

27163 Minneapolis Washington MN 752.2 1,287.7 4,813 42.4%

9001 New York Fairfield CT 497.8 269.3 13,775 5.6%

9005 New York Litchfield CT 112.5 37.9 2,050 7.3%

34003 New York Bergen NJ 778.5 164.7 11,244 8.4%

34013 New York Essex NJ 153.0 43.8 11,579 1.7%

34017 New York Hudson NJ 620.9 1,217.1 8,314 22.1%
Regulatory Impact Analysis 

3-144

2020 NOx 

FIPS MSA County ST Diesel 

Locomotive

Diesel 

Marine 

Total 

Mobile 

LM 

Percent 

34019 New York Hunterdon NJ 218.2 14.0 2,859 8.1%

34023 New York Middlesex NJ 393.0 235.8 9,099 6.9%

34025 New York Monmouth NJ 216.0 617.7 8,620 9.7%

34027 New York Morris NJ 149.1 22.5 6,081 2.8%

34029 New York Ocean NJ 13.6 500.0 6,071 8.5%

34031 New York Passaic NJ 139.0 21.8 5,226 3.1%

34035 New York Somerset NJ 368.6 0.7 3,670 10.1%

34037 New York Sussex NJ 40.5 26.7 1,901 3.5%

34039 New York Union NJ 278.8 880.2 7,151 16.2%

36005 New York Bronx NY 4.3 170.4 8,855 2.0%

36047 New York Kings NY 0.0 1,397.7 18,231 7.7%

36059 New York Nassau NY 0.0 506.2 11,407 4.4%

36061 New York New York NY 0.0 980.8 31,145 3.1%

36071 New York Orange NY 270.9 70.5 6,487 5.3%

36081 New York Queens NY 2.0 1,679.8 22,109 7.6%

36085 New York Richmond NY 0.0 1,942.3 4,992 38.9%

36087 New York Rockland NY 219.1 19.6 2,500 9.6%

36103 New York Suffolk NY 0.0 1,342.6 14,755 9.1%

36119 New York Westchester NY 0.0 117.2 7,870 1.5%

10003 Philadelphia New Castle DE 803.4 2,069.6 11,598 24.8%

24015 Philadelphia Cecil MD 213.9 56.2 2,142 12.6%

24029 Philadelphia Kent MD 1.8 53.8 541 10.3%

24031 Philadelphia Montgomery MD 627.6 15.6 12,024 5.3%

34005 Philadelphia Burlington NJ 0.0 963.9 6,299 15.3%

34007 Philadelphia Camden NJ 111.6 385.6 7,049 7.1%

34011 Philadelphia Cumberland NJ 23.4 1,063.5 3,128 34.8%

34015 Philadelphia Gloucester NJ 38.0 520.5 6,743 8.3%

34021 Philadelphia Mercer NJ 133.5 119.1 5,604 4.5%

34033 Philadelphia Salem NJ 9.3 315.8 1,442 22.5%

42017 Philadelphia Bucks PA 65.3 40.8 6,119 1.7%

42029 Philadelphia Chester PA 304.0 6.8 5,242 5.9%

42045 Philadelphia Delaware PA 125.2 4,798.6 12,519 39.3%

42101 Philadelphia Philadelphia PA 215.5 8,420.9 28,921 29.9%

4013 Phoenix Maricopa AZ 3,043.5 33.6 36,074 8.5%

4021 Phoenix Pinal AZ 1,689.8 7.5 4,626 36.7%

6019 San Joaquin Fresno CA 596.5 38.7 9,566 6.6%

6029 San Joaquin Kern CA 2,751.1 14.4 11,518 24.0%

6031 San Joaquin Kings CA 72.7 1.4 1,747 4.2%

6039 San Joaquin Madera CA 652.3 10.6 2,530 26.2%

6047 San Joaquin Merced CA 574.3 30.5 3,697 16.4%

6077 San Joaquin San Joaquin CA 980.7 496.4 7,856 18.8%

6099 San Joaquin Stanislaus CA 401.8 14.8 4,881 8.5%

6107 San Joaquin Tulare CA 905.5 10.6 5,493 16.7%

53029 Seattle Island WA 0.0 1,709.1 2,406 71.1%

53033 Seattle King WA 935.0 4,874.1 26,130 22.2%

53035 Seattle Kitsap WA 0.0 54.6 2,268 2.4%
Emission Inventory 

3-145

2020 NOx 

FIPS MSA County ST Diesel 

Locomotive

Diesel 

Marine 

Total 

Mobile 

LM 

Percent 

53045 Seattle Mason WA 0.1 28.7 541 5.3%

53053 Seattle Pierce WA 586.2 4,359.7 12,505 39.6%

53061 Seattle Snohomish WA 1,050.1 779.7 7,046 26.0%

53067 Seattle Thurston WA 267.7 317.1 3,088 18.9%

17027 St. Louis Clinton IL 653.3 3.4 1,223 53.7%

17083 St. Louis Jersey IL 54.0 473.6 1,104 47.8%

17119 St. Louis Madison IL 321.8 257.9 3,094 18.7%

17133 St. Louis Monroe IL 1,017.1 415.5 2,060 69.5%

17163 St. Louis St. Clair IL 339.2 491.8 3,360 24.7%

29055 St. Louis Crawford MO 149.4 1.7 640 23.6%

29071 St. Louis Franklin MO 1,005.6 63.6 2,226 48.0%

29099 St. Louis Jefferson MO 273.6 424.7 2,736 25.5%

29113 St. Louis Lincoln MO 451.5 172.5 1,301 48.0%

29183 St. Louis St. Charles MO 543.8 393.2 3,393 27.6%

29189 St. Louis St. Louis MO 867.4 489.5 12,921 10.5%

29219 St. Louis Warren MO 92.1 61.3 595 25.8%

29510 St. Louis St. Louis MO 874.5 6,486.7 13,766 53.5%
Regulatory Impact Analysis 

3-146

Table 3-117 2030 Locomotive and Diesel Marine NOx Tons/Year and Percent of Total Mobile Sources 

2030 NOx 

FIPS MSA County ST Diesel 

Locomotive 

Diesel 

Marine 

Total 

Mobile 

LM 

Percent 

13013 Atlanta Barrow GA 186.5 0.7 583 32.1%

13015 Atlanta Bartow GA 665.4 9.2 1,596 42.3%

13045 Atlanta Carroll GA 180.2 3.9 1,168 15.8%

13057 Atlanta Cherokee GA 0.0 8.8 1,502 0.6%

13063 Atlanta Clayton GA 350.2 1.3 2,912 12.1%

13067 Atlanta Cobb GA 918.9 3.6 5,714 16.1%

13077 Atlanta Coweta GA 462.3 2.6 1,676 27.7%

13089 Atlanta DeKalb GA 426.2 2.3 5,791 7.4%

13097 Atlanta Douglas GA 165.0 0.7 1,146 14.5%

13113 Atlanta Fayette GA 119.7 1.6 1,109 10.9%

13117 Atlanta Forsyth GA 0.0 18.3 1,115 1.6%

13121 Atlanta Fulton GA 1,251.8 4.9 13,644 9.2%

13135 Atlanta Gwinnett GA 318.0 3.3 4,804 6.7%

13139 Atlanta Hall GA 185.4 30.1 1,581 13.6%

13149 Atlanta Heard GA 0.0 4.3 100 4.3%

13151 Atlanta Henry GA 472.0 1.6 1,860 25.5%

13217 Atlanta Newton GA 53.5 2.3 800 7.0%

13223 Atlanta Paulding GA 391.3 1.3 1,152 34.1%

13237 Atlanta Putnam GA 10.3 13.8 169 14.2%

13247 Atlanta Rockdale GA 75.8 1.3 848 9.1%

13255 Atlanta Spalding GA 20.4 1.3 597 3.6%

13297 Atlanta Walton GA 64.1 0.7 550 11.8%

24003 Baltimore Anne Arundel MD 285.6 76.7 8,572 4.2%

24005 Baltimore Baltimore MD 896.2 48.2 11,329 8.3%

24013 Baltimore Carroll MD 145.3 1.7 2,442 6.0%

24025 Baltimore Harford MD 242.3 45.7 3,508 8.2%

24027 Baltimore Howard MD 347.0 10.7 3,770 9.5%

24510 Baltimore Baltimore MD 1,142.1 1,365.5 17,705 14.2%

1073 Birmingham Jefferson AL 4,081.7 223.2 10,639 40.5%

1117 Birmingham Shelby AL 1,005.0 13.6 2,211 46.1%

1127 Birmingham Walker AL 648.6 99.0 1,403 53.3%

9007 Boston Middlesex CT 105.1 127.5 234 99.4%

25001 Boston Barnstable MA 232.0 518.9 4,797 15.7%

25005 Boston Bristol MA 435.3 220.7 7,523 8.7%

25007 Boston Dukes MA 0.0 1,350.2 1,773 76.2%

25009 Boston Essex MA 567.2 212.4 9,820 7.9%

25019 Boston Nantucket MA 0.0 265.1 551 48.1%

25021 Boston Norfolk MA 679.2 142.8 10,138 8.1%

25023 Boston Plymouth MA 362.5 205.8 6,197 9.2%

25025 Boston Suffolk MA 360.0 710.3 16,310 6.6%

25027 Boston Worcester MA 1,368.9 47.6 11,980 11.8%

33011 Boston Hillsborough NH 35.8 19.5 6,461 0.9%

33015 Boston Rockingham NH 27.0 940.3 6,892 14.0%

47065 Chattanooga Hamilton TN 1,305.7 757.2 5,151 40.1%
Emission Inventory 

3-147

2030 NOx 

FIPS MSA County ST Diesel 

Locomotive 

Diesel 

Marine 

Total 

Mobile 

LM 

Percent 

47115 Chattanooga Marion TN 183.1 150.6 932 35.8%

47153 Chattanooga Sequatchie TN 0.0 0.0 56 0.0%

13047 Chattanooga Catoosa GA 395.9 0.3 830 47.8%

13083 Chattanooga Dade GA 376.2 0.0 699 53.8%

13295 Chattanooga Walker GA 0.0 0.3 438 0.1%

17031 Chicago Cook IL 18,514.9 5,645.1 63,116 38.3%

17043 Chicago DuPage IL 4,720.6 6.5 10,269 46.0%

17063 Chicago Grundy IL 427.7 163.2 1,168 50.6%

17089 Chicago Kane IL 1,750.8 4.6 3,281 53.5%

17093 Chicago Kendall IL 250.2 0.4 641 39.1%

17097 Chicago Lake IL 906.2 955.2 6,310 29.5%

17111 Chicago McHenry IL 488.4 7.6 1,548 32.0%

17197 Chicago Will IL 4,733.7 124.5 7,002 69.4%

18089 Chicago Lake IN 4,451.2 562.4 12,715 39.4%

18127 Chicago Porter IN 1,230.3 477.1 4,520 37.8%

18029 Cincinnati Dearborn IN 171.1 569.5 1,694 43.7%

21015 Cincinnati Boone KY 272.4 856.3 3,615 31.2%

21037 Cincinnati Campbell KY 514.0 592.5 2,128 52.0%

21117 Cincinnati Kenton KY 995.8 295.2 2,456 52.6%

39017 Cincinnati Butler OH 1,215.2 2.2 2,901 42.0%

39025 Cincinnati Clermont OH 52.6 1,124.0 3,076 38.2%

39061 Cincinnati Hamilton OH 1,200.0 3,327.7 12,598 35.9%

39165 Cincinnati Warren OH 187.1 4.2 1,261 15.2%

39007 Cleveland Ashtabula OH 826.5 4,523.2 10,335 51.8%

39035 Cleveland Cuyahoga OH 2,374.0 3,348.9 17,334 33.0%

39085 Cleveland Lake OH 563.9 800.5 3,676 37.1%

39093 Cleveland Lorain OH 1,350.4 2,952.3 8,584 50.1%

39103 Cleveland Medina OH 435.4 2.7 1,508 29.1%

39133 Cleveland Portage OH 844.5 11.2 2,012 42.5%

39153 Cleveland Summit OH 696.4 7.8 3,944 17.9%

26093 Detroit Livingston MI 79.5 2.5 1,589 5.2%

26099 Detroit Macomb MI 123.3 156.2 6,116 4.6%

26115 Detroit Monroe MI 582.6 234.6 2,409 33.9%

26125 Detroit Oakland MI 484.2 119.1 10,112 6.0%

26147 Detroit St. Clair MI 234.3 559.6 4,539 17.5%

26161 Detroit Washtenaw MI 135.7 1.7 3,199 4.3%

26163 Detroit Wayne MI 1,061.8 292.0 21,886 6.2%

48039 Houston Brazoria TX 606.1 6,200.9 13,541 50.3%

48071 Houston Chambers TX 34.6 213.6 964 25.8%

48157 Houston Fort Bend TX 841.8 4.3 3,437 24.6%

48167 Houston Galveston TX 483.0 14,191.0 27,937 52.5%

48201 Houston Harris TX 2,459.9 36,874.9 91,005 43.2%

48291 Houston Liberty TX 926.1 78.5 1,679 59.8%

48339 Houston Montgomery TX 721.7 12.6 3,561 20.6%

48473 Houston Waller TX 210.1 1.9 497 42.7%

21019 Huntington Boyd KY 355.3 457.1 1,606 50.6%
Regulatory Impact Analysis 

3-148

2030 NOx 

FIPS MSA County ST Diesel 

Locomotive 

Diesel 

Marine 

Total 

Mobile 

LM 

Percent 

21127 Huntington Lawrence KY 310.0 148.8 704 65.2%

39001 Huntington Adams OH 10.5 1,313.4 2,628 50.4%

39053 Huntington Gallia OH 92.3 577.8 1,377 48.7%

39087 Huntington Lawrence OH 335.2 857.5 2,351 50.7%

39145 Huntington Scioto OH 749.9 831.1 2,788 56.7%

54011 Huntington Cabell WV 775.3 634.9 13,900 10.1%

54053 Huntington Mason WV 174.8 1,000.4 2,292 51.3%

54099 Huntington Wayne WV 981.9 1,507.4 3,047 81.7%

18011 Indianapolis Boone IN 175.6 2.7 922 19.3%

18057 Indianapolis Hamilton IN 6.6 29.4 1,804 2.0%

18059 Indianapolis Hancock IN 136.6 1.6 816 16.9%

18063 Indianapolis Hendricks IN 486.8 1.6 1,616 30.2%

18081 Indianapolis Johnson IN 37.8 9.9 1,158 4.1%

18095 Indianapolis Madison IN 440.3 5.6 1,721 25.9%

18097 Indianapolis Marion IN 845.5 62.9 9,848 9.2%

18109 Indianapolis Morgan IN 17.2 10.4 785 3.5%

18145 Indianapolis Shelby IN 195.8 1.1 797 24.7%

20091 Kansas City Johnson KS 1,793.4 1.8 5,960 30.1%

20103 Kansas City Leavenworth KS 460.0 13.6 1,012 46.8%

20121 Kansas City Miami KS 2,627.2 7.1 2,928 90.0%

20209 Kansas City Wyandotte KS 969.7 112.5 2,648 40.9%

29037 Kansas City Cass MO 534.4 5.7 1,248 43.3%

29047 Kansas City Clay MO 980.2 120.2 2,864 38.4%

29049 Kansas City Clinton MO 0.0 7.6 320 2.4%

29095 Kansas City Jackson MO 3,078.5 843.5 10,916 35.9%

29107 Kansas City Lafayette MO 748.8 111.3 1,515 56.8%

29165 Kansas City Platte MO 730.6 26.2 2,855 26.5%

29177 Kansas City Ray MO 1,515.9 102.4 1,995 81.1%

6037 Los Angeles Los Angeles CA 8,037.8 34,907.8 110,332 38.9%

6059 Los Angeles Orange CA 2,064.0 1,951.1 22,503 17.8%

6065 Los Angeles Riverside CA 3,176.5 73.4 12,138 26.8%

6071 Los Angeles San 

Bernardino 

CA 10,729.1 34.3 20,287 53.1%

6111 Los Angeles Ventura CA 379.6 3,359.2 8,627 43.3%

27003 Minneapolis Anoka MN 677.4 359.0 3,678 28.2%

27019 Minneapolis Carver MN 1.7 31.1 683 4.8%

27037 Minneapolis Dakota MN 405.5 330.0 3,860 19.1%

27053 Minneapolis Hennepin MN 1,018.8 979.0 15,108 13.2%

27123 Minneapolis Ramsey MN 384.3 313.5 5,585 12.5%

27139 Minneapolis Scott MN 86.9 50.7 871 15.8%

27163 Minneapolis Washington MN 740.1 1,300.7 4,730 43.1%

9001 New York Fairfield CT 484.0 285.7 13,975 5.5%

9005 New York Litchfield CT 114.5 41.2 2,010 7.7%

34003 New York Bergen NJ 756.3 167.5 11,281 8.2%

34013 New York Essex NJ 146.0 44.6 13,693 1.4%

34017 New York Hudson NJ 596.4 1,227.0 11,022 16.5%
Emission Inventory 

3-149

2030 NOx 

FIPS MSA County ST Diesel 

Locomotive 

Diesel 

Marine 

Total 

Mobile 

LM 

Percent 

34019 New York Hunterdon NJ 210.5 15.2 2,703 8.4%

34023 New York Middlesex NJ 377.1 238.9 10,943 5.6%

34025 New York Monmouth NJ 196.7 637.0 8,926 9.3%

34027 New York Morris NJ 138.6 24.4 5,958 2.7%

34029 New York Ocean NJ 12.6 539.0 6,186 8.9%

34031 New York Passaic NJ 129.1 23.6 5,198 2.9%

34035 New York Somerset NJ 358.4 0.8 3,620 9.9%

34037 New York Sussex NJ 41.2 29.0 1,794 3.9%

34039 New York Union NJ 265.7 885.6 8,205 14.0%

36005 New York Bronx NY 4.3 172.6 9,872 1.8%

36047 New York Kings NY 0.0 1,407.8 23,002 6.1%

36059 New York Nassau NY 0.0 516.6 11,386 4.5%

36061 New York New York NY 0.0 987.0 17,781 5.6%

36071 New York Orange NY 263.2 72.3 6,601 5.1%

36081 New York Queens NY 1.9 1,692.5 24,125 7.0%

36085 New York Richmond NY 0.0 1,955.3 6,930 28.2%

36087 New York Rockland NY 215.1 21.3 2,459 9.6%

36103 New York Suffolk NY 0.0 1,408.8 14,851 9.5%

36119 New York Westchester NY 0.0 121.2 8,399 1.4%

10003 Philadelphia New Castle DE 781.2 2,083.0 12,157 23.6%

24015 Philadelphia Cecil MD 210.9 59.2 2,059 13.1%

24029 Philadelphia Kent MD 1.8 57.6 506 11.7%

24031 Philadelphia Montgomery MD 593.4 16.1 12,274 5.0%

34005 Philadelphia Burlington NJ 0.0 971.5 6,198 15.7%

34007 Philadelphia Camden NJ 104.7 388.6 7,322 6.7%

34011 Philadelphia Cumberland NJ 23.8 1,083.2 3,125 35.4%

34015 Philadelphia Gloucester NJ 36.9 525.2 7,922 7.1%

34021 Philadelphia Mercer NJ 131.1 120.3 5,616 4.5%

34033 Philadelphia Salem NJ 9.4 320.5 1,393 23.7%

42017 Philadelphia Bucks PA 63.2 43.1 6,003 1.8%

42029 Philadelphia Chester PA 290.2 7.4 5,004 5.9%

42045 Philadelphia Delaware PA 120.5 4,827.0 13,735 36.0%

42101 Philadelphia Philadelphia PA 213.8 8,470.2 31,412 27.6%

4013 Phoenix Maricopa AZ 3,019.1 36.5 18,989 16.1%

4021 Phoenix Pinal AZ 1,660.1 8.1 4,001 41.7%

6019 San Joaquin Fresno CA 590.6 42.0 5,860 10.8%

6029 San Joaquin Kern CA 2,741.2 15.7 7,256 38.0%

6031 San Joaquin Kings CA 71.7 1.5 902 8.1%

6039 San Joaquin Madera CA 644.9 11.5 1,488 44.1%

6047 San Joaquin Merced CA 573.0 33.1 2,108 28.7%

6077 San Joaquin San Joaquin CA 974.8 501.1 5,322 27.7%

6099 San Joaquin Stanislaus CA 398.9 16.0 2,978 13.9%

6107 San Joaquin Tulare CA 898.7 11.5 3,414 26.7%

53029 Seattle Island WA 0.0 1,720.9 2,318 74.2%

53033 Seattle King WA 919.1 4,923.0 23,930 24.4%

53035 Seattle Kitsap WA 0.0 59.2 1,921 3.1%
Regulatory Impact Analysis 

3-150

2030 NOx 

FIPS MSA County ST Diesel 

Locomotive 

Diesel 

Marine 

Total 

Mobile 

LM 

Percent 

53045 Seattle Mason WA 0.1 30.6 449 6.8%

53053 Seattle Pierce WA 576.1 4,394.7 12,254 40.6%

53061 Seattle Snohomish WA 1,033.3 793.9 6,039 30.3%

53067 Seattle Thurston WA 266.9 322.4 2,775 21.2%

17027 St. Louis Clinton IL 645.5 3.7 1,056 61.4%

17083 St. Louis Jersey IL 53.2 476.4 1,134 46.7%

17119 St. Louis Madison IL 312.6 259.6 2,469 23.2%

17133 St. Louis Monroe IL 1,008.1 417.9 2,049 69.6%

17163 St. Louis St. Clair IL 328.1 494.8 2,832 29.1%

29055 St. Louis Crawford MO 149.3 1.9 526 28.7%

29071 St. Louis Franklin MO 988.2 64.9 1,850 56.9%

29099 St. Louis Jefferson MO 269.4 428.0 2,271 30.7%

29113 St. Louis Lincoln MO 444.6 174.7 1,179 52.5%

29183 St. Louis St. Charles MO 535.5 399.3 2,847 32.8%

29189 St. Louis St. Louis MO 853.1 494.2 11,003 12.2%

29219 St. Louis Warren MO 90.7 62.4 503 30.4%

29510 St. Louis St. Louis MO 880.1 6,524.3 14,654 50.5%
Emission Inventory 

3-151

References

 

1

 "Calculation of Age Distributions in the Nonroad Model: Growth and Scrappage," EPA420-R-05-018, 

December 2005. Docket Document EPA-HQ-OAR-2003-0190-0388. The report is available online at 

http://epa.gov/otaq/models/nonrdmdl/nonrdmdl2005/420r05018.pdf 

2

 “Exhaust and Crankcase Emission Factors for Nonroad Engine Modeling—Compression-Ignition,” EPA420-P04-009, April 2004. Docket Document EPA-HQ-OAR-2003-0190-0410. The report is available online at 

http://epa.gov/otaq/models/nonrdmdl/nonrdmdl2004/420p04009.pdf 

3

 "Final Regulatory Impact Analysis: Control of Emissions from Nonroad Diesel Engines, Chapter 3" EPA420-

R-04-007, May 2004. Docket Document EPA-HQ-OAR-2003-0012-1032. The RIA is also available online at 

http://epa.gov/nonroad-diesel/2004fr/420r04007.pdf 

4

 “Exhaust and Crankcase Emission Factors for Nonroad Engine Modeling—Compression-Ignition,” EPA420-P04-009, April 2004. Docket Document EPA-HQ-OAR-2003-0190-0410. The report is available online at 

http://epa.gov/otaq/models/nonrdmdl/nonrdmdl2004/420p04009.pdf 

5

 "Final Regulatory Impact Analysis: Control of Emissions from Nonroad Diesel Engines, Chapter 3" EPA420-

R-04-007, May 2004. Docket Document EPA-HQ-OAR-2003-0012-1032. The RIA is also available online at 

http://epa.gov/nonroad-diesel/2004fr/420r04007.pdf 

6

 “Conversion Factors for Hydrocarbon Emission Components,” EPA420-R-05-015, December 2005. Docket 

Document EPA-HQ-OAR-2003-0190-0411. The report is available online at 

http://epa.gov/otaq/models/nonrdmdl/nonrdmdl2005/420r05015.pdf 

7

 “Exhaust and Crankcase Emission Factors for Nonroad Engine Modeling—Compression-Ignition,” EPA420-P04-009, April 2004. Docket Document EPA-HQ-OAR-2003-0190-0410. The report is available online at 

http://epa.gov/otaq/models/nonrdmdl/nonrdmdl2004/420p04009.pdf 

8

 "Final Regulatory Impact Analysis: Control of Emissions from Nonroad Diesel Engines, Chapter 3" EPA420-

R-04-007, May 2004. Docket Document EPA-HQ-OAR-2003-0012-1032. The RIA is also available online at 

http://epa.gov/nonroad-diesel/2004fr/420r04007.pdf 

9

 "National Scale Modeling of Air Toxics for the Mobile Source Air Toxics Rule; Technical Support Document," 

EPA-454/R-06-002, January 2006. Docket Document EPA-HQ-OAR-2003-0190-0427. The report is available 

online at http://www.epa.gov/otaq/regs/toxics/454r06002.pdf 

10 "Final Regulatory Impact Analysis: Control of Emissions from Marine Diesel Engines, Chapter 5" EPA-420-

R-99-026, November 1999. Docket Document EPA-HQ-OAR-2004-0308-0003. The RIA is also available 

online at http://www.epa.gov/otaq/regs/nonroad/marine/ci/fr/ria.pdf 

11 Telephone conversation with Doug Scheffler, American Waterways Operators, May 4, 2006. Docket 

Document EPA-HQ-OAR-2003-0190-0389. 

12 "Annual Energy Outlook 2006," Energy Information Administration, Report #:DOE/EIA-0383(2006), 

February 2006, Table A7. Docket Document EPA-HQ-OAR-2003-0190-0386. The report is available online at 

http://www.eia.doe.gov/oiaf/archive/aeo06/pdf/0383(2006).pdf 
Regulatory Impact Analysis 

3-152

 

13 "Final Regulatory Impact Analysis: Control of Emissions from Marine Diesel Engines, Chapter 5" EPA-420-

R-99-026, November 1999. Docket Document EPA-HQ-OAR-2004-0308-0003. The RIA is also available 

online at http://www.epa.gov/otaq/regs/nonroad/marine/ci/fr/ria.pdf 

14 “Exhaust and Crankcase Emission Factors for Nonroad Engine Modeling—Compression-Ignition,” EPA420-

P-04-009, April 2004. Docket Document EPA-HQ-OAR-2003-0190-0410. The report is available online at 

http://epa.gov/otaq/models/nonrdmdl/nonrdmdl2004/420p04009.pdf 

15 Swedish Methodology for Environmental Data (SMED), “Methodology for calculating emissions from ships: 

1. Update of emission factors,” November 4, 2004. Docket Document EPA-HQ-OAR-2003-0190-0418. 

16 Eastern Research Group, Inc. (ERG) 2007. Category 1 and 2 Marine Propulsion Engine Activity, 

Port/Underway Splits and Category 2 County Allocation. Prepared for U.S Environmental Protection Agency, 

Office of Transportation and Air Quality. Docket Document EPA-HQ-OAR-2003-0190-0423. 

17 American Public Transportation Association (APTA), “2006 Public Transportation Fact Book,” Ferryboat 

chapter. Docket Document EPA-HQ-OAR-2003-0190-0739. 

18 American Waterways Operators, Letter from Jennifer A. Carpenter to Docket, July 2, 2007. Docket 

Document EPA-HQ-OAR-2003-0190-0574. 

19 “Commercial Marine Emissions Inventory for EPA Category 2 and 3 Compression Ignition Marine Engines in 

the United States and Continental Waterways,” EPA420-R-98-020, August, 1998. Docket Document EPA-HQOAR-2003-0190-0417. The report is also available online at 

http://www.epa.gov/otaq/regs/nonroad/marine/ci/fr/r98020.pdf 

20 "Final Regulatory Impact Analysis: Control of Emissions from Marine Diesel Engines, Chapter 5" EPA-420-

R-99-026, November 1999. Docket Document EPA-HQ-OAR-2004-0308-0003. The RIA is also available 

online at http://www.epa.gov/otaq/regs/nonroad/marine/ci/fr/ria.pdf 

21 EPA, “Control of Emissions of Air Pollution From Nonroad Diesel Engines,” 63 FR 56967, October 23, 1998. 

Docket Document EPA-HQ-OAR-2005-0119-0002. The Federal Register notice is also available online at 

http://www.epa.gov/fedrgstr/EPA-AIR/1998/October/Day-23/a24836.htm 

22 “Exhaust and Crankcase Emission Factors for Nonroad Engine Modeling—Compression-Ignition,” EPA420-

P-04-009, April 2004. Docket Document EPA-HQ-OAR-2003-0190-0410. The report is available online at 

http://epa.gov/otaq/models/nonrdmdl/nonrdmdl2004/420p04009.pdf 

23 Ibid. 

24 Ibid. 

25 "National Scale Modeling of Air Toxics for the Mobile Source Air Toxics Rule; Technical Support 

Document," EPA-454/R-06-002, January 2006. Docket Document EPA-HQ-OAR-2003-0190-0427. The report 

is available online at http://www.epa.gov/otaq/regs/toxics/454r06002.pdf 

26 “Inland River Record Sample Analysis,” U.S. EPA, Memorandum from Amy Kopin to Docket No. EPA-HQOAR-2003-0190, January 11, 2008. Docket Document EPA-HQ-OAR-2003-0190-0847. 

27 "NONROAD2005 CI Marine NPRM," U.S. EPA. Version used to estimate emissions for the recreational 

marine and less than 37 kW commercial marine categories. Docket Document EPA-HQ-OAR-2003-0190-0385. 
Emission Inventory 

3-153

 

28 "Nonroad Engine Growth Estimates," NR-008c, EPA420-P-04-008, April 2004. Docket Document EPA-HQOAR-2003-0190-0387. The report is available online at 

http://www.epa.gov/otaq/models/nonrdmdl/nonrdmdl2004/420p04008.pdf 

29 "Calculation of Age Distributions in the Nonroad Model: Growth and Scrappage," EPA420-R-05-018, 

December 2005. Docket Document EPA-HQ-OAR-2003-0190-0388. The report is available online at 

http://epa.gov/otaq/models/nonrdmdl/nonrdmdl2005/420r05018.pdf 

30 “Exhaust and Crankcase Emission Factors for Nonroad Engine Modeling—Compression-Ignition,” EPA420-

P-04-009, April 2004. Docket Document EPA-HQ-OAR-2003-0190-0410. The report is available online at 

http://epa.gov/otaq/models/nonrdmdl/nonrdmdl2004/420p04009.pdf 

31 "Final Regulatory Impact Analysis: Control of Emissions from Nonroad Diesel Engines, Chapter 3" EPA420-

R-04-007, May 2004. Docket Document EPA-HQ-OAR-2003-0012-1032. The RIA is also available online at 

http://epa.gov/nonroad-diesel/2004fr/420r04007.pdf 

32 Ibid. 

33 "National Scale Modeling of Air Toxics for the Mobile Source Air Toxics Rule; Technical Support 

Document," EPA-454/R-06-002, January 2006. Docket Document EPA-HQ-OAR-2003-0190-0427. The report 

is available online at http://www.epa.gov/otaq/regs/toxics/454r06002.pdf 

34 EPA, “Control of Emissions of Air Pollution From Nonroad Diesel Engines,” 63 FR 56967, October 23, 1998. 

Docket Document EPA-HQ-OAR-2005-0119-0002. The Federal Register notice is also available online at 

http://www.epa.gov/fedrgstr/EPA-AIR/1998/October/Day-23/a24836.htm 

35 “Exhaust and Crankcase Emission Factors for Nonroad Engine Modeling—Compression-Ignition,” EPA420-

P-04-009, April 2004. Docket Document EPA-HQ-OAR-2003-0190-0410. The report is available online at 

http://epa.gov/otaq/models/nonrdmdl/nonrdmdl2004/420p04009.pdf 

36 Ibid. 

37 Ibid. 

38 "National Scale Modeling of Air Toxics for the Mobile Source Air Toxics Rule; Technical Support 

Document," EPA-454/R-06-002, January 2006. Docket Document EPA-HQ-OAR-2003-0190-0427. The report 

is available online at http://www.epa.gov/otaq/regs/toxics/454r06002.pdf 

39 “Conversion Factors for Hydrocarbon Emission Components,” EPA420-R-05-015, December 2005. Docket 

Document EPA-HQ-OAR-2003-0190-0411. The report is available online at 

http://epa.gov/otaq/models/nonrdmdl/nonrdmdl2005/420r05015.pdf 

40 “Exhaust and Crankcase Emission Factors for Nonroad Engine Modeling—Compression-Ignition,” EPA420-

P-04-009, April 2004. Docket Document EPA-HQ-OAR-2003-0190-0410. The report is available online at 

http://epa.gov/otaq/models/nonrdmdl/nonrdmdl2004/420p04009.pdf 

41 "Annual Energy Outlook 2006," Energy Information Administration, Report #:DOE/EIA-0383(2006), 

February 2006, Table A7. Docket Document EPA-HQ-OAR-2003-0190-0386. The report is available online at 

http://www.eia.doe.gov/oiaf/archive/aeo06/pdf/0383(2006).pdf 

42 “Railroad Ten-Year Trends, 1995-2004”, AAR 2205. 
Regulatory Impact Analysis 

3-154

 

43 EPA, 2002 National Emissions Inventory (NEI). County-level fractions of locomotive and commercial marine 

diesel emissions. Docket Document EPA-HQ-OAR-2003-0190-0412. NEI documentation is available online at 

http://www.epa.gov/ttn/chief/net/2002inventory.html 

44 Clean Air Interstate Rule (CAIR). Docket EPA-HQ-OAR-2003-0053. Documentation is also available online 

at http://www.epa.gov/air/interstateairquality/index.html 
Technological Feasibility 

CHAPTER 4: LOCOMOTIVE AND MARINE TECHNOLOGICAL FEASIBILITY

......................................................................................................................................... 2

4.1 Overview of Emissions Standards and Emission Control Technologies.......................2

4.2 Emission Control Technologies for Remanufactured Engine Standards and for Tier 

3 New Engine Interim Standards............................................................................................4

4.2.1 Diesel Combustion and Pollutant Formation...........................................................6

4.2.2 Engine-out Emission Control...................................................................................13

4.3 Feasibility of Tier 4 Locomotive and Marine Standards..............................................23

4.3.1 Selective Catalytic Reduction (SCR) NOx Control Technology ...........................24

4.3.2 PM and HC Exhaust Aftertreatment Technology.................................................31

4.3.3 SCR and CDPF Packaging Feasibility....................................................................38

4.3.4 Mechanical Durability of Aftertreatment Components........................................40

4.3.5 Stakeholder Concerns Regarding Locomotive NOx Standard Feasibility ..........41

4.4 Feasibility of Marine NTE Standards.............................................................................55

4.5 Lead Time..........................................................................................................................56

4.6 Conclusions .......................................................................................................................57

4-1 
Regulatory Impact Analysis 

CHAPTER 4: Locomotive and Marine Technological Feasibility 

In this chapter we describe in detail the analysis of emission control technologies we 

used to develop the standards we are finalized in this rulemaking. Because of the range of 

engines and applications we cover in this proposal, the standards span a range of emissions 

levels. Correspondingly, we have identified a number of different emission control 

technologies we expect will be used to meet these standards. These technologies range from 

incremental improvements of existing engine components to highly advanced catalytic 

exhaust aftertreatment systems. 

We first summarize our current locomotive and marine diesel engine standards and 

provide an overview of existing and future emission control technologies. We believe that 

further improvements in existing technologies will be used to meet the standards for existing 

engines that are remanufactured as new (i.e., Tier 0, 1, and 2 locomotives and marine engines 

greater than 600 kW manufactured from 1973 through 2011). We then describe how 

technologies similar to some of those already being implemented to meet our current and 

upcoming heavy-duty highway and nonroad diesel engine emissions standards can be applied 

to meet the standards for new engines (i.e., Tier 3). Throughout this chapter, we also address 

many of the comments submitted by stakeholders concerning the feasibility, applicability, 

performance, and durability of the emission control technologies we presented in the NPRM. 

We conclude this section with a discussion of catalytic exhaust aftertreatment technologies 

that we believe will be used to meet the Tier 4 standards. 

All of our analyses in this chapter include how we expect these technologies to 

perform throughout their useful life as well as how we believe they will be implemented 

specifically into locomotive and marine applications. Note that much of this chapter’s content 

is based upon the performance of currently available emission control technologies and results 

from testing that has already been completed. In most cases the already-published results 

show that currently available emission control technologies can be implemented without 

further improvements to meet the standards. In a few cases, we are projecting that further 

improvements to these technologies will be made between now and the Tier 4 standards 

implementation dates. These projected improvements will enable engine manufacturers to 

meet the standards finalized in this rulemaking. 

4.1 Overview of Emissions Standards and Emission Control Technologies 

Our current locomotive and marine diesel engine standards have already decreased 

NOx emissions from unregulated levels. For example, since 1997, NOx emissions standards 

for diesel locomotive engines have been reduced from an unregulated level of about 13.5 

g/bhp-hr to the current Tier 2 level of 5.5 g/bhp-hr – a 60% reduction when evaluated over the 

locomotive line-haul duty cycle. Similar NOx reductions have been realized for Category 1 & 

2 (C1 & C2) commercial marine diesel engines. Our Tier 1 marine standards are equivalent to 

the International Maritime Organization’s NOx regulation known as MARPOL Annex VI. 

Beginning in 2004, these standards became mandatory for C1 & C2 Commercial vessels, and 

were voluntary in prior years. Beginning in 2007, EPA Tier 2 standards for C1 & C2 

Commercial vessels superseded these MARPOL-equivalent standards. For a high-speed 

4-2 
Technological Feasibility 

marine diesel engine, NOx emissions have been reduced from a Tier 1 level of 9.8 g/kW-hr to 

7.5 g/kW-hr - a 23% reduction. While these reductions in locomotive and marine NOx

emissions are significant, they do not keep pace with the 90% NOx reduction (from 2.0 g/bhphr to 0.2 g/bhp-hr) set forth in the 2007 Heavy-Duty Highway Rule.1

 Neither do these 

reductions keep pace with the approximately 85% NOx reductions set forth in the Nonroad 

Tier 4 Standards for 56 kW to 560 kW engines and for generator sets above 560 kW.2,3 In a 

similar manner, locomotive and marine particulate matter (PM) emission reductions also lag 

behind the Heavy-Duty Highway and Nonroad Tier 4 Rules. For line-haul and switcher 

locomotives, a 67% reduction in PM already has been achieved in going from the Tier 0 to the 

Tier 2 standards. On the marine side, PM emissions for C1 & C2 Commercial have been 

reduced from essentially unregulated levels prior to May 2005, to a 0.2-0.4 g/kW-hr level for 

Tier 2.A 

In contrast, the 2007 Heavy-Duty Highway Rule set forth PM reductions of 90% - 

from 0.1 g/bhp-hr to 0.01 g/bhp-hr. Similarly, post-2014 Nonroad Tier 4 PM emissions will 

be reduced 85 to 95% compared to Tier 3 Nonroad PM emissions for 56 kW to 560 kW 

engines and for generator sets above 560 kW.2,3

 In the timeframe of the Tier 3 and 4 

Locomotive Standards that we are proposing, NOx and PM emissions will continue to be a 

serious threat to public health, and, on a percentage basis, the locomotive and marine 

contributions to the nationwide inventory of these pollutants would continue to increase 

relative to today’s levels if current Tier 2 emission levels were maintained. Please refer to 

Chapter 3 of the Regulatory Impact Analysis for a more detailed discussion of the 

contribution of locomotive and marine emissions to the NOx and PM inventory. 

To date, the Tier 0 through Tier 2 locomotive and Tier 1 through Tier 2 marine 

emissions reductions have been achieved largely through engine calibration optimization and 

engine hardware design changes (e.g. improved fuel injectors, increased injection pressure, 

intake air after-cooling, combustion chamber design, injection timing, reduced oil 

consumption, etc.) To achieve the Tier 3 PM emission standards we are proposing, further 

reductions in lubricating oil consumption will be required. This will most likely be achieved 

via improvements to piston, piston ring, and cylinder liner design, as well as improvements to 

the crankcase ventilation system. To further reduce NOx and PM emission beyond Tier 3 

levels, an exhaust aftertreatment approach will be necessary. 

Selective catalytic reduction (SCR) is a common catalytic exhaust emission control 

used for meeting more stringent NOx emissions standards in worldwide diesel applications. 

Stationary, coal-fired power plants have used SCR for three decades as a means of controlling 

NOx emissions, and currently, European heavy-duty truck manufacturers are using this 

technology to meet the Euro IV and Euro V limits. In the Category 2 and Category 3 marine 

sector, at least 100 vessels are equipped with SCR systems to control NOx emissions.4

 To a 

lesser extent, SCR has been introduced on diesels in the U.S. market, but the applications 

have been limited to marine ferryboat and stationary power generation demonstration projects 

in California and several northeast states. However, by 2010, when 100% of the heavy-duty 

 

A Marine Tier 2 PM emission standards are dependent on an engine's volumetric displacement-per-cylinder. 

4-3 
Regulatory Impact Analysis 

diesel trucks are required to meet the NOx limits of the 2007 heavy-duty Highway Rule, 

several heavy-duty truck engine manufacturers have indicated that they will use SCR 

technology to meet these standards.5,6

 While other promising NOx-reducing technologies 

such as lean NOx catalysts, NOx adsorbers, and advanced combustion control continue to be 

developed - and may be viable approaches to the standards we are proposing today - our 

analysis projects that SCR will be the technology chosen by the locomotive and marine diesel 

industries to meet the Tier 4 NOx standards we are proposing. For a complete review of these 

other alternative NOx emission control technologies, please refer to the Regulatory Impact 

Analysis from our Clean Air Nonroad diesel rule.7

The most effective exhaust aftertreatment used for diesel PM emission control is the 

diesel particulate filter (DPF). More than a million light diesel vehicles that are OEMequipped with DPF systems have been sold in Europe, and over 200,000 DPF retrofits to 

diesel engines have been conducted worldwide.8

 Broad application of catalyzed diesel 

particulate filter (CDPF) systems with greater than 90% PM control is beginning with the 

introduction of 2007 model year heavy-duty diesel trucks in the United States. These systems 

use a combination of both passive and active soot regeneration. Our analysis projects that 

CDPF systems with a combination of passive and active backup regeneration will be the 

primary technology chosen by the locomotive and marine diesel industries to meet the Tier 4 

PM standards. 

4.2 Emission Control Technologies for Remanufactured Engine Standards 

and for Tier 3 New Engine Interim Standards 

To meet the locomotive and marine remanufactured engine and Tier 3 standards, we 

believe engine manufacturers will utilize incremental improvements of existing engine 

components to reduce engine-out emissions. This will be accomplished primarily via 

application of technology originally developed to meet our current and upcoming standards 

for heavy-duty on-highway trucks and nonroad diesel equipment. This is especially true for 

many of the Category 1 and Category 2 marine engines, which are based on nonroad engine 

designs. This will allow introduction of technology originally developed to meet nonroad 

Tier 3 and Tier 4 standards to be used to meet the Tier 3 marine standards. Table 4-1, Table 

4-2 and Table 4-3 provide summaries of the technologies that we believe will be used meet 

the remanufactured engine and Tier 3 new engine interim standards for switch locomotives, 

line-haul locomotives and marine engines, respectively. The technologies described in Tables 

4-1 and 4-2 can also be applied to remanufactured marine engines >600 kW. 

4-4 
Technological Feasibility 

Table 4-1: Technologies for switch locomotive standards through Tier 3 

Year Standard NOx

(g/bhp-hr) 

PM 

(g/bhp-hr) Technology added to engine 

2010 T0-

Remanufactured 11.8 0.26 

New power assemblies to improve oil 

consumption, improved mechanical unit 

injectors 

2010 T1-

Remanufactured 11.0 0.26 

New power assemblies to improve oil 

consumption, electronic unit injection, new unit 

injector cam profile 

 

2013 T2-

Remanufactured 8.1 0.13 

For high-speed engines: Same as Tier 3 

nonroad engines 

For medium-speed engines: Further 

improvements to power assembly and closed 

crankcase ventilation system to reduce oil 

consumption; new turbocharger (t/c), engine 

calibration, and unit injector cam profile 

2011 T3 5.0 0.10 

For high-speed engines: Same as Tier 3 

nonroad engines 

For medium-speed engines: Further 

improvements to power assembly and CCV to 

reduce oil consumption, high pressure common 

rail injection with post-injection PM clean-up, 

injection timing retard, new t/c 

Table 4-2: Technologies for Line Haul Locomotive Standards up to Tier 3 

Year Standard 

NOx

(g/bhphr) 

PM 

(g/bhphr) 

Technology added to engine 

2010 

(2008 if 

available) 

T0-

Remanufactured 7.4 0.22 

New power assemblies to improve oil 

consumption, improved mechanical unit 

injectors or switch to electronic unit injection, 

new t/c 

2010 

(2008 if 

available) 

T1-

Remanufactured 7.4 0.22 

New power assemblies to improve oil 

consumption, electronic unit injection, new unit 

injector cam profile, new t/c 

2013 T2-

Remanufactured 5.5 0.10 

Further improvements to power assembly and 

CCV to reduce oil consumption, electronic unit 

injection or high pressure common rail injection 

2012 T3 5.5 0.10 

Further improvements to power assembly to 

reduce oil consumption, electronic unit injection 

or high pressure common rail injection 

4-5 
Regulatory Impact Analysis 

Table 4-3: Technologies for Marine Category 1 and Category 2 to meet Tier 3 Standards 

Year Standard 

HC+NOx

(g/bhphr) 

PM (g/bhphr) Technology added to engine 

2009-

2014 

Category 1 Tier 

3 Marine 

(< 75 kW) 

3.5 – 5.6 0.22 – 0.33 

Same engine-out NOx technologies as Tier 4 

nonroad—with no Tier 4 PM aftertreatment 

technologies 

2012-

2018 

Category 1 Tier 

3 Marine 

 (75-3700 kW) 

4.0 – 4.3 0.07 – 0.11 Recalibration on nonroad Tier 4 engines 

without aftertreatment 

2013 

Category 2 Tier 

3 Marine 

7 – 15 liters/cyl. 

5.5 0.10 

Same engine-out NOx technologies as pre2014, non-generator-set, Tier 4 nonroad—

with no Tier 4 PM aftertreatment technologies 

2012 

Category 2 Tier 

3 Marine 

15 – 30 liters/cyl. 

6.5 – 8.2 0.20 

Further improvements to power assembly to 

reduce oil consumption, electronic unit 

injection or high pressure common rail 

injection, new t/c 

In section 4.2.1.1 we will describe some of the fundamentals of diesel combustion and 

pollutant formation. In section 4.2.2 we describe the manner in which engine-out emissions 

can be controlled in order to meet the locomotive and marine remanufactured engine 

standards and Tier 3 standards. 

4.2.1 Diesel Combustion and Pollutant Formation 

In this section we describe the mechanisms of pollutant formation. In order to lay the 

foundation for this discussion, we begin with a review of diesel combustion, especially as it is 

related to 2-stroke cycle and 4-stroke cycle diesel engine operation. We describe both of 

these types of diesel engine operation because both 2-stroke and 4-stroke engines are used in 

locomotive and marine applications. We then describe NOx, PM, HC, and CO formation 

mechanisms. 

4.2.1.1 Diesel Combustion 

Category 1 marine diesel engines operate on a four-stroke cycle. The larger 

displacement Category 2 marine diesel engines and locomotive diesel engines operate on 

either a two-stroke cycle or a four-stroke cycle. The four-stroke cycle consists of an intake 

stroke, a compression stroke, an expansion (also called the power or combustion) stroke, and 

an exhaust stroke. The two-stroke cycle combines the intake and exhaust functions by using 

forced cylinder scavenging. Figure 1 provides an overview and brief comparison of the twostroke and four-stroke cycles used by marine and locomotive diesel engines. 

The diesel combustion event provides the energy for piston work. An example of the 

relationship between the different phases of diesel combustion and the net energy released 

from the fuel is shown in Figure 4.2. Combustion starts near the end of compression and 

continues through a portion of the expansion stroke. Near the end of the piston compression 

stroke, fuel is injected into the cylinder at high pressure and mixes with the contents of the 

cylinder (air + any residual combustion gases). This period of premixing is referred to as 

4-6 
Technological Feasibility 

ignition delay. Ignition delay ends when the premixed cylinder contents self-ignite due to the 

high temperature and pressure produced by the compression stroke in a relatively short, 

homogenous, premixed combustion event. Immediately following premixed combustion, 

diesel combustion becomes primarily non-homogeneous and diffusion-controlled. The rate of 

combustion is limited by the rate of fuel and oxygen mixing. During this phase of 

combustion, fuel injection continues creating a region that consists of fuel only. The fuel 

diffuses out of this region and air is entrained into this region creating an area where the fuel 

to air ratio is balanced (i.e., near stoichiometric conditions) to support combustion. The fuel 

burns primarily in this region. One way to visualize this is to roughly divide the cylinder 

contents into fuel-rich and fuel-lean sides of the reaction-zone where combustion is taking 

place as shown in Figure 3. As discussed in the following subsections, the pollutant rate of 

formation in a diesel engine is largely defined by these combustion regions and how they 

evolve during the combustion process.9

 

4-7 
Regulatory Impact Analysis 

Figure 1: A comparison of 2 complete revolutions of the four-stroke (top) and two-stroke diesel 

combustion cycles. Note that the two-stroke cycle relies on intake air-flow to scavenge the exhaust 

products from the cylinder. In the case of uniflow scavenged two-stroke diesel engines, cylinder 

scavenging is assisted by the use of a centrifugal or positive displacement blower to pressurize the intake 

ports located on the sides of the cylinder. Exhaust exits the cylinder through cam-actuated poppet valves 

in the cylinder head. Four-stroke diesel engines are the predominant type of Category 1 marine engine. 

Both four-stroke and uniflow-scavenged two-stroke diesel engines are used for Category 2 marine and 

locomotive applications. 

4-8 
Technological Feasibility 

Figure 2: An idealized example of the net apparent rate of combustion heat release (derived from highspeed cylinder pressure measurements) for a direct injection diesel engine with indication of the major 

events and phases of combustion. 

Figure 3: An idealized physical schematic of the diesel combustion process. 

4-9 
Regulatory Impact Analysis 

4.2.1.2 NOx Emissions 

Nitrogen oxides (NOx) are formed in diesel engines by the oxidation of molecular 

nitrogen (N2) in the stoichiometric combustion regions of the diffusion-controlled and 

premixed diesel combustion phases, described in the previous section. During the premixed 

phase of combustion, ignition and flame propagation occurs at high temperatures and at near 

stoichiometric mixtures of fuel and air. During diffusion-controlled combustion, the reaction 

zone is also near stoichiometric conditions. At the high temperatures present during premixed 

combustion or in the diffusion-controlled combustion reaction zone, a fraction of the nitrogen 

and oxygen can dissociate, forming radicals which then combine through a series of reactions 

to form nitric oxide (NO), the primary NOx constituent. Nitrogen dioxide (NO2), the other 

major NOx constituent, is formed from oxidation of NO in the flame region. NO2 formed 

during combustion rapidly decomposes to NO and molecular oxygen unless the reaction is 

quenched by mixing with cooler cylinder contents. Engine-out emissions of NO are typically 

80% or more of total NOx from direct injection diesel engines. The NOx formation rate has a 

strong exponential relationship to temperature. Therefore, high temperatures result in high 

NOx formation rates.9,10 Any changes to engine design that can lower the peak temperature 

realized during combustion, the partial pressures of dissociated nitrogen and oxygen, or the 

duration of time at these peak temperatures can lower NOx emissions. Most of the engine-out 

NOx emission control technologies discussed in the following sections reduce NOx emissions 

by reducing the peak combustion temperatures while balancing impacts on PM emissions, 

fuel consumption and torque output. 

4.2.1.3 PM Emissions 

Particulate matter (PM) emitted from diesel engines is a multi-component mixture 

composed chiefly of elemental carbon (or soot), semi-volatile organic carbon compounds, 

sulfate compounds (primarily sulfuric acid) with associated water, and trace quantities of 

metallic ash. 

During diffusion-controlled combustion, fuel diffuses into a reaction zone and burns. 

Products of combustion and partial products of combustion diffuse away from the reaction 

zone where combustion occurs. At temperatures above 1300 K, fuel compounds on the fuelrich side of the reaction zone can be pyrolized to form elemental carbon particles11. Most of 

the elemental carbon formed by fuel pyrolysis (80% to 98%) is oxidized during later stages of 

combustion.12,13 The remaining elemental carbon agglomerates into complex chain-aggregate 

soot particles and leaves the engine as a component of PM emissions. 

From this description, the formation of elemental carbon particles during combustion 

and emission as PM following the combustion event can be summarized as being dependent 

upon three primary factors: 

 1. Temperature 

 2. Residence time 

 3. Availability of oxidants 

4-10 
Technological Feasibility 

Thus, in-cylinder control of elemental carbon PM is accomplished by varying engine 

parameters that affect these variables while balancing the resultant effects on NOx emissions 

and torque output. 

The combinations of organic compounds (volatile and semi-volatile) that contribute to 

PM are referred to as the volatile organic fraction (VOF), the soluble organic fraction (SOF), 

or as organic carbon PM, depending upon the analytical procedure used to measure the 

compounds. Organic carbon PM primarily consists of lubricating oil and partial combustion 

products of lubricating oil. Some of the higher molecular weight fuel compounds from 

unburned or partially burned diesel fuel also contribute to organic carbon PM. Oil can be 

entrained into the cylinder contents from cylinder liner surfaces as they are uncovered by the 

piston and by leakage into the cylinder past the valve stems. Uniflow-scavenged two-stroke 

diesel engines typically have somewhat higher oil consumption and organic carbon PM 

emissions in part due to the lubricating oil entrained into the scavenging flow from around the 

intake ports in the cylinder wall. Compliance with the closed crankcase ventilation provisions 

in the Tier 0 and later locomotive and Tier 2 marine standards has typically been 

accomplished by using coarse filtration to separate a fraction of the oil aerosol from the 

crankcase flow and then entraining the crankcase flow directly into the exhaust downstream 

of the turbocharger exhaust turbine (Figure 4). Incomplete separation of the oil aerosol from 

the crankcase flow can increase the amount of lubricating oil directly entrained into the 

exhaust with subsequent formation of organic carbon PM. 

Both organic carbon and sulfate PM are formed after cooling and air-dilution of the 

exhaust. Sulfur dioxide (SO2) is formed via combustion of sulfur compounds from the fuel 

and lubricating oil burned during combustion. In the absence of post-combustion catalytic 

aftertreatment of the exhaust, approximately 1 to 3 % of fuel sulfur is oxidized to ionic sulfate 

(SO3

-

) and upon further cooling is present primarily as a hydrated sulfuric acid aerosol. For 

example, sulfate PM currently accounts for approximately 0.06 to 0.08 g/bhp-hr over the linehaul cycle for locomotive engines using 3000 ppm sulfur nonroad diesel fuel. 

Diesel oxidation catalysts (DOC) and catalyzed diesel particulate filters (CDPF) using 

platinum catalysts can oxidize the organic compounds thereby lowering PM emissions but 

they can also oxidize 50% or more of the SO2 emissions to sulfate PM, depending on the 

exhaust temperature and the platinum content of the catalyst formulation that is used. 

4-11 
Regulatory Impact Analysis 

 Figure 4: Crankcase ventilation system for a medium speed locomotive diesel engine. An eductor uses 

compressed air to draw crankcase gases through a coarse coalescing filter (top left photo). The outlet of 

the crankcase ventilation system can be clearly seen from the outlet of the locomotive’s exhaust stack (top 

right photo). The bottom photo shows tubing from a crankcase ventilation system removed from 

downstream of a similar coarse coalescing filter. There was considerable wetting of the inner wall of the 

tubing with lubricating oil. 

4.2.1.4 HC Emissions 

Hydrocarbon (HC) emissions from diesel engines are generally much lower compared 

to other mobile sources due to engine operation that, on a bulk-cylinder-content basis, is 

significantly fuel-lean of the stoichiometric air-to-fuel ratio. HC emissions primarily occur 

due to fuel and lubricant trapped in crevices (e.g., at the top ring land and the injector sac) 

which prevents sufficient mixing with air for complete combustion. Fuel related HC can also 

be emitted due to "over mixing" during ignition delay, a condition where fuel in the induced 

swirl flow has mixed beyond the lean flammability limit. Higher molecular weight HC 

compounds adsorb to soot particles or nucleate and thus contribute to the organic carbon PM. 

Lower molecular weight HC compounds are primarily emitted in the gas phase. During 

engine start-up under cold ambient conditions or following prolonged engine idling, fuelrelated HC can be emitted as a concentrated, condensed aerosol ("white smoke"). 

4.2.1.5 CO Emissions 

Carbon monoxide emissions (CO) from diesel engines are generally low compared to 

other mobile sources due to engine operation that, on a bulk-cylinder-content basis, is 

4-12 
Technological Feasibility 

significantly fuel-lean of the stoichiometric air-to-fuel ratio. Catalytic emission controls that 

effectively oxidize PM constituents and HC emissions are also effective for oxidation of CO, 

reducing CO emissions to even lower levels. 

4.2.2 Engine-out Emission Control 

Control of diesel emissions via modification of combustion processes is often 

characterized by trade-offs in NOx emission control vs. other parameters such as PM 

emissions, fuel consumption, and lubricating oil soot loading. For example, lower oxygen 

content (lowering the air-to-fuel ratio) lowers NOx formation but increases PM formation. 

Advanced (earlier) injection timing reduces PM emissions but increases NOx formation. 

Retarded (later) injection timing reduces NOx formation but increases PM formation, 

increases fuel consumption, and at high torque output levels can increase soot accumulation 

within the lubricating oil. During engine development, these trade-offs are balanced against 

each other in order to obtain effective NOx and PM control while maintaining acceptable 

power output, fuel efficiency and engine durability. The introduction of more-advanced fuel 

injection systems and improved turbocharging can improve these tradeoffs, allowing for 

reduced emisssions of both NOx and PM. 

4.2.2.1 Ultra Low Sulfur Diesel Fuel 

We estimate that the use of ultra low sulfur diesel (ULSD) fuel (<15 ppm S) will 

reduce sulfate PM emissions from locomotive and marine engines by approximately 0.06 to 

0.08 g/bhp-hr, as compared to PM emissions when ~3000 ppm S fuel is used. The use of 

ULSD fuel also reduces depletion of TBN in the oil and substantially reduces condensation of 

acidic aerosols within cooled exhaust gas recirculation systems (see section 4.2.2.5). In 

addition to the direct sulfate PM emissions reductions realized through the use of ULSD, this 

fuel is also necessary to enable the use of advanced catalytic exhaust aftertreatment 

technologies, as discussed later in this chapter. While we describe the emission reductions 

due to the use of lower sulfur diesel fuel here, we should be clear that these reductions are part 

of our baseline emissions inventory because ULSD is already in place and this rule does not 

change the fuel sulfur standard. 

4.2.2.2 Turbocharger Improvements 

The majority of Category 1 and 2 marine diesel engines and Tier 0 and later 

locomotive diesel engines are equipped with turbocharging and aftercooling. Tier 0 and later 

two-stroke locomotive engines (and some Tier 1 and later marine engines) are equipped with 

a hybrid mechanical centrifugal supercharger/exhaust turbocharger system. This system is 

gear driven up to approximately the notch 6 operating mode and is exhaust driven at higher 

operating modes or higher numbered notches (e.g., notches 7 and 8). This arrangement helps 

to provide sufficient scavenging boost at lower notch settings where there is insufficient 

exhaust energy for the exhaust turbine to drive the compressor. Significant improvements 

have been made in recent years in matching turbocharger turbine and compressor 

performance to the highway, nonroad, marine, and locomotive diesel engines. Improvements 

to turbochargers and the match of the turbocharger’s design to the engine reduce the incidence 

of insufficient oxygen during transients and help maintain sufficient air flow to the engine 

4-13 
Regulatory Impact Analysis 

during high load operation. The corresponding improvements in oxygen availability 

throughout the operational range of the engine reduce the formation of elemental carbon PM. 

We expect that new Tier 0 and Tier 1 (remanufactured) locomotive engines will include 

improvements to turbocharger design that are similar to those of current Tier 2 locomotive 

designs. We also expect that engine manufacturers will continue with incremental 

improvements in turbochargers and the match of the turbocharger’s design to Tier 3 

locomotive and marine engines. 

4.2.2.3 Charge Air Cooling 

Improvements in engine-out NOx emissions to meet the locomotive and marine 

remanufactured engine and Tier 3 standards will be accomplished in part via lowering charge 

air cooling temperature. This was one of the primary methods used by locomotive engine 

manufacturers to reduce NOx emissions to meet the Tier 1 and Tier 2 locomotive standards 

and the Tier 3 nonroad diesel standards. Lowering the intake manifold temperature lowers the 

peak temperature of combustion and thus NOx emissions. The NOx reduction realized from 

lowering the intake manifold temperature can vary depending upon the engine design but one 

estimate suggests NOx emissions can be reduced by five to seven percent with every 10 °C 

decrease in intake manifold temperature.14 Typically the intake manifold temperature is 

lowered by cooling the intake gases through a heat exchanger, also known as a charge air 

cooler or aftercooler, located between the turbocharger compressor outlet and the intake 

manifold. Locomotive applications typically use air-to-air aftercoolers. Locomotive 

aftercoolers use electrically powered auxiliary fans since oftentimes conditions at high torque 

output require significant intake air heat rejection, especially at speeds too low for effective 

passive air-flow. Operation of the locomotive in multi-engine train configurations or 

“consists” can also impede air-flow to heat exchangers. Increased cooling capacity in 

locomotive applications can be accomplished via increased air-flow through the air-to-air 

after cooler, often through use of either variable speed or multiple-staged electric fans. 

Marine applications with access to sea-water heat-exchanger coolant loops typically have 

excess heat rejection capacity with respect to charge air cooling. This cooling capacity can be 

limited within certain existing hull designs, but new hull designs can typically overcome these 

existing hull limitations. 

4.2.2.4 Injection Timing 

Electronic control of injection timing has been used by locomotive and marine engine 

manufacturers to balance NOx emissions, PM emissions, fuel efficiency, engine performance 

and engine durability for engines certified to the Tier 2 locomotive and marine engine 

standards, Tier 3 nonroad standards, and the 1994 and later heavy-duty highway standards. 

We expect similar systems to be used to comply with the remanufactured engine standards 

and will continue to be used to comply with the Tier 3 locomotive and marine standards. 

Delaying the start of fuel injection and thus the start of combustion can significantly 

reduce NOx emissions from a diesel engine. The effect of injection timing on emissions and 

performance is well established.15,16,17,18 Delaying the start of combustion by retarding

injection timing aligns the heat release from the fuel combustion with the portion of the power 

(or combustion) stroke of the engine cycle after the piston has begun to move down. This 

4-14 
Technological Feasibility 

means that the cylinder volume is increasing and that work (and therefore heat) is being 

extracted from the hot gases. The removal of this heat through expansion lowers the 

temperature in the combustion gases. NOx is reduced because the premixed burning phase is 

shortened and because cylinder temperature and pressure are lowered. Timing retard typically 

increases HC, CO, PM, and fuel consumption because the end of injection comes later in the 

combustion stroke where the time for extracting energy from fuel combustion is shortened 

and the cylinder temperature and pressure are too low for more complete oxidation of PM. 

This can be offset by increasing injection pressure, allowing an earlier end of injection at the 

same torque output (i.e., shorter injection duration for the same quantity of fuel injected), and 

by using multiple injection events following the primary diffusion-combustion event to 

enhance soot oxidation (see 4.2.2.6 High Pressure Injection, Fuel injection Rate Shaping, 

Multiple Injections and Induced Charge Motion). We expect that these strategies will 

continue to be used to meet the locomotive and marine remanufactured engine and Tier 3 

diesel engine standards. 

4.2.2.5 Exhaust Gas Recirculation 

Exhaust gas recirculation (EGR) reintroduces or retains a fraction of the exhaust gases 

in the cylinder. Most highway diesel engine manufacturers used cooled external EGR to meet 

the 2004 and later Heavy-Duty Highway emission standards of 2.5 g/bhp-hr HC + NOx and 

0.10 g/bhp-hr PM. EGR has been a key technology used to reduce engine-out NOx emissions 

to near 1.0 g/bhp-hr for CDPF-equipped 2007 heavy-duty truck and bus engines in the U.S. 

Although we do not expect that EGR will be needed to meet the Tier 3 locomotive and marine 

standards for remanufactured engines, we expect that some Category 1 marine diesel engines 

and high-speed locomotive switch engines that are based on Tier 3 and Tier 4 nonroad engine 

families that already use EGR, will also use EGR for their marine or switch locomotive 

applications of these engines to provide additional engine calibration flexibility. 

The use of EGR decreases NOx formation in three different ways: 

1. EGR can thermally reduce peak combustion temperature. Increasing the mass 

of the cylinder contents by increasing carbon dioxide (CO2) and water vapor 

concentrations reduces peak cylinder temperatures during combustion.19

2. A fraction of the air within the cylinder is replaced with inert exhaust, 

primarily CO2 and water vapor. This reduces the amount of molecular oxygen 

available for dissociation into atomic oxygen, an important step in NOx

formation via the Zeldovich mechanism.10 

3. The high temperature dissociation of CO2 and water vapor is highly 

endothermic, and thus can reduce temperatures via absorption of thermal 

energy from the combustion process.20

EGR often is routed externally from the exhaust system to the induction system. The use of 

externally plumbed EGR can increase the intake manifold temperature substantially. This 

reduces intake charge density and lowers the fresh air/fuel ratio for a given level of 

turbocharger boost pressure. The result can be a large increase in PM emissions if the boost 

4-15 
Regulatory Impact Analysis 

pressure cannot be increased to compensate for the lower intake charge density. For this 

reason, external EGR systems typically cool the exhaust gases using a heat exchanger in the 

exhaust recirculation loop. The introduction of ULSD fuel substantially reduces the risk of 

sulfuric acid condensation within an EGR cooler. EGR can also be accomplished entirely incylinder (internal EGR) through the use of camshaft phasing or other electronically controlled 

variable geometry valve-train systems, particularly when applied to varying two-stroke diesel 

engine exhaust scavenging, although it’s use is limited by the inability to effectively cool the 

residual gases in-cylinder. For both internal and external EGR systems, the EGR rate is 

electronically controlled to prevent temporary, overly fuel-rich conditions that can lead to 

high PM emissions during transient engine operation. 

 Although we don’t expect that EGR will be required to meet the remanufacturing 

standards or the Tier 3 locomotive and marine standards, we do believe that some engine 

manufacturers could select EGR as an effective NOx emission control strategy. EGR can also 

provide increased flexibility in how engines are calibrated to meet emissions standards with 

the potential for improvement in part-load fuel consumption. 

4.2.2.6 High Pressure Injection, Fuel injection Rate Shaping, Multiple Injections and 

Induced Charge Motion 

Inducing turbulent mixing is one means of increasing the likelihood of soot particles 

interacting with oxidants within the cylinder to decrease PM emissions. Turbulent mixing can 

be induced or increased by a number of means including: 

• Changes to intake port/valve design and/or piston bowl design 

• Increased (high) injection pressure 

• Multiple/split injections using high pressure common rail injection or late post 

injection using electronic unit injection 

As diesel fuel is injected into the cylinder during combustion, the high pressure fuel 

spray causes increased motion of the air and fuel within the cylinder. This increased motion 

leads to greater air and fuel interaction and reduced particulate matter emissions. Increasing 

fuel injection pressure increases the velocity of the fuel spray and therefore increases the 

mixing introduced by the fuel spray. 

The most recent advances in fuel injection technology are high-pressure common rail 

injection systems with the ability to use rate shaping or multiple injections to vary the delivery 

of fuel over the course of a single combustion event. These systems are in widespread use in 

heavy-duty on-highway diesel engines, and they are used in many current nonroad diesel 

engines. These systems provide both NOx and PM reductions. Igniting a small quantity of 

fuel early limits the rapid increase in pressure and temperature characteristic of premixed 

combustion and its associated NOx formation. Injecting most of the fuel into an established 

flame then allows for a steady burn that limits NOx emissions. Rate shaping can be done 

either mechanically or electronically, and has been shown to reduce NOx emissions by up to 

20 percent.21 Multiple injection/split injection have also been shown to significantly reduce 

4-16 
Technological Feasibility 

particulate emissions, most notably in cases that use retarded injection timing or a 

combination of injection timing retard and EGR to control NOx.

22,23,24,25 The typical 

diffusion-burn combustion event is broken up into two events. A main injection is 

terminated, and then followed by a short dwell period with no injection, which is in turn 

followed by another short post-injection event, see Figure 4-5. The second pulse of injected 

fuel induces late-combustion turbulent mixing. The splitting of the injection event into two 

events aids in breaking up and entraining the “soot cloud” formed from the first injection 

event into the bulk cylinder contents. 

Figure 5: An example of using multiple fuel injection events to induce late-combustion mixing and 

increase soot oxidation for PM control (Adapted form Pierpont, Montgomery and Reitz, 1995). 

Increasing the turbulence of the intake air entering the combustion chamber (i.e., 

inducing swirl) can also reduce PM by improving the mixing of air and fuel in the combustion 

chamber. Historically, swirl was induced by routing the intake air to achieve a circular 

motion in the cylinder. Heavy-duty on-highway and nonroad engine manufacturers are 

increasingly using variations of "reentrant" piston designs in which the top surface of the 

piston is cut out to allow fuel injection and air motion in a smaller cavity in the piston to 

induce additional turbulence (Figure 4-6). Manufacturers have also changed to three or four 

valves per cylinder for on-highway and nonroad high-speed diesel engines, and to four valves 

per cylinder for medium-speed locomotive engines, which reduces pumping losses and can 

also allow for additional intake air charge motion generation. This valve arrangement also 

offers better positioning of the fuel injector by allowing it to be placed in-line with the 

centerline axis of the piston. 

At low loads, increased swirl reduces HC, PM, and smoke emissions and lowers fuel 

consumption due to enhanced mixing of air and fuel. NOx emissions might increase slightly 

at low loads as swirl increases. At high loads, swirl causes slight decreases in PM emissions 

and fuel consumption, but NOx may increase because of the higher temperatures associated 

with enhanced mixing and reduced wall impingement.26 A higher pressure fuel system can be 

used to offset some of the negative effects of swirl, such as increased NOx, while enhancing 

positive effects like increased PM oxidation. Intake air turbulence such as “swirl” can be 

induced using shrouded intake valves or by use of a helical-shaped air intake port. Swirl is 

important in promoting turbulent mixing of fuel and soot with oxidants, but can also reduce 

volumetric efficiency. 

4-17 
Regulatory Impact Analysis 

Piston bowl design can be used to increase turbulent mixing. Reentrant bowl designs 

induce separation of the flow over the reentrant “ledge” of the piston and help to maintain 

swirl through the compression stroke and into the expansion stroke.10 

Figure 6: Schematic examples of a straight-sided piston-bowl (A), a reentrant piston bowl (B), and a deep, 

square reentrant piston bowl (C) for high-speed diesel engines. 

 A) B) C) 

To meet our locomotive and marine remanufactured engine standards, we expect that 

manufacturers will use high pressure electronically controlled unit injection and 

improvements to piston bowl design. To meet the Tier 3 locomotive and marine standards, 

we expect that manufacturers of high-speed Category 1 and 2 marine diesel engines, highspeed switch locomotive engines and some Category 2 marine and locomotive medium speed 

engines will use advanced electronic fuel systems, including in many cases high-pressure 

common rail fuel injection systems. 

4.2.2.7 Reduced Oil Consumption 

Reducing oil consumption not only decreases maintenance costs, but also VOF and 

PM emissions. Reducing oil consumption has been one of the primary ways that heavy-duty 

truck diesel engines have complied with the 1994 U.S. PM standard. Reducing oil 

consumption also reduces poisoning of exhaust catalysts from exposure to zinc and 

phosphorous oil additives. 

Redesign of the power assembly (pistons, piston rings and cylinder liner) played an 

important role in reducing organic carbon PM emissions from locomotive engines in order to 

meet the Tier 2 locomotive standards. Piston rings can be designed to improve the removal of 

oil from the cylinder liner surface and drainage back into the crankcase, reducing the amount 

of oil consumed. Valve stem seals can be used to reduce oil leakage from the lubricated 

regions of the engines valve train into the intake and exhaust ports of the engine. 

Improvements to the closed-crankcase ventilation systems that incorporate drain-back to the 

crankcase of oil separated from the crankcase flow and the use of high-efficiency filtration, 

either with replaceable high-efficiency coalescing filters or multiple-disc inertial separation, 

will reduce oil consumption and can remove oil-aerosol from the crankcase flow sufficiently 

to allow introduction of the crankcase gases into the turbocharger compressor inlet with little 

or no fouling of the turbocharger compressor, aftercooler or the remainder of the induction 

system. Euro IV and U.S. 2004 and 2007 heavy-duty truck engine designs that incorporate 

these technologies have significantly reduced engine-out organic carbon PM emissions. 

4-18 
Technological Feasibility 

 Particularly in the case of medium-speed engines, which have a relatively high 

fraction of PM emissions due to organic carbon PM, reduced oil consumption will be an 

effective means of meeting the locomotive and marine remanufactured engine and Tier 3 PM 

standards. We expect Tier 0 and Tier 1 remanufactured locomotive engines to receive power 

assembly designs similar to those of current Tier 2 locomotives. We expect that 

remanufactured Tier 2 locomotive engines and new Tier 3 locomotive and marine engines 

will receive incremental improvements in the design of the power assembly, valve stem seals 

and improved crankcase ventilation systems—especially if the crankcase ventilation system 

routes the crankcase vent to the turbocharger inlet and incorporates high-efficiency oil 

separation from the crankcase flow. When applying catalytic exhaust controls to meet the 

Tier 4 standards, reduced oil consumption will improve the durability of catalyst systems by 

reducing their exposure to zinc- and phosphorous-containing oil additives. 

4.2.2.8 Application Specific Differences in Emissions and Emission Control 

In much of the preceding discussion we have relied on previous experience primarily 

from high-speed (approximately >1600 rpm rated speed) on-highway and nonroad engines to 

provide specific examples of emissions formation and engine-out emission control. There 

are, however, some important operational and design differences between these engines and 

locomotive and marine diesel engines, particularly the medium speed locomotive and marine 

engines. 

High-speed diesel engines used in on-highway and nonroad applications (with the 

exception of generator applications) undergo significant transient operation that can create 

temporary conditions of insufficient availability of oxidants due to the inability of the airsupply from the turbocharger to follow engine transients. For these applications, the majority 

of elemental carbon PM is emitted during these transients of insufficient oxygen availability. 

Such transients are greatly reduced in locomotive and marine applications. Marine propulsion 

engines operate primarily along a propeller curve that effectively forms a narrower outer 

boundary within which engine operation occurs. Marine generators and locomotive engines 

operate within even narrower bounds. Generators generally operate at close to a fixed engine 

speed with varying load. Locomotives operate at 8 distinct speed-load operational notches 

with gradual transitions between each notch. Figure 7 illustrates the speed and power ranges 

over which typical locomotives and marine engines operate. 

4-19 
Regulatory Impact Analysis 

Figure 7: A comparison engine power output versus engine speed for a locomotive engine operated over 

notches one through eight and for a Category 2 marine engine operated over the E3 marine cycle, which 

approximates a propeller curve with a cubic relationship between speed and load. A cubic fit through the 

locomotive notch points is remarkably similar to the E3 prop-curve. The specific example shown is for 

two similar versions of the EMD two-stroke medium-speed diesel engine. 

In addition to operational differences, medium-speed diesel engines (750 to 1200 rpm 

rated speed) are the predominant type used in Category 2 marine and line-haul locomotive 

applications. Medium-speed diesel engines are also predominant in older switch locomotives, 

although the majority of locomotive switch families certified to the Tier 2 locomotive 

standards now use high-speed diesel engines. Medium speed diesel engines typically have 

even lower elemental carbon PM emissions due to increased residence time available at high 

load conditions for late-cycle burn-up of elemental carbon PM as compared to high-speed 

diesel applications such as heavy-duty on-highway engines. The increased duration of 

combustion also increases NOx formation for medium-speed diesel engines. 

Large-bore locomotive and Category 2 medium speed diesel engines also have 

significantly higher lubricating oil consumption than many high-speed diesel engines. 

Lubricating oil consumption for current 2007 on-highway diesel truck engines is 

approximately 0.09 to 0.13% of fuel consumed versus approximately 0.30 to 0.35% for 2-

stroke medium-speed diesel locomotive and marine engines and approximately 0.25% for 4-

stroke medium-speed locomotive engines. To some degree, this higher consumption of 

lubricating oil is by design. Higher lubricating oil consumption allows for a reduced 

frequency of complete oil changes, while at the same time the resulting frequent topping off 

4-20 
Technological Feasibility 

of oil replenishes lubricant additives that maintain the lubricating oil’s total base number 

(TBN) to prevent acidic corrosion. Frequent topping off also maintains the oil’s oxidation 

stability to maintain oil viscosity. Because improvements in high-pressure fuel injection 

systems and electronic engine management were used to reduce carbon PM emissions to meet 

Tier 2 locomotive and marine engine PM standards, only moderate improvements in 

lubricating oil consumption were necessary to meet the Tier 2 PM emission standards. 

Reduced elemental carbon PM, coupled with still moderately high lubricating oil 

consumption, results in a PM composition of medium-speed diesel engines that different from 

that of on-highway diesel engines and many nonroad diesel engines. PM emissions from 

medium-speed diesel engines have a higher fraction of organic carbon PM emissions than 

what has been previously measured from on-highway and most non-road diesel engines. 

Figure 4-8 shows the relative contributions of elemental carbon, organic carbon, and sulfate 

PM emission from recent testing by AAR of Tier 0, Tier 1 and Tier 2 locomotives using 

solvent extraction with gravimetric analysis for determining soluble organic carbon PM and 

water extraction and ion chromatography for determining sulfate PM.27 The AAR data shows 

soluble organic carbon PM dominating the PM composition. EPA recently conducted testing 

at Southwest Research Institute using a newly developed semi-continuous method for direct 

mass measurement of organic carbon and elemental carbon (OC-EC). The new OC-EC data 

shows somewhat different relative contributions of organic carbon and elemental carbon PM 

than the results determined via filter extraction but confirms that Tier 2 locomotives have a 

higher fraction of organic carbon PM emissions than other diesel engines, particularly at highload conditions.28

Crankcase ventilation flow is considerably higher from very large displacement 

medium-speed diesel engine compared with smaller, high-speed engines. This has 

complicated the design of crankcase ventilation systems with effective oil-aerosol separation. 

Higher capacity, high efficiency inertial disc-type separators are now being introduced in 

medium-speed marine applications to reduce bilge water contamination and oil consumption. 

Inertial disc-type oil separators originally developed for Euro IV and 2007 U.S. Heavy-duty 

On-highway applications have provided sufficient oil separation to allow introduction of 

filtered crankcase gases into the turbocharger inlet without oil fouling of the turbocharger or 

aftercooler system. Similar systems are now optionally available on Wärtsilä medium-speed 

stationary generator and marine engines (Figure 9). We expect that similar systems will be 

used on Tier 3 and Tier 4 Category 2 marine engines and remanufactured Tier 2 and new Tier 

3 and Tier 4 locomotive systems. Recent data shows the potential to reduce PM emissions 

from Tier 2 locomotives by 10 to 15% via improvements to the crankcase ventilation 

system.28

Improvements in oil formulation, including switching from Group 1 to Group 2 base 

oils with greatly improved oxidation stability also reduce the need for oil top-off to replenish 

lubricant additives. As Group 1 become unavailable in Europe, we expect increased use of 

Group 2 base oil formulations for use with EMD medium-speed engines in Europe. Future 

reductions in fuel sulfur for Tier 3 and Tier 4 locomotive and marine engines will also reduce 

the need for TBN control. 

4-21 
Regulatory Impact Analysis 

Figure 8: Emissions for 7 locomotives tested using 2800 ppm sulfur nonroad diesel fuel. 

2006 AAR Data

0.000

0.050

0.100

0.150

0.200

0.250

0.300

0.350

0.400

0.450

0.500

0.550

0.600

16-645F3 7FDL16 645 (Roots-blown) 16-710G3B 7FDL16 16-710G3C GEVO12LDB5

EMD SD50 Tier 0 GE DASH9-44CW

Tier 0

EMD GP38-2 Tier

0

EMD SD70M Tier

1

GE AC4400 Tier 1 EMD SD70Ace

Tier 2

GE ES44DC Tier 2

KCS7017 CN2508 CP4404 NS2630 CSXT595 UP8353 BNSF7736

Line-Haul Brake-specific PM Emissions (g/bhp-hr)

Sulfate PM (mostly sulfuric acid+associated water)

Organic PM (mostly lube oil)

Elemental Carbon PM (soot)

0.20 g/bhp-hr (Tier 2 PM Standard)

0.10 g/bhp-hr (Tier 3 PM Standard)

0.03 g/bhp-hr (Tier 4 PM Standard)

0.45 g/bhp-hr (Tier 1 PM Standard)

0.60 g/bhp-hr (Tier 0 PM Standard)

4-22 
Technological Feasibility 

 Figure 9: Alfa Laval disc-type inertial oil-aerosol separation systems for use with closed crankcase 

ventilation systems. The unit on the left is Alfdex system originally developed for Euro IV and U.S. 2007 

heavy-duty on-highway applications. This system was designed as “fit for life”, or essentially maintenance 

free for the useful life of the engine. A much higher volume system (right) was recently developed for 

Wärtsilä medium-speed engines. 

4.3 Feasibility of Tier 4 Locomotive and Marine Standards 

In this section we describe the emission control technologies that we believe will be 

used to meet the Tier 4 locomotive and marine diesel engine standards. In general, these 

technologies involve the use of catalytic exhaust aftertreatment devices placed in an engine’s 

exhaust system, downstream of an engine’s exhaust manifold or turbocharger turbine outlet. 

The catalytic coatings of these aftertreatment devices are oftentimes sensitive to other 

constituents in diesel exhaust. For example, sulfur compounds within diesel fuel can decrease 

the effectiveness or useful life of a catalyst. For this reason, we will require the use of ULSD 

fuel in engines that will be designed to meet the Tier 4 emissions standards. We also expect 

that engine manufacturers will specify new lubricating oil formulations for these Tier 4 

engines because of other trace compounds in some currently used lubricating oils,. These 

new oil formulations will help ensure that catalytic exhaust aftertreatment devices will operate 

properly throughout their useful life. Because we have already finalized and begun 

implementation of similar aftertreatment-forcing standards for both heavy-duty on-highway 

and nonroad diesel engines, we are confident that the application of similar, but appropriately 

designed, aftertreatment systems for locomotive and marine applications is technologically 

feasible, especially given the implementation timeframe of this rulemaking. 

4-23 
Regulatory Impact Analysis 

4.3.1 Selective Catalytic Reduction (SCR) NOx Control Technology 

Recent studies have shown that an SCR system is capable of providing well in excess 

of 80% NOx reduction efficiency in high-power, heavy-duty diesel applications.29, 30, 31 As 

shown in Figure 10, Vanadium and base-metal (Cu or Fe) SCR catalysts can achieve 

significant NOx reduction throughout much of exhaust gas temperature operating range 

observed in heavy-duty diesel engines used in locomotive and marine applications. 

Collaborative research and development activities between diesel engine manufacturers, truck 

manufacturers, and SCR catalyst suppliers have also shown that SCR is a mature, costeffective solution for NOx reduction on heavy-duty diesel engines. While many of the 

published studies have focused on heavy-duty highway truck applications, similar trends, 

operational characteristics, and NOx reduction efficiencies have been reported for heavy-duty 

marine and stationary electrical power generation applications as well.32 An example of the 

performance capability of SCR in marine applications is the Staten Island Ferry Alice Austen. 

This demonstration project reports that 90-95% NOx reduction is possible under steady-state 

conditions (where the exhaust gas temperature is above 270 °C.)33 Given the preponderance 

of studies and data - and our analysis summarized here - we believe that this technology is 

appropriate for both locomotive and marine diesel applications. 

4-24 
Technological Feasibility 

Figure 10: SCR Catalyst NOx Reduction versus Exhaust Gas Temperature Using an Ammonia-to-NOx

Ratio of 1:134,35,B

0

10

20

30

40

50

60

70

80

90

100

100 150 200 250 300 350 400 450 500

Exhaust Gas T @ Catalyst Inlet (°C)

NOx Conversion Efficiency (%)

Low-T Base Metal

Vanadium

High-T Base Metal

An SCR catalyst reduces nitrogen oxides to N2 and water by using ammonia (NH3) as 

the reducing agent. The most-common method for supplying ammonia to the SCR catalyst is 

to inject an aqueous urea-water solution into the exhaust stream. In the presence of hightemperature exhaust gasses (>250 °C), the urea hydrolyzes to form NH3 and CO2 - the NH3 is 

stored on the surface of the SCR catalyst where it is used to complete the NOx-reduction 

reaction. In theory, it is possible to achieve 100% NOx conversion if the NH3-to-NOx ratio (α) 

is 1:1 and the space velocity within the catalyst is not excessive (i.e. there is ample time for 

the reactions to occur). However, given the space limitations in packaging exhaust 

aftertreatment devices in mobile applications, an α of 0.85-1.0 is often used to balance the 

need for high NOx conversion rates against the potential for NH3 slip (where NH3 passes 

through the catalyst unreacted). 

Another approach to prevent NH3 slip is to use an oxidation catalyst downstream of 

the SCR. This catalyst, also referred to as a slip catalyst, is able to oxidize the NH3 which 

passes through (or is released from) the SCR. When this approach is used, it is possible to 

operate the SCR system at near-peak efficiency by optimizing the urea dosing rate to achieve 

the highest-possible level NOx control (i.e. providing adequate NH3 for optimum NOx

 

B

 The “High-T Base Metal” curve is based on a composite of low and high-space-velocity data provided by 

catalyst manufacturers. It is meant to represent high-hour performance of a system at a space velocity of 40,000 

hr-1. 

4-25 
Regulatory Impact Analysis 

reduction). A properly-designed slip catalyst, with good selectivity to nitrogen (N2), can 

convert most of the excess NH3 released from the SCR catalayst into N2 and water. Recent 

studies have shown that an aged SCR system, equipped with a slip catalyst, can achieve 

tailpipe NH3 levels of less of than 10 ppm when tested on the European Stationary Cycle 

(ESC) and European Transient Cycle (ETC).36,37 In one study, the system was aged on a 

engine dynamometer test cycle which included 400 hours of high-temperature engine 

operation at 650 °C (to simulate active DPF regeneration events). Comments received from 

MECA stated that 90% NOx conversion can be maintained between 250 °C and 550 °C 

following 2000 hours of hydrothermal aging.38 Our analysis of the locomotive engine 

operating conditions presumes a primarily “passive” DPF regeneration approach and 

maximum, post-turbine exhaust temperatures of 560 °C (during operation in non-ventilated 

tunnels).39 Under these conditions, we expect slip catalysts to be durable and effective in 

reducing NH3 slip. 

The urea dosing strategy and the desired α are dependent on the conditions present in 

the exhaust gas; namely temperature and the quantity of NOx present (which can be 

determined by engine mapping, temperature sensors, and NOx sensors). Overall NOx

conversion efficiency, especially under low-temperature exhaust gas conditions, can be 

improved by controlling the ratio of two NOx species within the exhaust gas; NO2 and NO. 

This can be accomplished through use of an oxidation catalyst upstream of the SCR catalyst 

to promote the conversion of NO to NO2. The physical size and catalyst formulation of the 

oxidation catalyst are the principal factors which control the NO2:NO ratio, and by extension, 

improve the low-temperature performance of the SCR catalyst. 

Published studies show that SCR systems will experience very little deterioration in 

NOx conversion throughout the life-cycle of a diesel engine.36,37,40 The principal mechanism 

of deterioration in an SCR catalyst is thermal sintering - the loss of catalyst surface area due 

to the melting and growth of active catalyst sites under high-temperature conditions (as the 

active sites melt and combine, the total number of active sites at which catalysis can occur is 

reduced). This effect can be minimized by design of the SCR catalyst washcoat and substrate 

for the exhaust gas temperature window in which it will operate. Another mechanism for 

catalyst deterioration is catalyst poisoning - the plugging and/or chemical de-activation of 

active catalytic sites. Phosphorus from the engine oil and sulfur from diesel fuel are the 

primary components in the exhaust stream which can de-activate a catalytic site. The risk of 

catalyst deterioration due to sulfur poisoning will be all but eliminated with the 2012 

implementation of ULSD fuel (<15 ppm S) for locomotive/marine applications. Catalyst 

deterioration due to phosphorous poisoning can be reduced through the use of lubricating oil 

with low sulfated-ash, phosphorus, and sulfur content (commonly referred to as “low-SAPS” 

oil) and through reduced oil consumption (as discussed in 4.2.2.7). Previous oil formulations 

for heavy-duty, on-highway engines, such as API CI-4, did not specify a limit for sulfur 

content, and allowed higher levels of phosphorous (0.14% vs 0.12%) and ash (1.2~1.5% vs. 

1.0%) content.41 We expect the use of low-SAPS oil improve the performance of durability 

of catalyzed-DPF and SCR aftertreatment components in locomotive and marine applications. 

The high ash content in current locomotive and marine engine oils is related to the need for a 

high total base number (TBN) in the oil formulation. This high-TBN oil has been necessary 

because of the high sulfur levels typically present in diesel fuel - a high TBN is necessary to 

neutralize the acids created when fuel-borne sulfur migrates to the crankcase. With the use of 

4-26 
Technological Feasibility 

ULSD fuel, acid formation in the crankcase will not be a significant concern. This oil will be 

available for use in heavy-duty highway engines by October 2006 and is specified by the 

American Petroleum Institute as "CJ-4."42 The durability of other exhaust aftertreatment 

devices, namely the DOC and DPF, will also benefit from the use of ULSD fuel and lowSAPS engine oil - less sulfur and phosphorous will improve DOC effectiveness and less ash 

will increase the DPF ash-cleaning intervals. 

The migration of low-SAPS engine oil properties to future locomotive and marine oil 

formulations - while beneficial and directionally helpful in regards to the durability, 

performance, and maintenance of the exhaust aftertreatment components we reference – is not 

a required element of our feasibility analysis. European truck and marine applications have 

shown that SCR is a durable technology with low-SAPs oil. Several comments to our NPRM 

suggested that these newer, low-SAPS oil formulations, developed for use in on-highway and 

nonroad diesel engines, may not be appropriate for locomotive or marine applications. While 

we acknowledge that the exact oil formulation for locomotive and marine applications using 

ULSD fuel is not known today, we do believe that there is adequate time to develop an 

appropriate oil formulation. For example, in the State of California, all intra-state 

locomotives, marine vessels (in the SCAQMD), and nonroad engines have been operating 

with ULSD fuel since June, 2006 – so there should already be field data/experience available 

today to begin developing an oil formulation for ULSD in advance of the implementation date 

for aftertreatment-forcing standards. In addition, the nonroad sector will have transitioned to 

ULSD fuel nationwide by June, 2010, followed by the locomotive and marine sectors by June, 

2012. The staggered introduction of ULSD fuel across these sectors (on-highway, nonroad, 

and finally, locomotive/marine), leaves ample time to develop oil formulations which do not 

contain any more sulphated-ash than necessary to neutralize crankcase acids. By adjusting 

amount of sulfated ash for each sector and the expected fuel sulfur level, it may be possible to 

reduce the ash quantity, resulting in extended ash cleaning intervals. 

The onboard storage of the aqueous urea solution on locomotives and marine vessels 

can be accomplished through segmenting of the existing fuel tanks or fitment of a separate 

stainless steel or plastic urea tank. To assure consistent SCR operation between refueling 

stops, the volume of urea-water solution carried onboard will need to be at least 5% of the 

diesel fuel tank capacity. At the appropriate intervals, the crews will need to refill the urea 

tank. For the railroad and marine industries, the distribution and dispensing of urea is 

expected to benefit from any solutions put in place by the trucking industry and heavy-duty 

highway engine and vehicle manufacturers well in advance of the Tier 4 locomotive and 

marine regulations. 

We project that locomotive and marine diesel engine manufacturers will benefit from 

any development taking place to implement DPF and SCR technologies in advance of the 

heavy-duty truck NOx standards in Europe and the U.S. The Manufacturers of Emission 

Controls Association (MECA) supports the feasibility and timing of the Tier 4 locomotive and 

marine standards we are finalizing, and has stongly stated development work for SCR designs 

can begin today, with full implementation of this technology by 2015.43 In addition, today’s 

urea dosing systems for SCR - already in widespread use across many different diesel 

applications - are expected to become more-refined/robust/reliable in advance of the Tier 4 

locomotive and marine standards. Given the steady-state operating characteristics of 

4-27 
Regulatory Impact Analysis 

locomotive and marine engines, DPF regeneration strategies and urea dosing controls will 

certainly be capable of controlling PM and NOx at the levels necessary to meet the standards 

finalized in this rulemaking. 

4.3.1.1 Urea Infrastructure and Feasibility & Cost 

The preferred concentration for the aqueous urea solution is 32.5% urea, which is the 

eutectic concentration (provides the lowest freezing point and the urea concentration does not 

change if the solution is partially frozen).44 With a freezing temperature of -11 °C (12 °F), 

heaters and/or insulation may be necessary in Northern regions for urea storage/dispensing 

equipment and the urea dosing apparatus (tank, pump, and lines) on the on the engine. The 

centralized nature of locomotive and marine refueling from either large centralized fuel 

storage tanks or from tanker trucks with long-term purchase agreements provides a working 

example of how urea could also be distributed from storage tanks at centralized fueling 

facilities, tanker trucks and/or multi-compartment fuel-oil/urea tanker trucks at remote fueling 

sites. Given that only a small percentage of the locomotive and marine fleet will require urea 

prior to 2017, EPA believes that the infrastructure for supplying urea from centralized 

refueling points and tank trucks can be established to serve the rail and marine industries. 

Discussions concerning the urea infrastructure and specifications for an emissions-grade urea 

solution are beginning to take place amongst stakeholders in the light-duty and heavy-duty 

highway diesel industry. It is possible that these discussions will result in a fully-developed 

urea infrastructure for light-duty and heavy-duty diesel highway engine and vehicle 

applications by 2010. This will allow time to expand and develop this framework and support 

the needs of the railroad and marine industries. Even without these developments underway 

in the light-duty and heavy-duty highway industry, the centralized fueling nature of the 

locomotive and marine industries lends itself well to adaptation to support a supply of urea at 

their normal fueling locations. 

In 2015, urea cost is expected to be ~$0.75/gallon for retail facilities dispensing 

200,000 - 1,000,000 gallons/month, and ~$1.00/gallon for those dispensing 80,000 - 200,000 

gallons/month.45 The additional operating cost incurred by the rail industry will also be 

dependent on the volume of urea dispensed at each facility, with smaller refueling sites 

experiencing higher costs. It is estimated that 87% of the locomotive fleet is refueled at fixed 

facilities and 13% at direct truck-to-locomotive facilities.46 The type of urea 

storage/dispensing equipment, and the ultimate cost-per-gallon, for railroad and marine 

industries will depend on the volume of fuel & urea dispensed at each site. High-volume 

fixed sites may choose to mix emissions-grade dry urea (or urea liquor) and de-mineralized 

water on-site, whereas others may choose bulk or container delivery of a pre-mixed 32.5% 

urea-water solution. Again, with the possible implementation of SCR for light-duty and 

heavy-duty highway applications in 2010, the economic factors for each urea supply option 

may be well-known prior to implementation of the 2017 standards. Even without these 

developments underway in the light-duty and heavy-duty highway industry, we believe that 

the urea supply options for the locomotive and marine industries will be numerous. 

 Urea production capacity in the U.S. is more than sufficient to meet the additional 

needs of the rail and marine industries. For example, in 2003, the total diesel fuel 

consumption for Class I railroads was approximately 3.8 billion gallons.47 If 100% of the 

4-28 
Technological Feasibility 

Class I locomotive fleet were to be equipped with SCR catalysts, approximately 190 million 

gallons-per-year of 32.5% urea-water solution would be required.45 It is estimated that 190 

million gallons of urea solution requires 0.28 million tons of dry urea (1 ton dry urea is 

needed to produce 667 gallons of 32.5% urea-water solution).45 Currently, the U.S. consumes 

14.7 million tons of ammonia resources per year, and relies on imports for 41% of that total 

(of which, urea is the principal derivative). In 2005 domestic ammonia producers operated 

their plants at 66% of rated capacity, resulting in 4.5 million tons of reserve production 

capacity.48 In the hypothetical situation above, where 100% of the locomotive fleet required 

urea, only 6.2% of the reserve domestic capacity would be needed to satisfy the additional 

demand. A similar analysis applied to the marine industry, with a yearly diesel fuel 

consumption of 2.2 billion gallons per year, would not significantly impact the urea demandto-reserve capacity equation. Since the rate at which urea-SCR technology is introduced to 

the railroad and marine markets will be gradual, the reserve urea production will be adequate 

to meet the expected demand in the 2015 timeframe for implementation of the Tier 4 

standards. 

4.3.1.2 Establishing the Tier 4 NOx Standard 

The basis for the locomotive Tier 4 Line-Haul NOx standard is the Tier 3 NOx

emission standard (5.5 g/bhp-hr) reduced by the following SCR catalyst efficiency estimates 

at full useful life of the engine; 60% efficiency in operating mode notch 2 (where exhaust gas 

temperature is near the minimum-level for NOx conversion), 85% conversion efficiency in 

operating modes notches 3 and 4 (where lower catalyst space velocities allow optimum 

reaction rates), and 83% conversion efficiency in the high-load operating modes, notches 5 

through 8.C When these efficiencies are weighted according to the line-haul duty cycle 

emissions test, an overall NOx reduction of 78% is obtained. 

Figure 11 illustrates EPA's projection of an "aged" locomotive/marine SCR system at 

full useful life. When these levels of NOx reduction are applied to engine out emissions from a 

typical Tier 2, 4-stroke-cycle locomotive diesel engine producing 5.5 g/bhp-hr of NOx on the 

line-haul duty cycle, the worst-case, full useful life standard is established at 1.3 g/bhp-hr.D 

This standard includes a compliance margin and we expect that emissions of a new engine – 

and the emissions throughout much of the engine’s life – will be closer to 0.8 g/bhp-hr. 

Because marine diesel engines will also operate under similar engine load/exhaust gas 

temperature conditions over their respective cycles, they also will be capable of similar NOx

reductions. As shown in the shaded area of Figure 4-11, the E3 Marine Test Cycle lies within 

the peak performance range of an SCR catalyst. 

 

C

 For conditions present in Tier 0-2 locomotives, extended SCR operation (and hence, NOx reduction) is not 

possible at the low power notches (NI, LI, DB, and N1) due to low exhaust gas temperatures. 

D With an overall, duty-cycle-weighted, NOx conversion efficiency of 78%, the remainder NOx emissions will be 

22% of the engine-out level (i.e. the Tier 2 Standard is 5.5 g/bhp-hr; 5.5 x 0.22 = 1.2 g/bhp-hr). 

4-29 
Regulatory Impact Analysis 

Figure 11: Typical 4-Stroke Diesel Locomotive Exhaust Gas Temperatures and Projected SCR Catalyst 

Efficiency at Full Useful Life. 

 For applications requiring improved SCR performance at lower exhaust gas 

temperatures, several options are available; throttling the engine airflow to increase exhaust 

gas temperature, using an SCR formulation designed for the low-temperature NOx conversion, 

or a heated urea dosing system (or some combination of all three options). Throttling of the 

intake airflow on refuse trucks – which often operate under light-load conditions - has been 

shown to substantially increase exhaust gas temperatures.49 Increasing the exhaust gas 

temperature at light load not only provides an opportunity for extended SCR operation, it is 

also improves performance of the DOC and DPF components. Low-temperature NOx

conversion can also be enhanced by use of a base-metal (Fe or Cu) zeolite SCR catalyst (see 

Figure 4-12). Systems for dosing urea at exhaust temperatures below 250 °C are being 

developed for heavy-duty, highway truck applications. One such system utilizes an 

electrically-heated bypass to hydrolyze the urea-water solution and produce NH3 when 

exhaust gas temperatures are as low as 160 °C – providing an additional 5-25% NOx

reduction relative to a system which stops urea dosing at 250 °C.50 Use of a pre-turbocharger 

location for a DOC located upstream of the SCR system can also improve low temperature 

performance by driving NO to NO2 conversion at lighter engine loads than would be possible 

with more remote mounting of the DOC. Use of air-gap or other types of insulated 

construction for exhaust system components can also improve thermal management and 

increase exhaust gas and catalyst temperatures. For further discussion of manifold-mounting 

of the DOC and exhaust system thermal management, see section 4.3.2 PM and HC Exhaust 

Aftertreatment Technology. 

4-30 
Technological Feasibility 

If no improvements were made to technologies which exist today, the 1.3 g/bhp-hr 

locomotive standard is technologically feasible. With projected improvements (that are 

currently more-difficult to quantify), we are confident in-use operation and end of useful life 

NOx emission levels will be less than the 1.3 g/bhp-hr standard finalized in this rulemaking. 

4.3.2 PM and HC Exhaust Aftertreatment Technology 

The most effective exhaust aftertreatment used for diesel PM emission control is the 

diesel particulate filter (DPF). More than a million light diesel vehicles that are OEMequipped with DPF systems have been sold in Europe, and over 200,000 DPF retrofits to 

diesel engines have been conducted worldwide.8

 Broad application of catalyzed diesel 

particulate filter (CDPF) systems with greater than 90% PM control is beginning with the 

introduction of 2007 model year heavy-duty diesel trucks in the United States. These systems 

use a combination of both passive and active soot regeneration. CDPF systems utilizing metal 

substrates are a further development that trades off a degree of elemental carbon soot control 

for reduced backpressure, greater design and packaging flexibility, improvements in the 

ability of the trap to clear oil ash, and better scaling to the large sizes needed for locomotive 

and marine applications. Metal-CDPFs were initially introduced as passive-regeneration 

retrofit technologies for diesel engines designed to achieve approximately 50 to 60% control 

of PM emissions.51 Recent data has shown that metal-CDPF trapping efficiency for 

elemental carbon PM can exceed 70% for engines with inherently low elemental carbon 

emissions.52 Data from locomotive testing (Figure 12) confirms a relatively low elemental 

carbon fraction and relatively high organic fraction for PM emissions from medium-speed 

Tier 2 locomotive engines.27 The use of a highly oxidizing PGM catalyst coated directly to 

the CPDF combined with a highly oxidizing DOC mounted upstream of the CDPF can 

provide 95% or greater removal of HC, including the semi-volatile organic compounds that 

contribute to PM. 

n 

nted 

ion 

f 

that is similar to what is already in common use with dry exhaust manifold configurations in 

A functional schematic of a metal-CDPF is shown in Figure 13. In this particular 

example, flow restrictions divert a portion of the particle laden exhaust flow through the 

porous sintered metal walls. The openings in the flow restrictions are sufficient to allow 

accumulated ash to migrate through the CDPF substrate, either reducing or eliminating the 

need for periodic ash cleaning.53 The metal-CDPF will most likely be used in combinatio

with an upstream diesel oxidation catalyst (DOC). A diesel oxidation catalyst mou

upstream of the metal-CDPF improves NO to NO2 oxidation for both passive soot 

regeneration within the CDPF and to increase the NOx reduction efficiency of the SCR 

system, particularly during light-load and/or under cold ambient conditions. The DOC can 

also assist with oxidation of organic carbon PM, particularly at lower notch positions. The

DOC effectively becomes mass transport limited for NO2 oxidation at notch 6 and above 

(approximately 80,000-hr space velocity), but at that point exhaust temperatures at the locat

of the metal-CDPF would be sufficient for NO to NO2 oxidation and thus for passive soot 

regeneration and also for oxidation of organic carbon. Some or all of the DOC volume can be 

installed in a close-coupled position within the exhaust manifold, immediately downstream o

the exhaust ports and upstream of the turbocharger's exhaust turbine (Figure 14) and within 

the “vee” of V-type locomotive and marine engines. Air-gapped construction can be used to 

provide faster warm-up and retention of heat within exhaust components. Thermal insulation

4-31 
Regulatory Impact Analysis 

Category 2 marine applications can be used to increase exhaust and catalyst temperatures 

(Figure 15). 

Figure 16 and Figure 17 shows the expected line-haul locomotive PM reductions for 

the following scenarios: 

• A 4-stroke line-haul Tier 2 locomotive due to reducing fuel sulfur content to 15 

ppm 

• A 4-stroke line-haul Tier 3 locomotive with oil consumption reduced 

approximately 50% relative to Tier 2 via improvements to the power assembly and 

closed-crankcase ventilation system 

• A 4-stroke line-haul Tier 4 locomotives with application of a DOC and metalCDPF to the Tier 3 engine 

• A 4-stroke line-haul Tier 4 locomotives with application of a DOC and wall-flowCDPF to the Tier 3 engine 

Figure 18 and Figure 19 shows the expected marine PM reductions (on the E3 General 

Marine Duty Cycle) for the following scenarios: 

• A 2-stroke medium-speed Category 2 marine diesel engine due to reducing fuel 

sulfur content to 15 ppmE

• A 2-stroke medium-speed Category 2 marine diesel engine with oil consumption 

reduced approximately 50% relative to Tier 2 via improvements to the power 

assembly and closed-crankcase ventilation system 

• A 2-stroke medium-speed Category 2 marine diesel engine with application of a 

DOC and metal-CDPF to the Tier 3 engine 

For both the 4-stroke line-haul locomotive and 2-stroke Category 2 marine examples, 

post-control PM was calculated using both AAR and EPA speciated PM test results. The 

relative contributions of elemental carbon and organic carbon to PM mass differed between 

the EPA and AAR test results, but the level of expected PM control was similar for the 

emissions control systems that were evaluated. Due to the relatively high organic carbon 

fraction and low elemental carbon fraction in the PM emissions, the difference in PM 

emissions between the metal-CDPF and the wall-flow-CDPF is less than 0.01 g/bhp-hr . The 

 

E

 For this specific example, speciated data from an EMD 16-710G3C-T2 2-stroke medium speed locomotive 

engine was used. This engine is offered in both Category 2 marine and line-haul locomotive applications. The 

locomotive application has a slightly higher speed rating and lower NOx emissions. A fit of the data to E3 

points for the lower 4000 bhp @ 900 rpm EMD 16-710G7C-T2 marine rating was used to model PM emissions 

instead of the 4300 bhp @ 950 rpm rating. The G3C-T2 and G7C-T2 engines are remarkably similar, if not 

identical, designs with very similar NOx and PM emissions and appear to differ only with respect to rated power 

and rated speed. 

4-32 
Technological Feasibility 

advantages of the metal-CDPF relative to the wall-flow-CDPF are greatly reduced 

maintenance requirements and reduced exhaust back-pressure. We estimate that the use of a 

metal CDPF will result in PM emissions of approximately 0.020 to 0.022 g/bhp-hr over the 

line-haul cycle. The results from a ceramic wall-flow trap would be simlilar at 0.015 to 0.17 

g/bhp-hr. Either system would provide sufficient compliance margin to meet the 0.03 g/bhphr Tier 3 line-haul locomotive standard. We expect comparable PM reductions from 

Category 2 marine engines and locomotive engines that utilize similar CDPF technology due 

to similarities in PM composition between these engines. 

Fig 4-20 shows the expected PM removal efficiency of going from Tier 3 to Tier 4 

plotted vs. exhaust temperature for all notch positions. The Tier 3 levels were calculated 

based on a 4-stroke Tier 2 locomotive engine with improved lubricating oil control. The Tier 

4 levels were calculated based on the efficiency of a DOC and metal-CDPF combination at 

the end of useful life and taking into account removal efficiency for elemental and organic 

carbon and expected sulfate make from fuel and lubricant sulfur. Efficiency is similar or 

higher for Category 2 marine applications due to a narrower range of exhaust temperatures 

(approximately 250 °C to 350 °C over the E3 cycle) that are generally above the light-off 

temperatures for HC and NO oxidation for typical precious-metal DOC and CDPF 

formulations and yet are largely below the temperatures at which peak sulfate-make occurs. 

Figure 12: Brake-specific PM emissions speciated into soluble organic, soluble sulfate, and insoluble 

elemental carbon over the Federal Line-Haul duty cycle. 

2006 AAR Data

0.000

0.050

0.100

0.150

0.200

0.250

0.300

0.350

0.400

0.450

0.500

0.550

0.600

16-645F3 7FDL16 645 (Roots-blown) 16-710G3B 7FDL16 16-710G3C GEVO12LDB5

EMD SD50 Tier 0 GE DASH9-44CW

Tier 0

EMD GP38-2 Tier

0

EMD SD70M Tier

1

GE AC4400 Tier 1 EMD SD70Ace

Tier 2

GE ES44DC Tier 2

KCS7017 CN2508 CP4404 NS2630 CSXT595 UP8353 BNSF7736

Line-Haul Brake-specific PM Emissions (g/bhp-hr)

Sulfate PM (mostly sulfuric acid+associated water)

Organic PM (mostly lube oil)

Elemental Carbon PM (soot)

0.20 g/bhp-hr (Tier 2 PM Standard)

0.10 g/bhp-hr (Tier 3 PM Standard)

0.03 g/bhp-hr (Tier 4 PM Standard)

0.45 g/bhp-hr (Tier 1 PM Standard)

0.60 g/bhp-hr (Tier 0 PM Standard)

4-33 
Regulatory Impact Analysis 

Figure 13: Cross-sectional functional schematic for a metal-CDPF (not to scale). Flow restrictions force 

part of the particle laden exhaust flow through the porous sintered metal layers. High efficiencies are 

possible with engines having relatively low elemental carbon PM emissions. 

4-34 
Technological Feasibility 

Figure 14: Metal-monolith diesel oxidation catalysts (DOC) mounted within the exhaust manifold of an 

EMD 710-series locomotive diesel engine. Use of a close-coupled DOC extends the range of light-load 

operation where NO to NO2 oxidation can occur. Oxidation of engine-out NO to NO2 assists with passive 

regeneration of the CDPF and increases the low temperature performance of the urea SCR system. The 

system also improves oxidation of organic carbon PM at light load conditions (locomotive notches 1 

through 6). 

Figure 15: A two-stroke medium-speed Category 2 marine diesel engine with an insulated exhaust 

manifold and exhaust turbine in use in New York Harbor. 

4-35 
Regulatory Impact Analysis 

Figure 16: Brake-specific PM emissions over the line-haul duty cycle for a Tier 2 locomotive and the 

expected reductions in PM emissions due to reduced fuel sulfur levels and application of PM emissions 

controls. PM composition based on 2006 AAR testing.27

0.000

0.010

0.020

0.030

0.040

0.050

0.060

0.070

0.080

0.090

0.100

0.110

0.120

0.130

0.140

0.150

0.160

0.170

0.180

0.190

0.200

Tier 2 GE

Locomotive, 3000

ppm S Fuel

Tier 2 GE

Locomotive, 15 ppm

S Fuel

Tier 3 GE

Locomotive, 15 ppm

S Fuel

Tier 4 GE

Locomotive, 15 ppm

S Fuel, Metalsubstrate CDPF

Tier 4 GE

Locomotive, 15 ppm

S Fuel, Wall-flow

CDPF

Line-Haul Cycle Brake Specific PM Emissions (g/bhp-hr)_

Sulfate (mostly sulfuric acid + water)

Organic (mostly lube oil)

Elemental Carbon (soot)

Fuel

sulfur

reduction

Reduced

lubricating

oil

consumption

Oxidation

by DOC

& CDPF

Soot trapping Soot Trapping 

Tier 2 0.20 g/bhp-hr PM Standard

Tier 3 0.10 g/bhp-hr PM Standard

Tier 4 0.03 g/bhp-hr PM Standard

60% reduction 

from Tier 2

85% reduction 

from Tier 2

Figure 17: Brake-specific PM emissions over the line-haul duty cycle for a Tier 2 locomotive and the 

expected reductions in PM emissions due to reduced fuel sulfur levels and application of PM emissions 

controls. PM composition based on 2007 EPA Testing.28

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.1

0.11

0.12

0.13

0.14

0.15

0.16

0.17

0.18

0.19

0.2

Tier 2 GE

Locomotive, 3000

ppm S Fuel

Tier 2 GE

Locomotive, 15 ppm

S Fuel

Tier 3 GE

Locomotive, 15 ppm

S Fuel

Tier 4 GE

Locomotive, 15 ppm

S Fuel, Metalsubstrate CDPF

Tier 4 GE

Locomotive, 15 ppm

S Fuel, Wall-flow

CDPF

Line-Haul Cycle Brake Specific PM Emissions (g/bhp-hr)_

Total PM

Sulfate (mostly sulfuric acid + water)

Organic (mostly lube oil)

Elemental Carbon (soot)

Fuel

sulfur

reduction

Reduced

lubricating oil

consumption

Oxidation

by DOC

& CDPF

Soot trapping Soot Trapping 

Tier 2 0.20 g/bhp-hr PM Standard

Tier 3 0.10 g/bhp-hr PM Standard

Tier 4 0.03 g/bhp-hr PM Standard

60% reduction 

from Tier 2

85% reduction 

from Tier 2

4-36 
Technological Feasibility 

Figure 18: Brake-specific PM emissions over the E3 General Marine Duty Cycle for a Tier 2 mediumspeed Category 2 diesel engineE

 and the expected reductions in PM emissions due to reduced fuel sulfur 

levels and application of PM emissions controls. PM composition based on 2006 AAR testing.27

0.000

0.010

0.020

0.030

0.040

0.050

0.060

0.070

0.080

0.090

0.100

0.110

0.120

0.130

0.140

0.150

0.160

0.170

0.180

0.190

0.200

Tier 2 EMD C2 Marine,

3000 ppm S Fuel

Tier 2 EMD C2 Marine, 15

ppm S Fuel

Tier 3 EMD C2 Marine, 15

ppm S Fuel

Tier 4 EMD C2 Marine, 15

ppm S Fuel, Metalsubstrate CDPF

Line-Haul Cycle Brake Specific PM Emissions (g/bhp-hr)_

Sulfate (mostly sulfuric acid + water)

Organic (mostly lube oil)

Elemental Carbon (soot)

Fuel

sulfur

reduction

Reduced

lubricating

oil

consumption

Oxidation

by DOC

& CDPF

Soot trapping

Tier 2 0.20 g/bhp-hr (0.27 g/kW-hr) PM Standard

Tier 3

0.10 g/bhp-hr

(0.13 g/kW-hr) 

PM Standard

Tier 4

0.03 g/bhp-hr 

(0.04 g/kW-hr) 

PM Standard

55% reduction 

from Tier 2

90% reduction 

from Tier 2

Figure 19: Brake-specific PM emissions over the E3 General Marine Duty Cycle for a Tier 2 mediumspeed Category 2 diesel engineE

 and the expected reductions in PM emissions due to reduced fuel sulfur 

levels and application of PM emissions controls. PM composition based on 2007 EPA Testing.28

0.000

0.010

0.020

0.030

0.040

0.050

0.060

0.070

0.080

0.090

0.100

0.110

0.120

0.130

0.140

0.150

0.160

0.170

0.180

0.190

0.200

Tier 2 EMD C2 Marine,

3000 ppm S Fuel

Tier 2 EMD C2 Marine, 15

ppm S Fuel

Tier 3 EMD C2 Marine, 15

ppm S Fuel

Tier 4 EMD C2 Marine, 15

ppm S Fuel, Metalsubstrate CDPF

Line-Haul Cycle Brake Specific PM Emissions (g/bhp-hr)_

Total PM

Sulfate (mostly sulfuric acid + water)

Organic (mostly lube oil)

Elemental Carbon (soot)

Fuel

sulfur

reduction

Reduced

lubricating oil

consumption

Soot trapping

Tier 2 0.20 g/bhp-hr (0.27 g/kW-hr) PM Standard

Tier 3

0.10 g/bhp-hr

(0.13 g/kW-hr) 

PM Standard

Tier 4

0.03 g/bhp-hr 

(0.04 g/kW-hr) 

PM Standard

50% reduction 

from Tier 2

85% reduction 

from Tier 2

Fuel

sulfur

reduction

4-37 
Regulatory Impact Analysis 

Figure 20: Expected PM reduction versus exhaust temperature for a combined DOC and Metal-CDPF 

system using 15 ppm sulfur fuel when applied to a Tier 3 locomotive. Below 200 °C, PM is dominated by 

organic carbon emissions, which can only be removed via catalytic oxidation and not by filtration since 

they are in the gas-phase in the raw exhaust. Thus (organic) PM removal is limited by the kineticallylimited HC oxidation rates over the precious metal catalyst applied to the DOC and the CDPF. The level 

of PM control is reduced at temperatures between 300 and 450 °C due to the formation of sulfate PM over 

the CDPF. Note that the percentage reduction is relative to the emissions of a Tier 3 locomotive, not a 

Tier 2 locomotive. 

% PM Reduction (Tier 3 to Tier 4)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

50 100 150 200 250 300 350 400 450

Post-turbine Exhaust T (degC)

% PM Reduction Relative to Engine-out (Tier 3)

_

Based on PM composition from 2006 filter extraction data

Based on PM composition from 2007 OC-EC data

4.3.3 SCR and CDPF Packaging Feasibility 

We expect that locomotive and marine manufacturers will design exhaust, 

turbocharger, and intake air aftercooling systems to accommodate the aftertreatment 

components. It is acknowledged that the existing overall length, width, and height 

dimensions of the locomotive are constrained by the existing infrastructure such as tunnel 

height, but our analysis shows the packaging requirements are such that they can be 

accommodated within the constraints of a locomotive. For commercial marine vessels, our 

discussions with marine architects and engineers, along with our review of vessel 

characteristics, leads us to conclude for engines >600 kW on-board commercial marine 

vessels, adequate engine room space can be made available to package aftertreatment 

components. Packaging of these components, and analyzing their mass/placement effect on 

vessel characteristics, will become part of design process undertaken by naval architecture 

and marine engineering firms.54 

To achieve an acceptable balance between SCR performance and exhaust system 

backpressure, we estimate the volume of the SCR will need to be approximately 2.5 times the 

engine displacement. This volume includes the volume required for an ammonia-slip-catalyst 

4-38 
Technological Feasibility 

zone coated to the final 15% of the volume of the SCR monoliths. The SCR volume is 

determined by sizing the device so that pollutants/reductants have adequate residence time 

within catalyst to complete the chemical reactions under peak exhaust flow (maximum power) 

conditions. The term used by the exhaust aftertreatment industry to describe the relationship 

between exhaust flow rate and catalyst residence time is "space velocity". Space velocity is 

the ratio of an engine's peak exhaust flow (in volume units-per-hour) to the volume of the 

aftertreatment device - this ratio is expressed as "inverse hours", or -hr. For example, an 

engine with a displacement of 200 liters (L), 300,000 L/min of exhaust flow, and a 450 L 

SCR would have a space velocity of 40,000-hr and a catalyst-to-engine displacement ratio of 

2.25:1.F

 Typical space velocities for SCR on existing Euro 5 heavy-duty truck applications 

range from 60,000 to 80,000-hr. 

To achieve acceptable elemental carbon PM capture efficiency, organic carbon PM 

oxidation efficiency and exhaust system backpressure, the volume of a metal-CDPF for 

locomotive applications will need to be approximately 1.7 times the engine displacement, 

which results in a maximum space velocity of approximately 60,000-hr. The exhaustmanifold-mounted DOC located upstream of the metal CDPF will need to be approximately 

0.8 times the engine displacement with a maximum space velocity of approximately 80,000-hr 

in notch 6 (approximately 120,000 hr in notch 8). Typical space velocity for combined 

DOC/CDPF systems for Euro 4, Euro 5, and U.S. 2007 heavy-duty truck applications range 

from approximately 60,000 to 80,000-hr. 

The volume of the space above the engine available for packaging of exhaust 

aftertreatment components on a locomotive is approximately 2300 L. For a 200L engine, 

with DOC, DPF, and SCR volume-to-displacement ratios of 0.8, 1.7, and 2.5 respectively, 

approximately 1000-L of space would be occupied by these aftertreatment components, 

leaving 1300 L of space available for the ducts, urea dosing/mixing hardware, and catalyst 

support structures. An example of an aftertreatment system design concept which satisfies the 

packaging space and volume criteria was developed by Tenneco® and is shown in Figure 21. 

 

F

 Space Velocity =300,000 L/min * 1/450 L * 60 min/hr, Catalyst-to-Engine Displacement Ratio = 450 L/200 L. 

4-39 
Regulatory Impact Analysis 

Figure 21: Tenneco® DPF + SCR aftertreatment concept for line-haul locomotive applications. 

Urea mixing section 

SCR substrates (ammonia slip 

catalysts shown in dark brown) 

Exhaust flow from turbine 

outlet 

DPF section with ducts/valves 

(light brown) to balance flow 

between filter elements 

 

One commenter stated that the EPA projections for space requirements of 

aftertreatment components was underestimated by a factor of 2. Our projection for 

catalyst/component volume was based on assigning a “reasonable” space velocity (one which 

provided a good balance between emissions reduction and exhaust backpressure) to each 

component – 120,000-hr for the DOC, 60,000-hr for the DPF, and 40,000-hr for the SCR. In our 

analysis, we used engine data from the AAR’s locomotive in-use emissions test program to 

calculate the exhaust flow rate in units of “standard” cubic feet per minute (or SCFM, the 

conventional unit for volumetric flow used by catalyst manufacturers to calculate space 

velocity). In the commenter’s analysis, the “actual” exhaust flow rate instead of one corrected 

to “standard” conditions, as is industry practice. This method of analysis resulted in a flow 

rate which was approximately twice that of the value used in the EPA analysis, resulting in a 

space velocity which was two times greater as well. Once corrected to “standard” conditions, 

the commenter’s results for space velocity (and by extension, component volume) are very 

close to the EPA analysis. 

4.3.4 Mechanical Durability of Aftertreatment Components 

The exhaust components in any diesel application are subject to stresses from thermal 

expansion, vibration, and shock loads – all of which can affect the durability of the 

aftertreatment system. These stresses – and their associated affect on component durability - 

can be managed through the selection of proper materials and the design of support and 

mounting structures which are capable of withstanding them throughout the exected useful 

life and service conditions of a particular engine application. One commenter to our NPRM 

stated that shock loading for a locomotive catalyst is estimated to be 10-12 g. This level of 

shock loading is consistent with the levels that catalyst substrate manufacturers, catalyst 

canners, and exhaust system manufacturers are currently designing to (for OEM 

4-40 
Technological Feasibility 

aftertreatment systems and components subject to the durability requirements of on-highway, 

marine, and nonroad applications). Nonroad applications such as logging equipment are 

subject to shock loads in excess of 10 g and on-highway applications can exceed 30 g (with 

some OEM applications specifying a 75 g shock load requirement).55 In addition, the 

American Bureau of Shipping (ABS) specification for exhaust manifolds on diesel engines 

states that these parts may need to withstand vibration levels as high as +/-10 g at 600 °C for 

90 minutes.56 Given these examples of shock and vibration requirements for today’s nonroad, 

on-highway, and marine environments , we believe that appropriate support structures can be 

designed and developed for the aftertreatment devices we expect to be used on Tier 4 

locomotives. 

4.3.5 Stakeholder Concerns Regarding Locomotive NOx Standard Feasibility 

One stakeholder has expressed a number of concerns regarding the feasibility of the 

1.3 g/bhp-hr Tier 4 locomotive NOx standard. The issues raised by the stakeholder can be 

summarized into three broad areas of concern: 

1. Ammonia (urea) dosing 

2. Deterioration of SCR catalyst NOx control 

3. Locomotive parity with the marine Tier 4 NOx standard 

4.3.5.1 Ammonia/Urea dosing 

The dosing concern specified that variability in urea quality (concentration), urea 

delivery (dosing), and engine-out NOx level limits the maximum NOx reduction potential of 

the SCR system in order to control ammonia slip to a level <20 ppm. This concern is valid 

only if urea dosing is controlled in an “open-loop” manner (or operated without consideration 

of - or inputs from – actual conditions present in the exhaust system and within the SCR 

catalysts.) If the urea dosing is controlled in a “closed-loop” manner, where feedback from 

NOx and exhaust gas temperature sensors before/after the SCR catalyst is used to adjust the 

urea dosing rate, the SCR catalyst can operate at near-peak NOx conversion efficiency while 

minimizing NH3 slip. The use of an NH3 slip catalyst can clean up any ammonia released 

from the SCR to levels less than 10 ppm, providing an additional level of robustness to the 

closed-loop urea dosing system.36 For example, if exhaust gas and SCR temperature 

conditions at a particular engine speed/load point allowed for a maximum of 60% NOx

conversion efficiency, it is not be necessary to dose urea at an NH3-to-NOx ratio (α) of 1:1 

(which could allow up to 40% of the NH3 to slip) when an α of ~0.6 could achieve nearly the 

same level of NOx control while minimizing NH3 slip.57 As shown in Figure 22, the 

relationship between dosing ratio and NOx conversion is linear up to a ratio of ~0.95 (i.e. an α

of 0.7 yields a NOx conversion of 70%, an α of 0.8 yields a NOx conversion of 80%, and so 

on). If the dosing ratio is increased beyond 0.95, the additional NH3 injected will not produce 

a corresponding increase in NOx conversion, but will begin to result in NH slip. An effective 

urea dosing system will operate at this “knee” in curve to maximize NOx conversion while 

keeping slip below a designated target value. 

4-41 
Regulatory Impact Analysis 

Figure 22: Effect of dosing ratio on NOx conversion efficiency and NH3 slip.57

A NOx sensor before (or upstream of) the SCR can be used as a “feed forward” control 

input to set the target urea dosing rate and a sensor after (or downstream of) the SCR can be 

used as “feedback” to fine-tune the dosing rate for optimum NOx reduction while limiting 

ammonia slip. In addition, the feedback control provided by a closed-loop urea dosing system 

also mitigates any variation in concentration of the urea-water solution and engine-out NOx

levels by adjusting the control sytem to compensate by increasing/decreasing the urea dosing 

rate. The closed-loop system can also adjust to changes in the NOx conversion efficiency as 

the SCR ages – as efficiency drops, the α can adapt downward, preventing excessive ammonia 

slip. 

Closed-loop urea injection systems are already under development for 2010 U.S. 

heavy-duty highway diesel engines, U.S. and European light-duty diesel vehicles, and Euro V 

on-highway diesel trucks, and these applications have similar—if not more dynamic—engine 

operation as compared to locomotive and marine engine operation. Figure 23 illustrates a 

closed-loop urea-SCR control system for onroad diesel applications.58 Figure 24 illustrates a 

urea-SCR system concept developed by Volkswagen to meet U.S Tier 2, Bin 5 passenger car 

emission standards.59 

4-42 
Technological Feasibility 

Figure 23: Adapted from “SCR Technology for NOx Reduction: Series Experience and State of 

Development”.58

4-43 
Regulatory Impact Analysis 

Figure 24: Adapted from “LNT or Urea SCR Technology: Which is the right technology for TIER 2 BIN 

5 passenger vehicles?”59

To ensure accurate urea injection across all engine operating conditions, these systems 

utilize NOx sensors to maintain closed-loop feedback control of urea dosing. These NOxsensor-based feedback control systems are similar to oxygen-sensor-based systems that are 

used with three-way catalytic converters on virtually every gasoline vehicle on the road today. 

The control logic to which the sensors provide input allows for correction of urea dosing to 

adequately compensate for both production variation and in-use catalyst degradation. We 

believe these NOx–sensor-based control systems are directly applicable to locomotive and 

marine engines. 

Urea dosing systems under development to meet the light-duty Tier 2 and Heavy-duty 

2010 diesel emissions standards also use sophisticated models that predict the mass of NH3

stored within the catalyst system and continuously adjust dosing while taking into account 

NH3 storage and release. Prediction of stored NH3 reduces NH3 slip and allows additional 

NOx reduction to occur by using stored NH3 for NOx reduction at light load and idle 

conditions where exhaust temperatures are typically too low for urea injection and hydrolysis. 

EPA’s analysis of SCR NOx efficiency in section 4.3.1.2 is thus somewhat conservative since 

the ability to reduce NOx emissions using stored NH3 under many conditions where urea 

injection and urea hydrolysis are problematic (e.g., operation in notch one and under dynamic 

brake conditions) was not taken into consideration. Internal EPA engine testing shows that 

using a closed-loop controlled urea dosing system with prediction of stored NH3 can allow 

sustained operation at exhaust conditions equivalent to notch 1 operation with 40 to 50% NOx

control.60

4-44 
Technological Feasibility 

Ammonia emissions, which are already minimized through the use of closed-loop 

feedback urea injection, can be all-but-eliminated with an ammonia slip catalyst downstream 

of the SCR catalyst. Such catalysts are in use today and have been shown to be 95% effective 

at reducing ammonia emissions. Ammonia slip catalysts that have been developed for Euro V 

and U.S. 2010 truck applications have reduced selectivity for NOx formation from ammonia 

oxidation and can provide additional SCR NOx conversion via reaction with ammonia within 

the slip catalyst itself. 

4.3.5.2 Deterioration of NOx Control with Urea-SCR Systems 

A concern has been raised by the stakeholder that the iron-zeolite catalysts (as 

compared to the vanadium-based catalyst used in trucks in Europe) age rapidly in the 

presence of real exhaust and when exposed to elevated temperatures. Part of this concern is 

related to data provided by the stakeholder that had originally been presented by researchers 

at Ford and General Motors.35,61 The data was characterized as reaching two conclusions: 

1. Fe-zeolite catalysts have NOx reduction efficiency of only 55% to 65% when NOx

emissions are predominantly NO.61

2. The NO to NO2 conversion efficiency of PGM-based DOC’s would rapidly 

degrade to zero, and thus could not be relied upon to provide any degree of NO to NO2

oxidation to improve the efficiency of Fe-zeolite SCR catalysts. 

The first point may be the case for some Fe-zeolite catalysts when operated at catalyst 

space velocities much higher than those that would be used for locomotive applications (see 

Figure 25). The research cited intentionally undersized the SCR catalyst to accentuate the 

impact of NO:NO2 ratio on NOx conversion When comparing the Fe-Zeolite SCR catalyst 

example in Figure 25 to a similar, aged Fe-Zeolite system at a lower space velocity (Figure 

26), the NOx conversion efficiency increases to approximately 80% to 90% over the exhaust 

temperature range for a line-haul locomotive application for the lower space velocity example 

with no conversion of NO to NO2. There are two likely reasons for the differences seen 

between the results in Figure 25 and the results in Figure 26: 

1. Differences in space velocity between the two SCR catalyst systems. 

2. Differences in catalyst formulation and/or the supplier of the SCR catalyst 

system. 

For an appropriately sized locomotive SCR system, >80% NOx conversion for notches 2 

through 8 is still possible even with no oxidation of NO to NO2 upstream of the SCR catalyst. 

Even when taking into consideration that the catalyst in Figure 25 is undersized, it was 

capable of greater than 75% NOx conversion with NO2 as 25% of NOx and greater than 90% 

NOx conversion with NO2 as 50% of NOx. 

The second point cites NO2 conversion of only 5-30% at the end of life for a passenger 

car and then further extrapolates this conversion to near-zero over the life of a locomotive. 

4-45 
Regulatory Impact Analysis 

Upon reviewing the research in question, it was apparent that the 5 to 30% range referred to 

average conversion over the light-duty FTP cycle, and that the lower end of the range (5%) 

referred to results achieved when saturating the catalyst with fuel-hydrocarbons. The graph in 

Figure 27 is from the same research cited by the stakeholder, and shows the level of reduced 

effectiveness for NO to NO2 of the up-front DOC in a compact-SCR system. The four 

conditions plotted on the curve all represent NO to NO2 oxidation performance at the same 

level of thermal aging but with increasing injection of hydrocarbons. The lowest NO2

oxidation levels reported are for a condition during which the catalyst is completely saturated 

with hydrocarbons from direct fuel injection into the exhaust. Once fuel injection ceased, 

NO2 oxidation returned to the efficiency represented by the upper curve on the chart. The test 

was meant to show how NO2 oxidation degrades if the catalyst becomes temporarily 

hydrocarbon saturated during PM trap forced-regeneration or during cold start, and does not 

represent aged vs. non-aged DOC results for NO2 oxidation since all of the conditions shown 

represent approximately the same thermally-aged condition. Furthermore, in the range of 

post-turbine exhaust temperatures encountered by 4-stroke line-haul locomotive engines in 

notches 2 through 8 (approximately 275 °C to 450 °C), NO to NO2 oxidation ranged from 

approximately 20% to 50%. 

Figure 25: A comparison of zeolite-based and vanadium based urea-SCR catalyst formulations at a space 

velocity of 50,000 hr-1 while varying NO2 as a percentage of NOx. Adapted from “Evaluation of Supplier 

Catalyst Formulations for the Selective Catalytic Reduction of NOx with Ammonia”.61 

4-46 
Technological Feasibility 

Figure 26: NOx conversion efficiency for an Fe-Zeolite urea-SCR catalyst system while varying NO2 as a 

percentage of NOx.

62 Note that the black line represents the case of NOx that is 100% NO (0% NO2). 

Figure 27: Oxidation of NO to NO2 using a PGM-containing DOC and increasing levels of direct fuel 

hydrocarbon injection into the exhaust. Exhaust temperatures representative of operation of a 4-stroke 

line-haul locomotive are marked in red. Adapted from “Urea SCR and DPF System for Tier 2 Diesel 

Light-Duty Truck”. 35

4-47 
Regulatory Impact Analysis 

Figure 28 shows SCR system performance from the same work by Ford researchers, 

which shows greater than 90% NOx control over exhaust temperatures consistent with 

locomotive operation in notches 2 through 8. The results shown following 20 hours of 

thermal aging at 700 °C are approximately representative of the maximum thermal aging that 

a stakeholder claimed could be encountered during the useful life of a locomotive. The 

results for 40 hours of thermal aging at 700 °C (or roughly double the thermal conditions 

encountered due to locomotive consist operation in tunnels) still shows nearly identical NOx

performance to the 20 hour results in the range of temperatures representative of locomotive 

notches 2 through 8 and are generally consistent with the results shown in Figure 26 at 

comparable NO2 as a percentage of NOx. However, the temperature used for aging is still 

much higher than what could be achieved even under the most severe locomotive operation. 

The typical maximum exhaust temperature for a locomotive is 450 °C. During tunnel 

operation in a consist, a stakeholder claimed that this temperature can reach 700 °C. Recent 

work by EPA has shown that the peak exhaust temperature encountered during consist 

operation in non-ventilated tunnels will not exceed 560 °C – 140 degrees less that previously 

stated.39 Futhermore, the peak temperature achieved during the EPA testing was limited by 

the locomotives electronic controls to prevent damage to the engine or locomotive, thus it 

represents a self-limiting upper bound to in-use post-turbine exhaust temperatures. 

Under this lower peak temperature scenario, thermal sintering of the SCR catalyst is 

diminished, and durability of the system is enhanced, relative to on-highway applications of 

this technology. Therefore, we do not believe that deterioration of the PM and NOx 

aftertreatment technologies will be any greater than that stated in our NPRM. Given this 

recent information, we are confident that locomotive manufacturers will be able to meet the 

Tier 4 standards by the 2015 implementation date. 

Comments from a locomotive manufacturer claimed that operation in tunnels for 

limited numbers of locomotives could be as high as 50 hours per year.63 Figure 29 shows 

NOx conversion and ammonia slip for a combined DPF and Fe-zeolite SCR system before 

and after 400 hours of engine dynamometer aging. Engine exhaust temperatures at the SCR 

inlet were elevated to 650 °C using the DPF’s forced regeneration system for the entire 400 

hour duration.36 The truck engine used for the tests was a Euro IV configuration with higher 

engine-out NOx emissions than that of a Tier 2 line-haul locomotive. NOx efficiencies of 

approximately 85 to 95% were achieved at the end of aging. This would represent thermal 

degradation beyond the end of useful life even for the limited number of locomotives that 

operate in unventilated tunnels for 50 hours per year, especially when considering that the 

temperatures during the aging tests exceeded what could be attained during locomotive 

operation. 

A stakeholder also provided citations to recent SCR durability data generated by the 

Ford Motor Company during development of combined base-metal zeolite SCR and CDPF 

emission control systems under development to meet Tier 2 bin 5 standards with future lightduty diesel vehicles. This recent data provided useful insight into the performance 

characteristics of some base-metal zeolite SCR formulations at temperatures more severe than 

those that could be attained during locomotive operation.64,65,66 High temperature 

hydrothermal aging and aging that combined hydrothermal and wet urea injection effects 

showed virtually little or no degradation from the fresh condition for temperatures of 670 °C 

4-48 
Technological Feasibility 

and below.64,65 Engine durability testing to high hours showed that oil poisoning via 

deposition of phosphorus, zinc, and calcium compounds was limited to the first 1” to 1.5” 

from the catalyst face, which is the catalyst substrate region often impacted by direct oil 

deposition via turbulent diffusion.66 NOx efficiency downstream of this region was effectively 

maintained for the duration of the aging tests. The use of a CH-4 lubricant with no limits on 

chemical properties during the engine durability testing was confirmed for the Ford engine 

tests at the 2007 DEER Conference in Detroit, Michigan.67 Considering the chemical limits 

on zinc, phosphorus, and sulfonated ash specified within the current LMOA-approved 

locomotive lubricating oils and within the API CJ-4 service category specifications developed 

for use with aftertreatment-equipped heavy-duty diesels engines, EPA expects even less 

impact from oil poisoning than was seen during the Ford testing using CH-4 lubricant. The 

physical layout of the Ford system necessitated by the stringent light-duty Tier 2 cold-start 

NOx requirements (DOC followed by SCR with CDPF at the rear) also accentuated the 

impact of oil poisoning on the SCR substrates in a manner that would not occur with systems 

under development for heavy-duty, nonroad, locomotive or marine applications that typically 

place the less oil sensitive DOC and CDPF components upstream of the SCR monoliths. 

Such heavy-duty diesel engine system configurations have demonstrated good durability 

following extended engine aging using CJ-4 type lubricants.36,37 

With respect to combined thermal and lubricating oil consumption effects, EPA 

recently conducted engine dynamometer emissions tests with four different combined DPF 

and base-metal zeolite SCR systems following engine dynamometer aging for approximately 

700 to 1200 hours (hour levels differed among the four systems) at conditions equivalent to 

locomotive “notch 8”. The operation also included over 100 hours of operation at 580 °C 

using a forced regeneration system and either 600 to 1100 hours of operation with oil ash 

accumulation accelerated by a factor of six relative to the ash accumulation expected from 

normal oil consumption. Modifications were made to the electronic engine management 

system of a heavy-duty truck engine to provide exhaust temperature and gaseous composition 

equivalent to that of a Tier 2 GE 7FDL “GEVO” engine at each of the 8 loaded locomotive 

“throttle notch” positions. SCR catalyst volume for the four systems tested was sized for a 

maximum space velocity of approximately 40,000 hr-1 at the simulated locomotive “throttle 

notch 8” condition, and exhaust flow was scaled relative to “ throttle notch 8” for the 

remaining loaded conditions. The tested systems were each configured with a DOC and 

partial-flow, metal-substrate DPF immediately downstream of the exhaust turbine, followed 

by a urea dosing system, static mixer, SCR substrates, and an ammonia slip catalysts. SCR 

NOx efficiency was assumed to be negligible for the dynamic brake and idle conditions. NOx 

efficiencies of 85% to 98% were observed for the simulated “notch 2” through “notch 8” 

conditions following the engine-dynamometer aging procedures (see Figure 30 and Figure 

31). This corresponded to emissions of 0.4 to 0.6 g/bhp-hr NOx emissions over the line-haul 

cycle for the tested systems.60 Ammonia slip in the exhaust stack outlet did not exceed 10 

ppm for any of the tested conditions (Figure 32). 

4-49 
Regulatory Impact Analysis 

Figure 28: NOx conversion efficiency with 20% conversion of NO to NO2 for Fe-Zeolite SCR following 

different thermal aging conditions. The condition of 20 hours at 700 °C represents a condition with 

considerably higher temperatures than highest temperatures encountered during line-haul locomotive 

tunnel operation as part of a consist. Adapted from “Urea SCR and DPF System for Tier 2 Diesel LightDuty Truck”. 35

4-50 
Technological Feasibility 

Figure 29: NOx conversion efficiency and NH3 slip before and after 400 hours of engine dynamometer 

aging at the SCR inlet of 650 °C using forced DPF regeneration to increase the exhaust temperature, 

adapted from G. Smedler’s presentation at the 2007 SAE Heavy Duty Diesel Emissions Symposium, 

Gothenburg, Sweden.36 Testing was conducted with a maximum SCR space velocity of 60,000 hr-1 using a 

high-engine-out NOx Euro IV HD truck engine. 

0

10

20

30

40

50

60

70

80

90

200 250 300 350 400 450

Temp (C)

NOx conversion (%); NH3 slip (ppm)

100

DPF + SCR - with slip cat (NOx), 400 hrs. @ 650 °C 

DPF + SCR - with slip cat (NH3), 400 hrs. @ 650 °C 

DPF + SCR - with slip cat (NH3), low hrs. 

DPF + SCR - with slip cat (NOx), low hrs. 

0

10

20

30

40

50

60

70

80

90

200 250 300 350 400 450

Temp (C)

NOx conversion (%); NH3 slip (ppm)

100

DPF + SCR - with slip cat (NOx), 400 hrs. @ 650 °C 

DPF + SCR - with slip cat (NH3), 400 hrs. @ 650 °C 

DPF + SCR - with slip cat (NH3), low hrs. 

DPF + SCR - with slip cat (NOx), low hrs. 

4-51 
Regulatory Impact Analysis 

Figure 30: NOx reduction efficiency for an Fe-zeolite urea SCR systems. Simulated throttle-notch, space 

velocity and turbine outlet temperature are shown on the X-axis. The 1270-hour point corresponds to 

1160 hours of notch 8 operation (390 °C, ~40,000 hr-1 space velocity) with oil consumption and ash 

poisoning accelerated 6-fold and 110 cumulative hours of operation at an elevated temperature of 580 °C. 

Urea was dosed throughout the hour accumulation. No significant change in line-haul cycle brake specific 

NOx emissions was observed for the Fe-zeolite system during hour accumulation. 

"System A" SCR NOx Reduction Efficiency

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

NOx Reduction (%)

System A @ 100 hours

System A @ 285 hours

System A @ 560 hours

System A @1270 hours

Throttle Notch

Inlet Temperature

Space Velocity

1

170 °C

5,200 hr-1

2

270 °C

5,700 hr-1

3

360 °C

11,000 hr-1

4

430 °C

13,000 hr-1

5

410 °C

20,000 hr-1

6

390 °C

26,000 hr-1

7

390 °C

32,000 hr-1

8

400 °C

38,000 hr-1

4-52 
Technological Feasibility 

Figure 31: NOx reduction efficiency for Cu-zeolite urea SCR systems. The 730-hour point corresponds to 

610 hours of notch 8 operation (390 °C, ~40,000 hr-1 space velocity) with oil consumption and ash 

poisoning accelerated 6-fold and 100 cumulative hours of operation at an elevated temperature of 580 °C. 

Urea was dosed throughout the hour accumulation. A significant NOx reduction was observed at the 

“notch 1” condition at low hours. Reduction at this condition was dependent on NH3 storage on the 

catalyst sufaces since urea dosing was only used at conditions of “notch 2” and above. The ability to 

reduce NOx with stored ammonia at “Notch 1” was less at high hours. NOx reduction over the line-haul 

cycle at high hours was comparable to the Fe-Zeolite formulation even though SCR catalyst volume was 

20% smaller for the Cu-zeolite formulation (note the higher maximum space velocity in Throttle Notch 8). 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

NOx Reduction (%)

System D @ 50 hrs.

System D @ 100 hrs.

System D @ 390 hrs.

System D @ 730 hrs.

Throttle Notch

Inlet Temperature

Space Velocity

1

180 °C

6,500 hr-1

2

280 °C

7,000 hr-1

3

360 °C

13,000 hr-1

4

430 °C

17,000 hr-1

5

400 °C

25,000 hr-1

6

390 °C

33,000 hr-1

7

380 °C

39,000 hr-1

8

390 °C

48,000 hr-1

System "D" SCR NOx Reduction Efficiency

4-53 
Regulatory Impact Analysis 

Figure 32: Ammonia slip for each locomotive “throttle notch” condition following hour accumulation for 

the Fe-zeolite urea SCR system (A) and the Cu-zeolite urea SCR system (D). Each system was configured 

with an ammonia slip catalyst. 

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

18.0

20.0

1234567

Locomotive Throttle Notch

NH3 - after slip catalyst (ppm)

8

System A @ 560 hours

System D @ 390 hours

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

18.0

20.0

1234567

Locomotive Throttle Notch

NH3 - after slip catalyst (ppm)

8

System A @ 560 hours

System D @ 390 hours

4.3.5.3 Locomotive Parity with the Marine Tier 4 NOx Standard 

The stakeholder also expressed concern that with everything else being equal, a 

marine engine capable of achieving the 1.3 g/bhp-hr NOx when tested to the marine duty 

cycle would only meet 1.7 g/bhp-hr NOx when tested to the locomotive duty cycle. This 

would be due primarily to the way that the respective duty cycles used for emissions testing 

are conducted and weighted. The E3 Marine Duty Cycle operational points have exhaust 

temperatures that correspond to relatively high NOx reduction efficiency with urea-SCR 

catalyst systems. The line-haul locomotive test cycle includes some operational points with 

exhaust temperatures that may be too low for high SCR NOx reduction efficiency (low idle, 

high idle, dynamic brake and Notch 1). But, all things aren’t equal. The locomotive 

emissions test cycle allows adjustments for reduced idle emissions from the new electronic 

control systems such as “automated start/stop” that we expect to be used by all manufacturers. 

The Category 2 marine engines that are comparable to, or larger than, line-haul locomotive 

engines will meet the same 1.3 Tier 4 NOx standard with SCR three years sooner. They will 

also be meeting the Tier 4 NOx standard from a higher engine-out NOx emissions baseline 

since many Category 2 Tier 2 Marine engines are currently meeting a 7.3 g/bhp-hr NOx

standard versus current Tier 2 locomotive standard at 5.5 g/bhp-hr NOx. Thus the Tier 4 

standards actually represent a slightly higher 82% NOx reduction for Tier 4 marine engines vs. 

77% for Tier 4 locomotives. Therefore we believe that the Tier 4 NOx standards for marine 

diesel engines are appropriate and represent roughly the same level of emissions stringency. 

4-54 
Technological Feasibility 

4.4 Feasibility of Marine NTE Standards 

Changes to the marine diesel engine NTE standards based upon our understanding of 

in-use marine engine operation and based upon the underlying Tier 3 and Tier 4 duty cycle 

emissions standards that we are finalizing. As background, we determine NTE compliance by 

first applying a multiplier to the corresponding duty-cycle emission standard, and then we 

compare to that value an emissions result that is recorded when an engine runs within a 

certain range of engine operation. This range of operation is called an NTE zone. Refer to 40 

CFR 94.106 for details on how we currently define this zone and how we currently apply the 

NTE multipliers within that zone. 

Based upon our best information of in-use marine engine operation, we will broaden 

certain regions of the marine NTE zones, while narrowing other regions. It should be noted 

that the first regulation of ours that included NTE standards was the commercial marine diesel 

regulation, finalized in 1999. After we finalized that regulation, we promulgated other NTE 

regulations for both heavy-duty on-highway and nonroad diesel engines. We also finalized a 

regulation that requires heavy-duty on-highway engine manufacturers to conduct field testing 

to demonstrate in-use compliance with the on-highway NTE standards. Throughout our 

development of these other regulations, we have learned many details about how best to 

specify NTE zones and multipliers that help ensure the greatest degree of in-use emission 

control, while at the same time help avoid disproportionately stringent requirements for 

engine operation that has only a minor contribution to an engine’s overall impact on the 

environment. Specifically, we are broadening the NTE zones in order to better control 

emissions in regions of engine operation where an engine’s emissions rates (i.e. grams/hour, 

tons/day) are greatest; namely at high engine speed and high engine load. This is especially 

important for controlling emissions from commercial marine engines because they typically 

operate at steady-state at high-speed and high-load. This also will make our marine NTE 

zones much more similar to our on-highway and nonroad NTE zones. 

Additionally, we analyzed different ways to define the marine NTE zones, and we 

determined a number of ways to improve and simplify the way we define and calculate the 

borders of these zones. We feel that these improvements will help clarify when an engine is 

operating within a marine NTE zone. We are also finalizing NTE zones for auxiliary marine 

engines for both Tier 3 and Tier 4 standards. Because these engines are very similar to 

constant-speed nonroad engines, we will adopt the same NTE provisions for auxiliary marine 

engines that have already been adopted for constant-speed nonroad engines. Note that we 

currently specify different duty cycles to which a marine engine may be certified, based upon 

the engine’s specific application (e.g., fixed-pitch propeller, controllable-pitch propeller, 

constant speed, etc.). Correspondingly, we also have a unique NTE zone for each of these 

duty cycles. These different NTE zones are intended to best reflect an engine’s real-world 

range of operation for that particular application. Refer to the figures in 40 CFR Part 1042, 

Appendix III, for illustrations of the changes we are finalizing as part of this rulemaking. 

We are also including changes to the NTE multipliers. We have analyzed how the 

Tier 3 and Tier 4 emissions standards will affect the stringency of our current marine NTE 

standards, especially in comparison to the stringency of the underlying duty cycle standards. 

We recognized that in certain sub-regions of our NTE zones, slightly higher multipliers are 

4-55 
Regulatory Impact Analysis 

necessary because of the way that our more-stringent Tier 3 and Tier 4 emissions standards 

affect the stringency of the NTE standards. For comparison, our current marine NTE 

standards contain multipliers that range in magnitude from 1.2 to 1.5 times the corresponding 

duty cycle standard. In the changes we have finalized, the new multipliers range from 1.2 to 

1.9 times the standard. Refer to the figures in 40 CFR Part 1042, Appendix III, for 

illustrations of the changes we are finalizing.68

We are also adopting other NTE provisions for marine engines that are similar to our 

existing heavy-duty on-highway and nonroad diesel NTE standards. These particular changes 

to account for the implementation of catalytic exhaust aftertreatment devices on marine 

engines and to account for when a marine engine rarely operates within a limited region of the 

NTE zone. 

Aftertreatment systems generally utilize metallic catalysts, which become highly 

efficient at treating emissions above a minimum exhaust temperature. For the most 

commonly used metallic catalysts, this minimum temperature occurs in the range of about 

(150 to 250) °C. In our recent on-highway and nonroad regulations, we identified NOx

adsorber-based aftertreatment technology as the most likely type of technology for onhighway and nonroad NOx aftertreatment. This NOx adsorber technology utilizes barium 

carbonate metals that become active and efficient at temperatures at or above 250 °C. Also, 

in our on-highway and nonroad rulemakings we identified platinum and platinum/palladium 

diesel oxidation catalyst technology for hydrocarbon emission control. This technology also 

becomes active and efficient at temperatures at or above 250 °C. Therefore, in our onhighway and nonroad rulemakings for NOx and hydrocarbons emissions, we set a lower 

exhaust temperature NTE limit of 250 °C, as measured at the outlet of the last aftertreatment 

device. We only considered engine operation at or above this temperature as potential NTE 

operation. 

For marine applications we have hydrocarbon aftertreatment emission control 

technology similar to that used in on-highway engines (i.e. diesel oxidation catalyst or DOC). 

However, we have identified different aftertreatment technology for NOx control, as 

compared to our on-highway and nonroad rulemakings. Specifically, we have identified 

selective catalytic reduction (SCR) NOx control technology, which we discussed in detail 

earlier in this chapter. We believe that the performance of this different technology needs to 

be considered in setting the proper exhaust temperature limits for the marine NTE standards. 

While some testing has shown that it is possible to dose urea when exhaust temperatures are 

as low as 150 °C, the majority of SCR applications require a minimum exhaust gas 

temperature of 250 °C for effective urea hydrolysis.50 That is why our NTE standards for 

both NOx and HC will remain consistent with our on-highway and nonroad regulations (see 

40 CFR 86.1370-2007 (g)) and apply only when exhaust gas temperatures are equal to or 

greater than 250 °C, as measured within 12 inches of the outlet of the aftertreatment device. 

4.5 Lead Time 

In selecting the models years for which each of the new standards will begin, we 

sought to apply them as soon as possible considering the amount of lead time needed by the 

manufacturers to design, evaluate, and certify the modified or new engines. For marine 

4-56 
Technological Feasibility 

engines, this was relatively straightforward, since most marine engines have land-based 

counterparts that are already scheduled to be subject to new standards. In general, the marine 

standards closely follow the land-based standards. 

With respect to the amount of lead time needed to develop new locomotive designs, it 

is helpful to consider how locomotive development compares to the development process for 

highway truck engines and high-speed nonroad diesel engines. For these other categories, we 

generally provide the manufacturers at least four years of lead time to develop new engines, 

and even more when it requires a major technological shift. For both categories, when 

adopting our Tier 4 standards that required the addition of catalysts, we provided 

approximately nine years of lead time between the time the standards were adopted and the 

first model year the NOx and PM standards had to be fully met (from 2001 to 2010 for heavyduty trucks and from 2004 to 2014 for nonroad equipment). 

While locomotive manufacturers will benefit to some degree from the transfer of 

technologies from these categories, there are two key factors that will offset these advantages. 

First, locomotive manufacturers have more limited engineering resources and testing 

facilities, and they will need to use these resources to redesign their locomotives to meet the 

Tier 4 standards at the same time that they are developing lower emission kits for their Tier 0, 

1 and 2 locomotives. Locomotive manufacturers generally have research staffs of a few 

dozen engineers and have a handful of test cells. For comparison, highway truck engine 

manufacturers typically have thousands of engineers and dozens of test cells. Equally 

important, the long useful lives and heavy normal use patterns for locomotives means that 

merely proving the durability of new designs can take more than three years even in the best 

case. 

For these reasons, we believe that 2015 is the earliest that we can apply catalyst-based 

Tier 4 standards for locomotives. Moreover, we believe that by requiring compliance this 

soon, we also need to adopt the interim flexibilities described in §1033.150. This timing will 

allow manufacturers to finalize their designs over the next few years and perform field testing 

before 2015. Since manufacturers are much closer to having their Tier 3 locomotive designs 

ready for field testing, those standards can begin in 2012, and those technologies can be 

retrofitted to Tier 2 locomotives the following year. Finally, we do not believe that field 

testing will be required for the Tier 0 and Tier 1 technologies, so those standards can begin by 

2010. 

4.6 Conclusions 

Even though this rulemaking covers a wide range of engines - and thus requires the 

implementation of a range of emission controls technologies - we believe we have identified a 

range of technologically feasible emission control technologies that likely will be used to 

meet standards. Some of these technologies are incremental improvements to existing engine 

components, and many of these improved components have already been applied to similar 

engines. The other technologies we identified involve catalytic exhaust aftertreatment 

systems. For these technologies we carefully examined the catalyst technology, its 

applicability to locomotive and marine engine packaging constraints, its durability with 

respect to the lifetime of today’s locomotive and marine engines, its impact on the 

4-57 
Regulatory Impact Analysis 

infrastructure of the rail and marine industries, and the safety of its use. From our analysis, 

based upon numerous data from automotive, truck, locomotive, and marine industries, we 

conclude that incremental improvements to engine components and the implementation of 

catalytic PM and NOx exhaust aftertreatment technology are technologically feasible for 

locomotive and marine applications, and thus can be used to meet the emissions standards 

finalized in this rulemaking. 

4-58 
Technological Feasibility 

4-59 

 

REFERENCES 

1

 Title 40, U.S. Code of Federal Regulations, Part 86, §86.007-11 “Emission standards and supplemental 

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2

 Title 40, U.S. Code of Federal Regulations, Part 1039, §1039.101, Table 1, 2005. 

3

 Title 40, U.S. Code of Federal Regulations, Part 89, §89.112, Table 1, 2005. 

4

 Existing installations of Selective Catalytic Reduction systems for NOx control from marine vessels, memo to 

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5

 "Review of SCR Technologies for Diesel Emission Control: European Experience and Worldwide 

Perspectives," presented by Dr. Emmanuel Joubert, 10th DEER Conference, July 2004. 

6

 Lambert, C., “Technical Advantages of Urea SCR for Light-Duty and Heavy-Duty Diesel Vehicle 

Applications,” SAE 2004-01-1292, 2004. 

7

 Final Regulatory Analysis: Control of Emissions from Nonroad Diesel Engines, U.S. EPA Document number 

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8

 “Diesel Particulate Filter Maintenance: Current Practices and Experience”, Manufacturers of Emission Controls 

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 Flynn, P., et al, “Minimum Engine Flame Temperature Impacts on Diesel and Spark-Ignition Engine NOx 

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10 Heywood, John B., “Internal Combustion Engine Fundamentals”, McGraw Hill 1988. 

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12 Kittelson, et al, “Particle concentrations in a diesel cylinder: comparison of theory and experiment”, SAE 

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13 Foster, D.E. and D.R. Tree, “Optical measurements of soot particle size, number density and temperature in a 

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14 Dickey, D., Matheaus,A., Ryan, T., “NOx Control in Heavy-Duty Diesel Engines - What is the Limit?”, SAE 

980174, 1998. 

15 Herzog, P., et al, "NOx Reduction Strategies for DI Diesel Engines," SAE 920470, 1992. 

16 Uyehara, O., "Factors that Affect NOx and Particulates in Diesel Engine Exhaust," SAE 920695, 1992. 

17 Durnholz, M., G. Eifler, and H. Endres, "Exhaust-Gas Recirculation - A Measure to Reduce Exhaust Emission 

of DI Diesel Engines," SAE 920725, 1992. 

18 Bazari, Z. and B. French, "Performance and Emissions Trade-Offs for a HSDI Diesel Engine - An 

Optimization Study," SAE 930592, 1993. 
Regulatory Impact Analysis 

4-60 

 

19 Röpke, S., G.W. Schweimer, and T.S. Strauss, "NOx Formation in Diesel Engines for Various Fuels and 

Intake Gases" SAE 950213, 1995. 

20 Kreso, A.M., et al, “A Study of the Effects of Exhaust Gas Recirculation on Heavy-Duty Diesel Engine 

Emissions” SAE 981422, 1998. 

21 Ghaffarpour, M. and R. Baranescu, “NOx Reduction Using Injection Rate Shaping and Intercooling in Diesel 

Engines,” SAE 960845, 1996. 

22 Tow, T.C., D.A. Pierpont, and R.D. Reitz, “Reducing Particulate and NOx Emissions by Using Multiple 

Injections in a Heavy Duty D.I. Diesel Engine”, SAE 940897, 1994. 

23 Pierpont, D.A., D.T. Montgomery, and R.D. Reitz, “Reducing Particulate and NOx Emissions Using Multiple 

Injections and EGR in a D.I. Diesel Engine”, SAE 950217, 1995 

24 Ricart, L.M. and R.D. Reitz, “Visualization and Modeling of Pilot Injection and Combustion in Diesel 

Engines”, SAE 960833, 1996. 

25 Mather, D.K. and R.D. Reitz, “Modeling the Influence of Fuel Injection Parameters on Diesel Engine 

Emissions”, SAE 980789, 1998. 

26 Bazari, Z. and B. French, "Performance and Emissions Trade-Offs for a HSDI Diesel Engine - An 

Optimization Study", SAE 930592, 1993. 

27 Smith, B., Osborne, D., Fritz, S. “AAR Locomotive Emissions Testing 2006 Final Report” Association of 

American Railroads, Document # LA-023. 

28 McDonald, J. “Particulate Matter Emissions from Two Tier 2 Locomotives.” This document is available in 

Docket EPA-HQ-OAR-2003-0190. 

29 Walker, A.P. et al., “The Development and In-Field Demonstration of Highly Durable SCR Catalyst Systems,” 

SAE 2004-01-1289. 

30 Conway, R. et al., “Combined SCR and DPF Technology for Heavy Duty Diesel Retrofit,” SAE 2005-01-

1862, 2005. 

31 “The Development and On-Road Performance and Durability of the Four-Way Emission Control SCRT®

System,” presented by Andy Walker, 9th DEER Conference, August, 2003. 

32 Telephone conversation with Gary Keefe, Argillon, June 7, 2006. 

33 M.J. Bradley & Associates, "Alice Austen Vessel SCR Demonstration Project - Final Report," August 2006, 

www.mjbradley.com/documents/Austen_Alice_Report_Final_31Aug06.pdf. 

34 "SCRT® Technology for Retrofit of Heavy Duty Diesel Applications," presented by Ray Conway, 11th DEER 

Conference, August, 2005. 

35 “Urea SCR and DPF System for Tier 2 Diesel Light-Duty Trucks,” presented by Christine Lambert, 12th

DEER Conference, August 2006. 

36 Smedler, Gudmund, “NOx Emission Control Options”, 2007 HDD Emission Control Symposium – 

Gothenberg, Sweden, September 11, 2007. 
Technological Feasibility 

4-61 

 

37 Searles, R.A., et. al., “Investigation of the Feasibility of Achieving EURO V Heavy-Duty Emission Limits 

with Advanced Emission Control Systems,” 2007 AECC Conference - Belgium, Paper Code: F02E310. 

38 “Written Comments of the Manufactureres of Emission Controls Association on the U.S. Environmental 

Protection Agency’s Control of Emissions of Air Pollution from New Locomotive Engines and New Marine 

Compression-ignition Engines Less Than 30 Liters Per Cylinder – Notice of Proposed Rulemaking.” This 

document is available in Docket EPA-HQ-OAR-2003-0190-0572.1 

39 “Locomotive Exhaust Temperatures During High Altitude Tunnel Operation In Donner Pass,” U.S. EPA, 

August 29, 2007. This document is available in Docket EPA-HQ-OAR-2003-1090-0736. 

40 Conway, R. et al., "NOx and PM Reduction Using Combined SCR and DPF Technology in Heavy Duty 

Diesel Applications," SAE 2005-01-3548, 2005. 

41 “API CJ-4 Performance Specifications,” Amercian Petroleum Institute, online at http://apicj4.org/performance_spec.html. This document is available in Docket EPA-HQ-OAR-2003-0190-0738. 

42 “CJ-4 Performance Specification: Frequently Asked Questions,” Lubrizol, online at 

http://www.lubrizol.com/cj-4/faq.asp. This document is available in Docket EPA-HQ-OAR-2003-0190-0741. 

43 Letter from Joseph Kubsh of MECA, September 27, 2007. This document is available in Docket EPA-HQOAR-2003-0190-0730. 

44 Miller, W. et al., "The Development of Urea-SCR Technology for US Heavy Duty Trucks," SAE 2000-01-

0190, 2000. 

45 "Viability of Urea Infrastructure for SCR Systems," presented by M.D. Jackson, U.S. EPA Clean Diesel 

Engine Implementation Workshop, August 6, 2003. 

46 Email message from Mike Rush, Association of American Railroads, to Jeff Herzog, U.S. Environmental 

Protection Agency, July, 15, 2002. 

47 “National Transportation Statistics - 2004,” Table 4-5, U.S. Bureau of Transportation Statistics. 

48 “Mineral Commodity Summaries 2006,” page 118, U.S. Geological Survey, 

www.minerals.usgs.gov/minerals/pubs/mcs/mcs2006.pdf. 

49 “Diesel Particulate Filter Technology for Low-Temperature and Low-NOx/PM Applications”, presented by 

Sougato Chatterjee, 10th DEER Conference, July 2004. 

50 Kowatari, T. et al., “A Study of a New Aftertreatment System (1): A New Dosing Device for Enhancing Low 

Temperature Performance of Urea-SCR,” SAE 2006-01-0642. 

51 Jacobs, T., Chatterjee, S., Conway, R. Walker, A., Kramer, J., Mueller-Hass, K. "Development of Partial Filter 

Technology for HDD Retrofit", SAE Technical Paper Series, No. 2006-01-0213, 2006. 

52 Jacob, E., Lämmerman, R., Pappenheimer, A., Rothe, D. "Exhaust Gas Aftertreatment System for Euro 4 

Heavy-duty Engines", MTZ, June, 2005. 

53 Pace, L., Konieczny, R., Presti, M. "Metal Supported Particulate Matter-Cat, A Low Impact and Cost Effective 

Solution for a 1.3 Euro IV Diesel Engine", SAE Technical Paper Series, No. 2005-01-0471, 2005. 
Regulatory Impact Analysis 

4-62 

 

54 Telephone conversation between Brian King, Elliot Bay Design Group, and Brian Nelson, EPA, July 24, 2006. 

55 Correspondence from Adam Kotrba of Tenneco. This document is available in Docket EPA-HQ-OAR-2003-

0190-0742. 

56 “ABS Rules for Building and Classing – Steel Vessels Under 90 Meters (295 Feet) In Length,” Part 4 - Vessel 

Systems and Machinery, American Bureau of Shipping, 2006. 

57 Ming, C. et al., “Modelling and Optimization of SCR-Exhaust Aftertreatment Systems,” SAE 2005-01-0969, 

2005. 

58 “SCR Technology for NOx Reduction: Series Experience and State of Development,” presented by Manuel 

Hesser, 11th DEER Conference, August 2005. 

59 “LNT or Urea SCR Technology: Which is the right technology for TIER 2 BIN 5 passenger vehicles?,” 

presented by Richard Dorenkamp, 12th DEER Conference, August 2006. 

60 McDonald, J. “EPA Evaluation of Base-metal Zeolite Urea SCR Systems.” This document is available in 

Docket EPA-HQ-OAR-2003-0190. 

61 “Evaluation of Supplier Catalyst Formulations for the Selective Catalytic Reduction of NOx with Ammonia”, 

Presented by Steven J. Schmieg and Jong H. Lee at the U.S. DOE 9th CLEERS Workshop, May 2-5, 2006. 

62 Data provided to the U.S. EPA by Johnson Matthey Catalytic Systems Division, November 6, 2006. 

63 “Comments of General Electric Transportation on Proposed Rule Regarding Control of Emissions of Air 

Pollution From Locomotive Engines and Marine Compression-Ignition Engines Less Than 30 Liters per 

Cylinder,” page A-9. This document is available in Docket EPA-HQ-OAR-2003-0190-0590.1 

64 Cheng, Y, Hoard, J., Lambert, C., Kwak, J.H., Peden, C.H.F. “Impact of Urea on Hydrothermally Aged 

Cu/Zeolite SCR Catalysts.” North American Catalysis Society – 20th North American Meeting, June 17-22, 

2007. 

65 Cavataio, G., Girard, J., Patterson, J.E., Montreuil, C., Cheng, Y. Lambert, C.K. “Laboratory testing of UreaSCR Formulations to Meet Tier 2 Bin 5 Emissions.” SAE Technical Paper Series, No. 2007-01-1575, 2007. 

66 Cheng, Y., Xu, L., Jagner, M., Lamber, C. “Laboratory Postmortem Analysis of 120k mi Engine Aged Urea 

SCR Catalyst.” SAE Technical Paper Series, No. 2007-01-1579, 2007. 

67 Lambert, C. “Post-Mortem of DOC-SCR-DPF System for Tier 2 Light-Duty Diesel Truck.” Presentation at 

the 2007 Diesel Engine-Efficiency & Emissions Research Conference, August 14, 2007. 

68 Online at http://www.gpoaccess.gov/cfr/index.html. 
Engineering Cost Estimates 

5-1 

CHAPTER 5: ENGINEERING COST ESTIMATES .......................................................5-2

5.1 Methodology for Estimating Engine and Equipment Engineering Costs..................5-3

5.2 Engineering Costs for Freshly Manufactured Engines................................................5-5

5.2.1 Fixed Engineering Costs..........................................................................................5-5

5.2.2 Variable Engineering Costs ..................................................................................5-24

5.3 Engineering Costs for Freshly Manufactured Equipment ........................................5-51

5.3.1 Fixed Engineering Costs........................................................................................5-52

5.3.2 Variable Engineering Costs ..................................................................................5-56

5.4 Operating Costs for Freshly Manufactured Tier 4 Engines......................................5-64

5.4.1 Increased Operating Costs Associated with Urea Use .......................................5-64

5.4.2 Increased Operating Costs Associated with DPF Maintenance ........................5-65

5.4.3 Increased Operating Costs Associated with Fuel Consumption Impacts.........5-65

5.4.4 Total Increased Operating Costs Associated with Freshly Manufactured Tier 4 

Engines.............................................................................................................................5-67

5.5 Engineering Hardware Costs and Operating Costs Associated with the Locomotive 

and Marine Remanufacturing Programs..........................................................................5-73

5.6 Summary of Final Program Engineering Costs .........................................................5-82

5.6.1 Engineering Costs for Freshly Manufactured Engines......................................5-82

5.6.2 Engineering Costs for Freshly Manufactured Equipment ................................5-83

5.6.3 Operating Costs for Freshly Manufactured Tier 4 Engines..............................5-84

5.6.4 Engineering Hardware and Operating Costs for Remanufactured Engines...5-85

5.6.5 Total Engineering and Operating Costs Associated with the Final Program..5-85

5.7 Engineering Costs and Savings Associated with Idle Reduction Technology..........5-88

5.8 Analysis of Energy Effects ............................................................................................5-94

5.9 Cost Effectiveness ..........................................................................................................5-97
Regulatory Impact Analysis 

5-2 

CHAPTER 5: Engineering Cost Estimates 

This chapter presents the engine and equipment engineering costs we have estimated 

for meeting the new engine emissions standards.A Section 5.1 includes a brief outline of the 

methodology used to estimate the engine and equipment costs. Sections 5.2 and 5.3 present 

the projected costs of the individual technologies we expect manufacturers to use to comply 

with the new emissions standards, along with a discussion of fixed costs such as research, 

tooling, certification, and equipment/vessel redesign. Section 5.4 presents our estimate of 

changes in the operating costs that would result from the program and section 5.5 presents 

costs associated with the locomotive and marine remanufacturing programs. Section 5.6 

summarizes these costs and presents the total program costs. Section 5.7 presents our analysis 

of the potential of costs and savings associated with idle reduction technology. Section 5.8 

presents our analysis of energy effects and section 5.9 presents our cost-effectiveness 

calculations associated with the costs presented in sections 5.2 through 5.6. 

To maintain consistency in the way our emission reductions, costs, and costeffectiveness estimates are calculated, our cost methodology relies on the same projections of 

locomotive and marine engine growth as those used in our emissions inventory projections. 

Our emission inventory analyses for marine engines and for locomotives include estimates of 

future engine populations that are consistent with the future engine sales used in this cost 

analysis. 

Note that the costs here do not reflect changes to the fuel used to power locomotive 

and marine engines. Our Nonroad Tier 4 rule controlled the sulfur level in all nonroad fuel, 

including that used in locomotives and marine engines.B

 The sulfur level in the fuel is a 

critical element of the locomotive and marine program. However, since the costs of 

controlling locomotive and marine fuel sulfur have been considered in our Nonroad Tier 4 

rule, they are not considered here. This analysis considers only those costs associated with 

the locomotive and marine program. 

Additionally, the costs presented here do not reflect any savings that are expected to 

occur because of the engine ABT program and the various flexibilities included in the 

program. These program features have the potential to provide savings for both engine and 

locomotive/vessel manufacturers. While we fully expect companies to use them to reduce 

compliance costs, we do not factor them into the cost analysis because they are voluntary 

programs. This analysis of compliance costs relates to regulatory requirements that are part of 

the final rule for Tiers 3 and 4 emissions standards for locomotive and marine engines. 

Unless noted otherwise, all costs are in 2005 dollars. 

 

A We use the term “engineering costs” to differentiate from “social costs.” Social costs are discussed in Chapter 

7 of this final RIA. For simplicity, the terms “cost” and “costs” throughout the discussion in this Chapter 5 

should be taken as referring to “engineering costs.” 

B

 See the Regulatory Impact Analysis for the Nonroad Tier 4 final rule, EPA420-R-04-007, May 2004. 
Engineering Cost Estimates 

5-3 

5.1 Methodology for Estimating Engine and Equipment Engineering Costs 

This analysis makes several simplifying assumptions regarding how manufacturers 

will comply with the new emission standards. First, for each tier of emissions standards 

within a given category of engine, we assume a single technology recipe. For example, all 

Tier 4 engines in the locomotive category are estimated to be fitted with a selective catalytic 

reduction (SCR) system, a diesel particulate filter (DPF), and a diesel oxidation catalyst 

(DOC). However, we expect that each manufacturer will evaluate all possible technology 

avenues to determine how to best balance costs while ensuring compliance. As noted, for 

developing cost estimates, we have assumed that the industry does not make use of the 

averaging, banking, and trading program, even though this program offers industry the 

opportunity for significant cost reductions. Given these simplifying assumptions, we believe 

the projections presented here overestimate the costs associated with different compliance 

approaches manufacturers may ultimately take. 

Through our background work for this locomotive and marine rule, our past 

locomotive and marine rules, and our recent highway and nonroad diesel rules, we have 

sought input from a large section of the regulated community regarding the future costs of 

applying the emission control technologies expected for diesel engines within the context of 

this final program. Under contract with EPA, ICF International (formerly ICF Consulting) 

provided questions to several engine and parts manufacturers regarding costs associated with 

emission control technologies for diesel engines. The responses to these questions were used 

to estimate costs for “traditional” engine technologies such as EGR, fuel-injection systems, 

and for marinizing systems for use in a marine environment.1,2

 

Costs for exhaust emission control devices (e.g., catalyzed DPFs, SCR systems, and 

DOCs) were estimated using the methodology used in our 2007 heavy-duty highway 

rulemaking. In that rulemaking effort, surveys were provided to nine engine manufacturers 

seeking information relevant to estimating the costs for and types of emission-control 

technologies that might be enabled with low-sulfur diesel fuel. The survey responses were 

used as the first step in estimating the costs for advanced emission control technologies 

anticipated for meeting the 2007 heavy-duty highway standards. We then built upon these 

costs based on input from members of the Manufacturers of Emission Controls Association 

(MECA). We also used this approach as the basis for estimating costs for our recent nonroad 

tier 4 (NRT4) rulemaking effort. Because the anticipated emission control technologies for 

use on locomotive and marine engines are the same as, or similar to, those expected for 

highway and nonroad engines, and because the suppliers of the technologies are the same for 

of these engines, we have used that analysis as the basis for estimating the costs of these 

technologies in this rulemaking.3

 

Costs of control include variable costs (for new hardware, its assembly, and associated 

markups) and fixed costs (for tooling, research, redesign efforts, and certification). For 

technologies sold by a supplier to the engine manufacturers, costs are either estimated based 

on a direct cost to manufacture the system components plus a 29 percent markup to account 

for the supplier’s overhead and profit or, when available, based on estimates from suppliers 

on expected total costs to the manufacturers (inclusive of markups).4

 Estimated variable costs 

for new technologies include a markup to account for increased warranty costs. Variable 
Regulatory Impact Analysis 

5-4 

costs are additionally marked up to account for both manufacturer and dealer overhead and 

carrying costs. The manufacturer carrying cost—estimated to be four percent of the direct 

costs—accounts for the capital cost of the extra inventory and the incremental costs of 

insurance, handling, and storage. The dealer carrying cost—estimated to be three percent of 

their direct costs—accounts for the cost of capital tied up in extra inventory. We adopted this 

same approach to markups in the 2007 heavy-duty highway rule and the NRT4 rule, based on 

industry input.5

We have also identified various factors that cause costs to decrease over time, making 

it appropriate to distinguish between near-term and long-term costs. Research on the costs of 

manufacturing has consistently shown that, as manufacturers gain experience in production, 

they are able to apply innovations to simplify machining and assembly operations, use lower 

cost materials, and reduce the number or complexity of component parts. This analysis 

incorporates the effects of this learning curve as described in Section 5.2.2.6

Fixed costs for engine research are estimated to be incurred over the five-year period 

preceding introduction of the engine. Fixed costs for engine tooling and certification are 

estimated to be incurred one year ahead of initial production. Fixed costs for equipment 

redesign are also estimated to be incurred one year ahead of production. We have also 

included lifetime operating costs where applicable. These include costs associated with fuel 

consumption impacts and urea use, and increased maintenance demands resulting from the 

addition of new emission-control hardware. We have also included incremental costs 

associated with an increase in remanufacturing costs due to the inclusion of additional 

hardware as part of the remanufactured engine. 

A simplified overview of the methodology used to estimate engine and equipment 

costs is as follows: 

• For engine research, we have estimated the total dollars that we believe each engine 

manufacturer will spend on research to make DPF and SCR systems work together. 

We refer to such efforts as corporate research. Also for engine research, we have 

estimated the dollars spent to tailor the corporate research to each individual engine 

line in the manufacturer’s product mix. We refer to such efforts as engine-line 

research. 

• For engine-related tooling costs, we have estimated the dollars that we believe each 

engine manufacturer will spend on tooling for each of its engine lines. This amount 

varies depending on whether the manufacturer makes only locomotive and/or marine 

engines or also makes highway and/or nonroad engines. This amount also varies 

depending on the emissions standards to which the engine line is certified (i.e., Tier 3 

or 4). 

• For engine variable costs (i.e., emission-control hardware), we use a three-step 

approach: 

• First, we estimate the cost per piece of technology/hardware. As described in detail in 

Section 5.2.2, emission-control hardware costs tend to be directly related to engine 
Engineering Cost Estimates 

5-5 

characteristics—for example, most emission control devices are sized according to 

engine displacement so costs vary by displacement. Because of this relationship, we 

are able to determine a variable cost equation as a function of engine displacement. 

• Second, we determine a sales weighted baseline technology package using a database 

from Power Systems Research of all locomotive and marine engines sold in the United 

States.7

 That database lists engine characteristics for every one of over 40,000 

locomotive and marine engines sold in the United States in any given year. Using the 

baseline engine characteristics of each engine, the projected technology package for 

that engine, and the variable cost equations described in Section 5.2.2, we calculate a 

variable cost for the sales weighted average engine in each of several different engine 

categories. 

• Third, this weighted average variable cost is multiplied by the appropriate projected 

sales in each year after the new standards take effect to give total annual costs for each 

engine category. The sum total of the annual costs for all engines gives the fleetwide 

variable costs per year. 

• Equipment related costs—i.e., marine vessels or locomotives—are generated using the 

same methodology to estimate the fixed costs for equipment redesign efforts and the 

variable costs for new brackets, bolts, and sheet metal that we expect will be required. 

This chapter addresses a number of costs including: Engine costs – fixed costs then 

variable costs; equipment costs – fixed costs then variable costs; and, operating costs – urea, 

maintenance, and fuel consumption impacts; and, remanufacturing program costs. A 

summation of these costs is presented in Section 5.6. Variable cost estimates for both engines 

and equipment represent an expected incremental cost of the engine or piece of equipment in 

the model year of introduction. Variable costs per engine decrease in subsequent years as a 

result of several factors, as described below, although these factors do not apply to equipment 

variable costs. All costs are presented in 2005 dollars. 

5.2 Engineering Costs for Freshly Manufactured Engines 

5.2.1 Fixed Engineering Costs 

Engine fixed costs consist of research, tooling, and certification. For these costs, we 

have made a couple of simplifying assumptions with regard to the timing of marine-related 

expenditures due to the complexity of the roll out of the marine engine standards. We have 

estimated that, in general, the marine engine fixed costs would be incurred during the years 

prior to 2012 (for Tier 3 related costs) and 2016 (for Tier 4 related costs). While this 

approach impacts the timing of marine-related expenditures and, thus, the annual costs during 

the early years of implementation, it has no impact on the total costs we would estimate in 

association with the new standards. However, while having no impact on the total costs we 

estimate would be incurred, this approach does have a very minor impact on the net present 

value of costs since some early costs (e.g., those for <75 kW Tier 3 engines and >3,700 kW 

Tier 4 NOx) are effectively pushed back a couple of years. We believe that the approach 
Regulatory Impact Analysis 

5-6 

taken makes it easier to follow the presentation of costs while having no impact on the results 

of the analysis. 

5.2.1.1 Engine and Emission Control Device Research 

As noted, we estimate costs for two types of engine research—corporate research, or 

that research conducted by manufacturers using test engines to learn how NOx and PM control 

technologies work and how they work together in a system; and, engine line research, or that 

research done to tailor the corporate knowledge to each particular engine line. For the Tier 3 

standards, we are estimating no corporate research since the technologies expected for Tier 3 

are “existing” technologies and are well understood. However, we have estimated engine-line 

research associated with Tier 3 since those technologies will still need to be tailored to each 

engine-line. For Tier 4, we have estimated considerable corporate research since the 

technologies expected for Tier 4 are still considered “new” technologies in the diesel engine 

market. We have also estimated more engine-line research for Tier 4 so that the corporate 

research may be tailored to each engine. 

We start this discussion with the more global corporate research. The technologies 

described in Chapter 4 represent those technologies we believe will be used to comply with 

the new emission standards. These technologies are also part of an ongoing research and 

development effort geared toward compliance with the 2007 heavy-duty highway and the 

nonroad Tier 4 standards and, to some extent, the current and future light-duty diesel vehicle 

standards in the US and Europe. Those engine manufacturers making research expenditures 

toward compliance with either highway or nonroad emission standards will have to undertake 

some research effort to transfer emission-control technologies to engines they wish to sell into 

the locomotive and/or marine markets. These research efforts will allow engine 

manufacturers to develop and optimize these new technologies for maximum emission control 

effectiveness, while continuing to design engines with good performance, durability, and fuel 

efficiency characteristics. However, many engine manufacturers are not part of the ongoing 

research effort toward compliance with highway and/or nonroad emission standards because 

they do not sell engines into the highway or nonroad markets. These manufacturers–i.e., the 

locomotive/marine-only manufacturers–are expected to learn from the research work that has 

already occurred and will continue through the coming years through their contact with 

highway and nonroad manufacturers, emission-control device manufacturers, and the 

independent engine research laboratories conducting relevant research. Despite these 

opportunities for learning, we expect the research expenditures for these loco/marine-only 

manufacturers to be higher than for those manufacturers already conducting research in 

response to the highway and nonroad rules. 

We are projecting that SCR systems and DPFs will be the most likely technologies 

used to meet the new Tier 4 emission standards. Because these technologies are being 

researched for implementation in the highway and nonroad markets well before the 

locomotive and marine emission standards take effect, and because engine manufacturers will 

have had several years complying with the highway and nonroad standards, we believe that 

the technologies used to comply with the locomotive and marine Tier 4 standards will have 

undergone significant development before reaching locomotive and marine production. This 

ongoing research will likely lead to reduced costs in three ways. First, we expect research 
Engineering Cost Estimates 

5-7 

will lead to enhanced effectiveness for individual technologies, allowing manufacturers to use 

simpler packages of emission-control technologies than we would predict today, given the 

current state of development. Second, we anticipate that the continuing efforts to improve the 

emission-control technologies will include innovations that allow lower-cost production. And 

finally, we believe manufacturers will focus research efforts on any drawbacks, such as fuel 

economy impacts or maintenance costs, in an effort to minimize or overcome any potential 

negative effects. 

We anticipate that manufacturers will introduce a combination of primary technology 

upgrades to meet the new emission standards. Achieving very low NOx emissions requires 

basic research on NOx emission-control technologies and improvements in engine 

management. Manufacturers are expected to address this challenge by optimizing the engine 

and exhaust emission-control system to realize the best overall performance. This will entail 

optimizing the engine and emission control system for both emissions and fuel economy 

performance in light of the presence of the new exhaust emission control devices and their 

ability to control pollutants previously controlled only via in-cylinder means or with exhaust 

gas recirculation. The NOx control technology in particular is expected to benefit from reoptimization of the engine management system to better match the NOx catalyst’s 

performance characteristics. The majority of the dollars we have estimated for corporate 

engine research is expected to be spent on developing this synergy between the engine and 

NOx exhaust emission-control systems. Therefore, for engines where we project use of 

exhaust aftertreatment devices, we have attributed two-thirds of the research expenditures to 

NOx+NMHC control, and one-third to PM control. This approach is consistent with that 

taken in our 2007 heavy-duty highway and NRT4 rules. 

To estimate corporate research costs, we begin with our 2007 heavy-duty highway 

rule. In that rule, we estimated that each engine manufacturer would expend $35 million for 

corporate research toward successfully implementing diesel particulate filters (DPF) and NOx

control catalysts. For this locomotive/marine analysis, we express all monetary values in 

2005 dollars which means our starting point equates to just under $39 million.8 For their 

locomotive/marine research efforts, engine manufacturers that also sell into the highway 

and/or nonroad markets will incur some level of research expense but not at the level incurred 

for the highway rule. In many cases, the engines used by highway/nonroad manufacturers in 

marine products are based on the same engine platform as those engines used in their 

highway/nonroad products. This is also true for locomotive switchers. However, power and 

torque characteristics are often different, so manufacturers will need to expend some effort to 

accommodate those differences. For these manufacturers, we assume that they will incur an 

average corporate research expense of roughly $4 million. This $4 million expense allows for 

the transfer of learning from highway/nonroad research to their locomotive/marine engines. 

For reasons noted above, two-thirds of this money is attributed to NOx+NMHC control and 

one-third to PM control. 

For those engine manufacturers that sell engines only into the locomotive and/or 

marine markets, and where those engines will be meeting the new Tier 4 standards, we 

believe they will incur a corporate research expense approaching that incurred by highway 

manufacturers for the 2007 highway rule although not quite at the same level. These 

manufacturers will be able to learn from the research efforts already underway for both the 
Regulatory Impact Analysis 

5-8 

2007 highway and nonroad Tier 4 rules (66 FR 5002 and 69 FR 38958, respectively), and for 

the Tier 2 light-duty highway rule (65 FR 6698) and analogous rules in Europe. This learning 

may come from seminars, conferences, technical publications regarding diesel engine 

technology (e.g., Society of Automotive Engineers technical papers), and contact with 

highway manufacturers, emission-control device manufacturers, and the independent engine 

research laboratories conducting relevant research. In the NRT4 rule, we estimated that this 

learning would result in nonroad-only manufacturers incurring 70 percent of the expenditures 

as highway manufacturers for the 2007 highway rule. Similarly, we would expect that 

locomotive/marine-only manufacturers would incur that same 70 percent of the expenditures 

incurred by highway manufacturers for the 2007 highway rule. This number—roughly $27 

million versus $39 million in the highway rule—reflects the transfer of knowledge to 

locomotive/marine-only manufacturers from the many stakeholders in the diesel industry. 

Two-thirds of this corporate research is attributed to NOx+NMHC control and one-third to 

PM control. 

The $4 million and $27 million estimates represent our estimate of the average 

corporate research expenditures for engine manufacturers. Any particular manufacturer may 

incur more or less than these average figures. 

These corporate research estimates are outlined in Table 5-1. 

Table 5-1 Estimated Corporate Research Expenditures by Type of Engine Manufacturer Totals per 

Manufacturer over Five Years ($Million, 2005 dollars) 

Manufacturer sells only Tier 3 

engines 

Manufacturer sells Tier 4 

engines 

Manufacturer sells into highway and/or 

nonroad markets 

$0 $4 

Manufacturer sells only into locomotive 

and/or marine markets 

$0 $27 

% allocated to PM n/a 33% 

% allocated to NOx+NMHC n/a 67% 

Note: Since we expect that the majority of the costs we have estimated for corporate engine research would be 

spent on developing the synergy between the engine and NOx exhaust emission-control systems, we have 

attributed two-thirds of the corporate research expenditures to NOx+NMHC control and one-third to PM 

control. 

The PSR database shows that there were 47 engine manufacturers that sold engines 

into the locomotive and marine markets in 2002. Of these 47, 12 sold engines into the market 

segments required to meet the Tier 4 standards (i.e., expected to need exhaust aftertreatment 

devices and, therefore, need to conduct this research). Of those 12, three sold exclusively 

into the locomotive and/or marine markets, while the other nine sold engines into the highway 

and/or nonroad markets in addition to the locomotive and/or marine markets. As a result, we 

estimate that three manufacturers will need to spend the full $27 million conducting research 

and nine will spend $4 million, for a total corporate research expenditure of just under $117 

million. 
Engineering Cost Estimates 

5-9 

Further, six of these 12 manufacturers sold into both the locomotive and marine 

markets and, therefore, will spend a portion of their corporate research dollars during the five 

years prior to 2015 (to support locomotive engines), and a portion during the five years prior 

to 2016 (to support marine engines). Of the six remaining manufacturers, five sold only into 

the marine market so will spend their dollars during the five years prior to 2016. The 

remaining manufacturer sold only into the locomotive switcher market and will spend its 

corporate research dollars during the five years prior to 2015. Further allocation of corporate 

research into marine C1, marine C2, locomotive switcher, and locomotive line-haul segments 

based on the segments into which each manufacturer sold in 2002 results in the total corporate 

research expenditures by market segment shown in Table 5-2. We then spread these costs 

over the five years in advance of the applicable standards to get the annual costs shown in 

Table 5-3. Note that the corporate research expenditures for manufacturers that sell into both 

the locomotive line-haul and marine C2 categories are split equally between those two 

categories. The same approach is taken for those manufacturers that sell engines across other 

categories. 

Table 5-2 Estimated Corporate Research Expenditures Allocated by Market Segment ($Million, 2005 

dollars) 

Market Segment Total Corporate Research Expenditure PM NOx+NMHC 

Locomotive Switcher/Passenger $ 10.4 $ 3.4 $ 7.0 

Locomotive Line-Haul $ 27.2 $ 9.0 $ 18.2 

Marine C1 $ 45.4 $ 15.0 $ 30.4 

Marine C2 $ 33.7 $ 11.1 $ 22.6 

Total Industry Expenditure $ 116.6 $ 38.5 $ 78.1 

Notes: Since we expect that the majority of the dollars we have estimated for corporate engine research would 

be spent on developing the synergy between the engine and NOx exhaust emission-control systems, we have 

attributed two-thirds of the corporate research expenditures to NOx+NMHC control and one-third to PM 

control. 
Regulatory Impact Analysis 

5-10 

Table 5-3 Estimated Corporate Research Expenditures by Year ($Millions, 2005 dollars) 

Locomotive Switchers Locomotive Line-Haul Marine C1 Marine C2 

Calendar 

Year PM NOx+ 

NMHC 

Subtotal PM NOx+ 

NMHC 

Subtotal PM NOx+ 

NMHC 

Subtotal PM NOx+ 

NMHC 

Subtotal T

S

2006 2007 2008 2009 2010 $0.7 $1.4 $2.1 $1.8 $3.6 $5.4 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2011 $0.7 $1.4 $2.1 $1.8 $3.6 $5.4 $3.0 $6.1 $9.1 $2.2 $4.5 $6.7 $2012 $0.7 $1.4 $2.1 $1.8 $3.6 $5.4 $3.0 $6.1 $9.1 $2.2 $4.5 $6.7 $2013 $0.7 $1.4 $2.1 $1.8 $3.6 $5.4 $3.0 $6.1 $9.1 $2.2 $4.5 $6.7 $2014 $0.7 $1.4 $2.1 $1.8 $3.6 $5.4 $3.0 $6.1 $9.1 $2.2 $4.5 $6.7 $2015 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $3.0 $6.1 $9.1 $2.2 $4.5 $6.7 $2016 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2017 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2018 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2019 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2020 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2021 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2022 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2023 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2024 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2025 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2026 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2027 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2028 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2029 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2030 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2031 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2032 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2033 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2034 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2035 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2036 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2037 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2038 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2039 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2040 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 Total $3.4 $7.0 $10.4 $9.0 $18.2 $27.2 $15.0 $30.4 $45.4 $11.1 $22.6 $33.7 $

NPV at 7% $2.1 $4.3 $6.5 $5.6 $11.4 $17.0 $8.8 $17.8 $26.5 $6.5 $13.2 $19.7 $NPV at 3% $2.8 $5.7 $8.4 $7.3 $14.8 $22.1 $11.8 $24.0 $35.8 $8.8 $17.8 $26.6 $
Engineering Cost Estimates 

5-11 

As shown in Table 5-3, the net present value of the corporate research is estimated at 

$93 million using a three percent discount rate, and $70 million using a seven percent 

discount rate.C

 We can estimate these expenditures on a per engine basis considering the time 

value of money and engine sales for 2006 through 2040, as shown in Table 5-4. 

Table 5-4 Estimated Corporate Research per Engine (2005 dollars) 

Estimated Cost Allocation 

($Millions) 

Estimated Sales from 2006 to 

2040 

$/engine

Locomotive 

Switcher/Passenger 

$ 8.4 3,212 $ 2,630 

Locomotive Line Haul $ 22.1 19,453 $ 1,140 

Marine C1 >600 kW $ 35.8 20,039 $ 1,790 

Marine C2 $ 26.6 6,647 $ 4,000 

Total $ 93.0 49,352 $ 1,880 

Note: Net present values of sales are calculated using zero as the sales figure for 2006. 

For engine line research—those engine research efforts done to tailor the corporate 

research to each particular engine line—we have first determined the number of engine lines 

by considering that, typically, the same basic diesel engine design can be increased or 

decreased in size by simply adding or subtracting cylinders. As a result, a four-, six-, or eightcylinder engine may be produced from the same basic engine design. While these engines 

have different total displacement, they each have the same displacement per cylinder. Using 

the PSR database, we grouped each engine manufacturer’s engines into distinct engine lines 

using increments of 0.5 liters per cylinder. This way, engines having similar displacements 

per cylinder are grouped together and are considered to be one engine line. Doing this, we 

found there to be 88 engine lines that will need Tier 3 engine line research and 31 engine lines 

that will need Tier 4 engine line research. Of the 88 Tier 3 engine lines, eight are locomotive 

switcher lines, two are locomotive line haul lines, 13 are C2 marine lines, and 65 are other 

marine lines which, due to their size, generally span at least two of the three categories of C1 

marine, recreational, and small commercial marine. For these 65 marine lines, we have 

weighted each manufacturer’s estimated engine line research costs according to total engine 

lines sold into each of these three categories by the particular manufacturer. Of the 31 Tier 4 

engine lines, four engine lines had sales in both the locomotive and the marine markets, so we 

have split evenly the engine line research between the appropriate segments; two of these four 

 

C

 Throughout Chapter 5 of this RIA, net present value (NPV) calculations are based on the period 2006-2040, 

reflecting the period when the NPRM analysis was completed. This has the consequence of discounting the 

current year costs, 2007, and all subsequent years are discounted by an additional year. The result is a slightly 

smaller NPV of engineering costs than by calculating the NPV over 2007-2040 (3% smaller for 3% NPV and 7% 

smaller for 7% NPV). The same convention applies for the emission inventories as shown in Table 5-66. We 

have used 2006 because we intended to publish the proposal in 2006. For the final analysis, we have chosen to 

continue with 2006 to make comparisons between proposal and final analyses more clear. 
Regulatory Impact Analysis 

5-12 

were marine-C1/locomotive-switcher engine lines, while the other two were marineC2/locomotive-line haul engine lines. 

Consistent with our NRT4 rule, for those engine lines adding aftertreatment devices 

(i.e., the Tier 4 engine lines) we have estimated the engine line research at $3.2 million per 

line for those engines under 600 kW, $6.5 million per line for non-locomotive line-haul 

engines over 600 kW, and $55 million per line for locomotive line-haul engines. The 

locomotive line-haul estimate is considerably higher than the others because of the high cost 

of prototypes for that category. For engine line research associated with the Tier 3 standards, 

we have estimated the expenditure per locomotive line-haul engine line at $15 million and at 

$1.6 million for all other engine lines. These values are lower than the amount estimated for 

Tier 4 since the Tier 3 effort should amount to recalibration work which is less costly than the 

work expected for Tier 4 engine lines. The estimated engine line research expenditures by 

type of engine manufacturer are shown in Table 5-5 and by market segment for Tier 3 in 

Table 5-6 and for Tier 4 in Table 5-7. 

Table 5-5 Estimated Engine Line Research Expenditures by Type of Engine Manufacturer Totals per 

Engine Line for Tiers 3 & 4 ($Million, 2005 dollars) 

Tier 3 engine line Tier 4 engine line <600 

kW 

Tier 4 engine line >600 

kW 

Manufacturer sells into highway 

and/or nonroad markets 

$ 1.6 $ 3.2 $ 6.5 

Manufacturer sells only into 

locomotive and/or marine markets 

$ 1.6 $ 3.2 $ 6.5 

Locomotive Line-haul engine line $ 15.0 $ 55.0 

% allocated to PM 33% 33% 33% 

% allocated to NOx+NMHC 67% 67% 67% 

Note: Since we expect that the majority of the dollars we have estimated for engine line research would be spent 

on developing the synergy between the engine and NOx exhaust emission-control systems, we have attributed 

two-thirds of the engine line research expenditures to NOx+NMHC control and one-third to PM control. 
Engineering Cost Estimates 

5-13 

Table 5-6 Tier 3 Engine Line Research Expenditures by Market Segment ($Million, 2005 dollars) 

Segment Engine Lines 

<600 kW 

Engine Lines 

>600kW 

Tier 3 

$/line 

Total 

Small Commercial Marine 

Recreational Marine 

Marine C1 

65 $ 1.6 $ 104 

Marine C2 0 13 $ 1.6 $ 20.8 

Locomotive Switcher/Passenger 6a 2 $ 1.6 $ 12.8 

Locomotive Line Haul 0 2 $ 15.0 $ 30.0 

Total 63 25 $ 167.6 

a

 Note that we have developed hardware costs for switchers based on a single large engine of, generally, over 

2000 hp. However, many switchers are powered by several nonroad engines placed in series to arrive at a large 

horsepower locomotive. Perhaps it would have been more appropriate to assume research costs for those 

engines to be $0 since the effort is, presumably, being done for the nonroad Tier 4 rule. However, to be 

conservative, we have included engine line research costs for these engines. 

Table 5-7 Tier 4 Engine Line Research Expenditures by Market Segment ($Million, 2005 dollars) 

Segment Engine Lines <600 

kW 

Engine Lines >600 

kW 

Tier 4 

$/line 

Total 

Marine C1 n/a 10 $ 6.5 $ 65.0 

Marine-C1/LocoSwitcher/Passenger 0 2 $ 6.5 $ 13.0 

Locomotive Switcher/Passenger 6a 0 $ 3.2 $ 19.2 

Marine C2 0 12 $ 6.5 $ 78.0 

Loco-LineHaul 0 2 $ 55.0 $ 110.0 

Total 6 25 $ 285.2 

a

 Note that we have developed hardware costs for switchers based on a single large engine of, generally, over 

2000 hp. However, many switchers are powered by several nonroad engines placed in series to arrive at a large 

horsepower locomotive. We could have assumed research costs for those engines to be $0 since the effort is, 

presumably, being done for the nonroad Tier 4 rule. However, to be conservative, we have included engine line 

research costs for these engines. 

We estimate that these engine line research expenditures will be made over a five year 

period in advance of the standard for which the cost is incurred. Spreading the costs this way 

results in the annual cost streams shown in Table 5-8 for Tier 3 and Table 5-9 for Tier 4 and 

Table 5-10 for the final program (i.e., Tiers 3 and 4). D

 

D Note that we show the Tier 3 engine-line research costs beginning in calendar year 2007 even though this rule 

will not be final until the end of 2007 at the earliest. While we usually do not account for investments made 

prior to a rule being finalized, we understand that manufacturers have begun spending money that, arguably, 

could be considered costs associated with this rule and believe it is appropriate that this rule reflect those 

estimates. 
Regulatory Impact Analysis 

5-14 

Table 5-8 Estimated Tier 3 Engine Line Research Expenditures by Year ($Millions, 2005 dollars) 

Locomotive Switchers Locomotive Line Haul Marine C1; Recreational; Small Commercial Marine C2 

Calendar 

Year PM NOx+ 

NMHC 

Subtotal PM NOx+ 

NMHC 

Subtotal PM NOx+ 

NMHC 

Subtotal PM NOx+ 

NMHC 

Subtotal 

2006 2007 $0.8 $1.7 $2.6 $2.0 $4.0 $6.0 $6.9 $13.9 $20.8 $1.4 $2.8 $4.2 2008 $0.8 $1.7 $2.6 $2.0 $4.0 $6.0 $6.9 $13.9 $20.8 $1.4 $2.8 $4.2 2009 $0.8 $1.7 $2.6 $2.0 $4.0 $6.0 $6.9 $13.9 $20.8 $1.4 $2.8 $4.2 2010 $0.8 $1.7 $2.6 $2.0 $4.0 $6.0 $6.9 $13.9 $20.8 $1.4 $2.8 $4.2 2011 $0.8 $1.7 $2.6 $2.0 $4.0 $6.0 $6.9 $13.9 $20.8 $1.4 $2.8 $4.2 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 Total $4.2 $8.6 $12.8 $9.9 $20.1 $30.0 $34.3 $69.7 $104.0 $6.9 $13.9 $20.8 $

NPV at 7% $3.2 $6.6 $9.8 $7.6 $15.4 $23.0 $26.3 $53.4 $79.7 $5.3 $10.7 $15.9 $NPV at 3% $3.8 $7.6 $11.4 $8.8 $17.9 $26.7 $30.5 $62.0 $92.5 $6.1 $12.4 $18.5 $
Enginee

5-15 

Table 5-9 Estimated Tier 4 Engine Line Research Expenditures by Year ($Millions, 2005 dollars) 

Locomotive Switchers Locomotive Line Haul Marine C1 > 600 kW Marine C2 

Calendar 

Year PM NOx+ 

NMHC 

Subtotal PM NOx+ 

NMHC 

Subtotal PM NOx+ 

NMHC 

Subtotal PM NOx+ 

NMHC 

Subtotal 

S

2006 2007 2008 2009 2010 $1.7 $3.4 $5.1 $7.3 $14.7 $22.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $2011 $1.7 $3.4 $5.1 $7.3 $14.7 $22.0 $4.7 $9.6 $14.3 $5.1 $10.5 $15.6 $2012 $1.7 $3.4 $5.1 $7.3 $14.7 $22.0 $4.7 $9.6 $14.3 $5.1 $10.5 $15.6 $2013 $1.7 $3.4 $5.1 $7.3 $14.7 $22.0 $4.7 $9.6 $14.3 $5.1 $10.5 $15.6 $2014 $1.7 $3.4 $5.1 $7.3 $14.7 $22.0 $4.7 $9.6 $14.3 $5.1 $10.5 $15.6 $2015 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $4.7 $9.6 $14.3 $5.1 $10.5 $15.6 $2016 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2017 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2018 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2019 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2020 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2021 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2022 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2023 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2024 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2025 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2026 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2027 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2028 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2029 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2030 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2031 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2032 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2033 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2034 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2035 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2036 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2037 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2038 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2039 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2040 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 Total $8.5 $17.2 $25.7 $36.3 $73.7 $110.0 $23.6 $47.9 $71.5 $25.7 $52.3 $78.0 $

NPV at 7% $5.3 $10.8 $16.1 $22.7 $46.1 $68.8 $13.8 $28.0 $41.8 $15.0 $30.6 $45.6 $NPV at 3% $6.9 $14.0 $20.9 $29.5 $60.0 $89.5 $18.6 $37.8 $56.5 $20.3 $41.3 $61.6 $
Regulatory Impact Analysis 

5-16 

Table 5-10 Estimated Tier 3 & Tier 4 Engine Line Research Expenditures by Year ($Millions, 2005 dolla

Locomotive Switchers Locomotive Line Haul Marine C1; Recreational; Small Commercial Marine C2 

Calendar 

Year PM NOx+ 

NMHC 

Subtotal PM NOx+ 

NMHC 

Subtotal PM NOx+ 

NMHC 

Subtotal PM NOx+ 

NMHC 

Subtotal 

S

2006 2007 $0.8 $1.7 $2.6 $2.0 $4.0 $6.0 $6.9 $13.9 $20.8 $1.4 $2.8 $4.2 $2008 $0.8 $1.7 $2.6 $2.0 $4.0 $6.0 $6.9 $13.9 $20.8 $1.4 $2.8 $4.2 $2009 $0.8 $1.7 $2.6 $2.0 $4.0 $6.0 $6.9 $13.9 $20.8 $1.4 $2.8 $4.2 $2010 $2.5 $5.2 $7.7 $9.2 $18.8 $28.0 $6.9 $13.9 $20.8 $1.4 $2.8 $4.2 $2011 $2.5 $5.2 $7.7 $9.2 $18.8 $28.0 $11.6 $23.5 $35.1 $6.5 $13.2 $19.8 $2012 $1.7 $3.4 $5.1 $7.3 $14.7 $22.0 $4.7 $9.6 $14.3 $5.1 $10.5 $15.6 $2013 $1.7 $3.4 $5.1 $7.3 $14.7 $22.0 $4.7 $9.6 $14.3 $5.1 $10.5 $15.6 $2014 $1.7 $3.4 $5.1 $7.3 $14.7 $22.0 $4.7 $9.6 $14.3 $5.1 $10.5 $15.6 $2015 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $4.7 $9.6 $14.3 $5.1 $10.5 $15.6 $2016 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2017 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2018 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2019 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2020 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2021 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2022 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2023 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2024 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2025 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2026 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2027 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2028 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2029 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2030 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2031 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2032 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2033 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2034 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2035 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2036 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2037 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2038 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2039 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2040 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 Total $12.7 $25.8 $38.5 $46.2 $93.8 $140.0 $57.9 $117.6 $175.5 $32.6 $66.2 $98.8 $

NPV at 7% $8.5 $17.3 $25.9 $30.3 $61.5 $91.8 $40.1 $81.4 $121.5 $20.3 $41.2 $61.5 $NPV at 3% $10.7 $21.6 $32.3 $38.3 $77.9 $116.2 $49.2 $99.8 $149.0 $26.4 $53.7 $80.1 $
Engineering Cost Estimates 

5-17 

Table 5-10 shows the total estimated costs associated with engine line research. This 

table combines the costs for Tier 3 (Table 5-8) and Tier 4 (Table 5-9). As shown in Table 

5-10, the net present value of the engine line research is estimated at $378 million using a 

three percent discount rate and $301 million using a seven percent discount rate. We can 

estimate these expenditures on a per engine basis considering the time value of money and 

engine sales for 2006 through 2040, as shown in Table 5-11. 

Table 5-11 Estimated Engine Line Research per Engine (2005 dollars) 

Estimated Cost Allocation 

($Millions) 

Estimated Sales from 

2006 to 2040 

$/engine

Locomotive Switcher/Passenger $ 32.3 3,212 $ 10,050

Locomotive Line Haul $ 116.2 19,453 $ 5,970 

Small Commercial Marine $ 7.1 228,857 $ 30 

Recreational Marine $ 23.8 561,291 $ 40 

Marine C1 <600 kW $ 44.5 303,024 $ 150 

Marine C1 >600 kW $ 73.6 20,039 $ 3,670 

Marine C2 $ 80.1 6,647 $12,050 

Total $ 377.6 1,142,525 $ 330 

Note: Net present values of sales are calculated using zero as the sales figure for 2006. 

5.2.1.2 Engine-Related Tooling Costs 

Once engines are ready for production, new tooling will be required to accommodate 

the assembly of the freshly manufactured engines. In the 2007 heavy-duty highway rule, we 

estimated approximately $1.6 million per engine line for tooling costs associated with 

DPF/NOx aftertreatment systems. For the NRT4 rule, we estimated that a manufacturer that 

sold only into the landbased nonroad market would incur the same amount – $1.65 million 

expressed in 2002 dollars – for each engine line that required a DPF/NOx aftertreatment 

system. In this rule, we estimate the same level of tooling costs associated with DPF/NOx

aftertreatment for those manufacturers selling only into the locomotive/marine markets, or 

$1.8 million in 2005 dollars. We have estimated the same level of tooling costs as in the 2007 

highway and NRT4 rules because we expect freshly manufactured locomotive/marine engines 

to use technologies with similar tooling needs (i.e., a DPF and a NOx aftertreatment device). 

For those manufacturers that sell into the highway and/or nonroad markets and have, 

therefore, already made considerable tooling investments, we have estimated an expenditure 

of 25 percent of this amount, or $450,000, for those engine lines that will require DPF/NOx

aftertreatment systems for the locomotive/marine market. These costs are assigned equally to 

NOx+NMHC control and PM control since the tooling for one should be no more costly than 

that for the other. 

The tooling estimates discussed above represent our estimates, per engine line, for 

engine lines expected to meet the Tier 4 requirements. As noted above in our discussion of 

engine line research, we estimate 31 engine lines that will incur these costs. Of those 31 lines, 

we estimate that five belong to manufacturers selling exclusively into the locomotive and/or 
Regulatory Impact Analysis 

5-18 

marine markets. The remaining 26 lines belong to manufacturers that also sell into the 

highway and/or nonroad markets. The resultant tooling expenditures associated with the Tier 

4 standards are then $22.1 million. 

For meeting the Tier 3 requirements, we have estimated lower costs per line because 

the engines will require far less in terms of new hardware and, in fact, are expected only to 

require upgrades to existing hardware (i.e., new fuel systems). As such, we have estimated 

that those manufacturers selling exclusively into the locomotive and/or marine markets will 

spend $450,000 per engine line, while manufacturers that also sell into the highway and/or 

nonroad markets will spend $180,000 per engine line. The PSR database shows 88 engine 

lines that we expect to meet the Tier 3 standards, 13 of which belong to manufacturers that 

sell only into the locomotive and/or marine markets. The resultant tooling expenditures 

associated with the Tier 3 standards are then $19.4 million. As with the Tier 4 tooling costs, 

these costs are assigned equally to NOx control and PM control. 

We have applied tooling costs by engine line assuming that engines in the same line 

are produced on the same production line. Typically, the same basic diesel engine design can 

be increased or decreased in size by simply adding or subtracting cylinders. As a result, a 

four-, six-, or eight-cylinder engine may be produced from the same basic engine design. 

While these engines have different total displacement, they each have the same displacement 

per cylinder. Using the PSR database, we grouped each engine manufacturer’s engines into 

distinct engine lines using increments of 0.5 liters per cylinder. This way, engines having 

similar displacements per cylinder are grouped together and are considered to be built on the 

same production line. Note that a tooling expenditure for a single engine line may cover 

engines over several market segments. To allocate the tooling expenditure for a given 

production line to a specific market segment, we have divided costs equally among the 

segments (i.e., an engine line used in both the marine C1 and the locomotive switchers 

segments would have its tooling costs split evenly between those two segments). 

We estimate that the tooling expenditures would be made one year in advance of 

meeting the standards for which the money is spent. A summary of the tooling costs per 

manufacturer are shown in Table 5-12. The tooling costs by market segment are shown in 

Table 5-13 and the annual cost streams are shown in Table 5-14. 

Table 5-12 Estimated Tooling Expenditures by Type of Engine Manufacturer Totals per Engine Line 

($Million, 2005 dollars) 

Tier 3 engine lines Tier 4 engine lines 

Manufacturer sells into highway and/or nonroad markets $ 0.18 $ 0.45 

Manufacturer sells only into locomotive and/or marine markets $ 0.45 $ 1.8 

% allocated to PM 50% 50% 

% allocated to NOx+NMHC 50% 50% 

Note: We have attributed the tooling costs equally to NOx+NMHC and PM control because we have no 

reason to believe that the tooling costs would be greater for one than the other. 
Engineering Cost Estimates 

5-19 

Table 5-13 Estimated Engine Tooling Expenditures by Market Segment and Tier ($Million, 2005 dollars) 

Segment Tier 3 Tier 4 Total 

Marine C1 <600 kW $ 7.9 $ 0 $ 7.9 

Marine C1 >600 kW $ 1.9 $ 7.8 $ 9.7 

Marine C2 $ 2.6 $ 8.9 $ 11.5

Recreational Marine $ 4.2 $ 0 $ 4.2 

Small Commercial Marine $ 1.2 $ 0 $ 1.2 

Locomotive Switcher $ 1.0 $ 3.1 $ 4.1 

Locomotive Line Haul $ 0.6 $ 2.3 $ 2.8 

Total $ 19.4 $ 22.1 $ 41.4
Regulatory Impact Analysis 

5-20 

Table 5-14 Estimated Tier 3 and Tier 4 Engine Tooling Expenditures by Year ($Millions, 2005 dollars) 

Locomotive Marine 

Calendar Year Switchers Line-Haul Subtotal Marine C1 Marine C2 Recreational Small Commercial Subtotal Total Spen

2006 2007 2008 2009 2010 2011 $1.0 $0.6 $1.6 $9.8 $2.6 $4.2 $1.2 $17.8 $19.4 2012 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2013 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2014 $3.1 $2.3 $5.4 $0.0 $0.0 $0.0 $0.0 $0.0 $5.4 2015 $0.0 $0.0 $0.0 $7.8 $8.9 $0.0 $0.0 $16.7 $16.7 2016 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2017 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2018 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2019 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2020 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2021 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2022 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2023 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2024 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2025 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2026 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2027 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2028 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2029 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2030 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2031 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2032 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2033 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2034 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2035 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2036 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2037 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2038 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2039 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 2040 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 Total $4.1 $2.8 $6.9 $17.6 $11.5 $4.2 $1.2 $34.5 $41.4 

NPV at 7% $2.4 $1.6 $4.0 $10.5 $6.2 $2.8 $0.8 $20.3 $24.3 NPV at 3% $3.2 $2.2 $5.4 $14.0 $8.8 $3.5 $1.0 $27.3 $32.7 
Engineering Cost Estimates 

5-21 

As shown in Table 5-14, the net present value of the engine tooling expenditures are 

estimated at $33 million using a three percent discount rate, and $24 million using a seven 

percent discount rate. We can estimate these expenditures on a per engine basis considering 

the time value of money and engine sales for 2006 through 2040, as shown in Table 5-15. 

Table 5-15 Estimated Engine Tooling Costs per Engine (2005 dollars) 

 Estimated Cost 

Allocation ($Millions) 

Estimated Sales from 2006 to 2040 $/engine

Locomotive Switcher/Passenger $ 3.2 3,212 $ 1,000 

Locomotive Line Haul $ 2.2 19,453 $ 110 

Small Commercial Marine $ 1.0 228,857 $ 4 

Recreational Marine $ 3.5 561,291 $ 10 

Marine C1 <600 kW $ 8.2 303,024 $ 30 

Marine C1 >600 kW $ 5.8 20,039 $ 290 

Marine C2 $ 8.8 6,647 $ 1,320 

Total $ 32.7 1,142,525 $ 30 

Note: Net present values of sales are calculated using zero as the sales figure for 2006. 

5.2.1.3 Engine Certification Costs 

Manufacturers would incur more than the normal level of certification costs during the 

first few years of implementation because all engines would need to be fully certified to the 

new emission standards rather than using the normal practice of carrying certification data 

over from prior years.E

 Consistent with our past locomotive and marine standard setting 

regulations, we have estimated engine certification costs as shown in Table 5-16. These costs 

are consistent with past rulemakings, but have been updated to 2005 dollars. Certification 

costs (for engines in all market segments) apply equally to all engine families for all 

manufacturers regardless of the markets into which the manufacturer sells. 

 

E

 Note that all engines are certified every year, but most annual certifications involve carrying over test data from 

prior years since the engine being certified has not changed in an “emissions-meaningful” way. Since new 

standards preclude use of carry-over data, we estimate new certification costs for all engines. Note that this is, 

effectively, a conservative estimate since some engines would have changed sufficiently absent our new 

standards to require new certification data. 
Regulatory Impact Analysis 

5-22 

Table 5-16 Certification Costs per Engine Family (2005 dollars) 

 $/engine family # of engine families 

Locomotive $ 42,000 46 

Small Commercial Marine $ 32,000 24 

Marine C1 0.9<L/cyl<1.2 $ 32,000 7 

Marine C1 1.2<L/cyl<2.5 $ 43,000 19 

Marine C1 L/cyl>2.5 $ 54,000 13 

Marine C2 L/cyl>5 $ 54,000 5 

To determine the number of engine families to be certified, we looked at our 

certification databases for the 2004 model year. For marine engines, our database provides 

the number of engine families, the liters per cylinder for each, and specifies whether it is 

certified as a C1 or a C2 engine. For locomotive engines, the database provides the engine 

displacement. We have also split the Marine C1 certification costs evenly between the C1 

Marine and Recreational Marine market segments in the Tier 3 timeframe. In the Tier 4 

timeframe, only those C1 Marine engines over 600 kW would incur certification costs since 

those C1 engines under 600 kW will not be meeting the Tier 4 standards. For the small 

commercial marine segment, we have estimated the number of engine families at 24 based on 

an estimated two families per each of 10 manufacturers selling into that market, and then 

another four families sold by marinizers. The costs for small commercial marine would be 

incurred only in the Tier 3 timeframe since they will not be meeting the Tier 4 standards. 

Similarly, the locomotive certification costs have been split evenly between locomotive 

switchers and locomotive line haul for both Tiers 3 and 4. The resultant annual cost streams 

are shown in Table 5-17. As shown in the table, the Tier 3 certification costs are estimated at 

$4.7 million, while the Tier 4 certification costs are estimated at around $2.8 million. 

The total certification expenditures are estimated at $7.4 million, or $6.0 million at a 

three percent discount rate and $4.6 million at a seven percent discount rate. The table also 

makes clear what portion of the costs are allocated to NOx+NMHC and PM, with a 50/50 

allocation. 

We can estimate these expenditures on a per engine basis considering the time value 

of money and engine sales for 2006 through 2040, as shown in Table 5-18. 
Engineering Cost Estimates 

5-23 

Table 5-17 Estimated Engine Certification Costs by Year ($Millions, 2005 dollars) 

Locomotive Marine Totals 

Calenda

r Year Switchers LineHaul 

Marine 

C2 

Marine 

C1 Recreational 

Small 

Commercial 

Total 

Spent PM NOx+ 

NMHC 

2006 

2007 

2008 

2009 

2010 

2011 $1.0 $1.0 $0.3 $0.9 $0.9 $0.8 $4.7 $2.4 $2.4 

2012 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 

2013 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 

2014 $1.0 $1.0 $0.0 $0.0 $0.0 $0.0 $1.9 $1.0 $1.0 

2015 $0.0 $0.0 $0.3 $0.4 $0.0 $0.0 $0.7 $0.4 $0.4 

2016 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 

2017 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 

2018 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 

2019 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 

2020 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 

2021 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 

2022 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 

2023 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 

2024 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 

2025 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 

2026 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 

2027 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 

2028 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 

2029 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 

2030 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 

2031 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 

2032 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 

2033 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 

2034 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 

2035 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 

2036 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 

2037 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 

2038 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 

2039 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 

2040 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 

Total $1.9 $1.9 $0.5 $1.3 $0.9 $0.8 $7.4 $3.7 $3.7 

NPV at 7% $1.2 $1.2 $0.3 $0.8 $0.6 $0.5 $4.6 $2.3 $2.3 

NPV at 3% $1.5 $1.5 $0.4 $1.1 $0.7 $0.6 $6.0 $3.0 $3.0 

Table 5-18 Estimated Engine Certification Costs per Engine (2005 dollars) 

Estimated Total Cost 

Allocation ($Millions) 

Estimated Sales from 2006 to 2040 $/engine

Locomotive 

Switcher/Passenger 

$ 1.5 3,212 $ 480 

Locomotive Line Haul $ 1.5 19,453 $ 80 

Small Commercial Marine $ 0.7 228,857 $ 3 

Recreational Marine $ 0.7 561,291 $ 1 

Marine C1 $ 1.1 323,064 $ 3 

Marine C2 $ 0.4 6,647 $ 60 

Total $ 6.0 1,142,525 $ 10 

Note: Net present values of sales are calculated using zero as the sales figure for 2006. 
Regulatory Impact Analysis 

5-24 

Note that these certification costs may overestimate actual costs because they assume 

all engines would be certified as a result of the new emission standards. However, some 

engines would have been scheduled for new certification independent of the new standards 

due to design changes or power increases among other possible reasons. For such engines, 

the incremental certification cost would be zero. However, to remain conservative, here we 

have applied the certification costs to all engine families. 

5.2.2 Variable Engineering Costs 

Engine variable costs are those costs for new hardware required to meet the new Tier 4 

emission standards. We have estimated no incremental hardware costs associated with the 

Tier 3 standards. Unlike the Tier 4 standards, the Tier 3 standards are not based on the 

introduction of new emission control technologies on locomotive or marine diesel engines. 

Rather, the Tier 3 standards represent the largest level of emission reductions possible from 

the emission control systems we project that locomotive and marine engines will already have 

in the timeframe of Tier 3 implementation. For example, the marine Tier 3 standards are 

predicated on the use of the most modern nonroad Tier 4 base engine technologies without the 

use of the nonroad Tier 4 aftertreatment based emission solutions. While these base engines 

may represent significant technical advances from the marine Tier 2 engines they replace—

having better high pressure fuel systems, better injectors, improved turbochargers, and more 

sophisticated electronic control units—we do not expect the manufacturing costs for these 

individual components to increase over the cost of the Tier 2 components they will replace. 

In fact, the shift from the Tier 2 engine’s electronic unit pump system to the Tier 3 engine’s 

common rail fuel system may actually result in a fuel system that is cheaper to produce, not 

more expensive. Similarly, while the processing power of the Tier 3 engine control computer 

may increase significantly, the cost of the computer chip that makes this possible is likely to 

be lower. This does not mean that the Tier 3 emission controls come for free. We project 

there will be costs incurred to optimize the control strategies to meet the stringent Tier 3 

standards and further to test and certify these engines. These costs are accounted for as fixed 

costs described further in section 5.2.1 of this RIA.F

For the variable cost estimates presented here, we have used the same methodology to 

estimate costs as was used in our 2007 highway and our NRT4 rules. Because of the wide 

variation of engine sizes in the locomotive and marine markets, we have chosen an approach 

that results not in a specific cost per engine for engines within a given power range or market 

segment, but rather a set of equations that can be used to determine the variable costs for any 

engine provided its displacement and number of cylinders are known. Using the equations 

 

F

 To clarify, we have analyzed the fixed costs associated with the switch from unit injectors to common rail fuel 

systems reflecting our belief that this transition will come in part because of our regulation. Because we estimate 

that common rail fuel systems will be no more expensive than unit injector systems, and may in fact be cheaper, 

we have made no estimate of an incremental increase in variable costs due to this switch. Similarly, we have not 

made an estimate of what savings (if any) might be realized from this switch. 
Engineering Cost Estimates 

5-25 

presented in this section, we have then estimated the engine variable costs for the sales 

weighted average engine in different power ranges within each market segment.G

The discussion here considers both near-term and long-term cost estimates. We 

believe there are factors that cause hardware costs to decrease over time, making it 

appropriate to distinguish between near-term and long-term costs. Research in the costs of 

manufacturing has consistently shown that as manufacturers gain experience in production, 

they are able to apply innovations to simplify machining and assembly operations, use lower 

cost materials, and reduce the number or complexity of component parts, all of which allows 

them to lower the per-unit cost of production. These effects are often described as the 

manufacturing learning curve.9

The learning curve is a well documented phenomenon dating back to the 1930s. The 

general concept is that unit costs decrease as cumulative production increases. Learning 

curves are often characterized in terms of a progress ratio, where each doubling of cumulative 

production leads to a reduction in unit cost to a percentage “p” of its former value (referred to 

as a “p cycle”). Organizational learning, which brings about a reduction in total cost, is 

caused by improvements in several areas. Areas involving direct labor and material are 

usually the source of the greatest savings. Examples include, but are not limited to, a 

reduction in the number or complexity of component parts, improved component production, 

improved assembly speed and processes, reduced error rates, and improved manufacturing 

process. These all result in higher overall production, less scrappage of materials and 

products, and better overall quality. As each successive p cycle takes longer to complete, 

production proficiency generally reaches a relatively stable plateau, beyond which increased 

production does not necessarily lead to markedly decreased costs. 

Companies and industry sectors learn differently. In a 1984 publication, Dutton and 

Thomas reviewed the progress ratios for 108 manufactured items from 22 separate field 

studies representing a variety of products and services.10 The distribution of these progress 

ratios is shown in Figure 5-1. Except for one company that saw increasing costs as 

production continued, every study showed cost savings of at least five percent for every 

doubling of production volume. The average progress ratio for the whole data set falls 

between 81 and 82 percent. Other studies (Alchian 1963, Argote and Epple 1990, Benkard 

1999) appear to support the commonly used p value of 80 percent, i.e., each doubling of 

cumulative production reduces the former cost level by 20 percent. 

 

G For example, if two engines are sold with one being 100 hp and having 5 sales, the other being 200 hp and 

having 20 sales, the sales weighted horsepower of engines sold would not be 150 hp but would instead be 180 hp 

(100x5 + 200x20 = 4,500; 4,500/25 = 180). 
Regulatory Impact Analysis 

5-26 

Figure 5-1 Distribution of Progress Ratios (Dutton and Thomas 1984) 

55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99 101 103 105 107

Progress Ratio

0

5

10

15

Frequency

From 22 field studies (n = 108).

Distribution of Progress Ratios

The learning curve is not the same in all industries. For example, the effect of the 

learning curve seems to be less in the chemical industry and the nuclear power industry where 

a doubling of cumulative output is associated with 11 percent decrease in cost (Lieberman 

1984, Zimmerman 1982). The effect of learning is more difficult to decipher in the computer 

chip industry (Gruber 1992). 

We believe the learning curve is appropriate to consider in assessing the cost impact of 

diesel engine emission controls. The learning curve applies to new technology, new 

manufacturing operations, new parts, and new assembly operations. Neither locomotive nor 

marine diesel engines currently use any form of NOx or PM aftertreatment except in very 

limited retrofit applications. Therefore, these are new technologies for these engines and will 

involve some new manufacturing operations, new parts, and new assembly operations beyond 

those anticipated in response to the 2007 highway and NRT4 rules. Since this will be a 

freshly manufactured product, we believe this is an appropriate situation for the learning 

curve concept to apply. Opportunities will exist to reduce unit labor and material costs and 

increase productivity as discussed above. We believe a similar opportunity exists for the new 

control systems that will integrate the function of the engine and emission-control 

technologies. While impacted diesel engines beginning with Tier 3 compliance are expected 

to have the basic components of this system—advanced engine control modules (computers), 

advanced engine air management systems (cooled EGR, and variable geometry 

turbocharging), and advanced electronic fuel systems including common rail systems—they 

will be applied in some new ways in response to the Tier 4 standards. Additionally some new 

components will be applied for the first time. These freshly manufactured parts and 

assemblies will involve new manufacturing operations. As manufacturers gain experience 

with these systems, comparable learning is expected to occur with respect to unit labor and 
Engineering Cost Estimates 

5-27 

material costs. These changes require manufacturers to start new production procedures, 

which will improve with experience. 

We have applied a p value of 80 percent beginning with the first year of introduction 

of any new technology. That is, variable costs were reduced by 20 percent for each doubling 

of cumulative production following the year in which the technology was first introduced in a 

given market segment. Because the timing of the emission standards in this final rule follows 

that of the 2007 highway and NRT4 rules, we have used the first stage of learning done via 

those rules collectively as the starting point of learning for locomotive and marine engines. In 

other words, one learning phase is factored into the baseline costs for locomotive/marine 

engines. We have then applied one additional learning step from that baseline. In the 2007 

highway rule, we applied a second learning step following the second doubling of production 

estimated to occur at the end of the 2010 model year. We could have chosen that point as our 

baseline case for this rule and then applied a single learning curve effect from there. Instead, 

to remain conservative, we have chosen to use only the first learning step from the 

highway/nonroad rules. The approach taken here is consistent with the approaches taken in 

our Tier 2 light-duty highway rule and the 2007 highway rule for heavy-duty gasoline 

engines. There, compliance was being met through improvements to existing technologies 

rather than the development of new technologies. We argued in those rules that, with existing 

technologies, there is less opportunity for lowering production costs. For that reason, we 

applied only one learning curve effect. The situation is similar for locomotive and marine 

engines. Because these will be existing technologies by the time they are introduced into the 

market, there would arguably be less opportunity for learning than there will be for the 

highway engines on which the technologies were first introduced. 

Another factor that plays into our near-term and long-term cost estimates is that for 

warranty claim rates. In our 2007 highway rule, we estimated a warranty claim rate of one 

percent. Subsequent to that rule, we learned from industry that repair rates can be as much as 

two to three times higher during the initial years of production for a new technology relative 

to later years.11 As a result, in our NRT4 rule, we applied a three percent warranty claim rate 

during the first two years and then one percent warranty claim rate thereafter. We have used 

the same approach here as used in the NRT4 rule. This difference in warranty claim rates, in 

addition to the learning effects discussed above, is reflected in the different long-term costs 

relative to near-term costs. 

5.2.2.1 SCR System Costs 

The NOx aftertreatment system anticipated for the Tier 4 standards is the selective 

catalytic reduction (SCR) system. For the SCR system to function properly, a systems 

approach that includes a reductant metering system and control of engine-out NOx emissions 

is necessary. Many of the new air handling and electronic system technologies developed to 

meet past locomotive and marine standards, and past highway and nonroad standards can be 

applied to accomplish the SCR system control functions as well. Some additional hardware 

for exhaust NOx or oxygen sensing may also be required. 

We have used the same methodology to estimate costs associated with SCR systems as 

was used in our 2007 highway and NRT4 rulemakings for other aftertreatment devices. The 
Regulatory Impact Analysis 

5-28 

basic components of the SCR system are well known and include the following material 

elements:H

• a ceramic substrate upon which a NOx catalyst washcoating is applied; 

• a can to hold and support the substrate; 

• a reductant injector and associated plumbing; 

• an exhaust gas sensor (e.g., a NOx sensor) used for control. 

Examples of these material costs are summarized in Table 5-19 and represent costs to 

the engine manufacturers inclusive of supplier markups. The manufacturer costs shown in 

Table 5-19 include additional markups to account for both manufacturer and dealer overhead 

and carrying costs. The application of overhead and carrying costs is consistent with the 

approach taken in the 2007 highway and NRT4 rulemakings. In those rules, we estimated the 

markup for catalyzed emission-control technologies based on input from catalyst 

manufacturers. Specifically, we were told that device manufacturers could not mark up the 

cost of the individual components within their products because those components consist of 

basic commodities (for example, precious metals used in the catalyst could not be arbitrarily 

marked up because of their commodity status). Instead, manufacturing entities could mark up 

costs only where they add a unique value to the product. In the case of catalyst systems, the 

underlying cost of precious metals, catalyst substrates, PM filter substrates, and canning 

materials were well known to both buyer and seller and no markup or profit recovery for 

those component costs could be realized by the catalyst manufacturer. In essence, these are 

components to which the supplier provides little value-added engineering. 

The one component that is unique to each catalyst manufacturer (i.e., the component 

where they add a unique value) is the catalyst washcoat support materials. This mixture 

(which is effectively specialized clays) serves to hold the catalytic metals in place and to 

control the surface area of the catalytic metals available for emission control. Although the 

price for the materials used in the washcoat is almost negligible (i.e., perhaps one or two 

dollars), we have estimated a substantial cost for washcoating based on the engineering value 

added by the catalyst manufacturer in this step. This is reflected in the costs presented for 

SCR systems and DPF systems. This portion of the cost estimate – the washcoating – is 

where the catalyst manufacturer recovers the fixed cost for research and development as well 

as realizes a profit. To these manufacturer costs, we have added a four percent carrying cost 

to account for the capital cost of the extra inventory, and the incremental costs of insurance, 

handling, and storage. A dealer carrying cost is also included to cover the cost of capital tied 

up in extra inventory. Considering input received from industry, we have adopted this 

 

H Note that our draft cost analysis included costs for the urea storage tank and computer controller in the SCR 

system costs. For our final cost analysis, we have removed those costs from the SCR system and are, instead, 

accounting for those costs in our discussion of equipment related variable costs. We have also made a 

corresponding reduction in the labor costs for SCR systems since the urea tank and controller labor is now being 

considered at the equipment level. That discussion is in section 5.3.2 of this RIA. 
Engineering Cost Estimates 

5-29 

approach of estimating individually the manufacturer and dealer markups in an effort to better 

reflect the value each entity adds at various stages of the supply chain.12 Also included is our 

estimate of warranty costs for the system.
Regulatory Impact Analysis 

5-30 

Table 5-19 SCR System Costs (costs shown are costs per SCR system for the given engine power/displacement, 20

Typical Engine Power (kW) 7 25 57 187 375 Typical Engine Displacement (Liter) 0.4 1.5 3.9 7.6 18.0 Material and component costs  Catalyst Volume (Liter) 1.0 3.8 9.8 19.1 45.0  Substrate $29 $113 $294 $573 $1,350  Washcoating and Canning $423 $517 $721 $1,035 $1,910  Platinum $0 $0 $0 $0 $0  Catalyst Can Housing $12 $12 $13 $15 $20  Urea Injection Assembly $500 $527 $585 $674 $922  Reductant Solution Tank & Controls (see section 5.3.2) $0 $0 $0 $0 $0  NOx sensor (1 sensor/engine) $200 $200 $200 $200 $200  DOC for cleanup $236 $255 $297 $362 $543 Direct Labor Costs  Estimated Labor hours 3 3 3 3 3  Labor Rate ($/hr) $18 $18 $18 $18 $18  Labor Cost $54 $54 $54 $54 $54  Labor Overhead @ 40% $22 $22 $22 $22 $22 Total Direct Costs to Mfr. $1,476 $1,699 $2,186 $2,935 $5,021  Warranty Cost (3% claim rate) $111 $128 $164 $220 $377  Mfr. Carrying Cost - Near term $59 $68 $87 $117 $201  Total Cost to Dealer - Near term $1,646 $1,895 $2,438 $3,273 $5,599  Dealer Carrying Cost - Near term $49 $57 $73 $98 $168  Baseline Cost to Buyer - Near term $1,695 $1,952 $2,511 $3,371 $5,767 Loco/Marine Cost to Buyer (includes highway learning) - Near term $1,356 $1,561 $2,009 $2,697 $4,613  Warranty Cost (1% claim rate) $37 $43 $55 $73 $126  Mfr. Carrying Cost - Long term $59 $68 $87 $117 $201  Total Cost to Dealer - Long term $1,572 $1,810 $2,329 $3,126 $5,348  Dealer Carrying Cost - Long term $47 $54 $70 $94 $160  Baseline Cost to Buyer - Long term $1,619 $1,864 $2,399 $3,220 $5,508  Baseline Cost to Buyer (includes Highway Learning) - Long term $1,295 $1,491 $1,919 $2,576 $4,406 Loco/Marine Cost to Buyer (includes Loco/Marine learning) - Long term $1,036 $1,193 $1,535 $2,061 $3,525 
Engineering Cost Estimates 

5-31 

We have estimated the cost of this system based on information from several 

reports.13, 14, 15 The individual estimates and assumptions used to estimate the cost for the 

system are touched upon in the following paragraphs. 

SCR Catalyst Volume 

During development of this rule, engine and aftertreatment device manufacturers have 

indicated that SCR catalyst volumes could be from one to three times engine displacement for 

locomotive and marine applications. As explained in Chapter 4 of this RIA, we have used a 

ratio of SCR volume to engine displacement equal to 2.5:1. 

SCR Catalyst Substrate 

The ceramic flow-through substrates used for the SCR catalyst were estimated to cost 

$30 per liter. 

SCR Catalyst Washcoating and Canning 

We have estimated a “value-added” engineering and material product, called 

washcoating and canning, based on feedback from members of the Manufacturers of Emission 

Control Association (MECA). By using a value-added component that accounts for fixed 

costs (including R&D), overhead, marketing and profits from likely suppliers of the 

technology, we can estimate this fraction of the cost for the technology apart from other 

components that are more widely available as commodities (e.g, precious metals and catalyst 

substrates). Based on conversations with MECA, we understand this element of the product 

to represent the catalyst manufacturer’s value added and, therefore, their opportunity for 

markup. As a result, the washcoating and canning costs shown in Table 5-19 represent costs 

with manufacturer markups included. The washcoating and canning costs can be expressed as 

$34(x) + $390, where x is the catalyst volume in liters. This washcoating cost is higher than 

our past rulemakings because of dual washcoating process we anticipate will be used to “zone 

coat” the diesel oxidation function onto a portion of the SCR catalyst (as discussed below). 

SCR Catalyst Precious Metals 

We expect that the SCR catalysts used in locomotive and marine applications will 

contain no precious metals (e.g., the platinum group metals platinum, palladium, and 

rhodium). As a result, we have estimated zero costs associated with these commodities. 

SCR Can Housing 

The material cost for the can housing is estimated based on the catalyst volume plus 

20 percent for transition (inlet/outlet) cones, plus 20 percent for scrappage (material 

purchased but unused in the final product) and a price of $1 per pound for 18 gauge stainless 

steel as estimated in a contractor report to EPA and converted into $2005.16
Regulatory Impact Analysis 

5-32 

Reductant Injection Assembly 

The costs for the reductant injection assembly are based in part on our past contractor 

report that estimated the costs at $250 to $300 for units meant for 12 to 26 liter catalysts. 

Here, we have adjusted the numbers based on recent conversations with industry by 

estimating the costs for the smallest engines at $500 and the largest at $5,000. We then used a 

linear interpolation to arrive at the costs for engines in between. 

Reductant Solution Tank and Brackets 

 These costs are now addressed in section 5.3.2 where we present equipment-related 

hardware costs. 

NOx Sensor Cost 

We believe that one sensor will be needed per catalyst and have used an estimated cost 

of $200 per sensor based on today’s cost of $300 for use in retrofit applications (retrofit 

applications are typically considerably more costly than new). With increased NOx sensor 

sales volumes in future locomotive, marine, highway, and nonroad markets, we believe that 

NOx sensor costs may well be in the $50 to $100 range, if not lower. For this analysis, 

reflecting the relatively low sales volumes of locomotive and marine engines relative to 

highway engines, we have chosen to remain conservative by using the $200 per sensor 

estimate. 

DOC for Cleanup 

Included in the costs for the SCR system are costs for a diesel oxidation catalyst 

(DOC) for clean-up of possible excess ammonia emissions that might occur as a result of 

excessive urea usage. The methodology used to estimate DOC costs is consistent with the 

SCR system cost methodology and is presented below in Table 5-20. These cost estimates 

use a DOC to engine displacement ratio of 0.8:1 because the low emissions conversion 

demand placed on the DOC is not expected to require a larger device. This ratio is higher 

than the ratio used in the draft cost analysis where we used a ratio of 0.5:1. The 0.8:1 ratio is 

consistent with our technological feasibility discussion in Chapter 4 of this RIA. 
Enginee

5-33 

Table 5-20 Diesel Oxidation Costs (costs shown are costs per SCR system for the given engine power/displacement, 

Typical Engine Power (kW) 7 25 57 187 375  Typical Engine Displacement (Liter) 0.4 1.5 3.9 7.6 18.0  Material and component costs  Catalyst Volume (liter) 0.3 1.2 3.1 6.1 14.4  Substrate $0 $0 $0 $0 $0  Washcoating and Canning $189 $201 $228 $270 $385  Platinum $2 $6 $16 $31 $73  Catalyst Can Housing $0 $0 $0 $0 $0 Direct Labor Costs  Estimated Labor hours 0.5 0.5 0.5 0.5 0.5  Labor Rate ($/hr) $18 $18 $18 $18 $18  Labor Cost $9 $9 $9 $9 $9  Labor Overhead @ 40% $4 $4 $4 $4 $4 Total Direct Costs to Mfr. $203 $220 $257 $313 $471  Warranty Cost - Near Term (3% claim rate) $17 $19 $21 $26 $37  Mfr. Carrying Cost - Near Term $8 $9 $10 $13 $19  Total Cost to Dealer - Near Term $229 $247 $289 $352 $527  Dealer Carrying Cost - Near Term $7 $7 $9 $11 $16 Loco/Marine Cost to Buyer $236 $255 $297 $362 $543 
Regulatory Impact Analysis 

5-34 

Important to note here is that we expect the DOC function to be fulfilled within the 

confines of the SCR catalyst using a process known as “zone coating” by which the DOC 

washcoat is applied to the tail end of the SCR catalyst substrate. By doing this, a physically 

separate DOC is not necessary. We have remained conservative in our cost analysis by 

including costs associated with canning of the DOC. 

Direct Labor Costs 

The direct labor costs for the catalyst are estimated based on an estimate of the number 

of hours required for assembly and established labor rates. Additional overhead for labor was 

estimated as 40 percent of the labor costs. 

SCR Warranty Costs 

We have estimated both near-term and long-term warranty costs. Near-term warranty 

costs are based on a three percent claim rate and an estimate of parts and labor costs per 

incident, while long-term warranty costs are based on a one percent claim rate and an estimate 

of parts and labor costs per incident.17 The labor rate is assumed to be $50 per hour with four 

hours required per claim, and parts costs are estimated to be 2.5 times the original 

manufacturing cost for the component. The calculation of near-term warranty costs for the 7 

kW engine shown in Table 5-19 is as follows: 

[($29+$423+$12+$500+$200+$236)(2.5) + ($50)(4hours)](3%) = $111 

Manufacturer and Dealer Carrying Costs 

The manufacturer’s carrying cost was estimated at 4 percent of the direct costs. This 

reflects primarily the costs of capital tied up in extra inventory, and secondarily the 

incremental costs of insurance, handling and storage. The dealer’s carrying cost was 

estimated at 3 percent of the incremental cost, again reflecting primarily the cost of capital 

tied up in extra inventory. 

SCR System Cost Estimation Function 

Using the example SCR system costs shown in the table, we calculated a linear 

regression to determine the SCR system cost as a function of engine displacement. This way, 

the function can be applied to the wide array of engines in the locomotive line haul and 

marine fleets to determine the total or per engine costs for SCR hardware. The functions 

calculated for SCR system costs in line-haul locomotives and marine applications are shown 

in Table 5-21. 

For locomotive switcher applications, we have used the costs developed for our NRT4 

rulemaking because locomotive switchers tend to be powered by land based nonroad engines. 

For this reason, it seemed most appropriate to use the same costs developed for that rule. 

These costs are also shown in Table 5-21.
Engineering Cost Estimates 

5-35 

Table 5-21 SCR System Costs as a Function of Engine Displacement, x, in Liters (2005 dollars) 

 Linear Regression R2

Line haul locomotive; marine Near-term cost function $185(x) + $1,293 0.999 

Long-term cost function $142(x) + $988 0.999 

Switcher locomotive Near-term cost function $103(x) + $183 0.999 

Long-term cost function $83(x) + $160 0.999 

Note: Near term costs include a 3 percent warranty claim rate while long term costs include a 1 percent 

warranty claim rate and the learning effect. 

This table shows both a near-term and a long-term cost function for SCR system costs. 

The near-term function incorporates the near-term warranty costs determined using a three 

percent claim rate, while the long-term function incorporates the long-term warranty costs 

determined using a one percent claim rate. Additionally, the long-term function incorporates 

learning curve effects. 

5.2.2.2 DPF System Costs 

One means of meeting the Tier 4 PM standard is to use a diesel particulate filter (DPF) 

system like that expected to be used for highway and NRT4 applications. However, as 

explained in Chapter 4 of this RIA, here we are projecting a DPF volume to engine 

displacement ratio of 1.7:1. In the highway and nonroad rules, we projected ratios of 1.5:1. 

For the DPF to function properly, a systems approach that includes precise control of engine 

air-fuel ratio is also necessary. Many of the new air handling and electronic fuel system 

technologies developed in order to meet the highway, nonroad, and past locomotive/marine 

standards can be applied to accomplish the DPF control functions as well. 

We have used the same methodology to estimate costs associated with DPF systems as 

was used in our 2007 highway and NRT4 rulemakings. The basic components of the DPF are 

well known and include the following material elements: 

• An oxidizing catalyst, typically platinum; 

• a substrate upon which the catalyst washcoating is applied and upon which PM is trapped; 

• a can to hold and support the substrate. 

Examples of these material costs are summarized in Table 5-22 and represent costs to 

the engine manufacturers inclusive of supplier markups. The total direct cost to the 

manufacturer includes an estimate of warranty costs for the DPF system. Hardware costs are 

additionally marked up to account for both manufacturer and dealer overhead and carrying 

costs. The manufacturer’s carrying cost was estimated to be four percent of the direct costs 

accounting for the capital cost of the extra inventory, and the incremental costs of insurance, 

handling, and storage. The dealer’s carrying cost was marked up three percent reflecting the 

cost of capital tied up in inventory. We have adopted this approach of estimating individually 

the manufacturer and dealer markups in an effort to better reflect the value added at each 
Regulatory Impact Analysis 

5-36 

stage of the supply chain based on industry input.18 Note that our final costs for DPF systems 

are identical to those presented in the draft cost analysis.
Enginee

5-37 

Table 5-22 DPF System Costs (costs shown are costs per DPF system for the given engine power/displacement, 20

Typical Engine Power (kW) 7 25 57 187 375  Typical Engine Displacement (Liter) 0.4 1.5 3.9 7.6 18.0  Material and component costs  Filter Volume (Liter) 0.7 2.6 6.7 13.0 30.6  Filter Trap $46 $176 $461 $898 $2,117  Washcoating and Canning $96 $111 $143 $192 $328  Platinum $41 $156 $408 $796 $1,874  Filter Can Housing $9 $10 $11 $12 $16  Differential Pressure Sensor $52 $52 $52 $52 $52 Direct Labor Costs  Estimated Labor hours 4 4 4 4 4  Labor Rate ($/hr) $18 $18 $18 $18 $18  Labor Cost $72 $72 $72 $72 $72  Labor Overhead @ 40% $29 $29 $29 $29 $29 Total Direct Costs to Mfr. $345 $606 $1,175 $2,051 $4,488  Warranty Cost -- Near Term (3% claim rate) $21 $41 $84 $149 $332  Mfr. Carrying Cost -- Near Term $14 $24 $47 $82 $180  Total Cost to Dealer -- Near Term $380 $671 $1,306 $2,282 $4,999  Dealer Carrying Cost -- Near Term $11 $20 $39 $68 $150  Savings by removing silencer ($52) ($52) ($52) ($52) ($52)  Baseline Cost to Buyer -- Near Term $340 $640 $1,293 $2,298 $5,098 Loco/Marine Cost to Buyer (includes highway learning) - Near term $272 $512 $1,035 $1,839 $4,078  Warranty Cost -- Long Term (1% claim rate) $7 $14 $28 $50 $111  Mfr. Carrying Cost -- Long Term $14 $24 $47 $82 $180  Total Cost to Dealer -- Long Term $366 $644 $1,250 $2,182 $4,778  Dealer Carrying Cost -- Long Term $11 $19 $38 $65 $143  Savings by removing muffler ($52) ($52) ($52) ($52) ($52)  Baseline Cost to Buyer -- Long Term $325 $611 $1,236 $2,196 $4,870  Baseline Cost to Buyer (includes Highway Learning) - Long term $260 $489 $989 $1,757 $3,896 Loco/Marine Cost to Buyer (includes Loco/Marine learning) - Long term $208 $391 $791 $1,405 $3,117 
Regulatory Impact Analysis 

5-38 

DPF Volume 

During development of this rule, engine manufacturers have suggested that DPF 

volumes could be up to three times engine displacement. The size of the DPF is based largely 

on the maximum allowable flow restriction for the engine. Generically, the filter size is 

inversely proportional to its resistance to flow (a larger filter is less restrictive than a similar 

smaller filter). In the 2007 highway and NRT4 rules, we estimated that the DPF would be 

sized to be 1.5 times the engine displacement based on the responses received from EMA and 

on-going research aimed at improving filter porosity control to give a better trade-off between 

flow restrictions and filtering efficiency. As explained in Chapter 4 of this RIA, here we have 

estimated a ratio of 1.7:1. 

DPF Substrate 

The DPF can be made from a wide range of filter materials including wire mesh, 

sintered metals, fibrous media, or ceramic extrusions. The most common material used for 

DPFs for heavy-duty diesel engines is cordierite. Here we have based our cost estimates on 

the use of silicon carbide (SiC) even though it is more expensive than other filter materials. 

In the 2007 highway rule, we estimated that DPFs would consist of a cordierite filter costing 

$30 per liter. To remain conservative in our cost estimates for nonroad applications, we 

assumed the use of silicon carbide filters costing double that amount, or $60 per liter, because 

silicon carbide filters are more durable. As discussed in Chapter 4 of this RIA, we believe 

that metal substrates may be choice for locomotive and marine DPFs, which would cost less 

than a silicon carbide substrate. Nonetheless, to be conservative in our cost estimates, we 

have assumed use of silicon carbide filters for locomotive and marine applications, so have 

based costs on the $60 per liter cost estimate. This cost is directly proportional to filter 

volume, which is proportional to engine displacement. We have converted the $60 value to 

$2005 using the Producer Price Index (PPI) for manufacturing industries; the end result being 

a cost of $62 per liter.19

DPF Washcoating and Canning 

These costs are based on costs developed under contract for our 2007 highway rule.20 

We converted those costs to $2005 using the PPI for manufacturing industries. We then 

calculated a linear “best fit” to express the washcoating and canning costs as $8(x) + $91, 

where x is the DPF volume in liters. 

DPF Precious Metals 

The total precious metal content for DFPs is estimated to be 60 g/ft3

 with platinum as 

the only precious metal used in the filter. In our NRT4 rule, we used a price of $542 per troy 

ounce for platinum. Here we have used the 2005 average monthly price of $899 per troy 

ounce for platinum.21

DPF Can Housing 

The material cost for the can housing is estimated based on the DPF volume plus 20 

percent for transition (inlet/outlet) cones, plus 20 percent for scrappage (material purchased 
Engineering Cost Estimates 

5-39 

but unused in the final product) and a price of $1 per pound for 18 gauge stainless steel as 

estimated in a contractor report to EPA and converted into $2005. 

DPF Differential Pressure Sensor 

We believe that the DPF system will require the use of a differential pressure sensor to 

provide a diagnostic monitoring function of the filter. A contractor report to EPA estimated 

the cost for such a sensor at $45.22 A PPI adjusted cost of $52 per sensor has been used in 

this analysis. 

DPF Direct Labor 

Consistent with the approach for SCR systems, the direct labor costs for the DPF are 

estimated based on an estimate of the number of hours required for assembly and established 

labor rates. Additional overhead for labor was estimated as 40 percent of the labor costs. 

DPF Warranty 

Consistent with the approach taken for SCR system costs, we have estimated both 

near-term and long-term warranty costs. Near-term warranty costs are based on a three 

percent claim rate and an estimate of parts and labor costs per incident, while long-term 

warranty costs are based on a one percent claim rate and an estimate of parts and labor costs 

per incident. The labor rate is estimated to be $50 per hour with two hours required per claim, 

and parts cost are estimated to be 2.5 times the original manufacturing cost for the component. 

DPF Manufacturer and Dealer Carrying Costs 

Consistent with the approach for SCR systems, the manufacturer’s carrying cost was 

estimated at four percent of the direct costs. This reflects primarily the costs of capital tied up 

in extra inventory, and secondarily the incremental costs of insurance, handling and storage. 

The dealer’s carrying cost was estimated at three percent of the incremental cost, again 

reflecting primarily the cost of capital tied up in extra inventory. 

Savings Associated with Silencer Removal 

DPF retrofits are often incorporated in, or are simply replacements for, the silencer 

(muffler) for diesel-powered vehicles and equipment. We believe that the DPF could be 

mounted in place of the silencer, although it may have slightly larger dimensions. We have 

estimated that applying a DPF allows for the removal of the silencer due to the noise 

attenuation characteristics of the DPF. We have accounted for this savings and have 

estimated a silencer costs at $52. The $52 estimate is an average for all engines; the actual 

savings will be higher for some and lower for others. 

DPF System Cost Estimation Function 

Using the example DPF costs shown in Table 5-22, we calculated a linear regression 

to determine the DPF system cost as a function of engine displacement. This way, the 

function can be applied to the wide array of engines in the locomotive line haul and/or marine 
Regulatory Impact Analysis 

5-40 

fleets to determine the total or per engine costs for DPF system hardware. The functions 

calculated for DPF system costs for locomotive line-haul and marine applications are shown 

in Table 5-23. 

For locomotive switcher applications, we have used the costs developed for our NRT4 

rulemaking because locomotive switchers tend to be powered by land based nonroad engines 

making it appropriate to use the same costs developed for that rule. These costs are also 

shown in Table 5-23. 

Table 5-23 DPF System Costs as a function of Engine Displacement, x, in Liters (2005 dollars) 

 Linear Regression R2

Line-haul locomotive; marine Near-term cost function $217(x) + $199 0.999 

Long-term cost function $166(x) + $153 0.999 

Switcher locomotive Near-term cost function $146(x) + $75 0.999 

Long-term cost function $112(x) + $57 0.999 

Note: Near term costs include a 3 percent warranty claim rate while long term costs include a 1 percent 

warranty claim rate and the learning effect. 

The near-term and long-term costs shown in Table 5-23 change due to the different 

warranty claim rates and the application of a 20 percent learning curve effect. 

5.2.2.3 Aftertreatment Marinization Costs 

For marine engines, the Tier 4 requirements will entail increased costs associated with 

marinizing the engines for the marine environment. Marine C1 and C2 engines are typically 

land based nonroad engines that are marinized for the marine environment. This marinization 

can take many forms, but is generally a matter of altering the cooling system to make use of 

sea or lake water rather than relying on ambient air since marine engines tend to be enclosed 

within vessels where ambient air radiators like those used in land based engines cannot 

operate efficiently. Such marinization efforts have been done for years and will continue but 

do not represent incremental costs associated with the new standards. Marinization costs 

associated with the new aftertreatment devices that would be added to comply with the Tier 4 

standards—to control the surface temperatures in the typically tight space constraints onboard 

a vessel—do represent incremental costs associated with the final program and, thus, they 

must be considered. 

Under contract to EPA, ICF International conducted a study that considered the costs 

associated with marinizing aftertreatment devices.23 In their study, ICF looked at the costs 

associated with two methods of marinization: triple wall stainless steel; and, insulating 

blankets. Both methods could be used to control the surface temperature of the aftertreatment 

device such that accidental touching would not cause burns or otherwise compromise safety. 

The triple wall insulation method proved more cost efficient. Using this method, the device 

would, essentially, have three layers of stainless steel surrounding the substrate rather than the 
Engineering Cost Estimates 

5-41 

single layer normally used on land based engines. These layers would be separated by a few 

millimeters to provide an insulating air gap. 

The ICF study looked at aftertreatment marinizing costs for a range of engine sizes in 

a manner similar to that discussed above for SCR and DPF systems. The details of these 

estimates are contained in the final report.24 In the report, ICF calculated costs using a 1:1 or a 

1.5:1 device volume to engine displacement ratio. However, as noted earlier, our analysis 

leads us to believe that a 2.5:1 ratio (SCR) and 1.7:1 ratio (DPF) are more applicable. As a 

result, we have adjusted the ICF results somewhat higher to reflect a larger sized device being 

insulated; these adjustments are reflected in Table 5-24 for marinization of SCR systems and 

in Table 5-25 for marinization of DPF systems. The resultant linear regression best fit curves 

for marinization costs as a function of engine displacement are shown in Table 5-26. 
Regulatory Impact Analysis 

5-42 

Table 5-24 SCR System Marinization Costs (2005 dollars) 

Typical Engine Power (kW) 64 93 183 620 968 1425 1902 38Typical Engine Displacement (L) 4.2 7 10.5 27 34.5 51.8 111 2SCR Catalyst Marinization Hardware Cost $23 $28 $29 $65 $77 $91 $173 $2Assembly $0 $0 $0 $0 $0 $0 $0  Labor @ $28/hr $3 $3 $3 $3 $3 $3 $3  Overhead @ 40% $1 $1 $1 $1 $1 $1 $1 Total Assembly Cost $4 $4 $4 $4 $4 $4 $4 Markup on Hardware and Assembly @ 29% $8 $9 $9 $20 $24 $28 $51 $Total SCR Catalyst Marinization Costs - Near term $34 $42 $42 $90 $105 $123 $228 $3Total SCR Catalyst Marinization Costs - Long term $27 $33 $34 $72 $84 $98 $182 $3

Table 5-25 DPF System Marinization Costs (2005 dollars) 

Typical Engine Power (kW) 64 93 183 620 968 1425 1902 38Typical Engine Displacement (L) 4.2 7 10.5 27 34.5 51.8 111 2DPF Marinization Hardware Cost $15 $22 $29 $52 $61 $75 $112 $2Assembly $0 $0 $0 $0 $0 $0 $0  Labor @ $28/hr $3 $3 $3 $3 $3 $3 $3  Overhead @ 40% $1 $1 $1 $1 $1 $1 $1 Total Assembly Cost $4 $4 $4 $4 $4 $4 $4 Markup on Hardware and Assembly @ 29% $6 $8 $9 $16 $19 $23 $34 $Total DPF Marinization Costs - Near term $25 $34 $42 $72 $84 $102 $150 $2Total DPF Marinization Costs - Long term $20 $27 $34 $58 $67 $81 $120 $2
Engineering Cost Estimates 

5-43 

Table 5-26 Marinization Costs as a function of Engine Displacement, x, in Liters (2005 dollars) 

 Linear Regression R2

SCR System Marinization Near-term cost function $1(x) + $42 0.990 

Long-term cost function $1(x) + $34 0.990 

DPF System Marinization Near-term cost function $1(x) + $35 0.991 

Long-term cost function $1(x) + $28 0.991 

Note: Near term costs include a 3 percent warranty claim rate while long term costs include a 1 percent 

warranty claim rate and the learning effect. 

5.2.2.4 Summary of Engine Variable Cost Equations 

Engine variable costs are discussed in detail in sections 5.2.2.1 through 5.2.2.3. As 

described in those sections, we have generated cost estimation equations for SCR systems, 

DPF systems, and aftertreatment marinization as a function of engine displacement. These 

equations are summarized in Table 5-27. Note that not all equations were used for all engines 

and all market segments; equations were used in the manner shown in the table. We have 

calculated the aggregate engine variable costs and present them later in this chapter. 
Regulatory Impact Analysis 

5-44 

Table 5-27 Summary of Cost Equations for Engine Variable Costs (x represents the dependent variable, 

2005 dollars) 

Engine Technology Time Frame Cost Equation Dependent 

Variable 

How Used 

SCR System Costs Near term 

Long term 

$185(x) + $1,293 

$142(x) + $988 

Engine 

Displacement 

(Liters) 

Tier 4 

Locomotive 

Line-haul and 

Marine Engines 

SCR System Costs Near term 

Long term 

$103(x) + $183 

$83(x) + $160 

Engine 

Displacement 

(Liters) 

Tier 4 

Locomotive 

Switcher 

Engines 

DPF System Costs Near term 

Long term 

$217(x) + $199 

$166(x) + $153 

Engine 

Displacement 

(Liters) 

Tier 4 

Locomotive 

Line-haul and 

Marine Engines 

DPF System Costs Near term 

Long term 

$146(x) + $75 

$112(x) + $57 

Engine 

Displacement 

(Liters) 

Tier 4 

Locomotive 

Switcher 

Engines 

SCR Marinization Costs Near term 

Long term 

$1(x) + $42 

$1(x) + $34 

Engine 

Displacement 

(Liters) 

Tier 4 Marine 

Engines 

DPF Marinization Costs Near term 

Long term 

$1(x) + $35 

$1(x) + $28 

Engine 

Displacement 

(Liters) 

Tier 4 Marine 

Engines 

Using these equations, we can calculate the variable costs associated with the Tier 4 

standards for any engine provided we know its displacement, power, and intended application. 

We could do this for every compliant engine expected to be sold in the years following 

implementation of the new standards, total the results, and we would have the total annual 

variable costs associated with the rule. We can achieve essentially the same thing by 

calculating a sales weighted variable cost. This could be done for a single engine that could 

represent the entire fleet provided we sales weighted the critical characteristics of that engine. 

Doing this for one engine would not provide a particularly good look at the impact of the new 

standards on costs since the sizes of engines, their power, and use varies so much. Therefore, 

we have broken the fleet first into the market segments according to our regulatory definitions 

(i.e., marine C1, marine C2, locomotive, etc.). We have further broken each market segment 

into several power ranges, some of which are arbitrary and meant only to provide more 

stratification of the results, and some of which are chosen to align properly with the structure 

of the new standards (e.g., marine C1 has a power cutpoint at 600 kW since the Tier 4 

standards apply to marine engines above 600 kW). 

The necessary engine characteristics for sales weighting are engine displacement, 

power, and application. We have used the PSR database and sales figures from 2002. The 

resultant sales weighted engines within given market segments and power ranges are shown in 

Table 5-28. For example, the sales weighted engine in the marine C1 segment, power range 
Engineering Cost Estimates 

5-45 

800 to 2000 hp, has an engine displacement of 33.4 liters and is 1266 hp (944 kW). Empty 

cells in the table mean that there are no engines in that power range and market segment. 

Table 5-28 Sales Weighted Engine Characteristics by Market Segment and Power Range 

Power Range Loco-LineHaul Loco-Switcher Marine C1 Marine C2 Marine 

Recreational 

Small 

Commercial 

Marine 

Sales Weighted Displacement (Liters) 

0<hp<25 0.0 0.0 0.0 0.0 0.0 0.6 

25<=hp<50 0.0 0.0 0.0 0.0 0.0 1.6 

50<=hp<75 0.0 2.7 2.5 0.0 2.6 0.0 

75<=hp<200 0.0 5.8 5.5 0.0 5.0 0.0 

200<=hp<400 0.0 7.7 10.5 0.0 4.9 0.0 

400<=hp<800 0.0 18.9 17.6 0.0 8.8 0.0 

800<=hp<2000 0.0 51.8 33.4 93.0 28.9 0.0 

>=2000hp 174.2 69.0 61.0 176.4 60.6 0.0 

Sales Weighted Horsepower 

0<hp<25 0.0 0.0 0.0 0.0 0.0 15.8 

25<=hp<50 0.0 0.0 0.0 0.0 0.0 36.0 

50<=hp<75 0.0 67.0 58.2 0.0 61.1 0.0 

75<=hp<200 0.0 157.7 149.6 0.0 159.1 0.0 

200<=hp<400 0.0 227.3 301.1 0.0 269.7 0.0 

400<=hp<800 0.0 660.0 553.2 0.0 457.2 0.0 

800<=hp<2000 0.0 1500.0 1266.3 1508.6 1226.1 0.0 

>=2000hp 4895.2 2000.0 2144.4 4014.5 2935.1 0.0 

Using these sales weighted engines shown in Table 5-28 and the variable cost 

equations shown in Table 5-27, we can calculate the individual piece costs for the various 

hardware elements expected to be added to engines to comply with the new standards. Those 

elements, as discussed above, being SCR systems, DPF systems, and costs associated with 

marinizing the SCR and the DPF systems (for marine engines only). The resultant piece costs 

are shown in Table 5-29. The table includes costs for engines in power ranges that are 

expected to add the new hardware or upgrade existing hardware. Empty cells reflect our 

belief that the technology will not be added as a result of our final rule. The rows containing 

data for “All engines” are costs for the sales weighted engine within each market segment. 

For Marine C1, we have also broken out the sales weighted costs for engines below and above 

600 kW (805 hp). We use these values—those for “All engines” or, for the C1 marine 

segment, those for “<600 kW” or “>600 kW”—for our total cost calculations presented in 

section 5.6. 
Regulatory Impact Analysis 

5-46 

Table 5-29 Piece Costs for Engine Hardware by Market Segment and Power Range (2005 dollars) 

Power Range Line-Haul Switchers Marine C1 Marine C2 Rec Marine Power Range Line-Haul Switchers 

SCR System Costs - Near term SCR System C

 0<hp<25 0<hp<25  25<=hp<50 25<=hp<50  50<=hp<75 $460 50<=hp<75 $381  75<=hp<200 $778 75<=hp<200 $635  200<=hp<400 $979 200<=hp<400 $796  400<=hp<800 $2,140 400<=hp<800 $1,723 800<=hp<2000 $5,532 $7,485 $18,527 00<=hp<2000 $4,431 2000+ $33,567 $7,315 $12,591 $33,988 2000+ $25,651 $5,855 All engines $33,567 $1,639 $30,476 All engines $25,651 $1,323 <800 hp only $852 <800 hp only $695 >800 hp only $6,449 $8,782 >800 hp only $5,163 

SCR Marinization Costs - Near term SCR Marinization 

 0<hp<25 0<hp<25  25<=hp<50 25<=hp<50  50<=hp<75 50<=hp<75  75<=hp<200 75<=hp<200 

200<=hp<400 200<=hp<400 400<=hp<800 400<=hp<800 

800<=hp<2000 $91 $178 800<=hp<2000 2000+ $131 $300 2000+ All engines $272 All engines <800 hp only <800 hp only >800 hp only $101 >800 hp only 

DPF System Costs - Near term >2000kW only DPF System C

 0<hp<25 0<hp<25  25<=hp<50 25<=hp<50  50<=hp<75 $467 50<=hp<75 $357  75<=hp<200 $918 75<=hp<200 $702  200<=hp<400 $1,203 200<=hp<400 $920  400<=hp<800 $2,847 400<=hp<800 $2,177 800<=hp<2000 $7,650 $7,437 $20,344 800<=hp<2000 $5,850 2000+ $37,924 $10,175 $13,405 $38,416 2000+ $28,982 $7,781 All engines $37,924 $2,137 $34,312 All engines $28,982 $1,634 <800 hp only $1,023 <800 hp only $782 >800 hp only $8,949 $8,953 >800 hp only $6,843 

DPF Marinization Costs - Near term DPF Marinization 

 0<hp<25 0<hp<25  25<=hp<50 25<=hp<50  50<=hp<75 50<=hp<75  75<=hp<200 75<=hp<200 

200<=hp<400 200<=hp<400 400<=hp<800 400<=hp<800 

800<=hp<2000 $71 $135 800<=hp<2000 2000+ $101 $225 2000+ All engines $205 All engines <800 hp only <800 hp only >800 hp only $79 >800 hp only 
Engineering Cost Estimates 

5-47 

5.2.2.5 Annual Engine Variable Engineering Costs 

Using the hardware piece costs shown in Table 5-29, we can calculate the annual costs 

for each market segment by multiplying piece costs by estimated future sales. Table 5-30 

through Table 5-34 show these costs. These costs are associated with the Tier 4 standards 

since only Tier 4 engines are expected to incur new hardware costs. The PM/NOx+NMHC 

cost allocations for engine variable costs used in this cost analysis are as follows: SCR 

systems including marinization costs on marine applications are attributed 100% to 

NOx+NMHC control; and DPF systems including marinization costs on marine applications 

are attributed 100% to PM control. 

Table 5-30 Annual Locomotive Line-haul Engine Variable Costs; Freshly Manufactured Tier 4 Engines 

Only ($Millions, 2005 dollars) 

Calendar 

Year Sales DPF SCR Total PM NOx+ 

NMHC 

2006 

2007 

2008 

2009 

2010 

2011 

2012 767 

2013 765 

2014 780 

2015 816 $30.9 $27.4 $58.3 $30.9 $27.4 

2016 854 $32.4 $28.7 $61.1 $32.4 $28.7 

2017 877 $25.4 $22.5 $47.9 $25.4 $22.5 

2018 894 $25.9 $22.9 $48.8 $25.9 $22.9 

2019 917 $26.6 $23.5 $50.1 $26.6 $23.5 

2020 948 $27.5 $24.3 $51.8 $27.5 $24.3 

2021 979 $28.4 $25.1 $53.5 $28.4 $25.1 

2022 1007 $29.2 $25.8 $55.0 $29.2 $25.8 

2023 1034 $30.0 $26.5 $56.5 $30.0 $26.5 

2024 1048 $30.4 $26.9 $57.2 $30.4 $26.9 

2025 1078 $31.2 $27.6 $58.9 $31.2 $27.6 

2026 1096 $31.8 $28.1 $59.9 $31.8 $28.1 

2027 1119 $32.4 $28.7 $61.1 $32.4 $28.7 

2028 1136 $32.9 $29.1 $62.1 $32.9 $29.1 

2029 1150 $33.3 $29.5 $62.8 $33.3 $29.5 

2030 1158 $33.6 $29.7 $63.3 $33.6 $29.7 

2031 1173 $34.0 $30.1 $64.1 $34.0 $30.1 

2032 1190 $34.5 $30.5 $65.0 $34.5 $30.5 

2033 1209 $35.0 $31.0 $66.0 $35.0 $31.0 

2034 1223 $35.5 $31.4 $66.8 $35.5 $31.4 

2035 1231 $35.7 $31.6 $67.3 $35.7 $31.6 

2036 1197 $34.7 $30.7 $65.4 $34.7 $30.7 

2037 1172 $34.0 $30.1 $64.0 $34.0 $30.1 

2038 1144 $33.2 $29.3 $62.5 $33.2 $29.3 

2039 1112 $32.2 $28.5 $60.8 $32.2 $28.5 

2040 1078 $31.2 $27.7 $58.9 $31.2 $27.7 

NPV at 7% $196.5 $173.9 $370.4 $196.5 $173.9 

NPV at 3% $426.6 $377.6 $804.2 $426.6 $377.6 
Regulatory Impact Analysis 

5-48 

Table 5-31 Annual Locomotive Switcher & Passenger Engine Variable Costs; Freshly Manufactured Tier 

4 Engines Only ($Millions, 2005 dollars) 

Calendar 

Year Sales DPF SCR Total PM NOx+ 

NMHC 

2006 

2007 

2008 

2009 

2010 

2011 

2012 92 

2013 92 

2014 93 

2015 93 $0.9 $0.7 $1.6 $0.9 $0.7 

2016 94 $1.0 $0.7 $1.6 $1.0 $0.7 

2017 94 $0.7 $0.5 $1.3 $0.7 $0.5 

2018 94 $0.7 $0.5 $1.3 $0.7 $0.5 

2019 94 $0.7 $0.6 $1.3 $0.7 $0.6 

2020 94 $0.7 $0.6 $1.3 $0.7 $0.6 

2021 94 $0.7 $0.6 $1.3 $0.7 $0.6 

2022 95 $0.7 $0.6 $1.3 $0.7 $0.6 

2023 160 $1.2 $0.9 $2.2 $1.2 $0.9 

2024 183 $1.4 $1.1 $2.5 $1.4 $1.1 

2025 201 $1.6 $1.2 $2.7 $1.6 $1.2 

2026 212 $1.6 $1.2 $2.9 $1.6 $1.2 

2027 227 $1.8 $1.3 $3.1 $1.8 $1.3 

2028 239 $1.9 $1.4 $3.3 $1.9 $1.4 

2029 247 $1.9 $1.4 $3.4 $1.9 $1.4 

2030 263 $2.0 $1.5 $3.6 $2.0 $1.5 

2031 281 $2.2 $1.6 $3.8 $2.2 $1.6 

2032 292 $2.3 $1.7 $4.0 $2.3 $1.7 

2033 296 $2.3 $1.7 $4.0 $2.3 $1.7 

2034 305 $2.4 $1.8 $4.2 $2.4 $1.8 

2035 302 $2.3 $1.8 $4.1 $2.3 $1.8 

2036 294 $2.3 $1.7 $4.0 $2.3 $1.7 

2037 287 $2.2 $1.7 $3.9 $2.2 $1.7 

2038 278 $2.2 $1.6 $3.8 $2.2 $1.6 

2039 269 $2.1 $1.6 $3.7 $2.1 $1.6 

2040 263 $2.0 $1.5 $3.6 $2.0 $1.5 

NPV at 7% $8.6 $6.4 $15.0 $8.6 $6.4 

NPV at 3% $20.4 $15.3 $35.8 $20.4 $15.3 
Engineering Cost Estimates 

5-49 

Table 5-32 Annual C2 Marine Engine Variable Costs; Freshly Manufactured Tier 4 Engines Only 

($Millions, 2005 dollars) 

Calendar 

Year Sales DPF SCR Marinization Total PM NOx+ 

NMHC 

2006 

2007 

2008 

2009 

2010 

2011 

2012 299 

2013 301 

2014 304 

2015 307 

2016 309 $10.6 $9.4 $0.1 $20.2 $10.7 $9.5 

2017 312 $10.7 $9.5 $0.1 $20.4 $10.8 $9.6 

2018 315 $8.3 $7.3 $0.1 $15.7 $8.3 $7.4 

2019 318 $8.3 $7.4 $0.1 $15.9 $8.4 $7.5 

2020 321 $8.4 $7.5 $0.1 $16.0 $8.5 $7.5 

2021 324 $8.5 $7.5 $0.1 $16.1 $8.5 $7.6 

2022 327 $8.6 $7.6 $0.1 $16.3 $8.6 $7.7 

2023 330 $8.6 $7.7 $0.1 $16.4 $8.7 $7.7 

2024 332 $8.7 $7.7 $0.1 $16.6 $8.8 $7.8 

2025 335 $8.8 $7.8 $0.1 $16.7 $8.9 $7.9 

2026 338 $8.9 $7.9 $0.1 $16.9 $8.9 $7.9 

2027 342 $9.0 $8.0 $0.1 $17.0 $9.0 $8.0 

2028 345 $9.0 $8.0 $0.1 $17.2 $9.1 $8.1 

2029 348 $9.1 $8.1 $0.1 $17.3 $9.2 $8.2 

2030 351 $9.2 $8.2 $0.1 $17.5 $9.3 $8.2 

2031 354 $9.3 $8.2 $0.1 $17.7 $9.4 $8.3 

2032 357 $9.4 $8.3 $0.1 $17.8 $9.4 $8.4 

2033 360 $9.5 $8.4 $0.1 $18.0 $9.5 $8.5 

2034 364 $9.5 $8.5 $0.1 $18.1 $9.6 $8.5 

2035 367 $9.6 $8.5 $0.1 $18.3 $9.7 $8.6 

2036 370 $9.7 $8.6 $0.1 $18.5 $9.8 $8.7 

2037 374 $9.8 $8.7 $0.1 $18.6 $9.9 $8.8 

2038 377 $9.9 $8.8 $0.1 $18.8 $10.0 $8.9 

2039 380 $10.0 $8.9 $0.1 $19.0 $10.0 $8.9 

2040 384 $10.1 $8.9 $0.1 $19.1 $10.1 $9.0 

NPV at 7% $54.4 $48.3 $0.8 $103.4 $54.7 $48.7 

NPV at 3% $119.3 $106.0 $1.7 $227.0 $120.2 $106.8 
Regulatory Impact Analysis 

5-50 

Table 5-33 Annual C1 Marine (>600 kW/805 hp) Engine Variable Costs; Freshly Manufactured Tier 4 

Engines Only ($Millions, 2005 dollars) 

Calendar 

Year Sales DPF SCR Marinization Total PM NOx+ 

NMHC 

2006 

2007 

2008 

2009 

2010 

2011 

2012 900 

2013 908 

2014 916 

2015 925 

2016 933 $8.4 $8.2 $0.2 $16.7 $8.4 $8.3 

2017 941 $8.4 $8.3 $0.2 $16.9 $8.5 $8.4 

2018 950 $6.5 $6.4 $0.1 $13.0 $6.6 $6.4 

2019 958 $6.6 $6.4 $0.1 $13.1 $6.6 $6.5 

2020 967 $6.6 $6.5 $0.1 $13.2 $6.7 $6.6 

2021 976 $6.7 $6.5 $0.1 $13.4 $6.7 $6.6 

2022 985 $6.7 $6.6 $0.1 $13.5 $6.8 $6.7 

2023 993 $6.8 $6.7 $0.1 $13.6 $6.9 $6.7 

2024 1002 $6.9 $6.7 $0.1 $13.7 $6.9 $6.8 

2025 1011 $6.9 $6.8 $0.1 $13.9 $7.0 $6.9 

2026 1020 $7.0 $6.8 $0.1 $14.0 $7.1 $6.9 

2027 1030 $7.0 $6.9 $0.1 $14.1 $7.1 $7.0 

2028 1039 $7.1 $7.0 $0.1 $14.2 $7.2 $7.0 

2029 1048 $7.2 $7.0 $0.2 $14.4 $7.2 $7.1 

2030 1058 $7.2 $7.1 $0.2 $14.5 $7.3 $7.2 

2031 1067 $7.3 $7.2 $0.2 $14.6 $7.4 $7.2 

2032 1077 $7.4 $7.2 $0.2 $14.7 $7.4 $7.3 

2033 1086 $7.4 $7.3 $0.2 $14.9 $7.5 $7.4 

2034 1096 $7.5 $7.4 $0.2 $15.0 $7.6 $7.4 

2035 1106 $7.6 $7.4 $0.2 $15.2 $7.6 $7.5 

2036 1116 $7.6 $7.5 $0.2 $15.3 $7.7 $7.6 

2037 1126 $7.7 $7.6 $0.2 $15.4 $7.8 $7.6 

2038 1136 $7.8 $7.6 $0.2 $15.6 $7.9 $7.7 

2039 1146 $7.8 $7.7 $0.2 $15.7 $7.9 $7.8 

2040 1157 $7.9 $7.8 $0.2 $15.8 $8.0 $7.8 

NPV at 7% $42.8 $41.9 $0.9 $85.6 $43.2 $42.4 

NPV at 3% $93.9 $92.1 $2.0 $187.9 $94.8 $93.0 
Engineering Cost Estimates 

5-51 

Table 5-34 Total Annual Engine Variable Costs; Freshly Manufactured Tier 4 Engines Only ($Millions, 

2005 dollars) 

Calendar 

Year Locomotive Marine C1 

Marine 

C2 

Recreational 

Marine 

Small 

Commercial 

Marine 

Total PM NOx+ 

NMHC 

2006 

2007 

2008 

2009 

2010 

2011 

2012 

2013 

2014 

2015 $60.0 $60.0 $31.9 $28.1 

2016 $62.7 $16.7 $20.2 $99.6 $52.5 $47.1 

2017 $49.2 $16.9 $20.4 $86.4 $45.4 $41.0 

2018 $50.1 $13.0 $15.7 $78.9 $41.5 $37.3 

2019 $51.4 $13.1 $15.9 $80.4 $42.3 $38.1 

2020 $53.1 $13.2 $16.0 $82.3 $43.4 $39.0 

2021 $54.8 $13.4 $16.1 $84.3 $44.4 $39.9 

2022 $56.3 $13.5 $16.3 $86.1 $45.4 $40.7 

2023 $58.7 $13.6 $16.4 $88.7 $46.8 $41.9 

2024 $59.7 $13.7 $16.6 $90.1 $47.5 $42.6 

2025 $61.6 $13.9 $16.7 $92.2 $48.6 $43.6 

2026 $62.8 $14.0 $16.9 $93.7 $49.4 $44.2 

2027 $64.2 $14.1 $17.0 $95.4 $50.3 $45.0 

2028 $65.3 $14.2 $17.2 $96.7 $51.1 $45.7 

2029 $66.2 $14.4 $17.3 $97.9 $51.7 $46.2 

2030 $66.9 $14.5 $17.5 $98.9 $52.2 $46.7 

2031 $67.9 $14.6 $17.7 $100.2 $52.9 $47.3 

2032 $69.0 $14.7 $17.8 $101.6 $53.6 $47.9 

2033 $70.1 $14.9 $18.0 $102.9 $54.4 $48.6 

2034 $71.0 $15.0 $18.1 $104.2 $55.0 $49.1 

2035 $71.4 $15.2 $18.3 $104.8 $55.4 $49.5 

2036 $69.4 $15.3 $18.5 $103.1 $54.5 $48.7 

2037 $67.9 $15.4 $18.6 $102.0 $53.8 $48.1 

2038 $66.3 $15.6 $18.8 $100.7 $53.1 $47.5 

2039 $64.4 $15.7 $19.0 $99.1 $52.3 $46.8 

2040 $62.5 $15.8 $19.1 $97.5 $51.4 $46.1 

NPV at 7% $385.5 $85.6 $103.4 $574.5 $303.1 $271.4 

NPV at 3% $839.9 $187.9 $227.0 $1,254.8 $662.1 $592.8 

Table 5-34 shows the net present value of the annual engine variable costs through 

2040 as $1.3 billion at a three percent discount rate or $0.6 billion at a seven percent discount 

rate. These costs are fairly evenly split between NOx+NMHC and PM. 

5.3 Engineering Costs for Freshly Manufactured Equipment 

In this section, we present our estimated costs associated with the piece of equipment 

into which the freshly manufactured engines are placed—i.e., the locomotive itself or the 

marine vessel itself. In general, we refer generically to equipment rather than specifically to 

locomotives or vessels. Costs of control to equipment manufacturers include fixed costs 

(those costs for equipment redesign), and variable costs (for new hardware and increased 

equipment assembly time). 
Regulatory Impact Analysis 

5-52 

5.3.1 Fixed Engineering Costs 

5.3.1.1 Equipment Redesign Costs 

The projected modifications to equipment resulting from the new emission standards 

relate to the need to package emission control hardware that engine manufacturers will 

incorporate into their engines. As discussed above, the additional emission control hardware 

for equipment into which a Tier 4 engine is installed is proportional in size to engine 

displacement by roughly a 4:1 ratio (2.5x engine displacement for the SCR system and 1.7x 

engine displacement for the DPF system). We expect that equipment manufacturers will have 

to redesign their equipment to accommodate this new volume of hardware. As such, we 

would expect such costs for only those pieces of equipment that will be installing a Tier 4 

engine since Tier 3 engines are expected to incorporate controls that will not result in a larger 

engine or otherwise require any more space within the piece of equipment. 

To determine marine-related redesign costs, our first step was to determine the number 

of vessels sold each year. To estimate vessel sales, we looked first at the number of engines 

being sold as marine engines. Since only C2 engines and C1 engines >600 kW (805 hp) 

would be complying with the Tier 4 standards, we limited ourselves to those engines. 

Further, we eliminated those engines sold as auxiliary engines since we know that there exists 

a direct correlation between vessel sales and propulsion engine sales because every freshly 

manufactured vessel will have at least one propulsion engine while having anywhere from 

zero to many auxiliary engines. Based on the 2002 PSR database and our marine engine sales 

growth rates, our analysis estimates that, in the year 2015—one year before vessels would be 

adding engines equipped with aftertreatment devices and, hence, being redesigned—there will 

be 751 marine C1 propulsion engines >600 kW and 147 marine C2 propulsion engines. 

We know that most vessels in these larger marine categories are fitted with more than 

one engine. In our draft cost analysis, we estimated 1.5 propulsion engines, on average, per 

vessel. For our final cost analysis, we have increased that to 2 propulsion engines per vessel 

based on our industry characterization work (see Chapter 1 of this RIA). This results in an 

estimated 375 marine C1 and 74 marine C2 vessels sold in 2015. 

We believe that not every vessel will require a full redesign. Instead, we believe that, 

while some vessels truly are a one-design/one-vessel effort, many vessels are a onedesign/five-vessel or even ten or more-vessel effort. To be conservative and as was done in 

our draft cost analysis, we have estimated that a redesign effort will accommodate two freshly 

manufactured vessels. That is, on average, a fleet of 74 freshly manufactured C2 vessels 

would require 37 redesign efforts. We have estimated the costs per redesign at $50,000 for 

C1 vessel redesigns and $100,000 for C2 vessel redesigns. These estimates are summarized 

in Table 5-35. 
Engineering Cost Estimates 

5-53 

Table 5-35 Estimated Vessel Redesigns in 2015 and Costs per Redesign (2005 dollars) 

Power 

Range 

Propulsion 

Engines in 

2015 

Engines / 

Vessel Vessels Vessels / Redesign Redesigns $/Redesign 

Marine-C1 

propulsion >800hp 751 2 375 2 188 $50,000 

Marine-C2 

propulsion All 147 2 74 2 37 $100,000 

Total 898 449 225 

Using these estimates, we can estimate the annual total costs associated with vessel 

redesigns. But first it is important to note that we do not believe that the vessel fleets will 

require these redesign efforts every year. Nor will the need to redesign vessels cease once the 

Tier 4 standards are implemented. Instead, in the second year of implementation we would 

expect vessel sales to be similar but in many ways different than in year one. Such is the 

nature of the marine fleet in contrast to say, the automotive fleet where a freshly 

manufactured vehicle design is typically carried-over for four to six years with no significant 

redesign. Nonetheless, a first year redesign effort will no doubt make a second year redesign 

effort less costly given what was learned by redesign and construction firms during the first 

year. To estimate this effect, we considered year two to require half the effort of year one, 

year three half again, and year four half again. We then carried this effort forward until we 

had accumulated at least 1,000 redesigns which, we believe, is sufficient to have fully 

redesigned the applicable fleet. The number of marine redesign efforts and the annual total 

costs are shown in Table 5-36. 
Regulatory Impact Analysis 

5-54 

Table 5-36 Estimated Total Number of Vessel Redesigns and the Associated Annual Costs; Freshly Manufactured Tier 4 Equ

entries are in $Millions, 2005 dollars) 

Calendar 

Year 

C1 

Redesigns 

C2 

Redesigns 

Annual 

Total 

Redesigns 

Cumulative 

Redesigns 

C1 

Redesign 

Costs 

C2 

Redesign 

Costs 

Annual 

Total Costs PM 

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 188 37 224 224 $9.4 $3.7 $13.1 $6.5 2016 90 20 110 334 $4.5 $2.0 $6.5 $3.3 2017 50 10 60 394 $2.5 $1.0 $3.5 $1.8 2018 30 10 40 434 $1.5 $1.0 $2.5 $1.3 2019 30 10 40 474 $1.5 $1.0 $2.5 $1.3 2020 30 10 40 514 $1.5 $1.0 $2.5 $1.3 2021 30 10 40 554 $1.5 $1.0 $2.5 $1.3 2022 30 10 40 594 $1.5 $1.0 $2.5 $1.3 2023 30 10 40 634 $1.5 $1.0 $2.5 $1.3 2024 30 10 40 674 $1.5 $1.0 $2.5 $1.3 2025 30 10 40 714 $1.5 $1.0 $2.5 $1.3 2026 30 10 40 754 $1.5 $1.0 $2.5 $1.3 2027 30 10 40 794 $1.5 $1.0 $2.5 $1.3 2028 30 10 40 834 $1.5 $1.0 $2.5 $1.3 2029 30 10 40 874 $1.5 $1.0 $2.5 $1.3 2030 30 10 40 914 $1.5 $1.0 $2.5 $1.3 2031 30 10 40 954 $1.5 $1.0 $2.5 $1.3 2032 30 10 40 994 $1.5 $1.0 $2.5 $1.3 2033 30 10 40 1,034 $1.5 $1.0 $2.5 $1.3 2034 2035 2036 2037 2038 2039 2040 Total $40.4 $22.7 $63.1 $31.5 

NPV at 7% $14.3 $7.5 $21.8 $10.9 NPV at 3% $25.2 $13.7 $38.9 $19.4 
Engineering Cost Estimates 

5-55 

For locomotive redesign efforts, we believe that the cost per redesign should be 

roughly equivalent to that for a C2 marine vessel, or $100,000 dollars per redesign, since the 

engine sizes and corresponding aftertreatment sizes should be roughly the same. Unlike the 

marine industry, the locomotive industry generally sells many of units of the same design. In 

fact, we estimate that there are only seven locomotive models—two line haul and five 

switcher—that comprise the hundreds of locomotives sold each year. Therefore, we have 

estimated that one redesign effort per model will suffice. The number of locomotive redesign 

efforts and the annual total costs are shown in Table 5-37. 

Table 5-37 Estimated Total Number of Locomotive Redesigns and the Associated Annual Costs; Freshly 

Manufactured Tier 4 Equipment only (monetary entries are in $Millions, 2005 dollars) 

Calendar 

Year 

Line haul 

Redesigns 

Switcher 

Redesigns 

Line haul 

Redesign 

Costs 

Switcher 

Redesign 

Costs 

Annual 

Total 

Costs 

PM NOx+ 

NMHC 

2006 

2007 

2008 

2009 

2010 

2011 

2012 

2013 

2014 2 5 $0.2 $0.5 $0.7 $0.4 $0.4 

2015 

2016 

2017 

2018 

2019 

2020 

2021 

2022 

2023 

2024 

2025 

2026 

2027 

2028 

2029 

2030 

2031 

2032 

2033 

2034 

2035 

2036 

2037 

2038 

2039 

2040 

Total $0.2 $0.5 $0.7 $0.4 $0.4 

NPV at 7% $0.1 $0.3 $0.4 $0.2 $0.2 

NPV at 3% $0.2 $0.4 $0.5 $0.3 $0.3 
Regulatory Impact Analysis 

5-56 

The net present value of the vessel redesign costs are estimated at $39 million using a 

three percent discount rate and at $22 million using a seven percent discount rate. The net 

present value of the locomotive redesign costs are estimated at $0.5 million using a three 

percent discount rate and at $0.4 million using a seven percent discount rate. In total, the net 

present value of the equipment redesign costs are estimated at $40 million using a three 

percent discount rate and at $22 million using a seven percent discount rate. These equipment 

redesign costs are split evenly between NOx+NMHC and PM control. 

5.3.2 Variable Engineering Costs 

As discussed above, we are projecting that SCR systems and DPFs will be the most 

likely technologies used to comply with the Tier 4 standards. Upon installation in a freshly 

manufactured locomotive or a freshly manufactured marine vessel, these devices would 

require some new equipment related hardware in the form of brackets and/or new sheet metal. 

Based on engineering judgement, we estimated this cost as shown in Table 5-38. Since the 

equipment variable costs are linked closely with the size of aftertreatment devices being 

installed (i.e., the large the diesel engine being installed in the piece of equipment, the larger 

the aftertreatment devices and, therefore, the larger the necessary brackets and/or greater the 

necessary sheet metal), it makes sense to scale the equipment hardware costs accordingly. 

Note that these costs would be incurred by only those pieces of equipment required to comply 

with the Tier 4 standards. Note also that we termed these costs “equipment variable costs” in 

our draft cost analysis and have more precisely named them “aftertreatment housing” costs for 

our final cost analysis. This helps to distinguish them from the reductant system costs, 

discussed below, that were included as engine-related costs in our draft analysis but are 

considered equipment-related costs in the final analysis. These two cost elements – 

aftertreatment housing and reductant system – now constitute the overall equipment variable 

costs. 

Table 5-38 Estimated Aftertreatment Housing Costs per Piece of Freshly Manufactured Equipment (2005 

dollars) 

 $/piece of equipment 

Locomotive Line-haul $4,000 

Locomotive Switcher $4,000 

Marine C1 (600-1492 kW; 805-2000 hp) $2,000 

Marine C1 (>1492 kW; >2000 hp) $4,000 

Marine C2 (600-1492 kW; 805-2000 hp) $2,000 

Marine C2 (>1492 kW; >2000 hp) $4,000 

For our final cost analysis, we have removed reductant tank and controller costs from 

the engine-related SCR system costs and are accounting for these costs at the equipment level. 

We have chosen to do so because we believe it is most appropriate given that some 

equipment, especially vessels, would be equipped with more than one engine but probably 

only one reductant tank and dosing controller to accommodate all engines. 
Engineering Cost Estimates 

5-57 

For the reductant tank, just as was done in our draft cost analysis, the estimated costs 

for the reductant solution tank and brackets is based on industry input that fuel tank size is 

roughly one gallon per engine horsepower and reductant dosing rate is roughly four percent of 

the fueling rate. We also estimated that a reductant tank would cost $60 per 10 gallons of 

capacity. Using these estimates, the needed reductant tank size and associated cost can be 

estimated. 

We have increased the final cost estimates for the reductant dosing controller based on 

input from EMA, although we have not used all of the EMA estimates directly. In their 

comments, EMA estimated the cost of a dosing panel at $10,000 to ~$15,000 per vessel, 

depending on power, and the cost of the dosing controls at $20,000 per vessel. We believe 

that those estimates were based on discussions with retrofit companies who, we have no 

doubt, are today charging those types of prices for those items. However, we believe that 

once the 2007/2010 HD highway truck program is underway, control and dosing system 

hardware will be available at a small fraction of today’s retrofit price. In our draft analysis, 

we estimated the cost of the controller and reductant injector at $500. After considering 

EMA’s comments, we believe that cost estimate was more applicable to a higher production 

quantity market (i.e., highway/nonroad production). For our final analysis, while the 

reductant injector itself remains part of the SCR catalyst system (described in section 5.2.2.1), 

we have estimated that the reductant dosing panel and controller will cost 10% of the amounts 

estimated by EMA. We base that on the cost of new DPF and/or SCR systems being roughly 

10% of the cost of typical retrofit systems. For the same reasons, we have applied the same 

10% factor to EMA’s estimates for a reductant tank heater (EMA estimated at $500) and a 

reductant pump (EMA estimated at $4,400), neither of which were included in our draft cost 

analysis but are included in our final cost analysis. We used the same methodology as 

described in section 5.2.2 (with the exception that we have not estimated any learning from 

the highway program that will reduce costs in the locomotive and marine program) to 

determine the final cost of the reductant system including labor and markups, etc., and these 

are shown in Table 5-39. 
Regulatory Impact Analysis 

5-62 

Table 5-39. Estimated Reductant Tank and Dosing System Cost per Piece of Freshly Manufactured 

Equipment (2005 dollars) 

Typical Equipment Power (kW) 1400 10000

Material and Component Costs Reductant Solution Tank & Brackets $450 $3,217

 Reductant Tank Heaters & Pumps $490 $490

 Reductant dosing panel & controls $3,000 $3,518

Direct Labor Costs Estimated Labor hours $2 $2

 Labor Rate ($/hr) $18 $18

 Labor Cost $36 $36

 Labor Overhead @ 40% $14 $14

Total Direct Costs to Mfr. $3,991 $7,276

 Warranty Cost (3% claim rate) $302 $548

 Mfr. Carrying Cost - Near term $160 $291

 Total Cost to Dealer - Near term $4,452 $8,115

 Dealer Carrying Cost - Near term $134 $243

 Baseline Cost to Buyer - Near term $4,586 $8,358

L/M Cost to Buyer (includes no Highway Learning) - Near term $4,586 $8,358

 Warranty Cost (1% claim rate) $101 $183

 Mfr. Carrying Cost - Long term $160 $291

 Total Cost to Dealer - Long term $4,251 $7,749

 Dealer Carrying Cost - Long term $128 $232

 Baseline Cost to Buyer - Long term $4,379 $7,982

 Baseline Cost to Buyer (includes no Highway Learning) - Long term $4,379 $7,982

L/M Cost to Buyer (includes Loco/Marine Learning) - Long term $3,503 $6,385

We have used the lower costs shown in Table 5-39 for locomotive switchers and 

vessels equipped with propulsion engines in the 800 to 2000 horsepower range (~600-

1500kW) and the higher costs for locomotive line-haul and vessels equipped with propulsion 

engines over 2000 horsepower. Note that, for marine, we estimate two Tier 4 propulsion 

engines per vessel in those power ranges and nearly two (1.9 on average) auxiliary engines 

per vessel.25 For vessels in the 800 to 2000 horsepower range, we anticipate that Tier 3 

auxiliary engines will be used which will not require reductant tank capacity. Looking at the 

sales weighted horsepowers shown in Table 5-28 we can estimate total vessel power requiring 

urea which, for vessels powered by 800 to 2000 horsepower engines, we estimate at just under 

2000 kW for C1 vessels and just under 2300 kW for C2 vessels. For the over 2000 

horsepower vessels, we similarly anticipate two Tier 4 propulsion engines and nearly two Tier 

4 auxiliary engines (rather than Tier 3) which will require urea tank capacity. Looking at the 

sales weighted horsepowers shown in Table 5-28 we can estimate total vessel power requiring 

reductant which, for vessels powered by over 2000 horsepower engines, we estimate at just 

under 6300 kW for C1 vessels and just under 12,000 kW for C2 vessels. Therefore, the costs 

shown in Table 5-39 are roughly appropriate. 

Using these costs and estimated future sales of locomotives and vessels, we can 

estimate the annual costs for the fleet. These costs are shown in Table 5-40 for locomotives, 

Table 5-41 for C1 marine, and Table 5-42 for C2 marine. Table 5-43 summarizes these costs 

and presents the total costs. As shown, we estimate the net present value of annual equipment 

variable costs at $220 million using a three percent discount rate and $100 million using a 
Engineering Cost Estimates 

5-59 

seven percent discount rate. These costs are split evenly between NOx+NMHC and PM 

control.
Regulatory Impact Analysis 

5-60 

Table 5-40 Annual Locomotive Variable Costs; Freshly Manufactured Tier 4 Equipment Only ($Millions, 2005 

Locomotive Line Haul Locomotive Switcher 

Calendar Year 

Sales Aftertreatment Housing Costs Reductant System Costs Subtotal Sales Aftertreatment Housing Costs Reductant System Costs 

2006 0 $0.0 $0.0 $0.0 0 $0.0 $0.0 2007 $0.0 $0.0 $0.0 $0.0 $0.0 2008 $0.0 $0.0 $0.0 $0.0 $0.0 2009 $0.0 $0.0 $0.0 $0.0 $0.0 2010 $0.0 $0.0 $0.0 $0.0 $0.0 2011 $0.0 $0.0 $0.0 $0.0 $0.0 2012 $0.0 $0.0 $0.0 $0.0 $0.0 2013 $0.0 $0.0 $0.0 $0.0 $0.0 2014 $0.0 $0.0 $0.0 $0.0 $0.0 2015 816 $3.3 $6.8 $10.1 93 $0.4 $0.4 2016 854 $3.4 $7.1 $10.6 94 $0.4 $0.4 2017 877 $3.5 $5.6 $9.1 94 $0.4 $0.3 2018 894 $3.6 $5.7 $9.3 94 $0.4 $0.3 2019 917 $3.7 $5.9 $9.5 94 $0.4 $0.3 2020 948 $3.8 $6.1 $9.8 94 $0.4 $0.3 2021 979 $3.9 $6.2 $10.2 94 $0.4 $0.3 2022 1,007 $4.0 $6.4 $10.5 95 $0.4 $0.3 2023 1,034 $4.1 $6.6 $10.7 160 $0.6 $0.6 2024 1,048 $4.2 $6.7 $10.9 183 $0.7 $0.6 2025 1,078 $4.3 $6.9 $11.2 201 $0.8 $0.7 2026 1,096 $4.4 $7.0 $11.4 212 $0.8 $0.7 2027 1,119 $4.5 $7.1 $11.6 227 $0.9 $0.8 2028 1,136 $4.5 $7.3 $11.8 239 $1.0 $0.8 2029 1,150 $4.6 $7.3 $11.9 247 $1.0 $0.9 2030 1,158 $4.6 $7.4 $12.0 263 $1.1 $0.9 2031 1,173 $4.7 $7.5 $12.2 281 $1.1 $1.0 2032 1,190 $4.8 $7.6 $12.4 292 $1.2 $1.0 2033 1,209 $4.8 $7.7 $12.6 296 $1.2 $1.0 2034 1,223 $4.9 $7.8 $12.7 305 $1.2 $1.1 2035 1,231 $4.9 $7.9 $12.8 302 $1.2 $1.1 2036 1,197 $4.8 $7.6 $12.4 294 $1.2 $1.0 2037 1,172 $4.7 $7.5 $12.2 287 $1.1 $1.0 2038 1,144 $4.6 $7.3 $11.9 278 $1.1 $1.0 2039 1,112 $4.4 $7.1 $11.6 269 $1.1 $0.9 2040 1,078 $4.3 $6.9 $11.2 263 $1.1 $0.9 

NPV at 7% $26.1 $43.3 $69.4 $4.3 $3.9 NPV at 3% $57.4 $94.0 $151.4 $10.3 $9.2 
Enginee

5-61 

Table 5-41 Annual C1 Marine Vessel Variable Costs; Freshly Manufactured Tier 4 Equipment Only ($Millions, 2

C1 800-2000hp C1 >2000hp 

Calendar Year Vessel 

Sales 

Aftertreatment 

Housing Costs 

Reductant 

System 

Costs 

Subtotal Vessel Sales Aftertreatment Housing Costs Reductant System Costs S

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 $0.0 $0.0 $0.0 $0.0 $0.0 2016 268 $0.5 $1.2 $1.8 111 $0.4 $0.9 2017 270 $0.5 $1.2 $1.8 112 $0.4 $0.9 2018 272 $0.5 $1.0 $1.5 113 $0.5 $0.7 2019 275 $0.5 $1.0 $1.5 114 $0.5 $0.7 2020 277 $0.6 $1.0 $1.5 115 $0.5 $0.7 2021 280 $0.6 $1.0 $1.5 116 $0.5 $0.7 2022 282 $0.6 $1.0 $1.6 117 $0.5 $0.7 2023 285 $0.6 $1.0 $1.6 118 $0.5 $0.8 2024 287 $0.6 $1.0 $1.6 119 $0.5 $0.8 2025 290 $0.6 $1.0 $1.6 120 $0.5 $0.8 2026 293 $0.6 $1.0 $1.6 122 $0.5 $0.8 2027 295 $0.6 $1.0 $1.6 123 $0.5 $0.8 2028 298 $0.6 $1.0 $1.6 124 $0.5 $0.8 2029 301 $0.6 $1.1 $1.7 125 $0.5 $0.8 2030 303 $0.6 $1.1 $1.7 126 $0.5 $0.8 2031 306 $0.6 $1.1 $1.7 127 $0.5 $0.8 2032 309 $0.6 $1.1 $1.7 128 $0.5 $0.8 2033 312 $0.6 $1.1 $1.7 129 $0.5 $0.8 2034 314 $0.6 $1.1 $1.7 131 $0.5 $0.8 2035 317 $0.6 $1.1 $1.7 132 $0.5 $0.8 2036 320 $0.6 $1.1 $1.8 133 $0.5 $0.8 2037 323 $0.6 $1.1 $1.8 134 $0.5 $0.9 2038 326 $0.7 $1.1 $1.8 135 $0.5 $0.9 2039 329 $0.7 $1.2 $1.8 137 $0.5 $0.9 2040 332 $0.7 $1.2 $1.8 138 $0.6 $0.9 

NPV at 7% $3.4 $6.3 $9.7 $2.8 $4.8 NPV at 3% $7.6 $13.8 $21.4 $6.3 $10.4 
Regulatory Impact Analysis 

5-62 

Table 5-42 Annual C2 Marine Vessel Variable Costs; Freshly Manufactured Tier 4 Equipment Only ($Millions, 2

C2 800-2000hp C2 >2000hp 

Calendar 

Year Vessel Sales Aftertreatment Housing Costs Reductant System Costs Subtotal Vessel Sales Aftertreatment Housing Costs Reductant System Costs S

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 $0.0 $0.0 $0.0 $0.0 $0.0 2016 3 $0.0 $0.0 $0.0 71 $0.3 $0.6 2017 3 $0.0 $0.0 $0.0 71 $0.3 $0.6 2018 3 $0.0 $0.0 $0.0 72 $0.3 $0.5 2019 3 $0.0 $0.0 $0.0 73 $0.3 $0.5 2020 4 $0.0 $0.0 $0.0 73 $0.3 $0.5 2021 4 $0.0 $0.0 $0.0 74 $0.3 $0.5 2022 4 $0.0 $0.0 $0.0 75 $0.3 $0.5 2023 4 $0.0 $0.0 $0.0 75 $0.3 $0.5 2024 4 $0.0 $0.0 $0.0 76 $0.3 $0.5 2025 4 $0.0 $0.0 $0.0 77 $0.3 $0.5 2026 4 $0.0 $0.0 $0.0 77 $0.3 $0.5 2027 4 $0.0 $0.0 $0.0 78 $0.3 $0.5 2028 4 $0.0 $0.0 $0.0 79 $0.3 $0.5 2029 4 $0.0 $0.0 $0.0 80 $0.3 $0.5 2030 4 $0.0 $0.0 $0.0 80 $0.3 $0.5 2031 4 $0.0 $0.0 $0.0 81 $0.3 $0.5 2032 4 $0.0 $0.0 $0.0 82 $0.3 $0.5 2033 4 $0.0 $0.0 $0.0 83 $0.3 $0.5 2034 4 $0.0 $0.0 $0.0 83 $0.3 $0.5 2035 4 $0.0 $0.0 $0.0 84 $0.3 $0.5 2036 4 $0.0 $0.0 $0.0 85 $0.3 $0.5 2037 4 $0.0 $0.0 $0.0 86 $0.3 $0.5 2038 4 $0.0 $0.0 $0.0 86 $0.3 $0.6 2039 4 $0.0 $0.0 $0.0 87 $0.3 $0.6 2040 4 $0.0 $0.0 $0.0 88 $0.4 $0.6 

NPV at 7% $0.0 $0.1 $0.1 $1.8 $3.0 NPV at 3% $0.1 $0.2 $0.3 $4.0 $6.7 
Engineering Cost Estimates 

5-63 

Table 5-43 Annual Equipment Variable Costs; Freshly Manufactured Tier 4 Equipment Only ($Millions, 

2005 dollars) 

Calendar Year Locomotive Marine Annual Total 

Costs PM NOx+ NMHC 

2006 

2007 

2008 

2009 

2010 

2011 

2012 

2013 

2014 

2015 $10.9 $10.9 $5.4 $5.4 

2016 $11.4 $4.0 $15.4 $7.7 $7.7 

2017 $9.8 $4.1 $13.9 $6.9 $6.9 

2018 $10.0 $3.4 $13.4 $6.7 $6.7 

2019 $10.2 $3.5 $13.7 $6.9 $6.9 

2020 $10.6 $3.5 $14.1 $7.0 $7.0 

2021 $10.9 $3.5 $14.4 $7.2 $7.2 

2022 $11.2 $3.6 $14.7 $7.4 $7.4 

2023 $11.9 $3.6 $15.5 $7.8 $7.8 

2024 $12.3 $3.6 $15.9 $7.9 $7.9 

2025 $12.7 $3.7 $16.4 $8.2 $8.2 

2026 $13.0 $3.7 $16.7 $8.3 $8.3 

2027 $13.3 $3.7 $17.1 $8.5 $8.5 

2028 $13.6 $3.8 $17.4 $8.7 $8.7 

2029 $13.8 $3.8 $17.6 $8.8 $8.8 

2030 $14.0 $3.8 $17.8 $8.9 $8.9 

2031 $14.3 $3.9 $18.2 $9.1 $9.1 

2032 $14.6 $3.9 $18.5 $9.2 $9.2 

2033 $14.8 $3.9 $18.7 $9.4 $9.4 

2034 $15.0 $4.0 $19.0 $9.5 $9.5 

2035 $15.0 $4.0 $19.1 $9.5 $9.5 

2036 $14.6 $4.0 $18.7 $9.3 $9.3 

2037 $14.3 $4.1 $18.4 $9.2 $9.2 

2038 $14.0 $4.1 $18.1 $9.0 $9.0 

2039 $13.6 $4.2 $17.7 $8.9 $8.9 

2040 $13.2 $4.2 $17.4 $8.7 $8.7 

NPV at 7% $77.6 $22.3 $99.9 $49.9 $49.9 

NPV at 3% $170.9 $49.1 $220.0 $110.0 $110.0 
Regulatory Impact Analysis 

5-64 

5.4 Operating Costs for Freshly Manufactured Tier 4 Engines 

We anticipate an increase in costs associated with operating locomotives and marine 

vessels. We anticipate three sources of increased operating costs: urea use; DPF 

maintenance; and a fuel consumption impact. Increased operating costs associated with urea 

use would occur only in those locomotives/vessels equipped with a urea SCR engine. 

Maintenance costs associated with the DPF (for periodic cleaning of accumulated ash 

resulting from unburned material that accumulates in the DPF) would occur in those 

locomotives/vessels that are equipped with a DPF engine. The fuel consumption impact is 

anticipated to occur more broadly—we expect that a one percent fuel consumption increase 

would occur for all freshly manufactured Tier 4 locomotive and marine engines due to higher 

exhaust backpressure resulting from aftertreatment devices. We also expect a one percent 

fuel consumption increase would occur for remanufactured Tier 0 locomotives and two 

percent for C2 marine engines due to our expectation that the tighter NOx standard may in 

part be met using retarded fuel injection timing. The operating costs associated with the 

remanufacturing program are presented in section 5.5 

5.4.1 Increased Operating Costs Associated with Urea Use 

Freshly manufactured Tier 4 engines are expected to be equipped with SCR systems. 

The costs associated with the SCR system, including the reductant tank and dosing system, 

are discussed in section 5.2.2.1 of this chapter. To estimate the costs associated with 

reductant use, we first considered the dosage rate. For this analysis, we have used a dosing 

rate of four percent reductant to every gallon of fuel burned. Using our marine and 

locomotive emissions analysis work (see Chapter 3 of this RIA), we can determine the gallons 

of fuel burned every year by SCR equipped pieces of equipment. The amount of reductant 

used each year is then four percent of those gallons. 

The cost per gallon of reductant would be dependent on the volume dispensed at each 

facility, with smaller refueling sites experiencing higher costs. The type of reductant 

storage/dispensing equipment, and the ultimate cost-per-gallon, for railroad and marine 

industries will depend on the volume of fuel and reductant dispensed at each site. We expect 

that the most common reductant will be urea and estimate that high-volume fixed sites may 

choose to mix emissions-grade dry urea (or urea liquor) and de-mineralized water on-site, 

whereas others may choose bulk or container delivery of a pre-mixed 32.5 percent urea-water 

solution.I

 In 2015, one source suggests that urea cost is expected to be ~$0.75/gallon for 

retail facilities dispensing 200,000 - 1,000,000 gallons/month, and ~$1.00/gallon for those 

dispensing 80,000 - 200,000 gallons/month.26 With the implementation of SCR for the onhighway truck fleet in 2010, the economic factors for each urea supply option will be wellknown prior to implementation of the 2015 and 2016 NOx standards for locomotive and 

 

I

 While the discussion here is focussed on urea as a reductant, other reductants may be used and, if used, we 

would expect them to be used only if they result in lower costs than urea. As such, we believe our estimates, 

based on urea as the reductant, are conservative. 
Engineering Cost Estimates 

5-65 

marine engines, respectively.J

 In our draft cost analysis, we used a value of $1.00/gallon of 

urea. To remain conservative and based on input from comments, for the final cost analysis 

we have used a urea cost of $1.34/gallon. This cost should cover the costs associated with 

distributing urea to the necessary point of transfer to locomotive and/or vessel (i.e., the 

necessary infrastructure). The resultant increased operating costs associated with urea use are 

presented in section 5.4.4. The costs associated with urea use are attributed solely to 

NOx+NMHC control. 

5.4.2 Increased Operating Costs Associated with DPF Maintenance 

The maintenance demands associated with the addition of DPF hardware are discussed 

in Chapter 4 of this RIA. For this analysis, we have estimated a maintenance interval of 

200,000 gallons of fuel burned between DPF ash maintenance events. For a typical 

locomotive engine having ~4000 hp this equates to roughly 7000 hours of operation between 

maintenance events. By comparison, our NRT4 rule estimated a maintenance interval of 

3,000 hours for engines under 175 hp and 4,500 hours for engines over 750 hp. We believe 

that the estimate of nearly 7,000 hours for the size engines used in applicable marine vessels 

and locomotives is appropriate, especially given potential use of “flow-through” DPF 

technologies as discussed in Chapter 4 of this RIA. We have also estimated the ash 

maintenance event to take four hours per event at $50 per hour for labor, or $200 per event. 

By using only those gallons burned in DPF equipped engines, we are then able to 

calculate the maintenance costs associated with DPF maintenance. These costs are presented 

in section 5.4.4. The costs associated with DPF maintenance are attributed solely to PM 

control. 

5.4.3 Increased Operating Costs Associated with Fuel Consumption Impacts 

The high efficiency emission-control technologies expected to be used to meet the 

Tier 4 standards involve wholly new system components integrated into engine designs and 

calibrations and, as such, would be expected to change the fuel consumption characteristics of 

the overall engine design. After reviewing the likely technology options available to the 

engine manufacturers, we believe the integration of the engine and exhaust emission-control 

systems into a single synergistic emission-control system will lead to locomotive and marine 

engines that can meet demanding emission-control targets with only a small impact on fuel 

consumption. Technology improvements have historically eliminated these marginal impacts 

in the past and it is our expectation that this kind of continuing improvement will eliminate 

the modest impact estimated here. However, because we cannot project the time frame for 

 

J

 Note that some marine C2 engines will meet the Tier 4 standards beginning in 2014. The operating costs 

presented here in section 5.4 reflect that implementation schedule while the engine and equipment costs 

presented in sections 5.2 and 5.3 do not. In those earlier sections, we chose to present all marine costs as though 

their standards began in 2016 for ease of presentation. However, the operating costs are linked directly to 

gallons burned which are, in turn, linked directly to emission inventories and reductions. Because our inventory 

and emission reduction analysis must correspond directly to the actual implementation of the standards, our 

operating costs are likewise presented according to the actual implementation of standards. Therefore, the 

marine C2 operating costs are shown as beginning in 2014. 
Regulatory Impact Analysis 

5-66 

when this improvement would be realized, we have included this impact in our cost estimates 

for the full period of the program to avoid underestimating costs. 

Diesel particulate filters are anticipated to provide a step-wise decrease in PM 

emissions by trapping and oxidizing the PM. The trapping of the very fine diesel PM is 

accomplished by forcing the exhaust through a porous filtering media with extremely small 

openings and long path lengths. This approach, called a wall flow filter, results in filtering 

efficiencies for diesel PM greater than 90 percent but requires additional pumping work to 

force the exhaust through these small openings. The impact of this additional pumping work 

on fuel consumption is dependent on engine operating conditions. At low exhaust flow 

conditions (i.e., low engine load, low turbocharger boost levels), the impact is so small that it 

typically cannot be measured, while at very high load conditions, with high exhaust flow 

conditions, the fuel economy impact can be as large as one to two percent. In our NRT4 rule, 

for wall flow filters, we estimated that the average impact of this increased pumping work 

was equivalent to an increased fuel consumption of approximately one percent. To be 

conservative in this analysis, we have used this one percent impact regardless of DPF 

technology even though the flow through technology that may be used is expected to have a 

lower impact on fuel consumption because it results in less pumping work to force the exhaust 

through the device. 

As for the urea SCR system, we do not expect a fuel consumption increase associated 

with this device. Urea SCR catalysts are flow through devices and while they do indeed 

represent a slight increase in backpressure (i.e., increased pumping work to force exhaust 

through the device), we expect that impact to be easily offset through engine control changes 

that take advantage of the high NOx conversion afforded by the SCR system. Therefore, in 

total, we expect a one percent fuel consumption increase for all freshly manufactured Tier 4 

engines. 

Using the gallons burned in freshly manufactured DPF equipped engines and, for linehaul and passenger locomotives, the gallons burned in remanufactured Tier 0 engines, along 

with an estimated diesel fuel price less taxes of $1.57/gallon, the costs associated with a fuel 

consumption impact can be calculated.K These costs are presented in section 5.4.4 of this 

chapter. The costs associated with the fuel consumption impact are split evenly between NOx

and PM control. 

 

K To estimate the diesel fuel price, we started with the annual average nationwide price for 2006 for high sulfur 

diesel fuel (excluding taxes) sold to commercial consumers from Table 41 of the Energy Information 

Administration (EIA) Petroleum Marketing Annual 2006. We adjusted this 2006 price of $2.03/gallon to a 2012 

price using the ratio of projected consumer purchased diesel fuel price in 2012 to the consumer purchased diesel 

fuel price in 2006 as reported in Table 12 of the Annual Energy Outlook (AEO) 2007. We chose to use the 

actual price in 2006 as the basis for estimating the future price, instead of directly relying on the projected prices 

in AEO 2007, because refinery models, like the one used for the AEO, are better at estimating changes in prices 

than they are at estimating actual prices. The actual price is a function of all the market forces that shape the 

price, whereas refinery models can only approximately estimate these effects. Note that, in the draft cost 

analysis, we used a value of $1.24/gallon of diesel fuel. As a result, if we exclude C2 marine remanufacturing 

program fuel consumption impact which were not considered in the draft cost analysis, the fuel consumption cost 

estimates are roughly 27% higher in the final cost analysis. 
Engineering Cost Estimates 

5-67 

Note that, as discussed in sections 4.2 and 5.2.2, we expect marine Tier 3 engines to 

generally be recalibrated and marinized nonroad engines originally developed to meet 

nonroad Tier 4 standards, except for the application of aftertreatment needed to meet nonroad 

Tier 4 but not marine Tier 3. These advanced engines represent significant technical advances 

from the marine Tier 2 engines— having better high pressure fuel systems, better injectors, 

improved turbochargers, and more sophisticated electronic control units. Likewise, they are 

expected to have brake specific fuel consumption that is as good as, or better than, that of 

marine Tier 2 engines, while producing significantly less NOx. We have therefore 

conservatively assumed no impact of the marine Tier 3 standards on fuel consumption. 

5.4.4 Total Increased Operating Costs Associated with Freshly Manufactured 

Tier 4 Engines 

The increased annual operating costs for each applicable market segment—locomotive 

line haul; switcher/passenger; marine C1>600 kW; marine C2—are presented in Table 5-44, 

Table 5-45, Table 5-46, and Table 5-47, respectively. These costs are summarized to give the 

total increased operating costs in Table 5-48. Table 5-49 shows the increased operating costs 

by cost element—reductant, DPF maintenance, and fuel consumption impact. 

Note that operating costs are attributed as follows: costs associated with reductant use 

are attributed solely to NOx+NMHC control; costs associated with DPF maintenance are 

attributed solely to PM control; and, costs associated with the fuel consumption impact are 

split evenly between NOx+NMHC and PM control.
Regulatory Impact Analysis 

5-68 

Table 5-44 Estimated Increased Operating Costs for Freshly Manufactured Tier 4 Line Haul Locomotives (monetary entr

Calendar 

Year 

SCR 

Equipped 

Fuel Usage 

(MM gal) 

Reductant 

Usage 

(MM gal) 

Annual 

Reductant 

Cost 

($MM) 

DPF 

Equipped 

Fuel Usage 

(MM gal) 

# of DPF 

Maintenance 

Events/Year 

Annual DPF 

Maintenance 

Cost 

($MM) 

Tier 4 

Fuel 

Usage 

(MM gal) 

Increased 

Fuel 

Consumption 

at 1 percent 

(MM gal) 

Annu

of 

Consu

Im

($M

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 207 8 $11.1 207 1036 $0.2 207 2 2016 423 17 $22.7 423 2117 $0.4 423 4 2017 645 26 $34.6 645 3224 $0.6 645 6 2018 870 35 $46.6 870 4350 $0.9 870 9 2019 1100 44 $58.9 1100 5499 $1.1 1100 11 2020 1337 53 $71.6 1337 6683 $1.3 1337 13 2021 1581 63 $84.7 1581 7905 $1.6 1581 16 2022 1833 73 $98.2 1833 9163 $1.8 1833 18 2023 2091 84 $112.1 2091 10456 $2.1 2091 21 2024 2349 94 $125.9 2349 11747 $2.3 2349 23 2025 2611 104 $139.9 2611 13055 $2.6 2611 26 2026 2873 115 $154.0 2873 14365 $2.9 2873 29 2027 3136 125 $168.1 3136 15682 $3.1 3136 31 2028 3400 136 $182.2 3400 17001 $3.4 3400 34 2029 3663 147 $196.4 3663 18317 $3.7 3663 37 2030 3925 157 $210.4 3925 19623 $3.9 3925 39 2031 4185 167 $224.3 4185 20926 $4.2 4185 42 2032 4445 178 $238.3 4445 22227 $4.4 4445 44 2033 4706 188 $252.2 4706 23529 $4.7 4706 47 2034 4965 199 $266.1 4965 24825 $5.0 4965 50 2035 5221 209 $279.8 5221 26105 $5.2 5221 52 2036 5466 219 $293.0 5466 27329 $5.5 5466 55 2037 5702 228 $305.6 5702 28510 $5.7 5702 57 2038 5929 237 $317.8 5929 29647 $5.9 5929 59 2039 6147 246 $329.5 6147 30736 $6.1 6147 61 2040 6355 254 $340.6 6355 31777 $6.4 6355 64 

NPV at 7% $814.1 $15.2 NPV at 3% $2,100.0 $39.2 
Enginee

5-69 

Table 5-45 Estimated Increased Operating Costs for Freshly Manufactured Tier 4 Switcher & Passenger Locomotives (mon

dollars) 

Calendar 

Year 

SCR 

Equipped 

Fuel Usage 

(MM gal) 

Reductant 

Usage 

(MM gal) 

Annual 

Reductant 

Cost 

($MM) 

DPF 

Equipped 

Fuel Usage 

(MM gal) 

# of DPF 

Maintenanc

e 

Events/Year 

Annual DPF 

Maintenance 

Cost 

($MM) 

Tier 4 

Passenger 

Fuel Usage 

(MM gal) 

Increased 

Passenger 

Fuel 

Consumptio

n at 1 

percent (MM 

gal) 

Annu

of 

Cons

n Im

($M

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 10 0 $0.6 10 51 $0.0 7 0 2016 21 1 $1.1 21 104 $0.0 14 0 2017 31 1 $1.7 31 156 $0.0 22 0 2018 42 2 $2.2 42 209 $0.0 29 0 2019 53 2 $2.8 53 263 $0.1 36 0 2020 63 3 $3.4 63 317 $0.1 44 0 2021 74 3 $4.0 74 371 $0.1 51 1 2022 85 3 $4.6 85 426 $0.1 58 1 2023 101 4 $5.4 101 505 $0.1 66 1 2024 119 5 $6.4 119 594 $0.1 73 1 2025 138 6 $7.4 138 691 $0.1 80 1 2026 159 6 $8.5 159 793 $0.2 88 1 2027 180 7 $9.7 180 902 $0.2 95 1 2028 203 8 $10.9 203 1016 $0.2 102 1 2029 227 9 $12.2 227 1137 $0.2 109 1 2030 253 10 $13.6 253 1265 $0.3 116 1 2031 281 11 $15.0 281 1404 $0.3 123 1 2032 310 12 $16.6 310 1549 $0.3 131 1 2033 340 14 $18.2 340 1699 $0.3 138 1 2034 371 15 $19.9 371 1856 $0.4 146 1 2035 403 16 $21.6 403 2014 $0.4 154 2 2036 434 17 $23.3 434 2170 $0.4 161 2 2037 464 19 $24.9 464 2322 $0.5 168 2 2038 494 20 $26.5 494 2472 $0.5 173 2 2039 524 21 $28.1 524 2619 $0.5 178 2 2040 553 22 $29.6 553 2764 $0.6 183 2 

NPV at 7% $52.1 $1.0 NPV at 3% $141.3 $2.6 
Regulatory Impact Analysis 

5-70 

Table 5-46 Estimated Increased Operating Costs for Freshly Manufactured Tier 4 Marine C1 Engines >600 kW (monetary e

Calendar 

Year 

SCR 

Equipped 

Fuel 

Usage 

(MM gal) 

Reductant 

Usage (MM 

gal) 

Annual 

Reductant 

Cost 

($MM) 

DPF 

Equipped 

Fuel Usage 

(MM gal) 

# of DPF 

Maintenance 

Events/Year 

Annual DPF 

Maintenance 

Cost 

($MM) 

Tier 4 

Fuel 

Usage 

(MM gal) 

Increased 

Fuel 

Consumption 

at 1 percent 

(MM gal) 

Annua

of F

Consu

Imp

($M

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 54 2 $2.9 54 272 $0.1 54 1 2017 149 6 $8.0 149 745 $0.1 149 1 2018 281 11 $15.0 281 1403 $0.3 281 3 2019 412 16 $22.1 412 2058 $0.4 412 4 2020 542 22 $29.0 542 2709 $0.5 542 5 2021 671 27 $36.0 671 3356 $0.7 671 7 2022 800 32 $42.9 800 3999 $0.8 800 8 2023 927 37 $49.7 927 4635 $0.9 927 9 2024 1052 42 $56.4 1052 5262 $1.1 1052 11 2025 1176 47 $63.0 1176 5880 $1.2 1176 12 2026 1297 52 $69.5 1297 6486 $1.3 1297 13 2027 1415 57 $75.9 1415 7077 $1.4 1415 14 2028 1529 61 $82.0 1529 7645 $1.5 1529 15 2029 1629 65 $87.3 1629 8144 $1.6 1629 16 2030 1708 68 $91.6 1708 8542 $1.7 1708 17 2031 1771 71 $94.9 1771 8853 $1.8 1771 18 2032 1822 73 $97.7 1822 9112 $1.8 1822 18 2033 1869 75 $100.2 1869 9345 $1.9 1869 19 2034 1911 76 $102.5 1911 9557 $1.9 1911 19 2035 1949 78 $104.5 1949 9746 $1.9 1949 19 2036 1983 79 $106.3 1983 9917 $2.0 1983 20 2037 2015 81 $108.0 2015 10074 $2.0 2015 20 2038 2044 82 $109.6 2044 10219 $2.0 2044 20 2039 2071 83 $111.0 2071 10355 $2.1 2071 21 2040 2096 84 $112.4 2096 10482 $2.1 2096 21 

NPV at 

7% 

 $322.0 $6.0 

NPV at 

3% 

 $825.6 $15.4 
Enginee

5-71 

Table 5-47 Estimated Increased Operating Costs for Freshly Manufactured Tier 4 Marine C2 Engines (monetary entrie

Calendar 

Year 

SCR 

Equipped 

Fuel 

Usage 

(MM gal) 

Reductant 

Usage (MM 

gal) 

Annual 

Reductant 

Cost 

($MM) 

DPF 

Equipped 

Fuel Usage 

(MM gal) 

# of DPF 

Maintenance 

Events/Year 

Annual DPF 

Maintenance 

Cost 

($MM) 

Tier 4 Fuel 

Usage 

(MM gal) 

Increased 

Fuel 

Consumption 

at 1 percent 

(MM gal) 

Annu

of 

Consu

Im

($M

2006 2007 2008 2009 2010 2011 2012 2013 2014 110 4 $5.9 110 551 $0.1 110 1 2015 212 8 $11.3 212 1058 $0.2 212 2 2016 313 13 $16.8 313 1566 $0.3 313 3 2017 417 17 $22.3 417 2083 $0.4 417 4 2018 520 21 $27.9 520 2602 $0.5 520 5 2019 624 25 $33.5 624 3121 $0.6 624 6 2020 728 29 $39.0 728 3639 $0.7 728 7 2021 831 33 $44.6 831 4156 $0.8 831 8 2022 934 37 $50.1 934 4672 $0.9 934 9 2023 1037 41 $55.6 1037 5187 $1.0 1037 10 2024 1140 46 $61.1 1140 5700 $1.1 1140 11 2025 1243 50 $66.6 1243 6213 $1.2 1243 12 2026 1345 54 $72.1 1345 6725 $1.3 1345 13 2027 1447 58 $77.6 1447 7235 $1.4 1447 14 2028 1549 62 $83.0 1549 7745 $1.5 1549 15 2029 1650 66 $88.5 1650 8251 $1.7 1650 17 2030 1751 70 $93.8 1751 8753 $1.8 1751 18 2031 1850 74 $99.2 1850 9250 $1.9 1850 19 2032 1949 78 $104.4 1949 9743 $1.9 1949 19 2033 2045 82 $109.6 2045 10227 $2.0 2045 20 2034 2140 86 $114.7 2140 10702 $2.1 2140 21 2035 2233 89 $119.7 2233 11165 $2.2 2233 22 2036 2321 93 $124.4 2321 11604 $2.3 2321 23 2037 2391 96 $128.2 2391 11957 $2.4 2391 24 2038 2446 98 $131.1 2446 12232 $2.4 2446 24 2039 2497 100 $133.9 2497 12487 $2.5 2497 25 2040 2546 102 $136.4 2546 12728 $2.5 2546 25 

NPV at 

7% 

 $386.7 $7.2 

NPV at 

3% 

 $964.4 $18.0 
Regulatory Impact Analysis 

5-72 

Table 5-48 Estimated Increased Operating Costs by Market Segment; Freshly Manufactured Tier 4 

Engines ($Millions, 2005 dollars) 

Calendar 

Year 

Locomotive 

Line-haul 

Locomotive 

Switcher & 

Passenger 

Marine C1 

>600kW 

Marine 

C2 Total PM NOx+ 

NMHC 

2006 

2007 

2008 

2009 

2010 

2011 

2012 

2013 

2014 $0.0 $0.0 $0.0 $7.7 $7.7 $1.0 $6.8 

2015 $14.6 $0.7 $0.0 $14.9 $30.1 $3.8 $26.3 

2016 $29.8 $1.4 $3.8 $22.0 $57.0 $7.1 $49.8 

2017 $45.3 $2.0 $10.5 $29.3 $87.1 $10.9 $76.2 

2018 $61.1 $2.7 $19.7 $36.6 $120.2 $15.0 $105.1 

2019 $77.3 $3.4 $28.9 $43.9 $153.5 $19.2 $134.3 

2020 $93.9 $4.1 $38.1 $51.2 $187.3 $23.5 $163.9 

2021 $111.1 $4.9 $47.2 $58.4 $221.6 $27.8 $193.9 

2022 $128.8 $5.6 $56.2 $65.7 $256.3 $32.1 $224.2 

2023 $147.0 $6.5 $65.2 $72.9 $291.6 $36.5 $255.1 

2024 $165.1 $7.6 $74.0 $80.1 $326.9 $40.9 $286.0 

2025 $183.5 $8.8 $82.7 $87.3 $362.3 $45.3 $317.1 

2026 $202.0 $10.0 $91.2 $94.5 $397.7 $49.6 $348.1 

2027 $220.5 $11.3 $99.5 $101.7 $433.0 $54.0 $379.0 

2028 $239.0 $12.7 $107.5 $108.9 $468.1 $58.3 $409.8 

2029 $257.5 $14.1 $114.5 $116.0 $502.1 $62.5 $439.6 

2030 $275.9 $15.6 $120.1 $123.1 $534.6 $66.5 $468.2 

2031 $294.2 $17.3 $124.5 $130.0 $565.9 $70.3 $495.7 

2032 $312.5 $19.0 $128.1 $137.0 $596.5 $74.0 $522.5 

2033 $330.8 $20.7 $131.4 $143.8 $626.7 $77.7 $549.0 

2034 $349.0 $22.6 $134.4 $150.5 $656.4 $81.3 $575.1 

2035 $367.0 $24.4 $137.0 $157.0 $685.4 $84.8 $600.6 

2036 $384.2 $26.2 $139.4 $163.1 $713.0 $88.1 $624.8 

2037 $400.8 $28.0 $141.6 $168.1 $738.5 $91.2 $647.3 

2038 $416.8 $29.7 $143.7 $172.0 $762.1 $94.0 $668.1 

2039 $432.1 $31.4 $145.6 $175.5 $784.6 $96.7 $687.9 

2040 $446.7 $33.0 $147.4 $178.9 $806.1 $99.3 $706.8 

NPV at 7% $1,067.6 $60.3 $422.2 $507.1 $2,057.2 $255.9 $1,801.3 

NPV at 3% $2,754.0 $162.4 $1,082.7 $1,264.7 $5,263.7 $653.9 $4,609.9 
Engineering Cost Estimates 

5-73 

Table 5-49 Estimated Increased Operating Costs by Cost Element Associated with the Final Program 

($Millions, 2005 dollars) 

Calendar 

Year 

Reductant 

Use 

DPF 

Maintenance 

Fuel 

Impact Total PM NOx+ 

NMHC 

2006 

2007 

2008 

2009 

2010 

2011 

2012 

2013 

2014 $5.9 $0.1 $1.7 $7.7 $1.0 $6.8 

2015 $23.0 $0.4 $6.7 $30.1 $3.8 $26.3 

2016 $43.5 $0.8 $12.6 $57.0 $7.1 $49.8 

2017 $66.6 $1.2 $19.3 $87.1 $10.9 $76.2 

2018 $91.8 $1.7 $26.7 $120.2 $15.0 $105.1 

2019 $117.3 $2.2 $34.1 $153.5 $19.2 $134.3 

2020 $143.1 $2.7 $41.6 $187.3 $23.5 $163.9 

2021 $169.3 $3.2 $49.2 $221.6 $27.8 $193.9 

2022 $195.8 $3.7 $56.9 $256.3 $32.1 $224.2 

2023 $222.8 $4.2 $64.7 $291.6 $36.5 $255.1 

2024 $249.8 $4.7 $72.4 $326.9 $40.9 $286.0 

2025 $277.0 $5.2 $80.2 $362.3 $45.3 $317.1 

2026 $304.1 $5.7 $87.9 $397.7 $49.6 $348.1 

2027 $331.2 $6.2 $95.6 $433.0 $54.0 $379.0 

2028 $358.1 $6.7 $103.2 $468.1 $58.3 $409.8 

2029 $384.3 $7.2 $110.6 $502.1 $62.5 $439.6 

2030 $409.3 $7.6 $117.7 $534.6 $66.5 $468.2 

2031 $433.4 $8.1 $124.4 $565.9 $70.3 $495.7 

2032 $457.0 $8.5 $131.0 $596.5 $74.0 $522.5 

2033 $480.3 $9.0 $137.4 $626.7 $77.7 $549.0 

2034 $503.2 $9.4 $143.8 $656.4 $81.3 $575.1 

2035 $525.6 $9.8 $150.0 $685.4 $84.8 $600.6 

2036 $546.9 $10.2 $155.8 $713.0 $88.1 $624.8 

2037 $566.7 $10.6 $161.2 $738.5 $91.2 $647.3 

2038 $585.0 $10.9 $166.2 $762.1 $94.0 $668.1 

2039 $602.4 $11.2 $170.9 $784.6 $96.7 $687.9 

2040 $619.1 $11.6 $175.4 $806.1 $99.3 $706.8 

NPV at 7% $1,574.8 $29.4 $453.0 $2,057.2 $255.9 $1,801.3 

NPV at 3% $4,031.2 $75.2 $1,157.3 $5,263.7 $653.9 $4,609.9 

As shown in Table 5-49, the net present value of the annual operating costs is 

estimated at $5.3 billion at a three percent discount rate or $2.1 billion at a seven percent 

discount rate. The primary increased operating cost is associated with reductant use which 

accounts for nearly three quarters of the estimated costs. Since reductant use is meant for 

NOx+NMHC control, most of the increased operating costs are attributed to NOx+NMHC 

control. 

5.5 Engineering Hardware Costs and Operating Costs Associated with the 

Locomotive and Marine Remanufacturing Programs 

Our program also contains requirements that remanufactured locomotives meet more 

stringent standards than those to which they were designed originally. For the final rule, we 

have included an analogous requirement for remanufactured marine engines. Because the 

standards for those engines are more stringent, they cannot necessarily be remanufactured to 
Regulatory Impact Analysis 

5-74 

their original configuration but must, instead, include some new technology and/or engine 

controls to ensure compliance with the more stringent standards. The incremental costs 

associated with those new technologies must be considered as part of this rule. The 

remanufacturing process is not a low cost endeavor. However, it is much less costly than 

purchasing a freshly manufactured engine. The costs we have estimated for the 

remanufacturing program are meant to capture the incremental costs associated with 

remanufacturing. 

To summarize the requirements, the existing fleet of locomotives that are currently 

subject to Tier 0 standards would need to comply with a new Tier 0 PM standard and a new 

Tier 0 NOx line-haul standard, except that Tier 0 locomotives that were freshly manufactured 

before 1994 would remain subject to the existing Tier 0 NOx standards. In general, these new 

Tier 0 standards would apply when the locomotive is remanufactured as early as January 1, 

2008. For locomotives currently subject to Tier 1 and Tier 2 standards, more stringent PM 

standards would apply at the point of next remanufacture as early as January 1, 2008, but not 

later than 2010. For marine engines, where an appropriate locomotive remanufacture kit 

exists, one must be used when the marine engine is remanufactured. 

To meet the locomotive remanufactured engine standards, we project that engine 

manufacturers will utilize incremental improvements to existing engine components. In many 

cases, similar improvements have already been implemented on freshly manufactured 

locomotives to meet our current locomotive standards. To meet the lower NOx standard 

required for Tier 0 locomotives, we expect possible improvements in the fuel system, the 

turbo charger, and the engine calibration. Such changes are expected to increase fuel 

consumption by one percent as discussed below. We have estimated the incremental 

hardware costs associated with the remanufacture of a Tier 0 locomotive to be $33,800 for the 

first remanufacture and $22,300 for the second one. The lower cost for the second 

remanufacture is because not all of the new technology would have to be remanufactured 

during the second effort. We have estimated that first remanufacture would occur through 

2016 with the second one occurring after 2016. 

To meet the PM standards for the Tier 1 remanufacturing program, we expect that 

lubricating oil consumption controls will be implemented, along with the ultra low sulfur 

diesel fuel requirement for locomotive engines (which was previously finalized in our 

nonroad clean diesel rulemaking). Because of the significant fraction of lubricating oil 

present in PM from today’s locomotives, we believe that existing low-oil-consumption piston 

ring-pack designs, when used in conjunction with improvements to closed crankcase 

ventilation systems, will provide significant, near-term PM reductions. We have estimated 

these hardware costs to be roughly equivalent to the hardware costs associated with the Tier 0 

remanufacturing. We have also estimated the first remanufacture would occur through 2016 

with the second one occurring after 2016. We do not expect a fuel consumption impact for 

these remanufactured engines. 

To meet the more stringent PM standards for the Tier 2 remanufacturing program, we 

expect use of improved fuel systems. Based on work previously done for our NRT4 rule, we 

have estimated the incremental hardware cost of a new fuel system on a line haul locomotive 

at $11,750 and on a switcher at $8,700. This cost differential exists because the line haul 
Engineering Cost Estimates 

5-75 

locomotives have larger engines and, hence, larger fuel rails and pumps, etc. We have not 

estimated an incremental hardware cost associated with a second remanufacture for Tier 2 

locomotives because we would not expect the fuel system would need a second 

remanufacture. We have estimated that the first remanufacture would occur prior to 2020. 

We do not expect a fuel consumption impact for these remanufactured engines. 

We have not estimated any incremental costs for Tier 3 remanufacturing because these 

locomotives would not meet a remanufactured standard more stringent than their original 

design. Therefore, while costs would be incurred to remanufacture these engines, those costs 

would not be different from current remanufacturing kits. 

In the case of our locomotive standards, it is worthwhile to note the difference in how 

we have handled variable costs for the remanufactured Tier 2 engines versus the new Tier 3 

standards. In some cases, we believe manufacturers may choose to introduce more modern 

common rail fuel systems for both their freshly manufactured Tier 3 products and for 

application to their existing Tier 2 products at the time of remanufacturing. In the case of the 

freshly manufactured Tier 3 engine, we are projecting no increase in engine variable cost 

because, for example, we expect the common rail fuel system to be no more expensive (and 

perhaps cheaper) than the fuel system that would have been used absent our new standards. 

However, we have accounted for these higher costs for the remanufactured Tier 2 engines 

reflecting the fact that the new fuel system is an incremental cost for the rebuild that would 

not have occurred absent our new standard (because the existing fuel system could be reused 

at remanufacture absent the new standard). 

For Tier 4 remanufacturing, we have estimated that locomotive engines would need a 

new set of aftertreatment devices and a remanufactured fuel system. We have estimated the 

aftertreatment device costs at slightly lower than the original equipment costs because we 

would expect that precious metals would be recycled from the device being removed and 

replaced. This results in remanufactured DPF and SCR system costs of 60 percent and 97 

percent, respectively, relative to the original cost. The 60/97 differential occurs because of 

the larger amount of precious metals contained in the DPF versus the SCR catalyst which 

contains only a small amount of precious metal for the DOC function. For the 

remanufactured fuel system, we have included the hardware costs already mentioned above 

associated with costs for Tier 2 remanufacturing (i.e., $11,750 or $8,700). We do not expect a 

fuel consumption impact for these remanufactured engines since they will not be meeting a 

more stringent standard than their original design. 

 For marine engines, we expect the same kits to be used as are used for locomotives. 

For C2 engines, we have used identical hardware costs as used for Tier 0 locomotives. For 

C1 engines, we have used half that value simply because the engines are smaller. We do not 

expect a second remanufacturing event on these engines as is expected for locomotives. 

However, by the time that some of the C1 engines have been remanufactured, we expect the 
Regulatory Impact Analysis 

5-76 

available locomotive kits to be less costly. We do not expect a fuel consumption impact for 

the C1 engines, but do expect one for the C2 engines, as discussed below.L

These estimated incremental remanufacturing hardware costs are summarized in Table 

5-50. 

 

L

 Note that the costs associated with the marine remanufacturing program are consistent with the inventory 

reductions discussed in section 3.1.3 of this RIA. Our estimate of the number of remanufactured engines is 

presented in a memorandum from Amy Kopin to the docket for this rule (see Docket Item No. EPA-HQ-OAR2003-0190-0847). 
Engineering Cost Estimates 

5-79 

Table 5-50 Estimated Incremental Hardware Costs Associated with the Locomotive Remanufacturing 

Program ($/remanufacture, 2005 dollars) 

Segment Tier 1st Remanufacture 2nd Remanufacture 

Tier 0 $33,800 $22,300 

Tier 1 $33,800 $22,300 

Tier 2 $11,750 $0 

Tier 3 $0 $0 

Locomotive Line-haul 

Tier 4 $66,000 $66,000 

Tier 0 $33,800 $22,300 

Tier 1 $33,800 $22,300 

Tier 2 $8,700 $0 

Tier 3 $0 $0 

Locomotive Switcher/Passenger 

Tier 4 $21,700 $21,700 

C1 Marine Tier 0 $16,900/$11,150 N/A 

C2 Marine Tier 0 $33,200 N/A 

We have also estimated an incremental operating cost associated with the locomotive 

and marine remanufacturing programs. We expect a fuel consumption impact would occur 

for those engines remanufactured to a more stringent NOx standard than the NOx standard to 

which they were designed originally. We would expect this because those engines are 

expected to employ engine control changes—retarded injection timing—to help control NOx

emissions. The result of such a change is slightly higher fuel consumption on the order of one 

percent. For locomotives, only Tier 0 locomotives would be remanufactured to a more 

stringent NOx standard than that for which they were originally designed. Therefore, we have 

estimated a one percent fuel consumption increase for remanufactured Tier 0 locomotives. 

On the marine side, only C2 marine remanufactured engines are expected to experience a fuel 

consumption impact. As noted, the locomotives are expected to see a one percent fuel 

consumption impact. However, the impacted marine C2 engines will be going from an 

uncontrolled state to a controlled state resulting in a larger impact relative to the locomotives 

for which the kits are actually made. Therefore, we have estimated a two percent fuel 

consumption increase for remanufactured C2 marine engines. 

Of note in the following tables is the annual reduction of gallons consumed by 

remanufactured Tier 0 locomotives and pre-Tier 3 C2 marine engines. This is a result of older 

Tier 0 locomotives and C2 vessels slowly being retired from duty and being replaced by 

freshly manufactured Tier 4 locomotives and vessels. There are no fuel consumption impacts 

shown for remanufactured Tier 1, 2 and 3 locomotives or C1 marine engines because we 

expect no fuel consumption impacts for them as a result of this program (no new 

aftertreatment devices so no urea nor DPF maintenance costs and no fuel consumption 

impact). 

Using these remanufacturing kit hardware costs and estimated fuel consumption 

impacts, we can calculate the total costs associated with the final remanufacturing program. 

These costs are presented in Table 5-51 for line haul locomotives, Table 5-52 for switchers 

and passenger locomotives, and Table 5-53 for marine engines. See Chapter 3 of this final 

RIA for how we determined the rate at which locomotives are remanufactured. The number 
Regulatory Impact Analysis 

5-78 

remanufactured and the calendar years in which they occur are also shown in the tables. As 

shown, the net present value of the annual remanufacturing costs is estimated at $1.5 billion 

and $0.8 billion for line haul locomotives at a three percent and seven percent discount rate, 

respectively. For switchers and passenger locomotives, we have estimated the net present 

value of the annual costs at $157 million and $90 million at a three and seven percent discount 

rate, respectively. For marine engines, these costs are $450 million and $289 million, 

respectively. Note that, while not shown in Table 5-51 through Table 5-53, the costs 

associated with the locomotive remanufacturing program are split evenly between 

NOx+NMHC and PM control. This split is shown in Table 5-59. 
Enginee

5-79 

Table 5-51 Estimated Annual Costs Associated with the Remanufacturing Program for Line Haul Locomotives (monetary e

Tier 0 Tier 1 Tier 2 

Calendar 

Year Remans $/reman 

Hardware 

Costs 

($MM) 

Reman 

Tier 0 

Fuel 

Usage 

(MMgal) 

Increased 

Fuel 

Consumption 

(MM gal) 

Fuel 

Costs 

($MM) 

Subtotal 

($MM) Remans $/reman 

Hardware 

Costs 

($MM) 

Remans $/reman 

Hardware 

Costs 

($MM) 

2006 0 $0 $0 0.0 0.0 $0.0 $0.0 0 $0 $0.0 0 $ - $0.0 2007 0 $0 $0.0 0.0 0.0 $0.0 $0.0 0 $0 $0.0 0 $ - $0.0 2008 1651 $33,800 $55.8 146.7 1.5 $2.3 $58.1 0 $0 $0.0 0 $ - $0.0 2009 0 $0 $0.0 145.5 1.5 $2.3 $2.3 803 $33,800 $27.1 0 $ - $0.0 2010 1220 $33,800 $41.2 375.4 3.8 $5.9 $47.1 0 $0 $0.0 0 $ - $0.0 2011 2096 $33,800 $70.8 779.8 7.8 $12.2 $83.1 489 $33,800 $16.5 0 $ - $0.0 2012 984 $33,800 $33.3 947.0 9.5 $14.9 $48.1 931 $33,800 $31.5 0 $ - $0.0 2013 1310 $33,800 $44.3 1,174.3 11.7 $18.4 $62.7 0 $0 $0.0 719 $11,749 $8.4 2014 624 $33,800 $21.1 1,227.5 12.3 $19.3 $40.4 0 $0 $0.0 791 $11,749 $9.3 2015 393 $33,800 $13.3 1,232.2 12.3 $19.3 $32.6 0 $0 $0.0 693 $11,749 $8.1 2016 1186 $33,800 $40.1 1,401.1 14.0 $22.0 $62.1 0 $0 $0.0 712 $11,749 $8.4 2017 1179 $22,300 $26.3 1,554.2 15.5 $24.4 $50.7 0 $0 $0.0 737 $11,749 $8.7 2018 1284 $22,300 $28.6 1,511.8 15.1 $23.7 $52.4 0 $0 $0.0 770 $11,749 $9.0 2019 231 $22,300 $5.2 1,443.7 14.4 $22.7 $27.8 803 $22,300 $17.9 791 $11,749 $9.3 2020 370 $22,300 $8.2 1,334.6 13.3 $20.9 $29.2 0 $0 $0.0 0 $0 $0.0 2021 0 $0 $0.0 1,219.3 12.2 $19.1 $19.1 489 $22,300 $10.9 0 $0 $0.0 2022 579 $22,300 $12.9 1,108.5 11.1 $17.4 $30.3 931 $22,300 $20.8 0 $0 $0.0 2023 1103 $22,300 $24.6 1,002.3 10.0 $15.7 $40.3 0 $0 $0.0 719 $0 $0.0 2024 501 $22,300 $11.2 900.6 9.0 $14.1 $25.3 0 $0 $0.0 791 $0 $0.0 2025 646 $22,300 $14.4 804.3 8.0 $12.6 $27.0 0 $0 $0.0 693 $0 $0.0 2026 0 $0 $0.0 710.3 7.1 $11.1 $11.1 0 $0 $0.0 712 $0 $0.0 2027 0 $0 $0.0 622.0 6.2 $9.8 $9.8 0 $0 $0.0 737 $0 $0.0 2028 622 $22,300 $13.9 539.1 5.4 $8.5 $22.3 0 $0 $0.0 770 $0 $0.0 2029 610 $22,300 $13.6 462.4 4.6 $7.3 $20.9 0 $0 $0.0 791 $0 $0.0 2030 505 $22,300 $11.3 393.2 3.9 $6.2 $17.4 0 $0 $0.0 0 $0 $0.0 2031 0 $0 $0.0 330.1 3.3 $5.2 $5.2 442 $22,300 $9.8 0 $0 $0.0 2032 0 $0 $0.0 272.8 2.7 $4.3 $4.3 0 $0 $0.0 0 $0 $0.0 2033 0 $0 $0.0 220.6 2.2 $3.5 $3.5 0 $0 $0.0 0 $0 $0.0 2034 0 $0 $0.0 168.3 1.7 $2.6 $2.6 220 $22,300 $4.9 0 $0 $0.0 2035 0 $0 $0.0 123.9 1.2 $1.9 $1.9 419 $22,300 $9.3 0 $0 $0.0 2036 0 $0 $0.0 88.0 0.9 $1.4 $1.4 0 $0 $0.0 324 $0 $0.0 2037 0 $0 $0.0 57.7 0.6 $0.9 $0.9 0 $0 $0.0 356 $0 $0.0 2038 0 $0 $0.0 33.4 0.3 $0.5 $0.5 0 $0 $0.0 312 $0 $0.0 2039 0 $0 $0.0 15.7 0.2 $0.2 $0.2 0 $0 $0.0 320 $0 $0.0 2040 0 $0 $0.0 4.8 0.0 $0.1 $0.1 0 $0 $0.0 332 $0 $0.0 

NPV at 

7% 

 $260.3 $139.9 $400.2 $72.1 $29.3 

NPV at 

3% 

 $365.3 $230.4 $595.7 $105.2 $44.3 
Regulatory Impact Analysis 

5-80 

Table 5-52 Estimated Annual Costs Associated with the Remanufacturing Program for Switcher and Passenger Locomotive

2005 dollars) 

Tier 0 & Tier 1 Tier 2 

Calendar 

Year Remans $/reman 

Hardware 

Costs 

($MM) 

Reman 

Tier 0 

Passenger 

Fuel 

Usage 

(MMgal) 

Increased Fuel 

Consumption 

(MM gal) 

Fuel 

Costs 

($MM) 

Subtotal 

($MM) Remans $/reman 

Hardware 

Costs 

($MM) 

Remans 

2006 0 $ - $0.0 0.0 0.0 $0.0 $0.0 0 $0 $0.0 0 2007 0 $ - $0.0 0.0 0.0 $0.0 $0.0 0 $0 $0.0 0 2008 31 $33,800 $1.1 4.8 0.0 $0.1 $1.1 0 $0 $0.0 0 2009 78 $33,800 $2.6 16.9 0.2 $0.3 $2.9 0 $0 $0.0 0 2010 314 $33,800 $10.6 28.9 0.3 $0.5 $11.1 0 $0 $0.0 0 2011 312 $33,800 $10.5 40.0 0.4 $0.6 $11.2 0 $0 $0.0 0 2012 309 $33,800 $10.5 44.4 0.4 $0.7 $11.2 0 $0 $0.0 0 2013 307 $33,800 $10.4 48.0 0.5 $0.8 $11.1 0 $0 $0.0 0 2014 307 $33,800 $10.4 50.9 0.5 $0.8 $11.2 0 $0 $0.0 0 2015 269 $33,800 $9.1 47.2 0.5 $0.7 $9.8 112 $8,728 $1.0 0 2016 271 $33,800 $9.2 48.5 0.5 $0.8 $9.9 154 $8,728 $1.3 0 2017 273 $22,300 $6.1 49.3 0.5 $0.8 $6.9 88 $8,728 $0.8 0 2018 274 $22,300 $6.1 43.7 0.4 $0.7 $6.8 89 $8,728 $0.8 0 2019 276 $22,300 $6.2 38.4 0.4 $0.6 $6.8 90 $8,728 $0.8 0 2020 278 $22,300 $6.2 33.0 0.3 $0.5 $6.7 91 $8,728 $0.8 0 2021 279 $22,300 $6.2 27.6 0.3 $0.4 $6.7 0 $0 $0.0 0 2022 281 $22,300 $6.3 22.2 0.2 $0.3 $6.6 0 $0 $0.0 0 2023 318 $22,300 $7.1 17.5 0.2 $0.3 $7.4 0 $0 $0.0 0 2024 315 $22,300 $7.0 13.5 0.1 $0.2 $7.2 0 $0 $0.0 0 2025 311 $22,300 $6.9 9.8 0.1 $0.2 $7.1 57 $0 $0.0 47 2026 266 $22,300 $5.9 6.8 0.1 $0.1 $6.0 169 $0 $0.0 93 2027 260 $22,300 $5.8 4.3 0.0 $0.1 $5.9 86 $0 $0.0 94 2028 253 $22,300 $5.6 2.5 0.0 $0.0 $5.7 88 $0 $0.0 94 2029 245 $22,300 $5.5 1.2 0.0 $0.0 $5.5 89 $0 $0.0 94 2030 236 $22,300 $5.3 0.4 0.0 $0.0 $5.3 90 $0 $0.0 94 2031 226 $22,300 $5.0 0.0 0.0 $0.0 $5.0 45 $0 $0.0 94 2032 190 $22,300 $4.2 0.0 0.0 $0.0 $4.2 0 $0 $0.0 94 2033 179 $22,300 $4.0 0.0 0.0 $0.0 $4.0 0 $0 $0.0 160 2034 166 $22,300 $3.7 0.0 0.0 $0.0 $3.7 0 $0 $0.0 183 2035 154 $22,300 $3.4 0.0 0.0 $0.0 $3.4 57 $0 $0.0 201 2036 142 $22,300 $3.2 0.0 0.0 $0.0 $3.2 114 $0 $0.0 212 2037 132 $22,300 $2.9 0.0 0.0 $0.0 $2.9 46 $0 $0.0 230 2038 123 $22,300 $2.7 0.0 0.0 $0.0 $2.7 46 $0 $0.0 238 2039 114 $22,300 $2.5 0.0 0.0 $0.0 $2.5 46 $0 $0.0 246 2040 105 $22,300 $2.3 0.0 0.0 $0.0 $2.3 46 $0 $0.0 263 

NPV at 7% $75.0 $4.6 $79.6 $2.4 NPV at 3% $123.9 $6.8 $130.7 $3.8 
Enginee

5-81 

Table 5-53 Estimated Annual Costs Associated with the Remanufacturing Program for Marine Engines (monetary entri

C1 Marine C2 Marine 

Calendar 

Year Remans $/reman 

Reman 

Hardware 

Costs 

($MM) 

Remans $/reman 

C2 Reman 

Hardware 

Costs ($MM) 

%C2 

controlled 

Pre-T3 

C2 fuel 

(MMgal) 

Controlle

d C2 fuel 

(MMgal) 

Increase

d fuel 

due to 

FE 

penalty 

(MMgal) 

2006 0 $0 $0.0 0 $0 $0.0 0% 0 0.0 2007 0 $0 $0.0 0 $0 $0.0 0% 0 0.0 2008 0 $0 $0.0 325 $33,800 $11.0 9% 2,044 158 3.2 2009 0 $0 $0.0 331 $33,800 $11.2 18% 2,063 319 6.4 2010 0 $0 $0.0 337 $33,800 $11.4 26% 2,081 483 9.7 2011 0 $0 $0.0 343 $33,800 $11.6 35% 2,100 650 13.0 2012 403 $16,900 $6.8 349 $33,800 $11.8 44% 2,119 820 16.4 2013 369 $16,900 $6.2 324 $33,800 $11.0 53% 2,105 978 19.6 2014 298 $16,900 $5.0 241 $33,800 $8.1 62% 2,021 1,095 21.9 2015 241 $16,900 $4.1 0 $0 $0.0 62% 1,937 1,050 21.0 2016 184 $16,900 $3.1 0 $0 $0.0 62% 1,853 1,004 20.1 2017 128 $11,150 $1.4 0 $0 $0.0 62% 1,769 959 19.2 2018 73 $11,150 $0.8 0 $0 $0.0 62% 1,685 913 18.3 2019 0 $0 $0.0 0 $0 $0.0 62% 1,602 868 17.4 2020 0 $0 $0.0 0 $0 $0.0 62% 1,519 823 16.5 2021 0 $0 $0.0 0 $0 $0.0 62% 1,436 778 15.6 2022 0 $0 $0.0 0 $0 $0.0 62% 1,354 734 14.7 2023 0 $0 $0.0 0 $0 $0.0 62% 1,272 690 13.8 2024 0 $0 $0.0 0 $0 $0.0 62% 1,191 646 12.9 2025 0 $0 $0.0 0 $0 $0.0 62% 1,110 602 12.0 2026 0 $0 $0.0 0 $0 $0.0 62% 1,030 558 11.2 2027 0 $0 $0.0 0 $0 $0.0 62% 949 515 10.3 2028 0 $0 $0.0 0 $0 $0.0 62% 870 471 9.4 2029 0 $0 $0.0 0 $0 $0.0 62% 791 429 8.6 2030 0 $0 $0.0 0 $0 $0.0 62% 713 387 7.7 2031 0 $0 $0.0 0 $0 $0.0 62% 637 345 6.9 2032 0 $0 $0.0 0 $0 $0.0 62% 562 304 6.1 2033 0 $0 $0.0 0 $0 $0.0 62% 488 265 5.3 2034 0 $0 $0.0 0 $0 $0.0 62% 417 226 4.5 2035 0 $0 $0.0 0 $0 $0.0 62% 349 189 3.8 2036 0 $0 $0.0 0 $0 $0.0 62% 289 157 3.1 2037 0 $0 $0.0 0 $0 $0.0 62% 247 134 2.7 2038 0 $0 $0.0 0 $0 $0.0 62% 218 118 2.4 2039 0 $0 $0.0 0 $0 $0.0 62% 193 104 2.1 2040 0 $0 $0.0 0 $0 $0.0 62% 170 92 1.8 

NPV at 7% $15.1 $51.5 21,279 7,089 141.8 NPV at 3% $21.1 $64.0 30,735 11,628 232.6 
Regulatory Impact Analysis 

5-82 

5.6 Summary of Final Program Engineering Costs 

Details of our engine and equipment cost estimates were presented in Sections 5.2 and 

5.3. Here we summarize the cost estimates. Section 5.6.1 summarizes the engine-related 

costs associated with the new Tier 4 standards for freshly manufactured engines. Section 

5.6.2 summarizes the equipment-related costs associated with the new Tier 4 standards for 

freshly manufactured equipment. Section 5.6.3 summarizes the operating costs associated 

with the freshly manufactured Tier 4 engines and equipment. Section 5.6.4 summarizes the 

hardware costs and operating costs associated with the locomotive and marine 

remanufacturing programs. Section 5.6.5 summarizes all these costs and presents the total 

estimated costs for the final program. Note that all present value costs presented here are 

2006 through 2040 numbers (the net present values in 2006 of the stream of costs occurring 

from 2006 through 2040, expressed in $2005). 

5.6.1 Engineering Costs for Freshly Manufactured Engines 

5.6.1.1 Fixed Engineering Costs 

Engine fixed costs include costs for engine R&D, tooling, and certification. These 

costs are discussed in detail in Section 5.2.1. The total estimated engine fixed costs are 

summarized in Table 5-54. The table also includes net present values using both a three 

percent and a seven percent discount rate. 

Table 5-54 Summary of Engine-Related Fixed Costs for Freshly Manufactured Tier 4 Engines ($Millions, 

2005 dollars) 

Costs Incurred 2006-2040 NPV at 3% 2006-2040 NPV at 7% 

Engine and Emission Control Research $ 569 $ 471 $ 371 

Engine Tooling $ 41 $ 33 $ 24 

Engine Certification $ 7 $ 6 $ 5 

Total Engine Fixed Costs $ 618 $ 509 $ 399 

Total Allocated to PM $ 212 $ 175 $ 137 

Total Allocated to NOx+NMHC $ 406 $ 335 $ 263 

Note: As explained in the text, we have attributed engine fixed costs to NOx+NMHC and PM control as 

follows: engine research costs are split two-thirds to NOx+NMHC control and one-third to PM control; engine 

tooling costs are split equally; engine certification costs are split equally except where new standards are 

implemented in different years (e.g., for Tier 4 locomotive standards). 

5.6.1.2 Variable Engineering Costs 

Engine variable, or hardware, costs are discussed in detail in Section 5.2.2. For engine 

variable costs, we have generated cost estimation equations as a function of engine 

displacement (see Table 5-27). Using these equations, we have calculated the hardware costs 

for freshly manufactured engines meeting the Tier 4 standards for each year through 2040. 

We present those annual engine variable costs in Section 5.2.2. Table 5-55 shows the net 
Engineering Cost Estimates 

5-83 

present value of those annual costs using a three percent discount rate and a seven percent 

discount rate. 

Table 5-55 Summary of Engine-Related Variable Costs for Freshly Manufactured Tier 4 Engines 

($Millions, 2005 dollars) 

2006-2040 NPV at 3% 2006-2040 NPV at 7% 

Locomotive $ 840 $ 386 

C1 Marine $ 188 $ 86 

C2 Marine $ 227 $ 103 

Small Commercial Marine & Recreational Marine $ 0 $ 0 

Total Engine Variable Costs $ 1,255 $ 575 

Total Allocated to PM $ 662 $ 303 

Total Allocated to NOx+NMHC $ 593 $ 271 

Note: The PM/NOx+NMHC cost allocations for engine variable costs are as follows: SCR systems including 

marinization costs on marine applications are attributed 100% to NOx+NMHC control; and, DPF systems 

including marinization costs on marine applications are attributed 100% to PM control. 

5.6.2 Engineering Costs for Freshly Manufactured Equipment 

5.6.2.1 Fixed Engineering Costs 

Equipment fixed costs are discussed in detail in Section 5.3.1. Table 5-56 shows the 

estimated equipment fixed costs—for redesign efforts—associated with the Tier 4 program. 

The table also includes net present values of the annual costs using both a three percent and a 

seven percent discount rate. 

Table 5-56 Summary of Equipment-Related Fixed Costs for Freshly Manufactured Tier 4 Equipment 

($Millions, 2005 dollars) 

 Costs 

Incurred 

2006-2040 NPV at 

3% 

2006-2040 NPV at 

7% 

Locomotive $ 0.7 $ 0.5 $ 0.4 

C1 Marine $ 40 $ 25 $ 14 

C2 Marine $ 23 $ 14 $ 8 

Small Commercial Marine & Recreational 

Marine 

$ 0 $ 0 $ 0 

Total Equipment Fixed Costs $ 64 $ 39 $ 22 

Total Allocated to PM $ 32 $ 20 $ 11 

Total Allocated to NOx+NMHC $ 32 $ 20 $ 11 

Note: Equipment fixed costs are split evenly between NOx+NMHC and PM control. 
Regulatory Impact Analysis 

5-84 

5.6.2.2 Variable Engineering Costs 

Equipment variable costs are discussed in detail in Section 5.3.2. Using the costs 

presented there we have calculated the hardware costs for new pieces of equipment—

locomotives and vessels—meeting the new Tier 4 standards for each year through 2040. We 

present those annual equipment variable costs in Section 5.3.2. Table 5-57 shows the net 

present value of those annual costs using a three percent and a seven percent discount rate. 

Table 5-57 Summary of Equipment-Related Variable Costs for Freshly Manufactured Tier 4 Equipment 

($Millions, 2005 dollars) 

2006-2040 NPV at 3% 2006-2040 NPV at 7% 

Locomotive $ 171 $ 78 

C1 Marine $ 38 $ 17 

C2 Marine $ 11 $ 5 

Small Commercial Marine & RecMarine $ 0 $ 0 

Total Equipment Variable Costs $ 220 $ 100 

Total Allocated to PM $ 110 $ 50 

Total Allocated to NOx+NMHC $ 110 $ 50 

Note: Equipment variable costs are split evenly between NOx+NMHC and PM control. 

5.6.3 Operating Costs for Freshly Manufactured Tier 4 Engines 

Operating costs are discussed in detail in Section 5.4 where we present the operating 

costs for each year through 2040. Operating costs consist of costs associated with urea use, 

DPF maintenance, and a fuel consumption impact on some engines. Table 5-58 shows the net 

present value of those annual operating costs using a three percent and a seven percent 

discount rate. 

Table 5-58 Summary of Operating Costs for Freshly Manufactured Tier 4 Engines ($Millions, 2005 

dollars) 

2006-2040 NPV at 3% 2006-2040 NPV at 7% 

 Reductant DPF 

Maint. 

Fuel Total Reductant DPF 

Maint. 

Fuel Total 

Locomotive $2,241 $42 $633 $2,916 $866 $16 $246 $1,128 

C1 Marine $826 $15 $242 $1,083 $322 $6 $94 $422 

C2 Marine $964 $18 $282 $1,265 $387 $7 $113 $507 

Small 

Commercial 

Marine & 

RecMarine 

$ 0 $0 $ 0 $ 0 $ 0 $ 0 $ 0 $ 0 

Total 

Operating 

Costs 

$4,031 $75 $1,157 $5,264 $1,575 $29 $453 $2,057 

Total 

Allocated to 

PM 

$ 0 $75 $579 $654 $ 0 $29 $227 $256 

Total $4,031 $ 0 $579 $4,610 $1,575 $ 0 $227 $1,801 
Engineering Cost Estimates 

5-85 

Allocated to 

NOx+NMHC 

Note: Operating costs are attributed as follows: costs associated with reductant use are attributed solely to 

NOx+NMHC control; costs associated with DPF maintenance are attributed solely to PM control; and, costs 

associated with the fuel consumption impact are split evenly between NOx+NMHC and PM control. 

5.6.4 Engineering Hardware and Operating Costs for Remanufactured Engines 

Costs associated with the locomotive and marine remanufacturing programs are 

discussed in detail in Section 5.5 where we present the costs for each year through 2040. 

These costs include the hardware costs that are incremental to current remanufacturing 

practices and any increased operating costs. Table 5-59 shows the net present value of those 

annual remanufacturing costs using a three percent and a seven percent discount rate. 

Table 5-59 Summary of Remanufacturing Program Costs ($Millions, 2005 dollars) 

2006-2040 NPV at 3% 2006-2040 NPV at 7% 

Line Haul $ 1,512 $ 775 

Switcher & Passenger $ 157 $ 90 

C1 Marine $ 21 $ 15 

C2 Marine $ 429 $ 274 

Total Remanufacturing Costs $ 2,120 $ 1,153 

Total Allocated to PM $ 1,060 $ 577 

Total Allocated to NOx+NMHC $ 1,060 $ 577 

Note: Costs associated with the locomotive and marine remanufacturing programs are split evenly between 

NOx+NMHC and PM control. 

5.6.5 Total Engineering and Operating Costs Associated with the Final Program 

Table 5-60 shows the total annual costs for each market segment—locomotive line 

haul, C2 marine, etc—for the final program. Table 5-61 shows the total annual costs for each 

cost element—engine, equipment, operating, etc.—on an annual basis for the final program. 

As shown, the net present value of the annual costs is estimated at $9.4 billion at a three 

percent discount rate and $4.3 billion at a seven percent discount rate. In the year 2030, the 

annual costs are estimated at $759 million. 

Note that costs throughout this cost analysis have been allocated as follows: engine 

research costs are split two-thirds to NOx+NMHC control and one-third to PM control; engine 

tooling costs are split equally; engine certification costs are split equally except where new 

standards are implemented in different years (e.g., for Tier 4 locomotive standards); SCR 

systems including marinization costs on marine applications are attributed 100% to 

NOx+NMHC control; DPF systems including marinization costs on marine applications are 
Regulatory Impact Analysis 

5-86 

attributed 100% to PM control; equipment fixed and variable costs are split evenly between 

NOx+NMHC and PM control; costs associated with reductant use are attributed solely to 

NOx+NMHC control; costs associated with DPF maintenance are attributed solely to PM 

control; costs associated with the fuel consumption impact are split evenly between 

NOx+NMHC and PM control; and, costs associated with the locomotive remanufacturing 

program are split evenly between NOx+NMHC and PM control. 

Table 5-60 Estimated Annual Engineering Costs by Market Segment for the Final Program ($Millions, 

2005 dollars) 

Locomotive Marine 

Calendar 

Year Line Haul Switcher & 

Passenger 

C2 

Marine 

C1 

Marine 

>600kW 

C1 

Marine 

<600kW 

Recreational Small 

Commercial 

Total 

2006 

2007 $6.0 $2.6 $4.2 $2.3 $10.9 $5.8 $1.7 $33.5 

2008 $64.1 $3.7 $20.1 $2.3 $10.9 $5.8 $1.7 $108.7 

2009 $35.4 $5.5 $25.4 $2.3 $10.9 $5.8 $1.7 $87.1 

2010 $80.6 $20.8 $30.7 $2.3 $10.9 $5.8 $1.7 $152.9 

2011 $134.6 $22.9 $61.3 $31.0 $16.2 $10.9 $3.7 $280.7 

2012 $107.0 $18.4 $59.9 $30.2 $0.0 $0.0 $0.0 $215.4 

2013 $98.6 $18.4 $64.0 $29.6 $0.0 $0.0 $0.0 $210.5 

2014 $80.5 $22.9 $72.6 $28.4 $0.0 $0.0 $0.0 $204.5 

2015 $123.7 $13.9 $83.0 $44.8 $0.2 $0.0 $0.0 $265.7 

2016 $171.8 $15.1 $76.6 $31.3 $0.0 $0.0 $0.0 $294.8 

2017 $161.7 $11.7 $81.7 $34.4 $0.0 $0.0 $0.0 $289.4 

2018 $180.7 $12.3 $82.7 $37.7 $0.0 $0.0 $0.0 $313.4 

2019 $192.0 $13.0 $88.8 $46.3 $0.0 $0.0 $0.0 $340.0 

2020 $184.8 $13.6 $94.8 $55.5 $0.0 $0.0 $0.0 $348.8 

2021 $204.8 $13.5 $100.8 $64.8 $0.0 $0.0 $0.0 $383.9 

2022 $245.4 $14.2 $106.8 $74.0 $0.0 $0.0 $0.0 $440.3 

2023 $310.8 $17.3 $112.8 $83.1 $0.0 $0.0 $0.0 $524.0 

2024 $317.2 $18.7 $118.8 $92.0 $0.0 $0.0 $0.0 $546.8 

2025 $340.7 $21.2 $124.8 $100.9 $0.0 $0.0 $0.0 $587.5 

2026 $345.4 $22.6 $130.8 $109.5 $0.0 $0.0 $0.0 $608.4 

2027 $365.4 $24.1 $136.7 $118.0 $0.0 $0.0 $0.0 $644.2 

2028 $399.4 $25.5 $142.7 $126.1 $0.0 $0.0 $0.0 $693.8 

2029 $419.5 $26.9 $148.6 $133.3 $0.0 $0.0 $0.0 $728.3 

2030 $436.9 $28.5 $154.6 $139.0 $0.0 $0.0 $0.0 $759.1 

2031 $455.7 $30.3 $160.4 $143.6 $0.0 $0.0 $0.0 $790.0 

2032 $465.2 $31.5 $166.2 $147.4 $0.0 $0.0 $0.0 $810.3 

2033 $542.2 $34.5 $171.9 $150.8 $0.0 $0.0 $0.0 $899.4 

2034 $569.2 $36.7 $176.6 $152.5 $0.0 $0.0 $0.0 $934.9 

2035 $594.3 $38.6 $182.1 $155.3 $0.0 $0.0 $0.0 $970.3 

2036 $601.6 $40.2 $187.4 $157.8 $0.0 $0.0 $0.0 $987.1 

2037 $618.5 $42.0 $191.8 $160.2 $0.0 $0.0 $0.0 $1,012.6 

2038 $634.8 $43.6 $195.4 $162.4 $0.0 $0.0 $0.0 $1,036.2 

2039 $651.1 $45.0 $198.7 $164.5 $0.0 $0.0 $0.0 $1,059.3 

2040 $666.5 $46.7 $201.9 $166.5 $0.0 $0.0 $0.0 $1,081.5 

NPV at 

7% 

$2,393.9 $209.3 $984.7 $639.5 $45.4 $25.7 $8.0 $4,306.5 

NPV at 

3% 

$5,364.1 $420.7 $2,061.2 $1,468.2 $53.0 $30.2 $9.4 $9,406.8 
Engineering Cost Estimates 

5-87 

Table 5-61 Estimated Annual Engineering Costs by Cost Element for the Final Program ($Millions, 2005 

dollars) 

Calendar 

Year 

Tier 4 

Engine 

Costs 

Tier 4 

Equipment 

Costs 

Reman 

Program 

Costs 

Tier 4 

Operating 

Costs 

Total PM NOx+NMHC 

2006 

2007 $33.5 $0.0 $0.0 $0.0 $33.5 $11.1 $22.5 

2008 $33.5 $0.0 $75.2 $0.0 $108.7 $48.7 $60.1 

2009 $33.5 $0.0 $53.5 $0.0 $87.1 $37.8 $49.2 

2010 $68.2 $0.0 $84.7 $0.0 $152.9 $64.9 $88.0 

2011 $137.9 $0.0 $142.8 $0.0 $280.7 $121.0 $159.7 

2012 $80.4 $0.0 $135.1 $0.0 $215.4 $94.1 $121.4 

2013 $80.4 $0.0 $130.2 $0.0 $210.5 $91.6 $118.9 

2014 $87.6 $0.7 $108.4 $7.7 $204.5 $85.7 $118.8 

2015 $123.1 $23.9 $88.6 $30.1 $265.7 $115.7 $150.0 

2016 $99.6 $21.9 $116.3 $57.0 $294.8 $128.7 $166.1 

2017 $86.4 $17.4 $98.5 $87.1 $289.4 $114.3 $175.1 

2018 $78.9 $15.9 $98.5 $120.2 $313.4 $113.8 $199.7 

2019 $80.4 $16.2 $89.8 $153.5 $340.0 $114.6 $225.4 

2020 $82.3 $16.6 $62.5 $187.3 $348.8 $106.4 $242.4 

2021 $84.3 $16.9 $61.1 $221.6 $383.9 $111.2 $272.7 

2022 $86.1 $17.2 $80.7 $256.3 $440.3 $126.4 $313.9 

2023 $88.7 $18.0 $125.6 $291.6 $524.0 $155.1 $368.9 

2024 $90.1 $18.4 $111.4 $326.9 $546.8 $153.3 $393.5 

2025 $92.2 $18.9 $114.1 $362.3 $587.5 $160.4 $427.1 

2026 $93.7 $19.2 $97.8 $397.7 $608.4 $157.6 $450.8 

2027 $95.4 $19.6 $96.2 $433.0 $644.2 $162.2 $481.9 

2028 $96.7 $19.9 $109.1 $468.1 $693.8 $173.8 $519.9 

2029 $97.9 $20.1 $108.2 $502.1 $728.3 $178.3 $550.0 

2030 $98.9 $20.3 $105.2 $534.6 $759.1 $181.4 $577.6 

2031 $100.2 $20.7 $103.1 $565.9 $790.0 $185.1 $604.8 

2032 $101.6 $21.0 $91.2 $596.5 $810.3 $183.8 $626.5 

2033 $102.9 $21.2 $148.6 $626.7 $899.4 $217.0 $682.5 

2034 $104.2 $19.0 $155.4 $656.4 $934.9 $223.5 $711.4 

2035 $104.8 $19.1 $161.1 $685.4 $970.3 $230.2 $740.1 

2036 $103.1 $18.7 $152.3 $713.0 $987.1 $228.1 $759.0 

2037 $102.0 $18.4 $153.7 $738.5 $1,012.6 $231.1 $781.5 

2038 $100.7 $18.1 $155.4 $762.1 $1,036.2 $233.9 $802.4 

2039 $99.1 $17.7 $157.9 $784.6 $1,059.3 $236.8 $822.5 

2040 $97.5 $17.4 $160.6 $806.1 $1,081.5 $239.7 $841.9 

NPV at 7% $973.8 $122.0 $1,153.4 $2,057.2 $4,306.5 $1,333.4 $2,973.1 

NPV at 3% $1,764.1 $259.5 $2,119.5 $5,263.7 $9,406.8 $2,680.0 $6,726.8 
Regulatory Impact Analysis 

5-88 

5.7 Engineering Costs and Savings Associated with Idle Reduction 

Technology 

Locomotives idle for many reasons, not all of which can be avoided. The primary 

reason they idle is to protect their engines. Locomotives use water, rather than antifreeze for 

engine cooling because water is more efficient at removing heat despite its higher freezing 

point. Therefore, by keeping the locomotive engine idling, the cooling water is kept from 

freezing and damaging the engine block. Engineers may also idle a locomotive to maintain 

critical system parameters: the batteries must maintain a certain charge in order to be able to 

restart the engine, the air brake system must be kept pressurized, and in some cases the 

locomotive is left to idle in order to properly cool down after heavy use. It may also be 

necessary to idle a locomotive to provide and maintain cab comfort for the crew or to 

otherwise comply with applicable government regulations.. Idling locomotives can be found 

both inside and outside of the switchyard, for example, line-hauls may idle while waiting on 

sidings for other trains to pass, during crew changes, or while moving (when some 

locomotives in a consist pulling a train are not needed to provide power). 

There are several technologies currently available to reduce unnecessary locomotive 

idling or idling emissions. First, shore power systems allow for the locomotive engine to be 

plugged into a stationary power source to keep the batteries charged, and to heat and circulate 

the water and oil. They range in price from $4,000 - $14,000 depending on the options 

installed.M These systems are most widely used on passenger trains that return to the same 

location at night, but are not always practical for switchers that idle in different locations 

throughout a switchyard, or for line-hauls that generally stop in many locations outside a 

switchyard. Second, Low Emission Idle Systems (LEI) made by Energy Conversions Inc. 

work by alternating the banks of cylinders that fire during idle. LEI runs the engine on half of 

its cylinders at idle which increases the load on the firing cylinders and causes them to burn 

fuel more efficiently, however, while this system may reduce some idling emissions it does 

not eliminate idling. The cost of the system is approximately $4000, and it can be installed in 

just two hours.N Third, an Auxiliary Power Unit (APU) is an idle reduction technology that 

reduces the amount of time when locomotive engine idling is necessary. APUs are small (less 

than 50 hp) diesel engines that stop and start themselves as needed to provide heat to both 

engine coolant and engine oil, and power to charge the batteries and run cab accessories, 

instead of this work being done by the much larger (2,000-4,000 hp) locomotive engine. 

There are two main manufacturers of APUs, EcoTrans which makes the K9 APU and Kim 

Hotstart which makes the Diesel Driven Heating System (DDHS). APUs can provide 

substantial fuel savings depending on a number of variables, such as: what the function of a 

locomotive is (e.g. a switcher or a line-haul), where it operates (i.e. geographical area), and 

what its operating characteristics are (e.g. number of hours per day that it operates). The cost 

of an APU ranges from $25,000 - $32,000, depending on the options installed, although Kim 

Hotstart has just developed a “Junior” model with a smaller engine that is priced around 

 

M Docket ID # OAR-2003-0190-0588.1 

N www.energyconversions.com/lei1.htm 
Engineering Cost Estimates 

5-89 

$16,000.l,O Fourth, a more complex solution has been demonstrated by the Advanced 

Locomotive Emissions Control Systems (ALECS). It uses emission reduction technology 

developed for stationary sources to capture the emissions from both stationary and slow 

moving trains in a railyard. Its cost can be upwards of one million dollars, but it can be used 

on any locomotive that enters the railyard.P Fifth, locomotive engines can be replaced with 

two or three smaller non-road engines, referred to as gensets.Q This configuration allows the 

locomotive to idle using only one small engine, while the other engines only operate when 

more power is needed. Sixth, a hybrid-electric system has been designed for switch yard 

purposes only (known as the GreenGoat.)R

 The hybrid-electric switcher engine only operates 

when the batteries need to be charged. The locomotive is powered by the batteries and can sit 

in a state of readiness without idling, similar to an electric car. 

Finally, one of the most cost effective onboard solutions that can provide idle 

reduction benefits to both line-haul and switcher locomotives nearly anywhere they operate is 

an automatic engine stop/start system (AESS). AESS is an electronic control system that 

reduces idling by shutting down a locomotive engine when it is idling unnecessarily e.g. it is 

not needed to maintain critical system parameters such as brake system pressure. AESS is a 

microprocessor technology that operates by continually monitoring certain operating 

parameters such as: reverser and throttle position, engine coolant and ambient air temperature, 

battery charge, brake system pressure, and time spent idling. The AESS will shutdown the 

locomotive engine after a prescribed period of time spent idling, usually fifteen to thirty 

minutes, if conditions meet a preprogrammed set of values (for example the ambient 

temperature must be greater than 32oF, and the water temperature must be greater than 100oF), 

and will restart the engine if one of the aforementioned parameters goes out of its specified 

range in order to both protect the locomotive engine and keep it in a ready-to-use state. 

AESS is limited in its ability to provide idle reduction in cold weather as it can only 

monitor the conditions under which the locomotive engine is operating and the condition of 

the engine itself. An APU can provide further reductions for those locomotives operating in 

colder climates by actually maintaining the necessary engine parameters, and is included as 

part of some Tier 0 certified kits. In fact, EPA demonstrated an APU/AESS combined 

systems approach in one of its grant projects using a Kim Hotstart DDHS.S

 An AESS alone 

can provide some fuel savings during the cold winter, but when combined with an APU will 

achieve considerable fuel savings. AESS systems will be required on all freshly 

manufactured Tier 3 and Tier 4 locomotives, and on all existing locomotives when they are 

 

O http://www.epa.gov/otaq/smartway/idlingtechnologies.htm#loco-mobile-apu 

P Tom Christofk, "Statewide Railyard Agreement" Presentation given at Second Public Meeting 7/13/06 for 

Placer County Air Pollution Control District 

htttp://www.placer.ca.gov/upload/apc/documents/up/up_arb_public_meeting_7_13_06.pdf 

Q www.northeastdiesel.org, "Multi-Engine GenSet Ultra Low Emissions Road-Switcher Locomotive" 

presentation by National Railway Equipment Co., Jan, 2006. 

R

 www.railpower.com 

S

 See “Case Study: Chicago Locomotive Idle Reduction Project” (EPA420-R-04-003) (March, 2004), available 

at http://www.epa.gov/smartway/documents/420r04003.pdf 
Regulatory Impact Analysis 

5-90 

first remanufactured under the revised remanufacturing program (see section III.B.(1)(c) of 

the Preamble for more details on the idle reduction program). 

If installed at the time of remanufacture, the AESS installation costs vary depending 

on the age and characteristics of the locomotive. On average, the cost of a basic system is 

approximately $10,000, and in some cases volume discounts may be available.M,T

 This cost 

estimate includes $2,500 in labor costs for installation, and $7,500 for the hardware costs for a 

basic AESS microprocessor system and monitoring equipment (systems including GPS or 

satellite uplink optional features are more expensive).O,T The cost may also vary depending 

on whether the locomotive is already equipped with the necessary sensors, and whether the 

AESS would require a stand alone electronic control unit as may be the case for older 

locomotives that are completely mechanical and do not have electronic controls. If installed 

on a new locomotive, costs should be much lower since the equipment could be installed at 

the factory and integrated with the original design of the locomotive. 

Idle reduction technology (e.g., AESS systems) can provide substantial emission 

reductions as well as cost savings by reducing fuel consumption. We estimated these cost 

savings for both a line-haul and switcher locomotive using 4,350 annual hours of operation 

for a line-haul or 36,500 hours over one useful life, and 4,450 annual hours for a switcher or 

101,000 hours over one useful life (see section 3.3.2 of this RIA for more details on the useful 

life of locomotives). The regulatory duty cycle (see 40CFR 92.132) indicates that a line-haul 

locomotive idles 38% of its operating time, and that a switcher locomotive idles 59.8% of its 

operating time. Using these values, we can estimate that a line-haul locomotive idles 

approximately 1,650 hours annually or nearly 14,000 hours over one useful life, and a 

switcher locomotive idles approximately 2,660 hours annually or slightly over 60,000 hours 

over one useful life (cost and emission savings estimates used the net present value of these 

idle hour values). 

These duty cycles include two types of idling: normal idle and low idle. Low idle 

indicates that there is no accessory load on the engine where normal idle indicates a load on 

the engine (for example, an accessory load occurs when the locomotive engine is charging a 

battery). As a conservative estimate, we are calculating that AESS provides a 50% reduction 

in low idling, although additional reductions in both low and normal idling may be 

possibleU,V,W. Using this reduction value, we have estimated that AESS will reduce 

unnecessary idling by over 410 hours a year on a line-haul locomotive, and approximately 

660 hours a year on a switcher locomotive. This means that over the useful life of a line-haul 

locomotive, we expect at least 2,900 hours of idling at a 3% net present value (2,500 at 7% 

 

T

 Jessica Montañez and Matthew Mahler, “Reducing Idling Locomotives Emissions”, North Carolina 

Department of Environment and Natural Resources, DAQ http://daq.state.nc.us/planning/locoindex.shtml

U David E. Brann, “Locomotive Idling Reduction”, 

http://www1.eere.energy.gov/vehiclesandfuels/pdfs/idling_2004/brann.pdf 

V http://www.arb.ca.gov/railyard/ryagreement/aess_electromotive.pdf 

W Draft Maryland Locomotive Idle Reduction Program Demonstration Project – DE-FG36-02GO12022 

http://www.osti.gov/bridge/servlets/purl/838872-D6MxUD/838872.PDF 
Engineering Cost Estimates 

5-91 

net present value) to have been eliminated, and at least 11,000 hours of idling at a 3% net 

present value (7,400 hours at 7% net present value) over the course of one useful life for a 

switcher locomotive. 

Using a fuel consumption value of three gallons per hour idled from Tier 2 

Certification data, a cost value of $1.57 per gallon of diesel fuel (see section 5.4.3 for 

information on how this cost was derived) and the yearly amount of idle hours avoided, we 

can estimate that this technology will pay for itself in just over three years on a switcher 

locomotive, and slightly over five years on a line-haul locomotive. It is important to note that 

locomotives typically operate for more than one useful life, and this technology does not have 

to be replaced upon remanufacture of the locomotive and therefore, it should continue to 

provide savings throughout the additional useful lives of that locomotive. It is also important 

to note that our estimates are conservative when compared to estimates by other groups, and 

when compared to data from locomotives equipped with AESS in the field. For comparative 

purposes, Table 5-62 shows the different payback times associated with the different savings 

estimates. Data from locomotives in the field indicate that this technology will pay for itself 

in eight months. That figure is derived from data that has been collected from a large number 

of locomotives, over many years of operation, in several different geographical regions of the 

country, and averaged separately for both line-haul and switcher locomotives. 

Table 5-62 Estimates of Typical AESS Payback Time by other SourcesX (monetary entries in 2005 

dollars) 

Source of Estimate 

Hours of 

Idle per 

switcher 

locomotive 

per year 

AESS 

reduced 

hours of 

idle 

Fuel 

Usage 

during 

idle 

(gal/hour)

Gallons 

Saved 

per 

Year 

Cost 

of 

Fuelb

Fuel 

Savings 

($) 

Payback 

time of 

AESSc 

RIA for this rule 2,650 665 3d 2,000 $1.57 $3,120 3.2 years 

DOE 5,300 2,650 4.5 12,000 $1.57 $18,800 6 months 

SmartStart Reports 3,840a 2,050 4.5 9,200 $1.57 $14,400 8 months 

Notes: 

a This average value comes from data accumulated over at least three years on both line-haul and switcher 

locomotives 

b

 The $1.57 cost of a gallon of diesel is calculated in Chapter 5 of this RIA 

c

 Payback time of AESS is based on average price of $10,000 which includes installation costs 

d

 3 gal/hr is based on Tier 2 Certification Data 

For simplicity we are presenting savings and emission reductions for a single useful 

life, even though locomotives are typically remanufactured at least three times before being 

scrapped. The AESS hardware would generally be expected to last for the remainder of a 

 

X These values have changed since the NPRM due to the increase estimate in the price of diesel fuel, see section 

5.4.3 of this chapter for more information. 
Regulatory Impact Analysis 

5-92 

locomotive's service life, which could be as little as one useful life for a very old locomotive 

being remanufactured for the last time, to more than four useful lives for a freshly 

manufactured locomotive. Thus actual cost savings will be significantly higher than the 

single useful life values presented here, even when discounted. 

It is also important to note that while we present annual and per-useful life emission 

reductions in this analysis, in Table 5-63, and in Table -5-64, these reductions are not 

aggregated into our final program as part of the emission reductions from the finalized 

program. In the past and in this recently finalized program, locomotives are tested and 

emissions are calculated to reflect the emission reductions associated with idle reduction 

technologies. AESS systems are currently being used by some manufacturers and 

remanufacturers as part of their certified locomotive emission controls. From both a 

regulatory and inventory perspective, the use of AESS is considered the same as installing 

aftertreatment or recalibrating the engine. The emission reductions are presented here merely 

to show the environmental significance of AESS. 

Reduced idling time means reduced fuel consumption. Tier 2 certification data 

indicates that modern locomotives typically burn 3 gallons of fuel an hour during low-idle. 

We estimated the cost savings of using an AESS based on an estimated diesel fuel cost (less 

taxes) of $1.57/gallon. For a line-haul locomotive, use of an AESS is estimated to provide 

fuel cost savings of almost $1,900 annually. Over the useful life, this would mean a net 

present value savings of nearly $13,700 at a three percent discount rate ($11,600 at a seven 

percent discount rate). For a switcher locomotive, an AESS could provide fuel savings of 

nearly $3,100 annually or, over its useful life, a net present value savings of approximately 

$50,000 at a three percent discount rate ($35,000 at a seven percent discount rate). 

Reduced idling time also means reducing idle emissions. Tier 2 certification data 

suggests that locomotives emit an average of 10g/hr of PM and 600g/hr of NOx during low 

idle. This means that a line-haul locomotive’s emissions could be reduced by over 0.005 tons 

of PM and 0.27 tons of NOx annually. Over the useful life, the net present value of PM 

reductions could be 0.032 tons at a three percent discount rate (0.027 tons at a seven percent 

discount rate). Likewise, the net present value of NOx reductions could be 1.9 tons at a three 

percent discount rate (1.5 tons at a seven percent discount rate). A switcher locomotive’s 

emissions can be reduced by over 0.007 tons of PM and 0.44 tons NOx annually. Over the 

useful life of the switcher, the net present value of PM reductions could be 0.12 tons at a three 

percent discount rate (0.08 tons at a seven percent discount rate) and, for NOx reductions, 7.0 

tons at a three percent discount rate (4.9 tons at a seven percent discount rate), older switchers 

would be expected to emit more pollutants than the Tier 2 estimates given here. 

Table 5-63 shows the annual fuel savings, the associated cost savings, and the 

emissions reductions we estimate would result from the AESS requirements. These values 

would be expected to be consistent for newer locomotives, although older locomotives may 

provide greater savings as they may consume more fuel at idle. Table -5-64 shows this 

information on a useful life basis along with net present value information and a net cost. The 

idle emission reductions are particularly important considering that we do not expect 

aftertreatment technologies to reduce NOx emissions for extended periods of idle, and further, 

we expect PM control to be reduced due to poor oxidation efficiency at idle. The ability of 
Engineering Cost Estimates 

5-93 

aftertreatment technologies to control emissions during idle operation is discussed in more 

detail in Chapter 4 of this RIA. Because of the limitations of the aftertreatment technology at 

idle, idle reduction via an AESS system is one of the best methods to ensure control of 

emissions at idle. 

Table 5-63 Annual Effects of Using AESS on Line-Haul and Switcher LocomotivesY (monetary entries in 

2005 dollars) 

Annual Estimates for a Typical Tier 2 Locomotive 

Type of 

Locomotive 

Time 

Spent 

Idlinga

(hrs)

Idling 

Reduced 

Using AESSb

(hrs) 

Fuel 

Savingsc 

(gals) 

Fuel 

Savingsd

($) 

PM Emission 

Reductionse

(tons) 

NOx Emission 

Reductionsf

(tons) 

Line-Haul 1,650 413 1,238 1,943 0.005 0.27 

Switcher 2,650 663 1,988 3,120 0.007 0.44 

Notes: 

a

 Using 38% idling time for line-hauls and 59.8% for switchers from Duty-Cycle (see 40CFR 92.132) 

b

 Assuming 50% of low-idle is reduced by AESS 

c

 Using 3 gallons of fuel burned per hour at low-idle (estimated from Tier 2 Certification Data) 

d

 Using diesel fuel price less taxes of $1.57/gallon (see section 5.4.3) e

 Using PM estimate of 10g/hr emitted during low idle (estimated from Tier 2 Certification Data) 

f

 Using NOx estimate of 600g/hr emitted during low idle (estimated from Tier 2 Certification Data) 

 

Y These values have changed since the NPRM due to the increase estimate in the cost of diesel fuel, see section 

5.4.3 of this chapter for more information. 
Regulatory Impact Analysis 

5-94 

Table -5-64 NPV 3% & 7% Effects of Using AESS Over the First Useful Life on Line-Haul and Switcher 

LocomotivesZ

 (monetary entries in 2005 dollars) 

Estimates Over the Firsta

 Useful Life of a Typical Tier 2 Locomotive 

Type of 

Locomotive 

NPV 

Factor 

Time 

Spent 

Idlingb

(hrs) 

Idling 

Reduced 

Using 

AESSc

(hrs) 

Fuel 

Savingsd

(gals) 

Fuel 

Savingse

($) 

Average 

Installation 

Cost of 

AESS($)f 

Net 

Savings 

($) 

PM 

Emission 

Reductionsg

(tons) 

NOx

Emission 

Reductionsh

(tons) 

NPV 3% 12,000 2,900 8,700 13,700 10,000 3,700 0.032 1.9 Line-Haul 

NPV 7% 9,900 2,500 7,400 11,600 10,000 1,600 0.027 1.6 

 

NPV 3% 42,000 11,000 32,000 50,000 10,000 40,000 0.12 7.0 Switcher 

NPV 7% 29,000 7,400 22,000 35,000 10,000 25,000 0.08 4.9 

Notes: 

a

 Additional savings not accounted for in this analysis include: reduced wear on engine components, reduced oil 

consumption, and fuel savings over subsequent useful lives of a locomotive’s full lifetime. 

b

 Using 38% idling time for line-hauls and 59.8% for switchers from Duty-Cycle (see 40CFR 92.132) 

c

 Assuming 50% of low-idle is reduced by AESS 

d

 Using 3 gallons of fuel burned per hour at low-idle (estimated from Tier 2 Certification Data) e

 Using diesel fuel price less taxes of $1.57 gallon (see section 5.4.3) 

f

 Average cost assumes AESS was bought and paid for the first year of installation 

g

 Using PM estimate of 10g/hr emitted during low idle (estimated from Tier 2 Certification Data) 

h

 Using NOx estimate of 600g/hr emitted during low idle (estimated from Tier 2 Certification Data)

Note that we have not included the costs and savings associated with AESS systems in 

the overall cost analysis of the program summarized in Section 5.6. The primary reason for 

this is the expectation that these systems would be in widespread use absent a requirement 

from EPA, even in retrofit applications on existing locomotives. We did not believe it would 

be appropriate to assume no one would employ these systems absent a requirement, nor did 

we want to assume that everyone would absent a requirement. Further, as shown in Table -

5-64, a net savings is likely, which would in effect, reduce the overall cost of our final 

program were we to include the costs and savings associated with AESS systems. Because of 

the difficulty and uncertainty involved in estimating their use absent a requirement, and their 

net effect of providing savings to users, we decided to present the costs and savings separately 

from the overall program. 

5.8 Analysis of Energy Effects 

Under E.O. 13211, a “significant energy action” is any regulatory action that might 

have a significant adverse effect on the supply, distribution, or use of energy. A significant 

adverse effect is, along with several other factors, any outcome that could reduce crude oil 

supply in excess of 10,000 barrels per day, reduce fuel production in excess of 4,000 barrels 

 

Z

 These values have changed since the NPRM due to the increase estimate in the price of diesel fuel, see section 

5.4.3 of this chapter for more information. 
Engineering Cost Estimates 

5-95 

per day, or increase energy usage in excess of either of those thresholds. The final locomotive 

and marine program is projected to have an impact on fuel usage in excess of one of these 

thresholds. 

Sections 5.4.3 and 5.5 of this RIA present our analysis of the increased costs 

associated with fuel consumption impacts that would result from both the addition of diesel 

particulate filters to some locomotive and marine engines, and the remanufacture of Tier 0 

locomotive and C2 marine engines. Table 5-44 through Table 5-47 show the increased 

number of gallons we have estimated would be consumed as a result of the final program. 

Table 5-51 through Table 5-53 show the increased number of gallons we have estimated 

would be consumed as a result of the remanufacturing programs. Using the metrics of 42 

gallons of fuel per barrel of crude oil and 365 days in a year, the projected number of barrels 

of oil per day can be calculated as shown in Table 5-65. As shown, in the year 2022, our 

program is projected to result in excess of 4,000 barrels of oil per day in increased energy 

usage. Note that the fuel consumption estimates shown in Table 5-65 do not reflect the 

potential fuel savings associated with automatic engine stop/start (AESS) systems or other 

idle reduction technologies. As discussed in section 5.7, such technologies can provide 

significant fuel savings which could offset the increased fuel consumption estimates shown in 

Table 5-65.
Regulatory Impact Analysis 

5-96 

Table 5-65 Estimated Increase in Fuel Consumed in Million Gallons per Year and Average Barrels per 

Day 

Increase in Fuel Consumed 

Calendar (Million gallons per year)

Year Tier 4 

Locomotive 

Tier 4 

Marine 

Locomotive 

Reman 

Marine 

Reman Total 

Barrels/day 

2006 0 0 0 0 0 0 

2007 0 0 0 0 0 0 

2008 0 0 2 3 5 305 

2009 0 0 2 6 8 523 

2010 0 0 4 10 14 894 

2011 0 0 8 13 21 1383 

2012 0 0 10 16 26 1717 

2013 0 0 12 20 32 2073 

2014 0 1 13 22 36 2335 

2015 2 2 13 21 38 2482 

2016 4 4 14 20 43 2781 

2017 7 6 16 19 48 3101 

2018 9 8 16 18 51 3315 

2019 11 10 15 17 54 3516 

2020 14 13 14 16 57 3695 

2021 16 15 12 16 59 3874 

2022 19 17 11 15 62 4060 

2023 22 20 10 14 65 4253 

2024 24 22 9 13 68 4449 

2025 27 24 8 12 71 4649 

2026 30 26 7 11 74 4851 

2027 32 29 6 10 77 5055 

2028 35 31 5 9 81 5260 

2029 38 33 5 9 84 5461 

2030 40 35 4 8 87 5653 

2031 43 36 3 7 89 5838 

2032 46 38 3 6 92 6020 

2033 48 39 2 5 95 6203 

2034 51 41 2 5 98 6382 

2035 54 42 1 4 101 6562 

2036 56 43 1 3 103 6740 

2037 59 44 1 3 106 6915 

2038 61 45 0 2 109 7086 

2039 63 46 0 2 111 7253 

2040 65 46 0 2 114 7416 
Engineering Cost Estimates 

5-97 

5.9 Cost Effectiveness 

As discussed in Chapter 6, this rule is very cost beneficial, with social benefits far 

outweighing social costs. However, this does not shed light on how cost effective this control 

program is compared to other control programs at providing the expected emission reductions. 

One tool that can be used to assess the value of the final program is the ratio of engineering costs 

incurred per ton of emissions reduced and comparing that ratio to other control programs. As we 

show in this section, the PM and NOx emissions reductions from the new locomotive and marine 

diesel program compare favorably—in terms of cost effectiveness—to other mobile source 

control programs that have been or will soon be implemented. We note that the locomotive and 

marine final rule builds upon the efforts undertaken by the engine manufacturing industry to 

comply with our recent 2007/2010 heavy-duty highway and nonroad Tier 4 (NRT4) 

rulemakings. As such, and as discussed at length in section 5.2.1 of this final RIA, much of the 

research and development associated with diesel emission controls builds upon the work done to 

comply with those earlier rules. This does not change the conclusion that the cost effectiveness 

of the locomotive and marine standards compares favorably with other actions deemed 

appropriate for society. 

Table 5-66 shows the emissions reductions associated with the final locomotive and 

marine program. These reductions are discussed in more detail in Chapter 3 of this final RIA. 

Table 5-66 Estimated Emissions Reductions Associated with the Final Locomotive and Marine Program 

(Short tons) 

Year PM2.5 PM10a

 NOx NMHC 

2015 7,000 8,000 161,000 14,000 

2020 14,000 15,000 371,000 26,000 

2030 27,000 27,000 795,000 40,000 

2040 37,000 38,000 1,144,000 52,000 

NPV at 3% 308,000 318,000 8,757,000 492,000 

NPV at 7% 134,000 139,000 3,708,000 221,000 

a

 Note that, PM2.5 is estimated to be 97 percent of the more inclusive PM10 emission inventory. In 

Chapter 3 we generate and present PM2.5 inventories since recent research has determined that these 

are of greater health concern. Similarly, NMHC is estimated to be 93 percent of the more inclusive 

VOC emission inventory. Traditionally, we have used PM10 and NMHC in our cost effectiveness 

calculations. Since cost effectiveness is a means of comparing control measures to one another, we 

use PM10 and NMHC in our cost effectiveness calculations for comparisons to past control 

measures. 

Using the costs associated with PM and NOx control shown in Table 5-61 and the 

emission reductions shown in Table 5-66, we can calculate the $/ton associated with the final 

program. These are shown in Table 5-67. The resultant cost per ton numbers depend on how the 

costs are allocated to each pollutant. We have allocated costs as closely as possible to the 

pollutants for which they are incurred. These allocations are also discussed in detail in Section 

5.6 of this final RIA. 
Regulatory Impact Analysis 

5-98 

Table 5-67 Final Program Aggregate Cost per Ton and Long-Term Annual Cost per Ton (2005 dollars) 

Pollutant 2006 Thru 2040 

Discounted Lifetime 

Cost Per Ton At 3% 

2006 Thru 2040 

Discounted Lifetime 

Cost Per Ton At 7% 

Cost Per Ton In 2030 Cost Per Ton In 2040 

NOx+NMHC $730 $760 $690 $700 

PM $8,440 $9,620 $6,620 $6,360 

The costs per ton shown in Table 5-67 for 2006 through 2040 use the net present value of 

the annualized costs and emissions reductions associated with the program for the years 2006 

through 2040. We have also calculated the costs per ton of emissions reduced in the years 2030 

and 2040 using the annual costs and emissions reductions in those specific years. These numbers 

are also shown in Table 5-67. All of the costs per ton include costs and emission reductions that 

will occur from the locomotive and marine remanufacturing programs. 
Engineering Cost Estimates 

5-99 

REFERENCES 

 

1

 “Electronic Systems and EGR Costs for Nonroad Engines,” Final Report, ICF Consulting, December, 2002, 

Public Docket No. A-2001-28, Docket Item II-A-10. 

2

 “Aftertreatment Marinizing Costs for CI Engines <30 L/cyl,” Final Report, ICF International, September 2006; 

Docket ID Number EPA-HQ-OAR-2003-0190. 

3

 “Economic Analysis of Diesel Aftertreatment System Changes Made Possible by Reduction of Diesel Fuel 

Sulfur Content,” Engine, Fuel, and Emissions Engineering, Incorporated, December 15, 1999, Public Docket No. 

A-2001-28, Docket Item II-A-76. 

4

 “Update of EPA’s Motor Vehicle Emission Control Equipment Retail Price Equivalent (RPE) Calculation 

Formula," Jack Faucett Associates, Report No. JACKFAU-85-322-3, September 1985, Public Docket No. A2001-28, Docket Item II-A-74. 

5

 “Economic Analysis of Diesel Aftertreatment System Changes Made Possible by Reduction of Diesel Fuel 

Sulfur Content,” Engine, Fuel, and Emissions Engineering, Incorporated, December 15, 1999, Public Docket No. 

A-2001-28, Docket Item II-A-76. 

6

 “Learning Curves in Manufacturing," Linda Argote and Dennis Epple, Science, February 23, 1990, Vol. 247, 

pp. 920-924. 

7

 Power Systems Research, OELink Sales Version, 2002. 

8

 Bureau of Labor Statistics at http://data.bls.gov, Producer Price Index for Total Manufacturing Industries, 

series ID PCUOMFG--OMFG, shows an annual PPI value for 2005 of 150.8 versus a March 2004 value 

(publication of the NRT4 rule) of 140.3 for a PPI adjustment of 1.075 (150.8/140.3). 

9

 “Learning Curves in Manufacturing,” Linda Argote and Dennis Epple, Science, February 23, 1990, Vol. 247, 

pp. 920-924. 

10 “Treating Progress Functions As Managerial Opportunity”, J.M Dutton and A. Thomas, Academy of 

Management Review, Rev. 9, 235, 1984, Public Docket A-2001-28, Docket Item II-A-73. 

11 Nonconformance Penalty Final Rule, 67 FR 51464, August 8, 2002. 

12 “Economic Analysis of Diesel Aftertreatment System Changes Made Possible by Reduction of Diesel Fuel 

Sulfur Content,” Engine, Fuel, and Emissions Engineering, Incorporated, December 15, 1999, Public Docket No. 

A-2001-28, Docket Item II-A-76. 

13 “Estimated Economic Impact of New Emission Standards for Heavy-Duty On-Highway Engines,” March 

1997, EPA420-R-97-009, Public Docket A-2001-28, Docket Item II-A-136. 

14 Estimates for Heavy-Duty Gasoline Vehicles,” Arcadis Geraghty & Miller, September 1998, EPA Air Docket 

A-2001-28, Docket Item II-A-77. 

15 “Economic Analysis of Diesel Aftertreatment System Changes Made Possible by Reduction of Diesel Fuel 

Sulfur Content,” Engine, Fuel, and Emissions Engineering, Incorporated, December 15, 1999, Public Docket No. 

A-2001-28, Docket Item II-A-76. 
Regulatory Impact Analysis 

5-100 

 

16 “Economic Analysis of Diesel Aftertreatment System Changes Made Possible by Reduction of Diesel Fuel 

Sulfur Content,” Engine, Fuel, and Emissions Engineering, Incorporated, December 15, 1999, Public Docket No. 

A-2001-28, Docket Item II-A-76. 

17 Nonconformance Penalty Final Rule, 67 FR 51464, August 8, 2002. 

18 “Economic Analysis of Diesel Aftertreatment System Changes Made Possible by Reduction of Diesel Fuel 

Sulfur Content,” Engine, Fuel, and Emissions Engineering, Incorporated, December 15, 1999, Public Docket No. 

A-2001-28, Docket Item II-A-76. 

19 Bureau of Labor Statistics at http://data.bls.gov, Producer Price Index for Total Manufacturing Industries, 

series ID PCUOMFG--OMFG, shows an annual PPI value for 2005 of 150.8 versus a January 2000 value 

(publication of the 2007 HD Highway rule) of 130.8 for a PPI adjustment of 1.153 (150.8/130.8). 

20 “Economic Analysis of Diesel Aftertreatment System Changes Made Possible by Reduction of Diesel Fuel 

Sulfur Content,” Engine, Fuel, and Emissions Engineering, Incorporated, December 15, 1999, Public Docket No. 

A-2001-28, Docket Item II-A-76. 

21 Johnson Matthey, www.platinum.matthey.com. 

22 “Economic Analysis of Diesel Aftertreatment System Changes Made Possible by Reduction of Diesel Fuel 

Sulfur Content,” Engine, Fuel, and Emissions Engineering, Incorporated, December 15, 1999, Public Docket No. 

A-2001-28, Docket Item II-A-76. 

23 “Aftertreatment Marinizing Costs for CI Engines <30 L/cyl,” Final Report, ICF International, September 

2006; Docket ID Number EPA-HQ-OAR-2003-0190. 

24 “Aftertreatment Marinizing Costs for CI Engines <30 L/cyl,” Final Report, ICF International, September 

2006; Docket ID Number EPA-HQ-OAR-2003-0190. 

25 “Marine Vessel Sales” Memorandum from John Mueller, Office of Transportation and Air Quality, to docket 

EPA-HQ-OAR-2003-0190, February 28, 2007. 

26 “Viability of Urea Infrastructure for SCR Systems,” presented by M.D. Jackson, at the U.S. EPA Clean Diesel 

Implementation Workshop, August 6, 2003. 
Cost-Benefit Analysis 

6-1 

CHAPTER 6: COST-BENEFIT ANALYSIS ............................................................. 6-2

6.1 Overview...........................................................................................................................6-2

6.2 Air Quality Impacts for Benefits Analysis ....................................................................6-6

6.2.1 Converting CMAQ Outputs to Full-Season Profiles for Benefits Analysis........6-6

6.2.2 Ozone and PM2.5 Air Quality Results ....................................................................6-7

6.3 Characterizing Uncertainty: Moving Toward a Probabilistic Framework for Benefits 

Assessment............................................................................................................................6-11

6.4 Health Impact Functions ..............................................................................................6-13

6.4.1 Potentially Affected Populations..........................................................................6-13

6.4.2 Effect Estimate Sources.........................................................................................6-13

6.4.3 Baseline Incidence Rates .......................................................................................6-31

6.5 Economic Values for Health Outcomes.......................................................................6-33

6.5.1 Mortality Valuation...............................................................................................6-34

6.5.2 Hospital Admissions Valuation ............................................................................6-34

6.5.3 Asthma-Related Emergency Room Visits Valuation..........................................6-34

6.5.4 Minor Restricted Activity Days Valuation..........................................................6-35

6.5.5 School Absences.....................................................................................................6-35

6.6 Benefits Analysis Results for the Final Standards......................................................6-41

6.7 Comparison of Costs and Benefits...............................................................................6-51

APPENDIX 6A: HEALTH-BASED COST EFFECTIVENESS ANALYSIS ..6-54

APPENDIX 6.B: SENSITIVITY ANALYSES OF KEY PARAMETERS IN THE 

BENEFITS ANALYSIS.........................................................................................6-63
Regulatory Impact Analysis 

6-2 

CHAPTER 6: Cost-Benefit Analysis 

6.1 Overview 

This chapter presents our analysis of the health and environmental benefits that are 

estimated to occur as a result of the final locomotive and marine engine standards throughout 

the period from initial implementation through 2030. Nationwide, the engines subject to the 

final emission standards in this rule are a significant source of mobile source air pollution. 

The final standards will reduce exposure to direct PM2.5, NOx and air toxics emissions and 

help avoid a range of adverse health effects associated with ambient ozone and PM2.5 levels. 

EPA is required by Executive Order (E.O.) 12866 to estimate the benefits and costs of 

major new pollution control regulations. Accordingly, the analysis presented here attempts to 

answer three questions: (1) what are the physical health and welfare effects of changes in 

ambient air quality resulting from particulate matter (PM) and ozone precursor emission 

reductions (direct PM and NOx)? (2) what is the monetary value of the changes in these 

effects attributable to the final rule? and (3) how do the monetized benefits compare to the 

costs? It constitutes one part of EPA’s thorough examination of the relative merits of this 

regulation. 

The benefits analysis relies on three major components to answer these questions: 

• Calculation of the impact of the final rule on the national nonroad emissions inventory of 

precursors to ozone and PM2.5, specifically NOx, and direct PM, for two future years 

(2020 and 2030). 

• Air quality modeling for 2020 and 2030 to determine changes in ambient concentrations 

of ozone and PM2.5, reflecting baseline and post-control emissions inventories. 

• A benefits analysis to determine the changes in human health and welfare, both in terms 

of physical effects and monetary value, that result from the projected changes in ambient 

concentrations of ozone and PM2.5 for the modeled standards. 

 A wide range of human health and welfare effects are linked to the emissions of direct 

PM and NOx and the resulting impact on ambient concentrations of ozone and PM2.5. Recent 

studies have linked short-term ozone exposures with premature mortality. Exposure to ozone 

has also been linked to a variety of respiratory effects including hospital admissions and 

illnesses resulting in school absences. Potential human health effects associated with PM2.5

range from premature mortality to morbidity effects linked to long-term (chronic) and shorterterm (acute) exposures (e.g., respiratory and cardiovascular symptoms resulting in hospital 

admissions, asthma exacerbations, and acute and chronic bronchitis). Welfare effects 
Cost-Benefit Analysis 

6-3 

potentially linked to PM include materials damage and visibility impacts, while ozone can 

adversely affect the agricultural and forestry sectors by decreasing yields of crops and forests. 

The benefits modeling is based on peer-reviewed studies of air quality and health and 

welfare effects associated with improvements in air quality and peer-reviewed studies of the 

dollar values of those public health and welfare effects. All of the benefit estimates for the 

control options in this analysis are based on an analytical structure and sequence consistent 

with benefits analyses performed for the recent analysis of the proposed Ozone NAAQS and 

the final PM NAAQS analysis.1,2

 For a more detailed discussion of the principles of benefits 

analysis used here, we refer the reader to those documents, as well as to the EPA Guidelines 

for Economic Analysis. 

Table 6.1-1 summarizes the annual monetized health and welfare benefits associated 

with the final standards for two years, 2020 and 2030. The estimates in Table 6.1-1, and all 

monetized benefits presented in this chapter, are in year 2006 dollars. There are a few items 

to note about these benefits: 

• Using the most conservative benefits estimate, the 2020 benefits outweigh the costs by a 

factor of 10. Using the upper end of the benefits range, the benefits could outweigh the 

costs by a factor of 25. Likewise, in 2030 benefits outweigh the costs by at least a factor 

of 10 and could be as much as a factor of 28. Thus, even taking the most conservative 

benefits assumptions, benefits of the final standards clearly outweigh the costs. 

• Emissions and air quality modeling decisions are made early in the analytical process. For 

this reason, the emission control scenarios used in the air quality and benefits modeling 

are slightly different than the final emission control program. The differences reflect 

further refinements of the regulatory program since we performed the air quality modeling 

for this rule. Chapter 3 of the RIA describes the changes in the inputs and resulting 

emission inventories between the preliminary assumptions used for the air quality 

modeling and the final regulatory scenario. 

• The RIA for the proposal for this rulemaking only quantified benefits from PM; in the 

current RIA we quantify and monetize the ozone-related health and environmental 

impacts associated with the final rule. The science underlying the analysis is based on the 

current ozone criteria document.3

 The analytic approach to characterizing uncertainty is 

consistent with the analysis used in the RIA for the proposed O3 NAAQS. 

• The range of ozone benefits associated with the final standards is based on risk reductions 

using several sources of ozone-related mortality effect estimates. There is considerable 

uncertainty in the magnitude of the association between ozone and premature mortality. 

This analysis presents four alternative estimates for the association based upon different 

functions reported in the scientific literature. Recognizing that additional research is 

necessary to clarify the underlying mechanisms causing these effects, we also consider the 

possibility that the observed associations between ozone and mortality may not be causal 

in nature. 
Regulatory Impact Analysis 

6-4 

 

• For this analysis, we observed two urban areas that, to some degree, experience ozone 

disbenefits related to NOx control: Southern California and the metropolitan Chicago 

area.

A Ozone disbenefits associated with NOx control typically occur during nighttime 

and early morning hours, when NOx titrates ozone. Because human exposure to ozone is 

a function of the temporal and spatial patterns of ambient concentrations of ozone in the 

atmosphere, the ozone-related health impacts analysis is sensitive to which ozone 

exposure metric we use in the health impact functions. For example, the 24-hour average 

incorporates both nighttime and daytime hours, which means the decrease in ozone 

titration caused by reduced NOx emissions can cause relatively large increases in 24-hour 

average ozone. This is not the most relevant ozone exposure metric to characterize 

population-level exposure given that the majority of the people tend to be outdoors during 

the daylight hours. Furthermore, concentrations are highest during the daylight hours. 

Together, this means that the most biologically relevant metric, and the one used in the 

ozone NAAQS since 1997, is the 8-hour maximum standard. Thus, although 

epidemiological studies often present their results in terms of 24-hour average ozone 

levels, for the final rule analysis, we have converted health impact functions that use a 24-

hour average ozone metric to 8-hour maximum ozone concentration using standard 

conversion functions. 

A In areas prone to ozone disbenefits, our ability to draw conclusions based on air quality modeling conducted 

for the final rule is limited. Marginal ozone changes in these areas are much more dependent upon baseline air 

quality conditions than PM due to nonlinearities present in the chemistry of ozone formation. A marginal 

decrease in NOx emissions modeled on its own in these areas, as was done for this analysis, may yield a very 

different ambient ozone concentration than if it were modeled in combination with other planned or future 

controls. This is because “yet-to-occur” emission reductions in these areas, associated with local controls and 

other unknown measures, are not accounted for in our analytical approach. Within these regions, we expect that 

the additional NOx reductions from SIP-based controls will lead to fewer ozone disbenefits from the marginal 

changes modeled here. 
Cost-Benefit Analysis 

6-5 

Table 6.1-1. Estimated Monetized PM- and Ozone-Related Health Benefits of the Final 

Locomotive and Marine Engine Standards 

2030 Total Ozone and PM Benefits – PM Mortality Derived from American Cancer Society Analysisa

Premature Ozone 

Mortality Function or 

Assumption 

Reference Mean Total Benefits 

(Billions, 2006$, 3% 

Discount Rate)c,d 

Mean Total Benefits 

(Billions, 2006$, 7% 

Discount Rate) c,d

NMMAPS Bell et al., 2004 $9.7 $8.9 

Bell et al., 2005 $11 $9.8 

Ito et al., 2005 $11 $10 

Meta-analysis 

Levy et al., 2005 $11 $10 

Assumption that association is not causal $9.2 $8.4 

2030 Total Ozone and PM Benefits – PM Mortality Derived from Expert Elicitationb

Premature Ozone 

Mortality Function or 

Assumption 

Reference Mean Total Benefits 

(Billions, 2006$, 3% 

Discount Rate) c,d

Mean Total Benefits 

(Billions, 2006$, 7% 

Discount Rate) c,d

NMMAPS Bell et al., 2004 $5.2 to $37 $4.8 to $34 

Bell et al., 2005 $6.2 to $38 $5.8 to $35 

Ito et al., 2005 $6.7 to $39 $6.3 to $35 

Meta-analysis 

Levy et al., 2005 $6.7 to $39 $6.4 to $35 

Assumption that association is not causal $4.7 to $37 $4.4 to $33 a

 Total includes ozone and PM2.5 benefits. Range was developed by adding the estimate from the ozone 

premature mortality function to the estimate of PM2.5-related premature mortality derived from the American 

Cancer Society analysis (Pope et al., 2002). b

 Total includes ozone and PM2.5 benefits. Range was developed by adding the estimate from the ozone 

premature mortality function to both the lower and upper ends of the range of the PM2.5 premature mortality 

functions characterized in the expert elicitation. The effect estimates of five of the twelve experts included in the 

elicitation panel fall within the empirically-derived range provided by the ACS and Six-Cities studies. One of 

the experts fall below this range and six of the experts are above this range. Although the overall range across 

experts is summarized in this table, the full uncertainty in the estimates is reflected by the results for the full set 

of 12 experts. The twelve experts’ judgments as to the likely mean effect estimate are not evenly distributed 

across the range illustrated by arraying the highest and lowest expert means. c

 Note that total benefits presented here do not include a number of unquantified benefits categories. A 

detailed listing of unquantified health and welfare effects is provided in Table 6.4-1. d

 Results reflect the use of both a 3 and 7 percent discount rate, as recommended by EPA’s Guidelines 

for Preparing Economic Analyses and OMB Circular A-4. Results are rounded to two significant digits for ease 

of presentation and computation. 

 Table 6.1-1 reflects those human health and welfare effects we are able to quantify and 

monetize. However, the full complement of human health and welfare effects associated with 

PM, ozone and air toxics remain unquantified because of current limitations in methods or 

available data. We have not quantified a number of known or suspected health effects linked 

with ozone and PM for which appropriate health impact functions are not available or which 

do not provide easily interpretable outcomes (i.e., changes in heart rate variability). 

Additionally, we are unable to quantify a number of known welfare effects, including reduced 

acid and particulate deposition damage to cultural monuments and other materials, and 

environmental benefits due to reductions of impacts of acidification in lakes and streams and 

eutrophication in coastal areas. As a result, we may underestimate the total benefits 

attributable to the implementation of the final standards. 
Regulatory Impact Analysis 

6-6 

 

This chapter is organized as follows. In Section 6.2, we provide an overview of the air 

quality impacts modeled for the final standards that are used as inputs to the benefits analysis. 

In Section 6.3, we discuss how uncertainty is characterized in this analysis. Section 6.4 

discusses the literature on ozone- and PM-related health effects and describes the specific set 

of health impact functions we used in the benefits analysis. Section 6.5 describes the 

economic values selected to estimate the dollar value of ozone- and PM-related health 

impacts. In Section 6.6, we report the results of the analysis for human health and welfare 

effects. Finally, Section 6.7 presents a comparison of the costs and benefits associated with 

the final standards. 

6.2 Air Quality Impacts for Benefits Analysis 

In Chapter 2, we summarize the methods for and results of estimating air quality for 

the 2020 and 2030 base case and final control scenario. These air quality results are in turn 

associated with human populations and ecosystems to estimate changes in health and welfare 

effects. For the purposes of the benefits analysis, we focus on the health effects that have 

been linked to ambient changes in ozone and PM2.5 related to emission reductions estimated 

to occur due to the final standards. We estimate ambient PM2.5 and ozone concentrations 

using the Community Multiscale Air Quality model (CMAQ). The air quality modeling 

Technical Support Document (TSD), which can be found in the docket for this rule, contains 

detailed information about the modeling conducted for this rule. In this section, we describe 

how the modeled air quality results were used for the benefits analysis. 

We remind the reader that the emission control scenarios used in the air quality and 

benefits modeling are slightly different than the final emission control program. The 

differences reflect further refinements of the regulatory program since we performed the air 

quality modeling for this rule. Emissions and air quality modeling decisions are made early in 

the analytical process. Chapter 3 of the RIA describes the changes in the inputs and resulting 

emission inventories between the preliminary assumptions used for the air quality modeling 

and the final regulatory scenario. 

6.2.1 Converting CMAQ Outputs to Full-Season Profiles for Benefits Analysis 

This analysis extracted hourly, surface-layer PM and ozone concentrations for each 

grid cell from the standard CMAQ output files. For ozone, these model predictions are used 

in conjunction with the observed concentrations obtained from the Aerometric Information 

Retrieval System (AIRS) to generate ozone concentrations for the entire ozone season.B,C 

B

 The ozone season for this analysis is defined as the 5-month period from May to September.

C

 Based on AIRS, there were 961 ozone monitors with sufficient data (i.e., 50 percent or more days reporting at 
Cost-Benefit Analysis 

6-7 

 

The predicted changes in ozone concentrations from the future-year base case to future-year 

control scenario serve as inputs to the health and welfare impact functions of the benefits 

analysis (i.e., the Environmental Benefits Mapping and Analysis Program [BenMAP]). 

To estimate ozone-related health and welfare effects for the contiguous United States, 

full-season ozone data are required for every BenMAP grid-cell. Given available ozone 

monitoring data, we generated full-season ozone profiles for each location in two steps: (1) 

we combined monitored observations and modeled ozone predictions to interpolate hourly 

ozone concentrations to a grid of 12-km by 12-km population grid cells for the contiguous 48 

states, and (2) we converted these full-season hourly ozone profiles to an ozone measure of 

interest, such as the daily 8-hour maximum.D,E

For PM2.5, we also use the model predictions in conjunction with observed monitor 

data. CMAQ generates predictions of hourly PM species concentrations for every grid. The 

species include a primary coarse fraction (corresponding to PM in the 2.5 to 10 micron size 

range), a primary fine fraction (corresponding to PM less than 2.5 microns in diameter), and 

several secondary particles (e.g., sulfates, nitrates, and organics). PM2.5 is calculated as the 

sum of the primary fine fraction and all of the secondarily formed particles. Future-year 

estimates of PM2.5 were calculated using relative reduction factors (RRFs) applied to 2002 

ambient PM2.5 and PM2.5 species concentrations. A gridded field of PM2.5 concentrations was 

created by interpolating Federal Reference Monitor ambient data and IMPROVE ambient 

data. Gridded fields of PM2.5 species concentrations were created by interpolating EPA 

speciation network (ESPN) ambient data and IMPROVE data. The ambient data were 

interpolated to the CMAQ 12 km grid. 

The procedures for determining the RRFs are similar to those in EPA’s draft guidance 

for modeling the PM2.5 standard (EPA, 1999). The guidance recommends that model 

predictions be used in a relative sense to estimate changes expected to occur in each major 

PM2.5 species. The procedure for calculating future-year PM2.5 design values is called the 

“Speciated Modeled Attainment Test (SMAT).” EPA used this procedure to estimate the 

ambient impacts of the final emissions controls. Full documentation of the revised SMAT 

methodology is contained in the Air Quality Modeling TSD. 

6.2.2 Ozone and PM2.5 Air Quality Results 

This section provides a summary of the predicted ambient PM2.5 and ozone 

concentrations from the CMAQ model for the 2020 and 2030 base cases and changes 

least nine hourly observations per day [8 am to 8 pm] during the ozone season).

D The 12-km grid squares contain the population data used in the health benefits analysis model, BenMAP. 

E

 This approach is a generalization of planar interpolation that is technically referred to as enhanced Voronoi 

Neighbor Averaging (EVNA) spatial interpolation. See the BenMAP manual for technical details, available for 

download at http://www.epa.gov/air/benmap. 
Regulatory Impact Analysis 

6-8 

associated with the final rule. Table 6.2-1 provides those ozone and PM2.5 metrics for grid 

cells in the modeled domain that enter the health impact functions for health benefits 

endpoints. The population-weighted average reflects the baseline levels and predicted 

changes for more populated areas of the nation. This measure better reflects the potential 

benefits through exposure changes to these populations. 

Table 6.2-1. Summary of CMAQ-Derived Population-Weighted Ozone and PM2.5 Air 

Quality Metrics for Health Benefits Endpoints Due to the Final Locomotive and Marine 

Engine Standards 

2020 2030 

Statistica Baseline Changeb Baseline Changeb

Ozone Metrics: National Population-Weighted Average (ppb)c

Daily 1-Hour Maximum Concentration 49.13 0.060 48.78 0.202 

Daily 8-Hour Maximum Concentration 42.89 0.033 42.65 0.137 

Daily 8-Hour Average Concentration 41.71 0.031 41.49 0.130 

Daily 24-Hour Average Concentration 28.16 -0.033 28.11 -0.039 

PM2.5 Metrics: National Population-Weighted Average (ug/m3) 

Annual Average Concentration 11.85 0.061 11.87 0.122 a

 Ozone and PM2.5 metrics are calculated at the CMAQ grid-cell level for use in health effects estimates 

based on the results of spatial and temporal Voronoi Neighbor Averaging. Ozone metrics are calculated over 

relevant time periods during the daylight hours of the “ozone season” (i.e., May through September). For the 8-

hour average, for example, the relevant time period is 9 am to 5 pm. b

 The change is defined as the base-case value minus the control-case value. 

c

 Calculated by summing the product of the projected CMAQ grid-cell population and the estimated 

CMAQ grid cell seasonal ozone concentration and then dividing by the total population.

6.2.2.1 Modeled Ozone-Related Disbenefits and Treatment in Benefits Analysis 

While this rule will reduce ozone levels generally and provide national ozone-related 

health benefits, as demonstrated in Table 6.2-1, this is not always the case at the local level. 

Due to the complex photochemistry of ozone production, reductions in NOx emissions lead to 

both the formation and destruction of ozone, depending on the relative quantities of NOx, 

VOC, and ozone catalysts such as the OH and HO2 radicals. In areas dominated by fresh 

emissions of NOx, ozone catalysts are removed via the production of nitric acid which slows 

the ozone formation rate. Because NOx is generally depleted more rapidly than VOC, this 

effect is usually short-lived and the emitted NOx can lead to ozone formation later and further 

downwind. The terms “NOx disbenefits” or “ozone disbenefits” refer to the ozone increases 

that can result from NOx emissions reductions in these localized areas. According to the 

North American Research Strategy for Tropospheric Ozone (NARSTO) Ozone Assessment, 

these disbenefits are generally limited to small regions within specific urban cores and are 
Cost-Benefit Analysis 

6-9 

 

surrounded by larger regions in which NOx control is beneficial.F

 For this analysis, we 

observed two urban areas that experience ozone disbenefits: Southern California and, to a 

lesser degree, the metropolitan Chicago area. Full documentation of these disbenefits is 

contained in the Air Quality Modeling TSD, located in the docket for this rule. 

Marginal changes in ozone in these areas are much more dependent upon baseline air 

quality conditions than PM due to nonlinearities present in the chemistry of ozone formation. 

A marginal decrease in NOx emissions modeled on its own in these areas, as was done for 

this analysis, may yield a very different ambient ozone concentration than if it were modeled 

in combination with other planned or future controls. For example, recent California SIP 

modeling indicates that with a combined program of national and local controls, including the 

final locomotive and marine controls, Southern California can reach ozone attainment by 

2024 through a mixture of substantial NOx (and VOC) reductions.G 

In areas prone to ozone disbenefits, such as Southern California, our ability to draw 

conclusions based on air quality modeling conducted for the final rule is limited because the 

yet-to-occur emission reductions in these areas are not accounted for in our analytical 

approach. Within these regions, it is expected that the additional NOx reductions from SIPbased controls would lead to fewer ozone disbenefits from the marginal changes modeled 

here. The ozone benefits in these regions may therefore be underestimated. 

EPA has been aware of the issue of ozone disbenefits for a number of years. We have 

recognized the implications of this issue for cost-benefit analyses of mobile source strategies 

in our recent rulemakings for heavy duty onroad and nonroad diesel engines. For example, in 

the Nonroad Diesel rule RIA, we noted that "our ozone air quality modeling showed that the 

NOx emissions reductions from the preliminary modeled standards are projected to result in 

increases in ozone concentrations for certain hours during the year, especially in urban, NOxlimited areas. Most of these increases are expected to occur during hours where ozone levels 

are low (and often below the one-hour ozone standard)." 

EPA has always incorporated ozone-related disbenefits into our estimates of total 

benefits. In the Nonroad Diesel rule RIA, we provided this statement regarding disbenefits: 

"Ozone benefits arising from this rule are in aggregate positive for the nation. However, due 

to ozone increases occurring during certain hours of the day in some urban areas, in 2020 the 

F

 The NARSTO Assessment Document synthesizes the scientific understanding of ozone pollution, giving 

special consideration to behavior on expanded scales over the North American continent, encompassing Canada, 

the United States, and Mexico. Successive drafts of this Assessment Document experienced progressive stages 

of review by its authors and by outside peers, and transcripts were recorded containing the review comments and 

the corresponding actions. This included an external review by the NRC, the comments of which were 

addressed and incorporated in the final draft. NARSTO, 2000. An Assessment of Tropospheric Ozone Pollution 

– A North American Perspective. NARSTO Management Office (Envair), Pasco, Washington. http://narsto.org/ G SCAQMD (2007). Final 2007 Air Quality Management Plan. Available at: 

http://www.aqmd.gov/aqmp/07aqmp/index.html. Accessed November 8, 2007. 
Regulatory Impact Analysis 

6-10 

net effect is an increase in ozone-related minor restricted activity days (MRAD), which are 

related to changes in daily average ozone (which includes hours during which ozone levels 

are low, but are increased relative to the baseline based on the preliminary modeling). 

However, by 2030, there is a net decrease in ozone-related MRAD consistent with widespread 

reductions in ozone concentrations from the increased NOx emissions reductions. Note that 

in both years, the overall impact of changes in both PM and ozone is a large decrease in the 

number of MRAD. Overall, ozone benefits are low relative to PM benefits for similar 

endpoint categories because of the increases in ozone concentrations during some hours of 

some days in certain urban areas." 

The addition of ozone mortality to our health impacts analysis has led to an increased 

focus on the issue of ozone disbenefits for two related reasons: (1) The monetized value of 

ozone-related benefits, in terms of ozone’s contribution to total rule-related benefits, has 

increased due to the inclusion of ozone mortality; and (2) The overall ozone impacts of NOx 

reductions in certain geographic regions of the U.S., when modeled on the margin, may be 

negative. 

Figure 1 shows the diurnal pattern of ozone concentrations in the 2030 baseline and 

post-control scenarios for a grid cell in Orange County, CA during July. From this figure it is 

clear that the disbenefits (points when the control case ozone levels are higher than the 

baseline) are occurring primarily during nighttime hours when ozone is generally low. 

This diurnal pattern means that the extent of the disbenefits is not as large as one 

might have thought. Our conversion from using a 24-hour metric to using the maximum 8-

hour average metric in the ozone mortality studies (see page 6-4 and the health impacts 

section) excludes the nighttime hours when NOx-related disbenefits are most likely to occur. 
Cost-Benefit Analysis 

Figure 1. July 2030 time-series of CMAQ base and control modeling for Orange County, CA 

6.3 Characterizing Uncertainty: Moving Toward a Probabilistic 

Framework for Benefits Assessment 

The National Research Council (NRC)4 highlighted the need for EPA to conduct 

rigorous quantitative analysis of uncertainty in its benefits estimates and to present these 

estimates to decision makers in ways that foster an appropriate appreciation of their inherent 

uncertainty. In response to these comments, EPA’s Office of Air and Radiation (OAR) is 

developing a comprehensive strategy for characterizing the aggregate impact of uncertainty in 

key modeling elements on both health incidence and benefits estimates. Components of that 

process include emissions modeling, air quality modeling, health effects incidence estimation, 

and valuation. 

In benefit analyses of air pollution regulations conducted to date, the estimated impact 

of reductions in premature mortality has accounted for 85% to 95% of total benefits. 

Therefore, it is particularly important to characterize the uncertainties associated with 

reductions in premature mortality. The health impact functions used to estimate avoided 

premature deaths associated with reductions in ozone have associated standard errors that 

represent the statistical errors around the effect estimates in the underlying epidemiological 

studies.H In our results, we report credible intervals based on these standard errors, reflecting 

the uncertainty in the estimated change in incidence of avoided premature deaths. We also 

provide multiple estimates, to reflect model uncertainty between alternative study designs. In 

 

H Health impact functions measure the change in a health endpoint of interest, such as hospital admissions, for a 

given change in ambient ozone or PM concentration. 

6-11 
Regulatory Impact Analysis 

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addition, we characterize the uncertainty introduced by the inability of existing empirical 

studies to discern whether the relationship between ozone and pre-mature mortality is causal 

by providing an effect estimate preconditioned on an assumption that the effect estimate for 

pre-mature mortality from ozone is zero. 

 For premature mortality associated with exposure to PM, we follow the same approach 

that has been used in several recent RIAs.I,J,K First, we use Monte Carlo methods for 

estimating random sampling error associated with the concentration response functions from 

epidemiological studies and economic valuation functions. Monte Carlo simulation uses 

random sampling from distributions of parameters to characterize the effects of uncertainty on 

output variables, such as incidence of premature mortality. Specifically, we used Monte 

Carlo methods to generate confidence intervals around the estimated health impact and dollar 

benefits. Distributions for individual effect estimates are based on the reported standard 

errors in the epidemiological studies. Distributions for unit values are described in Table 6.5-

1. 

Second, we use the results of our expert elicitation of the concentration response 

function describing the relationship between premature mortality and ambient PM2.5

concentration.L, M Incorporating only the uncertainty from random sampling error omits 

important sources of uncertainty (e.g., in the functional form of the model; whether or not a 

threshold may exist). This second approach attempts to incorporate these other sources of 

uncertainty. 

Use of the expert elicitation and incorporation of the standard errors approaches 

provide insights into the likelihood of different outcomes and about the state of knowledge 

regarding the benefits estimates. Both approaches have different strengths and weaknesses, 

which are fully described in Chapter 5 of the PM NAAQS RIA. 

I

 U.S. Environmental Protection Agency, 2004a. Final Regulatory Analysis: Control of Emissions from Nonroad 

Diesel Engines. EPA420-R-04-007. Prepared by Office of Air and Radiation. Available at 

http://www.epa.gov/nonroad-diesel/2004fr/420r04007.pdf 

J

 U.S. Environmental Protection Agency, 2005. Regulatory Impact Analysis for the Clean Air Interstate Rule. 

EPA 452/-03-001. Prepared by Office of Air and Radiation. Available at: 

http://www.epa.gov/interstateairquality/tsd0175.pdf

K U.S. Environmental Protection Agency, 2006. Regulatory Impact Analysis for the PM NAAQS. EPA Prepared 

by Office of Air and Radiation. Available at: http://www.epa.gov/ttn/ecas/regdata/RIAs/Chapter%205--

Benefits.pdf 

L

 Expert elicitation is a formal, highly structured and well documented process whereby expert judgments, 

usually of multiple experts, are obtained (Ayyb, 2002). 

M Industrial Economics, Inc. 2006. Expanded Expert Judgment Assessment of the Concentration-Response 

Relationship Between PM2.5 Exposure and Mortality. Prepared for EPA Office of Air Quality Planning and 

Standards, September. Available at: http://www.epa.gov/ttn/ecas/regdata/Uncertainty/pm_ee_report.pdf 
Cost-Benefit Analysis 

These multiple characterizations, including confidence intervals, omit the contribution 

to overall uncertainty of uncertainty in air quality changes, baseline incidence rates, 

populations exposed and transferability of the effect estimate to diverse locations. 

Furthermore, the approach presented here does not yet include methods for addressing 

correlation between input parameters and the identification of reasonable upper and lower 

bounds for input distributions characterizing uncertainty in additional model elements. As a 

result, the reported confidence intervals and range of estimates give an incomplete picture 

about the overall uncertainty in the estimates. This information should be interpreted within 

the context of the larger uncertainty surrounding the entire analysis. 

6.4 Health Impact Functions 

Health impact functions measure the change in a health endpoint of interest, such as 

hospital admissions, for a given change in ambient ozone or PM concentration. Health impact 

functions are derived from primary epidemiology studies, meta-analyses of multiple 

epidemiology studies, or expert elicitations. A standard health impact function has four 

components: 1) an effect estimate from a particular study; 2) a baseline incidence rate for the 

health effect (obtained from either the epidemiology study or a source of public health 

statistics such as the Centers for Disease Control); 3) the size of the potentially affected 

population; and 4) the estimated change in the relevant ozone or PM summary measures. 

A typical health impact function might look like: 

( 1) 0 eyy −⋅=Δ β Δ⋅ x , 

where y0 is the baseline incidence (the product of the baseline incidence rate times the 

potentially affected population), ∃ is the effect estimate, and )x is the estimated change in the 

summary pollutant measure. There are other functional forms, but the basic elements remain 

the same. Section 6.2 described the ozone and PM air quality inputs to the health impact 

functions. The following subsections describe the sources for each of the other elements: 

size of potentially affected populations; effect estimates; and baseline incidence rates. 

6.4.1 Potentially Affected Populations 

The starting point for estimating the size of potentially affected populations is the 

2000 U.S. Census block level dataset.5 Benefits Modeling and Analysis Program (BenMAP) 

incorporates 250 age/gender/race categories to match specific populations potentially affected 

by ozone and other air pollutants. The software constructs specific populations matching the 

populations in each epidemiological study by accessing the appropriate age-specific 

populations from the overall population database. BenMAP projects populations to 2020 

using growth factors based on economic projections.6

6.4.2 Effect Estimate Sources 

The most significant monetized benefits of reducing ambient concentrations of ozone 

6-13 
Regulatory Impact Analysis 

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and PM are attributable to reductions in human health risks. EPA’s Ozone and PM Criteria 

Documents7,8

 and the World Health Organization’s 2003 and 20049,10 reports outline 

numerous health effects known or suspected to be linked to exposure to ambient ozone and 

PM. EPA recently evaluated the PM literature for use in the benefits analysis for the 2006 

PM NAAQS RIA. Because we used the same literature for the PM benefits analysis in this 

RIA, and also in the RIA for the proposed rule, we do not provide a detailed discussion of 

individual effect estimates for PM in this section. Instead, we refer the reader to the 2006 PM 

NAAQS RIA and the proposed Locomotive and Marine RIA for details.N 

The RIA for the proposal for this rulemaking only quantified benefits from PM; in the 

current RIA we quantify and monetize the ozone-related health and environmental impacts 

associated with the final rule using an approach consistent with the proposed ozone NAAQS 

RIA. More than one thousand new ozone health and welfare studies have been published 

since EPA issued the 8-hour ozone standard in 1997. Many of these studies investigated the 

impact of ozone exposure on health effects such as: changes in lung structure and 

biochemistry; lung inflammation; asthma exacerbation and causation; respiratory illnessrelated school absence; hospital and emergency room visits for asthma and other respiratory 

causes; and premature death. We provide a discussion of those ozone-related impacts in this 

section. For a more detailed discussion of the health effects of ozone exposure, we point the 

reader to EPA’s ozone Criteria Document.11 

It is important to note that we were not able to separately quantify all of the PM and 

ozone health effects that have been reported in the ozone and PM criteria documents in this 

analysis for four reasons: (1) the possibility of double counting (such as hospital admissions 

for specific respiratory diseases); (2) uncertainties in applying effect relationships that are 

based on clinical studies to the potentially affected population; (3) the lack of an established 

concentration-response relationship; or 4) the inability to appropriately value the effect (for 

example, changes in forced expiratory volume) in economic terms. Table 6.4-1 lists the 

human health and welfare effects of pollutants affected by the alternate standards. Table 6.4-2 

lists the health endpoints included in this analysis.

N U.S. Environmental Protection Agency, 2005. Regulatory Impact Analysis for the PM NAAQS. EPA Prepared 

by Office of Air and Radiation. Available at: http://www.epa.gov/ttn/ecas/regdata/RIAs/Chapter%205--

Benefits.pdf pp. 5-29.
Cost-Benefit Analysis 

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Table 6.4-1 Human Health and Welfare Effects of Pollutants Affected by the Final 

Standards

Pollutant/Effect 

Quantified and Monetized in Base 

Estimatesa Unquantified Effects - Changes in: 

PM/Healthb

Premature mortality based on both 

cohort study estimates and on expert 

elicitationc,d

Bronchitis: chronic and acute 

Hospital admissions: respiratory 

and cardiovascular 

Emergency room visits for asthma 

Nonfatal heart attacks (myocardial 

infarction) 

Lower and upper respiratory illness 

Minor restricted-activity days 

Work loss days 

Asthma exacerbations (asthmatic 

population) 

Respiratory symptoms (asthmatic 

population) 

Infant mortality 

Subchronic bronchitis cases 

Low birth weight 

Pulmonary function 

Chronic respiratory diseases other than chronic bronchitis 

Nonasthma respiratory emergency room visits 

UVb exposure (+/-)e

PM/Welfare 

Visibility in Southeastern Class I areas 

Visibility in northeastern and Midwestern Class I areas 

Household soiling 

Visibility in western U.S. Class I areas 

Visibility in residential and non-Class I areas 

UVb exposure (+/-)e

Ozone/Healthf

Premature mortality: short-term 

exposures 

Hospital admissions: respiratory 

Emergency room visits for asthma 

Minor restricted-activity days 

School loss days 

Asthma attacks 

Acute respiratory symptoms 

Cardiovascular emergency room visits 

Chronic respiratory damageg 

Premature aging of the lungsg 

Nonasthma respiratory emergency room visits 

UVb exposure (+/-)e

Ozone/Welfare 

Decreased outdoor worker 

productivity 

Yields for commercial crops 

Yields for commercial forests and noncommercial crops 

Damage to urban ornamental plants 

Recreational demand from damaged forest aesthetics 

Ecosystem functions 

UVb exposure (+/-)e

CO Health Behavioral effects 
Regulatory Impact Analysis 

6-16 

Pollutant/Effect 

Quantified and Monetized in Base 

Estimatesa Unquantified Effects - Changes in: 

Nitrogen 

Deposition/ 

Welfare 

Commercial forests due to acidic sulfate and nitrate 

deposition 

Commercial freshwater fishing due to acidic deposition 

Recreation in terrestrial ecosystems due to acidic 

deposition 

Commercial fishing, agriculture, and forests due to 

nitrogen deposition 

Recreation in estuarine ecosystems due to nitrogen 

deposition 

Ecosystem functions 

Passive fertilization 

NOx/Health 

Lung irritation 

Lowered resistance to respiratory infection 

Hospital admissions for respiratory and cardiac diseases 

HC/Toxics 

Healthh

Cancer, including lung (benzene, 1,3-butadiene, 

formaldehyde, acetaldehyde, naphthalene) 

Anemia (benzene) 

Disruption of production of blood components (benzene) 

Reduction in the number of blood platelets (benzene) 

Excessive bone marrow formation (benzene) 

Depression of lymphocyte counts (benzene) 

Reproductive and developmental effects (1,3-butadiene) 

Irritation of eyes and mucus membranes (formaldehyde) 

Respiratory irritation (formaldehyde) 

Asthma attacks in asthmatics (formaldehyde) 

Asthma-like symptoms in non-asthmatics (formaldehyde) 

Irritation of the eyes, skin, and respiratory tract 

(acetaldehyde) 

Upper respiratory tract irritation and congestion (acrolein) 

Neurotoxicity (n-hexane, toluene, xylenes) 

HC/Toxics 

Welfareh

Direct toxic effects to animals 

Bioaccumulation in the food chain 

Damage to ecosystem function 

Odor a

 Primary quantified and monetized effects are those included when determining the primary estimate of 

total monetized benefits of the final standards. 

b

 In addition to primary economic endpoints, there are a number of biological responses that have been 

associated with PM health effects including morphological changes and altered host defense mechanisms. The 

public health impact of these biological responses may be partly represented by our quantified endpoints. c

 Cohort estimates are designed to examine the effects of long term exposures to ambient pollution, but 

relative risk estimates may also incorporate some effects due to shorter term exposures (see Kunzli, 2001 for a 

discussion of this issue). 

d

 While some of the effects of short-term exposure are likely to be captured by the cohort estimates, 

there may be additional premature mortality from short-term PM exposure not captured in the cohort estimates 

included in the primary analysis. e

 May result in benefits or disbenefits. 
Cost-Benefit Analysis 

6-17 

f

 The public health impact of biological responses such as increased airway responsiveness to stimuli, 

inflammation in the lung, acute inflammation and respiratory cell damage, and increased susceptibility to 

respiratory infection are likely partially represented by our quantified endpoints. g

 The public health impact of effects such as chronic respiratory damage and premature aging of the 

lungs may be partially represented by quantified endpoints such as hospital admissions or premature mortality, 

but a number of other related health impacts, such as doctor visits and decreased athletic performance, remain 

unquantified. h

 The categorization of unquantified toxic health and welfare effects is not exhaustive.
Regulatory Impact Analysis 

6-18 

Table 6.4-2. Ozone- and PM-Related Health Endpoints 

Endpoint Pollutant Study Study Population 

Premature Mortality 

Premature mortality – 

daily time series, nonaccidental 

O3 Bell et al (2004) (NMMAPS study)12

Meta-analyses:

Bell et al (2005)13

Ito et al (2005)14

Levy et al (2005)15

All ages 

Premature mortality 

—cohort study, allcause 

PM2.5 Pope et al. (2002)16

Laden et al. (2006)17 >29 years 

>25 years 

Premature mortality, 

total exposures 

PM2.5 Expert Elicitation (IEc, 2006)18 >24 years 

Premature mortality 

— all-cause 

PM2.5 Woodruff et al. (1997)19 Infant (<1 year) 

Chronic Illness 

Chronic bronchitis PM2.5 Abbey et al. (1995)20 >26 years 

Nonfatal heart attacks PM2.5 Peters et al. (2001)21 Adults (>18 years) 

Hospital Admissions 

Pooled estimate: 

Schwartz (1995) - ICD 460-519 (all resp)22

Schwartz (1994a; 1994b) - ICD 480-486 

(pneumonia)23,24 

Moolgavkar et al. (1997) - ICD 480-487 

(pneumonia)25

Schwartz (1994b) - ICD 491-492, 494-496 

(COPD) 

Moolgavkar et al. (1997) – ICD 490-496 

(COPD) 

>64 years 

O3 

Burnett et al. (2001)26 <2 years 

PM2.5 Pooled estimate:

Moolgavkar (2003)—ICD 490-496 (COPD)27

Ito (2003)—ICD 490-496 (COPD)28

>64 years 

PM2.5 Moolgavkar (2000)—ICD 490-496 (COPD)29 20–64 years 

PM2.5 Ito (2003)—ICD 480-486 (pneumonia) >64 years 

Respiratory 

PM2.5 Sheppard (2003)—ICD 493 (asthma)30 <65 years 

PM2.5 Pooled estimate: 

Moolgavkar (2003)—ICD 390-429 (all 

cardiovascular) 

Ito (2003)—ICD 410-414, 427-428 (ischemic 

heart disease, dysrhythmia, heart failure) 

Cardiovascular >64 years 

PM2.5 Moolgavkar (2000)—ICD 390-429 (all 

cardiovascular) 

20–64 years 
Cost-Benefit Analysis 

6-19 

Endpoint Pollutant Study Study Population 

Asthma-related ER 

visits 

O3 

Pooled estimate:

Jaffe et al (2003)31

Peel et al (2005)32

Wilson et al (2005)33

5–34 years 

All ages 

All ages 

Asthma-related ER 

visits (con’t) 

PM2.5 Norris et al. (1999)34 0–18 years 

Other Health Endpoints 

Acute bronchitis PM2.5 Dockery et al. (1996)35 8–12 years 

Upper respiratory 

symptoms 

PM2.5 Pope et al. (1991)36 Asthmatics, 9–11 

years 

Lower respiratory 

symptoms 

PM2.5 Schwartz and Neas (2000)37 7–14 years 

Asthma exacerbations PM2.5 Pooled estimate: 

Ostro et al. (2001)38 (cough, wheeze and 

shortness of breath) 

Vedal et al. (1998)39 (cough) 

6–18 yearsa

Work loss days PM2.5 Ostro (1987)40 18–65 years 

School absence days 

O3 

Pooled estimate:

Gilliland et al. (2001)41

Chen et al. (2000)42

5–17 yearsb

O3 Ostro and Rothschild (1989)43 Minor Restricted 18–65 years 

Activity Days 

(MRADs) 

PM2.5 Ostro and Rothschild (1989) 18–65 years 

a

 The original study populations were 8 to 13 for the Ostro et al. (2001) study and 6 to 13 for the Vedal et al. 

(1998) study. Based on advice from the Science Advisory Board Health Effects Subcommittee (SAB-HES), 

we extended the applied population to 6 to 18, reflecting the common biological basis for the effect in 

children in the broader age group. See: U.S. Science Advisory Board. 2004. Advisory Plans for Health 

Effects Analysis in the Analytical Plan for EPA’s Second Prospective Analysis –Benefits and Costs of the 

Clean Air Act, 1990—2020. EPA-SAB-COUNCIL-ADV-04-004. See also National Research Council 

(NRC). 2002. Estimating the Public Health Benefits of Proposed Air Pollution Regulations. Washington, 

DC: The National Academies Press. b

 Gilliland et al. (2001) studied children aged 9 and 10. Chen et al. (2000) studied children 6 to 11. Based on 

recent advice from the National Research Council and the EPA SAB-HES, we have calculated reductions in 

school absences for all school-aged children based on the biological similarity between children aged 5 to 17.

In selecting epidemiological studies as sources of effect estimates, we applied several 

criteria to develop a set of studies that is likely to provide the best estimates of impacts in the 

U.S. To account for the potential impacts of different health care systems or underlying 

health status of populations, we give preference to U.S. studies over non-U.S. studies. In 

addition, due to the potential for confounding by co-pollutants, we give preference to effect 

estimates from models including both ozone and PM over effect estimates from singlepollutant models.44,45

A number of endpoints that are not health-related also may significantly contribute to 

monetized benefits. Potential welfare benefits associated with ozone exposure include: 

increased outdoor worker productivity; increased yields for commercial and non-commercial 

crops; increased commercial forest productivity; reduced damage to urban ornamental plants; 
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increased recreational demand for undamaged forest aesthetics; and reduced damage to 

ecosystem functions.46,47 While we include estimates of the value of increased outdoor 

worker productivity, estimation of other welfare impacts is beyond the scope of this analysis. 

6.4.2.1 Ozone Exposure Metric 

Because several of the ozone mortality epidemiology studies report their base results 

relating mortality to 24-hour average ozone, the importance of understanding the biological 

relevance of the 24-hour metric relative to other possible metrics is critical. 

As mentioned above, ozone-related disbenefits are projected to occur in small regions 

within specific urban cores (though yet-to-occur emissions reductions related to ozone 

attainment efforts are not accounted for in our current air quality modeling approach). When 

NOx reductions increase ozone in these regions, however, it is typically during nighttime and 

early morning hours, when NOx titrates ozone. Because human exposure to ozone is a 

function of the temporal and spatial patterns of ambient concentrations of ozone in the 

atmosphere, the ozone-related health impacts analysis is especially sensitive to which ozone 

exposure metric we use in the health impact functions.O 

Prior to the addition of ozone-related premature mortality functions to the health 

impacts analysis, most of our ozone health impact functions have used metrics which are less 

sensitive to ozone disbenefits (e.g., 8-hour daily average). For example, emergency 

department visits for asthma are related to 1- or 8-hour maxima. School absences are based 

on 8-hour mean and 1-hour maximum ozone levels. It should be noted that ozone disbenefits 

that occur during daylight hours, when ozone is higher, are accounted for in these averages. 

Epidemiology studies are retrospective in nature and focus on identifying a statistical 

relationship between some measure of ozone and a health outcome. The specific 

epidemiological studies that form the basis for the ozone mortality impact estimates use timeseries statistical methods that estimate the relationship between daily ozone levels and 

mortality based on day to day variations in ozone and mortality. The focus of these studies is 

not as much on a specific ozone averaging time as on the day to day variation in the ozone 

metrics. In fact, epidemiologists often analyze and report results for multiple ozone metrics, 

but may report results for only one metric in the abstract of an article. 

In most cases, the day to day variation in different metrics (24-hour average vs 8-hour 

maximum, for example) is highly correlated. As such, the relationships between mortality 

O An exposure metric is a measure of air quality calculated as the average or maximum of modeled ambient 

concentrations over a relevant time period, such as during the daylight hours of the “ozone season” (which is 

May through September for this analysis). The 24-hour average is therefore calculated as the average of all 

hourly ozone concentrations throughout the day (from 12am to 11:59pm). The 8-hour maximum is the 

maximum hourly value observed between 9am and 5pm each day. The 1-hour maximum is the maximum hourly 

value observed throughout an entire day. 
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and different ozone metrics will be highly correlated as well. However, when we apply the 

mortality impact functions derived from these time-series results to evaluate the impacts of a 

specific control measure, we do not focus on the day to day variation in ozone levels so much 

as the shift in the overall distribution of ozone concentrations over an entire season. Because 

specific emission control strategies might result in a different diurnal profile than was 

observed in the monitored ozone data used in the studies, it is important to choose an ozone 

metric that is best suited to capturing changes in ozone that are likely to occur during hours 

where populations are likely to be exposed to the ozone. 

To address this issue in the final rule analysis, we have used standard conversion 

functions to convert ozone-related premature mortality health impact functions that use a 24-

hour average or 1-hour maximum ozone metric to functions that use an 8-hour maximum 

ozone concentration instead. This is consistent both with the available exposure modeling 

and with the form of the current ozone standard. This conversion also does not affect the 

relative magnitude of the health impact function. An equivalent change in the 24-hour 

average and 8-hour maximum will provide the same overall change in incidence of a health 

effect. The conversion ratios are based on observed relationships between the 24-hour 

average and 8-hour maximum ozone values. For example, in the Bell et al., 2004 analysis of 

ozone-related premature mortality, the authors found that the relationship between the 24-

hour average, the 8-hour maximum, and the 1-hour maximum was 2:1.5:1, so that the derived 

health impact effect estimate based on the 1-hour maximum should be half that of the effect 

estimate based on the 24-hour values (and the 8-hour maximum three-quarters of the 24-hour 

effect estimate). 

The conversion of ozone metrics does not require adjustment to the air quality 

modeling. It preserves the observed patterns of ozone-related disbenefits, and allows for 

disbenefits to occur in the health impact estimates if those disbenefits occur during hours 

when populations are likely to be exposed. In future analyses, we will also convert the ozone 

exposure metrics in morbidity studies that do not use an 8-hour exposure metric. 

6.4.2.2 Premature Mortality Effect Estimates 

While particulate matter is the criteria pollutant most clearly associated with 

premature mortality, recent research suggests that short-term repeated ozone exposure likely 

contributes to premature death. The 2006 Ozone Criteria Document states: “Consistent with 

observed ozone-related increases in respiratory- and cardiovascular-related morbidity, several 

newer multi-city studies, single-city studies, and several meta-analyses of these studies have 

provided relatively strong epidemiologic evidence for associations between short-term ozone 

exposure and all-cause mortality, even after adjustment for the influence of season and PM” 

(EPA, 2006: E-17).48 The epidemiologic data are also supported by newly available 

experimental data from both animal and human studies which provide evidence suggestive of 

plausible pathways by which risk of respiratory or cardiovascular morbidity and mortality 

could be increased by ambient ozone. With respect to short-term exposure, the ozone Criteria 

Document concludes: “This overall body of evidence is highly suggestive that ozone directly 

or indirectly contributes to non-accidental and cardiopulmonary-related mortality, but 
Regulatory Impact Analysis 

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additional research is needed to more fully establish underlying mechanisms by which such 

effects occur” (pg. E-18). 

With respect to the time-series studies, the conclusion regarding the relationship 

between short-term exposure and premature mortality is based, in part, upon recent cityspecific time-series studies such as the Schwartz (2004) analysis in Houston and the Huang et 

al. (2004) analysis in Los Angeles.P

 This conclusion is also based on recent meta-analyses by 

Bell et al. (2005), Ito et al. (2005), and Levy et al. (2005), and a new analysis of the National 

Morbidity, Mortality, and Air Pollution Study (NMMAPS) data set by Bell et al. (2004), 

which specifically sought to disentangle the roles of ozone, PM, weather-related variables, 

and seasonality. The 2006 Criteria Document states that “the results from these metaanalyses, as well as several single- and multiple-city studies, indicate that co-pollutants 

generally do not appear to substantially confound the association between ozone and 

mortality” (p. 7-103). However, CASAC raised questions about the implications of these 

time-series results in a policy context. Specifically, CASAC emphasized that “...while the 

time-series study design is a powerful tool to detect very small effects that could not be 

detected using other designs, it is also a blunt tool” (Henderson, 2006: 3). They point to 

findings (e.g., Stieb et al., 2002, 2003) that indicated associations between premature 

mortality and all of the criteria pollutants, indicating that “findings of time-series studies do 

not seem to allow us to confidently attribute observed effects to individual pollutants” (id.). 

They note that “not only is the interpretation of these associations complicated by the fact that 

the day-to-day variation in concentrations of these pollutants is, to a varying degree, 

determined by meteorology, the pollutants are often part of a large and highly correlated mix 

of pollutants, only a very few of which are measured” (id.). Even with these uncertainties, the 

CASAC Ozone Panel, in its review of EPA’s Staff Paper, found “...premature total nonaccidental and cardiorespiratory mortality for inclusion in the quantitative risk assessment to 

be appropriate.” 

Consistent with the methodology used in the ozone risk assessment found in the 

Characterization of Health Risks found in the Review of the National Ambient Air Quality 

Standards for Ozone: Policy Assessment of Scientific and Technical Information, we included 

ozone mortality in the primary health effects analysis, with the recognition that the exact 

magnitude of the effects estimate is subject to continuing uncertainty. We used effect 

estimates from the Bell et al. (2004) NMMAPS analysis, as well as effect estimates from the 

three meta-analyses. In addition, we include the possibility that there is not a causal 

association between ozone and mortality, i.e., that the effect estimate for premature mortality 

could be zero. 

P

 For an exhaustive review of the city-specific time-series studies considered in the ozone staff paper, see: U.S. 

Environmental Protection Agency, 2007. Review of the National Ambient Air Quality Standards for Ozone: 

Policy Assessment of Scientific and Technical Information. Prepared by the Office of Air and Radiation. 

Available at http://www.epa.gov/ttn/naaqs/standards/ozone/data/2007_01_ozone_staff_paper.pdf. pp. 5-36. 
Cost-Benefit Analysis 

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We estimate the change in mortality incidence and estimated credible intervalQ

resulting from application of the effect estimate from each study and present them separately 

to reflect differences in the study designs and assumptions about causality. However, it is 

important to note that this procedure only captures the uncertainty in the underlying 

epidemiological work, and does not capture other sources of uncertainty, such as uncertainty 

in the estimation of changes in air pollution exposure (Levy et al., 2000). 

6.4.2.3 Respiratory Hospital Admissions Effect Estimates 

Detailed hospital admission and discharge records provide data for an extensive body 

of literature examining the relationship between hospital admissions and air pollution. This is 

especially true for the portion of the population aged 65 and older, because of the availability 

of detailed Medicare records. In addition, there is one study (Burnett et al., 2001) providing 

an effect estimate for respiratory hospital admissions in children under two. 

Because the number of hospital admission studies we considered is so large, we used 

results from a number of studies to pool some hospital admission endpoints. Pooling is the 

process by which multiple study results may be combined in order to produce better estimates 

of the effect estimate, or β. For a complete discussion of the pooling process, see Abt (2005).R

To estimate total respiratory hospital admissions associated with changes in ambient ozone 

concentrations for adults over 65, we first estimated the change in hospital admissions for 

each of the different effects categories that each study provided for each city. These cities 

included Minneapolis, Detroit, Tacoma and New Haven. To estimate total respiratory 

hospital admissions for Detroit, we added the pneumonia and COPD estimates, based on the 

effect estimates in the Schwartz study (1994). Similarly, we summed the estimated hospital 

admissions based on the effect estimates the Moolgavkar study reported for Minneapolis 

(Moolgavkar et al., 1997). To estimate total respiratory hospital admissions for Minneapolis 

using the Schwartz study (1994), we simply estimated pneumonia hospital admissions based 

on the effect estimate. Making this assumption that pneumonia admissions represent the total 

impact of ozone on hospital admissions in this city will give some weight to the possibility 

that there is no relationship between ozone and COPD, reflecting the equivocal evidence 

represented by the different studies. We then used a fixed-effects pooling procedure to 

combine the two total respiratory hospital admission estimates for Minneapolis. Finally, we 

used random effects pooling to combine the results for Minneapolis and Detroit with results 

from studies in Tacoma and New Haven from Schwartz (1995). As noted above, this pooling 

approach incorporates both the precision of the individual effect estimates and between-study 

variability characterizing differences across study locations. 

Q A credible interval is a posterior probability interval used in Bayesian statistics, which is similar to a 

confidence interval used in frequentist statistics. 

R

 Abt Associates, Incorporated. Environmental Benefits Mapping and Analysis Program, Technical Appendices. 

May 2005. pp. I-3 
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6.4.2.4 Asthma-Related Emergency Room Visits Effect Estimates 

We used three studies as the source of the concentration-response functions we used to 

estimate the effects of ozone exposure on asthma-related emergency room (ER) visits: Peel et 

al. (2005); Wilson et al. (2005); and Jaffe et al. (2003). We estimated the change in ER visits 

using the effect estimate(s) from each study and then pooled the results using the random 

effects pooling technique (see Abt, 2005). The study by Jaffe et al. (2003) examined the 

relationship between ER visits and air pollution for populations aged five to 34 in the Ohio 

cities of Cleveland, Columbus and Cincinnati from 1991 through 1996. In single-pollutant 

Poisson regression models, ozone was linked to asthma visits. We use the pooled estimate 

across all three cities as reported in the study. The Peel et al. study (2005) estimated asthmarelated ER visits for all ages in Atlanta, using air quality data from 1993 to 2000. Using 

Poisson generalized estimating equations, the authors found a marginal association between 

the maximum daily 8-hour average ozone level and ER visits for asthma over a 3-day moving 

average (lags of 0, 1, and 2 days) in a single pollutant model. Wilson et al. (2005) examined 

the relationship between ER visits for respiratory illnesses and asthma and air pollution for all 

people residing in Portland, Maine from 1998-2000 and Manchester, New Hampshire from 

1996-2000. For all models used in the analysis, the authors restricted the ozone data 

incorporated into the model to the months ozone levels are usually measured, the springsummer months (April through September). Using the generalized additive model, Wilson et 

al. (2005) found a significant association between the maximum daily 8-hour average ozone 

level and ER visits for asthma in Portland, but found no significant association for 

Manchester. Similar to the approach used to generate effect estimates for hospital 

admissions, we used random effects pooling to combine the results across the individual study 

estimates for ER visits for asthma. The Peel et al. (2005) and Wilson et al. (2005) Manchester 

estimates were not significant at the 95 percent level, and thus, the confidence interval for the 

pooled incidence estimate based on these studies includes negative values. This is an artifact 

of the statistical power of the studies, and the negative values in the tails of the estimated 

effect distributions do not represent improvements in health as ozone concentrations are 

increased. Instead these should be viewed as a measure of uncertainty due to limitations in 

the statistical power of the study. Note that we included both hospital admissions and ER 

visits as separate endpoints associated with ozone exposure, because our estimates of hospital 

admission costs do not include the costs of ER visits, and because most asthma ER visits do 

not result in a hospital admission. 

6.4.2.5 Minor Restricted Activity Days Effects Estimate 

Minor restricted activity days (MRADs) occur when individuals reduce most usual 

daily activities and replace them with less-strenuous activities or rest, but do not miss work or 

school. We estimated the effect of ozone exposure on MRADs using a concentrationresponse function derived from Ostro and Rothschild (1989). These researchers estimated 

the impact of ozone and PM2.5 on MRAD incidence in a national sample of the adult working 

population (ages 18 to 65) living in metropolitan areas. We developed separate coefficients 

for each year of the Ostro and Rothschild analysis (1976-1981), which we then combined for 
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use in EPA’s analysis. The effect estimate used in the impact function is a weighted average 

of the coefficients in Ostro and Rothschild (1989, Table 4), using the inverse of the variance 

as the weight. 

6.4.2.6 School Absences Effect Estimate 

Children may be absent from school due to respiratory or other acute diseases caused, 

or aggravated by, exposure to air pollution. Several studies have found a significant 

association between ozone levels and school absence rates. We use two studies (Gilliland et 

al., 2001; Chen et al., 2000) to estimate changes in school absences resulting from changes in 

ozone levels. The Gilliland et al. study estimated the incidence of new periods of absence, 

while the Chen et al. study examined daily absence rates. We converted the Gilliland et al. 

estimate to days of absence by multiplying the absence periods by the average duration of an 

absence. We estimated 1.6 days as the average duration of a school absence, the result of 

dividing the average daily school absence rate from Chen et al. (2000) and Ransom and Pope 

(1992) by the episodic absence duration from Gilliland et al. (2001). Thus, each Gilliland et 

al. period of absence is converted into 1.6 absence days. 

Following recent advice from the National Research Council (2002), we calculated 

reductions in school absences for the full population of school age children, ages five to 17. 

This is consistent with recent peer-reviewed literature on estimating the impact of ozone 

exposure on school absences (Hall et al. 2003). We estimated the change in school absences 

using both Chen et al. (2000) and Gilliland et al. (2001) and then, similar to hospital 

admissions and ER visits, pooled the results using the random effects pooling procedure. 

6.4.2.7 Worker Productivity 

To monetize benefits associated with increased worker productivity resulting from 

improved ozone air quality, we used information reported in Crocker and Horst (1981). 

Crocker and Horst examined the impacts of ozone exposure on the productivity of outdoor 

citrus workers. The study measured productivity impacts. Worker productivity is measuring 

the value of the loss in productivity for a worker who is at work on a particular day, but due to 

ozone, cannot work as hard. It only applies to outdoor workers, like fruit and vegetable 

pickers, or construction workers. Here, productivity impacts are measured as the change in 

income associated with a change in ozone exposure, given as the elasticity of income with 

respect to ozone concentration. The reported elasticity translates a ten percent reduction in 

ozone to a 1.4 percent increase in income. Given the national median daily income for 

outdoor workers engaged in strenuous activity reported by the U.S. Census Bureau (2002), 

$68 per day (2000$), a ten percent reduction in ozone yields about $0.97 in increased daily 

wages. We adjust the national median daily income estimate to reflect regional variations in 

income using a factor based on the ratio of county median household income to national 

median household income. No information was available for quantifying the uncertainty 

associated with the central valuation estimate. Therefore, no uncertainty analysis was 

conducted for this endpoint. 
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6.4.2.8 Unquantified Effects 

6.4.2.8.1 Direct Ozone Effects on Vegetation 

The Ozone Criteria Document notes that “current ambient concentrations in many 

areas of the country are sufficient to impair growth of numerous common and economically 

valuable plant and tree species.” (U.S. EPA, 2006, page 9-1). Changes in ground-level ozone 

resulting from the implementation of alternative ozone standards are expected to affect crop 

and forest yields throughout the affected area. Recent scientific studies have also found the 

ozone negatively impacts the quality or nutritive value of crops (U.S. EPA, 2006, page 9-16). 

Well-developed techniques exist to provide monetary estimates of these benefits to 

agricultural producers and to consumers. These techniques use models of planting decisions, 

yield response functions, and the supply of and demand for agricultural products. The 

resulting welfare measures are based on predicted changes in market prices and production 

costs. Models also exist to measure benefits to silvicultural producers and consumers. 

However, these models have not been adapted for use in analyzing ozone-related forest 

impacts. Because of resource limitations, we are unable to provide agricultural or benefits 

estimates for the final rule. 

An additional welfare benefit expected to accrue as a result of reductions in ambient 

ozone concentrations in the United States is the economic value the public receives from 

reduced aesthetic injury to forests. There is sufficient scientific information available to 

reliably establish that ambient ozone levels cause visible injury to foliage and impair the 

growth of some sensitive plant species (U.S. EPA, 2006, page 9-19). However, present 

analytic tools and resources preclude EPA from quantifying the benefits of improved forest 

aesthetics. 

Urban ornamentals (floriculture and nursery crops) represent an additional vegetation 

category likely to experience some degree of negative effects associated with exposure to 

ambient ozone levels and likely to affect large economic sectors. In the absence of adequate 

exposure-response functions and economic damage functions for the potential range of effects 

relevant to these types of vegetation, no direct quantitative economic benefits analysis has 

been conducted. The farm production value of ornamental crops was estimated at over $14 

billion in 2003 (USDA, 2004). This is therefore a potentially important welfare effects 

category. However, information and valuation methods are not available to allow for 

plausible estimates of the percentage of these expenditures that may be related to impacts 

associated with ozone exposure. 
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6.4.2.8.2 Nitrogen Deposition 

Deposition to Estuarine and Coastal Waters 

Excess nutrient loads, especially of nitrogen, cause a variety of adverse consequences 

to the health of estuarine and coastal waters. These effects include toxic and/or noxious algal 

blooms such as brown and red tides, low (hypoxic) or zero (anoxic) concentrations of 

dissolved oxygen in bottom waters, the loss of submerged aquatic vegetation due to the lightfiltering effect of thick algal mats, and fundamental shifts in phytoplankton community 

structure (Bricker et al., 1999). A recent study found that for the period 1990-2002, 

atmospheric deposition accounted for 17 percent of nitrate loadings in the Gulf of Mexico, 

where severe hypoxic zones have been existed over the last two decades (Booth and 

Campbell, 2007)S

. 

Reductions in atmospheric deposition of NOx are expected to reduce the adverse 

impacts associated with nitrogen deposition to estuarine and coastal waters. However, direct 

functions relating changes in nitrogen loadings to changes in estuarine benefits are not 

available. The preferred WTP-based measure of benefits depends on the availability of these 

functions and on estimates of the value of environmental responses. Because neither 

appropriate functions nor sufficient information to estimate the marginal value of changes in 

water quality exist at present, calculation of a WTP measure is not possible. 

Deposition to Agricultural and Forested Land 

Implementation strategies for alternative standards which reduce NOX emissions, will 

also reduce nitrogen deposition on agricultural land and forests. There is some evidence that 

nitrogen deposition may have positive effects on agricultural output through passive 

fertilization. Holding all other factors constant, farmers’ use of purchased fertilizers or 

manure may increase as deposited nitrogen is reduced. Estimates of the potential value of this 

possible increase in the use of purchased fertilizers are not available, but it is likely that the 

overall value is very small relative to other health and welfare effects. The share of nitrogen 

requirements provided by this deposition is small, and the marginal cost of providing this 

nitrogen from alternative sources is quite low. In some areas, agricultural lands suffer from 

nitrogen over-saturation due to an abundance of on-farm nitrogen production, primarily from 

animal manure. In these areas, reductions in atmospheric deposition of nitrogen from PM 

represent additional agricultural benefits. 

Information on the effects of changes in passive nitrogen deposition on forests and 

other terrestrial ecosystems is very limited. The multiplicity of factors affecting forests, 

including other potential stressors such as ozone, and limiting factors such as moisture and 

S

 Booth, M.S., and C. Campbell. 2007. Spring Nitrate Flux in the Mississippi River Basin: A Landscape Model 

with Conservation Applications. Environ. Sci. Technol.; 2007; ASAP Web Release Date: 20-Jun-2007; (Article) 

DOI: 10.1021/es070179e 
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other nutrients, confound assessments of marginal changes in any one stressor or nutrient in 

forest ecosystems. However, reductions in deposition of nitrogen could have negative effects 

on forest and vegetation growth in ecosystems where nitrogen is a limiting factor (US EPA, 

1993). Moreover, any positive effect that nitrogen deposition has on forest productivity would 

enhance the level of carbon dioxide sequestration as well.T,U,V 

On the other hand, there is evidence that forest ecosystems in some areas of the United 

States (such as the western U.S.) are nitrogen saturated (US EPA, 1993). Once saturation is 

reached, adverse effects of additional nitrogen begin to occur such as soil acidification which 

can lead to leaching of nutrients needed for plant growth and mobilization of harmful 

elements such as aluminum. Increased soil acidification is also linked to higher amounts of 

acidic runoff to streams and lakes and leaching of harmful elements into aquatic ecosystems. 

6.4.2.8.3 Ultraviolet Radiation 

Atmospheric ozone absorbs a harmful band of ultraviolet radiation from the sun called 

UV-B, providing a protective shield to the Earth's surface. The majority of this protection 

occurs in the stratosphere where 90% of atmospheric ozone is located. The remaining 10% of 

the Earth's ozone is present at ground level (referred to as tropospheric ozone) (NAS, 1991; 

NASA). Only a portion of the tropospheric fraction of UV-B shielding is from anthropogenic 

sources (e.g., power plants, byproducts of combustion). The portion of ground level ozone 

associated with anthropogenic sources varies by locality and over time. Even so, it is 

reasonable to assume that reductions in ground level ozone would lead to increases in the 

same health effects linked to in UV-B exposures. These effects include fatal and nonfatal 

melanoma and non-melanoma skin cancers and cataracts. The values of $15,000 per case for 

non-fatal melanoma skin cancer, $5,000 per case for non-fatal non-melanoma skin cancer, and 

$15,000 per case of cataracts have been used in analyses of stratospheric ozone depletion 

(U.S. EPA, 1999). Fatal cancers are valued using the standard VSL estimate, which for 2020 

is $6.6 million (1999$). UV-B has also been linked to ecological effects including damage to 

crops and forest. For a more complete listing of quantified and unquantified UV-B radiation 

effects, see Table G-4 and G-7 in the Benefits and Costs of the Clean Air Act, 1990-2010 

(U.S. EPA, 1999. UV-B related health effects are also discussed in the context of 

stratospheric ozone in a 2006 report by ICF Consulting, prepared for the U.S. EPA. 

There are many factors that influence UV-B radiation penetration to the earth’s 

surface, including latitude, altitude, cloud cover, surface albedo, PM concentration and 

T

 Peter M. Vitousek et. al., “Human Alteration of the Global Nitrogen Cycle: Causes and Consequences” Issues 

in Ecology No. 1 (Spring) 1997. 

U Knute J. Nadelhoffer et. al., “Nitrogen deposition makes a minor contribution to carbon sequestration in 

temperate forests” Nature 398, 145-148 (11 March 1999) V Martin Köchy and Scott D. Wilson, “Nitrogen deposition and forest expansion in the northern Great Plains 

Journal of Ecology Journal of Ecology 89 (5), 807–817 
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composition, and gas phase pollution. Of these, only latitude and altitude can be defined with 

small uncertainty in any effort to assess the changes in UV-B flux that may be attributable to 

any changes in tropospheric O3 as a result of any revision to the O3 NAAQS. Such an 

assessment of UV-B related health effects would also need to take into account human habits, 

such as outdoor activities (including age- and occupation-related exposure patterns), dress and 

skin care to adequately estimate UV-B exposure levels. However, little is known about the 

impact of these factors on individual exposure to UV-B. 

Moreover, detailed information does not exist regarding other factors that are relevant 

to assessing changes in disease incidence, including: type (e.g., peak or cumulative) and time 

period (e.g., childhood, lifetime, current) of exposures related to various adverse health 

outcomes (e.g., damage to the skin, including skin cancer; damage to the eye, such as 

cataracts; and immune system suppression); wavelength dependency of biological responses; 

and interindividual variability in UV-B resistance to such health outcomes. Beyond these well 

recognized adverse health effects associated with various wavelengths of UV radiation, the 

Criteria Document (section 10.2.3.6) also discusses protective effects of UV-B radiation. 

Recent reports indicate the necessity of UV-B in producing vitamin D, and that vitamin D 

deficiency can cause metabolic bone disease among children and adults, and may also 

increase the risk of many common chronic diseases (e.g., type I diabetes and rheumatoid 

arthritis) as well as the risk of various types of cancers. Thus, the Criteria Document 

concludes that any assessment that attempts to quantify the consequences of increased UV-B 

exposure on humans due to reduced ground-level O3 must include consideration of both 

negative and positive effects. However, as with other impacts of UVB on human health, this 

beneficial effect of UVB radiation has not previously been studied in sufficient detail. The 

Agency is currently exploring the feasibility of estimating the effects of increased UVB 

exposures resulting from reductions in tropospheric ozone. 

6.4.2.8.4 Climate Implications of Tropospheric Ozone 

Although climate and air quality are generally treated as separate issues, they are 

closely coupled through atmospheric processes. Ozone, itself, is a major greenhouse gas and 

climate directly influences ambient concentrations of ozone. 

The concentration of tropospheric ozone has increased substantially since the preindustrial era and has contributed to warming. Tropospheric ozone is (after CO2 and CH4) 

the third most important contributor to greenhouse gas warming. The National Academy of 

Sciences recently statedW that regulations targeting ozone precursors would have combined 

benefits for public health and climate. As noted in the OAQPS Staff Paper, the overall body 

of scientific evidence suggests that high concentrations of ozone on a regional scale could 

have a discernible influence on climate. However, the Staff Paper concludes that insufficient 

information is available at this time to quantitatively inform the secondary NAAQS process 

W National Academy of Sciences, “Radiative Forcing of Climate Change: Expanding the Concept and 

Addressing Uncertainties,” October 2005. 
Regulatory Impact Analysis 

6-30 

 

with regard to this aspect of the ozone-climate interaction. 

Climate change can affect tropospheric ozone by modifying emissions of precursors, 

chemistry, transport and removal.X Climate change affects the sources of ozone precursors 

through physical response (lightning), biological response (soils, vegetation, and biomass 

burning) and human response (energy generation, land use, and agriculture). Increases in 

regional ozone pollution are expected due to higher temperatures and weaker circulation. 

Simulations with global climate models for the 21st century indicate a decrease in the lifetime 

of tropospheric ozone due to increasing water vapor which could decrease global background 

ozone concentrations. 

The Intergovernmental Panel on Climate Change (IPCC) recently released a reportY

which projects, with “virtual certainty,” declining air quality in cities due to warmer and 

fewer cold days and nights and/or warmer/more frequent hot days and nights over most land 

areas. The report states that projected climate change-related exposures are likely to affect the 

health status of millions of people, in part, due to higher concentrations of ground level ozone 

related to climate change. 

The IPCC also reportsZ that the current generation of tropospheric ozone models is 

generally successful in describing the principal features of the present-day global ozone 

distribution. However, there is much less confidence in the ability to reproduce the changes 

in ozone associated with perturbations of emissions or climate. There are major discrepancies 

with observed long-term trends in ozone concentrations over the 20th century, including after 

1970 when the reliability of observed ozone trends is high. Resolving these discrepancies is 

needed to establish confidence in the models. 

The EPA is currently leading a research effort with the goal of identifying changes in 

regional US air quality that may occur in a future (2050) climate, focusing on fine particles 

and ozone. The research builds first on an assessment of changes in US air quality due to 

climate change, which includes direct meteorological impacts on atmospheric chemistry and 

XDenman, K.L., G. Brasseur, A. Chidthaisong, P. Ciais, P.M. Cox, R.E. Dickinson, D. Hauglustaine, C. Heinze, 

E. Holland, D. Jacob, U. Lohmann, S Ramachandran, P.L. da Silva Dias, S.C. Wofsy and X. Zhang, 2007: 

Couplings Between Changes in the Climate System and Biogeochemistry. In: Climate Change 2007: The 

Physical Science Basis. Contribution of Working Group I to the Fourth Assessment 

Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. 

Marquis, K.B. Averyt, M.Tignor and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United 

Kingdom and New York, NY, USA. 

Y IPCC, Climate Change 2007: Climate Change Impacts, Adaptation and Vulnerability, 

Summary for Policymakers 

Z

 Denman, et al, 2007: Couplings Between Changes in the Climate System and Biogeochemistry. In: Climate 

Change 2007: The Physical Science Basis. 
Cost-Benefit Analysis 

6-31 

transport and the effect of temperature changes on air pollution emissions. Further research 

will result in an assessment that adds the emission impacts from technology, land use, 

demographic changes, and air quality regulations to construct plausible scenarios of US air 

quality 50 years into the future. As noted in the Staff Paper, results from these efforts are 

expected to be available for consideration in the next review of the ozone NAAQS. 

6.4.3 Baseline Incidence Rates 

Epidemiological studies of the association between pollution levels and adverse health 

effects generally provide a direct estimate of the relationship of air quality changes to the 

relative risk of a health effect, rather than estimating the absolute number of avoided cases. 

For example, a typical result might be that a 100 ppb decrease in daily ozone levels might, in 

turn, decrease hospital admissions by 3 percent. The baseline incidence of the health effect is 

necessary to convert this relative change into a number of cases. A baseline incidence rate is 

the estimate of the number of cases of the health effect per year in the assessment location, as 

it corresponds to baseline pollutant levels in that location. To derive the total baseline 

incidence per year, this rate must be multiplied by the corresponding population number. For 

example, if the baseline incidence rate is the number of cases per year per 100,000 people, 

that number must be multiplied by the number of 100,000s in the population. 

Table 6.4-3 summarizes the sources of baseline incidence rates and provides average 

incidence rates for the endpoints included in the analysis. For both baseline incidence and 

prevalence data, we used age-specific rates where available. We applied concentrationresponse functions to individual age groups and then summed over the relevant age range to 

provide an estimate of total population benefits. In most cases, we used a single national 

incidence rate, due to a lack of more spatially disaggregated data. Whenever possible, the 

national rates used are national averages, because these data are most applicable to a national 

assessment of benefits. For some studies, however, the only available incidence information 

comes from the studies themselves; in these cases, incidence in the study population is 

assumed to represent typical incidence at the national level. Regional incidence rates are 

available for hospital admissions, and county-level data are available for premature mortality. 

We have projected mortality rates such that future mortality rates are consistent with our 

projections of population growth (Abt Associates, 2005). 
Regulatory Impact Analysis 

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Table 6.4-3. National Average Baseline Incidence Ratesa

Rate per 100 people per yeard Endpoint Source Notes by Age Group 

<18 18-24 25-34 35-44 45-54 55-64 65+

Mortality CDC Compressed Mortality File, 

accessed through CDC Wonder 

(1996-1998) 

nonaccidental 0.025 0.022 0.057 0.150 0.383 1.006 4.937 

Respiratory 

Hospital 

Admissions. 

1999 NHDS public use data filesb incidence 0.043 0.084 0.206 0.678 1.926 4.389 11.62

9 

Asthma ER visits 2000 NHAMCS public use data 

filesc

; 1999 NHDS public use data 

filesb

incidence 1.011 1.087 0.751 0.438 0.352 0.425 0.232 

Minor Restricted 

Activity Days 

(MRADs) 

Ostro and Rothschild 

(1989, p. 243) 

incidence – 780 780 780 780 780 – 

School Loss 

Days 

National Center for Education 

Statistics (1996) and 1996 HIS 

(Adams et al., 1999, Table 47); 

estimate of 180 school days per 

year 

all-cause 990.0 – – – – – – 

a

 The following abbreviations are used to describe the national surveys conducted by the National Center for 

Health Statistics: HIS refers to the National Health Interview Survey; NHDS - National Hospital Discharge Survey; 

NHAMCS - National Hospital Ambulatory Medical Care Survey. b

 See ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NHDS/ c

 See ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NHAMCS/ d

 All of the rates reported here are population-weighted incidence rates per 100 people per year. Additional details 

on the incidence and prevalence rates, as well as the sources for these rates are available upon request. 
Cost-Benefit Analysis 

Table 6.4-3 National Average Baseline Incidence Rates (continued)

Endpoint Source Notes Rate per 100 people per 

year 

Ostro et al. (2001) Incidence (and 

prevalence) 

among 

asthmatic 

AfricanAmerican 

children 

Daily wheeze 

Daily cough 

Daily dyspnea 

0.076 (0.173) 

0.067 (0.145) 

0.037 (0.074) 

Asthma Exacerbations 

Vedal et al. (1998) Incidence (and 

prevalence) 

among 

asthmatic 

children 

Daily wheeze 

Daily cough 

Daily dyspnea 

0.038 

0.086 

0.045 

6.5 Economic Values for Health Outcomes 

Reductions in ambient concentrations of air pollution generally lower the risk of future 

adverse health effects for a large population. Therefore, the appropriate economic measure is 

willingness-to-pay (WTP) for changes in risk of a health effect rather than WTP for a health 

effect that would occur with certainty (Freeman, 1993). Epidemiological studies generally 

provide estimates of the relative risks of a particular health effect that is avoided because of a 

reduction in air pollution. We converted those to units of avoided statistical incidence for ease 

of presentation. We calculated the value of avoided statistical incidences by dividing 

individual WTP for a risk reduction by the related observed change in risk. For example, 

suppose a pollution-reduction regulation is able to reduce the risk of premature mortality from 

2 in 10,000 to 1 in 10,000 (a reduction of 1 in 10,000). If individual WTP for this risk 

reduction is $100, then the WTP for an avoided statistical premature death is $1 million 

($100/0.0001 change in risk). 

WTP estimates generally are not available for some health effects, such as hospital 

admissions. In these cases, we used the cost of treating or mitigating the effect as a primary 

estimate. These cost-of-illness (COI) estimates generally understate the true value of 

reducing the risk of a health effect, because they reflect the direct expenditures related to 

treatment, but not the value of avoided pain and suffering (Harrington and Portney, 1987; 

Berger, 1987). We provide unit values for health endpoints (along with information on the 

distribution of the unit value) in Table 6.5-1. All values are in constant year 2000 dollars, 

adjusted for growth in real income out to 2020 using projections provided by Standard and 

Poor’s. Economic theory argues that WTP for most goods (such as environmental protection) 

will increase if real income increases. Many of the valuation studies used in this analysis 

were conducted in the late 1980s and early 1990s. Because real income has grown since the 

6-33 
Regulatory Impact Analysis 

6-34 

studies were conducted, people’s willingness to pay for reductions in the risk of premature 

death and disease likely has grown as well. We did not adjust cost of illness-based values 

because they are based on current costs. Similarly, we did not adjust the value of school 

absences, because that value is based on current wage rates. Table 6.5-1 presents the values 

for individual endpoints adjusted to year 2020 income levels. The discussion below provides 

additional details on ozone related endpoints not previously included in the proposal for this 

rule. For details on valuation estimates for PM related endpoints, see the 2006 PM NAAQS 

RIA and the proposed Locomotive and Marine RIA. 

6.5.1 Mortality Valuation 

To estimate the monetary benefit of reducing the risk of premature death, we used the 

“value of statistical lives” saved (VSL) approach, which is a summary measure for the value 

of small changes in mortality risk for a large number of people. The VSL approach applies 

information from several published value-of-life studies to determine a reasonable monetary 

value of preventing premature mortality. The mean value of avoiding one statistical death is 

estimated to be roughly $5.5 million at 1990 income levels (2000 $), and $6.6 million at 2020 

income levels. This represents an intermediate value from a variety of estimates in the 

economics literature (see the 2006 PM NAAQS RIA for more details on the calculation of 

VSL). 

6.5.2 Hospital Admissions Valuation 

In the absence of estimates of societal WTP to avoid hospital visits/admissions for 

specific illnesses, estimates of total cost of illness (total medical costs plus the value of lost 

productivity) typically are used as conservative, or lower bound, estimates. These estimates 

are biased downward, because they do not include the willingness-to-pay value of avoiding 

pain and suffering. 

The International Classification of Diseases (ICD-9, 1979) code-specific COI 

estimates used in this analysis consist of estimated hospital charges and the estimated 

opportunity cost of time spent in the hospital (based on the average length of a hospital stay 

for the illness). We based all estimates of hospital charges and length of stays on statistics 

provided by the Agency for Healthcare Research and Quality (AHRQ 2000). We estimated 

the opportunity cost of a day spent in the hospital as the value of the lost daily wage, 

regardless of whether the hospitalized individual is in the workforce. To estimate the lost 

daily wage, we divided the 1990 median weekly wage by five and inflated the result to year 

2000$ using the CPI-U “all items.” The resulting estimate is $109.35. The total cost-ofillness estimate for an ICD code-specific hospital stay lasting n days, then, was the mean 

hospital charge plus $109 Χ n. 

6.5.3 Asthma-Related Emergency Room Visits Valuation 

To value asthma emergency room visits, we used a simple average of two estimates 

from the health economics literature. The first estimate comes from Smith et al. (1997), who 

reported approximately 1.2 million asthma-related emergency room visits in 1987, at a total 

cost of $186.5 million (1987$). The average cost per visit that year was $155; in 2000$, that 
Cost-Benefit Analysis 

cost was $311.55 (using the CPI-U for medical care to adjust to 2000$). The second estimate 

comes from Stanford et al. (1999), who reported the cost of an average asthma-related 

emergency room visit at $260.67, based on 1996-1997 data. A simple average of the two 

estimates yields a (rounded) unit value of $286. 

6.5.4 Minor Restricted Activity Days Valuation 

No studies are reported to have estimated WTP to avoid a minor restricted activity 

day. However, one of EPA’s contractors, IEc (1993) has derived an estimate of willingness to 

pay to avoid a minor respiratory restricted activity day, using estimates from Tolley et al. 

(1986) of WTP for avoiding a combination of coughing, throat congestion and sinusitis. The 

IEc estimate of WTP to avoid a minor respiratory restricted activity day is $38.37 (1990$), or 

about $52 ($2000). 

Although Ostro and Rothschild (1989) statistically linked ozone and minor restricted 

activity days, it is likely that most MRADs associated with ozone exposure are, in fact, minor 

respiratory restricted activity days. For the purpose of valuing this health endpoint, we used 

the estimate of mean WTP to avoid a minor respiratory restricted activity day. 

6.5.5 School Absences 

To value a school absence, we: (1) estimated the probability that if a school child 

stays home from school, a parent will have to stay home from work to care for the child; and 

(2) valued the lost productivity at the parent’s wage. To do this, we estimated the number of 

families with school-age children in which both parents work, and we valued a school-loss 

day as the probability that such a day also would result in a work-loss day. We calculated this 

value by multiplying the proportion of households with school-age children by a measure of 

lost wages. 

We used this method in the absence of a preferable WTP method. However, this 

approach suffers from several uncertainties. First, it omits willingness to pay to avoid the 

symptoms/illness that resulted in the school absence; second, it effectively gives zero value to 

school absences that do not result in work-loss days; and third, it uses conservative 

assumptions about the wages of the parent staying home with the child. Finally, this method 

assumes that parents are unable to work from home. If this is not a valid assumption, then 

there would be no lost wages. 

For this valuation approach, we assumed that in a household with two working 

parents, the female parent will stay home with a sick child. From the Statistical Abstract of 

the United States (U.S. Census Bureau, 2001), we obtained: (1) the numbers of single, 

married and “other” (widowed, divorced or separated) working women with children; and (2) 

the rates of participation in the workforce of single, married and “other” women with 

children. From these two sets of statistics, we calculated a weighted average participation rate 

of 72.85 percent. 

Our estimate of daily lost wage (wages lost if a mother must stay at home with a sick 

child) is based on the year 2000 median weekly wage among women ages 25 and older (U.S. 

6-35 
Regulatory Impact Analysis 

6-36 

Census Bureau, 2001). This median weekly wage is $551. Dividing by five gives an estimated 

median daily wage of $103. To estimate the expected lost wages on a day when a mother has 

to stay home with a school-age child, we first estimated the probability that the mother is in 

the workforce then multiplied that estimate by the daily wage she would lose by missing a 

work day: 72.85 percent times $103, for a total loss of $75. This valuation approach is 

similar to that used by Hall et al. (2003). 
Co

Table 6.5-1. Unit Values Used for Economic Valuation of Health Endpoints (2000$)a

Central Estimate of Value Per Statistical 

Incidence 

Health Endpoint 

1990 Income 

Level 

2020 Income 

Levelb

2030 Income 

Levelb Derivation of Estimates 

Premature Mortality (Value of a 

Statistical Life): PM2.5- and 

Ozone-related 

$5,500,000 $6,600,000 $6,800,000 Point estimate is the mean of a normal distri

confidence interval between $1 and $10 milis based on two meta-analyses of the wage-rmillion represents the lower end of the interMrozek and Taylor (2002)49 meta-analysis a

represents the upper end of the interquartile and Aldy (2003)50 meta-analysis. The VSL 

small change in mortality risk aggregated ovpopulation. 

Chronic Bronchitis (CB) $340,000 $420,000 $430,000 Point estimate is the mean of a generated dis

avoid a case of pollution-related CB. WTP pollution-related CB is derived by adjusting Viscusi et al., [1991]51) to avoid a severe ca

difference in severity and taking into accounwith respect to severity of CB. 

Nonfatal Myocardial Infarction 

(heart attack) 

 3% discount rate  Age 0–24  Age 25–44  Age 45–54  Age 55–65  Age 66 and over  7% discount rate  Age 0–24  Age 25–44  Age 45–54  Age 55–65  Age 66 and over 

$66,902 $74,676 $78,834 $140,649 $66,902 $65,293 $73,149 $76,871 $132,214 $65,293 

$66,902 $74,676 $78,834 $140,649 $66,902 $65,293 $73,149 $76,871 $132,214 $65,293 

$66,902 $74,676 $78,834 $140,649 $66,902 $65,293 $73,149 $76,871 $132,214 $65,293 

Age-specific cost-of-illness values reflect lo

medical costs over a 5-year period following earnings estimates are based on Cropper and 

Direct medical costs are based on simple avRussell et al. (1998)53 and Wittels et al. (199

Lost earnings: Cropper and Krupnick (1990). Present disco

lost earnings: 

age of onset: at 3% at 7% 25-44 $8,774 $7,855 45-54 $12,932 $11,578 55-65 $74,746 $66,920 Direct medical expenses: An average of: 1. Wittels et al. (1990) ($102,658—no disco2. Russell et al. (1998), 5-year period ($22,3

$21,113 at 7% discount rate) 

 (continued) 

6-37 
Regulatory Impact Analysis 

6-38 

Table 6.5-1. Unit Values Used for Economic Valuation of Health Endpoints (2000$)a (continued)

Central Estimate of Value Per Statistical 

Incidence 

Health Endpoint 

1990 Income 

Level 

2020 Income 

Levelb

2030 Income 

Levelb Derivation of Estimates 

Hospital Admissions Chronic Obstructive Pulmonary 

Disease (COPD) 

(ICD codes 490-492, 494-496) 

$12,378 $12,378 $12,378 The COI estimates (lost earnings plus direc

on ICD-9 code-level information (e.g., aveaverage length of hospital stay, and weightcategory illnesses) reported in Agency for Quality (2000)55 (www.ahrq.gov). 

Pneumonia (ICD codes 480-487) 

$14,693 $14,693 $14,693 The COI estimates (lost earnings plus direc

on ICD-9 code-level information (e.g., aveaverage length of hospital stay, and weightpneumonia category illnesses) reported in Research and Quality (2000) (www.ahrq.g

Asthma Admissions $6,634 $6,634 $6,634 The COI estimates (lost earnings plus direc

on ICD-9 code-level information (e.g., aveaverage length of hospital stay, and weightcategory illnesses) reported in Agency for Quality (2000) (www.ahrq.gov). 

All Cardiovascular (ICD codes 390-429) 

$18,387 $18,387 $18,387 The COI estimates (lost earnings plus direc

on ICD-9 code-level information (e.g., aveaverage length of hospital stay, and weightcardiovascular category illnesses) reported Research and Quality (2000) (www.ahrq.g

Emergency Room Visits for 

Asthma 

$286 $286 $286 Simple average of two unit COI values: 

(1) $311.55, from Smith et al. (1997)56 and 

(2) $260.67, from Stanford et al. (1999).57

 (continued)
Co

6-39 

Table 6.5-1. Unit Values Used for Economic Valuation of Health Endpoints (2000$)a (conti

Central Estimate of Value Per Statistical 

Incidence 

Health Endpoint 

1990 Income 

Level 

2020 Income 

Levelb

2030 Income 

Levelb Derivation of Estimates 

Respiratory Ailments Not Requiring Hospitalization Upper Respiratory Symptoms 

(URS) 

$25 $27 $27 Combinations of the three symptoms for w

available that closely match those listed by different “symptom clusters,” each describdollar value was derived for each type of Uestimates of WTP (IEc, 1994)58 to avoid e

and assuming additivity of WTPs. The doaverage of the dollar values for the seven d

Lower Respiratory Symptoms 

(LRS) 

$16 $17 $17 Combinations of the four symptoms for wh

available that closely match those listed by 11 different “symptom clusters,” each descdollar value was derived for each type of Lestimates of WTP (IEc, 1994) to avoid eacand assuming additivity of WTPs. The doaverage of the dollar values for the 11 diffe

Asthma Exacerbations $42 $45 $45 Asthma exacerbations are valued at $42 pe

mean of average WTP estimates for the fou“bad asthma day,” described in Rowe and 

study surveyed asthmatics to estimate WTP asthma day,” as defined by the subjects. Fan asthma attack is assumed to be equivaleasthma is moderate or worse as reported in (1986) study. 

Acute Bronchitis $360 $380 $390 Assumes a 6-day episode, with daily value 

low and high values for related respiratory in Neumann et al. (1994).60

 (continued) 
Regulatory Impact Analysis 

6-40 

Table 6.5-1. Unit Values Used for Economic Valuation of Health Endpoints (2000$)a (conti

Central Estimate of Value Per Statistical 

Incidence 

Health Endpoint 

1990 Income 

Level 

2020 Income 

Levelb

2030 Income 

Levelb Derivation of Estimates 

Restricted Activity and Work/School Loss Days Work Loss Days (WLDs) Variable 

(national median = ) 

County-specific median annual wages div

of vacation) and then by 5—to get median Census, compiled by Geolytics, Inc. 

School Absence Days $75 $75 $75 Based on expected lost wages from parent 

Estimated daily lost wage (if a mother muchild) is based on the median weekly wagolder in 2000 (U.S. Census Bureau, StatisStates: 2001, Section 12: Labor Force, ETable No. 621). This median wage is $55estimated median daily wage of $103. 

The expected loss in wages due to a day o

mother would have to stay home with her probability that the mother is in the workfwould lose if she missed a day = 72.85% 

Worker Productivity $0.95 per 

worker per 10% change in ozone per day 

$0.95 per 

worker per 10% change in ozone per day 

$0.95 per 

worker per 10% change in ozone per day 

Based on $68 – median daily earnings of w

and fishing – from Table 621, Statistical A(“Full-Time Wage and Salary Workers – 2000") (Source of data in table: U.S. BurBulletin 2307 and Employment and Earni

Minor Restricted Activity Days 

(MRADs) 

$51 $54 $55 Median WTP estimate to avoid one MRA

a Although the unit values presented in this table are in year 2000 dollars, all monetized annual benefit estimates associated with the f

inflated to reflect values in year 2006 dollars. We use the Consumer Price Indexes to adjust both WTP- and COI-based benefits estimfrom 2000 dollars.62 For WTP-based estimates, we use an inflation factor of 1.17 based on the CPI-U for “all items.” For COI-based 

inflation factor of 1.29 based on the CPI-U for medical care. 

b Our analysis accounts for expected growth in real income over time. Economic theory argues that WTP for most goods (such as en

will increase if real incomes increase. Benefits are therefore adjusted by multiplying the unadjusted benefits by the appropriate adjus

for income growth over time. For a complete discussion of how these adjustment factors were derived, we refer the reader to the PM 

impact analysis. Note that similar adjustments do not exist for cost-of-illness-based unit values. For these, we apply the same unit va

future year of analysis.
Cost-Benefit Analysis 

6-41 

6.6 Benefits Analysis Results for the Final Standards 

 Applying the impact and valuation functions described previously in this chapter to 

the estimated changes in PM2.5 and ozone associated with the final standards results in 

estimates of the changes in health damages (e.g., premature mortalities, cases, admissions) 

and the associated monetary values for those changes. Estimates of physical health impacts 

are presented in Table 6.6-1. Monetized values for those health endpoints are presented in 

Table 6.6-2. Total aggregate monetized benefits are presented in Table 6.6-3 and Table 6.6-4 

using either a 3 percent or 7 percent discount rate, respectively. All of the monetary benefits 

are in constant-year 2006 dollars. For each endpoint presented in Tables 6.6-1 and 6.6-2, we 

provide both the mean estimate and the 90% confidence interval. 

In addition to omitted benefits categories such as air toxics and various welfare 

effects, not all known PM2.5- and ozone-related health and welfare effects could be quantified 

or monetized. The estimate of total monetized health benefits of the final standards is thus 

equal to the subset of monetized PM2.5- and ozone-related health benefits we are able to 

quantify plus the sum of the nonmonetized health and welfare benefits. We believe the total 

benefits are therefore likely underestimated. 

 Total monetized benefits are dominated by benefits of mortality risk reductions. We 

provide results based on concentration response functions from the American Cancer Society 

Study (ACS), Six Cities, and Expert Elicitation to give an indication of the sensitivity of the 

benefits estimates to alternative assumptions. Following the recommendations of the NRC 

report (NRC, 2002), we identify those estimates which are based on empirical data, and those 

which are based on expert judgments. EPA intends to ask its Science Advisory Board to 

evaluate how EPA has incorporated expert elicitation results into the benefits analysis, and the 

extent to which they find the presentation in this RIA responsive to the NRC (2002) guidance 

to incorporate uncertainty into the main analysis and further, whether the agency should move 

toward presenting a central estimate with uncertainty bounds or continue to provide separate 

estimates for each of the 12 experts as well as from the ACS and Six Cities studies, and if so, 

the appropriateness of using Laden et al 2006, the most recently published update, as the 

estimate for the Six Cities based model. 

Using the ACS and Six-Cities results, we estimate that the final standards would result 

in between 490 and 1,100 cases of avoided PM2.5-related premature deaths annually in 2020 

and between 1,100 and 2,600 avoided premature deaths annually in 2030. When the range of 

expert opinion is used, we estimate between 220 and 2,200 fewer premature mortalities in 

2020 and between 500 and 4,900 fewer premature mortalities in 2030. Note that in the case 

of the premature mortality estimates derived from the expert elicitation, we report the 90% 

credible interval, which encompasses a broader representation of uncertainty relative to the 

statistical confidence intervals provided for the effect estimates derived from the 

epidemiology literature. 
Regulatory Impact Analysis 

6-42 

The range of ozone benefits associated with the final standards is estimated based on 

risk reductions estimated using several sources of ozone-related mortality effect estimates. 

There is considerable uncertainty in the magnitude of the association between ozone and 

premature mortality. This analysis presents four alternative estimates for the association 

based upon different functions reported in the scientific literature. We also consider the 

possibility that the observed associations between ozone and mortality may not be causal in 

nature. EPA has requested advice from the National Academy of Sciences on how best to 

quantify uncertainty in the relationship between ozone exposure and premature mortality in 

the context of quantifying benefits associated with ozone control strategies. 

For ozone-related premature mortality, we estimate a range of between 13 to 62 fewer 

premature mortalities as a result of the final rule in 2020 and between 54 to 250 in 2030, 

assuming that there is a causal relationship between ozone exposure and mortality. The 

increase in annual benefits from 2020 to 2030 reflects additional emission reductions from the 

final standards, as well as increases in total population and the average age (and thus baseline 

mortality risk) of the population. 

Our estimate of total monetized benefits in 2020 for the final standards, using the ACS 

and Six-Cities PM mortality studies and the range of ozone mortality assumptions, is between 

$3.7 billion and $8.8 billion, assuming a 3 percent discount rate, or between $3.6 billion and 

$8.0 billion, assuming a 7 percent discount rate. In 2030, we estimate the monetized benefits 

to be between $9.2 billion and $22 billion, assuming a 3 percent discount rate, or between 

$8.4 billion and $20 billion, assuming a 7 percent discount rate. The monetized benefit 

associated with reductions in the risk of both ozone- and PM2.5-related premature mortality 

ranges between 90 to 98 percent of total monetized health benefits, in part because we are 

unable to quantify a number benefits categories (see Table 6.4-1). These unquantified 

benefits may be substantial, although their magnitude is highly uncertain. 

The next largest benefit is for reductions in chronic illness (chronic bronchitis and 

nonfatal heart attacks), although this value is more than an order of magnitude lower than for 

premature mortality. Hospital admissions for respiratory and cardiovascular causes, minor 

restricted activity days, and work loss days account for the majority of the remaining benefits. 

The remaining categories each account for a small percentage of total benefit; however, they 

represent a large number of avoided incidences affecting many individuals. A comparison of 

the incidence table to the monetary benefits table reveals that there is not always a close 

correspondence between the number of incidences avoided for a given endpoint and the 

monetary value associated with that endpoint. For example, there are over 100 times more 

work loss days than PM-related premature mortalities (based on the ACS study), yet work 

loss days account for only a very small fraction of total monetized benefits. This reflects the 

fact that many of the less severe health effects, while more common, are valued at a lower 

level than the more severe health effects. Also, some effects, such as hospital admissions, are 

valued using a proxy measure of willingness-to-pay (e.g., cost-of-illness). As such, the true 

value of these effects may be higher than that reported in Table 6.6-2. 
Cost-Benefit Analysis 

6-43 

 Following these tables, we also provide a more comprehensive presentation of the 

distributions of incidence generated using the available information from empirical studies 

and expert elicitation. Tables 6.6-5 and 6.6-6 present the distributions of the reduction in 

PM2.5-related premature mortality based on the C-R distributions provided by each expert, as 

well as that from the data-derived health impact functions, based on the statistical error 

associated with the ACS study (Pope et al., 2002) and the Six-cities study (Laden et al., 2006). 

The 90% confidence interval for each separate estimate of PM-related mortality is also 

provided. 

 The effect estimates of five of the twelve experts included in the elicitation panel fall 

within the empirically-derived range provided by the ACS and Six-Cities studies. One of the 

experts fall below this range and six of the experts are above this range. Although the overall 

range across experts is summarized in these tables, the full uncertainty in the estimates is 

reflected by the results for the full set of 12 experts. The twelve experts’ judgments as to the 

likely mean effect estimate are not evenly distributed across the range illustrated by arraying 

the highest and lowest expert means. 
Regulatory Impact Analysis 

6-44 

Table 6.6-1. Estimated Reduction in Incidence of Adverse Health Effects Related to the 

Final Standardsa

2020 2030 

Health Effect Mean Incidence Reduction 

(5th – 95th %ile) 

PM-Related Endpoints 

Adult, age 30+ - ACS cohort 

study (Pope et al., 2002) 

490 

(190 - 790) 

1,100 

(440 – 1,800) 

Adult, age 25+ - Six-Cities 

study (Laden et al., 2006) 

1,100 

(610 - 1,600) 

2,600 

(1,400 – 3,700) 

Premature Mortality – 

Derived from Epidemiology 

Literature 

Infant, age <1 year – 

Woodruff et al. 1997 

1 

(1 - 2) 

2 

(1 – 3) 

Adult, age 25+ - Lower 

Bound (Expert K) 

220 

(0 - 1,100) 

500 

(0 – 2,400) Premature Mortality – 

Derived from Expert 

Elicitationb Adult, age 25+ - Upper Bound 

(Expert E) 

2,200 

(1,100 - 3,300) 

4,900 

(2,500 – 7,500) 

Chronic bronchitis (adult, age 26 and over) 310 

(56 - 560) 

680 

(130 – 1,200) 

Acute myocardial infarction (adults, age 18 and older) 1,000 

(550 - 1,500) 

2,500 

(1,300 – 3,600) 

Hospital admissions—respiratory (all ages)c 120 

(58 - 170) 

270 

(130 – 400) 

Hospital admissions—cardiovascular (adults, age >18)d 240 

(150 - 330) 

600 

(380 – 820) 

Emergency room visits for asthma (age 18 years and younger) 410 

(240 - 580) 

890 

(520 – 1,300) 

Acute bronchitis (children, age 8–12) 1,000 

(-35 – 2,100) 

2,300 

(-77 – 4,600) 

Lower respiratory symptoms (children, age 7–14) 9,200 

(4,400 – 14,000) 

20,000 

(9,700 – 31,000) 

Upper respiratory symptoms (asthmatic children, age 9–18) 6,700 

(2,100 – 11,000) 

15,000 

(4,600 – 25,000) 

Asthma exacerbation (asthmatic children, age 6–18) 8,400 

(920 – 24,000) 

19,000 

(2,000 – 53,000) 

Work loss days (adults, age 18–65) 59,000 

(51,000 – 67,000) 

120,000 

(110,000 – 140,000) 

Minor restricted-activity days (adults, age 18–65) 350,000 

(290,000 – 400,000) 

720,000 

(610,000 – 830,000) 

Ozone-Related Endpoints 

Premature Mortality, All ages 

– Derived from NMMAPS

Bell et al., 2004 13 

(-22 - 49) 

54 

(-43 – 150) 

Bell et al., 2005 44 

(-47 - 140) 

180 

(-69 – 420) 

Ito et al., 2005 60 

(-34 - 150) 

240 

(-14 – 500) 

Premature Mortality, All ages 

– Derived from Meta-analyses 

Levy et al., 2005 62 

(-14 – 138) 

250 

(44 - 450) 

Premature Mortality – Assumption that association between 0 0 
Cost-Benefit Analysis 

6-45 

ozone and mortality is not causal 

Hospital admissions- respiratory causes (children, under 2; 

adult, 65 and older)e 14 

(-146 - 170) 

260 

(-350 – 890) 

Emergency room visit for asthma (all ages) 69 

(-89 - 270) 

250 

(-190 – 830) 

Minor restricted activity days (adults, age 18-65) 84,000 

(43,000 – 120,000) 

290,000 

(150,000 – 430,000) 

School absence days 33,000 

(-17,000 – 77,000) 

110,000 

(-15,000 – 240,000) a

 Incidence is rounded to two significant digits. PM and ozone estimates represent impacts from the 

final standards nationwide. 

b

 Based on effect estimates derived from the full-scale expert elicitation assessing the uncertainty in the 

concentration-response function for PM-related premature mortality (IEc, 2006).AA The effect estimates of five 

of the twelve experts included in the elicitation panel fall within the empirically-derived range provided by the 

ACS and Six-Cities studies. One of the experts fall below this range and six of the experts are above this range. 

Although the overall range across experts is summarized in this table, the full uncertainty in the estimates is 

reflected by the results for the full set of 12 experts. The twelve experts’ judgments as to the likely mean effect 

estimate are not evenly distributed across the range illustrated by arraying the highest and lowest expert means. c

 Respiratory hospital admissions for PM include admissions for COPD, pneumonia, and asthma. 

d

 Cardiovascular hospital admissions for PM include total cardiovascular and subcategories for 

ischemic heart disease, dysrhythmias, and heart failure. e

 Respiratory hospital admissions for ozone include admissions for all respiratory causes and 

subcategories for COPD and pneumonia. 

 

AA Industrial Economics, Incorporated (IEc). 2006. Expanded Expert Judgment Assessment of the 

Concentration-Response Relationship Between PM2.5 Exposure and Mortality. Peer Review Draft. Prepared 

for: Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle 

Park, NC. August. 
Regulatory Impact Analysis 

6-46 

Table 6.6-2. Estimated Monetary Value in Reductions in Incidence of Health and 

Welfare Effects (in millions of 2005$)a,b

2020 2030 

PM2.5-Related Health Effect Estimated Mean Value of Reductions 

(5th and 95th %ile) 

Adult, age 30+ - ACS study 

(Pope et al., 2002) 

 3% discount rate 

 7% discount rate 

$3,400 

($810 - $7,00) 

$3,100 

($730 - $6,300) 

$8,100 

($1,900 - $16,000) 

$7,300 

($1,700 - $15,000) 

Adult, age 25+ - Six-cities study 

(Laden et al., 2006) 

 3% discount rate 

 7% discount rate 

$7,800 

($2,200 - $15,000) 

$7,000 

($1,900 - $13,000) 

$18,000 

($5,100 - $35,000) 

$17,000 

($4,600 - $32,000) 

Premature Mortality – 

Derived from 

Epidemiology 

Studiesc,d,

Infant Mortality, <1 year – 

(Woodruff et al. 1997) 

 3% discount rate 

 7% discount rate 

$7 

($2 - $14) 

$7 

($2 - $13) 

$13 

($3.5 - $26) 

$12 

($3.1 - $23) 

Adult, age 25+ - Lower bound 

(Expert K) 

 3% discount rate 

 7% discount rate 

$1,500 

($0 - $7,700) 

$1,400 

($0 - $7,000) 

$3,600 

($0 - $18,000) 

$3,200 

($0 - $16,000) 

Premature mortality – 

Derived from Expert 

Elicitationc,d,e Adult, age 25+ - Upper bound 

(Expert E) 

 3% discount rate 

 7% discount rate 

$15,000 

($4,100 - $30,000) 

$14,000 

($3,700 - $27,000) 

$36,000 

($9,500 - $70,000) 

$32,000 

($8,600 - $63,000) 

Chronic bronchitis (adults, 26 and over) $150 

($12 - $500) 

$340 

($28 - $1,100) 

Non-fatal acute myocardial infarctions 

 3% discount rate 

 7% discount rate 

$110 

($34 - $230) 

$110 

($31 - $230) 

$260 

($74 - $550) 

$250 

($69 - $540) 

Hospital admissions for respiratory causes $2.1 

($1.0 - $3.2) 

$4.9 

($2.4 - $7.3) 

Hospital admissions for cardiovascular causes $6.7 

($4.2 - $9.2) 

$17 

($11 - $23) 

Emergency room visits for asthma $0.15 

($0.08 - $0.23) 

$0.33 

($0.18 - $0.49) 

Acute bronchitis (children, age 8–12) $0.08 

($0 - $0.2) 

$0.17 

($0 - $0.42) 

Lower respiratory symptoms (children, 7–14) $0.18 $0.40 
Cost-Benefit Analysis 

6-47 

($0.07 - $0.33) ($0.15 - $0.73) 

Upper respiratory symptoms (asthma, 9–11) $0.21 

($0.06 - $0.46) 

$0.46 

($0.13 - $1.0) 

Asthma exacerbations $0.45 

($0.05 - $1.3) 

$1.0 

($0.11 - $2.9) 

Work loss days $8.9 

($7.7 - $10) 

$18 

($16 - $21) 

Minor restricted-activity days (MRADs) $22 

($13 – $32) 

$46 

($27 - $66) 

Recreational Visibility, 86 Class I areas $ 

(na)f 

$ 

(na) 

Ozone-related Health Effect 

Premature Mortality, All 

ages – Derived from 

NMMAPS

Bell et al., 2004 $100 

(-$170 - $420) 

$440 

(-$340 - $1,400) 

Bell et al., 2005 $340 

(-$360 - $1,200) 

$1,400 

(-$550 - $3,900) 

Ito et al., 2005 $460 

(-$260 - $1,400) 

$1,900 

(-$120 - $4,700) 

Premature Mortality, All 

ages – Derived from Metaanalyses 

Levy et al., 2005 $480 

(-$110 - $1,300) 

$2,000 

($280 - $4,400) 

Premature Mortality – Assumption that association between 

ozone and mortality is not causal 

$0 $0 

Hospital admissions- respiratory causes (children, under 2; 

adult, 65 and older) 

-$0.54 

(-$4.6 - $3.3) 

$2.7 

(-$11 - $17) 

Emergency room visit for asthma (all ages) $0.03 

(-$0.03 - $0.1) 

$0.09 

(-$0.07 - $0.30) 

Minor restricted activity days (adults, age 18-65) $2.5 

(-$4.0 - $9.9) 

$8.8 

(-$7.8 - $28) 

School absence days $2.9 

(-$1.5 - $6.8) 

$11 

(-$1.3 - $21) 

Worker Productivity $0.53 

(na)f 

$2.9 

(na)f 

a

 Monetary benefits are rounded to two significant digits for ease of presentation and computation. PM 

and ozone benefits are nationwide. 

b

 Monetary benefits adjusted to account for growth in real GDP per capita between 1990 and the 

analysis year (2020 or 2030) c

 Valuation assumes discounting over the SAB recommended 20 year segmented lag structure. Results 

reflect the use of 3 percent and 7 percent discount rates consistent with EPA and OMB guidelines for preparing 

economic analyses (EPA, 2000; OMB, 2003). 

d

 The valuation of adult premature mortality, derived either from the epidemiology literature or the 

expert elicitation, is not additive. Rather, the valuations represent a range of possible mortality benefits. 
Regulatory Impact Analysis 

6-48 

e

 Based on effect estimates derived from the full-scale expert elicitation assessing the uncertainty in the 

concentration-response function for PM-related premature mortality (IEc, 2006).BB The effect estimates of five 

of the twelve experts included in the elicitation panel fall within the empirically-derived range provided by the 

ACS and Six-Cities studies. One of the experts fall below this range and six of the experts are above this range. 

Although the overall range across experts is summarized in this table, the full uncertainty in the estimates is 

reflected by the results for the full set of 12 experts. The twelve experts’ judgments as to the likely mean effect 

estimate are not evenly distributed across the range illustrated by arraying the highest and lowest expert means.

f We are unable at this time to characterize the uncertainty in the estimate of benefits of worker 

productivity and improvements in visibility at Class I areas. As such, we treat these benefits as fixed and add 

them to all percentiles of the health benefits distribution.

Table 6.6-3 Total Monetized Benefits of the Final Locomotive and Marine Engine Rule – 

3% Discount Rate 

Total Ozone and PM Benefits (billions, 2006$) – 

PM Mortality Derived from Epidemiology Studies (ACS and Six Cities) 

2020 2030 

Ozone 

Mortality 

Function 

Reference Mean Total 

Benefits 

Ozone 

Mortality 

Function 

Reference Mean Total 

Benefits 

NMMAPS Bell et al., 

2004 

$4.0 to $8.4 NMMAPS Bell et al., 

2004 

$9.7 to $20 

Bell et al., 

2005 

$4.2 to $8.6 Bell et al., 

2005 

$11 to $21 

Meta-analysis Ito et al., 2005 $4.4 to $8.8 Ito et al., 2005 $11 to $21 

Levy et al., 

2005 

$4.4 to $8.8 

Meta-analysis 

Levy et al., 

2005 

$11 to $22 

Assumption that association is 

not causal 

$3.9 to $8.3 Assumption that association is 

not causal 

$9.2 to $20 

Total Ozone and PM Benefits (billions, 2006$) – 

PM Mortality Derived from Expert Elicitation (Lowest and Highest Estimate) 

2020 2030 

Ozone 

Mortality 

Function 

Reference Mean Total 

Benefits 

Ozone 

Mortality 

Function 

Reference Mean Total 

Benefits 

NMMAPS Bell et al., 

2004 

$2.1 to $16 NMMAPS Bell et al., 

2004 

$5.2 to $37 

Bell et al., 

2005 

$2.4 to $16 Bell et al., 

2005 

$6.2 to $38 

Meta-analysis Ito et al., 2005 $2.5 to $16 Ito et al., 2005 $6.7 to $39 

Levy et al., 

2005 

$2.5 to $16 

Meta-analysis 

Levy et al., 

2005 

$6.7 to $39 

 

BB Industrial Economics, Incorporated (IEc). 2006. Expanded Expert Judgment Assessment of the 

Concentration-Response Relationship Between PM2.5 Exposure and Mortality. Peer Review Draft. Prepared 

for: Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle 

Park, NC. August. 
Cost-Benefit Analysis 

6-49 

Assumption that association is 

not causal 

$2.0 to $16 Assumption that association is 

not causal 

$4.7 to $37 

Table 6.6-4 Total Monetized Benefits of the Final Locomotive and Marine Engine Rule – 

7% Discount Rate 

Total Ozone and PM Benefits (billions, 2006$) – 

PM Mortality Derived from Epidemiology Studies (ACS and Six Cities) 

2020 2030 

Ozone 

Mortality 

Function 

Reference Mean Total 

Benefits 

Ozone 

Mortality 

Function 

Reference Mean Total 

Benefits 

NMMAPS Bell et al., 

2004 

$3.7 to $7.6 NMMAPS Bell et al., 

2004 

$8.9 to $18 

Bell et al., 

2005 

$3.9 to $7.9 Bell et al., 

2005 

9.8 to $19 

Meta-analysis Ito et al., 2005 $4.0 to $8.0 Ito et al., 2005 $10 to $20 

Levy et al., 

2005 

$4.0 to $8.0 

Meta-analysis 

Levy et al., 

2005 

$10 to $20 

Assumption that association is 

not causal 

$3.6 to $7.5 Assumption that association is 

not causal 

$8.4 to $18 

Total Ozone and PM Benefits (billions, 2006$) – 

PM Mortality Derived from Expert Elicitation (Lowest and Highest Estimate) 

2020 2030 

Ozone 

Mortality 

Function 

Reference Mean Total 

Benefits 

Ozone 

Mortality 

Function 

Reference Mean Total 

Benefits 

NMMAPS Bell et al., 

2004 

$2.0 to $14 NMMAPS Bell et al., 

2004 

$4.8 to $34 

Bell et al., 

2005 

$2.2 to $15 Bell et al., 

2005 

$5.8 to $35 

Meta-analysis Ito et al., 2005 $2.3 to $15 Ito et al., 2005 $6.3 to $35 

Levy et al., 

2005 

$2.3 to $15 

Meta-analysis 

Levy et al., 

2005 

$6.4 to $35 

Assumption that association is 

not causal 

$1.9 to $14 Assumption that association is 

not causal 

$4.4 to $33 
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Table 6.6-5. Results of Application of Expert Elicitation: Annual Reductions in 

Premature Mortality in 2020 Associated with the Final Standards 

Source of Mortality 2020 Primary Option 

Estimate 

5th Percentile Mean 95th Percentile 

Pope et al. (2002) 190 490 790 

Laden et al. (2006) 610 1,100 1,600 

Expert A 320 1,700 3,200 

Expert B 180 1,300 2,800 

Expert C 240 1,300 2,800 

Expert D 190 920 1,500 

Expert E 1,100 2,200 3,300 

Expert F 840 1,200 1,700 

Expert G 0 770 1,400 

Expert H 4 980 2,300 

Expert I 210 1,300 2,300 

Expert J 310 1,100 2,300 

Expert K 0 220 1,100 

Expert L 150 930 1,800 
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Table 6.6-6. Results of Application of Expert Elicitation: Annual Reductions in 

Premature Mortality in 2030 Associated with the Final Standards 

Source of Mortality 2030 Primary Option 

Estimate 

5th Percentile Mean 95th Percentile 

Pope et al. (2002) 440 1,100 1,800 

Laden et al. (2006) 1,400 2,600 3,700 

Expert A 730 4,000 7,200 

Expert B 410 3,000 6,500 

Expert C 540 3,000 6,500 

Expert D 440 2,100 3,400 

Expert E 2,500 4,900 7,500 

Expert F 1,900 2,700 3,900 

Expert G 0 1,800 3,200 

Expert H 8 2,200 5,100 

Expert I 470 3,000 5,300 

Expert J 720 2,400 5,300 

Expert K 0 500 2,400 

Expert L 340 2,100 4,000 

6.7 Comparison of Costs and Benefits 

In estimating the net benefits of the final standards, the appropriate cost measure is 

‘social costs.’ Social costs represent the welfare costs of a rule to society. These costs do not 

consider transfer payments (such as taxes) that are simply redistributions of wealth. Table 

6.7-1 contains the estimates of monetized benefits and estimated social welfare costs for the 

final rule and each of the final control programs. The annual social welfare costs of all 

provisions of this final rule are described more fully in Chapter 7 of this RIA. 

 The results in Table 6.7-1 suggest that the 2020 monetized benefits of the final 

standards are greater than the expected social welfare costs. Specifically, the annual benefits 

of the total program will range between $3.9 to $8.8 billion annually in 2020 using a three 

percent discount rate,or between $3.6 to $8.0 billion assuming a 7 percent discount rate, 

compared to estimated social costs of approximately $330 million in that same year. These 

benefits are expected to increase to between $9.2 and $22 billion annually in 2030 using a 

three percent discount rate,or between $8.4 and $20 billion assuming a 7 percent discount 

rate, while the social costs are estimated to be approximately $740 million. Though there are 

a number of health and environmental effects associated with the final standards that we are 
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6-52 

unable to quantify or monetize (see Table 6.4-1), the benefits of the final standards far 

outweigh the projected costs. 

 Using the most conservative benefits estimate, the 2020 benefits outweigh the costs by 

a factor of 10. Using the upper end of the benefits range, the benefits could outweigh the 

costs by a factor of 25. Likewise, in 2030 benefits outweigh the costs by at least a factor of 

10 and could be as much as a factor of 28. Thus, even taking the most conservative benefits 

assumptions, benefits of the final standards clearly outweigh the costs. 

 

Table 6.7-1. Summary of Annual Benefits and Costs of the Final Standardsa

 

(Millions of 2006 dollars) 

Description 

2020 

(Millions of 2006 dollars) 

2030 

(Millions of 2006 

dollars) 

Estimated Social Costsb

 Locomotive 

 Marine 

Total Social Costs 

$200 

$140 

$330 

$460 

$280 

$740 

Estimated Health Benefits of the Final Standardsc,d,e,f

 Locomotive 

 3 percent discount rate 

 7 percent discount rate 

 Marine 

 3 percent discount rate 

 7 percent discount rate 

Total Benefits 

 3 percent discount rate 

 7 percent discount rate 

$2,000 to $4,400 

$1,900 to $4,000 

$1,900 to $4,400 

$1,700 to $4,000 

$3,900 to $8,800 

$3,600 to $8,000 

$4,300 to $11,000 

$4,000 to $10,000 

$4,900 to $11,000 

$4,400 to $10,000 

$9,200 to $22,000 

$8,400 to $20,000 

Annual Net Benefits (Total Benefits – Total Costs) 

 3 percent discount rate 

 7 percent discount rate 

$3,600 to $8,500 

$3,300 to $7,700 

$8,500 to $21,000 

$7,700 to $19,000 

a

 All estimates represent annualized benefits and costs anticipated for the years 2020 and 2030. Totals 

may not sum due to rounding. 

b

 The calculation of annual costs does not require amortization of costs over time. Therefore, the 

estimates of annual cost do not include a discount rate or rate of return assumption (see Chapter 7 of the RIA). 

In Chapter 7, however, we do use both a 3 percent and 7 percent social discount rate to calculate the net present 

value of total social costs consistent with EPA and OMB guidelines for preparing economic analyses. c

 Total includes ozone and PM2.5 benefits. Range was developed by adding the estimate from the ozone 

premature mortality function, including an assumption that the association is not causal, to both estimates of 

PM2.5-related premature mortality derived from the ACS (Pope et al., 2002) and Six-Cities (Laden et al., 2006) 

studies, respectively. 
Cost-Benefit Analysis 

6-53 

 

d Annual benefits analysis results reflect the use of a 3 percent and 7 percent discount rate in the 

valuation of premature mortality and nonfatal myocardial infarctions, consistent with EPA and OMB guidelines 

for preparing economic analyses (US EPA, 2000 and OMB, 2003).CC,DD e

Valuation of premature mortality based on long-term PM exposure assumes discounting over the SAB 

recommended 20-year segmented lag structure described in the Regulatory Impact Analysis for the Final Clean 

Air Interstate Rule (March, 2005). 

f Not all possible benefits or disbenefits are quantified and monetized in this analysis. Potential benefit 

categories that have not been quantified and monetized are listed in Table 6.4-1. 

CCU.S. Environmental Protection Agency, 2000. Guidelines for Preparing Economic Analyses. 

www.yosemite1.epa.gov/ee/epa/eed/hsf/pages/Guideline.html. 

DD Office of Management and Budget, The Executive Office of the President, 2003. Circular A-4. 

http://www.whitehouse.gov/omb/circulars. 
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6-54 

Appendix 6A: Health-Based Cost Effectiveness Analysis 

 Health-based cost-effectiveness analysis (CEA) and cost-utility analysis (CUA) have 

been used to analyze numerous health interventions but have not been widely adopted as tools 

to analyze environmental policies. The Office of Management and Budget (OMB) recently 

issued Circular A-4 guidance on regulatory analyses, requiring federal agencies to “prepare a 

CEA for all major rulemakings for which the primary benefits are improved public health and 

safety to the extent that a valid effectiveness measure can be developed to represent expected 

health and safety outcomes.” Environmental quality improvements may have multiple health 

and ecological benefits, making application of CEA more difficult and less straightforward. 

For the recently finalized PM NAAQS analysis, CEA provided a useful framework for 

evaluation: non-health benefits were substantial, but the majority of quantified benefits came 

from health effects. EPA included in the PM NAAQS RIA a preliminary and experimental 

application of one type of CEA—a modified quality-adjusted life-years (QALYs) approach. 

A detailed description of this QALY approach is provided in Appendix G of the final PM 

NAAQS RIA. For the analysis presented here, we use the same modified QALY approach to 

characterize the health-based cost effectiveness of the final standards. 

QALYs were developed to evaluate the effectiveness of individual medical treatments, 

and EPA is still evaluating the appropriate methods for CEA of environmental regulations. 

Agency concerns with the standard QALY methodology include the treatment of people with 

fewer years to live (the elderly); fairness to people with preexisting conditions that may lead 

to reduced life expectancy and reduced quality of life; and how the analysis should best 

account for nonhealth benefits, such as improved visibility. 

The Institute of Medicine (a member institution of the National Academies of Science) 

established the Committee to Evaluate Measures of Health Benefits for Environmental, 

Health, and Safety Regulation to assess the scientific validity, ethical implications, and 

practical utility of a wide range of effectiveness measures used or proposed in CEA. This 

committee prepared a report titled “Valuing Health for Regulatory Cost-Effectiveness 

Analysis,” which concluded that CEA is a useful tool for assessing regulatory interventions to 

promote human health and safety, although not sufficient for informed regulatory decisions 

(Miller, Robinson, and Lawrence, 2006).63 They emphasized the need for additional data and 

methodological improvements for CEA analyses, and urged greater consistency in the 

reporting of assumptions, data elements, and analytic methods. They also provided a number 

of recommendations for the conduct of regulatory CEA analyses. EPA is evaluating these 

recommendations and will determine a response for upcoming analyses. 

 The methodology derived from the final PM NAAQS analysis is not intended to stand 

as precedent either for future air pollution regulations or for other EPA regulations where it 

may be inappropriate. It is intended solely to demonstrate one particular approach to 

estimating the cost-effectiveness of reductions in ambient PM2.5 in achieving improvements in 
Cost-Benefit Analysis 

6-55 

public health. Reductions in ambient PM2.5 likely will have other health and environmental 

benefits that will not be reflected in this CEA. Other EPA regulations affecting other aspects 

of environmental quality and public health may require additional data and models that may 

preclude the development of similar health-based CEAs. A number of additional 

methodological issues must be considered when conducting CEAs for environmental policies, 

including treatment of nonhealth effects, aggregation of acute and long-term health impacts, 

and aggregation of life extensions and quality-of-life improvements in different populations. 

The appropriateness of health-based CEA should be evaluated on a case-by-case basis subject 

to the availability of appropriate data and models, among other factors. 

 The final locomotive and marine standards are expected to result in substantial 

reductions in potential population exposure to ambient concentrations of PM by 2030. The 

benefit-cost analysis presented in Chapter 6 of the RIA shows that the standards will achieve 

substantial health benefits whose monetized value far exceeds costs (net benefits are between 

$8.5 and $21 billion in 2030, based on empirically derived estimates of PM mortality and 

using a 3 percent discount rate). Despite the risk of oversimplifying benefits, cautiouslyinterpreted cost-effectiveness calculations may provide further evidence of whether the costs 

associated with the final standards are a reasonable health investment for the nation. 

 This analysis provides estimates of commonly used health-based effectiveness 

measures, including lives saved, life years saved (from reductions in mortality risk), and 

QALYs saved (from reductions in morbidity risk) associated with the reduction of ambient 

PM2.5 due to the final standards. In addition, we use an alternative aggregate effectiveness 

metric, Morbidity Inclusive Life Years (MILY) to address some of the concerns about 

aggregation of life extension and quality-of-life impacts. It represents the sum of life years 

gained due to reductions in premature mortality and the QALY gained due to reductions in 

chronic morbidity. This measure may be preferred to existing QALY aggregation approaches 

because it does not devalue life extensions in individuals with preexisting illnesses that reduce 

quality of life. However, the MILY measure is still based on life years and thus still 

inherently gives more weight to interventions that reduce mortality and morbidity impacts for 

younger populations with higher remaining life expectancy. This analysis focuses on life 

extensions and improvements in quality of life through reductions in two diseases with 

chronic impacts: chronic bronchitis (CB) and nonfatal acute myocardial infarctions. Monte 

Carlo simulations are used to propagate uncertainty in several analytical parameters and 

characterize the distribution of estimated impacts. While the benefit-cost analysis presented 

in the RIA characterizes mortality impacts using a number of different sources for the PM 

mortality effect estimate, for this analysis, we focus on the mortality results generated using 

the effect estimate derived from the Pope et al. (2002) study. 

Presented in three different metrics, the analysis suggests the following: 

• In 2020, the locomotive and marine standards will result in: 
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6-56 

• 490 (95% CI: 190 – 790) premature PM-related deaths avoided, or 

• 5,500 (95% CI: 2,100 – 8,800) PM-related life years gained (discounted at 3 

percent), or 

• 7,800 (95% CI: 2,600 – 14,000) MILYs gained (discounted at 3 percent). 

• In 2030, the final standards will result in: 

• 1,100 (95% CI: 4400 – 1,800) premature PM-related deaths avoided, or 

• 12,000 (95% CI: 4,800 – 20,000) PM-related life years gained (discounted at 3 

percent), or 

• 17,000 (95% CI: 5,700 – 31,000) MILYs gained (discounted at 3 percent). 

• Using a 7 percent discount rate, mean discounted life years gained are 4,100 for the 

final standards in 2020 and 9,300 in 2030; mean MILYs gained are 5,800 in 2020 and 

13,000 in 2030. (The estimates of premature deaths avoided are not affected by the 

discount rate.) 

• The associated reductions in CB and nonfatal acute myocardial infarctions will reduce 

medical costs by approximately $150 million in 2020 and $340 million in 2030 based 

on a 3 percent discount rate, or $130 million in 2020 and $300 million in 2030 based 

on a 7 percent discount rate. 

• Other health and visibility benefits are valued at $210 million in 2020 and $490 

million in 2030. 

Direct private compliance costs for the final standards are $340 million in 2020 and $750 

million in 2030 (see Chapter 7 of this RIA for more discussion of the cost estimates). 

Therefore, the net costs (private compliance costs minus avoided cost of illness minus other 

benefits) are negative, indicating that the final standards result in cost savings. As such, 

traditional cost-effectiveness ratios are not informative. However, it is possible to calculate 

the maximum costs for the rule that would still result in cost-effective improvements in public 

health compared with standard benchmarks of $50,000 and $100,000 per MILY: 

• Taking into account avoided medical costs and other benefits, annual costs of the final 

standards would need to exceed $750 million (95% CI: $360 million – $1,200 million) in 

2020 and $1.7 billion (95% CI: $0.8 billion – $2.8 billion) in 2030 to have a cost per 

MILY that exceeds a benchmark of $50,000, based on a 3 percent discount rate. 

• Annual costs of the final standards would need to exceed $1.1 billion (95% CI: $0.5 

billion – $1.9 billion) in 2020 and $2.6 billion (95% CI: $1.1 billion – $4.3 billion) in 
Cost-Benefit Analysis 

6-57 

2030 to have a cost per MILY that exceeds a benchmark of $100,000, based on a 3 

percent discount rate. 

• Using a 7 percent discount rate, annual costs of the final standards would need to exceed 

$630 million in 2020 and $1.4 billion in 2030 to have a cost per MILY that exceeds a 

benchmark of $50,000, and would need to exceed $0.9 billion in 2020 and $2.1 billion in 

2030 to have a cost per MILY that exceeds a benchmark of $100,000. 

Given costs of $340 million and $750 million in 2020 and 2030, respectively, the 

locomotive and marine standards are clearly a very cost-effective way to achieve 

improvements in public health. 

 Tables 6.A-1 through 6.A-9 present the intermediate and summary results of the 

health-based CEA of the final standards. Note that the methods used to generate these 

estimates follow the same methods as those explained in Appendix G of the final PM NAAQS 

RIA. We refer the reader to that document for more details about this modified QALY 

approach to health-based CEA. 

Table 6.A-1: Estimated Reduction in Incidence of All-cause Premature Mortality Associated with the 

Final Standards in 2020 and 2030 

Reduction in All-Cause Premature Mortality 

(95% CI) 

Age Interval 2020 2030 

30 – 34 4 

(2-7) 

8 

(3-13) 

35 – 44 13 

(5-21) 

26 

(10-42) 

45 – 54 26 

(10-43) 

47 

(18-75) 

55 – 64 65 

(25-105) 

110 

(44-180) 

65 – 74 106 

(41-170) 

250 

(98-400) 

75 – 84 120 

(48-200) 

350 

(140-550) 

85+ 150 

(59-240) 

340 

(130-540) 

Total 490 

(190-790) 

1,100 

(440-1,800) 
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Table 6.A-2: Estimated Life Years Gained from All-cause Premature Mortality Risk Reductions 

Associated with the Final Standards in 2020 and 2030 

Life Years Gained from Mortality Risk Reduction, 3% Discount Rate 

(95% CI) 

Age Interval 2020 2030 

25 – 34 120 

(45-190) 

210 

(83-340) 

35 – 44 330 

(130-530) 

650 

(260-1,100) 

45 – 54 580 

(230-930) 

1,000 

(400-1,600) 

55 – 64 1,200 

(470-1,900) 

2,000 

(800-3,300) 

65 – 74 1,500 

(600-2,400) 

3,500 

(1,400-5,700) 

75 – 84 1,200 

(460-1,900) 

3,400 

(1,300-5,400) 

85+ 600 

(230-960) 

1,300 

(520-2,100) 

Total 5,500 

(2,100-8,800) 

12,000 

(4,800-20,000) 

Table 6.A-3: Estimated Reduction in Incidence of Chronic Bronchitis Associated with the Final 

Standards in 2020 and 2030 

Reduction in Incidence (95% Confidence Interval) 

Age Interval 2020 2030 

25 – 34 57 

(10-100) 

120 

(21-210) 

35 – 44 62 

(11-110) 

140 

(26-250) 

45 – 54 58 

(11-110) 

120 

(22-210) 

55 – 64 59 

(11-110) 

110 

(21-210) 

65 – 74 41 

(8-74) 

110 

(19-190) 

75 – 84 20 

(4-37) 

62 

(12-110) 

85+ 9 

(2-16) 

22 

(4-39) 

Total 310 

(56-560) 

680 

(130-1,200) 
Cost-Benefit Analysis 

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Table 6.A-4: QALYs Gained per Avoided Incidence of CB 

Age Interval QALYs Gained per Incidence 

Start Age End Age Undiscounted Discounted (3%) 

25 34 12.15 

(4.40-19.95) 

6.52 

(2.36-10.71) 

35 44 9.91 

(3.54-16.10) 

5.94 

(2.12-9.66) 

45 54 7.49 

(2.71-12.34) 

5.03 

(1.82-8.29) 

55 64 5.36 

(1.95-8.80) 

4.03 

(1.47-6.61) 

65 74 3.40 

(1.22-5.64) 

2.84 

(1.02-4.71) 

75 84 2.15 

(0.77-3.49) 

1.92 

(0.69-3.13) 

85+ 0.79 

(0.27-1.29) 

0.77 

(0.26-1.25) 

Table 6.A-5: Estimated Reduction in Nonfatal Acute Myocardial Infarctions Associated with the Final 

Standards in 2020 and 2030 

Reduction in Incidence (95% Confidence Interval) 

Age Interval 2020 2030 

18 – 24 1 

(0-1) 

1 

(1-2) 

25 – 34 1 

(0-1) 

1 

(1-2) 

35 – 44 33 

(18-48) 

73 

(40-110) 

45 – 54 100 

(56-150) 

210 

(110-310) 

55 – 64 250 

(130-360) 

470 

(260-690) 

65 – 74 290 

(160-430) 

760 

(410-1,100) 

75 – 84 220 

(120-320) 

670 

(360-970) 

85+ 120 

(64-170) 

290 

(160-430) 

Total 1,000 

(550-1,500) 

2,500 

(1,300-3,600) 
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Table 6.A-6: QALYs Gained per Avoided Nonfatal Myocardial Infarction 

Age Interval QALYs Gained per Incidence 

Start Age End Age Undiscounted Discounted (3%) 

18 24 4.18 

(1.24-7.09) 

2.17 

(0.70-3.62) 

25 34 3.48 

(1.09-5.87) 

2.00 

(0.68-3.33) 

35 44 2.81 

(0.88-4.74) 

1.79 

(0.60-2.99) 

45 54 2.14 

(0.67-3.61) 

1.52 

(0.51-2.53) 

55 64 1.49 

(0.42-2.52) 

1.16 

(0.34-1.95) 

65 74 0.97 

(0.30-1.64) 

0.83 

(0.26-1.39) 

75 84 0.59 

(0.20-0.97) 

0.54 

(0.19-0.89) 

85+ 0.32 

(0.13-0.50) 

0.31 

(0.13-0.49) 

Table 6.A-7. Estimated Gains in 3 Percent Discounted MILYs Associated with the Final Standards in 

2020a

Age Life Years Gained 

from Mortality Risk 

Reductions 

(95% CI) 

QALY Gained from 

Reductions in Chronic 

Bronchitis 

(95% CI) 

QALY Gained from 

Reductions in Acute 

Myocardial Infarctions 

(95% CI) 

Total Gain in 

MILYs 

(95% CI) 

18–24 - - 1 

(0-3) 

1 

(0-3) 

25–34 120 

(45-190) 

370 

(52-850) 

1 

(0-2) 

490 

(97-1,100) 

35–44 330 

(130-530) 

370 

(50-870) 

58 

(15-120) 

750 

(190-1,500) 

45–54 580 

(230-930) 

290 

(41-700) 

160 

(40-320) 

1,000 

(310-1,900) 

55–64 1,200 

(470-1,900) 

240 

(32-560) 

280 

(70-570) 

1,700 

(570-3,000) 

65–74 1,500 120 240 1,800 
Cost-Benefit Analysis 

6-61 

(580-2.400) (16-280) (53-490) (660-3,200) 

75–84 1,200 

(460-1,900) 

39 

(5-92) 

120 

(31-230) 

1,300 

(500-2,200) 

85+ 600 

(230-960) 

7 

(1-18) 

35 

(10-69) 

640 

(250-1,000) 

Total 5,500 

(2,100-8,800) 

1,400 

(200-3,400) 

880 

(230-1,800) 

7,800 

(2,600-14,000) a

 Note that all estimates have been rounded to two significant digits. 

Table 6.A-8: Estimated Gains in 3 Percent Discounted MILYs Associated with the Final Standards in 

2030a

Age Life Years Gained 

from Mortality Risk 

Reductions 

(95% CI) 

QALY Gained from 

Reductions in 

Chronic Bronchitis 

(95% CI) 

QALY Gained from 

Reductions in Acute 

Myocardial Infarctions 

(95% CI) 

Total Gain in 

MILYs 

(95% CI) 

18–24 - - 3 

(1-5) 

3 

(1-5) 

25–34 210 

(83-340) 

760 

(110-1,800) 

2 

(1-5) 

970 

(190-2,100) 

35–44 650 

(260-1,100) 

820 

(110-2,000) 

130 

(34-260) 

1,600 

(400-3,300) 

45–54 1,000 

(400-1,600) 

600 

(82-1,400) 

310 

(80-640) 

1,900 

(560-3,700) 

55–64 2,000 

(800-3,300) 

460 

(63-1,100) 

530 

(130-1,100) 

3,000 

(1,000-5,400) 

65–74 3,500 

(1,400-5,700) 

300 

(42-710) 

620 

(160-1,300) 

4,500 

(1,600-7,700) 

75–84 3,400 

(1,300-5,400) 

120 

(16-290) 

350 

(94-710) 

3,800 

(1,400-6,400) 

85+ 1,300 

(520-2,100) 

16 

(2-39) 

86 

(25-170) 

1,400 

(550-2,400) 

Total 12,000 

(4,800-20,000) 

3,100 

(420-7,200) 

2,000 

(530-4,100) 

17,000 

(5,700-31,000) a

 Note that all estimates have been rounded to two significant digits. 
Regulatory Impact Analysis 

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Table 6.A-9: Summary of Health-Based Cost Effectiveness Results for the Final Standards in 2020 and 

2030a

Result Using 3% Discount Rate (95% Confidence Interval) 

2020 2030 

Life years gained from mortality 

risk reductions 

5,500 

(2,100-8,800) 

12,000 

(4,800-20,000) 

QALY gained from reductions in 

chronic bronchitis 

1,400 

(200-3,400) 

3,100 

(420-7,200) 

QALY gained from reductions in 

acute myocardial infarctions 

880 

(230-1,800) 

2,000 

(530-4,100) 

Total gain in MILYs 7,800 

(2,600-14,000) 

17,000 

(5,700-31,000) 

Avoided cost of illness 

 Chronic bronchitis $37 Million 

($6.9 - $68 Million) 

$78 Million 

($14 - $140 Million) 

 Nonfatal AMI $110 Million 

($29 - $250 Million) 

$260 Million 

($64 - $580 Million) 

Other benefits (based on COI and 

WTP estimates) 

$210 Million 

($190 - $220 Million) 

$490 Million 

($460 – $520 Million) 

Implementation strategy costsb $340 Million $750 Million 

Net cost per MILY Cost Savings Cost Savings a

 All summary results are reported at a precision level of two significant digits to reflect limits in the 

precision of the underlying elements. b

 Costs are the private firm costs of control, as discussed in Chapter 7, and reflect discounting using firm 

specific costs of capital. 
Cost-Benefit Analysis 

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Appendix 6.B: Sensitivity Analyses of Key Parameters in the Benefits Analysis 

The primary analysis presented in Chapter 6 is based on our current interpretation of 

the scientific and economic literature. That interpretation requires judgments regarding the 

best available data, models, and modeling methodologies and the assumptions that are most 

appropriate to adopt in the face of important uncertainties and resource limitations. The 

majority of the analytical assumptions used to develop the primary estimates of benefits have 

been used to support similar rulemakings and approved by EPA=s Science Advisory Board 

(SAB). Both EPA and the SAB recognize that data and modeling limitations as well as 

simplifying assumptions can introduce significant uncertainty into the benefit results and that 

alternative choices exist for some inputs to the analysis, such as the mortality C-R functions. 

This appendix supplements our primary estimates of benefits with a series of 

sensitivity calculations that use other sources of health effect estimates and valuation data for 

key benefits categories. The supplemental estimates examine sensitivity to both valuation 

issues and for physical effects issues. These supplemental estimates are not meant to be 

comprehensive. Rather, they reflect some of the key issues identified by EPA or commentors 

as likely to have a significant impact on total benefits. The individual adjustments in the 

tables should not simply be added together because: 1) there may be overlap among the 

alternative assumptions; and 2) the joint probability among certain sets of alternative 

assumptions may be low. 

6.B.1 Premature Mortality – Alternative Threshold Analysis 

To consider the impact of a threshold in the response function for the chronic 

mortality endpoint, we have constructed a sensitivity analysis by assigning different cutpoints 

below which changes in PM2.5 are assumed to have no impact on premature mortality. In 

applying the cutpoints, we have adjusted the mortality function slopes accordingly.EE Five 

cutpoints (including the base case assumption) were included in the sensitivity analysis: (a) 14 

μg/m3 (assumes no impacts below the alternative annual NAAQS), (b) 12 μg/m3 (c) 10 

μg/m3 (reflects comments from CASAC, 2005) 64, (d) 7.5 μg/m3 (reflects recommendations 

from SAB-HES to consider estimating mortality benefits down to the lowest exposure levels 

considered in the Pope 2002 study used as the basis for modeling chronic mortality) 65 and (e) 

background or 3 μg/m3 (reflects NRC recommendation to consider effects all the way to 

background).66 We repeat this sensitivity analysis for the RIA of the final standards, the 

results of which can be found in Table 6B-1. 

EE Note that this analysis only adjusted the mortality slopes for the 10 μg/m3

, 12 μg/m3 and 14 μg/m3 cutpoints 

since the 7.5 μg/m3

 and background cutpoints were at or below the lowest measured exposure levels reported in 

the Pope et al. (2002) study for the combined exposure dataset. 
Regulatory Impact Analysis 

Table 6B-1. PM-Related Mortality Benefits of the Final Standards: Cutpoint Sensitivity 

Analysis Using the ACS Study (Pope et al., 2002)a

PM Mortality Incidence Certainty that Benefits 

are At Least Specified 

Value 

Level of 

Assumed 

Threshold 

2020 2030 

More Certain that 

Benefits Are at Least 

as Large 

14 μg/m3 b

 140 320 

12 μg/m3 200 

6-64 

490 

10 μg/m3 c 490 1,100 

7.5 μg/m3 d 600 1,400 

Less Certain that 

Benefits Are at Least 

as Large 

3 μg/m3 e 640 1,500 

a

 Note that this table only presents the effects of a cutpoint on PM-related mortality incidence. 

b

 Alternative annual PM NAAQS. 

c Primary threshold assumption based on CASAC (2005).85

d

 SAB-HES (2004)86

e

 NAS (2002)87

6.B.2 Premature Mortality - Alternative Lag Structures 

Over the last ten years, there has been a continuing discussion and evolving advice 

regarding the timing of changes in health effects following changes in ambient air pollution. 

It has been hypothesized that some reductions in premature mortality from exposure to 

ambient PM2.5 will occur over short periods of time in individuals with compromised health 

status, but other effects are likely to occur among individuals who, at baseline, have 

reasonably good health that will deteriorate because of continued exposure. No animal 

models have yet been developed to quantify these cumulative effects, nor are there 

epidemiologic studies bearing on this question. 

The SAB-HES has recognized this lack of direct evidence. However, in early advice, 

they also note that “although there is substantial evidence that a portion of the mortality effect 

of PM is manifest within a short period of time, i.e., less than one year, it can be argued that, 

if no lag assumption is made, the entire mortality excess observed in the cohort studies will be 

analyzed as immediate effects, and this will result in an overestimate of the health benefits of 

improved air quality. Thus some time lag is appropriate for distributing the cumulative 

mortality effect of PM in the population,” (EPA-SAB-COUNCIL-ADV-00-001, 1999, p. 9).67 

In recent advice, the SAB-HES suggests that appropriate lag structures may be developed 

based on the distribution of cause-specific deaths within the overall all-cause estimate (EPASAB-COUNCIL-ADV-04-002, 2004). They suggest that diseases with longer progressions 
Cost-Benefit Analysis 

6-65 

should be characterized by longer-term lag structures, while air pollution impacts occurring in 

populations with existing disease may be characterized by shorter-term lags. 

A key question is the distribution of causes of death within the relatively broad 

categories analyzed in the long-term cohort studies. Although it may be reasonable to assume 

the cessation lag for lung cancer deaths mirrors the long latency of the disease, it is not at all 

clear what the appropriate lag structure should be for cardiopulmonary deaths, which include 

both respiratory and cardiovascular causes. Some respiratory diseases may have a long period 

of progression, while others, such as pneumonia, have a very short duration. In the case of 

cardiovascular disease, there is an important question of whether air pollution is causing the 

disease, which would imply a relatively long cessation lag, or whether air pollution is causing 

premature death in individuals with preexisting heart disease, which would imply very short 

cessation lags. 

The SAB-HES provides several recommendations for future research that could 

support the development of defensible lag structures, including using disease-specific lag 

models and constructing a segmented lag distribution to combine differential lags across 

causes of death (EPA-SAB-COUNCIL-ADV-04-002, 2004). The SAB-HES indicated 

support for using “a Weibull distribution or a simpler distributional form made up of several 

segments to cover the response mechanisms outlined above, given our lack of knowledge on 

the specific form of the distributions,” (EPA-SAB-COUNCIL-ADV-04-002, 2004, p. 24). 

However, they noted that “an important question to be resolved is what the relative 

magnitudes of these segments should be, and how many of the acute effects are assumed to be 

included in the cohort effect estimate,” (EPA-SAB-COUNCIL-ADV-04-002, 2004, p. 24-25). 

Since the publication of that report in March 2004, EPA has sought additional clarification 

from this committee. In its follow-up advice provided in December 2004, the SAB suggested 

that until additional research has been completed, EPA should assume a segmented lag 

structure characterized by 30 percent of mortality reductions occurring in the first year, 50 

percent occurring evenly over years 2 to 5 after the reduction in PM2.5, and 20 percent 

occurring evenly over the years 6 to 20 after the reduction in PM2.5

(EPA-COUNCIL-LTR-05-001, 2004).68 The distribution of deaths over the latency period is 

intended to reflect the contribution of short-term exposures in the first year, cardiopulmonary 

deaths in the 2- to 5-year period, and long-term lung disease and lung cancer in the 6- to 20-

year period. Furthermore, in their advisory letter, the SAB-HES recommended that EPA 

include sensitivity analyses on other possible lag structures. In this appendix, we investigate 

the sensitivity of premature mortality-reduction related benefits to alternative cessation lag 

structures, noting that ongoing and future research may result in changes to the lag structure 

used for the primary analysis. 

In previous advice from the SAB-HES, they recommended an analysis of 0-, 8-, and 

15-year lags, as well as variations on the proportions of mortality allocated to each segment in 

the segmented lag structure (EPA-SAB-COUNCIL-ADV-00-001, 1999, 
Regulatory Impact Analysis 

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(EPA-COUNCIL-LTR-05-001, 2004). The 0-year lag is representative of EPA=s assumption 

in previous RIAs. The 8- and 15-year lags are based on the study periods from the Pope et al. 

(1995)69 and Dockery et al. (1993)70 studies, respectively.FF However, neither the Pope et al. 

nor Dockery et al. studies assumed any lag structure when estimating the relative risks from 

PM exposure. In fact, the Pope et al. and Dockery et al. analyses do not support or refute the 

existence of a lag. Therefore, any lag structure applied to the avoided incidences estimated 

from either of these studies will be an assumed structure. The 8- and 15-year lags implicitly 

assume that all premature mortalities occur at the end of the study periods (i.e., at 8 and 15 

years). 

 In addition to the simple 8- and 15-year lags, we have added two additional sensitivity 

analyses examining the impact of assuming different allocations of mortality to the segmented 

lag of the type suggested by the SAB-HES. The first sensitivity analysis assumes that more of 

the mortality impact is associated with chronic lung diseases or lung cancer and less with 

acute cardiopulmonary causes. This illustrative lag structure is characterized by 20 percent of 

mortality reductions occurring in the first year, 50 percent occurring evenly over years 2 to 5 

after the reduction in PM2.5, and 30 percent occurring evenly over the years 6 to 20 after the 

reduction in PM2.5. The second sensitivity analysis assumes the 5-year distributed lag 

structure used in previous analyses, which is equivalent to a three-segment lag structure with 

50 percent in the first 2-year segment, 50 percent in the second 3-year segment, and 0 percent 

in the 6- to 20-year segment. 

The estimated impacts of alternative lag structures on the monetary benefits associated 

with reductions in PM-related premature mortality (estimated with the Pope et al. ACS impact 

function) are presented in Table 6B-2. These estimates are based on the value of statistical 

lives saved approach (i.e., $5.5 million per incidence) and are presented using both a 3 percent 

and 7 percent discount rate over the lag period. 

FF Although these studies were conducted for 8 and 15 years, respectively, the choice of the duration of the study 

by the authors was not likely due to observations of a lag in effects but is more likely due to the expense of 

conducting long-term exposure studies or the amount of satisfactory data that could be collected during this time 

period. 
Cost-Benefit Analysis 

Table 6B-2. Sensitivity of Benefits of Premature Mortality Reductions to Alternative Lag Assumptions 

(Relative to Primary Benefits Estimates of the Final Standards) 

Avoided Incidences 

(ACS; Pope et al., 2002)a Value 

(million 2006$)b

Description of Sensitivity Analysis 2020 2030 2020 2030 

Alternative Lag Structures for PM-Related Premature Mortality 

Primary 

30 percent of incidences occur in 1st

year, 50 percent in years 2 to 5, and 

20 percent in years 6 to 20 

 3% Discount Rate 490 1,100 $3,400 $8,100 

 7% Discount Rate 490 1,100 $3,100 $7,300 

None Incidences all occur in the first year 490 1,100 $3,800 $8,900 

8-year Incidences all occur in the 8th year 

 3% Discount Rate 490 1,100 $3,100 $7,300 

 7% Discount Rate 490 1,100 $2,400 $5,600 

15-year Incidences all occur in the 15th year 

 3% Discount Rate 490 1,100 $2,500 $5,900 

 7% Discount Rate 490 1,100 $1,500 $3,500 

Alternative 

Segmented 

20 percent of incidences occur in 1st 

year, 50 percent in years 2 to 5, and 

30 percent in years 6 to 20 

 3% Discount Rate 490 1,100 $3,300 $7,800 

 7% Discount Rate 490 1,100 $2,900 $6,800 

5-Year 

Distributed 

50 percent of incidences occur in 

years 1 and 2 and 50 percent in years 

2 to 5 

 3% Discount Rate 490 1,100 $3,600 $8,500 

 7% Discount Rate 490 1,100 $3,400 $8,000 a

 Incidences rounded to two significant digits. 

b

 Dollar values rounded to two significant digits. The alternative lag structure analysis presents benefits 

calculated using both a 3 percent and 7 percent discount rate. 

The results of the scaled alternative lag sensitivity analysis demonstrate that choice of 

lag structure can have a large impact on benefits. Because of discounting of delayed benefits, 

the lag structure may have a large downward impact on monetized benefits if an extreme 

assumption that no effects occur until after 15 years is applied. However, for most reasonable 

distributed lag structures, differences in the specific shape of the lag function have relatively 

small impacts on overall benefits. 

6.B.3 Visibility Benefits in Additional Class I Areas 

The Chestnut and Rowe (1990)71 study from which the primary visibility valuation 

estimates are derived only examined WTP for visibility changes in Class I areas (national 

parks and wilderness areas) in the southeast, southwest, and California. To obtain estimates 

of WTP for visibility changes at national parks and wilderness areas in the northeast, 

northwest, and central regions of the U.S., we have to transfer WTP values from the studied 

regions. This introduces additional uncertainty into the estimates. However, we have taken 

steps to adjust the WTP values to account for the possibility that a visibility improvement in 

6-67 
Regulatory Impact Analysis 

parks in one region is not necessarily the same environmental quality good as the same 

visibility improvement at parks in a different region. This may be due to differences in the 

scenic vistas at different parks, uniqueness of the parks, or other factors, such as public 

familiarity with the park resource. To take this potential difference into account, we adjusted 

the WTP being transferred by the ratio of visitor days in the two regions. 

Based on this benefits transfer methodology (implemented within the preference 

calibration framework discussed in Chapter 5 and Appendix I of the final PM NAAQS RIA), 

estimated additional visibility benefits in the northwest, central, and northeastern U.S. are 

provided in Table 6B-3. 

Table 6.B-3: Monetary Benefits Associated with Improvements in Visibility in Additional Federal Class I 

Areas in 2020 and 2030 (in millions of 2006$)a

Year Northwestb Centralc Northeastd Total 

2020 $13 $27 $9 $49 

2030 $34 $41 $32 $110 a All estimates are rounded to 2 significant digits. All rounding occurs after final summing of unrounded 

estimates. As such, totals will not sum across columns b Northwest Class I areas include Crater Lake, Mount Rainier, North Cascades, and Olympic national parks, 

and Alpine Lakes, Diamond Peak, Eagle Cap, Gearhart Mountain, Glacier Peak, Goat Rocks, Hells 

Canyon, Kalmiopsis, Mount Adams, Mount Hood, Mount Jefferson, Mount Washington, Mountain Lakes, 

Pasayten, Strawberry Mountain, and Three Sisters wilderness areas. c Central Class I areas include Craters of the Moon, Glacier, Grand Teton, Theodore Roosevelt, Badlands, 

Wind Cave, and Yellowstone national parks, and Anaconda-Pintlar, Bob Marshall, Bridger, Cabinet 

Mountains, Fitzpatrick, Gates of the Mountain, Lostwood, Medicine Lake, Mission Mountain, North 

Absaroka, Red Rock Lakes, Sawtooth, Scapegoat, Selway-Bitterroot, Teton, U.L. Bend, and Washakie 

wilderness areas. d Northeast Class I areas include Acadia, Big Bend, Guadalupe Mountains, Isle Royale, Voyageurs, and 

Boundary Waters Canoe national parks, and Brigantine, Caney Creek, Great Gulf, Hercules-Glades, Lye 

Brook, Mingo, Moosehorn, Presidential Range-Dry Roosevelt Campobello, Seney, Upper Buffalo, and 

Wichita Mountains wilderness areas. 

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Cost-Benefit Analysis 

6-69 

 

REFERENCES 

1

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This Page Intentionally Left Blank 
Economic Impact Analysis 

7-1 

CHAPTER 7: ECONOMIC IMPACT ANALYSIS 

7.1 Overview and Results ..............................................................................................7-2 

7.1.1 What is an Economic Impact Analysis?.........................................................7-2

7.1.2 What Methodology Did EPA Use in this Economic Impact Analysis? .......7-3

7.1.3 What Economic Sectors are Included in the Economic Impact Model?.....7-4

7.1.4 Summary of Results .........................................................................................7-9

7.2 Economic Methodology .........................................................................................7-20 

7.2.1 Behavioral Economic Models........................................................................7-20

7.2.2 What is the Economic Theory Underlying the EIM? .................................7-21

7.2.3 How Is the EIM Used to Estimate Economic Impacts?..............................7-28

7.3 EIM Data Inputs and Model Solution..................................................................7-37 

7.3.1 Market Equilibrium Conditions ...................................................................7-38

7.3.2 Compliance Costs ...........................................................................................7-46

7.3.3 Behavioral Parameters...................................................................................7-62

7.3.4 Economic Impact Model Structure...............................................................7-68

7.3.5 Estimating Impacts.........................................................................................7-70

7.4 Methods for Describing Uncertainty....................................................................7-70 

Appendix 7A: Impacts on Marine Engine Markets............................................7-76 

Appendix 7B: Impacts on the Equipment Markets............................................7-83 

Appendix 7C: Impacts on Transportation Service Markets............................7-105 

Appendix 7D: Time Series of Social Costs.........................................................7-108 

Appendix 7E: Model Equations..........................................................................7-112 

7E.1 Economic Model Equations..............................................................................7-112 

7E.1.1 Supply Equations.......................................................................................7-112

7E.1.2 Demand Equations ...................................................................................7-113

7E.1.3 Market Equilibrium Conditions..............................................................7-114

7E.2 Computing With-Regulation Equilibrium Conditions within Spreadsheet.7-114 

7E.3 Social Costs: Consumer and Producer Economic Welfare Calculations.....7-116 

Appendix 7F: Elasticity Parameters for Economic Impact Modeling ............7-118 

Appendix 7G: Initial Market Equilibrium - Price Forecasts..........................7-124 

Appendix 7H: Sensitivity Analysis .....................................................................7-126 

7H.1 Model Elasticity Parameters............................................................................7-127 

7H.2 Fixed Cost Shift Scenario .................................................................................7-131 

7H.3 Marine Operating Cost Scenario.....................................................................7-134 
Regulatory Impact Analysis 

7-2 

CHAPTER 7: Economic Impact Analysis 

We prepared an Economic Impact Analysis (EIA) to estimate the economic impacts of 

the new emission control program on the locomotive and marine diesel engine and vessel 

markets. In this chapter we describe the Economic Impact Model (EIM) we developed to 

estimate the market-level changes in prices and outputs for affected markets, the social costs 

of the program, and the expected distribution of those costs across stakeholders. We also 

present the result of our analysis. 

We estimate the social costs of the new program to be approximately $738 million in 

2030.A, B

 The rail sector is expected to bear about 62.5 percent of the social costs of the 

program in 2030, and the marine sector is expected to bear about 37.5 percent. In each of 

these two sectors, these social costs are expected to be born primarily by producers and users 

of locomotive and marine transportation services (62 and 36 percent, respectively). The 

remaining 2 percent is expected to be borne by locomotive, marine engine, and marine vessel 

manufacturers and fishing and recreational vessel users. 

The impact of these costs on society are expected to be minimal, with the prices of rail 

and marine transportation services in 2030 estimated to increase by less about 0.6 percent for 

locomotive transportation services and about 1.1 percent for marine transportation services. 

7.1 Overview and Results 

7.1.1 What is an Economic Impact Analysis? 

An EIA is prepared to inform decision makers about the potential economic 

consequences of a regulatory action. The analysis consists of estimating the social costs of a 

regulatory program and the distribution of these costs across stakeholders. These estimated 

social costs can then be compared with estimated social benefits (as presented in Chapter 6). 

As defined in EPA's Guidelines for Preparing Economic Analyses, social costs are the value 

of the goods and services lost by society resulting from a) the use of resources to comply with 

and implement a regulation and b) reductions in output.1

 In this analysis, social costs are 

explored in two steps. In the market analysis, we estimate how prices and quantities of goods 

and services affected by the new emission control program can be expected to change once 

the program goes into effect. In the economic welfare analysis, we look at the total social 

costs associated with the program and their distribution across key stakeholders. 

 

A All estimates presented in this section are in 2005$. 

B

 The estimated 2030 social welfare cost of $738 million is based on draft compliance costs for this final rule, 

which estimated $740 million engineering costs in 2030 (see Table 7-3). The final compliance cost estimate for 

2030 is somewhat higher, at $759 million; see Section 7.3.2 for an explanation of the difference. This difference 

is not expected to have an impact on the results of the market analysis or on the expected distribution of social 

costs among stakeholders. 
Economic Impact Analysis 

7-3 

7.1.2 What Methodology Did EPA Use in this Economic Impact Analysis? 

The EIM is the behavioral model we developed to estimate market-level impacts 

(price and quantity changes) and social welfare costs associated with an emission control 

program. The model relies on basic microeconomic theory to simulate how producers and 

consumers of products and services affected by the emission requirements can be expected to 

respond to an increase in production costs as a result of the new emission control program. 

The economic theory that underlies the model is described in detail in Section 7.2. 

The EIM is designed to estimate the economic impacts of the new program by 

simulating economic behavior. This is done by creating a model of the initial, pre-control 

market for a product, shocking that model by the estimated compliance costs, and observing 

the impacts on the market. At the initial, pre-control market equilibrium, a market is 

characterized by a price and quantity combination at which producers are willing to produce 

the same amount of a product that consumers are willing to purchase at that price (supply is 

equal to demand). The control program under consideration would increase the production 

costs of affected goods by the amount of the compliance costs. This generates a "shock" to 

the initial equilibrium market conditions (a change in supply). Producers of affected products 

will try to pass some or all of the increased production costs on to the consumers of these 

goods through price increases. In response to the price increases, consumers will decrease 

their demand for the affected good (a change in the quantity demanded). Producers will react 

to the decrease in quantity demanded by decreasing the quantity they produce; the market will 

react by setting a higher price for those fewer units. These interactions continue until a new 

market equilibrium price and quantity combination is achieved. The amount of the 

compliance costs that can be passed on to consumers is ultimately limited by the price 

sensitivity of purchasers and producers in the relevant market (represented by the price 

elasticity of demand and supply). The EIM explicitly models these behavioral responses and 

estimates new equilibrium prices and output and the resulting distribution of social costs 

across these stakeholders (producers and consumers). 

The EIM is a behavioral model. The estimated social costs of this emission control 

program are a function of the ways in which producers and consumers of the engines and 

equipment affected by the standards change their behavior in response to the costs incurred in 

complying with the standards. These behavioral responses are incorporated in the EIM 

through the price elasticity of supply and demand (reflected in the slope of the supply and 

demand curves), which measure the price sensitivity of consumers and producers. An 

“inelastic” price elasticity (less than one) means that supply or demand is not very responsive 

to price changes (a one percent change in price leads to less than one percent change in 

demand). An “elastic” price elasticity (more than one) means that supply or demand is 

sensitive to price changes (a one percent change in price leads to more than one percent 

change in demand). A price elasticity of one is unit elastic, meaning there is a one-to-one 

correspondence between a change in price and change in demand. The price elasticities used 

in this analysis are described in Section 7.3 and are either from peer-reviewed literature or 

were estimated using well-established econometric methods. It should be noted that the price 

elasticity of demand for the locomotive and marine engine and vessel markets is internally 

derived from the rail and marine transportation service markets as part of the process of 

running the model. This is an important feature of the EIM, which allows it to link the engine 
Regulatory Impact Analysis 

7-4 

and equipment components of each model and simulate how compliance costs can be 

expected to ripple through the affected markets. 

7.1.3 What Economic Sectors are Included in the Economic Impact Model? 

In this EIA we estimate the impacts of the new emission control program on two broad 

sectors: rail and marine. The characteristics of the markets analyzed that are relevant to the 

EIM are summarized in Table 7-1, and described in more detail in Section 7.3. 

Table 7-1. Summary of Markets in Economic Impact Model 

Model Dimension Rail Sector Marine Sector 

Description of Markets: 

Supply 

Locomotive: locomotive 

manufacturers (integrated 

manufacturers); 3 categories 

 Line Haul 

 Passenger 

 Switcher 

Note: Passenger and switcher markets 

are combined into one market in this 

analysis 

Rail Transportation Services: Entities 

that provide rail transportation 

services (railroads, primarily Class I) 

Marine Engines: 

 3 Applications

 Commercial Propulsion 

 Recreational Propulsion 

 Auxiliary 

 7 Engine Sizes 

 Small: 

 < 50 hp 

 Category 1: 

 50-200 hp 

 200-400 hp 

 400-800 hp 

 800-2,000 hp 

 > 2,000 hp 

 Category 2: 

 800-2,000 hp 

 > 2,000 hp 

Marine Vessels: 7 Applications 

 Tug/tow/pushboats 

 Cargo vessels 

 Ferry vessels 

 Supply/crew boats 

 Other commercial vessels 

 Fishing boats 

 Recreational boats 

Marine Transportation Services: Entities 

that provide marine transportation services 

(excludes small fishing and recreational 

vessels) 

 

Description of Markets: 

Demand 

Locomotive: Railroads (primarily 

Class I) 

Rail transportation services: Entities 

that use rail transportation services 

(power, chemical, agricultural 

companies; personal transportation) 

Marine Engines: Vessel manufacturers 

Marine Vessels: Marine vessel users 

(owners of all types of marine vessels) 

Marine transportation services: Entities 

that use marine transportation services 

(power, chemical, agricultural companies; 

personal transportation) 

Geographic Scope 50 states 50 states 

Market Structure Perfectly competitive Perfectly competitive 
Economic Impact Analysis 

7-5 

Model Dimension Rail Sector Marine Sector 

Baseline Population Same as locomotive inventory 

analysis 

PSR 2002 OE Link Sales Database 

Growth Projections Based on projected fuel consumption 

from Energy Information Agency

Commercial marine: 0.9% (0.009); 

Recreational marine: Based on EPA’s 

Nonroad Model 

Supply Elasticity Locomotives (all): 2.7 (elastic) 

Rail Transportation Market: 1.6 

(inealastic) 

Engines: 3.8 (elastic) 

Vessels: 2.3 (elastic) 

Marine Transportation Market: 1.6 

(inelastic) 

Demand Elasticity Locomotives (all): Derived 

Rail Transportation Market: -0.5 

(inelastic) 

Engines: Derived 

Vessels: 

Commercial: Derived 

Recreational and small Fishing : -2.0 

 (elastic) 

Marine Transportation Market: -0.5 

(inelastic) 

Regulatory Shock Locomotive Market: direct engine 

and equipment compliance costs cause 

shift in supply function 

Rail Transportation Market: direct 

operating and remanufacturing 

compliance costs, in addition to higher 

locomotive prices, cause shift in 

supply function 

Marine diesel engine: direct engine 

compliance costs cause shift in supply 

function 

Marine vessels: direct vessel compliance 

costs, in addition to higher engine prices, 

cause shift in supply function 

Marine Transportation Market: direct 

operating costs in addition to higher vessel 

prices cause shift in supply function 

7.1.3.1 Rail Sector Component 

The rail sector component of the EIM is a two-level model consisting of suppliers and 

users of locomotives and rail transportation equipment and services. 

Locomotive Market. The locomotive market consists of locomotive manufacturers 

(line haul, switcher, and passenger) on the supply side and railroads on the demand side. The 

vast majority of locomotives built annually are for line haul applications; a small number of 

passenger locomotives are built annually, and even fewer switchers. The locomotive market 

is characterized by integrated manufacturers (the engine and locomotive are made by the same 

manufacturer) and therefore the engine and equipment impacts are modeled together. The 

EIM does not distinguish between power bands for locomotives. This is because while there 

is some variation in power for different engine models, the range is not large. On average line 

haul locomotives are typically about 4,000 hp, passenger locomotives are about 3,000 hp, and 

switchers are about 2,000 hp. 
Regulatory Impact Analysis 

7-6 

Recently, a new switcher market is emerging in which manufacturers are expected to 

be less integrated, with the manufacturer of the engine expected to be different from the 

manufacturer of the switcher.C Because the characteristics of this new market are speculative 

at this time, the switcher market component of the EIM is modeled in the same way as line 

haul locomotives for future years (integrated manufacturers; same behavioral parameters), but 

with different baseline equilibrium prices and quantities and different compliance costs. 

Consistent with the cost analysis, the passenger market is combined with the switcher 

market in this EIA. 

Rail Transportation Services. The rail transportation services market consists of 

entities that provide and utilize rail transportation services. On this supply side, these are the 

railroads. On the demand side, these are rail transportation service users such as the chemical 

and agricultural industries and the personal transportation industry. Most of the goods moved 

by rail are bulk goods such as coal, chemicals, minerals, petroleum, and the like. About 26 

percent of the carloads in 2004 were miscellaneous mixed shipments (mostly intermodal, e.g., 

containers) and about 6 percent were motor vehicles and equipment. This means that about 

68 percent of the goods moved by rail are production inputs.2

 The EIM does not estimate the 

economic impact of the new emission control program on ultimate finished goods markets 

that use rail transportation services as inputs. This is because transportation services are only 

a small portion of the total variable costs of goods and services manufactured using these bulk 

inputs. Also, changes in prices of transportation services due to the estimated compliance 

costs are not expected to be large enough to affect the prices and output of goods that use rail 

transportation services as an input. 

7.1.3.2 Marine Sector Component 

The marine sector component of the EIM distinguishes between engine, vessel, and 

ultimate user markets (marine transportation service users, fishing users, recreational users). 

This is because, in contrast to the locomotive market, manufacturers in the diesel marine 

market are not integrated. Marine diesel engines and vessels are manufactured by different 

entities. 

Marine Engine Market. The marine engine markets consist of marine engine 

manufacturers on the supply side and vessel manufacturers on the demand side. The model 

distinguishes between three types of engines, commercial propulsion, recreational propulsion, 

and auxiliary. Engines are broken out into eight categories based on horsepower and 

displacement. 

• Small marine diesel engines 

 

C

 Until recently, switchers have typically been converted line haul locomotives and very few, if any, new 

dedicated switchers were built in any year. Recently, however, the power and other characteristics of line haul 

locomotives have made them less attractive for switcher usage. Their high power means they consume more 

fuel than smaller locomotives, and they have less attractive line-of-sight characteristics than what is needed for 

switchers. Therefore, the industry is anticipating a new market for dedicated switchers. 
Economic Impact Analysis 

7-7 

• <50 hp 

• C1 engines 

• 50-200 hp 

• 200-400 hp 

• 400-800 hp 

• 800-2,000 hp 

• >2,000 hp 

• C2 engines 

• 800-2,000 hp 

• >2,000 hp 

The engine categories used in this EIA are different from the categories used for the 

emission limits, both in term of units (horsepower instead of kilowatt) and in terms of the 

range of power included in each group of engines. The EIA categories were chosen for ease 

of analysis. Note, however, that the power threshold for the Tier 4 standards is 600 kW or 

900 hp, which is consistent with this analysis. 

For the purpose of the EIA, the C1/C2 threshold is 5 l/cyl displacement, even though 

the new C1/C2 threshold is 7 l/cyl displacement. The 5 l/cyl threshold was used because it is 

currently applicable limit. In addition, there is currently only one engine family in the 5 to 7 

l/cyl range, and it is not possible to project what future sales will be in that range or if more 

engine families will be added. 

Marine Vessel Market. The marine vessel market consists of marine vessel 

manufacturers on the demand side and marine vessel users on the supply side. The model 

distinguishes between seven vessel categories. Each of these vessels would have at least one 

propulsion engine and at least one auxiliary engine, although many may have more: 

• Recreational 

• Fishing 

• Tow/tug/push 

• Ferry 

• Supply/crew 

• Cargo 

• Other commercial 

For recreational applications, the purchasers of those vessels are the end users, and so 

the EIM is a two-level model for that market. Demand for vessels comes directly from 

households that use these vessels for recreational activities and acquire them for the personal 

enjoyment of the owner. For the other commercial vessel markets (tow/tug/push, ferry, 

supply/crew, cargo, other), demand for those vessels is derived from the transportation 

services they provide. Therefore it is necessary to include a marine transportation services 

market in the model. 

For the fishing applications, we use a dual approach. Small fishing vessels, those that 

use engines below 800 hp, are treated like recreational vessels, as a two-level market. The 
Regulatory Impact Analysis 

7-8 

physical and operating characteristics of these vessels are very similar to recreational vessels. 

Small fishing vessels often have fiberglass hulls, have fewer hours of use per year than large 

commercial fishing vessels, and their engines have higher power density. Smaller fishing 

vessels are typically operated for day fishing, their catch is more likely subject to seasonal 

restrictions, and they have very small or no crew apart from the owner. Demand for these 

vessels is less likely to be tied to a downstream market like the industrial fish processing or 

other food or industrial markets and be more a function of the local fish market, the demand 

for fresh fish for restaurants, and the individual preferences of the owner/operator. 

Large fishing vessels, those that use engines above 800 hp, are treated like marine 

transportation service vessels, as a three-level market. These vessels tend to be uniquely built, 

used for offshore fishing, and are manned by professional crews. They may be at sea for 

extended period of time, and many have fish processing facilities onboard. Demand for these 

vessels is tied more directly to the fish consumption market, including industrial fish 

processing companies. 

Marine Transportation Services. The marine transportation services market consists 

of entities that provide and utilize marine transportation services: vessel owners on the supply 

side and marine transportation service users on the demand side. The firms that use these 

marine transportation services are very similar to those that use locomotive transportation 

services: those needing to transport bulk chemicals and minerals, coal, agricultural products, 

etc. These transportation services are production inputs that depend on the amount of raw 

materials or finished products being transported and thus marine transportation costs are 

variable costs for the end user. Demand for these transportation services will determine the 

demand for vessels used to provide these services (tug/tow/pushboats, cargo, ferries, 

supply/crew, other commercial vessels). 

7.1.3.3 Market Linkages 

The submarkets in each of the marine and rail markets are linked; this provides 

feedback mechanism between consumers and producers in the relevant markets, which 

simulates dynamic interactions in the actual markets. The locomotive and marine components 

of the EIM are not linked however, meaning there is no feedback mechanism between the 

locomotive and marine sectors. Although locomotives and marine vessels such as tugs, 

towboats, cargo, and ferries provide the same type of transportation service, the characteristics 

of these markets are quite different and are subject to different constraints that limit switching 

from one type of transportation service to the other. For example, switching from rail services 

to marine services requires having access to a port and the waterway system; if the production 

facility is not located on a waterway it would also be necessary to transport the goods to and 

from port. Similarly, users of marine transportation services typically transport bulk goods in 

large quantities (by barge or by container); these quantities may be more complicated and 

costly to transport by rail. Because the services provided by the locomotives and marine 

markets are not completely interchangeable, a change in the price of one is not expected to 

have an impact on the price for the other. 

For the limited number of cases where there is direct competition between rail and 

marine transportation services, we do not expect this rule to change the dynamics of the 
Economic Impact Analysis 

7-9 

choice between marine or rail providers of these services because 1) the estimated compliance 

costs imposed by this rule are relatively small in comparison with the total production costs of 

providing transportation services, and 2) both sectors would be subject to the new standards. 

So, for example, while an increase in the price of marine diesel engines may lead to an 

increase in the price of marine transportation services, this will not likely have much impact 

on the demand for rail services because the rail sector is also expected to see increased costs. 

7.1.4 Summary of Results 

The EIA consists of two parts: a market analysis and welfare analysis. The market 

analysis looks at expected changes in prices and quantities for affected products. The welfare 

analysis looks at economic impacts in terms of annual and present value changes in social 

costs. 

We performed a market analysis for all years and all engines and equipment. The 

detailed results can be found in the appendices to this chapter. On the marine side, all 

propulsion markets were modeled even though marine engines below 800 hp are not expected 

to be affected by the program (they are not subject to Tier 4 standards and the only 

compliance costs associated with Tier 3 standards are fixed costs). The results for engines 

less than 800 hp can be found in a Technical Support Document.3

 In addition, to facilitate 

and accommodate computer programming constraints, only auxiliary engines above 800 hp 

were included in the model (see section 7.3.2.2). 

In this section we present summarized results for selected years: 2012, which 

illustrates the impacts of the marine and locomotive remanufacture programs (there are no 

variable costs for the Tier 3 standards, and therefore the Tier 3 standards do not affect market 

impacts in that year); 2016, which illustrates the market impacts of the Tier 4 standards; and 

2030, when per unit compliance costs are stabilized. 

Due to the structure of the program, the sources of the estimated social costs impacts 

of the program change over time. In the early years of the program, prior to 2016, the social 

costs are due to the fixed and variable costs associated with the phase-in of the new emission 

standards. By 2016, operating costs and the remanufacture program costs are about half the 

total costs of the program; the share of these two segments increases to about 83 percent of 

total program costs in 2030 and 90 percent in 2040. The remainder is due to variable costs for 

the engine standards. Consequently, a large share of the long-term social costs of the program 

fall on the marine and rail transportation service sectors. Results for all years can be found in 

the appendices to this Chapter. 

The results of the economic impact analysis presented below are based on an earlier 

version of the engineering costs developed for this rule (see Section 7.3.2). 

7.1.4.1 Market Analysis Results 

In the market analysis, we estimate how prices and quantities of goods affected by the 

new emission control program can be expected to change once the program goes into effect. 

The analysis relies on the baseline equilibrium prices and quantities for each type of 
Regulatory Impact Analysis 

7-10 

equipment and the price elasticity of supply and demand. It predicts market reactions to the 

increase in production costs due to the new compliance costs (variable, operating, and 

remanufacturing costs). It should be noted that this analysis does not allow any other factors 

to vary. In other words, it does not consider that manufacturers may adjust their production 

processes or marketing strategies in response to the control program. 

The market data presented below is aggregated across sub-markets. The absolute 

price changes and relative price/quantity changes reported below are production-weighted 

averages of the individual market-level estimates generated by the model for each group of 

engine/equipment markets, and not the expected price or quantity change for a particular 

engine model or vessel market. So, for example, the estimated price changes for marine 

diesel engines are production-weighted averages of the estimated results for all of the marine 

diesel engine markets included in that group.D The absolute change in quantity reported for 

each group of engine/equipment markets, on the other hand, is the sum of the decrease in 

units produced across sub-markets within each engine/equipment group. The aggregated data 

presented in Table 7-2 is intended to provide a broad overview of the expected market 

impacts that is useful when considering the impacts of the rule on the economy as a whole and 

not the impacts on a particular engine or equipment category. 

More detailed results for each of the submarkets are presented in the appendices to this 

chapter and in a Technical Support Document.4 As explained in Section 7.2.2.1, this is a 

market-level analysis and the results are not intended to reflect expected price or quantity 

changes for a particular engine or equipment model. 

Locomotive Sector Impacts. On the locomotive side, the new program is expected to 

have a negligible impact on locomotive prices and quantities. In 2012, the expected impacts 

are mainly the result of the operating costs associated with locomotive remanufacturing 

standards. These standards impose an operating cost on railroad transportation providers and 

are expected to result in a slight increase in the price of locomotive transportation services 

(about 0.1 percent, on average) and a slight decrease in the quantity of services provided 

(about 0.1 percent, on average). Due to the decrease in quantity of services provided, the 

locomotive remanufacturing program is also expected to have a small impact on the new 

locomotive market. The remanufacturing program will increase railroad operating costs, 

which expected to result in an increase in the price of transportation services. This increase 

will results in a decrease in demand for rail transportation services and ultimately in a 

decrease in the demand for locomotives and a decrease in their price. In other words, the 

market will contract slightly. We estimate a reduction in the price of locomotives of about 

0.03 percent on average. 

Beginning in 2016, the market impacts are affected by both the operating costs and the 

direct costs associated with the Tier 4 standards. As a result of both of these impacts, the 

price of a new locomotive is expected to increase by about 4.2 percent for a line haul 

locomotive and one percent for a switcher; the quantity produced of either is expected to 

 

D As a result, estimates for specific types of engines and equipment may be different than the reported group 

average. The detail results for markets are reported in the Appendices to Chapter 7 of the RIA. 
Economic Impact Analysis 

7-11 

decrease by about 0.1 percent, on average. Locomotive transportation service prices are 

expected to increase by about 0.3 percent. By 2030, the price increase for a new line haul 

locomotive is expected to decrease to about 3.2 percent; prices for switchers are expected to 

increase by about 1.5 percent. The expected quantity decrease for either is approximately 0.3 

percent. The price of rail transportation services is expected to increase by about 0.6 percent. 

Marine Sector Impacts. On the marine engine side, the expected impacts are different 

for engines above and below 800 hp. 

With regard to engines above 800 hp and the vessels that use them, there are 

negligible impacts in the early years of the program. The slight expected decrease in prices 

and in quantity produced is due to the market impacts of the remanufacture program; there are 

no market impacts for the Tier 3 standards. Beginning in 2016, market impacts due to the 

Tier 4 standards begin to occur, with expected price increases up to 17 percent for engines and 

7 percent for vessels expected to occur. The impact on marine transportation markets, 

however, is expected to be small, at less than 0.5 percent. The results in 2030 are similar, 

with expected price increases up to 13 percent for engines and 5 percent for vessels, but 

marine transportation market price increase of only about 1 percent. 

It should be noted that the actual social welfare impacts for producers and consumers 

of vessels is likely to be less that these estimated impacts, however. By allocating all of the 

auxiliary engines above 800 hp to the vessels that will be affected by this program, this 

analysis over-estimates the vessel impacts of the program. In fact, not all of the very large 

auxiliary engines are actually used on the commercial vessels that are subject to this program; 

some will be installed on vessels with Category 3 marine diesel engines. While it is 

appropriate to consider these costs in the economic impact analysis for this program, it is clear 

that not all of these social costs will be passed on to the producers and users of vessels 

directly affected by this program. 

With regard to engines below 800 hp, the market impacts of the program are expected 

to be negligible.E

 This is because there are no variable costs associated with the standards for 

these engines. The market impacts associated with the program are indirect effects that stem 

from the impacts on the marine service markets for the larger engines that would be subject to 

direct compliance costs. Changes in the equilibrium outcomes in those marine service 

markets may lead to reductions for marine services in other marine engine and vessel markets, 

including the markets for smaller marine diesel engines and vessels. The result is that in some 

years there may be small declines in the equilibrium price in the markets for marine diesel 

engines less than 800 hp. This would occur because an increase in the price and a decrease in 

the quantity of marine transportation services provided by vessels with engines above 800 hp 

that results in a change in the price of marine transportation services may have follow-on 

effects in other marine markets and lead to decreases in prices for those markets. For 

 

E

 The market results for engines and vessels below 800 hp are provided in a Technical Support Document. See 

U.S. EPA. Technical Support Document for the Final Locomotive /Marine Rule: Detailed Results from 

Economic Impact Model (EIM). EPA420-R-07-014. December 2007. A copy of this document can be found in 

the docket for this rule, EPA-HQ-OAR-2004-0190. 
Regulatory Impact Analysis 

7-12 

example, the large vessels used to provide transportation services are affected by the rule. 

Their compliance costs lead to a higher vessel price and a reduced demand for those vessels. 

This reduced demand indirectly affects other marine transportation services that support the 

larger vessels, and leads to a decrease in price for those markets as well. 

 

Table 7-2. Summary of Estimated Market Impacts for 2011, 2016, 2030 (2005$) 

Market Average Change in Price Change in Quantity 

Variable 

Engineering 

Cost Per Unit 

Absolute Percent Absolute Percent 

2012 

Rail Sector 

Locomotives $0 -$535 -0.03% -1 -0.1% 

Switcher/Passenger $0 -$348 -0.03% 0 -0.1% 

Transportation Services NA NAa 0.1% NAa -0.1% 

Marine Sector 

Engines 

 Auxiliary >800 hp $0 -$47 0.00% 0 -0.1% 

 Propulsion C1>800 hp $0 -$8 0.00% 0 0.0% 

 Propulsion C2>800 hp $0 -$139 -0.03% 0 - 0.1% 

 Other marine $0 $0 0.00% 0 0.0% 

Vessels 

 C1>800 hp $0 -$174 -0.01% 0 0.0% 

 C2>800 hp $0 -$2,419 -0.07% 0 -0.1% 

 Other marine $0 -$3 0.00% 1 0.0% 

Transportation Services NA NAa 0.2% NAa -0.1% 

2016 

Rail Sector 

Locomotives $84,274 $83,227 4.2% -1 -0.1% 

Switcher/Passenger $14,175 $13,494 1.0% 0 -0.1% 

Transportation Services NA NAa 0.3% NAa -0.1% 

Marine Sector

Engines 

 Auxiliary >800 hp $37,097 $35,569 17.1% -11 -3.4% 

 Propulsion C1>800 hp $18,483 $16,384 8.5% -15 -3.7% 

 Propulsion C2>800 hp $71,806 $71,602 16.3% 0 -0.2% 

 Other marine $0 $0 0.00% 0 0.0% 

Vessels 

 C1>800 hp $8,277 $34,043b 2.1% -14 -3.7% 

 C2>800 hp $12,107 $255,143b 7.0% 0 -0.2% 

 Other marine $0 -$4 0.00% -1 0.0% 

Transportation Services NA NAa 0.4% NAa -0.2% 

2030 

Rail Sector 
Economic Impact Analysis 

7-13 

Market Average Change in Price Change in Quantity 

Variable 

Engineering 

Cost Per Unit 

Absolute Percent Absolute Percent 

Locomotives $65,343 $63,019 3.2% -4 -0.3% 

Switcher/Passenger $21,139 $19,628 1.5% -1 -0.3% 

Transportation Services NA NAa 0.6% NAa -0.3% 

Marine Sector 

Engines 

 Auxiliary >800 hp $28,359 $27,021 13.0% -11 -2.8% 

 Propulsion C1>800 hp $14,131 $12,479 6.5% -13 -2.9% 

 Propulsion C2>800 hp $54,893 $54,264 12.3% -1 -0.5% 

 Other marine $0 -$1 0.0% 0 0.0% 

Vessels 

 C1>800 hp $6,933 $25,768b 1.6% -12 -2.9% 

 C2>800 hp $10,169 $164,774b 5.1% 0 -0.5% 

 Other marine $0 -$12 0.0% -4 0.0% 

Transportation Services NA NAa 1.1% NAa -0.5% a The prices and quantities for transportation services are normalized ($1 for 1 unit of services 

provided) and therefore it is not possible to estimate the absolute change price or quantity; see 

7.3.1.5. 

b The estimated vessel impacts include the impacts of direct vessel compliance costs and the indirect 

impacts of engine markets for both propulsion and auxiliary engines. See Chapter 7 of the RIA.

7.1.4.2 Economic Welfare Analysis 

In the economic welfare analysis we look at the costs to society of the new emission 

control program in terms of losses to key stakeholder groups that are the producers and 

consumers in the rail and marine markets. The estimated surplus losses presented below 

reflect all engineering costs associated with the new program (fixed, variable, operating, and 

remanufacturing costs). Detailed economic welfare results for the new program for all years 

are presented in the Appendices to this chapter and are summarized below. 

A summary of the estimated annual net social costs is presented in Table 7-3 and 

Figure 7-1. Table 7-3 shows that total social costs for each year are slightly less than the total 

engineering costs. This is because the total engineering costs do not reflect the decreased 

sales of locomotives, engines and vessels that are incorporated in the total social costs. In 

addition, in the early years of the program the estimated social costs of the program are not 

expected to increase regularly over time. This is because the compliance costs for the 

locomotive remanufacture program are not constant over time. 
Regulatory Impact Analysis 

7-14 

Table 7-3 Estimated Annual Engineering and Social Costs Through 2040 (2005$, $million) 

 Marine 

operating costs 

Marine 

Remanuf. 

Costs 

Marine Engine 

and Vessel 

costs 

Rail operating 

costs 

Rail Remanuf. 

costs 

Rail New 

Locomotive 

costs 

Total 

Engineerin

2007 $0.0 $22.4 $0.0 $0.0 $0.0 $8.6 $2008 $0.0 $22.4 $18.1 $0.0 $25.8 $8.6 $2009 $0.0 $22.4 $24.1 $0.0 $32.3 $8.6 $2010 $0.0 $22.4 $30.2 $0.0 $58.2 $38.4 $12011 $0.0 $71.0 $36.4 $0.0 $110.1 $41.9 $22012 $0.0 $29.7 $50.4 $0.0 $90.3 $34.7 $22013 $0.0 $29.7 $54.4 $0.0 $82.3 $34.7 $22014 $0.0 $29.7 $54.1 $0.0 $61.0 $40.4 $22015 $0.0 $53.8 $42.1 $14.3 $50.9 $74.9 $22016 $7.2 $35.2 $39.3 $29.3 $80.8 $80.7 $22017 $21.3 $32.5 $35.8 $45.5 $66.1 $59.5 $22018 $38.1 $25.1 $33.5 $61.8 $68.2 $60.6 $22019 $54.8 $25.3 $31.0 $78.4 $62.1 $61.9 $32020 $71.4 $25.5 $29.4 $95.6 $36.7 $63.9 $32021 $88.1 $25.8 $27.8 $113.3 $36.7 $65.9 $32022 $104.6 $26.0 $26.2 $131.9 $57.7 $67.8 $42023 $121.0 $26.2 $24.6 $151.3 $102.8 $71.0 $52024 $137.3 $26.4 $23.0 $170.3 $90.2 $72.3 $52025 $153.5 $26.6 $21.5 $189.7 $94.3 $74.7 $52026 $169.5 $26.8 $19.9 $209.1 $79.6 $76.1 $52027 $185.2 $27.0 $18.4 $228.8 $79.6 $77.9 $62028 $200.7 $27.3 $16.8 $248.5 $94.1 $79.3 $62029 $215.1 $25.0 $15.3 $268.1 $94.5 $80.3 $72030 $228.1 $25.2 $13.8 $287.7 $92.8 $81.3 $72031 $239.8 $25.4 $12.3 $307.4 $92.0 $82.6 $72032 $250.8 $25.7 $10.9 $327.1 $81.4 $83.9 $72033 $261.3 $25.9 $9.4 $346.8 $138.8 $85.2 $82034 $271.5 $26.1 $8.1 $366.5 $147.0 $86.4 $92035 $281.2 $26.4 $6.7 $386.0 $153.9 $86.8 $92036 $290.6 $26.6 $5.6 $404.6 $146.3 $84.4 $9
Econom

7-15 

2037 $299.6 $26.8 $4.8 $422.6 $148.5 $82.6 $92038 $308.2 $27.1 $4.2 $440.0 $150.7 $80.6 $1,02039 $316.0 $27.3 $3.7 $456.7 $153.5 $78.4 $1,02040 $322.4 $27.6 $3.3 $472.6 $156.6 $76.0 $1,0

2040 NPV at 3%a,b $9,1

2040 NPV at 7% a,b $4,12030 NPV at 3% a,b $5,32030 NPV at 7% a,b $2,9

a EPA presents the present value of cost and benefits estimates using both a three percent and a seven percent social discount rate. Ac

Circular A-4, “the 3 percent discount rate represents the ‘social rate of time preference’... [which] means the rate at which ‘society’ dconsumption flows to their present value”; “the seven percent rate is an estimate of the average before-tax rate of return to private cap... [that] approximates the opportunity cost of capital.” 

b Note: These NPV calculations are based on the period 2006-2040, reflecting the period when the analysis was completed. This has 

discounting the current year costs, 2007, and all subsequent years are discounted by an additional year. The result is a smaller stream 

calculating the NPV over 2007-2040 (3% smaller for 3% NPV and 7% smaller for 7% NPV).
Regulatory Impact Analysis 

7-16 

Table 7-4 shows how the social costs are expected to be shared across stakeholders, 

for selected years. According to these results, the rail sector is expected to bear most of the 

costs of the program, ranging from 56.5 percent in 2012 to 67.0 percent in 2016. Producers 

and consumers of locomotive transportation services are expected to bear most of those costs, 

ranging from 31.0 percent in 2012 to 48.8 percent in 2016. The marine sector is expected to 

bear the remaining social costs, ranging from 43.5 percent in 2012 to 33.0 percent in 2016. 

Producers of marine diesel engines are expected to bear more of the program costs in the early 

years (20.7 percent in 2012), but by 2020 producers and consumers in the marine 

transportation services market are expected to bear a larger share of the social costs, 37.3 

percent. 

Figure 7-1. Estimated Annual Social Costs, 2007-2040 (2005$, $million) 

Total Loco/Marine Program Costs

$0

$200,000,000

$400,000,000

$600,000,000

$800,000,000

$1,000,000,000

$1,200,000,000

20062008201020122014201620182020202220242026

20282030

2032

203420362038

Total Costs
Economic Impact Analysis 

7-17 

Table 7-4. Summary of Estimated Net Social Costs for 2011, 2016, 2020, 2030 (2005$, $million) 

 Surplus 

Change 

Percent Surplus 

Change 

Percent 

 2012 2016 

Locomotives

Locomotive Producers -$35.1 15.9% -$8.3 2.9% 

 Line haul producers -$27.8 12.6% -$0.9 0.3% 

 Switcher/Passenger producers -$7.2 3.3% -$7.4 2.6% 

Rail transportation service providers -$21.4 9.7% -$43.4 15.3% 

Rail transportation service consumers -$68.4 31.0% -$138.9 48.8% 

Total locomotive sector -$124.9 56.6% -$190.6 67.0% 

Marine

Marine engine producers -$45.8 20.7% -$2.1 0.7% 

 Auxiliary >800 hp -$16.0 7.3% -$0.5 0.2% 

 C1 > 800 hp -$19.0 8.6% -$1.6 0.5% 

 C2 > 800 hp -$10.7 4.9% $0.0 0.0% 

 Other marine $0.0 0.0% $0.0 0.0% 

Marine vessel producers -$0.3 0.1% -$15.8 5.6% 

 C1 > 800 hp -$0.1 0.0% -$13.5 4.7% 

 C2 > 800 hp -$0.1 0.1% -$2.2 0.8% 

 Other marine -$0.1 0.0% -$0.1 0.0% 

 Recreational and fishing vessel consumers $0.0 0.0% $0.0 0.0% 

Marine transportation service providers -$11.9 5.4% -$18.1 6.4% 

Marine transportation service consumers -$38.1 17.3% -$57.9 20.3% 

Auxiliary Engines <800 hp -$0.0 0.0% $0.0 0.0% 

Total marine sector -$96.1 43.5% -$93.8 33.0% 

TOTAL PROGRAM -$221.0 -$284.4 

 Surplus 

Change 

Percent Surplus 

Change 

Percent 

2020 2030 

Locomotives

Locomotive Producers -$1.1 0.3% -$3.1 0.4% 

 Line haul producers -$1.0 0.3% -$2.7 0.4% 

 Switcher/Passenger producers -$0.1 0.0% -$0.4 0.1% 

Rail transportation service providers -$46.4 14.0% -$109.0 14.8% 

Rail transportation service consumers -$148.6 44.8% -$348.9 47.3% 

Total locomotive sector -$196.1 59.1% -$461.1 62.5% 

Marine

Marine engine producers -$1.8 0.5% -$2.0 0.3% 

 Auxiliary >800 hp -$0.4 0.1% -$0.5 0.1% 

 C1 > 800 hp -$1.3 0.4% -$1.4 0.2% 

 C2 > 800 hp $0.0 0.0% -$0.1 0.0% 

 Other marine $0.0 0.0% $0.0 0.0% 

Marine vessel producers -$10.3 3.1% -$9.2 1.2% 

 C1 > 800 hp -$8.8 2.7% -$8.2 1.1% 

 C2 > 800 hp -$1.3 0.4% -$0.7 0.1% 

 Other marine -$0.1 0.0% -$0.3 0.0% 

 Recreational and fishing vessel consumers $0.0 0.0% $0.0 0.0% 

Marine transportation service providers -$29.5 8.9% -$63.3 8.6% 
Regulatory Impact Analysis 

7-18 

 Surplus 

Change 

Percent Surplus 

Change 

Percent 

Marine transportation service consumers -$94.4 28.4% -$202.5 27.4% 

Auxiliary Engines <800 hp $0.0 0.0% $0.0 0.0% 

Total marine sector -$135.9 40.9% -277.0 37.5% 

TOTAL PROGRAM $332.0 $738.1 

 Table 7-5 provides additional detail about the sources of surplus changes, for 2020 

when the per unit compliance costs are stable. On the marine side, this table shows that 

engine and vessel producers are expected to pass along much of the engine and vessel 

compliance costs to the marine transportation service providers who purchase marine vessels. 

These marine transportation service providers, in turn, are expected to pass some of the costs 

to their customers. This is also expected to be the case in the rail sector. 

Table 7-5. Distribution of Estimated Surplus Changes by Market and Stakeholder for 2020 (2005$, 

$million) 

 Total Engineering 

Costs 

Surplus Change 

Marine Markets 

Engine Producers $29.4 –$1.8 

Vessel Producers $6.0 –$10.3 

Engine price changes –$1.7

Equipment cost changes –$8.6

Recreational and Fishing Consumers $0 $0 

Engine price changes $0

Equipment cost changes $0

Transportation Service Providers $100.8 –$34.3 

Increased price vessels –$6.0

Operating costs –$16.7

Remanufacture costs $6.9

Transportation Service Consumers –$41.2 

Increased price vessels –$19.1

Operating costs –$53.4

Remanufacture costs $21.9

Rail Markets 

Locomotive Producers $63.9 –$1.1 

Rail Service Providers $132.3 –$46.4 

Increased price new locomotives –$15.1

Operating costs –$22.6

Remanufacture costs –$8.7

Rail Transportation Service Consumers –$148.6 

Increased price new locomotives –$48.4

Operating costs –$72.4

Remanufacture costs –$27.8

TOTAL $332.5 $332.0 
Economic Impact Analysis 

7-19 

The present value of net social costs of the new standards through 2040, shown in Table 

7-3, is estimated to be is estimated to be $9.1 billion (2005$).F This present value is 

calculated using a social discount rate of 3 percent and the stream of social welfare costs from 

2006 through 2040. We also performed an analysis using a 7 percent social discount rate.G 

Using that discount rate, the present value of the net social costs through 2040 is estimated to 

be $4.2 billion (2005$). 

Table 7-6 shows the distribution of total surplus losses for the program from 2007 

through 2040. This table shows that the rail sector is expected to bear about 62 percent of the 

total program social costs through 2040 (NPV 3%), and that most of the costs are expected to 

be borne by the rail transportation consumers. The marine sector is expected to bear about 38 

percent of the total program social costs through 2040 (NPV 3%), most of which are also 

expected to be borne by the marine transportation consumers. This is consistent with the 

structure of the program, which leads to high compliance costs for those stakeholder groups. 

Table 7-6 Estimated Net Social Costs Through 2040 by Stakeholder ($million, 2005$) 

Stakeholder Groups Surplus 

Change 

Percent of 

Total Surplus 

Surplus 

Change 

Percent of 

Total Surplus 

Locomotives NPV 3% NPV 7% 

Locomotive producers -$221.1 2.4% -$160.4 3.8% 

 Line Haul -$172.2 -$124.5 

 Switcher/Passenger -$48.9 -$35.9 

Rail trans. service providers -$1,302.7 14.2% -$568.6 13.6% 

Rail trans. service consumers -$4,168.7 45.6% -1,819.5 43.5% 

Total locomotive sector -$5,692.6 62.2% -$2,548.5 61.0% 

Marine 

Marine engine producers -$307.5 3.4% -$229.4 5.5% 

 Auxiliary >800 hp -$87.3 -$64.0 

 C1 > 800 hp -$106.8 -$74.6 

 C2 > 800 hp -$56.8 -$42.6 

 Other marine -$56.7 -$48.1 

Marine vessel producers -$150.0 1.6% -$72.5 1.7% 

 C1 > 800 hp -$126.8 -$60.8 

 C2 > 800 hp -$19.7 -$10.2 

 Other marine -$3.5 -$1.5 

 

F

 Note: These NPV calculations are based on the period 2006-2040, reflecting the period when the analysis was 

completed. This has the consequence of discounting the current year costs, 2007, and all subsequent years are 

discounted by an additional year. The result is a smaller stream of social costs than by calculating the NPV over 

2007-2040 (3% smaller for 3% NPV and 7% smaller for 7% NPV). 

G EPA has historically presented the present value of cost and benefits estimates using both a 3 percent and a 7 

percent social discount. The 3 percent rate represents a demand-side approach and reflects the time preference 

of consumption (the rate at which society is willing to trade current consumption for future consumption). The 7 

percent rate is a cost-side approach and reflects the shadow price of capital. 
Regulatory Impact Analysis 

7-20 

 Recreational and small 

 fishing vessel consumers 

$0.2 $0.1 

Marine trans. service providers -$704.6 7.7% -$308.4 7.4% 

Marine trans. service consumers -$2,254.7 24.6% -$986.9 23.6% 

Auxiliary Engines <800 hpa -$40.2 0.4% -$34.2 -0.8% 

Total marine sector $3,456.7 37.8% -$1,631.3 39.0% 

TOTAL PROGRAM -$9.149.2 -$4,179.8 a Marine auxiliary engines <800 hp are not subject to Tier 4 standards, and there are no variable costs 

associated with the Tier 3 standards. Consequently, there would be no direct compliance impacts for 

producers or users of these engines. Social costs are limited to fixed costs associated with tooling and 

certification for Tier 3 standards (those costs occur 2007-2011). 

7.2 Economic Methodology 

Economic impact analysis uses a combination of theory and econometric modeling to 

evaluate potential behavior changes associated with a new regulatory program. As noted 

above, the goal is to estimate the impact of the regulatory program on affected markets (prices 

and quantities) and stakeholder groups (the share of total social costs to be borne by producers 

and consumers). This is done by creating a mathematical model based on economic theory 

and populating the model using publicly available price and quantity data. A key factor in 

this type of analysis is the responsiveness of the quantity of engines, equipment, and 

transportation services demanded by consumers or supplied by producers to a change in the 

price of that product. This relationship is called the price elasticity of demand or supply. 

The EIM’s methodology is rooted in applied microeconomic theory and was 

developed following the OAQPS Economic Analysis Resource Document.

5

 This section 

discusses the economic theory underlying the modeling for this EIA and several key issues 

that affect the way the model was developed. 

7.2.1 Behavioral Economic Models 

Models incorporating different levels of economic decision making can generally be 

categorized as with-behavior responses or without-behavior responses. The EIM is a 

behavioral model. 

 Engineering cost analysis is an example of the latter and provides detailed estimates of 

the cost of a regulation based on the projected number of affected units and engineering 

estimates of the annualized costs. The result is an estimate of the total compliance costs for a 

program. However, these models do not attempt to estimate how a regulatory program will 

change the prices or output of an affected industry. Therefore, the results may over-estimate 

the total costs of a program because they do not take decreases in quantity produced into 

account. In addition, engineering cost analysis does not address which stakeholders are 

expected to bear the costs of the regulation. 

The with-behavior response approach builds on the engineering cost analysis and 

incorporates economic theory related to producer and consumer behavior to estimate changes 
Economic Impact Analysis 

7-21 

in market conditions. As Bingham and Fox note, this framework provides “a richer story” of 

the expected distribution of economic welfare changes across producers and consumers.6 In 

behavioral models, manufacturers of goods affected by a regulation are economic agents who 

can make adjustments, such as changing production rates or altering input mixes, that will 

generally affect the market environment in which they operate. As producers change their 

production levels in response to a new regulation, consumers of the affected goods are 

typically faced with changes in prices that cause them to alter the quantity that they are 

willing to purchase. These changes in price and output resulting from the market adjustments 

are used to estimate the distribution of social costs between consumers and producers. 

 If markets are competitive and per-unit regulatory costs are small, the behavioral 

approach will yield approximately the same total cost impact as the engineering cost 

approach. However, the advantage of the with-behavior response approach is that it illustrates 

how the costs flow through the economic system and it identifies which stakeholders 

(producers and consumers) are most likely to be affected. 

7.2.2 What is the Economic Theory Underlying the EIM? 

The EIM is a multi-market partial equilibrium numerical simulation model that 

estimates price and quantity change in the intermediate run under competitive market 

conditions. Each of these model features is described in this section. 

7.2.2.1 Partial Equilibrium Multi-Market Model 

In the broadest sense, all markets are directly or indirectly linked in the economy, and 

a new regulatory program will theoretically affect all commodities and markets to some 

extent. However, not all regulatory programs have noticeable impacts on all markets. For 

example, a regulation that imposes significant per unit direct compliance costs on the 

production of an important manufacturing input, such as steel, would be expected to have a 

large impact on the national economy. However, a regulation that imposes a small direct 

compliance cost on an important input, or any direct compliance costs on an input that is only 

a small share of production costs would be expected to have less of an impact on all markets 

in the economy. 

The appropriate level of market interactions to be included in an economic impact 

analysis is determined by the number of industries directly affected by the requirements and 

the ability of affected firms to pass along the regulatory costs in the form of higher prices. 

There are at least three alternative approaches for modeling interactions between economic 

sectors, which reflect three different levels of analysis. 

In a partial equilibrium model, individual markets are modeled in isolation. The only 

factor affecting the market is the cost of the regulation on facilities in the industry being 

modeled; there are no interaction effects with other markets. Conditions in other markets are 

assumed either to be unaffected by a policy or unimportant for cost estimation. 

In a multi-market model, a subset of related markets is modeled together, with sector 

linkages, and hence selected interaction effects, explicitly specified. This approach represents 
Regulatory Impact Analysis 

7-22 

an intermediate step between a simple, single-market partial equilibrium approach and a full 

general equilibrium approach. This technique has most recently been referred to in the 

literature as "partial equilibrium analysis of multiple markets."7 

In a general equilibrium model, all sectors of the economy are modeled together, 

incorporating interaction effects between all sectors included in the model. General 

equilibrium models operationalize neoclassical microeconomic theory by modeling not only 

the direct effects of control costs but also potential input substitution effects, changes in 

production levels associated with changes in market prices across all sectors, and the 

associated changes in welfare economy-wide. A disadvantage of general equilibrium 

modeling is that substantial time and resources are required to develop a new model or tailor 

an existing model for analyzing regulatory alternatives. 

This analysis uses a multi-market partial equilibrium approach in that it models only 

those markets that are directly affected by the new emission control program: producers and 

consumers in the rail and marine sectors. These two sectors are modeled separately, and the 

locomotive and marine components of the EIM are not linked (there is no feedback 

mechanism between the locomotive and marine diesel market segments; see Section 7.1.3.3). 

The results of the analysis will be estimated price and quantity changes in the locomotive and 

rail transportation services markets and in the marine engine, vessel, and transportation 

services markets, as well as estimates of how the compliance costs will be shared between 

producers and consumers in the relevant markets. 

The EIM does not estimate the economic impact of the new emission control program 

on finished goods that use rail or marine transportation services as inputs. For example, while 

we look at the impacts of the program on locomotive transportation costs, we do not look at 

the impacts of the controls on electricity produced using coal transported by rail, or on 

manufactured products that use that electricity. Similarly, while we look at the impacts of the 

control program on the price of large fishing vessels, we do not look at the impacts of the 

controls on the prices of food products that use fish as an input. This is because these inputs 

(rail transportation, fishing vessel) are only a small portion of the total inputs of the final 

goods and services produced using them. Therefore, a change in the price of these inputs on 

the order anticipated by this program would not be expected to significantly affect the prices 

and quantities of finished products that use transportation or other services provided by 

locomotives or marine vessels as an input. 

It should also be noted that the economic impact model employed for this analysis 

estimates the aggregate economic impacts of the control program on the relevant markets. It 

is not a firm-level analysis and therefore the supply elasticity or individual compliance costs 

facing any particular manufacturer may be different from the market average. This difference 

can be important, particularly where the rule affects different firms’ costs over different 

volumes of production. However, to the extent there are differential effects, EPA believes 

that the wide array of flexibilities provided in this rule are adequate to address any cost 

inequities that may arise. 
Economic Impact Analysis 

7-23 

7.2.2.2 Perfect Competition Model 

For all markets that are modeled, the analyst must characterize the degree of 

competition within each market. The discussion generally focuses on perfect competition 

(price-taking behavior) versus imperfect competition (the lack of price-taking behavior). This 

EIM relies on an assumption of perfect competition. This means that consumers and firms are 

price takers and do not have the ability to influence market prices. 

In a perfectly competitive market at equilibrium the market price equals the value 

society (consumers) places on the marginal product, as well as the marginal cost to society 

(producers). Producers are price takers, in that they respond to the value that consumers put 

on the product. It should be noted that the perfect competition assumption is not primarily 

about the number of firms in a market. It is about how the market operates: whether or not 

individual firms have sufficient market power to influence the market price. Indicators that 

allow us to assume perfect competition include absence of barriers to entry, absence of 

strategic behavior among firms in the market, and product differentiation.H Finally, according 

to contestable market theory, oligopolies and even monopolies will behave very much like 

firms in a competitive market if it is possible to enter particular markets costlessly (i.e., there 

are no sunk costs associated with market entry or exit). This would be the case, for example, 

when products are substantially similar (e.g., a recreational vessel and a commercial vessel). 

In contrast, imperfect competition implies firms have some ability to influence the 

market price of output they produce. One of the classic reasons firms may be able to do this 

is their ability to produce commodities with unique attributes that differentiate them from 

competitors’ products. This allows them to limit supply, which in turn increases the market 

price, given the traditional downward-sloping demand curve. Decreasing the quantity 

produced increases the monopolist’s profits but decreases total social surplus because a less 

than optimal amount of the product is being consumed. In the monopolistic equilibrium, the 

value society (consumers) places on the marginal product, the market price, exceeds the 

marginal cost to society (producers) of producing the last unit. Thus, social welfare would be 

increased by inducing the monopolist to increase production. Social cost estimates associated 

with an emission control program would be larger with monopolistic market structures and 

other forms of imperfect competition because the regulation exacerbates the existing social 

inefficiency of too little output from a social perspective. The Office of Management and 

Budget (OMB) explicitly mentions the need to consider these market power-related welfare 

costs in evaluating regulations under Executive Order 12866.8

Perfect competition is widely accepted for this type of analysis and only in rare cases 

are other approaches used.9

 For the markets under consideration in this EIA we assume the 

perfectly competitive market structure. This is because these markets do not exhibit evidence 

of noncompetitive behavior: there are no indications of barriers to entry, the firms in these 

markets are not price setters, and there is no evidence of high levels of strategic behavior in 

the price and quantity decisions of the firms. 

 

H The number of firms in a market is not a necessary condition for a perfectly competitive market. See Robert 

H. Frank, Microeconomics and Behavior, 1991, McGraw-Hill, Inc., p 333. 
Regulatory Impact Analysis 

7-24 

On the marine side, the markets included in this analysis do not exhibit evidence of 

noncompetitive behavior. On the engine side, these markets are matured, as evidenced by 

unit sales growing at the rate of population increases. Pricing power in such markets is 

typically limited. There is also excess capacity, especially on the engine side. Marine diesel 

engines are typically marinized land-based highway or nonroad engines, and it is possible for 

marine diesel engine manufacturers to produce additional marine engines with minimal 

production constraints if a high demand is present. On the vessel side, there are hundreds of 

shipyards that can be engaged in the production of vessels, and vessels from one firm can be 

purchased instead of engines and vessels from another firm. Finally, there are hundreds of 

marine transportation service providers, ranging from individuals who own their own tug or 

supply boat to firms that employ a fleet. It is also not uncommon for owners to move vessels 

among coasts and waterways to take advantage of local markets. For all of these reasons it is 

appropriate to model the market markets as competitive. 

The locomotive markets are also modeled as competitive. While there are two main 

locomotive producers, EMD and GE, their products are homogeneous and railroads can easily 

purchase locomotives from one or the other. The high cost of fuel for the rail transportation 

services sector also contributes to competition among locomotive manufacturers, in that 

railroads will shift their purchases from one manufacturer to the other if they can achieve a 

reduction in fuel costs. The new switcher market will add to the competitive pressure in this 

market as well. On the rail transportation side, although the Government Accountability 

Office (GAO) has expressed concerns regarding the amount of competition in the rail road 

industry due to mergers over the past decades, it also acknowledges that a more “rigorous 

analysis of competitive markets” was needed to show the industry was not competitive.10 The 

Association of American Railroads (AAR), a trade group representing the freight railroads of 

North America, has suggested that mergers have actually made the rail road industry more 

competitive. According to the AAR, most mergers have been “end-to-end” mergers that 

reduce the need to interchange traffic to a connecting railroad (creating a single line service), 

as opposed to the merger of competing railroads with parallel lines. These mergers increase 

competition by creating more efficient, lower cost railway networks.11 AAR also argues that 

recent mergers have not given railroads excessive market power that would come with 

uncompetitive markets. They note that productivity is up, prices are down, innovative new 

operating strategies are being tested, profits are not in excess of a competitive rate of return, 

and they do not have an excessive share of the national transportation market.12

7.2.2.3 Intermediate-Run Model 

In developing a multi-market partial equilibrium model, the choices available to 

producers must be considered. For example, are producers able to increase their factors of 

production (e.g., increase production capacity) or alter their production mix (e.g., substitution 

between materials, labor, and capital)? These modeling issues are largely dependent on the 

time horizon for which the analysis is performed. Three benchmark time horizons are 

discussed below: the very short run, the long run, and the intermediate run. This discussion 

relies in large part on the material contained in the OAQPS Economic Analysis Resource 

Guide.

13
Economic Impact Analysis 

7-25 

The EIM models market impacts in the intermediate run. The use of the intermediate 

run means that some factors of production are fixed and some are variable. This modeling 

period allows analysis of the economic effects of the rule's compliance costs on current 

producers. As described below, a short-run analysis imposes all compliance costs on 

producers, while a long-run analysis imposes all costs on consumers. The use of the 

intermediate time frame is consistent with economic practices for this type of analysis. 

In the very short run, all factors of production are assumed to be fixed, leaving 

producers with no means to respond to the increased costs associated with the regulation (e.g., 

they cannot adjust labor or capital inputs). Within a very short time horizon, regulated 

producers are constrained in their ability to adjust inputs or outputs due to contractual, 

institutional, or other factors and can be represented by a vertical supply curve, as shown in 

Figure 7-2. In essence, this is equivalent to the nonbehavioral model described earlier. 

Neither the price nor quantity changes and the manufacturer’s compliance costs become fixed 

or sunk costs. Under this time horizon, the impacts of the regulation fall entirely on the 

regulated entity. Producers incur the entire regulatory burden as a one-to-one reduction in 

their profit. This is referred to as the “full-cost absorption” scenario and is equivalent to the 

engineering cost estimates. Although there is no hard and fast rule for determining what 

length of time constitutes the very short run, it is inappropriate to use this time horizon for this 

type of analysis because it assumes economic entities have no flexibility to adjust factors of 

production. 

Figure 7-2. Short Run: All Costs Borne By Producers 

In the long run, all factors of production are variable, and producers can be expected to 

adjust production plans in response to cost changes imposed by a regulation (e.g., using a 

different labor/capital mix). Figure 7-3 illustrates a typical, if somewhat simplified, long-run 

industry supply function. The supply function is horizontal, indicating that the marginal and 
Regulatory Impact Analysis 

7-26 

average costs of production are constant with respect to output.I

 This horizontal slope reflects 

the fact that, under long-run constant returns to scale, technology and input prices ultimately 

determine the market price, not the level of output in the market. 

Figure 7-3 Long-Run: Full-Cost Pass-Through 

Unit Cost Increase } Price

Increase }

Q1 Q0

S1

: With Regulation

S0

: Without Regulation

P1

P0

D

c b

a

$

Output

d

Market demand is represented by the standard downward-sloping curve. The market 

is assumed here to be perfectly competitive; equilibrium is determined by the intersection of 

the supply and demand curves. In this case, the upward shift in the market supply curve 

represents the regulation’s effect on production costs. The shift causes the market price to 

increase by the full amount of the per-unit control cost (i.e., from P0 to P1). With the quantity 

demanded sensitive to price, the increase in market price leads to a reduction in output in the 

new with-regulation equilibrium (i.e., Q0 to Q1). As a result, consumers incur the entire 

regulatory burden as represented by the loss in consumer surplus (i.e., the area P0ac P1). In 

the nomenclature of EIAs, this long-run scenario is typically referred to as “full-cost passthrough” and is illustrated in Figure 7-3. 

Taken together, impacts modeled under the long-run/full-cost-pass-through scenario 

reveal an important point: under fairly general economic conditions, a regulation's impact on 

producers is transitory. Ultimately, the costs are passed on to consumers in the form of higher 

prices. However, this does not mean that the impacts of a regulation will have no impact on 

 

I

 The constancy of marginal costs reflects an underlying assumption of constant returns to scale of production, 

which may or may not apply in all cases. 
Economic Impact Analysis 

7-27 

producers of goods and services affected by a regulation. For example, the long run may 

cover the time taken to retire today’s entire capital equipment, which could take decades. 

Therefore, transitory impacts could be protracted and could dominate long-run impacts in 

terms of present value. In addition, to evaluate impacts on current producers, the long-run 

approach is not appropriate. Consequently a time horizon that falls between the very shortrun/full-cost-absorption case and the long-run/full-cost-pass-through case is most appropriate 

for this EIA. 

The intermediate run time frame allows examination of impacts of a regulatory 

program during the transition between the short run and the long run. In the intermediate run, 

there is some resource immobility which may cause producers to suffer producer surplus 

losses. Specifically, producers may be able to adjust some, but not all, factors of production, 

and they therefore will bear some portion of the costs of the regulatory program. The 

existence of fixed production factors generally leads to diminishing returns to those fixed 

factors. This typically manifests itself in the form of a marginal cost (supply) function that 

rises with the output rate, as shown in Figure 7-4. 

Figure 7-4 Intermediate Run: Partial-Cost Pass-Through 

Again, the regulation causes an upward shift in the supply function. The lack of 

resource mobility may cause producers to suffer profit (producer surplus) losses in the face of 

regulation; however, producers are able to pass through some of the associated costs to 

consumers, to the extent the market will allow. As shown, in this case, the market-clearing 

process generates an increase in price (from P0 to P1) that is less than the per-unit increase in 

costs, so that the regulatory burden is shared by producers (net reduction in profits) and 

consumers (rise in price). In other words, there is a loss of both producer and consumer 

surplus. 
Regulatory Impact Analysis 

7-28 

Consistent with other economic impact analyses performed by EPA, this EIM uses an 

intermediate run approach. This approach allows us to examine the market and social welfare 

impacts of the program as producers adjust their output and consumers adjust their 

consumption of affected products in response to the increased production costs. During this 

period, the distribution of the welfare losses between producer and consumer depends in large 

part on the relative supply and demand elasticity parameters used in the model. For example, 

if demand for marine vessels or locomotives is relatively inelastic (i.e., demand does not 

decrease much as price increases), then most of the direct compliance costs on vessel or 

locomotive manufacturers will be passed along to the owners and operators of this equipment 

in the form of higher prices. 

7.2.3 How Is the EIM Used to Estimate Economic Impacts? 

7.2.3.1 Estimation of Market Impacts (Single Market) 

A graphical representation of a general economic competitive model of price 

formation, as shown in Figure 7-5(a), posits that market prices and quantities are determined 

by the intersection of the market supply and market demand curves. Under the baseline 

scenario, a market price and quantity (p,Q) are determined by the intersection of the 

downward-sloping market demand curve (DM) and the upward-sloping market supply curve 

(SM). The market supply curve reflects the sum of the domestic (Sd) and import (Sf) supply 

curves. 
Economic Impact Analysis 

7-29 

Figure 7-5 Market Equilibrium Without and With Regulation 

With the regulation, the costs of production increase for suppliers. The imposition of 

these regulatory control costs is represented as an upward shift in the supply curve for 

domestic and import supply by the estimated compliance costs. As a result of the upward 

shift in the supply curve, the market supply curve will also shift upward as shown in Figure 7-

5(b) to reflect the increased costs of production. 

At baseline without the new standards, the industry produces total output, Q, at price, 

p, with domestic producers supplying the amount qd and imports accounting for Q minus qd, 

or qf. With the regulation, the market price increases from p to p′, and market output (as 

determined from the market demand curve) decreases from Q to Q′. This reduction in market 

output is the net result of reductions in domestic and import supply. 

As indicated in Figure 7-5, when the new standards are applied the supply curve will 

shift upward by the amount of the estimated compliance costs. The demand curve, however, 

does not shift in this analysis. This is explained by the dynamics underlying the demand 

curve. The demand curve represents the relationship between prices and quantity demanded. 

Changes in prices lead to changes in the quantity demanded and are illustrated by movements 

along a constant demand curve. In contrast, changes in any of the other variables would lead 
Regulatory Impact Analysis 

7-30 

to change in demand and are illustrated as shifts in the position of the demand curve.J

 For 

example, an increase in the number of consumers in a market would cause the demand curve 

to shift outward because there are more individuals willing to buy the good at every price. 

Similarly, an exogenous increase in average income would also lead the demand curve to shift 

outward as people choose to buy more of a good at a given price. Changes in the prices of 

related good and tastes or preferences can also lead to demand curve shifts. 

The new standards are expected to increase the costs of production in all the affected 

markets (locomotive, rail transportation services, marine engines, marine vessels, and marine 

transportation services) and ultimately lead to higher equilibrium prices in the affected 

markets. As these prices increase, the quantity demanded falls (i.e., the price change leads to 

a movement along the demand curve). However, the new emission control program is not 

expected to lead to shifts in the locomotive and marine transportation service market demand 

curves for several reasons. First, the demand for transportation services is determined by the 

national economy. The growth in the size of the national economy determines the demand for 

transportation services. We presume the cost of the new standards will not change the size of 

the national economy in measurable ways since these sectors are relatively small contributors 

to GDP. Therefore, we do not expect a change in demand in these sectors. Second, the 

business decisions of users of rail and marine transportation services will not be changed due 

to the new standards. These users will still need to use rail and marine transportation services 

to ship their products to their destinations for intermediate or final users of those products. In 

this sense, transportation services are part of an integrated production process that will not be 

changed by this program. For all of these reasons, it would be inappropriate to shift the 

demand curve for this analysis. 

7.2.3.2 Incorporating Multi-Market Interactions 

The above description is typical of the expected market effects for a single product 

markets considered in isolation (for example the locomotive or engine markets). However, 

the markets considered in this EIA are more complicated because of the need to investigate 

impacts on each component of the affected markets (engine, vessel and transportation services 

on the marine side and locomotives and transportation services on the locomotive side) and 

the relationships between those components. 

For example, with regard to the commercial vessel markets, the new emission control 

program is expected to affect vessel producers in two ways. First, these producers are 

affected by higher input costs (increases in the price of marine diesel engines) associated with 

the rule. Second, the standards will also impose additional production costs on vessel 

producers associated with vessel changes necessary to accommodate compliant engines. 

Similarly, the rail and marine transportation services markets will be affected by increases in 

the price of engines and equipment (locomotives and marine vessels) as well as direct 

increases in operating costs. 

 

J

 An accessible detailed discussion of these concepts can be found in Chapters 5-7 of Nicholson’s (1998) 

intermediate microeconomics textbook. 
Economic Impact Analysis 

7-31 

In the marine market case, the demand for engines is directly linked to the production 

of vessels that uses those engines.K For this reason, it is reasonable to assume that the inputoutput relationship between the marine diesel engines and vessels is strictly fixed and that the 

demand for engines varies directly with the demand for vessels. A demand curve specified in 

terms of its downstream consumption is referred to as a derived demand curve. Figure 7-6 

illustrates how a derived demand curve is identified. 

 

K In the marine vessel market, one or two engines are used per vessel, depending on its intrinsic design, and this 

configuration is insensitive to small changes in the engine used. 
Regulatory Impact Analysis 

7-32 

Figure 7-6 Derived-Demand Curve for Engines 

Consider an event in the marine equipment market (vessel market) that causes the 

price of equipment to increase by ΔP (such as an increase in the price of engines). This 

increase in the price of equipment will cause the supply curve in the equipment market to shift 

up, leading to a decreased quantity (ΔQE). The change in equipment production leads to a 

decrease in the demand for engines (ΔQEng). The new point (QE – ΔQE, P – ΔP) traces out the 

derived demand curve. Note that the supply and demand curves in the marine equipment 

markets are needed to identify the derived demand in the engine market. All of the market 

supply and demand curves and the elasticity parameters are described in Appendix 7F. 
Economic Impact Analysis 

7-33 

7.2.3.3 Estimation of Social Costs 

The economic welfare implications of the market price and output changes with the 

regulation can be examined by calculating consumer and producer net “surplus” changes 

associated with these adjustments. This is a measure of the negative impact of an 

environmental policy change and is commonly referred to as the “social cost” of a regulation. 

It is important to emphasize that this measure does not include the benefits that occur outside 

of the market, that is, the value of the reduced levels of air pollution with the regulation. 

Including this benefit will reduce the net cost of the regulation and even make it positive. 

The demand and supply curves that are used to project market price and quantity 

impacts can be used to estimate the change in consumer, producer, and total surplus or social 

cost of the regulation (see Figure 7-7). 
Regulatory Impact Analysis 

7-34 

Figure 7-7. Economic Welfare Calculations: Changes in Consumer, Producer, and Total Surplus 

P1

P2

Q2 Q1

S

S′

D

Q/t

$/Q

(a) Change in Consumer Surplus with

Regulation

P1

P2

Q2 Q1

S

S′

D

Q/t

$/Q

(b) Change in Producer Surplus with

Regulation

P1

P2

Q2 Q1

S

S′

D

Q/t

$/Q

(c) Net Change in Economic Welfare with

Regulation

A

B

C

D

The difference between the maximum price consumers are willing to pay for a good 

and the price they actually pay is referred to as “consumer surplus.” Consumer surplus is 

measured as the area under the demand curve and above the price of the product. Similarly, 

the difference between the minimum price producers are willing to accept for a good and the 

price they actually receive is referred to as “producer surplus.” Producer surplus is measured 

as the area above the supply curve below the price of the product. These areas can be thought 

of as consumers’ net benefits of consumption and producers’ net benefits of production, 

respectively. 
Economic Impact Analysis 

7-35 

In Figure 7-7, baseline equilibrium occurs at the intersection of the demand curve, D, 

and supply curve, S. Price is Pl with quantity Ql. The increased cost of production with the 

regulation will cause the market supply curve to shift upward to S′. The new equilibrium 

price of the product is P2. With a higher price for the product there is less consumer welfare, 

all else being unchanged. In Figure 7-7(a), area A represents the dollar value of the annual 

net loss in consumers’ welfare associated with the increased price. The rectangular portion 

represents the loss in consumer surplus on the quantity still consumed due to the price 

increase, Q2, while the triangular area represents the foregone surplus resulting from the 

reduced quantity consumed, Ql – Q2. 

In addition to the changes in consumers’ welfare, there are also changes in producers’ 

welfare with the regulatory action. With the increase in market price, producers receive 

higher revenues on the quantity still purchased, Q2. In Figure 7-7(b), area B represents the 

increase in revenues due to this increase in price. The difference in the area under the supply 

curve up to the original market price, area C, measures the loss in producer surplus, which 

includes the loss associated with the quantity no longer produced. The net change in 

producers’ welfare is represented by area B – C. 

The change in economic welfare attributable to the compliance costs of the regulations 

is the sum of consumer and producer surplus changes, that is, –(A) + (B–C). Figure 7-7(c) 

shows the net (negative) change in economic welfare associated with the regulation as area D. 

7.2.3.4 Fixed and Variable Costs in a Competitive Market 

The estimated engineering compliance costs, consisting of fixed costs (R&D 

capital/tooling, certification costs), variable costs, and operational costs provide an initial 

measure of total annual compliance costs without accounting for behavioral responses. The 

starting point for assessing the social costs and market impacts of a regulatory action is to 

incorporate the regulatory compliance costs into the production decision of the firm. 
Regulatory Impact Analysis 

7-36 

Figure 7-8 Modeling Fixed Regulatory Costs 

In general, shifting the supply curve by the total cost per unit implies that both capital 

and operating costs vary with output levels. At least in the case of capital, this raises some 

questions. In the long run, all inputs (and their costs) can be expected to vary with output. 

But a short(er)-run analysis typically holds some capital factors fixed. For instance, to the 

extent that a market supply function is tied to existing facilities, there is an element of fixed 

capital (or one-time R&D). As indicated above, the current market supply function might 

reflect these fixed factors with an upward slope. As shown in Figure 7-8, the marginal cost 

(MC) curve will only be affected, or shift upwards, by the per-unit variable compliance costs 

(c1=TVCC/q), while the average total cost (ATAC) curve will shift up by the per-unit total 

compliance costs (c2=TCC/q). Thus, the variable costs will directly affect the production 

decision (optimal output rate), and the fixed costs will affect the closure decision by 

establishing a new higher reservation price for the firm (i.e., pm′). In other words, the fixed 

costs are important in determining whether the firm will stay in this line of business (i.e., 

produce anything at all), and the variable costs determine the level (quantity) of production. 

Depending on the industry type, fixed costs associated with complying with a new 

regulation are generally treated differently in an analysis of market impacts. In a competitive 

market, the industry supply curve is generally based on the market's marginal cost curve; 

fixed costs do not influence production decisions at the margin. Therefore, the market 

analysis for a competitive market is based on variable costs only. 
Economic Impact Analysis 

7-37 

Implicit in this approach is the assumption that manufacturers do not recover their 

production fixed costs by passing all or part of them to consumers through new price 

increases. Yet, production fixed costs must be recovered; otherwise, manufacturers would go 

out of business. Manufacturers in any industry are likely to have ongoing product 

development programs the costs of which are included in the current market price structure. 

It is expected that the resources for those programs would be re-oriented toward compliance 

with the regulatory program until those costs are recovered for each manufacturer. If this is 

the case, then the rule would have the effect of shifting product development resources to 

regulatory compliance from other market-based investment decisions. Thus, fixed costs are a 

cost to society because they displace other product development activities that may improve 

the quality or performance of engines and equipment. In this EIA, fixed costs are accounted 

for in the year in which they occur and are attributed to the respective locomotive, marine 

engine, and vessel manufacturers. These manufacturers are expected to see losses of producer 

surplus as early as 2007. 

7.3 EIM Data Inputs and Model Solution 

The EIM is a computer model comprised of a series of spreadsheet modules that 

simulate the supply and demand characteristics of the markets under consideration. The 

model equations, presented in Appendix 7E, are based on the economic relationships 

described in Section 7.2. The EIM analysis consists of four basic steps: 

 

• Define the initial market equilibrium conditions of the markets under consideration 

(equilibrium prices and quantities and behavioral parameters; these yield equilibrium 

supply and demand curves). 

• Introduce a policy "shock" into the model based on estimated compliance costs that 

shift the supply functions. 

• Use a solution algorithm to estimate a new, with-regulation equilibrium price and 

quantity for all markets. 

• Estimate the change in producer and consumer surplus in all markets included in the 

model. 

Supply responses and market adjustments can be conceptualized as an interactive 

process. Producers facing increased production costs due to compliance are willing to supply 

smaller quantities at the baseline price. This reduction in market supply leads to an increase 

in the market price that all producers and consumers face, which leads to further responses by 

producers and consumers and thus new market prices, and so on. The new with-regulation 

equilibrium reflects the new market prices where total market supply equals market demand. 

This section describes the markets and data used to construct the EIM: initial 

equilibrium market conditions (equilibrium prices and quantities), compliance cost inputs, and 

model elasticity parameters. Also included is a brief discussion of the solution algorithm used 

to estimate with-regulation market conditions. 
Regulatory Impact Analysis 

7-38 

7.3.1 Market Equilibrium Conditions 

The starting point for the economic impact analysis is initial market equilibrium 

conditions (prices and quantities) that exist prior to the implementation of the new standards. 

At pre-control market equilibrium conditions, consumers are willing to purchase the same 

amount of a product that producers are willing to produce at that market price. 

7.3.1.1 Locomotive Initial Equilibrium Quantities 

For equilibrium baseline sales for the locomotive markets, the EIM uses the same 

locomotive sales quantities that are used in the locomotive engineering cost analysis presented 

in Chapter 5. These sales were derived using the inputs for our locomotive emissions 

inventory analysis. In that analysis, we projected future locomotive populations and the 

number of locomotives remanufactured for given years. An estimated sales figure can be 

derived from those projected populations by comparing the given year’s population to the 

prior year’s population. The difference, after backing out the number of older locomotives 

that are projected to be removed from services, can be considered the new sales for the given 

year. Locomotive sales for all years of the analysis are contained in Table 7-7. Note that, to 

be consistent with the engineering costs analysis, passenger locomotives sales are included 

with the switcher locomotive sales.

Table 7-7 Locomotive Sales (2007 through 2040) 

Year Line Haul Sales Switcher/Passenger 

Sales 

2007 646 92 

2008 666 92 

2009 693 92 

2010 729 92 

2011 751 92 

2012 767 92 

2013 765 92 

2014 780 93 

2015 816 93 

2016 854 94 

2017 877 94 

2018 894 94 

2019 917 94 

2020 948 94 

2021 979 94 

2022 1,007 95 

2023 1,034 160 

2024 1,048 183 

2025 1,078 201 

2026 1,096 212 

2027 1,119 227 

2028 1,136 239 

2029 1,150 247 

2030 1,158 263 

2031 1,173 281 
Economic Impact Analysis 

7-39 

Year Line Haul Sales Switcher/Passenger 

Sales 

2032 1,190 292 

2033 1,209 296 

2034 1,223 305 

2035 1,231 302 

2036 1,197 294 

2037 1,172 287 

2038 1,144 278 

2039 1,112 269 

2040 1,078 263 

7.3.1.2 Locomotive Initial Equilibrium Prices 

The baseline equilibrium price used for new line-haul locomotives used in the EIM is 

$2 million (2005$). The baseline equilibrium price for the switcher/passenger category is 

$1.3 million (2005$). These prices are based on conversations with the locomotive 

manufacturers. These prices are used for all years of the analysis. The analysis assumes a 

constant (real) price of goods and services over time and so equilibrium prices for future years 

are the same as the initial year equilibrium prices. This is reasonable because, in the absence 

of shocks to the economy or the supply of raw materials, economic theory suggests that the 

equilibrium market price for goods and services should remain constant over time (see 

Appendix 7G for a discussion of the constant price assumption). 

7.3.1.3 Marine Engine and Vessel Initial Equilibrium Quantities 

Propulsion Engine Quantities. For baseline equilibrium sales for the marine 

propulsion engine markets, the EIM uses the same marine engine sales quantities that are used 

in the marine engineering cost analysis presented in Chapter 5. These are based on the Power 

Systems Research OELink database, for 2002. These sales figures are reproduced in Table 7-

8. 

Table 7-8. Marine Diesel Engine Sales (2002) 

Marine Diesel Engine 

Categories (by hp) 

Annual Sales 

Auxiliary 

Annual Sales 

Commercial 

Propulsion 

Annual Sales 

Recreational 

Propulsion Total 

< 50 hpa 9,332 67 3,924 13,323 

50-200 hp 4,019 2,665 6,294 12,978 

200-400 hp 1,773 1,398 2,663 5,834 

400-800 hp 956 1,634 4,220 6,810 

C1 800-2,000 hp 142 472 598 1,212 

C1 >2,000 hp 13 196 177 386 

C2 800-2,000 hp 56 6 0 62 
Regulatory Impact Analysis 

7-40 

C2 >2,000 hp 86 125 0 211 

Total 16,377 6,563 17,876 40,816 

a

The cost analysis does not differentiate between auxiliary, commercial propulsion, and recreational 

propulsion engines <50 hp; these engines were allocated to the engine categories based on PSR 

OELink sales splits for 2002. 

Vessel Quantities. Baseline equilibrium vessel sales for the commercial vessel 

markets were derived by apportioning the commercial propulsion engine sales in Table 7-8 to 

vessel types based on the characteristics of the current vessel populations. This yields the 

number of propulsion engines by application. We then assumed the average number of 

propulsion engines per vessel and applied that value to the number of engines by application 

(one or two) to obtain the number of vessels by application.14 

For the recreational vessel market, baseline equilibrium vessels sales were estimated 

by assuming one engine per vessel for engines up to 200 hp, and 2 engines per vessel for 

larger vessels. There are no Category 2 recreational vessels because our program does not 

allow Category 2 engines to be categorized as recreational. Consequently, any recreational 

vessels with Category 2 engines would be included in the commercial vessel categories. This 

approach is not expected to affect the results of the analysis because the number of vessels 

that use Category 2 marine diesel engines is small (less than 200 in 2002) and it is unlikely 

that recreational vessels that use engines of this size would number more than a few, if any. 

The estimated vessel sales for 2002 are reproduced in Table 7-9. 

Table 7-9. Marine Vessel Sales (2002) 

Hp Bin Fishing 

Tow/Tug

/ Push Ferries 

Supply/ 

Crew Cargo 

Other 

Commerc’l Recreatn’l Total 

0-50 65 0 1 0 0 1 3,924 3,991

50–200 2,293 247 40 41 13 31 6,294 8,959

200–400 602 65 10 11 3 8 1,332 2,031

400–800 702 76 12 13 4 10 2,110 2,927

C1 800–2,000 202 22 4 4 1 3 299 535

C1 >2,000 85 9 1 2 0 1 89 187

C2 800–2,000 0 1 0 2 0 0 0 3

C2 >2,000 10 29 3 16 4 1 0 63

Total 3,958 449 71 88 25 55 14,047 18,695

Auxiliary Engine Quantities. In general, every marine vessel has at least one auxiliary 

engine (and often two, in the case of commercial vessels) to provide power in case of 

emergency and/or to power various electric equipment that the operator may want to use 

without running the main propulsion engine (for example, galley equipment on a recreational 

vessel or various crew support equipment on a commercial vessel). The 16,377 auxiliary 

engines set out in Table 7-8, are those specifically produced and identified as marine auxiliary 
Economic Impact Analysis 

7-41 

engines. This is fewer that the minimum number that would be required to provide two 

auxiliary engines for each commercial vessels and one for each fishing and recreational 

vessel. This discrepancy is explained by the fact that not all auxiliary engines used onboard 

marine vessels are marine-specific; instead, some may be non-installed nonroad engines, 

which are not required to be certified as marine diesel engines in our marine diesel engine 

emission control program. 

The existence of separate marine auxiliary engines creates a complication when 

estimating the economic impacts of the new standards, since increases in auxiliary engine 

prices will have a separate impact on vessel markets. To estimated the combined effects of 

the indirect impacts of propulsion and auxiliary engine prices increases and the direct impacts 

of vessel compliance costs, it is necessary to allocate auxiliary as well as propulsion engines 

to vessels. 

Because there are no variable costs associated with auxiliary engine below 800 hp, it is 

not necessary to allocate those engines to vessels.L

 The only auxiliary engines that must be 

allocated to vessels are those larger than 800 hp (those that will need to comply with the Tier 

4 standards). These auxiliary engines are distributed as follows, for the purpose of this 

anlaysis: 

• All auxiliary engines from 800 to 2,000 hp are allocated to vessels with C1 

propulsion engines above 2,000 hp (except tows) and supply and crew vessels 

with C2 propulsion engines above 2,000 hp 

• All auxiliary engines above 2,000 hp are allocated to vessels with C2 

propulsion engines above 2,000 hp (except supply and crew vessels and those 

in the “other” category). 

The results of this allocation scheme are set out in Table 7-10. Note that this approach 

modifies the auxiliary engine sales from the Power Systems Research OELink database, for 

2002 set out in Table 7-8, although the total number of auxiliary engines from 600 to 2,000 hp 

(198 engines) and above 2,000 hp (99 engines) are the same. 

Table 7-10 Adjusted Auxiliary Engine Sales (2002) 

Vessel Type Number of Vessels Auxiliary Engine Per 

Vessel 

Total Auxiliary Engines 

Auxiliary engines 600-2,000 hp 198 

 C1 >2000 hp Fishing 85 1.9 159 

 C1 >2000 hp Ferries 1 1.9 2 

 C1 >2000 hp Supply/Crew 2 1.9 4 

 

L

 There are no variable costs associated with marine auxiliary engines <800 hp because there are no variable 

costs associated with the Tier 3 standard and these engines are not subject to Tier 4 standards. Since there are no 

direct compliance costs, it is not necessary to allocate these auxiliary engines to vessels. There are fixed costs 

associated with the Tier 3 standards, however, and these costs are appropriately included in the total social 

welfare impacts of the program. These costs are incurred in 2007 through 2011. 
Regulatory Impact Analysis 

7-42 

 C1 >2000 hp Other 1 1.9 2 

 C2 >2000 hp Supply/Crew 16 1.9 30 

 C2 > 2000 hp Other 1 1.9 2 

 

Auxiliary Engines >2000 hp 99 

 C2 >2000 hp Fishing 10 2.2 22 

 C2 >2000 hp Tow/Tug 29 2.2 62 

 C2 >2000 hp Ferries 3 2.2 6 

 C2 >2000 Cargo 4 2.2 9 

By allocating all of the auxiliary engines above 800 hp to the vessels that will be 

affected by this program, this analysis over-estimates the vessel impacts of the program. In 

fact, not all of the very large auxiliary engines are actually used on the commercial vessels 

that are subject to this program; some will be installed on vessels with Category 3 marine 

diesel engines. However, it is appropriate to consider these costs in the economic impact 

analysis for this program. 

Projected Sales for Future Years. To project marine engine sales for future years, the 

EIM uses the same technique as is used in the cost analysis, which consists of applying a 

1.009 growth factor to the 2002 sales, for commercial marine diesel engines, and by applying 

the NONROAD model growth rate to the 2002, for recreational marine engines. Note that for 

the purpose of this analysis, small engine projections are estimated using only the commercial 

growth factor of 1.009. 

To project marine vessel sales for future years, the same technique was used as for the 

baseline equilibrium vessel sales described above. 

7.3.1.4 Marine Engine and Vessel Initial Equilibrium Prices 

Propulsion Engine Prices. The baseline equilibrium engine prices for C1 commercial 

propulsion engines used in the EIM were obtained from an internet search of engine prices.15 

The baseline equilibrium prices for C2 propulsion engines were estimated by multiplying the 

C1 commercial propulsion engine prices by 1.5. This reflects the larger cylinder displacement 

of these engines and the fact that they are built for longer hours of use. The baseline 

equilibrium prices for recreational propulsion engines were estimated by multiplying the C1 

commercial propulsion engine prices by 1.25. This reflects the fact that while recreational 

engines are often similar to commercial engines they are designed for higher power and use at 

higher engine load. Recreational engines also often have esthetic features (e.g., chrome 

fixtures) that set them apart from their recreational counterparts. There are no prices for 

Category 2 recreational vessels; all engines with per cylinder displacement above 7 liters are 

considered to be commercial regardless of the application in which they are used. The 

baseline equilibrium prices for marine diesel propulsion engines are set out in Table 7-11. 
Economic Impact Analysis 

7-43 

Table 7-11. Per Unit Marine Diesel Propulsion Engine Prices (2005$) 

Marine Diesel Engine 

Categories (by hp) 

Commercial 

Propulsion 

Recreational 

< 50 hp $7,000 $8,750 

50-200 hp $16,000 $20,000 

200-400 hp $21,000 $26,250 

400-800 hp $50,000 $62,500 

C1 800-2,000 hp $155,000 $193,750 

C1 > 2,000 hp $300,000 $375,000 

C2 800-2,000 hp $230,000 NA 

C2 > 2,000 hp $450,000 NA 

Auxiliary Engine Prices. With the exception of auxiliary engine above 2,000 hp, the 

estimated baseline equilibrium prices for auxiliary engines are the same as similar propulsion 

engines. Engine manufacturers indicated that for engines in this size range, the propulsion 

and auxiliary engines are typically very similar packages. For the larger engines, those above 

2,000 hp, engine manufacturers informed us that the price of an auxiliary engine is typically 

slightly less than for propulsion, with the price of an auxiliary engine at about 85 percent of 

the price for a propulsion engine. While they are the same base engine, they have a different 

package of accessories. 

 As described above, auxiliary engines are allocated among commercial vessels based 

on power and not based on engine category. Therefore, it is necessary to adjust the auxiliary 

engine baseline equilibrium prices for auxiliary engines to reflect a weighted average price for 

Category 1 and Category 2 auxiliary engines. The weights used in the calculation reflect the 

estimated share of engines for each hp category in the baseline population. It is not necessary 

to construct a weighted price for engines above 2,000 hp; all of these engines are assumed to 

be Category 2 engines, and their price is $385,000 (85 percent of the price of a propulsion 

engine, $450,000). 

Average auxiliary engine price (800-2,000 hp) = 0.84×$155,000 + 0.16×$230,000 = $167,000 

Table 7-12 Per Unit Marine Auxiliary Diesel Engine Prices 

Auxiliary Engine 

Market in EIM 

Propulsion 

Engine Baseline 

Price 

Auxiliary Engine 

Baseline 

Quantity 

Share Auxiliary Engine 

Baseline Price 

800-2,000 hp 198 engines 100% $167,000 

 C1 800 to 2000 hp $155,000 166 engines (166/198) = 84% 

 C2 800 to 2000 hp $230,000 32 engines (32/198) =16% 

 >2,000 hp $385,000 99 engines 100% $385,000 

Vessel Prices. The estimated baseline equilibrium price for marine vessels used in the 

EIM were estimated based on the engine prices, by applying an assumed ratio of the price of a 
Regulatory Impact Analysis 

7-44 

vessel to the price of the propulsion engines onboard. Table 7-13 sets out the ratios used to 

estimate the vessel prices, and Table 7-14 sets out the vessel prices used in the EIA. 

Table 7-13. Ratio of Vessel Price to Marine Diesel Engine Price 

Hp Bin Fishing Tow/Tug/

Push Boat 

Ferries Supply/ 

Crew 

Cargo Other 

Commercial 

Recreational

0-50 5 6 5 6

50-200 5 6 6 6 6 5 6

200-400 3.5 4 4 8 4 3.5 4

400-800 3.5 4.5 4.5 9 4.5 3.5 4

C1 800-2,000 3.5 5 5 10 10 3.5 4

C1 >2,000 3.5 5 5 10 10 3.5 4

C2 800-2,000 3.5 5 5 10 10 3.5 4

C2 >2,000 3.5 5 5 10 10 3.5 4

Table 7-14. Per Unit Marine Vessel Prices (2005$) 

Hp Bin Fishing Tow/Tug/ 

Push Boat 

Ferries Supply/ 

Crew 

Cargo Other 

Commercial 

Recreational 

0-50 $35,000 $42,000 $35,000 $52,500 

50-200 $80,000 $96,000 $96,000 $96,000 $96,000 $80,000 $120,000 

200-400 $147,000 $168,000 $168,000 $336,000 $168,000 $147,000 $210,000 

400-800 $350,000 $450,000 $450,000 $900,000 $450,000 $350,000 $500,000 

C1 800-

2,000 

$1,085,000 $1,550,000 $1,550,000 $3,100,000 $3,100,000 $1,085,000 $1,550,000 

C1 >2,000 $2,100,000 $3,000,000 $3,000,000 $6,000,000 $6,000,000 $2,100,000 $3,000,000 

C2 800-

2,000 

$1,610,000 $2,300,000 $2,300,000 $2,300,000 $4,600,000 $1,610,000 NA 

C2 >2,000 $3,150,000 $4,500,000 $4,500,000 $4,500,000 $9,000,000 $3,150,000 NA 

With respect to future prices, this analysis assumes a constant (real) price of goods and 

services over time and the equilibrium prices for future years are the same as the baseline 

equilibrium prices. This is reasonable because, in the absence of shocks to the economy or 

the supply of raw materials, economic theory suggests that the equilibrium market price for 

goods and services should remain constant over time (see Appendix 7G for a discussion of the 

constant price assumption). 

7.3.1.5 Baseline Quantities and Equilibrium Prices for Transportation Markets 

The nature of the locomotive and marine transportation services markets makes it 

difficult to identify the baseline equilibrium prices and quantities for this analysis. Instead of 

trying to estimate these values, the EIM uses an alternative approach based on total revenues 

for each sector. In this approach, annual revenue data is used as a proxy for production data. 

This data is normalized such that the baseline price is set equal to $1/unit and the baseline 

quantity is then equal to the annual revenue. This allows estimation of the relative price 

change and the relative quantity change due to the new standards, although it does not allow 

estimation of the absolute price and absolute quantity change. 
Economic Impact Analysis 

7-45 

Baseline data for the EIM’s railroad and marine service revenues are reported in Table 

7-15.16 Revenue data for the rail transportation services market for 2005 comes from the 

Association of American Railroads Freight Railroad Statistics, Condensed Income 

Statement.17 The data for revenue for freight and passenger services are used.M. 

Revenue data for the marine transportation services sector come from the U.S. Census 

reports revenues for the marine service sector for 2002.N Revenue data for 2002 was obtained 

for the marine transportation sectors that are likely to be affected by this rule.18 These are: 

• 4831: Deep sea, coastal, and Great Lakes transportation – 2 sectors were used: 

o 483113: Coast and Great Lakes Freight 

o 483114: Coast and Great Lakes Passenger 

• 4832: Inland water transportation 

• 4872: Scenic & sightseeing transportation, water 

• 4883: Support activity for water transportation – the revenue for this sector 

was adjusted to reflect only the portion of support activity that would be 

associated with sectors 4831 and 4832 (water transportation excluding deep 

sea), by applying the ratio of affected water transportation revenue to total 

revenues for Sectors 4831 and 4832. 

Table 7-15. Railroad and Marine Service Markets Baseline Revenue Data ($billions) 

Transportation Service Market 2002 Annual Growth Rate 2005 

Railroad Services Market 

 Freight revenue: $44,457M 

 Passenger revenue: $65M 

NR 0.9% $44.5 

Marine Services Market $13.8 0.9% $15.4 

The 2002 marine revenue data was adjusted for 2005 using the GDP deflator index. 

To estimate revenue for 2005, we applied growth rates used for engine sales. Revenue for all 

future years of the analysis (2007 to 2040) were calculated by applying annual growth rates to 

the 2005 data set as follows: 

Revenue200X = Revenue2005 × (1+0.009)(200X-2005)

 

M It should be noted that this revenue estimate includes a return on investment, which reflects the prices for 

transportation services experienced by rail transportation service consumers. 

N The revenue estimate for the marine transportation sector used in this analysis is based on an earlier estimate of 

revenues from the affected marine transportation sectors. Our final draft estimate is slightly lower. The 

difference is less than 5 percent and will not change the conclusions of this economic impact analysis . 
Regulatory Impact Analysis 

7-46 

This data suggests that the rail transportation sector is much larger than the marine 

transportation sector. However, the difference in the amount of tons of goods moved is 

smaller. According to AAR, the rail transportation sector moved about 1,844.2 million tons 

of freight in 2004.19 The marine sector accounted for about 1,047.1 million tons in that 

year.20 This suggests that while some of the difference in revenue is due to differences in the 

amount of freight transported, part of the difference is due to differences in the characteristics 

of each sector. For example, railroads are responsible for maintaining the rail system; they 

pass some of those costs to their customers through higher prices. The marine system, in 

contrast, is maintained by public authorities (U.S. Army Corps of Engineers, state and local 

governments), and so those costs would not be reflected in the prices of marine transportation 

services. Similarly, while rail yards are maintained by railroads, ports are owned and 

operated by various public and private authorities. Finally, marine transportation is somewhat 

more fuel efficient than rail, with one tug or towboat able to transport more goods than one 

locomotive. 

7.3.2 Compliance Costs 

The social costs of the new standards are estimated by shocking the initial market 

equilibrium conditions by the amount of the compliance costs. 

The engineering costs we used in the EIA are an earlier version of the estimated 

compliance costs developed for this rule. The net present value of the engineering costs used 

in the EIA is estimated to be approximately $9.17 billion (NPV over the period of analysis at 

3 percent discount rate), which is about $240 million less than the net present value of the 

final estimated engineering costs of about $9.41 billion. This difference is the sum of various 

cost adjustments, the largest of which are an increase of about $222 million in operating costs 

for the marine markets and $42 million in the operating costs for the rail markets (NPV over 

the period of analysis at 3 percent discount rate). These changes are not expected to have a 

substantial impact on the market level results because the differences are relatively small on 

an annual basis. For example, operating costs for C2 marine markets increase by about 15 

percent in 2030 (from $107 million to $123 million). The previous estimate of $107 million 

was associated with an increase of approximately 1.1 in the price of marine transportation 

services and a decrease of approximately 0.5 percent in the quantity of marine transportation 

services provided. A small increase in operating costs is not likely to change those results by 

very much. The market-level impacts on the other downstream markets are also likely to be 

very small and not economically significant. Finally, the difference in compliance costs will 

not affect the distribution of social costs, which is a function of the price elasticity of supply 

and demand. 

Table 7-16 summarizes how the compliance costs are applied to each component of 

the EIM to simulate the effect of the emission control program. On the supply side, only 

variable costs are used to shift the supply curve in the relevant markets (see Section 7.2.3.4). 

Fixed costs are added to the social costs of the program as a separate line item. In this model, 

the demand curves are not shifted because the program does not regulate consumers or 

impose direct compliance costs on them and therefore would not cause a change in demand 

(see Section 7.2.3.1). 
Economic Impact Analysis 

7-47 

Table 7-16. Summary of Types of Compliance Costs 

Market Category Supply Shift 

Entity Direct Costs Indirect Costs 

Demand Shift 

Rail Locomotive Loco Mfr Variable costs N/A 

Transportation 

Services 

Railroad Reductant, fuel, 

remanufacture kit 

Higher 

locomotive 

prices 

<800 hp Engine Mfr Variable costs = 0 N/A 

Vessel Variable costs = 0 Higher engine 

prices 

>800 hp Engine mfr Variable costs N/A 

Vessel Variable costs Higher engine 

prices 

Marine 

Transportation 

Services 

Vessel 

Owner 

Reductant, fuel, 

remanufacture kit 

Higher engine 

and vessel prices 

No demand 

shift; see 

7.2.3.1 

The compliance costs used in the EIM are based on the estimated engineering 

compliance costs described in Chapter 5. 

For marine diesel engine variable costs, we used the piece costs shown in Table 5-29. 

Note that, as explained in Chapter 5, there are no variable costs associated with the Tier 3 

standards for the marine program. We do not expect the prices of engine components used to 

meet the Tier 3 standards will be different from those used to meet the Tier 2 standards. For 

marine diesel engine fixed costs in the EIA, we simply divided the annual engine fixed costs 

presented in Tables 5-3, 5-10, 5-14, and 5-17 by the projected sales for the given year. When 

doing this, it is important to stay within category (e.g., marine C1 annual costs should be 

divided by marine C1 annual sales). This makes the fixed costs per engine appear rather large 

in the EIA since those costs are being spread over a relatively small number of engines during 

the years in which the cost are incurred. 

On the vessel side, there are no compliance costs, fixed or variable, associated with the 

Tier 3 standards since the engine-based controls are not expected to affect the footprint of the 

engine. For the Tier 4 compliance costs we used the vessel hardware costs shown in Tables 

5-38 and 5-39. Importantly, the costs associated with engines (discussed above) are incurred 

for every engine (auxiliary and propulsion), while the vessel hardware costs show in Tables 5-

38 and 5-39 are incurred for every vessel. To arrive at a per vessel cost for the EIA, we added 

the aftertreatment housing costs shown in Table 5-38 to the reductant system costs shown in 

Table 5-39, keeping in mind the near-term (years one and two) and long-term (years three and 

later) costs presented in Table 5-39. The vessel fixed costs are the annual redesign costs 

shown in Table 5-36 divided by the projected number of vessel sales during the given years as 

shown in Tables 5-41 and 5-42. Tier 4 marine vessel compliance costs (costs for vessel 

redesign) are incurred over an 18-year period that is derived from the number of vessel types 

that will have to be modified (see Chapter 5 for an explanation of how these costs are 

calculated). 
Regulatory Impact Analysis 

7-48 

For locomotives, we used essentially the same methodology. The variable costs are 

taken from Tables 5-29, 5-38, and 5-39. Annual fixed costs are simply divided by the sales 

for the given year in which the costs are incurred. In the EIA, since the locomotive and its 

engine are considered to be one in the same, there was no need to differentiate between purely 

engine costs and equipment costs. 

For all markets, fixed costs are allocated to the year in which they occur. For this 

analysis, fixed costs are spread over five years in advance of the applicable standards with the 

exception of tooling and certification costs, which are allocated to the year before the 

standards are effective. Variable costs begin to be incurred only when the programs go into 

effect. For simplicity of presentation, we have estimated marine engine and equipment costs 

as though all marine Tier 3 standards begin in 2011 and all marine Tier 4 standards begin in 

2016. For locomotives and marine diesel engines, this means a staggered set of fixed costs, as 

described in Table 7-17, with the compliance costs for the different tiers overlapping on some 

years. 

The annual compliance costs used to shock the model are the sum of the relevant 

compliance costs for a given year. The staggered nature of the emission control programs 

means that for the initial years of the program the annual estimated variable costs may include 

fixed costs for complying with both Tier 3 and Tier 4 requirements. Similarly, fixed costs 

may combine Tier 3 and Tier 4 compliance costs, depending on which programs are phased-in 

for that year. As a result, the compliance costs described below may be due to Tier 3 costs, 

Tier 4 costs, or both. This approach is appropriate because the EIA is intended to look at the 

social costs of the regulatory program as a whole and not by tier of standards. 

Table 7-17 Examples of Locomotive and Marine Engine Staggered Compliance Costs Schedule 

Line haul Locomotive Commercial Marine 

3.5-7 l/cyl, 1,400-2,000 hp 

Tier 3 Tier 4 Marine T3 Marine T4 

2007 Fixed Fixed 

2008 Fixed Fixed

2009 Fixed Fixed

2010 Fixed Fixed Fixed

2011 Fixed (inc. cert.) Fixed Fixed (inc. cert.) Fixed 

2012 Effective Date; 

Variable 

Fixed Effective Date; 

Variable 

Fixed

2013 Variable Fixed Variable Fixed

2014 Variable Fixed (inc. cert.) Variable Fixed

2015 Variable Effective Date; 

Variable 

Variable Fixed (inc. cert.)

2016 Variable Variable Variable Effective Date; 

Variable 

2017 Variable Variable Variable Variable
Economic Impact Analysis 

7-49 

7.3.2.1 Locomotive Compliance Costs 

The estimated per unit compliance costs for new locomotives used in the EIM are 

summarized in Table 7-18. These costs are dominated by fixed costs in the early years of the 

program. Variable costs do not occur until 2015, when the aftertreatment standards begin. 

This reflects the fact that there are no variable costs associated with the Tier 3 standards. 

Fixed costs reflect both the Tier 3 and Tier 4 costs. 

Table 7-18 Estimated Per Unit Compliance Costs – New Locomotives (2005$) 

 Line Haul Locomotive Switcher, Passenger Locomotive 

Year Variable Fixed Total Variable Fixed Total 

2007 $0 $9,294 $9,294 $0 $27,904 $27,904

2008 $0 $9,007 $9,007 $0 $27,904 $27,904

2009 $0 $8,657 $8,657 $0 $27,904 $27,904

2010 $0 $45,894 $45,894 $0 $53,856 $53,856

2011 $0 $46,534 $46,534 $0 $75,341 $75,341

2012 $0 $35,791 $35,791 $0 $78,642 $78,642

2013 $0 $35,878 $35,878 $0 $78,175 $78,175

2014 $0 $39,278 $39,278 $0 $104,927 $104,927

2015 $84,274 $0 $84,274 $14,175 $51,847 $66,023

2016 $84,274 $0 $84,274 $14,175 $78,520 $92,695

2017 $65,343 $0 $65,343 $23,682 $0 $23,682

2018 $65,343 $0 $65,343 $23,682 $0 $23,682

2019+ $65,343 $0 $65,343 $21,139 $0 $21,139

7.3.2.2 Marine Diesel Engine Compliance Costs 

The estimated per unit compliance costs for new marine diesel engines used in the 

EIM are summarized in Table 7-19 (C2 propulsion engines), Table 7-20 (C1 propulsion 

engines), Table 7-21 (recreational engines), and Table 7-22 (small engines). In the early 

years, 2007 through 2011, there are fixed costs associated with the Tier 3 standards. 

Beginning in 2012, there are no compliance costs associated with the Tier 3 standards because 

there are no variable costs for those standards. The Tier 4 standards apply only to engines 

above 800 hp. As a result, there are fixed costs attributed to those engines through 2015, after 

which time the only costs are variable costs associated with the aftertreatment devices.O 

In our engineering cost analysis, propulsion and auxiliary engines have the same 

compliance costs. To facilitate and accommodate computer programming constraints, 

however, it was necessary to run the model using a simplified approach for the auxiliary 

engine markets, in which the compliance costs are a weighted average of all compliance costs 

 

O For simplicity, the marine engine and equipment standards are estimated as though all marine Tier 4 standards 

begin in 2016 even though some Tier 4 standards for very large engines begin in 2014. While this affects the 

individual year results for early years, the differences disappear by 2016 at which time all marine diesel engines 

above 800 hp are subject to aftertreatment standards. 
Regulatory Impact Analysis 

7-50 

for engines above 800 hp. This weighted average is applied to auxiliary engines 800-2,000 hp 

and those above 2,000 hp. This means that the supply shift is the same for both of the 

auxiliary engine markets even though the actual program impacts are different for them. The 

weighted average compliance costs was obtained by dividing the total compliance costs for 

engines above 800 hp by the number of engines above 800 hp. This results in estimated 

compliance costs of $37,097 in 2016, the first year of the Tier 4 program. If separate 

compliance costs had been used for the two auxiliary engine markets, they would have been 

$19,073 for 800-2,000 hp, and $67,255 for above 2,000 in that year. The aggregated 

compliance costs approach results in a higher price impact for auxiliary engines 800-2,000 hp 

and a smaller price impact for auxiliary engines above 2,000 hp. These price impacts would 

be passed along to the vessel markets as indirect costs of the program, with associated 

impacts. We performed a sensitivity analysis that examines the impact of this approach; the 

results of that analysis, reported in Appendix H, suggest that the results of the analysis would 

not change very much if this simplifying approach were not used. 

Table 7-19 Estimated Per Unit Compliance Costs - C2 Commercial Propulsion Engines (2005$) 

Hp 

Category 

Cost Type 2007 2008 2009 2010 2011 2012 

800-2,000 Variable $0 $0 $0 $0 $0 $0

 Fixed $14,571 $14,441 $14,312 $14,184 $99,121 $74,808

 Total $14,571 $14,441 $14,312 $14,184 $99,121 $74,808

>2,000 Variable $0 $0 $0 $0 $0 $0

 Fixed $14,571 $14,441 $14,312 $14,184 $99,121 $74,808

 Total $14,571 $14,441 $14,312 $14,184 $99,121 $74,808

Hp 

Category 

Cost Type 2013 2014 2015 2016 2017 2018+ 

800-2,000 Variable $0 $0 $0 $39,428 $39,428 $30,142

 Fixed $74,140 $73,479 $102,680 $0 $0 $0

 Total $74,140 $73,479 $102,680 $39,428 $39,428 $30,142

>2,000 Variable $0 $0 $0 $73,360 $73,360 $56,081

 Fixed $74,140 $73,479 $102,680 $0 $0 $0

 Total $74,140 $73,479 $102,680 $73,360 $73,360 $56,081

Table 7-20 Estimated Per Unit Compliance Costs – C1 Commercial Propulsion Engines (2005$) 

Hp 

Category 

Cost Type 2007 2008 2009 2010 2011 2012 

50-200 Variable $0 $0 $0 $0 $0 $0

 Fixed $836 $829 $822 $814 $1,549 $0

 Total $836 $829 $822 $814 $1,549 $0

200-400 Variable $0 $0 $0 $0 $0 $0
Economic Impact Analysis 

7-51 

 Fixed $836 $829 $822 $814 $1,549 $0

 Total $836 $829 $822 $814 $1,549 $0

400-800 Variable $0 $0 $0 $0 $0 $0

 Fixed $836 $829 $822 $814 $1,549 $0

 Total $836 $829 $822 $814 $1,549 $0

800-2,000 Variable $0 $0 $0 $0 $0 $0

 Fixed $2,710 $2,686 $2,662 $2,638 $29,554 $25,963

 Total $2,710 $2,686 $2,662 $2,638 $29,554 $25,963

>2,000 Variable $0 $0 $0 $0 $0 $0

 Fixed $2,710 $2,686 $2,662 $2,638 $29,554 $25,963

 Total $2,710 $2,686 $2,662 $2,638 $29,554 $25,963

Hp 

Category 

Cost Type 2013 2014 2015 2016 2017 2018+ 

50-200 Variable $0 $0 $0 $0 $0 $0

 Fixed $0 $0 $0 $0 $0 $0

 Total $0 $0 $0 $0 $0 $0

200-400 Variable $0 $0 $0 $0 $0 $0

 Fixed $0 $0 $0 $0 $0 $0

 Total $0 $0 $0 $0 $0 $0

400-800 Variable $0 $0 $0 $0 $0 $0

 Fixed $0 $0 $0 $0 $0 $0

 Total $0 $0 $0 $0 $0 $0

800-2,000 Variable $0 $0 $0 $15,196 $15,196 $11,618

 Fixed $25,732 $25,502 $34,190 $0 $0 $0

 Total $25,732 $25,502 $34,190 $15,196 $15,196 $11,618

>2,000 Variable $0 $0 $0 $26,401 $26,401 $20,183

 Fixed $25,732 $25,502 $34,190 $0 $0 $0

 Total $25,732 $25,502 $34,190 $26,401 $26,401 $20,183

Table 7-21 Estimated Per Unit Compliance Costs – Recreational Engines (2005$) 

Hp 

Category 

Cost Type 2007 2008 2009 2010 2011 2012 

0-50 Variable $0 $0 $0 $0 $0 $0

 Fixed 286 279 272 266 486 $0

 Total 286 279 272 266 486 $0

50-200 Variable $0 $0 $0 $0 $0 $0

 Fixed $286 $279 $272 $266 $486 $0

 Total $286 $279 $272 $266 $486 $0

200-400 Variable $0 $0 $0 $0 $0 $0

 Fixed $286 $279 $272 $266 $486 $0

 Total $286 $279 $272 $266 $486 $0

400-800 Variable $0 $0 $0 $0 $0 $0

 Fixed $286 $279 $272 $266 $486 $0

 Total $286 $279 $272 $266 $486 $0

800-2,000 Variable $0 $0 $0 $0 $0 $0

 Fixed $286 $279 $272 $266 $486 $0

 Total $286 $279 $272 $266 $486 $0
Regulatory Impact Analysis 

7-52 

>2,000 Variable $0 $0 $0 $0 $0 $0

 Fixed $286 $279 $272 $266 $486 $0

 Total $286 $279 $272 $266 $486 $0

Hp 

Category 

Cost Type 2013 2014 2015 2016 2017 2018+ 

0-50 hp Variable $0 $0 $0 $0 $0 $0

 Fixed $0 $0 $0 $0 $0 $0

 Total $0 $0 $0 $0 $0 $0

50-200 Variable $0 $0 $0 $0 $0 $0

 Fixed $0 $0 $0 $0 $0 $0

 Total $0 $0 $0 $0 $0 $0

200-400 Variable $0 $0 $0 $0 $0 $0

 Fixed $0 $0 $0 $0 $0 $0

 Total $0 $0 $0 $0 $0 $0

400-800 Variable $0 $0 $0 $0 $0 $0

 Fixed $0 $0 $0 $0 $0 $0

 Total $0 $0 $0 $0 $0 $0

800-2,000 Variable $0 $0 $0 $0 $0 $0

 Fixed $0 $0 $0 $0 $0 $0

 Total $0 $0 $0 $0 $0 $0

>2,000 Variable $0 $0 $0 $0 $0 $0

 Fixed $0 $0 $0 $0 $0 $0

 Total $0 $0 $0 $0 $0 $0

Table 7-22 Estimated Per Unit Compliance Costs – Small Marine Engines (2005$) 

Hp 

Category 

Cost Type 2007 2008 2009 2010 2011 2012 

0-50 Variable $0 $0 $0 $0 $0 $0

 Fixed $178 $176 $175 $173 $365 $0

 Total $178 $176 $175 $173 $365 $0

Hp 

Category 

Cost Type 2013 2014 2015 2016 2017 2018+ 

0-50 Variable $0 $0 $0 $0 $0 $0

 Fixed $0 $0 $0 $0 $0 $0

 Total $0 $0 $0 $0 $0 $0

7.3.2.3 Marine Vessel Compliance Costs 

There are no direct vessel compliance costs associated with the Tier 3 standards. This 

is because the Tier 3 engine footprint (engine size and weight) is not expected to be modified 

from the Tier 2 configuration and therefore no vessel redesign or modification will be 

required to accommodate Tier 3 engines. There are also no indirect vessel compliance costs 
Economic Impact Analysis 

7-53 

associated with the Tier 3 standards as there are no variable costs to the engine manufacturers 

for the Tier 3 standards (the only Tier 3 compliance costs are fixed costs). 

Direct vessel compliance costs are associated with the Tier 4 aftertreatment standards. 

These compliance costs are not differentiated by vessel application; they are the same for all 

commercial vessels that use Tier 4 propulsion engines. The magnitude of the compliance 

costs varies depending on if they are Category 1 or Category 2 engines. The vessel 

compliance costs begin to occur in 2015, with the fixed costs, which continue through 2028, 

depending on the size of the engine.P

 Variable costs begin to occur in 2016 and continue for 

all years of the analysis. 

Table 7-23 Per Unit Compliance Costs – Vessels with C2 Propulsion Engines (2005$; vessel equipped with 

2 propulsion engines) 

Hp 

Category 

Cost 

Type 

2015 2016 2017 2018 2019 2020 2021 2022 

50-200 Variable $0 $0 $0 $0 $0 $0 $0 $0

 Fixed $0 $0 $0 $0 $0 $0 $0 $0

 Total $0 $0 $0 $0 $0 $0 $0 $0

200-400 Variable $0 $0 $0 $0 $0 $0 $0 $0

 Fixed $0 $0 $0 $0 $0 $0 $0 $0

 Total $0 $0 $0 $0 $0 $0 $0 $0

400-800 Variable $0 $0 $0 $0 $0 $0 $0 $0

 Fixed $0 $0 $0 $0 $0 $0 $0 $0

 Total $0 $0 $0 $0 $0 $0 $0 $0

800-2,000 Variable $0 $6,586 $6,586 $5,503 $5,503 $5,503 $5,503 $5,503

 Fixed $50,000 $26,935 $13,347 $13,228 $13,110 $12,993 $12,877 $12,762

 Total $50,000 $33,520 $19,933 $18,731 $18,613 $18,496 $18,380 $18,265

>2,000 Variable $0 $12,358 $12,358 $10,385 $10,385 $10,385 $10,385 $10,385

 Fixed $50,000 $26,935 $13,347 $13,228 $13,110 $12,993 $12,877 $12,762

 Total $50,000 $39,293 $25,705 $23,614 $23,496 $23,379 $23,263 $23,148

Hp 

Category 

Cost 

Type 

2023 2024 2025 2026 2027 2028 2029 2030+ 

50-200 Variable $0 $0 $0 $0 $0 $0 $0 $0

 Fixed $0 $0 $0 $0 $0 $0 $0 $0

 Total $0 $0 $0 $0 $0 $0 $0 $0

200-400 Variable $0 $0 $0 $0 $0 $0 $0 $0

 Fixed $0 $0 $0 $0 $0 $0 $0 $0

 Total $0 $0 $0 $0 $0 $0 $0 $0

400-800 Variable $0 $0 $0 $0 $0 $0 $0 $0

 Fixed $0 $0 $0 $0 $0 $0 $0 $0

 Total $0 $0 $0 $0 $0 $0 $0 $0

 

P

 For simplicity, the marine engine and equipment standards are estimated as though all marine Tier 4 standards 

begin in 2016 even though some Tier 4 standards for very large engines begin in 2014. While this affects the 

individual year results for early years, the differences disappear by 2016 at which time all marine diesel engines 

above 800 hp are subject to aftertreatment standards. 
Regulatory Impact Analysis 

7-54 

800-2,000 Variable $5,503 $5,503 $5,503 $5,503 $5,503 $5,503 $5,503 $5,503

 Fixed $12,649 $12,536 $12,424 $12,313 $12,203 $12,095 $0 $0

 Total $18,152 $18,039 $17,927 $17,816 $17,706 $17,597 $5,503 $5,503

>2,000 Variable $10,385 $10,385 $10,385 $10,385 $10,385 $10,385 $10,385 $10,385

 Fixed $12,649 $12,536 $12,424 $12,313 $12,203 $12,095 $0 $0

 Total $23,034 $22,921 $22,809 $22,699 $22,589 $22,480 $10,385 $10,385

Table 7-24 C1 Per Unit Compliance Costs – Vessels with C1 Propulsion Engines (2005$; vessel equipped 

with 2 propulsion engines) 

Hp 

Category 

Cost 

Type 

2015 2016 2017 2018 2019 2020 2021 2022 

50-200 Variable $0 $0 $0 $0 $0 $0 $0 $0

 Fixed $0 $0 $0 $0 $0 $0 $0 $0

 Total $0 $0 $0 $0 $0 $0 $0 $0

200-400 Variable $0 $0 $0 $0 $0 $0 $0 $0

 Fixed $0 $0 $0 $0 $0 $0 $0 $0

 Total $0 $0 $0 $0 $0 $0 $0 $0

400-800 Variable $0 $0 $0 $0 $0 $0 $0 $0

 Fixed $0 $0 $0 $0 $0 $0 $0 $0

 Total $0 $0 $0 $0 $0 $0 $0 $0

800-2,000 Variable $0 $6,586 $6,586 $5,503 $5,503 $5,503 $5,503 $5,503

 Fixed $25,000 $11,885 $6,544 $3,891 $3,857 $3,822 $3,788 $3,754

 Total $25,000 $18,470 $13,129 $9,394 $9,359 $9,325 $9,291 $9,257

>2,000 Variable $0 $12,358 $12,358 $10,385 $10,385 $10,385 $10,385 $10,385

 Fixed $25,000 $11,885 $6,544 $3,891 $3,857 $3,822 $3,788 $3,754

 Total $25,000 $24,243 $18,902 $14,277 $14,242 $14,208 $14,173 $14,140

Hp 

Category 

Cost 

Type 

2023 2024 2025 2026 2027 2028 2029 2030+ 

50-200 Variable $0 $0 $0 $0 $0 $0 $0 $0

 Fixed $0 $0 $0 $0 $0 $0 $0 $0

 Total $0 $0 $0 $0 $0 $0 $0 $0

200-400 Variable $0 $0 $0 $0 $0 $0 $0 $0

 Fixed $0 $0 $0 $0 $0 $0 $0 $0

 Total $0 $0 $0 $0 $0 $0 $0 $0

400-800 Variable $0 $0 $0 $0 $0 $0 $0 $0

 Fixed $0 $0 $0 $0 $0 $0 $0 $0

 Total $0 $0 $0 $0 $0 $0 $0 $0

800-2,000 Variable $5,503 $5,503 $5,503 $5,503 $5,503 $5,503 $5,503 $5,503

 Fixed $3,721 $3,688 $3,655 $3,622 $3,590 $3,558 $0 $0

 Total $9,224 $9,191 $9,158 $9,125 $9,093 $9,061 $5,503 $5,503

>2,000 Variable $10,385 $10,385 $10,385 $10,385 $10,385 $10,385 $10,385 $10,385

 Fixed $3,721 $3,688 $3,655 $3,622 $3,590 $3,558 $0 $0

 Total $14,106 $14,073 $14,040 $14,007 $13,975 $13,943 $10,385 $10,385

 In addition to the direct vessel costs for Tier 4, there are also indirect costs associated 

with the Tier 4 engine standards, for both propulsion and auxiliary engines. This means it is 

necessary to shift the supply curve by the increase in price for both the propulsion and 
Economic Impact Analysis 

7-55 

auxiliary engines on the vessel that occurs as a result of the engine compliance costs. As 

explained in Section 7.3.1.3, we assume each affected vessel has 2 propulsion engines and an 

average of about 2 marine auxiliary engines. We allocated all auxiliary engines above 800 hp 

to the vessels in this analysis even though some of these large auxiliary engines will be 

installed on vessels with Category 3 propulsion engines that are not part of this rule. As a 

result, while this rule considers the costs of all auxiliary engines, in fact not all of those costs 

will be borne by stakeholders included in this rulemaking, and the share of the costs for 

marine stakeholders is likely to be less. 

7.3.2.4 Operating Costs 

There are two types of operating costs that are affected by the control program: the 

additional costs associated with operating vessels and locomotives equipped with the emission 

control technologies that would be required by the program, and the additional costs 

associated with the locomotive and marine remanufacture programs. Each of these is 

described below. In the EIA, each of these is applied to the transportation markets because 

they are costs that accrue to the operators of the regulated locomotives or marine vessels. 
Regulatory Impact Analysis 

7-56 

Figure 7-9. Estimated Total Compliance Costs by Type, 2007-2040 

$0

$200,000,000$400,000,000$600,000,000$800,000,000

$1,000,000,000$1,200,000,000

200620082010201220142016201820202022202420262028203020322034

New Marine Engines New Marine Vessels New Locomotives Marine Loco operating Marine reman Loco reman Total C
Economic Impact Analysis 

7-57 

Operating Costs. As explained in Chapter 5, we anticipate three sources of increased 

costs associated with operating vessels and locomotives equipped with the emission control 

technologies that would be required by the program: reductant use, DPF maintenance, and 

fuel consumption. The costs associated with reductant use would affect only those 

locomotives or vessels equipped with a SCR engine. Maintenance costs associated with the 

DPF (for periodic cleaning of accumulated ash resulting from unburned material that 

accumulates in the DPF) would occur only in those locomotives or vessels equipped with a 

DPF engine. Thus, those costs are limited to Tier 4 engines. Fuel consumption impacts are 

limited to Tier 4 locomotives and marine engines and, to a much smaller extent, for 

remanufactured Tier 0 locomotives. As explained in section 5.4.3, and discussed in sections 

4.2 and 5.2.2, Tier 3 engine are not expected to have a fuel impact. As illustrated in Figure 7-

9, the estimated operating costs are substantial when compared with the compliance costs 

associated with engine and equipment modifications. 

The EIM applies the operational costs solely to the rail and marine transportation 

services markets.Q This is accomplished by shifting the transportation service sector supply 

curves by the amount of the operating costs for that sector for that year. This was done by 

dividing the total operating costs for each service sector by the revenue for that year, where 

revenue represents the quantity produced in each service sector (due to normalized costs; see 

7.3.1.4). The operating costs per unit are then interpreted as costs per dollar of output. 

Applying these costs to the locomotive transportation market, in the rail sector case, is 

appropriate because all locomotives built after the Tier 4 standards go into effect will incur 

these operating costs. On the marine side, the EIM uses a simplifying assumption that applies 

all marine operating costs to the marine transportation services market and large fishing sector 

(marine vessels that use engines >800 hp) and does not allocate any of these costs to the 

recreational or small fishing sectors. This approach is appropriate because the operating costs 

(fuel and reductant consumption) are estimated based on fuel consumption and most of the 

fuel consumed in the marine sector is by vessels in the marine transportation services sector. 

While many of the new non-recreational vessels built each year are small fishing vessels, the 

use of fishing vessels is highly seasonal and hence they would not be expected to use as much 

fuel as the other commercial vessels (tug/tow/pushboats, ferries, cargo vessels, and 

supply/crew boats) that are used extensively all year around. Nevertheless, there are expected 

to be some Tier 4 engines used in the fishing sector and depending on the extent to which this 

occurs the estimated impacts on the marine transportation service market may be somewhat 

over-estimated. 

 

Q As explained above, the marine transportation market also includes large fishing vessels. 
Regulatory Impact Analysis 

7-58 

 

Table 7-25 Marine and Locomotive Operating Costs 2007-2040 (2005$) 

 Marine C1>800Hp Marine C2 Loco-Line haul Loco-Switcher & 

Passenger 

Total 

2006 $0 $0 $0 $0 $0

2007 $0 $0 $0 $0 $0

2008 $0 $0 $0 $0 $0

2009 $0 $0 $0 $0 $0

2010 $0 $0 $0 $0 $0

2011 $0 $0 $0 $0 $0

2012 $0 $0 $0 $0 $0

2013 $0 $0 $0 $0 $0

2014 $0 $0 a $0 $0 $0

2015 $0 $0 a $14,185,772 $123,493 $14,309,265

2016 $3,870,419 $3,293,306 $29,032,183 $247,890 $36,443,798

2017 $10,570,247 $10,712,210 $44,269,726 $1,271,179 $66,823,362

2018 $19,868,812 $18,184,444 $59,812,022 $1,964,807 $99,830,086

2019 $29,127,012 $25,658,771 $75,760,270 $2,663,752 $133,209,805

2020 $38,333,253 $33,115,211 $92,240,224 $3,368,108 $167,056,796

2021 $47,486,990 $40,569,128 $109,253,188 $4,077,974 $201,387,280

2022 $56,568,548 $48,025,843 $126,758,381 $5,181,423 $236,534,195

2023 $65,562,936 $55,465,093 $144,738,379 $6,548,724 $272,315,132

2024 $74,437,500 $62,887,279 $162,686,068 $7,632,591 $307,643,438

2025 $83,177,448 $70,294,373 $180,873,965 $8,803,576 $343,149,362

2026 $91,751,957 $77,704,458 $199,101,463 $10,036,091 $378,593,969

2027 $100,108,209 $85,118,907 $217,434,039 $11,329,994 $413,991,150

2028 $108,149,904 $92,522,545 $235,760,387 $12,693,251 $449,126,087

2029 $115,215,712 $99,897,508 $254,018,232 $14,115,012 $483,246,465

2030 $120,856,169 $107,239,121 $272,104,628 $15,635,903 $515,835,820

2031 $125,288,414 $114,551,129 $290,126,726 $17,261,263 $547,227,532

2032 $128,976,388 $121,812,042 $308,100,481 $18,968,553 $577,857,463

2033 $132,310,485 $129,005,921 $326,059,441 $20,726,018 $608,101,865

2034 $135,336,373 $136,127,702 $343,920,178 $22,559,417 $637,943,670

2035 $138,043,673 $143,164,690 $361,555,878 $24,413,611 $667,177,853

2036 $140,480,248 $150,075,487 $378,402,501 $26,218,259 $695,176,495

2037 $142,716,999 $156,843,456 $394,649,956 $27,989,727 $722,200,137

2038 $144,794,853 $163,398,588 $410,285,116 $29,715,888 $748,194,446

2039 $146,732,986 $169,244,421 $425,260,693 $31,393,184 $772,631,284

2040 $148,542,610 $173,848,627 $439,565,559 $33,044,952 $795,001,748

a For simplicity, the marine engine and equipment standards are estimated as though all marine Tier 4 standards begin in 

2016 even though some Tier 4 standards for very large engines begin in 2014. While this affects the individual year results 

for early years, the differences disappear by 2016 at which time all marine diesel engines above 800 hp are subject to 

aftertreatment standards. 
Economic Impact Analysis 

7-59 

Remanufacturing Costs. The rail and marine transportation markets are also subject to 

costs associated with the remanufacture standards for their existing engines. Locomotive 

owners are currently required to use certified remanufacture kits when they rebuild engines 

originally built in 1973 through 2001 (called Tier 0 locomotives). This program will extend 

the remanufacturing requirements both to tighten the standards associated with Tier 0 

locomotives and to add requirements for engines built after 2001 (Tier 1 and Tier 2 

locomotives). The new emission control program also includes a program for existing marine 

diesel engines, according to which owners would be required to use a certified remanufacture 

system when they rebuild their engines if such a system is available. This program applies 

only to marine diesel engines above 800 hp that were manufactured from 1973 through Tier 2. 

In the EIM, these remanufacture costs are treated as operating costs and applied to the 

rail and marine transportation sectors along with the reductant and fuel costs. This approach 

was chosen because these costs are periodic and recurring throughout the life of the engine, at 

five to seven year intervals. An important consequence of this modeling approach is that it 

assumes that the owner bears the full cost of the remanufacturing kit and that the kit 

manufacturer does not bear any of the cost. This simplifying assumption is appropriate 

because the mandatory nature of the requirement results in a price elasticity of demand that is 

close to zero (inelastic): if a railroad owns a Tier 0, Tier 1, or Tier 2 locomotive it very 

simply must purchase a kit or it can no longer operate the locomotive. Similarly, if a vessel 

operator owns an engine for which a kit is available, he or she is required use that kit when 

rebuilding the engine. The cost of a remanufacture kit would have to be very high before the 

option of pulling a locomotive or marine vessel out of service or purchasing a new engine one 

would become attractive. 

As explained in Chapter 5, the remanufacturing costs for Tier 0 and Tier 1 

locomotives represent the difference between the cost of current remanufacture kits and those 

that will be required pursuant to the standards. For these kits, first time rebuilds will require 

additional fuel system components that are not required in subsequent rebuilds and therefore 

the cost for the initial rebuild is more than for future rebuilds. For Tier 2 locomotives, there 

are additional costs for the initial rebuild, but not for future rebuilds. There are no additional 

costs associated with Tier 3 rebuilds because these locomotives have all of the essential 

components when they are built new. Finally, there are rebuild costs for Tier 4 locomotives 

associated with the aftertreatment devices. Tier 4 locomotives begin to be rebuilt in 2023. 

For marine diesel engines, there is currently only one tier of requirements. 

Tables 7-26 and 7-27 set out the estimated compliance costs for the locomotive 

remanufacture program for line haul and switcher/passenger locomotives. These tables reflect 

both the year in which the costs apply and the cost per unit. Also included are the fuel costs 

associated with the use of Tier 0 remanufacture systems. 
Regulatory Impact Analysis 

7-60 

Table 7-26 Line Haul Locomotive Remanufacture Costs – Per Unit and Total Fuel Costs 

Year Tier 0 Tier 1 Tier 2 Tier 4 Tier 0 Fuel Costs 

($MM) 

2006 

$0 $0 

$ 

- $ - $0.0

2007 

$0 $0 

$ 

- $ - $0.0

2008 

$33,800 $0 

$ 

- $ - $2,302,054

2009 

$0 $33,800 

$ 

- $ - $2,282,427

2010 

$33,800 $0 

 $ 

- $ - $5,890,678

2011 

$33,800 $33,800 

$ 

- $ - $12,215,776

2012 

$33,800 $33,800 

$ 

- $ - $14,835,459

2013 $33,800 $0 $11,749 $ - $18,419,581

2014 $33,800 $0 $11,749 $ - $19,255,764

2015 $33,800 $0 $11,749 $ - $19,332,128

2016 $33,800 $0 $11,749 $ - $21,973,242

2017 $22,300 $0 $11,749 $ - $24,371,317

2018 $22,300 $0 $11,749 $ - $23,707,717

2019 $22,300 $22,300 $11,749 $ - $22,654,591

2020 $22,300 $0 $0 $ - $20,943,018

2021 $0 $22,300 $0 $ - $19,132,536

2022 $22,300 $22,300 $0 $ - $17,393,944

2023 $22,300 $0 $0 $67,534 $15,727,242

2024 $22,300 $0 $0 $67,534 $14,132,429

2025 $22,300 $0 $0 $67,534 $12,621,451

2026 $0 $0 $0 $67,534 $11,145,346

2027 $0 $0 $0 $67,534 $9,760,539

2028 $22,300 $0 $0 $67,534 $8,459,667

2029 $22,300 $0 $0 $67,534 $7,255,299

2030 $22,300 $0 $0 $67,534 $6,169,878

2031 $0 $22,300 $0 $67,534 $5,180,024

2032 $0 $0 $0 $67,534 $4,280,592

2033 $0 $0 $0 $67,534 $3,461,230

2034 $0 $22,300 $0 $67,534 $2,640,362

2035 $0 $22,300 $0 $67,534 $1,944,832

2036 $0 $0 $0 $67,534 $1,380,746

2037 $0 $0 $0 $67,534 $906,104

2038 $0 $0 $0 $67,534 $524,244

2039 $0 $0 $0 $67,534 $246,131

2040 $0 $0 $0 $67,534 $74,669
Economic Impact Analysis 

7-61 

Table 7-27 Switcher and Passenger Remanufacture Costs – Per Unit and Total Fuel Costs 

Year Tier 0 & 1 Tier 2 Tier 4 Tier 0 Fuel Costs 

($MM) 

2006 $ - $ $0 $0.0 

2007 $ - $0 $0.0 $0 

2008 $33,800 $0 $0.0 $75,733 

2009 $33,800 $0 $0.0 $265,064 

2010 $33,800 $0 $0.0 $452,881 

2011 $33,800 $0 $0.0 $627,823 

2012 $33,800 $0 $0.0 $695,983 

2013 $33,800 $0 $0.0 $752,782 

2014 $33,800 $0 $0.0 $798,979 

2015 $33,800 $0 $0.0 $740,665 

2016 $33,800 $8,728 $0.0 $761,113 

2017 $22,300 $8,728 $0.0 $773,987 

2018 $22,300 $8,728 $0.0 $685,380 

2019 $22,300 $8,728 $0.0 $602,074 

2020 $22,300 $8,728 $0.0 $518,011 

2021 $22,300 $8,728 $0.0 $433,190 

2022 $22,300 $0 $0.0 $347,613 

2023 $22,300 $0 $0.0 $274,152 

2024 $22,300 $0 $0.0 $212,051 

2025 $22,300 $0 $21,937 $153,737 

2026 $22,300 $0 $21,937 $106,026 

2027 $22,300 $0 $21,937 $68,159 

2028 $22,300 $0 $21,937 $39,381 

2029 $22,300 $0 $21,937 $18,933 

2030 $22,300 $0 $21,937 $6,059 

2031 $22,300 $0 $21,937 $0 

2032 $22,300 $0 $21,937 $0 

2033 $22,300 $0 $21,937 $0 

2034 $22,300 $0 $21,937 $0 

2035 $22,300 $0 $21,937 $0 

2036 $22,300 $0 $21,937 $0 

2037 $22,300 $0 $21,937 $0 

2038 $22,300 $0 $21,937 $0 

2039 $22,300 $0 $21,937 $0 

2040 $22,300 $0 $21,937 $0 

Table 7-28 sets out the estimated compliance costs for the marine remanufacture 

program locomotives. This table reflect both the year in which the costs apply and the cost 

per unit. Also included are the fuel costs associated with the program. 
Regulatory Impact Analysis 

7-62 

Table 7-28 Marine Remanufacture Costs - Per Unit and Total al Fuel Costs 

Year Category 1 Category 2 Fuel Costs ($MM) 

2006 $0 $0 $0 

2007 $0 $0 $0 

2008 $0 $33,800 $5,644,717 

2009 $0 $33,800 $11,391,038 

2010 $0 $33,800 $17,240,336 

2011 $0 $33,800 $23,193,999 

2012 $16,900 $33,800 $29,253,431 

2013 $16,900 $33,800 $34,882,042 

2014 $16,900 $33,800 $39,067,796 

2015 $16,900 $0 $37,438,192 

2016 $16,900 $0 $35,809,907 

2017 $11,150 $0 $34,190,014 

2018 $11,150 $0 $32,575,327 

2019 $0 $0 $30,962,228 

2020 $0 $0 $29,357,401 

2021 $0 $0 $27,761,110 

2022 $0 $0 $26,173,086 

2023 $0 $0 $24,593,388 

2024 $0 $0 $23,021,891 

2025 $0 $0 $21,458,087 

2026 $0 $0 $19,900,133 

2027 $0 $0 $18,351,377 

2028 $0 $0 $16,812,402 

2029 $0 $0 $15,290,458 

2030 $0 $0 $13,788,862 

2031 $0 $0 $12,311,399 

2032 $0 $0 $10,857,990 

2033 $0 $0 $9,442,047 

2034 $0 $0 $8,067,051 

2035 $0 $0 $6,749,194 

2036 $0 $0 $5,591,972 

2037 $0 $0 $4,774,057 

2038 $0 $0 $4,211,541 

2039 $0 $0 $3,723,095 

2040 $0 $0 $3,286,099 

7.3.3 Behavioral Parameters 

A key feature of the EIM is that it is a behavioral model in that it incorporates 

economic theory related to producer and consumer behavior to estimate changes in market 

conditions. As explained in 7.2.1, a behavioral model allows us to examine how 

manufacturers of affected goods make out adjustments in response to higher production costs 

due to complying with the control program, and how consumers can be expected to change 

their consumption choices in response to higher prices resulting from producers passing along 

at least some part of the compliance costs. The result of these market interactions determines 

both the new market equilibrium price and quantity and the portion of the compliance costs 
Economic Impact Analysis 

7-63 

that will be born by producers and consumers. Thus, the price elasticity of supply and 

demand are important parameters in behavioral models such as the EIM because they 

represent how much production and consumption can be expected to change as a result of a 

price increase. 

 Tables 7-29 and 7-30 provide a summary of the price elasticities of demand and 

supply that are used as behavioral parameters in the EIM. Elasticities from peer-reviewed 

literature were used when possible. If no peer-reviewed elasticities were available, they were 

estimated for this economic impact analysis using generally accepted empirical methods (see 

Appendix 7F for a discussion of how they were estimated). Several demand elasticities (those 

for locomotives, commercial marine vessels, and marine diesel engines) are derived internally 

by the EIM based on upstream markets (transportation markets). This is another behavioral 

feature of the model that allows linkages between the different components of the model. 

It should be noted that the price elasticities of supply and demand used in the model 

reflect intermediate-run behavioral changes. This is appropriate because the EIM is intended 

to estimate economic impacts as markets adjust to increases in compliance costs. In the long 

run, supply and demand behavioral responses are expected to be more elastic since more 

changes can be made to production processes. 

7.3.3.1 Price Elasticity of Demand for Affected Markets 

The EIM requires that values be specified for the price elasticity of demand for the rail 

and marine transportation markets, the recreational vessel market, and the small fishing vessel 

markets. 

The price elasticity for rail transportation services demand is from the peer-reviewed 

literature and is inelastic (-0.5).21 This means that the quantity demanded is not expected to 

be sensitive to price changes (a one percent increase in price is expected to result in a 0.5 

percent decrease in the quantity demand). This is reasonable because, as described above, 

users of these transportation services typically chose them because they are the best solution 

for transporting their goods. The decision to choose rail transportation services is a function 

of many things and the price may not be the most important factor. 

We were unable to find a price elasticity of demand for the marine transportation 

sector in the peer-reviewed literature. Due to difficulties in gathering the appropriate data to 

estimate this elasticity, we instead use the same demand elasticity as the rail transportation 

services market. This is reasonable because a significant portion of the marine transportation 

sector is engaged in the same basic activity, although with different geographic constraints. 

Like locomotives, vessels used in marine transportation services (e.g., cargo vessels, ferries, 

supply/crew boats, and tow/tug/pushboats) are engaged in transporting materials and people, 

and the demand for those services is likely to be inelastic because the users have few, if any, 

alternatives. 

For the recreational vessel market, we used the same price elasticity of demand used in 

the Economic Impact Analysis for our recently proposed SI marine standards.22 This price 

elasticity of demand is elastic (-2.0), meaning that consumers are expected to be sensitive to a 
Regulatory Impact Analysis 

7-64 

change in price (a one percent increase in price is expected to result in a two percent decrease 

in the quantity demanded). This is reasonable because recreational marine vessels are a 

discretionary purchase and consumers have other recreational alternatives. 

We were also unable to find a price elasticity of demand for the fishing sector in the 

peer-reviewed literature. In this analysis, we used a dual approach. Specifically, for large 

fishing vessels (those with propulsion engines above 800 hp) we applied the same price 

elasticity of demand as for commercial vessels (as explained elsewhere, this demand elasticity 

is derived as part of the model from the transportation services sector); for small fishing 

vessels (those with propulsion engines below 800 hp) we applied the same price elasticity of 

demand as for recreational vessels. This approach is appropriate because smaller fishing 

vessels are not used in the same way as larger fishing vessels. Smaller vessels are often 

personally operated by the owner of the vessel; if there is a crew it would be very small. 

These vessels are used for specific applications in which catches are small and speed is 

essential (e.g., lobster fishing). This market can very competitive: the catch is bought the 

same day as the vessel returns to port, with the earliest arrivers getting the best price. Because 

of these dynamics, the price elasticity of fishing vessels is likely to be fairly elastic (fish 

vessel purchasers likely to be sensitive to an increase in the price of a vessel). Vessels with 

larger engines, on the other hand, are likely to be part of a large commercial operation, 

making them less sensitive to vessel price changes. These vessels have larger crews and will 

go out for several weeks or months. The catch will be larger and may even be processed 

onboard the vessel or frozen for shore processing. For these vessels, we used the same 

approach as commercial vessels, in which the price elasticity of demand for the equipment is 

derived from the transportation market. This approach implies that the price elasticity of 

demand for final consumers of fish is inelastic (-0.5); this is reasonable given a previouslyestimated price elasticity of demand for the agricultural products market estimated for our 

2004 Nonroad Rule (-0.2).23 

7.3.3.2 Supply Elasticities 

Unlike the price elasticity of demand, the EIM requires that values be specified for the 

price elasticity of supply for each of the markets included in the model; none of these values 

are derived by the model. 

The price elasticity of supply for the rail transportation service market is the same as 

that estimated for the economic impact analysis for our Clean Air Nonroad Diesel (Nonroad 

Tier 4) rule. 24 This price elasticity of supply is elastic (1.6), meaning that producers are 

expected to be sensitive to a change in price (a 1 percent price increase is expected to result in 

a 1.6 increase in the quantity produced). This reflects extra production capacity in the market 

and the relative ease with which railroads can alter their production of transportation services 

(e.g., by making trains longer or shorter). 

A published estimate of the price elasticity of supply for the locomotive market was 

unavailable. Therefore we estimated a value for this parameter using the calibration method 

approach (this approach and the results are described in Appendix 7F). At 2.7, the price 

elasticity of supply for the locomotive market is elastic, meaning that producers are expected 

to be sensitive to changes in price (a one percent increase in price is expected to result in a 2.7 
Economic Impact Analysis 

7-65 

percent increase in quantity produced). This reflects extra production capacity in the market. 

The EIM uses the same value for the price elasticity of supply for all locomotive markets: 

line haul, switcher, and passenger. Using this estimated price elasticity of supply for the 

switcher market is somewhat speculative since that market is currently not very developed 

(most existing switchers are modified line haul locomotives; see above). 

For the marine transportation services market, we used the same approach as for the 

economic impact analysis for our Clean Air Nonroad Diesel rule and applied the price 

elasticity of supply used for the train transportation market. 25 This approach is reasonable 

because the marine transportation service sector provides a similar service, although with 

different geographic constraints. 

A published estimate of the price elasticity of supply for the commercial vessel market 

was unavailable. Therefore we estimated a value for this parameter using the calibration 

method approach (this approach and the results are described in Appendix 7F). At 2.3, the 

price elasticity of supply for the locomotive market is elastic, meaning that producers are 

expected to be sensitive to changes in price (a one percent increase in price is expected to 

result in a 2.3 percent increase in quantity produced). This reflects excess production capacity 

in the market. 

For the recreational vessel market, we used the same price elasticity of supply used in 

the Economic Impact Analysis for our recently proposed SI marine standards.26 This price 

elasticity of supply is elastic (2.3), meaning that producers are expected to be sensitive to a 

change in price (a one percent increase in price is expected to result in a 2.3 percent increase 

in the quantity produced). This is reasonable since recreational vessels are typically serially 

produced with no specific buyer in mind, using fiberglass molds. Therefore a price increase 

may have to be higher before affecting production. Also, to some extent, these vessels are 

more “portable” and can be inventoried, although model year and design may limit the ability 

of manufacturers to inventory large numbers of these vessels. 

For the fishing vessel market, we used the same approach described above for the 

supply elasticity of demand, applying the price elasticity of supply for commercial vessels to 

fishing vessels with engines greater than 800 hp, and the price elasticity of supply for 

recreational vessels for fishing vessels with engines less than 800 hp. This is reasonable 

because smaller vessels have many of the same characteristics as recreational vessels (highspeed planning vessels with fiberglass hulls), while larger vessels are produced more like 

commercial vessels (uniquely built for an identified purchaser based on designs that are 

typically modified by the purchaser before production). Because of these similarities, the 

processes used to produced small and large fishing vessels would be more like recreational 

and commercial vessels, respectively. It should be noted that, in this case, the price 

elasticities of supply for both markets are the same (2.3). 

For the marine diesel engine market, we used the same price elasticity of supply for 

diesel engines that was estimated for the economic impact analysis for our 2004 Clean Air 

Nonroad Diesel rule.27 This approach is reasonable because the vast majority of marine diesel 

engines affected by this rule are derived from land-based marine or highway diesel engines. 

This price elasticity of supply is elastic (3.8), meaning that producers are expected to be 
Regulatory Impact Analysis 

7-66 

sensitive to a change in price (a one percent increase in price is expected to result in a 3.8 

increase in supply). This is due to excess production capacity. 

Because the price elasticity of demand and supply are key inputs to the model, a 

sensitivity analysis was performed to consider the uncertainty that is associated with the 

estimation process. The sensitivity analysis includes alternative values for the price elasticity 

of supply for locomotives and marine vessels estimated using an alternative method in lieu of 

the calibration method. The results are presented in Appendix 7H. 

Table 7-29. Price Elasticities of Demand Used in EIM 

Market Estimate Source Method Data Source 

Rail 

Rail Transp. 

Svcs 

-0.5 Literature 

estimate 

Literature 

review 

Boyer, K.D. 1997. Principles of 

Transportation Economics. Reading, MA: 

Addison-Wesley. 

Locomotives Derived 

Marine 

Marine 

Transp. Svcs 

-0.5 Literature 

estimate 

Assumed 

value 

Uses the same elasticity as the locomotive 

transportation services sector. 

Vessels—

Commercial 

Fishing 

(>800 hp) 

Derived 

Vessels—

Fishing 

(<800 hp) 

-2.0 Econometric 

estimate 

Assumed 

value 

Uses the same elasticity as the recreation 

vessels sector. 

Vessels—

Recreational 

-2.0 Econometric 

estimate 

Previous 

EPA 

economic 

analysis 

U.S. Environmental Protection Agency 

(EPA). 2007. Control of Emissions from 

Marine SI and Small SI Engines, Vessels, 

and Equipment Draft Regulatory Impact 

Analysis. EPA420-D-07-004. Available at 

<http://www.epa.gov/otaq/regs/nonroad/ma

rinesi-equipld/420d07004.pdf>.

Engines Derived 

Table 7-30. Supply Elasticities Used in EIM 

Market Estimate Source Method Input Data Source 

Rail 
Economic Impact Analysis 

7-67 

Rail Transp. 

Svcs 

1.6 Literature 

estimate 

Method based 

on cost 

elasticities 

reported in 

Ivaldi and 

McCollough 

(2001) 

Ivaldi, M. and McCullough, G. 2001. 

"Density and Integration Effects on Class I 

U.S. Freight Railroads." Journal of Regulatory 

Economics 19:161-162. 

Locomotives 2.7 EPA 

estimate 

Calibration 

method 

U.S. Bureau of the Census. 2004a. “Railroad 

Rolling Stock Manufacturing: 2002.” 2002 

Economic Census Manufacturing Industry 

Series. EC02-31I-336510 (RV). Washington, 

DC: U.S. Bureau of the Census. Table 1. 

U.S. Bureau of the Census. 2005. “Statistics 

for Industry Groups and Industries: 2004” 

Annual Survey of Manufacturers. M04(AS)-1. 

Washington, DC: U.S. Bureau of the Census. 

Table 2. 

Marine 

Marine 

Transp. Svcs 

1.6 Assumed value; uses the same elasticity as the rail transportation services sector 

Vessels—

Commercial 

and large 

fishing 

2.3 EPA 

estimate 

Calibration 

method U.S. Bureau of the Census. 2004b. “Ship 

Building and Repairing: 2002.” 2002 

Economic Census Manufacturing Industry 

Series. EC02-31I-336611 (RV). Washington, 

DC: U.S. Bureau of the Census. Table 1. 

U.S. Bureau of the Census. 2005. “Statistics 

for Industry Groups and Industries: 2004” 

Annual Survey of Manufacturers. M04(AS)-1. 

Washington, DC: U.S. Bureau of the Census. 

Table 2. 

Vessels—

Recreational 

and small 

fishing 

2.3 Econometric 

estimate 

Previous EPA 

economic 

analysis 

U.S. Environmental Protection Agency 

(EPA). 2007. Control of Emissions from 

Marine SI and Small SI Engines, Vessels, and 

Equipment Draft Regulatory Impact Analysis. 

EPA420-D-07-004. Available at 

<http://www.epa.gov/otaq/regs/nonroad/marin

esi-equipld/420d07004.pdf>. 

Engines 3.8 Econometric 

estimate 

Previous EPA 

economic 

analysis 

U.S. Environmental Protection Agency 

(EPA). 2004. Final Regulatory Impact 

Analysis: Control of Emissions from Nonroad 

Diesel Engines. EPA420-R-04-007. 

Available at <http://www.epa.gov/nonroaddiesel/2004fr/420r04007.pdf>. 
Regulatory Impact Analysis 

7-68 

7.3.4 Economic Impact Model Structure 

7.3.3.1 Estimating With-Regulation Equilibrium Conditions 

The economic impact analysis is conducted using the data and the supply and demand 

framework described above. The price and quantity data, along with the supply and demand 

elasticities, are used to identify the market supply and demand curves. The regulatory costs 

are then used to shift the supply curve, and the resulting new equilibrium determines the 

market impacts and distribution of social impacts. 

Figure 7-10 illustrates the economic impact modeling structure. Point A represents the 

initial baseline equilibrium price and quantity (corresponding to the prices and quantities 

presented in section 7.3.1). The slope of the supply and demand curves passing through the 

baseline point A are determined by applying the appropriate supply and demand elasticities 

presented in section 7.3.2.6. These slopes reflect the responsiveness of producers and 

consumers when prices change and determine how much of the compliance costs producers 

are able to pass along to consumers in the with-regulation equilibrium. 

The compliance costs associated with the regulation (presented in Section 7.3.2) enter 

the model expressed as per-unit costs and result in an upward shift in the supply curve from S0

to S1 in Figure 7-10. Note that the demand curve does not shift because consumer preferences 

and income are not affected by the regulation (see Section 7.3.2.1) 

With the addition of the compliance costs, if prices were not allowed to adjust 

demanders would still want to consume the quantity at point A, but suppliers would only be 

willing to supply the quantity at point B (i.e., demand exceeds supply at the baseline price, P). 

The model then solves for the new equilibrium price (P*) where the quantity demanded 

equals the quantity supplied. The movement from the baseline equilibrium point A to withregulation equilibrium point C determines the market impacts (changes in price and quantity) 

as well as the distribution of social costs. Appendix 7E describes the set of supply and 

demand equations included in the model. Given the number of equations included in the 

model, the solution algorithm described below is used to identify the new with-regulation set 

of equilibrium prices and quantities (Point C). 

The analysis illustrated in Figure 7-10 is repeated for each year included in the period 

of analysis. For future years, a projected time series of prices and quantities are developed 

and used as the baseline (point A) from which market changes are evaluated. The engineering 

cost analysis provides quantities for future years using historical annual growth rates. In 

contrast, there is much more uncertainty surrounding future prices for these markets. As a 

result, we use a constant 2005 observed prices for the relevant markets during the period of 

analysis. 
Economic Impact Analysis 

7-69 

Figure 7-10 Estimating With-Regulation Equilibrium 

7.3.3.2 Solution Algorithm 

Supply responses and market adjustments can be conceptualized as an interactive 

process. Producers facing increased production costs due to compliance are willing to supply 

smaller quantities at the baseline price. This reduction in market supply leads to an increase 

in the market price that all producers and consumers face, which leads to further responses by 

producers and consumers and thus new market prices, and so on. The new with-regulation 

equilibrium is the result of a series of iterations in which price is adjusted and producers and 

consumers respond, until a set of stable market prices arises where total market supply equals 

market demand. Market price adjustment takes place based on a price-revision rule, described 

below, that adjusts price upward (downward) by a given percentage in response to excess 

demand (excess supply). 

The EIM model uses a similar type of algorithm for determining with-regulation 

equilibria and the process can be summarized by six recursive steps: 

1. Impose the control costs on affected supply segments, thereby affecting their supply 

decisions. 

2. Recalculate the market supply in each market. Excess demand currently exists. 
Regulatory Impact Analysis 

7-70 

3. Determine the new prices via a price revision rule. We use a rule similar to the factor 

price revision rule described by Kimbell and Harrison.28 Pi is the market price at 

iteration I, qd is the quantity demanded, and qs is the quantity supplied. The parameter 

z influences the magnitude of the price revision and speed of convergence. The 

revision rule increases the price when excess demand exists, lowers the price when 

excess supply exists, and leaves the price unchanged when market demand equals 

market supply. The price adjustment is expressed as follows: 

z

s

d

11i

q

q PP ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ + •= 

4. Recalculate market supply with new prices, 

5. Compute market demand in each market. 

6. Compare supply and demand in each market. If equilibrium conditions are not 

satisfied, go to Step 3, resulting in a new set of market prices. Repeat until 

equilibrium conditions are satisfied (i.e., the ratio of supply and demand is arbitrarily 

close to one). When the ratio is appropriately close to one, the market- clearing 

condition of supply equals demand is satisfied. 

7.3.5 Estimating Impacts 

Using the static partial equilibrium analysis, the EIM model loops through each year 

calculating new market equilibriums based on the projected baseline economic conditions and 

compliance cost estimates that shift the supply curves in the model. The model calculates 

price and quantity changes and uses these measures to estimate the social costs of the rule and 

partition the impact between producers and consumers. This approach follows the classical 

treatment of tax burden distribution in the public finance literature.29

7.4 Methods for Describing Uncertainty 

Every economic impact analysis examining the market and social welfare impacts of a 

regulatory program is limited to some extent by limitations in model capabilities, deficiencies 

in the economic literatures with respect to estimated values of key variables necessary to 

configure the model, and data gaps. In this EIA, there are three main potential sources of 

uncertainty: (1) uncertainty resulting from the way the EIM is designed, particularly from the 

use of a partial equilibrium model; (2) uncertainty resulting from the values for key model 

parameters, particularly the price elasticity of supply and demand; and (3) uncertainty 

resulting from the values for key model inputs, particularly baseline equilibrium price and 

quantities. Sources of uncertainty that have a bearing on the results of the EIA for the new 

emission control program are listed and described in more detail in Table 7-31. 
Economic Impact Analysis 

7-71 

The values used for the price elasticities of supply and demand are critical parameters 

in the EIM. The values of these parameters have an impact on both the estimated change in 

price and quantity produced expected as a result of compliance with the new standards and on 

how the burden of the social costs will be shared among producer and consumer groups. In 

selecting the values to use in the EIM it is important that they reflect the behavioral responses 

of the industries under analysis. 

The first source of values for elasticities of supply and demand is the published 

economic literature. These estimates are peer reviewed and generally constitute reasonable 

estimates for the industries in question. In this analysis, we use published elasticity from 

peer-reviewed literature for the rail transportation services sector, for both demand and 

supply.30 We used these elasticities for the marine transportation sector as well. 

When published elasticities of supply or demand are not available, it is necessary to 

estimate these values econometrically. 

We used previously-estimated values for the price elasticity of supply for engines and 

recreational vessels in this analysis, (see Appendix 7F). These estimates reflect a production 

function approach using data at the aggregate industry level. This method was chosen 

because of limitations with the available data: we were not able to obtain firm-level or plantlevel production data for companies that operate in the affected sectors. However, the use of 

aggregate industry level data may not be appropriate or an accurate way to estimate the price 

elasticity of supply compared to firm-level or plant-level data. This is because, at the 

aggregate industry level, the size of the data sample is limited to the time series of the 

available years and because aggregate industry data may not reveal each individual firm or 

plant production function (heterogeneity). There may be significant differences among the 

firms that may be hidden in the aggregate data but that may affect the estimated elasticity. In 

addition, the use of time series aggregate industry data may introduce time trend effects that 

are difficult to isolate and control. 

To estimate the price elasticity of supply for the locomotive and commercial marine 

vessel markets, we used a calibration method. This involves specifying an economic model 

of supply, treating some of the parameters of the model as fixed using secondary data, and 

solving for unknown parameters that replicate a benchmark data set (see Appendix F). This 

approach introduces uncertainty in that the results are dependent on the underlying 

assumptions and data used to construct the economic model. In our proposal, we noted that 

we intended investigate alternative estimates for the price elasticity of supply for these two 

industries, using the production function approach and cross-sectional data model at either the 

firm-level or plant level from the U.S. Census. Because the results of that analysis have not 

yet been peer reviewed, we did not use them in our primary analysis. However, we performed 

a sensitivity analysis using those alternative values (see Appendix H). 
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Table 7-31 Primary Sources of Uncertainty in the Economic Impact Analysis 

Source of Uncertainty Description Potential Impact 

UNCERTAINTIES ASSOCIATED WITH ECONIMIC IMPACT MODEL STRUCTURE 

Partial equilibrium model – The EIM domain is limited to the economic 

sectors directly affected by the emission control 

program; impacts on secondary markets are not 

accounted for. However, the impacts are not 

expected to be large as directly affected products 

and services (locomotives and marine engines and 

vessels) are production inputs (transportation 

services) and are not a large share of total 

production costs for final goods and services, or 

are final goods for household consumption 

Results may 

understate social 

costs; magnitude of 

impact is uncertain 

National level model The EIM considers only national-level impacts; 

regional impacts are not modeled. This is 

appropriate because locomotive and marine 

engine and vessel markets are national markets. 

While there may be some regional differences 

these are likely to be small due to the competitive 

nature of the transportation industry. 

Impacts uncertain 

Supply side assumptions On the supply side, industries are assumed to be 

mature and behave linearly within the range of 

analysis; no substitution between production 

inputs. This is appropriate because per unit 

compliance costs are not large enough to prompt a 

major change in product design or assembly. 

Impacts uncertain 

Demand side assumptions On the demand side, end consumer preferences or 

consumption patterns are assumed to be constant 

and behave linearly within the range of analysis. 

This is appropriate because all other factors in the 

demand function will not be changed by the new 

standards. 

Impacts uncertain 

Constant price assumption Prices are assumed to be constant across the 

period of analysis. This is a reasonable 

assumption since it is not possible to predict 

changes in these prices over time (see Appendix 

7H). 

Impacts uncertain 

Period of analysis Each period of analysis is assumed to be 

independent of previous period and producers are 

assumed to not engage in long-term planning. 

This means the impacts of multi-tier standards are 

not smoothed among periods. Because the new 

engine standards will not go into effect for several 

years after the program is finalized, producers 

may in fact take the full program into account in 

production plans to minimize their costs 

Estimated price 

changes may be too 

high for early 

periods, too low for 

later periods; 

magnitude of impact 

is uncertain 
Economic Impact Analysis 

7-73 

Market shock In the EIM, the market shocked by variable costs 

only; fixed costs do not disturb the market 

equilibrium. This is a result of the perfect 

competition assumption implies market supply 

curve is the industry average marginal cost curve. 

This is appropriate because producers in these 

industries generally plan for R&D and model 

changes. A sensitivity analysis performed that 

includes fixed costs in supply shift

Results may overstate 

distribution of social 

costs to some 

producers, understate 

market impacts; 

magnitude of impact 

is uncertain 

Sensitivity analysis 

performed 

UNCERTAINTIES ASSOCIATED WITH PRICE ELASTICITY ESTIMATION 

Uncertainty resulting from the functional form 

used in the estimation, the data used (aggregate or 

firm-level), the time period involved, sample size. 

Impacts on 

distribution of social 

costs among 

stakeholders (e.g., 

higher supply 

elasticity would result 

in less social costs for 

manufacturers and 

more social costs for 

consumers) 

Impacts on market 

analysis (change in 

price, change in 

quantity produced) 

; magnitude of impact 

is uncertain 

Sensitivity analysis 

performed

UNCERTAINTIES ASSOCIATED WITH DATA INPUTS 

Submarket groupings Submarket data is assumed to be representative 

and capture the range of affected equipment. 

However, the product groupings in NAICS or SIC 

4-digit categories may include other engines or 

equipment that may not have the same production 

or consumption characteristics; these groupings 

not behave the same way as the directly-affected 

industries. 

Impacts on social 

welfare and market 

analyses uncertain 

Baseline equilibrium prices Estimated baseline equilibrium prices are 

assumed to be representative and capture the 

range of affected equipment, and reflect actual 

transaction prices. However, the actual prices 

paid by consumers may be different. Also, the 

mix of products included in price analysis may 

not be representative of the population. 

Impacts on market 

analysis uncertain 

Baseline equilibrium quantities Estimated baseline equilibrium quantities and 

future quantities assumed to be representative; 

these are the same as the cost analysis 

Impacts on market 

analysis uncertain 
Regulatory Impact Analysis 

7-74 

To explore the effects of key sources of uncertainty, we performed a sensitivity 

analysis in which we examine the results of using alternative values for the price elasticity of 

supply and demand and alternative methods to shock to the market equilibrium (fixed and 

variable costs). We also examined the results of using alternative values for the equipment 

supply elasticities (locomotives and marine vessels) estimated using an alternative 

methodology, and we examined the impacts of using a weighted average compliance cost for 

auxiliary marine engines above 800 hp. These analysis and their results are described in more 

detail in Appendix 7H. A summary of the results are presented in Table 7-32. 

Table 7-32. Results of Sensitivity Analysis 

Parameter Year Change in 

Value 

Impact 

2020 More elastic Negligible impact on expected price increase and quantity decrease 

Higher value associated with increase in social cost burden for users 

of rail and marine transportation services 

Price 

Elasticity of 

Supply 

2020 Less elastic Negligible impact on expected price increase and quantity decrease 

Lower value associated with increase in social cost burden for 

suppliers of marine vessels and providers of rail and marine 

transportation services 

2020 More elastic Negligible impact on expected price increase and quantity decrease 

Higher value associated with increase in social cost burden for 

suppliers of marine vessels and providers of rail and marine 

transportation services 

Price 

Elasticity of 

Demand 

2020 Less elastic Negligible impact on expected price increase and quantity decrease 

Lower value associated with increase in social cost burden for users 

of rail and marine transportation services 

Market 

Supply Shift 

2014 Include 

fixed and 

variable 

costs; 

analysis 

performed 

for 

locomotive 

and 

propulsion 

marine >800 

hp only 

Price increase larger than primary case, but decrease in quantity 

produced remains small, less than 3.5 percent (less than 20 units) for 

commercial marine engines and vessels. Negligible change in 

locomotive markets. Distribution of social costs shifts from 

manufacturers to user groups. 
Economic Impact Analysis 

7-75 

Alternative 

Method of 

Estimating 

Price 

Elasticity of 

Supply – 

Equipment 

Markets 

2020 Supply 

elasticites 

for 

locomotives 

and marine 

vessels more 

elastic 

Differences in market impacts (price and quantity changes) and 

distribution of costs among stakeholders are negligible. The share of 

compliance costs borne by vessel manufacturers is slightly less, and 

the share of engine manufactures and rail transportation service 

providers and users increases slightly. Similarly, the share of 

locomotive producers is slightly less than the primary case, and the 

share of rail transportation service producers and consumers is 

slightly more. 

Alternative 

Compliance 

Cost for 

Auxiliary 

Marine 

Engines 

Above 800 

hp 

2016, 

2030 

Separate 

compliance 

costs for 

auxiliary 

engines 800-

2,000 hp and 

above 2,000 

hp 

Market impacts are different, with smaller price increase for 

auxiliary engines 800 to 2,000 hp and larger price increase for 

auxiliary engines above 2,000 hp. Slight reduction in the decrease in 

quantity produced for small engines; no change for larger engines. 

Slight shift in social welfare cost burden from marine engine and 

vessel producers to marine transportation service producers and 

consumers. 
Regulatory Impact Analysis 

7-76 

Appendix 7A: Impacts on Marine Engine Markets 

This appendix provides the time series of impacts from 2007 through 2040 for selected 

auxiliary and propulsion marine engines markets. R Table 7A-1 through Table 7A-6 provide 

the time series of impacts and include the following: 

• average engineering costs (variable) per engine 

• absolute change in the market price ($) 

• relative change in market price (%) 

• relative change in market quantity (%) 

• total engineering costs (variable and fixed) associated with each engine market 

• changes in engine manufacturer surplus 

All prices, costs, and surplus changes are presented in 2005 dollars, and real engine or 

equipment prices are assumed to be constant during the period of analysis. Net present values 

for 2006 were calculated using social discount rates of 3% and 7% over the 2007 and 2040 

time period. 

Results are presented for only those markets that are expected to incur direct variable 

costs under Tier 3 or Tier 4 standards. This means that results are not presented for marine 

engine markets less than 800 hp or for recreational propulsion engine markets. For these 

engine markets, the results are expected to be negligible and any change in price or quantity 

would be incidental to the changes in the larger engine markets. It should also be noted that 

all engine markets would incur fixed costs. However, as explained in 7.2.3.4, fixed costs are 

not included in the EIM. 

The NPV calculations presented in this Appendix are based on the period 2006-2040, 

reflecting the period when the analysis was completed. This has the consequence of 

discounting the current year costs, 2007, and all subsequent years are discounted by an 

additional year. The result is a smaller stream of social costs than by calculating the NPV 

over 2007-2040 (3% smaller for 3% NPV and 7% smaller for 7% NPV).

 

R

 The engineering costs we used in the EIA are an earlier version of the estimated compliance costs developed 

for this rule; see Section 7.3.2 for an explanation of the difference. This difference is not expected to have an 

impact on the results of the market analysis or on the expected distribution of social costs among stakeholders. 
Econom

7-77 

Table 7A-1. Impact on Auxiliary Engine Market: 800–2000 hp (Average Price per Engine = $167,000)a,b

Year 

Variable 

Engineering 

Cost/Unit 

Absolute Change 

in Price 

($) 

Change 

in Price 

(%) 

Change 

in Quantity 

(%) 

Total 

Engineering Costs 

(million $) 

2007 $0 $0 0.0% 0.0% $1.7 2008 $0 –$4 0.0% 0.0% $1.7 2009 $0 –$5 0.0% 0.0% $1.7 2010 $0 –$6 0.0% 0.0% $1.7 2011 $0 –$7 0.0% 0.0% $13.5 2012 $0 –$10 0.0% 0.0% $10.7 2013 $0 –$10 0.0% 0.0% $10.7 2014 $0 –$10 0.0% 0.0% $10.7 2015 $0 –$8 0.0% 0.0% $14.9 2016 $37,097 $34,894 20.9% –5.0% $8.3 2017 $37,096 $34,891 20.9% –5.0% $8.4 2018 $28,360 $26,653 16.0% –3.9% $6.5 2019 $28,359 $26,650 16.0% –3.9% $6.5 2020 $28,363 $26,652 16.0% –3.9% $6.6 2021 $28,360 $26,646 16.0% –3.9% $6.7 2022 $28,359 $26,643 16.0% –3.9% $6.7 2023 $28,363 $26,644 16.0% –3.9% $6.8 2024 $28,360 $26,639 15.9% –3.9% $6.8 2025 $28,361 $26,638 15.9% –3.9% $6.9 2026 $28,361 $26,635 15.9% –3.9% $7.0 2027 $28,359 $26,631 15.9% –3.9% $7.0 2028 $28,361 $26,632 15.9% –3.9% $7.1 2029 $28,360 $26,628 15.9% –3.9% $7.2 2030 $28,359 $26,626 15.9% –3.9% $7.2 2031 $28,360 $26,626 15.9% –3.9% $7.3 2032 $28,359 $26,624 15.9% –3.9% $7.3 2033 $28,359 $26,622 15.9% –4.0% $7.4 2034 $28,360 $26,622 15.9% –4.0% $7.5 2035 $28,359 $26,620 15.9% –4.0% $7.5 2036 $28,361 $26,622 15.9% –4.0% $7.6 2037 $28,359 $26,619 15.9% –4.0% $7.7 2038 $28,360 $26,619 15.9% –4.0% $7.8 2039 $28,357 $26,616 15.9% –4.0% $7.8 2040 $28,362 $26,620 15.9% –4.0% $7.9 NPV at 3% $147.5 NPV at 7% $83.4 

a Figures are in 2005 dollars. b Net present values for 2006 are calculated using a social discount rate of 3% and 7% over the 2007 to 2040 time period. 
Regulatory Impact Analysis 

7-78 

Table 7A-2. Impact on Auxiliary Engine Market: >2000 hp (Average Price per Engine = $385,000)a,b

Year 

Variable 

Engineering 

Cost/Unit 

Absolute Change 

in Price 

($) 

Change 

in Price 

(%) 

Change 

in Quantity 

(%) 

Total 

Engineering Costs 

(million $) 

2007 $0 $0 0.0% 0.0% $0.9 2008 $0 –$45 0.0% 0.0% $0.9 2009 $0 –$60 0.0% –0.1% $0.9 2010 $0 –$74 0.0% –0.1% $0.9 2011 $0 –$88 0.0% –0.1% $6.7 2012 $0 –$122 0.0% –0.1% $5.3 2013 $0 –$130 0.0% –0.1% $5.3 2014 $0 –$128 0.0% –0.1% $5.3 2015 $0 –$99 0.0% –0.1% $7.4 2016 $37,097 $36,919 9.6% –0.2% $4.2 2017 $37,096 $36,895 9.6% –0.2% $4.2 2018 $28,360 $28,142 7.3% –0.2% $3.2 2019 $28,359 $28,111 7.3% –0.2% $3.3 2020 $28,363 $28,083 7.3% –0.3% $3.3 2021 $28,360 $28,049 7.3% –0.3% $3.3 2022 $28,359 $28,017 7.3% –0.3% $3.4 2023 $28,363 $27,991 7.3% –0.4% $3.4 2024 $28,360 $27,959 7.3% –0.4% $3.4 2025 $28,361 $27,932 7.3% –0.4% $3.5 2026 $28,361 $27,904 7.2% –0.5% $3.5 2027 $28,359 $27,876 7.2% –0.5% $3.5 2028 $28,361 $27,853 7.2% –0.5% $3.5 2029 $28,360 $27,829 7.2% –0.5% $3.6 2030 $28,359 $27,809 7.2% –0.5% $3.6 2031 $28,360 $27,793 7.2% –0.6% $3.6 2032 $28,359 $27,778 7.2% –0.6% $3.7 2033 $28,359 $27,764 7.2% –0.6% $3.7 2034 $28,360 $27,753 7.2% –0.6% $3.7 2035 $28,359 $27,740 7.2% –0.6% $3.8 2036 $28,361 $27,732 7.2% –0.6% $3.8 2037 $28,359 $27,719 7.2% –0.6% $3.8 2038 $28,360 $27,710 7.2% –0.6% $3.9 2039 $28,357 $27,698 7.2% –0.7% $3.9 2040 $28,362 $27,697 7.2% –0.7% $3.9 NPV at 3% $73.8 NPV at 7% $41.7 

a Figures are in 2005 dollars. b Net present values for 2006 are calculated using a social discount rate of 3% and 7% over the 2007 to 2040 time period. 
Econom

7-79 

Table 7A-3. Impact on C1 Commercial Propulsion Engine Market: 800–2000 hp (Average Price per Eng

Year 

Variable 

Engineering 

Cost/Unit 

Absolute Change 

in Price 

($) 

Change 

in Price 

(%) 

Change 

in Quantity 

(%) 

Total 

Engineering Costs 

(million $) 

2007 $0 $0 0.0% 0.0% $1.3 2008 $0 –$2 0.0% 0.0% $1.3 2009 $0 –$3 0.0% 0.0% $1.3 2010 $0 –$4 0.0% 0.0% $1.3 2011 $0 –$5 0.0% 0.0% $15.1 2012 $0 –$7 0.0% 0.0% $13.4 2013 $0 –$7 0.0% 0.0% $13.4 2014 $0 –$7 0.0% 0.0% $13.4 2015 $0 –$5 0.0% 0.0% $18.1 2016 $15,196 $13,995 9.0% –2.9% $8.1 2017 $15,196 $13,993 9.0% –2.9% $8.2 2018 $11,618 $10,680 6.9% –2.3% $6.3 2019 $11,618 $10,678 6.9% –2.3% $6.4 2020 $11,618 $10,677 6.9% –2.3% $6.4 2021 $11,618 $10,675 6.9% –2.3% $6.5 2022 $11,618 $10,673 6.9% –2.3% $6.6 2023 $11,618 $10,672 6.9% –2.3% $6.6 2024 $11,618 $10,670 6.9% –2.3% $6.7 2025 $11,618 $10,669 6.9% –2.3% $6.7 2026 $11,618 $10,667 6.9% –2.3% $6.8 2027 $11,618 $10,666 6.9% –2.3% $6.9 2028 $11,618 $10,664 6.9% –2.3% $6.9 2029 $11,618 $10,663 6.9% –2.3% $7.0 2030 $11,618 $10,662 6.9% –2.3% $7.0 2031 $11,618 $10,661 6.9% –2.3% $7.1 2032 $11,618 $10,660 6.9% –2.3% $7.2 2033 $11,618 $10,660 6.9% –2.3% $7.2 2034 $11,618 $10,659 6.9% –2.4% $7.3 2035 $11,618 $10,658 6.9% –2.4% $7.4 2036 $11,618 $10,658 6.9% –2.4% $7.4 2037 $11,618 $10,657 6.9% –2.4% $7.5 2038 $11,618 $10,657 6.9% –2.4% $7.6 2039 $11,618 $10,656 6.9% –2.4% $7.6 2040 $11,618 $10,656 6.9% –2.4% $7.7 NPV at 3% $154.1 NPV at 7% $88.6 

a Figures are in 2005 dollars. b Net present values for 2006 are calculated using a social discount rate of 3% and 7% over the 2007 to 2040 time period. 
Regulatory Impact Analysis 

7-80 

Table 7A-4. Impact on C1 Commercial Propulsion Engine Market: >2000 hp (Average Price per Engine 

Year 

Variable 

Engineering 

Cost/Unit 

Absolute Change 

in Price 

($) 

Change 

in Price 

(%) 

Change 

in Quantity 

(%) 

Total 

Engineering Costs 

(million $) 

2007 $0 $0 0.0% 0.0% $0.6 2008 $0 –$4 0.0% 0.0% $0.6 2009 $0 –$6 0.0% 0.0% $0.6 2010 $0 –$7 0.0% 0.0% $0.6 2011 $0 –$8 0.0% 0.0% $6.3 2012 $0 –$11 0.0% 0.0% $5.6 2013 $0 –$12 0.0% 0.0% $5.6 2014 $0 –$12 0.0% 0.0% $5.6 2015 $0 –$9 0.0% 0.0% $7.5 2016 $26,401 $22,137 7.4% –5.4% $5.9 2017 $26,401 $22,134 7.4% –5.4% $5.9 2018 $20,183 $16,886 5.6% –4.2% $4.6 2019 $20,183 $16,884 5.6% –4.2% $4.6 2020 $20,183 $16,880 5.6% –4.2% $4.6 2021 $20,183 $16,878 5.6% –4.2% $4.7 2022 $20,183 $16,875 5.6% –4.2% $4.7 2023 $20,183 $16,872 5.6% –4.2% $4.8 2024 $20,183 $16,869 5.6% –4.2% $4.8 2025 $20,183 $16,867 5.6% –4.2% $4.9 2026 $20,183 $16,864 5.6% –4.2% $4.9 2027 $20,183 $16,862 5.6% –4.2% $4.9 2028 $20,183 $16,859 5.6% –4.2% $5.0 2029 $20,183 $16,857 5.6% –4.2% $5.0 2030 $20,183 $16,855 5.6% –4.2% $5.1 2031 $20,183 $16,854 5.6% –4.2% $5.1 2032 $20,183 $16,853 5.6% –4.2% $5.2 2033 $20,183 $16,851 5.6% –4.2% $5.2 2034 $20,183 $16,850 5.6% –4.2% $5.3 2035 $20,183 $16,849 5.6% –4.2% $5.3 2036 $20,183 $16,848 5.6% –4.2% $5.4 2037 $20,183 $16,847 5.6% –4.2% $5.4 2038 $20,183 $16,846 5.6% –4.2% $5.5 2039 $20,183 $16,845 5.6% –4.2% $5.5 2040 $20,183 $16,845 5.6% –4.2% $5.6 NPV at 3% $92.0 NPV at 7% $49.5 

a Figures are in 2005 dollars. b Net present values for 2006 are calculated using a social discount rate of 3% and 7% over the 2007 to 2040 time period. 
Econom

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Table 7A-5. Impact on C2 Commercial Propulsion Engine Market: 800–2000 hp (Average Price per Eng

Year 

Variable 

Engineering 

Cost/Unit 

Absolute Change 

in Price 

($) 

Change 

in Price 

(%) 

Change 

in Quantity 

(%) 

Total 

Engineering Costs 

(million $) 

2007 $0 $0 0.0% 0.0% $0.1 2008 $0 –$27 0.0% 0.0% $0.1 2009 $0 –$36 0.0% –0.1% $0.1 2010 $0 –$44 0.0% –0.1% $0.1 2011 $0 –$53 0.0% –0.1% $0.6 2012 $0 –$73 0.0% –0.1% $0.5 2013 $0 –$78 0.0% –0.1% $0.5 2014 $0 –$76 0.0% –0.1% $0.5 2015 $0 –$59 0.0% –0.1% $0.7 2016 $39,428 $39,322 17.1% –0.2% $0.3 2017 $39,428 $39,308 17.1% –0.2% $0.3 2018 $30,142 $30,012 13.0% –0.2% $0.2 2019 $30,142 $29,994 13.0% –0.2% $0.2 2020 $30,142 $29,975 13.0% –0.3% $0.2 2021 $30,142 $29,956 13.0% –0.3% $0.2 2022 $30,142 $29,938 13.0% –0.3% $0.2 2023 $30,142 $29,920 13.0% –0.4% $0.2 2024 $30,142 $29,902 13.0% –0.4% $0.2 2025 $30,142 $29,886 13.0% –0.4% $0.2 2026 $30,142 $29,869 13.0% –0.5% $0.2 2027 $30,142 $29,854 13.0% –0.5% $0.2 2028 $30,142 $29,838 13.0% –0.5% $0.2 2029 $30,142 $29,825 13.0% –0.5% $0.2 2030 $30,142 $29,813 13.0% –0.5% $0.2 2031 $30,142 $29,804 13.0% –0.6% $0.2 2032 $30,142 $29,795 13.0% –0.6% $0.2 2033 $30,142 $29,787 13.0% –0.6% $0.2 2034 $30,142 $29,779 12.9% –0.6% $0.2 2035 $30,142 $29,772 12.9% –0.6% $0.2 2036 $30,142 $29,766 12.9% –0.6% $0.2 2037 $30,142 $29,759 12.9% –0.6% $0.2 2038 $30,142 $29,753 12.9% –0.6% $0.2 2039 $30,142 $29,748 12.9% –0.7% $0.3 2040 $30,142 $29,745 12.9% –0.7% $0.3 NPV at 3% $5.6 NPV at 7% $3.3 

a Figures are in 2005 dollars. b Net present values for 2006 are calculated using a social discount rate of 3% and 7% over the 2007 to 2040 time period. 
Regulatory Impact Analysis 

7-82 

Table 7A-6. Impact on C2 Commercial Propulsion Engine Market: >2000 hp (Average Price per Engine 

Year 

Variable 

Engineering 

Cost/Unit 

Absolute Change 

in Price 

($) 

Change 

in Price 

(%) 

Change 

in Quantity 

(%) 

Total 

Engineering Costs 

(million $) 

2007 $0 $0 0.0% 0.0% $1.9 2008 $0 –$53 0.0% 0.0% $1.9 2009 $0 –$70 0.0% –0.1% $1.9 2010 $0 –$87 0.0% –0.1% $1.9 2011 $0 –$103 0.0% –0.1% $13.4 2012 $0 –$142 0.0% –0.1% $10.2 2013 $0 –$152 0.0% –0.1% $10.2 2014 $0 –$150 0.0% –0.1% $10.2 2015 $0 –$115 0.0% –0.1% $14.4 2016 $73,360 $73,152 16.3% –0.2% $10.4 2017 $73,360 $73,125 16.2% –0.2% $10.5 2018 $56,081 $55,827 12.4% –0.2% $8.1 2019 $56,081 $55,791 12.4% –0.2% $8.2 2020 $56,081 $55,753 12.4% –0.3% $8.2 2021 $56,081 $55,717 12.4% –0.3% $8.3 2022 $56,081 $55,681 12.4% –0.3% $8.4 2023 $56,081 $55,646 12.4% –0.4% $8.5 2024 $56,081 $55,612 12.4% –0.4% $8.5 2025 $56,081 $55,579 12.4% –0.4% $8.6 2026 $56,081 $55,547 12.3% –0.5% $8.7 2027 $56,081 $55,516 12.3% –0.5% $8.8 2028 $56,081 $55,487 12.3% –0.5% $8.8 2029 $56,081 $55,460 12.3% –0.5% $8.9 2030 $56,081 $55,437 12.3% –0.5% $9.0 2031 $56,081 $55,418 12.3% –0.6% $9.1 2032 $56,081 $55,401 12.3% –0.6% $9.2 2033 $56,081 $55,385 12.3% –0.6% $9.3 2034 $56,081 $55,371 12.3% –0.6% $9.3 2035 $56,081 $55,357 12.3% –0.6% $9.4 2036 $56,081 $55,345 12.3% –0.6% $9.5 2037 $56,081 $55,332 12.3% –0.6% $9.6 2038 $56,081 $55,320 12.3% –0.6% $9.7 2039 $56,081 $55,310 12.3% –0.7% $9.8 2040 $56,081 $55,303 12.3% –0.7% $9.9 NPV at 3% $169.9 NPV at 7% $93.4 

a Figures are in 2005 dollars. b Net present values for 2006 are calculated using a social discount rate of 3% and 7% over the 2007 to 2040 time period. 
Economic Impact Analysis 

/144 

7-83

Appendix 7B: Impacts on the Equipment Markets 

This appendix provides the time series of impacts from 2007 through 2040 for selected 

equipment markets (vessels and locomotives).S

 Results are presented for 21 separate 

equipment markets: 2 locomotive markets (line-haul and switchers) and 19 vessel markets. 

Table 7B-1 through Table 7B-21 provide the time series of impacts and include the following: 

• average engineering costs (variable) per equipment 

• absolute change in the market price ($) 

• relative change in market price (%) 

• relative change in market quantity (%) 

• total engineering costs (variable and fixed) associated with each engine market 

• changes in equipment manufacturer surplus (selected commercial vessel and locomotive 

markets) 

• changes in total surplus (fishing markets only) 

All prices, costs, and surplus changes are presented in 2005 dollars, and real 

equipment prices are assumed to be constant during the period of analysis. Net present values 

for 2006 were calculated using social discount rates of 3% and 7% over the 2007 and 2040 

time period. 

Results are presented for only those markets that are expected to incur direct variable 

costs under Tier 3 or Tier 4 standards. This means that results are not presented for marine 

vessel markets for vessels that have propulsion engines less than 800 hp or for recreational 

vessel markets. For these vessel markets, the results are expected to be negligible and any 

change in price or quantity would be incidental to the changes in the larger vessel markets. It 

should also be noted that fixed costs are limited to only the Tier 4 standards. There are no 

fixed costs associated with the Tier 3 standards because Tier 3 engines are expected to have 

the same engine footprint as Tier 2 engines. For Tier 4 vessels, as explained in 7.2.3.4, fixed 

costs are not included in the EIM. 

The NPV calculations presented in this Appendix are based on the period 2006-2040, 

reflecting the period when the analysis was completed. This has the consequence of 

discounting the current year costs, 2007, and all subsequent years are discounted by an 

additional year. The result is a smaller stream of social costs than by calculating the NPV 

over 2007-2040 (3% smaller for 3% NPV and 7% smaller for 7% NPV). 

 

S

 The engineering costs we used in the EIA are an earlier version of the estimated compliance costs developed 

for this rule; see Section 7.3.2 for an explanation of the difference. This difference is not expected to have an 

impact on the results of the market analysis or on the expected distribution of social costs among stakeholders. 
7-84 

Table 7B-1. Impact on Locomotive Market: Line-Haul (Average Price per Locomotive = $2,000,000)a,b

Year 

Variable 

Engineering 

Cost/Unit 

Absolute Change 

in Price 

($) 

Change 

in Price 

(%) 

Change 

in Quantity 

(%) 

Total 

Engineering Costs 

(million $) 

2007 $0 $0 0.0% 0.0% $6.0 2008 $0 –$158 0.0% 0.0% $6.0 2009 $0 –$197 0.0% 0.0% $6.0 2010 $0 –$351 0.0% 0.0% $33.4 2011 $0 –$658 0.0% –0.1% $35.0 2012 $0 –$535 0.0% –0.1% $27.4 2013 $0 –$483 0.0% –0.1% $27.4 2014 $0 –$355 0.0% 0.0% $30.7 2015 $84,274 $83,494 4.2% –0.1% $68.8 2016 $84,274 $83,227 4.2% –0.1% $72.0 2017 $65,343 $64,374 3.2% –0.1% $57.3 2018 $65,343 $64,273 3.2% –0.1% $58.4 2019 $65,343 $64,217 3.2% –0.2% $59.9 2020 $65,343 $64,261 3.2% –0.1% $61.9 2021 $65,343 $64,163 3.2% –0.2% $64.0 2022 $65,343 $63,950 3.2% –0.2% $65.8 2023 $65,343 $63,600 3.2% –0.2% $67.6 2024 $65,343 $63,574 3.2% –0.2% $68.5 2025 $65,343 $63,454 3.2% –0.3% $70.4 2026 $65,343 $63,439 3.2% –0.3% $71.6 2027 $65,343 $63,345 3.2% –0.3% $73.1 2028 $65,343 $63,181 3.2% –0.3% $74.2 2029 $65,343 $63,093 3.2% –0.3% $75.1 2030 $65,343 $63,019 3.2% –0.3% $75.7 2031 $65,343 $62,939 3.1% –0.3% $76.7 2032 $65,343 $62,909 3.1% –0.3% $77.8 2033 $65,343 $62,547 3.1% –0.4% $79.0 2034 $65,343 $62,431 3.1% –0.4% $79.9 2035 $65,343 $62,328 3.1% –0.4% $80.4 2036 $65,343 $62,313 3.1% –0.4% $78.2 2037 $65,343 $62,252 3.1% –0.4% $76.6 2038 $65,343 $62,196 3.1% –0.4% $74.8 2039 $65,343 $62,144 3.1% –0.4% $72.7 2040 $65,343 $62,095 3.1% –0.4% $70.4 NPV at 3% $1,102.4 NPV at 7% $553.6 

a Figures are in 2005 dollars. b Net present values for 2006 are calculated using a social discount rate of 3% and 7% over the 2007 to 2040 time period. 
7-85 

Table 7B-2. Impact on Locomotive Market: Switcher/Passenger (Average Price per Locomotive = $1,300

Year 

Variable 

Engineering 

Cost/Unit 

Absolute Change 

in Price 

($) 

Change 

in Price 

(%) 

Change 

in Quantity 

(%) 

Total 

Engineering Costs 

(million $) 

2007 $0 $0 0.0% 0.0% $2.6 2008 $0 –$103 0.0% 0.0% $2.6 2009 $0 –$128 0.0% 0.0% $2.6 2010 $0 –$228 0.0% 0.0% $4.9 2011 $0 –$428 0.0% –0.1% $6.9 2012 $0 –$348 0.0% –0.1% $7.2 2013 $0 –$314 0.0% –0.1% $7.2 2014 $0 –$231 0.0% 0.0% $9.7 2015 $14,175 $13,668 1.1% –0.1% $6.2 2016 $14,175 $13,494 1.0% –0.1% $8.7 2017 $23,682 $23,052 1.8% –0.1% $2.2 2018 $23,682 $22,986 1.8% –0.1% $2.2 2019 $21,139 $20,407 1.6% –0.2% $2.0 2020 $21,139 $20,436 1.6% –0.1% $2.0 2021 $21,139 $20,372 1.6% –0.2% $2.0 2022 $21,139 $20,233 1.6% –0.2% $2.0 2023 $21,139 $20,006 1.5% –0.2% $3.4 2024 $21,139 $19,989 1.5% –0.2% $3.9 2025 $21,139 $19,911 1.5% –0.3% $4.2 2026 $21,139 $19,901 1.5% –0.3% $4.5 2027 $21,139 $19,840 1.5% –0.3% $4.8 2028 $21,139 $19,734 1.5% –0.3% $5.0 2029 $21,139 $19,676 1.5% –0.3% $5.2 2030 $21,139 $19,628 1.5% –0.3% $5.6 2031 $21,139 $19,576 1.5% –0.3% $5.9 2032 $21,139 $19,557 1.5% –0.3% $6.2 2033 $21,139 $19,321 1.5% –0.4% $6.3 2034 $21,139 $19,246 1.5% –0.4% $6.4 2035 $21,139 $19,179 1.5% –0.4% $6.4 2036 $21,139 $19,169 1.5% –0.4% $6.2 2037 $21,139 $19,130 1.5% –0.4% $6.1 2038 $21,139 $19,093 1.5% –0.4% $5.9 2039 $21,139 $19,059 1.5% –0.4% $5.7 2040 $21,139 $19,028 1.5% –0.4% $5.6 NPV at 3% $99.0 NPV at 7% $56.7 

a Figures are in 2005 dollars. b Net present values for 2006 are calculated using a social discount rate of 3% and 7% over the 2007 to 2040 time period. 
7-86 

Table 7B-3. Impact on C1 Fishing Vessel Market: 800–2000 hp (Average Price per Vessel = $1,085,000)a,

Year 

Variable 

Engineering 

Cost/Unit 

Absolute Change 

in Price ($) 

Change in Price 

(%) 

Change in 

Quantity (%) 

Total 

Engineering 

Costs (million $) 

Change in Equip

Manufacturer

Surplus (million 

2007 $0 $0 0.0% 0.0% $0.0 $0.0 2008 $0 –$3 0.0% 0.0% $0.0 $0.0 

2009 $0 –$3 0.0% 0.0% $0.0 $0.0 

2010 $0 –$4 0.0% 0.0% $0.0 $0.0 

2011 $0 –$5 0.0% 0.0% $0.0 $0.0 

2012 $0 –$7 0.0% 0.0% $0.0 $0.0 

2013 $0 –$8 0.0% 0.0% $0.0 $0.0 

2014 $0 –$7 0.0% 0.0% $0.0 $0.0 

2015 $0 –$6 0.0% 0.0% $5.7 –$5.7 2016 $6,585 $18,493 1.7% –3.4% $4.2 –$6.3 2017 $6,587 $18,493 1.7% –3.4% $3.0 –$5.2 2018 $5,504 $14,369 1.3% –2.6% $2.2 –$3.8 2019 $5,501 $14,366 1.3% –2.6% $2.2 –$3.8 2020 $5,503 $14,365 1.3% –2.6% $2.2 –$3.8 2021 $5,504 $14,363 1.3% –2.6% $2.2 –$3.9 2022 $5,504 $14,362 1.3% –2.6% $2.2 –$3.9 2023 $5,504 $14,360 1.3% –2.6% $2.3 –$3.9 2024 $5,504 $14,358 1.3% –2.6% $2.3 –$3.9 2025 $5,503 $14,356 1.3% –2.6% $2.3 –$4.0 2026 $5,502 $14,354 1.3% –2.6% $2.3 –$4.0 2027 $5,504 $14,354 1.3% –2.6% $2.3 –$4.0 2028 $5,502 $14,351 1.3% –2.6% $2.3 –$4.1 2029 $5,504 $14,351 1.3% –2.6% $1.4 –$3.2 2030 $5,505 $14,350 1.3% –2.6% $1.4 –$3.2 2031 $5,501 $14,347 1.3% –2.6% $1.4 –$3.2 2032 $5,501 $14,347 1.3% –2.6% $1.5 –$3.3 2033 $5,505 $14,348 1.3% –2.6% $1.5 –$3.3 2034 $5,504 $14,346 1.3% –2.6% $1.5 –$3.3 2035 $5,503 $14,345 1.3% –2.6% $1.5 –$3.3 2036 $5,505 $14,346 1.3% –2.6% $1.5 –$3.4 2037 $5,503 $14,344 1.3% –2.6% $1.5 –$3.4 2038 $5,504 $14,344 1.3% –2.6% $1.5 –$3.4 2039 $5,504 $14,344 1.3% –2.6% $1.6 –$3.5 2040 $5,504 $14,343 1.3% –2.6% $1.6 –$3.5 NPV at 3% $31.5 –$54.5 

NPV at 7% $16.2 –$26.7 

a Figures are in 2005 dollars. b Net present values for 2006 are calculated using a social discount rate of 3% and 7% over the 2007 to 2040 time period. 
7-87 

Table 7B-4. Impact on C1 Fishing Vessel Market: >2000 hp (Average Price per Vessel = $2,100,000)a,b

Year 

Variable 

Engineering 

Cost/Unit 

Absolute Change 

in Price ($) 

Change in 

Price 

(%) 

Change in 

Quantity (%) 

Total 

Engineering 

Costs (million $) 

Change in Equip

Manufacturer

Surplus (million 

2007 $0 $0 0.0% 0.0% $0.0 $0.0 2008 $0 –$8 0.0% 0.0% $0.0 $0.0 2009 $0 –$11 0.0% 0.0% $0.0 $0.0 

2010 $0 –$13 0.0% 0.0% $0.0 $0.0 

2011 $0 –$16 0.0% 0.0% $0.0 $0.0 

2012 $0 –$22 0.0% 0.0% $0.0 $0.0 

2013 $0 –$23 0.0% 0.0% $0.0 $0.0 

2014 $0 –$23 0.0% 0.0% $0.0 $0.0 

2015 $0 –$18 0.0% 0.0% $2.4 –$2.4 2016 $12,359 $65,202 3.1% –6.2% $2.3 –$6.4 2017 $12,362 $65,198 3.1% –6.2% $1.8 –$6.0 2018 $10,383 $50,284 2.4% –4.8% $1.4 –$4.6 2019 $10,382 $50,278 2.4% –4.8% $1.4 –$4.6 2020 $10,389 $50,279 2.4% –4.8% $1.4 –$4.6 2021 $10,386 $50,270 2.4% –4.8% $1.4 –$4.7 2022 $10,382 $50,261 2.4% –4.8% $1.4 –$4.7 2023 $10,387 $50,262 2.4% –4.8% $1.4 –$4.8 2024 $10,382 $50,251 2.4% –4.8% $1.5 –$4.8 2025 $10,385 $50,248 2.4% –4.8% $1.5 –$4.8 2026 $10,387 $50,244 2.4% –4.8% $1.5 –$4.9 2027 $10,389 $50,239 2.4% –4.8% $1.5 –$4.9 2028 $10,389 $50,237 2.4% –4.8% $1.5 –$4.9 2029 $10,389 $50,231 2.4% –4.8% $1.1 –$4.6 2030 $10,388 $50,227 2.4% –4.8% $1.1 –$4.6 2031 $10,386 $50,224 2.4% –4.8% $1.1 –$4.7 2032 $10,384 $50,219 2.4% –4.8% $1.2 –$4.7 2033 $10,389 $50,219 2.4% –4.8% $1.2 –$4.8 2034 $10,385 $50,216 2.4% –4.8% $1.2 –$4.8 2035 $10,389 $50,215 2.4% –4.8% $1.2 –$4.9 2036 $10,383 $50,212 2.4% –4.8% $1.2 –$4.9 2037 $10,385 $50,209 2.4% –4.8% $1.2 –$4.9 2038 $10,387 $50,209 2.4% –4.8% $1.2 –$5.0 2039 $10,387 $50,205 2.4% –4.8% $1.2 –$5.0 2040 $10,387 $50,208 2.4% –4.8% $1.2 –$5.1 NPV at 3% $20.0 –$65.7 

NPV at 7% $9.9 –$30.7 

a Figures are in 2005 dollars. b Net present values for 2006 are calculated using a social discount rate of 3% and 7% over the 2007 to 2040 time period. 
7-88 

Table 7B-5. Impact on C2 Fishing Vessel Market: >2000 hp (Average Price per Vessel = $2,100,000)a,b

Year 

Variable 

Engineering 

Cost/Unit 

Absolute Change 

in Price ($) 

Change in 

Price 

(%) 

Change in 

Quantity (%) 

Total 

Engineering 

Costs (million $) 

Change in Equip

Manufacturer

Surplus (million 

2007 $0 $0 0.0% 0.0% $0.0 $0.0 2008 $0 –$613 0.0% 0.0% $0.0 $0.0 2009 $0 –$807 0.0% –0.1% $0.0 $0.0 2010 $0 –$1,002 0.0% –0.1% $0.0 $0.0 2011 $0 –$1,196 –0.1% –0.1% $0.0 $0.0 2012 $0 –$1,643 –0.1% –0.1% $0.0 $0.0 2013 $0 –$1,759 –0.1% –0.1% $0.0 $0.0 2014 $0 –$1,731 –0.1% –0.1% $0.0 $0.0 2015 $0 –$1,335 –0.1% –0.1% $0.5 –$0.5 2016 $12,398 $237,339 11.3% –0.2% $0.4 –$0.3 2017 $12,379 $237,000 11.3% –0.2% $0.3 –$0.2 2018 $10,360 $181,220 8.6% –0.2% $0.3 –$0.2 2019 $10,358 $180,801 8.6% –0.2% $0.3 –$0.2 2020 $10,355 $180,373 8.6% –0.3% $0.3 –$0.2 2021 $10,351 $179,939 8.6% –0.3% $0.3 –$0.2 2022 $10,346 $179,518 8.5% –0.3% $0.3 –$0.2 2023 $10,428 $179,203 8.5% –0.4% $0.3 –$0.2 2024 $10,421 $178,796 8.5% –0.4% $0.3 –$0.2 2025 $10,413 $178,410 8.5% –0.4% $0.3 –$0.2 2026 $10,405 $178,031 8.5% –0.5% $0.3 –$0.2 2027 $10,396 $177,662 8.5% –0.5% $0.3 –$0.2 2028 $10,386 $177,316 8.4% –0.5% $0.3 –$0.2 2029 $10,376 $176,996 8.4% –0.5% $0.1 –$0.1 2030 $10,365 $176,723 8.4% –0.5% $0.1 –$0.1 2031 $10,354 $176,490 8.4% –0.6% $0.1 –$0.1 2032 $10,422 $176,359 8.4% –0.6% $0.1 –$0.1 2033 $10,408 $176,162 8.4% –0.6% $0.1 –$0.1 2034 $10,394 $175,980 8.4% –0.6% $0.1 –$0.1 2035 $10,379 $175,807 8.4% –0.6% $0.1 –$0.1 2036 $10,364 $175,650 8.4% –0.6% $0.1 –$0.1 2037 $10,348 $175,486 8.4% –0.6% $0.1 –$0.1 2038 $10,408 $175,410 8.4% –0.6% $0.1 –$0.1 2039 $10,390 $175,271 8.3% –0.7% $0.1 –$0.1 2040 $10,372 $175,184 8.3% –0.7% $0.1 –$0.1 NPV at 3% $3.3 –$2.3 NPV at 7% $1.7 –$1.3 

a Figures are in 2005 dollars. b Net present values for 2006 are calculated using a social discount rate of 3% and 7% over the 2007 to 2040 time period. 
7-89 

Table 7B-6. Impact on C1 Tow/Tug/Push Vessel Market: 800–2000 hp (Average Price per Vessel = $1,55

Year 

Variable 

Engineering 

Cost/Unit 

Absolute Change 

in Price 

($) 

Change 

in Price 

(%) 

Change 

in Quantity 

(%) 

Total 

Engineering Costs 

(million $) 

2007 $0 $0 0.0% 0.0% $0.0 2008 $0 –$306 0.0% 0.0% $0.0 

2009 $0 –$404 0.0% –0.1% $0.0 

2010 $0 –$501 0.0% –0.1% $0.0 

2011 $0 –$598 0.0% –0.1% $0.0 

2012 $0 –$821 –0.1% –0.1% $0.0 

2013 $0 –$879 –0.1% –0.1% $0.0 

2014 $0 –$865 –0.1% –0.1% $0.0 

2015 $0 –$667 0.0% –0.1% $0.6 2016 $6,604 $33,409 2.2% –0.2% $0.5 2017 $6,585 $33,232 2.1% –0.2% $0.3 2018 $5,498 $25,412 1.6% –0.2% $0.2 2019 $5,488 $25,195 1.6% –0.2% $0.2 2020 $5,516 $25,007 1.6% –0.3% $0.2 2021 $5,506 $24,784 1.6% –0.3% $0.2 2022 $5,495 $24,566 1.6% –0.3% $0.2 2023 $5,521 $24,390 1.6% –0.4% $0.2 2024 $5,510 $24,182 1.6% –0.4% $0.2 2025 $5,498 $23,980 1.5% –0.4% $0.2 2026 $5,485 $23,783 1.5% –0.5% $0.2 2027 $5,509 $23,628 1.5% –0.5% $0.2 2028 $5,497 $23,445 1.5% –0.5% $0.3 2029 $5,519 $23,314 1.5% –0.5% $0.2 2030 $5,505 $23,170 1.5% –0.5% $0.2 2031 $5,492 $23,045 1.5% –0.6% $0.2 2032 $5,512 $22,967 1.5% –0.6% $0.2 2033 $5,498 $22,861 1.5% –0.6% $0.2 2034 $5,517 $22,796 1.5% –0.6% $0.2 2035 $5,502 $22,702 1.5% –0.6% $0.2 2036 $5,487 $22,613 1.5% –0.6% $0.2 2037 $5,504 $22,559 1.5% –0.6% $0.2 2038 $5,488 $22,474 1.4% –0.6% $0.2 2039 $5,505 $22,433 1.4% –0.7% $0.2 2040 $5,488 $22,377 1.4% –0.7% $0.2 NPV at 3% $3.4 

NPV at 7% $1.8 

a Figures are in 2005 dollars. b Net present values for 2006 are calculated using a social discount rate of 3% and 7% over the 2007 to 2040 time period. 
7-90 

Table 7B-7. Impact on C1 Tow/Tug/Push Vessel Market: >2000 hp (Average Price per Vessel = $3,000,00

Year 

Variable 

Engineering 

Cost/Unit 

Absolute Change 

in Price 

($) 

Change 

in Price 

(%) 

Change 

in Quantity 

(%) 

Total 

Engineering Costs 

(million $) 

2007 $0 $0 0.0% 0.0% $0.0 2008 $0 –$592 0.0% 0.0% $0.0 

2009 $0 –$780 0.0% –0.1% $0.0 

2010 $0 –$967 0.0% –0.1% $0.0 

2011 $0 –$1,155 0.0% –0.1% $0.0 

2012 $0 –$1,587 –0.1% –0.1% $0.0 2013 $0 –$1,698 –0.1% –0.1% $0.0 2014 $0 –$1,672 –0.1% –0.1% $0.0 2015 $0 –$1,289 0.0% –0.1% $0.3 2016 $12,321 $54,303 1.8% –0.2% $0.3 2017 $12,403 $54,079 1.8% –0.2% $0.2 2018 $10,387 $41,362 1.4% –0.2% $0.2 2019 $10,388 $40,963 1.4% –0.2% $0.2 2020 $10,389 $40,544 1.4% –0.3% $0.2 2021 $10,389 $40,135 1.3% –0.3% $0.2 2022 $10,389 $39,735 1.3% –0.3% $0.2 2023 $10,387 $39,342 1.3% –0.4% $0.2 2024 $10,385 $38,961 1.3% –0.4% $0.2 2025 $10,382 $38,589 1.3% –0.4% $0.2 2026 $10,378 $38,228 1.3% –0.5% $0.2 2027 $10,373 $37,879 1.3% –0.5% $0.2 2028 $10,368 $37,544 1.3% –0.5% $0.2 2029 $10,362 $37,241 1.2% –0.5% $0.1 2030 $10,355 $36,982 1.2% –0.5% $0.1 2031 $10,347 $36,760 1.2% –0.6% $0.1 2032 $10,423 $36,646 1.2% –0.6% $0.1 2033 $10,413 $36,460 1.2% –0.6% $0.1 2034 $10,403 $36,286 1.2% –0.6% $0.1 2035 $10,392 $36,123 1.2% –0.6% $0.1 2036 $10,380 $35,969 1.2% –0.6% $0.1 2037 $10,368 $35,818 1.2% –0.6% $0.1 2038 $10,355 $35,672 1.2% –0.6% $0.1 2039 $10,421 $35,627 1.2% –0.7% $0.1 2040 $10,406 $35,536 1.2% –0.7% $0.1 NPV at 3% $2.1 

NPV at 7% $1.1 

a Figures are in 2005 dollars. b Net present values for 2006 are calculated using a social discount rate of 3% and 7% over the 2007 to 2040 time period. 
7-91 

Table 7B-8. Impact on C2 Tow/Tug/Push Vessel Market: 800–2000 hp (Average Price per Vessel = $1,55

Year 

Variable 

Engineering 

Cost/Unit 

Absolute Change 

in Price 

($) 

Change 

in Price 

(%) 

Change 

in Quantity 

(%) 

Total 

Engineering Costs 

(million $) 

2007 $0 $0 0.0% 0.0% $0.0 2008 $0 –$355 0.0% 0.0% $0.0 

2009 $0 –$468 0.0% –0.1% $0.0 

2010 $0 –$581 0.0% –0.1% $0.0 

2011 $0 –$694 0.0% –0.1% $0.0 

2012 $0 –$953 –0.1% –0.1% $0.0 

2013 $0 –$1,020 –0.1% –0.1% $0.0 

2014 $0 –$1,005 –0.1% –0.1% $0.0 

2015 $0 –$775 0.0% –0.1% $0.1 

2016 $6,469 $83,929 5.4% –0.2% $0.1 2017 $6,411 $83,688 5.4% –0.2% Less than $0.1 

2018 $5,776 $64,356 4.2% –0.2% Less than $0.1 

2019 $5,725 $64,063 4.1% –0.2% Less than $0.1 

2020 $5,674 $63,761 4.1% –0.3% Less than $0.1 

2021 $5,623 $63,464 4.1% –0.3% Less than $0.1 

2022 $5,573 $63,173 4.1% –0.3% Less than $0.1 

2023 $5,523 $62,889 4.1% –0.4% Less than $0.1 

2024 $5,474 $62,612 4.0% –0.4% Less than $0.1 

2025 $5,425 $62,341 4.0% –0.4% Less than $0.1 

2026 $5,377 $62,079 4.0% –0.5% Less than $0.1 

2027 $5,329 $61,824 4.0% –0.5% Less than $0.1 

2028 $5,281 $61,578 4.0% –0.5% Less than $0.1 

2029 $5,758 $61,876 4.0% –0.5% Less than $0.1 

2030 $5,706 $61,673 4.0% –0.5% Less than $0.1 

2031 $5,655 $61,493 4.0% –0.6% Less than $0.1 

2032 $5,605 $61,329 4.0% –0.6% Less than $0.1 

2033 $5,555 $61,173 3.9% –0.6% Less than $0.1 

2034 $5,505 $61,025 3.9% –0.6% Less than $0.1 

2035 $5,456 $60,885 3.9% –0.6% Less than $0.1 

2036 $5,408 $60,751 3.9% –0.6% Less than $0.1 

2037 $5,359 $60,619 3.9% –0.6% Less than $0.1 

2038 $5,312 $60,491 3.9% –0.6% Less than $0.1 

2039 $5,264 $60,377 3.9% –0.7% Less than $0.1 

2040 $5,691 $60,759 3.9% –0.7% Less than $0.1 

NPV at 3% $0.4 

NPV at 7% $0.2 

a Figures are in 2005 dollars. b Net present values for 2006 are calculated using a social discount rate of 3% and 7% over the 2007 to 2040 time period. 
7-92 

Table 7B-9. Impact on C2 Tow/Tug/Push Vessel Market: >2000 hp (Average Price per Vessel = $3,000,00

Year 

Variable 

Engineering 

Cost/Unit 

Absolute Change 

in Price 

($) 

Change 

in Price 

(%) 

Change 

in Quantity 

(%) 

Total 

Engineering Costs 

(million $) 

2007 $0 $0 0.0% 0.0% $0.0 2008 $0 –$788 0.0% 0.0% $0.0 

2009 $0 –$1,038 0.0% –0.1% $0.0 2010 $0 –$1,288 0.0% –0.1% $0.0 2011 $0 –$1,538 –0.1% –0.1% $0.0 

2012 $0 –$2,113 –0.1% –0.1% $0.0 

2013 $0 –$2,261 –0.1% –0.1% $0.0 2014 $0 –$2,226 –0.1% –0.1% $0.0 2015 $0 –$1,717 –0.1% –0.1% $1.6 2016 $12,367 $236,621 7.9% –0.2% $1.3 2017 $12,349 $236,192 7.9% –0.2% $0.8 2018 $10,391 $180,411 6.0% –0.2% $0.8 2019 $10,388 $179,874 6.0% –0.2% $0.8 2020 $10,385 $179,322 6.0% –0.3% $0.8 2021 $10,381 $178,766 6.0% –0.3% $0.8 2022 $10,376 $178,226 5.9% –0.3% $0.8 2023 $10,399 $177,737 5.9% –0.4% $0.8 2024 $10,392 $177,219 5.9% –0.4% $0.8 2025 $10,385 $176,723 5.9% –0.4% $0.8 2026 $10,377 $176,240 5.9% –0.5% $0.8 2027 $10,396 $175,797 5.9% –0.5% $0.8 2028 $10,386 $175,354 5.8% –0.5% $0.8 2029 $10,376 $174,945 5.8% –0.5% $0.4 2030 $10,392 $174,625 5.8% –0.5% $0.4 2031 $10,381 $174,328 5.8% –0.6% $0.4 2032 $10,395 $174,087 5.8% –0.6% $0.4 2033 $10,382 $173,839 5.8% –0.6% $0.4 2034 $10,394 $173,635 5.8% –0.6% $0.4 2035 $10,379 $173,417 5.8% –0.6% $0.4 2036 $10,390 $173,243 5.8% –0.6% $0.4 2037 $10,374 $173,038 5.8% –0.6% $0.4 2038 $10,383 $172,872 5.8% –0.6% $0.4 2039 $10,390 $172,726 5.8% –0.7% $0.4 2040 $10,397 $172,641 5.8% –0.7% $0.4 NPV at 3% $9.9 

NPV at 7% $5.1 

a Figures are in 2005 dollars. b Net present values for 2006 are calculated using a social discount rate of 3% and 7% over the 2007 to 2040 time period. 
7-93 

Table 7B-10. Impact on C1 Ferries Vessel Market: 800–2000 hp (Average Price per Vessel = $1,550,000)a

Year 

Variable 

Engineering 

Cost/Unit 

Absolute Change 

in Price 

($) 

Change 

in Price 

(%) 

Change 

in Quantity 

(%) 

Total 

Engineering Costs 

(million $) 

2007 $0 $0 0.0% 0.0% $0.0 2008 $0 –$306 0.0% 0.0% $0.0 2009 $0 –$404 0.0% –0.1% $0.0 2010 $0 –$501 0.0% –0.1% $0.0 2011 $0 –$598 0.0% –0.1% $0.0 2012 $0 –$821 –0.1% –0.1% $0.0 2013 $0 –$879 –0.1% –0.1% $0.0 2014 $0 –$865 –0.1% –0.1% $0.0 2015 $0 –$667 0.0% –0.1% $0.0 2016 $6,644 $33,450 2.2% –0.2% Less than $0.1 

2017 $6,585 $33,232 2.1% –0.2% Less than $0.1 

2018 $5,438 $25,353 1.6% –0.2% Less than $0.1 

2019 $5,605 $25,313 1.6% –0.2% Less than $0.1 

2020 $5,555 $25,046 1.6% –0.3% Less than $0.1 

2021 $5,506 $24,784 1.6% –0.3% Less than $0.1 

2022 $5,457 $24,528 1.6% –0.3% Less than $0.1 

2023 $5,408 $24,277 1.6% –0.4% Less than $0.1 

2024 $5,566 $24,239 1.6% –0.4% Less than $0.1 

2025 $5,516 $23,998 1.5% –0.4% Less than $0.1 

2026 $5,467 $23,764 1.5% –0.5% Less than $0.1 

2027 $5,418 $23,537 1.5% –0.5% Less than $0.1 

2028 $5,569 $23,517 1.5% –0.5% Less than $0.1 

2029 $5,519 $23,314 1.5% –0.5% Less than $0.1 

2030 $5,470 $23,134 1.5% –0.5% Less than $0.1 

2031 $5,421 $22,975 1.5% –0.6% Less than $0.1 

2032 $5,565 $23,020 1.5% –0.6% Less than $0.1 

2033 $5,515 $22,878 1.5% –0.6% Less than $0.1 

2034 $5,466 $22,744 1.5% –0.6% Less than $0.1 

2035 $5,417 $22,617 1.5% –0.6% Less than $0.1 

2036 $5,554 $22,681 1.5% –0.6% Less than $0.1 

2037 $5,504 $22,559 1.5% –0.6% Less than $0.1 

2038 $5,455 $22,441 1.4% –0.6% Less than $0.1 

2039 $5,587 $22,515 1.5% –0.7% Less than $0.1 

2040 $5,537 $22,426 1.4% –0.7% Less than $0.1 

NPV at 3% $0.6 

NPV at 7% $0.3 

a Figures are in 2005 dollars. b Net present values for 2006 are calculated using a social discount rate of 3% and 7% over the 2007 to 2040 time period. 
7-94 

Table 7B-11. Impact on C1 Ferries Vessel Market: >2000 hp (Average Price per Vessel = $3,000,000)a,b

Year 

Variable 

Engineering 

Cost/Unit 

Absolute Change 

in Price 

($) 

Change 

in Price 

(%) 

Change 

in Quantity 

(%) 

Total 

Engineering Costs 

(million $) 

2007 $0 $0 0.0% 0.0% $0.0 2008 $0 –$598 0.0% 0.0% $0.0 

2009 $0 –$789 0.0% –0.1% $0.0 

2010 $0 –$979 0.0% –0.1% $0.0 

2011 $0 –$1,169 0.0% –0.1% $0.0 2012 $0 –$1,605 –0.1% –0.1% $0.0 

2013 $0 –$1,718 –0.1% –0.1% $0.0 

2014 $0 –$1,691 –0.1% –0.1% $0.0 

2015 $0 –$1,304 0.0% –0.1% $0.0 2016 $12,224 $119,473 4.0% –0.2% Less than $0.1 

2017 $12,115 $119,052 4.0% –0.2% Less than $0.1 

2018 $10,291 $91,121 3.0% –0.2% Less than $0.1 

2019 $10,199 $90,623 3.0% –0.2% Less than $0.1 

2020 $10,108 $90,114 3.0% –0.3% Less than $0.1 

2021 $10,018 $89,606 3.0% –0.3% Less than $0.1 

2022 $10,756 $89,937 3.0% –0.3% Less than $0.1 

2023 $10,660 $89,452 3.0% –0.4% Less than $0.1 

2024 $10,565 $88,968 3.0% –0.4% Less than $0.1 

2025 $10,471 $88,503 3.0% –0.4% Less than $0.1 

2026 $10,378 $88,048 2.9% –0.5% Less than $0.1 

2027 $10,285 $87,604 2.9% –0.5% Less than $0.1 

2028 $10,193 $87,183 2.9% –0.5% Less than $0.1 

2029 $10,102 $86,789 2.9% –0.5% Less than $0.1 

2030 $10,012 $86,444 2.9% –0.5% Less than $0.1 

2031 $10,686 $86,901 2.9% –0.6% Less than $0.1 

2032 $10,591 $86,612 2.9% –0.6% Less than $0.1 

2033 $10,497 $86,339 2.9% –0.6% Less than $0.1 

2034 $10,403 $86,081 2.9% –0.6% Less than $0.1 

2035 $10,310 $85,834 2.9% –0.6% Less than $0.1 

2036 $10,218 $85,602 2.9% –0.6% Less than $0.1 

2037 $10,127 $85,367 2.8% –0.6% Less than $0.1 

2038 $10,037 $85,144 2.8% –0.6% Less than $0.1 

2039 $10,658 $85,648 2.9% –0.7% Less than $0.1 

2040 $10,563 $85,483 2.8% –0.7% Less than $0.1 

NPV at 3% $0.2 

NPV at 7% $0.1 

a Figures are in 2005 dollars. b Net present values for 2006 are calculated using a social discount rate of 3% and 7% over the 2007 to 2040 time period. 
7-95 

Table 7B-12. Impact on C2 Ferries Vessel Market: >2,000 hp (Average Price per Vessel = $3,000,000)a,b

Year 

Variable 

Engineering 

Cost/Unit 

Absolute Change 

in Price 

($) 

Change 

in Price 

(%) 

Change 

in Quantity 

(%) 

Total 

Engineering Costs 

(million $) 

2007 $0 $0 0.0% 0.0% $0.0 2008 $0 –$788 0.0% 0.0% $0.0 

2009 $0 –$1,038 0.0% –0.1% $0.0 

2010 $0 –$1,288 0.0% –0.1% $0.0 

2011 $0 –$1,538 –0.1% –0.1% $0.0 2012 $0 –$2,113 –0.1% –0.1% $0.0 2013 $0 –$2,261 –0.1% –0.1% $0.0 2014 $0 –$2,226 –0.1% –0.1% $0.0 2015 $0 –$1,717 –0.1% –0.1% $0.0 2016 $12,491 $236,744 7.9% –0.2% Less than $0.1 

2017 $12,379 $236,223 7.9% –0.2% Less than $0.1 

2018 $10,360 $180,381 6.0% –0.2% Less than $0.1 

2019 $10,268 $179,754 6.0% –0.2% Less than $0.1 

2020 $10,444 $179,381 6.0% –0.3% Less than $0.1 

2021 $10,351 $178,737 6.0% –0.3% Less than $0.1 

2022 $10,259 $178,109 5.9% –0.3% Less than $0.1 

2023 $10,428 $177,766 5.9% –0.4% Less than $0.1 

2024 $10,335 $177,161 5.9% –0.4% Less than $0.1 

2025 $10,499 $176,837 5.9% –0.4% Less than $0.1 

2026 $10,405 $176,268 5.9% –0.5% Less than $0.1 

2027 $10,312 $175,713 5.9% –0.5% Less than $0.1 

2028 $10,470 $175,437 5.8% –0.5% Less than $0.1 

2029 $10,376 $174,945 5.8% –0.5% Less than $0.1 

2030 $10,284 $174,516 5.8% –0.5% Less than $0.1 

2031 $10,435 $174,382 5.8% –0.6% Less than $0.1 

2032 $10,342 $174,034 5.8% –0.6% Less than $0.1 

2033 $10,488 $173,945 5.8% –0.6% Less than $0.1 

2034 $10,394 $173,635 5.8% –0.6% Less than $0.1 

2035 $10,301 $173,339 5.8% –0.6% Less than $0.1 

2036 $10,442 $173,295 5.8% –0.6% Less than $0.1 

2037 $10,348 $173,013 5.8% –0.6% Less than $0.1 

2038 $10,484 $172,974 5.8% –0.6% Less than $0.1 

2039 $10,390 $172,726 5.8% –0.7% Less than $0.1 

2040 $10,298 $172,542 5.8% –0.7% Less than $0.1 

NPV at 3% $1.1 

NPV at 7% $0.6 

a Figures are in 2005 dollars. b Net present values for 2006 are calculated using a social discount rate of 3% and 7% over the 2007 to 2040 time period. 
7-96 

Table 7B-13. Impact on C1 Supply/Crew Vessel Market: 800–2,000 hp (Average Price per Vessel = $3,10

Year 

Variable 

Engineering 

Cost/Unit 

Absolute Change 

in Price 

($) 

Change 

in Price 

(%) 

Change 

in Quantity 

(%) 

Total 

Engineering Costs 

(million $) 

2007 $0 $0 0.0% 0.0% $0.0 2008 $0 –$608 0.0% 0.0% $0.0 

2009 $0 –$801 0.0% –0.1% $0.0 

2010 $0 –$994 0.0% –0.1% $0.0 

2011 $0 –$1,186 0.0% –0.1% $0.0 2012 $0 –$1,630 –0.1% –0.1% $0.0 

2013 $0 –$1,744 –0.1% –0.1% $0.0 

2014 $0 –$1,717 –0.1% –0.1% $0.0 

2015 $0 –$1,324 0.0% –0.1% $0.1 2016 $6,644 $32,266 1.0% –0.2% Less than $0.1 

2017 $6,585 $31,892 1.0% –0.2% Less than $0.1 

2018 $5,438 $23,908 0.8% –0.2% Less than $0.1 

2019 $5,605 $23,663 0.8% –0.2% Less than $0.1 

2020 $5,555 $23,183 0.7% –0.3% Less than $0.1 

2021 $5,506 $22,713 0.7% –0.3% Less than $0.1 

2022 $5,457 $22,252 0.7% –0.3% Less than $0.1 

2023 $5,408 $21,803 0.7% –0.4% Less than $0.1 

2024 $5,566 $21,571 0.7% –0.4% Less than $0.1 

2025 $5,516 $21,143 0.7% –0.4% Less than $0.1 

2026 $5,467 $20,728 0.7% –0.5% Less than $0.1 

2027 $5,418 $20,325 0.7% –0.5% Less than $0.1 

2028 $5,569 $20,137 0.6% –0.5% Less than $0.1 

2029 $5,519 $19,783 0.6% –0.5% Less than $0.1 

2030 $5,470 $19,475 0.6% –0.5% Less than $0.1 

2031 $5,421 $19,206 0.6% –0.6% Less than $0.1 

2032 $5,565 $19,154 0.6% –0.6% Less than $0.1 

2033 $5,515 $18,923 0.6% –0.6% Less than $0.1 

2034 $5,466 $18,705 0.6% –0.6% Less than $0.1 

2035 $5,417 $18,501 0.6% –0.6% Less than $0.1 

2036 $5,554 $18,492 0.6% –0.6% Less than $0.1 

2037 $5,504 $18,300 0.6% –0.6% Less than $0.1 

2038 $5,455 $18,114 0.6% –0.6% Less than $0.1 

2039 $5,587 $18,132 0.6% –0.7% Less than $0.1 

2040 $5,537 $18,004 0.6% –0.7% Less than $0.1 

NPV at 3% $0.6 

NPV at 7% $0.3 

a Figures are in 2005 dollars. b Net present values for 2006 are calculated using a social discount rate of 3% and 7% over the 2007 to 2040 time period. 
7-97 

Table 7B-14. Impact on C1 Supply/Crew Vessel Market: >2,000 hp (Average Price per Vessel = $6,000,00

Year 

Variable 

Engineering 

Cost/Unit 

Absolute Change 

in Price 

($) 

Change 

in Price 

(%) 

Change 

in Quantity 

(%) 

Total 

Engineering Costs 

(million $) 

2007 $0 $0 0.0% 0.0% $0.0 2008 $0 –$1,182 0.0% 0.0% $0.0 

2009 $0 –$1,557 0.0% –0.1% $0.0 

2010 $0 –$1,932 0.0% –0.1% $0.0 

2011 $0 –$2,308 0.0% –0.1% $0.0 

2012 $0 –$3,169 –0.1% –0.1% $0.0 2013 $0 –$3,392 –0.1% –0.1% $0.0 2014 $0 –$3,340 –0.1% –0.1% $0.0 2015 $0 –$2,575 0.0% –0.1% $0.0 

2016 $12,224 $117,183 2.0% –0.2% Less than $0.1 

2017 $12,547 $116,893 1.9% –0.2% Less than $0.1 

2018 $10,291 $88,324 1.5% –0.2% Less than $0.1 2019 $10,199 $87,431 1.5% –0.2% Less than $0.1 2020 $10,530 $86,930 1.4% –0.3% Less than $0.1 2021 $10,436 $86,014 1.4% –0.3% Less than $0.1 2022 $10,343 $85,120 1.4% –0.3% Less than $0.1 2023 $10,250 $84,253 1.4% –0.4% Less than $0.1 2024 $10,565 $83,806 1.4% –0.4% Less than $0.1 2025 $10,471 $82,977 1.4% –0.4% Less than $0.1 2026 $10,378 $82,170 1.4% –0.5% Less than $0.1 2027 $10,285 $81,387 1.4% –0.5% Less than $0.1 2028 $10,193 $80,641 1.3% –0.5% Less than $0.1 2029 $10,491 $80,344 1.3% –0.5% Less than $0.1 2030 $10,397 $79,746 1.3% –0.5% Less than $0.1 2031 $10,305 $79,225 1.3% –0.6% Less than $0.1 2032 $10,213 $78,752 1.3% –0.6% Less than $0.1 2033 $10,497 $78,683 1.3% –0.6% Less than $0.1 2034 $10,403 $78,264 1.3% –0.6% Less than $0.1 2035 $10,310 $77,867 1.3% –0.6% Less than $0.1 2036 $10,218 $77,495 1.3% –0.6% Less than $0.1 2037 $10,489 $77,485 1.3% –0.6% Less than $0.1 2038 $10,395 $77,127 1.3% –0.6% Less than $0.1 2039 $10,302 $76,808 1.3% –0.7% Less than $0.1 2040 $10,211 $76,572 1.3% –0.7% Less than $0.1 NPV at 3% $0.5 

NPV at 7% $0.2 

a Figures are in 2005 dollars. b Net present values for 2006 are calculated using a social discount rate of 3% and 7% over the 2007 to 2040 time period. 
7-98 

Table 7B-15. Impact on C2 Supply/Crew Vessel Market: 800–2,000 hp (Average Price per Vessel = $3,10

Year 

Variable 

Engineering 

Cost/Unit 

Absolute Change 

in Price 

($) 

Change 

in Price 

(%) 

Change 

in Quantity 

(%) 

Total 

Engineering Costs 

(million $) 

2007 $0 $0 0.0% 0.0% $0.0 2008 $0 –$657 0.0% 0.0% $0.0 

2009 $0 –$865 0.0% –0.1% $0.0 

2010 $0 –$1,074 0.0% –0.1% $0.0 2011 $0 –$1,283 0.0% –0.1% $0.0 2012 $0 –$1,762 –0.1% –0.1% $0.0 

2013 $0 –$1,886 –0.1% –0.1% $0.0 

2014 $0 –$1,856 –0.1% –0.1% $0.0 

2015 $0 –$1,432 0.0% –0.1% $0.0 2016 $6,469 $82,746 2.7% –0.2% Less than $0.1 

2017 $6,411 $82,348 2.7% –0.2% Less than $0.1 

2018 $5,776 $62,911 2.0% –0.2% Less than $0.1 

2019 $5,725 $62,414 2.0% –0.2% Less than $0.1 

2020 $5,674 $61,898 2.0% –0.3% Less than $0.1 

2021 $5,623 $61,393 2.0% –0.3% Less than $0.1 

2022 $5,573 $60,898 2.0% –0.3% Less than $0.1 

2023 $5,523 $60,415 1.9% –0.4% Less than $0.1 

2024 $5,474 $59,944 1.9% –0.4% Less than $0.1 

2025 $5,425 $59,487 1.9% –0.4% Less than $0.1 

2026 $5,377 $59,042 1.9% –0.5% Less than $0.1 

2027 $5,329 $58,611 1.9% –0.5% Less than $0.1 

2028 $5,281 $58,198 1.9% –0.5% Less than $0.1 

2029 $5,758 $58,345 1.9% –0.5% Less than $0.1 

2030 $5,706 $58,014 1.9% –0.5% Less than $0.1 

2031 $5,655 $57,725 1.9% –0.6% Less than $0.1 

2032 $5,605 $57,463 1.9% –0.6% Less than $0.1 

2033 $5,555 $57,217 1.8% –0.6% Less than $0.1 

2034 $5,505 $56,986 1.8% –0.6% Less than $0.1 

2035 $5,456 $56,769 1.8% –0.6% Less than $0.1 

2036 $5,408 $56,562 1.8% –0.6% Less than $0.1 

2037 $5,359 $56,359 1.8% –0.6% Less than $0.1 

2038 $5,312 $56,164 1.8% –0.6% Less than $0.1 

2039 $5,264 $55,993 1.8% –0.7% Less than $0.1 

2040 $5,691 $56,336 1.8% –0.7% Less than $0.1 

NPV at 3% $0.4 

NPV at 7% $0.2 

a Figures are in 2005 dollars. b Net present values for 2006 are calculated using a social discount rate of 3% and 7% over the 2007 to 2040 time period. 
7-99 

Table 7B-16. Impact on C2 Supply/Crew Vessel Market: >2,000 hp (Average Price per Vessel = $6,000,00

Year 

Variable 

Engineering 

Cost/Unit 

Absolute Change 

in Price 

($) 

Change 

in Price 

(%) 

Change 

in Quantity 

(%) 

Total 

Engineering Costs 

(million $) 

2007 $0 $0 0.0% 0.0% $0.0 2008 $0 –$1,279 0.0% 0.0% $0.0 

2009 $0 –$1,685 0.0% –0.1% $0.0 2010 $0 –$2,092 0.0% –0.1% $0.0 2011 $0 –$2,498 0.0% –0.1% $0.0 2012 $0 –$3,431 –0.1% –0.1% $0.0 2013 $0 –$3,672 –0.1% –0.1% $0.0 2014 $0 –$3,615 –0.1% –0.1% $0.0 2015 $0 –$2,788 0.0% –0.1% $0.9 2016 $12,380 $219,369 3.7% –0.2% $0.7 2017 $12,379 $218,705 3.6% –0.2% $0.5 2018 $10,360 $166,274 2.8% –0.2% $0.4 2019 $10,376 $165,421 2.8% –0.2% $0.4 2020 $10,391 $164,537 2.7% –0.3% $0.4 2021 $10,404 $163,660 2.7% –0.3% $0.4 2022 $10,364 $162,753 2.7% –0.3% $0.4 2023 $10,376 $161,927 2.7% –0.4% $0.4 2024 $10,387 $161,112 2.7% –0.4% $0.4 2025 $10,396 $160,327 2.7% –0.4% $0.4 2026 $10,405 $159,564 2.7% –0.5% $0.4 2027 $10,363 $158,773 2.6% –0.5% $0.4 2028 $10,370 $158,072 2.6% –0.5% $0.5 2029 $10,376 $157,435 2.6% –0.5% $0.2 2030 $10,382 $156,894 2.6% –0.5% $0.2 2031 $10,386 $156,435 2.6% –0.6% $0.2 2032 $10,390 $156,026 2.6% –0.6% $0.2 2033 $10,392 $155,647 2.6% –0.6% $0.2 2034 $10,394 $155,296 2.6% –0.6% $0.2 2035 $10,395 $154,968 2.6% –0.6% $0.2 2036 $10,395 $154,665 2.6% –0.6% $0.2 2037 $10,394 $154,361 2.6% –0.6% $0.2 2038 $10,393 $154,073 2.6% –0.6% $0.2 2039 $10,390 $153,825 2.6% –0.7% $0.2 2040 $10,387 $153,666 2.6% –0.7% $0.2 NPV at 3% $5.5 

NPV at 7% $2.8 

a Figures are in 2005 dollars. b Net present values for 2006 are calculated using a social discount rate of 3% and 7% over the 2007 to 2040 time period. 
7-100 

Table 7B-17. Impact on C1 Cargo Vessel Market: 800–2,000 hp (Average Price per Vessel = $3,100,000)a

Year 

Variable 

Engineering 

Cost/Unit 

Absolute Change 

in Price 

($) 

Change 

in Price 

(%) 

Change 

in Quantity 

(%) 

Total 

Engineering Costs 

(million $) 

2007 $0 $0 0.0% 0.0% $0.0 2008 $0 –$608 0.0% 0.0% $0.0 

2009 $0 –$801 0.0% –0.1% $0.0 

2010 $0 –$994 0.0% –0.1% $0.0 

2011 $0 –$1,186 0.0% –0.1% $0.0 2012 $0 –$1,630 –0.1% –0.1% $0.0 

2013 $0 –$1,744 –0.1% –0.1% $0.0 

2014 $0 –$1,717 –0.1% –0.1% $0.0 

2015 $0 –$1,324 0.0% –0.1% $0.0 2016 $6,201 $31,823 1.0% –0.2% Less than $0.1 

2017 $7,024 $32,331 1.0% –0.2% Less than $0.1 

2018 $5,221 $23,690 0.8% –0.2% Less than $0.1 

2019 $5,174 $23,232 0.7% –0.2% Less than $0.1 

2020 $5,128 $22,756 0.7% –0.3% Less than $0.1 

2021 $5,082 $22,289 0.7% –0.3% Less than $0.1 

2022 $5,876 $22,672 0.7% –0.3% Less than $0.1 

2023 $5,824 $22,219 0.7% –0.4% Less than $0.1 

2024 $5,772 $21,777 0.7% –0.4% Less than $0.1 

2025 $5,721 $21,348 0.7% –0.4% Less than $0.1 

2026 $5,670 $20,930 0.7% –0.5% Less than $0.1 

2027 $5,619 $20,526 0.7% –0.5% Less than $0.1 

2028 $5,569 $20,137 0.6% –0.5% Less than $0.1 

2029 $5,519 $19,783 0.6% –0.5% Less than $0.1 

2030 $5,470 $19,475 0.6% –0.5% Less than $0.1 

2031 $5,421 $19,206 0.6% –0.6% Less than $0.1 

2032 $5,373 $18,962 0.6% –0.6% Less than $0.1 

2033 $5,325 $18,733 0.6% –0.6% Less than $0.1 

2034 $5,277 $18,517 0.6% –0.6% Less than $0.1 

2035 $5,230 $18,314 0.6% –0.6% Less than $0.1 

2036 $5,184 $18,122 0.6% –0.6% Less than $0.1 

2037 $5,137 $17,933 0.6% –0.6% Less than $0.1 

2038 $5,819 $18,478 0.6% –0.6% Less than $0.1 

2039 $5,767 $18,312 0.6% –0.7% Less than $0.1 

2040 $5,716 $18,182 0.6% –0.7% Less than $0.1 

NPV at 3% $0.2 

NPV at 7% $0.1 

a Figures are in 2005 dollars. b Net present values for 2006 are calculated using a social discount rate of 3% and 7% over the 2007 to 2040 time period. 
7-101 

Table 7B-18. Impact on C2 Cargo Vessel Market: >2,000 hp (Average Price per Vessel = $6,000,000)a,b

Year 

Variable 

Engineering 

Cost/Unit 

Absolute Change 

in Price 

($) 

Change 

in Price 

(%) 

Change 

in Quantity 

(%) 

Total 

Engineering Costs 

(million $) 

2007 $0 $0 0.0% 0.0% $0.0 2008 $0 –$1,371 0.0% 0.0% $0.0 

2009 $0 –$1,806 0.0% –0.1% $0.0 

2010 $0 –$2,242 0.0% –0.1% $0.0 

2011 $0 –$2,677 0.0% –0.1% $0.0 

2012 $0 –$3,677 –0.1% –0.1% $0.0 2013 $0 –$3,935 –0.1% –0.1% $0.0 2014 $0 –$3,874 –0.1% –0.1% $0.0 2015 $0 –$2,988 0.0% –0.1% $0.2 2016 $12,283 $234,245 3.9% –0.2% $0.2 2017 $12,379 $233,630 3.9% –0.2% $0.1 2018 $10,429 $177,652 3.0% –0.2% $0.1 2019 $10,336 $176,629 2.9% –0.2% $0.1 2020 $10,444 $175,776 2.9% –0.3% $0.1 2021 $10,351 $174,728 2.9% –0.3% $0.1 2022 $10,456 $173,903 2.9% –0.3% $0.1 2023 $10,363 $172,913 2.9% –0.4% $0.1 2024 $10,464 $172,128 2.9% –0.4% $0.1 2025 $10,371 $171,184 2.9% –0.4% $0.1 2026 $10,469 $170,454 2.8% –0.5% $0.1 2027 $10,375 $169,559 2.8% –0.5% $0.1 2028 $10,470 $168,895 2.8% –0.5% $0.1 2029 $10,376 $168,111 2.8% –0.5% $0.1 2030 $10,467 $167,617 2.8% –0.5% $0.1 2031 $10,374 $167,027 2.8% –0.6% $0.1 2032 $10,462 $166,672 2.8% –0.6% $0.1 2033 $10,368 $166,170 2.8% –0.6% $0.1 2034 $10,453 $165,877 2.8% –0.6% $0.1 2035 $10,360 $165,431 2.8% –0.6% $0.1 2036 $10,442 $165,188 2.8% –0.6% $0.1 2037 $10,348 $164,769 2.7% –0.6% $0.1 2038 $10,427 $164,542 2.7% –0.6% $0.1 2039 $10,334 $164,185 2.7% –0.7% $0.1 2040 $10,410 $164,094 2.7% –0.7% $0.1 NPV at 3% $1.5 

NPV at 7% $0.8 

a Figures are in 2005 dollars. b Net present values for 2006 are calculated using a social discount rate of 3% and 7% over the 2007 to 2040 time period. 
7-102 

Table 7B-19. Impact on C1 Other Commercial Vessel Market: 800–2,000 hp (Average Price per Vessel = 

Year 

Variable 

Engineering 

Cost/Unit 

Absolute Change 

in Price 

($) 

Change 

in Price 

(%) 

Change 

in Quantity 

(%) 

Total 

Engineering Costs 

(million $) 

2007 $0 $0 0.0% 0.0% $0.0 2008 $0 –$216 0.0% 0.0% $0.0 

2009 $0 –$284 0.0% –0.1% $0.0 

2010 $0 –$353 0.0% –0.1% $0.0 

2011 $0 –$421 0.0% –0.1% $0.0 

2012 $0 –$579 –0.1% –0.1% $0.0 

2013 $0 –$620 –0.1% –0.1% $0.0 

2014 $0 –$610 –0.1% –0.1% $0.0 

2015 $0 –$470 0.0% –0.1% $0.0 

2016 $6,496 $33,657 3.1% –0.2% Less than $0.1 

2017 $6,731 $33,780 3.1% –0.2% Less than $0.1 

2018 $5,511 $25,859 2.4% –0.2% Less than $0.1 

2019 $5,462 $25,664 2.4% –0.2% Less than $0.1 

2020 $5,413 $25,462 2.3% –0.3% Less than $0.1 

2021 $5,365 $25,264 2.3% –0.3% Less than $0.1 

2022 $5,597 $25,350 2.3% –0.3% Less than $0.1 

2023 $5,547 $25,158 2.3% –0.4% Less than $0.1 

2024 $5,497 $24,970 2.3% –0.4% Less than $0.1 

2025 $5,448 $24,787 2.3% –0.4% Less than $0.1 

2026 $5,400 $24,608 2.3% –0.5% Less than $0.1 

2027 $5,619 $24,702 2.3% –0.5% Less than $0.1 

2028 $5,569 $24,531 2.3% –0.5% Less than $0.1 

2029 $5,519 $24,373 2.2% –0.5% Less than $0.1 

2030 $5,470 $24,232 2.2% –0.5% Less than $0.1 

2031 $5,421 $24,105 2.2% –0.6% Less than $0.1 

2032 $5,629 $24,243 2.2% –0.6% Less than $0.1 

2033 $5,578 $24,129 2.2% –0.6% Less than $0.1 

2034 $5,529 $24,019 2.2% –0.6% Less than $0.1 

2035 $5,479 $23,915 2.2% –0.6% Less than $0.1 

2036 $5,430 $23,814 2.2% –0.6% Less than $0.1 

2037 $5,382 $23,715 2.2% –0.6% Less than $0.1 

2038 $5,576 $23,861 2.2% –0.6% Less than $0.1 

2039 $5,527 $23,770 2.2% –0.7% Less than $0.1 

2040 $5,477 $23,693 2.2% –0.7% Less than $0.1 

NPV at 3% $0.5 

NPV at 7% $0.2 

a Figures are in 2005 dollars. b Net present values for 2006 are calculated using a social discount rate of 3% and 7% over the 2007 to 2040 time period. 
7-103 

Table 7B-20. Impact on C1 Other Commercial Vessel Market: >2,000 hp (Average Price per Vessel = $2,

Year 

Variable 

Engineering 

Cost/Unit 

Absolute Change 

in Price 

($) 

Change 

in Price 

(%) 

Change 

in Quantity 

(%) 

Total 

Engineering Costs 

(million $) 

2007 $0 $0 0.0% 0.0% $0.0 2008 $0 –$423 0.0% 0.0% $0.0 

2009 $0 –$558 0.0% –0.1% $0.0 

2010 $0 –$692 0.0% –0.1% $0.0 

2011 $0 –$827 0.0% –0.1% $0.0 

2012 $0 –$1,136 –0.1% –0.1% $0.0 2013 $0 –$1,216 –0.1% –0.1% $0.0 2014 $0 –$1,197 –0.1% –0.1% $0.0 2015 $0 –$923 0.0% –0.1% $0.0 

2016 $12,224 $120,160 5.7% –0.2% Less than $0.1 

2017 $12,115 $119,830 5.7% –0.2% Less than $0.1 

2018 $10,291 $91,960 4.4% –0.2% Less than $0.1 2019 $10,199 $91,580 4.4% –0.2% Less than $0.1 2020 $10,108 $91,196 4.3% –0.3% Less than $0.1 2021 $10,018 $90,808 4.3% –0.3% Less than $0.1 2022 $10,756 $91,258 4.3% –0.3% Less than $0.1 2023 $10,660 $90,889 4.3% –0.4% Less than $0.1 2024 $10,565 $90,517 4.3% –0.4% Less than $0.1 2025 $10,471 $90,161 4.3% –0.4% Less than $0.1 2026 $10,378 $89,811 4.3% –0.5% Less than $0.1 2027 $10,285 $89,469 4.3% –0.5% Less than $0.1 2028 $10,193 $89,146 4.2% –0.5% Less than $0.1 2029 $10,102 $88,840 4.2% –0.5% Less than $0.1 2030 $10,012 $88,569 4.2% –0.5% Less than $0.1 2031 $10,686 $89,090 4.2% –0.6% Less than $0.1 2032 $10,591 $88,857 4.2% –0.6% Less than $0.1 2033 $10,497 $88,636 4.2% –0.6% Less than $0.1 2034 $10,403 $88,427 4.2% –0.6% Less than $0.1 2035 $10,310 $88,224 4.2% –0.6% Less than $0.1 2036 $10,218 $88,034 4.2% –0.6% Less than $0.1 2037 $10,127 $87,840 4.2% –0.6% Less than $0.1 2038 $10,037 $87,657 4.2% –0.6% Less than $0.1 2039 $10,658 $88,193 4.2% –0.7% Less than $0.1 2040 $10,563 $88,051 4.2% –0.7% Less than $0.1 NPV at 3% $0.2 

NPV at 7% $0.1 

a Figures are in 2005 dollars. b Net present values for 2006 are calculated using a social discount rate of 3% and 7% over the 2007 to 2040 time period. 
7-104 

Table 7B-21. Impact on C2 Other Commercial Vessel Market: >2,000 hp (Average Price per Vessel = $2,

Year 

Variable 

Engineering 

Cost/Unit 

Absolute Change 

in Price 

($) 

Change 

in Price 

(%) 

Change 

in Quantity 

(%) 

Total 

Engineering Costs 

(million $) 

2007 $0 $0 0.0% 0.0% $0.0 2008 $0 –$521 0.0% 0.0% $0.0 

2009 $0 –$686 0.0% –0.1% $0.0 

2010 $0 –$852 0.0% –0.1% $0.0 

2011 $0 –$1,017 0.0% –0.1% $0.0 

2012 $0 –$1,397 –0.1% –0.1% $0.0 

2013 $0 –$1,496 –0.1% –0.1% $0.0 

2014 $0 –$1,472 –0.1% –0.1% $0.0 

2015 $0 –$1,135 –0.1% –0.1% $0.1 

2016 $12,491 $222,458 10.6% –0.2% Less than $0.1 2017 $12,379 $222,075 10.6% –0.2% Less than $0.1 2018 $10,633 $170,182 8.1% –0.2% Less than $0.1 

2019 $10,538 $169,733 8.1% –0.2% Less than $0.1 

2020 $10,444 $169,278 8.1% –0.3% Less than $0.1 

2021 $10,351 $168,819 8.0% –0.3% Less than $0.1 

2022 $10,259 $168,373 8.0% –0.3% Less than $0.1 

2023 $10,167 $167,944 8.0% –0.4% Less than $0.1 

2024 $10,076 $167,513 8.0% –0.4% Less than $0.1 

2025 $10,755 $167,869 8.0% –0.4% Less than $0.1 

2026 $10,659 $167,458 8.0% –0.5% Less than $0.1 

2027 $10,564 $167,057 8.0% –0.5% Less than $0.1 

2028 $10,470 $166,677 7.9% –0.5% Less than $0.1 

2029 $10,376 $166,319 7.9% –0.5% Less than $0.1 

2030 $10,284 $166,004 7.9% –0.5% Less than $0.1 

2031 $10,192 $165,724 7.9% –0.6% Less than $0.1 

2032 $10,101 $165,464 7.9% –0.6% Less than $0.1 

2033 $10,726 $165,934 7.9% –0.6% Less than $0.1 

2034 $10,630 $165,695 7.9% –0.6% Less than $0.1 

2035 $10,535 $165,466 7.9% –0.6% Less than $0.1 

2036 $10,442 $165,251 7.9% –0.6% Less than $0.1 

2037 $10,348 $165,032 7.9% –0.6% Less than $0.1 

2038 $10,256 $164,824 7.8% –0.6% Less than $0.1 

2039 $10,165 $164,629 7.8% –0.7% Less than $0.1 

2040 $10,074 $164,480 7.8% –0.7% Less than $0.1 

NPV at 3% $0.4 

NPV at 7% $0.2 

a Figures are in 2005 dollars. b Net present values for 2006 are calculated using a social discount rate of 3% and 7% over the 2007 to 2040 time period. 
7-105 

Appendix 7C: Impacts on Transportation Service Markets 

This appendix provides the time series of impacts from 2007 through 2040 for two 

transportation service markets (railroad and marine).T

 Table 7C-1 through Table 7C-2 provide 

the time series of impacts and include the following: 

• relative change in market price (%) 

• relative change in market quantity (%) 

• total engineering costs (variable and fixed) associated with each engine market 

• changes in service user surplus 

• changes in service provider surplus 

• changes in total surplus 

All costs and surplus changes are presented in 2005 dollars and real service prices are 

assumed to be constant during the period of analysis. Net present values for 2006 are 

calculated using a social discount rate of 3% and 7% over the 2007 and 2040 time period. 

The NPV calculations presented in this Appendix are based on the period 2006-2040, 

reflecting the period when the analysis was completed. This has the consequence of 

discounting the current year costs, 2007, and all subsequent years are discounted by an 

additional year. The result is a smaller stream of social costs than by calculating the NPV 

over 2007-2040 (3% smaller for 3% NPV and 7% smaller for 7% NPV). 

 

T

 The engineering costs we used in the EIA are an earlier version of the estimated compliance costs developed 

for this rule; see Section 7.3.2 for an explanation of the difference. This difference is not expected to have an 

impact on the results of the market analysis or on the expected distribution of social costs among stakeholders. 
7-106 

Table 7C-1. Impact on Railroad Services Market 

Year 

Change 

in Price 

(%) 

Change 

in Quantity 

(%) 

Total 

Engineering Costs 

(million $) 

Change in Service 

Consumer Surplus 

(million $) 

Change in Service 

Provider Surplus 

(million $) 

2007 0.0% 0.0% $0.0 $0.0 $0.0 2008 0.0% 0.0% $25.8 –$19.5 –$6.1 2009 0.1% 0.0% $32.3 –$24.5 –$7.7 2010 0.1% 0.0% $58.2 –$44.1 –$13.8 2011 0.2% –0.1% $110.1 –$83.5 –$26.1 2012 0.1% –0.1% $90.3 –$68.4 –$21.4 2013 0.1% –0.1% $82.3 –$62.4 –$19.5 2014 0.1% 0.0% $61.0 –$46.3 –$14.5 2015 0.2% –0.1% $65.2 –$102.5 –$32.0 2016 0.3% –0.1% $110.0 –$138.9 –$43.4 2017 0.3% –0.1% $111.6 –$129.6 –$40.5 2018 0.3% –0.1% $130.0 –$144.4 –$45.1 2019 0.3% –0.2% $140.5 –$153.3 –$47.9 2020 0.3% –0.1% $132.3 –$148.6 –$46.4 2021 0.3% –0.2% $150.0 –$163.5 –$51.1 2022 0.4% –0.2% $189.6 –$194.8 –$60.9 2023 0.5% –0.2% $254.1 –$245.9 –$76.8 2024 0.5% –0.2% $260.5 –$251.7 –$78.7 2025 0.5% –0.3% $284.0 –$271.2 –$84.7 2026 0.5% –0.3% $288.8 –$275.9 –$86.2 2027 0.5% –0.3% $308.4 –$292.0 –$91.3 2028 0.6% –0.3% $342.6 –$318.8 –$99.6 2029 0.6% –0.3% $362.6 –$334.8 –$104.6 2030 0.6% –0.3% $380.5 –$348.9 –$109.0 2031 0.6% –0.3% $399.4 –$364.2 –$113.8 2032 0.7% –0.3% $408.4 –$372.0 –$116.2 2033 0.8% –0.4% $485.6 –$431.1 –$134.7 2034 0.8% –0.4% $513.5 –$453.0 –$141.6 2035 0.8% –0.4% $539.9 –$473.2 –$147.9 2036 0.8% –0.4% $550.9 –$479.9 –$150.0 2037 0.8% –0.4% $571.1 –$493.9 –$154.3 2038 0.8% –0.4% $590.7 –$507.3 –$158.5 2039 0.9% –0.4% $610.2 –$520.3 –$162.6 2040 0.9% –0.4% $629.2 –$533.0 –$166.6 NPV at 3% $4,499.2 –$4,168.7 –$1,302.7 NPV at 7% $1,941.2 –$1,819.5 –$568.6 

a Figures are in 2005 dollars. b Net present values for 2006 are calculated using a social discount rate of 3% and 7% over the 2007 to 2040 time period. 
7-107 

Table 7C-2. Impact on Marine Services Market 

Year 

Change 

in Price 

(%) 

Change 

in Quantity 

(%) 

Total 

Engineering Costs 

(million $) 

Change in Service 

Consumer Surplus 

(million $) 

Change in Service 

Provider Surplus 

(million $) 

2007 0.0% 0.0% $0.0 $0.0 $0.0 2008 0.1% 0.0% $18.1 –$13.7 –$4.3 2009 0.1% –0.1% $24.1 –$18.2 –$5.7 2010 0.1% –0.1% $30.2 –$22.8 –$7.1 2011 0.2% –0.1% $36.4 –$27.5 –$8.6 2012 0.2% –0.1% $50.4 –$38.1 –$11.9 2013 0.3% –0.1% $54.4 –$41.2 –$12.9 2014 0.3% –0.1% $54.1 –$40.9 –$12.8 2015 0.2% –0.1% $42.1 –$31.8 –$9.9 2016 0.4% –0.2% $46.5 –$57.9 –$18.1 2017 0.4% –0.2% $57.1 –$66.1 –$20.6 2018 0.4% –0.2% $71.6 –$71.9 –$22.5 2019 0.5% –0.2% $85.7 –$82.8 –$25.9 2020 0.6% –0.3% $100.8 –$94.4 –$29.5 2021 0.6% –0.3% $115.8 –$105.9 –$33.1 2022 0.7% –0.3% $130.8 –$117.3 –$36.7 2023 0.7% –0.4% $145.6 –$128.7 –$40.2 2024 0.8% –0.4% $160.3 –$140.0 –$43.7 2025 0.8% –0.4% $174.9 –$151.1 –$47.2 2026 0.9% –0.5% $189.4 –$162.2 –$50.7 2027 1.0% –0.5% $203.6 –$173.1 –$54.1 2028 1.0% –0.5% $217.5 –$183.7 –$57.4 2029 1.0% –0.5% $230.4 –$193.7 –$60.5 2030 1.1% –0.5% $241.9 –$202.5 –$63.3 2031 1.1% –0.6% $252.2 –$210.4 –$65.7 2032 1.1% –0.6% $261.6 –$217.7 –$68.0 2033 1.2% –0.6% $270.8 –$224.8 –$70.2 2034 1.2% –0.6% $279.5 –$231.6 –$72.4 2035 1.2% –0.6% $288.0 –$238.1 –$74.4 2036 1.2% –0.6% $296.1 –$244.5 –$76.4 2037 1.3% –0.6% $304.3 –$250.8 –$78.4 2038 1.3% –0.6% $312.4 –$257.1 –$80.3 2039 1.3% –0.7% $319.7 –$262.8 –$82.1 2040 1.3% –0.7% $325.7 –$267.5 –$83.6 NPV at 3% $2,644.6 –$2,254.7 –$704.6 NPV at 7% $1,151.2 –$986.9 –$308.4 

a Figures are in 2005 dollars. b Net present values for 2006 are calculated using a social discount rate of 3% and 7% over the 2007 to 2040 time period. 
Regulatory Impact Analysis 

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Appendix 7D: Time Series of Social Costs 

This appendix provides a time series of the rule’s estimated social costs from 2007 

through 2040.U Costs are presented in 2005 dollars. In addition, this appendix includes the 

net present values by stakeholder for 2006 using social discount rates of 3% and 7% over the 

2007 and 2040 time period. As a result, it illustrates how the choice of discount rate 

determines the present value of the total social costs of the program. 

The NPV calculations presented in this Appendix are based on the period 2006-2040, 

reflecting the period when the analysis was completed. This has the consequence of 

discounting the current year costs, 2007, and all subsequent years are discounted by an 

additional year. The result is a smaller stream of social costs than by calculating the NPV 

over 2007-2040 (3% smaller for 3% NPV and 7% smaller for 7% NPV). 

 

U The engineering costs we used in the EIA are an earlier version of the estimated compliance costs developed 

for this rule; see Section 7.3.2 for an explanation of the difference. This difference is not expected to have an 

impact on the results of the market analysis or on the expected distribution of social costs among stakeholders. 
Econom

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Table 7D-1. Time Series of Social Costs: 2007 to 2040 ($2005, $million)a,b

Stakeholder Groups 2007 2008 2009 2010 2011 2012 2013 2014 2015 

Rail Sector 

Locomotive Producers –$8.6 –$8.7 –$8.7 –$38.7 –$42.4 –$35.1 –$35.1 –$40.7 –$5.5 

Line–Haul –$6.0 –$6.1 –$6.1 –$33.7 –$35.5 –$27.8 –$27.8 –$30.9 –$0.6 

Switcher/Passenger –$2.6 –$2.6 –$2.6 –$5.0 –$7.0 –$7.2 –$7.2 –$9.8 –$4.9 

Rail Transportation Service Providers 

$0.0 –$6.1 –$7.7 –$13.8 –$26.1 –$21.4 –$19.5 –$14.5 –$32.0 

Rail Transportation Service Consumers 

$0.0 –$19.5 –$24.5 –$44.1 –$83.5 –$68.4 –$62.4 –$46.3 –$102.5 

Total Locomotive Sector –$8.6 –$34.3 –$40.9 –$96.6 –$152.0 –$124.9 –$116.9 –$101.4 –$140.1 

Marine Sector 

Marine Engine Producers –$17.3 –$17.3 –$17.3 –$17.3 –$76.2 –$45.8 –$45.8 –$45.8 –$63.1 

Auxiliary >800 hp –$2.6 –$2.6 –$2.6 –$2.6 –$20.2 –$16.0 –$16.0 –$16.0 –$22.3 C1 >800 hp –$1.9 –$1.9 –$1.9 –$1.9 –$21.4 –$19.0 –$19.0 –$19.0 –$25.7 

C2 >800 hp –$2.0 –$2.0 –$2.0 –$2.0 –$14.1 –$10.7 –$10.7 –$10.7 –$15.1 

Other Marine –$10.8 –$10.8 –$10.8 –$10.8 –$20.5 $0.0 $0.0 $0.0 $0.0 

Marine Vessel Producers $0.0 –$0.1 –$0.1 –$0.2 –$0.2 –$0.3 –$0.3 –$0.3 –$13.3 

C1 >800 hp $0.0 $0.0 $0.0 $0.0 $0.0 –$0.1 –$0.1 –$0.1 –$9.4 C2 >800 hp $0.0 –$0.1 –$0.1 –$0.1 –$0.1 –$0.1 –$0.2 –$0.1 –$3.8 Other Marine $0.0 $0.0 $0.0 $0.0 $0.0 –$0.1 –$0.1 –$0.1 $0.0 

Recreation and Fishing Vessel Consumers 

$0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 

Marine Transportation Service Providers 

$0.0 –$4.3 –$5.7 –$7.1 –$8.6 –$11.9 –$12.9 –$12.8 –$9.9 

Marine Transportation Service Consumers 

$0.0 –$13.7 –$18.2 –$22.8 –$27.5 –$38.1 –$41.2 –$40.9 –$31.8 

Auxiliary Engines < 800 hpc –$7.6 –$7.6 –$7.6 –$7.6 –$15.0 $0.0 $0.0 $0.0 $0.0 

Total Marine Sector –$25.0 –$43.1 –$49.1 –$55.1 –$127.6 –$96.1 –$100.1 –$99.7 –$118.2 

Total Program –$33.5 –$77.4 –$89.9 –$151.7 –$279.5 –$221.0 –$217.0 –$201.1 –$258.3 
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Table 7D-1. Time Series of Social Costs: 2007 to 2040 ($2005, $million)a,b (continued) 

Stakeholder Groups 2019 2020 2021 2022 2023 2024 2025 2026 2027 2

Rail Sector 

Locomotive Producers –$1.1 –$1.1 –$1.2 –$1.5 –$2.0 –$2.1 –$2.3 –$2.3 –$2.5 –

Line–Haul –$1.0 –$1.0 –$1.2 –$1.4 –$1.8 –$1.9 –$2.0 –$2.1 –$2.2 –

Switcher/Passenger –$0.1 –$0.1 –$0.1 –$0.1 –$0.2 –$0.2 –$0.2 –$0.3 –$0.3 –Rail Transportation Service Providers 

–$47.9 –$46.4 –$51.1 –$60.9 –$76.8 –$78.7 –$84.7 –$86.2 –$91.3 –$

Rail Transportation Service Consumers 

–$153.3 –$148.6 –$163.5 –$194.8 –$245.9 –$251.7 –$271.2 –$275.9 –$292.0 –$

Total Locomotive Sector –$202.3 –$196.1 –$215.8 –$257.2 –$324.7 –$332.5 –$358.2 –$364.4 –$385.8 –$

Marine Sector 

Marine Engine Producers –$1.7 –$1.8 –$1.8 –$1.8 –$1.8 –$1.9 –$1.9 –$1.9 –$2.0 –

Auxiliary >800 hp –$0.4 –$0.4 –$0.4 –$0.4 –$0.4 –$0.5 –$0.5 –$0.5 –$0.5 –C1 >800 hp –$1.2 –$1.3 –$1.3 –$1.3 –$1.3 –$1.3 –$1.3 –$1.3 –$1.4 –

C2 >800 hp $0.0 $0.0 –$0.1 –$0.1 –$0.1 –$0.1 –$0.1 –$0.1 –$0.1 –Other Marine $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 

Marine Vessel Producers –$10.2 –$10.3 –$10.5 –$10.6 –$10.8 –$10.9 –$11.0 –$11.2 –$11.3 –$

C1 >800 hp –$8.7 –$8.8 –$8.9 –$9.0 –$9.1 –$9.2 –$9.2 –$9.3 –$9.4 –

C2 >800 hp –$1.3 –$1.3 –$1.4 –$1.4 –$1.5 –$1.5 –$1.6 –$1.6 –$1.6 –

Other Marine –$0.1 –$0.1 –$0.2 –$0.2 –$0.2 –$0.2 –$0.2 –$0.3 –$0.3 –Recreation and Fishing Vessel Consumers 

$0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 

Marine Transportation Service Providers 

–$25.9 –$29.5 –$33.1 –$36.7 –$40.2 –$43.7 –$47.2 –$50.7 –$54.1 –$

Marine Transportation Service Consumers 

–$82.8 –$94.4 –$105.9 –$117.3 –$128.7 –$140.0 –$151.1 –$162.2 –$173.1 –$

Auxiliary Engines < 800 hpc $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 

Total Marine Sector –$120.6 –$135.9 –$151.2 –$166.4 –$181.5 –$196.4 –$211.3 –$226.0 –$240.4 –$

Total Program –$322.9 –$332.0 –$367.0 –$423.5 –$506.1 –$528.9 –$569.5 –$590.4 –$626.2 –$
Econom

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Table 7D-1. Time Series of Social Costs: 2007 to 2040 ($2005, $million)a,b (continued) 

Stakeholder Groups 2031 2032 2033 2034 2035 2036 2037 2038 2039 2

Rail Sector 

Locomotive Producers –$3.3 –$3.4 –$3.9 –$4.1 –$4.3 –$4.2 –$4.2 –$4.2 –$4.1 

Line–Haul –$2.8 –$2.9 –$3.4 –$3.6 –$3.7 –$3.6 –$3.6 –$3.6 –$3.6 Switcher/Passenger –$0.4 –$0.5 –$0.5 –$0.6 –$0.6 –$0.6 –$0.6 –$0.6 –$0.6 Rail Transportation Service Providers 

–$113.8 –$116.2 –$134.7 –$141.6 –$147.9 –$150.0 –$154.3 –$158.5 –$162.6 –$

Rail Transportation Service Consumers 

–$364.2 –$372.0 –$431.1 –$453.0 –$473.2 –$479.9 –$493.9 –$507.3 –$520.3 –$

Total Locomotive Sector –$481.2 –$491.6 –$569.8 –$598.7 –$625.4 –$634.0 –$652.4 –$670.0 –$687.1 –$

Marine Sector 

Marine Engine Producers –$2.1 –$2.1 –$2.1 –$2.1 –$2.2 –$2.2 –$2.2 –$2.2 –$2.3 

Auxiliary >800 hp –$0.5 –$0.5 –$0.5 –$0.5 –$0.5 –$0.5 –$0.5 –$0.6 –$0.6 

C1 >800 hp –$1.4 –$1.4 –$1.4 –$1.4 –$1.5 –$1.5 –$1.5 –$1.5 –$1.5 C2 >800 hp –$0.1 –$0.1 –$0.1 –$0.1 –$0.1 –$0.1 –$0.1 –$0.1 –$0.1 Other Marine $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 

Marine Vessel Producers –$9.3 –$9.5 –$9.6 –$9.7 –$9.8 –$9.9 –$10.1 –$10.2 –$10.3 –

C1 >800 hp –$8.2 –$8.3 –$8.4 –$8.5 –$8.6 –$8.7 –$8.7 –$8.8 –$8.9 C2 >800 hp –$0.8 –$0.8 –$0.8 –$0.8 –$0.9 –$0.9 –$0.9 –$0.9 –$1.0 Other Marine –$0.3 –$0.3 –$0.4 –$0.4 –$0.4 –$0.4 –$0.4 –$0.4 –$0.4 

Recreation and Fishing Vessel Consumers 

$0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 

Marine Transportation Service Providers 

–$65.7 –$68.0 –$70.2 –$72.4 –$74.4 –$76.4 –$78.4 –$80.3 –$82.1 –

Marine Transportation Service Consumers 

–$210.4 –$217.7 –$224.8 –$231.6 –$238.1 –$244.5 –$250.8 –$257.1 –$262.8 –$

Auxiliary Engines < 800 hpc $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 

Total Marine Sector –$287.5 –$297.3 –$306.7 –$315.8 –$324.5 –$333.0 –$341.4 –$349.8 –$357.4 –$

Total Program –$768.7 –$788.9 –$876.5 –$914.4 –$949.9 –$967.0 –$993.8 –$1,019.8 –$1,044.5 –$1

a Figures are in 2005 dollars. b Net present values for 2006 are calculated using a social discount rate of 3% and 7% over the 2007 to 2040 time period. c Marine auxiliary engines <800 hp are not subject to Tier 4 standards, and there are no variable costs associated with the Tier 3 stan

there would be no direct compliance impacts for producers or users of these engines. Social costs are limited to fixed costs associacertification for Tier 3 standards (those costs occur 2007-2011). 
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Appendix 7E: Model Equations 

To develop the economic impact model, we use a set of nonlinear supply and demand 

equations for the affected markets and transform them into a set of linear supply and demand 

equations. These resulting equations describe stakeholder production and consumption 

responses to policy-induced cost and price changes in each market. They are also used to 

specify the conditions for a new with-policy equilibrium. We describe these equations in 

more detail below. 

7E.1 Economic Model Equations 

7E.1.1 Supply Equations 

First, we consider the formal definition of the elasticity of supply with respect to 

changes in own price: 

pdp

QdQ ss

s

/

/

ε = . (7E.1) 

Next, we can use “hat” notation to transform Eq. (7E.1) to proportional changes and 

rearrange terms: 

$Q p$ s s = ε (7E.1a) 

where 

Q$

s = percentage change in the quantity of market supply, 

γs = market elasticity of supply, and

p$ = percentage change in market price. 

As Fullerton and Metcalf note, this approach takes the elasticity definition and turns it 

into a linear behavioral equation for each market.31 

To introduce the direct impact of the regulatory program, we assume the direct perunit compliance cost (c) leads to a proportional shift in the marginal cost of production. Under 

the assumption of perfect competition (price equals marginal cost), we can approximate this 

shift at the initial equilibrium point as follows: 

MC$ c

MC

c

o o p = = . (7E.1b) 
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7-113 

The with-regulation supply response to price and cost changes can now be written as: 

Qs

ˆ = s ε ( pˆ – ˆCM ) (7E.1c) 

For equipment producers, the supply response also simultaneously accounts for 

changes in equilibrium input prices (engines). To do this, we modify Eq. (7E.1b) as follows: 

$ () ()

MC

c p

MC

c p

p

eng

o

eng

o

= + = α Δ + α Δ (7E.1d) 

where Δpengine is the equilibrium change in the engine price and α is the ratio of engines used 

per unit of equipment. For example, if one piece of equipment uses only one engine, then α = 

1. This equation can accommodate other input-output ratios by multiplying Δpeng by the 

appropriate input-to-output ratio (α). 

For transportation service providers, the supply response also simultaneously accounts 

for changes in equilibrium input prices (equipment). To do this, we use an equation similar to 

Eq. (7E.1.d): 

$ () ()

MC

c p

MC

c p

p

equip

o

equip

o

= + = α Δ + α Δ (7E.1e) 

where Δpequip is the equilibrium change in the equipment price and α is the ratio of equipment 

used per unit of transportation services. 

7E.1.2 Demand Equations 

Similar to supply, we can characterize services and selected equipmentV demand 

responses to price changes as: 

$

Q p$ d d = η (7E.2) 

where 

Q$

d = percentage change in the quantity of market demand, 

ηd = market elasticity of demand, and 

p$

 = percentage change in market price. 

 

V The equipment markets are recreational vessels and fishing vessels. The remaining vessel and locomotive 

demand curves are derived from the supply decisions of the appropriate downstream transportation service 

markets. 
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In contrast the demand for engines and selected equipment markets is a derived 

demand and is related to equipment or service supply decisions. In order to maintain a 

constant input-to-output ratio, the derived demand for inputs is specified as: 

Q Q $ $

input output = . (7E.3) 

7E.1.3 Market Equilibrium Conditions 

In response to the exogenous increase production costs, stakeholder responses are 

completely characterized by represented in Eq. (7E.1.c)(service, equipment and engine 

supply), Eq. (7E.2) (service and selected equipment demand), Eq. (7E.3) (derived demand for 

selected equipment and engine). Next, we specify the relationship that must hold for markets 

to “clear”, that is, supply in each market equals demand. Given the equations specified above, 

the new equilibrium satisfies the condition that for each market, the proportional change in 

supply equals the proportional change in demand: 

Q Q $ $

s d = . (7E.4) 

7E.2 Computing With-Regulation Equilibrium Conditions within the 

Spreadsheet 

The French economist Léon Walras proposed one early model of market price 

adjustment by using the following thought experiment. Suppose there is a hypothetical agent 

that facilitates market adjustment by playing the role of an “auctioneer.” He announces prices, 

collects information about supply and demand responses (without transactions actually taking 

place), and continues this process until market equilibrium is achieved. 

For example, consider the with-regulation supply and demand conditions at the 

without-regulation equilibrium price (P) (see Figure 7E-1). The auctioneer determines that the 

quantity demanded (A) exceeds the quantity supplied (B) at this price and calls out a new 

(higher) price (P′) based on the amount of excess demand. Consumers and producers make 

new consumption and production choices at this new price (i.e., they move along their 

respective demand and supply functions), and the auctioneer checks again to see if excess 

demand or supply exists. This process continues until P = P* (point C in Figure 7E-1) is 

reached (i.e., excess demand is zero in the market). A similar analysis takes place when 

excess supply exists. The auctioneer calls out lower prices when the price is higher than the 

equilibrium price. 
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P

D

$/Q

Q/t

S1: With Regulation

P*

A B

P Price

Increase

Unit Cost 

Increase

S0: Without 

Regulation

C

Figure 7E-1. Computing With-Regulation Equilibrium 

The economic model uses a similar type of algorithm for determining with-regulation 

equilibria, and the process can be summarized by six recursive steps: 

1. Impose the control costs on affected supply segments, thereby affecting their 

supply decisions. 

2. Recalculate the market supply in each market. Excess demand currently exists. 

3. Determine the new prices via a price revision rule. We use a rule similar to the 

factor price revision rule described by Kimbell and Harrison.32 Pi is the market 

price at iteration i, qd is the quantity demanded, and qs is the quantity supplied. The 

parameter z influences the magnitude of the price revision and the speed of 

convergence. The revision rule increases the price when excess demand exists, 

lowers the price when excess supply exists, and leaves the price unchanged when 

market demand equals market supply. The price adjustment is expressed as 

follows: 

z

s

d

11i

q

q PP ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ + •= (7E.5) 

4. Recalculate market supply with new prices. 

5. Compute market demand in each market. 
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6. Compare supply and demand in each market. If equilibrium conditions are not 

satisfied, go to Step 3, resulting in a new set of market prices. Repeat until 

equilibrium conditions are satisfied (i.e., the ratio of supply and demand is 

arbitrarily close to one). When the ratio is appropriately close to one, the marketclearing condition of supply equals demand is satisfied. 

7E.3 Social Costs: Consumer and Producer Economic Welfare Calculations 

The change in consumer surplus in the affected markets can be estimated using the 

following linear approximation method: 

)CS = – [Q1 × )p] + [0.5 × )Q × )p]. (7E.6) 

As shown, higher market prices and reduced consumption lead to welfare losses for 

consumers. A geometric representation of this calculation is illustrated in Figure 7E-2. 

For affected supply, the change in producer surplus can be estimated with the 

following equation: 

)PS = [Q1 × )p] – [Q1 × )MC] – [0.5 × )Q × ()p – )MC)]. (7E.7) 

Increased regulatory costs and output declines have a negative effect on producer surplus, 

because the net price change ()p – )MC) is negative. However, these losses are mitigated, to 

some degree, as a result of higher market prices. A geometric representation of this 

calculation is also illustrated in Figure 7E-2. 

Throughout this report, changes in surplus reflect the social costs of the emission 

control program. These calculations exclude any environmental benefits associated with the 

rule. 
Economic Impact Analysis 

7-117 

Q1 Q0

P1

P0

h

c

a

$

Output

e

d

S1 : With Regulation

S0 : Without Regulation Price

Increase

g Unit Cost Increase

f

b

) consumer surplus = –[fghd + dhc] 

) producer surplus = [fghd – aehb] – bdc 

) total surplus = –[aehb + dhc + bdc] 

Figure 7E-2. Economic Welfare Calculations: Changes in Consumer, Producer, 

and Total Surplus 
Regulatory Impact Analysis 

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Appendix 7F: Elasticity Parameters for Economic Impact 

Modeling 

Elasticities were obtained from peer-reviewed literature or were obtained from other 

EPA rule that estimated these parameters using empirical methods (i.e. econometrically). 

Table 7F-1 and Table 7F-2 summarize the price elasticities of supply and demand used in this 

analysis. The methodologies for estimating the supply and demand elasticities are described 

in the documents identified in the data source column. The unknown parameters for the 

analysis were the locomotive and commercial marine vessel supply elasticities. This 

appendix describes the methods and data used to identify these two supply elasticites used in 

the economic impact analysis. 

It should be noted that the methods we used to estimate the price elasticites have 

certain limitations. The production function approach that was previously used to estimate 

the supply elasticity or the approach described in this appendix was limited in available data. 

Specifically, firm level or plant level data was unavailable for the companies that operate in 

the affected sectors. As a result, the supply elasticities were estimated with industry level 

aggregate data. However, the use of aggregate industry level data may not be appropriate or 

an accurate way to estimate the price elasticity of supply compared to firm-level or plant-level 

data. This is because, at the aggregate industry level, the size of the data sample is limited to 

the time series of the available years and because aggregate industry data may not reveal each 

individual firm or plant production function (heterogeneity). There may be significant 

differences among the firms that may be hidden in the aggregate data but that may affect the 

estimated elasticity. In addition, the use of time series aggregate industry data may introduce 

time trend effects that are difficult to isolate and control. 

To address these concerns, EPA intends to investigate estimates for the price elasticity 

of supply for the affected industries for which published estimates are not available, using 

alternative methods and data inputs. This research program will use the cross-sectional data 

model at either the firm-level or plant level from the U.S. Census Bureau to estimate these 

elasticities. We plan to use the results of this research provided the results are robust and that 

they are available in time for the analysis for the final rule. 

Table 7F-1. Summary of Market Demand Elasticities Used in EIM 

Market Estimate Source Method Data Source 

Rail 

Rail Transp. 

Svcs 

-0.5 Literature 

estimate 

Literature 

review 

Boyer, K.D. 1997. Principles of 

Transportation Economics. Reading, MA: 

Addison-Wesley. 

Locomotives Derived 

Marine 
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Marine 

Transp. Svcs 

-0.5 Literature 

estimate 

Assumed 

value 

Uses the same elasticity as the locomotive 

transportation services sector. 

Vessels—

Commercial 

Fishing 

(>800 hp) 

Derived 

Vessels—

Fishing 

(<800 hp) 

-2.0 Econometric 

estimate 

Assumed 

value 

Uses the same elasticity as the recreation 

vessels sector. 

Vessels—

Recreational 

-2.0 Econometric 

estimate 

Previous 

EPA 

economic 

analysis 

U.S. Environmental Protection Agency 

(EPA). 2007. Control of Emissions from 

Marine SI and Small SI Engines, Vessels, 

and Equipment Draft Regulatory Impact 

Analysis. EPA420-D-07-004. Available at 

<http://www.epa.gov/otaq/regs/nonroad/ma

rinesi-equipld/420d07004.pdf>.

Engines Derived 

Table 7F-2. Summary of Supply Elasticities Used in EIM 

Market Estimate Source Method Input Data Source 

Rail 

Rail Transp. 

Svcs 

1.6 Literature 

estimate 

Method based 

on cost 

elasticities 

reported in 

Ivaldi and 

McCollough 

(2001) 

Ivaldi, M. and McCullough, G. 2001. 

"Density and Integration Effects on Class I 

U.S. Freight Railroads." Journal of Regulatory 

Economics 19:161-162. 

Locomotives 2.7 EPA 

estimate 

Calibration 

method 

U.S. Bureau of the Census. 2004a. “Railroad 

Rolling Stock Manufacturing: 2002.” 2002 

Economic Census Manufacturing Industry 

Series. EC02-31I-336510 (RV). Washington, 

DC: U.S. Bureau of the Census. Table 1. 

U.S. Bureau of the Census. 2005. “Statistics 

for Industry Groups and Industries: 2004” 

Annual Survey of Manufacturers. M04(AS)-1. 

Washington, DC: U.S. Bureau of the Census. 

Table 2. 

Marine 

Marine 

Transp. Svcs 

1.6 Assumed value; uses the same elasticity as the rail transportation services sector 
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Vessels—

Commercial 

and large 

fishing 

2.3 EPA 

estimate 

Calibration 

method U.S. Bureau of the Census. 2004b. “Ship 

Building and Repairing: 2002.” 2002 

Economic Census Manufacturing Industry 

Series. EC02-31I-336611 (RV). Washington, 

DC: U.S. Bureau of the Census. Table 1. 

U.S. Bureau of the Census. 2005. “Statistics 

for Industry Groups and Industries: 2004” 

Annual Survey of Manufacturers. M04(AS)-1. 

Washington, DC: U.S. Bureau of the Census. 

Table 2. 

Vessels—

Recreational 

and small 

fishing 

2.3 Econometric 

estimate 

Previous EPA 

economic 

analysis 

U.S. Environmental Protection Agency 

(EPA). 2007. Control of Emissions from 

Marine SI and Small SI Engines, Vessels, and 

Equipment Draft Regulatory Impact Analysis. 

EPA420-D-07-004. Available at 

<http://www.epa.gov/otaq/regs/nonroad/marin

esi-equipld/420d07004.pdf>. 

Engines 3.8 Econometric 

estimate 

Previous EPA 

economic 

analysis 

U.S. Environmental Protection Agency 

(EPA). 2004. Final Regulatory Impact 

Analysis: Control of Emissions from Nonroad 

Diesel Engines. EPA420-R-04-007. 

Available at <http://www.epa.gov/nonroaddiesel/2004fr/420r04007.pdf>. 

The technique we used to quantify the locomotive and commercial marine vessel 

industry supply elasticity is described as “calibration approach” in Handbook of of 

Econometrics, Volume 5.33 This approach involves specifying an economic model of supply, 

treating some of the parameters of the model as fixed using secondary data, and solving for 

unknown parameters that replicate a benchmark data set.W The specific procedure uses an 

analytical expression for a short-to-intermediate run supply elasticity derived by Rutherford 

and recent benchmark data sets from Economic Census data between 1997 and 2004.34 

As described by Rutherford, the procedure specifies that the functional form of the 

production function is the constant elasticity of substitution (CES). It also assumes there is a 

fixed capital input that makes it consistent with the intermediate-run time frame of the 

analysis. As Rutherford shows, the price elasticity of supply can be expressed as 

ε = (1 − θ) × σ/ θ,

where θ represents the value share of capital and σ represents the elasticity of substitution 

between inputs. For this analysis, we assume an elasticity of substitution of one (σ =1), which 

yields a Cobb-Douglas production technology that is a special case of the CES production 

 

W A complete discussion of the meaning, merits and criticism, and best practices of these types of techniques can 

be found in Dawkins, Christina & T. N. Srinivasan, & John Whalley, (2001). “Calibration” in Handbook of 

Econometrics, Volume 5, ed. J. J. Heckman & E. E. Leamer, (Amsterdam: Elsevier). 
Economic Impact Analysis 

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function. The Cobb-Douglas production function is one of the most commonly used 

production functions in economics studies. 

We collected the latest Economic Census data for NAICS 336510 (Railroad Rolling 

Stock Manufacturing) that provides an estimate of the value share of capital θ for 

locomotives. To compute this value share, we subtracted reported payroll costs from the 

reported industry value added and divided by the total value of shipments (see Table 7F-3). 

Using the elasticity formula, σ = 1, and annual value share data reported in Table 7F-3, we 

computed an average supply elasticity value of 2.7 for this industry. Accounting for 

variability of the value share parameter across 1997 to 2004, we computed a 95% confidence 

interval for the elasticity value that ranges from 1.9 to 3.4. 

Similarly, we estimated the value share of capital θ for commercial marine vessels 

from latest Economic Census data for NAICS 336611 (Ship Building and Repairing 

Manufacturing). Using the elasticity formula, σ = 1, and annual value share data reported in 

Table 7F-4, we computed an average supply elasticity value of 2.3 for this industry. By the 

value share parameter across 1997 to 2004, we computed a 95% confidence interval for the 

elasticity value that ranges from 1.3 to 3.2. 

The parameter estimates suggest both locomotive and commercial marine vessel 

supplies are elastic and firms can change production levels in response to changes in market 

prices. Two factors support an elastic supply estimate for this sector. First, industries that are 

less capital intensive typically have more flexibility to adjust variable inputs (e.g. labor and/or 

materials) and can change production levels in response to variations in market prices. The 

Census data for locomotive and ship building manufacturing are consistent with this 

observation and suggest the capital share of production costs in the locomotive or ship 

building industry is small relative to other inputs. The value share of capital is ranging from 

20% to 30% for locomotives and from 25% to 38% for ship building and repairing. Second, 

industries with excess production capacity also have more flexibility to change output levels 

in response to price changes. Data from the Census also suggest the locomotive 

manufacturing industry’s capacity utilization rates have been low, implying excess capacity 

exists. Data for the fourth quarters of 2000 to 2004 show utilization rates ranging from 45% to 

69%. For ship building and repairing industry, the production capacity utilization ratio for the 

fourth quarters of 2000 to 2004 is ranging around 50% to 80% according to U.S. Bureau of 

the Census data.
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Table 7F-3. Benchmark Supply Elasticities for NAICS 336510 (Railroad Rolling Stock 

Manufacturing): 1997–2004 ($1,000) 

Supply Elasticity 

ε = (1 − θ) × σ/ θ

Year 

Value of 

Shipments Value Added Payroll Costs 

Value Share 

of Capital (θ)

a σ=1 

(Cobb-Douglas) 

2004 $7,566,129 $3,216,704 $1,123,054 28% 2.6 

2003 $7,404,763 $2,909,834 $1,156,084 24% 3.2 

2002 $7,793,382 $3,741,703 $1,195,073 33% 2.1 

2001 $8,578,053 $3,824,449 $1,449,784 28% 2.6 

2000 $9,722,424 $4,360,089 $1,480,181 30% 2.4 

1999 $10,352,310 $4,460,735 $1,532,969 28% 2.5 

1998 $9,256,810 $3,848,408 $1,440,110 26% 2.8 

1997 $8,263,395 $3,345,283 $1,319,135 25% 3.1 

Parameter Statistics 

Average 2.7 

Standard deviation 0.4 

Upper bound (95% confidence interval) 3.4 

Lower bound (95% confidence interval) 1.9 

a

The value share of capital is computed by subtracting payroll costs from reported value added and dividing by 

the total value of shipments. 

Sources: U.S. Bureau of the Census. 2004. “Railroad Rolling Stock Manufacturing: 2002.” 2002 Economic 

Census Manufacturing Industry Series. EC02-31I-336510 (RV). Washington, DC: U.S. Bureau of the Census. 

Table 1. 

U.S. Bureau of the Census. 2005. “Statistics for Industry Groups and Industries: 2004.” Annual Survey of 

Manufacturers. M04(AS)-1. Washington, DC: U.S. Bureau of the Census. Table 2. 
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Table 7F-4. Benchmark Supply Elasticities for NAICS 336611 (Ship Building & 

Repairing): 1997–2004 ($1,000) 

Supply Elasticity 

ε = (1 − θ) × σ/ θ

Year Value of Shipments 

Value 

Added 

Payroll 

Costs 

Value Share 

of Capital (θ)

a σ=1 

(Cobb-Douglas) 

2004 $13,705,958 $8,573,286 $3,772,590 35% 1.9 

2003 $13,485,503 $8,679,730 $3,692,026 37% 1.7 

2002 $12,814,574 $8,449,010 $3,628,382 38% 1.7 

2001 $11,792,832 $6,968,749 $3,439,474 30% 2.3 

2000 $11,380,112 $6,324,192 $3,435,806 25% 2.9 

1999 $11,070,960 $6,328,784 $3,336,632 27% 2.7 

1998 $11,143,246 $6,728,975 $3,347,525 30% 2.3 

1997 $10,542,961 $6,202,797 $3,353,414 27% 2.7 

Parameter Statistics 

Average 2.3 

Standard Deviation 0.5 

Upper Bound (95% Confidence Interval) 3.2 

Lower Bound (95% Confidence Interval) 1.3 

a

The value share of capital is computed by subtracting payroll costs from reported value added and dividing by 

the total value of shipments. 

 Sources: U.S. Bureau of the Census. 2004b. “Ship Building and Repairing: 2002.” 2002 Economic Census 

Manufacturing Industry Series. EC02-31I-336611 (RV). Washington, DC: U.S. Bureau of the Census. Table 

1. 

U.S. Bureau of the Census. 2005. “Statistics for Industry Groups and Industries: 2004.” Annual Survey of 

Manufacturers. M04(AS)-1. Washington, DC: U.S. Bureau of the Census. Table 2. 
Regulatory Impact Analysis 

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Appendix 7G: Initial Market Equilibrium - Price Forecasts 

The EIM analysis begins with current market conditions: equilibrium supply and 

demand. To estimate the economic impact of a regulation, standard practice uses projected 

market equilibrium (time series of prices and quantities) as the baseline and evaluates market 

changes from this projected baseline. Consequently, it is necessary to forecast equilibrium 

prices and quantities for future years. 

Figure 7G-1. Prices and Quantities in Long Run Market Equilibrium 

Equilibrium price forecasts typically use one of two approaches.35 The first assumes a 

constant (real) price of goods and services over time. The second models a specific time 

series where prices may change over time due to exogenous factors. 

In the absence of shocks to the economy or the supply of raw materials, economic 

theory suggests that the equilibrium market price for goods and services should remain 

constant over time. As shown in Figure 7G-1, demand grows over time, in the long run, 

capacity will also grow as existing firms expand or new firms enter the market and eliminate 

any excess profits. This produces a flat long run supply curve. Note that in the short to 

medium run time frame the supply curve has a positive slope due to limitations in how 

quickly firms can react. 

If capacity is constrained (preventing the outward shift of the baseline supply curve) or 

if the price of production inputs increase (shifting the baseline supply curve upward over 

time), then prices may trend upward reflecting that either the growth in demand is exceeding 
Economic Impact Analysis 

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supply or the commodity is becoming more expensive to produce. 

It is very difficult to develop forecasts events (such as those mentioned above) that 

influence long run prices. As a result, the approach used in this analysis is to use a constant 

2005 observed price. 
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Appendix 7H: Sensitivity Analysis 

The economic impact analysis presented in this Chapter is based on an economic 

impact model developed specifically for this analysis. The EIM reflects specific assumptions 

about behavioral responses (represented the price elasaticities of supply and demand) and how 

the engineering compliance costs are included in the market supply function shift (supply 

affected by variable costs only, in accordance with the underlying assumption of perfect 

competition). This appendix examines the sensitivity of the market and social welfare 

impacts estimated by the model to the values used for these key parameters. Alternative 

values for these parameters are selected and the results are compared to the results of the 

primary analysis described in Section 7.1. Four model components are examined:X

• Scenario 1: alternative market supply and demand elasticity parameters for all markets 

• Scenario 2: alternative ways to treat the market supply shifts (variable and fixed costs) 

• Scenario 3: alternative supply elasticity parameter for equipment markets, based on 

supply elasticities estimated using an alternative methodology 

• Scenario 4: alternative way to treat compliance costs for auxiliary engines above 800 hp 

The results of these four sensitivity analysis scenarios are presented below. Although 

estimates of market impacts and total economic welfare changes for the different scenarios are 

similar for to the primary case, the different assumptions highlight the role the assumptions 

play in determining the distribution of welfare changes among stakeholders. 

 

X An additional sensitivity analysis was included in the draft RIA prepared for the NPRM in which we examined 

an alternative methodology of allocating operating costs (fuel costs associated with Tier 4 and marine 

remanufacture costs). In the primary case for the draft EIA, all marine operating costs were allocated to the 

marine transportation service providers; in the sensitivity analysis we explored a scenario that allocated 

operating costs to fishing and recreational vessels as well. This scenario is not relevant for the final rule for two 

reasons. First, recreational engines are not subject to the Tier 4 standards in the final rule, and recreational 

engines above 800 hp are not expected to come under the marine remanufacture program due to their operational 

characteristics. Second, fishing vessels below 800 hp are not subject to the Tier 4 standards or marine 

remanufacture requirements, and fishing vessels above 800 hp are now modeled as being directly consumed in 

the transportation services market (see 7.1.3.2). 
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7H.1 Model Elasticity Parameters 

Key model parameters include estimates of the price elasticity of supply and demand 

used by the model to characterize behavioral responses of producers and consumers in each 

market in response to a change in price. 

Consumer demand and producer supply responsiveness to changes in the commodity 

prices are referred to by economists as “elasticity.” The measure is typically expressed as the 

percentage change in quantity (demanded or supplied) brought about by a percent change in 

own price. A detailed discussion regarding the estimation and selection of the supply and 

demand elasticities used in the EIM are discussed in Appendix 10F. This component of the 

sensitivity analysis examines the impact of changes in selected elasticity values, holding other 

parameters constant. The goal is to determine whether alternative elasticity values 

significantly alter conclusions in this report. 

There are at least two ways to examine the sensitivity of the EIA results to 

assumptions about the price elasticity of supply or demand. The first is to choose upper and 

lower bounds for these variables based on the ranges of values reported in the literature or 

based on sensitivity analysis constructed around estimated values. This method was not 

available for this study because, as described in Appendix F, many of these parameters were 

obtained from secondary sources and information was not readily available to compute 

confidence intervals for them. Therefore, an alternative approach was used in which the 

supply or demand elasticity parameters were increased/decreased by about 25 percent while 

holding all other model parameters (including the other elasticities) constant. Table 7H-1 

reports the upper- and lower-bound demand and supply elasticity estimates used in this 

analysis. 

Parameter Elasticity Source Lower Bound Base Case Upper Bound 

DEMAND ELASTICITIES 

Rail and marine 

transportation 

services 

Literature 

estimate 

-0.4 -0.5 -0.6 

Locomotive Derived N/A 

Commercial 

vessels 

Derived N/A 

Recreational and 

fishing vessels 

Econometric 

Estimate 

-1.5 -2.0 -2.5 

Marine engines Derived N/A 

SUPPLY ELASTICITIES 

Rail and marine 

transportation 

services 

Literature 

estimate 

1.2 1.6 2.0 

Locomotives Calibration 

Estimate 

2.0 2.7 3.4 

Commercial 

marine vessels 

Calibration 

Estimate 

1.7 2.3 2.9 
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Recreational and 

fishing vessels 

Econometric 

Estimate 

1.7 2.3 2.9 

Marine engines Econometric 

Estimate 

2.9 3.8 4.8 

The results of this analysis for 2020 are presented in Tables 7H-2 and 7H-3. Varying 

the model’s elasticity parameters does not significantly change the estimated impacts on total 

economic welfare. However, varying the model parameters has an impact on how the 

regulatory program costs are distributed across stakeholders. The elasticity parameters play 

an important role in determining the economic incidence of the regulatory program. 

In scenarios in which the supply side of the service markets is more responsive to 

price changes (more elastic) users of services would bear more of the burden of the regulatory 

program. Thus, when the elasticity of supply is more elastic (producers are more sensitive to 

a change in price) and demand is held constant, the expected surplus loss to users of 

transportation services increases from 28 percent to 30 percent for marine and from 45 

percent to 47 percent for rail, respectively (see Table 7H-2). Similarly, when the elasticity of 

demand is less elastic (consumers are less sensitive to a change in price) and the supply 

elasticity is held constant, the expected surplus loss to users of transportation services 

increases from 28 percent to 31 percent for marine and from 45 percent to 48 percent for rail, 

respectively (see Table 7H-3). 

In contrast, when the supply side of the service market is less responsive to price 

changes (the elasticity of supply is less elastic) or the demand side of the service is more 

sensitive to price changes (the elasticity of demand is more elastic), service providers would 

bear more of the burden of the regulatory program. Here, when the elasticity of supply is 

decreased but the elasticity of demand is held constant, the expected surplus loss to providers 

of transportation services increases from 9 percent to 11 percent for marine and from 14 

percent to 17 percent for rail, respectively (see Table 7H-2). When the elasticity of demand is 

more elastic (consumers are more sensitive to a change in price) and the supply elasticity is 

held constant, the expected surplus loss to providers of transportation services increases from 

9 percent to 10 percent for marine and from 14 percent to 17 percent for rail, respectively (see 

Table 7H-3). 

With regard to locomotive, marine vessel, and marine diesel engine suppliers, their 

share of the surplus loss increases when the price elasticity of supply is less elastic (they are 

less sensitive to prices changes) or when the price elasticity of demand is more elastic 

(consumers are more sensitive to price changes). 

With regard to market effects, price increases and quantity decreases are somewhat 

higher when the price elasticity of supply is more elastic or the price elasticity of demand is 

less elastic and somewhat lower when the price elasticity of supply is less elastic or the price 

elasticity of demand is more elastic. 
7-129 

Table 7H-2. Sensitivity Analysis for Supply Elasticity Parameters: 2020a

Primary Case Supply Upper S

Change in Price Change in Quantity Change in Price Change in Quantity Change in Pr

Market–Level Impacts 

Variable 

Engineering 

Cost Per Unit Absolute Percent Absolute Percent Absolute Percent Absolute Percent Absolute Per

Rail Sector Locomotives  Line Haul $65,343 $64,261 3.2% -1 –0.1% $64,433 3.2% -1 –0.2% $64,009  Switcher/Passenger $21,139 $20,436 1.6% 0 –0.1% $20,548 1.6% 0 –0.2% $20,271 Transportation services NA NA 0.3% NA –0.1% NA 0.3% NA –0.2% NA Marine Sector Engines  Auxiliary >800 hp $28,363 $27,129 13.1% –9 –2.7% $27,267 13.1% –10 –3.0% $26,950  C1 >800 hp $14,131 $12,497 6.5% –12 –2.9% $12,676 6.6% –13 –3.2% $12,267  C2 >800 hp $54,893 $54,573 12.4% 0 –0.3% $54,621 12.4% 0 –0.3% $54,502  Other marine $0 -$1 0.0% 0 0.0% $0 0.0% 0 0.0% –$1 Equipment  C1 >800 hp $6,936 $26,158 1.6% –11 –2.9% $28,705 1.8% –12 –3.2% $22,890  C2 >800 hp $10,174 $170,164 5.3% 0 –0.3% $171,103 5.3% 0 –0.3% $168,818  Other marine $0 –$6 0.0% –2 0.0% –$5 0.0% –2 0.0% –$8 Transportation services NA NA 0.6% NA –0.3% NA 0.6% NA –0.3% NA Welfare Impacts (Million $) Surplus Change Share Surplus Change Share Surplus ChanLocomotives Locomotive producers –$1.1 0.3% –$0.9 0.3%  Line Haul –$1.0 0.3% –$0.9 0.3%  Switcher/Passenger –$0.1 0.0% –$0.1 0.0% Rail transport. service providers –$46.4 14.0% –$39.0 11.8% –Users of rail transport. services –$148.6 44.8% –$156.1 47.0% –$Total locomotive sector –$196.1 59.1% –$196.1 59.1% –$Marine Marine engine producers –$1.8 0.5% –$1.6 0.5%  Auxiliary >800 hp –$0.4 0.1% –$0.4  C1 >800 hp –$1.3 0.4% –$1.1 0.3%  C2 >800 hp $0.0 0.0% $0.0 0.0%  Other marine $0.0 0.0% $0.0 0.0% Marine vessel producers –$10.3 3.1% –$9.4 2.8% – C1 >800 hp –$8.8 2.7% –$8.0 2.4%  C2 >800 hp –$1.3 0.4% –$1.3 0.4%  Other marine –$0.1 0.0% –$0.1 0.0% Recreat’l and small /fishing vessel $0.0 0.0% $0.0 0.0% Marine transport. service providers –$29.5 8.9% –$25.0 7.5% –Users of marine transport. services –$94.4 28.4% –$99.9 30.1% –Fixed engineering costs (engines <800 hp) 

 $0.0 0.0% $0.0 0.0% 

Total marine sector –$135.9 40.9% –135.9 40.9% –$Total program –$332.0 100.0% –$332.0 100.0% –$

a Figures are in 2005 dollars.
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Table 7H-3. Sensitivity Analysis for Demand Elasticity Parameters: 2020a

Primary Case Demand Upper Bound 

Change in Price Change in Quantity Change in Price Change in Quantity Change in Pr

Market–Level Impacts 

Variable Engineering Cost Per Unit Absolute Percent Absolute Percent Absolute Percent Absolute Percent Absolute Per

Locomotives  Line Haul $65,343 $64,261 3.21% –1 –0.15% $64,068 3.20% –2 –0.17% $64,479  Switcher/Passenger $21,139 $20,436 1.57% 0 –0.15% $20,310 1.56% 0 –0.17% $20,577 Transportation services NA NA 0.29% NA –0.15% NA 0.28% NA –0.17% NA Marine  Engines  Auxiliary>800 hp $28,363 $27,129 13.07% –9 –2.69% $26,990 12.99% –10 –2.98% $27,306 1 C1 >800 hp $14,131 $12,497 6.52% –12 –2.86% $12,318 6.44% –13 –3.17% $12,727  C2 >800 hp $54,893 $54,573 12.42% 0 –0.28% $54,518 12.41% 0 –0.32% $54,634 1 Other marine $0 –$1 0.00% 0 –0.01% –$1 0.00% 0 –0.01% $0  Equipment  C1 >800 hp $6,936 $26,158 1.63% –11 –2.86% $23,616 1.47% –12 –3.17% $29,434  C2 >800 hp $10,174 $170,164 5.28% 0 –0.28% $169,132 5.25% 0 –0.32% $171,356  Other marine $0 –$6 0.00% –2 –0.01% –$7 0.00% –2 –0.01% –$5  Transportation services NA NA 0.55% NA –0.28% NA 0.52% NA –0.32% NA Welfare Impacts (Million $) Locomotives Surplus 

Change Share 

Surplus 

Change Share 

Surplus 

Change Share 

Locomotive producers –$1.1 0.3% –$1.3 0.4% –$0.9  Line Haul –$1.0 0.3% –$1.2 0.4% –$0.8  Switcher/Passenger –$0.1 0.0% –$0.1 0.0% –$0.1 Rail transport. service providers –$46.4 14.0% –$54.7 16.5% –$37.1 Users of rail transport.services –$148.6 44.8% –$140.1 42.2% –$158.2 Total locomotive sector –$196.1 59.1% –$196.1 59.1% –$196.1 Marine Marine engine producers –$1.8 0.5% –$1.9 0.6% –$1.5  Auxiliary >800 hp –$0.4 –$0.5 –$0.4 C1 >800 hp –$1.3 0.4% –$1.4 0.4% –$1.1  C2 >800 hp $0.0 0.0% –$0.1 0.0% $0.0  Other marine $0.0 0.0% $0.0 0.0% $0.0 Marine vessel producers –$10.3 3.1% –$11.2 3.4% –$9.2  C1 >800 hp –$8.8 2.7% –$9.6 2.9% –$7.8  C2 >800 hp –$1.3 0.4% –$1.4 0.4% –$1.3  Other marine –$0.1 0.0% –$0.2 0.1% –$0.1  Rec/fishing vessel consumers $0.0 0.0% $0.0 0.0% $0.0 Marine transport. service providers –$29.5 8.9% –$34.5 10.4% –$23.8 Users of marine transport services –$94.4 28.4% –$88.2 26.6% –$101.5 Fixed Engineering Costs (all engines <800 hp) 

 $0.0 0.0% $0.0 0.0% $0.0 

Total marine sector –$135.9 40.9% –135.8 40.9% –$136.0 Total program –$332.0 100.0% –$331.9 100.0% –$332.1 1

a Figures are in 2005 dollars. 
Economic Impact Analysis 

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7H.2 Fixed Cost Shift Scenario 

As discussed in 7.2.3.4, in the primary economic analysis only the variable costs are 

used to shift the supply curve in the engine and equipment markets. This is because in a 

competitive market the industry supply curve is generally based on the market’s marginal cost 

curve and fixed costs do not influence production decisions on the margin. 

In this scenario, the supply shift for engine and equipment producers includes fixed as 

well as variable costs that are incurred in 2014. The year 2014 was chosen because engine 

and equipment manufacturers will be incurring R&D and other fixed costs associated with the 

Tier 4 standards, but not the one-time certification costs that will be incurred in 2015. The 

analysis also includes the Tier 3 variable costs that will continue until the Tier 4 engines and 

equipment become available. 

Results are provided for all locomotive markets. For the marine sector, only 

propulsion marine diesel engines above 800 hp are included. There are two reasons for this. 

First, the Economic Impact Model (EIM) does not permit adding marine diesel engines below 

800 hp for this sensitivity analysis. Because the smaller marine engines are not subject to the 

Tier 4 standards, and there are no variable costs associated with the Tier 3 standards, those 

markets were omitted from the EIM to simplify model development. Therefore it is not 

possible to include them. Second, as explained in 7.3.2.3, the primary analysis uses one 

compliance cost, based on the weighted average compliance costs, for both auxiliary marine 

engine markets (800-2,000 hp and above 2,000 hp). This results in an over-estimate of the 

market impacts for auxiliary engines 800-2,000 hp and an under-estimate of the market 

impacts for auxiliary engines above 2,000 hp, and also affects the impacts on the vessel 

markets to which these auxiliary engines were allocated. These effects would be even more 

exaggerated if a weighted average of the fixed costs were used. Therefore, the analysis omits 

the large auxiliary engines and considers only marine propulsion engines above 800 hp. The 

results of this sensitivity analysis are compared with an adjusted primary case that includes 

locomotives and marine propulsion engines above 800 hp. 

The results of this analysis are presented in Table 7H-4. 

In 2014, the changes in the results are considerable. In the rail sector, the expected 

price change for line haul locomotives increases from $355 to $38,688 (a 1.9 pecent increase 

instead a small price decrease). The expected price change for switcher/passenger 

locomotives increases from -$231 to $104,544 for switcher/passenger locomotives (a 8.0 

percent price increase instead of a small price decrease. With regard to the social welfare 

costs, the burden of the program shifts from locomotive manufacturers to trail transportation 

service providers and consumers, as the locomotive manufacturers pass their costs to 

downstream consumers. The share of the social costs burden for locomotive manufacturers 

drops from 15 percent and 5 percent for line haul and switcher/passenger locomotives, 

respectively, to nearly zero. The share of the burden for transportation service providers and 

consumers increases from 7.2 percent and 23 percent to 12 percent and 38 percent, 

respectively. 
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The results are similar for the marine sector. In this case, engine and vessel prices 

change from a slight drop in prices (less than $150 for engines and less than $2,500 for 

vessels) to a price increase of 13 to 17 percent for engines ($23,800 and $73,275) and 2 to 5 

percent for vessels ($28,388 and $143,674), depending on engine size. With regard to the 

social welfare costs, there is also a shift to equipment and the marine transportation sectors 

similar to that in the locomotive sector. However, in the marine case equipment 

manufacturers are not able to pass along the entire amount of the fixed costs to the 

transportation markets, and therefore bear a larger share of the burden. Specifically, the share 

of the burden of the program for engine manufacturers drops from 15 percent ($30M) to less 

than 1 percent ($1M), while the share of the burden for vessel manufacturers increases from 

0.1 percent ($200,000) to 3.6 percent ($7.2M). The burden for marine transportation 

providers increases from 6 percent to 9 percent ($12.8M to $17.9M); the burden for marine 

transportation service consumers increases from 20 percent to 29 percent ($41 to $57.2M). 

Even with these cost and welfare shifts, the overall production of locomotives and 

marine diesel engines and vessels is not expected to decrease significantly, and prices of rail 

and marine transportation services are not expected to increase significantly. Locomotive 

sales would decrease by about 1 locomotive, and marine engines and vessels would decrease 

by about 16 engines and 13 vessels. The impact on rail and marine transportation service 

prices would be negligible, and both are below 0.5 percent even when fixed costs are 

included. This is because rail and marine transportation services are production inputs for 

other goods and services, and an increase in their prices would be a relatively small increase 

to the total production costs of goods and services using these inputs. 
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Table 7H-4. Sensitivity Analysis for Supply Shifts (Fixed and Variable Costs): 2014a

Variable Cost Only Supply Shift Fixed and Variable Cost Supply S

Change in Price Change in Quantity Change in Price Ch

Market–Level Impacts Absolute Percent Absolute Percent Absolute Percent Absolute Locomotives  Line Haul -$355 0.0% 0 0.0% $38,688 1.9%  Switcher/Passenger -$231 0.0% 0 0.0% $104,544 8.0% Transportation services NA 0.1% NA 0.0% NA 0.2% Marine  Engines  Auxiliary>800 hp Excluded from analysis; see text Excluded from analys C1 >800 hp -$9 0.0% 0 0.0% $23.842 13.2%  C2 >800 hp -$147 0.0% 0 -0.1% $73,275 17.0%  Other marine $0 0.0% 0 0.0% $0 0.0%  Equipment  C1 >800 hp -$181 0.0% 0 0.0% $28,388 2.1%  C2 >800 hp -$2,354 -0.1% 0 -0.1% $143,674 4.5%  Other marine $0 0.0% 0 0.0% $0 0.0%  Transportation services NA 0.3% NA -0.1% NA 0.4% Welfare Impacts (Million $) Surplus Change Share Surplus Change Share Locomotives Locomotive producers –$40.7 20.2% –$0.5  Line Haul –$30.9 15.4% –$0.5  Switcher/Passenger –$9.8 4.9% $0.0 Rail transport. service providers –$14.5 7.2% –$24.0 1Users of rail transport.services –$46.3 23.0% –$76.9 3Total locomotive sector –$101.4 50.4% –$101.4 5Marine Marine engine producers –$29.7 14.8% –$1.2  Auxiliary >800 hp Excluded from analysis; see text Excluded from analys C1 >800 hp –$19.0 9.4% –$1.2  C2 >800 hp –$10.7 5.3% $0.0  Other marine $0.0 0.0% $0.0 Marine vessel producers –$0.2 0.1% –$7.2  C1 >800 hp –$0.1 0.0% –$7.0  C2 >800 hp –$0.2 0.1% –$0.2  Other marine $0.0 0.0% $0.0  Rec/fishing vessel consumers $0.0 0.0% $0.0 Marine transport. service providers –$12.8 6.4% –$17.9 Users of marine transport services –$41.0 20.4% –$57.2 2Auxiliary marine >800 hpb –$16.0 8.0% –$16.0 Total marine sector –$99.7 49.6% –$99.5 4Total program –$201.1 100.0% –$200.9 10a Figures are in 2005 dollars. b Auxiliary engines above 800 were excluded from the model for this sensitivity analysis; this is a line item entry that reflects the engineeri
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7H.3 Alternative Equipment Supply Elasticity Scenario 

In the third sensitivity scenario, we use alternative estimates for the price elasticity of 

supply for the locomotive and marine vessel markets. 

The supply elasticities used in the primary analysis were estimated using a calibration 

approach as described in Appendix F, and aggregate industry data over the years 1997 

through 2004 for NAICS 336510 (railroad rolling stock manufacturing) and NAICS 336611 

(ship building and repairing). This method was chosen because of limitations with the 

available data: we were not able to obtain firm-level or plant-level production data for 

companies that operate in the affected sectors. However, as we noted in the proposal for this 

rule, the use of aggregate industry level data may not be appropriate or an accurate way to 

estimate the price elasticity of supply compared to firm-level or plant-level data. This is 

because, at the aggregate industry level, the size of the data sample is limited to the time 

series of the available years and because aggregate industry data may not reveal each 

individual firm or plant production function (heterogeneity). There may be significant 

differences among the firms that may be hidden in the aggregate data but that may affect the 

estimated elasticity. In addition, the use of time series aggregate industry data may introduce 

time trend effects that are difficult to isolate and control. 

To address these concerns, we investigated estimates for the price elasticity of supply 

for the affected industries for which published estimates are not available using alternative 

methods and data inputs.36 This analysis used a cross-sectional data model and 

establishment-level data from the U.S. Census Bureau to estimate these supply elasticities. It 

should be noted that this analysis is still in draft form and the estimated supply elasticities 

have not yet been peer-reviewed. Therefore, we are providing them as a sensitivity analysis 

and not incorporating them into the primary analysis. 

Supply elasticities were estimated for 3 markets: NAICS 336611: Ship Building and 

Repairing (for commercial marine vessels); NAICS industry 336612: Boat Building (for 

recreational marine vessels); and NAICS industry 366510: Railroad Rolling Stock 

Manufacturing (for locomotives). We used a panel data set comprised of confidential 

establishment-level data from the 1972, 1977, 1982, 1992, 1997 and 2002 Census of 

Manufacturers (COM). 

It should be noted that while the use of data at the establishment addresses uncertainty 

introduced in the analysis by using data aggregated on a yearly basis, it does not resolve 

uncertainty due to the variety of establishments included in each NAICS category. Each of 

the NAICS categories includes firms engaged in activities that are not affected by the new 

standards. In addition, the production processes and thus production function of the nonaffected firms may be very different from those firms that are affected by the rule. 

For example, NAICS 336611 includes repair yards as well as yards that produce new 

vessels. Repair yards, as well as yards that engaged in specialized services such as scaling, 

may not have the same production function as yards that produce new vessels. In addition, 

the production function of a yard that engages in all of these activities may be different from a 
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yard that engages solely in new vessel construction. In addition, NAICS 336611includes all 

shipyards, ranging from yards that make very large ocean-going vessels, to military vessels, to 

medium and small commercial craft such as tugs, fishing boats, supply boats, and passenger 

vessels, as well as non-self-propelled barges. This large variety of commercial vessels 

reflects a range of production processes, not all of which can be expected to be the same as 

those used to produce the vessels that will be affected by the rule. For example, the 

production processes used to make OGV and military vessels are likely to be different from 

those used to make non-self-propelled barges and these are likely to be different from those 

used to make commercial self-propelled vessels. In addition, commercial self-propelled 

vessels that use engines are likely to have different production processes from smaller 

commercial vessels. Large commercial vessels are more likely to be unique designs produced 

for a specific customer while small commercial vessels may be serially produced with 

minimal customization or even built with fiberglass hulls. 

With regard to NAICS 336612, that category includes all boat building, for gasoline as 

well as diesel powered vessels. The vast majority of recreational vessels are gasoline 

powered and therefore are not affected by this rule. In addition, gasoline-powered vessels are 

primarily fiberglass hull vessels. This construction process is very different from aluminum 

or wooden hulls used in higher end vessels that are more likely to have diesel engines. 

Finally, with regard to NAICS industry 366510: Railroad Rolling Stock 

Manufacturing, this category is comprised of roughly 100 establishments primarily involved 

in one or more of the following manufacturing activities: manufacturing and/or rebuilding 

locomotives, locomotive frames and parts; manufacturing railroad, street, and rapid transit 

cars and car equipment for operation on rails for freight and passenger service; and 

manufacturing rail layers, ballast distributors, rail tamping equipment and other railway track 

maintenance equipment. Many of these establishments are not engaged in locomotive 

manufacturing, such as track or rail manufacturing and maintenance, ancillary equipment and 

maintenance – these products may involve different technologies or production 

processes/functions by using different total labor or materials input from the locomotive 

manufacturing. In addition, for the emerging switcher market, we don’t know what their 

production process is. We assume they have the same production function as the line haul 

locomotive manufacturers. But, in fact, the production function of the switcher manufacturers 

may be different because they are expected to purchase the “merchant engine” from other 

non-road engine manufacturers. 

The alternative supply elasticities are set out in Table 7H-6. These supply elasticities 

are all more elastic than those used in the primary analysis, suggesting that manufacturers of 

locomotives and marine vessels are more sensitive to price changes (a one percent change in 

price leads to more than a 1 percent change in quantity supplied). 
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Table 7H-6. Alternative Supply Elasticities 

NAICS Primary Case Sensitivity Analysis 

NAICS 336510 (locomotives) 2.7 5.9 

NAICS 336611 (commercial marine) 2.3 7.1 

NAICS 336612 (recreational marine) 2.3 4.2 

The results of the sensitivity analysis for 2020 are set out in Table 7H-7. Despite the significant 

change in the locomotive and marine vessel supply elasticities, the results of the analysis are not affected by 

very much. This suggests that the main driver of the market response to this regulatory program is the 

inelastic price elasticity of demand in the affected transportation service markets. There are small increases 

in the expected market results, but the price and quantity changes are very small. The price change for 

locomotives increases from $64,261 to $64,843 for line haul and from $20,436 to $20,814 for 

switcher/passenger locomotives, with no change in the quantity produced. The price change for marine 

engines above 800 hp decreases, from $12,497 to $11,842 for C1 engines, and from $54,573 to $54,563 for 

C2 engines, with a small change in the quantity produced (less than 20). For vessels, the price change 

increases from $26,158 to $34,917 for vessels C1 engines and $170,164 to $172,803 for vessels with C2 

engines. 

 These small impacts occur because the only change in the analysis is to the supply elasticities for the 

equipment markets (locomotives and marine vessels). Notably, the supply and demand elasticities for the 

rail and marine transportation sectors as well as the supply elasticities for marine engines remain the same. 

The more elastic supply elasticities for the equipment markets means suppliers can pass on more of the direct 

compliance costs to consumers (in this case, the transportation services market) in the form of higher prices 

relative to the primary case. These higher prices lead to a larger decrease in the quantity of equipment 

demanded than in the primary case. In the case of marine vessels, the drop in demand is high enough to 

slightly offset the price increase associated with the direct compliance costs, resulting in a smaller increase in 

prices when compared to the primary case. 

There is also a change in the way the social costs are distributed among stakeholders, although these 

changes are small. In the rail sector, the share allocated to the transportation services market consumers 

increases from 44.8 percent to 44.9 percent, while the share for locomotive producers decreases from 0.3 

percent to 0.2 percent. In the marine sector, the share of social costs attributed to marine service providers 

and consumers increases from 8.9 percent to 9.1 percent, and 28.4 percent to 29.3 percent, respectively. The 

share attributed to engine manufacturers increases somewhat, from 0.5 percent to 0.7 percent, while the share 

attributed to vessel producers decreases from 3.1 percent to 1.8 percent.
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Table 7H-7. Sensitivity Analysis for Alternative Equipment Supply Elasticities: 2020a

 Primary Case Elasticities Alternative Locomotive and Ve

Change in Price Change in Quantity Change in Price 

Market–Level Impacts Absolute Percent Absolute Percent Absolute Percent AbRail Sector 

Locomotives Line Haul $64,261 3.2% -1 -0.1% $64,843 3.2% Switcher/Passenger $20,436 1.6% 0 -0.1% $20.814 1.6% Transportation services N/A 0.3% N/A -0.1% N/A 0.3% 

Marine Sector 

Engines 

Auxiliary >800 hp $27,129 13.1% -9 -2.7% $26,677 12.8% C1 >800 hp $12,497 6.5% -12 -2.9% $11,842 6.2% C2 >800 hp $54,573 12.4% 0 -0.3% $54,563 12.4% Other Marine -$1 0.0% 0 0.0% -$1 0.0% 

Equipment 

C1 >800 hp $26,158 1.6% -11 -2.9% $34,917 2.2% C2 >800 hp $170,164 5.3% 0 -0.3% $172,803 5.3% Other marine -$6 0.0% -2 0.0% -$3 0.0% 

Transportation services N/A 0.6% N/A -0.3% N/A 0.6% 

Welfare Impacts (Million $) Surplus Change Share Surplus Change 

Locomotives 

Locomotive Producers $1.1 0.3% -$0.5 Line Haul -$1.0 0.3% -$0.5 Switcher/Passenger -$0.1 0.0% $0.0 Rail transportation service providers -$46.4 14.0% -$46.6 Users of rail transportation service -$148.6 44.8% -$149.0 Total locomotive sector -$196.1 59.1% -$196.1 

Marine 

Marine engine producers -$1.8 0.5% -$2.4 Auxiliary >800 hp -$0.4 0.1% -$0.6 

C1 >800 hp -$1.3 0.4% -$1.8 C2 >800 hp $0.0 0.0% -$0.1 Other marine $0.0 0.0% $0.0 

Marine vessel producers -$10.3 3.1% -$6.0 

C1 >800 hp -$8.8 2.7% -$4.8 C2 >800 hp -$1.3 0.4% -$1.1 Other marine -$0.1 0.0% $0.0 

 Rec/fishing vessel consumers $0.0 0.0% $0.0 Marine transportation svc providers -$29.5 8.9% -$30.4 Users of marine transportation service -$94.4 28.4% -$97.2 Total marine sector -$135.9 40.9% -$135.9 Total program -$332.0 -$322.0 a Figures are in 2005 dollars
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7H.4 Alternative Auxiliary Engine Compliance Cost Scenario 

In the fourth sensitivity scenario, we use alternative compliance costs for marine 

auxiliary engines above 800 hp. 

As explained in section 7.3.2.2, the compliance costs for marine auxiliary engines above 

800 hp are a weighted average of all compliance costs for those engines. This weighted average 

is applied to both categories of auxiliary marine engines examined in this anlaysis: those 800-

2,000 hp and those above 2,000 hp. This means that the supply shift is the same for both of the 

auxiliary engine markets even though the actual program impacts are different for them. The 

weighted average compliance costs were obtained by dividing the total compliance costs for 

engines above 800 hp by the number of engines above 800 hp. This results in estimated 

compliance costs of $37,097 in 2016, the first year of the Tier 4 program. 

In this analysis, we apply separate compliance costs to each of the two auxiliary marine 

engine markets, using the compliance costs set out in Table 7H-8. We performed the analysis for 

2016 and 2030. The results are reported in Tables 7H-9 and 7H-10. 

Table 7H-7. Sensitivity Analysis for Alternative Compliance Costs: Marine Auxiliary 

Markets 

Engine Category Primary Case Sensitivity Analysis 

 2016 2030 

Aux. Marine 800-2,000 $37,097 $19,073 $67,255 

Aux Marine >2,000 hp $28,359 $14,582 $51,414 

 As expected, the market results for this analysis are different for marine auxiliary 

engines. In 2016, the expected price impacts for engines 800 to 2,000 hp is lower than the 

primary case (10.4 percent price increase instead of 20.9 percent) and the expected price impacts 

for engines above 2,000 hp being higher (18.3 percent price increase instead of 9.6 percent). 

There is only a small change in quantity produced, however, with a slightly smaller decrease for 

auxiliary engines 800 to 2,000 hp and no change for the larger engines. These results are similar 

for a 2030. 

 The social welfare impacts change only negligibly for the marine markets (less 

than 0.5 percent change), with marine engine and vessel producers bearing slightly less of the 

total social welfare costs and marine transportation service providers and users bearing slightly 

more. Specifically, in 2016 the share for engine manufacturers decreases to 0.6 percent from 0.7, 

while vessel manufacturers would see their share decrease from 5.8 percent to 5.1 percent. 

Marine transportation service providers would see their share increase from 6.4 percent to 6.5 

percent, and marine transportation service users would see their share increase from 20.3 percent 

to 20.7 percent. We expect that actual social welfare impacts would be less that these estimated 

impacts, however. By allocating all of the auxiliary engines above 800 hp to the vessels that will 
Economic Impact Analysis 

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be affected by this program, this analysis over-estimates the vessel impacts of the program. In 

fact, not all of the very large auxiliary engines are actually used on the commercial vessels that 

are subject to this program; some will be installed on vessels with Category 3 marine diesel 

engines. While it is appropriate to consider these costs in the economic impact analysis for this 

program, it is clear that not all of these social costs will be passed on to the producers and users 

of vessels directly affected by this program. 

 In conclusion, the simplifying method of using weighted average compliance costs for 

auxiliary engines above 800 hp does not change the results of the analysis. 

Table 7H-9. Sensitivity Analysis for Auxiliary Engine Compliance Costs: 2016a

Primary Scenario Sensitivity Scenario 

Change in Price Change in Quantity Change in Price Change in Quantity 

Market–Level 

Impacts 

Absolute Percent Absolute Percent Absolute Percent Absolute Percent 

Locomotives 

 Line Haul $84,227 4.2% -1 -0.1% $84,227 4.2% -1 -0.1% 

 Switcher/Passenger $13,494 1.0% 0 -0.1% $13,494 1.0% 0 -0.1% 

Transportation services NA 0.3% NA -0.1% NA 0.3% NA -0.1% 

Marine 

Engines 

 Aux 800 to 2000 hp $34,894 20.9% -11 -5.0% $17,417 10.4% -8 -3.8% 

 Aux > 2000 hp $36,919 9.6% 0 -0.2% $67,084 18.3% 0 -0.2% 

 C1 >800 hp $16,384 8.5% -15 -3.7% $16,696 8.6% -14 -3.3% 

 C2 >800 hp $71,602 16.3% 0 -0.2% $71,600 16.3% 0 -0.2% 

 Other marine $0 0.0% 0 0.0% $0 0.0% 0 0.0% 

 Equipment 

 C1 >800 hp $34,043 2.1% -14 -3.7% $29,574 1.9% -12 -3.3% 

 C2 >800 hp $225,143 7.0% 0 -0.2% $262,231 8.4% 0 -0.2% 

 Other marine -$4 0.0% -1 0.0% -$4 0.0% -1 0.0% 

 Transportation 

services 

NA 0.4% NA -0.2% NA 0.4% NA -0.2% 

Welfare Impacts 

(Million $) 

 Surplus 

Change 

 

Share 

Surplus 

Change 

 

Share 

Locomotives 

Locomotive producers –$8.3 2.9% –$8.3 2.9% 

 Line Haul –$0.9 0.3% –$0.9 0.3% 

 Switcher/Passenger –$7.4 2.6% –$7.4 2.6% 

Rail transport. service 

providers 

–$43.4 15.3% –$43.4 15.3% 

Users of rail 

transport.services 

–$138.9 48.8% –$138.9 48.8% 

Total locomotive 

sector 

–$190.6 67.0% –$190.6 67.0% 

Marine 

Marine engine 

producers 

–$2.1 0.7% –$1.8 0..6% 

 Aux 800 to 2000 hp –$0.4 0.1% –$0.3 0.1% 

 Aux > 2000 hp –$0.1 0.0% –$0.1 0.0% 

 C1 >800 hp –$1.6 0.5% –$1.3 0.5% 

 C2 >800 hp $0.0 0.0% –$0.0 0.0% 

 Other marine $0.0 0.0% $0.0 0.0% 

Marine vessel 

producers 

–$15.8 5.6% –$14.5 5.1% 

 C1 >800 hp –$13.5 4.7% –$12.2 4.3% 
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 C2 >800 hp –$2.2 0.8% –$2.2 0.8% 

 Other marine –$0.1 0.0% $0.1 0.0% 

 Rec/fishing vessel 

consumers 

$0.0 0.0% $0.0 0.0% 

Marine transport. 

service providers 

–$18.1 6.4% –$18.3 6.5% 

Users of marine 

transport services 

–$57.9 20.3% –$58.7 20.7% 

Total marine sector –$93.8 33.0 % –$93.3 32.9% 

Total program –$284.4 100.0% –$283.8 100.0% 

a

 Figures are in 2005 dollars 
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Table 7H-10. Sensitivity Analysis for Auxiliary Engine Compliance Costs: 2030a

Primary Scenario Sensitivity Scenario 

Change in Price Change in Quantity Change in Price Change in Quantity 

Market–Level 

Impacts 

Absolute Percent Absolute Percent Absolute Percent Absolute Percent 

Locomotives 

 Line Haul $63,019 3.2% -4 -0.3% $63,019 3.2% -4 -0.3% 

 Switcher/Passenger $19,628 1.5% -1 -0.3% $19,628 1.5% -1 -0.3% 

Transportation services NA 0.6% NA -0.3% NA 0.6% NA -0.3% 

Marine 

 Engines 

 Aux 800 to 2000 hp $26,626 15.9% -10 -3.9% $13,267 7.9% -8 -3.0% 

 Aux > 2000 hp $27,809 7.2% -1 -0.5% $50,890 13.9% -1 -0.5% 

 C1 >800 hp $12,479 6.5% -13 -2.9% $12,718 6.6% -12 -2.6% 

 C2 >800 hp $54,264 12.3% -1 -0.5% $54,262 12.3% -1 -0.5% 

 Other marine -$1 0.0% 0 0.0% -$1 0.0% 0 0.0% 

 Equipment 

 C1 >800 hp $25,768 1.6% -12 -2.9% $22,352 1.5% -11 -2.6% 

 C2 >800 hp $164,774 5.1% 0 -0.5% $193,158 6.2% 0 -0.5% 

 Other marine -$12 0.0% -4 0.0% -$12 0.0% -4 0.0% 

 Transportation 

services 

NA 1.1% NA -0.5% NA 1.0% NA -0.5% 

Welfare Impacts 

(Million $) 

 Surplus 

Change 

 

Share 

Surplus 

Change 

 

Share 

Locomotives 

Locomotive producers –$3.1 0.4% –$3.1 0.4% 

 Line Haul –$2.7 0.4% –$2.7 0.4% 

 Switcher/Passenger –$0.4 0.1% –$0.4 0.1% 

Rail transport. service 

providers 

–$109.0 14.8% –$109.0 14.8% 

Users of rail 

transport.services 

–$348.9 47.3% –$348.9 47.3% 

Total locomotive 

sector 

–$461.1 62.5% –$461.1 62.5% 

Marine 

Marine engine 

producers 

–$2.0 0..3% –$1.7 0.2% 

 Aux 800 to 2000 hp –$0.4 0.1% –$0.3 0.1% 

 Aux > 2000 hp –$0.1 0.0% –$0.1 0.0% 

 C1 >800 hp –$1.4 0.2% –$1.2 0.2% 

 C2 >800 hp –$0.1 0.0% –$0.1 0.0% 

 Other marine $0.0 0.0% $0.0 0.0% 

Marine vessel 

producers 

–$9.2 1.2% –$8.1 1.1% 

 C1 >800 hp –$8.2 1.1% –$7.0 1.0% 

 C2 >800 hp –$0.7 0.1% –$0.7 0.1% 

 Other marine $0.3 0.0% $0.3 0.0% 

 Rec/fishing vessel 

consumers 

$0.0 0.0% $0.0 0.0% 

Marine transport. 

service providers 

–$63.3 8.6% –$63.5 8.6% 

Users of marine 

transport services 

–$202.5 27.4% –$203.2 27.5% 

Total marine sector –$277.0 37.5% –$276.5 37.5% 

Total program –$738.1 100.0% –$737.5 100.0% 

a

 Figures are in 2005 dollars 
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REFERENCES 

 

1

 U.S. EPA “EPA Guidelines for Preparing Economic Analyses.” EPA 240-R-00-003. September 2000, p. 113. A 

copy of this document can be found at http://yosemite.epa.gov/ee/epa/eed.nsf/webpates/guidelines.html 

2

 Association of American Railroads (AAR). Railroad Facts, 2006 Edition. November 2006. Policy and 

Economics Department, Association of American Railroads, 50 F Street NW, Washington DC 20001. www.aar.org. 

3

 U.S. EPA. Technical Support Document for the Final Locomotive /Marine Rule: Detailed Results from Economic 

Impact Model (EIM). December 2007. A copy of this document can be found in the docket for this rule, EPA-HQOAR-2004-0190. 

4

 U.S. EPA. Technical Support Document for the Final Locomotive /Marine Rule: Detailed Results from Economic 

Impact Model (EIM). December 2007. A copy of this document can be found in the docket for this rule, EPA-HQOAR-2004-0190. 

5

 U.S. EPA. “OAQPS Economic Analysis Resource Document.” Research Triangle Park, NC: EPA 1999. A copy 

of this document can be found at http://www.epa.gov/ttn/ecas/econdata/6807-305.pdf

6

 Bingham, T.H., and T.J. Fox. “Model Complexity and Scope for Policy Analysis.” Public Administration 

Quarterly, 23(3), 1999. 

7

 Berck, P., and S. Hoffman. “Assessing the Employment Impacts.” Environmental and Resource Economics

22:133-156. 2002. 

8

 Office of Management and Budget. “Executive Analysis of Federal regulations Under Executive Order 12866.” 

Executive Office of the President, Office of Management and Budget. January 11, 1996. A copy of this document 

is available at http://www.whitehouse.gov/omb/inforeg/print/riaguide.html

9

 U.S. EPA “EPA Guidelines for Preparing Economic Analyses.” EPA 240-R-00-003. September 2000, p. 126. A 

copy of this document can be found at http://yosemite.epa.gov/ee/epa/eed.nsf/webpates/guidelines.html

10 U.S. Government Accountability Office. “Freight Railroads: Industry Health Has Improved, but Concerns about 

Competition and Capacity Should Be Addressed.” GAO-07-94, October 2006, pg. 42. A copy of this document can 

be found at www.gao.gov/new.items/d0794.pdf 

11 Association of American Railroads (AAR). Railroad Facts, 2006 Edition. November 2006. Policy and 

Economics Department, Association of American Railroads, 50 F Street NW, Washington DC 20001. www.aar.org. 

12 Association of American Railroads (AAR). Railroad Facts, 2006 Edition. November 2006. Policy and 

Economics Department, Association of American Railroads, 50 F Street NW, Washington DC 20001. www.aar.org. 

13 U.S. EPA. “OAQPS Economic Analysis Resource Document.” Research Triangle Park, NC: EPA 1999. A copy 

of this document can be found at http://www.epa.gov/ttn/ecas/econdata/6807-305.pdf

14 “Marine Vessel Sales” Memorandum from John Mueller, Office of Transportation and Air Quality, to docket 

EPA-HQ-OAR-2003-0190, February 28, 2007. 
Economic Impact Analysis 

7-143

 

15 “Marine Diesel Engine Prices” Memorandum from John Mueller, Office of Transportation and Air Quality, to 

docket EPA-HQ-OAR-2003-0190, February 28, 2007. 

16 RTI international. Memo to Chi Li, US EPA, “Documentation for EIM Baseline Data for Transportation Service 

Markets”, December 17, 2007. A copy of this document can be found in the docket for this rule, EPA-HQ-OAR2004-0190. 

17 Association of American Railroads (AAR). Railroad Facts, 2006 Edition. November 2006. Policy and 

Economics Department, Association of American Railroads, 50 F Street NW, Washington DC 20001. www.aar.org. 

18 Memorandum, Documentation for EIM Baseline Data for Transportation Services Markets, from Brooks Depro, 

RTI International, to Chi Li, U.S. EPA, Dated December 12, 2007. 

19 Association of American Railroads (AAR). Railroad Facts, 2006 Edition. November 2006. Policy and 

Economics Department, Association of American Railroads, 50 F Street NW, Washington DC 20001. www.aar.org. 

20 U.S. Army Corps of Engineers (USACE), Waterborne Commerce Statistics Center 2005. “Final Waterborne 

Commerce Statistics for Calendar Year 2004.” Washington, DC: U.S. Army Corps of Engineers. 

21 Boyer, K.D. 1997 Principles of Transportation Economics. Reading, MA: Addison-Wesley. 

22 U.S. Environmental Protection Agency (EPA). 2007. Control of Emissions from Marine SI and Small SI 

Engines, Vessels, and Equipment Draft Regulatory Impact Analysis. EPA420-D-07-004. Available at 

<http://www.epa.gov/otaq/regs/nonroad/marinesi-equipld/420d07004.pdf>. 

23 U.S. Environmental Protection Agency (EPA) 2004. Final Regulatory Impact Analysis: Control of Emissions 

from Nonroad Diesel Engines. EPA420-R-04-007. Available at http://www.epa.gov/nonroaddiesel/2004fr/420r04007.pdf 

24 U.S. Environmental Protection Agency (EPA) 2004. Final Regulatory Impact Analysis: Control of Emissions 

from Nonroad Diesel Engines. EPA420-R-04-007. Available at http://www.epa.gov/nonroaddiesel/2004fr/420r04007.pdf 

25 U.S. Environmental Protection Agency (EPA) 2004. Final Regulatory Impact Analysis: Control of Emissions 

from Nonroad Diesel Engines. EPA420-R-04-007. Available at http://www.epa.gov/nonroaddiesel/2004fr/420r04007.pdf 

26 U.S. Environmental Protection Agency (EPA). 2007. Control of Emissions from Marine SI and Small SI 

Engines, Vessels, and Equipment Draft Regulatory Impact Analysis. EPA420-D-07-004. Available at 

<http://www.epa.gov/otaq/regs/nonroad/marinesi-equipld/420d07004.pdf>. 

27 U.S. Environmental Protection Agency (EPA) 2004. Final Regulatory Impact Analysis: Control of Emissions 

form Nonroad Diesel Engines. EPA420-R-04-007. Available at http://www.epa.gov/otaq/nonroaddiesel/2004fr/420r04007.pdf 

28 Kimbell, L.J., and G.W. Harrison. 1986. “On the Solution of General Equilibrium Models.” Economic Modeling 

3:197-212. 

29 See, for example, Harberger, Arnold C. 1974. Taxation and Welfare. Chicago: University of Chicago Press. 
Regulatory Impact Analysis 

7-144

 

30 Ivaldi, M. and McCullough, G. 2001. "Density and Integration Effects on Class I U.S. Freight Railroads." 

Journal of Regulatory Economics 19:161-162; see also Boyer, K.D. 1997. Principles of Transportation Economics. 

Reading, MA: Addison-Wesley. 

31 Fullerton, D., and G. Metcalf. 2002. “Tax Incidence.” In A. Auerbach and M. Feldstein, eds., Handbook of Public 

Economics, Vol.4, Amsterdam: Elsevier 

32 Kimbell, L.J., and G.W. Harrison. 1986. “On the Solution of General Equilibrium Models.” Economic Modeling 

3:197-212. 

33 Handbook of of Econometrics, Volume 5, ed. J. J. Heckman & E. E. Leamer (2001, Amsterdam: Elsevier) 

34 Rutherford, T. 1998. “CES Preferences and Technology: A Practical Introduction.” GAMS MPSGE Guide. 

Washington, DC: GAMS Development Corporation. 

35 U.S. EPA. “OAQPS Economic Analysis Resource Document.” Research Triangle Park, NC: EPA 1999, pages 

5-25. A copy of this document can be found at http://www.epa.gov/ttn/ecas/econdata/6807-305.pdf

36 “Supply Elasticity Estimation Report,” Memorandum from Nathalie Simon, National Center for Environmental 

Economics, January 31, 2008. A copy of this document can be found in Public Docket EPA-HQ-OAR-2003-0190. 
Regulatory Alternatives 

CHAPTER 8: Regulatory Alternatives................................................................. 8-2

8.1 Alternatives Considered.................................................................................... 8-2

8.1.1 Alternative 1: Proposed Program from the NPRM............................... 8-3

8.1.2 Alternative 2: Exclusion of Remanufacturing Standards...................... 8-3

8.1.3 Alternative 3: Elimination of Tier 3......................................................... 8-3

8.1.4 Alternative 4: Tier 4 Exclusively in 2013................................................. 8-4

8.2 Emission Inventory Impacts............................................................................. 8-4

8.2.1 Methodology............................................................................................... 8-4

8.2.2 Analysis....................................................................................................... 8-5

8.3 Summary of Results ........................................................................................ 8-13

8.3.1 Alternative 1: Proposed Program from the NPRM.............................. 8-13

8.3.2 Alternative 2: Exclusion of Remanufacturing Standards.................... 8-13

8.3.3 Alternative 3: Elimination of Tier 3....................................................... 8-14

8.3.4 Alternative 4: Tier 4 Exclusively in 2013............................................... 8-14

8-1
Regulatory Impact Analysis 

CHAPTER 8: Regulatory Alternatives 

The program we are finalizing today represents a broad and comprehensive approach 

to reducing emissions from locomotive and marine diesel engines. As we developed this final 

rule, we considered a number of alternatives with regard to the scope and timing of the 

standards. After carefully evaluating these alternatives, we believe that our new program 

provides the best opportunity for achieving timely and substantial emission reductions from 

locomotive and marine diesel engines. Our final program balances a number of key factors: 

(1) achieving very significant emissions reductions as early as possible, (2) providing 

appropriate lead time to develop and apply advanced control technologies, and (3) 

coordinating requirements in this final rule with existing highway and nonroad diesel engine 

programs. The alternative scenarios described here were constructed to further evaluate each 

individual aspect of our program, and have enabled us to achieve the appropriate balance 

between these key factors. This chapter presents a detailed explanation of our analysis, 

including a year by year breakout of expected costs and emission reductions. 

8.1 Alternatives Considered 

Our final rule consists of a comprehensive three-part program to address emissions 

from diesel locomotive engines and marine diesel engines below 30 liters per cylinder 

displacement. First, we are adopting stringent emission standards for existing locomotives 

and standards for existing commercial marine diesel engines above 600 kW. These standards 

apply when an engine is remanufactured. Second, we are adopting a set of near-term 

emission standards, referred to as Tier 3, for newly-built locomotives and marine engines that 

reflect the application of technologies to reduce engine-out PM and NOx. Third, we are 

adopting longer-term standards, referred to as Tier 4, for newly-built locomotives and marine 

engines that utilize high-efficiency catalytic aftertreatment technology enabled by the 

availability of Ultra-Low Sulfur Diesel (ULSD). We are also adopting standards to eliminate 

emissions from unnecessary locomotive idling. As we developed this final rule, we evaluated 

alternative scenarios that looked at the impact of varying the timing and scope of our final 

standards. 

Table 8-1 gives a brief summary of the alternatives we considered. The first 

alternative compares our proposed program to our final program. Alternatives 1 and 2 look at 

the effects of eliminating both the remanufacturing programs and the Tier 3 near-term 

standards that our final rule includes. Finally, Alternative 4 examines the effects of moving 

Tier 4 ahead to 2013 and eliminating both the remanufacturing programs and the Tier 3 nearterm standards. 

8-2
Regulatory Alternatives 

Table 8-1 Summary of Alternatives and Standards 

Final Rule 

• Locomotive Remanufacturing 

• Marine Remanufacturing, 

• Tier 3 Near-term program, 

• Tier 4 Long-term standards 

Alternative 1: Proposed Program from the 

Notice of Proposed Rulemaking 

• Proposed Locomotive Remanufacturing program, 

• Proposed Tier 3 Near-term program, 

• Proposed Tier 4 Long-term standards 

Alternative 2: Exclusion of Remanufacturing 

Standards 

• Tier 3 Near-term program, 

• Tier 4 Long-term standards 

Alternative 3: Elimination of Tier 3 

• Locomotive Remanufacturing, 

• Marine Remanufacturing, 

• Tier 4 Long-term standards 

Alternative 4: Tier 4 Exclusively in 2013 • Tier 4 Long-term standards only in 2013

8.1.1 Alternative 1: Proposed Program from the Notice of Proposed Rulemaking 

Alternative 1 examines the differences between the program we proposed and the 

program we are finalizing in this rulemaking. The final rule makes a number of important 

changes to the program originally set out in the proposal which we believe will yield greater 

overall NOx and PM reductions, especially in the critical early years of the program. In 

particular, the adoption of standards for remanufactured marine engines and a 2-year pullahead of the Tier 4 NOx requirements for line-haul locomotives and for 2000-3700 kW 

marine engines provide greater near-term reductions than the proposal. The final rule also 

expands the remanufactured locomotive program to include Class II railroads. The analysis of 

this alternative illustrates the additional benefits gained through the development process 

which resulted in our final program. 

8.1.2 Alternative 2: Exclusion of Remanufacturing Standards 

Alternative 2 examines the potential impacts of the locomotive and marine 

remanufacturing programs by excluding them from the analysis (see sections III.B.(1)(a)(i), 

III.B.(1)(b), and III.B.(2)(b) of our Preamble for more details on the remanufacturing 

standards). Alternative 2 is identical to the final program with the exception of the removal of 

both the locomotive and marine remanufacturing standards, as the timing and scope of Tier 3 

and Tier 4 standards remain unchanged in this alternative. These results can be compared 

with the results of the primary program to estimate the benefits that would be lost if we did 

not finalize either the locomotive or marine remanufacturing standards. 

8.1.3 Alternative 3: Elimination of Tier 3 

Alternative 3 eliminates the Tier 3 standards, while retaining the Tier 4 standards and 

the combined locomotive and marine remanufacturing requirements. The timing and scope of 

8-3
Regulatory Impact Analysis 

both the Tier 4 and the locomotive and marine remanufacturing programs would remain 

unchanged. These results can be compared with the results of the final program to estimate 

the benefits that would be foregone if the near-term standards were not finalized. 

8.1.4 Alternative 4: Tier 4 Exclusively in 2013 

Alternative 4 most closely reflects the program described in our Advanced Notice of 

Proposed Rulemaking (ANPRM), whereby we would set new aftertreatment based emission 

standards as soon as possible. In this case, we believe the earliest that such standards could 

logically be started is in 2013 (three months after the introduction of 15 ppm ULSD in this 

sector). This alternative would eliminate the Tier 3 standards and both the locomotive and 

marine remanufacturing standards, while pulling the Tier 4 standards ahead to 2013 for all 

portions of the Tier 4 program. These results show the benefits of the comprehensive 

program we are finalizing today compared to the aggressive but narrow approach outlined in 

our ANPRM.

8.2 Emission Inventory Impacts 

8.2.1 Methodology 

8.2.1.1 Inventory Impacts 

Based on our primary case, we estimated inventory impacts using a methodology 

based on engine population, hours of use, average engine loads, and in-use emissions factors 

for each alternative. (Refer to Chapter 3 of this RIA for a more complete discussion of how 

the primary control inventories were generated). The results are shown in Table 8-2. 

8.2.1.2 Costs 

We have estimated the costs associated with each alternative using the same methods 

employed for the final rule. The cost estimates for the locomotive remanufacturing program 

include adjustments for costs associated with hardware requirements. The cost estimates for 

the marine remanufacturing program were generated in a similar manner as those generated 

for the locomotive remanufacturing program. We have estimated the cost per remanufactured 

marine engine as equal to that for a remanufactured locomotive engine because we would 

expect a similar or identical remanufacture kit to be used. At this time, for alternative 4 we 

are unable to make an accurate estimate of the cost for pulling ahead Tier 4 technologies, 

since we do not believe it to be feasible at this time. However, we have reported costs in the 

summary table reflecting the same cost estimation aaproach we have used for our final 

program and have denoted unestimated additional costs as ‘C’. These additional unestimated 

costs would include costs for additional engine test cells, engineering staff, and engineering 

facilities necessary to accelerate the development of Tier 4. The details of our estimated 

remanufacturing program costs can be found in Chapter 5 of this RIA. The results are shown 

in Table 8-2. 

8-4
Regulatory Alternatives 

8.2.1.3 Benefits 

To estimate the PM-related monetized benefits for each of the alternative scenarios, 

we used a benefits transfer approach to scale the PM benefits from the final Locomotive and 

Marine Engine control scenario. The PM benefits scaling approach is similar to the scaling 

approach conducted for the Clean Air Nonroad Diesel (CAND) Rule (see Chapter 9 of the 

CAND RIA). For the estimate of benefits generated for the final rule, we ran a sophisticated 

photochemical air quality model, the Community Multiscale Air Quality model (CMAQ), to 

estimate baseline and post-control ambient concentrations of PM for 2030. Benefits for the 

final standards were then generated using the inputs and methods described in Chapter 6 of 

the RIA for this rule. We then scaled these PM benefits to reflect the magnitude of the PM2.5

precursor emissions changes estimated to occur as a result of the alternative control scenarios. 

The results are shown in Table 8-2. 

8.2.2 Analysis 

Table 8-2 includes the expected yearly emission reductions associated with each 

alternative, including: the estimated PM and NOx reductions for years 2006-2040 expressed as 

a net present value (NPV) using discounting rates of 3% and 7%. The yearly estimated costs 

are also expressed in this table at both 3% and 7% NPV. The benefit analysis from 2020 and 

the analysis from 2030 are also included on this table. For further analysis, Table 8-3 and 

Table 8-4 summarize the PM and NOx emission reductions and costs for each alternative 

through 2040; and Table 8-5 and Table 8-6 summarize the emission reductions, costs and 

benefits for the year 2020 and the year 2030. Figure 8.2-1 and Figure 8.2-2 illustrate the 

inventory impacts of each alternative from 2006-2040 for comparison.

8-5
Regulatory Impact Analysis 

Table 8-2 Inventory, Cost, and Benefits year from 2006-2040 

 Final Program Alternative 1: Proposed Program from Notice of 

Proposed Rulemaking 

PM-only 

Benefitsa,b

(Billions) 

PM-only 

Benefitsa,b

(Billions) 

Calendar 

Year 

PM2.5

Emissions 

Reductions 

(tons) 

NOx

Emissions 

Reductions 

(tons) 

Total 

Costs 

(Millions) 

3% 7% 

PM2.5

Emissions 

Reductions 

(tons) 

NOx

Emissions 

Reductions 

(tons) 

Total 

Costs 

(Millions) 

3% 7% 

2006 0 0 $0 --- --- 0 0 $0 --- --- 

2007 0 0 $30 --- --- 0 0 $30 --- --- 

2008 320 14,000 $110 --- --- 60 5,100 $90 --- --- 

2009 930 24,000 $90 --- --- 390 7,300 $70 --- --- 

2010 1,700 44,000 $150 --- --- 840 20,000 $110 --- --- 

2011 2,800 70,000 $280 --- --- 1,700 39,000 $230 --- --- 

2012 4,200 87,000 $220 --- --- 2,700 51,000 $170 --- --- 

2013 5,200 110,000 $210 --- --- 3,300 65,000 $160 --- --- 

2014 6,300 130,000 $200 --- --- 4,300 82,000 $160 --- --- 

2015 7,300 160,000 $270 --- --- 5,300 95,000 $200 --- --- 

2016 8,900 210,000 $290 --- --- 6,900 120,000 $220 --- --- 

2017 11,000 250,000 $290 --- --- 8,600 180,000 $240 --- --- 

2018 12,000 290,000 $310 --- --- 10,000 220,000 $270 --- --- 

2019 13,000 330,000 $340 --- --- 11,000 260,000 $290 --- --- 

2020 14,000 370,000 $350 $3.9 $3.6 13,000 310,000 $300 $3.3 $3.0 

2021 16,000 410,000 $380 --- --- 14,000 350,000 $330 --- --- 

2022 17,000 450,000 $440 --- --- 15,000 390,000 $390 --- --- 

2023 18,000 490,000 $520 --- --- 17,000 460,000 $470 --- --- 

2024 19,000 540,000 $550 --- --- 18,000 520,000 $500 --- --- 

2025 21,000 580,000 $590 --- --- 20,000 570,000 $570 --- --- 

2026 22,000 620,000 $610 --- --- 21,000 610,000 $590 --- --- 

2027 23,000 670,000 $640 --- --- 22,000 660,000 $630 --- --- 

2028 24,000 710,000 $690 --- --- 23,000 700,000 $680 --- --- 

2029 25,000 750,000 $730 --- --- 25,000 740,000 $710 --- --- 

2030 27,000 790,000 $760 $9.2 $8.4 26,000 780,000 $750 $8.8 $8.0 

2031 28,000 830,000 $790 --- --- 27,000 820,000 $780 --- --- 

2032 29,000 870,000 $810 --- --- 28,000 860,000 $800 --- --- 

2033 30,000 910,000 $900 --- --- 29,000 900,000 $890 --- --- 

2034 31,000 950,000 $930 --- --- 31,000 940,000 $930 --- --- 

2035 32,000 990,000 $970 --- --- 32,000 980,000 $960 --- --- 

2036 33,000 1,000,000 $990 --- --- 33,000 1,000,000 $980 --- --- 

2037 34,000 1,100,000 $1,000 --- --- 34,000 1,100,000 $1,000 --- --- 

2038 35,000 1,100,000 $1,000 --- --- 35,000 1,100,000 $1,000 --- --- 

2039 36,000 1,100,000 $1,100 --- --- 36,000 1,100,000 $1,100 --- --- 

2040 37,000 1,100,000 $1,100 --- --- 36,000 1,100,000 $1,100 --- --- 

NPV 3% 308,000 8,760,000 $9,410 --- 286,000 8,140,000 $8,760 --- 

NPV 7% 134,000 3,710,000 $4,310 --- 121,000 3,320,000 $3,900 --- 

a

 Note that the range of PM-related benefits reflects the use of an empirically-derived estimate of PM mortality benefits, based on 

the ACS cohort study (Pope et al., 2002) and the extension of the Harvard Six-Cities study (Laden et al. 2006). b

 Annual benefits analysis results reflect the use of a 3 percent and 7 percent discount rate in the valuation of premature mortality 

and nonfatal myocardial infarctions, consistent with EPA and OMB guidelines for preparing economic analyses (US EPA, 2000 

and OMB, 2003).U.S. Environmental Protection Agency, 2000. Guidelines for Preparing Economic Analyses. 

http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/Guidelines.html. 

8-6
Regulatory Alternatives 

 Alternative 2: Exclusion of Remanufacturing 

Standards 

Alternative 3: Elimination of Tier 3 

PM-only 

Benefitsa,b

(Billions) 

PM-only 

Benefitsa,b

(Billions) 

 PM2.5

Emissions 

Reductions 

(tons) 

NOx

Emissions 

Reductions 

(tons) 

Total 

Costs 

(Millions) 

3% 7% 

PM2.5 Emissions 

Reductions 

(tons) 

NOx Emissions 

Reductions 

(tons) 

Total 

Costs 

(Millions) 

3% 7% 

2006 0 0 $0 --- --- 0 0 $0 --- --- 

2007 0 0 $30 --- --- 0 0 $0 --- --- 

2008 60 600 $30 --- --- 320 14,000 $80 --- --- 

2009 90 2,600 $30 --- --- 930 24,000 $50 --- --- 

2010 110 4,400 $70 --- --- 1,700 44,000 $120 --- --- 

2011 140 6,500 $140 --- --- 2,800 70,000 $220 --- --- 

2012 440 9,800 $80 --- --- 3,700 85,000 $220 --- --- 

2013 840 14,000 $80 --- --- 3,600 100,000 $210 --- --- 

2014 1,500 31,000 $100 --- --- 3,500 120,000 $200 --- --- 

2015 2,400 65,000 $180 --- --- 3,800 150,000 $270 --- --- 

2016 3,500 100,000 $180 --- --- 4,800 190,000 $290 --- --- 

2017 4,800 150,000 $190 --- --- 6,000 230,000 $290 --- --- 

2018 6,100 190,000 $210 --- --- 6,900 270,000 $310 --- --- 

2019 7,500 240,000 $250 --- --- 7,700 310,000 $340 --- --- 

2020 8,800 280,000 $290 $2.5 $2.3 8,800 350,000 $350 $2.8 $2.6 

2021 10,000 330,000 $320 --- --- 9,900 380,000 $380 --- --- 

2022 12,000 370,000 $360 --- --- 11,000 420,000 $440 --- --- 

2023 13,000 420,000 $450 --- --- 12,000 460,000 $520 --- --- 

2024 15,000 470,000 $490 --- --- 14,000 510,000 $550 --- --- 

2025 16,000 520,000 $530 --- --- 15,000 550,000 $590 --- --- 

2026 18,000 570,000 $570 --- --- 16,000 590,000 $610 --- --- 

2027 19,000 620,000 $610 --- --- 17,000 640,000 $640 --- --- 

2028 21,000 670,000 $650 --- --- 19,000 680,000 $690 --- --- 

2029 22,000 720,000 $690 --- --- 20,000 720,000 $730 --- --- 

2030 24,000 760,000 $720 $8.2 $7.5 21,000 760,000 $760 $7.8 $7.1 

2031 25,000 810,000 $760 --- --- 23,000 800,000 $790 --- --- 

2032 26,000 850,000 $790 --- --- 24,000 840,000 $810 --- --- 

2033 28,000 890,000 $880 --- --- 25,000 880,000 $900 --- --- 

2034 29,000 930,000 $920 --- --- 26,000 920,000 $930 --- --- 

2035 30,000 970,000 $950 --- --- 28,000 950,000 $970 --- --- 

2036 32,000 1,000,000 $980 --- --- 29,000 990,000 $990 --- --- 

2037 33,000 1,000,000 $1,000 --- --- 30,000 1,000,000 $1,000 --- --- 

2038 34,000 1,100,000 $1,000 --- --- 31,000 1,100,000 $1,000 --- --- 

2039 35,000 1,100,000 $1,100 --- --- 32,000 1,100,000 $1,100 --- --- 

2040 36,000 1,100,000 $1,100 --- --- 33,000 1,100,000 $1,100 --- --- 

NPV 3% 240,000 7,640,000 $8,080 --- 237,000 8,360,000 $9,240 --- 

NPV 7% 96,000 3,030,000 $3,430 --- 100,000 3,530,000 $4,160 --- 

a Note that the range of PM-related benefits reflects the use of an empirically-derived estimate of PM mortality benefits, based on the ACS 

cohort study (Pope et al., 2002) and the extension of the Harvard Six-Cities study (Laden et al. 2006). b

 Annual benefits analysis results reflect the use of a 3 percent and 7 percent discount rate in the valuation of premature mortality and 

nonfatal myocardial infarctions, consistent with EPA and OMB guidelines for preparing economic analyses (US EPA, 2000 and OMB, 

2003).U.S. Environmental Protection Agency, 2000. Guidelines for Preparing Economic Analyses. 

http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/Guidelines.html. 

8-7
Regulatory Impact Analysis 

 Alternative 4: Tier 4 Exclusively in 2013 

PM-only 

Benefitsb,c

(Billions) 

 PM2.5

Emissions 

Reductions 

(tons) 

NOx

Emissions 

Reductions 

(tons) 

Total Costsa

(Millions) 

3% 7% 

2006 0 0 $0 --- --- 

2007 0 0 $0 --- --- 

2008 60 600 $80 --- --- 

2009 90 2,600 $80 --- --- 

2010 120 4,400 $80 --- --- 

2011 150 6,300 $80 --- --- 

2012 200 8,700 $120 --- --- 

2013 1,500 51,000 $140 --- --- 

2014 2,600 93,000 $170 --- --- 

2015 3,800 140,000 $180 --- --- 

2016 5,000 180,000 $210 --- --- 

2017 6,300 220,000 $250 --- --- 

2018 7,500 270,000 $280 --- --- 

2019 8,800 310,000 $320 --- --- 

2020 10,000 350,000 $360 $3.0 $2.8 

2021 11,000 400,000 $450 --- --- 

2022 13,000 440,000 $490 --- --- 

2023 14,000 490,000 $530 --- --- 

2024 16,000 530,000 $570 --- --- 

2025 17,000 580,000 $610 --- --- 

2026 18,000 620,000 $640 --- --- 

2027 20,000 660,000 $680 --- --- 

2028 21,000 710,000 $710 --- --- 

2029 22,000 750,000 $750 --- --- 

2030 24,000 790,000 $780 $8.4 $7.6 

2031 25,000 830,000 $810 --- --- 

2032 26,000 870,000 $850 --- --- 

2033 28,000 900,000 $940 --- --- 

2034 29,000 940,000 $970 --- --- 

2035 30,000 980,000 $1,000 --- --- 

2036 31,000 1,000,000 $1,000 --- --- 

2037 32,000 1,000,000 $1,000 --- --- 

2038 33,000 1,100,000 $1,100 --- --- 

2039 34,000 1,100,000 $1,100 --- --- 

2040 34,000 1,100,000 $1,100 --- --- 

NPV 3% 249,000 8,320,000 $9,070 + C --- 

NPV 7% 101,000 3,420,000 $3,950 + C --- 

a

 The ‘C’ represents the additional costs necessary to accelerate the introduction of Tier 4 technologies that 

we are unable to estimate at this time, such additional engine test cells, engineering staff, and engineering 

facilites necessary to introduce Tier 4 one year earlier. b

 Note that the range of PM-related benefits reflects the use of an empirically-derived estimate of PM 

mortality benefits, based on the ACS cohort study (Pope et al., 2002) and the extension of the Harvard SixCities study (Laden et al. 2006). 

c

 Annual benefits analysis results reflect the use of a 3 percent and 7 percent discount rate in the valuation of 

premature mortality and nonfatal myocardial infarctions, consistent with EPA and OMB guidelines for 

preparing economic analyses (US EPA, 2000 and OMB, 2003).U.S. Environmental Protection Agency, 

2000. Guidelines for Preparing Economic Analyses. 

http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/Guidelines.html. 

8-8
Regulatory Alternatives 

Table 8-3 Summary of Total Inventory and Costs Through 2040 NPV 3% 

Program 

PM Emissions 

Redcutions (tons) 

2006-2040 

NPV 3% 

NOx Emissions 

Reductions 

(tons) 2006-

2040 

NPV 3% 

Total Costsa

(Millions) 

2006-2040 

NPV 3% 

Final Program 308,000 8,760,000 $9,410 

Alternative 1: Proposed Program from 

Notice of Proposed Rulemaking 286,000 8,140,000 $8,760 

Alternative 2: Exclusion of 

Remanufacturing Standards 240,000 7,640,000 $8,080 

Alternative 3: Elimination of Tier 3 237,000 8,360,000 $9,240 

Alternative 4: Tier 4 Exclusively in 

2013 249,000 8,320,000 $9,070 + C 

a ‘C’ represents additional costs necessary to accelerate the introduction of Tier 4 technologies that we are unable 

to estimate at this time.

Table 8-4 Summary of Total Inventory and Costs Through 2040 NPV 7% 

Program 

PM Emissions 

Redcutions (tons) 

2006-2040 

NPV 7% 

NOx Emissions 

Reductions 

(tons) 2006-

2040 

NPV 7% 

Total Costsa

(Millions) 

2006-2040 

NPV 7% 

Final Program 134,000 3,710,000 $4,310 

Alternative 1: Proposed Program from 

Notice of Proposed Rulemaking 121,000 3,320,000 $3,900 

Alternative 2: Exclusion of 

Remanufacturing Standards 96,000 3,030,000 $3,430 

Alternative 3: Elimination of Tier 3 100,000 3,530,000 $4,160 

Alternative 4: Tier 4 Exclusively in 

2013 101,000 3,420,000 $3,950 + C 

a ‘C’ represents additional costs necessary to accelerate the introduction of Tier 4 technologies that we are unable 

to estimate at this time.

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Regulatory Impact Analysis 

Table 8-5 Summary of Inventory, Costs, and Benefits for 2020 

2020 Benefitsbc

(Billions) PM 

Program only 

2020 PM 

Emissions 

Reductions 

(tons) 

2020 NOx 

Emissions 

Reductions 

(tons) 

2020 Total 

Costsa

(Millions) 

3% 7% 

Final Program 14,000 370,000 $350 $3.9 ($3.6) 

Alternative 1: Proposed Program 

from Notice of Proposed Rulemaking 13,000 310,000 $300 $3.3 ($3.0) 

Alternative 2: Exclusion of 

Remanufacturing Standards 8,800 280,000 $290 $2.5 ($2.3) 

Alternative 3: Elimination of Tier 3 8,800 350,000 $350 $2.8 ($2.6) 

Alternative 4: Tier 4 Exclusively in 

2013 10,000 350,000 $360 + C $3.0 ($2.8) 

a

 The ‘C’ represents the additional costs necessary to accelerate the introduction of Tier 4 technologies that we are 

unable to estimate at this time, such additional engine test cells, engineering staff, and engineering facilites necessary 

to introduce Tier 4 one year earlier. 

b

 Note that the range of PM-related benefits reflects the use of an empirically-derived estimate of PM mortality 

benefits, based on the ACS cohort study (Pope et al., 2002) and the extension of the Harvard Six-Cities study (Laden 

et al. 2006). 

c

 Annual benefits analysis results reflect the use of a 3 percent and 7 percent discount rate in the valuation of 

premature mortality and nonfatal myocardial infarctions, consistent with EPA and OMB guidelines for preparing 

economic analyses (US EPA, 2000 and OMB, 2003).U.S. Environmental Protection Agency, 2000. Guidelines for 

Preparing Economic Analyses. http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/Guidelines.html. 

Table 8-6 Summary of Inventory, Costs, and Benefits for 2030 

2030 Benefitsbc

(Billions) 

PM only 

3% (7%) 

Program 

2030 PM 

Emissions 

Reductions 

(tons) 

2030 NOx

Emissions 

Reductions 

(tons) 

2030 Total 

Costsa

(Millions) 

3% 7% 

Final Program 27,000 790,000 $760 $9.2 $8.4 

Alternative 1: Proposed Program from 

Notice of Proposed Rulemaking 26,000 780,000 $750 $8.8 $8.0 

Alternative 2: Exclusion of 

Remanufacturing Standards 24,000 760,000 $720 $8.2 $7.5 

Alternative 3: Elimination of Tier 3 21,000 760,000 $760 $7.8 $7.1 

Alternative 4: Tier 4 Exclusively in 

2013 24,000 790,000 $780 + C $8.4 $7.6 

a

 The ‘C’ represents the additional costs necessary to accelerate the introduction of Tier 4 technologies that we are 

unable to estimate at this time, such additional engine test cells, engineering staff, and engineering facilites necessary 

to introduce Tier 4 one year earlier. 

b

 Note that the range of PM-related benefits reflects the use of an empirically-derived estimate of PM mortality 

benefits, based on the ACS cohort study (Pope et al., 2002) and the extension of the Harvard Six-Cities study (Laden 

et al. 2006). 

c

 Annual benefits analysis results reflect the use of a 3 percent and 7 percent discount rate in the valuation of 

premature mortality and nonfatal myocardial infarctions, consistent with EPA and OMB guidelines for preparing 

economic analyses (US EPA, 2000 and OMB, 2003).U.S. Environmental Protection Agency, 2000. Guidelines for 

Preparing Economic Analyses. http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/Guidelines.html. 

8-10
Regulatory Alternatives 

Figure 8.2-1 PM2.5 Inventories for 2006-2040 

13

23

33

43

53

63

2006 2011 2016 2021 2026 2031 2036

PM2.5 (thousands of tons)

Base

A1: Proposed Program from Notice of Proposed Rulemaking

A2: Exclusion of Remanufacturing Standards

A3: Elimination of Tier 3

A4: Tier 4 Exclusivelyin 2013

Final Program

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Regulatory Impact Analysis 

Figure 8.2-2 NOx Inventories for 2006-2040 

500

675

850

1,025

1,200

1,375

1,550

1,725

1,900

2006 2011 2016 2021 2026 2031 2036

NOx (thousands of tons)

Base

A1: Proposed Program from Notice of Proposed Rulemaking

A2: Exclusion of Remanufacturing Standards

A3: Elimination of Tier 3

A4: Tier 4 Exclusively in 2013

Final Program

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Regulatory Alternatives 

8.3 Summary of Results 

8.3.1 Alternative 1: Proposed Program from the Notice of Proposed Rulemaking 

Table 8-2 shows the changes in inventory that arise from comparing our proposal to 

our final rulemaking. As a stand-alone program, through the year 2040 Alternative 1 provides 

PM2.5 reductions of 286,000 tons NPV 3%, or 121,000 tons NPV 7%, and NOx reductions of 

8,140,000 tons NPV 3%, or 3,320,000 tons NPV 7%. The cost of this alternative through 

2040 is estimated to be $8,760 million NPV 3%, or $3,900 million NPV 7%. In 2020, this 

alternative provides monetized health and welfare benefits of $3.3 billion at a 3% discount 

rate, or $3.0 billion at a 7% discount rate, and $8.8 billion in 2030 at a 3% discount rate or 

$8.0 billion at a 7% discount rate. Through 2040 our final program provides additional PM2.5 

reductions of 22,000 tons NPV 3%, or 13,000 tons NPV 7%, and additional NOx reductions 

of 620,000 tons NPV 3%, or 390,000 tons NPV 7%. Through 2040, the additional costs of 

our final program will be $650 million NPV 3%, or $410 million NPV 7%. The additional 

PM2.5 monetized health and welfare benefits in 2020 of our final program are $0.6 billion at a 

3% discount rate, or $0.6 billion at a 7% discount rate, while in 2030 the additional monetized 

health and welfare benefits total $0.4 billion at a 3% discount rate, or $0.4 billion at a 7% 

discount rate. Figures 8.1 and Figure 8.2 show the increase in emission reductions that our 

final program provides over our proposed program. The decrease in PM2.5 inventory of our 

final program as compared to the proposed program is almost immediate; the decrease in NOx

inventory is also greatest in the near-term as compared to our proposed program. Figure 8.1 

and Figure 8.2 demonstrate that the changes made to the proposed program result in a final 

program that provides greater overall NOx and PM reductions in the critical early years of the 

program. 

8.3.2 Alternative 2: Exclusion of Remanufacturing Standards 

Our analysis of this alternative shows the valuable emission reductions and health and 

welfare benefits that the locomotive and marine remanufacturing standards provide. The 

locomotive and marine remanufacturing programs provide inventory impacts and benefits 

both in the near-term and the long-term. As a stand-alone program, Alternative 2 provides 

PM2.5 reductions of 240,000 tons NPV 3%, or 96,000 tons NPV 7%, and NOx reductions of 

7,640,000 tons NPV 3%, or 3,030,000 tons NPV 7% through the year 2040. The cost of this 

alternative through 2040 is estimated to be $8,080 million NPV 3%, or $3,430 million NPV 

7%. In 2020, this alternative provides monetized health and welfare benefits of $2.5 billion at 

a 3% discount rate, or $2.3 billion at a 7% discount rate, and $8.2 billion in 2030 at a 3% 

discount rate, or $7.5 billion at a 7% discount rate. Compared to the final program, our 

analysis shows that by 2040 eliminating the locomotive and marine remanufacture programs 

lessen PM2.5 emission reductions by 68,000 tons NPV 3%, or 38,000 tons NPV 7%, and NOx

emission reductions by nearly 1,120,000 tons NPV 3% or 680,000 tons NPV 7%. The cost of 

this alternative, as compared to our final program through 2040, is estimated to be $1,330 

million less than our proposal at NPV 3%, or $880 million less at NPV 7%. Compared to our 

final program, eliminating the locomotive and marine remanufacture programs reduce the 

monetized health and welfare benefits by $1.4 billion at a 3% discount rate, or $1.3 billion at 

a 7% discount rate in 2020, and $1.0 billion at a 3% discount rate, or $0.9 billion at a 7% 

discount rate in 2030. Figure 8.1 shows that the remanufacturing programs provide PM2.5

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Regulatory Impact Analysis 

emission reductions throughout the entire length of the program. Eliminating the locomotive 

and marine remanufacturing programs would also reduce the NOx emission benefits that our 

final program provides. Figure 8.2-2 shows the loss of early NOx benefits that this alternative 

would result in as compared to our final program. This alternative shows that when our final 

program includes the locomotive and marine remanufacture programs it provides significant 

additional emission reductions, and over one-third more health and welfare benefits in 2020 

alone. 

8.3.3 Alternative 3: Elimination of Tier 3 

Alternative 3 eliminates the Tier 3 standards, while retaining Tier 4 and the combined 

locomotive and marine remanufacturing requirements. This alternative allows us to consider 

the value of combining the Tier 4 standards with the locomotive and marine remanufacturing 

standards together as one program, and conversely, allows us to see the additional benefits 

gained when combining them with the Tier 3 standards. Although the remanufacturing 

programs provide significant benefits in the near-term, as evidenced by the analysis of 

Alternative 2, it is clear that Tier 3 also plays an important role in providing both near-term 

and long-term emission reductions. As a stand-alone program, Alternative 3 provides PM2.5

reductions of 237,000 tons NPV 3%, or 100,000 tons NPV 7%, and NOx reductions of 

8,360,000 tons NPV 3%, or 3,530,000 tons NPV 7% through the year 2040. The cost of this 

alternative through 2040 is estimated to be $9,240 million NPV 3%, or $4,160 million NPV 

7%. In 2020, this alternative provides monetized health and welfare benefits of $2.8 billion at 

a 3% discount rate, or $2.6 billion at a 7% discount rate and $7.8 billion in 2030 at a 3% 

discount rate, or $7.1 billion at a 7% discount rate. Comparing this alternative to our final 

program allows us to consider the value of the Tier 3 standards on their own merits. 

Specifically, this alternative would lessen PM2.5 emissions reductions by nearly 71,000 tons 

NPV 3%, or 34,000 tons NPV 7%, and NOx emissions by 400,000 tons NPV 3 %, or 180,000 

tons NPV 7%. The cost of this alternative, as compared to our final program through 2040, is 

estimated to be $170 million less at NPV 3%, or $150 million less at NPV 7%. The 

monetized health and welfare benefits that would be forgone by eliminating Tier 3 are $1.1 

billion at a 3% discount rate, or $1.0 billion at a 7% discount rate in 2020, and $1.4 billion at 

a 3% discount rate, or $1.3 billion at a 7% discount rate in 2030. Figure 8.2-1 shows the 

decrease in PM2.5 emission reductions that this alternative results in. Figure 8.2-2 shows that 

this alternative also provides decreased long-term NOx reductions. This alternative shows that 

by eliminating Tier 3 from our final program, we would lose almost one-quarter of the total 

PM emissions reductions and over one-quarter of the PM health and welfare benefits in 2020 

alone. As these alternatives show, each element of our comprehensive program: the 

locomotive and marine remanufacturing programs, the Tier 3 emission standards, and the Tier 

4 emission standards, represents a valuable emission control program on its own, while the 

collective program results in the greatest emission reductions we believe to be possible giving 

consideration to all of the elements described in our final rule. 

8.3.4 Alternative 4: Tier 4 Exclusively in 2013 

Alternative 4 eliminates the Tier 3 standards along with the locomotive and marine 

remanufacturing standards, while pulling the Tier 4 standards ahead to 2013 for all portions of 

the Tier 4 program. As stated in our NPRM, we are concerned that it may not be feasible to 

8-14
Regulatory Alternatives 

introduce Tier 4 technologies on locomotive and marine diesel engines earlier than the 

proposal specifies. We have used the same cost estimation approach for this alternative as 

that of our final program, however, we are unable to make an accurate estimate of the cost for 

such an approach since we do not believe it to be technologically feasible at this time. 

Therefore, we have denoted the unestimated costs that are necessary to accelerate the 

development of Tier 4 technologies with a ‘C’ in the summary tables. These additional 

unestimated costs would include costs for additional engine test cells, engineering staff, and 

engineering facilities necessary to introduce Tier 4 one year earlier. As a stand-alone 

program, Alternative 4 provides PM2.5 reductions of 249,000 tons NPV 3%, or 101,000 tons 

NPV 7%, and NOx reductions of 8,320,000 tons NPV 3%, or 3,420,000 tons NPV 7% through 

the year 2040. In 2020, this alternative provides monetized health and welfare benefits of 

$3.0 billion at a 3% discount rate, or $2.8 billion at a 7% discount rate, and $8.4 billion in 

2030 at a 3% discount rate, or $7.6 billion at a 7% discount rate. Through 2040, this 

alternative, as compared to our final program, would decrease PM2.5 reductions by more than 

59,000 tons NPV 3%, or 33,000 tons NPV 7%, and NOx emissions by 440,000 tons NPV 3%, 

or 290,000 tons NPV 7%. Compared to our final program, the reduction in monetized health 

and welfare benefits of this alternative are $0.9 billion at a 3% discount rate or $0.8 billion at 

a 7% discount rate in 2020, while in 2030 the reductions in monetized benefits are $0.8 billion 

at a 3% discount rate, or $0.8 billion at a 7% discount rate. This alternative shows that in 

addition to the technical challenges necessary to introduce Tier 4 technologies, it would 

actually result in higher PM2.5 and NOx emissions and lower health and welfare benefits than 

our final program. 

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Regulatory Impact Analysis 

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